Project VRM - User contributions [en]

ListenLog Meeting Notes

2010-04-13T20:11:27Z

Khopper:

[[Category:Media Logging]]
[[Category:Conference call]]

Below are meeting notes and ongoing issues and action item lists. For a detailed description of ListenLog, visit the main [[ListenLog]] page.

==1/12==
===Parking Lot===
* Where is data stored (by default)? Does it sync to a local store or just stream out?
* Is there any concern from collaboration stations and partners that there's no exclusive access to data / analytics?
* What data do we capture? Application behavior data in addition to basic listen data? What about rating data? Location data?
** Should the standard by minimal for extensibility vs. maximal for enhanced value/functionality
** Privacy concerns (EFF Chair Brad Templeton)
* Security / encryption on the stored data? Which bits?
* How best to communicate and promote the ListenLog concept and where the key benefits and differentiators are? How do we address naysayers and differentiate from alternative approaches, e.g. APML?
* How do we do identity? How do we make it swappable? Do we use icards, openID, Oauth?
* How do we start the service without identity? (e.g. access to device ID?)
* Do users have control over what's stored?
* What's the absolute minimum of device-side functionality?
** Opt-out(?)
** Change repository
** Assign identity
* Where does legal and TOS come in? (see rights and contracts below)
* Does anyone enforce standards compliance?
* Who does the work / coding?
** PRX + who?
* Do we think about revenue / sustainability? PRX has two roles here - one to build codebase and standards for storage, the other to think about services and how we'd use the data.
* Do we do opt out for data capture? (probably yes)
* Do we provide "public by default," e.g. ubiquitious, anonymous access to the data out of the box (probably no)
* Can we open source iphone bit? Publicly available libraries?
* What's in the first release?
** Should we provide ability for users to release data? How and to whom? What capacity for sharing? What terms? Anon vs. nonanon?
* Does there need to be database legal protection underlying data rights access to drive user terms?

==1/13==
* How do we make the data inherently more anonymous?
** Match account data between logs
** Make timestamp and LAT-LONG fuzzy?
* What data rights can a user authorize for third parties?
** propagation rights (the grantee can't extend to someone else)
** public rights vs. directed/granted rights (e.g. for anyone to use vs. for specific entity to use)
*** anon/non-anonymous
*** Most rights issues/rats nests are associated with granted rights
** How long you can use the data for? Keep the data?
** Rights to cease use, remove data(?) + confirmation(?)
** Can't use this to try and find/identify someone - reverse-engineering rights
** commercial/non-commercial?
** Contact me (e.g. DNC)
** Compare to IRB
* Contract rights
** Investigate proactively - what is it that pandora might want to do? What is reasonable?
*** give me audio recommendations
*** use for product development
** Don't cross-correlate/aggregate (e.g. social network correlation, Ben Laurie) - piercing identity/privacy data
** Endorsement / assignment to my identity

===Core Requirements===
* what data is going to be captured
* where is it stored and in what format
* how does one identify oneself / assign identity
* what's the minimum functionality that needs to live on the device
* what's the minimum functionality that needs to live remotely
* additional / core functionality to prove value necessary?
* Determine protections for communication and storage between client app and repository authenticated, encrypted, etc.

===Action Items===
* Draft functional requirements
* Doc talk to Berkman legal re: user rights / terms

==1/20==
* There is a core set of things to figure out to proceed with this project. We'll focus on those:
** What data will we capture?
** What format will we send and store the data in?
** How is this data being transmitted and stored?
** How do we maintain integrity, privacy, and provide the required minimum of user control (e.g. "delete my data")?
** How do we assign or associate identity?

* In discussing data capture, Keith argued for being minimal and conservative and providing a mechanism for extending
* Agreement that our proposed data, format, and services will deal with listening attention data only; both for on-demand (file) audio as well as streaming audio
* Open question about whether Sound Exchange / RIAA requires specific formats, if so, might be nice to comply
* XRI be resolved / discoverability

* XDI can be used to transmit and/or store; X3 looks promising
* XRI = identifier - resolvable to an XDI endpoint
* XDI dictionary - like an XML schema
* XRI authority resolution server (open XRI)
* community iname registry
* inumber (this is how you handle reassignment)
* At what level will we register?
* How will we maintain this registry?
* How will other applications that write to LL handle XRI? Will they resolve to their own server?
*What about dupes?
**xri synonyms is the answer?
* Use iname registration for digital identity?
** might not need an icard selector on the iphone
** Simpler way?

===Action Items===
* Diagram high-level architecture
* Prep for discussing identity options next week
* Look into Sound Exchange reporting formats and PBcore formats

==2/26==
===Action Items===
* Make some design decisions to get us started with POC development
* Create a boxes and arrows diagram to help represent the data sets and functional entities and where they live
* Have a convo with berkman RE: servers and hosting the user data
* Identity + claimant - how best to handle user identity (focus on near-term)?
* XDI dictionary - designing what info is sent to log, registrants, etc.
* Revisit user functionality on wiki - what do we really need to do? What role should XRI/XDI (and existing OpenXRI code) do for us here?

==3/3==
===Notes on Identity Requirements===

When does identity happen? There are at least three contexts where we handle identity

#Capture data implicitly
##No registration
##Linked to phone
#Access to data provisioned through phone
#Sending listen activity to other data store providers instead of the default store

Perhaps there are others, but these three seem clear so far.

By way of thinking about identity, it is worth noting that there are four distinct functional aspects of identity. Something I call the Identity Quartet (blog post imminent!)

# For authentication, logging into services (username:jandrieu)
# For presentation, e.g., as handle on MySpace or Facebook or WOrld of Warcraft (name: Thor the Destroyer)
# Internally for database level handling of the attributes & privileges associated with a user (users.primaryKey=1023304)
# As a service endpoint, e.g., joe@andrieu.net

Flexible identity systems separate these four elements. Lazy ones combine, such as using my email address as my username. Or displaying my email address when I comment on a bulletin board system. A good system has distinct identifiers for each of these roles, and in fact, sophisticated ones could/should allow multiple different identifiers of the same functional class, for the same user, such as allowing an individual to have multiple characters on WoW, Iain Henderson calls this aspect of identity "personnas".

So, between the first set of contexts and the four functional types, we should be able to map out what we need for ListenLog.

-j

===Identity Workflow===
How identity in the LL app(s) might work. This is a proposed workflow based on my limited understanding of how identity systems work:

[[User:Khopper|Khopper]] 15:19, 10 March 2009 (UTC)

# ID the data: iPhone app by default will create a unique ID for each install (likely created by combining application ID + device ID) in order to appropriately key the log data to a unique instance (individual)
# Claim the data: In order to get access to this data beyond the phone, the individual must associate the existing unique ID in #1 with a more friendly and portable identity (i.e. user ID). This could be through a unique external or internal identification process. External might be something like OpenID, internal might be created and assigned through an integrated user registration process. This would have to happen on the phone.
# To complete the association, the user must authenticate (internally or externally)
# The LL datastore must associate the UniqueID in #1 with the userID in #2. Ideally, this will be obfuscated in some way to protect the identity of the user (is this possible?).
# To retrieve the data remotely (e.g. on a website that lets you browse your LL data), you must provide identity credentials and authenticate. This will locate the data and validate your access to it.
# To write to the data remotely through another application or device (e.g. Pandora), you must follow steps 1-4 above. This should be standardized as part of the LL specification. It is conceivable that there are use cases where data needs to be merged or split by application ID, by user ID, or by Unique ID.

==10/31==
== Current Project Status ==
10/31/2009
* Due to funding status and application performance issues, the [http://publicradioplayer.org Public Radio Player (PRP)] might not be the ideal (or at least the initial) platform for LL deployment
* Investigations into [[The Mine! Project]] suggest that this platform may work for prototyping media logging
* An initial proof of concept on The Mine! has been identified: Using the Last.fm API to log listening data in a proposed LL format into The Mine!
** Note: The Mine!, by design, is broad in it's applicability (i.e. supports any personal data), but fairly shallow in it's capability (e.g. embedding in devices, strong encryption, native support for specific logging formats, etc.). LL needs much of this deep yet narrow functionality to be deployable in the market
* Several months of discussions have led to the conclusion that getting market acceptance of an opt-in logging functionality will require the following:
# Data stored fully encrypted
# Data stored fully distributed (i.e. no vendor silos, regardless of portability and substitutability)
# Early functionality focused on attracting an early adopter developer community, e.g. data access APIs on day one

ListenLog

2010-04-13T20:10:15Z

Khopper:

[[Category:Media Logging]]

== Overview ==

The VRM ListenLog is a proposed method for integrating simple user-driven functionality into an online audio player device or application. The ListenLog is a consolidated and documented history of an individual's online listening activity. It is simply a recorded activity log, in a standard and open format, capturing an individual's listening actions from multiple online applications. The ListenLog is unique in that its aim is to give the user the ability to accumulate and control the use of their listener activity data. The data is the user's. It does not belong to any vendor or intermediary. The user alone should have control over where the data lives, what applications write to it, what gets logged, who to share it with, and how it can be used. The ListenLog concept was devised in part for the [http://publicradiotuner.com Public Radio Tuner iPhone project], where it will likely be first introduced.

== Purpose & Deployment ==
ListenLog, or [[Listen Log]] is a form of [[Media Logging]]. It is required for [[EmanciPay]] to work. EmanciPay is a new business model for otherwise free media goods -- one that sharply reduces the frictions involved in paying for media. By increasing the number of people who pay for free media, PayChoice also helps stigmatize non-payment for those goods.

Logging of listening is required to answer all these common questions:
#What was that (program, segment, piece of music)?
#Who was that (host, guest)?
#Who produced that (program, segment, piece of music)?
#When and where did I listen to that?
#How often did I (listen to, watch or read) that in the past?
#Do I have a relationship with the source? (Such as membership, or past transactions.)

ListenLog is a simple utility that does nothing more than record listening data for the user. It will store it in a form that can be crunched in many ways and for many purposes -- all by the user's choice and at the user's discretion. It will be data that can go in any database or spreadsheet. It can also be shared in a selectively disclosed way. (See [[VRM Principles]].) This will make possible a marketplace for third-party services that help both listeners and the institutions with which they relate. (Starting with stations, but also including program producers, artists and others.)

With ListenLog data, listeners can make fully informed choices about how they support streams, podcasts and on-demand sources of programming.

Listeners can also form better relationships with stations and program sources. Membership can come to mean far more than serving as a future target for pitches and billing.

Both Media Logging and EmanciPay are being pioneered with the Public Radio Tuner, which is currently being developed by [[http://prx.org PRX]] and other partners, including NPR and ProjectVRM at the [[http://cyber.law.harvard.edu Berkman Center]]. Neither, however, are to be limited to the iPhone, or any other platform. But the iPhone and the Public Radio Tuner are where we are starting.

There are challenges posed both by the pioneering nature of the work and the limitations of the iPhone.

For example, the iPhone has a tiny memory for data and no persistent and user- (or program-) accessible data store on the host computer with which it syncs through iTunes. But iPhone does provide means for that data to be stored remotely, in a trusted "cloud." For this purpose, PRX has created ListenLog.org as a site/service for storing each user's data and making it available through standard web service interfaces.

Because this distances the user's data from the user's devices, and requires trust in PRX (or any host), questions of trust are naturally raised. These are the same issues raised by any cloud service, including Gmail and Amazon S3. Establishing trust mechanisms -- both technical and contractual -- are non-trivial. But they are also essential for laying the groundwork required for this new business model to work.

== History ==
While tracking listener behavior data is not a new concept, the ListenLog is a novel approach to deploying early VRM functionality. While a simple activity log might not appear as the killer app, it succeeds by putting in place a small piece of user-driven infrastructure into a larger application - one with a promise of relatively wide distribution. Since this infrastructure component will write, store, and share listener activity in an open and standard format, we hope that such a log will become significantly more useful as other devices and tools leverage the standard to increase what an individual can do with their ListenLog data. This type of sideways approach holds the promise of planting the seeds of VRM onto lots of devices without requiring the primary application functionality (i.e. audio listening) be purely user-driven.

A user-driven activity log works well for an application that pulls together audio streams and files from a number of different sources. Of course, online audio providers (vendors in the VRM model) can already track and aggregate listening behavior data, but only for the audio they control. When the user acts as the sole point of integration, pulling together audio from multiple sources, their own consolidated log becomes uniquely powerful. Only when the listener is the point of integration does such an approach yield a new type of value.

== Specifics ==

Initially, the ListenLog will capture online audio stream and file listening behavior occurring through a specific application and device in a standard format (see the [[ListenLog XDI]] page for an example). At minimum, the log will capture audio stream/file identification, user agent, listen start time, listen duration and device location information. Extending this data to incorporate unique device IDs, audio meta data, rating (e.g. thumbs-up/down), referral, or other application behavior data is under consideration.

The application supporting ListenLog recording capability will host the minimum of user-facing functionality. Specifically, the individual user will be able to choose a digital identity(s) with which to associate their listening data, and can also choose where the data is stored, as it will be dynamically written by the device application to any ListenLog standards compliant service provider (host).

A separate ListenLog Service interface, perhaps integrated with the ListenLog storage provider, will enable the user to choose if and how to share their ListenLog data, either anonymously or as associated with digital identities. For example, an individual may choose to share all data publicly and anonymously. All users selecting this option would, in aggregate, create a single, public "firehose" feed of all listening activity. It is our hope that third-party developers will offer user-driven functionality based on your ListenLog functionality, helping you share selected data conditionally with vendors for example, or providing agnostic recommendations based on your listening habits.

== Enabled Functionality ==

The power of ListenLog is not in the log itself, but what the activity history might enable. As an open standard, the ListenLog format will enable third-party relationship services to be developed for users to interact with. Possible examples include:

==== [[EmanciPay]] ====
By keeping track of what files, tracks and programs are listened to, users can more easily allocate voluntary payments across properties without resorting to the transaction burden of micropayments.

==== Detached Recommendations ====

Audio recommendation systems are typically integrated into a single vendor offering and based on your (and your peer's) internal listening behavior. A third party recommendation system could avoid lock-in of data and stimulate competition from pure-play recommendation systems. Additionally, listening behavior would be aggregated across devices, applications, and formats to provide a greater depth and breadth of material to work from.

==== Personal History Search ====

The ability to apply an "I've listened to this" filter when you perform an audio search.

== More ==
* [[ListenLog Meeting Notes]]
* [[ListenLog Identity]]
* [[ListenLog XDI]]
* [[Listen Log]] (a higher-level more general page on this topic)

VRM Research opportunities

2009-12-23T14:29:26Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that explores the hypothesis that '''a free customer is worth more than a captive one''' by testing specific behaviors of individuals placed into "free customer" vs. "captive customer" scenarios. The experimental scenarios will use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with guidance from Dave Rand, Jason Callina, Joe Andrieu, Tim Hwang and Rob Faris. External funding, while not required, is being explored, as it would greatly expand the scope of testable treatments and number of participants while reducing the overall timeframe.

=== Proposed Experimental Scenario ===
This scenario entails an information gathering process, where participants are placed in either a free or captive customer scenario and then subsequently asked to enter a variety of personal information. Different treatments within this experiment will test participants' willingness to engage, exchange information, and offer the experience to their friends.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $0.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal non-identifiable demographic information (e.g. sex, age, salary, zip, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# For the Free Customer Scenario:
## Before information gathering begins, the free group is informed that they will be testing a new user-driven information collection tool that lets individuals gather together their own data to later share if and how they choose to. The information gathering process will involve letting them generate a list of their favorite musical artists along with some other personal information. It will be made clear that the information that is collected will be strictly for their own use and will not be shared without their permission. At the end of the information gathering process, participants are then offered the chance to share their data with a specific vendor (e.g. Last.fm) in exchange for targeted music recommendations based on their favorite artists (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).
## Upon completion of all selected actions, the participant is then informed that this study was to test their willingness to provide personal information. They will be asked brief survey questions to determine the depth of their belief they were in a "free" situation and the data would not, in fact, be used, shared, or investigated without their permission. Data from participants who did not trust the process might be discarded. Participants will then be asked if they're willing to anonymously share the personal data collected with the research team.
# For the Captive Customer Scenario:
## Before information gathering begins, the captive group is informed that they will be testing a new vendor-driven information collection
tool that gathers together individual's data for a vender (e.g. Last.fm) for the purpose of generating music recommendations. The information gathering process will be identical to the "free customer" process and involve letting them generate a list of their favorite musical artists along with some other personal information. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and targeted artist and song recommendations are provided in return (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?
** What is the most effective way to present a free scenario so that it feels free?
** What percentage of participants don't trust online data gathering efforts and what affects this perception

* Potential issues / items to resolve:
** Are we really measuring FREE vs. CAPTIVE customer experiences? In other words, is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is offering targeted music a valuable enough proposition to encourage data sharing, listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** Is the subtle deception involved in this experiment appropriate, presented appropriately, and functionally the best way to structure this experiment?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Show we throw out data from "free" customers who don't trust the information gathering process?
** Should captive customers be told what vendor they are sharing the data with - if so, should we test multiple vendors?
** Should we disclose which data is being shared with vendors if it all doesn't go into the API request?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman/Harvard) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-23T14:27:35Z

Khopper: /* Specific Research Proposals */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that explores the hypothesis that '''a free customer is worth more than a captive one''' by testing specific behaviors of individuals placed into "free customer" vs. "captive customer" scenarios. The experimental scenarios will use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with guidance from Dave Rand, Jason Callina, Joe Andrieu, Tim Hwang and Rob Faris. External funding, while not required, is being explored, as it would greatly expand the scope of testable treatments and number of participants while reducing the overall timeframe.

=== Proposed Experimental Scenario ===
This scenario entails an information gathering process, where participants are placed in either a free or captive customer scenario and then subsequently asked to enter a variety of personal information. Different treatments within this experiment will test participants' willingness to engage, exchange information, and offer the experience to their friends.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $0.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# For the Free Customer Scenario:
## Before information gathering begins, the free group is informed that they will be testing a new user-driven information collection tool that lets individuals gather together their own data to later share if and how they choose to. The information gathering process will involve letting them generate a list of their favorite musical artists along with some other personal information. It will be made clear that the information that is collected will be strictly for their own use and will not be shared without their permission. At the end of the information gathering process, participants are then offered the chance to share their data with a specific vendor (e.g. Last.fm) in exchange for targeted music recommendations based on their favorite artists (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).
## Upon completion of all selected actions, the participant is then informed that this study was to test their willingness to provide personal information. They will be asked brief survey questions to determine the depth of their belief they were in a "free" situation and the data would not, in fact, be used, shared, or investigated without their permission. Data from participants who did not trust the process might be discarded. Participants will then be asked if they're willing to anonymously share the personal data collected with the research team.
# For the Captive Customer Scenario:
## Before information gathering begins, the captive group is informed that they will be testing a new vendor-driven information collection
tool that gathers together individual's data for a vender (e.g. Last.fm) for the purpose of generating music recommendations. The information gathering process will be identical to the "free customer" process and involve letting them generate a list of their favorite musical artists along with some other personal information. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and targeted artist and song recommendations are provided in return (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?
** What is the most effective way to present a free scenario so that it feels free?
** What percentage of participants don't trust online data gathering efforts and what affects this perception

* Potential issues / items to resolve:
** Are we really measuring FREE vs. CAPTIVE customer experiences? In other words, is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is offering targeted music a valuable enough proposition to encourage data sharing, listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** Is the subtle deception involved in this experiment appropriate, presented appropriately, and functionally the best way to structure this experiment?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Show we throw out data from "free" customers who don't trust the information gathering process?
** Should captive customers be told what vendor they are sharing the data with - if so, should we test multiple vendors?
** Should we disclose which data is being shared with vendors if it all doesn't go into the API request?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman/Harvard) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-23T14:27:14Z

Khopper: /* Specific Research Proposals */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that explores the hypothesis that '''a free customer is worth more than a captive one''' by testing specific behaviors of individuals placed into "free customer" vs. "captive customer" scenarios. The experimental scenarios will use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with guidance from Dave Rand, Jason Callina, Joe Andrieu, Tim Hwang and Rob Faris. External funding, while not required, is being explored, as it would greatly expand the scope of testable treatments and number of participants while shrinking the overall timeframe.

=== Proposed Experimental Scenario ===
This scenario entails an information gathering process, where participants are placed in either a free or captive customer scenario and then subsequently asked to enter a variety of personal information. Different treatments within this experiment will test participants' willingness to engage, exchange information, and offer the experience to their friends.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $0.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# For the Free Customer Scenario:
## Before information gathering begins, the free group is informed that they will be testing a new user-driven information collection tool that lets individuals gather together their own data to later share if and how they choose to. The information gathering process will involve letting them generate a list of their favorite musical artists along with some other personal information. It will be made clear that the information that is collected will be strictly for their own use and will not be shared without their permission. At the end of the information gathering process, participants are then offered the chance to share their data with a specific vendor (e.g. Last.fm) in exchange for targeted music recommendations based on their favorite artists (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).
## Upon completion of all selected actions, the participant is then informed that this study was to test their willingness to provide personal information. They will be asked brief survey questions to determine the depth of their belief they were in a "free" situation and the data would not, in fact, be used, shared, or investigated without their permission. Data from participants who did not trust the process might be discarded. Participants will then be asked if they're willing to anonymously share the personal data collected with the research team.
# For the Captive Customer Scenario:
## Before information gathering begins, the captive group is informed that they will be testing a new vendor-driven information collection
tool that gathers together individual's data for a vender (e.g. Last.fm) for the purpose of generating music recommendations. The information gathering process will be identical to the "free customer" process and involve letting them generate a list of their favorite musical artists along with some other personal information. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and targeted artist and song recommendations are provided in return (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?
** What is the most effective way to present a free scenario so that it feels free?
** What percentage of participants don't trust online data gathering efforts and what affects this perception

* Potential issues / items to resolve:
** Are we really measuring FREE vs. CAPTIVE customer experiences? In other words, is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is offering targeted music a valuable enough proposition to encourage data sharing, listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** Is the subtle deception involved in this experiment appropriate, presented appropriately, and functionally the best way to structure this experiment?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Show we throw out data from "free" customers who don't trust the information gathering process?
** Should captive customers be told what vendor they are sharing the data with - if so, should we test multiple vendors?
** Should we disclose which data is being shared with vendors if it all doesn't go into the API request?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman/Harvard) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-15T21:03:56Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that explores the hypothesis that '''a free customer is worth more than a captive one''' by testing specific behaviors of individuals placed into "free customer" vs. "captive customer" scenarios. The experimental scenarios will use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Dave Rand, Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw. External funding, while not required, is being explored, as it would greatly expand the scope of testable treatments and number of participants while shrinking the overall timeframe.

=== Proposed Experimental Scenario ===
This scenario entails an information gathering process, where participants are placed in either a free or captive customer scenario and then subsequently asked to enter a variety of personal information. Different treatments within this experiment will test participants' willingness to engage, exchange information, and offer the experience to their friends.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $0.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# For the Free Customer Scenario:
## Before information gathering begins, the free group is informed that they will be testing a new user-driven information collection tool that lets individuals gather together their own data to later share if and how they choose to. The information gathering process will involve letting them generate a list of their favorite musical artists along with some other personal information. It will be made clear that the information that is collected will be strictly for their own use and will not be shared without their permission. At the end of the information gathering process, participants are then offered the chance to share their data with a specific vendor (e.g. Last.fm) in exchange for targeted music recommendations based on their favorite artists (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).
## Upon completion of all selected actions, the participant is then informed that this study was to test their willingness to provide personal information. They will be asked brief survey questions to determine the depth of their belief they were in a "free" situation and the data would not, in fact, be used, shared, or investigated without their permission. Data from participants who did not trust the process might be discarded. Participants will then be asked if they're willing to anonymously share the personal data collected with the research team.
# For the Captive Customer Scenario:
## Before information gathering begins, the captive group is informed that they will be testing a new vendor-driven information collection
tool that gathers together individual's data for a vender (e.g. Last.fm) for the purpose of generating music recommendations. The information gathering process will be identical to the "free customer" process and involve letting them generate a list of their favorite musical artists along with some other personal information. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and targeted artist and song recommendations are provided in return (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?
** What is the most effective way to present a free scenario so that it feels free?
** What percentage of participants don't trust online data gathering efforts and what affects this perception

* Potential issues / items to resolve:
** Are we really measuring FREE vs. CAPTIVE customer experiences? In other words, is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is offering targeted music a valuable enough proposition to encourage data sharing, listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** Is the subtle deception involved in this experiment appropriate, presented appropriately, and functionally the best way to structure this experiment?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Show we throw out data from "free" customers who don't trust the information gathering process?
** Should captive customers be told what vendor they are sharing the data with - if so, should we test multiple vendors?
** Should we disclose which data is being shared with vendors if it all doesn't go into the API request?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman/Harvard) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-15T15:41:59Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that explores the hypothesis that '''a free customer is worth more than a captive one''' by testing specific behaviors of individuals placed into "free customer" vs. "captive customer" scenarios. The experimental scenarios will use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Dave Rand, Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw. External funding, while not required, is being explored, as it would greatly expand the scope of testable treatments and number of participants while shrinking the overall timeframe.

=== Proposed Experimental Scenario ===
This scenario entails an information gathering process, where participants are placed in either a free or captive customer scenario and then subsequently asked to enter a variety of personal information. Different treatments within this experiment will test participants' willingness to engage, exchange information, and offer the experience to their friends.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $0.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# For the Free Customer Scenario:
## Before information gathering begins, the free group is informed that they will be testing a new user-driven information collection tool that lets individuals gather together their own data to later share if and how they choose to. The information gathering process will involve letting them generate a list of their favorite musical artists along with some other personal information. It will be made clear that the information that is collected will be strictly for their own use and will not be shared without their permission. At the end of the information gathering process, participants are then offered the chance to share their data with a specific vendor (e.g. Last.fm) in exchange for targeted music recommendations based on their favorite artists (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).
## Upon completion of all selected actions, the participant is then informed that this study was to test their willingness to provide personal information. They will be asked brief survey questions to determine the depth of their belief they were in a "free" situation and the data would not, in fact, be used, shared, or investigated without their permission. Data from participants who did not trust the process might be discarded. Participants will then be asked if they're willing to anonymously share the personal data collected with the research team.
# For the Captive Customer Scenario:
## Before information gathering begins, the captive group is informed that they will participating in a test of a music recommendation service, where they will be providing information directly to a vendor (e.g. Last.fm). The information gathering process will be identical to the "free customer" process and involve letting them generate a list of their favorite musical artists along with some other personal information. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and targeted artist and song recommendations are provided in return (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?
** What is the most effective way to present a free scenario so that it feels free?
** What percentage of participants don't trust online data gathering efforts and what affects this perception

* Potential issues / items to resolve:
** Are we really measuring FREE vs. CAPTIVE customer experiences? In other words, is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is offering targeted music a valuable enough proposition to encourage data sharing, listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** Is the subtle deception involved in this experiment appropriate, presented appropriately, and functionally the best way to structure this experiment?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Show we throw out data from "free" customers who don't trust the information gathering process?
** Should captive customers be told what vendor they are sharing the data with - if so, should we test multiple vendors?
** Should we disclose which data is being shared with vendors if it all doesn't go into the API request?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman/Harvard) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-15T15:33:51Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that explores the hypothesis that '''a free customer is worth more than a captive one''' by testing specific behaviors of individuals placed into "free customer" vs. "captive customer" scenarios. The experimental scenarios will use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Dave Rand, Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw. External funding, while not required, is being explored, as it would greatly expand the scope of testable treatments and number of participants while shrinking the overall timeframe.

=== Proposed Experimental Scenario ===
This scenario entails an information gathering process, where participants are placed in either a free or captive customer scenario and then subsequently asked to enter a variety of personal information. Different treatments within this experiment will test participants' willingness to engage, exchange information, and offer the experience to their friends.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $0.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# For the Free Customer Scenario:
## Before information gathering begins, the free group is informed that they will be testing a new user-driven information collection tool that lets individuals gather together their own data to later share if and how they choose to. The information gathering process will involve letting them generate a list of their favorite musical artists along with some other personal information. It will be made clear that the information that is collected will be strictly for their own use and will not be shared without their permission. At the end of the information gathering process, participants are then offered the chance to share their data with a specific vendor (e.g. Last.fm) in exchange for targeted music recommendations based on their favorite artists (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).
## Upon completion of all selected actions, the participant is then informed that this study was to test their willingness to provide personal information. They will be asked brief survey questions to determine the depth of their belief they were in a "free" situation and the data would not, in fact, be used, shared, or investigated without their permission. Data from participants who did not trust the process might be discarded.
# For the Captive Customer Scenario:
## Before information gathering begins, the captive group is informed that they will participating in a test of a music recommendation service, where they will be providing information directly to a vendor (e.g. Last.fm). The information gathering process will be identical to the "free customer" process and involve letting them generate a list of their favorite musical artists along with some other personal information. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and targeted artist and song recommendations are provided in return (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?
** What is the most effective way to present a free scenario so that it feels free?
** What percentage of participants don't trust online data gathering efforts and what affects this perception

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-15T15:33:35Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that explores the hypothesis that '''a free customer is worth more than a captive one''' by testing specific behaviors of individuals placed into "free customer" vs. "captive customer" scenarios. The experimental scenarios will use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Dave Rand, Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw. External funding, while not required, is being explored, as it would greatly expand the scope of testable treatments and number of participants while shrinking the overall timeframe.

=== Proposed Experimental Scenario ===
This scenario entails an information gathering process, where participants are placed in either a free or captive customer scenario and then subsequently asked to enter a variety of personal information. Different treatments within this experiment will test participants' willingness to engage, exchange information, and offer the experience to their friends.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $0.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# Differences between free and captive scenarios are as follows...
# For the Free Customer Scenario:
## Before information gathering begins, the free group is informed that they will be testing a new user-driven information collection tool that lets individuals gather together their own data to later share if and how they choose to. The information gathering process will involve letting them generate a list of their favorite musical artists along with some other personal information. It will be made clear that the information that is collected will be strictly for their own use and will not be shared without their permission. At the end of the information gathering process, participants are then offered the chance to share their data with a specific vendor (e.g. Last.fm) in exchange for targeted music recommendations based on their favorite artists (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).
## Upon completion of all selected actions, the participant is then informed that this study was to test their willingness to provide personal information. They will be asked brief survey questions to determine the depth of their belief they were in a "free" situation and the data would not, in fact, be used, shared, or investigated without their permission. Data from participants who did not trust the process might be discarded.
# For the Captive Customer Scenario:
## Before information gathering begins, the captive group is informed that they will participating in a test of a music recommendation service, where they will be providing information directly to a vendor (e.g. Last.fm). The information gathering process will be identical to the "free customer" process and involve letting them generate a list of their favorite musical artists along with some other personal information. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and targeted artist and song recommendations are provided in return (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?
** What is the most effective way to present a free scenario so that it feels free?
** What percentage of participants don't trust online data gathering efforts and what affects this perception

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-15T15:32:53Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that explores the hypothesis that '''a free customer is worth more than a captive one''' by testing specific behaviors of individuals placed into "free customer" vs. "captive customer" scenarios. The experimental scenarios will use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Dave Rand, Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw. External funding, while not required, is being explored, as it would greatly expand the scope of testable treatments and number of participants while shrinking the overall timeframe.

=== Proposed Experimental Scenario ===
This scenario entails an information gathering process, where participants are placed in either a free or captive customer scenario and then subsequently asked to enter a variety of personal information. Different treatments within this experiment will test participants' willingness to engage, exchange information, and offer the experience to their friends.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $0.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# There are important difference between free and captive scenarios
# For the Free Customer Scenario:
## Before information gathering begins, the free group is informed that they will be testing a new user-driven information collection tool that lets individuals gather together their own data to later share if and how they choose to. The information gathering process will involve letting them generate a list of their favorite musical artists along with some other personal information. It will be made clear that the information that is collected will be strictly for their own use and will not be shared without their permission. At the end of the information gathering process, participants are then offered the chance to share their data with a specific vendor (e.g. Last.fm) in exchange for targeted music recommendations based on their favorite artists (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).
## Upon completion of all selected actions, the participant is then informed that this study was to test their willingness to provide personal information. They will be asked brief survey questions to determine the depth of their belief they were in a "free" situation and the data would not, in fact, be used, shared, or investigated without their permission. Data from participants who did not trust the process might be discarded.
# For the Captive Customer Scenario:
## Before information gathering begins, the captive group is informed that they will participating in a test of a music recommendation service, where they will be providing information directly to a vendor (e.g. Last.fm). The information gathering process will be identical to the "free customer" process and involve letting them generate a list of their favorite musical artists along with some other personal information. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and targeted artist and song recommendations are provided in return (this will leverage a music recommendation API, such as from Last.fm). These recommendations can be listened to and specific tracks can be downloaded and purchased (e.g. with the AMT revenue generated from participation).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?
** What is the most effective way to present a free scenario so that it feels free?
** What percentage of participants don't trust online data gathering efforts and what affects this perception

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-15T15:18:13Z

Khopper: /* Specific Research Proposals */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that explores the hypothesis that '''a free customer is worth more than a captive one''' by testing specific behaviors of individuals placed into "free customer" vs. "captive customer" scenarios. The experimental scenarios will use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Dave Rand, Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw. External funding, while not required, is being explored, as it would greatly expand the scope of testable treatments and number of participants while shrinking the overall timeframe.

=== Proposed Experimental Scenario ===
This scenario entails an information gathering process, where participants are placed in either a free or captive customer scenario and then subsequently asked to enter a variety of personal information. Different treatments within this experiment will test participants' willingness to engage, exchange information, and offer the experience to their friends.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that they will be testing a new user-driven information collection tool that lets individuals gather together their own data to later share if and how they choose to. The test will involve letting them generate a list of their favorite musical artists along with some other personal information. It will be made clear that the information that is collected will be strictly for their own use and will not be shared without their permission. For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?
** What is the most effective way to present a free scenario so that it feels free?
** What percentage of participants don't trust online data gathering efforts and what affects this perception

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-15T15:06:37Z

Khopper: /* Specific Research Proposals */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that explores the hypothesis that '''a free customer is worth more than a captive one''' by testing specific behaviors of individuals placed into "free customer" vs. "captive customer" scenarios. The experimental scenarios will use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Dave Rand, Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw. External funding, while not required, is being explored, as it would greatly expand the scope of testable treatments and number of participants while shrinking the overall timeframe.

=== Proposed Experimental Scenario ===
This scenario entails a music recommendation process, where participants are asked to share information to online music vendors in exchange for personalized song recommendations. Different scenarios within this experiment will test participants' willingness to engage, exchange information, and share the experience with others.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-15T15:01:28Z

Khopper: /* Objectives */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies and software -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
This scenario entails a music recommendation process, where participants are asked to share information to online music vendors in exchange for personalized song recommendations. Different scenarios within this experiment will test participants' willingness to engage, exchange information, and share the experience with others.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T14:14:09Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
This scenario entails a music recommendation process, where participants are asked to share information to online music vendors in exchange for personalized song recommendations. Different scenarios within this experiment will test participants' willingness to engage, exchange information, and share the experience with others.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations
** Can the cash reward be exchanged for a music download (that possibly is of higher value) to test effectiveness of the recommendation and ultimately test if free participants provide information that results in better vendor offerings.

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T14:12:09Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
This scenario entails a music recommendation process, where participants are asked to share information to online music vendors in exchange for personalized song recommendations. Different scenarios within this experiment will test participants' willingness to engage, exchange information, and share the experience with others.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive too arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T14:11:36Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
This scenario entails a music recommendation process, where participants are asked to share information to online music vendors in exchange for personalized song recommendations. Different scenarios within this experiment will test participants' willingness to engage, exchange information, and share the experience with others.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics (e.g. age) that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T14:10:11Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
This scenario entails a music recommendation process, where participants are asked to share information to online music vendors in exchange for personalized song recommendations. Different scenarios within this experiment will test participants' willingness to engage, exchange information, and share the experience with others.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. listening, sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T14:09:17Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
This scenario entails a music recommendation process, where participants are asked to share information to online music vendors in exchange for personalized song recommendations. Different scenarios within this experiment will test participants' willingness to engage, exchange information, and share the experience with others.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T14:08:53Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
This scenario entails a music recommendation process, where participants are asked to share information to online music vendors in exchange for personalized song recommendations. Different scenarios within this experiment will test participants' willingness to engage with the experience, exchange information, and share the experience with others.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T14:08:27Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
This scenario entails a music recommendation process, where participants are asked to share information to online music vendors in exchange for personalized song recommendations. Different scenarios within this experiment will test participants willingness to engage with the experience, exchange information, and share the experience with others.
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** What types of data might free participants be more willing to provide?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T14:03:25Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor (user is not given a choice), and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T14:01:26Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.10)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor, and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T13:59:05Z

Khopper: /* Specific Research Proposals */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and Berkman-developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.20)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor, and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-10T02:41:05Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.20)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete (privacy policy available?). For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor, and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-09T21:17:38Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.20)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).
# The difference between free and captive experiences are as follows:
## Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete. A privacy policy is available that confirms this. For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
## Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor, and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-09T21:16:12Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.20)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete. A privacy policy is available that confirms this.
# For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
# Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by and for a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor, and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential issues / items to resolve:
** Is this a compelling enough experiment? Will we learn meaningful things? Are we really measuring FREE vs. CAPTIVE customer experiences?
*** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Can we collect information and disclose its purposes in such a way as to accurately leverage recommendation APIs and not be deceitful, yet still create a clear and compelling delineation between free and captive participation?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-09T21:07:53Z

Khopper: /* Proposed Experimental Scenario */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.20)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal demographic information (e.g. name, address, sex, age, etc.). All questions/fields are optional.
# Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete. A privacy policy is available that confirms this.
# For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
# Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor, and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential Issues:
** Is this a compelling enough experiment? Will we learn meaningful things?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

VRM Research opportunities

2009-12-09T21:06:53Z

Khopper: /* Specific Research Proposals */

== Project Overview ==
=== Objectives ===
Our primary goal is to test one or more basic VRM principles (e.g. benefits of vendor openness, willingness of users to pay for perceived value in the absence of existing payment mechanisms provided by the seller). Results of research efforts will guide the expression of VRM principles, and, presumably, drive their adoption.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research methodologies an dsoftware -- and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with a scenario that tests specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Jason Callina, Joe Andrieu, Tim Hwang and Aaron Shaw.

=== Proposed Experimental Scenario ===
# Participant selects the Amazon HIT and agrees to complete an associated online process in exchange for a small amount of money (e.g. $.20)
# Participant is randomly assigned to one of two groups (free or captive)
# Both groups are presented with an identical, multi-step information gathering process - specifically, to provide music preference information (e.g. favorite artists and tracks) along with personal information (e.g. name, address, sex, etc.). All questions/fields are optional.
# Before information gathering begins, the free group is informed that the information that they are being asked for is collected strictly for their own use, with the option to share it with one or more vendors once the process is complete. A privacy policy is available that confirms this.
# For each vendor the free participant chooses to share their data with, the vendor provides recommendations for artists and songs which they can choose to listen to or purchase (or ignore).
# Before information gathering begins, the captive group is informed that the information that they are being asked for is being collected by a single vendor for the purpose of providing music recommendations that will be presented for listening and purchase. Upon completion of the information gathering process, relevant data is automatically shared with the vendor, and artist and song recommendations are provided in return. The participant can choose to listen to or purchase these tracks.
# At the end of the information gathering process, both groups are informed they have completed the requirements to redeem their earnings. Additional steps taken at this point (e.g. sharing) are not required.
# Upon completion of the entire process, both groups are provided the option to share a link to this project with a friend (or on twitter, facebook, etc).

* Some aspects to test:
** Will free participants be more likely to complete the process than captive ones?
** Will free participants provide more data to more vendors than captive ones?
** Will free participants be more likely to share the experience with their friends?
** Will free participants be more likely to listen and purchase recommended songs?
** How will specifics of the experience and specific wording affect individual's willingness to participate?
** Are there certain individual characteristics that predict willingness to participate?

* Potential Issues:
** Is this a compelling enough experiment? Will we learn meaningful things?
** Is the fundamental recommendation experience valuable enough to encourage listening and purchasing of music tracks and ultimately, sharing with friends? Is there a way to make this more compelling?
** Is our definition of free and captive so arbitrary or context-specific as to lose experimental merit?
** How will we control for willingness to purchase/listen if one process potentially alters the quality of the music recommendations? Is this necessary to control for?
** Should captive participants also have the post-process option of sharing the data with multiple vendors?
** Should other free group options exist, such as the ability to download your entered information in a standard format or share your preferences and recommendations with a friend?
** How will participants be paid so as not to influence whether or not they choose to provide information (or alternatively, simply skip the process and collect their $.20)?
** What music recommendation APIs are available, what types of data do they require to generate quality recommendations (and is this standard)?
** How might trust issues with the data collector (i.e. Berkman) influence outcomes?
** How will the music services themselves (e.g. perceived brand trust and value) affect outcomes (and how might we control for this)?
** What are the experimental disclosure requirements here - esp as it relates to personal information gathering that likely won't be used to generate music recommendations

=== Additional Scenario Possibilities ===
'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios
* (NOTE: Can eyebrowse allow for non-sharing of data?)

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Sources/Background ==
* [http://www.goodreads.com/book/show/2527900.Nudge_Improving_Decisions_About_Health_Wealth_and_Happiness Nudge: Improving Decisions About Health, Wealth, and Happiness], by Richard H. Thaler, Cass R. Sunstein
* [http://swoopo.com Swoop.com], "entertainment shopping"

Research Meeting Notes

2009-12-09T20:07:33Z

Khopper: New page: == Notes from Meeting on 10/29 == * Before / after with VRM implementations * Self-tracking - what sorts of changes occur * Look at companies that are willing to share info and what change...

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-12-09T20:06:49Z

Khopper: Moving meeting notes to a new page

Information flow

2009-12-01T13:51:21Z

Khopper: Undo revision 4061 by Hiphopalemi (Talk)

As it emerged at the white board during a VRM session at [http://iiw.windley.com/wiki/Workshop2006b Internet Identity Workshop].

http://cyber.law.harvard.edu/projectvrm/images/c/ca/Vendor-relationship-management-flow.jpg

Some prose describing this diagram on [http://netmesh.info/jernst/Digital_Identity/doc-searls-vendor-relationship-management.html Johannes Ernst's Blog].

[http://zgp.org/~dmarti/blosxom/business/upside-down-bg.html Upside-down buyers' guide]: a simple flow for, for example, ordering servers, using an RFQ microformat and the existing Technorati tag system.

Taxonomy issues

2009-12-01T13:50:49Z

Khopper: Undo revision 4062 by Hiphopalemi (Talk)

Very much a stub...sorry for the current "stream of consciousness" character of this -- will be cleaning it all up as soon as possible.

There's <strong>a lot</strong> of work here. RFPs should allow for widely varying degrees of specificity. Ideally, a seeker should be able to create an RFP for:
<ul><li>a blackberry 7130c</li>
<li>a cell phone with bluetooth and EDGE support</li>
<li>a cell phone costing less than $250</li>
<li>a cell phone</li></ul>
Even before considering the different attributes required by different types of requests (travel planning vs. product purchase, for example) it's clear that creating a workable microformat for RFPs will be a fascinating (and frustrating) process. It's worth noting, though, that work in this area pays off in many ways.

Desirable: RFP attribute "package," optional, linking multiple RFPs. Indicator to vendors that the seeker is interested in the entire package (implicitly asserting that offers that cover the entire package are preferable/required? Or explicit flag?) -- plane ticket RFP, car rental RFP, hotel RFP, linked by package ID. Five different books, linked by package ID. Possibly worthwhile on both ends: seeker only wants A if they can also get B, vendor can tailor offer/discounts based on total package.

Note to self: Marti's "fulfillment" RFP attribute is an elegant addition. Does seem to suggest that authoritative, verifiable identity for vendors goes from "extremely useful" to "non-negotiable," though. Note also that it makes authoritative seeker identity even more significant: if seekers provide feedback that (is | may be) used to establish vendor reputation as an evaluation factor, there's significant incentive to astroturf.

Suggests the desirability of a parallel reputation system that covers both vendors and seekers. Consider how long an entity has had a public presence, RFP/feedback patterns. Unfortunately seems to also suggest that there could be an actual need for some third-part(y|ies), handling the seeker/vendor DB stuff outlined in the VRM diagram: in addition to pointers to requests/offers, the archive retains history. In this case I guess one would want as many competing versions as possible, so that clients can poll multiple and use their own systems to resolve differing data.

The Matrix (Blue Pill)

2009-12-01T13:50:01Z

Khopper: Undo revision 4063 by Hiphopalemi (Talk)

'''VRM Scenarios - "The Matrix (Blue Pill)"'''

http://www.socialcustomer.com/images/bluepill.jpg

Vendors control production, allocation and distribution, and at the same time understand that a connected customer is a lifetime customer. Supply chain models such as [http://www.inventoryops.com/ConsignmentInventory.htm vendor managed inventory and consignments] are used. The vendor controls what purchase options are given to the customer, and realizes that he must be equitable, or the customer will terminate the relationship. The vendor has perfect information on the behavior of his customers, including purchase history. Vendors use this information to continually refine and model the selection and quantity of goods and services made available to each customer to not only maximize profits, but also to ensure continued access to that customer. Customers select their vendors based on the belief that they will have an ongoing relationship with the vendors they choose, and give them feedback as to what they'd like to see.

Misc

2009-11-28T17:52:00Z

Khopper: Undo revision 3913 by KruGer (Talk)

== Categories of VRM data ==
What you've done, like, and want

Done: Transaction history. Your copy of your transaction/interaction record categorized

Like: Preferences. Where do you like to sit on a plane, car, color, and size

Dislike: Preferences, allergies, etc.

Want: Personal RFP. What things do you want to buy and how do you describe your need

Perhaps there is a service to intelligently inform one category from another. For instance, by looking at a transaction history you can deduce preferences and suggest wants. You always want to sit in the aisle seat and buy milk every week.

== VRM meets DRM ==
Perhaps VRM should stand for Vendor Rights Management as well as Vendor Relationship Management.

Perhaps rights management and relationship management should be the same thing.

Perhaps companies doing DRM wouldn't have to bear the full responsibility for managing the rights involved in relationships. Things could be done by mutual agreement by both sides.

We need to make "rights management" something that is mutual in operation, and mutually beneficial. How should we approach that?

User:Khopper

2009-11-28T17:51:13Z

Khopper: Undo revision 3910 by KruGer (Talk)

My [http://www.hopperanalytical.com/blog blog].

VRM Research opportunities

2009-11-05T03:04:54Z

Khopper: /* Specific Research Proposals */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles (i.e. vendor openness). Hopefully, results of a research effort can be used to make a case for adopting VRM principles and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang.

'''Scenario 1'''
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

'''Scenario 2'''
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

'''Scenario 3'''
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T03:04:28Z

Khopper: /* Specific Research Proposals */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles (i.e. vendor openness). Hopefully, results of a research effort can be used to make a case for adopting VRM principles and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''. Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 2
* Assign users to either the role of Vendor or Customer and pair them up. Customers gather music listening preferences and habits about themselves through either a user-driven, open tool and process or through a vendor-driven, choice-free process.
* The results of these processes are shared with their vendor partners who are asked to make a music download recommendation to their customer based on the information shared. The vendor receives a larger reward if the customer selects their recommended download over a (smaller) cash prize.
* This scenario goes beyond demonstrating increased sharing to test the idea that openness has the potential to generate less guesswork and increased sales for the vendor

Scenario 3
* Require AMT participants to use [http://eyebrowse.csail.mit.edu/ Eyebrowse] software to collect browser history data.
* Create two scenarios - one that puts the user in charge of sharing what/how/to whom and another where the data is uploaded to a commercial vendor as part of the HIT.
* Measure willingness of participants to complete the task and subsequently to upload their data for the two scenarios

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:48:27Z

Khopper: /* Objectives */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles (i.e. vendor openness). Hopefully, results of a research effort can be used to make a case for adopting VRM principles and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:46:47Z

Khopper: /* Testable Principles */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts,
* Development of an ecosystem of value around vendor services, creating the open version of customer lock-in
** e.g. Good services based on open transaction data encourage continued use of open transaction data provider

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:44:39Z

Khopper: /* Testable Principles */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill (longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts, thereby making the open vendor more attractive for plugging into this value ecosystem

'''Open Questions'''
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:43:24Z

Khopper: /* Testable Principles */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Decreased cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Decreased guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill(longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts, thereby making the open vendor more attractive for plugging into this value ecosystem

Open Questions
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:42:40Z

Khopper: /* Testable Principles */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor for freeing a customer?'''
* Eliminate cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Less guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill(longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts, thereby making the open vendor more attractive for plugging into this value ecosystem

Open Questions
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:41:55Z

Khopper: /* Testable Principles */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer-generated data must be portable
*** Customer-supplied data must be retractable
*** Customer-supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor to freeing a customer?'''
* Eliminate cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Less guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill(longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts, thereby making the open vendor more attractive for plugging into this value ecosystem

Open Questions
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:40:33Z

Khopper: /* Testable Principles */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily, that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer data must be portable
*** Customer supplied data must be retractable
*** Customer supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor to freeing a customer?'''
* Eliminate cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Less guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill(longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts, thereby making the open vendor more attractive for plugging into this value ecosystem

Open Questions
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:40:18Z

Khopper: /* Testable Principles */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

'''What characterizes a free customer?'''
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer data must be portable
*** Customer supplied data must be retractable
*** Customer supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

'''What are the potential benefits to a vendor to freeing a customer?'''
* Eliminate cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Less guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill(longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts, thereby making the open vendor more attractive for plugging into this value ecosystem

Open Questions
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:38:31Z

Khopper: /* Objectives */

== Project Overview ==
=== Objectives ===
Our primary goal here is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally at Berkman and related communities of interest, and externally with the public at large. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

What characterizes a free customer?
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer data must be portable
*** Customer supplied data must be retractable
*** Customer supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

What are the potential benefits to a vendor to freeing a customer?
* Eliminate cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Less guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill(longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts, thereby making the open vendor more attractive for plugging into this value ecosystem

Open Questions
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:37:06Z

Khopper: /* Testable Principles */

== Project Overview ==
=== Objectives ===
Our primary goal with a VRM research effort is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally and externally. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

What characterizes a free customer?
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer data must be portable
*** Customer supplied data must be retractable
*** Customer supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

What are the potential benefits to a vendor to freeing a customer?
* Eliminate cost/hassle of gathering, storing, and managing customer data where the customer is relying on their own tools
* Increased attention / visibility to vendor for being open, i.e. being the open alternative in the market
* Increased participation from customers wanting to engage with open businesses
** Both initial willingness and ongoing enagement
* Increased sharing / customer WOM around open products / services
* Increased volume and quality of customer-supplied data
* Less guesswork by the vendor if the customer is telling them exactly what the want when they want it - or at least more/better information about themselves
* Increased customer trust / loyalty / goodwill(longer term?)
* Increased external innovation and value being generated around vendor services / data
** e.g. if a vendor opens their transaction data, a 3rd-party service might help customers better manage their electronic receipts, thereby making the open vendor more attractive for plugging into this value ecosystem

Open Questions
* Similarity to "free culture" arguments, e.g. what are the benefits to CC Licensing. Prior research already done here?
* What aspects of the benefits above are perceptual vs. technical? How might we measure and test these?

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T02:21:41Z

Khopper: /* Project Overview */

== Project Overview ==
=== Objectives ===
Our primary goal with a VRM research effort is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally and externally. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
Generally speaking, VRM's vision is equip individuals with tools that make them independent leaders and not just captive followers in their relationships with vendors and other parties on the supply side of markets. VRM is successful when customers see direct benefits from taking control of their relationships, and vendors see alternatives to customer lock-in for gaining loyalty and generating profit.

This vision makes several assumptions. Primarily that '''a free customer is more valuable than a captive one.''' Testing this hypothesis (or more accurately, specific versions and aspects of this hypothesis) should be our primary goal. This hypothesis begs at least two important questions:

What characterizes a free customer?
* Able to choose how to relate to a vendor
** Customer relies on tools and data under their control to relate to and manage vendors
** Choose what information to share and when
** Choose how this information can be used (i.e. under what terms), for example:
*** Customer data must be portable
*** Customer supplied data must be retractable
*** Customer supplied data can't be used for targeted advertising / marketing messages
*** etc.
** Customer receives a copy of data that is provided or generated as part of doing business, e.g. transaction data
** Full disclosure on how customer supplied-data is being used (privacy policy)
** Options for terminating relationship at will and without penalty

What are the potential benefits for a vendor to freeing a customer?
*
*

Potential issues
*

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T01:51:32Z

Khopper:

== Project Overview ==
=== Objectives ===
Our primary goal with a VRM research effort is to complete some meaningful research within the 2009-2010 school year that builds visibility and credibility around Project VRM internally and externally. A research initiative should help test one or more basic VRM principles. Hopefully, results of a research effort can be used to make a case for adopting VRM principles of openness and be used to demonstrate the benefits of these principles to vendors and/or customers.

Additional benefits include bringing together passionate participants around a research project, demonstrating and furthering Berkman research software, and forcing some clarity and learning around testable characteristics of VRM.

=== Testable Principles ===
*
*

== Specific Research Proposals ==
Use mechanical Turk and internally developed web and measurement software tools for completing web-based, personal data-gathering scenarios - Doc Searls and Keith Hopper with help from Aaron Shaw, Jason Callina, Joe Andrieu, and Tim Hwang

Present users with different web-based scenarios that test specific aspects of the hypothesis that '''a free customer is worth more than a captive one'''
* Upon completion of a scenario, users will be receive a small amount of money (or free music download) in exchange for their effort.

Scenario 1
* A proposed web-based scenario begins by asking users to provide or produce meaningful personal data (e.g. music preferences, audio listening history, etc.). Each scenario represents three differing degrees of personal control over the experience and the data. At the end of each process, the user is presented with various options for sharing the data, using the data (for example, to deploy against a music recommendation API) and various commercial activities (e.g. music downloads).
* The specific hypotheses to be tested will be whether an individual is more willing to complete the data gathering effort and subsequently to engage with the system and with vendors if they have more personal control / autonomy over the data and experience

Scenario 2
* randomly assigning users to the role of Vendor or Customer

Scenario 3
* Eyebrowse

== Project Status ==
* Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
* Additional meetings (11/2, 11/3) with Keith Hopper and Jason Callina and Keith Hopper and Tim Hwang to discuss possible scenarios and where to seek additional advice/support
* There are clear benefits to producing research not only for the VRM community but also for the business community. Both Zeo and Personal Black Box (interestingly, both startup orgs) have expressed a strong interest in research that helps clarify and "prove" the benefits of vendors opening up control to the user.
* Specific research proposal is shaping up involving the use of Amazon Mechanical Turk and based on code and data acquisition mechanisms already constructed and tested by Berkman staff for other research projects (cooperation project). See [http://cyber.law.harvard.edu/projectvrm/VRM_Research_opportunities#Specific_Research_Proposals Specific Research Proposals].

== Notes from Meeting on 10/29 ==
* Before / after with VRM implementations
* Self-tracking - what sorts of changes occur
* Look at companies that are willing to share info and what changes this brings to them
* Game: profiles of companies (mirror existing companies) - users interacting and measuring
* User research around learning perceptions and actions
** How does behavior change when people are ignorant vs. once they understand
** Increase visibility around what users "give away" + then add-in control
* People's willingness to give data with control vs. without control
* Max - existing research: ranked shopping list based on privacy levels
** Can we use real money? Mechanical Turk?
* Testing what people say vs. what they do
* How do we do the "invention is the mother of necessity" aspect of VRM - can we test something that doesn't exist yet?
* Research Hypotheses:
** Will people be more willing to yield their information if they control it?
** How will people behave if we give them more control over/with their data
* Realworld retailer - at checkout, they're given some choices
** give up information in exchange
* How do we test removal of guesswork?
* Turk experiment with movie recommendations
* Test
** 1st group: You have money to buy some movies (simulated stores)
** 2nd group: Create basket and share with stores
* Education of customers is an important aspect of example
* Problem with secondary markets, e.g. users aggregating data without vendor buy-in
* LastFM, scrobbling test (e.g. what will people do with their data?)
** What would you pay per song? Then collect data and present it to them, will it change what the song is worth?
* Don't forget about selection bias with these types of experiments

== Notes from Workshop on 10/13 ==
=== Should be... ===
* testable, concrete, measurable
* of use for doc in his new book
* appease the berkman gods with productive research efforts
* relatively easy and completable with volunteers, internal resources and a limited time-frame
* provide businesses with fodder that they need to help make the case internally for opening up user control
** Ben from Zeo needs a list of benefits to openness to bring to his investors: We need to provide that!
** What are the benefits to vendors of VRM?
** What is user data control? Define.
** Relationships -> what are the vendor benefits?
*** eliminate guesswork

=== For example... ===
* Talk to organizations who have opened up and have them describe the http://cyber.law.harvard.edu/projectvrm/edit/VRM_Research_opportunitiesbenefits
** What's measurable here?
* Test the hypothesis: A free customer is worth more than a captive one
** "worth more" means defining customer value and measuring it
** is the customer valuing the vendor more?
** what is free vs. captive?
* before and after giving users their data
* altimeter group
** engagementdb.com: report that shows that those who do social media are most profitable
* case studies (e.g. HBS format)
* backward analysis?
* report on personal informatics
** what data comes out of personal informatics?
* Company experiences with launching api's?
* interviews, surveys
* What are some open data efforts?
** VRM spotting -> what are some user-driven organizations?
* What are possible frameworks / scenarios for measurement / testing / research
* What are the intention economy principles?

=== What is VRMness (user-drivenness?) that might be testable? ===
* individual is the POI
* individual gets a copy of their data
* individual controls use of data
* users initiate
* user contribution is a core value
* user choice
* belongs to user - user owns / controls the system
* service portability / substitutability

VRM Research opportunities

2009-11-05T01:49:43Z

Khopper: /* Specific Research Proposals */