VRM Research opportunities

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

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

Specific Research Proposals

1. Mechanical Turk for completing web-based, personal data-gathering scenarios

• Randomly present web users with ~3 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.
• 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 radically 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
• The research project is currently being run out of Harvard's Berkman Center, led by Doc Searls and Keith Hopper with help from
  • Aaron Shaw: Experimental Design
  • Jason Callina: Scenario Construction, measurement
  • Joe Andrieu: VRM / User-driven expertise
  • You!

Project Status

10/31/2009

• Meeting with geeks on 10/29 produced some rough research directions and commitment from Berkman staffers to helping execute
• 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 to 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 Specific Research Proposals above.