1. Who’s watching you? Power, personalization and on-line compliance. Adam Joinson Institute of Educational Technology, The Open University Acknowledgements: Ulf Reips, Alan Woodley, Tom Buchanan. CSI: Miami

2. “You have zero privacy anyway... Get over it” (Scott McNealy, CEO, SUN Microsystems, 1999) What are the main threats to privacy? –Personalization, commodification, ubiquity, data-mining Implications for online behaviour, specifically compliance to survey requests

3. Personalization and privacy Personalized online experiences have some (questionable) benefits – e.g. special offers, e-learning profiles But, vast quantities of personal data collected in the name of personalization Used extensively in survey methodology –Difficult to personalize and maintain privacy..

5. Commodification How much is your personal data worth? –Safety from terror attack? –Entry to the USA? –Access to the NY Times? –Access to community? Expressive vs. Informational privacy (DeCew, 1999) –$2.50 reduction in shopping? Generally, we undervalue our personal information – 70% will give it away for a raffle.

7. Ubiquity Always on networks (phone, WLANs, bluetooth) RFID tags (products, passports) Camera phones Identity theft and authentication –Biometrics, live databases –Number plate recognition Ubicomp aims to make life easier … which means seamless authentication.

9. Data-mining Storage is cheap How long are the following kept for? –Your Amazon purchase history? –Your google searches? –Your fingerprint, photo and personal information on entry to the US? –(ever, until 2038, 75-100 years). Ever more sophisticated software Amazon – profiling at who you send gifts to. Social software, meta-tagging and semantic web pose new issues

12. Privacy and online behavior Internet solves the privacy- intimacy paradox (Ben Ze’ev, 2003) –Require privacy for intimacy, intimacy reduces privacy DeCew (1998): Expressive vs. Informational privacy –More useful to talk about pseudonmyity? But, it’s not clear exactly how this will influence behaviour –Buying embarrassing items online vs. in a shop –Rejection risk and e-mail vs. a quick chat (Joinson, 2004) –Why leave a data-trail? Examining the micro-environment of surveys.

13. Surveys, privacy and social influence Surveys pose a privacy challenge for respondents –Most surveys stress anonymity / confidentiality – for this reason. –Technological mediation and improved responses (Tourangeau, 2004) Personalization and status used widely to improve falling response rates (Dillman, 1992; 2000; Tourangeau, 2004) –But both fairly unstable predictors. –Reasons for this variability unknown.. –Status usually confounded with power –Anonymity reduced? (Andreasen, 1970) Powerful audience + identifiability to out-group = –Suppression of in-group norms (Reicher & Levine, 1994) –Compliance / conformity to request? –Resistance? (Levine, 2000) – by exaggerating non-punishable behaviours.

14. Overview of experiments PRESTO –Mass personalized e-mailing –Automated web surveys –Automated reporting Used for institutional research with panels –Open University students recruited Studying personalization, privacy, disclosure, power and response rates This paper: Initial 7 experiments. Manipulation checks difficult (but, new panel recruited to allow)

15. Experiment 1 Stratified sample of 10,000 OU students (Panel 2) E-mail signed by vice-chancellor Four salutations used Signing onto panel = dependent variable Overall sign-up rate: 15.5% (22% Panel 1)

16. Experiment 1: Results –Dear Student31713.9% –Dear OU Student31613.7% –Dear John Doe37816.4% –Dear John41818.2% Chi-square = 24.39, df (3), p <.000. Odds-ratios: Dear John vs. Dear Student (1.4, p <.001). Personalized salutation increased odds of response by almost 40%

17. Overall model Logistic regression Age, salutation, loyalty, gender Overall model: Chi-Square = 199.96 (df = 6), p <.001 –Age (Wald = 105.3, p <.000) –Salutation (Wald = 27.53, p <.000) –Gender (Wald = 19.58, p < 0.00) –Loyalty (Wald = 13.27, p <.000)

18. Experiment 2 Staff survey – Professors – Cleaners. 4226 e-mail invitations sent February – March 2005. 74% Response Rate (from two reminders). Signed by pro-vice chancellor Quite a personalization effect…

19. Experiment 2: Results Dear John: 1738 (82% RR) Dear Colleague: 1391 (66% RR) –Effect across staff categories Reminder manipulation: ‘1700’ vs. ‘some’ –No impact of personal responsibility (some responses vs. over 1700 responses). –Salutation effect remained.

20. Why the strong effect? Mindless action in response to personal salutation? Reciprocal response to effort? Increased sense of responsibility / ‘specialness’? Strategic response? More likely to be read?

21. Experiment Three: Tested mindless / likely to read hypotheses Used Panel 1 (N = 2247). Recruited late 2002 A reverse replication of experiment one: –Email from vice-chancellor (same word count, sign-off etc) –Asking if they want to leave the panel –Same web form as signing on, but to sign off Salutation manipulated as per experiment one

22. Experiment Three: Results

23. Experiment three: Analyses Personalized vs. non-personalized combined Odds ratio = 1.416 (Chi-Square = 2.93, p = 0.05). Logistic regression: salutation (p = 0.06), age (ns) and gender (ns). Overall model: ns (p =.11)

24. Power? Personalized salutation might serve to reduce sense of privacy / anonymity (Andreason, 1970) Power / status may be crucial when anonymity removed (Levine, 2000) When identifiability is combined with high power / status (and possible sanction), then there is a strong incentive to comply.

25. For instance… Sender: Professor Fred Perry, President To: A.N.Joinson Date: 6th Feb 2004 Dear Adam, I’m writing to remind you to complete the latest version of the staff survey. The original invitation was sent two weeks ago. Professor Fred Perry, President, The Open University

26. As opposed to… Sender: F.Perry To: A.N.Joinson Date: 6th Feb 2004 Dear colleague, I’m writing to remind you to complete the latest version of the staff survey. The original invitation was sent two weeks ago. Fred Perry, The Open University

27. Experiment 5 Another panel (n=2137) Salutation manipulated (as per usual) Power / status manipulated Invitation signed by pro-vice chancellor: either – (Strategy and Planning) The Open University –Professor Pro-vice chancellor, (Strategy and Planning), The Open University Manipulation placed at top and bottom of e-mail body (but not e-mail address).

28. Experiment 5: Results (response rates)

30. Experiment 5: Analyses Salutation (Chi-Square (df = 2) = 8.92, p <.02) Power (Chi-Square (df =1) = 1.25, p =.23, ns). Logistic regression (Method: Stepwise Forward Conditional). DV = response rate –Power*Salutation interaction significant (SE = 0.27, Wald = 9.3, df = 1, p<.01) Effect size of salutation by power: –high power (X = 10.97 (df = 2), Contingency co-efficient = 0.10, p <.01) – low power (X = 3.24 (df = 2), Contingency co-efficient = 0.05, p =.2, ns).

31. Experiments 6 and 7 Examine the impact of different personalization techniques on self-disclosure to a sensitive question Two types of non-disclosure: Good (I don’t want to say) vs Bad (Submission of no response) Study 6: Power and Personalized salutation Study 7: Personalized URL vs. Lon-on procedure

32. Experiment 6 Another panel (combined 3544 people) 1,617 (45.6%) response rate 2 x 2 design –Power of sponsor (high vs. neutral) In e-mail invitation and front sheet Title and job given (or not) –Salutation (Dear John vs. Presto panel member) DV - Salary disclosure and response rates.

33. Response Rates (raw & %) Power: (  2 = 2.71, df = 1, p =.053, Odds Ratio = 1.12) Salutation: (  2 = 0.96, df = 1, p =.17, ns, Odds Ratio = 1.07). Salutation / High power: (  2 = 2.18, df = 1, p =.077, Odds Ratio = 1.15). Salutation / Low power: (  2 = 0.01, df = 1, p >.9, Odds Ratio = 0.99).

34. Non-disclosure and salutation Salutation: (  2 = 4.47, df = 2, p = 0.05) Power: (  2 = 2.80, df = 2, p = 0.10)

35. Summary Personalized salutation increases response rates when combined with high power This has two effects –Increased ‘good’ non-disclosure via ‘don’t want to say’ –Reduced ‘bad’ non-disclosure via submission of no option Difference is the type of non-disclosure, not non- disclosure itself Evidence of a privacy bind?

36. Privacy bind? Completion of surveys poses a privacy problem for people when it’s sensitive information Use of anonymity mitigates this, but also enables ‘poor responding’ (via skipped questions) When it’s a powerful source, and personalized salutation, the participant is in a bind between compliance and privacy concerns This doesn’t exist for non-personalized, low power senders. The provision of ‘don’t want to say’ options provides a way out of this bind…compliant resistance?

37. Experiment 7 Question: Confirming the role of identifiability? Authentication method (Log-on vs. Encoded URL) Presto panel (1144 people) 633 women and 507 men (data missing for 4 participants), Mean age of 43.6 years (SD = 10.44) DV - Salary disclosure.

38. Disclosure Disclosed salary‘I prefer not to say’ Encoded URL587 (97.0%)18 (3.0%) Log-on488 (94.4%)29 (5.6%) (  2 = 4.18, df = 1, p =.041, Odds Ratio = 1.94).

39. General Summary Personalization removes anonymity –Compliance behavior in surveys –Reduced disclosure (in a nice way), greater social desirability Loss of informational privacy also reduces expressive privacy –Unless the informational privacy resides elsewhere? –Pseudonymity rather than anonymity (Identity dissimulation)? The issue is who is watching – power critical, not just removal of anonymity –And inevitably, trust.

40. Thank you! adam@joinson.com http://www.joinson.comhttp://www.joinson.com(slides next week) http://www.prisd.nethttp://www.prisd.net (privacy project)