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Social Networks, Personalized Advertising, and Privacy Controls

This paper investigates how internet users' perception of control over their personal information affects how likely they are to click on online advertising. The paper uses data from a randomized field experiment that examined the relative effectiveness of personalizing ad copy to mesh with existing personal information on a social networking website. The website gave users more control over their personally identifiable information in the middle of the field test. The website did not change how advertisers used anonymous data to target ads. After this policy change, users were twice as likely to click on personalized ads. There was no comparable change in the effectiveness of ads that did not signal that they used private information when targeting. The increase in effectiveness was larger for ads that used less commonly available private information to personalize their message. This suggests that giving users the perception of more control over their private information can be an effective strategy for advertising-supported websites.

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Paper provided by NET Institute in its series Working Papers with number 10-07.

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Length: 41 pages
Date of creation: Sep 2010
Date of revision:
Handle: RePEc:net:wpaper:1007
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References listed on IDEAS
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  1. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
  2. Avi Goldfarb & Catherine E. Tucker, 2011. "Privacy Regulation and Online Advertising," Management Science, INFORMS, vol. 57(1), pages 57-71, January.
  3. repec:tpr:qjecon:v:119:y:2004:i:1:p:249-275 is not listed on IDEAS
  4. Kihlstrom, Richard E & Riordan, Michael H, 1984. "Advertising as a Signal," Journal of Political Economy, University of Chicago Press, vol. 92(3), pages 427-50, June.
  5. Bharat Anand & Ron Shachar, 2009. "Targeted advertising as a signal," Quantitative Marketing and Economics, Springer, vol. 7(3), pages 237-266, September.
  6. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2002. "How Much Should We Trust Differences-in-Differences Estimates?," NBER Working Papers 8841, National Bureau of Economic Research, Inc.
  7. Meghan Busse & Jorge Silva-Risso & Florian Zettelmeyer, 2006. "$1,000 Cash Back: The Pass-Through of Auto Manufacturer Promotions," American Economic Review, American Economic Association, vol. 96(4), pages 1253-1270, September.
  8. McAfee, R Preston & McMillan, John, 1987. "Auctions and Bidding," Journal of Economic Literature, American Economic Association, vol. 25(2), pages 699-738, June.
  9. N. Lesca, 2010. "Introduction," Post-Print halshs-00640602, HAL.
  10. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
  11. Puneet Manchanda & Ying Xie & Nara Youn, 2008. "The Role of Targeted Communication and Contagion in Product Adoption," Marketing Science, INFORMS, vol. 27(6), pages 961-976, 11-12.
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