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Go With the Flow: Effects of Transparency and User Control on Targeted Advertising Using Flow Charts

Targeted advertising reaches users based on various traits,
such as demographics or behaviour. However, users are often
reluctant to accept ads. We hypothesise that users are
more open to targeted advertising if they can inspect, control
and thereby understand the process of ad selection. We
conducted a between-subjects study (N=200) to investigate
to what extent four key aspects of ads (Quality, Behavioural
Intention, Understanding and Attitude) may be affected by
transparency and user control using a flow chart. Our results
indicate that positive effects of flow charts reported from
other domains may also be applicable to advertising: Using
flow charts to provide transparency together with user control
is found to have more positive e ffects on domain-specfi c
quality measures than established, text-based approaches
and using either of the techniques in isolation. The paper
concludes with recommendations for practitioners aiming to
improve user response to ads.

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Go With the Flow: Effects of Transparency and User Control on Targeted Advertising Using Flow Charts

  1. 1. Go With the Flow: Effects of Transparency and User Control on Targeted Advertising Using Flow Charts Yucheng Jin, Karsten Seipp, Erik Duval✝, Kartrien Verbert Augment group HCI @ KU Leuven 9 June 2016
  2. 2. http://www.gazmuth2.com/wp-content/uploads/2015/06/Denver-City-Council-No-Advertising-Ban-Please.jpg 1
  3. 3. http://zeendo.com/info/wp-content/uploads/2013/02/ch1.png 2
  4. 4. 3
  5. 5. http://www.xmlgrrl.com/blog/2008/05/11/practical-human-centering-and-vrm/ http://cyber.law.harvard.edu/projectvrm/Main_Page 4 • manage relationships with organizations • share data selectively • control how their data is used (Searls, 2006)
  6. 6. 5 Related work
  7. 7. 6 importance and potential benefits of transparency (TR) and user control (UC) for targeted ads. K. O'Donnell and H. Cramer. People's perceptions of personalized ads. In Proc. WWW '15 Companion, pages 1293-1298. WWW Steering Committee, 2015. B. Ur, P. G. Leon, L. F. Cranor, R. Shay, et al. Smart, useful, scary, creepy: Perceptions of online behavioral advertising. In Proc. SOUPS '12, pages 4:1-4:15. ACM, 2012. L. F. Cranor. Can users control online behavioral advertising effectively? Security & Privacy, IEEE,10(2):93-96, 2012. • Transparency and user control
  8. 8. https://www.facebook.com/business/products/ads 7
  9. 9. https://www.facebook.com/business/products/ads 8
  10. 10. Positive effects of transparency facilities on trust, agreement, satisfaction and acceptance of E-Commerce recommendations. (Gregor, 1999; Wang, 2007) 9
  11. 11. • Visualizing recommender systems Talk Explorer (Verbert, 2013) 10
  12. 12. TasteWeights (Bostandjiev,2012) 11
  13. 13. 12 System Design
  14. 14. 13 • simple • visual representation • intuitive
  15. 15. 14 FLINT (Crews, 1998) RetroGuide (Huser, 2010) • Applications of flowchart
  16. 16. Transparency User control 15
  17. 17. 16
  18. 18. 17 User profile Flow of selection Control panel
  19. 19. 18 Evaluation
  20. 20. We conducted a between-subjects study on Amazon Mechanical Turk (MTurk). - 200 subjects - $1 for each study - average time 11 minutes. We created four experimental conditions: - Condition 1 (C1): (No-TR & No-UC) base condition - Condition 2 (C2): (TR & No-UC). - Condition 3 (C3): (No-TR & UC). - Condition 4 (C4): (TR & UC) 19
  21. 21. 20 C1: (No-TR & No-UC) base condition C2: (TR & No-UC) C3: (No-TR & UC) C4: (TR & UC)
  22. 22. • Subjects ~80% subjects noticed online targeted ads. ~10% subjects configured targeted ads. 21
  23. 23. Pu, Pearl, Li Chen, and Rong Hu. "A user-centric evaluation framework for recommender systems." Proceedings of the fifth ACM conference on Recommender systems. ACM, 2011. • Materials We used ResQue and tailored the questionnaire to evaluate four aspects of targeted advertisement: - Quality - Behavioral intention - Understanding - Attitude Log file 22
  24. 24. 5-point Likert scale, Strongly agree - Strongly disagree Quality Behavioral intention Understanding Attitude 23
  25. 25. • Evaluation steps 1. Introduce web app to subjects 2. Log in to the app with their Facebook accounts. 3. During the trailer, subjects can rate the ads and configure ads if they wish. 4. After the trailer, subjects were asked to complete the questionnaire. 24
  26. 26. 25 Results and discussion
  27. 27. • Quality 26 (H=14.49, df=3, p=.002) C4 > C1 - Interest match: TR & UC - Context match: limited - Attractiveness: limited - Annoyance: TR / UC, TR & UC
  28. 28. • Behavioral intention 27 (H=11.42, df=3, p=.01) C4 > C1 (H=11.74, df=3, p=.008) C4 > C1; C2 > C1 - Willingness to click: TR & UC (Log file: 61% subjects click the ads) - Willingness to purchase: limited (personal and motivational aspects) - Willingness to see: TR , TR & UC
  29. 29. • Understanding 28 (H=13.68, df=3, p=.003) C4 > C1; C3 > C1 - Understanding: TR / UC, TR & UC
  30. 30. • Quality 29 - Satisfaction: limited (privacy) - Confidence: limited - Trust: limited (company credibility and company trust) P42 said that “personalized ads make me feel like spying or a violation of my privacy.” R. E. Goldsmith, B. A. Laerty, and S. J. Newell. The impact of corporate credibility and celebrity credibility on consumer reaction to advertisements and brands. Journal of Advertising, 29(3):43{54, 2000
  31. 31. 30 Conclusion
  32. 32. - first implementation of flow charts for targeted ads - new insights in TR and UC for ads o Providing only TR improves a user's Behavioral Intention o Providing only UC improves a user's Understanding of the ad selection process o Providing both TR and UC improves the aspects Quality, Behavioral Intention, and Understanding o Attitude does not appear to be affected by either approach. 31
  33. 33. • Limitation - Studies conducted via MTurk may suffer from inattentive or “gaming" users. - A small size of data set of ads. 70 elements of 7 ad categories. A. Kittur, E. H. Chi, and B. Suh. Crowdsourcing user studies with mechanical turk. In Proc. CHI '08, pages 453{456. ACM, 2008. 32
  34. 34. Thank you for your attention. Yucheng Jin yucheng.jin@cs.kuleuven.be Questions? IWT (IWT-SBO-Nr. 110067).

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  • hsuanyeelee

    Mar. 31, 2017

Targeted advertising reaches users based on various traits, such as demographics or behaviour. However, users are often reluctant to accept ads. We hypothesise that users are more open to targeted advertising if they can inspect, control and thereby understand the process of ad selection. We conducted a between-subjects study (N=200) to investigate to what extent four key aspects of ads (Quality, Behavioural Intention, Understanding and Attitude) may be affected by transparency and user control using a flow chart. Our results indicate that positive effects of flow charts reported from other domains may also be applicable to advertising: Using flow charts to provide transparency together with user control is found to have more positive e ffects on domain-specfi c quality measures than established, text-based approaches and using either of the techniques in isolation. The paper concludes with recommendations for practitioners aiming to improve user response to ads.

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