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 effects 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.

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. http://www.gazmuth2.com/wp-content/uploads/2015/06/Denver-City-Council-No-Advertising-Ban-Please.jpg
1

3. http://zeendo.com/info/wp-content/uploads/2013/02/ch1.png
2

4. 3

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. 5
Related work

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. https://www.facebook.com/business/products/ads
7

9. https://www.facebook.com/business/products/ads
8

10. Positive effects of transparency facilities on trust,
agreement, satisfaction and acceptance of E-Commerce
recommendations.
(Gregor, 1999; Wang, 2007)
9

11. • Visualizing recommender systems
Talk Explorer (Verbert, 2013)
10

12. TasteWeights (Bostandjiev,2012)
11

13. 12
System Design

14. 13
• simple
• visual representation
• intuitive

15. 14
FLINT (Crews, 1998) RetroGuide (Huser, 2010)
• Applications of flowchart

16. Transparency
User control
15

17. 16

18. 17
User profile
Flow of selection
Control panel

19. 18
Evaluation

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. 20
C1: (No-TR & No-UC) base condition C2: (TR & No-UC)
C3: (No-TR & UC) C4: (TR & UC)

22. • Subjects
~80% subjects noticed online targeted ads.
~10% subjects configured targeted ads.
21

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. 5-point Likert scale, Strongly agree - Strongly disagree
Quality
Behavioral
intention
Understanding
Attitude
23

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. 25
Results and
discussion

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. • 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. • Understanding
28
(H=13.68, df=3, p=.003)
C4 > C1; C3 > C1
- Understanding: TR / UC, TR & UC

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. 30
Conclusion

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. • 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. Thank you for your attention.
Yucheng Jin
yucheng.jin@cs.kuleuven.be
Questions?
IWT (IWT-SBO-Nr. 110067).