research-article

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

Authors:

Katrien VerbertAuthors Info & Claims

AVI '16: Proceedings of the International Working Conference on Advanced Visual Interfaces

Pages 68 - 75

https://doi.org/10.1145/2909132.2909269

Published: 07 June 2016 Publication History

Abstract

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-specific 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.

References

[1]

About facebook advertising. https://www.facebook.com/about/ads/. Accessed: 2015-10-05.

[2]

Iab internet advertising revenue report. http://www.iab.net/about_the_iab/recent_press_releases/press_release_archive/press_release/pr-061115. Accessed: 2015-09-04.

[3]

The ibm watson personality insights service. https://watson-pi-demo.mybluemix.net/. Accessed: 2015-09-08.

[4]

L. Agarwal, N. Shrivastava, S. Jaiswal, and S. Panjwani. Do not embarrass: Re-examining user concerns for online tracking and advertising. In Proc. SOUPS '13, pages 8:1--8:13. ACM, 2013.

Digital Library

[5]

H. Badenes, M. N. Bengualid, J. Chen, L. Gou, et al. System u: automatically deriving personality traits from social media for people recommendation. In Proc. RecSys '14, pages 373--374. ACM, 2014.

Digital Library

[6]

F. Bakalov, M.-J. Meurs, B. König-Ries, B. Sateli, et al. An approach to controlling user models and personalization effects in recommender systems. In Proc. IUI '13, pages 49--56. ACM, 2013.

Digital Library

[7]

H. Beales. The value of behavioral targeting. Network Advertising Initiative, 2010.

[8]

D. Bogdanov, M. Haro, F. Fuhrmann, A. Xambó, et al. Semantic audio content-based music recommendation and visualization based on user preference examples. Inform. Proc. & Management, 49(1):13--33, 2013.

Digital Library

[9]

S. Bostandjiev, J. O'Donovan, and T. Höllerer. Tasteweights: A visual interactive hybrid recommender system. In Proc. RecSys '15, pages 35--42. ACM, 2012.

Digital Library

[10]

M. Burke, A. Hornof, E. Nilsen, and N. Gorman. High-cost banner blindness: Ads increase perceived workload, hinder visual search, and are forgotten. ACM TOCHI, 12(4):423--445, 2005.

Digital Library

[11]

D. E. Campbell and R. T. Wright. Shut-up i don't care: Understanding the role of relevance and interactivity on customer attitudes toward repetitive online advertising. Journal of E-commerce Research, 9(1):62--76, 2008.

[12]

H. Cramer. Effects of ad quality & content-relevance on perceived content quality. In Proc. CHI '15, pages 2231--2234. ACM, 2015.

Digital Library

[13]

L. F. Cranor. Can users control online behavioral advertising effectively? Security & Privacy, IEEE, 10(2):93--96, 2012.

Digital Library

[14]

T. Crews and U. Ziegler. The flowchart interpreter for introductory programming courses. In Frontiers in Education Conference, 1998. FIE'98. 28th Annual, volume 1, pages 307--312. IEEE, 1998.

Digital Library

[15]

C. A. Eldering. Advertisement selection system supporting discretionary target market characteristics, Apr. 10 2001. US Patent 6,216,129.

[16]

A. Farahat and M. C. Bailey. How effective is targeted advertising? In Proc. WWW '12, pages 111--120. ACM, 2012.

Digital Library

[17]

R. E. Goldsmith, B. A. Lafferty, 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.

[18]

D. G. Goldstein, R. P. McAfee, and S. Suri. The cost of annoying ads. In Proc WWW '13, pages 459--470, 2013.

Digital Library

[19]

J. B. Hirsh, S. K. Kang, and G. V. Bodenhausen. Personalized persuasion tailoring persuasive appeals to recipients' personality traits. Psychological science, 23(6):578--581, 2012.

[20]

S. B. Insights. Us framework and vals types, 2013.

[21]

J. J. Kacen and J. A. Lee. The influence of culture on consumer impulsive buying behavior. Journal of consumer psychology, 12(2):163--176, 2002.

[22]

W. Kamarulzaman and M. S. Nordin. Confirmatory factor analysis on the big 5 personality test inventory. Online Submission, 2012.

[23]

A. Kangasrääsiö, D. Glowacka, and S. Kaski. Improving controllability and predictability of interactive recommendation interfaces for exploratory search. In Proc. IUI '15, pages 247--251. ACM, 2015.

Digital Library

[24]

A. Kardan and M. Hooman. Targeted advertisement in social networks using recommender systems. In Proc. ECDC '13, pages 1--13. IEEE, 2013.

[25]

J. Kennedy, M. Graham, T. Paterson, and A. Law. Visual cleaning of genotype data. In Proc. BioVis '13, pages 105--112. IEEE, 2013.

[26]

A. Kittur, E. H. Chi, and B. Suh. Crowdsourcing user studies with mechanical turk. In Proc. CHI '08, pages 453--456. ACM, 2008.

Digital Library

[27]

P. J. Lavrakas. An evaluation of methods used to assess the effectiveness of advertising on the internet. Interactive Advertising Bureau Research Papers, 2010.

[28]

P. G. Leon, B. Ur, Y. Wang, M. Sleeper, et al. What matters to users?: Factors that affect users' willingness to share information with online advertisers. In Proc SOUPS '13, pages 7:1--7:12. ACM, 2013.

Digital Library

[29]

B. Liu, A. Sheth, U. Weinsberg, J. Chandrashekar, et al. Adreveal: Improving transparency into online targeted advertising. In Proc. HotNets '13, page 12. ACM, 2013.

Digital Library

[30]

M. Malheiros, C. Jennett, S. Patel, S. Brostoff, et al. Too close for comfort: A study of the effectiveness and acceptability of rich-media personalized advertising. In Proc. CHI '12, pages 579--588. ACM, 2012.

Digital Library

[31]

G. S. K. Niazi, J. Siddiqui, B. Alishah, and A. I. Hunjra. Effective advertising and its influence on consumer buying behavior. Information management and business review, 4(3):114--119, 2012.

[32]

K. O'Donnell and H. Cramer. People's perceptions of personalized ads. In Proc. WWW '15 Companion, pages 1293--1298. WWW Steering Committee, 2015.

Digital Library

[33]

J. O'Donovan, B. Smyth, B. Gretarsson, S. Bostandjiev, et al. Peerchooser: Visual interactive recommendation. In Proc. CHI '08, pages 1085--1088. ACM, 2008.

Digital Library

[34]

P. Pu, L. Chen, and R. Hu. A user-centric evaluation framework for recommender systems. In Proc. RecSys '11, pages 157--164. ACM, 2011.

Digital Library

[35]

M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: Estimating the click-through rate for new ads. In Proc. ACM'07, pages 521--530. ACM.

Digital Library

[36]

D. Searls. Project vrm. http://cyber.law.harvard.edu/projectvrm/Main_Page/. Accessed: 2015-09-04.

[37]

J. Stávková, L. Stejskal, and Z. Toufarová. Factors influencing consumer behaviour. ZEMEDELSKA EKONOMIKA-PRAHA-, 54(6):276, 2008.

[38]

V. Toubiana, A. Narayanan, D. Boneh, H. Nissenbaum, et al. Adnostic: Privacy preserving targeted advertising. In Proc. NDSS '11, 2010.

[39]

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.

Digital Library

[40]

K. Van Heel, B. Knegtering, and A. Brombacher. Safety lifecycle management. a flowchart presentation of the iec 61508 overall safety lifecycle model. Quality and Reliability Engineering International, 15(6):493--500, 1999.

[41]

K. Verbert, D. Parra, P. Brusilovsky, and E. Duval. Visualizing recommendations to support exploration, transparency and controllability. In Proc. IUI '13, pages 351--362. ACM, 2013.

Digital Library

[42]

W. Wang and I. Benbasat. Recommendation agents for electronic commerce: Effects of explanation facilities on trusting beliefs. Journal of Management Information Systems, 23(4):217--246, 2007.

Digital Library

[43]

X. Wu, J. Yan, N. Liu, S. Yan, et al. Probabilistic latent semantic user segmentation for behavioral targeted advertising. In Proc. DDAIA, pages 10--17. ACM, 2009.

Digital Library

[44]

J. Yan, N. Liu, G. Wang, W. Zhang, et al. How much can behavioral targeting help online advertising? In Proc. WWW '09, pages 261--270. ACM, 2009.

Digital Library

[45]

J. H. Yu and B. Cude. "hello, mrs. sarah jones! we recommend this product!"consumers' perceptions about personalized advertising: comparisons across advertisements delivered via three different types of media. International journal of consumer studies, 33(4):503--514, 2009.

[46]

P. W. Yue and R. Anderson. Capturing electronic journals management in a flowchart. The Serials Librarian, 51(3-4):101--118, 2007.

Cited By

Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Medert FOtto JPerczel L(2024)Enhancing Transparency of Political Micro-targeting on FacebookCode and Conscience10.1007/978-3-031-52082-2_4(47-62)Online publication date: 25-Oct-2024
https://doi.org/10.1007/978-3-031-52082-2_4
Guesmi MChatti MJoarder SAin QSiepmann CGhanbarzadeh HAlatrash R(2023)Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender SystemInformation10.3390/info1407040114:7(401)Online publication date: 14-Jul-2023
https://doi.org/10.3390/info14070401
Show More Cited By

Recommendations

Is Combining Contextual and Behavioral Targeting Strategies Effective in Online Advertising?

Online targeting has been increasingly used to deliver ads to consumers. But discovering how to target the most valuable web visitors and generate a high response rate is still a challenge for advertising intermediaries and advertisers. The purpose of ...
An economic analysis of online advertising using behavioral targeting

Online publishers and advertisers have recently shown increasing interest in using targeted advertising online. Such targeting allows them to present users with advertisements that are a better match, based on their past browsing and search behavior and ...
An introduction to online targeted advertising: principles, implementation, controversies
IUI '11: Proceedings of the 16th international conference on Intelligent user interfaces

Online user interaction is becoming increasingly personalized both via explicit means: customizations, options, add-ons, skins, apps, etc. and via implicit means, that is, deep data mining of user activities that allows automated selection of content ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

AVI '16: Proceedings of the International Working Conference on Advanced Visual Interfaces

June 2016

400 pages

ISBN:9781450341318

DOI:10.1145/2909132

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Agentschap voor Innovatie door Wetenschap en Technologie

Conference

AVI '16

Sponsor:

AVI '16: International Working Conference on Advanced Visual Interfaces

June 7 - 10, 2016

Bari, Italy

Acceptance Rates

AVI '16 Paper Acceptance Rate 20 of 96 submissions, 21%;

Overall Acceptance Rate 128 of 490 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

25
Total Citations
View Citations
570
Total Downloads

Downloads (Last 12 months)41
Downloads (Last 6 weeks)2

Reflects downloads up to 25 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Chatti MGuesmi MMuslim A(2024)Visualization for Recommendation Explainability: A Survey and New PerspectivesACM Transactions on Interactive Intelligent Systems10.1145/367227614:3(1-40)Online publication date: 11-Jun-2024
https://dl.acm.org/doi/10.1145/3672276
Medert FOtto JPerczel L(2024)Enhancing Transparency of Political Micro-targeting on FacebookCode and Conscience10.1007/978-3-031-52082-2_4(47-62)Online publication date: 25-Oct-2024
https://doi.org/10.1007/978-3-031-52082-2_4
Guesmi MChatti MJoarder SAin QSiepmann CGhanbarzadeh HAlatrash R(2023)Justification vs. Transparency: Why and How Visual Explanations in a Scientific Literature Recommender SystemInformation10.3390/info1407040114:7(401)Online publication date: 14-Jul-2023
https://doi.org/10.3390/info14070401
Jin YChen LCai WZhao X(2023)CRS-Que: A User-centric Evaluation Framework for Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/36315342:1(1-34)Online publication date: 2-Nov-2023
https://dl.acm.org/doi/10.1145/3631534
van Kuijk KMahmoudi SWen YBarile FRienstra T(2023)An Argumentative Framework for Generating Explainable Group RecommendationsAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597387(266-274)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597387
Im JWang RLyu WCook NHabib HCranor LBanovic NSchaub F(2023)Less is Not More: Improving Findability and Actionability of Privacy Controls for Online Behavioral AdvertisingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580773(1-33)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580773
Guesmi MChatti MTayyar AAin QJoarder S(2022)Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design ApproachMultimodal Technologies and Interaction10.3390/mti60600426:6(42)Online publication date: 30-May-2022
https://doi.org/10.3390/mti6060042
Alvarado OHtun NJin YVerbert K(2022)A Systematic Review of Interaction Design Strategies for Group Recommendation SystemsProceedings of the ACM on Human-Computer Interaction10.1145/35551616:CSCW2(1-51)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555161
Freller LKeck MNeumayr TAugstein MBottoni PPanizzi E(2022)Towards a Construction Kit for Visual Recommender SystemsProceedings of the 2022 International Conference on Advanced Visual Interfaces10.1145/3531073.3534484(1-3)Online publication date: 6-Jun-2022
https://dl.acm.org/doi/10.1145/3531073.3534484
Guesmi MChatti MVorgerd LNgo TJoarder SAin QMuslim ABellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Explaining User Models with Different Levels of Detail for Transparent Recommendation: A User StudyAdjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3511047.3537685(175-183)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3511047.3537685
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents