skip to main content
10.1145/3503252.3534359acmconferencesArticle/Chapter ViewAbstractPublication PagesumapConference Proceedingsconference-collections
extended-abstract

Personalizing Persuasive Principles to Improve Credibility

Published: 04 July 2022 Publication History

Abstract

The role of credibility in persuasive systems is undoubted. It is therefore important that the determinants of credibility are clearly understood. Studies have asserted that personalizing persuasive principles, for instance, based on users’ personality, age, or gender is more effective in influencing behavior. However, there is little knowledge on how credibility determinants can be adapted to increase a system's credibility and system persuasiveness for specific users. This paper describes ongoing research that attempts to categorize credibility determinants based on their effectiveness for different users and contexts. The paper presents the research plan and findings from three initial studies.

References

[1]
Ifeoma Adaji. 2017. Towards improving e-commerce users experience using personalization & persuasive technology. In UMAP 2017 - Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery, Inc, 318–321.
[2]
Ifeoma Adaji and Julita Vassileva. 2017. Perceived Effectiveness, Credibility and Continuance Intention in E-commerce: A Study of Amazon. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10171 LNCS, (2017), 293–306.
[3]
Noora Aldenaini, R. Orji, and S. Sampalli. 2020. How Effective is Personalization in Persuasive Interventions for Reducing Sedentary Behavior and Promoting Physical Activity: A Systematic Review. undefined (2020).
[4]
Sean Andrist, Micheline Ziadee, Halim Boukaram, Bilge Mutlu, and Majd Sakr. 2015. Effects of Culture on the Credibility of Robot Speech: A Comparison between English and Arabic. In ACM/IEEE International Conference on Human-Robot Interaction, IEEE Computer Society, 157–164.
[5]
Jacqueline Corbett. 2013. Designing and Using Carbon Management Systems to Promote Ecologically Responsible Behaviors. J Assoc Inf Syst 14, 7 (July 2013), 2.
[6]
Filip Drozd, Tuomas Lehto, and Harri Oinas-Kukkonen. 2012. Exploring perceived persuasiveness of a behavior change support system: a structural model. In International Conference on Persuasive Technology, 157–168.
[7]
B, J. Fogg. 2002. The Web Credibility Project: Guidelines - Stanford University. Retrieved July 20, 2021 from https://credibility.stanford.edu/guidelines/index.html
[8]
B J Fogg. 2003. Prominence-Interpretation Theory: Explaining How People Assess Credibility Online. In CHI ’03 extended abstracts on Human factors in computing systems  - CHI ’03, ACM Press, New York, New York, USA, 722–723.
[9]
BJ Fogg. 2009. A behavior model for persuasive design. In Proceedings of the 4th International Conference on Persuasive Technology, ACM Press, New York, New York, USA, 40.
[10]
Carl I. Hovland and Walter Weiss. 1951. The Influence of Source Credibility on Communication Effectiveness. Public Opinion Quarterly 15, 4 (January 1951), 635–650.
[11]
Anne Hsu, Jing Yang, Yigit Yilmaz, Md Sanaul Haque, Cengiz Can, and Ann Blandford. 2014. Persuasive technology for overcoming food cravings and improving snack choices. Conference on Human Factors in Computing Systems - Proceedings (2014), 3403–3412.
[12]
Xi Hu, Xiayu Chen, and Robert Davidson. 2019. Social Support, Source Credibility, Social Influence, and Impulsive Purchase Behavior in Social Commerce. International Journal of Electronic Commerce 23, 3 (July 2019), 297–327.
[13]
Felix Koranteng, Jaap Ham, Isaac Wiafe, and Uwe Matzat. 2021. The Role of Usability, Aesthetics, Usefulness and Primary Task Support in Predicting the Perceived Credibility of Academic Social Networking Sites. Behaviour & Information Technology (2021).
[14]
Felix N. Koranteng, Jaap Ham, and Isaac Wiafe. 2021. Investigating User Perceptions of Persuasive Design Elements that Influence Perceived Credibility. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12684 LNCS, (April 2021), 164–177.
[15]
Felix Nti Koranteng, Jaap Ham, Uwe Matzat, and Isaac Wiafe. 2022. Supporting to be Credible: Investigating Perceived Social Support as a Determinant of Perceived Credibility. In International Conference on Persuasive Technology.
[16]
Tuomas Lehto and Harri Oinas-Kukkonen. 2011. Persuasive Features in Web-Based Alcohol and Smoking Interventions: A Systematic Review of the Literature. J Med Internet Res 2011;13(3):e46 https://www.jmir.org/2011/3/e46 13, 3 (July 2011), e1559.
[17]
Marieluise Merz and Lena Ackermann. 2021. Design Principles of Persuasive Systems – Review and Discussion of the Persuasive Systems Design Model. AMCIS 2021 Proceedings (August 2021). Retrieved February 15, 2022 from https://aisel.aisnet.org/amcis2021/sig_hci/sig_hci/3
[18]
Michael Oduor and Harri Oinas-Kukkonen. 2017. Commitment devices as behavior change support systems: A study of users’ perceived competence and continuance intention. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 201–213.
[19]
Harri Oinas-Kukkonen and Marja Harjumaa. 2009. Persuasive systems design: Key issues, process model, and system features. Communications of the Association for Information Systems 24, 1 (2009), 28.
[20]
Kiemute Oyibo. 2021. EMVE-DeCK: A Theory-Based Framework for Designing and Tailoring Persuasive Technology. UMAP 2021 - Adjunct Publication of the 29th ACM Conference on User Modeling, Adaptation and Personalization (June 2021), 257–267.
[21]
Chanthika Pornpitakpan. 2004. The Persuasiveness of Source Credibility: A Critical Review of Five Decades’ Evidence. Journal of Applied Social Psychology 34, 2 (February 2004), 243–281.
[22]
Kristian Torning and Harri Oinas-Kukkonen. 2009. Persuasive system design: State of the art and future directions. ACM International Conference Proceeding Series 350, (2009).
[23]
Isaac Wiafe. 2013. A Unified Approach To Persuasive Systems Development. International Journal of Conceptual Structures and Smart Applications 1, 2 (2013), 6–16.
[24]
Isaac Wiafe, Muna M. Alhammad, Keiichi Nakata, and Stephen R. Gulliver. 2012. Analyzing the Persuasion Context of the Persuasive Systems Design Model with the 3D-RAB Model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7284 LNCS, (2012), 193–202.

Index Terms

  1. Personalizing Persuasive Principles to Improve Credibility
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    UMAP '22: Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
    July 2022
    360 pages
    ISBN:9781450392075
    DOI:10.1145/3503252
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 04 July 2022

    Check for updates

    Author Tags

    1. credibility
    2. personalization
    3. persuasive systems

    Qualifiers

    • Extended-abstract
    • Research
    • Refereed limited

    Conference

    UMAP '22
    Sponsor:

    Acceptance Rates

    Overall Acceptance Rate 162 of 633 submissions, 26%

    Upcoming Conference

    UMAP '25

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 97
      Total Downloads
    • Downloads (Last 12 months)25
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 15 Sep 2024

    Other Metrics

    Citations

    View Options

    Get Access

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media