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Consumers’ Attitudes towards Online Advertising: A Model of Personalization, Informativeness, Privacy Concern and Flow Experience

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  • Li Mo

    (School of Communication, Fujian Normal University, Fuzhou 350117, China)

  • Xiaosan Zhang

    (Research Center for Belt and Road Financial and Economic Development, Xiamen National Accounting Institute, Xiamen 361000, China)

  • Yabin Lin

    (School of Communication, Fujian Normal University, Fuzhou 350117, China)

  • Zhenghui Yuan

    (Research Center for Belt and Road Financial and Economic Development, Xiamen National Accounting Institute, Xiamen 361000, China)

  • Zengjun Peng

    (Department of Mass Communications, St. Cloud State University, St. Cloud, MN 56301, USA)

Abstract

Online personalized advertising has been widely adopted in China in the recent years, leading to both positive and negative results. This study endeavors to examine the impact of perceived personalization of online advertising on consumers’ attitudes toward advertising. A total of 472 questionnaires were administered and analyzed using Structural Equation Modeling. The results show that perceived personalization exerts a positive impact through perceived informativeness, and a negative impact through privacy concerns. However, the positive effect was determined to be stronger and thus perceived personalization had an overall positive impact. Additionally, the results showed that both effects were mediated by flow experience. The practical and theoretical implications of the findings were discussed.

Suggested Citation

  • Li Mo & Xiaosan Zhang & Yabin Lin & Zhenghui Yuan & Zengjun Peng, 2023. "Consumers’ Attitudes towards Online Advertising: A Model of Personalization, Informativeness, Privacy Concern and Flow Experience," Sustainability, MDPI, vol. 15(5), pages 1-12, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:5:p:4090-:d:1078503
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    References listed on IDEAS

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    1. Marios Koufaris, 2002. "Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior," Information Systems Research, INFORMS, vol. 13(2), pages 205-223, June.
    2. Aguirre, Elizabeth & Mahr, Dominik & Grewal, Dhruv & de Ruyter, Ko & Wetzels, Martin, 2015. "Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness," Journal of Retailing, Elsevier, vol. 91(1), pages 34-49.
    3. Thomas P. Novak & Donna L. Hoffman & Yiu-Fai Yung, 2000. "Measuring the Customer Experience in Online Environments: A Structural Modeling Approach," Marketing Science, INFORMS, vol. 19(1), pages 22-42, May.
    4. Tiffany White & Debra Zahay & Helge Thorbjørnsen & Sharon Shavitt, 2008. "Getting too personal: Reactance to highly personalized email solicitations," Marketing Letters, Springer, vol. 19(1), pages 39-50, March.
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    Cited by:

    1. Zhou, Tao & Zhang, Chunlei, 2024. "Examining generative AI user addiction from a C-A-C perspective," Technology in Society, Elsevier, vol. 78(C).

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