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Categorizing consumer behavioral responses and artifact design features: The case of online advertising

Author

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  • Jian Tang

    (Syracuse University)

  • Ping Zhang

    (Syracuse University)

  • Philip Fei Wu

    (Royal Holloway, University of London)

Abstract

Many consumers encounter and interact with digital artifacts and services on a daily basis, either willingly or unwillingly. This paper conceptualizes consumer online behaviors into more refined categories in the context of online advertising. Behaviors can be differentiated by their directions and intensities. Approach and avoidance are two directions of behavioral responses when consumers encounter online advertisements. Active and passive behaviors reflect the levels of intensity of behavioral efforts consumers put when dealing with online advertisements. Active behavioral responses mean that consumers make effort to act upon online ads, either approaching or avoiding them. Passive behavioral responses indicate that consumers make little effort to change the current status, and would approach or avoid in a passive way. We posit that consumers’ behavioral responses can be generally categorized into four major types across these two dimensions: active approach, passive approach, active avoidance, and passive avoidance. In addition, we categorize the design features of online advertisements with a three-facet framework: ad content, ad form, and ad action. Ad content is concerned with the message or meaning that an ad carries; ad form is about materializing content based on presentation styles such as media, location, color, audio, etc.; and ad action is concerned with the behaviors of an ad such as movement, onset timing, frequency, etc. Due to the novelty of the categorizations, an exploratory study was conducted to provide empirical evidence on the categorizations of the four behavior types and three design feature types. Our findings indicate that all four types of consumer behaviors were present, and all behaviors identified by our study can be classified into one of the four types. The same is true for the design feature categorization. We illustrate that the categorization of the three types of ad design features can also guide the understanding of consumers’ judgments of ads, which may function as a bridge of ad design features’ influence on consumer behaviors. This study contributes to a stronger, more refined understanding of how consumers react to online advertising services, and how such responses relate to various types of design features. It also has practical implications for the design, delivery, and management of digital artifacts and services in general.

Suggested Citation

  • Jian Tang & Ping Zhang & Philip Fei Wu, 2015. "Categorizing consumer behavioral responses and artifact design features: The case of online advertising," Information Systems Frontiers, Springer, vol. 17(3), pages 513-532, June.
  • Handle: RePEc:spr:infosf:v:17:y:2015:i:3:d:10.1007_s10796-014-9508-3
    DOI: 10.1007/s10796-014-9508-3
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    4. Kizgin, Hatice & Jamal, Ahmad & Rana, Nripendra & Dwivedi, Yogesh & Weerakkody, Vishanth, 2019. "The impact of social networking sites on socialization and political engagement: Role of acculturation," Technological Forecasting and Social Change, Elsevier, vol. 145(C), pages 503-512.
    5. Le Thanh Tam & Huong Xuan Ho & Dong Phong Nguyen & Arun Elias & Angelina Nhat Hanh Le, 2021. "Receptivity of Governmental Communication and Its Effectiveness During COVID-19 Pandemic Emergency in Vietnam: A Qualitative Study," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 22(1), pages 45-64, June.
    6. Hongjuan Song & Yushi Jiang, 2017. "Online Personalized Advertising Avoidance by Chinese Consumers: The Effect of Consumer Good Types," Noble International Journal of Business and Management Research, Noble Academic Publsiher, vol. 1(6), pages 107-117, June.
    7. Jaeki Song & Jeff Baker & Fred D. Davis, 2015. "Special section on the role of information technology in service delivery," Information Systems Frontiers, Springer, vol. 17(3), pages 471-474, June.
    8. Xiongfei Cao & Ali Nawaz Khan & Ahsan Ali & Naseer Abbas Khan, 2020. "Consequences of Cyberbullying and Social Overload while Using SNSs: A Study of Users’ Discontinuous Usage Behavior in SNSs," Information Systems Frontiers, Springer, vol. 22(6), pages 1343-1356, December.
    9. Kizgin, Hatice & Jamal, Ahmad & Dwivedi, Yogesh K. & Rana, Nripendra P., 2021. "The impact of online vs. offline acculturation on purchase intentions: A multigroup analysis of the role of education," Journal of Business Research, Elsevier, vol. 130(C), pages 724-735.
    10. Hatice Kizgin & Ahmad Jamal & Bidit Lal Dey & Nripendra P. Rana, 2018. "The Impact of Social Media on Consumers’ Acculturation and Purchase Intentions," Information Systems Frontiers, Springer, vol. 20(3), pages 503-514, June.
    11. Debora Dhanya Amarnath & Uma Pricilda Jaidev, 2021. "Toward an integrated model of consumer reactance: a literature analysis," Management Review Quarterly, Springer, vol. 71(1), pages 41-90, February.
    12. Ningyan Cao & Normalisa Md Isa & Selvan Perumal, 2023. "Effects of Risk Attitude and Time Pressure on the Perceived Risk and Avoidance of Mobile App Advertising among Chinese Generation Z Consumers," Sustainability, MDPI, vol. 15(15), pages 1-18, July.
    13. Xiongfei Cao & Ali Nawaz Khan & Ahsan Ali & Naseer Abbas Khan, 0. "Consequences of Cyberbullying and Social Overload while Using SNSs: A Study of Users’ Discontinuous Usage Behavior in SNSs," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
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