Artificial Intelligence (AI) in Advertising

Understanding and Schematizing the Behaviors of Social Media Users


Nowadays, information technology is not only widely used in all walks of life but also fully applied in the marketing and advertisement sector. In particular, Artificial Intelligence (AI) has received growing attention worldwide because of its impact on advertising. However, it remains unclear how social media users react to AI advertisements. The purpose of this study is to examine the behavior of social media users towards AI-based advertisements. This study used a qualitative method, including a semi-structured interview. A total of 23 semi-structured interviews were conducted with social media users aged 18 and over, using a purposive sampling method. The interviews lasted between 27.05–50.39 minutes on average (Mean: 37.48 SD: 6.25) between August and October 2021. We categorized the findings of the current qualitative research into three main process themes: I) reception; II) diving; and III) break-point. While 'reception' covers positive and negative sub-themes, 'diving' includes three themes: comparison, timesaving, and leaping. The final theme, 'break-point', represents the decision-making stage and includes negative or positive opinions. This study provides content producers, social media practitioners, marketing managers, advertising industry, AI researchers, and academics with many insights into AI advertising.
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Aaker, D. A., and Stayman, D. M., 1992. Implementing the concept of transformational advertising. Psychology & Marketing, 9(3): 237–253.

Argan, M., Argan, M. T., and İpek, G., 2018. I Wish I Were! Anatomy of a fomsumer. Journal of Internet Applications and Management, 9(1): 43–57.

Ashraf, S., Gao, M., Chen, Z., Naeem, H., Ahmad, A., and Ahmed, T., 2020a. Underwater pragmatic routing approach through packet reverberation mechanism. IEEE Access, 8, 163091–163114.

Ashraf, S., Muhammad, D., Shuaeeb, M., and Aslam, Z., 2020b. Development of Shrewd Cosmetology Model through Fuzzy Logic. Journal of Research in Engineering and Applied Sciences, 5(3): 93–99.

Batra, R., and Keller, K. L., 2016. Integrating marketing communications: New findings, new lessons, and new ideas. Journal of Marketing, 80(6): 122–145.

Braun, V., and Clarke, V., 2006. «Using thematic analysis in psychology». Qualitative research in Psychology, 3(2): 77–101.

Campbell, C., Plangger, K., Sands, S., and Kietzmann, J., 2022. Preparing for an era of deepfakes and AI-generated ads: A framework for understanding responses to manipulated advertising. Journal of Advertising, 51(1): 22–38.

Charmaz, K., 2006. Constructing grounded theory, a practical guide through qualitative analysis. London: Sage.

Choi, J. A., and Lim, K., 2020. Identifying machine learning techniques for classification of target advertising. ICT Express, 6(3): 175–180.

Churcher, P. R., 1991. The impact of artificial intelligence on leisure. AI & Society, 5(2): 147–155.

Creswell, J. W., 2013. Qualitative enquiry & research design, choosing among five approaches. 3rd ed. Los Angeles, CA: Sage.

Glaser, B. G., and Strauss, A. L., 2017. Discovery of grounded theory: Strategies for qualitative research. Routledge.

Hodkinson, C., 2019. ‘Fear of Missing Out’(FOMO) marketing appeals: A conceptual model. Journal of Marketing Communications, 25(1): 65–88.

Hou, C. I., 2013. Traffic flow forecasting in leisure farm areas using artificial neural networks. Przegląd Elektrotechniczny, 89(1b): 205–207.

JWT Intelligence., 2012. Fear of missing out (FOMO). Retrieved November 28, 2017 from JWT:

Kietzmann, J., and Canhoto, A., 2013. Bittersweet! Understanding and managing electronic word of mouth. Journal of Public Affairs, 13(2): 146–159.

Kietzmann, J., Paschen, J., and Treen, E., 2018. Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3): 263–267.

Lai, Z., 2020. Research on Advertising Core Business Reformation Driven by Artificial Intelligence. Journal of Physics: Conference Series: 1–9.

Lashua, B. D., 2014. DWYL? YOLO. Annals of Leisure Research, 17(2): 121–126.

Lashua, B. D., 2018. The time machine: Leisure science (Fiction) and futurology. Leisure Sciences, 40(1-2): 85–94.

Li, H., 2019. Special section introduction: Artificial intelligence and advertising. Journal of advertising, 48(4): 333–337.

Li, H., Edwards, S. M., and Lee, J. H., 2002. Measuring the intrusiveness of advertisements: Scale development and validation. Journal of Advertising, 31(2): 37–47.

Malterud, K., Siersma, V. D., and Guassora, A. D., 2016. Sample size in qualitative interview studies: guided by information power. Qualitative Health Research, 26(13): 1753–1760.

McCarthy, J., and Hayes, P. J., 1981. Some philosophical problems from the standpoint of artificial intelligence. In Readings in artificial intelligence (pp. 431–450). Morgan Kaufmann.

Mogaji, E., 2018. Emotional Appeals in Advertising Banking Services. London: Emeald.

Murgai, A., 2018. Transforming digital marketing with artificial intelligence. International Journal of Latest Technology in Engineering, Management & Applied Science, 7(4): 259–262.

Przybylski, A. K., Murayama, K., DeHaan, C. R., and Gladwell, V., 2013. Motivational, Emotional, and Behavioral Correlates of Fear of Missing Out. Computers in Human Behavior, 29(4): 1841–1848.

Rodgers, S., 2021. Themed issue introduction: Promises and perils of artificial intelligence and advertising, Journal of Advertising, 50(1): 1–10.

Rodgers, W., and Nguyen, T. (2022). Advertising benefits from ethical artificial intelligence algorithmic purchase decision pathways. Journal of Business Ethics: 1–19.

Shah, N., Engineer, S., Bhagat, N., Chauhan, H., and Shah, M., 2020. Research trends on the usage of machine learning and artificial intelligence in advertising. Augmented Human Research, 5(1): 1–15.

Shumanov, M., Cooper, H., and Ewing, M., 2021. Using AI predicted personality to enhance advertising effectiveness. European Journal of Marketing: 1–20.

Vakratsas, D., and Wang, X., 2020. Artificial intelligence in advertising creativity. Journal of Advertising, 50(1): 39–51.

Wu, L., Dodoo, N. A., Wen, T. J., and Ke, L., 2021. Understanding Twitter conversations about artificial intelligence in advertising based on natural language processing. International Journal of Advertising: 1–18.

Xian, X., 2021. Psychological Factors in Consumer Acceptance of Artificial Intelligence in Leisure Economy: A Structural Equation Model. Journal of Internet Technology, 22(3): 697–705.

Zhao, H., Lyu, F, and Luo, Y., 2020. Research on the effect of online marketing based on multimodal fusion and artificial intelligence in the context of big data. Security and Communication Networks: 1–9.
Argan, M., Dinç, H., Kaya, S., & Tokay Argan, M. (2023). Artificial Intelligence (AI) in Advertising: Understanding and Schematizing the Behaviors of Social Media Users. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(3), 331–348.


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