Artificial Intelligence, Content Recommendation, Biases, and Consumer Behavior: An Analysis of the Impact of Artificial Intelligence on Consumer Behavior
Keywords:
Artificial Intelligence, Content Recommendation, Biases, And Consumer BehaviourAbstract
Purpose: Information plays very important role in decision making. Awareness of brand, price, discounts, post sales activities like guarantee, warrantee, and maintenance must be advertised to influence the buying behaviour. But what if this information creates a bias. Does any type of bias generated by this information, in the form of advertisement, influence the buying behaviour? The present research is exploring the fact that how artificial intelligence-based advertisement suggestions and content recommendations create certain type of bias and how it affects the buying behaviour. This research is based upon a survey of consumers.
Design/methodology/approach: The methodology emphasised to eliminate the errors in measurement. Respondents were approached twice, in a gap of 30 days for collecting data. They were asked to retake the survey and data in both the attempts have been examined for any major deviation. The average of scores have been consolidated as final data of the research analysis.
Findings: The linear regression equation coefficients for the various model variables. The "B" values are the coefficients for each variable. In model 04 we could predict buying behaviour as BB (y) = 0.589 + .403 Anchoring bias + .284 Conformity Bias + .259 Heuristic Bias+ .233 FOMO.
Originality: Researchers have emphasis on exploring a new set of influencing factors for consumer behaviour rather following the key factors in systematic review of previous works. Thus, the work ensures the originality in research.
Downloads
References
Bissell, J. J. (2015). Conformity bias and catastrophic social change. Tipping Points: Modelling Social Problems and Health, 168-182.
Solomon, M., Russell-Bennett, R., & Previte, J. (2012). Consumer behaviour. Pearson Higher Education AU.
Frederiks, E. R., Stenner, K., & Hobman, E. V. (2015). Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour. Renewable and Sustainable Energy Reviews, 41, 1385-1394.
Connolly, T., & Zeelenberg, M. (2002). Regret in decision making. Current directions in psychological science, 11(6), 212-216.
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42.
Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International journal of information management, 48, 63-71.
Erdfelder, E., Faul, F., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149.
Gupta, M., Parra, C. M., & Dennehy, D. (2021). Questioning racial and gender bias in AI-based recommendations: Do espoused national cultural values matter? Information Systems Frontiers, 1-17.
Ji, C., Mieiro, S., & Huang, G. (2022). How social media advertising features influence consumption and sharing intentions: the mediation of customer engagement. Journal of Research in Interactive Marketing, 16(1), 137-153.
Kenning, P. H., & Plassmann, H. (2008). How neuroscience can inform consumer research. IEEE transactions on neural systems and rehabilitation engineering, 16(6), 532-538.
Lam, S. K. T., Frankowski, D., & Riedl, J. (2006). Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In Emerging Trends in Information and Communication Security: International Conference, ETRICS 2006, Freiburg, Germany, June 6-9, 2006. Proceedings (pp. 14-29). Springer Berlin Heidelberg.
Lee, C., Kim, J., & Chan-Olmsted, S. M. (2011). Branded product information search on the Web: The role of brand trust and credibility of online information sources. Journal of Marketing Communications, 17(5), 355-374.
Liao, C., To, P. L., Wong, Y. C., Palvia, P., & Kakhki, M. D. (2016). The impact of presentation mode and product type on online impulse buying decisions. Journal of Electronic Commerce Research, 17(2), 153.
Madhavan, M., & Kaliyaperumal, C. (2015). Consumer buying behavior-an overview of theory and models. St. Theresa Journal of Humanities and Social Sciences, 1(1), 74-112.
Osterwalder, A., Pigneur, Y., Bernarda, G., & Smith, A. (2015). Value proposition design: How to create products and services customers want. John Wiley & Sons.
Ratchford, B. T., Talukdar, D., & Lee, M. S. (2007). The impact of the Internet on consumers' use of information sources for automobiles: A re-inquiry. Journal of Consumer Research, 34(1), 111-119.
Rabby, F., Chimhundu, R., & Hassan, R. (2021). Artificial intelligence in digital marketing influences consumer behaviour: a review and theoretical foundation for future research. Academy of Marketing Studies Journal, 25(5), 1-7.
Schwarz, E. (2022). Emotion Index: When Do Emotions Trigger Buying Impulses?. In Neuro-Advertising: Brain-friendly advertising for more success in your market (pp. 29-66). Wiesbaden: Springer Fachmedien Wiesbaden.
Schmidt, J., & Bijmolt, T. H. (2020). Accurately measuring willingness to pay for consumer goods: a meta-analysis of the hypothetical bias. Journal of the Academy of Marketing Science, 48, 499-518
Srivastava, B., & Rossi, F. (2019). Rating AI systems for bias to promote trustable applications. IBM Journal of Research and Development, 63(4/5), 5-1.
Srivastava, G., & Singh, N. (2021). Artificial intelligence to predict consumer behaviour: A literature survey. Recent Trends in Communication and Electronics, 367-371.
Yang, F., Tang, J., Men, J., & Zheng, X. (2021). Consumer perceived value and impulse buying behavior on mobile commerce: The moderating effect of social influence. Journal of Retailing and Consumer Services, 63, 102683.
Reisch, L. A., & Zhao, M. (2017). Behavioural economics, consumer behaviour and consumer policy: state of the art. Behavioural Public Policy, 1(2), 190-206.
Liang, X., Hu, X., Meng, H., Jiang, J., & Wang, G. (2022). How does model type influence consumer and online fashion retailing? International Journal of Retail & Distribution Management, 50(6), 728-743.
Hakan, B. O. Z. (2019). Anchoring Effect: A Myth or Reality?. Ekonomik ve Sosyal Araştırmalar Dergisi, 15(1), 33-47.
Alwosheel, A., van Cranenburgh, S., & Chorus, C. G. (2018). Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis. Journal of Choice Modelling, 28(September 2018), 167–182. https://doi.org/ 10.1016/j.jocm.2018.07.002.
Good, M. C., & Hyman, M. R. (2021). Direct and indirect effects of fear‐of‐missing‐out appeals on purchase likelihood. Journal of Consumer Behaviour, 20(3), 564-576.
Hodkinson, C. (2019). ‘Fear of Missing Out’(FOMO) marketing appeals: A conceptual model. Journal of Marketing Communications, 25(1), 65-88.
Przybylski, A. K., Murayama, K., DeHaan, & C. R.,Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841- 1848.
Niza Braga, J., & Jacinto, S. (2022). Effortless online shopping? How online shopping contexts prime heuristic processing. Journal of Consumer Behaviour, 21(4), 743-755.
Kuruppu, G. N., & De Zoysa, A. (2020). COVID-19 and panic buying: an examination of the impact of behavioural biases. Available at SSRN 3596101.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
