The Influence of Artificial Intelligence-Generated Content on Consumers' Purchase Decision in E-commerce: An Action Mechanism Study

Yuchen Hou

doi:10.54097/579fsh69

Authors

Yuchen Hou Business School, Newcastle university, Newcastle, United Kingdom

DOI:

https://doi.org/10.54097/579fsh69

Keywords:

AIGC, e-commerce, recommender systems, consumer trust, urgency cues.

Abstract

Consumers making choices in e-commerce are now organized by artificial intelligence-generated and -orchestrated content (AIGC). This study looks at two typical surfaces, namely, algorithmic recommendations and urgency cues, through an exploratory secondary analysis of a survey (N=102) of young and digitally active shoppers. This paper has constructed concise variables on the basis of the questionnaire, and reports descriptive statistics, chi-square screens as well as two parsimonious logistic regressions. Results indicate that reported influence by recommendations is positively associated with comfort about data collection and shopping frequency, while chatbot exposure shows a negative association; dynamic-pricing exposure shows a borderline positive signal. Reported influence by urgency messages is predicted by perceived preference accuracy and trust in AI over human service. AI familiarity, perceived intrusiveness, perceived bias, overall satisfaction, and basic demographics do not reach conventional significance in either model; chi-square tests similarly show no detectable segmentation by gender or education in the tested outcomes. Together, the findings suggest that influence depends less on generic awareness and more on situated perceptions (accuracy, trust, data comfort) and context of use (task readiness). This paper translates these patterns into actionable guidance on transparency, explainability, pricing fairness cues, chatbot orchestration, and responsible urgency, while noting limitations of sample scope, self-reporting, and model convergence.

Downloads

Download data is not yet available.

References

[1] Shankar V. What an artificial intelligence (AI) is reshaping retailing. Journal of Retailing, 2018, 94 (4): vi - xi.

[2] Ricci F, Rokach L, Shapira B. Recommender systems: Techniques, applications, and challenges. Recommender Systems Handbook, 2021: 1 - 35.

[3] Cloarec J. The personalization–privacy paradox in the attention economy. Technological Forecasting and Social Change, 2020, 161: 120299.

[4] Häubl G, Trifts V. Consumer decision making in online shopping environments: The effects of interactive decision aids. Marketing Science, 2000, 19 (1): 4 - 21.

[5] Tintarev N, Masthoff J. Explaining recommendations: Design and evaluation. Recommender Systems Handbook, 2015: 353 - 382.

[6] Huang M H, Rust R T. A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 2021, 49 (1): 30 - 50.

[7] Zhang Y, Chen X. Explainable recommendation: A survey and new perspectives. Foundations and Trends in Information Retrieval, 2020, 14 (1): 1 - 101.

[8] McKnight D H, Choudhury V, Kacmar C. Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 2002, 13 (3): 334 - 359.

[9] Luo X, Tong S, Fang Z, Qu Z. Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 2019, 38 (6): 937 - 947.

[10] Soleimani M. Buyers' trust and mistrust in e-commerce platforms: a synthesizing literature review. Information Systems and e-Business Management, 2022, 20 (1): 57 - 78.

[11] Jannach D, Zanker M, Felfernig A, Friedrich G. Recommender systems: an introduction. Cambridge University Press, 2010.

[12] Cloarec J, Meyer-Waarden L, Munzel A. Transformative privacy calculus: Conceptualizing the personalization‐privacy paradox on social media. Psychology & Marketing, 2024, 41 (7): 1574 - 1596.

[13] Riedel A S, Weeks C S, Beatson A T. Dealing with intrusive ads: a study of which functionalities help consumers feel agency. International Journal of Advertising, 2024, 43 (2): 361 - 387.

[14] Vomberg A, Homburg C, Sarantopoulos P. Algorithmic pricing: Effects on consumer trust and price search. International Journal of Research in Marketing, 2024.

[15] Barton B, Zlatevska N, Oppewal H. Scarcity tactics in marketing: A meta-analysis of product scarcity effects on consumer purchase intentions. Journal of Retailing, 2022, 98 (4): 741 - 758.

[16] Xu X Y, Jia Q D, Tayyab S M U. Exploring the stimulating role of augmented reality features in E-commerce: A three-staged hybrid approach. Journal of Retailing and Consumer Services, 2024, 77: 103682.

[17] Wang Y, Ma W, Zhang M, Liu Y, Ma S. A survey on the fairness of recommender systems. ACM Transactions on Information Systems, 2023, 41 (3): 1 - 43.

[18] Ribeiro M T, Singh S, Guestrin C. "Why should i trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 1135 - 1144.

[19] Chatti M A, Guesmi M, Muslim A. Visualization for recommendation explainability: a survey and new perspectives. ACM Transactions on Interactive Intelligent Systems, 2024, 14 (3): 1 - 40.

[20] Zhang K Z, Zhao S J, Cheung C M, Lee M K. Examining the influence of online reviews on consumers' decision-making: A heuristic–systematic model. Decision Support Systems, 2014, 67: 78 - 89.