The Transformative Role of AI in Modern Consumer Decision-Making

The digital age has ushered in a transformative era where artificial intelligence (AI) plays an increasingly central role in consumer decision-making. As AI technologies continue to evolve, their influence on how consumers interact with products and services is profound. From recommendation systems to AI-powered chatbots, these technologies are reshaping the consumer landscape, offering both opportunities and challenges that warrant thorough examination.

AI-driven recommendation systems have become a staple in various industries, including retail, entertainment, and education. These systems, as highlighted by Resnick and Varian (1997), emerged from the understanding that consumers often rely on the advice of others when making decisions. Over time, they have evolved into sophisticated tools that use algorithms to analyse user preferences and predict the most suitable products or services. The effectiveness of these systems in enhancing consumer satisfaction and driving sales has been well-documented (Ricci et al., 2010; Jan et al., 2023; Hu & Pan, 2023).

The ethical implications of AI in consumer decision-making are a crucial area of concern. As AI systems become more integrated into daily life, issues related to privacy, data security, and algorithmic transparency become increasingly important. The balance between providing personalized experiences and protecting consumer autonomy is delicate, and the potential for AI to exacerbate social inequalities must be addressed (Chen et al., 2022; Paul et al., 2023).

As AI continues to advance, its influence on consumer decision-making is likely to grow, presenting new challenges and opportunities for both businesses and consumers. This special issue invites contributions that delve into these issues, offering insights that can guide the responsible development and implementation of AI technologies in the consumer market.

List of Topic Areas

1. Historical Evolution: How has the role of recommendation systems evolved since their inception in the mid-1990s to their current state, and what has driven these changes?
2. Efficacy of Chatbots: How effective are ChatGPT and similar AI chatbots in influencing consumer purchase decisions compared to more traditional recommendation engines?
3. Ethical Implications: In what ways might AI-driven recommendation systems contribute to or mitigate “information cocooning,” and what are the broader societal implications of this?
4. Consumer Trust: How do consumers perceive the accuracy and trustworthiness of AI-generated recommendations versus human-generated advice?
5. Personalization vs. Homogenization: Is there a risk that over-reliance on personalized AI-generated recommendations might lead to a homogenization of consumer choices, thus stifling diversity in consumer behavior?
6. Industry Applications: How do the impacts and effectiveness of AI recommendation systems vary across different industries, such as retail, entertainment, and services?
7. Consumer Psychology: How do AI recommendation systems affect consumer perceptions of choice, freedom, and autonomy in their decision-making processes
8. Emerging Technologies: In what ways might emerging conversational recommender systems (CRS) and interactive recommendation agents (IRAs) reshape the consumer experience and decision-making processes?
9. Commercial Implications: How do businesses perceive the value of AI recommendation systems in terms of sales, user satisfaction, and understanding user needs, and how do these perceptions align with the actual outcomes?
10. Future Trajectories: With the constant evolution of AI and ML technologies, what potential shifts can be anticipated in consumer decision-making patterns, and what implications could these shifts have for businesses, society, and individual autonomy?

These research questions can be refined further based on specific academic or industry needs and have the potential to provide a comprehensive starting point encouraging more in-depth investigations into this domain.

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available here.
Author guidelines must be strictly followed. Please see here.
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to ““Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Submit your paper here!

Key Deadlines

Opening date for manuscripts submissions: 01/09/2024
Closing date for manuscripts submission: 01/06/2025

Guest Editors

Dr. Hassan Kalantari Daronkola, Senior Lecturer, Swinburne University of Technology, Australia, [email protected]
Professor Lester Johnson, Swinburne University of Technology, Australia, [email protected]
Dr. Civilai Leckie, Swinburne University of Technology, Australia, [email protected]

References

Aksoy, L., Cooil, B., & Lurie, N.H. (2011). Decision quality measures in recommendation agents research. Journal of Interactive Marketing, 25(2), 110-122.
Chen, S., Qiu, H., Zhao, S., Han, Y., He, W., Siponen, M., & Xiao, H. (2022). When more is less: the other side of artificial intelligence recommendation. Journal of Management Science and Engineering, 7(2), 213-232.
Dwivedi, Y.K., Kshetri, N., Hughes, L., Slade, E.L., Jeyaraj, A., Kar, A.K., et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges, and implications of generative conversational AI for research, practice, and policy. International Journal of Information Management, 71, 102642.
Hu, Q., & Pan, Z. (2023). Can AI benefit individual resilience? The mediation roles of AI routinization and infusion. Journal of Retailing and Consumer Services, 73, 103339.
Jan, I.U., Ji, S., & Kim, C. (2023). What (de)motivates customers to use AI-powered conversational agents for shopping? The extended behavioral reasoning perspective. Journal of Retailing and Consumer Services, 75, 103440.
Resnick, P., & Varian, H.R. (1997). Recommender systems. Communications of the ACM, 40(3), 56-58.

Ricci, F., Rokach, L., & Shapira, B. (2010). Introduction to recommender systems handbook. In Recommender Systems Handbook. Springer, Boston, MA.
Sunstein, C.R. (2006). Infotopia: How Many Minds Produce Knowledge. Oxford University Press, Oxford.
Zhai, X. (2022). ChatGPT User Experience: Implications for Education. Social Science Research

Network. https://doi.org/10.2139/ssrn.4312418