AI in marketing, consumer research and psychology: A systematic literature review and research agenda

Abstract

This study is the first to provide an integrated view on the body of knowledge of artificial intelligence (AI) published in the marketing, consumer research, and psychology literature. By leveraging a systematic literature review using a data‐driven approach and quantitative methodology (including bibliographic coupling), this study provides an overview of the emerging intellectual structure of AI research in the three bodies of literature examined. We identified eight topical clusters: (1) memory and computational logic; (2) decision making and cognitive processes; (3) neural networks; (4) machine learning and linguistic analysis; (5) social media and text mining; (6) social media content analytics; (7) technology acceptance and adoption; and (8) big data and robots. Furthermore, we identified a total of 412 theoretical lenses used in these studies with the most frequently used being: (1) the unified theory of acceptance and use of technology; (2) game theory; (3) theory of mind; (4) theory of planned behavior; (5) computational theories; (6) behavioral reasoning theory; (7) decision theories; and (8) evolutionary theory. Finally, we propose a research agenda to advance the scholarly debate on AI in the three literatures studied with an emphasis on cross‐fertilization of theories used across fields, and neglected research topics.

Alexa, what do we know about conversational commerce? Insights from a systematic literature review

“…Finally , a comprehensive and systematic review of conversational commerce is arguably best undertaken and published by Psychology & Marketing because (1) conversational commerce is a marketing phenomenon that reflects the deployment of conversational agents for value creation and service delivery in the marketplace, and in the process of doing so, influences customer behavior, which can be explained by the psychological process involving a myriad of perception and evaluation influencing factors, (2) the journal is home to psychological studies of marketing phenomenon, (3) the journal has explicitly called for papers in this area (Mariani & Perez‐Vega, 2020; Pentina & Bailey, 2022), and (4) the journal has a prolific and recent track record of publishing comprehensive and systematic reviews (Hassan et al, 2022; Mariani et al, 2022), including those relying on a bibliometric methodology (Adler & Sarstedt, 2021; Donthu et al, 2021a; Khan et al, 2020), with a review on conversational commerce or conversational agents virtually nonexistent.…”

“…Noteworthily, the use of bibliometric analysis is well suited for large-scale reviews because it is a quantitative analysis that is exploratory yet data driven, thereby enabling the development of an objective evaluation of the performance of the field and the mapping of the scientific knowledge in that field in its entirety (Donthu et al, 2021c;Kumar et al, 2022aKumar et al, , 2022bMariani et al, 2022). Alternatives such as integrative and narrative reviews typically rely on qualitative analysis and thus tend to be subjective and prone to researcher bias, whereas meta-analysis, which is the other quantitative alternative to bibliometric analysis, is usually focused on a series of highly researched statistical relationships and thus discounting insights emerging from nonquantitative studies (e.g., conceptual and qualitative) in the field (Donthu et al, 2021c;Paul et al, 2021).…”

Psychological Analysis for Depression Detection from Social Networking Sites

“…The reviews provided by people for a product or a movie follow a collect-and-pass-through recommendation system, which depicts the polarity of psychology that shows whether people like the product or the movie or not. People's opinions and reviews can be analyzed thoroughly, which helps boost business performance and design future services [ 3 ].…”

How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign

“…However, the last two decades have seen a surge of predictive ML applications in business analytics, which appear to be increasingly dominating causal analysis in marketing as well. In a keyword-search-based literature review, Mariani, Perez-Vega, and Wirtz (2021) find that the number of publications on predictive ML and Artificial Intelligence (AI) in marketing, consumer research and psychology has grown exponentially in the last decade . The systematic literature reviews by Mustak, Salminen, Plé, and Wirtz (2021) and Ma and Sun (2020) paint a similar picture, with the latter stating that the rise of ML in marketing began with applications of support vector machines, a specific type of ML algorithm.…”