Artificial Intelligence Decision-Making in Shopping Patterns: Consumer Values, Cognition, and Attitudes
Artificial Intelligence Decision-Making in Shopping Patterns: Consumer Values, Cognition, and Attitudes

Summary/Abstract: This article reviews and advances existing literature concerning artificial intelligence decision-making in shopping patterns. In this research, previous findings were cumulated showing that machine learning techniques can accurately predict consumer preferences, and we contribute to the literature by indicating that customer purchase intention can be configured through artificial neural network algorithms. Throughout January 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “artificial intelligence” + “shopping pattern,” “consumer value,” “consumer cognition,” and “consumer attitude.” As research published in 2022 was inspected, only 144 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 20 mainly empirical sources. Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, MMAT, ROBIS, and SRDR.

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