Data-driven Machine Learning and Neural Network Algorithms in the Retailing Environment: Consumer Engagement, Experience, and Purchase Behaviors
Data-driven Machine Learning and Neural Network Algorithms in the Retailing Environment: Consumer Engagement, Experience,and Purchase Behaviors

Summary/Abstract: Based on an in-depth survey of the literature, the purpose of the paper is to explore data-driven machine learning and neural network algorithms in the retailing environment. In this research, previous findings were cumulated showing that customer brand perception and satisfaction can be carried out according to machine learning algorithms and big data, and we contribute to the literature by indicating that user decision-making algorithms throughout the online environment can be pivotal in artificial intelligence technologies to more thoroughly grasp the consumer journey. Throughout January 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “retail” + “data-driven machine learning,” “neural network algorithm,” “consumer engagement,” “consumer experience,” and “purchase behavior.” As research published in 2022 was inspected, only 148 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 22 mainly empirical sources. Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

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