ABSTRACT. I draw on a substantial body of theoretical and empirical research on machine learning tools, algorithms, and techniques in retail business operations. With increasing evidence of online purchase decision-making algorithms, there is an essential demand for comprehending whether self-service technologies and machine learning algorithms have altered consumption and buying habits across the retailing environment. In this research, prior findings were cumulated indicating that artificial intelligence technologies assist in dynamic assessment by use of machine learning algorithms and computer vision technologies to determine user trends and patterns and grasp customer attitudes and feelings. I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout January 2022, with search terms including “retail” + “machine learning tool/algorithm/technique,” “consumer perception,” “consumer expectation,” and “consumer habit.” As I analyzed research published in 2022, only 137 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, I decided on 20, chiefly empirical, sources. Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR.
JEL codes: D12; D22; D91; L66; E71

Keywords: machine learning; algorithm; retail; consumer; perception; habit

How to cite: Hopkins, E. (2022). “Machine Learning Tools, Algorithms, and Techniques in Retail Business Operations: Consumer Perceptions, Expectations, and Habits,” Journal of Self-Governance and Management Economics 10(1): 43–55. doi: 10.22381/jsme10120223.

Received 25 January 2022 • Received in revised form 25 March 2022
Accepted 26 March 2022 • Available online 30 March 2022

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