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

• Author: Nica, Elvira

1. Introduction

   The purpose of our systematic review is to examine the recently published literature on artificial intelligence decision-making in shopping patterns and integrate the insights it configures on consumer values, cognition, and attitudes. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, our paper has attempted to prove that machine learning techniques can accurately predict consumer preferences. The actuality and novelty of this study are articulated by addressing artificial intelligence customer service (Hallows et al, 2022; Hu et al, 2022; Rodgers and Nguyen, 2022; Yang et al, 2022; Zhao et al, 2022), that is an emerging topic involving much interest. Our research problem is whether artificial intelligence chatbots can optimize customer experience and sales performance (Andronie et al., 2021a, b, c; Birtus and Lazaroiu, 2021; Lazaroiu, 2013; Musova et al, 2021) as regards human-machine interaction. In this review, prior findings have been cumulated indicating that decision support tools and robust data accuracy in artificial intelligence systems can minimize error and improve algorithmic outputs. The identified gaps advance adequate quality decisions across online retail stores. Our main objective is to indicate that tailored business intelligence and machine learning tools (Battisti et al., 2022; Fan et al., 2022b; Kim et al., 2022; Rodgers and Nguyen, 2022; Subero-Navarro et al., 2022) improve user engagement and assist in social value creation. This systematic review contributes to the literature on purchase decision route algorithms (Rodgers and Nguyen, 2022; SadighZadeh and Kaedi, 2022; Schepers and Streukens, 2022; Yuan et al, 2022; Zhao et al, 2022) by clarifying that optimization of artificial intelligence customer service in responsiveness and service attitude may result in a beneficial emotional experience for users. This research endeavors to elucidate whether artificial intelligence-based chatbots integrate frontline service performance and adjustability shaping customer-oriented behaviors as functional and relational operations. Our contribution is by integrating research findings indicating that, to fortify their companies' position throughout a competitive environment, retailers should be able to accurately predict prospective customers in relation to their purchase behavior.

2. Theoretical Overview of the Main Concepts

   Augmented and virtual reality tools increase competitive advantage (Alimamy and Gnoth, 2022; Fan et al, 2022a; Klaus and Zaichkowsky, 2022; Rausch et al., 2022; Sharma and Shafiq, 2022) across the digitally connected customer and retail environment. Anthropomorphic traits of digital customer service agents may be instrumental in customer satisfaction and retention. The COVID-19 outbreak has led users to take an interest in frontline robots' capacity to offer services in manners that keep consumers protected from contracting the virus, optimizing transactions through technology infusion. Enhancement of artificial intelligence proficiency in emotional grasp and satisfaction can optimize user intention (Alimamy and Gnoth, 2022; Crolic et al., 2022; Rodgers and Nguyen, 2022; Shaikh et al., 2022; Zhao et al., 2022) of continuous use and enjoyment. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), integrating customer purchase behavior and engagement levels by machine learning tools, algorithms, and techniques (section 4), decision support tools and robust data accuracy in artificial intelligence customer service (section 5), purchase decision algorithms for business intelligence and machine learning tools (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10).

3. Methodology

   Throughout January 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was carried out, with search terms comprising "artificial intelligence" + "shopping pattern," "consumer value," "consumer cognition," and "consumer attitude." The search terms were determined as being the most employed words or phrases across the analyzed literature. As research published in 2022 was analyzed, only 144 articles met the suitability criteria. By removing questionable or indeterminate findings (insubstantial/inconsequent data), results unconfirmed by replication, too imprecise content, or having quite similar titles, 20, chiefly empirical, sources were selected (Tables 1 and 2). Extracting and inspecting publicly accessible files (scholarly sources) as evidence, before the research began no institutional ethics approval was required. (Figures 1 and 2)

4. Integrating Customer Purchase Behavior and Engagement Levels by Machine Learning Tools, Algorithms, and Techniques

   Consumers are likely to outsource decisions to home voice bots and algorithms (Alimamy and Gnoth, 2022; Klaus and Zaichkowsky, 2022; Rausch et al., 2022; Sharma and Shafiq, 2022), leading to low involvement and convenience addiction developed on reduced time and effort. To fortify their companies' position throughout a competitive environment, retailers should be able to accurately predict prospective customers in relation to their purchase behavior. Inspecting online user-generated data as text-based reviews can configure customer purchase intention through artificial neural network algorithms in terms of prediction capacity. Risk and trust perceptions integrate customization and co-creation intentions in augmented reality shopping.

   Machine learning tools, algorithms, and techniques integrate customer purchase behavior and engagement levels (Fan et al., 2022a; Shaikh et al., 2022; Yang et al., 2022) concerning forecasting product and service demand. In terms of low perceived control, users identify a more relevant threat in interacting with artificial intelligence service agents having more human-like designs and opt for less anthropomorphic ones...