Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR‐4‐SLR)

In this digital era, data is new oil and artificial intelligence (AI) is new electricity, which is needed in different elements of operations management (OM) such as manufacturing, product development, services and supply chain. This study explores the feasibility of AI utilization within an organization on six factors such as job-fit, complexity, long-term consequences, affect towards use, social factors and facilitating conditions for different elements of OM by mining the collective intelligence of experts on Twitter and through academic literature. The study provides guidelines for managers for AI applications in different components of OM and concludes by presenting the limitations of the study along with future research directions.

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Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions

The compromise effect denotes the finding that brands gain share when they become the intermediate rather than an extreme option in a choice set (Simonson 1989). Despite the robustness and importance of this phenomenon, choice modelers have neglected to incorporate the compromise effect within formal choice models and to test whether such models outperform the standard value maximization model. In this article, we suggest four context-dependent choice models that can conceptually capture the compromise effect. Although these models are motivated by theory from economics and behavioral decision research, they differ with respect to the particular mechanism that underlies the compromise effect (e.g., contextual concavity vs. loss aversion). Using two empirical applications, we (1) contrast the alternative models and show that incorporating the compromise effect by modeling the local choice context leads to superior predictions and fit relative to the traditional value maximization model and a stronger (naive) model that adjusts for possible biases in utility measurement; (2) generalize the compromise effect by demonstrating that it systematically affects choice in larger sets of products and attributes than previously shown; (3) show the theoretical and empirical equivalence of loss aversion and local (contextual) concavity; and (4) demonstrate the superiority of models that use a single reference point over "tournament models" in which each option serves as a reference point. We discuss the theoretical and practical implications of this research, as well as the ability of the proposed models to predict other behavioral context effects.

Alternative Models for Capturing the Compromise Effect

• Explores the importance of multiple sources for pre-purchase information of CAVs among Tennessee residents. • Utilizes latent segmentation to identify public’s scepticism of CAVs. • Indifferent individuals: stronger reliance on information sources than skeptical individuals. • Residents interested in owning a CAV rely on their peers for the information. • Residents interested in autonomous public transit rely on the advertisements for the information. Recent academic research and industrial commitments highlight the potential of connected and autonomous vehicles (CAVs) in transforming the way we travel. However, there are some anticipated barriers associated with the widespread consumer adoption of CAVs, including but not limited to low user acceptance, cybersecurity, safety, and legislation. The existing literature is non-existent in capturing the impact of information received from multiple sources (peers, car dealers, media advertisements, and personal research) on the consumers’ acceptance of CAVs while considering these barriers associated with CAV. In this direction, we quantify the impact of the multiple information sources on the individuals concerned and indifferent about the anticipated barriers of CAVs to boost their acceptance while utilizing a two-step econometric framework based on an online survey of 4,448 Tennesseans. The two-step modeling framework utilizes latent class analysis followed by a multivariate ordered logit model. Results indicated that the elderly, three or more person households, and residents interested in owning a CAV are more likely to be concerned about CAV barriers. In contrast, males, physically challenged, and residents willing to pay higher for CAV are more likely to be indifferent toward CAV barriers. Among concerned individuals, the elderly, females, and residents willing to pay more than $30 k for a conventional car are more likely to rely on car dealers when purchasing a CAV. The results of this study are expected to assist automakers, technology companies, policymakers, and third-party agencies to advertise, promote and introduce CAVs effectively through appropriate information channel(s) to boost their consumer acceptance. 

Quantifying the consumer’s dependence on different information sources on acceptance of autonomous vehicles

Social commerce (SC) is an upcoming trend that has changed the online shopping experience by allowing e-retailers to develop long-term relationships with customers and increase sales. Empowered by Web 3.0, SC offers many-to-many interactions, enhancing the quality and quantity of social interaction related to the seller–customer, information searches, and product/service delivery. The customer experience (CEX) has been well developed both in the online and offline contexts. However, limited attention has been paid to examining CEX in the SC setting. This study aims to conduct a systematic review of the literature to develop a conceptual framework exploring both the antecedents and consequences of CEX in the SC setting. In the process, we make three significant contributions to academia and practice. First, the study contributes to our understanding of CEX in the context of SC. Second, it proposes a conceptual framework by identifying antecedents of CEX and potential consequences using the consumer culture theory. Finally, it highlights a subject relevant to academia and practice while proposing recommendations for further research. 

Customer experience in social commerce: A systematic literature review and research agenda

Reference points in consumer choice models: A review and future research agenda

Understanding the process of consumer decision making is important for many decision support systems. Consumers evaluate different alternatives and then come to a decision. Prior research suggests that consumer evaluations leading to choice are comparative in nature and can be affected by other alternatives or reference products. This study proposes a mixtures-of-experts model framework to examine the role of different reference products in consumer choice of multi-attribute products. While multiple external and internal reference points have been proposed, previous studies have very rarely investigated more than one reference point in the same model. Using data from a choice-based conjoint experiment, our empirical model enables us to identify which product consumers tend to use as the reference product by incorporating four different reference products and includes consumer characteristics to examine how consumers differ in their utilization of different reference products. The results show that our model outperforms other reference-dependent models in prior literature. In our empirical context of smartphone choices, the most commonly used reference product is the most preferred product in the choice set, while the least preferred product and the average product are rarely used. We also examine the role of consumer characteristics such as gender, product familiarity, and product interest in utilizing reference products. This paper provides insights into the unobserved comparison process in consumer choice, which can be applied to decision support systems such as recommendation engines.

Incorporating reference products into modeling consumer choice decision: A mixtures-of-experts model

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Reference points in consumer choice models: A review and future research agenda

Incorporating reference products into modeling consumer choice decision: A mixtures-of-experts model