Journal of Business Research最新文献

Generative Artificial Intelligence (GAI) is witnessing a lot of adoption across industries, but literature is yet to fully document the nuances of these applications. We develop a comprehensive framework for understanding the factors that affect trust in online grocery shopping (OGS) using GAI chatbots. Our exploratory study was conducted via interviews, which helped to build our model. We integrate the Elaboration Likelihood Model (ELM) and Status Quo Bias (SQB) theory to develop the Unified Framework for Trust on Technology Platforms. In our confirmatory study, by analyzing 372 responses from users, using structural equation modelling (SEM), we initially validate our path model. Subsequently, we used fuzzy set qualitative comparative analysis (fsQCA) to check the causal combinations to explain different trust levels. Apart from perceived regret avoidance, all of the other factors had a significant effect on attitude and trust. Perceived anthropomorphism moderated the associations between interaction quality, credibility, threat, and attitude.

Literature suggests that chief executive officers (CEOs) often discount long-term returns more than short-term gains, resulting in firm short-termism. However, the findings from previous studies are not conclusive. Drawing on upper echelons theory, we argue that a CEO’s optimism about the firm’s future returns is a critical factor that influences the likelihood of CEO temporal myopia leading to firm short-termism. We further propose that the perceived opportunity costs of being short-term oriented can reshape the role of CEO optimism in converting temporal myopia to firm short-termism. Specifically, when a CEO’s compensation is dependent on the firm’s stock market performance, the perceived opportunity cost of being short-term focused is high, mitigating the negative impact of CEO optimism. However, when the CEO faces high environmental discretion that can help the firm maintain competitive advantage through alternative means, the perceived opportunity cost becomes lower, amplifying the adverse impact of CEO optimism.

Since the introduction of ChatGPT, the leading example of Generative Artificial Intelligence (GenAI), the research community and the general public have been captivated by GenAI’s remarkable advances in performance, and its ability to both imitate and, in some respects, surpass human capabilities. This paper offers a comprehensive analysis of the impact of AI on consumer behavior, focusing on the two pivotal phases of AI development over the past 15 years. We start by reviewing the extensively researched, yet still growing, field of algorithmic predictions and decision-making, alongside the varied positive and negative consumer reactions it elicits. Subsequently, we delve into the just emerging field of GenAI. Here, we differentiate between Convergent Thinking GenAI, which is more domain-specific and geared towards pre-defined task completion, and Divergent Thinking GenAI, which is more domain-general and oriented towards new task fulfillment. For each of these realms, we identify key areas for future investigation.

Brand activism (BA) has attracted substantial attention due to consumers’ rising expectations for brands to address social and political issues. However, empirical research on consumers’ understanding of activist brands (i.e., brands that engage in brand activism) remains limited, mainly due to the absence of a valid measurement tool. To fill this gap, the authors introduce a two-dimensional consumer Activist Brand Perception (ABP) scale with eight items. Drawing from extensive literature analysis, relevant manifestations of BA are identified as the foundation for the scale. Through a rigorous scale development process involving qualitative and quantitative assessment, the (ABP) scale emerges as a reliable instrument to capture consumer perceptions of activist brands, enabling researchers to comprehend how consumers view activist brands.

Professional generated content (PGC) serves as a vital and reliable online source that provides large-scale information about various aspects of brands and products. This study focuses on acquiring product-level competitive intelligence from large-scale PGCs. Specifically, we aim to simultaneously identify competitive relationships among products, extract representative topics shared by competing products, and estimate content preferences. To this end, we present a topic model that jointly leverages textual content and their associated product tags in PGCs. Owing to large-scale and lengthy PGCs, we propose a collapsed variational Bayesian inference algorithm to improve the model learning. We analyze over 100,000 PGCs and 3,000 associated products for empirical application in automobiles. Experimental results show that the proposed approach can accurately analyze market competition. Our findings have significant implications for product managers, enabling them to identify competitors, assess experts’ opinions on their products and competitors, and select high-quality content creators to improve promotions.

Exploiting a novel measure of firm-specific exposure to climate change generated from cutting-edge machine learning algorithms, we explore the effect of climate change vulnerability on shareholder wealth using Donald Trump’s unexpected election victory in 2016. Our results demonstrate that companies more vulnerable to climate change experienced significantly more adverse market reactions when Trump was elected. Considering Trump’s public skepticism on climate change, investors expected him to oppose actions that seriously addressed climate change, resulting in more negative consequences for firms with higher climate change exposure. Our results provide compelling evidence that the positions taken by politicians on climate change directly impact firm value and shareholder wealth. Our findings suggest that elections have significant ramifications on financial and capital markets and that climate change is a crucially important issue for shareholders and investors.

We examine the effects of short- and long-term as well as positive and negative ESG reputation of companies on aggressive short selling. Our results suggest that aggressive short selling is most associated with recent and negative ESG-related news. These findings are consistent with the recency and negativity biases of reputation literature. When we separate the companies into positive and negative long-term ESG reputation, we find that aggressive short selling occurs most frequently in the subgroup of companies that possess a long-term, negative ESG reputation jointly with recent, negative ESG-related information. Interestingly, however, we do not find that the aggressive short selling of this subgroup is followed by significantly negative abnormal returns. Instead, we find significant negative abnormal returns following aggressive shorting of the subgroup with a long-term, positive ESG reputation coupled with recent, negative ESG-related information, which is consistent with the expectancy violation theory of reputation literature.

Previous studies have investigated the positive effects of inspiration, but its influence on consumer patience in intertemporal choice remains largely unknown. To bridge this gap, we conducted six studies to examine the relationship between inspiration and consumer patience in intertemporal choice. Our findings indicate that inspiration can increase consumer patience in intertemporal choice, with meaning in life mediating the observed positive effects. Furthermore, we discover that regulatory focus moderates the direct and indirect effects of inspiration on consumer patience in intertemporal choice through meaning in life. Specifically, for promotion-focused consumers, inspiration enhances their sense of meaning in life, leading to increased patience in intertemporal choice. However, the positive effect of inspiration on patience through meaning in life is not observed in prevention-focused consumers. These findings contribute to the literature by directly investigating the link between inspiration and patience in intertemporal choice while providing valuable insights for marketers and policymakers.

Despite the increasing use of robots in marketing to improve consumers’ perceptions of brand image, relatively little is known about how the positioning of robots (i.e., as either partners or servants) influences consumer evaluations of brands employing service robots. In this paper, we examine the effect of robot role positioning on consumers in four studies within the context of time-honored brands. Study 1 demonstrates that brands using robots positioned as partners (vs. servants) will enhance consumers’ brand evaluations. Study 2 shows that psychological distance is a mechanism underlying the effect between robot role positioning and consumers’ brand evaluations. Studies 3 and 4 consider power distance belief (PDB) and service type to explore the premise’s boundary and find that these factors moderate the effect of robot role positioning on consumer brand evaluations. The findings offer theoretical and managerial implications for human-robot interaction in marketing.

With the emergence of the digital economy, the occupational landscape in many countries has undergone major transformations. While scholars have started to study the working conditions of digital economy occupations such as app-based food delivery couriers and social media influencers, assessing societal perceptions of these occupations remains uncharted territory. This article provides a substantive contribution through an in-depth analysis of occupational prestige and occupational social value perceptions across 76 UK digital economy occupations. Leveraging two expansive surveys with more than 2400 respondents, the findings show that these nascent occupations tend to have modest prestige, and that their perceived social value is lower than that of analogous non-digital occupations. Socio-economic factors and attitudes foster variability in societal perceptions. The research thus advances a nuanced understanding of the evolving digital economy, providing evidence for fellow researchers, policymakers, and the larger public, for whom the results help contextualize career choices and occupational identities.