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While algorithms increasingly outperform human experts and gain widespread adoption, many individuals still resist using them due to algorithmic aversion. Although prior research has examined the appreciation and avoidance of algorithmic advice, the underlying mechanisms driving these decisions remain underexplored. This paper investigates the role of individuals’ readiness to act, specifically whether they adopt a deliberative or implemental mindset, in shaping their openness to algorithmic advice. Across three hypothetical studies and one incentive-compatible study, results show that individuals in a deliberative mindset, characterized by thoughtful evaluation, tend to prefer advice from human sources. In contrast, those in an implemental mindset, characterized by action-oriented thinking, are more likely to prefer algorithmic advice. Additionally, the findings reveal that perceived uncertainty moderates the influence of mindset on algorithmic receptiveness. These findings offer nuanced insights into the psychological mechanisms that drive engagement with algorithms and suggest practical strategies to enhance collaboration with both algorithmic and human recommendations. © 2025 Elsevier Ltd

Prior literature has reported a tendency for consumers to rate peer-to-peer (P2P) services more positively than the same services provided by traditional businesses (e.g. Airbnb vs. hotel). We first show that consumers experience greater empathy toward a P2P service provider (a person) than a traditional service provider (a business). We then show that P2P services enjoy higher evaluations because empathy for the provider leads consumers to tolerate minor negative elements in those settings as compared to traditional business settings. Further, we show that this effect is moderated by the perceived size of the business. The implications of this type of rating bias on traditional businesses and consumer welfare, and the limitations of this research, are discussed. © 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Academic dishonesty of students is a problem that threatens the integrity of educational institutions. Understanding the sources of academic dishonesty has become an urgent need, which compels higher educational institutions to evaluate and redesign approaches to address this problem. To develop new and important insights about this this form of student misconduct, this paper takes an integrative social cognitive perspective. It explores students’ attitudes toward various forms of academic dishonesty. The central research question concerns the impact of individual differences in moral disengagement and Machiavellianism on academic dishonesty tendencies. The study is based on a sample of 195 students at a public university in northeastern United States. Analysis was conducted by partial least squares equation modeling (SmartPLS-SEM). The analysis disclosed that, in sum, moral disengagement was strongly associated with academic dishonesty attitudes of fabricating information and both moral disengagement and Machiavellianism were associated with obtaining unfair academic dishonesty advantages. Data supported nearly all aspects of a structural model of academic dishonesty tendencies, with the exception of an association between Machiavellianism and receiving or abetting academic dishonesty, as well as an association between moral disengagement and ignoring prevalent practices that were in the predicted direction but were not significant. These findings provide a general understanding of the process by which academic dishonesty is determined. Study implications for ameliorating the impact of academic dishonesty are as follows: students should be engaged in an atmosphere full of communal morality, dissuasive of justificatory rationalizations and social arrangements that negate students’ use of moral disengagement.

Despite the ethical concerns over the datafication and surveillance of individuals and groups, companies are making ever greater investments in big data. The assumptions underpinning this movement are: (1) organizations are passive implementers of big data—more data is the inevitable consequence of technology and a competitive necessity for business, (2) more data offers a more objective and accurate picture of reality and (3) more data enables better prediction. We argue that this perspective is strategically unsustainable and abdicates ethical responsibility.

Motivated by the ongoing debate on the costs and benefits of corporate social responsibility (CSR), we explore how talented managers view CSR investments. Based on nearly 20,000 observations across 17 years, our evidence reveals a nonmonotonic effect of managerial talent on CSR. Exploiting a novel measure of managerial ability, we find that talented managers view CSR investments favorably. However, only those with especially strong talent are in favor of CSR investments. For executives ranked above the 75th percentile in terms of managerial talent, an increase in managerial ability leads to more CSR investments, suggesting that these strongly talented managers perceive CSR as enhancing firm performance. In contrast, for those with weaker talent, CSR investments are negatively associated with managerial ability, implying that these weakly talented managers view CSR as a wasteful deployment of resources. Further evidence shows that our conclusion is unlikely confounded by endogeneity.

Motivated by recent productivity-based theories of diversification, we argue that only conglomerates with an optimal degree of diversification can utilize their comparative advantages across various industries and achieve economies of scope by eliminating redundancies. Evidence from both corporate bond and equity markets suggests that optimally diversified conglomerates consist of either (1) approximately five equally weighted divisions, or (2) one large core business segment that roughly accounts for 75 % sales. Moreover, the relative size of divisions has a critical impact on how diversification affects credit spreads and excess values. Nonparity among divisions correlates with greater costs that increase with the number of divisions.

Request PDF | Induction of Construal-Level Mindset via Experience of Surprise: An Abstract: Proceedings of the 2018 Academy of Marketing Science (AMS) Annual Conference | An experience of surprise is often an outcome of disconfirmation of expectations and can be associated with positive or negative affect depending... | Find, read and cite all the research you need on ResearchGate

In this research, we find that incentive valence and construal-level mindsets can interact to influence behavioral persistence on challenging tasks. An abstract mindset improves persistence in response to positively framed incentives whereas a concrete mindset improves persistence in response to negatively framed incentives. This interaction effect can be observed even when the cues inducing construal-level mindsets are not related to the incentives or the incentivized tasks. Participants in our studies were either positively or negatively incentivized to solve a set of difficult anagrams, and were primed with an abstract or a concrete mindset using spatial (Study 1) and social (Study 2) cues. The participants persisted longer in response to the positively framed incentive when primed with spatially or socially remote cues. In contrast, for the negatively framed incentive, participants persisted longer when primed with spatially or socially proximal cues. Copyright © 2017 John Wiley & Sons, Ltd.

This research examines the impact of generative artificial intelligence (AI) on the perception of educational content quality, specifically by comparing AI-generated and human-generated course syllabi in marketing education. Results from four studies indicate a general preference for AI-generated syllabi, attributed to their greater perceived objectivity. This preference is more pronounced in conventional courses but diminishes in unconventional ones, suggesting that the unique aspects of these courses may reduce the advantages of generative AI. In addition, disclosing the AI authorship of syllabi significantly affects their perceived quality negatively, underscoring the impact of transparency on the acceptance of AI-generated educational materials. These findings highlight the potential of generative AI in educational content creation and its limitations in certain contexts. They offer valuable insights for enhancing educational practices and shaping policy decisions to enrich student experiences in the era of AI integration.

Today's practicing marketers and scholars are confronted with a wide array of conflicting and imprecise information about best practices by which to search, gather, consolidate and interpret market information. Consequently, the need has never been greater to optimize market sensing to generate managerial actions that efficiently and effectively utilize knowledge of emerging consumer needs and competitive threats. This book addresses these urgent concerns. In essence, Market Sensing Today will cover, in ground-breaking ways, the following marketing managerial areas: * marketing opportunities associated with conventional and progressive bases of segmentation. * trends in market segment size and growth affecting long-range planning. * strategic direction for reaching future goals. * managerial understanding of assumptions competitors make about themselves. * the direction of current market strategies. * adding to the knowledge of a firm's core competencies. * how new market knowledge is best integrated into a firm's market intelligence system. * best ways to ensure the quality of information underlying decisions. * how benchmarking improves with market sensing. * best approaches for translating business issues into projects. * ways that key information may be disseminated within firms. * how proposed strategic changes are promoted by market sensing. * roles customer satisfaction insights play in policy. This book will address these key issues and more, to advance theory, research and practice based on latest developments in this vital field. It will show how to re-formulate traditional models that no longer work.

Political polarization is a marked political division in the population, characterized by multiple manifestations. The authors argue that it can affect consumer psychology, which in turn influences marketers, policy makers, and consumer welfare. The present work introduces the construct of political polarization to the marketing literature and shows how it serves as a novel challenge for various marketing stakeholders. For consumers, the authors propose that political polarization increases the salience of political identities, alters inter- and intragroup dynamics, and amplifies cognitive biases. These effects negatively affect consumer welfare, including financial welfare, relationships, mental and physical health, and societal interests. For marketers, polarization introduces a challenge to both be more sociopolitically engaged while also navigating competing political interests. Polarization also creates new opportunities and challenges for segmentation, targeting, loyalty, and product offerings. For policy makers, political polarization creates policy gaps, impedes the implementation of policy, and obstructs governance. Building from these insights, the authors consider the drawbacks and overlooked benefits of political polarization, potential remedies, and directions for future research.

Fear of COVID-19 has been understandably widespread, given continual exposure to dire information from pandemic media coverage and interpersonal communications. The present study addresses a limitation of the extended parallel process model in predicting fear of COVID-19 by inclusion of the concept of emotional contagion. The main gap in the literature is filled by the study’s distinctive contribution that broadens and upgrades the extended parallel process model. The model is extended by its integration with the theory of emotional processing. The study is based on a national panel of adults (N = 206). The methods include path modeling by SmartPLS. In addition, multigroup analyses examine overall model differences between gender classifications. Findings and conclusions can be used to minimize excessive fear, and at the same time to promote confidence in following official public guidance and protective regulations to cope with the pandemic. © The Author(s) 2023.

This handy guide to excellent business communications is perfect for both college students and business professionals. Whether preparing for a career, launching a career, or advancing in a career, the savvy professional understands that every organization expects employees to be exceptional business communicators. Today's Business Communication: A How-to Guide for the Modern Professional leads readers through the most frequently encountered business communication situations. Two business partners who are also business school professors share their combined 30 years of marketing and communication experience with readers in this accessible, entertaining, and informative guide. The authors enhance the readers' experience through anecdotes from business professionals from different industries.

This study presents an original model that features the emotion of fear of COVID-19 as a direct effect on vaccination intentions. A central research question addressed in the study is what roles do the emotion of fear of contracting COVID-19 and the threat posed by uptake of the COVID-19 vaccination play in levels of vaccination intention? The study used a structural equation model (SEM) and applied the SmartPLS 3.2.6 data analysis tool for model estimation and multivariate analysis variables. A key finding is that vaccination resistance is strongest when fear of COVID-19 is lower, and vaccination threat higher. Vaccination threat appraisal and vaccination intention were found to have a negative relationship. Response costs at higher levels lessen motivation for COVID-19 vaccination. Research implications include research-based targeting of differing segments by their primary fear, either fear of COVID-19 or of the preventative vaccine.

With the increasing use of AI in marketing, ethical repercussions are beginning to emerge. From privacy issues, through discrimination of marginalized groups, to emergent systemic social distortions, AI is changing the marketing ethical landscape. In this paper we conduct a structured literature review of the emerging ethical issues posed by AI in the domains of marketing and consumer behavior. We identify three clusters of ethical issues (algorithm, society and existential) and map these to the marketing domains of systems, brands, and consumers. We conclude that the field of ethical marketing AI is still very much in its infancy, but such is the rate of development ethical marketing AI is likely to become an important field for academics and practitioners alike. © 2023 IEEE Computer Society. All rights reserved.

Social media platforms have become more polarizing with the emergence of polarizing influencers. This research investigates how polarizing influencers can improve the effectiveness of brand-posts with the help of three experiments and field-data from Instagram. The results of the first experiment suggest that the polarizing nature of the communication source triggers defensive motivated reasoning among fans, even when the message being communicated is non-polarizing. This, in turn, has downstream consequences on post engagement and purchase intention. Analysis of 779 brandposts of Instagram influencers suggests that the polarization effect on post engagement is stronger for mega (vs. macro) influencers. By exploring the role of motivated reasoning, this research expands our understanding of the factors that drive consumers to engage with brand content on social media. The findings suggest that marketers can take advantage of the existing polarization among online users regarding polarizing influencers to enhance the effectiveness of their brand communication.