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on Neuroeconomics |
| By: | Bugarčić, Milica; Hanić, Hasan |
| Abstract: | The paper presents the results of a meta-analysis of empirical literature focused on the study of behavioral biases in financial decision-making. The findings indicate that biases such as loss aversion, present bias, status quo bias, overconfidence, and herding systematically shape both individual and collective financial behavior. Their influence is evident across diverse institutional and cultural contexts, confirming that behavioral deviations are structural rather than incidental features of economic behavior. The study highlights the need to integrate longitudinal and neuroeconomic approaches to achieve a deeper understanding of the interaction between cognitive, emotional, and contextual factors in decision-making processes. By incorporating psychological dimensions into economic theory, behavioral economics provides a more comprehensive and realistic framework for interpreting market dynamics and enhancing the quality of financial decision-making. |
| Keywords: | behavioral economics, behavioral finance, financial decion-making, cognitive biases |
| JEL: | D14 G53 I22 G41 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esconf:343874 |
| By: | Isaac Cheah; Ethan Pancer; Jianping Huang; Aimee Pink; Béatrice Parguel (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique); Fong Keng-Highberger |
| Abstract: | Research examining artificial intelligence's (AI) role in food choice largely asks whether AI helps people make better decisions, treating it as an information source that improves the accuracy or personalization of recommendations. We suggest that this framing captures one point on a broader continuum of how AI impacts food decisions. AI can act, often simultaneously, as an advisor consulted for information, a decision partner that actively shares in constructing the choice, or an ambient influence that shapes what appears desirable and available before deliberate choice begins. As AI becomes more conversational and more embedded across the food environment, this continuum is increasingly populated in less visible regions, yet the field remains concentrated on the advisor role. We focus on the decision-partner role, where AI participates in value construction, goal clarification, attribute weighting, and choice justification, raising questions about agency, responsibility, and delegation. We articulate four mechanisms that describe this participation, namely preference offloading, responsibility reallocation, confidence calibration, and food-decision skill erosion. Because food choice is frequent, often habitual, and commonly involves trade-offs among hedonic and utilitarian goals, it is an ideal domain in which these process-level effects can compound. We draw on evidence from consumer psychology, automation research, and cognitive science, noting where boundary conditions remain untested in food contexts, and close with a research agenda organized around agency, reliance, responsibility, confidence, and cognitive effort. When AI shares in constructing a food choice, the values being optimized may no longer be entirely the consumer's own. |
| Keywords: | Artificial intelligence, Food choice, Decision-making, Consumer psychology, Algorithm reliance |
| Date: | 2027–01 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05752256 |
| By: | Benjamin Balzer; Benjamin Young |
| Abstract: | Consumers can invest resources to understand and avoid their behavioral mistakes, and their incentives to do so depend on the market consequences of remaining naive. We incorporate this feedback between consumers' cognitive states and market outcomes into a general contracting model. Firms face a trade-off between the depth and reach of exploitation: deeper exploitation raises profit from a naive consumer but induces greater cognitive investment, promoting sophistication and shrinking the exploitable consumer base. This trade-off disciplines exploitation and can cause policies that benefit consumers when cognition is fixed to backfire when cognition is endogenous. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2609.25509 |
| By: | Nikos Bozionelos (EM - EMLyon Business School); Sumona Mukhuty (Leeds Beckett University (United Kingdom, Leeds)); Sanjay Kumar Singh (University of Dundee); Georgios Bozionelos (Sotiria General Hospital (Greece, Athens)) |
| Abstract: | Purpose: To provide a comprehensive investigation into the relationship of cultural intelligence (CQ) with job performance using primary data. Of special interest were the interactions between meta-cognitive CQ (MCQ) with the other three facets of CQ. Design/methodology/approach: Participants were 182 expatriates working in Abu Dhabi. Their line managers evaluated them on the measures of job performance: Organizational citizenship behaviour-Help (OCB-H), OCB-Change (OCB-C) and task performance. Findings: All testing took place with Emotional Intelligence as a control. Most interactions of MCQ with cognitive CQ (CCQ × MCQ), motivational CQ (MCQ × MoCQ) and behavioural CQ (BCQ × MCQ) were related to the measures of job performance. However, interaction patterns were more nuanced than expected. For example, lower motivational CQ was associated with higher OCB-H and task performance when MCQ was weak. Global CQ was related to all performance measures, but the relationships of the four facets of CQ differed across measures, with cognitive CQ showing the greatest consistency. Finally, global CQ was more strongly related to OCB-H and OCB-C than to task performance, with the results for CQ facets demonstrating the same tendency. Practical implications: Higher scores on all facets of CQ may not always be advantageous for job performance in highly culturally diverse environments. Originality/value: The first study with specifically collected data to test the interactions between CQ facets, providing evidence for their interplay. The study adds to evidence on how cultural intelligence relates to job performance, along with its nomological validity. |
| Keywords: | Task performance, Organizational citizenship, Emotional intelligence, Person-environment fit, Adaptive expertise, Interactions, Facets, Cultural intelligence |
| Date: | 2026–09–09 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05756282 |
| By: | Kurz, Konstantin |
| Abstract: | Entrepreneurial and nonentrepreneurial life is about making decisions. For centuries, the process of making such decisions was viewed as purely rational and cognitive – an understanding that was long echoed in the entrepreneurship literature. However, at the beginning of this millennium, this non-emotional, utility-maximizing decision-making concept was challenged by the rise of manifold studies from social psychology and neuroscience providing empirical evidence for the role of emotions in decision making. These findings have then also been incorporated into the literature on entrepreneurship, which has begun to capture entrepreneurial action as an emotional journey. Yet, little is known empirically about the interplay of these emotional and cognitive processes during entrepreneurial decision making. Inspired by multiple calls for research on this topic, this dissertation investigates when emotions facilitate or impair effective entrepreneurial and ethical decision making. For this, this manuscript summarizes three studies, each with a distinct, unique, real-world dataset. The first research setting analyzes the effect of emotions relived through empathy on the level of entrepreneurial action in the form of new product launches. The empirical analysis on 4, 425 real entrepreneurs reveals that relived emotions affect these entrepreneurs’ new product launches in an inverted U-shaped relationship, suggesting a novel “too-much-of-a-good-thing” logic for entrepreneurial empathy. The second study investigates the effect of emotions on the resource acquisition effectiveness of survival-driven crowdfunding campaigns, an entirely new campaign type. In this study, we demonstrate that for this new campaign type, negative emotional tone strategies can facilitate resource acquisition, contrasting the thus far prevailing understanding that positive emotional cues are universally beneficial for improving crowdfunding success. Finally, the last study of this dissertation analyzes the rational and emotional motives for sharing electricity during a long-lasting power outage. The empirical results of this third study show that emotional motives play a surprisingly strong role relative to rational factors for facilitating prosocial contribution behavior in such a crisis setting. With these three multifaceted research settings, this dissertation as a whole clearly illustrates the need for close interaction of both cognitive and emotional factors for effective entrepreneurial and ethical decision making. |
| Date: | 2026–09–10 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:162281 |
| By: | Arkarup Basu Mallik; Mihir Bhattacharya; Anuj Bhowmik |
| Abstract: | We study an attribute-based model of stochastic choice in which attention to each attribute is limited, and characterize two choice rules within it. The Multiplicative Attention Rule (MAR) rewards an alternative only for the attributes on which it ranks first, combining attention to these multiplicatively across attributes; it captures choice deferral driven by the cognitive load of attending to `too many' attributes. The Additive Attention Rule (AAR) instead averages attention across attributes and credits an alternative by its full ordinal rank within the menu. This difference in aggregation is what separates the two rules behaviourally: AAR reproduces the Attraction Effect while holding the relevant attributes fixed across menus, something MAR can do only when the attribute set itself changes, though AAR, in turn, cannot accommodate the Compromise Effect. Both rules are characterized by axioms on observable choice data, and in both cases the underlying attention parameters and attribute-based preferences are uniquely identified from that data. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.01333 |