About

Martin Reimann is the McClelland Associate Professor of Marketing at the Eller College of Management. His ongoing research focuses on how new technology is changing consumers and markets, including how humans are adopting and evolving with AI. He investigates an experience-based account of judgment and choice, demonstrating how affect and autobiographical memory shape perceived value beyond money. Dr. Reimann’s work has advanced understanding of how leaders and organizations can leverage trust in the age of AI to enhance decision-making through business strategies, technology, and leadership. His research has been published in several leading journals and supported by notable organizations such as DARPA, Google, the Marketing Science Institute, the National Endowment for the Arts, and the National Science Foundation. He specializes in utilizing methodologies including behavioral experiments, functional neuroimaging (fMRI), and deep-learning models, contributing significantly to the field of consumer neuroscience. Reimann holds affiliations as an associate professor in the Department of Psychology, the College of Veterinary Medicine, and the Cognitive Science Graduate Interdisciplinary Program at the University of Arizona, and is a visiting affiliated faculty at Stanford University.

Research topics

• Computer Science
• Political Science
• Social Science
• Sociology
• Psychology
• Marketing
• Business
• Social psychology
• Epistemology
• Knowledge management
• Public relations
• Aesthetics
• Cognitive science
• Philosophy
• Advertising
• Economics
• Art
• Microeconomics

Selected publications

• How does AI disclosure shape trust? Unpacking the role of legitimacy

  2026-04-07

  articleOpen accessSenior author

  As generative artificial intelligence (AI) is increasingly adopted, understanding how its usage is perceived has become crucial for theory and practice. Our investigation highlights how disclosing AI usage reduces trust by triggering legitimacy concerns arising from deviations from taken-for-granted human-centered norms. Drawing on a micro-institutional perspective, we unpack legitimacy into its dimensions and propose that they operate via three context-specific processes—perceived typicality, commitment, and authenticity—which jointly account for the erosion of trust resulting from AI disclosure. An initial structured content-analytic study of directed written interviews reveals that people indeed voice these legitimacy concerns when scrutinizing AI usage and addresses research questions about how such concerns manifest across facets. A subsequent vignette experiment shows that disclosing AI usage sequentially diminishes perceptions of typicality, commitment, and authenticity, ultimately lowering trust. A supplementary replication experiment confirms this pattern. Altogether, our investigation clarifies the paradoxical nature of transparency, advances empirical testing of legitimacy theory, and helps bridge the literatures on trust and institutional theory.

  PublisherDOI

• Sustainability Transformation Monitor 2026

  Bertelsmann Stiftung · 2026-03-05

  articleOpen access

  Bereits zum vierten Mal in Folge liefert der Sustainability Transformation Monitor fundierte Einblicke in den Stand der Nachhaltigkeitstransformation – sowohl in der Realwirtschaft als auch in der Finanzwirtschaft. Im Fokus steht das Zusammenspiel beider Welten: Wie kann grüne Finanzierung gezielt eingesetzt werden, um die Transformation von Unternehmen wirksam zu beschleunigen? Und welche Rahmenbedingungen braucht es, damit Sustainable Finance und unternehmerische Praxis Hand in Hand wirken?

  PublisherDOI

• How does AI disclosure shape trust? Unpacking the role of legitimacy

  SocArXiv (OSF Preprints) · 2026-04-07

  preprintOpen access1st authorCorresponding

  As generative artificial intelligence (AI) is increasingly adopted, understanding how its usage is perceived has become crucial for theory and practice. Our investigation highlights how disclosing AI usage reduces trust by triggering legitimacy concerns arising from deviations from taken-for-granted human-centered norms. Drawing on a micro-institutional perspective, we unpack legitimacy into its dimensions and propose that they operate via three context-specific processes—perceived typicality, commitment, and authenticity—which jointly account for the erosion of trust resulting from AI disclosure. An initial structured content-analytic study of directed written interviews reveals that people indeed voice these legitimacy concerns when scrutinizing AI usage and addresses research questions about how such concerns manifest across facets. A subsequent vignette experiment shows that disclosing AI usage sequentially diminishes perceptions of typicality, commitment, and authenticity, ultimately lowering trust. A supplementary replication experiment confirms this pattern. Altogether, our investigation clarifies the paradoxical nature of transparency, advances empirical testing of legitimacy theory, and helps bridge the literatures on trust and institutional theory.

  Publisher

• The transparency dilemma: how AI disclosure erodes trust

  PsyArXiv (OSF Preprints) · 2026-04-07

  preprintOpen access1st authorCorresponding

  As generative artificial intelligence (AI) has found its way into various work tasks, questions about whether its usage should be disclosed and the consequences of such disclosure have taken center stage in public and academic discourse on digital transparency. This article addresses this debate by asking: Does disclosing the usage of AI compromise trust in the user? We examine the impact of AI disclosure on trust across diverse tasks—from communications via analytics to artistry—and across individual actors such as supervisors, subordinates, professors, analysts, and creatives, as well as across organizational actors such as investment funds. Thirteen experiments consistently demonstrate that actors who disclose their AI usage are trusted less than those who do not. Drawing on micro-institutional theory, we argue that this reduction in trust can be explained by reduced perceptions of legitimacy, as shown across various experimental designs (Studies 6–8). Moreover, we demonstrate that this negative effect holds across different disclosure framings, above and beyond algorithm aversion, regardless of whether AI involvement is known, and regardless of whether disclosure is voluntary or mandatory, though it is comparatively weaker than the effect of third-party exposure (Studies 9–13). A within-paper meta analysis suggests this trust penalty is attenuated but not eliminated among evaluators with favorable technology attitudes and perceptions of high AI accuracy. This article contributes to research on trust, AI, transparency, and legitimacy by showing that AI disclosure can harm social perceptions, emphasizing that transparency is not straightforwardly beneficial, and highlighting legitimacy’s central role in trust formation.

  Publisher

• How Does AI Disclosure Shape Trust? Unpacking the Role of Legitimacy

  Social Psychology Quarterly · 2026-04-14

  articleSenior author

  As generative artificial intelligence (AI) is increasingly adopted, understanding how its usage is perceived has become crucial for theory and practice. Our investigation highlights how disclosing AI usage reduces trust by triggering legitimacy concerns arising from deviations from taken-for-granted human-centered norms. Drawing on a micro-institutional perspective, we unpack legitimacy into its dimensions and propose that they operate via three context-specific processes—perceived typicality, commitment, and authenticity—that jointly account for the erosion of trust resulting from AI disclosure. An initial structured content-analytic study of directed written interviews reveals that people indeed voice these legitimacy concerns when scrutinizing AI usage and addresses research questions about how such concerns manifest across facets. A subsequent vignette experiment shows that disclosing AI usage sequentially diminishes perceptions of typicality, commitment, and authenticity, ultimately lowering trust. A supplementary replication experiment confirms this pattern. Altogether, our investigation clarifies the paradoxical nature of transparency, advances empirical testing of legitimacy theory, and helps bridge the literatures on trust and institutional theory.

  PublisherDOI

• The transparency dilemma: How AI disclosure erodes trust

  Organizational Behavior and Human Decision Processes · 2025-04-23 · 105 citations

  articleOpen accessSenior author

  As generative artificial intelligence (AI) has found its way into various work tasks, questions about whether its usage should be disclosed and the consequences of such disclosure have taken center stage in public and academic discourse on digital transparency. This article addresses this debate by asking: Does disclosing the usage of AI compromise trust in the user? We examine the impact of AI disclosure on trust across diverse tasks—from communications via analytics to artistry—and across individual actors such as supervisors, subordinates, professors, analysts, and creatives, as well as across organizational actors such as investment funds. Thirteen experiments consistently demonstrate that actors who disclose their AI usage are trusted less than those who do not. Drawing on micro-institutional theory, we argue that this reduction in trust can be explained by reduced perceptions of legitimacy, as shown across various experimental designs (Studies 6–8). Moreover, we demonstrate that this negative effect holds across different disclosure framings, above and beyond algorithm aversion, regardless of whether AI involvement is known, and regardless of whether disclosure is voluntary or mandatory, though it is comparatively weaker than the effect of third-party exposure (Studies 9–13). A within-paper meta analysis suggests this trust penalty is attenuated but not eliminated among evaluators with favorable technology attitudes and perceptions of high AI accuracy. This article contributes to research on trust, AI, transparency, and legitimacy by showing that AI disclosure can harm social perceptions, emphasizing that transparency is not straightforwardly beneficial, and highlighting legitimacy’s central role in trust formation.

  PublisherDOI

• Being honest about using AI at work makes people trust you less, research finds

  2025-05-06

  articleSenior author
  PublisherDOI

• The Transparency Dilemma: How AI Disclosure Erodes Trust

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  articleOpen accessSenior author
  PublisherDOI

• A Crowdsourced Study of ChatBot Influence in Value-Driven Decision Making Scenarios

  ArXiv.org · 2025-11-19

  preprintOpen access

  Similar to social media bots that shape public opinion, healthcare and financial decisions, LLM-based ChatBots like ChatGPT can persuade users to alter their behavior. Unlike prior work that persuades via overt-partisan bias or misinformation, we test whether framing alone suffices. We conducted a crowdsourced study, where 336 participants interacted with a neutral or one of two value-framed ChatBots while deciding to alter US defense spending. In this single policy domain with controlled content, participants exposed to value-framed ChatBots significantly changed their budget choices relative to the neutral control. When the frame misaligned with their values, some participants reinforced their original preference, revealing a potentially replicable backfire effect, originally considered rare in the literature. These findings suggest that value-framing alone lowers the barrier for manipulative uses of LLMs, revealing risks distinct from overt bias or misinformation, and clarifying risks to countering misinformation.

  PublisherOA PDFDOI

• The Sociology of Trust

  2024-01-12 · 2 citations

  book-chapterOpen access

  This chapter surveys sociological approaches to trust, defining trust as a willingness to accept vulnerability to another actor on the expectation that this vulnerability will not be exploited. It reviews two dominant traditions: generalized trust, which concerns broad confidence in unfamiliar others and is linked to socialization, self-reinforcement, and biological factors; and particularized trust, which concerns familiar actors and specific domains and is shaped by prior interaction, future dependence, and network embeddedness. The authors argue that this dichotomy is too narrow and propose a more precise framework built around three independent “radiuses” of trust: the trustor, the trustee, and the trust domain. This multidimensional model highlights neglected forms such as categorical trust and clarifies how trust varies across social settings. The chapter concludes by outlining eight priorities for future research, including better measurement, links between trust forms, affective and moral foundations, trust outcomes, digital trust, and the distinct study of trustworthiness.

  PublisherDOI

Frequent coauthors

• Oliver Schilke

  University of Arizona

  59 shared

• Jacquelyn S. Thomas

  Southern Methodist University

  18 shared

• Karen S. Cook

  14 shared

• Antoine Bechara

  University of Southern California

  12 shared

• Bernd Weber

  University of Bonn

  11 shared

• Carolin Neuhaus

  University of Bonn

  10 shared

• Judith Lynne Zaichkowsky

  Simon Fraser University

  8 shared

• Malte Brettel

  WHU – Otto Beisheim School of Management

  6 shared

Awards & honors

• MSI Scholar
• multiple early career awards from the American Marketing Ass…