About

Bryan Bonner is a professor at the David Eccles School of Business, University of Utah, holding the Frederick I. Herzberg Professorship in the Department of Management. His areas of expertise include organizational behavior, group decision-making, expertise in groups, and social influence within team settings. Bonner has received multiple awards for his teaching excellence, including the Brady Superior Teaching Award in 2012, the Honors Faculty Teaching Award in 2009, and the Doctoral Faculty Teaching Excellence Award in 2008. His research focuses on understanding how expertise, social context, incentives, and group dynamics influence decision-making, knowledge transfer, and performance in organizational settings. Bonner has contributed extensively to the academic literature through numerous refereed publications in journals such as the Journal of Behavioral Decision Making, Group Dynamics, and Organizational Behavior and Human Decision Processes. He has also authored articles in prominent outlets like Harvard Business Review. In addition to his research, Bonner is actively involved in professional service, serving on editorial boards and research groups related to group dynamics and organizational behavior.

Research topics

• Political Science
• Artificial Intelligence
• Machine Learning
• Computer Science
• Social psychology
• Psychology
• Economics
• Management science
• Communication
• Management
• Data science
• Cognitive psychology
• Knowledge management

Selected publications

• Registered Report: A Replication Examining Occupational Experience and Performance on the Water-Level Task

  Psychological Science · 2026-02-01

  articleSenior author

  This is a registered report to directly replicate the primary finding in Hecht and Proffitt (1995). Hecht and Proffitt found that those with occupational experience handling liquid in containers performed worse at solving a water-level problem than those in occupations that did not require handling liquids. Shortly after, Vasta et al. (1997) found the opposite: Experience was associated with superior performance on the task. The conflicting findings and the small sample sizes in each study leave the relationship between experience and water-level-task performance uncertain. We addressed these concerns with a high-powered direct replication of Hecht and Proffitt with adults in Germany ( N = 407). We failed to replicate Hecht and Proffitt’s results, finding that their study had less than 33% power to detect the small, nonsignificant difference that we observed between groups.

  PublisherDOI

• Author response for "Registered Report: A Replication Examining Occupational Experience and Performance on the Water Level Task"

  2025-11-11

  peer-reviewSenior author
  PublisherDOI

• Author response for "Registered Report: A Replication Examining Occupational Experience and Performance on the Water Level Task"

  2025-09-07

  peer-reviewSenior author
  PublisherDOI

• Author response for "Registered Report: A Replication Examining Occupational Experience and Performance on the Water Level Task"

  2025-06-23

  peer-reviewSenior author
  PublisherDOI

• ScholarOne - When Power and Expertise Collide, How Do Groups Decide?

  2024-04-09

  preprintOpen accessSenior author

  This research examines the effects of the alignment/misalignment of decision power with member expertise. In Study 1, 324 participants worked in face-to-face groups. We manipulated whether group members were provided with veridical performance feedback and whether decision power was assigned to the best/worst performing member. We found it was the combination of providing performance feedback and assigning power to the worst member that led to negative outcomes, not merely that the worst performer had decision power or that feedback was provided. In Study 2 and Study 3, participants reacted to vignettes that differed with respect to whether performance feedback was provided to team members and to which member decision power was assigned. We found that only assessments of fairness and expectations of success varied as a consequence of the assignment of decision power based on member expertise.

  PublisherOA PDFDOI

• Unaware and unaccepting: Human biases and the advent of artificial intelligence.

  Technology Mind and Behavior · 2024-01-01 · 4 citations

  articleOpen accessSenior author

  Generating accurate assessments of future technological capabilities becomes increasingly difficult as the rate at which technological progress accelerates. The current research reviews the rapid growth of artificial intelligence (AI) and examines the human biases that impede its assessment. In two online experiments (N = 161; N = 151), we find evidence that people are prone to underestimate AI capabilities due to the exponential growth bias (i.e., the tendency to underestimate exponential growth). Moreover, we find evidence that people reject the aversive implications of rapid technological progress even in cases in which they themselves predict the growth rate, due to the motivated reasoning bias (i.e., the desire to search for and interpret information in ways consistent with one’s desires).

  PublisherOA PDFDOI

• When Power and Expertise Collide, How Do Groups Decide?

  Small Group Research · 2024-10-11

  article

  This research examines outcomes associated with the misalignment of power and expertise. Using interactive groups, we found that it was specifically the combination of providing performance feedback and then assigning power to the worst member that led to negative outcomes, not merely that the worst performer held decision power. A follow-up study verified that veridical feedback improved people’s ability to identify expertise. Across two additional studies, we found that known misalignment of decision power and performance decreased perceived fairness and expectations of success. We posit that when members know that power and expertise are misaligned, decision making and performance suffer.

  PublisherDOI

• Author response for "Registered Report: A Replication Examining Occupational Experience and Performance on the Water Level Task"

  2024-06-20

  peer-reviewSenior author
  PublisherDOI

• Differences in the Fundamental Epistemic Foundations of Problem Solving

  Academy of Management Proceedings · 2023-07-24

  articleSenior author

  The tasks that organizational teams undertake are typically in the areas analyzing, collaborating, considering, and deciding within defined problem domains. This theory-building work seeks to synthesize and integrate core concepts from classical philosophy and the modern problem-solving literature toward building a stronger foundation from which problem solving may be understood. This is done with an eye toward organizational application, particularly in the context of problem-solving teams. This level of abstraction is more basic and fundamental than individual differences with respect to decision styles, the preference for more or less effortful cognition, or the need to find closure, and provides new avenues for thought and research. Members of teams, even if they share the motivation to come to the best possible solutions to organizational problems, will vary with respect to their perspectives on how that should be done, (i.e., their epistemic predilections). These predilections vary with respect to the a priori or a posteriori nature of preferred knowledge and the source of that knowledge (i.e., self, proximal, or distal). The fundamental divide between problem solvers who are a priori- as opposed to a posteriori-motivated is that the former believes that a given problem requires only insight (although not necessarily their own) whereas the latter believes that the problem requires evidence (although not necessarily from their own experience). In the current work we introduce, define, and provide the nomological network related to epistemic predilection and show its potential with respect to opening new avenues of scientific inquiry.

  PublisherDOI

• Demonstrability Measure

  PsycTESTS Dataset · 2022-01-01

  dataset1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Michael R. Baumann

  The University of Texas at San Antonio

  15 shared

• Patrick R. Laughlin

  8 shared

• Kathryn A. Coll

  7 shared

• Daniel Shannahan

  Northern State University

  7 shared

• Kristin Bain

  6 shared

• Nathan Meikle

  University of Kansas

  6 shared

• Gerardo A. Okhuysen

  University of California, Irvine

  5 shared

• Sheli Sillito

  Brigham Young University

  4 shared

Education

• Ph.D., Management

  University of Utah

  2005

• M.S., Management

  University of Utah

  2001

• B.S., Management

  University of Utah

  1999

Awards & honors

• Brady Superior Teaching Award (2012)
• Faculty Teaching Award (2009)
• Doctoral Faculty Teaching Excellence Award (2008)
• David Eccles Faculty Fellow (2006)