About

Brett Hollenbeck is an Associate Professor of Marketing at UCLA Anderson School of Management. His research focuses on empirical industrial organization and quantitative marketing, with recent studies examining economies of scale in retail and service industries, the relationship between mergers and innovation, the economics of online reviews and ratings on online platforms, the regulation of rating manipulation, and the role of imperfect competition in optimizing taxation and regulation of the legal cannabis industry. Before pursuing his Ph.D. in economics at the University of Texas at Austin, he worked as an economist in the Office of Tax Policy at the U.S. Department of the Treasury and as a research fellow at the U.S. Chamber of Commerce. He teaches Customer Assessment and Analytics at Anderson, equipping his MBA students with quantitative tools to measure customer preferences and analyze data. Originally from Missouri, Hollenbeck is a film buff, an avid traveler, and a supporter of Texas Longhorns football.

Research topics

• Computer Science
• Public economics
• Biology
• Industrial organization
• Microeconomics
• Economics
• Business

Selected publications

• Misinformation and Mistrust: The Equilibrium Effects of Fake Reviews on Amazon.com

  National Bureau of Economic Research · 2025-08-01

  reportOpen access

  Fake product reviews-and the manipulation of reputation systems by sellers more broadly-are a widespread issue for two-sided platforms.We study two primary channels through which such manipulation can affect market outcomes: (i) creating misinformation about the reviewed product, and (ii) breeding mistrust in ratings system overall.To examine these in the Amazon.commarketplace, we measure misinformation by observing products purchasing fake reviews and measure mistrust by eliciting shoppers' beliefs about the prevalence of fake reviews on Amazon through an incentivized survey experiment.We incorporate these into a structural model of demand in which consumers form beliefs about product quality based on observed reviews and perceptions about their trustworthiness.Counterfactual policy simulations indicate that fake reviews reduce consumer welfare, shift sales from honest to dishonest sellers, and ultimately harm the platform.Welfare losses arise primarily from misinformation that leads to worse purchases.While mistrust also leads to purchasing mistakes, the consumer harms of mistrust are largely offset by increased price competition under a weakened ratings system.Finally, we identify key limitations in platforms' incentives to police manipulation and evaluate enforcement alternatives.

  PublisherDOI

• The Impact of LLM Adoption on Online User Behavior 

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

  preprintOpen accessSenior author
  PublisherDOI

• Misinformation and Mistrust: The Equilibrium Effects of Fake Reviews on Amazon.Com

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• The Financial Consequences of Legalized Sports Gambling

  2025-07-02

  articleOpen access1st authorCorresponding

  We study the impact on consumer financial health of the widespread legalization of sports betting in the United States, which began in 2018. We use the state-by-state rollout of legal betting combined with a large representative dataset on consumer credit outcomes to identify effects. We find that states with legal sports betting, particularly online or mobile betting, experienced small decreases in credit scores, as well as large increases in some indicators of excessive debt, including bankruptcies and debt sent to collection agencies. These adverse outcomes are overwhelmingly concentrated among consumers with poor financial health prior to gambling legalization.

  PublisherOA PDFDOI

• Evaluating the Impact of Privacy Regulation on E-Commerce Firms: Evidence from Apple’s App Tracking Transparency

  Management Science · 2025-11-20 · 4 citations

  article

  Assembling novel data sets on online advertiser spending, performance, and revenue, we quantify the economic effects of Apple’s App Tracking Transparency (ATT) privacy policy on e-commerce firms. We find that conversion-optimized Meta advertisements, affected most by ATT, saw a 37% reduction in click-through rates after ATT. Although firms responded by shifting ad spending from Meta to the Google ecosystem, firms with higher baseline Meta dependence nevertheless experienced a substantial decline in firm-wide revenue relative to firms with lower baseline Meta dependence. We quantify these effects using a variety of methods, finding revenue decreases in the range between 8% and 40% relative to less exposed firms. These declines were primarily borne by smaller e-commerce firms, raising questions about the tradeoffs between consumer privacy and the ability of smaller e-commerce and direct-to-consumer firms to succeed in the product market. This paper was accepted by Jean-Pierre Dube, marketing. Funding: This work was supported by the LEC Program on Economics & Privacy, MSI Research Grant [4001820]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.06600 .

  PublisherDOI

• Identification of Preference “Phenotypes” in Men With Prostate Cancer

  Urology Practice · 2024-06-20 · 1 citations

  article

  INTRODUCTION: Patient preference assessment is key to high-quality decision-making in men with prostate cancer. We aimed to determine if "phenotypes" could be identified among men with prostate cancer, with each phenotype representing a cohort with a distinct combination of preferences. We wished to learn if there was an association between phenotype and treatment selection. METHODS: A prospective cohort of men with prostate cancer received a pre-visit decision aid. This software used conjoint analysis to quantify relative patient preferences for treatment-associated survival, quality of life outcomes, and recovery time. We collected patient clinical data, physician recommendation for active treatment or surveillance, and treatments received. Preferences were analyzed using latent class analysis to identify distinct classes of preference phenotypes. We compared patient characteristics and treatment choice across phenotypes, both univariately and in a multivariable logistic regression. RESULTS: In 250 men who used the decision aid as part of routine care, latent class analysis revealed 3 phenotypic classes. Men in Class 1 had the highest concerns around recovery time and the lowest value on improving lifespan. Men in Class 2 had relatively evenly distributed concerns. Men in Class 3 had the lowest concerns around recovery time and risk of surgical complications. On multivariate analysis, treatment choice was not associated with preference-based phenotype. Only physician recommendation was associated with choice of active treatment. CONCLUSIONS: We identified the existence of 3 patient preference-based phenotypes in men with prostate cancer. Each phenotype had a unique combination of trade-offs when considering competing treatment outcomes. These phenotypes were not associated with treatment. Physician recommendation was the only factor determining treatment choice.

  PublisherDOI

• Retailer Price Competition and Assortment Differentiation: Evidence from Entry Lotteries

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Dynamic Entry & Spatial Competition: An Application to Dollar Store Expansion

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Retailer Price Competition and Assortment Differentiation: Evidence from Entry Lotteries <br>

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• The Financial Consequences of Legalized Sports Gambling

  SSRN Electronic Journal · 2024-01-01 · 8 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

Frequent coauthors

• Sherry He

  25 shared

• Renato Giroldo

  17 shared

• Davide Proserpio

  University of Southern California

  17 shared

• Sridhar Moorthy

  University of Toronto

  6 shared

• Kosuke Uetake

  5 shared

• Wayne Taylor

  4 shared

• Ali Tosyali

  Rochester Institute of Technology

  4 shared

• Gijs Overgoor

  Rochester Institute of Technology

  4 shared