Key Areas of Research
The referral penalty: Decreased perceptions of merit undermine helping behavior towards referred employees
Journal of Applied Psychology
Employee referrals are commonly used by organizations due to their numerous benefits. However, it remains unclear how organizational incumbents, who are uninvolved in the hiring process, perceive and react to referral beneficiaries. Although traditional views suggest that the presence of a referral signals merit, incumbents’ perceptions may differ. We theorize that incumbents are more likely to perceive referral beneficiaries as less merited than non-referred employees, due to perceived legitimacy concerns stemming from a simplified view that reliance on network contacts de facto compensates for lower qualifications. Drawing on equity theory, we then theorize that low merit perceptions lead to less positive and more negative behaviors towards referral beneficiaries, as an attempt to restore the equilibrium between beneficiaries’ perceived inputs (e.g., driven by perceived lower merit) and outputs (e.g., being on payroll). Sampling employees from industries in which referrals are normative (Study 1a) and from a cultural context that is positively predisposed toward referrals (Study 1b) confirmed our theorizing. In a subsequent study, aiming to enhance the generalizability of our findings, we found supporting evidence for perceived equity violations, leading incumbents to engage in corrective behaviors toward referral beneficiaries (Study 2). Finally, testing our hypotheses more conservatively, we found that negative attributions toward referral beneficiaries persisted even when the referred employees had demonstrated high performance, thereby underscoring the robustness of our findings (Study 3). This paper elucidates important unintended consequences of one of the most popular hiring methods - employee referrals - and draws implications for both theory and practice.
Tomova Shakur, Teodora, Texas Christian University and Derfler-Rozin, Rellie, University of Maryland
Should I Stand Up for My Mistreated Colleague? When and Why High-Status Team Members Stand Up for Their Coworkers
Organizational Behavior and Human Decision Processes, January 2026
Supervisory mistreatment has adverse consequences for its victims. Coworkers, as observers, can shape victims’ experiences by standing up for them. Yet doing so entails the risk of supervisory retaliation. High-status coworkers should be well-positioned to stand up for victims as they have greater social capital at work. However, such retaliation risks may loom large for them because they are highly motivated to protect what they have. Thus, prior research reports both positive and negative links between status markers and various forms of standing up. We suggest that these inconclusive findings stem from examining individuals’ status only within a single domain (e.g., work) while neglecting how their standing in other groups may shape their experiences in that focal domain. Building on status inconsistency theory (Lenski, 1954) and the concept of status portfolios (Fernandes et al., 2021), we argue that status variance (i.e., inconsistency of status across groups) shapes how high-status employees react to mistreatment. Specifically, we hypothesize that high-status employees with high (compared to low) status variance will experience greater fear of retaliation and reduced willingness to stand up. We argue that this occurs because they perceive their status portfolios as unstable and become more vigilant in protecting their elevated standing at work. Four complementary studies provided support for our hypotheses. We discuss implications for research on bystander intervention, supervisory mistreatment, and status.
Gencay, Oguz, PhD., Bilkent University., Derfler-Rozin, Rellie, PhD. University of Maryland, Arman, Gamze, UWE Bristol
Does earnings management matter for strategy research?
Strategic Managment Journal, August 2025
Strategic management research often uses accounting data, despite well-known concerns that earnings management could obscure the link between actual and measured performance. We apply methods from the econometric literature on bunching to estimate that around 15 percent of firm-year observations in Compustat manipulate accounting earnings to achieve profitability. We show that cash-based performance measures are less susceptible to manipulation and that the choice of accrual versus cash-based measures “matters” for two classic strategy research questions: a decomposition of ROA variance and an analysis of persistence in firm performance. These findings underscore the importance of robustness testing and contribute to an emerging literature that reconsiders the link between theoretical constructs and empirical performance measures.
Gibbs (Purdue), Simcoe (Boston U), and Waguespack (Maryland)
Unlocking Forecast Quality: The Power of Material Sustainability Disclosures
Accounting and Business Research
In 2013, the Sustainability Accounting Standards Board (SASB) began releasing guidelines to identify material (or financially relevant) sustainability metrics. This study investigates the effects of material and immaterial sustainability activities on analyst forecast error and dispersion. We further examine how these effects are influenced by the issuance of stand-alone sustainability reports and the release of SASB’s material sustainability standards. Using a sample of US firms from 2005 to 2018, we find that material sustainability activities are associated with more accurate and less dispersed analyst forecasts when firms issue stand-alone sustainability reports. Among firms that do not release such reports, material sustainability activities improve forecast quality only after the initial release of the SASB standards. Immaterial sustainability activities appear to add noise to information in the financial market and confound earnings forecasts, especially during the pre-SASB period, but this confounding effect reverses in the post-SASB period. Overall, our findings provide empirical evidence that classifying and disclosing corporate sustainability activities yield economic and informational benefits in capital markets.
Sue A. Cooper PhD EA CMA MEd MBA, Visiting Associate Clinical Professor of Accounting, University of Maryland, College Park, and Jennifer Yin PhD, Professor of Accounting, University of Texas at San Antonio, and Harrison Liu PhD, Associate Professor of Accounting University of Texas at San Antonio
Effects of Greenhouse Gas Emissions and Climate Change Policy on Audit Fees
Accounting and the Public Interest, December 2025
This study explores the correlation between greenhouse gas (GHG) emissions from U.S. companies and their audit fees, driven by the escalating frequency and intensity of extreme weather events. Building on prior research that connects climate risk, regulation, and audit fees, our investigation uses a sample of companies with Scope 1 GHG emissions sourced from the U.S. Environmental Protection Agency’s (EPA’s) Greenhouse Gas Reporting Program (GHGRP). Our results show a positive association between GHG emissions and audit fees. Additionally, we find that regulatory uncertainty surrounding U.S. climate policy intensifies this relationship. Our findings are robust to alternate model and variable specifications. This research benefits managers and policymakers by highlighting some of the financial consequences of corporate GHG emissions, especially when combined with inconsistent climate policies. It is also beneficial to accountants in practice or researchers interested in refining their audit fee models.
Sue A. Cooper PhD EA CMA MEd MBA, Visiting Associate Clinical Professor, University of Maryland, College Park, and Jared B. Cooper MEd, Certified MD Educator, Wicomico County Board of Education
User Innovation and Product Stickiness: Evidence from Video Games
Journal of Economics & Management Strategy
Prior research on user innovation fails to explain its low adoption rate and neglects its impact on increased product stickiness. To bridge these gaps, we conducted an empirical investigation into user innovations within the video game sector. Our study reveals that embracing user innovation leads to an upsurge in the number of active players for a game. Furthermore, the marginal effect of user innovations varies depending on their recency and quality, with low-quality user innovations leading to user attrition. The effect is also contingent on the stage in the product life cycle in which user innovation is adopted.
Yunfei Wang, UMD and Peng Huang, UMD
Seed Accelerators, Information Asymmetry, and Corporate Venture Capital Investments
Management Science
Beyond financial incentives, investments by Corporate Venture Capitalists (CVCs) are often motivated by strategic objectives, such as gaining early exposure to emerging technologies. However, in the presence of information asymmetry, CVCs tend to invest in startups with a high degree of business relatedness—startups that are less risky but lacking in knowledge novelty—which are not ideal for achieving their strategic objectives. With startup accelerators showing promise in mitigating the information asymmetry problem, we examine how a CVC’s investment pattern in a region shifts following a startup accelerator’s entry, with a particular interest in the degree of business relatedness between the CVC’s parent corporation and its portfolio companies. Analyses reveal that CVCs increase investments in startups that are dissimilar to their parent’s business following the entry of startup accelerators. We show that the two pathways through which accelerators reduce information asymmetry—quality signals, and mentorship and training—likely contribute to this change. In addition, the change is most pronounced for CVCs whose parent firm operates in an IT-using—rather than an IT-producing—industry, suggesting that accelerators help IT-using firms gain a foothold in the technology space through CVC investments. These findings deepen the understanding of the role that startup accelerators play in the entrepreneurial ecosystem against the backdrop of digital transformation occurring in nearly every industry.
Raveesh Mayya, NYU and Peng Huang, UMD
Prompt Adaptation as a Dynamic Complement in Generative AI Systems
As generative AI systems rapidly improve, a key question emerges: How do users keep up—and what happens if they fail to do so. Drawing on theories of dynamic capabilities and IT complements, we examine prompt adaptation—the adjustments users make to their inputs in response to evolving model behavior—as a mechanism that helps determine whether technical advances translate into realized economic value. In a preregistered online experiment with 1,893 participants, who submitted over 18,000 prompts and generated more than 300,000 images, users attempted to replicate a target image in 10 tries using one of three randomly assigned models: DALL-E 2, DALL-E 3, or DALL-E 3 with automated prompt rewriting. We find that users
with access to DALL-E 3 achieved higher image similarity than those with DALL-E 2—but only about half of this gain (51%) came from the model itself. The other half (49%) resulted from users adapting their prompts in response to the model’s capabilities. This adaptation emerged across the skill distribution, was driven by trial-and-error, and could not be replicated by automated prompt rewriting, which erased 58% of the performance improvement associated with DALL-E 3. Our findings position prompt adaptation as a dynamic complement to generative AI—and suggest that without it, a substantial share of the economic value created when models advance may go unrealized.
Eaman Jahani, UMD
Benjamin Manning, MIT
Hong-Yi TuYe, MIT
Mohammed Alsobay, MIT
Christos Nicolaides, University of Cyprus
Siddharth Suri, Microsoft Research
David Holtz, Columbia
Tracking-Based Advertising After Apple's App Tracking Transparency: Firm-Level Evidence and Policy Implications
TechREG CHRONICLE, November 2025
We discuss the impact of Apple’s App Tracking Transparency's (“ATT”) on targeted, online advertising. We overview the empirical results of Aridor, Che, Hollenbeck, Kaiser & McCarthy (2025) that measured the impact of ATT on e-commerce firms. The results point to a large reduction in the efficacy of targeted advertising and subsequently large revenue losses, borne primarily by smaller firms. We discuss the competition policy implications of this by highlighting the potentially anticompetitive implications of privacy measures implemented by private firms and the lack of substitutability between advertising networks, despite a large exogenous shock in the efficacy of Meta advertising.
Aridor, Guy: Assistant Professor of Marketing, Northwestern University
Hollenbeck, Brett: Associate Professor of Marketing, UCLA
McCarthy, Daniel: Associate Professor of Marketing, University of Maryland
AdGazer: Improving Contextual Advertising with Theory-Informed Machine Learning
Journal of Marketing
Contextual advertising involves matching features of ads to features of the media context where they appear. We propose AdGazer, a new machine learning procedure to support contextual advertising. It comprises a theoretical framework organizing high and low-level features of ads and contexts, feature engineering models grounded in this framework, an XGBoost model predicting ad and brand attention, and an algorithm optimally assigning ads to contexts. AdGazer includes a Multimodal Large Language Model to extract high-level topics predicting the ad-context match. Our research uses a unique eye-tracking database containing 3531 digital display ads and their contexts, and aggregate ad and brand gaze times. We compare AdGazer’s predictive performance to two feature learning models, VGG16 and ResNet50. AdGazer predicts highly accurately with hold-out correlations of 0.83 for ad gaze and 0.80 for brand gaze, outperforming both feature learning models and generalizing better to out-of-distribution ads. Context features jointly contributed at least 33% to predicted ad gaze and about 20% to predicted brand gaze, good news for managers practicing or considering contextual advertising. We demonstrate that the theory-informed AdGazer effectively matches ads to advertising vehicles and their contexts, optimizing ad gaze more than current practice and alternatives like text-based and native contextual advertising.
Michel Wede (UMD Smith) Jianping Ye (UMD PhD student); and Rik Pieters (Tilburg University, the Netherlands)