Creating a More Inclusive and Sustainable Future

Unintended Consequences of Closing Pay Gaps Across Multiple Groups: A Formal Modeling and Simulation Analysis of Allocation Methods
Organization Science, October 2025

In recent years, many firms have prioritized both pay equity (i.e., closing pay gaps associated with target groups such as women and racial minorities) and equitable representation (i.e., ensuring these target groups are fairly represented across a firm’s hierarchy). We use formal modeling and simulations to show how efforts to close pay gaps across multiple groups can undermine equitable representation. Specifically, our analysis suggests that pressure for pay equity creates a cost-based financial incentive to enact a subtle form of tokenism: A firm may minimize the cost of closing pay gaps if it maintains a workforce with a small number of minority women whom it pays well in order to compensate for underpaying larger numbers of majority women and minority men who resemble each other in terms of job attributes and personal qualifications. A firm can avoid these outcomes if it focuses on ensuring that employees from target groups are equitably rewarded for job attributes and personal qualifications rather than minimizing cost. But an equitable-rewards approach can be substantially more expensive than a cost-minimization approach, especially if pay gaps are larger in high-wage jobs or if there are many target groups. We conclude by offering testable empirical predictions and recommending a practical solution, namely to include terms for intersectional categories (e.g., minority women) in the regressions used to estimate pay gaps.

David Anderson (Villanova University); Margret Bjarnadottir (University of Maryland); 
David Ross (University of Florida)

• Diverse and Inclusive Workplace
• Diverse Workforce Development
• Fairness and Equity

The GenAI Future of Consumer Research
Journal of Consumer Research

We develop a novel generative AI (GenAI) trajectory, “democratization-average trap-model collapse,” to identify data and model challenges posed by GenAI, from which we project the GenAI future of consumer research. This trajectory consists of three key phenomena: democratization broadens consumer participation, the average trap produces generic responses, and model collapses occurs when GenAI outputs lose human sensibilities. Data and model challenges arise as democratization enhances data representation but embeds real-world biases; the average trap, caused by next-token prediction algorithms, leads to generic outputs that lack individuality; and model collapse occurs when GenAI increasingly learns from its own outputs, amplifying machine bias and diverging from human behavior. To address these challenges, researchers can leverage democratization to study marginalized consumers and prioritize human-centered research over purely data-driven methods. The average trap can be mitigated by fine-tuning models with task-specific and marginalized consumption data while engineering responses for uniqueness. Preventing model collapse requires integrating human-machine hybrid data and applying theories of mind to realign AI with human-centric consumption. Finally, we outline three future research directions: preserving data distribution tails to support consumption democratization, countering the average trap in next-token prediction, and reversing the trajectory from democratization to model collapse.

Ming-Hui Huang, Distinguished University Professor, National Taiwan University
Roland T. Rust, Distinguished Professor and David Bruce Smith Chair in Marketing, University of Maryland

• AI
• Fairness and Equity
• Feelings Economy
• Personalization

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 

• Fairness and Equity
• Leadership
• Organizational Culture and Change