Key Areas of Research
Conflicted About Coworkers: How Coworker Support Influences Engagement After Status Loss
Personnel Psychology, February 2025
People's needs for status and support are theoretically distinct, yet little research has considered how people cope with having one but not the other. We examine how people react to losing status as a function of whether they typically perceive their coworkers as supportive. Although social support is documented as a resource people can draw on to cope with failure at work, we argue that in the case of failures that implicate status (i.e., status loss), experiencing these events in a more supportive work group may not aid recovery and reengagement. Specifically, we predict that when the preexisting group context is one of more (rather than less) supportive coworkers, status loss may elicit greater ambivalence about those coworker relationships, triggering psychological reactions that undermine engagement. Consistent with this model, in a weekly experience sampling study of working adults (Study 1), having more supportive coworkers led to a stronger negative effect of weekly status loss on subsequent engagement. In scenario-based (Study 2) and high-involvement laboratory (Study 3) experiments featuring different manipulations of coworker support and status loss, we found that when individuals experienced status loss in more (rather than less) supportive work groups, status loss led to lower engagement because it heightened ambivalence about their coworker relationships, which triggered anxiety (Study 2), and self-threat and hurt feelings (Study 3). Theoretical and practical implications are discussed.
Jennifer Carson Marr (UMD), Edward P. Lemay (UMD), Hyunsun Park (Georgia Tech)
Public Pension Contract Minimalism
American Business Law Journal, November 2024
The national pension debt and COVID crises have collided. Post-pandemic economic decline has escalated existing financial strains on state and local pension plans, impacting workers and the public welfare. With unfunded obligations exceeding one trillion dollars, many of these plans are in jeopardy. But the movement to reform government pension contracts has yet to adopt an anchoring idea, leaving judicial decisions in disarray and policymakers without guidance about how to shore up troubled retirement systems. The crux of the problem is the many meanings of contract under state and U.S. Contract Clauses that prevent pension reform. This Essay endorses a promising path forward—contract minimalism. “Contract minimalism” concentrates on the duration of government pension contracts. It posits that public and private employment law should be treated the same. Like its private law counterpart, public sector employment at-will ought to consist of a daily contract interval. A contract-a-day concept entitles employers to change the plan prospectively, with employees receiving a proportionate share of benefits for work performed. Just as several agreements safeguard salaries for labor, they should also mirror the protection afforded to deferred benefits like pensions. Contract minimalism additionally puts public and private sector employers on the same legal footing as to the authority to change pension plan terms. Thus, it aligns public pension benefits with overlapping fields of law, placing them on a firm conceptual foundation. The minimalist approach also has the advantage over approaches that are insufficiently attentive to scarce government resources or employee old-age security. By protecting pension benefits early and incrementally, it advances a middle path with fairer, more coherent results. In the present post-pandemic era of hard choices, minimalism provides an equilibrium between the over and under-protection of pension benefits.
T. Leigh Anenson, Professor of Business Law, University of Maryland and Hannah R. Weiser, Assistant Professor of Law, Bentley University
The Impact of App Crashes on Consumer Engagement
Journal of Marketing
The authors develop and test a theoretical framework to examine the impact of app crashes on app engagement. The framework predicts that consumers increase engagement after encountering a single crash due to their need-for-closure and curiosity, yet reduce engagement after experiencing repeated and concentrated crashes, primarily because of frustration and perceived task unattainability; the recency of crashes moderates these effects. Field data analysis reveals that while a crash truncates a session and reduces content consumption, it increases page views in the following session. However, this increase in page views does not compensate for the loss during the crashed session. Frequent and more concentrated crashes curtail engagement. Three experiments in which crashes are exogenously manipulated in a different context support the validity and generalizability of these findings, confirm the proposed mediators, and demonstrate how to lessen the negative impact of repeated crashes with post-crash messages. The research adds new dimensions to the task pursuit literature and provides managers with a framework to quantify the economic impact of crashes, analyze content substitution behavior, and assess the bias of a transactional view of crash incidents. Additionally, it offers insights into targeted feature release to more tolerant users and strategic design of post-crash messages.
Savannah Wei Shi, Associate Professor of Marketing & J.C. Penney Research Professor, Leavey School of Business Santa Clara University
Seoungwoo Lee*, Assistant Professor, Yonsei School of Business, Yonsei University Seoul
Kirthi Kalyanam, L.J. Skaggs Distinguished Professor Leavey School of Business, Santa Clara University
Michel Wedel, PepsiCo Chaired Professor of Consumer Science, Robert H. Smith School of Business, University of Maryland
AI-powered Analysts
We explore how brokerage firms’ investments in artificial intelligence (AI) affect their analysts’ information production. We find that analysts employed at brokerage firms with greater AI integration issue more accurate earnings forecasts. Cross-sectional analyses reveal that AI’s benefits are more pronounced for analysts with less firm-specific experience and when the firm’s disclosures are more readable. Further tests indicate that a key mechanism driving the improvement in forecast accuracy is that AI adoption helps mitigate the adverse effects of analyst decision fatigue and optimism bias. Finally, we find that forecast revisions made by AI-powered analysts are more informative to capital markets. Overall, our evidence points to the advantageous impact of AI on information production capabilities of financial analysts.
Michael Kimbrough, Musa Subasi, Yang Liu
Do Credit Rating Agencies Learn from the Options Market?
Management Science, November 2024
Do credit rating agencies (CRAs) learn from the options market? We examine this question by exploring the relation between options trading activity and credit rating accuracy. We find that as options trading volume increases, credit ratings become more responsive to expected credit risk and exhibit greater ability to predict future defaults. We also find that CRAs rely more on the options market as a source of ratings-related information when firm default risk is higher, options trading is more informative, manager-provided information is of lower quality, and firm uncertainty is higher. Our results are robust to a number of sensitivity tests, including alternative measures of options trading and credit rating accuracy. We reach similar inferences using various approaches to address endogeneity issues, including difference-in-difference analyses and an instrumental variables approach. Overall, our findings are consistent with the view that CRAs incorporate unique information from the options market into their rating decisions which, in turn, improves credit rating accuracy.
Musa Subasi, University of Maryland-College Park
Paul Brockman, Lehigh University
Jeff Wang, San Diego State University
Eliza Zhang, University of Washington-Tacoma
Equity Term Structures without Dividend Strips Data
Journal of Finance
We use a large cross section of equity returns to estimate a rich affine model of equity prices, dividends, returns, and their dynamics. Our model prices dividend strips of the market and equity portfolios without using strips data in the estimation. Yet model-implied equity yields closely match yields on traded strips. Our model extends equity term-structure data over time (to the 1970s) and across maturities, and generates term structures for various equity portfolios. The novel cross section of term structures from our model covers 45 years and includes several recessions, providing a novel set of empirical moments to discipline asset pricing models.
Stefano Giglio, Yale School of Management
Bryan Kelly, Yale School of Management
Serhiy Kozak, R.H. Smith School of Business, University of Maryland
Seductive Language for Narcissists in Job Postings
Management Science
Prior research indicates that narcissistic executives engage in earnings management and other negative organizational behaviors, and many studies ponder why firms hire such individuals, especially into corporate accounting positions. Utilizing a selection of terms from real-world job postings that we characterize as either describing a “Rule-Bender” or “Rule-Follower" candidate, we first conduct several validation studies which reveal that these terms vary predictably across types of job postings, that people generally agree with our categorization of these terms, and that Rule-Benders are viewed as possessing worse managerial skills but a higher proclivity for unethical behavior. We then demonstrate that narcissistic job seekers are more attracted to job postings that describe the ideal candidate using Rule-Bender terms for both general positions (Experiment 1) and senior accounting positions (Experiment 2). Finally, we examine firm characteristics that might lead professional recruiters to incorporate Rule-Bender language into Chief Accounting Officer job postings and find that Rule-Bender terms are preferred for higher-growth, higher-innovation firms (Experiment 3), and when more aggressive reporting would benefit the firm (Experiment 4). Our results suggest that recruiters’ language choices can attract Rule-Bending narcissists to firms, perhaps even in unintended circumstances.
Jonathan Gay (University of Mississippi), Scott Jackson (University of South Carolina), Nick Seybert (University of Maryland)
Transforming Products into Platforms: Unearthing New Avenues for Business Innovation
NIM Marketing Intelligence Review, October 2024
It is impossible for brands to ignore digital platform opportunities. Network effects are one of the strongest sources of power and defensibility ever invented and underlie some of the most valuable businesses in the world. Managers and entrepreneurs can leverage the power of platforms by adding some platform elements to their existing products or services, by distributing their brands via existing platforms or by developing their own new platforms. By using one’s own brands as platforms requires creativity but can help businesses unlock new value and build resilient ecosystems around their products. There are three key methods. The first is to invite third-party sellers to enhance existing products. Examples include selling advertising space around products or creating app stores to extend offers. The second is to connect one’s customers by enabling interactions among users to add value. Third, brands might reach out to customers’ customers by enhancing the end-user experience in a way that benefits both themselves and their direct customers. If thoughtfully implemented, any platform strategy will create self-reinforcing feedback loops sparking growth and keeping competitors at bay.
Andrei Hagiu, Associate Professor of Information System, Boston University; Bobby Zhou, Associate Professor of Marketing, University of Maryland
The Theory-Based View and Strategic Pivots: The Effects of Theorization and Experimentation on the Type and Nature of Pivots
Strategy Science
We examine how formalization in cognitive processes (theorization) and evidence evaluation (experimentation) influence the type (frequency and radicalness) and nature (impetus, clarity, and coherence) of entrepreneurial pivots. We use a mixed-method research design to analyze rich data from over 1,600 interviews with 261 entrepreneurs within a randomized control trial in London. A quantitative analysis that complements human-coded and machine learning-coded measures reveals that conditional on pivoting, theorization and experimentation are complementary in their association with making single radical pivots. The extensive qualitative-case comparison further elucidates interactions between theorization and experimentation that generate differences in the nature of pivots that range from purposeful (clear and coherent rationale deriving from articulated theory and experimentation), postulatory (informed by articulated theory but not incorporating nuances or surprises generated from experimentation), and remedial (stemming from adjustments to preformed theories that drew on prior experiences) to reactive (driven by environmental stimuli absent a clear theory of value). These insights contribute to the theory-driven strategic decision-making literature and offer practical insights for entrepreneurs, incubators, and policymakers on the benefits of a scientific approach to entrepreneurship.
Valentine, Jacob (Doctoral Candidate, University of Maryland); Novelli, Elena (Professor, Bayes Business School); Agarwal, Rajshree (Lamone Professor of Strategy and Entrepreneurship, University of Maryland)
Improved LISA Analysis for Zero-Heavy Crack Cocaine Seizure Data
INFORMS Journal of Data Science
Local Indicators of Spatial Association (LISA) analysis is a useful tool for analyzing and extracting meaningful insights from geographic data. It provides informative statistical analysis that highlights areas of high and low activity. However, LISA analysis methods may not be appropriate for zero-heavy data, as without the correct mathematical context the meaning of the patterns identified by the analysis may be distorted. We demonstrate these issues through statistical analysis and provide the appropriate context for interpreting LISA results for zero-heavy data. We then propose an improved LISA analysis method for spatial data with a majority of zero values. This work constitutes a possible path to a more appropriate understanding of the underlying spatial relationships. Applying our proposed methodology to crack cocaine seizure data in the U.S., we show how our improved methods identify different spatial patterns, which in our context could lead to different real-world law enforcement strategies. As LISA analysis is a popular statistical approach that supports policy analysis and design, and as zero-heavy data is common in these scenarios, we provide a framework that is tailored to zero-heavy contexts, improving interpretations and providing finer categorization of observed data, ultimately leading to better decisions in multiple fields where spatial data is foundational.
Eunseong Jang, The Robert H. Smith School of Business, University of Maryland
Margret Bjarnadottir, The Robert H. Smith School of Business, University of Maryland
Marcus Boyd, National Consortium for the Study of Terrorism and Responses to Terrorism, University of Maryland
S. Raghavan, The Robert H. Smith School of Business & Institute for Systems Research, University of Maryland