Research

Working Papers

“How Does Sales Experience Affect Life Satisfaction?” (Under review at Journal of Marketing Research) with Michael Ahearne, Arpit Agrawal, and Johannes Habel.

Awards: 2025 Sales SIG Doctoral Dissertation Proposal Award Winner
Accepted at MSI Working Paper Series

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Business-to-business (B2B) sales is perhaps the most ambivalent career the marketing discipline offers, entailing extreme advantages and disadvantages that might change individuals’ lives for the better or worse. Thus, it is unclear how experience in a B2B sales role impacts an individual’s life satisfaction, a terminal outcome of utmost importance given our pursuit of better marketing for a better world. We test this effect in three studies including cross-sectional data of 64,090 individuals, longitudinal panel data of over 12,500 individuals, and qualitative interviews with salespeople. Interestingly, we find that having experience in a B2B sales role on average enhances life satisfaction. This is because sales experience equips individuals with distinct interpersonal skills that they use to find greater satisfaction outside the work context, such as helping others, enforcing own interests, and building social relationships. The effect of sales experience on life satisfaction is more positive for individuals with a plastic personality, who are more likely to gain and transfer skills. Thereby, this study adds a neglected antecedent to life satisfaction theory and offers actionable implications for managers and educators on improving life satisfaction within organizations and society at large.

“So Near Yet So Far: The Impact of Near Misses on Salesperson Turnover” (Invited for 3rd round revision at Journal of Marketing Research) with Michael Ahearne, Arpit Agrawal, Yashar Atefi, and Johannes Habel.

Awards: Best Paper Award at 2024 OFR Symposium, Sales Management Track at 2024 AMA Winter Academic Conference

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The role of sales is unique within organizations, marked by its high-profile nature and objective performance metrics. Meeting sales targets is vitally important for salespeople, and failure to do so can lead to significant frustration. It is well-established that quota attainment is a key factor influencing a salesperson’s decision to quit. Research consistently shows that the lower the quota attainment, the higher the likelihood of the salesperson quitting. However, our study offers a new perspective and adds a subtle complexity to this understanding. Drawing from the concept of counterfactual thinking, we propose that salespeople who narrowly miss sales targets are more likely to quit, even if they are generally high performers. We examine this with two studies: 1) a panel dataset of over 2,900 retail salespeople and 2) cross-industry data on over 25,000 salespeople. Our results confirm that repeated quota near misses cause turnover and reveal three sources of heterogeneity: the effect of quota near misses on turnover increases with higher variable pay percentage and decreases with higher maximum possible pay and higher group performance diversity. Our findings extend sales force turnover theory and provide actionable implications for managers who aim at reducing turnover.

“Build vs. Acquire: The Impact of Internal vs. External Sales Hiring on Firm Performance” (Preparing for submission to Marketing Science) with Michael Ahearne, Johannes Habel, and Jim Hess.

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In business-to-business (B2B) sales, firms face critical decisions about salesforce compensation and skill development that significantly impact performance. This paper examines two key questions: (1) What are the performance implications of hiring salespeople internally versus externally? And (2) Among the salespeople hired internally versus externally who are like to stay longer with the firm? We develop an analytical model that captures the key trade-offs in these decisions and test it using a unique dataset of 40 companies. For hiring source decisions, we show that while internal hires initially outperform due to firm-specific knowledge, external hires exhibit steeper learning curves and may eventually match or exceed internal hire performance. Our empirical analysis validates these theoretical predictions and quantifies the magnitude of these effects across different industry contexts. The findings provide actionable insights for sales managers making hiring decisions under uncertainty, while contributing to theories of salesforce management and human capital development.

“Unemployment and Service Quality” (Preparing for submission to Management Science) with Michael Ahearne, Johannes Habel, and Jim Hess.

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It is well established that service employees decisively shape service quality. Therefore, research has clarified how service firms should manage these employees, typically conceiving of them as a generally available and controllable resource. However, this general viewpoint faces severe limitations considering recent labor market disruptions, which pose severe challenges for service firms filling vacant positions. Building on this observation, we propose a theoretical model to explore the role of labor market and wages on expected service quality at a facility. Our model predicts that higher unemployment rates improve service quality by reducing employee turnover, higher wages enhance service quality through increased effort, with these effects demonstrating a significant negative interaction. Additionally, we predict these factors have a negative interaction, whereby high-wage facilities experience less benefit from rising unemployment than their low-wage counterparts. We empirically validate these predictions using two natural experiments in a retailing and fast-food restaurant context. The results suggest that as unemployment falls and labor supply becomes shorter, service quality decreases. Intriguingly, this effect particularly emerges for service firms that offer lower wages, while service firms that offer higher wages maintain their service quality levels. We discuss in detail how to evolve service quality theory based on these findings. Furthermore, we offer actionable guidance to managers and policymakers on how to safeguard service quality as the war for talent rages on.

“Freedom Isn’t Free: How Self-Employment Reshapes Work, Health, and Happiness” (Preparing for submission to Journal of Business Venturing) with Michael Ahearne and Johannes Habel.

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Self-employment represents a fundamental trade-off between autonomy and personal well-being, yet its impact on individuals’ lives remains incompletely understood. This research examines three critical questions: (1) How does transitioning to self-employment affect key life outcomes including income, work hours, life satisfaction, job satisfaction, health satisfaction, and leisure time? (2) How do individual characteristics—specifically gender and age—moderate these relationships? (3) How does business size influence the trade-offs between economic success and personal well-being in self-employment? Using longitudinal data from the German Socio-Economic Panel (SOEP) tracking over 92,000 individuals. Our findings reveal that self-employment improves job satisfaction and life satisfaction but creates significant trade-offs, particularly around work hours, leisure time, and health. The effects are strongly moderated by gender – with men experiencing higher income gains but worse health outcomes, while women achieve better work-life balance at the cost of lower earnings. Age and business size further moderate these relationships, with older entrepreneurs and those managing smaller ventures better able to preserve work-life balance. These results enhance our understanding of how entrepreneurship shapes individual well-being and highlight important boundary conditions around gender, age, and organizational context. The findings have implications for theories of occupational choice and work-life balance, while offering practical insights for potential entrepreneurs and policymakers.

Work In Progress

“A Predictive Framework for Forecasting Long-Term Video Game Performance Using Key Launch Period Metrics” with Sam Hui and Rahul Suhag.

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This study develops a predictive framework to evaluate the performance of video games by analyzing metrics from their initial release period. By leveraging early data on streamer engagement, subscription trends, and tipping behavior, the model forecasts future subscriber growth and tipping potential. The approach combines machine learning techniques with key performance indicators such as user retention, playtime, and monetization patterns. These insights can guide developers and publishers in optimizing game strategies, enhancing user experience, and planning future content updates or marketing campaigns to maximize long-term success.