David P. Daniels
I am a Presidential Young Professor in NUS Business School's Department of Management and Organisation. I hold a Ph.D. in Business Administration from the Stanford Graduate School of Business, an M.A. in Economics from Stanford University, and an A.B. summa cum laude from Harvard University.
My research focuses on diversity in groups and organizations, negotiation and influence, and motivation (e.g., prosocial behavior, engagement, and turnover). I study these topics using both field and lab data, with a focus on causal field research in naturalistic settings. I draw on perspectives and tools ranging from management, psychology, and economics, to statistics and machine learning.
In 2022-2023, I won the NUS Business School Early Career Research Excellence Award and was selected as an APS Rising Star by the Association for Psychological Science. In 2021, I won the Best Paper Award from the Academy of Management (Conflict Management Division). I am also a National Science Foundation Graduate Research Fellow.
My research has been published in top academic outlets such as Proceedings of the National Academy of Sciences, Organizational Behavior and Human Decision Processes, and the Journal of Consumer Research. My research has been covered by media outlets such as the Harvard Business Review, Forbes, PBS, NPR, and NBC.
I can be reached at bizdpd (at) nus (dot) edu (dot) sg.
Here are some articles that describe my research:
Selected Recent Research and Working Papers
Do investors value workforce gender diversity?
Daniels, D. P., Dannals, J. E., Lys, T., and Neale, M.A. (Working paper). Invited for resubmission at Organization Science.
We theorize that investors will respond positively to workforce gender diversity in major U.S. firms because diversity has large potential upsides for such firms (e.g., increased creativity), whereas diversity’s potential downsides (e.g., increased conflict) can be mitigated if they are effectively managed. To test our theory’s predictions, we conduct the first causal field research regarding the impact of workforce gender diversity on firm market value, by examining how investors respond to news about workforce gender diversity numbers. We show that U.S. technology firms and U.S. financial firms experience more positive stock price reactions when it is revealed that they have relatively higher (vs. lower) workforce gender diversity numbers compared to other firms. These stock price reactions are both economically and statistically significant. For example, we estimate that if a technology firm had revealed gender diversity numbers that were one standard deviation higher, its market valuation would have increased by approximately $1.05 billion. Furthermore, we find parallel investor reactions in randomized experiments with investors, and we show that these reactions are mediated by investors’ beliefs about potential upsides of diversity for the firm (e.g., creativity) but not by investors’ beliefs about potential downsides of diversity for the firm (e.g., conflict). We also document several interesting boundary conditions; for instance, investors seem to infer that firms who keep their workforce gender diversity numbers “secret” in fact have low diversity, and investors care more about diversity in upper-level roles rather than lower-level roles. Our results support our theory and point towards a new type of business case for diversity, driven by investors: if firms had more gender diversity, investors would “reward” them with substantially higher valuations.
The streak-end rule: How past experiences shape decisions about future behaviors in a large-scale natural field experiment with volunteer crisis counselors
Kang, P., Daniels, D. P., and Schweitzer, M. E. (2022). Proceedings of the National Academy of Sciences, 119(45).
Decisions about future behaviors are clearly shaped by the content of past experiences, but whether the order of past experiences matters remains controversial. By analyzing the largest field experiment about prosocial behavior to date, a natural field experiment involving 14,383 volunteer crisis counselors over five years, we examine how the content and order of past experiences causally influence decisions about future behaviors – whether individuals continue volunteering or quit. Volunteers were repeatedly and randomly assigned to perform 1,976,649 prosocial behaviors that were either harder (suicide conversations) or easier (non-suicide conversations). We found that the content of past experiences mattered: Harder (versus easier) behaviors encouraged quitting. However, the order of past experiences mattered far beyond their content alone: Harder behaviors caused disproportionately more quitting if they came in long “streaks” or at the “end.” These “streak”/“end” effects reveal important practical insights for leaders and policymakers seeking to boost prosocial behavior. For instance, a reordering intervention – assigning behaviors so as to avoid creating hard “streaks” – would reduce volunteer quitting rates by at least 22%, boosting prosocial behavior and likely saving lives.
The magnitude heuristic: Larger differences increase perceived causality
Daniels, D. P. and Kupor, D. (2022). Journal of Consumer Research.
With the rise of machine learning and “big data,” many large yet spurious relationships between variables are discovered, leveraged by marketing communications, and publicized in the media. Thus, consumers, managers, and other people are increasingly exposed to many large-magnitude relationships between variables that do not signal causal effects. This exposure may carry a substantial cost. In seven studies, we demonstrate that the magnitudes of relationships between variables can distort people’s judgments about whether those relationships reflect causal effects. Specifically, people often use a magnitude heuristic: People infer that relationships with larger perceived magnitudes are more likely to reflect causal effects, even when this is not true (and even when relationships’ correlations are held constant). In many situations, relying on the magnitude heuristic will distort causality judgments, such as when large-magnitude relationships between variables are spurious, or when normatively extraneous factors (e.g., reference points) distort perceptions of magnitudes. Moreover, we show that magnitude-distorted (mis)perceptions of causality in turn distort people’s choices. Since people often encounter spurious relationships with large magnitudes in the health domain and in other consequential domains, the magnitude heuristic is likely to lead to biases in some of people’s most important decisions.