Daniel Lee
Associate Professor of Entrepreneurship University of Delaware
- Newark DE
Prof. Lee specializes in experimental and behavioral approaches to entrepreneurship and innovation.
Media
Biography
Industry Expertise
Areas of Expertise
Media Appearances
Terrarium startup OBEnaturelle tops 2023 Hen Hatch
Delaware Business Times online
2023-12-28
Hen Hatch is UD Horn Entrepreneurship’s “premier pitch competition,” explained faculty coordinator Daniel Lee, an assistant professor in the Alfred Lerner College of Business & Economics. The multi-round contest starts in the summer with students submitting a multi-page business plan, then moves on to the semifinals and mentoring before whittling down to the four final entrepreneurs who presented this fall.
New Study Explores The Role of Grades In MBA Recruiting
Poets&Quants online
2022-09-26
“Between the extracurriculars and the difficult classes, what we say isn’t all time spent away from academics is lost time,” author Daniel Lee, of the University of Delaware – Alfred Lerner College of Business and Economics, tells The Wall Street Journal. “But we can’t really comment on the good or bad effects.”
What Happens When M.B.A. Students Don’t Tell Recruiters Their Grades?
The Wall Street Journal online
2022-09-17
The paper, written by Eric Floyd, an assistant professor of accounting at the University of California San Diego, Daniel Lee, assistant professor of entrepreneurship at the University of Delaware, and Sorabh Tomar, assistant professor of accounting at Southern Methodist University, found evidence to support both arguments.
Common test of ‘implicit’ racial bias fails to predict prosocial behavior in new study
PsyPost online
2018-10-19
“I first became interested in the IAT during my second year in grad school. My father (who is an employment lawyer) was at a conference where he and his colleagues took one as a didactic exercise. In telling me about it, he stressed that he could feel himself slowing down when the less associated categories were paired,” explained Daniel J. Lee of Rice University, the author of the study.
Articles
Learning to Quit? A Multi-Year, Multi-Site Field Experiment with Innovation-Driven Entrepreneurs
Journal of Financial EconomicsEsther Bailey, Daniel Fehder, Eric Floyd, Yael Hochberg, Daniel J. Lee
2026
We use a randomized experiment with 553 science- and technology-based startups in 12 co-working spaces across the US to evaluate the effects of intensive, short-term entrepreneurial training programs on survival and performance for innovation-driven startups. Treated startups are more likely to shut down their businesses and do so sooner than control startups. Conditional on survival, however, treated startups are more likely to raise external funding for their ventures, raise funding faster, and raise more funding than the control group; they also exhibit higher employment and revenue. Treated founders are less likely to found a new startup after shutdown. Our findings are consistent with practitioner arguments that early entrepreneurship training interventions can help entrepreneurs with less viable ventures “rationally quit” (“fail fast”). We use machine learning techniques (causal random forest) to provide exploratory insights on the most impacted subgroups.
The impact of workers' compensation laws on entrepreneurial activity
Strategic Entrepreneurship JournalIndu Khurana, Daniel J. Lee, Julio O. de Castro
2026
Government policy that aims to stimulate business activity often overlooks its indirect impacts on entrepreneurial entry. In particular, the role of free time, especially in concert with liquidity constraints, remains an underexplored factor. In this paper, we exploit two exogenous shocks to workers' free time to furnish plausibly causal effects on entrepreneurial activity: (random) injury and the 2011 amendments to the Illinois workers' compensation laws. Utilizing a two‐way fixed effects estimation, we find that as workers' compensation becomes less generous, that is, by limiting both financial resources and an employee's time away from work, entrepreneurial activity within a specific geographical region is significantly reduced.
Making the grade (but not disclosing it): How withholding grades affects student behavior and employment
Management ScienceEric Floyd, Sorabh Tomar, Daniel J Lee
2024
We study the effects of grade nondisclosure (GND) policies implemented within master of business administration (MBA) programs at highly ranked business schools. GND precludes students from disclosing their grades and grade point averages (GPAs) to employers. In the labor market, we find that GND weakens the positive relation between GPA and employer desirability. During the MBA program, we find that GND reduces students’ academic effort for a given course by approximately 4.9% relative to comparable students not subject to the policy. Consistent with our model, in which abilities are potentially correlated and students can substitute effort toward other activities in order to signal GPA-focused abilities, students participate in more extracurricular activities and enroll in more difficult courses under GND.
Reproducibility in Management Science
Management ScienceMiloš Fišar, Ben Greiner, Christoph Huber, Elena Katok, Ali I Ozkes, Management Science Reproducibility Collaboration
2024
With the help of more than 700 reviewers, we assess the reproducibility of nearly 500 articles published in the journal Management Science before and after the introduction of a new Data and Code Disclosure policy in 2019. When considering only articles for which data accessibility and hardware and software requirements were not an obstacle for reviewers, the results of more than 95% of articles under the new disclosure policy could be fully or largely computationally reproduced. However, for 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced.
Gender bias in high stakes pitching: an NLP approach
Small Business EconomicsIndu Khurana, Daniel J. Lee
2023
Investors use heuristics and biases which may disproportionately impact entrepreneurial teams with women when hearing pitches and evaluating early-stage ventures. However, merely entertaining a pitch is not enough—the tone of the conversation also matters. To investigate, we explore non-rationality in the funding process by applying Natural Language Processing (NLP) to Shark Tank: a high-impact television program fosr entrepreneurial pitching. Using sentiment analysis, we show that male judges react more positively to pitches from entrepreneurial teams with women. Importantly, these positive reactions are not indicative of increased deal flow. The opposite is true for female judges who, while no more likely to react positively to teams with female entrepreneurs, are significantly more likely to ink a deal when they do.
Research Grants
Faculty Research Grant
University of Delaware Lerner College
2024
Advancing Research Translation Through Social Capital
NSF iCorps, Northeast Region
2024
GUR Grant
University of Delaware
2022
Accomplishments
Mednick Fellowship, Virginia Foundation for Independent Colleges
2020
Education
Georgia State University
PhD
Economics
2016
Georgia State University
MA
Economics
2013
Emory University
BA
Math, Economics
2010
Affiliations
- Academy of Management
- Strategic Management Society
- American Economic Association
- Southern Economic Association
- Economic Science Association
