创业
背景(考古学)
弱势群体
人力资本
回调函数
风险投资
质量(理念)
营销
首次公开发行
业务
经济
管理
市场经济
经济增长
计算机科学
财务
古生物学
哲学
认识论
生物
程序设计语言
作者
Tristan L. Botelho,Melody Chang
出处
期刊:Organization Science
[Institute for Operations Research and the Management Sciences]
日期:2023-01-01
卷期号:34 (1): 484-508
被引量:20
标识
DOI:10.1287/orsc.2022.1592
摘要
Organizations tout the importance of innovation and entrepreneurship. Yet, when hiring it remains unclear how they evaluate entrepreneurial human capital—namely, job candidates with founder experience. How hiring firms evaluate this experience—and especially how this evaluation varies by entrepreneurial success and failure—reveals insights into the structures and processes within organizations. Organizations research points to two perspectives related to the evaluation of founder experience: Former founders may be advantaged, due to founder experience signaling high-quality capabilities and human capital, or disadvantaged, due to concerns related to fit and commitment. To identify the dominant class of mechanisms driving the evaluation of founder experience, it is important to consider how these evaluations differ, depending on whether the founder’s venture failed or succeeded. To isolate demand-side mechanisms and hold supply-side factors constant, we conducted a field experiment. We sent applications varying the candidate’s founder experience to 2,400 software engineering positions in the United States at random. We find that former founders received 43% fewer callbacks than nonfounders and that this difference is driven by older hiring firms. Further, this founder penalty is greatest for former successful founders, who received 33% fewer callbacks than former failed founders. Our results highlight that mechanisms related to concerns about fit and commitment, rather than information asymmetry about quality, are most influential when hiring firms evaluate former founders in our context. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1592 .
科研通智能强力驱动
Strongly Powered by AbleSci AI