竞争
显著性(神经科学)
利用
选择(遗传算法)
竞赛(生物学)
营销
经济
内生性
过程(计算)
业务
产业组织
微观经济学
计算机科学
计量经济学
计算机安全
生态学
人工智能
生物
操作系统
作者
Panos Markou,Stylianos Kavadias,Nektarios Oraiopoulos
出处
期刊:Management Science
[Institute for Operations Research and the Management Sciences]
日期:2023-01-04
卷期号:69 (9): 5298-5315
被引量:32
标识
DOI:10.1287/mnsc.2022.4642
摘要
Project selection decisions are complex because they must balance not only financial returns, project risk, and fit with strategy, but also competitive circumstances. A rival’s project development efforts provide two pieces of information: a market rivalry signal, indicating potentially heightened competition in a market, and a technological signal, indicating a possible solution to a problem in that market. We hypothesize that these signals affect a firm’s likelihood of project selection in opposite directions, and that the timing of the signals matters for selection. We examine the drug development pipelines of the top 15 pharmaceutical companies from 1999 to 2016 to examine how rival projects drive the decision to progress a drug from preclinical laboratory trials to clinical trials in humans. Early-stage rival projects provide a stronger market rivalry signal, and they are associated with a decreased likelihood of the firm selecting its own project to compete in the same market. Late-stage rival projects signal technological feasibility and are associated with an increase in the likelihood of selection. We then exploit heterogeneity in market potential (i.e., disorder prevalence/incidence) and a molecular compound’s technology (i.e., therapeutic modality) to independently manipulate the salience of the two signals. Finally, we provide evidence on how selection based on rival signals informs project success. Information from rival projects prompts the selection of more successful drugs, but only after a threshold when sufficient uncertainty has been resolved. This paper was accepted by Jayashankar Swaminathan, operations management. Supplemental Material: Data and the online appendix are available at https://doi.org/10.1287/mnsc.2022.4642 .
科研通智能强力驱动
Strongly Powered by AbleSci AI