杠杆(统计)
生产力
剑
经济
产业组织
业务
计算机科学
经济增长
操作系统
机器学习
作者
Amit Jain,Will Mitchell
摘要
Abstract Research Summary Organizational learning studies demonstrate that specialization conditions multiple aspects of firm performance, including productivity and financial returns, through its effect on skill development and coordination. We know little, however, about how specialization may influence a firm's R&D performance, including both R&D productivity and innovation impact. We propose that specialization is a double‐edged sword for R&D performance that can be influenced via changing scientists' collaborators: specialization increases scientist and firm R&D productivity but decreases the impact of innovations, while changing collaborators in a team reverses how specialization relates to productivity and impact. We validate this argument using a long panel (1970–2017) from the biotechnology industry. Specialization and collaborator change may thus serve as mechanisms to manage the trade‐off between productivity and impact in R&D activities. Managerial Summary This article studies how managers in firms may leverage their R&D workers' specialization to optimize their R&D performance. Our study shows that specialization is a double‐edged sword for R&D performance: it facilitates R&D productivity at the detriment of R&D impact, while the trade‐off shifts when collaborators within a scientist's team change. Thus, specialization and collaborator change condition R&D performance, with two implications for strategy. First, a firm's managers can recruit specialists or generalists depending on whether they want to prioritize productivity or impact in R&D activities. Second, job rotation practices that create periodic collaborator change may disrupt R&D productivity, yet invigorate explorative activity and increase the likelihood of impactful innovation.
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