已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Specialization as a double‐edged sword: The relationship of scientist specialization with R&D productivity and impact following collaborator change

杠杆(统计) 生产力 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
出处
期刊:Strategic Management Journal [Wiley]
卷期号:43 (5): 986-1024 被引量:13
标识
DOI:10.1002/smj.3357
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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
ding应助dogontree采纳,获得10
刚刚
乐乐应助lklk采纳,获得10
1秒前
2531发布了新的文献求助10
2秒前
伊笙完成签到 ,获得积分10
6秒前
shanyuee应助Veee采纳,获得10
7秒前
shanyuee应助Veee采纳,获得10
7秒前
shanyuee应助Veee采纳,获得10
7秒前
景__发布了新的文献求助10
7秒前
8秒前
AUV完成签到,获得积分10
10秒前
11秒前
11秒前
14秒前
17秒前
螃蟹One完成签到 ,获得积分10
17秒前
左焦发布了新的文献求助10
17秒前
科研通AI2S应助siyuezhi采纳,获得10
17秒前
海洋岩土12138完成签到 ,获得积分10
18秒前
玖梦恨别离完成签到 ,获得积分10
19秒前
景__发布了新的文献求助10
20秒前
舍断离完成签到,获得积分10
21秒前
dogontree发布了新的文献求助10
21秒前
rick3455完成签到 ,获得积分10
22秒前
DreamMaker完成签到,获得积分10
22秒前
22秒前
老王家的二姑娘完成签到 ,获得积分10
24秒前
炎星语完成签到,获得积分10
24秒前
Smiley完成签到 ,获得积分10
25秒前
我爱学习完成签到 ,获得积分20
26秒前
26秒前
奔跑的蒲公英完成签到,获得积分10
27秒前
迷路冰安完成签到 ,获得积分10
28秒前
充电宝应助山山而川采纳,获得10
29秒前
开心幻悲完成签到 ,获得积分10
33秒前
34秒前
爆米花应助简单酒窝采纳,获得30
34秒前
元神完成签到 ,获得积分10
36秒前
景__发布了新的文献求助10
36秒前
耿宇航完成签到 ,获得积分10
36秒前
38秒前
高分求助中
System in Systemic Functional Linguistics A System-based Theory of Language 1000
Дружба 友好报 (1957-1958) 1000
The Data Economy: Tools and Applications 1000
Essentials of thematic analysis 700
Mantiden - Faszinierende Lauerjäger – Buch gebraucht kaufen 600
PraxisRatgeber Mantiden., faszinierende Lauerjäger. – Buch gebraucht kaufe 600
A Dissection Guide & Atlas to the Rabbit 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3117383
求助须知:如何正确求助?哪些是违规求助? 2767503
关于积分的说明 7690900
捐赠科研通 2422835
什么是DOI,文献DOI怎么找? 1286437
科研通“疑难数据库(出版商)”最低求助积分说明 620404
版权声明 599856