控制重构
资源(消歧)
软件部署
业务
探索性研究
投资(军事)
动态能力
知识管理
计算机科学
产业组织
政治
操作系统
计算机网络
社会学
人类学
嵌入式系统
法学
政治学
作者
Ari Dothan,Dovev Lavie
出处
期刊:Advances in strategic management
日期:2016-08-31
卷期号:: 319-369
被引量:17
标识
DOI:10.1108/s0742-332220160000035011
摘要
Resource reconfiguration enables firms to adapt in dynamic environments by supplementing, removing, recombining, or redeploying resources. Whereas prior research has underscored the merits of resource reconfiguration and the modes for implementing it, little is known about the antecedents of this practice. According to prior research, under given industry conditions, resource reconfiguration is prompted by a firm’s corporate strategy and by characteristics of its knowledge assets. We complement this research by identifying learning from performance feedback as a fundamental driver of resource reconfiguration. We claim that performance decline relative to aspiration motivates the firm’s investment in knowledge reconfiguration, and that this investment is reinforced by the munificence of complementary resources in its industry, although uncertainty about the availability of such resources limits that investment. Testing our conjectures with a sample of 248 electronics firms during the period 1993–2001, we reveal a clear distinction between exploitative reconfiguration, which combines existing knowledge elements, and exploratory reconfiguration, which incorporates new knowledge elements. We demonstrate that performance decline relative to aspiration motivates a shift from exploitative reconfiguration to exploratory reconfiguration. Moreover, munificence of complementary resources mitigates the tradeoff between exploratory and exploitative reconfigurations, whereas uncertainty weakens the motivation to engage in both types of reconfiguration, despite the performance gap. Nevertheless, codeployment, which extends the deployment of knowledge assets to additional domains, is more susceptible to uncertainty than redeployment, which withdraws those assets from their original domain and reallocates them to new domains. Our study contributes to emerging research on resource reconfiguration, extends the literature on learning from performance feedback, and advances research on balancing exploration and exploitation.
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