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引用次数: 8
摘要
最优独特性是一种强调行为者“同时保持相同和不同”的动力的理论(Brewer, 1991, p. 475)。这一理论最初是作为一种解释个人自我意识的方法,随着时间的推移,它已经扩展到组织层面甚至更远的地方,成为组织理论家和战略学者可以交流的一个主要研究领域。在本文中,我们简要回顾了历史上和当代研究最优独特性的方法,并指出了将最优独特性语境化的趋势。虽然令人鼓舞,但这一趋势没有考虑到四个重要的偶然性,这些偶然性显著地塑造了最优独特性及其基础机制:组织杂交性、社会文化、时间偶然性和衡量最优独特性的基准。我们对这四种偶然事件进行了讨论,并提出了相应的会话启动方式,以指导今后的研究。这些对话的启动者有可能进一步增强我们对最佳独特性的理解,将最佳独特性研究扩展到新的领域,并帮助告知和解决组织在追求最佳独特性时面临的挑战。
Optimal Distinctiveness: On Being the Same and Different
Optimal distinctiveness is a theory that emphasizes actors’ drive to be both “the same and different at the same time” (Brewer, 1991, p. 475). Originating as an approach to explain individuals’ self-construals, the theory has expanded over time to embrace the organizational level and beyond, becoming a major area of research where organization theorists and strategy scholars can converse. In this paper, we briefly review the historical and contemporaneous approaches to optimal distinctiveness and note an increasing trend of contextualizing optimal distinctiveness. While encouraging, this trend has fallen short of accounting for four important contingencies that significantly shape optimal distinctiveness and its underpinning mechanisms: organizational hybridity, societal culture, temporal contingencies, and benchmarks for gauging optimal distinctiveness. We discuss these four contingencies and propose corresponding conversation starters to guide future research. These conversation starters have the potential of further enhancing our understanding of optimal distinctiveness, broadening optimal distinctiveness scholarship into new domains, and helping inform and resolve challenges organizations face in pursuing optimal distinctiveness.
期刊介绍:
Computational and Mathematical Organization Theory provides an international forum for interdisciplinary research that combines computation, organizations and society. The goal is to advance the state of science in formal reasoning, analysis, and system building drawing on and encouraging advances in areas at the confluence of social networks, artificial intelligence, complexity, machine learning, sociology, business, political science, economics, and operations research. The papers in this journal will lead to the development of newtheories that explain and predict the behaviour of complex adaptive systems, new computational models and technologies that are responsible to society, business, policy, and law, new methods for integrating data, computational models, analysis and visualization techniques.
Various types of papers and underlying research are welcome. Papers presenting, validating, or applying models and/or computational techniques, new algorithms, dynamic metrics for networks and complex systems and papers comparing, contrasting and docking computational models are strongly encouraged. Both applied and theoretical work is strongly encouraged. The editors encourage theoretical research on fundamental principles of social behaviour such as coordination, cooperation, evolution, and destabilization. The editors encourage applied research representing actual organizational or policy problems that can be addressed using computational tools. Work related to fundamental concepts, corporate, military or intelligence issues are welcome.