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Achieving Organizational Ambidexterity: An Exploratory Model, Using Fuzzy Cognitive Maps

机译：实现组织的二元性：使用模糊认知图的探索性模型

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摘要

Over the course of three to four decades, most well-established companies lose their dominating position in the market or fail entirely. Their failure occurs even though they have resources for sensing shifting market trends, skills and assets to develop next-generation technologies, and the financial means to fill skill gaps and afford risky investments. Nevertheless, incumbents obviously find it very difficult to invest in innovation that takes attention and resources away from a highly successful core business. A solution to this "innovator's dilemma" is the concept of "organizational ambidexterity", which has garnered considerable attention among researchers in organization and innovation. According to empirical findings and emergent theory, companies can improve their financial performance and ensure their long-term survival by balancing their innovation activities, so that they are equally focused on exploratory (discontinuous) and exploitative (incremental, continuous) innovations. But how can such a balance be achieved? The literature on the organizational theory and related fields (product innovation, knowledge management, creativity, etc.) identifies more than 300 contributing factors to innovation and ambidexterity: many are interdependent so that their impacts compound or cancel each other. Moreover, for many factors, there is limited empirical data and the size of impacts is unknown. To understand which managerial actions lead to ambidexterity, this dissertation develops a novel approach to the study and analysis of complex casual systems with high uncertainty: exploratory fuzzy cognitive mapping.;Fuzzy Cognitive Mapping (FCM) is a semi-quantitative system modeling and simulation technique. It is used to represent qualitative information about complex systems as networks of casual relationships that can be studied computationally. Exploratory modeling and analysis (EMA) is a new approach to modeling and simulation of complex systems when there is high uncertainty about the structural properties of the system. This work is the first to combine both approaches.;The work makes several contributions: First, it shows that only a small fraction of management interventions will actually lead to ambidexterity while most will, at best, improve one type of innovation at the expense of the other. Second, it provides a simulation tool to management researchers and practitioners that allows them to test ideas for improving ambidexterity against a model that reflects our current collective knowledge about innovation. And third, it develops a range of techniques (and software code) for exploratory FCM modeling, such as methods for transforming qualitative data to FCM, for exploratory simulation of large and complex FCM models, and for data visualization. They can be utilized to study other similarly complex and uncertain systems.
机译：在三到四个十年的过程中，大多数信誉良好的公司失去了在市场上的主导地位或完全倒闭。即使他们有足够的资源来感知不断变化的市场趋势，开发下一代技术的技能和资产，以及填补技能差距并提供风险投资的财务手段，他们的失败也会发生。然而，老牌企业显然发现很难投资于创新，因为创新需要注意力和资源从非常成功的核心业务中转移。解决“创新者困境”的方法是“组织灵活性”概念，该概念已引起组织和创新研究人员的极大关注。根据经验发现和新兴理论，公司可以通过平衡创新活动来提高财务绩效并确保长期生存，从而使他们同样专注于探索性（不连续）和开发性（增量，连续）创新。但是如何实现这种平衡呢？有关组织理论和相关领域（产品创新，知识管理，创造力等）的文献确定了300多个促成创新和模棱两可的因素：许多因素是相互依存的，因此它们的影响相互补充或抵消。此外，由于许多因素，经验数据有限，影响的大小未知。为了理解哪些管理行为会导致歧义，本文提出了一种新的方法来研究和分析具有高度不确定性的复杂休闲系统：探索性模糊认知映射。；模糊认知映射（FCM）是一种半定量的系统建模和仿真技术。 。它用来表示有关复杂系统的定性信息，作为可以通过计算研究的偶然关系网络。探索性建模和分析（EMA）是在复杂的系统结构特性存在高度不确定性时对复杂系统进行建模和仿真的一种新方法。这项工作是将这两种方法结合起来的第一项工作。这项工作做出了一些贡献：第一，它表明只有一小部分管理干预措施实际上会导致模棱两可，而大多数最多只能改善一种创新方式，却以牺牲另一个。其次，它为管理研究人员和从业人员提供了一种仿真工具，使他们可以根据反映我们当前有关创新的集体知识的模型测试可提高灵活性的想法。第三，它开发了一系列用于探索性FCM建模的技术（和软件代码），例如用于将定性数据转换为FCM，用于大型复杂FCM模型的探索性仿真以及数据可视化的方法。它们可用于研究其他类似的复杂和不确定的系统。

著录项

  • 作者

    Alizadeh, Yasser.;

  • 作者单位

    Portland State University.;

  • 授予单位 Portland State University.;
  • 学科 Management.;Engineering.;Computer engineering.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 332 p.
  • 总页数 332
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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