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Entrepreneurial discovery and information complexity in knowledge-intensive industries.

机译:知识密集型行业的创业发现和信息复杂性。

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摘要

Why are some firms better able than others to exploit new opportunities? I posit that differences in the type and level of complexity of the information obtained through the entrepreneurial discovery process may be a meaningful indicator of the likelihood that a firm is able to exploit a new opportunity. Specifically, I investigate knowledge reproduction processes for product replication (internal copying) and imitation (external copying) as a means of exploiting opportunities and building competitive advantage. Integrating concepts from information theory and the knowledge-based view of the firm, I introduce a generalized model and quantitative methods for estimating the inherent complexity of any unit of knowledge, such as a strategy, technology, product, or service, as long as the unit is represented in algorithm form. Modeling organizations as information processing systems, I develop measures of the information complexity of an algorithm representing a unit of knowledge in terms of the minimum amount of data (algorithmic complexity) and the minimum number of instructions (computational complexity) required to fully describe and execute the algorithm. I apply this methodology to construct and analyze a unique historical dataset of 91 firms (diversifying and de novo entrants) and 853 new product introductions (1974–2009), in a knowledge-intensive industry, digital signal processing. I find that: (1) information complexity is negatively and significantly related to product replication and imitation; (2) replicators have the greatest advantage over imitators at moderate levels of information complexity; (3) intellectual property regimes strengthening the patentability of algorithms significantly increase product replication, without significantly decreasing imitation; (4) outbound licensing of patented technologies decreases product replication and increases imitation; (5) products introduced by de novo entrants are less likely to be replicated and more likely to be imitated than products introduced by diversifying entrants; and (6) diversifying entrants have the greatest advantage over de novo entrants at high and low levels of information complexity; neither type of entrant has a significant advantage at moderate levels of complexity. These empirical findings support and extend predictions from earlier simulation studies. The model is applicable to other aspects of organizational strategy and has important implications for researchers, managers, and policymakers.
机译:为什么有些公司比其他公司更有能力开发新机会?我认为,通过企业家发现过程获得的信息的类型和复杂程度的差异可能是企业能够利用新机会的有意义的指标。具体来说,我研究了用于产品复制(内部复制)和模仿(外部复制)的知识复制过程,以此来利用机会和建立竞争优势。结合信息理论和企业基于知识的观点的概念,我介绍了一种通用模型和定量方法,用于估计任何知识单元(例如战略,技术,产品或服务)的固有复杂性,只要单位以算法形式表示。我将组织建模为信息处理系统,我根据完整描述和执行所需的最小数据量(算法复杂度)和最小指令数(计算复杂度)来开发表示知识单元的算法的信息复杂度的度量算法。我使用这种方法来构建和分析知识密集型行业数字信号处理中91家公司(多元化和新兴企业)和853种新产品(1974-2009年)的独特历史数据集。我发现:(1)信息复杂性与产品复制和仿制成负相关,并且与产品仿制密切相关; (2)在中等程度的信息复杂度下,复制者比模仿者具有最大的优势; (3)加强算法可专利性的知识产权制度大大增加了产品复制,而没有明显减少模仿; (4)专利技术的对外许可减少了产品复制并增加了仿制; (5)与多元化参与者引入的产品相比,新生参与者引入的产品被复制和模仿的可能性较小; (6)在信息复杂程度高低的情况下,多元化的参与者比新生的参与者具有最大的优势;在中等程度的复杂性上,这两种类型的进入者都没有明显的优势。这些经验发现支持并扩展了早期模拟研究的预测。该模型适用于组织策略的其他方面,对研究人员,管理人员和决策者具有重要意义。

著录项

  • 作者

    Joshi, Amol Madhukar.;

  • 作者单位

    The University of North Carolina at Chapel Hill.;

  • 授予单位 The University of North Carolina at Chapel Hill.;
  • 学科 Business Administration Entrepreneurship.;Business Administration Management.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 172 p.
  • 总页数 172
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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