Doctoral Dissertation Research: Knowledge Flow and Value Creation: Integrating Structural Embeddedness and Knowledge Embeddedness in Alliance Networks

学者们刚刚开始研究企业的联盟关系和创新成果之间的关系，并报告了有趣但相互矛盾的结果。为了进一步了解这一领域，我研究了企业联盟伙伴的特征和联盟网络的结构如何共同影响其创新能力。企业是“结构嵌入”在组织间的网络，这些模式的企业间的关系告诉我们，通过网络的知识交流，从而表明特定的组织信息优势，由于企业的网络位置。然而，结构方法忽略了企业知识占有的异质性。本研究引入了“知识嵌入”的概念，以解决网络成员可能拥有非常不同类型的专业知识这一事实。知识嵌入性描述了核心企业的知识与其合作伙伴的知识之间的关系，以及网络成员之间的专业知识分布。本文试图将这两种观点结合起来，提出网络结构影响知识流动和交换的模式，而企业间的知识关系影响交换的内容。 本研究以医药产业为例，探讨结构嵌入、知识嵌入与企业创新之间的关系。 几十年来，制药行业经历了快速的技术进步，产品创新对于企业的稳定增长至关重要。然而，由于所涉及的技术的复杂性，企业往往发现他们缺乏必要的知识和资源，因此寻求联盟伙伴合作开发产品。我使用主要的企业名称，以确定人口的228家公司在药物制剂行业（SIC 2834）所列的国家统计局在1990年至2000年期间。这些公司的联盟活动将从各种来源收集：资本，SEC文件，以及行业报纸和期刊列表（如药店新闻，医疗保健财务管理，今日健康产业，制药执行，制药技术，研发焦点药物新闻）。我将从USPTO收集专利数据，USPTO是美国官方专利数据库，包含可搜索的全文专利记录。本研究将有助于现有的研究开发一个综合的解释网络关系和合作伙伴的知识特性如何共同影响创新绩效，并通过调查是否某些网络的位置或合作伙伴的属性提高了创新的速度和企业的创新价值。通过确定公司之间的结构和知识关系对创新成果的独特和共同贡献，这项研究将使管理人员和决策者能够更有效地为研究和开发活动分配资源。此外，这项研究对我们理解知识和信息如何通过组织网络传播具有广泛的意义。这对于预测网络如何影响新的组织形式、管理实践和新技术的传播和采用等结果非常重要，这些结果对经济增长和社会福利具有广泛的影响。研究结果还将用于编制管理联盟网络和创新课程的教材。

Scholars are just beginning to examine the relationship between firms' alliance connections and their innovation outcomes and have reported intriguing yet conflicting results. To further understanding in this area, I investigate how characteristics of firms' alliance partners and the structure of alliance networks jointly affect their capabilities to innovate. Firms are 'structurally embedded' in inter-organizational networks, and these patterns of inter-firm relations inform us about the knowledge exchange through a network, and hence indicate specific organizational informational advantages due to firms' network positions. However, structural approaches ignore firm heterogeneity in knowledge possession. This study introduces the concept of 'knowledge embeddedness' to address the fact that members of a network may possess very different types of expertise. Knowledge embeddedness describes the relationships between a focal firm's knowledge and its partners' knowledge, as well as the distribution of expertise among the members of a network. I seek to integrate these two perspectives in my thesis by proposing that network structure influences the pattern of knowledge flow and exchange, while knowledge relations among firms affect the content of exchange. I examine the relationships between structural embeddedness, knowledge embeddedness, and firms' innovativeness in the pharmaceutical industry. The pharmaceutical industry has undergone fast technology advancement over the decades and product innovation is critical for firms' steady growth. Nevertheless due to the complexity of technology involved, firms often find that they lack necessary knowledge and resources, and hence seek alliance partners to cooperate in product development. I used primary business designation to identify a population of 228 firms in pharmaceutical preparation industry (SIC2834) as listed by COMPUSTAT during the period from 1990 to 2000. The alliance activities of these firms will be gathered from a variety of sources: Recombination Capital, SEC filings, and a list of industrial newspapers and journals (such as Drug Store News, Healthcare Financial Management, Health Industry Today, Pharmaceutical Executive, Pharmaceutical Technology, R & D Focus Drug News). I will collect patent data from USPTO, the official U.S. patent database containing the searchable full-text patent records. This study will contribute to existing research by developing an integrated explanation of how network relations and partner knowledge characteristics jointly affect innovation performance, and by investigating whether certain network positions or partner attributes enhance both the rate of innovation and the value of a firm's innovations. By identifying the unique and joint contributions of structural and knowledge relationships among firms to innovation outcomes, the study will enable managers and policy makers to more efficiently and effectively allocate resources to research and development initiatives. Moreover, the study has broad implications for our understanding of how knowledge and information travel through networks of organizations. This is important for predicting how networks influence such outcomes as the diffusion and adoption of novel organizational forms, management practices, and new technologies, outcomes that have wide-reaching implications for economic growth and social welfare. The results will also be used to develop teaching materials for courses on managing alliance networks and innovation.