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联盟关系与其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企业的创新能力。企业在结构上嵌入于组织间网络中，这些企业间关系的模式告诉我们通过网络进行的知识交换，从而表明由于企业的网络位置而具有特定的组织信息优势。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。知识嵌入性描述了焦点企业的知识与其合作伙伴的知识之间的关系，以及专业知识在网络成员之间的分布。我试图在我的论文中整合这两种观点，提出网络结构影响知识流动和交换的模式，而企业之间的知识关系影响交换的内容。我研究了结构嵌入性、知识嵌入性和制药行业企业创新之间的关系。制药行业在过去的几十年里经历了快速的技术进步，产品创新对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进行产品开发。我使用主要业务名称来确定由COMPUSTAT在1990年至2000年期间列出的228家制药制剂行业公司（SIC2834）。这些公司的联盟活动将从各种来源收集：重组资本，美国证券交易委员会文件，以及一系列行业报纸和期刊（如药店新闻，医疗保健财务管理，健康产业今日，制药执行官，制药技术，研发焦点药物新闻）。我将从USPTO收集专利数据，这是美国官方专利数据库，包含可搜索的全文专利记录。本研究对网络关系和合作伙伴知识特征如何共同影响创新绩效进行了综合解释，并探讨了某些网络位置或合作伙伴属性是否同时提高了企业的创新速度和创新价值，从而对现有研究做出了贡献。通过确定企业之间的结构关系和知识关系对创新成果的独特和共同贡献，该研究将使管理者和政策制定者能够更有效地为研发计划分配资源。此外，该研究对我们理解知识和信息如何在组织网络中传播具有广泛的意义。这对于预测网络如何影响诸如新组织形式、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。研究结果还将用于开发联盟网络管理和创新课程的教材。

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.