Knowledge processing in Manufacturing Industries: The Evaluation of Documents as Boundary Objects in Implementing Knowledge - Cai, Ruiting (2012)
Knowledge is paid more attention in the manufacturing industries, where individuals are required to process knowledge efficiently and effectively. As tactic boundary objects, knowledge is difficult to be codified and the performance of which has significant influence on the companies’ operation. The purpose of this study is to evaluate the efficient and effective knowledge process approaches, which make knowledge can be processed by transparent and visible formats. This study assessed through a Chinese manufacturing company, Shanghai Cooltech Power CO., LTD, which provides essential and useful primary and secondary data for analyzing the efficiency and effectiveness of coding, transferring, interpretation and creating in knowledge processing, which evaluating the practical processes that can be implemented in companies. The investigation evaluated, using a qualitative research, different factors likely to affect the performance of knowledge processing: organizational structure, collaboration of human and material agencies, performance of boundary objects and the roles of gatekeepers. Through observations, interviews and document reviews, the knowledge is processed by the keeping developing with the living and socialized documents. Meanwhile, the types of organizational structure influences the transferring channels, which results to the performance of knowledge sharing. In addition, interactions of human and material agencies refer to the knowledge interpretation. Furthermore, the performance of boundary objects has significant impacts on knowledge coding and transforming, which are improved through negotiations and communications. Additionally, the roles of gatekeepers also affect the development of knowledge with regards to controlling and deploying knowledge. Therefore, with the influences of each factor, knowledge is developed continuously and consistently through a systematic model.