Border-crossing knowledge networking among multinational firms : the case of R&D facilities

Abstract

(cont.) which is expected, given their additional knowledge resources. The respondents' urgency in looking for the relevant knowledge is the primary explanation for this difference between the two networks. This difference in knowledge networking among the different groups did not result in more similar knowledge networking, within each group, implying that there are additional factors that influence the networks. These factors are, (1) respondents' personal, job-related, and social resources; (2) resource distribution among R&D facilities; (3) management initiatives; (4) level of trust; and (5) facilities' histories and mandates, and how they are influenced by government policies. This study contributes to the current literature on globalization of R&D, knowledge management, and social network theory.

I use social network methods, to (1) describe the extent of knowledge networks of R&D researchers at three facilities of a large Multinational Enterprise (MNE), dubbed AMBE Corp., located in Brazil, Canada, and China; (2) to analyze the role of personal attributes in the manner in which the individuals utilize their knowledge resources; and (3) to determine the influence of other internal and external factors that affect the respondents' knowledge networks. There are two knowledge networks under analysis are, (1) Knowledge Networks for Problem Solving, and (2) Knowledge Networks for Central R&D Innovation. The third network, Communication Tools for Knowledge Networking, examines the communication tools that support the above knowledge networks. The descriptive results show that although the respondents in the three facilities utilize a different mix of knowledge resources, they do a relatively effective job of bridging the gap between AMBE's global knowledge resources, particularly that of AMBE's Central R&D, with the external knowledge resources. In all three facilities, the external knowledge networking is conducted through specialized gatekeepers with the exception of knowledge networking. The quantitative results show that personal attributes influence knowledge networks differently. For problem solving, all of the personal attributes, with the exception of gender, influence the groups so that they have different knowledge-networking profiles, with none of the groups having more extensive knowledge networks than their comparison groups. However, for Central R&D innovation networks, personal attributes affect the knowledge networks so that men, managers, and respondents with more than 15 years of tenure, have more extensive knowledge networks,