The knowledge transfer patterns of multinational firms: The evidence from Korean firm-level analysis

The paper explores the factors driving Outward Foreign Direct Investment (OFDI) by Emerging multinational enterprisess (EMNEs) and the patterns of knowledge transfer in six cases of EMNEs from three BRICS’ economies (India, China and South Africa). It found that there are significant differences between the OFDI from EMNEs and Developed multinational enterprise (DMNEs), which cannot be explained by using traditional FDI models. The way that EMNEs enter and operate in developed and developing countries are different. Knowledge transfers between EMNEs and developing host economies are predominantly one way and the former transfers more technology and knowledge than they gain. In the case of EMNEs and developed host economies, the knowledge and technology transfers appears to be more evenly matched, a two-way street benefitting both parties. The paper makes two major contributions: (i) it attempts to identify and distinguish the factors driving OFDI and patterns of knowledge transfer of OFDI from EMNEs and shows how they differ from DMNEs; (ii) it highlights aspects of OFDI by EMNEs such as expansion into countries outside their respective regions, and different patterns of technology and knowledge transfer in the South and North respectively.

The paper explores the factors driving Outward Foreign Direct Investment (OFDI) by Emerging multinational enterprisess (EMNEs) and the patterns of knowledge transfer in six cases of EMNEs from three BRICS’ economies (India, China and South Africa). It found that there are significant differences between the OFDI from EMNEs and Developed multinational enterprise (DMNEs), which cannot be explained by using traditional FDI models. The way that EMNEs enter and operate in developed and developing countries are different. Knowledge transfers between EMNEs and developing host economies are predominantly one way and the former transfers more technology and knowledge than they gain. In the case of EMNEs and developed host economies, the knowledge and technology transfers appears to be more evenly matched, a two-way street benefitting both parties. The paper makes two major contributions: (i) it attempts to identify and distinguish the factors driving OFDI and patterns of knowledge transfer of OFDI from EMNEs and shows how they differ from DMNEs; (ii) it highlights aspects of OFDI by EMNEs such as expansion into countries outside their respective regions, and different patterns of technology and knowledge transfer in the South and North respectively.

This study examines the relationship between the structure of professional networks and patterns of inter-professional knowledge transfer in the healthcare setting. Collecting survey data and qualitative evidence from 118 professionals in a hospital department, we used theory on the sociology of professions and social networks to investigate patterns of knowledge transfer between doctors and nurses. First we found that members of different professions tend to be embedded in distinctive professional cliques, which in turn inhibit effective inter-professional knowledge transfer. Network structure, however, combines with individual characteristics in predicting knowledge transfer patterns. By occupying central positions in closely knit networks, clinical directors can facilitate knowledge transfer patterns between doctors and nurses. And actors who are legitimated both intra-professionally and inter-professionally to occupy brokerage positions in social networks, namely junior doctors and nurse managers, are more likely to gain access to non-redundant, valuable knowledge. The overall picture is one of network structure interplaying with the characteristics of individual actors in shaping the dynamics of professional interactions.

People learn from prior experiences. We first learn how to use a spoon and then know how to use a different size of spoon. We first learn how to sew and then learn how to embroider. Transferring knowledge from one situation to another related situation often increases the speed of learning. This observation is relevant to human learning, as well as machine learning. This thesis focuses on the problem of knowledge transfer — an area of study in machine learning. The goal of knowledge transfer is to train a system to recognize and apply knowledge acquired from previous tasks to new tasks or new domains. An effective knowledge transfer system facilitates learning processes for novel tasks, where little information is available. For example, the ability to transfer knowledge from a model that identifies writers born in the U.S. to identify writers born in Kiribati, a much lesser known country, would increase the speed of learning to identify writers born in Kiribati from scratch. In this thesis, we investigate three dimensions of knowledge transfer: what, how, and why. We present and elaborate on these questions: What type of knowledge to transfer? How to transfer knowledge across entities? Why a certain pattern of knowledge transfer is observed? We first propose Segmented Transfer — a novel knowledge transfer model — to identify and learn from the most informative partitions from prior tasks. The proposed model is applied to Wikipedia vandalism detection problem and to entity search and retrieval problem and improves the predictions. Based on the foundation of knowledge transfer and network theory, we propose Knowledge Transfer Network (KTN), a novel type of network describing transfer learning relationships among problems. KTN is not only a knowledge representation, but also a framework to select an effective and efficient ensemble of learners to improve a predictive model. This novel type of network provides insights on identifying ontological connections that were initially obscured. For example, we may observe knowledge transfer occurs among dissimilar tasks, such as transferring from using a knife and fork to using chopsticks.

This article examines the relationship between teams, reputation and culture in knowledge transfer. It is argued that knowledge transfer, at its core, is highly dependent upon individual attitudes and can neither be mandated nor incentivised. Given the need to focus on individual attitudes, we must pay very close attention to their evolution, and what factors are at work, and how they impact knowledge transfer. We contend that insight into these matters can be gleaned through the factors we examine here. Our tests find strong support for these hypotheses within the sample studied and suggest that these factors help to explain why certain patterns of knowledge transfer have emerged.

Knowledge transfer across hierarchical lines is essential for organizations to function. Yet, how managers transfer different types of knowledge to their subordinates, in different organizational structures, remains understudied. Drawing on knowledge work and management literature, we adopt a case- based approach using sports teams (football, and ice hockey) as archetypal context to explore the nature of the knowledge transfer that occurs in both tall and flat organizational structures. Based on in-depth interviews with players and coaches, observations of team meetings, practices, and games, our findings revealed different knowledge transfer patterns in the two cases. Football (tall) involved declarative knowledge transferred through three mechanisms: scripting, simplification and individual directives. In contrast, ice hockey (flat) involved procedural knowledge transferred through three different mechanisms: general principles, exemplifying and experiential learning. The relevance and generalizability for practitioners and managerial research is discussed and future research is addressed.

Interdisciplinary training for doctoral students is an emerging scenario in higher education. The learning relations of knowledge transfer (KT: transferring knowledge from one person to another) and knowledge integration (KI: integrating or synthesizing perspectives from different disciplines) built by doctoral students play an important role in the process of interdisciplinary training. This study explores how doctoral students from a wide range of disciplines build learning relations of KT and KI during online interdisciplinary training. A mixed-methods approach was applied. First, longitudinal social network analysis (SNA) was employed to investigate the patterns of KT and KI relations over a seven-month training period. Subsequently, interviews were conducted to triangulate the SNA findings. SNA results show that the establishment of KI relations is significantly less than that of KT. Qualitative results show three themes explaining the challenges of KI relations, including challenges of online settings, the design of the training modules, and student involvement. Practical implications are drawn for the improvement of online interdisciplinary training, such as suggestions to promote KI and strategies for group learning.

The patent citation networks are described using critical node, core network, and network topological analysis. The main objective is understanding of the knowledge transfer processes between technical fields, institutions and countries. This includes identifying key influential players and subfields, the knowledge transfer patterns among them, and the overall knowledge transfer efficiency. The proposed framework is applied to the field of nanoscale science and engineering (NSE), including the citation networks of patent documents, submitting institutions, technology fields, and countries. The NSE patents were identified by keywords “full-text” searching of patents at the United States Patent and Trademark Office (USPTO). The analysis shows that the United States is the most important citation center in NSE research. The institution citation network illustrates a more efficient knowledge transfer between institutions than a random network. The country citation network displays a knowledge transfer capability as efficient as a random network. The technology field citation network and the patent document citation network exhibit a␣less efficient knowledge diffusion capability than a random network. All four citation networks show a tendency to form local citation clusters.

Managing the Embedded Multinational: A Business Network View, Mats Forsgren, Ulf Holm, Jan Johansson, Edward Elgar, Cheltenham, 2005

Do subsidiaries of foreign MNEs invest more in R&D than domestic firms?

Knowledge transfer upon repatriation

Purpose – The purpose of this paper is to explore how translation activities influence knowledge transfer across cultures in Chinese multinational enterprises (MNEs). Although translation is recognized as a critical instrument for MNEs to enhance cross-national knowledge flow, scholars have not put much emphasis on the importance of translation in international business research. Design/methodology/approach – This paper proposes a novel hierarchical framework to delineate the five major boundary-spanning functions regarding translation for knowledge transfer in China (i.e. exchanging, linking, manipulating, facilitating and intervening). Due to the paucity of relevant literature, the authors used exploratory case studies investigating two large Chinese MNEs to illustrate how individuals as boundary spanners handle the translation requirements associated with cross-cultural knowledge transmission within a MNE’s business network. The data coding approach was used to examine the assumed model. Findings – The findings demonstrate that translators indeed play a vital role in cross-border knowledge exchanging, linking people with crucial knowledge, manipulating the flow of knowledge for protecting confidentiality, facilitating the cross-cultural interaction of various knowledge sources and intervening to prevent the occurrence of misunderstanding in MNE contexts. The authors also reveal how translators overcome the three constraints of language interpretation concerning knowledge transfer (i.e. lack of equivalence, cultural interference and ambiguity). The proposed research framework was fully supported. Research limitations/implications – The results provide insightful implications for MNEs to treat translation as a significant “re-codification” rather than a mundane task. Knowledge transfer within MNEs involves not only knowledge regarding products, technology and operations but also involves “whole organizations” including business models, organizational visions, missions and strategies. Originality/value – The main value of this paper is to propose a novel model regarding the role of translation in cross-cultural knowledge transfer in China. Language is a container of contexts; the translation procedure in MNEs is actually dynamic and contingent in nature and can be seen as an act of knowledge creation per se.

Semi-automatic image interpretation systems utilize interactions between users and computers to adapt and update interpretation algorithms. We have studied the influence of human inputs on image interpretation by examining several knowledge transfer models. Experimental results show that the quality of the system performance depended not only on the knowledge transfer patterns but also on the user input, indicating how important it is to develop user-adapted image interpretation systems.

Reverse Knowledge Transfers Multinational enterprises (MNEs) are, sina qua non, the world’s most efficient and prolific producers of knowledge (process and product technology, management skills, intangible assets). Once produced, exploiting that knowledge is a primary motivation for foreign direct investment (FDI) as MNEs go abroad to earn rents on their knowledge-based assets created at home. Through the process of FDI, MNEs transfer knowledge and capital to their foreign affiliates in host countries. This picture – MNEs as creators, exploiters and transferrors of knowledge – suggests a one-way transfer from the parent firm in the home country out to foreign affiliates in host countries. However, this picture has been getting old for some time and is now close to obsolete. Most MNEs are now horizontally and vertically integrated networks where knowledge flows in all directions within the network, including the parent firm. MNEs from emerging economies in Asia and Latin America now engage in ‘‘springboard FDI’’, going abroad to explore for knowledge and bringing it home. Metanationals – MNEs ‘‘born in the wrong place’’ – depend on knowledge learned abroad that is transferred back to the parent firm. As a result, knowledge-seeking FDI now commands as much attention from scholars as knowledge-exploiting FDI did in the 1980s. IB scholars have increasingly focused their attention on inbound or reverse knowledge transfers – antecedents, processes and consequences. However, research on knowledge transfers within and by MNEs faces a high hurdle: how does one measure the direction, size and impact of knowledge transfers? The most popular approach has been to track patent data citations. Patent data have provided a relatively easy ‘‘trail’’ for mapping intrafirm and interfirm knowledge flows, both planned transfers and spillovers. Most scholars in this area have used patent data, building on the seminal works of Patel and Pavitt (1991) and Journal of International Business Studies (2009) 40, 177–180 & 2009 Academy of International Business All rights reserved 0047-2506

PurposeThe success of knowledge transfer very much depends on a company's ability to effectively manage their knowledge transfer process. The purpose of this paper is to argue that a critical component in understanding knowledge transfer in the international arena is the speed of that knowledge transfer (and those factors that influence that speed) within a multinational enterprise (MNE).Design/methodology/approachIn this paper, social capital theory is used to argue that social capital is related to the speed of knowledge transfer within an MNE. The three dimensions of social capital, i.e. relational, dimensional, and cognitive, facilitate the transfer process and effect the rapidity of technology transfer.FindingsThe role of knowledge transfer speed in MNEs knowledge management has been neglected and, yet, the speed of knowledge transfer is critical for MNE organizations to build or maintain their competitive advantage. A critical component in understanding knowledge transfer in the international arena is the speed of that knowledge transfer (and those factors that influence that speed) between different units.Originality/valueThis study examines social capital to better understand knowledge management at the intra‐firm level of an MNE. The success of knowledge transfer very much depends on a company's ability to effectively manage that knowledge transfer process. Using social capital theory, we argued that the three dimensions of social capital (relational, dimensional, and cognitive) are related to the speed of knowledge transfer from the parent company to the foreign subsidiary.