Factors of stickiness in transfers of know-how between MNC units

Montazemi, Ali Reza; Pittaway, Jeff J.; Qahri‐Saremi, Hamed; Wei, Yongbin

doi:10.1016/j.jsis.2012.01.001

Cited by 57 publications

(51 citation statements)

References 112 publications

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“…It involves the application of mangers' tacit knowledge about the organization for its future requirements (Dyerson & Roper, 1991), and it is, therefore, organization-specific tacit knowledge (Montazemi, Pittaway, Saremi & Wei, 2012). In HE, management know-how is a key contributor to a university's success.…”

Section: Knowledge Attributes and Knowledge Sharing In Strategic Allimentioning

confidence: 99%

Knowledge sharing in China–UK higher education alliances

Roberts

Yan

et al. 2014

International Business Review

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Knowledge sharing through cross-border strategic alliances has been seen by firms as one of the critical strategies to pursue sustainable competitive advantage. However, empirical investigations on how knowledge sharing occurs in strategic alliances are limited and are rarely concerned with strategic alliances in the higher education industry. Based on an empirical investigation of China-UK educational alliances, this research sheds light on this under explored area. Findings reveal that the scale of academic and organizational knowledge sharing is affected by knowledge attributes and partner characteristics. While knowledge sharing in China-UK higher education alliances displays numerous similarities with that occurring in other industries, this study reveals features that are distinct to this important and increasingly international sector. In so doing, this paper offers valuable insights for managers and policy makers concerned with the internationalisation of higher education.

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Section: Knowledge Attributes and Knowledge Sharing In Strategic Allimentioning

confidence: 99%

Knowledge sharing in China–UK higher education alliances

Roberts

Yan

et al. 2014

International Business Review

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“…In the 131 MASEM studies located, 123 of them used this approach. Seven studies (Fan, Jackson, Yang, Tang, & Zhang, 2010;Krieg & Xu, 2015;Montazemi, Pittaway, Saremi, & Wei, 2012;Murayama & Elliot, 2012;Simons, Leroy, Collewaert, & Masschelein, 2014;Trivette, Dunst, & Hamby, 2010;Yufik & Simms, 2010) used the two-stage SEM (TSSEM) approach, which will be introduced later. The remaining study (Grace et al, 2007) is the only study that used an approach similar to the parameter-based MASEM presented in the present paper.…”

Section: Applications Of Meta-analytic Structural Equation Modeling Imentioning

confidence: 99%

Random‐effects models for meta‐analytic structural equation modeling: review, issues, and illustrations

Cheung

2016

Research Synthesis Methods

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Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM. Random-effects models are well known in conventional meta-analysis but are less studied in MASEM. The primary objective of this paper was to address issues related to random-effects models in MASEM. Specifically, we compared two different random-effects models in MASEM-correlation-based MASEM and parameter-based MASEM-and explored their strengths and limitations. Two examples were used to illustrate the similarities and differences between these models. We offered some practical guidelines for choosing between these two models. Future directions for research on random-effects models in MASEM were also discussed. Copyright © 2016 John Wiley & Sons, Ltd.

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“…However, this ignores knowledge boundaries that can mean that even when individuals share their knowledge this may not be effective. Thus, the knowledge literature has highlighted that knowledge does not flow smoothly between people and organizational units, especially where they have different backgrounds and experience (Montazemi et al, 2012); knowledge is thus described as being 'sticky' (Szulanski, 1996). Some of the knowledge literature has obscured this because it has focused on knowledge processes within particular communities (i.e., all the literature on communities of practice that refers to the seminal work of Lave and Wenger, 1991).…”

Section: Recent Developments: It As An Active Participant In Knowledgmentioning

confidence: 99%

Managing Knowledge and Managing Knowledge Work: What we know and what the Future holds

Newell

2015

Journal of Information Technology

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In this paper we review the recent IS literature on knowledge and consider different assumptions that underpin different approaches to this broad research area. In doing this we contrast those who focus on knowledge management with those who focus on knowing as practice and examine how contexts, processes and purposes need to be considered whichever approach to knowledge one is adopting. We also identify how recent IT developments, especially in relation to social software and the digitization of everything, are presenting new opportunities (and challenges) for how organizations can manage both knowledge and knowledge work. This presents IS scholars with new research agendas for examining and understanding the relationships between technology, organization and society.

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Factors of stickiness in transfers of know-how between MNC units

Cited by 57 publications

References 112 publications

Knowledge sharing in China–UK higher education alliances

Knowledge sharing in China–UK higher education alliances

Random‐effects models for meta‐analytic structural equation modeling: review, issues, and illustrations

Managing Knowledge and Managing Knowledge Work: What we know and what the Future holds

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