Understanding the structures, antecedents and outcomes of organisational learning and knowledge transfer: a multi-theoretical and multilevel network analysis

European Journal of International Management

Volumn 4, Issue 6, 2010, Pages 576-601

  Su, Chunke ^a   Huang, Meikuan ^b   Contractor, Noshir ^c  

^a UNIVERSITY OF TEXAS AT ARLINGTON   (United States)

^b CALIFORNIA STATE UNIVERSITY   (United States)

^c Northwestern University   (United States)

Author keywords

ERGM analysis; information allocation; information retrieval; knowledge transfer; organisational learning

Indexed keywords

EID: 84969452477     PISSN: 17516757     EISSN: 17516765     Source Type: Journal    
DOI: 10.1504/EJIM.2010.035590     Document Type: Article

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99. The MTML framework is different from the traditional multilevel approach which focuses on explaining cross-level variations in hierarchically nested data (Kozlowski and Klein, 2000; Hox, 2002). The multilevel facet in the MTML framework examines various configurations of individuals as the unit of analysis, and how these configurations lead to diverse network structures.
100. The nature of our network data was multilevel. There were three levels associated with the information retrieval and allocation networks: knowledge domains nested within each team, and teams nested within each organisation. Since the goal of this paper was not focused on a cross-level analysis to examine the variations between knowledge domains and teams, we performed a meta-analysis at the team level only. We combined network data across all knowledge domains into a single network for each team, and used this single network as data input for ERGM analysis for each team. Specifically, we put knowledge-domain level networks on the diagonal of one large network, and filled the rest cells with structural zeros. We fixed the effects of these structural zeros in XPNet so that we only estimated the effects of the actual networks across multiple knowledge domains in each team. As an exploration, we did combine the network data across all knowledge domains of all teams into a single grand network (the highest level of our data). However, when we tried to perform ERGM analysis on the grand-level network, the XPNet program crashed due to the enormous size of the network (a 1207 by 1207 matrix), accompanied by a large number of parameters to be estimated (ten). In future research, one possible alternative is to use a multilevel analytic program MLwiN (Rasbash et al., 2005), which is designed specifically for meta-analysis of multilevel network data (cf. Lubbers, 2003; Lubbers and Snijders, 2007).
101. This hypothesis was strongly supported by the unanimously significant estimation of the in-k- star parameter, which tests the degree to which one member retrieves information from multiple others. In addition, our study reports (see Table 1) that the information retrieval network in our participating teams has a low mean density of 0.30 (compared to 0.52 of the information allocation network), which provides further support to H3a. Such results suggest that, although overall members did not actively retrieve information from others, those who did were most likely to retrieve information from the same member (the ‘star’). This tendency has led to a highly centralised information retrieval structure in our participating teams.
102. We found the third form of decentralisation to be prevalent in our participating teams. The mean density (see Table 1) of the information allocation network across all knowledge domains and all teams is 0.52 (the maximum density of a network is 1). Network density is defined as the total number of ties divided by the total number of possible ties in a given network (Borgatti et al., 1999). It provides a descriptive measure of the frequency of the occurrence of network ties. A network density of 0.52 would reject the conjecture that the decentralised structure found in our study was simply the result of a sparse network of information allocation in our participating teams.