Elsevier

Social Networks

Volume 44, January 2016, Pages 295-306
Social Networks

Organizational learning across multi-level networks

https://doi.org/10.1016/j.socnet.2015.03.003Get rights and content

Highlights

  • We show that organizations would be better examined in a multilevel network perspective.

  • We provide an analytic approach for addressing the influence of the intraorganizational structure on interpersonal interaction.

  • We assess the diversity of knowledge sampled in organizational learning.

  • We show how Multilevel Exponential Random Graph Models could alleviate limitations of current multilevel network methods.

Abstract

This paper examines organizational learning through a multilevel network lens. We assess how interpersonal knowledge transfer is sustained by the organizational structure of interunit work-flow ties and by the level of specialism of the connected units.

To do this, we apply Multilevel Exponential Random Graph Models on data collected in a multiunit government institution in Italy.

Results indicate that our approach allows simplifying and better understanding of organizational learning. Units are more likely to retain knowledge transfer ties within their boundaries. Unit boundary-spanning tends to occur only when knowledge transfer ties are sustained by hierarchical interunit work-flow ties.

Introduction

Organizational learning, the process by which organizations generate, disseminate and exploit knowledge, translating it into innovation (March and Simon, 1958, Cyert and March, 1963, March, 1991) is a key topic in organizational studies.

An extensive literature has demonstrated that learning is a never-ending process, which contributes significantly to organizational growth, performance and survival (March, 1996). Because of these benefits, significant attention has been devoted to understand how learning occurs (Argote et al., 2003). A crucial mechanism consists in learning from the experience of others, either within or across the organizational boundaries. Learning from others requires some form of knowledge transfer, which is made possible mainly by interpersonal interaction among organizational members (Krackhardt and Hanson, 1993, Tushman, 1977). Interpersonal interaction, in fact, allows people to search for (i.e., ‘look and identify’) knowledge available in some parts of the organization and to transfer (‘move and incorporate’) it to other parts (Hansen, 1999, p. 83).

In examining knowledge transfer within the organization – which is the focus of this paper – the capability of interpersonal relations to connect different units, divisions and departments has been particularly investigated. Because organizational units, divisions and departments are pools of homogenous knowledge (Reagans and McEvily, 2003, Tortoriello and Krackhardt, 2010), searching in different units across organizational boundaries increases the heterogeneity of knowledge available and promotes learning (Beckman and Haunschild, 2002).

Since people are generally reluctant to cross-cut the boundaries defined around their units, several papers have attempted to identify how boundary spanning can be facilitated (Argote and Epple, 1990, Tortoriello et al., 2012). These studies have focused mostly on characteristics of the people as well as on types and structures of the relations among them (Burt, 2004, Dokko et al., 2014), disregarding the context in which relations occur.

This paper assesses whether and how the characteristics of the organization, and of the organizational structure in particular, can affect the presence of boundary-spanning ties. We examine the effect of the existence of work-flow ties connecting the units among which people are expected to search and transfer knowledge. The purpose of such an investigation is to provide a better understanding of the extent to which boundary-spanning relations of informal knowledge search-transfer at interpersonal level can be sustained by the formal work-flow ties between organizational units (Reagans and McEvily, 2003).

We emphasize the benefits of addressing these purposes by conceiving organizations as hierarchical systems of nested relations – i.e., multilevel network systems. We show how such an approach would allow a better representation of the interdependences between formal and informal relations and a clear assessment of the role of both.

We provide empirical evidence on this claim specifying and estimating newly developed Multilevel Exponential Random Graph Models (MERGMs – Wang et al., 2013, Wang et al., 2015). They represent a significant improvement on previous multilevel methods that are unable to analyze hierarchical systems of nested relations in depth. Standard Hierarchical Linear Models (HLMs) account for individual membership to a unit only. Assuming that either lower- (i.e., individual) or higher- (i.e., unit) level actors are independent from one another, conditional on the nesting structure of individuals within units, HLMs do not fit the structure of network then, do HLMs allow modeling interdependences among actors either within levels (i.e., interpersonal and interunit network separately) or between levels (i.e., associations and overlaps between the two network structures). The structural linked design (Lazega et al., 2008), popular in network studies, relaxes some assumptions of HLMs and, therefore, addresses some of their limitations. This approach respects the multi-level nature of the data, but models only some kinds of interdependences between the ties at the two levels.

In the empirical part of the paper, we specify and estimate MERGMs on original relational data that we have collected in a multiunit division of a regional government institution based in Northern Italy. Comparing the results of MERGM estimations with those of simpler ERGMs (Robins et al., 2007, Snijders et al., 2006), we show the advantages of addressing organizational learning through the multilevel network lens.

Section snippets

Organizational learning as interpersonal knowledge sharing

Informal interpersonal networks are one of the main conduits through which knowledge flows within the organization (Krackhardt and Hanson, 1993). Informal ties of advice seeking and knowledge searching, in particular, allow individuals to have access to knowledge accumulated by close contacts (Reagans and Zuckerman, 2001, Zaheer and McEvily, 1999), either inside or outside the organization (Argote et al., 2003).

Indeed, informal interpersonal networks can provide access to heterogeneous others (

Representing multilevel network data

Multilevel Exponential Random Graph Models (Wang et al., 2013, Wang et al., 2015) are the only existing method which directly assesses network interdependences across levels. MERGMs are a new class of ERGMs for multilevel network data.

Let M = [A,X,B] denote the network variable for a (u,v) two-level network, and m = [a,x,b] the corresponding realizations. M consists of a network A = [Ahu] representing a relation among a set U of higher-level actors with h, u nodes in U; a network B = [Biv] representing

Results

We specified the effects in an increasing order of complexity (Table 4). Model 1 is the baseline model, Model 2 is the intermediate model, and Model 3 is the multilevel complete model. We comment on the latter, drawing attention to its ability to either improve or simplify the representation of knowledge transfer.

First, we look at the interpersonal network. The positive Reciprocity estimate indicates that knowledge seeking is likely to be a mutual relation, based on knowledge sharing (McFadyen

Discussion and conclusions

Studies on organizational learning have long emphasized the role of interpersonal ties spanning unit boundaries as the main conduit through which knowledge flows within the organization. Although recognizing the hierarchical nature of organizations, these studies have treated organizational units as independent, and investigated relational and individual characteristics that allow organizational members to cross-cut boundaries. In this paper we have taken a different perspective, focusing on

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