How Far do Network Effects Spill Over? Evidence from an Empirical Study of Performance Differentials in Interorganizational Networks

• Author: Alessandro Lomi,Paola Tubaro,Francesca Pallotti
• Date: 01 September 2015
• DOI: http://doi.org/10.1111/emre.12052
• Published date: 01 September 2015

How Far do Network Effects Spill Over?

Evidence from an Empirical Study of

Performance Differentials in

Interorganizational Networks

Francesca Pallotti1,2,Paola Tubaro1, and Alessandro Lomi2

1Department of International Business and Economics, University of Greenwich, London, UK

2University of Lugano, Switzerland

Organizations join interorganizational networks in the hope of gaining exposure to learning opportunities, and

accessing valuable extramural resources and knowledge. In this paper we argue that participation in

interorganizational networks also reduces performance differentials among organizational nodes. We examine

three alternative mechanisms capable of sustaining this prediction. The first (strength of ties) operates at a strictly

local level defined in terms of dyadic relations linking organizations. The second mechanism (social proximity)

operates at an intermediate – or meso level of interdependence defined in terms of membership in overlapping

cliques into which interorganizational networks are typically organized. The third mechanism (structural equiva-

lence) is global and pertains to jointly occupied network positions. The objective of this paper is to examine at

which of these levels network effects operate to reduce performance differentials among members of

interorganizational networks. Our empirical analysis of performance differentials between hospitals in a regional

community supports the following conclusions: (i) performance spillover effects are highly differentiated and vary

significantly across network levels; (ii) organizations occupying similar positions within the network are more

similar in terms of performance; (iii) joint membership in multiple sub-groups (or cliques) reduces performance

differentials up to a limit; after this limit is reached, the performance of organizationalpartners begins to diverge;

(iv) the strength of direct collaboration between organizational partners does not necessarily reduce

interorganizational performance differentials. The results of the study are new because available research on

interorganizational networks says little about the range of network effects, i.e., about how far the performance

spillover effects that operate through networks propagate throughoutorganizational fields and communities. These

results are also consequential because they suggest that network effects on performance differentials are sensitive

to the specification of network boundaries.

Keywords: cliques; interorganizational networks; performance differentials; social influence; structural

equivalence

Introduction

Students of organizations and management demonstrate

an increased interest in the role of interorganizational

relations as joint problem solving arrangements estab-

lished by interdependent organizations to support,

control, and coordinate flows of material and symbolic

resources (Cropper et al., 2008). The basic argument

behind this interest is that connected organizations are

better able to manage the uncertainty inherent in their

mutual dependencies, recognize and access key

resources across corporate boundaries, and learn from

each other by sharing and transferring relevant knowl-

edge and information (Powell et al., 1996).

Interorganizational networks provide access to the com-

petitive and operational experience of others resulting in

best practices that can then be more easily understood,

evaluated and, possibly, assimilated (Ingram and Baum,

1997).

Examples of research building on this perspective

include studies of strategic alliances (Gulati and

Correspondence: Francesca Pallotti, Department of International Busi-

ness and Economics, Centre for Business Network Analysis, University

of Greenwich, Old Royal Naval College, Park Row, London SE10 9LS,

UK. E-mail: f.pallotti@gre.ac.uk

European Management Review, Vol. 12, 189–208 (2015)

DOI: 10.1111/emre.12052

© 2015 European Academy of Management

Gargiulo, 1999; Stuart, 2000), collaborative manufactur-

ing (Helper et al., 2000), interlocking directorates

(Haunschild, 1994; Haunschild, and Beckman, 1998),

joint ventures (Polidoro et al., 2011), and models speci-

fying organizational outcomes as a function of the

quality, status, and prestige of network associates (Baum

and Oliver, 1991; Stuart et al., 1999).

In all these examples of interorganizational networks,

organizations are assumed to establish collaborative

agreements in the hope to learn and benefit from the

experience, resources, and capabilities of network part-

ners (Powell et al., 1996). Establishing collaborative

relations is attractive precisely because network ties

may: ‘Provide unique opportunities to learn from peer

firms, and (have) a distinctive capacity to motivate

members to strive for higher performance’ (Sgourev and

Zuckerman, 2011: 13). Thus, organizations establish

collaborative relations under expectations that assimila-

tion of extramural resources, competencies and skills

that network ties afford will eventually yield higher

levels of individual performance (Cohen and Levinthal,

1990). However,if this expectation is generally satisfied,

then the aggregate outcome is that performance differ-

entials between connected organizations will decrease as

a direct consequence of the reduced diversity in the set

of organizational capabilities and skills present in the

network (Han, 1994).

In this paper, we seek to clarify how – and to what

extent – this assimilation process sustained by network

ties affects performance differentials among participants

in interorganizational networks. Our work extends prior

research in two ways. First, we show that network

effects on interorganizational performance differentials

operate differently across structural levels. With few

exceptions (Provan and Sebastian, 1998), available

research has been conducted at one single level of analy-

sis (Provan et al., 2007). We estimate models that

specify how the strength of direct partnership (social

interaction), co-membership in cliques (social proxim-

ity), and shared network position (structural equiva-

lence) affect interorganizational differences in

performance. The underlying idea guiding our analysis

is that if interorganizational relations have implications

for organizational performance, they should be revealed

by variations in performance differentials between con-

nected organizations. Because networks are multi-level

constructions, this objective involves identifying the

mechanisms that bound performance assimilation

effects across network levels. How far do network effect

on performance spill over? We know that networks influ-

ence behavior. We simply do not know the range of such

influence which may vary from the very local (individual

network ties) to the global (network positions, or roles).

To the best of our knowledge no research is available

that has addressed this question directly in the context of

organizational fields and communities.

Second, while most prior research has focused on

‘network effects’ on individual performance, our inter-

est in this paper is on differences in performance

between connected organizations – with unconnected

organizations used as case controls. This stance is con-

sistent with our objective of establishing the level at

which network relations affect interorganizational

performance differentials. Are organizations connected

by network ties more similar in terms of performance

than organizations that are not connected? And if this

is the case, at what level are these differences more

clearly observable? With the partial exception of

Mizruchi and Marquis (2006) we are not aware of

research that has attempted to address these questions

directly despite the clear tendency during the last

decade or so to study networks at the level of dyads

(Stuart, 1998; Rivera et al., 2010). The analysis we

present provides unambiguous answers to these ques-

tions while allowing for the possibility that

interorganizational performance differentials – which

are necessarily dyadic – may not be best explained at

the dyadic level.

We situate our study in the context of original field-

work and data that we have collected on an

interorganizational regional community of hospital

organizations. Interorganizational networks are particu-

larly relevant for understanding organizational outcomes

in the health care industry where: ‘Many organizations

in a community may well be considered part of a broadly

defined delivery system and connected to one another in

a variety of ways’ Provan and Sebastian (1998: 454).

More specifically, our analysis focuses on the network of

patient transfer relations between hospitals. Wefocus on

this specific and well-studied interorganizational rela-

tion because prior literature has demonstrated that

patient transfers represent an important occasion for

reciprocal learning as they imply collaboration, knowl-

edge transfer and information sharing between partner

hospitals involved in the transfer (Lee et al., 2011;

Iwashyna, 2012).

In our study, the relational content is unambiguously

specified and the strength of the relation accurately

measured. Patient transfer between hospitals would not

be possible without considerable investments and

explicit arrangements in support of joint decision

making (Lomi et al. 2014). As we explain later in the

paper, patient flows represent the physical traces of

complex arrangements established to support and facili-

tate interorganizational collaboration and joint decision

making. For this reason patient flows represent a signal

of an underlying collaboration and knowledge sharing

between partner hospitals. Inter-organizational knowl-

edge sharing is particularly important for organizations

in fields where the knowledge base is complex and

expanding (Powell et al., 1996; Uzzi, 1997) – such as

healthcare.

190 F. Pallotti et al.

© 2015 European Academy of Management