Inter-organizational network studies - A literature review

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Inter-Organizational Network Studies—ALiterature ReviewCarsten Bergenholtz a & Christian Waldstrøm aa Department of Business Administration , Business and SocialSciences, Aarhus University , Aarhus, DenmarkPublished online: 25 Aug 2011.

To cite this article: Carsten Bergenholtz & Christian Waldstrøm (2011) Inter-OrganizationalNetwork Studies—A Literature Review, Industry and Innovation, 18:6, 539-562, DOI:10.1080/13662716.2011.591966

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Research Paper

Inter-Organizational NetworkStudies—A Literature Review

CARSTEN BERGENHOLTZ & CHRISTIAN WALDSTRØM

Department of Business Administration, Business and Social Sciences, Aarhus University, Aarhus, Denmark

ABSTRACT Research on inter-organizational networks is generally fragmented which renders some of the

studies incompatible and hinders a greater understanding and coherence of the field. The major distinction—

which is not clearly stated in most research—is between the metaphorical description of some type of

interaction across organizational boundaries, or whether the term refers to specific social structures between

organizations. Whereas the metaphorical approach has previously dominated research, there has been a rise

in the use of more structured and quantifiable research, most notably in the use of social network analysis.

However, this has not been without serious theoretical and methodological issues. Most notably, a number of

the concepts, methods and theories used within the field of inter-organizational networks originate from

research in interpersonal and intra-organizational networks where some of the methodological issues (e.g. unit

of analysis and boundary specification) are more easily addressed. In order to map the different

methodological approaches in the field of inter-organizational networks, this paper presents a large-scale

systematic literature review of the last 12 years’ research on inter-organizational networks, with a focus on the

methodological features. Some of the main variables relate to the unit of analysis, whether social network

analysis is applied and what concept of a network is involved. The main findings of this paper are that few of the

previous studies have used the full methodological (and thus theoretical) scope of the available data, the most

cited papers and those appearing in top-ranked journals are more prone to using social network analysis than

papers in general and there is a recent tendency among influential papers to go beyond a narrow application of

social network analysis, and rely on multiplex relational data and whole networks.

KEY WORDS: Inter-organizational networks, literature review, methodology, social network analysis, citations

1. Introduction

“Networks” is clearly a term in vogue and the value of networks is emphasized in very

different theoretical approaches to inter-organizational networks, for example, access to

resources (Powell et al., 1996; Uzzi, 1997; Laursen and Salter, 2006), trust (Gulati, 1995;

Zaheer et al., 1998), power (Cook, 1977; Burt, 1992) and status (Kim and Higgins, 2007;

1366-2716 Print/1469-8390 Online/11/060539–24 q 2011 Taylor & Francis

DOI: 10.1080/13662716.2011.591966

Correspondence Address: Carsten Bergenholtz, Department of Business Administration, Business and Social

Sciences, Aarhus University, Haslegaardsvej 10, 8210 Aarhus, Denmark. Email: [email protected]

Industry and Innovation,

Vol. 18, No. 6, 539–562, August 2011

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Jensen, 2008); see the literature review by Zaheer et al. (2010). Within innovation research,

the impact of inter-organizational networks on innovation performance is also receiving

increasing attention, although a major part of the literature has network formations as the

dependent variable, rather than organizational performance (Ahuja et al., 2008).

A decade ago, Oliver and Ebers provided an overview of the “cacophony of

heterogeneous concepts, theories, and research results” (1998: 549) within the field of inter-

organizational networks. Their systematic literature review focuses on the links between

different theoretical perspectives, while also including some methodological considerations.

Since then few systematic, that is, transparent and replicable, literature reviews on inter-

organizational networks have been published, mainly focusing on parts of the whole picture.

Provan et al. (2007), for example, look specifically at whole networks while Knoben et al.

(2006) focus on inter-organizational networks and change.

A clear and commonly accepted definition of inter-organizational networks does not

exist, partly due to the term’s metaphorical origin and the wide number of research fields

applying network research (Borgatti et al., 2009). Methodologically speaking a number of

approaches are possible, since networks can be studied from an ego-network, dyadic,

triadic or whole network perspective (Wasserman and Faust, 1994). Inter-organizational

networks entail particular methodological challenges though, compared to, for example,

intra-organizational settings, since inter-organizational networks lack clear network

boundaries (Laumann et al., 1983). Furthermore, the kinds of relations to be studied are

also very diverse, since empirical interactions as joint ventures, alliances, franchising, patent

licensing, strategic networks, interlocks, loosely coupled systems, strictly dyadic relations

and whole networks have been framed via network concepts (Kilduff and Tsai, 2003).

Finally, the difference between an organizational and individual level of analysis also

constitutes a relevant distinction (Zaheer and Usai, 2004; Sedita, 2008). Nonetheless, we

argue that the main difference between different conceptualizations of networks depends on

whether the network is considered a metaphor for some kind of interaction across an

organizational boundary, or whether the term refers to the specific social structure between

organizations and is being analyzed via social network analysis (Mitchell, 1969; Wasserman

and Faust, 1994) or different variations of these archetypes. Furthermore, within an

analytical approach a narrow conception of social network analysis (SNA) can be applied,

relying, for example, on uniplex, dichotomous relational data that is constrained to ego-

networks, rather than multiplex, valued relational data that draws in overall network

structures. In the corporate world (Cross and Parker, 2004; Knoben et al., 2006) and in

science in general (Barabasi, 2002; Borgatti et al., 2009) SNA is becoming a well-known

tool. However, an unfortunate lack of integration between the scientific community of inter-

organizational network research and of SNA has been argued to exist (Knoben et al., 2006;

Roijakkers and Hagedoorn, 2006).

The purpose of this paper is to explore the field of inter-organizational network research

with a focus on the methodological features and developments over time. First, we present

some general reflections on inter-organizational networks and how they can be measured

and studied. Hereafter we present previous systematic literature reviews within this field and

the different ways such a study can be performed, including reflections on what the main

methodological issues are. We then document the search and coding process and via a

large-scale systematic literature review of papers published in the years 1997–2008, we

review inter-organizational network studies and the applied methodologies, by looking at

540 C. Bergenholtz & C. Waldstrøm

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whether SNA is applied, what kind of network structure is conceptualized, what the level and

unit of analysis is, whether qualitative or quantitative methods are applied and whether the

measurements embrace multiplex and valued relational variables. Based on this systematic

data collection, we draw theoretical implications and point to the main methodological

challenges and future trends in this research field.

2. Networks: Metaphor or Mapping the Social Structure?

It is a recurring problem across research fields to reach consensus on definitions,

particularly within the social sciences. Within the field of inter-organizational research,

“networks” can refer to as different phenomena as whole networks, interlocks and strictly

dyadic relations. One of the most cited definitions in the SNA literature limits networks to: “a

set of nodes (e.g. persons, organizations) linked by a set of social relationships (e.g.

friendships, transfer of funds, overlapping membership) of a specified type” (Laumann et al.,

1978: 458). This definition forces a degree of explicitness about both the type and number of

nodes and form and content of the ties that sets it apart from the purely metaphorical notions

such as connectedness, interdependence or embeddedness. In order to embrace all kinds

of inter-organizational studies, the present paper relies on Oliver and Ebers’ broader

definition: “studies dealing with any type of inter-organizational relations” (1998: 551).

The term “inter-organizational network” can be conceptualized as a metaphor for the

interdependent nature of organizations with interactions across organizational boundaries

or be a reference to an analytical perspective where the specific social structure between

organizations can be analyzed (Mitchell, 1969; Wasserman and Faust, 1994) (cf. Figure 1).

Between these two ends of the continuum, many interim positions exist. Some studies

ask focal companies how they handle their customers or suppliers in general (Dodd and

Patra, 2002; Heide, 2003) which provides information on networks and networking on a very

general level. Other studies focus solely on the dyadic relation between two given

companies (e.g. Fletcher and Barrett, 2001), while some research uses information on one

specific supplier/customer to generalize from (e.g. Santoro and Chakrabarti, 2002; Cousins

et al., 2006; Knudsen, 2007). This is certainly interesting and relevant information, and

provides insight into the role of an important external actor, but the studies do not uncover

the social structure, opportunities and constraints present in the network. Given that network

structure and position have turned out to have significant impact on organizational

performance (Ahuja et al., 2008) it could seem to be problematic to make conclusions on

organizational performance in an inter-organizational framework, without including the

analytical perspective. Furthermore, some studies perform network analyses based on

information on roles, where a node refers to “family”, “friend”, “venture capitalist”, etc. (e.g.

Aidis et al., 2008; Klyver, 2008). These latter studies do gain some insight into the network

structure of different roles, but do not contain information on the network structure of

individual actors.

An analytical approach relies on the fundamental proposition that an organization’s

structural position in a network influences its opportunities and constraints (Burt, 1992;

Borgatti et al., 2009). An analytical approach can apply SNA as a specific methodological

tool to analyze networks on the individual, dyadic, triadic or whole network level (Wellman,

1988; Wasserman and Faust, 1994) and thus provide a structural approach to studying

social contexts. Thus, an analytical perspective is in contrast to all forms of metaphor

Inter-Organizational Network Studies—A Literature Review 541

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perspectives. Not all analytical studies are based on SNA though, since an analysis of three

actors can be based on analytical reflections, without applying SNA.

2.1 The Depth of Relational Data vs. Network Structure: A Trade-Off

An analytical approach hence assumes that the network structure and interdependence of

actors constitutes a significant feature of any explanation of inter-organizational interactions.

But even though structure is emphasized, any study needs to balance how much information

to collect and analyze on (a) the structure of the social network and (b) in-depth information

on the content of the actual organizational relations. More technically, the issue relates to

structural and relational attributes such as relational content, relational properties, individual

structural measures/roles and whole network measures (Burt and Minor, 1983; Monge and

Eisenberg, 1987; Marsden, 2005).

Most relations between organizations consist not only of one kind of relation, but will

involve multiple relational contents and various levels of analysis, for example, competition,

money- or knowledge-transfer, coordination, inter-personal networks or formal interlocks.

Thus, by studying one organization’s relations with one specific supplier, it is possible to gain

in-depth information on this particular dyadic relation, while missing the overall network

structure. On the other hand, solely getting information on interlock relations among Fortune

Figure 1. The analytical and metaphor perspective


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500 companies only provides insight on an overall network structure among very large

companies, and as Zaheer and Usai argue: “Organizations being large, complex and nested

entities, the social processes that are implied in network research at the level of the

individual are often questionable at the inter-organizational level” (2004: 67). In this context,

Zaheer and Usai emphasize that social network research is “relying on an essentially

individually oriented methodology being applied to the level of organizations” (2004: 67). As

an example they refer to the significant difference between an argument for the triadic

structure of three individuals knowing each other (e.g. within an organization), compared to

the triad of Toyota and an American automaker and Japanese companies within the

automobile industry. The organizational relation might primarily hinge on two particular

individuals or department and some departments might collaborate, while others compete.

In order to map these relations, a narrow application of SNA and analysis of an inter-

organizational network might merely rely on whether there is a relation or not, while a more

encompassing application will also consider the institutional setting and rely on multiplex

relational data (Minor, 1983), involving several layers of content (for example, interlocks and

formal knowledge-transfer) within one relation. The complexity of how to fully measure and

conceptualize inter-organizational relations is thus significantly higher than in an intra-

organizational setting, although large, multi-sited organizations face some of the same

challenges (Tsai, 2002; Zaheer and Usai, 2004). These issues have been most notably

addressed within the field of entrepreneurial networks (Hoang and Antoncic, 2003; Coviello,

2005), with criticism pointing to the need for a more complete and multi-level approach to

studying inter-organizational networks among entrepreneurs.

This resonates in the general field of inter-organizational networks, where Knoben et al.

(2006) refers to one of the most cited studies within this field (Ahuja, 2000a), to highlight that

the level of the aggregation of data is vital. In that particular study, the level is quite abstract,

since the study mainly involves information on the density of ego-networks. Thus, there is no

information on whether networks are formed with previous partners, or what kinds of

relations are being formed (ibid.). The question of the depth of the relational data relates

both to studies uncovering social structures and metaphorical approach.

The level of aggregation and what kind of information (on depth and structure) is

collected in the research, furthermore relates directly to the boundedness of networks.

Neither single firms, nor dyadic relations or indeed whole networks exist in a vacuum.

However, when is a network sufficiently bounded to be properly analyzed? Uzzi (1997)

considers the apparel industry in New York as one bounded network, Higgins and Gulati

(2003) examine the American biotech industry, while Gay and Dousset (2005) include the

global biotech industry. No natural and nominal boundaries exist, as in an intra-

organizational context, which entails significant methodological challenges (Laumann et al.,

1983). The challenges posed by the unbounded nature of inter-organizational networks may

be one of the reasons why dyadic relationships are examined in so many papers (for a

distinction on network level of analysis, see Fombrun, 1982). Through a realist approach

(actors themselves defining the social boundaries) or the nominal approach (imposing the

boundary conceptually based on the analytic purpose), it is possible to make a meaningful

boundary specification of the nodes and relations in a study (Laumann et al., 1983).

Powell’s seminal article (1990) discusses inter-organizational networks in generic

terms, but research has indicated that there are significant differences between inter-

organizational networks in different empirical settings (Ahuja, 2000a; Rowley et al., 2000;


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Provan et al., 2007). This leads to a question about how generic the constructs in network

research are, and, for example, whether the results from the often studied volatile

biotechnological sectors can be transferred to other sectors. More specifically, it can be

investigated whether the same methodological approaches are being used, across sectors.

This point is valid not just for industries, but also national cultures, since substantial studies

have shown that different national cultures imply different managerial and networking

practices (Hofstede, 2001; Cook et al., 2005). However, the highly contextual nature of

relations makes it very difficult to assess whether measured differences across cultures are

actual, structural differences in networks, or whether these differences are due to the

culturally embedded ways of describing relations and networks (Chow et al., 1999; Pachucki

and Breiger, 2010).

Given these methodological challenges and the extensive amount of research on inter-

organizational networks, it is time to take stock on the methodological basis of network

research.

2.2 Inter-organizational Network Studies

Originally, it was the purpose of this paper to create a meta-analysis of the field of inter-

organizational networks in order to systematize and present the main findings of the vast

body of research. However, this proved impossible, as it was quickly clear that the field is so

fragmented and plagued by the issues mentioned in the paper, that such an analysis was not

in fact meaningful for the literature on inter-organizational networks.

Literature reviews that intend to give an overview of a field can be divided into two kinds:

either they are systematic, that is, performed via documented search terms and analyzed on

the basis of an explicit coding, or they are based on the researchers’ general perspective on

the field (see, e.g. Brass et al., 2004; Kilduff et al., 2006; Mizruchi and Marquis, 2006). Each

approach has its advantage, depending on the purpose of the research. In order to get a

large-scale overview of the methodological issues in inter-organizational network theory

over a given time period, it seems suitable to conduct a systematic literature review. In this

way the reader knows (a) what journals have been searched, (b) what search terms have

been applied and (c) how the papers have been coded, providing a quantified image of years

of inter-organizational research.

Oliver and Ebers (1998) performed a large-scale literature review of inter-organizational

networks, based on papers from 1980 to 1996 in four leading journals. Their intention was

wide-ranging, since they covered all “studies dealing with any type of inter-organizational

relations” (Oliver and Ebers, 1998: 551). The authors culled through all abstracts in the given

journals and analyzed the papers following a coding, which was iterated along the

way. Theoretical aspects are at the centre of attention, although some strictly

methodological features were part of the study as well; whether the paper was qualitative

or quantitative, longitudinal and at what level the analysis was performed (Oliver and Ebers,

1998: 551). Based on a small selection of journals, their study attempts to provide a

full-scale overview of the most influential inter-organizational network research over 16

years.

Borgatti and Foster (2003) provided another extensive review of network research,

mainly based on articles from 1998 to 2003. They identified a number of dimensions on

which network studies vary: direction of causality, level of analysis, explanatory goals and


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explanatory mechanisms. Their scope was very wide-ranging, including not only inter-

organizational networks, but also networks in general.

Knoben et al.’s study (2006) is also systematic and replicable and focuses on networks

and change. Articles from 1984 to 2005 in five top journals were searched based on

documented keywords. Based on a reading of abstracts, 20 papers were examined

thoroughly with a purpose of identifying different ways of investigating longitudinal inter-

organizational network studies. In this sense, Knoben et al.’s literature review (2006)

provides an in-depth examination of a small part of all inter-organizational research.

Provan et al. (2007) performed a wide search in the Web of Science and Scopus

databases, thus including a large amount of journals from 1985 to 2005 in their attempt to

uncover research on whole inter-organizational networks. After discarding irrelevant articles

by culling through the abstracts, the authors performed an in-depth examination of 27

articles on whole inter-organizational networks. Their study hence searched through a large

part of inter-organizational research but chose to focus on a small part.

Thus, some literature reviews of this field exist, while few large-scale, systematic and

replicable studies have been performed. The studies performed by Provan et al. (2007)

and Knoben et al. (2006) focus on a subset of inter-organizational research, and while Oliver

and Ebers (1998) do include all kinds of inter-organizational research, they restrict

themselves to searching relatively few journals.

3. Method of Enquiry

The main purpose of the above brief descriptions is to provide an insight to what type of studies

have previously been performed, in order to position the approach of the present paper. Our

study provides a transparent, large-scale and explorative overview, based on a wide search of

the Web of Science database and thus not restricted to a few journals. The selected time

period is 1997–2008, which is a direct extension of the time period in Oliver and Ebers’ sample

(1998). The subject areas are Sociology, Business and Management. Search terms consist of

different combinations, wordings and spellings (UK/US) of: inter-organizational, inter-firm,

organization, networks, industry and business. The title, abstract and keywords are searched.

It would be impossible to include all papers related to the overall topic in question. As an

example, the inclusion of the search term “alliance” would have given an even wider starting

point (10,000 þ articles). Since this literature review is centered on the methodological

aspects of research that positions itself in the framework of inter-organizational networks we

chose the above-mentioned terms. Note, however, that papers were not excluded for having

the keyword “alliance”—indeed a sizeable proportion of the papers in the review did have

alliances as their primary focus.

This search provides 1,017 hits, an amount of papers that would be unfeasible to

investigate properly. Since we are interested in a wide overview of inter-organizational

research, we chose to include the top 20 journals that publish the most research on inter-

organizational networks, in order to get an insight on the most high-profile discussions in this

field of research. Even though the Web of Science database already is fairly exclusive in its

attempt only to include quality journals, we chose to take another step to include the most

influential research. Relevant papers from the top 20 most cited journals in Sociology,

Business and Management, based on the Journal Citation Reports’ impact factor (Reuters,

2008) are therefore added to the previous top 20. See Appendix B for an exhaustive list of


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journals. This procedure resulted in a provisional sample of 357 articles. One could argue

that we risk omitting the most cited papers, since we select papers based on journal criteria.

We have therefore compared our final sample 1a (306) with the top 50 most cited papers in

our basic sample (1,017) and only four papers would have been added. It can therefore be

argued that we have included (i) journals that publish the most in this field, (ii) journals with

the highest impact factor and (iii) the most cited papers.

After a reading of abstracts a further 51 papers were discarded because they did not

involve any kind of inter-organizational data or conceptualizations and the basic sample thus

consists of 306 papers (sample 1a). In this way the search is fairly transparent and broad,

based on a wide database search, which results in a large sample based on a variety of

journals, both in terms of topics, institutional connections and geographical location of

journals, authors and studies. Due to this variety, differences between the included journals

are therefore to be expected, unlike Oliver and Ebers’ study (1998) that showed basic

similarities between the four chosen journals. The differences will be part of the story told

about tendencies in the inter-organizational network literature. Additionally, since the four

journals from Oliver and Ebers’ study (1998) are included and some of the variables overlap,

and the present paper’s time period intentionally follows directly after their study, a direct

comparison can be made for a small part of the study.

3.1 Variables

Speaking overall, a systematic literature review as this has to deal with a trade-off between

sample size and the number of variables. In this study, we have focused on a few relevant

methodological categories, in order to achieve a wider search frame and sample than a

limited number of top journals could have provided. A total of 306 articles is a substantial

number and significantly higher than previous literature studies based on a systematic

coding. We deemed this approach relevant and possible since the present study disregards

considerations on what theory the articles rely on. Therefore, we can base our approach on

fewer variables, 15, compared to 77 in Oliver and Ebers (1998). A full explanation of each

variable is provided in Appendix A. In addition to the coding, we also have information on

how often each paper has been cited in the Web of Science (Thomson Reuters, 2009)

database, as of December 2009.

The variables are chosen in order to (un)cover methodological issues and limitations

and are based on previous literature reviews and initial readings of the selected articles, as

well as a focus on the methodological parameters necessary to perform a SNA. These were

based on methodological parameters presented in classic works on SNA (Marsden, 1990;

Wasserman and Faust, 1994; Marsden, 2005) and inspired by Oliver and Ebers’ approach

(1998). Naturally, the list does not exhaust all possible methodological considerations and a

few variables were modified along the way, as new insights in this process were gained. The

modifications primarily consisted of leaving out a few variables (e.g. “directed/undirected

ties” that turned out to be too specific, since hardly any research had directed ties) and

merging others into the same variables (e.g. “data source” which was too specific in the

beginning). This modification involved a re-coding of previously read papers and does not

affect our analysis. We have had a strong focus on continuous discussion of how to perform

the actual coding and conducted a formal test of the inter-rater reliability: based on a random

selection of 30 papers (450 codings) the inter-rater reliability is 93.6 per cent, which is very


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satisfactory (James, 1982; Collin et al., 1996). The two authors of this paper performed the

variable selection and coding process over a period of three months with roughly half of the

papers coded by each.

4. The Field of Inter-organizational Networks

The descriptive results are briefly presented in Tables 1 and 2, where after a longitudinal

analysis and inter-relations between different methodological features they are presented

and discussed in the following section.

As presented in Table 1 there has been a marked increase in the number of papers

dealing with this field in the period from 1997 to 2008, with more than twice (2.4) as many

articles matching our search criteria in the last four-year period than the first. This

development mirrors a broader trend in the rise in the number of research papers dealing with

network and SNA across various fields (Freeman, 2004; Borgatti and Halgin, forthcoming).

Table 2 lists the variables relevant for empirical papers.

Compared to Oliver and Ebers’ results (1998), a few differences can be identified.

There is only a minor difference in the number of empirical papers, 89.2 per cent compared

to 83.3 per cent in the present sample, which is in part due to the inclusion of journals such

as Academy of Management Review that only publishes conceptual papers. Oliver and

Ebers report 74.7 per cent quantitative studies, compared to 60.5 per cent in the present

sample. Another clear difference is in the proportion of longitudinal studies, 38 per cent

(Oliver and Ebers) vs. 49.6 per cent in this paper, which is to be expected since more and

more research heeds the call for more longitudinal studies within the field of networks

in general, not least to settle issues of causality, which are difficult to resolve within

cross-sectional studies (Snijders, 2005). Finally, there is a difference in the proportion of

Table 1. Number (n) and percentage (%) of the coded variables in 306 articles, including theoretical papers

Variable n % Variable n %

Empirical Network comparison

Conceptual 43 14.1 Comparison 16 5.2

Empirical 255 83.3 No comparison 261 85.3

Simulation or meta-data 8 2.6 NA 29 9.5

Year published Alliance

1997–2000 58 19.0 Alliance 109 35.7

2001–2004 106 34.6 Not alliance 197 64.3

2005–2008 142 46.4

Unit of analysis

Level of analysis One focal organization 34 11.1

Individual 7 2.3 Multiple focal organizations 19 6.2

Organizational 214 69.9 One focal network 20 6.5

Both 64 20.9 Neithera 202 66.0

Not available (NA) 21 6.9 NA 31 10.1

a The high number of “neither” is due to the fact that the first three categories do not exhaust all options. The aim with

this variable was to check how many studies focused on one focal network, rather than, for example, the global biotech

community.


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studies using the individual vs. the organizations as level of analysis, but this is primarily due

to a slightly different definition on this matter used in this paper.

While a Chi-square test shows that there is no significant change in the overall use of

different data sources, it is notable that only 6.4 per cent of the studies used surveys in the

first four years, in contrast to over 20 per cent in both of the following four-year periods.

Approximately one-third of the articles are based on alliances, a number that has remained

steady over the years. A total of 20.9 per cent of the studies apply a multi-level perspective,

Table 2. Number (n) and percentage (%) of the coded variables

Variable n % Variable n %

Industry SNA

High-tech 92 34.6 SNA applied 50 19.0

Low-tech 120 44.9 SNA not applied 213 81.0

Multiple 51 18.6

NA 5 1.9 Network focus

Metaphor 101 38.4

Geography Dyadic 52 19.8

Africa 2 0.8 Egonet 34 12.9

Asia 23 8.7 Egonet þ density 8 3.0

Australia 3 1.1 Whole network 68 25.9

Europe 85 32.3

International 57 21.7 Dependent variable

NA 10 3.8 Relational 59 22.4

South America 3 1.1 Firm level 96 36.5

North America 80 30.4 Network level 30 11.4

Multiple 5 1.9

Data source NA 73 27.8

Desk research 12 4.6

Qualitative 92 35.0 Relational content

Multiple 51 19.4 Formal organizational

(incl. alliance, supply)

49 18.6

Quantitative, survey 50 19.0 Financial 9 3.4

Quantitative, database 58 22.1 Formal individual 21 8.0

General 50 19.0

Time perspective Multiple 64 24.3

Snapshot 133 50.6 Research & development 62 23.6

Comparison of discrete

periods

12 4.6 Virtual ties 5 1.9

One continuous time

period

118 44.9 Decision-making 3 1.1

Multiplexity Dichotomous

Uniplex 51 19.4 Dichotomous 56 21.3

Multiplex 99 38.4 Valued 94 35.7

NA 113 43.0 NA 113 43.0

Note: Sample b, based on 263 articles, excluding theoretical papers. These papers are excluded, since a variable like

Data Source is not meaningful without empirical data.


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including both the organizational and individual in their study. Despite an increasing

awareness of the significance of applying both levels in thorough research (Zaheer and

Usai, 2004) the numbers do not change over the years. Contractor et al. (2006) highlight the

value of performing multi-level studies that involve the individual, organization and network

level and we would have wanted to highlight papers performing this perspective, but there

were only two or three studies that applied such an approach.

A stable and substantial amount of the studies relates to high-tech and more than 11 per

cent of all the empirical studies are based on the biotechnological industry. Additionally, 4 of

those are among the 15 most cited studies in the sample, according to Web of Science

(2009). Since biotech in some respects (in terms of fragmentation and collaboration) can be

argued to be a particular industry, any conclusions from these studies might be difficult to

translate into other industries (Pisano, 2006).

The explorative coding of relational content (Burt, 1983) turned out to be quite complex

due to a number of inconsistencies in the reviewed papers. Some papers refer to the form of

the relation, others to the content—some to both, while often not making this distinction

explicit. Such a lack of information makes any comparison across studies difficult, as

research has shown that there are significant differences between networks in different

institutional settings involving different kinds of knowledge exchanges (Walker et al., 1997;

Rowley et al., 2000; Owen-Smith and Powell, 2004).

Hardly any of the 306 papers in this literature review dealt with intra-organizational

networks at any level, confirming the assumption at the beginning of this paper that intra-

and inter-organizational studies are rarely combined, with few notable exceptions (e.g.

Lorenzoni and Lipparini, 1999). SNA also has a longer history within inter-personal and intra-

organizational studies (Freeman, 2004), than in inter-organizational studies. This in itself

constitutes a strong argument for one of the premises behind this special issue, since many

streams of literature have shown how inter-organizational challenges relate to intra-

organizational ones (cf., e.g. the literature on absorptive capacity (Cohen and Levinthal,

1990)).

This gap has partly to do with the level of analysis, since both in an intra- and inter-

setting an individual person can be the node, but most inter-firm studies rely on firms as the

node. Furthermore, the inter-firm setting implies a different challenge of setting the network

boundary (Laumann et al., 1983; Borgatti and Halgin, forthcoming) than intra-organizational

networks. In many of the reviewed papers, the inter-personal level is present in the data

collection, since many studies are conducted by interviewing key informants in various

organizations about their—or their organization’s—ties to other organizations or individuals

in other organizations. In the data analysis and subsequent conclusions, however, this

individual level is invariably abandoned for an organizational level of analysis.

4.1 Development over Time and Top Journals vs. General Sample

The above section highlights some general aspects of the samples. In the following, the data

will be examined more in-depth, by looking at cross-tabulations and longitudinal analyses.

Furthermore, two sub-samples are constructed to be able to distinguish between top journals

and the wide sample, in order to paint a more nuanced picture of the field of inter-

organizational network research. See Appendices C and D for an overview of the new sub-

samples.


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It is worth noting that sub-sample 2a that consists of Oliver and Ebers’ four journals

(Administrative Science Quarterly, Academy of Management, American Sociological Review

and Organization Studies) (1998) and sample 2b that in addition to sample 2a includes

Strategic Management Journal, American Journal of Sociology and Organization Science (86

articles) do not contain fewer articles in the first few years than later years, in contrast to the

entire sample 1a. The field of inter-organizational networks that is mapped in this study thus

seems to have gained recognition earlier in top journals, than in Web of Science in general.

Nineteen per cent of all the papers in our sample apply SNA, and in line with a more

general increase in the number of papers using SNA (Borgatti and Halgin, forthcoming), a

significant (at 0.1 significance level) increase from 13.3 per cent in the first six years to 22 per

cent of the studies in the last six years can be found. SNA is thus receiving increasing

attention and consistent with our expectation of the difficulty of creating a useable boundary

specification necessary for SNA, only 6 per cent of the studies using SNA are based on

surveys. A total of 38.4 per cent of all studies are based on a conceptualization of an inter-

organizational network as a metaphor, while 25.9 per cent include a whole network

perspective.

In top journals, the methodological choices concerning SNA and conceptualization of

the networks are far different. A total of 34.6 per cent apply SNA and a significant (at the 0.1

level) increase from 25.6 per cent in the first half to 44.7 per cent in the second half of the

period can be identified. The extent of the use of SNA in top journals thus seems to be

trickling down to journals in general. Furthermore, only 17.3 per cent apply a metaphor

perspective, and in the past six years a mere three (3.7 per cent) metaphor studies have

been published in these journals. This difference is also confirmed when comparing the

basic sample with the 15 most cited papers within the sample, since only 1 of these papers

involves a conceptualization of networks as a metaphor and 26.7 per cent apply SNA. Table

3 illustrates how often the variables SNA, whole network and metaphor are applied in the

sample in general, top journals, most cited and least cited papers.

Table 3. SNA, whole networks and metaphor in top journals, most cited and least cited journals and basic sample

Top journals (%) Most cited (%) Least cited (%) Basic sample (%)

SNA 34.6 26.7 6.0 19.0

Not SNA 65.4 73.3 94.0 81.0

Total 100 100 100 100

Whole network 42.9 60.0 14.0 25.9

Metaphor 17.3 6.7 44.0 38.4

Dyadic and egocentrica 39.8 33.3 42.0 35.7

Total 100 100 100 100

a Studies based on dyadic relations and egocentric networks are identifiable relations (hence in that sense not implying

a strict metaphor conception) but also don’t constitute whole networks, which means a proper SNA can’t be carried out.

We have therefore focused our analysis on the “whole network” and “metaphor” categories.

Note: For a list of “top journals”, see Appendices C and D. “Most cited” consist of the top 15 cited papers, minimum 115

in Web of Science, December 2009. “Least cited” consist of the 50 least cited papers with nine or less citations in Web

of Science, December 2009, all from 2005 or older.


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Since top journals are more likely to involve papers that are highly cited, data on the

least cited papers are to some extent co-varied with data on top journals. However, it seems

relevant and illustrative that it is not only the most highly cited papers and top journals that

stand out compared to the overall sample, but that the least cited papers also clearly stand

out. Of 50 papers with only nine citations or less (based on a sample from the years 1997 to

2005, after which other papers citing these papers have not had enough time to be

published), 44 per cent involve networks as a metaphor and only 6 per cent of these studies

involve SNA. Hence, as Table 3 illustrates, the use of SNA and a whole network perspective

is much more prevalent in the higher ranked journals and more cited papers.

This is related to the fact that databases, which make whole network analysis much

more accessible, are more frequently used as a data source. Whether researchers choose

databases as a data source in order to include the whole network perspective, can naturally

not be determined based on these data.

In relation to the measurement of the content of the relation, it is notable, but not

surprising, that qualitative and survey studies involve substantially more multiplex and

valued variables. Furthermore, a somewhat surprising number of studies (43 per cent) do

not even specify the variable making the relational foundation of the whole inter-

organizational network unclear.

Finally, it is a myth that the main methodological difference between studies carried out

in a European and North American context is mainly based on a choice between qualitative

vs. quantitative methodology, as argued by Usdiken and Pasadeos (1995) and Aldrich

(2000). Table 4 illustrates what data sources papers based in Europe and North America are

based on.

It is thus more fitting to claim that the difference mainly depends on the use of

databases, since the use of surveys is roughly equal in the two different continents. The

difference thus relies on whether the researcher intends to involve the organizations and rely

on answers provided by respondents in these organizations, either in terms of interviews or

survey-based data collection.

5. Discussion

The analysis clearly demonstrates a stable increase in the number of papers within the field

of inter-organizational networks in the period from 1997 to 2008—but interestingly enough

Table 4. Methodological differences between European and North American studies

Qualitative (%) Database (%) Survey (%) Othera (%)

Europe 51.2 5.9 18.8 23.5

North America 11.1 32.5 21.2 36.3

Other 37.7 61.6 60.0 40.2

Total 100 100 100 100

a The other column refers to studies where the data source was not available, the use of multiple data sources (both

quantitative and qualitative) and a few grounded and simulated studies. The use of multiple data sources is evenly

distributed across North America and Europe.

Note: The difference is significant at the 0.05 level.


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the number of papers matching those criteria are relatively constant in the top-ranked

journals, as if the field has already reached a level of maturity in those journals, that other

journals have yet to reach.

Furthermore, the most cited papers and papers in higher ranked journals—where high

standards of methodological clarity, transparency and rigor is indispensable—far more

frequently rely on an the analytical approach and use SNA to study networks than papers

from the sample in general. As a contrast, among the least cited papers, only three used

SNA. This stringent approach to distinguishing relational content might also explain why

more of the studies in top journals have a dual focus on both the individual and

organizational level of analysis, and use this explicit distinction to derive further insights into

the possible interplay between them.

At the other end of this methodological spectrum, there has been a similar decrease in

the number of studies merely applying a metaphorical framework to the networks studied,

and in the past years, hardly any top-ranked journals published papers based on a metaphor

approach. It thus seems a fair assumption that the higher ranked journals and highly cited

papers using SNA influence other researchers into also using a SNA approach in their

studies, leading to the above-mentioned increase in the proportion of SNA studies in the

sample in general.

The above conclusions are based on developments over time that can be identified on a

significant level. The following sections focus on the most influential (highly cited) papers in

order to identify additional and more subtle tendencies over time. Table 5 contains a list of

studies that illustrates the features that are to be discussed in the following.

Oliver and Ebers’ literature review (1998) begins in 1980, which also is an indicator of an

increased focus on how inter-organizational networks can explain performance. Powell’s

seminal paper (1990) generated additional attention on inter-organizational networks as

neither markets nor hierarchies, and combined with Granovetter’s emphasis on the

embeddedness of business transactions (1985), this led to a focus on the analytical network

approach and how SNA is a useful tool to explain network formation and organizational

performance (cf., e.g. Walker et al., 1997; Ahuja, 2000b). These studies emphasize how the

social network approach cannot stand alone (Ahuja, 2000b: 339) which fits with the fact that

no studies in our sample solely relies on SNA. Hence, SNA is used with traditional statistics

and social network-based theories are combined with other (e.g. resource-based view,

Ahuja, 2000b) theoretical explanations. This combination of course constitutes a challenge

of combining data that assumes interdependence of data (SNA) and an approach that

assumes independence of data (traditional statistics) (Moreno and Jennings, 1938).

While there is an increase in the use of an analytical approach in general, Table 5

further indicates a shift in the type of analysis applied. In the early years of our time period a

rather narrow conception of SNA is used. For example, Ahuja (2000a) relies on ego-

networks in his explanation of how an organization is to be the most innovative, and Walker

et al. (1997) merely relies on whether there is a relation or not (dichotomous). Even though it

is difficult to pinpoint when the trend changes, Owen-Smith and Powell (2004) present a

strong argument for going beyond a narrow application of SNA. The study clearly shows how

it is important to draw in institutional features, for example, via analyzing multiplex relational

data and draw into account the institutional and empirical settings of the given networks. An

ego-network-based study based on uniplex relational data would not have been able to offer

the same in-depth explanations, since different kinds of networks beyond the ego-network


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Table 5. Illustrations of different methodological conceptualizations of networks

Study Network conceptualization Objective

Santoro and Chakrabarti

(2002)

Metaphor Based on a study of industry–university collabor-

ations in the USA the authors attempt to show

the significance of firm size, technology

centrality and champions at the firm in

industry–university collaborations. No rela-

tional data in the analytical sense are included.

Sampson (2007) Dyadic Based on a study of 463 R&D alliances in the

telecommunication industry, moderate techno-

logical diversity within the individual alliance

dyad is shown to have the biggest impact on

firm innovation. The study does not include any

structural measures beyond the dyadic level.

Ahuja (2000a) Analytical: ego-network,

including density þ

SNA

Based on a study of 420 collaborations in the

chemical industry, different kinds of ties (direct,

indirect, structural holes) are shown to impact

innovation differently. Structural holes are

shown to have a negative effect on innovation,

which potentially might be due to the egocentric

limitations of the data and subsequent analysis.

Capaldo (2007) Analytical: egocentric,

valued and multiplex

relational data

Based on a study of three ego-networks in the

furnishing manufacturing industry, different

strength of ties—not just the existence of a tie

or not—are shown to imply different opportu-

nities, and the study proposes how a firm

should integrate a dual network structure of

weak and strong ties in order to enhance

innovation performance.

Casper (2007) Analytical: uniplex rela-

tional data þ SNA

Based on a study of the career histories of 923

managers in the San Diego biotech industry,

Casper examines the emergence of a dense

social network via individual inter-firm mobility.

As indicated by the author, a multiplex

examination of ties between different kinds of

actors (e.g. senior managers vs. company

founders) would have added value to the

analysis.

Walker et al. (1997) Analytical: dichotomous

relational data þ SNA

Based on a study of 451 cooperate relationships in

the biotech industry, it is shown how network

structure is path dependent, while the

institutional setting of an industry is considered

important, for example, relating to the duration

of common inter-organizational interactions.

The possible implications of strength of ties are

not addressed.


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level clearly influence the findings. Recently, several of these studies have emerged (Owen-

Smith and Powell, 2004; Dyer and Hatch, 2006; Sorenson and Stuart, 2008; Paier and

Scherngell, 2011) and they offer a richer picture of the network activities. Naturally, some

research settings might call for very formalized and simple setups, but generally, a richer

picture offers the opportunity of creating multiple layers of networks and makes different

network boundary decisions, which Owen-Smith and Powell (2004) proved to be vital in an

explanation of innovation performance. This resonates with one of the main topics in the field

of networks and innovation; compare the seminal discussion between Burt (1992) and

Coleman (1988) on whether open or closed networks enhance innovation the most, and

several of the studies in the sample explicitly point out that the Burt–Coleman discussion

probably needs to draw in institutional factors (Walker et al., 1997; Ahuja, 2000b; Owen-

Smith and Powell, 2004).

Ahuja (2000b: 339) argued that it was a logical step to combine network data with

traditional approaches, and now it could similarly be argued that the next logical step is to go

beyond a narrow conception of SNA. Parts of this step have already been taken, since we

see indications of a trend in highly cited papers to go from merely relying on uniplex, ego-

network-based and dichotomous data to a more encompassing approach, relying on

multiplex and whole network conceptualizations, and to a lesser extent valued relational

data. Related to this focus on multiplexity and the need to draw in whole networks, is the lack

of comparable content specifications of the relations. As Table 5 indicates, no uniform

approach exists and given the need to focus on institutional aspects, this will constitute a

significant obstacle for de-fragmenting the field of inter-organizational network studies. The

main proposal is to explicitly distinguish between form and content, and to be explicit about

the institutional setting and the actual contents of the interactions, in order to go beyond a

simplistic, formalized SNA.

Practical and methodological limitations constrain the use of SNA, but several points

are relevant to emphasize. First, a substantial number of studies collected data suitable for a

more advanced and encompassing-based SNA, but chose to aggregate or simplify data and

Table 5. Continued

Study Network conceptualization Objective

Gay and Dousset (2005) Analytical: whole network

þ SNA

Based on a study of 730 alliances among 557

organizations in the biotech industry, networks

are shown to be scale-free and contain small-

world effects which impact the future develop-

ment of firm linkages.

Owen-Smith and Powell

(2004)

Analytical: multiplex rela-

tional data, whole net-

work þ SNA

Based on a study of 1,559 relations among 740

organizations in the biotech industry, it is

shown how multiplex networks have varying

impact on innovation performance. It is thus

argued how and why it is important to offer

appropriate network boundaries and include

multiplex relational content in order to provide a

full explanation of the issue in question.


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opt for more regular and non-relational statistical methods to analyze the data (Anderson

et al., 1999). A typical example is a paper on formal alliances within a given sector, pulling

data from a database for that particular industry, which allows for a whole network, SNA

approach, but which chooses not to use the full data, and instead, for example, relies on a

number of relations (degree of centrality). Secondly, the less than optimal use of given data

is also to some extent constrained by fundamental methodological challenges. Only within

the recent decade has the field of SNA managed to analyze centrality based on valued data

(Freeman et al., 1991; Borgatti and Everett, 2000). Hence, while the field of inter-

organizational network studies relies more and more on SNA, the tools are continuously

being developed. Newer developments in SNA provide for analyses that were previously not

possible, and hence limited the usefulness of relational studies. This is the case with

Exponential Random Graph Models (ERGM) (Robins et al., 2007), longitudinal and dynamic

network modeling (Snijders, 2005), visualization (Moody et al., 2005) and inroads in the

effects of directional data (Bonacich and Lloyd, 2001).

Using the full potential of SNA on those data would allow for a broader set of tests, for

example, on betweenness centrality linked to innovation (Gilsing et al., 2008), structural

holes, core–periphery structures (Lazega et al., 2008) and overall structural network

changes over time (Ahuja, 2000a). It is not, therefore, an overly assertive claim that more

than a few of these papers could have published in higher ranking journals and/or had more

citations had they used the already collected and available data more fully.

As a final note, there is an ongoing discussion about whether the research on inter-

organizational networks constitutes a theory (Oliver and Ebers, 1998; Kilduff et al., 2006;

Borgatti and Halgin, forthcoming) rather than merely an analytical tool (Mitchell, 1969;

Zaheer et al., 2010) to be used in the social sciences. Based on this systematic literature

review of methodological features, the field of inter-organizational networks seems to be in

need of further development and coherence, if it is to be characterized as a theory.

To sum up, if innovation performance is to be explained, an analytical approach that

involves longitudinal, multiplex data and a whole network conceptualization that goes

beyond ego-networks and includes overall network structures are required as a minimum. In

this way, a less narrow conception of SNA is applied. A clear trend has been identified, and

hopefully the influential papers will continue to impact lower ranked journals, as the growing

trend of simply applying SNA has already proven possible.

5.1 Limitations and Implications for Future Research

A number of limitations apply. We have aimed for transparency, but any coding into

categories that are not universally defined is still a somewhat subjective endeavor.

Furthermore, even though the sample is based on a broad search, the sample is still a

sample. We do believe, however, to have captured the essence of the field of inter-

organizational network research.

It can be argued that this paper is guilty of the same shortcomings as we identify in the

study, since we do not use a full analysis of the citation data, merely counting the number of

citations as a measure of performance or status instead of doing a full citation analysis.

However, a full citation network and bibliometrical analysis of this number of studies is

beyond the scope of this paper, but would make for quite an obvious continuation of this

research.


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Furthermore, if a theoretical coding similar to Oliver and Ebers (1998) is integrated, the

methodological features could be interrelated with the different theoretical inter-linkages to

explore if the new and advanced analytical opportunities within SNA have provided for a

richer theoretical research. Finally, it could also be interesting to apply the same systematic

approach in other theoretical fields, in order to investigate how SNA is integrated in these

fields, seen from a methodological perspective.

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Appendix A: List of Variables

All variables are categorical, except for Nos. 10 and 16. It should be highlighted that whilesome of these categories are mutually exclusive, others are not, which we have chosen todeal with in the following manner.

(1) Is the paper empirical or conceptual?(a) Conceptual, (b) empirical, (c) simulation or meta-data.

(2) What industry is investigated?Data on specific branches has been collected. The division between high- and low-tech is based on NAISC definition (http://www.techamerica.org/naics-definition).Coded as not available if not specified.

(3) In what geographical area is the study based?Data on each geographical area is collected. If more than two countries areinvolved, the paper has been coded as “international”. Not available if not specified.

(4) Does the study examine alliances as an inter-organizational form?Not based on keywords, but abstracts and a reading of the article.

(5) Is the study based on organizational and/or individual relations?How is the relation measured or analyzed? (a) Individual, for example, interlock orpersonnel flow, (b) organizational, for example, supply or alliance relationship,(c) both.

(6) What is the organizational unit of analysis?(a) One focal organization, (b) multiple focal organizations, (c) one focal project,involving several organizations, (d) neither. Not available, if not specified.

(7) What is the content of the inter-organizational relation?Papers have been exploratively coded according to the main inter-organizationalrelation, and “multiple” if several apply. The content can be innovative activities or asupply relationship.

(8) What kind of data, qualitative or quantitative, is the study based on?(a) Qualitative, including interviews, archival data, observation and desk research,


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(b) simulation, (c) quantitative, survey, (d) quantitative, database, (e) multiple.(9) Is the study a snapshot or longitudinal, that is, can change potentially be studied?

(a) Snapshot, (b) comparison of different discrete time periods, (c) one continuoustime period. If the study involves both snapshot and longitudinal approaches, thepaper has been coded as either (b) or (c).

(10) If longitudinal data has been collected, how many years of data have beencollected? If several data sources, the shortest time period has been selected. Ratiovariable.

(11) Are methods traditionally associated with SNA (interdependent, structural,quantitative data analysis) used?(a) Yes, (b) no. Based on actual analysis of data, not just collected data.

(12) What concept of network is involved ranging from network as a metaphor to a wholenetwork approach? (b), (c) and (d) are all analytical perspectives.(a) Metaphor; includes data sets where some kind of data on inter-organizationalactivity is collected, but not used to analyze relational or structural properties.(b) Dyadic; includes studies that examine the relations between two specificcompanies, and studies that examine multiple dyadic relations and disregard triadicfeatures. (c) Ego-network; bounded network where every node has a relation to afocal actor. Typically based on a star-network. No information on relations betweenalters. (d) Ego-network including density; bounded network where every node has arelation to a focal actor. Information on relations between alters included. (e) Wholenetwork; based on all sets of possible relations between a given set oforganizations.

(13) What is the dependent variable?(a) Relational, when an inter-organizational relation is explained, (b) firm level, if thestudy focuses on explaining the behavior or performance of a focal firm, (c) networklevel, if a whole network is explained, (d) multiple if more than one dependentvariable applicable. Not available if not specified.

(14) Do the data involve multiplex relations?(a) Uniplex, (b) multiplex. Based on actual analysis of data, not just the collecteddata. Coded as multiplex if either the dependent or independent variable ismultiplex. Not available if not specified.

(15) Are the relational data dichotomous?(a) Dichotomous, (b) valued. Based on actual analysis of data, not just the collecteddata. Coded as multiplex if either the dependent or independent variable ismultiplex. Not available if not specified.

(16) How often has the paper been cited in the Web of Science database, as ofDecember 2009?Data has been collected on 10 and 11 December 2009. Nominal variable.


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Appendix B: List of Journals in Provisional Sample and Basic Samples 1a

and 1b

Journals

Provisional sample,

# of papers

Basic sample 1a,

# of papers

Basic sample 1b,

# of papers

Research Policy 30 28 26

Strategic Management Journal 24 24 23

International Journal of Technology Management 22 16 15

Organization Science 22 19 19

Journal of Business Research 19 16 10

Journal of Management Studies 18 15 12

Organization Studies 18 16 13

Industrial Marketing Management 17 16 12

Academy of Management Journal 15 12 11

Entrepreneurship and Regional Development 14 11 10

Administrative Science Quarterly 13 8 8

Technovation 12 12 10

Technology Analysis & Strategic Management 10 9 7

Journal of Management Information Systems 9 7 6

Academy of Management Review 8 7 2

International Journal of Operations & Production

Management

8 7 7

International Small Business Journal 8 7 5

Management Science 8 7 7

R&D Management 8 8 7

Journal of International Business Studies 7 5 5

Information & Management 7 5 5

Journal of Marketing 6 6 5

Technological Forecasting and Social Change 6 4 2

Journal of Business Venturing 5 5 4

Journal of Operations Management 5 5 5

Mis Quarterly 5 4 3

American Sociological Review 4 4 4

Journal of Management 4 4 3

Journal of Product Innovation Management 4 4 4

American Journal of Sociology 3 3 3

Economy and Society 3 2 1

Social Networks 3 3 3

British Journal of Management 2 2 2

Harvard Business Review 2 0 0

Social Problems 2 0 0

Annual Review of Sociology 1 1 0

Law & Society Review 1 0 0

Organizational Research Methods 1 1 1

Social Science Research 1 1 1

British Journal of Sociology 1 1 1

Sociologia Ruralis 1 1 1

Total 357 306 263


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Appendix C: List of Journals in Sample 2a, Including Theoretical Papers

Appendix D: List of Journals in Sample 2b, Including Theoretical Papers

Journals Sample 2a, # of papers

Organization Studies 16

Academy of Management Journal 12

Administrative Science Quarterly 8

American Sociological Review 4

Total 40

Journals Sample 2b, # of papers

Strategic Management Journal 24

Organization Science 19

Organization Studies 16

Academy of Management Journal 12

Administrative Science Quarterly 8

American Sociological Review 4

American Journal of Sociology 3

Total 86


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Inter-organizational network studies - A literature review

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