Sunday, 20 November 2011

Social Networks and their Limitations

My research interests, as applied to assessing green innovation, technology, clusters, strategic alliances or policy development, etc. is based upon understanding the social networks that afford the relationships on which these new ideas or innovations emerge. This month I’ll introduce a few academic considerations about social networks, while next month I’ll try to explain how I’m trying to develop a corrective to the limitations that social network thinking needs to solve if it is to be an effective explanation of the interdependencies that it describes.

Attempts to describe and explain the social interactions that support organisations and other social phenomena make use of a variety of models and methods. There are, though, a limited number of paradigms that dominate the literature, and among these, emerging as the market leader, is that of the network. Stephen Borgatti and Pacey Foster illustrate the exponential growth of network-based research outputs with bibliometric data (Borgatti and Foster 2003: 992), arguing that the network paradigm forms part of a more general move “away from individualist, essentialist and atomistic explanations toward more relational, contextual and systemic understandings” (Borgatti and Foster 2003: 991). In this moth’s blog I will briefly examine some of the key features of network theory. The network paradigm does not represent a unified approach to research; network models are themselves diverse but share characteristics and assumptions. The use of such models in addressing the issue of innovation (the theme of this paper) seems a sensible choice as networks are able to capture a sense of the interdependencies of organisations and the channels of exchange that enable the relationships necessary for innovation to develop and be maintained (Freeman 1991). Equally, network descriptions can be applied to a variety of innovation-related phenomena. Examples include Powell, Koput and Smith-Doerr (1996), who use network patterns to describe the growth in corporate partnerships and external collaboration and the purpose such relationships serve, while Bengt-Åke Lundvall, with a very different approach to organisational adaptation, uses network descriptions to exemplify the process of knowledge transfer and learning between different firms (see Lundvall 1992).

Network models have been used extensively in research questions addressing both internal organisational change and inter-organisational dynamics. This paper will focus on network research of both types of dynamics, and network approaches specifically addressing the topic of innovation, partly because innovation and change are particularly dynamic organisational themes, and also because they present a challenge for researchers and theorists due to the difficulty of setting the boundaries of the network, i.e. distinguishing what is included from what is excluded from the network. In addition, with an established, though rapidly growing, literature to justify new research, bold claims are being made about how networks are a fundamental part of an organisation’s relationship to the innovation process: “It now appears that inherent successful innovation can be explained by the influence of the networks and social capital” (Lewrick, Raeside and Peisl 2007: 38).

The key feature of network analysis in this literature is that it emphasises the interdependence of individuals within organisations rather than conceptualising them as sovereign elements that act autonomously. As such, the relationship between individuals is perceived to be the unit of analysis of social structures, with such relationships conceptualised as conduits for the flow of resources, and in particular, information (Wasserman and Faust 1994: 4-5). Network theorists therefore attempt to identify the relationship patterns that form network structures and analyse the network relationships to identify the conditions that enable or obstruct specific activity. While there are common features in the types of methods and metrics used to identify such patterns, and shared assumptions concerning the importance of ties of interaction in investigating behaviour and activity, there are fewer commonalities in the theoretical basis of such analysis.

Theoretical positions developed in the early stages of network research – approximately 1970-1990 (Borgatti and Foster 2003: 992) – can be defined in terms of their opposition to structural-functionalism, in addition to an implied opposition to perspectives that emphasise purposive action and non-relation characteristics (Wellman and Berkoviz 1997). However, explicit theoretical or ontological frameworks with which to support the assumptions of a network analysis are typically absent. Mustafa Emirbayer and Jeff Goodwin (1994) claim that there are, in fact, three implicit models or frameworks in network analysis, although all three models – structuralist determinism; structuralist instrumentalism; and, structuralist constructionism – seem to have fundamental problems (see Emirbayer and Goodwin 1994: 1425-1436).

A broad analysis of more recent network models (Borgatti and Foster 2003) identifies a number of research streams and research dimensions with which to categorise the literature. While the themes are useful in demonstrating the degree to which concepts such as social capital, embeddedness and social cognition have gained resonance with network researchers, their initial observation is that much of the research, especially that influenced by Burt: “seems to add a rational actor assumption to social capital theory to the effect that actors deliberately choose their ties (i.e. manipulate the network structure) specifically in order to maximise gain” (Borgatti and Foster 2003: 1002).

While these typologies are useful, the dearth of ontological considerations in the network literature leads to, and is compounded by, a number of methodological weaknesses. For example, in research designed to address networks, genuine theories are generally overlooked in preference to descriptions. In addition to this, some of the stronger claims supporting these descriptions rely on data sets such as patent data and citations, which are weak indicators of sophisticated networks, while other research relies wholly on surveys and questionnaires, often leading to perceived ties being treated as actual ties (Marsden 1990). In this way, such research is unable to develop findings through which new concepts or theories are able to emerge, thus perpetuating the choice of concentrating on description, or worse, on implicitly retaining internally inconsistent theories and concepts based on fragmented and unexamined models.

The problem is that while the authors of these explanations often support their claims with empirical evidence, demonstrating that networks form a condition of the organisational dynamics able to facilitate innovation, there is little actual theory to explain, rather than merely describe, why the processes function, what deeper mechanisms are at work, or how the changing components of the networks impact on the processes produced by earlier interdependencies or the process of feedback and emergence on changes within collaborative groupings. As Gerald Salancik argues, “network analysis has been used mainly as a tool for analysing data about organizations rather than for understanding organizations per se” (Salancik 1995: 345). Thus while it is true that describing the effects of network phenomena may be useful, in addition, it would be much more powerful if it were coupled with explanations concerning why they exist in the form that they exist, particularly if the research is to be applicable to other cases and, more ambitiously, to address the nature of the ontologically primitive elements that constitute network theory’s basic concepts (Parkhe, Wasserman, and Ralston 2006: 561-563). To address these gaps, and identify the range of actors and ties responsible for organisational change within such networks, an alternative perspective must be developed that can encompass all of the features of the typologies developed by Emirbayer and Goodwin, and Borgatti and Foster, and, in addition, offer an account of the ontological features so as to improve our conceptualisation of networks in general. I’ll try to explain the type of framework I have been developing in next month’s blog.

For a more complete overview of these issues and a full list of references to this blog articles see Haynes, P. (2011) Conceptualising Networks as Assemblages, Revista Internacional de Sociologia 69(2) 417-437


  1. How does this academic paper relate to climate change issues?

  2. Good question - I think we need to understand networks better in order to work out how to develop appropriate green technology