Networks and Causality Ben Farr-Wharton Robyn Keast

What has social network analysis taught us??? A Rousing question… What has social network analysis taught us???

Answer… Many Great Things Hierarchies in networks Path distance between actors Centrality of actors Network Density Actors’ relative position More things…

What has Social Network Analysis struggled to teach us???

Why do we form networks anyway? SNA doesn’t give an indication of why people form networks in the first place, or the direct impact of having a certain connection… it is theoretically limited for this reason! To answer this question, network researchers turn to social capital theory… though this de- instrumentalizes some of the great things that we have found out about networks from SNA, and instead focuses more on the ‘power of social interaction’

‘Network Resources’… A New Hope

‘Network Resources’… A New Hope Extending Lavie’s (2006) Resource-based view of the interconnected firm, Gulati et al. (2011) suggests that a persons’/firms network structure forms resources that can enhance their performance. Perspective Description Associated with The social embeddedness perspective ‘holds that the context of social relationships in which actors are embedded influences organizational behavior and economic outcomes’ (Gulati et al., 2011, p. 208) (Uzzi, 1996) (Uzzi, 1997) The relational embeddedness perspective ‘Encompass cohesive direct ties that reinforce collaboration by providing trusted channels for knowledge and information’ (Gulati et al., 2011, p. 209) (Dyer & Singh, 1998; Granovetter, 1985) The structural embeddedness perspective ‘considers the implications of the overall network structure in which an organization is situated, encompassing not just its direct ties but also its position in a larger network’ (Gulati et al., 2011, p. 209) (Burt, 1992) (Granovetter, 1992) (Kilduff & Tsai, 2003) The social capital perspective of networks Emphasises the ability of actors to secure benefits by virtue of membership in social networks or other social structures (Gulati et al., 2011, p. 209) (Burt, 2000) (Adler & Kwon, 2002) (Coleman, 1988)

More on the impact of Network Structure…

What has happened outside of ‘Social Network Analysis’ Unbeknownst to many ‘network theorists’, there is now a good 10 years of research that has incorporated elements of network structure in causal models!!! This field is often termed ‘entrepreneurial networks’

For Example… Network Size Findings Research Context/ Sample size Author Method New ventures with large alliance networks have more innovation Biotechnology ventures in Canada N = 369 Baum, Calabrese, and Silverman (2000) Multivariate analysis of Business observations The number of linkages held with peers has a positive correlation with the survival rates of start-up companies Software start-ups in Israel N = 100 Raz and Gloor (2007) Social network mapping and Multivariate analysis of survey data The network size can positively impact on survival and growth of firms, but only to a point; when networks are too big, and require too much investment (time and resources), the positive impact diminishes Australian SMEs N = 5014 Watson (2007) Multivariate analysis of survey and economic data The size of an entrepreneurs’ network is more significant for accessing resources than the quality of relationships held between network members German start-ups N = 379 Semrau and Werner (2013) Multivariate analysis of survey data

Principles behind causal analysis of network structure Research participants respond to surveys… Survey questions seek data on network structure, firm performance, and other factors… Data is subject to validity and reliability testing (confirmatory factor analysis) Multivariate analysis including hierarchical regressions and structural equation models are used to explore the relationship between the distribution of network structure factors and performance factors.

An Example My PhD Study – The impact of social network structure on the performance of creative practitioners in Australia

An Example My PhD Study – The impact of social network structure on the performance of creative practitioners in Australia Collected data from 289 creative practitioners Used Structural Equation Modeling to analyse the impact of network structure variables (structural hole, network size, non-local connection and industry clustering) on performance factors (exploitation, perceived earnings and labour precarity)

Results Model Fit was Good All Instruments were validated using CFA, only ‘exploitation’ had a sub-standard composite reliability score

Advantages of the approach over Social Network Analysis Has the potential to show causality, as well as mediation and moderation, of network structure on performance This is helping to build robust theory in regards to ‘impact of network structure’ Is not as vulnerable to actor non-response as SNA It is replicatable, transferable and comparable

Some issues with the approach There are no standard instruments for measuring network structure using psychometrics Approaches thus far have utilized self-report survey responses for acquiring network structure details

More recently… Guiliani (2013) has incorporated SNA/UCINET to develop a score of ‘network structure’ for firms, and has regressed these onto firm performance factors

Next Step Farr-Wharton, Chamberlain & Keast will be mining old datasets to operationalize SNA/UCINET in multivariate analysis – exploring the impact on performance* *performance is measured in many ways… Authorship, Problem Solving etc.

More information For any information on the literature behind this approach; including network structure measurement instruments, please contact me: ben.farr-wharton@scu.edu.au skype: bennywharton ph: +614 38 755 019

From Lee (2010, p805) – cited in Borgatti & Haglin 2011 Social networks research provide ample evidence that actors’ relative positions in a network correlate with their economic performance. Actors occupying valuable network positions-though researchers agree less on which positions qualify as valuable-are shown to outperform those who do not.

The Issue with Lee’s (2011) statement ‘Social networks research provide ample evidence that actors’ relative positions in a network correlate with their economic performance.’ The bulk of Social Network Research uses case-based social network analysis… Results for case analysis typically identify one, or several actors that perform better than other; however as sample size, scope, research design etc. differ from one case to another, results are inhibited in their overall transferability…. Hence… “researchers agree less on which positions qualify as valuable”

So… While we have an indication that certain network structures, such as being structural hole, has the potential to enhance the outcomes of an actor – we can’t say for certain, given any network, that a person occupying a structural hole will outperform others… thus… While we can say that ‘networks can impact on performance’ Our ability to say that ‘networks definitively impact on performance’ requires further qualification