Social network analysis

We complemented our in depth qualitative research by employing a social network analysis of the development CLAHRC actors’ ego networks of interaction over two points in time, for the four in-depth sites. Our SNA analysis complemented our in depth qualitative case studies through providing quantitative evidence as to the extent to which CLAHRCs had enabled the new patterns of working to bridge the T2 gap.

Ego networks relate to an individual actor’s network of relationships, and enable us to examine variation across actors’ networks, with a particular focus on the extent to which they bridge the research-practice divide. We examined actors’ ego networks at two points in time: (i) early on in the development of CLAHRCs, and (ii) during the run up to CLAHRC refinancing. In doing so we wanted to gain insights into actors’ ego networks across all levels of the CLAHRC, including those actors engaged in senior and more front line roles.

To carry out the SNA, we used a web-based socio-metric survey (Network Genie) to capture actors’ personal characteristics and networks, which was emailed to a list of CLAHRC staff

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as agreed with the Directors (or Deputy Directors) of the four CLAHRCs. Each actor was then sent a link to the survey, and we then followed up with a number of additional reminder emails. Network Genie enables us to ask actors (the egos) about the people they interact with (the alters). The questionnaire is automatically generated by each actor based on the names they select of people they interact with. The sample of names provided for each CLAHRC ranged from 35 to 48, and included actors from a range of different roles including: CLAHRC senior managers (both HEI and NHS employees, clinical and social scientists), theme leads (clinical scientists and social scientists) and other NHS staff involved in CLAHRCs including secondees and researchers. We present an abridged version of the SNA instrument in appendix 2 due to the problems of presenting a web-based survey.

The first wave of data collection in 2011 (Wave I) produced 81 complete responses, and the second wave in 2013 (Wave II) produced 86 responses. Sample sizes for each of the four CLAHRCs are presented in Table 1. In Wave I we asked actors to outline their networks and patterns of interaction at the inception of CLAHRCs, to capture actors’ actual ego networks as CLAHRCs were first established (i.e. looking backwards). In Wave II we captured actors’ current ego networks (at the end of CLAHRCs five years of funding).

Table 1: Respondents across the four CLAHRCs by survey wave

CLAHRC Survey Wave I Wave II CLAHRC A 22 20 CLAHRC B 17 27 CLAHRC C 27 23 CLAHRC D 15 16 Total 81 86

Our aim was to examine how individuals’ professional characteristics influenced the four different outcome measures of social interaction for understanding the KT processes in CLAHRCs: (i) formation of networks across academics and clinicians, (ii) integration of decision-making practices among CLAHRC academics and clinicians, (iii) formation of

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networks across members of research and implementation themes, and (iv) formation of networks across members of clinical and non-clinical departments involved in CLAHRC.

The professional characteristics we focused on were the respondents professional background (i.e. academic or clinician), their professional status (i.e. senior versus junior), the status of professionals in an actor’s ego network, the number of professionals in their network with whom the respondent has not worked with before joining the CLAHRC, and the number of professional connections from the same professional category.

Social network research has tended to focus on whole networks, and the extent to which actors find themselves in social structures characterised by dense, reciprocal, transitive, or strong ties. These approaches may tell us some interesting things about the entire network and its substructure but they do not tell us very much about the opportunities and constraints facing individuals. Thus, to understand the variation in the behaviour of individuals, we need to take a closer look at their local circumstances. Describing the variation across individuals in the way they are embedded in local social structures is the goal of the analysis of ego networks.

We collected actors’ ego network data using Network Genie web-based platform, which asks actors (the egos) about the people they interact with (in our case for the purposes of KT) (the alters). The typical way to generate ego network data is to create an exhaustive list of alters with whom the respondent has some type of relationship. Termed a name generator, the respondent was asked to list alters who occupy certain social roles, those with whom s/he shares interactions, or those with whom s/he exchanges resources. This approach is used in many classic studies of ego networks 147_.

To analyse the SNA data we employed regression analysis, and bivariate analyses where we were limited by sample size. Our analysis comprises of cross-sectional regression models for both waves of data collection independently, thereby providing two ‘snap shots’ of patterns of interaction. In addition, we conducted longitudinal analyses for the changes in behaviour over time. Specifically, we explore the effects of CLAHRC participants’ professional and organisational characteristics on the change in the measures of the bridging of academic-

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practitioner networking and decision-making gap and the formation of connections among members of research and implementation themes and members of clinical and non-clinical departments. The criterion change over time in all cases is measured as the difference between criterion scores in Waves II and I (CChange = CWave II – CWave I).

We drew on and adopted SNA analysis and in-depth qualitative research to explore institutional entrepreneurship across the different CLAHRCs in facilitating KT. In the following chapters we present our findings, beginning with the founding conditions of CLAHRCs.

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