Actor Level Ego Centric

The individuals or entities within a network are defined as actors or nodes. The focus of network studies is on the relationships held between each actor with other actors within the social system. This approach places an emphasis on the influence and diffusion of resources accessible to individuals in the network (Prell, 2011). While the emphasis of this form of analysis in on the relational linkages, individuals possess certain characteristics (or resources) and can characterise the opportunities (or constraints) that has the potential to become available. Therefore, it is common for researchers to gather a collection of demographic and compositional elements.

The ego-centric approach in another technique used by researcher to design, collect and analyse network data. Much of the literature employing social network data approaches utilizes an ego-centric approach to analysis (Kadushin 2012). This approach is like standard social science techniques in that it focuses on collecting data from an individual actor’s position to

121 reveal their own position within the network. The researchers may pursue the utilization snowball methods to define the boundaries of a social structure and the characteristics of the actors that lie within a specific social phenomenon (Hanneman & Riddle, 2005). This method adopts an exploratory stance to further explain specific streams of the much broader field.

There are limitations to employing this ego-centric approach in isolation. For instance, it is widely accepted that individuals are not always effective or accurate in reporting their extent of their relationships. Informant accuracy compromises the reliability of SNA data (Wasserman & Faust, 1994) due to studies that reflect that people are ineffective in reporting their interactions (Bernard & Killworth, 1977). Ego-centric data frequently requires the actor to simply state the people that they have specific relational ties through interviews or through the usage of name generators. This technique may also complicate the drawing of boundaries for the entire network as it may be difficult to conceptualize all actors (Hanneman & Riddle, 2005) or the prevalence of important yet latent ties (Marritotti & Delbridge, 2012). The data collection process may reveal complicated data sets if the size of the network under investigation includes multiple relational types or the actors reveal only a limited number of ties.

The ego-centric approach may be most useful when triangulated among the other approaches to collecting network data. This study will utilize the ego-centric approach to data collection following the collection of all names affiliated with direct interactions with the network on either side of the relational dyad. The ego-centric approach will also be utilized to determine the compositional elements of the network, such as human capital and the intensity of specific relational ties through the collection of electronic survey data (Dillman, 2000). This analysis began from an ego-centric approach, which employed a snowballing technique to identify further members and sources of data. The analysis then shifted to a review of archival documents to identify further members of the collaboration. Finally, a cross-sectional survey was distributed. This survey included a rooster design of all documented individuals within the network. However, it also included a name generator that allowed the participant to identify significant contributors to their project. Several names that emerged from the data collection existed outside the boundaries of the immediate network. This form of analysis has been used to inform the findings in all of the empirical investigation chapters.

3.9.4.1 Demographic Attributes

There were several demographic attributes that were collected on the actors in this network. The aim of this activity was to understand the composition of resources available

122 within the network by categorizing demographic attributes, as well as indicators of intellectual and relational capital.

Figure 10 - Actor Attributes (Demographic)

Attribute Description

Knowledge Content Area of Expertise

University-Business Prior Experience

Subgroups Departments

Organization Relationship Tenure Strong or Weak Tie Professional Status Career Level University-Business Experience Years

Demographic Age

Gender

Projects Volume

Date of commencement

3.9.4.2 Behavioural Attributes

Several behavioural attributes were collected as well. For instance, time commitment and individual trust development (Tsai & Ghoshal, 1998). Questions were centered on the reliability of partners, the belief that shared goals exist, and the belief that promises were being kept. The belief of project success and that the individual was operating effectively. These measures were based on Aijzen & Fishbein’s (1991) theory of planned behaviour, which was adapted to determine about the behavioural intent to remain involved in the network. There was also an assessment that was aimed to understand the frequency of new knowledge generation and whether the volume of learning experiences was satisfactory for the individual. Social capital and intra-organizational network development behaviours to promote knowledge access (Ferris et al., 2005; Ng & Feldman, 2010), knowledge sharing and collecting (Lin, 2007) and the extent to which members introduce external social capital.

3.9.4.3 Types of Relational Ties

The relational ties are conduits in which knowledge resources might be able to diffuse among the actors. Semi-structured interviews revealed several personal (friendship) relationships existed within the network. Chapter 6 presents the relational linkages that were strategically through contractual interdependence. However, the impact of previously collected evidence suggests significant deficiencies in reporting the density and cohesiveness of the network structure. This chapter also discusses the impact of informal linkages that are driven by behavioural factors rather than economic incentives.

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3.9.4.4 Network Size

An understanding of the general size of the overarching network was obtained in the early stages of the project. However, self-reported conceptions over the size of the network varied per individual actor. Additionally, by analysing the semi-structured interviews and participant observations evidence of informal (and undocumented) links were providing resources to the network.

3.9.4.5 External Influences and Network Development Behaviour

This attribute aimed to understand the extent to which the individuals in the network engage with other forms of expertise to enhance their knowledge capabilities outside of the focal network. It also aimed to collect the individual’s motivation to encourage new membership to the network. This attribute was aimed to understand the frequency of new knowledge generation and whether the volume of learning experiences was satisfactory for the individual.

Some researchers might seek to characterise the prevalence and influence of external members onto a particular network structure and how they access the resource in ‘external economies’ (Hoover and Vernon, 1962). Approaching SNA in this method looks toward an open system approach (Kadushin, 2011). These explorations of the external linkages may include a vast number of actors and complicated boundary specifications. For instance, research by Bernard and Killworth (2006) indicated that some individuals may have the cognitive capacity to manage approximately 280 interpersonal connections. This work also reveals that there is a large standard deviation within the populations of their study. Although they have been successful in gathering data, specifically from online networks, in other contexts the collection of data utilizing this technique is likely to require significant resources and research access may prove to be a challenge.

This study will acknowledge the open systems approach to illustrate the demands and potential transfer of social capital to actors on the external fringes of the network structure and how the interplay of authority, legitimacy, and leadership effects the actors internal to the relationship (Kadushin, 2012). An open systems approach may illustrate the extent to which weak ties and external novel resources characterise the ‘access’ the network has through a less bounded view (Jarillo, 1994; Cross & Parker, 2004), but can lack the density necessary to reveal multiple levels of relational channels and behaviours.

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3.9.4.6 Learning Frequency and Knowledge Goals

This attribute was aimed to understand the frequency of new knowledge generation and whether the volume of learning experiences was satisfactory for the individual. This attribute was collected to understand the underlying motives for new knowledge generation. This attribute was collected to understand how value is assessed within this network. Early analysis revealed that the patents and publications generated were not the only valuable learning outcome achieved within this network. This study developed measures that requested information on the volume learning experiences to assess the vitality of the knowledge exchange between organizations.