Knowledge creation cross-sectional studies

The use of SNA to investigate organisational efficiency and innovative capability drew a considerable amount of research effort in the 1970s and 1980s (Steve Conway and Steward 2009). In this time, seminal works established many SNA concepts that are commonly used today.

4.2.1.1. Strength of weak ties

Granovetter (1973) is one of the most cited papers in the social sciences with over 46,000 citations (as per Google Scholar on 03.01.2018) and established the concept of ‘the strength of weak ties’ (Conway and Steward 2009). The work is based on the concept that strong ties are generated on “homophilization” of source-receiver knowledge, whilst weak ties are best characterised by heterophilous knowledge, and are therefore valuable (Rogers and Bhowmik 1970). It is important to note that Granovetter (1973) does not define how a strong or weak tie should be measured, although most works citing the study take it to mean that a strong tie is characterized by frequent interactions among members (Freeman, White et al. 1992). Granovetter (1973) suggests that effective communication leads to greater homophily in knowledge, and therefore heterogeneous ties provide better diffusion. Granovetter (1973) concluded that the greatest increase in path length is when weak-ties are cut, as these serve as bridges between different communities. However, no studies could be found where specific bridges are found, and most real networks seldom have outliers in betweenness scores (Barabási and Pósfai 2016).

Whilst it is a central concept in SNA, the results have been mixed. Weak ties and structural holes have benefitted the innovative capability (Perry-Smith 2006). Zhou, Shin et al. (2009) largely concurs, but suggests an inverted U-shape relationship. However, many papers find that strong ties are better at sharing knowledge, thereby reducing the cost of knowledge transactions between individuals (Kachra and White 2008, Phelps, Heidl et al. 2012). However, the observation that heterophilous knowledge is conducive to both spreading and creating knowledge has been corroborated by several different studies (McFadyen and Cannella 2004, McFadyen and Cannella 2005, McFadyen, Semadeni et al. 2009, Backmann, Hoegl et al. 2015, Guan and Liu 2016).

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4.2.1.2. Network centrality

Knowledge creation is a process driven by individuals interacting with knowledge artefacts in themselves, other people, and through other communication mediums (Nonaka, Byosiere et al. 1994, Phelps, Heidl et al. 2012). Combining knowledge from different fields in novel ways has been shown to be conducive to developing knowledge (Burt 2004, Nelson 2009). This has been the documented reasoning behind the success of many organisations such as IDEO (Hargadon and Sutton 1997). It is the ability to transfer, understand, and create valuable synergy from these different knowledge bases that determines the success of this process (Nahapiet and Ghoshal 1998). It is through the success of such mechanisms that investigating the effect of interpersonal relationships and the development of knowledge becomes vital (Singh and Fleming 2010). Within organisations direct ties communication has been identified as being vital to sharing more reliable and more complex information (Singh 2005). Knowledge networks have been studied in many different contexts: diffusion of knowledge (Bothner 2003, Nerkar and Paruchuri 2005), knowledge production (Jackson 2010), team knowledge exchange and creation capabilities (Reagans and McEvily 2003), and interorganizational strategic alliances to improve knowledge transfer and innovation (Lane and Lubatkin 1998, Schilling and Phelps 2007).

However, where many knowledge networks findings show that there is a positive relationship between innovative capability of an individual and the number of links that person has (Audia and Goncalo 2007), other studies claim that the number of ties is negatively correlated to the quality of their research (Bordons, Aparicio et al. 2015). McFadyen and Cannella (2004) investigated a network of biomedical research scientists and found that both the number of social relations and the strength of the interpersonal relations had diminishing returns on knowledge creation (measured as the sum of journal impact factors researchers submit to and normalised by the number of co- authors). This is supported where a very similar correlation was found and attributed who correlated the collaborations network to exploitation and exploration innovative capability (Guan and Liu 2016). Both studies have found an inverted U-shaped correlation.

McFadyen and Cannella (2005) investigated the impact of geographic proximity and department interdepartmental collaboration and concluded that geographic proximity is not as strong a predictor as department; they found that the further the collaboration moves away from their own department, the greater the knowledge produced (measured as the sum of journal impact factors researchers submit to and normalised by the number of co-authors).

Guimera, Uzzi et al. (2005) establishes that there is generally a statistically significant positive correlation with the impact factor and the probability of including existing members, and negative correlation with the impact factor and the probability of selecting past collaborators. This suggests

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that introducing new knowledge into a network is conducive to knowledge creation and academic performance.

Some studies have used the centralities to classify nodes into organisational archetypes (e.g. by identifying stars, liaisons, gatekeepers) (Tichy, Tushman et al. 1979). Such an approach has been revisited more recently in Batallas and Yassine (2006) who propose brokerage indexes for the archetypes, which can be used to alter management strategies to suit the needs of the situation.

4.2.1.3. Network clustering

Other studies have investigated the local structure of the network, identifying the lack of local cluster (open triads) is associated with greater innovative capability, as it is associated with a structural hole indicating that diverse ideas flow to the person (Nerkar and Paruchuri 2005). Such approaches suggest that knowledge diversity is greater if their neighbours are not linked.

However, other studies have found that closed triads perform better with greater flow of idea between the individuals who can then collaborate, a concept close to IDR (Obstfeld 2015). McFadyen, Semadeni et al. (2009) offers a different perspective: researchers maintaining strong ties with researchers with a sparse ego-network provide the knowledge creation.

Guan and Liu investigate exploitative and exploratory innovation in both knowledge and collaboration networks, testing six hypotheses on each (Guan and Liu 2016). The main findings were that stronger integration across clusters provide greater capability for knowledge diffusion. There have been conflicting findings of high local clustering with regards to tie strength. Pan and Saramäki (2012) found that high local clustering were associated with weak ties, whilst the opposite has also been found (Uddin, Hossain et al. 2013). Degree and betweenness centralities were found to increase the citation count, and were central to developing strong ties, and that these were found in nodes with high local clustering (Uddin, Hossain et al. 2013).

4.2.1.4. Other knowledge diffusion methods

Some studies have focused on the effect that the average path length has on knowledge creation, finding that shorter path lengths increase knowledge transference and innovation performance (Fleming, King et al. 2007).

Other approaches have not been specific to scientific collaborations but provide analogous findings. In the fields of rumour propagation, Banerjee, Chandrasekhar et al. (2014) defined the geographic strength as 1/distance to find geographic centrality measures, to be correlated to the diffusion of gossip. It was found that a new diffusion centrality matched real events well. In the field of social

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capital, Ellison, Vitak et al. (2014) investigate the importance of maintaining relationships in order to develop social capital. The main finding of the paper was that merely being connected to other members in social networking sites did not produce social capital, but rather that the small efforts to maintain specific relationships did, making frequency weighting vital.