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Networking for innovation: how entrepreneurs' network behaviours help clusters to innovate

Periodic Reporting for period 2 - N4I_CLUSTERS (Networking for innovation: how entrepreneurs' network behaviours help clusters to innovate)

Reporting period: 2018-09-01 to 2020-02-29

My research program is based on the idea that the most innovative individuals may not simply have better networks, they may rather have more effective networking behavior. For example, such individuals build the right connections at the right time or make wise decisions on when to mobilize specific ties from their network. Likewise, clusters of entrepreneurial and innovative activity may thrive not simply because of the valuable social capital individual innovators and entrepreneurs may have access to; rather they may be home to network-savvy individuals that know how to build valuable social capital and how to leverage it successfully.

Existing research most often focuses on the role of network structure as drivers of information advantage and innovative success of individuals and clusters and tends to overlook the role of networking behaviour. To assess the role of networking behaviors in driving innovation and entrepreneurship outcomes, my research program aims to build a network behavioural approach to understanding the network-innovation relationship. At the micro-level, a better understanding of networking behaviors is important, as it will help shed light on some of the fundamental individual-level mechanisms through which networking facilitates innovation. At a macro-level, understanding the role of networking behaviors in capitalizing on local network opportunities is critical to better understand why some clusters are more vibrant and innovative than others.

My research programme is a mixed-method study that consists of five main phases with the following objectives:

1. Identify key networking behaviors of entrepreneurs and innovators
2. Monitor networking behaviors of entrepreneurs and innovators
3. Assess the relation between networking behavior and innovation outcomes at the micro level
4. Testing core mechanisms of the network-innovation relationship
5. Assess the relation between networking behavior and innovation outcomes at the macro level

Cumulatively, meeting the above objectives should lead to fundamentally new insights into why certain individuals contribute more to innovation outcomes than others, and why certain clusters thrive as hubs of innovation.
Over the reporting period, the research program has advanced along several dimensions. Below, I summarize the major steps taken, broken down by the five stepwise research program objectives described in the previous section.

OBJECTIVE 1. Identifying key networking behaviors of entrepreneurs and innovators

We have collected granular qualitative data of entrepreneurial networking behavior. This includes interviews with more than 20 experienced entrepreneurs in London’s entrepreneurial ecosystem TechCity and 230 hours of participant observation at entrepreneurial networking events. A working paper from this project, including preliminary findings, has been presented at the Babson entrepreneurship conference in 2019.

OBJECTIVE 2. Monitoring networking behaviors of entrepreneurs and innovators

We designed and implemented an experiment in the form of a networking game to emulate the search of information through networking in event-like settings. We monitored the dynamics of social network behavior among 70 participants using sociometric badges developed by the MIT Media Labs. A working paper from this project has been presented at various conferences, including the Academy of Management, DRUID and the Sunbelt Social Networks conference.

We also collected experimental data on network mobilization behaviours of boundary-spanners: individuals with job roles across two different domains (e.g. academia and industry). Using a framed field experimental setup, we analyse how boundary-spanners decide which contacts to rely on when. An article based on this project is currently under peer review.

OBJECTIVE 3. Assess the relation between networking behavior and innovation outcomes at the micro level

I published a first article in relation to this objective. This paper, accepted for publication in the Administrative Science Quarterly, focuses on networking behavior in a corporate innovation setting and seeks to determine the extent to which individuals dividing the work across roles can also benefit from dividing their network. We show that collaborating individuals benefit from connecting to the same groups but different individuals—an approach we label dual networking—rather than connecting to distinct groups. This advantage stems from the opportunity to engage in dual interpretation and dual influencing, leading to more effective elaboration and championing of innovative ideas.

In a new project, currently at the design stage, we will examine why some firms are better able than others to take advantage of the rich network opportunity space that entrepreneurial ecosystems provide. Combining Twitter data, Crunchbase data and data on the career histories from entrepreneurs based in London’s TechCity ecosystem, we analyse how a range of strategic networking actions help newly started ventures survive and grow.

Projects in relation to objectives 4 and 5 have not yet started.
“Networking is just a letter away from not working”, a phrase that perhaps reflects the pervasive negative sentiment associated with deliberate attempts to build, maintain or leverage one’s professional network. Despite this negative connotation, individuals continuously engage in behaviors – be they deliberate and strategic, or ad-hoc and spontaneous – that change their social network and the valuable resource it represents. Entrepreneurs and innovators in particular actively shape their professional networks to access key knowledge inputs, capital and other resources.

My research program foresees a large-scale and intensive data collection effort of network structural data, network behavioural data, and innovation achievement data at multiple points in time using a range of novel data collection methods. More specifically, I will use interviews, participant observation, sociometric badges, social science experiments and data from the Twitter use to identify and measure network behaviors.

If brought to a successful conclusion, my research should contribute to building the micro-foundations of networks research in the literature on innovation and entrepreneurship in economic geography and lead to fundamentally new insights into why certain entrepreneurs or other innovators contribute more to the innovation outcomes than others, and why certain clusters thrive as hubs of innovation whereas others do not.
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