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Commercializing academic research: a social network approach exploring the role of regions and distance

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Abstract

Relationships between firms and universities have been centre stage for some time. However, empirical studies on firms contracting research to universities remains limited. The likelihood of engaging in contract research depends on the characteristics of the firm and the university. Because existing literature further suggests that location is a key facilitator for knowledge transfer activities, the paper investigates the role played by regions and geographical distance between firms and universities when engaging in contract research. Hence, the analysis combines characteristics from both organisations and adds relationship-specific features with respect to the distance between them and the region they are located in. It also looks at the role played by cognitive distance. The paper contributes to the understanding of how academic research, commissioned by firms, is influenced by locational features: the ability to engage in contract research and the regional context, the regional embeddedness of research contract partners, and the geographical distance between these partners. It builds on an original dataset with information on contract research at firm. Based on a panel of three consecutive waves of R&D surveys in Belgium conducted in 2006, 2008 and 2010, the linkages of universities with R&D active firms are examined by linking a database on universities with one on firm R&D investments. Using the most recent insights in the social network approach, highlights the variables that impact the likelihood of firms engaging in research contracted to a university. Descriptive measurements are calculated from social network analysis to capture the basic structure of the firm-university network and construct an Exponential Random Graph model to predict firm-university relationships based on network characteristics and node attributes. Four main conclusions are drawn. First, more innovative regions do not show a higher likelihood of firms to engage in contract research with universities. Second, the likelihood for contract research is higher, if firms and universities are located in the same region. Third, geographical distance shows a negative relation to the likelihood of contract research suggesting cluster formation. Fourth, in the case of contract research cognitive distance complements geographic distance.

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Authors and Affiliations

  1. Belgian Science Policy Office, Avenue Simon Bolivar 30, 1000, Brussels, Belgium

    André Spithoven

  2. Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium

    André Spithoven, Jef Vlegels & Walter Ysebaert

  3. Ghent University, Tweekerkenstraat 2, 9000, Ghent, Belgium

    André Spithoven & Jef Vlegels

Authors
  1. André Spithoven
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  2. Jef Vlegels
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Correspondence to André Spithoven.

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Appendix: examining model fit

Appendix: examining model fit

As a first goodness-of-fit check, we compare the statistics of simulated networks of the model against the observed network in below table. Note that p values closer to one are better (Goodreau et al. 2008; Lusher et al. 2013).

Variable

Model 1

Model 2

Model 3a

Model 3b

p value

p value

p value

p value

Network level

    

 Edges

0.90

0.88

0.74

0.90

 gwb1degree

0.88

0.84

0.78

0.88

Node level: business

    

 Age (relative to 2009)

    

  < 6 years

0.98

0.76

1.00

0.88

  ≥ 6 years (ref)

    

 Size

    

  Micro (ref)

    

  Small

1.00

0.94

0.96

0.92

  Medium

1.00

0.88

0.74

0.78

  Large

1.00

0.96

0.82

0.86

 Sector

    

  No service (ref.)

    

  Service

1.00

0.80

  

 R&D intensity

    

  Low (ref.)

    

  Mid

0.78

0.82

0.86

0.88

  High

0.78

0.94

0.92

0.93

 Subsidized

    

  Low (ref.)

    

  Mid

1.00

0.86

0.98

0.74

  High

0.64

0.70

0.58

0.78

 Regio (nuts1)

    

  Brussels Capital Region (ref.)

    

  Flemish Region

 

0.76

0.94

0.88

  Walloon Region

 

1.00

0.86

0.98

 Concurrent

    

  Brussels Capital Region

 

1.00

0.76

1.00

  Flemish Region

 

0.72

0.80

0.84

  Walloon Region

 

1.00

0.90

1.00

Node level: university

    

 Bibliometric quality

    

  Low (ref.)

    

  Mid

1.00

0.96

0.98

1.00

  High

0.90

0.86

0.84

0.94

 R&D expenditures

    

  Low (ref.)

    

  Mid

0.96

1.00

0.94

1.00

  High

0.74

0.82

0.82

0.96

 Regio (nuts1)

    

  Brussels Capital Region (ref.)

    

  Flemish Region

 

0.80

0.92

0.98

  Walloon Region

 

1.00

0.82

0.98

Dyad level

    

 Regional embeddedness

    

  Brussels Capital Region

 

0.90

0.86

0.90

  Flemish Region

 

0.96

0.96

0.92

  Walloon Region

 

0.72

0.96

0.96

Distance (km)

0.88

0.82

0.96

 

 Cognitive distance based on bibliometric specialization

    

  0 Firm in services—University in social sciences (ref.)

    

  1 Firm in manufacturing—University in social sciences

  

0.70

 

  2 Firm in manufacturing—University in exact sciences

  

0.76

 

  3 Firm in services—University in exact sciences

  

0.96

 

 Cognitive distance based on R&D specialization

    

  0 Firm in services—University in social sciences (ref.)

    

  1 Firm in manufacturing—University in social sciences

   

0.92

  2 Firm in manufacturing—University in exact sciences

   

0.96

  3 Firm in services—University in exact sciences

   

1.00

A second typical GOF check for ERGMs is comparing a selection of network statistics of the simulated networks with the observed network. The network statistics do not necessarily have to present a predictor in the model itself. Below we plot the distribution of minimum geodesic distance, dyad-wise shared partners on the log-odds scale and degree for mode 1 (Morris et al. 2008). All plots show that the GOF of our models is at least reasonable.

1.1 Model 1 GOF plots

figure a

1.2 Model 2 GOF plots

figure b

1.3 Model 3a GOF plots

figure c

1.4 Model 3b GOF plots

figure d

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Spithoven, A., Vlegels, J. & Ysebaert, W. Commercializing academic research: a social network approach exploring the role of regions and distance. J Technol Transf 46, 1196–1231 (2021). https://doi.org/10.1007/s10961-019-09740-1

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