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Collaborative know-how and trust in university–industry collaborations: empirical evidence from ICT firms

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Abstract

This paper builds upon the knowledge-based view and organizational learning perspective. It develops and empirically tests a conceptual model to analyse the drivers and benefits of university–industry cooperation from the firm perspective. We used structural equation modeling to examine data collected from a sample of small and medium-sized Italian firms in the information and communication technology sector. We found that past collaborative experience increases the benefits drawn from university–industry cooperation. Both collaborative know-how and trust, however, play a significant mediating role on the relationship between collaborative experience and benefits. In particular, collaborative know-how is the main factor enhancing intangible benefits, such as knowledge transfer and learning, while trust is the main driver of tangible benefits, such as product and process innovations. Taken together, these findings suggest that firms should develop strategic competences to fully benefit from collaborations with universities because past collaborative experience alone is not sufficient. From the policy point of view, effort is needed to build channels and tools enhancing trust between industry and university, especially to support small firms.

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Notes

  1. Data were collected as part of the CLUOS project carried out by the University of Sannio, financed by Regione Campania, which aimed to study the ICT sector in Campania. The sample was stratified by sub-sector and administrative province.

  2. We identified and excluded 25 large firms. To select SMEs, we used the EU definition of (i) fewer than 250 employees; and (ii) turnover below 50 million euros or balance sheet total below 43 million euros.

  3. Stores selling ICT equipment were excluded from this population of firms.

  4. Iacobucci (2009) argued that “If the measurement is strong (three or four indicators per factor, and good reliabilities), [and the] path model [is] not overly complex….then samples of size 50 or 100 can be plenty”.

  5. In structural equation modeling, control variables should be linked to all latent variables. However, to save degree of freedom and to respect the threshold of five for the ratio between sample size and the number of model parameters suggested by Kline (2005), we only analysed the effect of Size and R&D intensity on Collaborative experience, Tangible benefits and Intangible benefits.

  6. To ensure the internal consistency of the scale used to measure a construct, the alpha value should be at least 0.7 (Flynn et al. 1990).

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Acknowledgements

We are grateful to the two anonymous referees for their valuable comments and suggestions and to Melissa Leffler, freelance language editor, for proofreading a draft of this manuscript. Luca Pennacchio acknowledges the funding received from Parthenope University of Naples through the research grants “Bando di sostegno alla ricerca individuale per il triennio 2015–2017” (years 2016 and 2017) and “Bando di Ateneo per il sostegno alla partecipazione ai bandi di ricerca competitiva per il triennio 2016–2018”.

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Authors and Affiliations

  1. University of Sannio, Benevento, Italy

    Emilio Bellini

  2. Polytechnic University of Milan, Milan, Italy

    Emilio Bellini

  3. European Commission, DG Employment, Social Affairs and Inclusion, Brussels, Belgium

    Giuseppe Piroli

  4. Parthenope University of Naples, Naples, Italy

    Luca Pennacchio

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  1. Emilio Bellini
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Correspondence to Luca Pennacchio.

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Appendix

Appendix

Measurement items for latent variables using a five-point Likert-type scale

Latent variable

Items (items code)

Collaborative experience

Contracts (CO1)

Joint research (CO2)

Patents (CO3)

Copyright licenses (CO4)

Direct recruitment (CO5)

Temporary exchange of staff (CO6)

Advice (CO7)

Informal information exchange (CO8)

Theses and dissertations (CO9)

Research publications and reports (CO10)

Collaborative selection (KH1)*

We use specific tools to monitor universities’ areas of expertise (KH1a)

We have internal staff dedicated to the selection of universities for collaboration (KH1b)

We have a defined process to select universities to work with (KH1c)

Collaborative management (KH2)*

There is a formal document for the specifications of the project in collaboration with university (KH2a)

To develop innovations in collaboration with a university, we activate a formal collaboration process (KH2b)

We identify a project manager for any collaboration with a university (KH2c)

We define a formal plan of activities (e.g. timing diagram or budget of activities) for collaboration with a university (KH2d)

We check the progress of the plan during the joint project and regularly update the estimates of time/cost and/or specifications (KH2e)

We identify resources for the joint project and formally allocate a portion of their time (KH2f)

We use software for project management (e.g. Microsoft Project) in collaboration with the university (KH2g)

We define plans for long-term collaboration with universities (KH2h)

fTrust

We feel that we need to protect ourselves from possible opportunistic behaviours (TR1)

We specify rigid objectives and constraints for university activities (TR2)

Tangible benefits

We have used the results of joint projects with universities to develop new products (TB1)

We have used the results of joint projects with universities to improve internal production processes (TB2)

We have used the results of joint projects with universities to develop our business (TB3)

Intangible benefits

Do you feel satisfied with your collaboration with universities? (ITB1)

We have acquired new knowledge/skills from the relationships with universities (ITB2)

  1. *Collaborative selection and Collaborative management are used to build the construct Collaborative know-how

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Bellini, E., Piroli, G. & Pennacchio, L. Collaborative know-how and trust in university–industry collaborations: empirical evidence from ICT firms. J Technol Transf 44, 1939–1963 (2019). https://doi.org/10.1007/s10961-018-9655-7

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