A nonlinear relationship between the team composition and performance in university spin-offs

Giulia Tagliazucchi, Gianluca Marchi and Bernardo Balboni

Technological Forecasting and Social Change, 2021, vol. 172, issue C

Abstract: University spin-offs are a peculiar type of venture that encapsulate a core technology or key scientific knowledge stemming from academic research activities and that are founded by university personnel, professors, or researchers. The high level of scientific knowledge of the team is a critical factor for the new academic venture, but previous research shows that the low cognitive variety of the team members, and in particular, the absence of market-based knowledge, may hinder subsequent performance rates. The aim of the paper is to understand how the degree of composition variety of the founding team affects the growth performance of the university spin-offs, given the founders’ roles in shaping the future development path of the new ventures and their origin within the scientific and academic domain. Based on a new hand-picked database of Italian university spin-offs, we evaluate the nonlinear relationship between founding teams with low, moderate, and high levels of composition variety and subsequent growth performance by studying also the role of external backers as moderating variables. Results show a higher performance with high or low levels of founding team variety and a lower performance with moderate levels of variety. In addition, venture capitalist funds play a positive moderating role.

Keywords: University spin-off; Team composition; Performance; Academic entrepreneurship (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162521004935
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:172:y:2021:i:c:s0040162521004935

DOI: 10.1016/j.techfore.2021.121061

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().