Introducing the T-shaped model of cluster competence – an integrative framework and first empirical evidence from the German craftsmen sector
Matthias Tomenendal,
Christian Raffer,
Stephanie Stockklauser and
Johannes Kirch
Industry and Innovation, 2018, vol. 25, issue 2, 144-166
Abstract:
Although cluster research has been an enormously vivid field in the past years, the idea that firms’ individual competences influence the knowledge gain from collocation is still under-researched. In this paper, we aim to fill this gap in two steps. First, we derive four firm-specific cluster competences from existing literature as key components of a T-shaped model which is based on the resource and the relational view on knowledge spillovers. Second, we evaluate the explanatory power of our model in terms of firm success by conducting empirical testing on the basis of several logistic regressions in the form of proportional odds models. For this purpose, we use a hitherto unexploited firm-level data set, gained from a survey conducted among small and medium-sized craft businesses in Germany. The results provide evidence that idiosyncratic cluster competences on the firm level are supportive of firm success in business clusters.
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/13662716.2017.1289837 (text/html)
Access to full text is restricted to subscribers.
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:taf:indinn:v:25:y:2018:i:2:p:144-166
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CIAI20
DOI: 10.1080/13662716.2017.1289837
Access Statistics for this article
Industry and Innovation is currently edited by Associate Professor Mark Lorenzen
More articles in Industry and Innovation from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().