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Informal Training in Chinese Small- and Medium-sized Enterprises
Univertsity of Bradford.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Business Studies.
2018 (English)In: International Journal of Entrepreneurship and Small Business, ISSN 1476-1297, E-ISSN 1741-8054Article in journal (Refereed) In press
Abstract [en]

This paper contributes to the research on collaborative entrepreneurship by studying consumers perception and evaluation of inter-industry collaborations between perfumery and gastronomy for the sake of new product development. First, we review the literature and secondary documents to identify potential variables about consumers perceived industry-level fit between collaborating partners and consumer knowledge, which we assume to influence consumers evaluation of collaboration. Second, we interview professionals in the two industries to further refine these variables, operationalise them into measurable constructs, and add new moderating variables specific to the context. Third, we conduct an online survey of consumers, testing our hypotheses. Our research reveals that the presence of consumers perceived industry-level similarities suffices to leverage their possible doubts on industry-level dissimilarities when they evaluate the relevance of inter-industry collaboration to develop a new product. Consumers relative openness to inter-industry collaborations provides an encouraging signal for companies to push the frontier and seek inspiration from seemingly distant sectors.

Place, publisher, year, edition, pages
2018.
Keyword [en]
informal training; small and medium-sized enterprises (SMEs); HRM; China
National Category
Economics and Business
Identifiers
URN: urn:nbn:se:uu:diva-335477DOI: www.inderscience.com/info/ingeneral/forthcoming.php?jcode=ijesbOAI: oai:DiVA.org:uu-335477DiVA, id: diva2:1163042
Available from: 2017-12-05 Created: 2017-12-05 Last updated: 2017-12-05

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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
  • html
  • text
  • asciidoc
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