A comprehensive performance measurement framework for business incubation centres: Empirical evidence in an Irish context. (9th March 2022)

• Record Type: Journal Article
• Authors: Azadnia, Amir Hossein; Stephens, Simon; Ghadimi, Pezhman; Onofrei, George
• Is Part Of: Business strategy and the environment. Volume 31:Number 5(2022)
• Page Start: 2437
• Page End: 2455
• Publication Date: 2022-03-09
• Subjects: business incubation -- Delphi panel -- fuzzy inference system -- performance measurement; Industrial management -- Environmental aspects -- Periodicals; Industries -- Environmental aspects -- Periodicals; 658.4083
• DOI: 10.1002/bse.3036
• Languages: English
• ISSNs: 0964-4733

Abstract

Abstract: During the last 20 years, there has been an increased interest among academics and practitioners in the area of business incubation. However, limited attention has been devoted to developing a comprehensive framework that can measure business incubators' performances. Therefore, there is an urgent need for an appropriate, robust and useable performance framework. In this paper, we present a comprehensive framework using a weighted fuzzy inference system for business incubation centres' (BIC) performance measurement. The proposed approach utilises the input of a Delphi panel to identify criteria and subcriteria. Then a fuzzy analytic hierarchy process is used to weigh the criteria. Subsequently, a weighted fuzzy inference system is developed and applied to provide results based on the identified criteria and subcriteria. To show the proficiency and applicability of the proposed framework, a case study of Irish BICs is applied. The comprehensive performance measurement framework presented in this paper provides for accurate evaluation and monitoring across six criteria. The six criteria are facilities and infrastructure; clients; networking and marketing; products and services; finance; and human capital. The results show that although most of the BICs focus on facilities and infrastructure, there is a need to concentrate more on factors such as networking, marketing and finance. The detailed approach presented in this paper can be used by academics and practitioners who wish to apply fuzzy inference systems to performance measurement. In addition, the results from our pilot can be used by BIC managers and policymakers to improve performance.