CAN NEURAL NETWORKS PREDICT BUSINESS FAILURE? EVIDENCE FROM SMALL HIGH TECH FIRMS IN THE UK

Densil A. Williams ()
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Densil A. Williams: Mona School of Business and Management, University of the West Indies, Mona, Jamaica, West Indies

Journal of Developmental Entrepreneurship (JDE), 2016, vol. 21, issue 01, 1-17

Abstract: This paper, using a flexible and more robust analytical tool, neural networks models, analyzes the most important factors in determining business failure among small, high-technology firms. Using the resource-based view and organizational ecology as the theoretical lenses through which to view the problem, the paper modeled a number of variables that stood as proxies for resources. The results suggest profit, in the form of retained earnings, is the most significant factor that determines failure among these firms. Other factors of importance are governance structure, location and firm size. Importantly, the issue of governance structure as a resource has received very little attention in existing works on business failure, and as such, this paper makes a contribution to the literature by adding governance as an important resource.

Keywords: Failure; small and medium enterprise (SME); high-technology; neural networks (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S1084946716500059

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