University of Hertfordshire

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By the same authors

Critical factors for insolvency prediction: towards a theoretical model for the construction industry

Research output: Contribution to journalArticlepeer-review

  • Hafiz A. Alaka
  • Lukumon O. Oyedele
  • Hakeem A. Owolabi
  • Azeez A. Oyedele
  • Olugbenga O. Akinade
  • Muhammad Bilal
  • Saheed O. Ajayi
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Original languageEnglish
Pages (from-to)25-49
Number of pages25
JournalInternational Journal of Construction Management
Volume17
Issue1
Early online date10 May 2016
DOIs
Publication statusPublished - 2 Jan 2017

Abstract

Many construction industry insolvency prediction model (CI-IPM) studies have arbitrarily employed or simply adopted from previous studies different insolvency factors, without justification, leading to poorly performing CI-IPMs. This is due to the absence of a framework for selection of relevant factors. To identify the most important insolvency factors for a high-performance CI-IPM, this study used three approaches. Firstly, systematic review was used to identify all existing factors. Secondly, frequency of factor use and accuracy of models in the reviewed studies were analysed to establish the important factors. Finally, using a questionnaire survey of CI professionals, the importance levels of factors were validated using the Cronbach's alpha reliability coefficient and significant index ranking. The findings show that the important quantitative factors are profitability, liquidity, leverage, management efficiency and cash flow. While important qualitative factors are management/owner characteristics, internal strategy, management decision making, macroeconomic firm characteristics and sustainability. These factors, which align with existing insolvency-related theories, including Porter's five competitive forces and Mintzberg's 5Ps (plan, ploy, pattern, position and perspective) of strategy, were used to develop a theoretical framework. This study contributes to the debate on the need to amalgamate qualitative and quantitative factors to develop a valid CI-IPM.

ID: 17743543