Abstract
In an attempt to stem the tide of mass failure of construction businesses, or support financiers and clients in identifying healthy construction firms for loans and contracts respectively, many researchers have developed construction industry bankruptcy prediction models (CI-BPMs). The effectiveness of such CI-BPMs is partly dependent on the methodology used in building it. Despite the usual claim of high accuracy by developers/authors, none of the CI-BPMs developed has gained wide acceptance in the construction industry, leading to development of new ones in succession. This study hence reviews the methodological positions in CI-BPM studies using the complete available population. After a critical content analysis of the features of CI-BPM studies, they all appeared to have used the positivism paradigm with realist ontology, objective epistemology and deductive approach. Although the main aim of CIBPM studies to 'predict' failure, an action (i.e. prediction) which is well ingrained in the positivism paradigm, makes the generally adopted positivism paradigm appear very appropriate, the aggressive dynamism of the construction industry and the experts' criticism of the methodology clearly makes it inappropriate. This work proposes pragmatism, in the methodological pluralism form, as the best paradigm for CI-BPM research with realist ontology, combined subjective and objective epistemology, mixed-method research choice, case study, archival and survey strategies, and the deductive research approach. A complete research design framework for executing the proposed methodology is presented.
Original language | English |
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Pages | 1291-1300 |
Number of pages | 10 |
Publication status | Published - 2015 |
Event | 31st Annual ARCOM Conference - Lincoln University, Lincoln, United Kingdom Duration: 7 Sept 2015 → 9 Sept 2015 |
Conference
Conference | 31st Annual ARCOM Conference |
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Country/Territory | United Kingdom |
City | Lincoln |
Period | 7/09/15 → 9/09/15 |
Keywords
- construction business failure
- bankruptcy prediction models