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Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends

Author

Listed:
  • Jun Wang

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Mao Li

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China
    PetroChina Huabei Oilfield Company, Cangzhou 062552, China)

  • Martin Skitmore

    (Faculty of Society and Design, Bond University, Robina, QLD 4226, Australia)

  • Jianli Chen

    (Department of Civil Engineering, University of Utah, Salt Lake City, UT 84112, USA)

Abstract

The construction industry is infamous for its high insolvent failure rate because construction projects require complex processes, heavy investment, and long durations. However, there is a lack of a comprehensive framework and a requirement for such a framework in predicting the financial distress of construction firms. This paper reviews relevant literature to summarize the existing knowledge, identify current problems, and point out future research directions needed in this area using a scientometric analysis approach. Based on a total of 93 journal articles relating to predicting construction company failure extracted from multiple databases, this study conducts a holistic review in terms of chronological trends, journal sources, active researchers, frequent keywords, and most cited documents. Qualitative analysis is also provided to explore the data collection and processing procedures, model selection and development process, and detailed performance evaluation metrics. Four research gaps and future directions for predicting construction company failure are presented: selecting a broader data sample, incorporating more heterogeneous variables, balancing model predictability and interpretability, and quantifying the causality and intercorrelation of variables. This study provides a big picture of existing research on predicting construction company insolvent failure and presents outcomes that can help researchers to comprehend relevant literature, directing research policy-makers and editorial boards to adopt the promising themes for further research and development.

Suggested Citation

  • Jun Wang & Mao Li & Martin Skitmore & Jianli Chen, 2024. "Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2290-:d:1354263
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    References listed on IDEAS

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    1. Rafał Balina & Marta Idasz-Balina & Noer Azam Achsani, 2021. "Predicting Insolvency of the Construction Companies in the Creditworthiness Assessment Process—Empirical Evidence from Poland," JRFM, MDPI, vol. 14(10), pages 1-16, September.
    2. Scott, James, 1981. "The probability of bankruptcy: A comparison of empirical predictions and theoretical models," Journal of Banking & Finance, Elsevier, vol. 5(3), pages 317-344, September.
    3. Becchetti, Leonardo & Sierra, Jaime, 2003. "Bankruptcy risk and productive efficiency in manufacturing firms," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2099-2120, November.
    4. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    5. Gordon D. Murray, 1977. "A Cautionary Note on Selection of Variables in Discriminant Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 26(3), pages 246-250, November.
    6. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    7. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    8. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    9. Micha, Bernard, 1984. "Analysis of business failures in France," Journal of Banking & Finance, Elsevier, vol. 8(2), pages 281-291, June.
    10. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    11. Altman, Edward I. & Saunders, Anthony, 1997. "Credit risk measurement: Developments over the last 20 years," Journal of Banking & Finance, Elsevier, vol. 21(11-12), pages 1721-1742, December.
    12. Graham Hall, 1994. "Factors Distinguishing Survivors From Failures Amongst Small Firms In The Uk Construction Sector," Journal of Management Studies, Wiley Blackwell, vol. 31(5), pages 737-760, September.
    13. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    14. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    15. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    16. Sueyoshi, Toshiyuki & Goto, Mika, 2009. "DEA-DA for bankruptcy-based performance assessment: Misclassification analysis of Japanese construction industry," European Journal of Operational Research, Elsevier, vol. 199(2), pages 576-594, December.
    17. Obaid Saad Al-Sobiei & David Arditi & Gul Polat, 2005. "Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques," Construction Management and Economics, Taylor & Francis Journals, vol. 23(4), pages 423-430.
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    Cited by:

    1. Zhongshuai Wang & Baocheng Bian & Jun Wang, 2025. "Influence of Green Credit Policy on Corporate Risk-Taking: The Mediating Effect of Debt Maturity Mismatch and the Moderating Effect of Executive Compensation," Sustainability, MDPI, vol. 17(7), pages 1-30, March.

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