Predicting Construction Company Insolvent Failure: A Scientometric Analysis and Qualitative Review of Research Trends
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1. Introduction
In 2020, the global construction industry reached a staggering market size of USD 10.7 trillion, which is expected to exceed USD 15.2 trillion by 2030 [1]. Compared to other industries, construction is particularly vulnerable to financial crises [2] and sensitive to economic cycles [3] due to its various specificities, including the uniqueness and long duration of construction projects, the complexity of the construction process, the involvement of multiple specific teams, and uncertainties surrounding construction activities [4]. Unsurprisingly, therefore, despite its large size, growing momentum, and notable economic contribution, the industry is infamous for its high business insolvent failure rate [5], making the accurate prediction of company failure important for both the companies themselves and such other stakeholders as investors, creditors, shareholders, and employees [6,7,8,9].
Business failure prediction, also known as bankruptcy prediction or default prediction, requires the quantitative analysis of a corporate enterprise to forecast the likelihood of its default, and much research has been conducted in different regions using various predictive approaches to achieve this goal. Earlier research, for instance, primarily focused on adopting statistical techniques to build linear models such as the multivariate discriminant analysis [10], multiple regression analysis [11], and logistic regression models [12]. Thanks to the recent development of artificial intelligence, emerging techniques such as machine learning [3], deep learning [13], and ensemble learning [2] have been adopted for prediction purposes and have enabled the use of more variables, larger sample sizes, and higher accuracy.
However, despite some fruitful academic results, limited reviews have been conducted that are specifically concerned with the construction industry. Of these, Edum-Fotwe et al. summarized how to utilize weighted financial ratios to construct a single index (known as a Z-score) that classified construction companies as failing, at risk, or non-failing [14]. Wong and Ng integrated the common causes of construction company failure and listed pertinent prediction techniques as ratio analysis, multiple discriminant analysis, conditional probability models, and subjective assessment [15]. Alaka et al. investigated 70 relevant journal articles and doctoral theses, summarizing their country, variables selected, techniques used, sample size, performance, etc., but incorporated articles from other industries like banking, IT, and manufacturing, and did not exclusively focus on the construction industry [16]. Alaka et al. summarized the critical factors for insolvency prediction regarding factor frequency and model accuracy and conducted a questionnaire survey of construction industry professionals to collect their feedback on those factors [17]. More recently, Assaad and El-adaway reviewed relevant research over the past 30 years to identify the failure factors impacting the business operations of construction firms using both simplified analysis and social network analysis [18].
Nonetheless, although these authors made a valuable contribution to the current body of knowledge, they primarily focused on evaluating and selecting variables involved and failed to consider other important technical aspects such as data processing methods, model development process, and performance evaluation criteria. In addition, they are essentially manual and ad hoc qualitative reviews and do not adopt any scientometric approach to conduct a systematic review. At the same time, recent research has found that humans are better at discovering and comprehending domain knowledge presented in graphical forms [19,20]. To update the research trends, this paper adopts the science mapping approach, which reveals the inherent relationships among existing research work using graphical representation and conducts a quantitative review of construction company failure prediction studies to complement existing qualitative work. The specific research objectives of this review include: (1) applying a science mapping approach to analyze the journals, keywords, researchers, and articles in the domain of predicting construction company insolvent failure; (2) analyzing the existing key research works related to predicting construction company insolvent failure; (3) revealing the recent research gaps and pointing out some possible future research directions of predicting construction company insolvent failure.
The paper is structured as follows. Section 2 lists the research methods used in the study with all the inclusion and exclusion criteria. Section 3 contains the results of the scientometric analysis. Section 4 includes further qualitative discussions by reviewing data collection and processing procedures, model selection and development process, and detailed performance evaluation metrics and identifying research gaps and future directions. Finally, Section 5 concludes the study.
5. Conclusions
This study reviewed 93 key journal articles relating to predicting construction company insolvent failure using both scientometric analysis and qualitative discussion. The results of the scientometric analysis reveal the proliferation of relevant research over the last 12 years or so. The Journal of Construction Engineering and Management, Construction Management and Economics, Engineering Construction and Architectural Management, and Expert Systems with Applications have made the most contributions in terms of article numbers. H.P. Tserng, D. Arditi, and P.C. Chen are the most influential researchers who have both produced the most articles and received the most citations. A chronological keywords analysis found that the research focus—business insolvent failure prediction—can also be considered as “bankruptcy prediction”, “financial distress prediction”, or “insolvency prediction”, but the “prediction models” have gradually shifted from “statistical techniques” such as “z-score”, “discriminant analysis model”, and “regression analysis” to more advanced “machine learning” techniques such as “neural networks” and “support vector machine”. Finally, the articles receiving the most citations were identified, visualized, and discussed.
A detailed qualitative discussion was conducted to set out data collection and processing procedures, compare different predictive models, summarize performance evaluation metrics, point out research gaps, and suggest future research directions. The extant research focuses heavily on studying publicly traded construction companies in developed regions. With the help of big data analytical techniques, the average sample size has significantly increased from 142 for articles on and before 2010 to 4005 for articles after 2010. It is also found that financial ratio variables are commonly utilized for model development, followed by variables representing company characteristics and economic conditions. Due to the imbalanced nature of construction company insolvent failure datasets, under-sampling and oversampling techniques need to be utilized to yield more balanced datasets. Artificial intelligence models have recently gained more popularity than statistical models due to their generally superior prediction ability. However, they are also criticized because of their “black boxes” nature and, thus, lack of interpretability. Different model performance evaluation metrics are also discussed in terms of usage and limitations, and it is suggested that multiple metrics should be used to evaluate a given (or novel) predictive model, since different metrics show different tradeoffs. Finally, several research gaps and corresponding future directions are identified in areas: selecting a broader data sample, incorporating more heterogeneous variables, balancing model predictability and interpretability, and quantifying the causality and intercorrelation of variables.
This review-based study combines a novel scientometric analysis approach and traditional qualitative discussion to provide a holistic review of research related to predicting construction company insolvent failure. It provides an overview of relevant research works from both visual and textual perspectives and outlines research gaps and directions for future studies. One limitation of this study is that it focused on only English language journal articles from Web of Science, Scopus and Engineering Village databases when selecting the literature sample. Future studies will require extending the inclusion criteria by considering other publication outlets (e.g., conference articles, books) and articles published in other languages and other sorts of databases (e.g., PubMed, Google Scholar).
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