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The purpose of this study is to analyze the business-failure-process risk from two perspectives. First, a simplified model of the loss-generation process in a failing firm is developed to show that the linear system embedded in accounting makes financial ratios to depend linearly on each other. Second, a simplified model of the development of the risk during the failure process is developed to introduce a new concept of failure-process-risk line (FPRL) to assess the systematic failure risk of a firm. Empirical evidence from Finnish firms is used to test two hypotheses.

This study makes use of simple mathematical modeling to depict the loss-generation process and the development of failure risk during the failure process. Hypotheses are extracted from the mathematical results for empirical testing. Time-series data originally from 13,082 non-failing and 515 failing Finnish are used to test the hypotheses. Analysis of variance F statistics and Mann–Whitney U test are used in testing of the hypotheses.

The findings show that the linear time-series correlations are generally higher in failing than in non-failing firms because of the loss-generation process. The FPRL depicted efficiently the systematic failure-process risk through the beta coefficient. Beta coefficient efficiently discriminated between failing and non-failing firms. The difference between the last-period risk estimate and FPRL was largely determined by the approximated growth rate of the periodic failure risk.

The loss-generation process is based on a simple cash-based approach ignoring the growth of the firm. In future research, the model could be generalized to a growing firm in an accrual-based framework. The failure-process risk is assumed to grow at a constant rate. In further studies, more general models could be applied. Empirical analyses are based on simple statistical methods and tests. More advanced methods could be used to analyze the data.

This study shows that failure process makes the time-series correlation between financial ratios to increase making their signals of failure consistent and allowing the use of static classification models to assess failure risk. The beta coefficient is a useful tool to reflect systematic failure-process risk. In addition, it can be used in practice to warn a firm about ongoing failure process.

To the best of the author’s knowledge, this is the first study analyzing systematically business-failure-process risk. It is first in introducing a mathematical loss-generation process and the FPRL based on the beta coefficient assessing the systematic failure risk.

This paper aims to detect failure processes of French exporting firms and study their contingency with export processes.

The sample consisted of 131 bankrupted exporting firms from Bureau van Dijk’s Amadeus database. Factor and cluster analyses of six financial variables from Laitinen’s (1991) model were used to detect failure processes. Export processes were detected with cluster analysis of export share in total turnover. Contingency between failure and export processes was studied with a statistical test.

Three different failure processes existed for exporting firms. Two of these processes, which accounted for 79 per cent of firms, were classified as gradual failure: a step-by-step worsening of financial performance before the bankruptcy was declared. One was a symbiotic process reflecting varying pre-bankruptcy behaviours of different financial variables. Two different types of exporters existed. Most firms (77 per cent) were occasional exporters, while 23 per cent were constantly and more strongly involved in international markets before their bankruptcy was declared. There was no contingency between failure and export processes.

This study is the first one to detect failure processes specifically for exporting firms based on financial variables. In line with previous literature about non-exporting firms, gradual failure processes were most characteristic to exporting firms. The study shows that different types of exporters were not characterized by any unique behaviour of financial variables before their bankruptcy was declared.

This paper has two purposes. First, it evaluates the extent to which different failure processes are present in failed UK SMEs, by considering non-financial metrics including director characteristics, in addition to the financial ones. Second, it analyses the determinants of the transition to failure in relation to the different failure processes that have been identified.

The study is based on a sample of failed UK SMEs. The data covers financial ratios, board characteristics, the macroeconomic environment, sectoral details and regional information. First, failure processes are identified using a combination of factor analysis and cluster analysis. Second, the determinants of firms' transition to failure for the whole sample and in the individual failure clusters are analysed using panel data analysis.

Four different firm failure processes were identified. Director characteristics differ between firm failure processes. We find evidence that director characteristics including director age and board gender structure, affect the transition to failure of UK SMEs. We also find that different factors affect the different failure processes.

The paper is the first to analyse the reasons for failure of UK SMEs in the firm failure process context by considering non-financial metrics such as the characteristics of the firms' directors. In addition the paper also identifies a number of different determinants that affect the various failure processes. This finding is important because it suggests that policies designed to reduce the incidence of firm failure should take account of the different failure processes.

The purpose of this paper is to find out which different failure processes exist among the young manufacturing micro firms, and whether the representation of those processes differs first, in European countries, and second, among exporting and non-exporting firms.

The study is based on financial data of 1,216 manufacturing micro firms from European countries. Failure processes have been detected with a two stage-method: by extracting latent dimensions from financial variables with factor analysis, and then, by clustering the established factor scores.

With firms’ age, the number of different failure processes reduces from four to two. Strong evidence was found about the dominance of different failure processes in different countries for most firm age groups. Failure processes are not strongly associated with (non-)exporting.

This paper is the first one determining young manufacturing micro firms’ failure processes and comparing the representation of those processes in different firm subsets, either based on their country of origin or (non-)exporting behavior. Moreover, previous studies have not encompassed specific sectors, young or very small firms.

This study aims to find out whether firm failure processes are age- and size-dependent.

The sample consists of 333 bankrupted Estonian firms. Failure processes are detected with consecutive factor and cluster analyses of six financial variables calculated for three pre-failure years. Multinomial logistic regression is applied to study the interconnections between failure processes (dependent variable) and firm size and age (independent variables). In addition, the contingency between detected failure processes and failure causes obtained from court judgements are studied.

Three failure processes are detected, of which the predominant one accounting for 55 per cent of cases is a gradual failure process, indicating a step-by-step decline in the values of financial variables. The two minority processes are mixed, meaning that some financial variables are poor for many years before the bankruptcy and others decrease only shortly before bankruptcy declaration. With an increase in firm size, the gradual failure process becomes more common, but in turn, the presence of the gradual failure process is not age-dependent. Failure causes detected by trustees are not associated with failure processes.

This paper is the first one to specifically outline the age and size dependencies of firm failure processes. In addition, the interconnection of failure causes and firm failure processes detected with financial variables are rarely studied topics.

In this study the factors behind the decision‐makers’ erroneous judgements regarding failure prediction (classification of firms as bankrupt and non‐bankrupt) are analysed. The purpose is to find out the factors causing incorrect responses, i.e. the cases in which the decision‐maker is for some reason incapable of using the given information to arrive at the correct classification. The following five possible sources of disturbance in this decision‐making were hypothesized: firm‐specific factors, data, decision‐maker‐specific factors, external factors, and failure process. In further analysis these factors were empirically operationalized and their significance was tested applying logistic (logit) analysis separately for the Type I and Type II classification errors identified in an HIP study. The results indicated that the effect of all of the five hypothesized factors on misclassifications is statistically significant. The inconsistency of the cues (firm‐specific factors) may be the main factor causing errors in evaluation. Moreover, the failure process is another important factor (Type I error). Thus, human bankruptcy prediction can be improved mainly by checking the consistency of financial statements (that they give a true view of the firm’s economic status) and by paying special attention to timely identification of the possible failure process. Future HIP studies on bankruptcy prediction and also other economic events should pay attention to control the kinds of sources of disturbance identified in this study, to maintain validity.

The purpose of this paper is to answer why some multinational enterprises (MNEs) fail within the international business (IB) domain.

Conceptually, the study takes an organismic approach to MNE failure. Methodologically, it adopts an elite interview approach derived from the Delphi technique. Respondents are 39 IB and strategic management academics.

The paper finds that MNE failure is rooted in strategic leadership and capabilities (i.e. internal deterioration of organizational resources and strategies) and institutional pressures and differences, and these factors lead to deterioration of institutional legitimacy for an MNE.

The paper conducts a review of the firm failure and foreign divestment literature and undertakes an organismic approach to the analysis of MNE failure in the IB context. The paper provides useful insights on developing and implementing both market and non-market strategies for overcoming MNE internationalization failure.

Conflicting results have emerged from several past studies as to whether bankruptcy prediction models are able to forecast corporate failure more accurately than auditors’ going‐concern opinions. Nevertheless, the last decade has seen improved modelling of the path‐to‐failure of financially distressed firms over earlier static models of bankruptcy. In the light of the current crisis facing the auditing profession, this study evaluates the efficacy of auditors’ going‐concern opinions in comparison to two bankruptcy prediction models. Bankrupt firms in the U.S. service and trade industry sectors were used to compare model predictions against the auditors’ going‐concern opinion for two years prior to firm failure. The two models are the well‐known Altman (1968) Multiple Discriminant Analysis (MDA) model that includes only financial ratio variables in its formulation and the newer, temporal logit model of Cybinski (2000, 2003) that includes explicit factors of the business cycles in addition to variables internal to the firm. The results show overall better bankruptcy classification rates for the temporal model than for the Altman model or audit opinion.

Corporate failure remains a critical financial concern, with implications for both firms and financial institutions; this paper aims to review the literature that proposes corporate failure prediction models for the twenty-first century.

This paper gathers information from 106 published articles that contain corporate failure prediction models. The focus of the analysis is on the elements needed to design corporate failure prediction models (definition of failure, sample approach, prediction methods, variables and evaluation metrics and performance). The in-depth review creates a synthesis of current trends, from the view of those elements.

Both consensus and divergences emerge regarding the design of corporate failure prediction models. On the one hand, authors agree about the use of bankruptcy as a definition of failure and that at least two evaluation metrics are needed to examine model performance for each class, individually and in general. On the other hand, they disagree about data collection procedures. Although several explanatory variables have been considered, all of them serve as complements for the primarily used financial information. Finally, the selection of prediction methods depends entirely on the research objective. These discrepancies suggest fundamental advances in discovery and establish valuable ideas for further research.

This paper reveals some caveats and provides extensive, comprehensible guidelines for corporate failure prediction, which researchers can leverage as they continue to investigate this critical financial subject. It also suggests fruitful directions to develop further experiments.

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Why do firms fail? The reasons behind given for corporate failure vary widely. During the financial crisis, firms failed because of the financial crisis, or during the Coronavirus pandemic firms failed due to the Coronavirus pandemic. When there are global market problems, those problems tend to get the blame for most things, including firms going under. But other reasons are sometimes given as well – that a firm is carrying too much debt to be sustainable, or a hypercompetitive market, or there are unsurmountable problems in the supply chain. Very often, these reasons are not scrutinized very closely as everyone moves on to invest in another company or try and find work elsewhere.

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