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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.

The purpose of this paper is to explore the non-financial causes of organizational success or failure, provide a better understanding of the symptoms of financial distress and improve the predictive capacity of financial failure models.

The paper utilizes exploratory case studies in investigating the relationship of non-financial factors to organizational success or failure across a sample of sector-specific Australian firms listed on the Australian Stock Exchange. A two-tailed study was designed, in which seven cases from both extremes were chosen from three Australian business sectors: finance, property and manufacturing.

Non-financial factors associated with the organizations studied impacted their success or failure. These factors included management skill, experience and involvement in organizational strategy, feedback and resultant activity, together with board of director composition. The identification of financial and non-financial factors and sound internal processes could be utilized for the development of an early warning predictor of organizational success or failure.

The use of this method is very time-consuming but is highly valuable in case study research, providing a more in-depth understanding of how non-financial factors impact organizational success or failure.

The research will provide a better understanding of the symptoms of financial distress and improve the predictive capacity of financial failure models. The improvement in prediction of organizational failure will reduce the costs of failure to all areas affected, from the large corporation to the small business. The inter-connectivity of all businesses to each other often results in a knock-on effect of failure with the cost being borne by all members of the community in some manner. The level of social impact and cost of failure can only be seen by the enormous costs of the Global Financial Crisis failures.

This paper contributes to the literature on effective qualitative research and explores important areas of consideration for those conducting qualitative multiple-case studies. It is intended to be of use to researchers investigating the area of predictors of organizational failure or success.

The purpose of this paper is to investigate the relationship of financial ratios and non-financial factors of successful and failed corporations in the USA. Specifically, the authors provide evidence on whether financial ratios and non-financial factors can be jointly included as indicators to improve the predictive capacity of organisational success or failure in different countries and sectors.

The paper utilises a mixed method exploratory case study focussing on listed corporations in the US and Australian manufacturing, agriculture, finance and property sectors.

The financial ratio findings demonstrate that (with the exception of the failed Australian manufacturing sector) the integrated multi-measure (IMM) ratio approach consistently provides a higher classification rate for the failed and successful groups than those provided by an individual measure. In all cases the IMM method scored higher for US companies (with the exception of the failed Australian property sector). The findings also show that irrespective of the country location or sector, non-financial factors such as board composition and managements’ involvement in organisational strategy impact on a corporation’s success or failure.

The findings reveal that non-financial factors occur prior to financial ratios when attempting to predict organisational success or failure and the IMM approach enables a more thorough examination of the predictive ability of financial ratios for US and Australian organisations. This intuitively indicates that when combined with financial ratios, non-financial factors may be a useful predictor of corporate success or failure across countries and sectors.

Sound internal processes and the identification of both financial ratios and non-financial factors can be utilised to improve the reliability of financial failure models, enable corrective and preventative steps to be implemented by management and potentially reduce the costs of failure for US and Australian organisations.

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 explore the relationship of non-financial and financial factors to firm survival, provide evidence of factors related to financial success and distress for prominent Australian agricultural firms, and improve the predictive capacity of financial failure models.

The paper utilizes mixed method exploratory case studies across four Australian agricultural firms (two successful and two failed) listed on the Australian Securities Exchange.

The authors found that the use of an Integrated Multi-Measured approach provided a higher classification rate for the failed group than those provided by an individual measure. We also discovered that non-financial factors associated with the agricultural organizations studied impacted their success or failure. These factors included managements’ involvement in organizational strategy and the composition of the board of directors. It was also apparent that management decision-making approaches may become frozen, or at best restricted, in the face of impending failure, dependent upon the stress level within the organization and the management skill base.

The cases studied indicated that non-financial factors of failure occurred prior to any financial predictors, intuitively indicating a relationship between non-financial and financial factors in Australian agricultural firms.

The identification of financial and non-financial factors and sound internal processes which distinguish successful and failing firms can be utilized for the development of an early warning predictor of organizational success or failure.

Introduction: Financial failure is a concept that may arise from many internal and external factors such as operational, financial, and economic items and may incur serious losses. Over-indebtedness arising from managerial misjudgments may cause high financial distress, insufficiency, and bankruptcy. In this regard, determination of effects of capital structure decisions on financial failure risk is crucial.

Aim: The main purpose of this study is to explore the relationship between capital structure decisions and financial failure risk. For this purpose, data from Borsa İstanbul (BIST) for listed food and beverage companies for the period from 2004 to 2019 is used. Another purpose of this study is to compare the financial failure models considering capital structure theories.

Method: In the study, capital structure decisions are associated with five different financial ratios; while the financial failure risk is proxied by financial failure scores of Altman (1968), Springate (1978), Ohlson (1980), Taffler (1983), and Zmijewski (1984). Therefore, five different panel data models are used for testing these hypotheses.

Findings: The results of panel data analysis reveal that capital structure decisions have statistically significant effects on financial failure risk for all models; however, those effects vary from one financial failure model to another. Also, the results show that in the models in which financial failure risk is proxied by the Altman (1968) and Taffler (1983) scores, the aggressive financial policies increase the financial failure risk. However, regarding the models in which financial failure risk is proxied by the Springate (1978), Ohlson (1980), and Zmijewski (1984) scores, aggressive financial policies decrease the financial failure risk.

Originality of the Study: To the best of our knowledge, this chapter is original and important in terms of revealing the effects of capital structure decisions on the financial failure risk and comparing the financial failure models.

Implications: The results revealed that the risk of financial failure models represented by Altman (1968) and Taffler (1983) scores are found to be statistically stronger and more successful in meeting theoretical expectations compared to other models. Therefore, it would be more appropriate to refer Altman’s (1968) and Taffler’s (1983) financial failure models in financial failure risk measurements.

Given the fierce competition in financial services, service failure management and trust restoration tactics are becoming strategic priorities. Studies investigating trust restoration have increased over the years due to the significance of trust in services and the frequency of trust violations. Drawing on the sense-making and defensive approaches of attribution theory, this study aims to explore the effectiveness of various trust recovery tactics (e.g. apology, explanation, and investigation) in financial services considering the prevalence of service failure severity.

Based on a scenario-based survey, this study gathered data from 402 consumers of different banks in Pakistan. The study analyzed the data using ordinary least square regressions and structural equation modeling.

The study indicated that explanation is more effective in repairing character-competence and commitment-based trust, while investigation remained highly effective for inducing congruence-based trust. Interestingly, an apology was more effective for communication-based trust repairing, while context-based trust recovery was unaffected against all recovery tactics. Despite the prevalence of severe service failure, recovery actions proved fully effective for character-competence and commitment-based trust while partially effective for congruence-based trust recovery. This study also found that severe service failure undermines the effectiveness of recovery actions in repairing communication and context-based trust.

The study extends the literature on trust recovery by integrating sense-making and defensive attribution theory. The sense-making approach contributes to the existing knowledge on trust recovery by elucidating how consumers and service providers develop a shared understanding to facilitate the recovery mechanism of multidimensional trust in financial services.

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.

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.

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.