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Social scientists have recently turned their attention to the important consequences of industrial districts or so-called agglomeration economies on economic growth and firm performance. This paper explores an important but unanswered question involving agglomeration economies: does geographic location within an agglomeration affect firm performance? I assess this question by examining the effects of different geographic office locations (by zip code) on the failure rates of all corporate law firms located in Silicon Valley from 1969 to 1998. Empirical estimates reveal that Silicon Valley corporate law firms benefit from the increased volume of client referrals that comes from being near mutualistic firms that offer a different range of legal services, the lower labor costs and more specialized division of labor that come from being near a large joint supply of lawyers, and the increased business that comes from being near important clients (i.e. venture capital firms).

In addition, corporate law firms that locate in certain municipalities of Silicon Valley, including Palo Alto, San Jose, and Santa Clara, have significantly increased failure rates, even controlling for many firm-specific differences. Younger corporate law firms (under the age of 11 years) are helped disproportionately by being near important environmental resources and harmed disproportionately by being in certain perilous areas of Silicon Valley. All told, a law firm’s office location within Silicon Valley has significant consequences for its survival.

This paper examines the relationship between poison pills, real earnings management and initial public offering (IPO) failure.

The authors sampled 2,997 IPO firms that went public during 1993-2015.

The authors find that IPO firms manipulate earnings upward using real earnings management. The authors also find that IPO firms exhibiting a higher level of real earnings management have a higher probability of IPO failure. In addition, the authors find that weak shareholders' governance is positively associated with IPO failure.

These results suggest that poor governance structures in failed firms open the door to manipulating real activities and increasing operational risk.

The study findings are of most significant interest to potential investors and other stakeholders affiliated with a firm going public, an auditor, an underwriter, the lawyers who consult with the firm and employees or executives who might consider joining that firm.

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.

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.

Available information for evaluating the possibility of hospitality firm failure in emerging countries is often deficient. Oversampling can compensate for this but can also yield mixed samples, which limit prediction models’ effectiveness. This research aims to provide a feasible approach to handle possible mixed information caused by oversampling.

This paper uses mixed sample modelling (MSM) when evaluating the possibility of firm failure on enlarged hospitality firms. The mixed sample is filtered out with a mixed sample index through control of the noisy parameter and outliner parameter and meta-models are used to build MSM models for hospitality firm failure prediction, with performances compared to traditional models.

The proposed models are helpful in predicting hospitality firm failure in the mixed information situation caused by oversampling, whereas MSM significantly improves the performance of traditional models. Meanwhile, only partial mixed hospitality samples matter in predicting firm failure in both rich- and poor-information situations.

This research is helpful for managers, investors, employees and customers to reduce their hospitality-related risk in the emerging Chinese market. The two-dimensional sample collection strategies, three-step prediction process and five MSM modelling principles are helpful for practice of hospitality firm failure prediction.

This research provides a means of processing mixed hospitality firm samples through the early definition and proposal of MSM, which addresses the ranking information within samples in deficient information environments and improves forecasting accuracy of traditional models. Moreover, it provides empirical evidence for the validation of sample selection and sample pairing strategy in evaluating the possibility of hospitality firm failure.

Studies on organizational failure learning have focused on whether and how organizations learn from failures but have paid limited attention on the persistence of failure learning. This study centers on failure recidivism and answers why organizations would fall into repeated failures after learning from them.

Based on a sample of Chinese publicly listed firms that once recovered from special treatment status, the authors use event history technique and Cox proportional hazards regression model.

The authors find that reviviscent firms with higher interlock centrality are less likely to decline again, and underperforming partners can strengthen the role of interlock tie in failure recidivism. By contrast, politically connected reviviscent firms are more likely to decline again, and this effect attenuates for firms located in more market-oriented regions.

The authors’ contribution comes from the close integration of literature on failure learning and network embeddedness perspective to examine how social networks affect the learning process of failure recidivism.

The study provides important practical implications for organizations, especially those that once experienced failures or are experiencing failures.

Combining organizational learning theory and network embeddedness perspective, the study provides novel insights into answering how firms embedded in different types of social networks affect failure learning persistence differently.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.