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The use of choice-based, matched, and other stratified sample designs is common in auditing research. However, it is not widely appreciated that the data analysis for these studies has to take into account the non-random nature of sample selection in these designs. A choice-based, matched or otherwise stratified sample is a nonrandom sample that must be analyzed using conditional analysis techniques. We review five research streams in the auditing area. These streams include work on determinants of audit litigation, audit fees, auditor reporting in financially distressed firms, audit quality and auditor switches. Cram, Karan, and Stuart (CKS) (2009) demonstrated the accuracy of conditional analysis, compared to unconditional analysis, of nonrandom samples through the use of simulations, replications, and mathematical proofs. Papers since published have continued to rely upon questionable research, however, and it is hard for researchers to identify what is the reliability of a given work. We complement and extend CKS (2009) by identifying audit papers in selected research streams whose results will likely differ if the data gathered are analyzed using conditional analysis techniques. Thus research can be advanced either by replication and reanalysis, or by refocus of new research upon issues that should no longer be viewed as settled.

Uses qualitative response models of occupational choice to investigate differences in the occupational structures of minority workers relative to white men. Compares the accuracy of multinomial logit and multiple discriminant analyses in predicting occupational distributions. Further, investigates whether these models yield consistent estimates of the level of occupational segregation of minority workers. The results suggest that logit and discriminant analysis are equally accurate and stable methods for comparing occupational structures across groups of workers.

To identify predictors of corporate financial distress, using the discriminant and logit models, in an emerging market over a period of economic turbulence and to reveal the comparative predictive and classification accuracies of the models in this different environmental setting.

The research relies on a sample of 27 failed and 27 non‐failed manufacturing firms listed in the Istanbul Stock Exchange over the 1996‐2003 period, which includes a period of high economic growth (1996‐1999) followed by an economic crisis period (2000‐2002). The two well‐known methods, discriminant analysis and logit, are compared on the basis of a better overall fit and a higher percentage of correct classification under changing economic conditions. Furthermore, this research attempts to reveal the changes, if any, in the bankruptcy predictors, from those found in the earlier studies that rested on the data from the developed markets.

The logistic regression model is found to have higher classification power and predictive accuracy, over the four years prior to bankruptcy, than the discriminant model. In this research, the discriminant and logit models identify the same number of significant predictors out of the total variables analyzed, and six of these are common in both. EBITDA/total assets is the most important predictor of financial distress in both models. The logit model identifies operating profit margin and the proportion of trade credit within total claims ratios as the second and third most important predictors, respectively.

This paper reveals the accuracy with which the discriminant and logit models work in an emerging market over a period when firms face high uncertainty and turbulence. This study may be extended to other emerging markets to eliminate the limitation of the small sample size in this study and to further validate the use of these models in the developing countries. This can serve to make the methods important decision tools for managers and investors in these volatile markets.

Previous studies investigate factors affecting project outcomes. Yet, it has not been fully explored regarding which factors differentiate healthy projects from distressed projects in the early stage of the project delivery process. The purpose of this study is to investigate the links between project-planning factors and project outcomes in the closing phase.

The authors use a longitudinal survey method to examine the predictability of project-planning factors. Subsequently, the authos employ confirmatory factor analysis and hierarchical logit regression to develop project-distress classification models.

Analysis of 90 capital projects shows that performance variation in the project planning phase explains a substantial portion of project distress at completion. Subsequent univariate logit analysis shows that S5 (quality of scope control system) and Tn1 (new practices and technologies) variables have the strongest predictive abilities. Hierarchical logit analysis further shows that a combination of 15 metrics in the project-distress measurement model produces strong and stable predictive power.

This study assesses how well performance variation in the project-planning phase predicts project distress before construction phase. It does not assume the reported results apply to all types of projects. Nonetheless, future studies could generalize our findings by incorporating more types of projects.

This study takes a systematic approach, combining longitudinal survey, measurement theory and hierarchical logit analysis to identify distressed projects early, offering managers an opportunity to take early corrective actions. Practitioners may use this approach to investigate other types of projects and further refine the project-distress classification model into a project-specific model, thereby reflecting projects' unique characteristics.

The purpose of this study is to identify antecedent factors in addition to merit that contribute to the designation of first author on a publication. A second purpose is to provide knowledge of the significance and implications of being designated first author on a research article in the management discipline. A third purpose is to propose directions for further research.

The study consists of an empirical analysis of archival data gathered from 780 authors of 260 coauthored articles from top-tier journals and uses logit regression to analyze the data.

The empirical analysis shows that under certain conditions author need and author power are factors that combine with merit as antecedents to the designation of being the first author of an article.

To the best of the authors’ knowledge, this is the first empirical study that identified antecedent factors that contribute to first authorship beyond the prescribed factor of merit which professional norms in management assume is the one and only factor that contributes to being designated as first author.

El propósito de este estudio es identificar los factores que anteceden, además del mérito, a la designación del primer autor en una publicación. Un segundo objetivo es proporcionar conocimiento sobre la importancia y las implicaciones de ser designado primer autor en un artículo de investigación en la disciplina de gestión. El tercer propósito es proponer direcciones para futuras investigaciones.

El estudio consiste en un análisis empírico de los datos de archivo recopilados de 780 autores de 260 artículos de revistas de primer nivel y utiliza la regresión logit para analizar los datos.

El análisis empírico muestra que, bajo ciertas condiciones, la necesidad y el poder del autor son factores que se combinan con el mérito como antecedentes de la designación como primer autor de un artículo.

Hasta donde alcanza nuestro conocimiento, este es el primer estudio empírico que identifica los factores que anteceden a la primera autoría más allá del factor de mérito, el cual es según las normas profesionales el único factor que contribuye a ser designado como primer autor.

O objetivo deste estudo é identificar fatores antecedentes, além do mérito, que contribuem para a designação do primeiro autor em uma publicação. Um segundo objetivo é fornecer conhecimento da importância e das implicações de ser designado primeiro autor em um artigo de pesquisa na disciplina de gerenciamento. Um terceiro objetivo é propor orientações para futuras pesquisas.

O estudo consiste em uma análise empírica dos dados de arquivo coletados de 780 autores de 260 artigos em coautoria de periódicos de primeira linha e usa a regressão logit para analisar os dados.

A análise empírica mostra que, sob certas condições, a necessidade e o poder do autor são fatores que se combinam com o mérito como antecedentes à designação de ser o primeiro autor de um artigo.

Até onde sabemos, este é o primeiro estudo empírico que identifica os fatores que precedem a primeira autoria além do fator de mérito, que, segundo as normas profissionais, é o único fator que contribui para ser designado como primeiro autor.

The purpose of this paper is to discuss and assess the structural characteristics (conceptual utility) of the most popular classification and predictive techniques employed in customer relationship management and customer scoring and to evaluate their classification and predictive precision.

A sample of customers' credit rating and socio‐demographic profiles are employed to evaluate the analytic and classification properties of discriminant analysis, binary logistic regression, artificial neural networks, C5 algorithm, and regression trees employing Chi‐squared Automatic Interaction Detector (CHAID).

With regards to interpretability and the conceptual utility of the parameters generated by the five techniques, logistic regression provides easily interpretable parameters through its logit. The logits can be interpreted in the same way as regression slopes. In addition, the logits can be converted to odds providing a common sense evaluation of the relative importance of each independent variable. Finally, the technique provides robust statistical tests to evaluate the model parameters. Finally, both CHAID and the C5 algorithm provide visual tools (regression tree) and semantic rules (rule set for classification) to facilitate the interpretation of the model parameters. These can be highly desirable properties when the researcher attempts to explain the conceptual and operational foundations of the model.

Most treatments of complex classification procedures have been undertaken idiosyncratically, that is, evaluating only one technique. This paper evaluates and compares the conceptual utility and predictive precision of five different classification techniques on a moderate sample size and provides clear guidelines in technique selection when undertaking customer scoring and classification.

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.

The paper aims to identify “problematic” agricultural credit co‐operatives (CCAM) and to evaluate their risk of insolvency as a function of financial indicators, providing regulators and other stakeholders with a set of tools that would be predictive of future insolvency and perhaps bankruptcy.

Using a database of CCAM failures in the period between 1995 and 2009, statistical models of failure of CCAM, are estimated and compared, using logistic regression analysis and multiple discriminant analysis for assessing the potential failure of CCAM as a function of financial/economical indicators.

The paper identified the variables customer resources growth, transformation ratio, credit overdue, expenses ratio, structural costs, liquidity, indebtedness and financial margin as determinants of CCAM failure. It suggests that CCAM take measures geared to boosting business, to shoring up the financial margin and the deposit base, to bolstering the complementary margin and to improving the credit recovery processes. Additionally it is necessary to increase cost efficiency, rationalizing structures and procedures consistent with reducing operating costs without detriment to the quality of service provided.

This paper helps to understand why agricultural credit co‐operatives fail.

The purpose of this paper is to do a performance comparison of three different data mining techniques.

Logit model, decision tree and random forest are applied in this study on British, French, German, Italian, Portuguese and Spanish balance sheet data from 2006 to 2012, which covers 446,464 firms. Because of the strong imbalance with regard to the solvency status, classification trees and random forests are modified to adapt to this imbalance. All three model specifications are optimized extensively using resampling techniques, relying on the training sample only. Model performance is assessed, strictly, based on out-of-sample predictions.

Random forest is found to strongly outperform the classification tree and the logit model in almost all considered years and countries, according to the quality measure in this study.

Obtaining reliable estimates of default propensity scores is of immense importance for potential credit grantors, portfolio managers and regulatory authorities. As the overwhelming majority of firms are not listed on stock exchanges, annual balance sheets still provide the most important source of information. The obtained ranking of the three models according to their predictive performance is relatively robust, due to the consideration of several countries and a relatively long time period.