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Describes the ability of modern computer‐driven multivariate statistical analysis to deal with complex data and the development of statistical models for predicting financial distress. Applies multivariate techniques to 1986‐1991 financial ratio data for Australian failed (29) and nonfailed (42) companies; and explains the techniques used (principal components analysis, factor analysis, discriminant analysis and cluster analysis) and the different types of information they can provide to help identify the distress levels of companies. Predicts that multivariate methods will change the way researchers think about problems and design their research. An unusually clear exposition of the application of multivariate methods.

Like all other natural fibers, the physical properties of cotton also vary owing to changes in the related genetic and environmental factors, which ultimately affect both the mechanics involved in yarn spinning and the quality of the yarn produced. However, information is lacking about the degree of influence that those properties impart on the spinnability of cotton fiber and the strength of the final yarn. This paper aims to discuss this issue.

This paper proposes the application of discriminant analysis as a multivariate regression tool to develop the causal relationships between six cotton fiber properties, i.e. fiber strength (FS), fiber fineness (FF), upper half mean length (UHML), uniformity index (UI), reflectance degree and yellowness and spinning consistency index (SCI) and yarn strength (YS) along with the determination of the respective contributive roles of those fiber properties on the considered dependent variables.

Based on the developed discriminant function, it can be revealed that FS, UI, FF and reflectance degree are responsible for higher YS. On the other hand, with increasing values of UHML and fiber yellowness, YS would tend to decrease. Similarly, SCI would increase with higher values of FS, UHML, UI and reflectance degree, and its value would decrease with increasing FF and yellowness.

The discriminant functions can effectively envisage the contributive role of each of the considered cotton fiber properties on SCI and YS. The discriminant analysis can also be adopted as an efficient tool for investigating the effects of various physical properties of other natural fibers on the corresponding yarn characteristics.

Today’s growing numbers of contractor selection methodologies reflect the increasing awareness of the construction industry for improving its procurement process and performance. This paper investigates contractor classification methods that link clients’ selection aspirations and contractor performance. Multivariate techniques were used to study the intrinsic link between clients’ selection preferences, i.e. project‐specific criteria (PSC) and their respective levels of importance assigned (LIA), during tender evaluation for modelling contractor classification models in a data set of 68 case studies of UK construction projects. The logistic regression (LR) and multivariate discriminant analysis (MDA) were used. Results revealed that both techniques produced a good prediction on contractor performance and indicated that suitability of the equipment, past performance in cost and time on similar projects, contractor relationship with local authority, and contractor reputation/image are the most predominant PSC in the LR and MDA models among the 34 PSC. Suggests contractor classification models using multivariate techniques could be developed further.

The purpose of this paper is to identify the performing and non‐performing companies by using multiple discriminant analysis (MDA) and multiple regression and the ratios that could distinguish between the performing and the under‐performing companies.

First, the study applied the α Jensen technique to classify the Shariah compliance companies into performing and non‐performing. Then, the results from the α Jensen technique with 20 financial ratios are applied to MDA in order to establish models that are used to identify non‐performing and performing companies.

The growth turnover ratio is the only ratio that could discriminate between the performing and non‐performing companies in the plantation industry.

The paper only investigates a sector in the main board of Bursa Malaysia, which is the plantation industry. Future research may look into the whole Shariah counters in Bursa Malaysia.

The paper could assist investors to evaluate and select an optimal investment portfolio.

The paper applies multivariate analysis which does not depend only on one variable. Using the multivariate analysis it provides an alternative to establish models that discriminate between the performing and non‐performing companies. This paper also investigates only the Shariah compliance counters in Bursa Malaysia.

Investigates the image of craftsmanship as a predictor influencing the purchase, by Americans, of European cars. Focuses also on the development of the factor model used in identifying differences between the purchase of European and American cars. Complements efforts of many others intent on the identification of factors giving the Europeans the competitive edge. Reports on a sample of 250 recent purchasers of US and European cars to find out the variables involved, of these 200 were returned and 140 were used for analysis – the remaining 60 were retained to be used as a final test of the model. Objectives were to determine whether such points as craftsmanship influenced Americans to purchase a European car and uses image analysis as well as discriminant analysis. Concludes that though this analysis and results are discussed it can in no way be described as definite or inclusive. Says this research may provide impetus for more extensive research into European image and impact on US consumers.

Considering the economic dimension of sustainability, the purpose of this paper is to analyze the risk of bankruptcy in the Pakistani firms of the non-financial sector from years 1995 to 2017.

Three techniques were used which include multivariate discriminant analysis (MDA), logit regression and multilayer perceptron artificial neural networks. The accounting data of firms were selected one year before the bankruptcy.

Findings were obtained by comparing and analyzing the methods which show that neural networks model outperforms in the prediction of bankruptcy. They further conclude that profitability and leverage indicators have the power of discrimination in bankruptcy prediction and the best variables to predict financial distress are also found and indicated.

Practically, this study may help the firms to better anticipate the risks of getting bankrupt by choosing the right method and to make effective decision making for organizational sustainability.

Three different techniques were used in this research to predict the bankruptcy of non-financial sector in Pakistan to make an effective prediction.

Through the credit they furnish, materials suppliers provide a form of working capital for most construction contractors. This paper considers the implications of this for crediting organizations (i.e. suppliers). It is shown that a supplier's financial turnover movement (or lack of it) can be modelled and predicted with some accuracy by considering a number of characteristics of their credit control department. The models are developed from analysis of data obtained from a survey of 55 UK materials suppliers' credit control and debt collection procedures. The statistical technique of multivariate‐discriminant analysis (MDA) is used. Predictive accuracy of the models is tested on an independent, hold‐out sample of 10 suppliers' characteristics. It is found that ‘risk‐taking’ suppliers who protect themselves from bad debt by using insurance; suppliers who employ a third‐party organization to evaluate potential debtors' creditworthiness; and suppliers who service only one construction trade with materials, achieve significantly greater financial growth than those suppliers who do not exhibit these characteristics.

Aims to distinguish among different levels of default risk for residential mortgage loans and to examine the significant factors for the different levels of default risk. Classifies the sample into default and non‐default groups and analyses the original mortgage loan data by factor and cluster analyses based on borrower characteristics, property characteristics and microeconomic variables in order to derive risk classifications from various likelihoods of default. Furthermore, applies logit, probit and discriminant analyses to examine the significant factors for all three clusters. The empirical results show that the three clusters may be ranked as follows, in order of risk, from the least to greatest likelihood of default: the owner‐occupied housing buyer, invester group and young buyer clusters. In addition, the factor “borrower’s education level” has negative impact for all three clusters.

This study investigates the characteristics of business and management consulting firms (firm size, international affiliation and scope of operation) affecting the adoption rate (i.e. recency of adopting big data analytics (BDA) as a new idea) and usage level of BDA. Ten critical areas of BDA application to business and management consulting were investigated, (1) Human Resource Management; (2) Risk Management; (3) Financial Advisory Services; (4) Innovation and Strategy; (5) Brand Building and Product Positioning; (6) Market Research/Diagnostic Studies; (7) Scenario-Based Planning/Business Simulation; (8) Information Technology; (9) Internal Control/Internal Audit; and (10) Taxation and Tax Management.

Survey data was obtained through a structured questionnaire from one hundred and eighteen (118) consultants in Nigeria from diverse consulting firm settings in terms of size, international affiliation and scope of operation (Big 4/non-Big 4 firms). Data was analyzed using descriptive statistics, cluster analysis, multivariate analysis of variance (MANOVA), multivariate discriminant analysis and multivariable logistic regression.

Whereas organizational characteristics such as firm size, international affiliation and scope of operation significantly determine the adoption rate of BDA, two attributes (international affiliation and scope of operation) significantly explain BDA usage level. Internationally affiliated consulting firms are more likely to record higher usage level of BDA than local firms. Also, the usage level of BDA by the Big 4 accounting/consulting firms is expected to be higher in comparison to non-Big 4 firms.

Contrary to common knowledge that firm size is positively associated with the adoption of an innovation, the study found no evidence to support this claim in respect of the diffusion of BDA. Overall, it appears that the scope of operation is the strongest organizational factor affecting the diffusion of BDA among consulting firms.

The study contributes to knowledge by exposing the factors promoting the uptake of BDA in a developing country. The originality of the current study stems from the consideration that it is the first, to the researchers' knowledge, to investigate the application of BDA by consulting firms in the Nigerian context. The study adds to literature on management accounting in the digital economy.

The purpose of this case study was to investigate the learning strategies of police officers. Participants were 80 police officers serving in a Midwestern police agency. Of these, 49 were assigned to patrol duties and 31 were assigned to community oriented policing duties. Each participant completed the “Assessing the Learning Strategies of Adults” (ATLAS) instrument. When individual variables were examined in describing learning strategies among police officers, no significant differences were found using both chi‐square and a one‐way ANOVA. A multivariate discriminant analysis produced a recognizable discriminant function, and three variables met the criteria to be included in the interpretation of the meaning of the discriminant function. Predominately, male police officers prescribed to the learning strategy traits that are desired in community oriented policing. Police officers who ascribed to the learning strategies which are more congruent with traditional policing were slightly younger than the officers who ascribed to the learning strategy appropriate for community policing. Furthermore, more females in this study ascribed to learning strategies more related to traditional policing.