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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.

The purpose of this paper is to compare influential factors of entrepreneurial activities over time in China and to compare China with other selected countries. The data are collected from Global Entrepreneurship Monitor (GEM). The method used is decision trees and chi-square automatic interaction detector (CHAID) analysis, which isolates important factors and examines entrepreneurship predictor importance.

The method used is decision trees and CHAID analysis which isolate important factors and examine entrepreneurship predictor importance. The original contribution of this paper is that this is the first time where artificial decision trees are applied to data to isolate factors that influence business startups and used across countries for comparative purposes. It is also the first application of this model to Chinese GEM. CHAID trees and predictor importance show the value of motivations of people who have already started businesses and shed light on how public policy can be influential in promoting entrepreneurship.

Results indicate that solid knowledge and skills of how to start a business and knowing someone who has already started a business are the most important factors in China and in most of the selected countries. Fear of failure is becoming less important for Chinese entrepreneurs over the years from 2003 to 2012. Results show that countries, including China, have to enhance skill and knowledge education if they want to promote small business entrepreneurship as a policy. The findings support human capital theory.

The limitations of this study are due to using aggregated data from GEM surveys, which do not allow the authors to examine individual or household behavior. The authors do not know the variance and the distribution of responses to the questions asked and the locations in which the surveys were conducted. Another limitation is that GEM data do not report regional variations which can be modeled. For future work, the authors suggest more detailed data availability which will lead to isolating entrepreneurial problems and highlighting relevant attitudes important to entrepreneurs.

Better data collection is needed at household and regional levels to understand business starts and to promote entrepreneurship.

Social implication of this research is to find out effective ways to increase entrepreneurial activities, therefore creating job opportunities and boosting economic growth. Educational programs will also decrease disparity of opportunity and incomes between different geographical regions in the country. The original contribution of this paper is that this is the first time artificial decision trees are applied to data to isolate factors that influence business startups across countries.

The original contribution of this paper is that this is the first time where artificial decision trees are applied to data to isolate factors that influence business startups and used across countries for comparative purposes. It is also the first application of this model to Chinese GEM. CHAID trees and predictor importance show the value of motivations of people who have already started businesses and shed light on how public policy can be influential in promoting entrepreneurship. This research modeled the breakdown of reasons people would start a business by using GEM data surveys.

Financial statement fraud (FSF) committed by companies implies the current status of the companies may not be healthy. As such, it is important to detect FSF, since such companies tend to conceal bad information, which causes a great loss to various stakeholders. Thus, the objective of the paper is to propose a novel approach to building a classification model to identify FSF, which shows high classification performance and from which human-readable rules are extracted to explain why a company is likely to commit FSF.

Having prepared multiple sub-datasets to cope with class imbalance problem, we build a set of decision trees for each sub-dataset; select a subset of the set as a model for the sub-dataset by removing the tree, each of whose performance is less than the average accuracy of all trees in the set; and then select one such model which shows the best accuracy among the models. We call the resulting model MRF (Modified Random Forest). Given a new instance, we extract rules from the MRF model to explain whether the company corresponding to the new instance is likely to commit FSF or not.

Experimental results show that MRF classifier outperformed the benchmark models. The results also revealed that all the variables related to profit belong to the set of the most important indicators to FSF and that two new variables related to gross profit which were unapprised in previous studies on FSF were identified.

This study proposed a method of building a classification model which shows the outstanding performance and provides decision rules that can be used to explain the classification results. In addition, a new way to resolve the class imbalance problem was suggested in this paper.

Determining the relevant information using financial measures is of great interest for various stakeholders to analyze the performance of the firm. This paper aims at identifying these financial measures (ratios) which critically affect the firm performance. The authors specifically focus on discovering the most prominent ratios using a two-step process. First, the authors use an exploratory factor analysis to identify the underlying dimensions of these ratios, followed by predictive modeling techniques to identify the potential relationship between measures and performance.

The study uses data of 25 financial variables for a sample of 1923 Indian manufacturing firms which exist continuously between 2011 and 2018. For prediction models, four popular decision tree algorithms [Chi-squared automatic interaction detector (CHAID), classification and regression trees (C&RT), C5.0 and quick, unbiased, efficient statistical tree (QUEST)] were investigated, and the information fusion-based sensitivity analyses were performed to identify the relative importance of these input measures.

Results show that C5.0 and CHAID algorithms produced the best predictive results. The fusion sensitivity results find that net profit margin and total assets turnover rate are the most critical factors determining the firm performance in an Indian manufacturing context. These findings may enable managers in their decision-making process and also have vital implications for investors in assessing the performance of the firm.

To the best of the authors’ knowledge, the current paper is the first to address the application of decision tree algorithms to predict the performance of manufacturing firms in an emerging economy such as India, with the latest data. This practical perspective helps the organizations in managing the critical parameters for the firm’s growth.

Bid/no-bid decision is a significant and strategic decision, which must be finalized at an early stage of the bidding process. Such decision-making may have significant impact on the performance of the contractors. Using Chi-square Automatic Interaction Detector (CHAID) and Classification and Regression (CRT) decision tree algorithms, this paper aims to develop bid/no-bid models for design-bid-build projects for contractors.

The models in this study have been developed using CHAID and CRT algorithms. Thirty-four bid/no-bid key factors were collected via extensive research. The bid/no-bid factors were listed based on their importance index as a result of a questionnaire distributed among the construction professionals. These factors were divided into five main risk categories – owner, project, bidding situation, contract and contractor – which were taken as inputs for the models. Split-sample validation was applied for testing and measuring the accuracy of the CHAID and CRT models. Moreover, Spearman's rank correlation and Analysis of Variance (ANOVA) tests were employed to identify the statistical features of the received 169 responses.

The key bid/no-bid factors in construction industry were categorized in five related groups and ranked based on the relative importance index. It was found that the top 6 ranked bid/no-bid factors were (1) current workload, (2) need for work, (3) previous experience with employer; (4) timely payment by the employer; (5) availability of other projects for bidding (6) reputation of employer in the industry. Matrix comparison between all bid/no-bid groups was performed using Spearman's correlation to measure the relationship between each of the two paired groups. It was concluded that all the relationships were positive.

Existing bidding models require many inputs and advanced understanding of mathematics and software to run the model. Contractors tend to use easy, fast and available support methods. Excluding a great number of the bid/no-bid factors may affect the final decision. This paper proposes a bid/no-bid decision tree models for contractors of different sizes. It is the first study in the literature, to the best of authors' knowledge, to study bid/no-bid decision with the proposed decision tree algorithm. The proposed models in this study overcome the shortfalls of most previous models such as avoiding the complexity and difficulties of applying the concept. The proposed model will provide the contractors with a bid/no-bid decision based on the input for the defined bid factor groups. The proposed models display the soft spots and hot spots between the independent and dependent variables, which leads to a better decision. The proposed models display the result effectively in visual terms, easy to understand and easy to apply. The proposed models are a form of multiple effect (or variable) analysis which allows the companies to explain, describe, predict or classify an outcome.

The purpose of this paper is to enhance the predictive power of bankruptcy prediction models by taking the past values of firms’ financial ratios as benchmark. For this purpose, the paper proposes an indicator variable expressing the time trends of financial ratios.

The proposed measure uses the minimum and the maximum of financial ratios from the previous period as benchmarks in order to give a more complete picture about the present financial performance of firms. The most popular classification methods of bankruptcy prediction were employed: discriminant analysis, logistic regression, decision trees. Sample specific results and conclusions were avoided by applying tenfold stratified cross-validation.

The empirical results suggest that the proposed measure can increase the predictive performance of bankruptcy prediction models compared to models based solely on static financial ratios. The results gave evidence for the fact that the firms’ past financial performance is a useful benchmark for evaluating the risk of future insolvency.

The proposed concept is completely new to the literature and practice of bankruptcy prediction. Similar concept has not been published to date. The suggested dynamization approach has three important advantages. It is easy to compute from time series of financial ratios. It is applicable within any classifier irrespective of its mathematical background. The performance of models can be enhanced without the necessity of giving up the interpretability of bankruptcy models, so the proposed measure may play very important role in the practice of credit scoring modeling as well.

This paper aims to empirically examine the main determinants of the compliance level of disclosure requirements for IAS 16, as well as factors that may explain the differences in the levels of compliance within companies.

The association between the level of compliance and various corporate characteristics was examined using Chi-square Automatic Interaction Detector (CHAID) analysis in financial disclosures for IAS 16. CHAID analysis was applied to the manufacturing companies listed in Borsa Istanbul for the years 2012 and 2013.

It was found that the most significant factor is the auditor reputation within different nodes such as size or free float rate. In most of the studies, correlation is used to determine the association between different factors, but this study is the first one that uses the CHAID analysis which offers an adjusted significance testing, and at the same time classification of the interaction between variables.

This paper provides insights into the primary factors of disclosure compliance that help to improve the structure of disclosures and the level of compliance in preparing future financial reports. The proposed improvements will also support further developments in financial reporting regulations regarding disclosures. The key limitation in this paper is that it concentrates on a specific standard and only covers two years. However, it provides suggestions for one of the most important standards that includes various disclosures.

In addition, this paper fills a gap in the literature about the compliance level of specific standards such as IAS 16 and the usage of CHAID analysis in such studies. The results were consistent with some previous studies regarding the relationship between compliance level, auditor reputation and size and it also highlight the effect of different disclosure items on compliance level.

The objective of this study is to describe financial profiles of firms via data mining method and obtain vital characteristics of performance level for the firms, which have the best financial profile of the health sector. In this study, public hospitals needed an urgent financial solution under the health sector reform steps were selected for implementation. The study covered 645 Ministry of Health hospitals, which have revolving fund in Turkey. Year of 2004 data was used in the study. As the methodology of this study, the Chi‐Square Automatic Interaction Detector or CHAID decision tree algorithm, one of the most efficient and up‐to‐date data mining method used for segmentation According to the results of the study, it was determined that financial performance of 9.15% (59 hospitals) of the covered hospitals was classified as “good” whereas 90.85% (586 hospitals) of them displayed as “bad” financial performance. Also hospitals were categorized in 12 different profiles in terms of level of financial performance by CHAID. These profiles show us what financial indicators of the hospitals should focus on for good financial performance as well as those profiles should take example to improve their financial performances. As a result of the findings, financial management policies, financial strategies and legal regulations were suggested to improve financial performance of the public hospitals and for the success of the Turkish health sector reform steps.

Entrepreneurial trait and behaviour approaches are used to identify differing entrepreneurial profiles. Specifically, this study aims to determine which entrepreneurial competencies (ECs) can predict entrepreneurial action (EA) for distinct profiles, such as male versus female, start-up versus established and for entrepreneurs within different age groups and educational levels.

The research was conducted using a survey method on a large sample of 1,150 South African entrepreneurs. Chi-squared automatic interaction detection (CHAID) algorithms were used to build decision trees to illustrate distinct entrepreneurial profiles.

Each profile has a different set of ECs that predict EA, with a growth mindset being the most significant predictor of action. Therefore, this study confirms that a “one-size-fits-all” approach cannot be applied when profiling entrepreneurs.

From a pedagogical standpoint, different combinations of these ECs for each profile provide priority information for identification of appropriate candidates (e.g. the highest potential for success) and training initiatives, effective pedagogies and programme design (e.g. which individual ECs should be trained and how should they be trained).

Previous work has mostly focused on demographic variables and included a single sample to profile entrepreneurs. This study maintains much wider applicability in terms of examining profiles in a systematic way. The large sample size supports quantitative analysis of the comparisons between different entrepreneurial profiles using unconventional analyses. Furthermore, as far as can be determined, this represents the first CHAID conducted in a developing country context, especially South Africa, focusing on individual ECs predicting EA.