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The purpose of this paper is to investigate the use of decision tree analysis in the identification of stakeholders based on their participation in tourism and political activities in a community.

A survey was sent to tourism stakeholders in two rural counties. Responses were collected and analyzed using the exhaustive chi‐square automatic interaction detection decision tree analysis.

Based on the results of the decision tree analysis four tourism stakeholder groups were identified based on their participation in tourism and political activities in a community: high participants, high‐moderate participants, low‐moderate participants, and low participants.

Owing to a low response rate, an issue of non‐response bias could exist, but the information from the respondents can give insight on stakeholders in these communities. Also, the specific results of this study can only be applied to eastern North Carolina and are not generalizable to other areas.

Results from this study demonstrate the use of decision tree analysis in identifying community stakeholders. Using decision tree analysis tourism planners can identify stakeholder groups that will participate in tourism and political activities. With this knowledge, tourism planners can identify which stakeholder groups will be the most influential and vocal in a community with regard to tourism development.

Decision tree analysis is a tool for partitioning a data set based on the relationships between a set of independent variables and a dependent variable. The research reported here tests the application of decision tree analysis, an analytical technique that is not traditionally used to segment stakeholders in tourism.

This paper seeks to define and describe decision tree analysis as a method for decision making when there are multiple, linearly or hierarchically related decision points that must be considered when determining the optimum choices within a project.

Using theory and example, the paper relates the use of decision tree analysis to the successful selection of decision points in complex, multistage digital library projects.

Decision tree analysis is useful as a method for both determining the various courses of action within a project and visualizing these choices in an easily understood format. Decision tree analysis can help a team understand the relationships between issues in a project as well as the consequences of various courses of action.

The paper fills a gap in the digital library project management literature by providing an overview of a tool used in operational research that helps identify an optimal course of action within a complex system of interrelated decisions.

The purpose of this paper is to analyze medium‐term risks faced by electrical generation companies in competitive environments. Market risks faced by generation companies are caused by several variables subject to uncertainty. Hydro conditions, fuel (coal and natural gas) prices, system demand, and CO₂ emission price are the risk factors considered in the paper. Taking into account these risk factors, generation companies have to take decisions that would affect their economic results and their risk exposure.

This paper proposes a methodology to support the risk‐analysis decision‐making process. Firstly, different scenarios of risk factors are generated. Then, a market equilibrium model is used in order to assess the impact of the different sources of uncertainty. Finally, decision trees are used in order to analyze the variables subject to interest, such as electricity prices or companies' profits.

The proposed methodology can be enhanced to take into account scenarios of more risk factors, such as equipment failure or agents' behavior. Another future enhancement could be a detailed study of correlation between different risk factors.

A realistic case study is presented, showing the advantages of these techniques for medium‐term risk‐analysis and decision‐making processes. Several decision trees have been generated to assess the impact of the different risk factors in electricity prices and companies' profits. These decision trees provide valuable information for companies when facing their risk‐management process.

The approach presented here constitutes a valuable support to gain useful information for wise decision making and to hedge against risk.

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.

Explores, using appropriate examples, the ways in which decision trees can be used by the manager to assist in the longitudinal decision‐making process. Since the mathematical concepts associated with decision trees are complex, managers can be reluctant to attempt to use decision tree models. A recognition that such models can be simply developed in a spreadsheet environment, and can then be used for sensitivity analysis using different decision criteria, demonstrates that decision trees can offer valuable insights into the structure of a strategic decision problem.

Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy but also by sensitivity (correctly classified legitimate emails) and specificity (correctly classified unsolicited emails) towards the accurate classification, captured by both false positive and false negative rates. This paper aims to present a comparative study between various decision tree classifiers (such as AD tree, decision stump and REP tree) with/without different boosting algorithms (bagging, boosting with re-sample and AdaBoost).

Artificial intelligence and text mining approaches have been incorporated in this study. Each decision tree classifier in this study is tested on informative words/features selected from the two publically available data sets (SpamAssassin and LingSpam) using a greedy step-wise feature search method.

Outcomes of this study show that without boosting, the REP tree provides high performance accuracy with the AD tree ranking as the second-best performer. Decision stump is found to be the under-performing classifier of this study. However, with boosting, the combination of REP tree and AdaBoost compares favourably with other classification models. If the metrics false positive rate and performance accuracy are taken together, AD tree and REP tree with AdaBoost were both found to carry out an effective classification task. Greedy stepwise has proven its worth in this study by selecting a subset of valuable features to identify the correct class of emails.

This research is focussed on the classification of those email spams that are written in the English language only. The proposed models work with content (words/features) of email data that is mostly found in the body of the mail. Image spam has not been included in this study. Other messages such as short message service or multi-media messaging service were not included in this study.

In this research, a boosted decision tree approach has been proposed and used to classify email spam and ham files; this is found to be a highly effective approach in comparison with other state-of-the-art modes used in other studies. This classifier may be tested for different applications and may provide new insights for developers and researchers.

A comparison of decision tree classifiers with/without ensemble has been presented for spam classification.

The purpose of this paper is to suggest important determinants for helpfulness from the reviews’ product data, review characteristics, and textual characteristics, and identify the more crucial factors among these determinants by using statistical methods. Furthermore, this study intends to propose a classification-based review recommender using a decision tree (CRDT) that uses a decision tree to identify and recommend reviews that have a high level of helpfulness.

This study used publicly available data from Amazon.com to construct measures of determinants and helpfulness. To examine this, the authors collected data about economic transactions on Amazon.com and analyzed the associated review system. The final sample included 10,000 reviews composed of 4,799 helpful and 5,201 not helpful reviews.

The study selected more crucial determinants from a comprehensive group of product, reviewer, and textual characteristics through using a t-test and logistics regression. The five important variables found to be significant in both t-test and logistic regression analysis were the total number of reviews for the product, the reviewer’s history macro, the reviewer’s rank, the disclosure of the reviewer’s name, and the length of the review in words. The decision tree method produced decision rules for determining helpfulness from the value of the product data, review characteristics, and textual characteristics. The prediction accuracy of CRDT was better than that of the k-nearest neighbor (kNN) method and linear multivariate discriminant analysis in terms of prediction error. CRDT can suggest better determinants that have a greater effect on the degree of helpfulness.

The important factors suggested as affecting review helpfulness should be considered in the design of websites, as online retail sites with more helpful reviews can provide a greater potential value to customers. The results of the study suggest managers and marketers better understand customers’ review and increase the value to customers by proving enhanced diagnosticity to consumers.

This study is different from previous studies in that it investigated the holistic aspect of determinants, that is, product, review, and textual characteristics for classifying helpful reviews, and selected more crucial determinants from a comprehensive group of product, reviewer, and textual characteristics by using a t-test and logistics regression. This study utilized a decision tree, which has rarely been used in predicting review helpfulness, to provide rules for identifying helpful online reviews.

Decision analysis, an approach developed to lend support in applying the concepts of decision theory, is particularly relevant for structuring and analysing complex decisions in which uncertainty plays a major part.

The purpose of this paper is to develop an easy and robust tool to develop a decision support system (DSS) for the inspection staff of oil pipelines. The aim is to predict “the class” of each spillage, with respect to some relevant variables such as, mechanical failure or system malfunction. The management will then be able to define which pipelines to monitor and to choose the most suitable monitoring policies, based on the decision tree analysis outcome.

A non‐parametric technique based on rule induction is proposed for the identification of the expected spill cause category of cross‐country oil pipelines. In particular, the classification and regression trees approach is used to automatically generate inspection or maintenance decision rules. The analysis that is described is based on an extended database concerning information about spill cause category in cross‐country oil pipelines in Western Europe.

The proposed technique represents an interesting added value tool for the management. The proposed methodology extrapolates rules for determining the expected spill cause category of cross‐country pipelines, depending on the boundary conditions.

The methodology here presented will assist maintenance managers of oil pipeline to better plan maintenance activity. In particular, the procedure makes it possible to determine which parts of a pipeline have to be submitted to a monitoring action or particular protection, with the aim of improving the efficiency and reducing the risk of spillages.

Effective planning, coordination, and scheduling of the maintenance function can be, and for many years was, accomplished without computer support. The proposed procedure may be included in an information systems tool (sound Computerized Maintenance Information Management System (CMMIS)), for more efficient and effective maintenance/inspection scheduling activities.

Alternative payment means have been expanding rapidly in recent years. The need to identify the segments of customers that are targetable for both financial and nonfinancial institutions is growing. The purpose of this paper is to use two different methods, discriminant analysis and decision trees, in order to compare the effectiveness of the two methods for segmentation and identify critical consumer characteristics which determine behavior and preference in relation to the use of payment means.

Using data from 321 bank customers, decision tree and discriminant analysis methods are used, first to test the same set of variables differentiating the customers and then to compare the respective results and prediction ability of the two methods.

Results show that discriminant analysis has a better model fit and segments the customers in a more effective way than the decision tree method. In addition, each method shows different variables to differentiate the customer groups.

The findings are limited to the sector and country of the study, as well as the convenience sample that has been used.

Suggestions for financial managers to better understand their customers’ behavior and target the right group are discussed.

This is the first attempt to compare decision trees and discriminant analysis as alternative segmentation methods for payment means.