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This paper aims to contribute to the extant literature in this field by examining nonprofit organizations’ fraud reporting compliance using logistic regression and decision tree induction algorithms.

This study used the data from 428 nonprofit organizations during 2009-2015 period, and analyzed 21 individual measures (obtained from these organizations’ Internal Revenue Service Form990 filings) using logistic regression and decision tree induction algorithms, to study the governance characteristics and fraud reporting.

The study found evidence that compliance with the law, board of directors’ independence, federal audit and using independent accountants to compile and review financial statements are the most prevailing factors affecting the odds of nonprofit organizations experiencing fraud reported as an asset diversion.

The argument associated with using governance to reduce the chances of fraud has been a popular topic in industry and academia but unfortunately has limited empirical evidence in the literature, especially when it relates to nonprofits. This study contributes to the literature in this respect.

The paper aims to identify and critically analyze the factors influencing cost system functionality (CSF) using several machine learning techniques including decision trees, support vector machines and logistic regression.

The study used a self-administered survey method to collect the necessary data from companies conducting business in Turkey. Several prediction models are developed and tested; a series of sensitivity analyses is performed on the developed prediction models to assess the ranked importance of factors/variables.

Certain factors/variables influence CSF much more than others. The findings of the study suggest that utilization of management accounting practices require a functional cost system, which is supported by a comprehensive cost data management process (i.e. acquisition, storage and utilization).

The underlying data were collected using a questionnaire survey; thus, it is subjective which reflects the perceptions of the respondents. Ideally, it is expected to reflect the objective of the practices of the firms. Second, the authors have measured CSF it on a “Yes” or “No” basis which does not allow survey respondents reply in between them; thus, it might have limited the choices of the respondents. Third, the Likert scales adopted in the measurement of the other constructs might be limiting the answers of the respondents.

Information technology plays a very important role for the success of CSF practices. That is, successful implementation of a functional cost system relies heavily on a fully integrated information infrastructure capable of constantly feeding CSF with accurate, relevant and timely data.

In addition to providing evidence regarding the factors underlying CSF based on a broad range of industries interesting finding, this study also illustrates the viability of machine learning methods as a research framework to critically analyze domain specific data.

The life span of an aircraft is usually around 30 years in the commercial aviation industry. During this time span, aircraft needs maintenance to stay in service. The cost of maintenance, repair and overhaul (MRO) activities in its pure nature is a significant portion of operations, accounting around 10 percent of all cost drivers. The purpose of this paper is to design/develop and critically assess a comprehensive model of operations at Turkish Technic – the MRO department of Turkish Airlines.

A comprehensive systems dynamics model is designed and developed to holistically represent and critically assess the different facets of MRO operations to help in analyzing various decision scenarios at Turkish Airlines.

The developed system dynamics (SD) model presented unique opportunities to test various MRO operations’ work load and aircraft fleet expansion policy alternatives. The model can also be used as a “learning laboratory” by altering various system parameters and testing different policies. The case study results suggested that MRO operations have a direct impact on the available number of airworthy aircrafts and hence, the usable fleet seat capacity; to sustain a profitable airline fleet, the airline companies should take into account the unique characteristics/needs of MRO operations for both existing and new/prospective aircrafts.

There are several SD studies in the literature focusing on the airline industry, but the MRO operations are virtually neglected in them. Hence, the proposed SD model contributed to the extant literature. The value of the developed model stems from its potential use in the critical analysis of decision scenarios as well as being leveraged as a training/learning laboratory.

Insurance frauds deeply affect insurance companies, policyholders, and the insurance industry as a whole. The cost of fraudulent damage affects the profitability of companies, and has negative effects on the society in terms of moral values. Increases in insurance costs can lead to increases in the premiums paid by policyholders, each family, and, ultimately, all of the insured. Recently, new legal regulations related to this issue have been performed in Turkey and higher institutions have been created. A regulation issued by the Under-secretariat of the Treasury, on June 1, 2011, defines insurance fraud as aggravated fraud. Insurance fraud in Turkey usually takes the form of intentional misrepresentations of facts to the insurance company to get the company to pay for something not actually covered by the policy. Studies examined the insurance industry in terms of the concept of financial crime, and inclusion of the concept of financial crime in insurance regulations was proposed since financial crimes have an important place in the current problems of the industry. In addition, it is seen that insurance frauds have changed over time as a result of studies.

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.

This paper aims to evaluate the collaboration quality performance of sugar company Z and its stakeholders (farmers and distributors) by considering the sustainability aspects. This assessment shall be able to integrate qualitative and quantitative factors in the model, which is critical in sugar supply chains involving multi-stakeholders.

Integration of quality function deployment (QFD), the fuzzy analytical network process (FANP) and the data envelopment analysis (DEA) is administered to assess the efficiency score of each stakeholder involved. The evaluation encompasses collaboration behaviour factors since the input of collaboration activities will result in sustainability aspects such as revenue, green house gas (GHG) emissions and social impact. The analysis has been conducted in two scenarios, those are the basic scenario by utilising original data and the extended scenario by using projection data.

The result clarifies that the most influential behaviour factor in the collaboration activities is commitment (0.116), while the least important behaviour factors are power (0.008) and adaptation (0.008). Furthermore, by using the extended scenario, the overall efficiency for each benchmarking is higher compared to the condition before the improvement (basic scenario).

The result of this study is only relevant to the particular sugar supply chain and involving limited sustainability variables. Therefore, in a further study, more variables such as technical and financial aspects could be explored further in the assessment process.

The result of this study is available for each stakeholder and can be fundamental for the constant improvement in sustainable supply chain (SSC) practices. It shows that an improvement of one stakeholder will positively impact the entire system.

Smallholders and sugarcane farmers will recognise the significance of collaboration behaviour. Thus, they can enhance their mutual benefits by using the existing resources.

This paper arranges for a practical contribution by implementing advanced assessment methods in the sugar supply chain by taking into account the economic, environmental and social aspects. This comprehensive assessment process in the sugar supply chain is the novelty of this paper.