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The aim of this study is to analyze the relationship between personality traits and students’ cheating behavior using the five-factor personality model and the fraud triangle factors. This chapter develops an evidential study that has the goal to determine the relationship between the students’ cheating behavior and personality traits by using fraud triangle factors. In this context, 251 surveys have been conducted on students of a foundation university located in Istanbul. As means of data collection, NEO – Five Factor Inventory and Academic Fraud Risk Factors have been used. Data have been analyzed by regression tree analysis. Risk and classification tables have been created before starting the study with a decision tree in which classification and regression trees algorithms were implemented. The results reveal that rationalization behind the cheating is the most important reason for students to copy and people who believed that they were extremely appropriate to copy were responsible ones when analyzed in terms of their personality traits. The results of this study contribute to the literature by discovering the characteristics of those who admit academic dishonesty and underlie the factors or predispositions for engaging in this behavior. For sure, three factors of the fraud triangle may have different levels of significance in this study; in addition, pressure is not associated with the cheating behavior.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Risk preferences play a critical role in almost every facet of economic activity. Experimental economists have sought to infer the risk preferences of subjects from choice behavior over lotteries. To help mitigate the influence of observable, and unobservable, heterogeneity in their samples, risk preferences have been estimated at the level of the individual subject. Recent work has detailed the lack of statistical power in descriptively classifying individual subjects as conforming to Expected Utility Theory (EUT) or Rank Dependent Utility (RDU). I discuss the normative consequences of this lack of power and provide some suggestions to improve the accuracy of normative inferences about individual-level choice behavior.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

In the current scenario, cybersecurity issues have emerged to be a major challenge for firms to deal with. The increased use of technologies has increased radically the volume and typology of information produced, exchanged, and managed by firms thus creating conditions for cybersecurity incidents or information breaches. In this situation, it becomes paramount for firms to recognize cybersecurity risks and be prepared to prevent them through the implementation of approaches and technologies able to ensure a high level of protection.

In this chapter, we provide a framework for analyzing and managing cybersecurity risks. We employed a case study strategy to understand how the risk analysis process is carried out within an Information Security company. The study and observations obtained from this case study have permitted to define a framework useful for SME to deal with cybersecurity issues.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

All firms operating in the global economy are exposed to a multitude of risks including financial crisis, cyberattack, social instability, governance failure, extreme weather events, etc. As a consequence, international organizations assume many (new and evolving) exposures that must be addressed, where some firms are able to adjust and thrive against these adverse odds, whereas many others fail. It appears like some (a few) firms are able to repeatedly outperform the market, where a great many of them struggle, and quite a few register negative returns every year. As a consequence, the authors typically observe leptokurtic negatively skewed distributions of financial returns with extreme negative tails of poor performing firms, where the performance data fall way beyond the requirements of a normal distribution. The authors investigate this phenomenon based on a comprehensive dataset of European firms retrieved from Compustat Global for the 25-year period 1995–2019. The analysis shows that there is indeed a consistent pattern of many underperforming firms across different industry classifications and time intervals and a few outperformers. This provides evidence of a regularly observed phenomenon that often is overlooked in mainstream management studies. The results have implications for academic research that often relies on assumptions of data normality in statistical analysis and for corporate management that has to deal with a risk-prone business environment.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.