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During the last two decades, there has been increasing emphasis on higher education institutions as agents promoting and advancing sustainability. This chapter addresses how sustainability is integrated into management education at higher education institutions. It is based on a systematic literature review that teases out governance, education, research, outreach and campus operations (GEROCO) as key elements for embedding sustainability in management education. In addition, it identifies the important role of having an overall governing strategic direction that serves to anchor sustainability. The chapter highlights that sustainability and responsible management education initiatives are interconnected and are complex to embed through the university system.

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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.

Much of the current literature on integrating sustainability into HEIs is focussed on why HEIs should embrace sustainable development (SD) and what is still missing or hindering work and the integration of efforts. There is much less exploration of how SD has been interpreted at the individual HEI level and action taken as a result. This case study reflects on important elements of the journey Nottingham Trent University (NTU) in the UK has taken to integrate sustainability, focussing on key decisions and activity in 2009/10. In highlighting this, the authors seek to empower those looking to support and/or lead the embedding of Education for Sustainable Development (ESD), separately or as part of an integrated effort, in their own institution. Today in 2019, NTU is a global leader in integrating ESD as part of a wider SD agenda. The work which this paper presents, to understand and establish a baseline of key elements of NTU's existing ESD activity and systems, was an important turning point. Activities undertaken to review and assess “where are we now?”, primarily through an institution-wide survey in 2009/10, led to important insights and supported dialogue, as well as the connection and underpinning of core administrative elements of the NTU SD framework and systems. Further recommendations are given in the final section of this paper on other drivers that can help to embed ESD within an HEI.

The purpose of this study is to investigate characteristics of apparel-related critical incidents that motivate both Generation Z and Y consumers to share electronic word-of-mouth (eWOM) via specific online channels.

The current research used an exploratory mixed-methods approach.

Qualitative findings of critical incidents revealed that the main situations that led to the spread of eWOM involved new purchases (49%), product quality (21%), pricing and promotions (19%), complaints (9%) and brand content (48%). Participants were motivated to spread information about the critical incidents by a desire to connect with friends and family (83%), help others (37%), influence others (48%) and express brand loyalty (32%). Quantitative results indicated significant relationships between critical incidents, motivations and eWOM channel choice.

This study has theoretical implications for apparel researchers attempting to gain insight into critical incidents that motivate consumers to engage in eWOM on specific channels in a positive or negative manner.

These findings are important for marketers as it appears that brand content does an efficient job at driving engagement on SM; marketers need to increase efforts to engage with consumers via feedback on websites, as this is an opportunity to counteract negative experiences and retain consumers’ loyalty.

To the best of the authors’ knowledge, the current research is the first to extend theories of communication and motivation to connect critical incidents with situational intrinsic and extrinsic motivations for spreading eWOM via online channels for Millennial and Generation Z consumers.

This study aims to examine the perceptions of undergraduate students studying early childhood education who took an academic course in which transformative pedagogy is adopted as part of a holistic approach designed to create transformative change and strengthen the students’ self-efficacy for sustainability, and thus, help reduce the environmental crisis in which we live.

By means of a phenomenological approach, this exploratory qualitative research used three research tools, reflection analysis, drawing analysis and analysis of course summary work, to identify changes in the perceptions of students undertaking the course. Data analysis was based on an inductive approach and included first- and second-cycle coding.

The results of the study show that the transformative pedagogy adopted in the course created transformative change in the students’ knowledge, attitudes, emotions and self-efficacy to act to reduce the climate crisis, not just through recycling.

The study was conducted with a relatively small, single class of undergraduate early childhood education students. The impact of certain activities may be different in larger classes. The gender imbalance, with the majority of students being female adds a further limitation. Male students may have different perspectives than female students, and those with different backgrounds and interests may respond differently.

This study provides some important insights into how sustainability education can be applied in a higher education curriculum. The study also contributes to the current dialogue on sustainability education by providing a rich description of how students experience alternative approaches to teaching in the field.

This study demonstrates how environmental action can be integrated in higher education.

Fraud transaction detection has become a significant factor in the communication technologies and electronic commerce systems, as it affects the usage of electronic payment. Even though, various fraud detection methods are developed, enhancing the performance of electronic payment by detecting the fraudsters results in a great challenge in the bank transaction.

This paper aims to design the fraud detection mechanism using the proposed Harris water optimization-based deep recurrent neural network (HWO-based deep RNN). The proposed fraud detection strategy includes three different phases, namely, pre-processing, feature selection and fraud detection. Initially, the input transactional data is subjected to the pre-processing phase, where the data is pre-processed using the Box-Cox transformation to remove the redundant and noise values from data. The pre-processed data is passed to the feature selection phase, where the essential and the suitable features are selected using the wrapper model. The selected feature makes the classifier to perform better detection performance. Finally, the selected features are fed to the detection phase, where the deep recurrent neural network classifier is used to achieve the fraud detection process such that the training process of the classifier is done by the proposed Harris water optimization algorithm, which is the integration of water wave optimization and Harris hawks optimization.

Moreover, the proposed HWO-based deep RNN obtained better performance in terms of the metrics, such as accuracy, sensitivity and specificity with the values of 0.9192, 0.7642 and 0.9943.

An effective fraud detection method named HWO-based deep RNN is designed to detect the frauds in the bank transaction. The optimal features selected using the wrapper model enable the classifier to find fraudulent activities more efficiently. However, the accurate detection result is evaluated through the optimization model based on the fitness measure such that the function with the minimal error value is declared as the best solution, as it yields better detection results.

Although the organizational practice of using “contingent or non-traditional workers” has been escalating since the mid-1980s, only recently has research begun to focus on the consequences of this practice. In unionized workplaces, labor leaders have begun to organize these workers. Although it is believed that contingent workers are responding positively to union organizing drives, little is known about the attitudes and behaviors of contingent workers as union members. Using the Union Commitment scale developed by Gordon, Philpot, Burt, Thompson and Spiller (1980), the research project reported here compares the Union Commitment of traditional faculty and three categories of adjunct faculty. The results reveal that there are no significant differences across these employee groups for the factors of Union Loyalty, Responsibility to the Union, Willingness to Work for the Union and Alienation from the Union. The implications of these findings for research and practice are discussed.