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Academic integrity is vital to the success and sustainability of the academic project and particularly critical in the training of ethical and informed health professionals. Yet studies have found that cheating in online exams was commonplace during the COVID-19 pandemic. With the increased use of online and blended learning post-COVID-19, an understanding of student cheating dynamics is essential for developing effective strategies to combat academic dishonesty in the rapidly changing educational landscape.

This study explored academic integrity and reasons for dishonesty from the perspective of health sciences students at a South African University of Technology (UOT) via the Fraud Diamond. To gain an in-depth understanding of the topic, a qualitative method was employed, and data were collected via focus group discussions with nine student class representatives. These data were analysed thematically using the Clarke and Braun approach.

Cheating during online assessment was common and innovative, with students manipulating others and exploiting friendships to facilitate dishonest practices. Student motivations for dishonesty included a lack of consequences and pressure due to a lack of time management, engagement and preparation.

This study underscores the need for institutions to adopt an adaptable, multi-faceted approach that addresses student cheating opportunity, motivation and normalisation of dishonest practices whilst strengthening academic integrity policies and enforcement thereof.

The findings contribute valuable insights into the ongoing academic integrity crisis in higher education in the South African context.

This paper aims to test the effectiveness of the Haryono Umar (HU)-model used in corruption prevention strategies through corruption detection as a tool for detecting corruption because the mode of corruption is increasingly dynamic and complex by focusing on the causes of corruption: pressure, opportunity, rationalization, capability and lack of integrity.

The research uses multiple regression methods, classification and regression trees and the HU-model application system developed by researchers. The research sample uses secondary data from financial reports on the Indonesia stock exchange according to organizational clustering (such as red, grey and green areas).

The research result showed that of the 470 sample companies, there were 445 companies, or 98.9%, in the red cluster (indicated corruption), 19 companies, or 4.04, in the green clusters or not indicated corruption and six companies, or 1.28%, were included in the grey cluster or potential corruption. By knowing the cluster of an organization, efforts to prevent corruption can be made effective and efficient. Implementing the HU-model proves that the amount of pressure, the abundance of opportunities, the ease of rationalization and the high level of position and authority strengthen the drive for corruption if there is a lack of integrity.

Each internal organization can use this model independently and find conditions related to corruption so that they can immediately take action to prevent it.

The application of the HU-model is a discovery in preventing corruption by focusing on the possibility of corruption occurring in each organization through organizational clustering.

Digital supply chains require nascent technologies like blockchain and Internet of Things (IoT). There is a need to develop a roadmap for the implementation of these technologies, as they require a huge amount of resources and infrastructure. The purpose of this paper is to analyze the challenges of implementing blockchain-IoT integrated architecture in the green supply chain and develop strategies for the same.

After a thorough literature survey of Scopus-indexed journals and books, 37 barriers were identified, which were then brought down to 15 barriers after confirming with industry and academic experts using the Delphi method. Using the total interpretive structural modeling (TISM) method and cross-impact matrix multiplication applied to classification (MICMAC) analysis, the barriers were modeled, and finally, strategies were formulated using a concept map to handle the barriers in the blockchain-IoT integrated architecture for a green supply chain.

This paper presents the research on barriers that can be considered for incorporating blockchain and IoT in the green supply chain. It was found from the TISM model that environmental concerns are Level-1 barriers and need to be addressed by developing appropriate technology and allocating funds for the same. An integrated ecosystem with blockchain and IoT is developed.

The focus of this study was on the challenges of blockchain and IoT; hence, it is required to extend the research and find challenges for different industries and also analyze the criteria using other multi-criteria decision-making (MCDM) methods. Further research is required for the integration of blockchain-IoT with supply chain functions.

The transformation of a traditional supply chain into a green supply chain is possible with the integration of technologies. This research work and the strategies developed are useful to managers and practitioners working on technology implementation. Planning resources and addressing key barriers is possible with the concept maps and architecture developed.

Green supply chain management (SCM) is gaining importance in industry as well as the academic sector due to government Policies and norms worldwide for reducing emissions and encouraging environment-friendly production systems. Incorporating blockchain and IoT in a green supply chain will further digitize and increase transparency in supply chains.

We have done a categorization of all barriers based on the expert survey by academicians and industry experts from industries in India. The concept map helps in identifying possible solutions for the challenges and initiatives to be taken for the smooth integration of technologies in the green supply chain.

Corruption is one of the main facilitators of increased wildlife crime in South Africa. It is important to look at the current legislative framework to determine how wildlife crime can be combated effectively. This paper aims to provide an analysis of the process of wildlife crime, focusing on wildlife poaching and trafficking role players and critically analyses the legislation in place to combat the phenomenon.

A critical analysis of existing literature such as case law, legislation, peer-reviewed publications and electronic articles was used to identify the legislation and processes for the prevention of wildlife crime in South Africa. Common denominators were identified in these sources to provide a baseline for examining wildlife crime.

Wildlife criminals rely extensively on corruption during all facets of their trafficking schemes. The use of traditional environmental laws is not effective in dismantling wildlife trafficking networks, as prosecuting those in the upper echelons is often challenging. The extensive reliance on corruption during all facets of wildlife trafficking provides an opportunity for law enforcement to use such corruption crimes as predicate offences in racketeering prosecutions, thus ensuring kingpins find it more difficult to escape prosecution, owing to their involvement in criminal enterprises that sustain themselves through patterns of racketeering.

This paper contributes knowledge to narrow the research gap with regard to wildlife criminals and how they function. It also addresses possibilities to improve prosecution and disrupt wildlife trafficking networks.

This study examines the impact of debt maturity structure on stock price crash risk (SPCR) in Asian economies and the moderating effect of firm age on this relationship.

The study utilized annual data from 432 nonfinancial firms publicly listed in six Asian countries: China, Hong Kong, Japan, Singapore, Pakistan and India. The observation period covers 14 years, from 2007 to 2020. The sample was categorized into three groups: the entire sample and one group each for developing and developed Asian economies. A generalized least squares panel regression method was employed to test the research hypotheses.

The results suggest that long-term debt has a significant negative influence on SPCR in Asian economies, indicating that firms with high long-term debt experience lower future SPCR. Moreover, firm age negatively moderates this relationship, implying that older firms may experience a more pronounced reduction in SPCR due to high long-term debt. Finally, firms in developed Asian economies with high long-term debt are more effective in mitigating the risk of a significant drop in their stock prices than firms in developing Asian economies.

This study contributes to the literature in several ways. To the best of the researcher’s knowledge, this is the first of such efforts to investigate the relationship between debt maturity structure and crash risk in Asia. Additionally, it reveals that long-term debt influences SPCR directly and indirectly in Asia through the moderating role of firm age. Lastly, it is likely one of the first studies by a research team in Asia to compare the nonfinancial markets of developed and developing Asian countries.

This paper offers an overview of the burgeoning literature on open banking, focusing on its implications for the financial sector.

The paper reviews the recent developments in the nascent literature of open banking. In particular, it discusses the following issues. (1) the extent to which open banking fosters competition, drives innovation and enhances financial inclusion; (2) the impact of institutional arrangements on the outcomes of open banking initiatives and (3) the critical role of government in promoting open banking and regulating banking activities.

The paper concludes with a discussion on potential directions for future research. First, open banking introduces significant challenges to the traditional banking model. Furthermore, the interplay between open banking and financial risk presents an area ripe for exploration. Lastly, the importance of consumer education in the context of open banking cannot be overstated.

Open innovation enables financial institutions generate productive innovations as well as provide customers with significantly better services, by getting access to previously restricted customer data. However, currently non-bank and fintech lenders often face significant barriers in accessing comprehensive customer data, which restricts their capacity to support non-standard credit models. More emphasis is required to be assigned to research on the economic impact of open banking.