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This chapter provides a comprehensive investigation of the literature on sustainability reporting in Islamic banks using a content analysis of 200 English language articles published between 2000 and 2023. The data were collected from the Web of Science (WOS) database and analyzed using Bibliometrix in R software. This chapter addresses the trends of the most influential authors, institutions, countries, and research hotspots. This chapter fills one of the gaps in sustainability reporting literature, setting up a statistical description of the principal features of sustainable Islamic banking research and carrying out an analysis of its knowledge structure via bibliometric analysis. This chapter found that the number of articles on sustainability reporting in Islamic banks has increased over time; however, it is concentrated on a few core sources and authors, mostly related to Islamic finance, accounting, and ethics, as well as sustainability and social responsibility. The analysis of the co-authorship network shows a limited degree of collaboration between researchers from different clusters. The most productive and collaborative countries are Malaysia, the United Kingdom, and Pakistan, reflecting their well-developed and regulated Islamic banking industries that follow sustainability guidelines and frameworks. Moreover, the co-word analysis identifies 11 clusters and 43 keywords that represent the main topics and themes in the field, such as corporate governance, performance, disclosure, and risk. This chapter suggests some directions for future research and action on sustainability reporting in Islamic banks. This chapter contributes to the literature on sustainability in Islamic banking and the UN 2030 Agenda for Sustainable Development.

The study aims to evaluate PubMed publications on ChatGPT or artificial intelligence (AI) involvement in scientific or medical writing and investigate whether ChatGPT or AI was used to create these articles or listed as authors.

This scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. A PubMed database search was performed for articles published between January 1 and November 29, 2023, using appropriate search terms; both authors performed screening and selection independently.

From the initial search results of 127 articles, 41 were eligible for final analysis. Articles were published in 34 journals. Editorials were the most common article type, with 15 (36.6%) articles. Authors originated from 27 countries, and authors from the USA contributed the most, with 14 (34.1%) articles. The most discussed topic was AI tools and writing capabilities in 19 (46.3%) articles. AI or ChatGPT was involved in manuscript preparation in 31 (75.6%) articles. None of the articles listed AI or ChatGPT as an author, and in 19 (46.3%) articles, the authors acknowledged utilizing AI or ChatGPT.

Researchers worldwide are concerned with AI or ChatGPT involvement in scientific research, specifically the writing process. The authors believe that precise and mature regulations will be developed soon by journals, publishers and editors, which will pave the way for the best usage of these tools.

This scoping review expressed data published on using AI or ChatGPT in various scientific research and writing aspects, besides alluding to the advantages, disadvantages and implications of their usage.

It is widely argued that money laundering (ML) is not a new phenomenon and the pervasiveness of ML is associated with some severe economic, social and political costs. Due to the lack of studies on the ML’s issue in the UAE, this study aims to examine the determinants of ML in the country between 1975 and 2020.

The autoregressive distributed lag bounds testing results demonstrate the presence of long-run relationship between ML and the selected macroeconomics variables. The analysis is validated by the dynamic ordinary least squares, the fully modified ordinary least squares and the canonical co-integration regression estimators.

The estimation result reveals that while the real estate market, outflow of money, arms procurement and size of the underground economy influences the size of ML positively, gold trade, the level of financial development and the size of economic activities are negatively associated with ML, both in the short- and long-run.

Up to date from a country-level analysis, no study has been devoted to the ML in UAE, except for Aljassmi et al. (2023). To the best of the authors’ knowledge, this study is the first to investigate the determinants of laundered money in the UAE economy. Based on these outcomes, strategies and measures which will deter the laundering of illicit funds through the real estate and gold market, remittance system, financial system and arms procurement contracts in the UAE are recommended.

Despite the vulnerability of rapidly developing and emerging market economies, researchers have paid less attention to the determination of the size of money laundering (ML) in these economies, including the United Arab Emirates (the UAE). Therefore, this paper aims to estimate the magnitude of ML in the UAE between 1975 and 2020 based on the currency demand approach (CDA).

The study uses the Gregory–Hansen cointegration technique alongside the autoregressive distributed lag bounds testing procedure to estimate the CDA model.

The results illustrate that an amount equivalent to about 19.034% of the GDP is laundered in the UAE between 1975 and 2020, on average, with the value lying between 15.129% and 23.121%. In addition, the results demonstrate the importance of the real estate market, gold trade, remittance channels and the size of the underground economy in facilitating the laundering of illicit funds in the country.

To the best of the authors’ knowledge, the study is the pioneering attempt at estimating the amount of illicit funds laundered in the UAE. Besides, the adoption of a novel, yet robust, approach based on the modification of the CDA technique also sets the study apart as it ensures a correct, clear, unambiguous and indisputable estimate of the magnitude of ML is obtained. In addition, it is expected that the outcome of the study will expand the frontiers of knowledge among policy makers and relevant agencies and ensure the adoption of the most efficient and effective measures to curb the ML menace in the country.

Reverse logistics (RL) has become integral in modern supply chains, with many companies investing in circular economy (CE), a recuperative and effective industrial economy. The traditional linear model triggered many negative environmental consequences such as climate change, ocean pollution, loss of biodiversity and land degradation. The development of RL strategies that support the transition between RL to CE is crucial. The purpose of this paper is to connect RL with CE in the context of Industry 4.0 and develop a hierarchal structure to explore the relationship between RL and CE critical success factors in the context of Industry 4.0.

This study used both qualitative and quantitative approach. Literature review in collaboration with the Delphi method is used to identify and validate critical success factors. Then, the ISM-based model and MICMAC method were used to determine the relationship between CE and RL success factors and its driving and dependence power.

This study result shows that waste reduction, skilled employees and expert's involvement and top management commitment and support will provide guidelines and paths for implementing CE and RL, leading to the competitiveness of a firm.

The findings provide managerial insight, particularly useful to third-party logistics companies' managers who are looking to implement RL and CE, to help prioritize where to invest company resources to generate prime difference. Furthermore, this study also identified Industry 4.0 technologies, which would tackle top identified critical success factors within the hierarchical model such as block chain and digital platforms.

This paper contributes to the literature by exploring the connection between RL and CE in the context of Industry 4.0 that determines the critical success factors enabling sustainable inter-firm collaboration.

This paper aims to apply the concept of traditional branding constructs, i.e. brand image, brand perceived quality, brand satisfaction, brand trust and brand loyalty to a less explored field of halal brand products – halal brand image, halal brand perceived quality, halal brand satisfaction, halal brand trust and halal brand loyalty. Second, the present research is an effort to empirically validate the interrelationships among branding constructs such as brand image, brand perceived quality, brand satisfaction, brand trust and brand loyalty in a holistic framework to confirm whether these branding constructs also work for the halal brand in the same way to gauge Chinese Muslims consumers’ purchasing intentions.

This research used cross-sectional data from 481 Chinese Muslim students at 9 universities located in 3 cities of China through face-to-face and online survey methods. Data were collected from the consumers of halal milk brand. A theoretical model with the hypothesized relationships was tested with the help of the structural equation modelling procedure.

The results suggest that halal brand image has a significant and positive influence on the halal brand perceived quality, halal brand satisfaction, halal brand trust and halal brand loyalty. Similarly, the halal brand perceived quality, halal brand satisfaction, halal brand trust and halal brand loyalty significantly influence consumer halal brand purchase intention.

This study is conducted in the halal food sector of China and specific religious and migration contexts. Further investigations of the halal food purchasing behaviour of local Muslims, as well as international Muslim students in those Western countries which are famous destinations for international students for education, could yield varying results.

The outcomes achieved are helpful for commerce and government organizations for policy development to better meet the burgeoning demand for halal products by Chinese Muslims. These are also very helpful for producers and exporters who intend to penetrate the halal market in non-Muslim-dominant countries such as China.

Studies on understanding Muslim consumers’ purchasing behaviours in non-Muslim countries are limited. Given the fact, numbers of Muslims seem a smaller amount of China’s total population, but their total numbers are large compared with total numbers in many Muslim countries. Therefore, understanding their purchasing behaviours for halal products and influential determinants concerning such purchasing behaviours adds to the literature and helps the industry to better serve and capitalise on the growing market.

This study aims to document the hypogea churches in Lay Gayint Woreda, South Gondar, to provide information for future tourism development practices and serve as insurance against loss of value due to unmanaged deteriorative factors.

The study followed a descriptive research design with qualitative research approach. Data were collected through field observations, interviews and written sources examination.

The study explored the lesser-known hypogea churches, which have significant tourist attraction values, including environmental, historical and architectural significance. However, the use of these potential cultural resources for tourism development is not yet attempted, and their conservation status is found to be critical. This documentation work is significant both for the sake of future tourism development plans and as insurance against looming cultural losses.

This study did not record the ancient treasures of the churches because of the current political instabilities that hindered the access of data. However, it has implication for the need of an extensive documentation activity to trace the cultural resources in the remote areas of the country for future tourism development and conservation practices.

This paper documented the remote hypogea churches not only for the purpose of future tourism development plan but also as an insurance of their values against unmanaged destructive factors.

The health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations.

The proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly.

The analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods.

This study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.

This paper aims to develop a new method of protein detection in fabric using purified henna extract targeted to be used as a precautionary step by protein contact dermatitis (PCD) patients.

Henna contains lawsone, which is known for its reaction with amino acid, resulting in the formation of a highly coloured compound. Dye exhaustion test of 2 per cent purified henna extract was done on both protein and non-protein fabric, and the results were analysed using independent samples t-test to assess the significant differences of the comparison. Then, protein fabric indicator was developed by incorporating purified henna extract. It was tested for its dye ability and stability.

Protein fabrics showed a significant higher dye exhaustion (p = 0.001) in which pure silk came in first with 21.70 per cent ± 2.53, silk/rayon, 13.96 per cent ± 0.55, viscose/wool, 9.57 per cent ± 1.36, cotton/spandex, 5.89 per cent ± 0.86, linen/cotton, 5.26 per cent ± 1.29, cotton, 4.87 per cent ± 1.51, polyester/viscose, 4.69 per cent ± 1.09, linen, 4.56 per cent ± 0.86, polyester/cotton, 3.90 per cent ± 0.29 and polyester and 3.88 per cent ± 0.66. Two different forms of protein fabric indicator were developed: capsule and tablet. Both indicators showed good observable dye exhaustion and fixation results. Orange henna dye was fixed on protein fabric while non-protein fabric returned to its original colour after washing procedure. However, the tablet form showed better performance in terms of functionality and stability.

This study will help general community to better understand fibre and its constituents, especially protein where clothing is an integral part of human life because it comes in direct contact with the human skin. As PCD caused by clothing and textile is an uncommon disease, it may also help in creating awareness on how some people may develop contact dermatitis through fabric. This is because the fabric’s protein content is a causative allergen, which most people tend to overlook. For the researcher, this study helps to elucidate some critical areas in PCD for clothing and textile as this topic can be considered an understudy. There is no standard method to detect protein in fabric, especially blended fabric. Thus, a portable kit to detect protein in fabric will be developed in this study. This kit will benefit PCD patients to ease them in finding the right fabric for their skin.