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From 1979 to 2023, the takaful structure has been adopted in many jurisdictions, making the documenting of its early days of establishment relatively difficult and somewhat unreliable. This is unlike conventional insurance, where the history and legislation are well documented and archived in various research (Hellwege, 2016; Marano and Siri, 2017). The purpose of this paper is to provide a chronology for the establishment and development of takaful via the takaful establishment in each jurisdiction, documenting its first takaful operator and first takaful regulation.

This paper has used a qualitative method in the form of reviewing literature and available data such as journals, books and official resources. The data is thoroughly analysed in order to build the chronology for takaful. It adopted an exploratory research design, which is deemed suitable in situations where few works of literature have examined the subject (Neuman, 2014). The paper explores the establishment and non-establishment of takaful in 57 countries. The paper categorises the countries into seven regions starting with the GCC, Levant, Asia, Central Asia, Africa, Europe and Others.

The takaful chronology presented in this paper shows that takaful operations exist in 47 jurisdictions, starting from Sudan and the UAE in 1979, with the most recent adopters being Morocco and Iran in December 2021. It is found that 22 jurisdictions do not have takaful regulations, and the Takaful Act 1984, issued in Malaysia, is considered the first takaful regulation that sets the basis for other regulations that follow.

The paper contributes to the literature by providing a comprehensive chronology of takaful, especially as the few existing timelines have been found to be incomplete and consist of contradictory information.

The escalating volume of electronic waste (e-waste) presents a significant environmental and health hazard, emphasizing the importance of promoting e-waste recycling. Therefore, this study aims to utilize a valence theory approach to comprehensively understand the factors influencing individuals' intention to recycle e-waste.

A survey-based research approach was employed to examine the factors influencing consumers' e-waste recycling intention. Data were collected through an online survey questionnaire from Malaysian individuals aged 18 and above. The hypotheses were tested using a sample of 300 respondents, employing partial least squares structural equation modeling as a symmetric analysis technique. Additionally, fuzzy-set Qualitative Comparative Analysis (fsQCA), an asymmetric analysis approach, was used to gain deeper insights. Non-probability purposive sampling was utilized in the sampling process.

The PLS-SEM analysis revealed that subjective norms and willingness to change significantly impact e-waste recycling intention. Furthermore, perceived convenience, environmental concerns and social media usage were found to support the intention to recycle e-waste. The fsQCA results enhanced the interpretation by uncovering intricate relationships among the antecedents and identifying specific configurations that accurately predict consumers' recycling intentions.

The practical implications of this study emphasize the need for policymakers and practitioners to raise awareness regarding the benefits of e-waste recycling, enhance convenience in the recycling process and strengthen personal and subjective norms to encourage individuals to recycle their e-waste.

This study's originality lies in its adoption of a valence theory framework to comprehend the intentions behind e-waste recycling, as well as its inclusion of control variables during the analysis. This unique approach enhances the understanding of factors influencing e-waste recycling intention and provides valuable insights for policymakers and practitioners in developing effective strategies to promote e-waste recycling behavior.

This study aims to conduct a comprehensive literature review to examine the relationship between corporate governance characteristics and firms’ engagement in environmental, social and governance (ESG) activities. The review focuses specifically on academic papers published in ranked accounting and finance journals.

The analysis combines a structured literature review with citation analysis, topic modeling using a machine learning (ML) approach and a manual review of selected articles published between 2000 and 2021.

This paper contributes to corporate governance and ESG literature by conducting an in-depth review, offering a comprehensive analysis of the existing findings and identifying future research directions. From the reviewed literature, this paper proposes the following thematic areas: board characteristics, ownership structure and their impact on a company’s engagement in ESG activities; CEO characteristics and their influence on a company’s involvement in ESG activities; corporate governance and ESG as sources for transparency and legitimacy; internal and external assurance of a company’s involvement in ESG activities; and gender diversity and a company’s involvement in ESG activities.

The study provides a comprehensive understanding of corporate governance and ESG literature. The innovative combination of methods, including ML and manual techniques, enhances the ability to identify key research topics and uncover research directions in the field. Moving forward, this paper suggests several promising directions for future research, including examining the influence of emerging technologies on ESG reporting and assessing the impact of regulatory changes and context on the link between corporate governance and firms’ involvement in ESG practices.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Through its advanced computational capabilities, cyber–physical systems (CPS) proffer solutions to some of the cultural challenges plaguing the effective delivery of facilities management (FM) mandates. This study aims to explore the drivers for the uptake of CPS for FM functions using a qualitative approach – the Delphi technique.

Using the Delphi technique, the study selected experts through a well-defined process entailing a pre-determined set of criteria. The experts gave their opinions in two iterations which were subjected to statistical analyses such as the measure of central tendency and interquartile deviation in ascertaining consensus among the experts and the Mann–Whitney U test in establishing if there is a difference in the opinions given by the experts.

The study’s findings show that six of the identified drivers of the uptake of CPS for FM were attributed to be of very high significance, while 12 were of high significance. Furthermore, it was revealed that there is no significant statistical difference in the opinions given by experts in professional practice and academia.

The study’s outcome provides the requisite insight into the propelling measures for the uptake of CPS for FM by organisations and, by extension, aiding digital transformation for effective FM delivery.

To the best of the authors’ knowledge, evidence from the literature suggests that no study has showcased the drivers of the incorporation of CPS for FM. Hence, this study fills this gap in knowledge by unravelling the significant propelling measures of the integration of CPS for FM functions.