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This study comprehensively aims to review the key influential and intellectual aspects of spillovers between Islamic and conventional financial markets.

The study uses the bibliometric and content analysis methods using the VOSviewer software to analyse 52 academic documents derived from the Web of Sciences (WoS) between 2015 and June 2022.

The results demonstrate the influential aspects of spillovers between Islamic and conventional financial markets, including the leading authors, journals, countries and institutions and the intellectual aspects of literature. These aspects are synthesised into four main streams: research between stock indexes; studies between stock indexes, oil and precious metal; works between Sukuk, bond and indexes; and empirical studies review. The authors also propose future research directions in spillovers between Islamic and conventional financial markets.

Our study is subject to several limitations. Firstly, the authors only used the WoS database. Secondly, the study only includes papers and reviews written in English from the WoS. This study assists academic scholars, practitioners and regulatory bodies in further exploring the suggested issues in future studies and improving and predicting economic and financial stability.

To the best of the authors’ knowledge, no extant empirical studies have been conducted in this area of research interest.

This study investigates the potential effectiveness of ChatGPT in enhancing the academic writing skills of Saudi EFL undergraduate students. It also examines the challenges associated with its use and suggests effective ways to address them in the education sector.

The study employed a sequential mixed-methods approach, which involved distributing questionnaires to gather data from students, followed by conducting semi-structured interviews with a purposeful selection of eight students and six teachers.

The findings revealed that students were generally satisfied with the effectiveness of ChatGPT in enhancing their academic writing skills. However, they also pinpointed some challenges associated with using ChatGPT, including plagiarism, overreliance, inadequate documentation, threats to academic integrity, and inaccurate information. To alleviate these challenges, effective strategies include deploying detection tools, equipping students and educators with training sessions, and revisiting academic policies and assessment methods. It is recommended that ChatGPT be used responsibly as an assistant tool, in conjunction with students' ideas and teachers' feedback. This approach can significantly enhance students' writing skills and facilitate completing their research projects and assignments.

ChatGPT can be a valuable tool in the educational landscape, but it is essential to use it judiciously. Therefore, teachers' effective integration of ChatGPT into their classrooms can significantly enhance students' writing abilities and streamline their research process.

This study contributes to recent AI-based research and provides practical insights on the responsible integration of ChatGPT into education while addressing potential challenges.

Women’s employment has been a contentious issue in the Arab world. However, in today’s changing work conditions, workplaces, and the growth in education, women have increasingly occupied higher-level roles, and their presence in diverse industries has expanded. The study takes a holistic approach to female labor force participation (FLFP) in Oman, encompassing various theories and factors influencing women’s engagement in private sector jobs.

The research employed an exploratory design followed by a qualitative analysis based on a constructivist grounded theory approach. The study involved 170 semi-structured face-to-face interviews with women workers in Oman’s private sector. It explored work–family conflict (WFC), work–life balance (WLB), organizational and family support, and societal bias impacting female labor force participation.

The low FLFP in Oman’s private sector is a complex issue shaped by various interconnected challenges. Our qualitative analysis synthesizes a conceptual framework that highlights WFC and WLB as pivotal factors behind low FLFP, as women in the private sector face long work hours, low salaries, and high responsibilities. Our study confirms that these women receive substantial familial support in dealing with WFC but lack organizational support and family-friendly policies (FFPs).

Addressing the low participation of women in Oman’s private sector labor force necessitates comprehensive strategies encompassing legislative measures, cultural shifts, and organizational reforms. Implementing these strategies creates an environment where women feel empowered to participate and actively thrive in the private sector workforce.

This study indicates the presence of some serious social issues, such as society’s bias, lack of support for working women, and the gender gap in the private sector workforce, which has broad implications and significance for Oman. The study indicates the positive role of support women receive from their families, organizations, and Oman labor law and what could improve their participation further.

This study addresses the limited research on challenges encountered by women in Oman’s private-sector workforce. Our research addresses this gap by answering key questions, collecting and interpreting data, and developing a comprehensive conceptual framework. This framework aims to elucidate the factors contributing to women’s reluctance to pursue private-sector employment, considering the diverse issues they confront.

Airline scheduling is an extremely complex process. Moreover, disruption in a single flight may damage the entire schedule tremendously. Using an efficient recovery scheduling strategy is vital for a commercial airline. The purpose of this paper is to present an integrated aircraft and crew recovery plans to reduce delay and prevent delay propagation on airline schedule with the minimum cost.

A mixed-integer linear programming model is proposed to formulate an integrated aircraft and crew recovery problem. The main contribution of the model is that recovery model is formulated based on individual flight legs instead of strings. This leads to a more accurate schedule and better solution. Also, some important issues such as crew swapping, reassignment of aircraft to other flights as well as ground and sit time requirements are considered in the model. Benders’ decomposition approach is used to solve the proposed model.

The model performance is also tested by a case including 227 flights, 64 crew, 56 aircraft and 40 different airports from American Airlines data for a 24-h horizon. The solution achieved the minimum cost value in 35 min. The results show that the model has a great performance to recover the entire schedule when disruption happens for random flights and propagation delay is successfully limited.

The authors confirm that this is an original paper and has not been published or under consideration in any other journal.

This study aims to explore the evolution of the Islamic capital market (ICM) from the perspective of research publications.

A bibliometric analysis was applied based on selected publications from the Web of Science Core Collection (WoSCC) database from 2000 to 2021. The study adopted VOSviewer software which was developed by Leiden University.

This study has some implications that need urgent action. Firstly, there are some areas that have received little attention among researchers, although they are relevant to the industry, for instance, in fintech and blockchain in ICM. Secondly, the inconsistent frequency of publications in some niche areas may suggest that there are unprecedented events that hinder further research; probably, the researcher may anticipate more information and progress in the industry. Thirdly, the need to strengthen the collaboration between industry and academia to advance research.

This study considered only the WoSCC database. The provider of WoSCC is Clarivate (formerly known as Thomson Reuters), where access to publications is limited to institutional subscribers. The implications of this study are to identify and propose future research trends in the field of ICM.

To the best of the authors’ knowledge, the present study is among the pioneer studies in analysing bibliometric focusing on ICM. Previous research has focused on Islamic finance and banking, and not specifically on ICM. Accordingly, this study sheds light on research gaps in ICM.

The continuous increase of energy demands is a critical worldwide matter. Jordan’s household sector accounts for 44% of overall electricity usage annually. This study aims to use artificial neural networks (ANNs) to assess and forecast electricity usage and demands in Jordan’s residential sector.

Four parameters are evaluated throughout the analysis, namely, population (P), income level (IL), electricity unit price (E$) and fuel unit price (F$). Data on electricity usage and independent factors are gathered from government and literature sources from 1985 to 2020. Several networks are analyzed and optimized for the ANN in terms of root mean square error, mean absolute percentage error and coefficient of determination (R²).

The predictions of this model are validated and compared with literature-reported models. The results of this investigation showed that the electricity demand of the Jordanian household sector is mainly driven by the population and the fuel price. Finally, time series analysis approach is incorporated to forecast the electricity demands in Jordan’s residential sector for the next decade.

The paper provides useful recommendations and suggestions for the decision-makers in the country for dynamic planning for future resource policies in the household sector.