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This study aims to find whether sukuk (Islamic bonds) possess a safe haven property for investors or not.

To analyze this statement, the study used data from MSCI World conventional and MSCI World Islamic indices from August 17, 2012 to June 8, 2022. The study used the generalized autoregressive conditional heteroskedasticity (GARCH) variance technique, the most common technique used in stock data analysis.

The results dictate the absence of sukuk as a safe haven for investors as both the conventional and Islamic markets show decoupling behavior. The study finds concrete evidence of a strong association between the debt-based bond market and the Islamic sukuk market. As these markets mostly like to move in a parallel direction, a recession in a conventional bond market likely means a recession in the Islamic sukuk market.

This study is unique in incorporating the MSCI World Islamic Index and other Islamic indices of several Muslim countries, which was absent in previous research. Second, this study is unique because it adds a separate regression for the COVID era to show whether the movement of indices changed during regression.

Islamic Insurance (Takaful) played a dynamic role in Islamic Social Finance (ISF). The popularity of the Takaful concept is being increased with each passing day. To describe the process, it is important to understand past areas of research, research differences, areas of unknown Takaful research, as well as the existing data. However, the purpose of the paper is to provide a comprehensive analysis of the development of qualitative research by carefully examining the nature of science and aspects of social relations in ISF.

Field, concerns with analysing and measuring the scholarly literature, quantitative characteristics of sciences and scientific technologies are known as scient metrics. This research includes queries such as “Islamic Insurance”, “Takaful” and “Shariah Insurance” as a reference in “Article title, Abstract and Keywords” based on Scopus from 2002 to 2022. This analysis was conducted in February 2022. The Bibliometrics, R-Studio, VOSviewer and Excel software are used to analyse the collected data and apply the bibliometric analysis.

The gist of the results and findings is that there are clear research gaps in the existing literature on Takaful. The available research on this subject does cover historical background, concept and models of Takaful and customer satisfaction towards Takaful model. Very important areas such as use of technology for bringing innovation in Takaful products and Shariah issues in existing practice and their solutions were not found in the present literature on Takaful.

This is the comprehensive research article to examine current literature on Takaful with bibliometric analysis. Results and potential areas of this research could be much helpful for scholars and researchers to create more dynamic improvements in the scientific development of Takaful in ISF.

This review paper aims to provide a comprehensive retrospective analysis of the research landscape in the Halal industry through a threefold approach, including bibliometric analysis, latent theme identification and examination of driving factors of research citations.

A total of 2,510 research documents, identified from the Scopus through a systematic search, were considered for review. The review methods included bibliometric analysis of the domain, application of the machine learning structural topic modeling (STM) to identify latent themes and negative binomial regression to estimate the impact of paper, author and geographical characteristics on citation rates.

In addition to bibliometric insights, the STM uncovered 10 key topics within the Halal industry literature, including certification processes, dietary practices, consumer behavior, ethical considerations, supply chain management, market dynamics, Halal tourism, verification methods, regulatory frameworks and Halal food production and marketing. Positive associations with citation rates were found for abstract length, number of keywords, paper age, number of references, funding, number of authors and international collaboration, while a negative association was observed for authorship from Malaysia.

This paper, besides providing insights into research dynamics and citation patterns, also guides future research avenues in the Halal industry.

This study aims to systematically assess and map the evolution, themes and trends within Islamic marketing research, with a focus on identifying areas of high growth potential and guiding future research directions.

Using a bibliometric analysis and advanced structured topic modeling (STM) techniques, this research analyzes Islamic marketing studies published between 2000 and 2024. The comprehensive database search, centered on Scopus-listed publications, provides a macroscopic view of the field’s development over the past two decades.

This study reveals significant insights into the field of Islamic marketing, including the identification of key research themes, the growth in literature and geographical hotspots of scholarly activity. Emerging trends suggest a growing focus on consumer behavior, brand loyalty, Halal certification and ethical marketing practices.

This research is limited by its reliance on the Scopus database for source materials, and by focusing primarily on English-language publications. These constraints may have excluded relevant research in other languages or published in nonindexed formats.

This study offers valuable insights for marketers, businesses and policymakers aiming to engage the Islamic market. Understanding the dynamics of Islamic marketing can help in developing strategies that align with Muslim consumers’ values and preferences.

This research contributes to the Islamic marketing literature by using bibliometric analysis and STM, offering a unique, comprehensive overview of the field’s evolution, trends and future directions. It underscores the importance and growth of Islamic marketing as an independent academic discipline.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

This paper aimed to shed light on the relationship between blockchain technology intensity and tax evasion and whether this relationship is moderated by good governance.

Data from a sample of 50 European companies selected from the STOXX 600 index between 2010 and 2019 were used to test the model via panel data and multiple regression. Here, we used the generalized least squares method estimated on panel data. A multivariate regression model was used to analyze the moderating effect of good governance on the association between blockchain technology intensity and tax evasion. For the robustness analyses, we included the comparative study of legal systems. We performed an additional analysis by testing the dynamic dimension of the data set using the generalized method of moments to control for the endogeneity problem.

Expectedly, the results showed a negative relationship between blockchain technology intensity and tax evasion. Furthermore, the findings suggest that the moderating variable negatively affects the relationship between blockchain technology and tax evasion.

To our knowledge, this study supports the existing literature. Firstly, it expands the scientific debate on tax evasion. Secondly, it extends the scope of the agency theory, which is used to explain the phenomena associated with tax evasion. This study is one of the first to examine the moderating effect of good governance on the association between blockchain technology intensity and tax evasion.