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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.

This study offers an in-depth examination of Google Bard, an advanced artificial intelligence chatbot created by Google, focusing specifically on its potential impact on academic research. This discussion aims to comprehensively explore the features of Google Bard, highlighting its capabilities in data management, facilitating collaborative discussions, and enhancing accessibility to complex research. In addition to the aforementioned positive characteristics, we will also delve into the limitations and ethical considerations associated with this innovative device. The functionality of the system is constrained by the limitations imposed by its pre-established algorithms and training data. In addition, there are significant concerns regarding data privacy, potential biases in its responses stemming from its training data, and the wider societal implications associated with a heavy reliance on machine-generated content. Ensuring responsible and ethical utilization of Bard necessitates Google's provision of transparent communication regarding its development process. In light of the prominent functionalities demonstrated by Google Bard, it is imperative for researchers to engage in a rigorous examination of the information it presents, thereby safeguarding against the inadvertent propagation of misinformation or biased viewpoints. This will lay the groundwork for its effective integration into the academic research methodology.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.

Neuromarketing is a marketing communication field that applies neuroscience and physiological research tools to study consumer behavior toward stimuli, viz., ads and brands. This study aims to assess research trends in the neuromarketing field on the most influential journals, authorships, countries, citations and co-occurrences. The Scopus database is used to analyze identified articles from 2013 to 2022 and for the eligible research articles, a “systematic methodological review” (SMR) on consumer behavior through neuromarketing approach was done. “Visualization of Science (VOS)” viewer and “Biblioshiny” by R-studio software have been used for mapping the keyword analysis, co-citation analysis and author occurrence analysis. It was further found that of the top 10 academic institutions, the list is dominated by the six Asian institutions. It was further witnessed that journal “Physiology and Behavior” is trending as the most dedicated and emerging journals on neuromarketing and consumer behavior. Asian nations such as Bangladesh, China, India, Indonesia, etc., are turning out to be an emerging collaborators and publishers in this niche area of research, thereby giving tough competition to most developed countries. The findings of the thematic mapping show that neuromarketing is itself a very novel and newest area of study and topics such as “human marketing,” “neuromarketing,” “consumer behavior” and “electroencephalography” are new dimensions that can be looked upon in future.

Purpose: As a result of the transition from the paradigm of ‘knowledge and skills’ learning to the university of uncertainty, the concept of VUCA has grown for the revision of various adaptive models of educational practices.

Need for Study: The primary goal is to explore the research field of the educational system and learning environments; the investigation of scientific knowledge is enabled by bibliometric analysis, revealing through it the fluctuations of the literature.

Methodology: To better view the historical evolution of publications in the educational system field, two data samples were integrated into this study, with the focus of the chapter being on the authors, keywords, articles, journals, subject analysis, word cloud analysis, and cluster analysis. The first includes 1,620 Web of Science-recorded documents published between 1991 and 2022, and the second sample comprises 159 Scopus-recorded papers published between 1978 and 2022.

Findings: The first empirical results show that interest in this subject escalated around 2008. The main concerns around this research field are the labour market, teaching-learning, technology, economic development, the medical field, and sustainability. After 2020, a new subject took amplitude, seemingly connected to the educational system and learning environment, that subject being ’COVID-19.

Practical Implications: The relationship between authors, keywords, and sources is illustrated through Sankey diagrams, from which valuable information can be extracted: nine of the Scopus authors have published articles in the ‘Journal of Higher Education Policy and Management’ documents that present the following list of keywords: ‘higher education’, ‘education’, ‘management’, ‘leadership’, and ‘tertiary education’.

The primary objective of this research is to conceptualize the integration of enterprise risk management (ERM) as a mechanism to enhance the connection between corporate sustainability (CS) reporting and financial performance. This study suggests that future researchers should validate the proposed conceptualization by conducting a comprehensive content analysis of sustainability reports of Malaysian oil and gas companies. This analysis will allow for the collection of pertinent data regarding CS reporting and ERM implementation. The present study takes a comprehensive approach by integrating legitimacy, stakeholder, and resource-based view (RBV) theories, proposing a robust conceptual design that emphasizes the role of ERM in the connection between CS reporting and firm performance. Drawing on theoretical foundations, this study proposes that CS reporting will have a direct effect on financial performance. Moreover, the integration of ERM serves to strengthen the nexus between CS reporting and financial performance. This study offers valuable insights for stakeholders in the oil and gas sector by providing strategic guidance to enhance financial performance not only through CS reporting but also by implementing ERM. Moreover, the framework proposed in this study is expected to bring tangible and intangible benefits to corporations, including reducing information asymmetry, improving the quality of disclosure, and creating value within the field of CS. The proposed conceptual framework holds great significance as it enhances the applicability of legitimacy, stakeholder, and RBV theories, while also creating value for stakeholders through CS reporting and the adoption of risk management practices to enhance financial performance.