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This study aims to mark the 50th anniversary of social marketing as an innovative social change discipline; this viewpoint reviews “epistemological diversity” within social marketing and calls for its incorporation in the expansion of the discipline.

Cognisant of the visible (e.g., gender, age, race) and invisible (e.g., epistemology, experience, socioeconomic status) dimensions of diversity, this viewpoint focuses on one invisible dimension of diversity – epistemology. Using secondary data, an epistemological review of social marketing is undertaken by selecting five aspects that serve as potential indicators: professional associations, global conferences, education and training, research and publication and practice. Several recommendations are made to expand epistemological diversity in social marketing.

The epistemological review of diversity within social marketing reveals disparities between the Global North and South. These disparities are due to varying opportunities for participation in associations, conferences, education and training, and research and publication. In addition, there exist “hidden” social change practitioners who implement programmes in the Global South that are consistent with social marketing practice, but continue to be unchronicled and unheralded.

For many years, social marketing scholars and practitioners have been working to expand the boundaries of the discipline, with a focus on theory and application. Although the authors acknowledge that diversity and equity are inherently central to this discipline, epistemological diversity, specifically as linked to the geographies of the Global South, has been largely overlooked. Our viewpoint nudges the social marketing community into including epistemological diversity in the ongoing discourse around broadening and deepening the discipline of social marketing.

The current study aims to investigate the effects of opportunities and threats of using blockchain on the input, processing and output components of the financial reporting process within the fintech landscape.

This study administered a questionnaire in Iran to 121 university lecturers in accounting and auditing, independent auditors, financial managers and internal auditors to better understand the effect of blockchain on financial reporting. The responses were analysed using SPSS and Smart PLS Software.

This study demonstrates how blockchain technology can improve the financial reporting process in the fintech industry by providing opportunities for remote labour, improved accountant roles and task automation. Threats include the requirements for blockchain expertise, standardisation, security issues and decreased flexibility. Limited R&D resources pose problems for small businesses. The main advantages of outputs are continuous, timely financial reporting and comparability; the risks associated with customised reports and regulatory difficulties in managing non-financial and financial data are the main disadvantages. The results show that all indicators of opportunities to use blockchain positively and significantly affect financial reporting opportunities within the fintech context. In addition, all the indicators of the threats of using blockchain have a positive and significant effect on the threats of financial reporting in the fintech context.

The present study is designed to meet the needs of a blockchain-based financial reporting system in the fintech context. Rapid growth and transformation into an advanced digital system has increased the importance of understanding the effects of the opportunities and threats of applying blockchain technology.

This chapter employs a single case study of Safeer (pseudonym) (25 years old) who has been part of the tribal conflict involving the Rind clan against another Rind clan and the Lashari tribe in the Dadu district of Sindh, Pakistan. It investigates how youth, in the name of tribal honor, actively resist rival tribes, serving as potential actors during conflicts. Safeer actively engaged in numerous tribal conflicts during broad daylight. Not only due to tribal conflicts but also out of fear of the police, Safeer led a conscious and vigilant life from childhood to protect himself and his extended clan members, reflecting a shared sentiment among the youth of his clan. Safeer’s case provides insight into the prevailing perception of youth across more than eight tribal districts in Sindh, excessively affected by conflict consequences yet actively resisting and challenging atrocities. He, like many others in his clan, felt compelled to seek revenge for the murder of an innocent clan member. Safeer’s perspective also highlights the police’s incompetence and the public’s lack of trust in formal justice (especially courts), as a major cause leading young individuals into tribal clashes. Safeer’s case extends beyond an individual narrative, illustrating a broader political landscape in the vast geography of Sindh. It reflects the adversity and resilience of rural youth facing immense challenges, including declining agricultural activities due to reduced irrigation water, rising agricultural input prices, inflation, unemployment, and a shrinking small-scale industrial base in rural Sindh.

The purpose of this paper is to evaluate existing studies on the relationship of corporate governance with firm performance in different regions and address the need for similar analysis for the Gulf Coperation Council (GCC) sector. The banking sector comprises the conventional and Islamic banks in the GCC sector and is important due to their ability to bring stability to this region. Existing studies that measure the relationship of GCC sector conventional banks and firm performance are limited. This study proposes a need for future research on corporate governance in the GCC region.

This paper will review and analyze the different empirical and theoretical contributions in establishing the relationship between corporate governance and firm performance.

This paper will create a focus for future research of measuring the impact of corporate governance mechanism on firm performance. The regulators will be encouraged to focus on more research studies for the GCC sector development in the field of corporate governance of the banking sector.

The existing studies are valid and practicable for the region under study, and the results need not be applicable for other business environments. In addition, the evolving business and economic environment have always brought about inconsistent conclusions; thus, the period of study can always give varied results.

The analysis undertaken in this paper will address the literature gaps for the GCC banking sector and play an instrumental role for future studies by theoreticians and regulators.

This paper identifies the literature gaps for the GCC region and analyses the most applicable existing studies that can be useful for the banking sector corporate governance improvement. This paper will create opportunities for the future researchers.

The purpose of this study is to explore learner-centered emerging technologies, methods of learning with artificial intelligence (AI), student engagement and concerns related to the assessment process at public and private universities in Bangladesh.

The study employed a qualitative research approach to investigate innovations in learning, student engagement and assessment concerns. Respondents were categorized into three clusters: Cluster-A comprised students from various academic levels, Cluster-B included faculty members with online teaching experience and Cluster-C consisted of information and communication technology (ICT) experts from university information technology (IT) departments. Qualitative data were collected via in-depth interviews.

The research findings indicate the widespread acceptance and utilization of learner-centered technologies, with online and hybrid learning offering flexibility, accessibility, self-paced learning and better student engagement opportunities. While AI platforms like ChatGPT and Gemini are valued for clarifying theoretical concepts, concerns exist regarding their ethical use, particularly in completing assignments. The assessment process faces challenges related to online exams, with concerns over cheating prompting calls for AI-supported solutions. However, there is a gap between commitment and action in regulating unethical AI usage, highlighting the need for clearer guidance and investment in technological solutions for an effective assessment process.

Limitations of this study include the inability to quantify the satisfaction level with the emergence of learner-centered technologies, which could be better explained through a quantitative study.

This study’s originality and value stem from its thorough investigation of innovations in learner-centered education considering the emergence of AI and student engagement, uniquely focusing on the perspective of a developing country.

This chapter reviews existing literature to discuss the intersection of gender and armed conflict in Kashmir, with a specific focus on social work interventions. It gives an overview of the history of conflict in Kashmir, the various types of violence experienced by women, men, and people who identify as gender non-conforming, and talks about how gendered norms and expectations shape resistance and resilience in Kashmir, as well as the difficulties people face in peacebuilding initiatives. The chapter concludes by highlighting how crucial it is to treat gender issues seriously in social work interventions in Kashmir and how policymakers, practitioners, and academics must strive to support gender equity and peacebuilding in the area.

Construction Hazard Prevention through Design (CHPtD) is recognized as a pivotal strategy for enhancing workplace safety. Despite its theoretical potential, practical implementation faces challenges, particularly regarding designers' familiarity with field-level operations and hazard recognition. This study aims to investigate whether ChatGPT can address these challenges by assisting in hazard recognition during CHPtD sessions.

The research utilized a randomized controlled experiment to assess ChatGPT as an intervention in supporting hazard recognition during CHPtD sessions. The study involves 162 civil and construction engineering student participants, representing future professionals. Participants engaged in hazard recognition during CHPtD sessions either with or without the assistance of ChatGPT.

Participants who utilized ChatGPT during CHPtD sessions demonstrated a significant improvement in hazard recognition, identifying approximately 40% more hazards compared to those who did not use ChatGPT. These findings underscore the efficacy of ChatGPT in supporting CHPtD efforts.

The results highlight the practical utility of harnessing ChatGPT in CHPtD sessions to enhance hazard recognition and ultimately promote workplace safety. By leveraging ChatGPT, designers and engineers can better anticipate and mitigate potential hazards during the design phase, thus creating a safer working environment for field workers.

This research contributes to addressing the challenges associated with implementing CHPtD by introducing ChatGPT as a valuable tool to support hazard recognition. By demonstrating the effectiveness of ChatGPT in enhancing hazard identification during CHPtD sessions, this study offers a novel approach to promoting the adoption of CHPtD and advancing workplace safety initiatives.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

The current research aims to investigate the impact of coronavirus 2019 (COVID-19) evolution, government stringency measures and economic resilience on stock market volatility in the Middle East and North African (MENA) emerging markets. Other macroeconomic factors were also taken into account.

Based on financial data from 10 selected MENA countries, we tested an integrated framework that has not yet been explored in prior research. The exponential generalized autoregressive conditional heteroskedasticity (E-GARCH) was adopted to analyze data from March 2020 to February 2022.

Our research illustrates the direct and indirect effects of the virus outbreak on stock market stability and reports that economic resilience could alleviate the volatility shock. This finding is robust across the various proxies of economic resilience used in this study. We also argue that the negative impact of the pandemic on equity market variation gets more pronounced in countries with higher level of stringency scores.

Policymakers ought to strengthen their economic structures and reinforce the economic governance at the national level to gain existing and potential investors’ trust and ensure lower stock market volatilities in times of crisis. Our study also recommends some key economic factors to consider while establishing efficient policies to tackle unexpected shocks and prevent financial meltdowns.

Our findings add to the evolving literature on the reaction of economic and financial markets to the sanitary crisis, particularly in developing countries where research is still scarce. This study is the first of its kind to investigate the stock market reaction to stringency measures in the understudied MENA region.