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This study examines the determinants of idiosyncratic risk (IR) or unsystematic risk. The study also examines the determinants of IR by dividing the firms into different categories: beta-based firms, liquid and illiquid firms and financially constrained (FC) and unconstrained (FUC) firms.

The fixed effects static panel data model specifications are formulated based on Hausman (1978) test for BRICS (Brazil, Russia, India, China, and South Africa) member countries over the period 2000–2019. Moreover, the t-test is applied to see whether the returns of different types of portfolios are significantly different.

The portfolio analysis results show that, on average, high IR firms tend to be small in size, highly leveraged, have low competitiveness, low profitability, less dividend yield and low returns for all the sampled countries. The sample paired t-test also confirms that a significant difference exists between extreme portfolios: small and large size and low IR and high IR portfolios. The panel regression results show that firm size, market power, price-to-earnings ratio, return on equity (ROE) and dividend yield negatively relates to IR. Yet, both leverage and liquidity are positively related to IR. However, the sign of momentum returns is mostly positive for the entire sample. The coefficient values for high-beta, FC and illiquid firms are more significant and large than the firms' counterparts for all BRICS member countries. These results support the hypothesis of an under-diversified portfolio and suggest that the above-mentioned firm-specific variables are the significant determinants of unsystematic risk.

The securities exchange commission, as the supervisor of the public limited companies, needs to increase its role in investor protection related to the uncertainty of investment in the capital market. Accordingly, in making investment decisions in a stock exchange, investors can use the information that captures unsystematic risk for investment decision-making.

This study is the first to explore the determinants of IR in top emerging countries. Second, none of the existing studies has focused on the determinants of the IR based on different categories of firms.

This study intends to explore the influence of emotional intelligence on employee self-efficacy in Pakistan's telecom industry. Besides, it explores the mediating effect of emotional labor (surface acting and deep acting) between them. This study also tests the relationship between emotional labor (surface acting and deep acting) and self-efficacy in the customer care of Pakistan's telecom division.

The study leads forward with a positivist approach to obtain data in two different waves as a time lag study from the big five telecom companies operating in Pakistan. The data was collected from 270 employees working in Customer Services in the Telecom sector.

The results reveal that there exists a positive relationship between emotional intelligence and self-efficacy in customer care employees in Pakistan's telecommunication division sector. Moreover, emotional labor (deep acting) partially mediates the relationship between emotional intelligence and self-efficacy, and surface acting could not mediate the relationship among the employees of customer care in Pakistan's telecom division.

Management of emotions at the workplace has been an immensely vital area in managing the performance of employees, especially in customer-centric jobs, where dealing with customers is the prime focus and achieving customer satisfaction is the utmost outcome. There is limited evidence of the relationship between emotional intelligence and self-efficacy specifically in the customer care of the Telecom sector.

This qualitative study deals with the career longevity phenomenon in the hospitality sector of Pakistan and aimed at exploring the factors which become the reason for continuing services in this sector for a longer period despite the prevailing perception of the short-term and unsatisfactory hospitality careers.

The study has taken up an interpretive social constructivism approach to carry out the research. The purposive sampling technique is used to solicit expert insights into the dynamics of the hospitality career. A thematic analysis was employed to identify the common themes, extract the meaning from the discussion patterns of the respondents, and outline viewpoints and ideas of the respondents.

The findings of the study are discussed at three levels of career, i.e. entry level, development level, and consolidation level. Long careers in the hospitality sector are a product of dedication and commitment to the job, professionalism, variety, complexity of the job, and healthy relationship with coworkers, supervisors, and guests.

The study links the belief of belonging and socialization attributes to the retention of employees in the hospitality sector jobs. Secondly, the study uses a qualitative approach to provide a diverse perspective of employee–industry loyalty rather than employee–organization loyalty. Thirdly, the study brings forth practical implications for personnel managers in the hospitality sector and proposes that the management should systematically stimulate the socialization of the workers to hold the talent despite providing workers with the opportunity to join another sector. Finally, the study informs about research limitations and directions for future research.

This study aims to examine the moderator effect of religiosity on the relationship between halal brand awareness and habit towards purchasing decisions of halal products.

The quantitative method is used in this study. Descriptive and statistical (multiple and moderated regression) analyses are employed to test the hypothesis according to the research model. The data is collected using a cross-sectional design from 197 respondents consisting of business owners in North Sumatera, Indonesia.

It is found that both halal brand awareness and habit have a positive and significant effect on the purchasing decision of halal products. Meanwhile, religiosity significantly acts as a moderating variable in the relationship between awareness and purchasing decisions, as well as habit and purchasing decisions.

This study revealed the important factor of religiosity as a moderating factor in purchase decisions of halal products. The government may need to collaborate with Islamic educational institutions to raise awareness of the halal concept and product awareness. It is assumed that individuals who know about the Islamic religion will have a higher degree of awareness of halal products compared to individuals with limited knowledge of Islam; thus, providers of Islamic education play a crucial role in raising the level of awareness of halal products. Schools may serve as catalysts for the dissemination of knowledge of halal products.

Developing halal product markets can be done by enhancing the religiosity level of consumers, one of them through attending formal or informal religious classes.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

The purpose of the paper is twofold. Firstly, this study aims to investigate the factors influencing stakeholders’ intention to invest in Shariah-compliant social impact bonds (SC SIBs) in Malaysia. Secondly, this study compares the differences in the perception of different stakeholders on the importance of the factors.

Using the extended theory of planned behaviour, the study undertakes a questionnaire survey on licensed capital market investors and individuals involved in the development of the financial market (developers). A total of 260 complete and valid responses were obtained from the survey. Multiple regression and Mann–Whitney tests were carried out to achieve the two objectives, respectively.

The results reveal that attitude (β = 0.447, p < 0.01), subjective norm (SN) (β = 0.255, p < 0.01) and moral norm (MN) (β = 0.163, p < 0.01) are significantly positive predictors of intention to invest in SC SIBs. In terms of the differences in the perceptions of the two parties, the results show that the factors have more effect towards developers than investors.

The empirical evidence from this study on the factors that influence stakeholders’ participation in SC SIBs is useful to the policymakers and interested parties in taking the next steps to develop, implement and promote SC SIBs to stakeholders in Malaysia. Fund managers can use the study’s insights to promote positive attitudes, SNs and MNs towards SC SIBs, especially targeting developers who are more influenced by these factors. More importantly, the results indicate a need for different strategies to influence the stakeholder investment behaviour of SC SIB in Malaysia to ensure that it is sustainable and viable in the long run.

The adverse environmental impacts of menstrual products are a significant concern because of their widespread use and non-biodegradability. With various global and regional initiatives on sustainability, there is now greater public awareness about environmental protection. This heightened awareness has resulted in the exploration of alternative methods to reduce waste, such as the development and use of sustainable menstrual products. This study aims to examine the factors that influence Indian women’s purchasing intention in relation to sustainable menstrual products. The study uses the Stimulus–Organism–Behavior–Consequence (SOBC) theory as the appropriate grounded theory to explain these determinants.

Cross-sectional data were collected from 720 women who have been using sustainable menstrual products. For analysing the model, the study performed structural equation modelling using AMOS.

The study’s results indicated that health consciousness, sustainability knowledge and environmental responsibility are positively associated with self-identity and mindfulness. Additionally, the study found that organism states have a positive impact on the purchase intention of sustainable menstrual products. Furthermore, there was a significant relationship observed between purchase intention and mindful consumption. The study also discovered significant positive relationships between satisfaction and key associations.

This study may be considered pioneering, as it establishes a connection between the usage of sustainable menstrual products and concepts such as mindfulness and mindful consumption.

Finance continuously evolves as the technological innovation progresses in the society. Numerous prior studies have discussed emerging financial services due to this innovation. However, limited scholarly work has evaluated the trends and state of the art of financial innovation. Therefore, this study aims to review recent literature on financial innovation by using a bibliometric and content-analysis approach.

Documents for this study are sampled from financial innovation, a journal focusing on recent innovations in finance. A total of 354 peer-reviewed articles published in eight years (2015–2022) are first examined and mapped using the bibliometrix package in RStudio software. Furthermore, content analysis was performed to investigate the adopted research methods and types, and produce directions for future studies.

The trend of financial innovation research kept increasing, with China as the leader in publication quantity, affiliation productivity and paper citation acquisition. Topics related to “FinTech,” “Bitcoin” and “Covid-19” have been the most discussed topics by financial innovation researchers. FinTech and Bitcoin studies are expected to grow in emerging countries like China, India and Pakistan. The study also indicates that most financial innovation studies use quantitative research methods and are categorized as empirical papers.

This study contributes to the finance literature by comprehensively evaluating current research on financial innovation using one specific journal in the field. Also, this study examines financial innovation literature using different approaches from previous bibliometric financial innovation studies.

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.

Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.

To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.

The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.

Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.

This research has not been published anywhere else.