The young Muslim generation’s embracing digital platforms for Zakat payments represents a dynamic fusion of enduring religious values with the modern digital landscape, heralding a new era in Islamic charitable practices. This trend illustrates a profound transformation within the Islamic world, where the pillars of faith are being reimagined and revitalized through the lens of technological advancement. The present study delved into the factors influencing the young Muslim generation’s preference for digital platforms in Zakat transactions across Indonesia and Malaysia. We examined variables such as Performance Expectancy, Effort Expectancy, Social Influence, Trust, Zakat Literacy, and Digital Infrastructure, aiming to discern their impact on the propensity for digital Zakat contributions with the extension of Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research encompassed a diverse sample of 382 participants and utilized advanced methodologies, specifically Partial Least Squares Structural Equation Modeling (PLS-SEM) and PLS Multi Group Analysis (PLS-MGA), for rigorous data analysis. The results indicated that Effort Expectancy, Social Influence, Digital Infrastructure, and Zakat Literacy notably influenced the use of digital platforms for Zakat. Furthermore, PLS-MGA uncovered significant cross-country differences where Digital Infrastructure showed a more pronounced positive impact in Malaysian context, whereas Social Influence had a greater effect in Indonesia. These findings offer critical insights into the young Muslim community’s digital engagement for religious financial obligations, underscoring the need for tailored digital Zakat solutions that cater to the unique preferences of this demographic. This research not only enriches the understanding of digital adoption in religious practices but also challenges the notion of a universal approach, advocating for context-specific strategies in the realm of digital religious financial services. Future researchers are suggested to consider longitudinal investigations as well as examining cross-regional contexts in this realm of research.

This paper presents an effective method for performing audio steganography, which would help in improving the security of information transmission. Audio steganography is one of the techniques for hiding secret messages within an audio file to maintain communication secrecy from unwanted listeners. Most of these conventional methods have several drawbacks related to distortion, detectability, and inefficiency. To mitigate these issues, a new scheme is presented which combines the techniques of image interpolation with LSB encoding. It selects non-seed pixels to allow reversibility and diminish distortion in medical images. Our technique also embeds a fragile watermarking scheme to identify any breach during transmission to recover data securely and reliably. A magic rectangle has also been used for encryption to enhance data security. This paper proposes a robust, low-distortion audio steganography technique that provides high data integrity with undetectability and will have wide applications in sectors like e-healthcare, corporate data security, and forensic applications. In the future, this approach will be refined for broader audio formats and overall system robustness.

This research analyses digital nomads’ relationship with tourism, their motivations for travelling and their expectations of the destinations they visit. In addition, it aims to understand the lifestyle of this public and their preference for sustainable destinations, as well as the implications for policies and the organisation of tourism infrastructure, in line with their specific needs. A questionnaire was administered to users of open-access social networks or members of online digital nomad communities (n = 34), between December 2022 and March 2023. Descriptive statistics, construct validations, reliability and internal consistency of the measures were carried out and Pearson’s linear correlation coefficient (r) was applied between items of the same scale and different scales. The results indicate that quality of life, life-work balance, living with other cultures, being in contact with nature, escaping from large urban centres, indulging in tourism all year round and travelling for long stays, are the main motivations of this public. The importance of quality Wi-Fi, flexible tourist services and support services is emphasised as the main attributes to be considered in tourist destinations.

Resisting the adoption of medical artificial intelligence (AI), it is suggested that this opposition can be overcome by combining AI awareness, AI risks, and responsibility displacement. Through effective integration of public AI dangers and displacement of responsibility, some of these major concerns can be alleviated. The United Kingdom’s National Health Service has adopted the use of chatbots to provide medical advice, whereas heart disease diagnoses can be made by IBM’s Watson. This has the ability to improve healthcare by increasing accuracy, efficiency, and patient outcomes. The resistance may be due to concerns about losing jobs, anxieties about misdiagnosis or medical mistakes, and the consciousness of AI systems drifting more responsibility away from medical professionals. There is hesitancy among healthcare professionals and the general public about the deployment of AI, despite the fact that healthcare is being revolutionised by AI, its uses are pervasive. Participants’ awareness of AI in healthcare, AI risk, resistance to AI, responsibility displacement and ethical considerations were gathered through questionnaires. Descriptive statistics, chi-square tests and correlation analyses were used to establish the relationship between resistance and medical AI. The study’s objective seeks to collect data on primary and public AI awareness, perceptions of risk and feelings of displacement that the professionals have regarding medical AI. Some of these concerns can be resolved when AI awareness is effectively integrated and patients, healthcare providers, as well as the general public are well informed about AI’s potential advantages. Trust is built when, AI related issues such as bias, transparency, and data privacy are critically addressed. Another objective is to develop a seamless integration of risk management, communication and awareness of AI. Lastly to assess how this comprehensive approach has affected hospital settings’ ambitions to use medical AI. Fusing AI awareness, risk management, and effective communication can be used as a comprehensive strategy to address and promote the application of medical AI in hospital settings. An argument made by Chen et al. is that providing training in AI can improve adoption intentions while lowering complexity through the awareness of AI.

This study investigates the influence of service quality, destination facilities, destination image, and tourist satisfaction on tourist loyalty in the Pasar Lama Chinatown area of Tangerang City. Utilizing data from 400 respondents, the study employed structured questionnaires analyzed through descriptive statistics, reliability analysis, exploratory and confirmatory factor analysis, and structural equation modeling (SEM). The results reveal that service quality (β = 0.47, p < 0.001), destination facilities (β = 0.33, p < 0.001), and destination image (β = 0.4, p < 0.001) all significantly enhance tourist satisfaction, which in turn has a strong positive effect on loyalty (β = 0.58, p < 0.001). Direct paths also show that service quality, destination facilities, and destination image independently contribute to tourist loyalty. Bootstrapping confirms satisfaction’s mediating role between these factors and loyalty. Practical recommendations suggest prioritizing service quality improvements, facility enhancements, and a positive destination image to foster loyalty and promote tourism sustainability in Pasar Lama, China. These insights assist tourism managers in developing strategies to enhance long-term visitor retention and engagement in the area.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.