Enhancing global health stands as a pivotal objective within the United Nations' Sustainable Development Goals (SDGs). In the wake of the ongoing digital transformation across various spheres of life, the incorporation of new digital technologies and the utilization of artificial intelligence hold the potential to contribute significantly to the attainment of these objectives. Leveraging the scalability inherent in digital business models, coupled with the widespread adoption of smartphones, facilitates the broad dissemination of digital healthcare services, even within emerging and developing nations. This inquiry adopts a quantitative research methodology to examine the implications of this phenomenon. In 2023, a cohort of 103 experts within German-speaking countries participated in an online survey, offering their insights into the impact of digitalization on health-related sustainability goals. The survey encompassed an assessment of the influence of digital technologies and AI on 13 sub-goals within the health domain, as well as on six additional SDGs. The comprehensive evaluation revealed that all 19 sub-goals exhibit a discernible medium to high impact. The analysis underscores that domains such as education and early warning systems are particularly amenable to digital interventions. Conversely, endeavors targeting the reduction of tobacco consumption or drug abuse may benefit from complementary measures. Conclusively, this study not only presents a developmental perspective on modeling but also formulates ten actionable recommendations, elucidating potential avenues for advancing the integration of digital technologies and artificial intelligence to enhance health-related sustainability goals.

An emerging application domain concerning content-based recommender systems provides a better consideration of the semantics behind textual descriptions. Traditional approaches often miss relevant information due to their sole focus on syntax. However, the Semantic Web community has enriched resources with cultural and linguistic background knowledge, offering new standards for word categorization. This paper proposes a framework that combines the information extractor ReVerb with the WordNet taxonomy to monitor global semantic relatedness scores. Additionally, an experimental validation confronts human-based semantic relatedness scores with theoretical ones, employing Mann–Kendall trend tests to reveal fairness and biases. Overall, our framework introduces a novel approach to semantic relatedness monitoring by providing valuable insights into fairness and biases.

The issue of accounting fraud presents a significant challenge within the business sector, prompting an increase in scholarly investigations across various contexts. Despite this growing interest, research specifically addressing the Thai context has remained scarce. Thus, this quantitative study aimed to bridge this gap by assessing the proficiency of Thai Gen Z accountants in detecting accounting fraud, with a particular emphasis on their digital, data science, and diagnostic skills. The study collected data from 150 participants using a structured survey questionnaire distributed to licensed accountants affiliated with the Thailand accounting program. It adopted a theoretical framework inspired by social learning theory and information processing theory to examine both direct and mediated relationships among the key variables under investigation. The results were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine these relationships. The results showed that digital competency have significant direct effects on the fraud detection skills, with diagnostic skills playing a key role in the process. The study revealed that digital competency not only furnishes accountants with necessary technological expertise but also bolsters their analytical skills, which are vital for identifying fraudulent activities. Likewise, data science literacy—encompassing skills in predictive analytics, big data management, and data insight communication—significantly enhances accountants' capacity to identify and understand fraudulent patterns. The emergent role of diagnostic skills as a key intermediary emphasizes the importance of comprehensive training programs that foster both technical prowess and critical analytical thinking.

Non-governmental organizations (NGOs) typically have restrained information and communication technology (ICT) budgets and resources. At the same time, they face high pressure to reduce administrative costs. A possible solution to the resulting conundrum could be user-driven technology. This term describes a selection of technologies, including intelligent process automation, low-code platforms, and business intelligence tools that push innovation and user-centricity by letting operational employees directly deploy comparably cheap solutions without the need for central ICT support. Practitioner literature indicates, however, that user-driven technologies are lagging in the social sector despite evidence from some individual success stories published by researchers. Thus, a systematic assessment of user-driven technologies within NGOs and of potential challenges in their introduction is necessary. To close this research gap, we employ the method of computationally intensive theory construction, combining data mining with qualitative interviews. Results indicate that user-driven technologies are indeed lagging and that forming a problem-mindset and creating adequate governance structures are the main challenges to their introduction within NGOs.

Manufacturing firms face an uncertain and continuosly changing environment because of innovations, technological changes, and globalization. To cope with this quick and uncertain environment, firms need to be flexible. Artificial Intelligence (AI) and Big Data Analytics (BDA) are must for manufacturing firms to achieve the flexibility in procurement to manufacturing to marketing. This study explores role of AI-BDA played between Supply Chain Flexibility (SCF) and Supply chain firms performance(SCFP) through six hypothesis. A sample data of 297 responses from forty Indian manufacturing firms were collected. Exploratory and confirmatory factorial analysis were used to analyse the collected data. Out of six hypothesis, four hypothesis are supported. The results show positive impact of AI, BDA and SCF on supply chain firm performance. Also AI positively impacts on BDA. However two hypothesis not supported are positive effect of AI and BDA on SCF needs further investigated. The study can guide decision makers to understand role of AI and BDA to improve supply chain performance.

In recent years, there have been massive changes to the government reporting requirements, which reflect the government's recognition of the need for a more open evidence-based practice. As a response, the U.S. government ordered to apply open government in all government agencies. The open government's objective is to have open government systems that include open access to their records, procedures, and data for public review and engagement. Currently, government agencies control and filter shared data with the public, limiting the ability to efficiently and effectively promote public oversight. This paper proposes a conceptual model, named GovBlockchain, that has the potential to achieve open government data objectives. The GovBlockchain is illustrated using the procurement cycle, and the results are subsequently compared with current open government practice. The results indicate that GovBlockchain provides stakeholders with a higher level of transparency.

This study investigates the influence of Omnichannel-Retailing (OCR) on customer-retention (CR) for Generation Z (Gen Z) in the nascent Industry 5.0 era. Employing a survey with South Asian respondents, the research explores their experiences with retailer-provided omnichannel services. A novel model examines the relationships between OCR, customer-satisfaction (CS), CR, and customer-engagement (CE). Additionally, the model investigates the moderating role of Man-Machine-Collaboration (MMC) on the OCR-CR association. Process macro analysis is employed to assess mediation and moderation effects. The findings reveal positive associations between OCR and both CR and CE. Interestingly, CS does not mediate the OCR-CR relationship, suggesting a more nuanced effect of OCR on retention. However, CE emerges as a significant mediator. Furthermore, the moderating role of MMC is confirmed. This research holds novelty by examining the early stages of Industry 5.0 and its potential disruption of marketing practices, particularly for CR strategies targeting Gen Z in South Asia.

In Vietnam's rapidly expanding e-commerce landscape, there is a critical need for advanced tools that can effectively analyze customer feedback to boost satisfaction and loyalty. This paper introduces a two-step predictive framework merging deep learning and traditional machine learning to analyze Vietnamese e-commerce reviews. Utilizing a dataset of 10,021 reviews on Tiki, Shopee, Sendo, and Hasaki between 2015 and 2023, the framework first employs fine-tuned deep learning models like BERT and Bi-GRU to extract aspect-based sentiments from reviews, tailored for the nuances of the Vietnamese language. Subsequently, machine learning algorithms like XGBoost predict customer satisfaction by integrating sentiment analysis with e-commerce data such as product prices. Results show BERT and Bi-GRU yield over 70% sentiment accuracy, while XGBoost achieves 80%+ satisfaction prediction accuracy. This framework offers a potent solution for discerning customer sentiments and enhancing satisfaction in Vietnam's dynamic e-commerce landscape.

Social media has swiftly established itself as a primary source of products’ information for customers. Nowadays, Small and Medium-size Enterprises (SMEs) can use social media to develop Customer Relationship Management platforms (social CRM). Firms, particularly SMEs in developing countries need to understand the factors affecting their performance by implementing social CRM. However, there is a dearth of awareness on the impact of social CRM on the performance of SMEs. This study proposes an integrated model that aims to investigate the effects of social CRM on SMEs’ performance. The model is constructed by incorporating three dominant theoretical frameworks: the Fit-Viability Model (FVM), Network Externalities, and the Resource-Based View (RBV). A cross-sectional survey was used to gather data from 149 SMEs managerial staff. Findings revealed that almost 50% of the variability in the performance of SMEs is explained by the fitness and viability of social CRM. In addition, network externalities of social media significantly impact the social CRM fitness in the context of SMEs with path coefficient 0.617. Furthermore, the internal financial resources factor makes sCRM viable for SMEs as the results show significant relationship between the internal financial resources and the sCRM viability with 0.536 path coefficient. Manager innovativeness, IT knowledge, top management support, and government assistance, on the other hand, do not contribute significantly to the viability of social CRM for SMEs. The model aids SMEs in making well-informed decisions regarding the adoption of social CRM by evaluating both the suitability of social media for CRM tasks and the enterprise preparedness to implement social CRM, leading to enhanced performance.

The research delves into the intricacies of green service consumption within the retail sector, concentrating specifically on delineating customer expectations and elucidating the transformative technological interface. The study elucidates the intricate dynamics of customer expectations and technological transformations to enhance the purchase experience in retail stores, focusing on the evolving paradigm in the retail landscape from a product-centric to a service-oriented economy and unveiling the pivotal role of societal influences, technology and customer expectations. The central theme revolves around understanding green service consumption within the retail sector, emphasizing the interplay between societal values, technological innovations and the dynamism of customer expectations. The research design employed is qualitative, leveraging a multidisciplinary approach and gleaning insights from sociotechnical perspectives, environmental sustainability, AI and consumer behavior. The sampling design involved experts in the field who participated in interviews and were purposefully selected based on their expertise in green service consumption and retail practices. The analysis utilized the fuzzy DEMANTEL method, involving a rudimentary exploration of dimensions. The research leverages extensive and in-depth deliberations involving theoretical explorations, connections among influencing factors and methodological insights from fuzzy logic and DEMATEL to dissect the complexities of green service consumption. The study elucidates the pivotal transition in the retail landscape, unveiling the changing nature of customer expectations, the surging significance of green service consumption and the influence of societal values and technological advancements. The discourse signifies the paramount role of societal values, particularly the increasing awareness of sustainability, ethical sourcing and eco-friendly strategies, in shaping retail strategies. The research presents a multifaceted view of green service consumption, thereby contributing to an enriched theoretical landscape of green service-focused retail experiences.