This paper aims to explore the influence of digital supply chain flexibility in five-star Hotels on smart quality systems together with its mediating effect through sustainable procurement practices within KSA. The methodology used is quantitative, based on data collected from a sample of five-star Hotels in KSA. Based on the above findings, this study argues about an integral part of DSCF (Digital Supply Chain Flexibility), SP and SQS for adopting theoretical framework. In this study, partial least squares-structural equation modelling (PLS-SEM) was used to validate the hypotheses. Digital supply chain flexibility has a significant positive relationship with sustainable procurement practices and smart quality systems in five-star Hotels. This means that actions such as fostering sustainable procurement practices could act on the ability of digital supply chain flexibility to boost smart quality systems, which play a role in enhancing operational efficiency and service quality. This paper contributes to the examination of various strategies for digital supply chains with sustainable procurement practices and creative quality systems within the hospitality sector; particularly focusing on KSA. Although the research findings are constrained to five-star Hotels from KSA they offer important directions for future research in different hospitality settings. These results provide practical information for hotels managers and policymakers on how digital supply chain flexibility, and sustainable procurement practices combined would contribute to the high-quality service provision as well as operational resilience of five-star Hotels. While these lessons have been drawn from the KSA market, they are also relevant to other hospitality markets with hospitality markets worldwide. This is the first study to examine Mediating Role of Sustainable Procurement and Digital Supply Chain Flexibility Impact on Smart Quality Systems Study in Five-Star Hospitals in KSA.

The aim of this study is to investigate the influence of logistics and distribution processes on business process reengineering by adopting green innovation for sustainable transformation. Guided by the Resource-Based View (RBV), the study examines logistics and distribution processes in relation to business process reengineering (BPR), through green innovation adoption, towards achieving sustainability goals. Drawing on a conceptual framework based in supply chain management and BPR theories, the study uses Structural Equation Modeling-Partial Least Squares to analyze data collected from respondents within the target industries. The results show that streamlined logistics and distribution processes help to ensure the success of Green Innovation as part of BPR, strengthening the trend towards sustainable transformation. The study accentuates the importance of logistics management in enabling environmental stewardship and enhanced operational efficiency, providing some key ideas for improving both theoretical developments and future directions as well on practice ground to adopt sustainable supply chain trends.

The purpose of the present paper was to explore whether total quality management (TQM) mediated the influence of market orientation (MO) on organizational performance (OP). The study was conducted using a quantitative way of investigation with the help of the survey instrument as the primary tool for data collection. Firm ICT managers completed the survey, and the data collected was used to explore the pathways proposed. After having been collected, the data were screened using the SPSS 26 version, and the hypothesized correlations were tested using the same data. The results indicate that both MO and TQM had significant impacts on OP. Such a connection was also impacted by the implementation of TQM. The information and communication technology (ICT) industry is popular among developing countries and is considered as one of the most employers and tools that fasten economic progress. The results provide new insights into the relationships and impacts of MO and TQM on organizational performance to owners/managers, practitioners, and academicians in the ICT sector. The owners and managers can serve as the guidelines to make better decisions in implementing MO with TQM standards to outperform in all aspects and sustain market competition. The owners and managers are advised to develop better strategies to adopt MO and TQM plans into more effective and performance-oriented approaches. This study is the first to do empirical research on the connections between overall quality management, market orientation, and organizational performance in the ICT sector of an Arab developing country.

As supply networks become more complex and international, the task of controlling associated risks becomes more difficult. This article investigates the usefulness of risk alert technologies in supply chain management, with a focus on Big Data Analytics (BDA) and Artificial Intelligence (AI). The study investigates the impact of BDA capabilities, solid IT infrastructure, managerial views, and AI-apprehensions on the effectiveness of risk alert tools using a questionnaire-based survey of 420 managerial personnel and Structural Equation Modeling (SEM) via SMART PLS. The work proposes the concept of AI-Apprehensions as a moderating variable, which is a relatively unexplored field. According to the findings, while BDA capabilities and IT infrastructure considerably improve the effectiveness of risk alert tools, AI-apprehensions can negate these advantages. The study provides useful insights for policymakers and practitioners, emphasizing the importance of balancing technical and human components for effective risk management.

This study investigates AI bias in financial technology (FinTech)-based supply chain management in Pakistan. The study employs Structural Equation Modeling (SEM) to analyze data from diverse respondents. Hypotheses examine the relationships between AI integration, algorithm diversity, employee training, data quality, regulatory compliance, organizational culture, and AI bias. The findings reveal that higher AI integration leads to increased AI bias, Algorithm diversity reduces AI bias, while employee training decreases bias, Quality and diversity of data negatively correlate with AI bias, and regulatory compliance lowers bias. In addition, organizational culture mediates the relationship between AI integration and AI bias. This research contributes a holistic understanding of AI bias factors, guiding ethical AI adoption. Policymakers can use these insights to shape regulations, and industry practitioners can make informed decisions.