Academic Industry Research Articles

Role of Islamic Banks in Increasing Financial Inclusion: A Systematic Literature Review

This article investigates the contribution of Islamic banks to enhancing access to financial services by looking at their unique method of offering Sharia-compliant goods and services. Islamic banking principles, the study claims, encourage profit-and-loss sharing and forbid riba (interest), which increases access to financial services, particularly for devout Muslims. In particular, this paper looks at how Islamic banks affect financial inclusion in Southeast Asia, the Middle East, and North Africa. This is accomplished by assessing academic literature, industry reports, and statistical data. It looks at potential substitutes, including mobile banking and microfinance, to reach those who need access to traditional banking services and delves into the difficulties Islamic banks have, such as a lack of knowledge about money and location. Data indicate that Islamic banks support economic development and stability by establishing trust via Sharia governance structures. In its last part, the report offers some suggestions on how governments may utilize Islamic banking to increase public access to financial services and promote social change.

Data Science Applications in Supply Chain Management Decision-making

This research views data science as the basis of the decision-making process at SCM. Tough international trade environment characterized by complex supply chain and inventory issues as well as unpredictable demand for goods necessitates powerful analytics tools. Using the latest technologies - machine learning, predictive analytics, and big data - data science generates data-driven decisions for more accurate, efficient, and prompt SCM decision-making. The study intends to study the current trends and evaluate the influence of data science in SCM decision-making processes. It also delves into the difficulties and advantages with the utilization of data science during these procedures. This study uses a synthesis approach by systematically going through a literature review to gather data from different academic journals and industry publications. According to the results of the thematic analysis, the themes will emerge, so the whole complexity and depth of data science applications in SCM will be properly revealed. Data science changes the business decision-making in a way that was impossible before with the advent of new information from the huge and complex data sources. Data analytics not only smoothens but also upgrades long-term trend forecasting and market readiness in SCM. Furthermore, the paper emphasizes the influence of the Internet of Things (IoT) and industry 4.0 technologies of SCM with an accent on how they are associated to increase efficiency and sustainability in the operations.

Data Science Applications in Supply Chain Management Decision-making

This research views data science as the basis of the decision-making process at SCM. Tough international trade environment characterized by complex supply chain and inventory issues as well as unpredictable demand for goods necessitates powerful analytics tools. Using the latest technologies - machine learning, predictive analytics, and big data - data science generates data-driven decisions for more accurate, efficient, and prompt SCM decision-making. The study intends to study the current trends and evaluate the influence of data science in SCM decision-making processes. It also delves into the difficulties and advantages with the utilization of data science during these procedures. This study uses a synthesis approach by systematically going through a literature review to gather data from different academic journals and industry publications. According to the results of the thematic analysis, the themes will emerge, so the whole complexity and depth of data science applications in SCM will be properly revealed. Data science changes the business decision-making in a way that was impossible before with the advent of new information from the huge and complex data sources. Data analytics not only smoothens but also upgrades long-term trend forecasting and market readiness in SCM. Furthermore, the paper emphasizes the influence of the Internet of Things (IoT) and industry 4.0 technologies of SCM with an accent on how they are associated to increase efficiency and sustainability in the operations.

Understanding the Impact of Artificial Intelligence (AI) on Traditional Businesses in Indonesia

The integration of artificial intelligence (AI) in Indonesia's business landscape has ushered in significant changes, presenting challenges and opportunities for traditional businesses across various sectors. Understanding the implications of AI is crucial for navigating these changes effectively. This study aims to investigate the transformative impact of AI on traditional Indonesian businesses, specifically analyzing the opportunities and challenges associated with AI adoption and its implications for business strategies and operations. Utilizing a qualitative method, this study examines the influence of AI technologies on traditional Indonesian businesses. The study compiles data from academic literature, industry reports, and real-world case studies to analyze the intricate dynamics between AI technologies and human behavior. The findings reveal that AI integration offers numerous opportunities for traditional businesses in Indonesia, such as enhanced operational efficiency, improved customer experience, and innovation potential. However, significant challenges, including high implementation costs, data privacy concerns, and the lack of skilled AI talent, hinder widespread adoption. Despite these challenges, businesses that successfully navigate them can gain a competitive advantage in the digital age. This study contributes to the existing literature by providing fresh insights into the transformative impact of AI on traditional Indonesian businesses. It synthesizes recent research findings and case studies, offering valuable guidance for businesses aiming to leverage AI for strategic advantage.

Advances in Polyvinyl Alcohol-Based Membranes for Fuel Cells: A Comprehensive Review on Types, Synthesis, Modifications, and Performance Optimization.

Fuel cell technology is at the forefront of sustainable energy solutions, and polyvinyl alcohol (PVA) membranes play an important role in improving performance. This article thoroughly investigates the various varieties of PVA membranes, their production processes, and the numerous modification tactics used to solve inherent problems. Various methods were investigated, including chemical changes, composite blending, and the introduction of nanocomposites. The factors impacting PVA membranes, such as proton conductivity, thermal stability, and selectivity, were investigated to provide comprehensive knowledge. By combining various research threads, this review aims to completely investigate the current state of PVA membranes in fuel cell applications, providing significant insights for both academic researchers and industry practitioners interested in efficient and sustainable energy conversion technologies. The transition from traditional materials such as Nafion to PVA membranes has been prompted by limitations associated with the former, such as complex synthesis procedures, reduced ionic conductivity at elevated temperatures, and prohibitively high costs, which have hampered their widespread adoption. As a result, modern research efforts are increasingly focused on the creation of alternative membranes that can compete with conventional technical efficacy and economic viability in the context of fuel cell technologies.

Influence of social capital, market orientation, and technological readiness on researchers’ interactions with companies

This study investigates the influence of social capital, market orientation, and technological readiness levels (TRLs) on the intensity of collaboration between researchers from a public research institution in Brazil and companies, and how TRLs moderate this relationship. Using a quantitative approach, we applied structural equation modeling (PLS-SEM) to analyze responses from researchers at this institution. The analysis highlights the critical roles of social capital and market orientation in fostering effective R&D interactions. Social capital enhances collaboration through trust and network strength, while market orientation aligns R&D efforts with market needs, ensuring that innovations are both relevant and timely. Importantly, this study explores how technological readiness levels (TRLs) moderate these relationships, offering insights into the varying impact of social capital and market orientation across different stages of technological development. Findings reveal that participation in projects with TRLs 4 to 6, known as the Valley of Death, significantly moderates the impact of market orientation on researchers' interaction intensity with companies, underscoring the importance of considering technological maturity in R&D collaborations. The study is framed within the open innovation approach, emphasizing the importance of leveraging external knowledge and collaborative networks to enhance innovation outcomes. Theoretically, this research extends the existing models of R&D collaboration by illustrating how TRLs modify the effects of social capital and market orientation. Practically, it offers actionable insights for R&D managers and policymakers on structuring environments that foster robust academic–industry partnerships, facilitating the successful transition of innovations from conceptualization to market readiness.

Emerging Trends and Challenges in Digital Crime: A Study of Cyber Criminal Tactics and Countermeasures

The rise of digital crime, particularly cybercrime, poses significant challenges to individuals, organizations, and societies worldwide. This study explores emerging trends and challenges in digital crime, focusing on the tactics employed by cybercriminals and the countermeasures developed to mitigate these threats. The urgency of this research lies in the increasing sophistication and frequency of cyberattacks, which threaten sensitive data, financial assets, and critical infrastructure. The objectives of this study are to analyze current trends in cybercrime, identify the most prevalent tactics used by cybercriminals, and examine effective countermeasures and strategies implemented by organizations and law enforcement agencies. This research adopts a literature review methodology, synthesizing findings from academic research, industry reports, and government publications. Key findings highlight the evolving nature of cyber threats, including phishing attacks, ransomware campaigns, and data breaches. Effective countermeasures include cybersecurity frameworks, incident response protocols, and technological advancements in threat detection and prevention. The study underscores the importance of ongoing research and collaboration among stakeholders to address the dynamic landscape of digital crime and safeguard digital ecosystems against emerging threats

Regulating digital security by design? Implications of the perspectives from DSbD programme stakeholders

Purpose As part of the growing necessity for inter-organisational and multi-disciplinary interaction to facilitate complex innovation in digital security, there needs to be greater engagement with regulation in the innovation process. This is particularly true in the case of security technologies that are embedded within wider systems and that are largely invisible to most of the users of that system. This paper aims to describe stakeholders’ perspectives on regulation in the digital security innovation process and evaluates the implications of these perspectives on anticipatory regulation in digital security. Design/methodology/approach Using a qualitative methodology based on semi-structured expert interviews and ethnographic participant observation, the study draws on the authors’ involvement in a formally organised programme of academia–industry–government collaboration called Digital Security by Design (DSbD). Findings The study highlights a relational dimension to establishing regulatory responsibilities that is enabled through interdisciplinary dialogue. The study contributes to understanding the multifaceted roles of regulation in digital security innovation across organisations and areas of expertise. It does so by identifying four themes in how regulation is perceived in the DSbD programme: ethical imperative, adding value, adoption lever and passive compliance. Practical implications Incorporating regulatory responsibilities through dialogue early in the innovation process, rather than only once a security technology’s deleterious effects are noticeable, which could make digital innovation and transformation safer and better regulated. It can also make regulation successfully adopted, rather than an exercise in damage control or an adversarial process between regulators and organisations. Originality/value This paper presents original empirical research on how regulation is considered by stakeholders in a novel multi-disciplinary digital security innovation process. It then uses these findings as a basis to evaluate the implications for establishing regulatory responsibilities for a class of security technologies that are embedded within wider systems and that are largely invisible to most of the users of those wider systems.

Enhancing IoT security: A comparative study of feature reduction techniques for intrusion detection system

Internet of Things (IoT) devices are extensively utilized but are susceptible to cyberattacks, posing significant security challenges. To mitigate these threats, machine learning techniques have been implemented for network intrusion detection in IoT environments. These techniques commonly employ various feature reduction methods, prior to inputting data into models, in order to enhance the efficiency of detection processes to meet real-time requirements. This study provides a comprehensive comparison of feature selection (FS) and feature extraction (FE) techniques for network intrusion detection systems (NIDS) in IoT environments, utilizing the TON-IoT and BoT-IoT datasets for both binary and multi-class classification tasks. We evaluated FS methods, including Pearson correlation and Chi-square, and FE methods, such as Principal Component Analysis (PCA) and Autoencoders (AE), across five classic machine learning models: Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), k-Nearest Neighbors (kNN), and Multi-Layer Perceptron (MLP). Our analysis revealed that FE techniques generally achieve higher accuracy and robustness compared to FS methods, with RF paired with AE delivering superior performance despite higher computational demands. DTs are most effective with smaller feature sets, while MLPs excel with larger sets. Chi-square is identified as the most efficient FS method, balancing performance and computational efficiency, whereas PCA outperforms AE in runtime efficiency. The study also highlights that FE methods are more effective for complex datasets and less sensitive to feature set size, whereas FS methods show significant performance improvements with more informative features. Despite the higher computational costs of FE methods, they demonstrate a greater capability to detect diverse attack types, making them particularly suitable for complex IoT environments. These findings are crucial for both academic research and industry applications, providing insights into optimizing detection performance and computational efficiency in NIDS for IoT networks.

Optimizing Sentiment Analysis Models for Customer Support: Methodology and Case Study in the Portuguese Retail Sector

Sentiment analysis is a cornerstone of natural language processing. However, it presents formidable challenges due to the intricacies of lexical diversity, complex linguistic structures, and the subtleties of context dependence. This study introduces a bespoke and integrated approach to analyzing customer sentiment, with a particular emphasis on a case study in the Portuguese retail market. Capitalizing on the strengths of SentiLex-PT, a sentiment lexicon curated for the Portuguese language, and an array of sophisticated machine learning algorithms, this research constructs advanced models that encapsulate both lexical features and the subtleties of linguistic composition. A meticulous comparative analysis singles out multinomial logistic regression as the pre-eminent model for its applicability and accuracy within our case study. The findings of this analysis highlight the pivotal role that sentiment data play in strategic decision-making processes such as reputation management, strategic planning, and forecasting market trends within the retail sector. To the extent of our knowledge, this work is pioneering in its provision of a holistic sentiment analysis framework tailored to the Portuguese retail context, marking an advancement for both the academic field and industry application.

Employment Skills Revisited: A Qualitative Exploration of Multi-Stakeholder Perspective in China’s “3+1” Higher Education Context

The issue of employment for university students is receiving significant attention in China, leading to educational reforms aimed at enhancing students’ employment skills and prospects. One such initiative is the “3+1” higher education programs, characterized by work-based learning. However, the effectiveness of this initiative falls short of expectations due to insufficient understanding of the up-to-date employment skills required by the industry. As such, this qualitative study explored stakeholders’ perceptions regarding the employment skills needed by students enrolled in “3+1” programs. Key challenges and strategies associated with developing students ‘ employment skills were identified through interviews with students, teachers, curriculum planners, industry leaders, and human resource professionals. Findings revealed a consensus among stakeholders on the importance of developing a holistic skill set, acquiring practical experience, increasing adaptability and resilience, embracing lifelong learning, and improving communication skills. Challenges included mismatches between academic learning and industry demands, limited industry exposure, soft skill deficiency, inadequate assessment, insufficient university-industry cooperation, and students’ low motivation, for which corresponding strategies were proposed. This study provides valuable insights into enhancing the employability of “3+1” graduates, emphasizing the importance of comprehensive education aligned with industry needs.

Environmental and Human Health Problems Associated with Hospital Wastewater Management in Zimbabwe.

Wastewater is a term used to describe water that has undergone degradation in quality owing to anthropogenic activities or natural processes. Wastewater encompasses liquid waste originating from academic institutions, households, agricultural sector, industries, mines and hospitals. Hospital wastewater contains potentially hazardous substances including residues of pharmaceuticals, radioisotopes, detergents and pathogens, with detrimental impacts to the environment and human health. Nevertheless, studies related to hospital waste management are limited in Africa, particularly in Southern Africa. This research offers an overview of aspects surrounding hospital wastewater in Southern Africa, focusing on Zimbabwe. Already published and grey literature was reviewed to compile the paper. Number of patients, nature of medical services offered and hospital size influences generation of hospital wastewater. Partially and non-treated hospital wastewater is managed together with municipal wastewater. Management of hospital wastewater is impeded by shortage of resources, lack of co-ordination among responsible authorities and ineffective legal framework enforcement, among other challenges. Inappropriate hospital wastewater management results in environmental contamination, causing human ailments. Attainment of sustainable hospital wastewater management requires clearly defined and enforced legislation, collaboration of accountable stakeholders, sufficient resources and enhanced awareness of involved stakeholders. Application of technologies that uphold recycling and reuse of wastewater is essential to reach Sustainable Development Goals, Zimbabwe Vision 2030 and National Development Strategy 1 targets, particularly those dealing with environmental protection while upholding human health.

Using Ai to Help Reduce the Effect of Global Warming

This paper explores the application of artificial intelligence (AI) in mitigating the effects of global warming, which stands as one of the most pressing and complex challenges of our time. The purpose of this research is to examine how various AI technologies, including machine learning, neural networks, and big data analytics, can be leveraged to enhance climate modeling, optimize energy systems, improve agricultural practices, and support carbon capture and storage efforts. By conducting a comprehensive literature review, this paper aims to highlight current advancements, practical applications, and relevant case studies that demonstrate the potential of AI to reduce greenhouse gas emissions and promote sustainable practices across different sectors. The study synthesizes findings from recent academic research, industry reports, and real-world implementations to provide an in-depth analysis of the benefits and challenges associated with integrating AI into climate action strategies. The methodology involves a thorough examination of the existing literature, identifying key areas where AI has shown significant promise in addressing various aspects of global warming. This includes enhancing the accuracy of climate predictions, optimizing the efficiency of renewable energy systems, improving precision agriculture techniques, and increasing the effectiveness of carbon capture and storage technologies. The conclusions drawn from this research underscore the transformative potential of AI in combating global warming. The findings highlight the necessity for interdisciplinary collaboration, advancements in AI technologies, and the development of supportive policy frameworks to maximize the impact of these innovations. The paper emphasizes that while AI offers significant potential to address global warming, realizing this potential requires addressing several challenges, including data quality and availability, integration with existing systems, ethical considerations, and economic and policy barriers. Furthermore, this paper discusses the critical role of AI in enabling more effective climate adaptation strategies. As the impacts of global warming become increasingly apparent, AI-driven tools and solutions can help communities and ecosystems adapt to changing environmental conditions. This includes providing early warning systems for natural disasters, optimizing resource allocation during climate-related crises, and supporting the development of resilient infrastructure. In addition to technological advancements, the paper also explores the importance of public engagement and citizen science in enhancing the effectiveness of AI applications in environmental monitoring and climate action. By involving citizens in data collection and environmental monitoring, AI models can access more diverse and localized data, improving their accuracy and relevance. Public engagement can also raise awareness about AI's role in addressing climate change and foster greater support for sustainable practices. Overall, this paper provides a comprehensive overview of the current state of AI applications in mitigating global warming, offering insights into the future directions and emerging trends in this rapidly evolving field. The research highlights the need for continued innovation, interdisciplinary collaboration, and supportive policy measures to fully harness the potential of AI in the fight against global warming and to ensure a sustainable future for all. DOI: https://doi.org/10.52783/pst.464

Joining forces: Combining police and external expertise for cold case reviews

Cold case reviews within police and law enforcement agencies are challenging, not the least owing to the amount of time required to carefully review documentation, forensic exhibit holdings and various other casefile information. Most federal and state agencies are time poor, meaning there are very few dedicated cold case teams fortunate enough to have an abundance of police and expert staff resources. Universities and education organisations, however, have large troves of various expertise, alongside expansive human resources, by way of their academic and student body. In certain circumstances, the academic expertise and course offerings of a university may be well suited to assisting law enforcement in reviewing cold cases.There is growing desire for university courses to generate job ready graduates. In the field of law enforcement and policing this is difficult, as safety and the security of sensitive material and evidence is paramount. Educators strive to create workplace simulations, and with the correct mix of academic expertise, course offerings and industry linkages, the emerging opportunity for real cold case collaboration is possible. One such example is the Cold Case Review @ Murdoch (CCR) initiative. Since 2020, CCR has worked with the Lower Saxony Police Academy in Germany to develop the novel International Cold Case Analysis Project (ICCAP), now incorporating over 25 member institutions, to assist in solving real cases from both Niedersachsen (Lower Saxony) and federal police jurisdictions. One case, known as “The North Sea Man” has shown great success and demonstrates the power of joining forces between law enforcement and external agencies to help advance cold cases.

The 2023 AUR Academic Radiology and Industry Leaders Roundtable

The 2023 AUR Academic Radiology and Industry Leaders Roundtable

Preface

Chairman’s Preface Dr. Talal Al-Wahaibi Chairman, ICES 2024 Dean, College of Engineering A’Sharqiyah University, Ibra, Oman Welcome to the International Conference on Energy and Sustainability (ICES 2024) — a nexus where academia’s brightest minds, industry pioneers, and passionate researchers converge to ignite discussions and exchange ideas that will shape the future of energy and sustainability. As we gather here, whether in the halls of A’Sharqiyah University or through the digital expanse connecting us across the world, we do so with a unified purpose: to explore avenues for a greener economy and a sustainable future, not just for Oman but for our global community. Under the wise directives of His Majesty, Oman is on an ambitious journey to establish the ‘Oman Sustainability Centre’ and achieve the goal of net-zero carbon emissions by 2040. It is a bold vision that requires equally bold actions, and through the collaborative effort of this conference, A’Sharqiyah University reaffirms its commitment to being a catalyst in this national endeavor. Please note that the proceedings here represent a selection of the work presented at ICES 2024. Some papers have been extended beyond this collection for publication in collaboration with esteemed journals. This curated volume captures the breadth of discussions and contributions made during our event, yet there is more to be found in the associated journal issues. In these proceedings, you will find the essence of ICES 2024 distilled into words and studies. Each page carries the weight of our collective aspiration and the detailed insights that emerged from our two-day engagement. The College of Engineering at A’Sharqiyah University is proud to host this event, which aligns perfectly with our vision to become a beacon of excellence in the domains of energy and sustainability. The objective of ICES 2024 extends beyond the dissemination of research; it is to cement relationships among the academic community and industry leaders, to spark innovative partnerships, and to foster a collaborative network that transcends borders. In this confluence of local wisdom and international expertise, we find the greatest potential for impactful change. The proceedings before you are a testament to the rigorous inquiry and innovative spirit that characterizes ICES 2024. They are a record of the thoughtful discourse that took place, the challenging questions raised, and the forward-thinking solutions proposed. I am deeply grateful to every participant, speaker, organizer, and attendee — your contributions have been the lifeblood of this conference. Together, we have created a platform for meaningful conversation and set the stage for impactful action. List of Steering Committee, Organizing Committee, Scientific Committee and About the Conference are available in this Pdf.

Building Trust in AI-Human Partnerships: Exploring Preferences and Influences in the Manufacturing Industry

Abstract The incorporation of artificial intelligence (AI) into industrial processes has seen substantial development, characterized by the shift from Industry 4.0 to the future concept of Industry 5.0. The article identifies a significant gap in knowledge regarding how openness in AI engagement influences consumer trust and confidence in news media. This gap highlights the need for further exploration into the relationship between transparency in AI processes and consumer perceptions. The research utilises a combination of qualitative and quantitative approaches, gathering insights from academic literature, industry viewpoints, and actual data. We conduct an extensive analysis of existing literature to investigate the process of incorporating artificial intelligence into news creation and its influence on the level of confidence consumers have in the news. We have identified a significant lack of knowledge about the impact of openness in AI engagement on consumer views and trust in news media. Expanding on this discrepancy, we suggest a systematic methodology that incorporates controlled experiments and surveys to evaluate the influence of different degrees of openness on consumer trust and involvement with AI-generated news content. In addition, the paper examines the difficulties in establishing confidence in artificial intelligence (AI) inside the European Union, including several aspects such as technological, ethical, social, and legal considerations. The document presents a thorough plan to guarantee the secure development and execution of AI, with a focus on the significance of transparency, ethics, and teamwork. The study’s results provide vital insights for politicians, news organisations, and industrial businesses as they navigate the intricate process of integrating AI. Comprehensive Plan for Secure AI Development, to address the challenges outlined, the article presents a thorough plan for ensuring the secure development and execution of AI within the European Union. This plan emphasizes the significance of transparency, ethics, and collaboration in building trust and confidence in AI technologies.

EXPLORING THE VIABILITY AND IMPACT OF VEGANISM IN LUXURY FASHION

As environmental concerns rise, luxury fashion brands are increasingly adopting vegan materials to create eco-friendly collections without compromising on quality or style. This study aims to explore the viability and impact of veganism in luxury fashion, focusing on its influence on consumer behavior, industry standards, and sustainability. A comprehensive review of secondary sources, including academic literature, industry reports, and sustainability assessments, was conducted to analyze the rise of vegan materials in luxury fashion. The study found that environmental awareness, ethical consumerism, and technological advancements are key drivers behind the increasing viability of vegan materials like Piñatex and Mylo in luxury fashion, despite challenges related to durability and environmental trade-offs. The adoption of vegan materials offers luxury brands a strategic opportunity to align with consumer demand for sustainability and ethical production, enhancing brand image and loyalty. Future research should explore the scalability of vegan materials, examine consumer perceptions of vegan luxury, and assess the long-term sustainability and lifecycle impacts of these alternatives.

From click to fork: An empirical analysis of system and food quality influences in online meal kit consumption

This research explores the determinants and implications of brand attachment in the fast-growing online meal-kit industry. The objectives were to explore the components constituting the system and food quality, establish their impact on brand attachment, and examine the subsequent impact on consumer behavioral intentions. Utilizing big data analytics, the study analyzed real-time app content sourced from consumer reviews using a text mining technique and employed structural model analysis to validate a proposed model with five emerging factors from the consumer review data. The findings suggest that system quality parameters—perceived ease of use and perceived usefulness—significantly influenced food quality. Enhanced food quality cultivated brand love yet reduced brand hate. Notably, brand love and hate exhibited opposing correlations with negative electronic word-of-mouth and continuous usage intention. The outcomes secure crucial insights for ongoing engagement with online meal-kit services, significantly contributing to academic research and industry practice.

Managing global supply chain teams: human resource strategies for effective collaboration and performance

This study examines the role of human resource (HR) strategies in enhancing collaboration and performance within global supply chains, an area of increasing importance in the context of globalized business operations. The primary objective is to identify and analyze current trends in HR practices that contribute to effective global supply chain management, focusing on adaptability, technological integration, sustainability, and ethical practices. Employing a systematic literature review and content analysis as its methodology, the study draws on academic journals, conference proceedings, and industry reports from databases such as Scopus, Web of Science, and Google Scholar. Key findings reveal that adaptability and flexible work arrangements, supported by digital HR technologies, are essential for operational resilience and employee engagement. The integration of sustainability and ethical considerations into HR practices is identified as crucial for maintaining competitive advantage and ensuring regulatory compliance. Furthermore, the development of cultural intelligence and inclusive leadership within global teams is highlighted as vital for fostering effective collaboration across diverse cultural landscapes. The study concludes that strategic HR management plays a pivotal role in navigating the challenges and leveraging the opportunities within global supply chains. Recommendations for practitioners emphasize the importance of continuous learning, embracing digital transformation, and integrating sustainability into HR practices. The study also outlines areas for further research, including the impact of emerging technologies on HR practices and the long-term effects of sustainable HR strategies on organizational performance. This research contributes valuable insights into optimizing HR strategies to enhance global supply chain collaboration and performance.