This systematic literature review critically examines the application of the Resource-Based View (RBV) theory within Supply Chain Management (SCM) across various industries. Developed by Jay B. Barney in 1991, RBV posits that a firm's sustained competitive advantage is driven by its unique resources and capabilities that are valuable, rare, inimitable, and non-substitutable. RBV suggests that a company’s long-term competitive advantage stems from its distinct resources and capabilities, which are valuable, rare, difficult to imitate, and not easily substitutable. Despite extensive utilization in strategic management, the direct application of RBV in SCM has been less explored, particularly in understanding how specific internal resources enhance SCM capabilities and outcomes. The review adopts a systematic approach, analyzing 97 peer-reviewed articles from diverse journals. This method includes a comprehensive search and evaluation process, ensuring the inclusion of significant studies that discuss the application of RBV in SCM across various industries. The articles were sourced from Scopus databases, with keywords related to RBV and SCM to ensure thorough topic coverage. The findings indicate a pronounced increase in related publications since 2010, reflecting a growing scholarly and practical interest in RBV’s application to SCM. The findings revealed that emerging trends like integrating advanced technologies like Blockchain, Artificial Intelligence and the Internet of Things are identified as strategic resources that redefine competitive landscapes by enhancing transparency, responsiveness and responsiveness within supply chains. The review also highlights the increasing importance of sustainability practices within SCM, aligning with RBV to potentially offer a sustainable competitive advantage. Conclusively, this review contributes to both academic knowledge and guides practitioners toward leveraging internal resources strategically to navigate contemporary challenges, setting a foundation for future inquiries into global supply chain resilience and dynamic capabilities.

The growing adoption of artificial intelligence (AI) in production systems has revolutionized quality assurance processes. Fault detection capability is a critical enabler of production performance and process variability reduction. This study explores the relationship between AI integration, represented by AI-supported decision-making, real-time processing, fault anticipation, and data integration, and production quality assurance. It also assesses the mediating role of fault detection capability in industrial production environments. A quantitative approach was used to gather data from staff in production, quality assurance, process monitoring, maintenance, and operational control units of industrial companies. Participants included production managers, quality assurance managers, process engineers, maintenance supervisors, data analysts, and operational specialists with experience in production process management and digital technologies. The results show that enhancing AI integration across the four dimensions positively impacts production quality assurance by increasing process visibility, facilitating real-time decisions, anticipating faults, and integrating production data across functional activities. The research also reveals that fault detection capability acts as a key mediator, allowing production units to detect anomalies early, prevent quality issues, and boost the stability of production outcomes. It concludes that AI integration positively impacts production quality assurance both directly and indirectly through fault detection capability, helping industrial organizations achieve higher quality standards, process reliability, and production control.

This paper examines the complex interdependencies between the big data analytics capabilities, competitive intelligence, organizational agility, and organizational outcomes in the telecommunication industry in Jordan. Using a quantitative design and data gathered through 350 participants and processed by SmartPLS. The results show that big data analytics capabilities influence organizational outcomes in a positive manner both directly and indirectly via competitive intelligence. Although competitive intelligence does not influence organizational outcomes directly, it mediates the relationship between the big data analytics capabilities and organizational outcomes significantly. Moreover, organizational agility is identified to moderate positively between big data analytics capabilities and organizational outcomes, which underscores its significant contribution to boosting the success of analytics. These findings present important theoretical insights by clarifying the processes by which big data analytics competencies are transformed to generate better organizational performance, as well as have practical implications to telecommunication companies seeking to better exploit their data assets.

This study sought to investigate the role of organizational capabilities on e-business successful implementation. The proposed conceptual framework was tested on a sample of 16 Jordanian companies with an online involvement, and a total of 263 valid returns were obtained in a questionnaire based survey. The results provide quite a strong support for the hypothesized relations: organizational capabilities, namely learning organizational capabilities and IT capabilities have significant impact on e-business implementation success. However, no statistical support was found for the significant impact of the knowledge management capabilities on e-business successful implementation. This study implies that the policy-makers should focus on formulating policies and targeting appropriate organizational capabilities to ensure effective e-business implementation, which will eventually yield positive results for the company as a whole. An organization needs a well-designed IT infrastructure to create and maintain the organizational knowledge deriving from organizational learning capabilities and enabling IT assimilation. In light of these results, the research presented many recommendations for future research and a set of limitations.