The objective of this study was to examine the impact of utilizing smart algorithms on enhancing the operational performance of sports facilities in the Kingdom of Saudi Arabia. These algorithms, based on principles and concepts of artificial intelligence, aim to achieve functions such as learning, decision-making, data analysis, pattern recognition, planning, and problem-solving. The study aimed to identify the extent to which smart algorithms are utilized in sports facilities, assess the level of operational performance, explore the correlation between the use of smart algorithms and operational performance, and predict the level of operational performance based on the use of smart algorithms. The study employed a descriptive approach, specifically utilizing a survey study method. Participants included chairmen and members of boards of directors, executive directors, sports directors, administrators, specialists, and members of various committees. The study sample was intentionally selected from different categories within the study population. Two questionnaires were used to collect data from 325 participants. The findings revealed a lack of utilization of smart algorithms in sports facilities in the Kingdom of Saudi Arabia, indicating a low level of operational performance. Additionally, a correlation was observed between the use of smart algorithms and operational performance, suggesting that the level of operational performance can be predicted based on the utilization of smart algorithms. The study concludes that the implementation of intelligent algorithms can enhance the operational performance of sports facilities in the Kingdom of Saudi Arabia. It provides valuable insights into the effects of utilizing smart algorithms on improving operational performance.

This study aims to investigate the relationship between internal and information integration within the supply chain (SCI-INTI and SCI-INFI), supply chain management (SCM) practices, and port operational performance (POP) in Oman’s container ports. Additionally, it explores the mediating role of SCM practices in the relationship between SCI-INTI, SCI-INFI, and POP in Oman. To meet the study’s objectives, a quantitative cross-sectional survey method was used. A total of 377 questionnaires were distributed to managers responsible for supply chain operations in the main departments at Sohar and Salalah ports, yielding 331 usable responses, with a response rate of 88 percent. The data collected were analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that both internal and information integration within the supply chain have positive and statistically significant effects on the operational performance of Oman’s container ports (POP). Specifically, Supply Chain Integration with Internal Integration (SCI-INTI) significantly impacts POP (β = 0.249, t = 5.039, p < 0.001), and Supply Chain Integration with Information Integration (SCI-INFI) also significantly affects POP (β = 0.259, t = 4.966, p < 0.001). Additionally, SCI-INTI positively influences Supply Chain Management Practices (SCMP) (β = 0.381, t = 7.674, p < 0.001), as does SCI-INFI (β = 0.484, t = 9.878, p < 0.001). Furthermore, SCMP positively and significantly influences the operational performance of Oman’s container ports (β = 0.424, t = 7.643, p < 0.001). These findings contribute to the literature by emphasizing the significance of internal and information integration within the supply chain and SCM practices as strategic internal resources and capabilities that enhance operational performance in container ports. Understanding these elements enables decision-makers and policymakers within government port authorities and port operating companies to optimize internal resources and capabilities to improve port operational performance.

The operational performance of container ports is crucial for efficient logistics and trade. However, there is limited understanding of how external integration through Customer and Supplier Integration (SCI-CI and SCI-SI) impacts port operational performance (POP), particularly in emerging markets like Oman. This study addresses this gap by examining the relationship between SCI-CI, SCI-SI, and POP, and explores the mediating role of supply chain management (SCM) practices in this context. Using the Resource-Based View (RBV) as the theoretical framework, the study employed a quantitative cross-sectional survey method. A total of 377 questionnaires were distributed to managers at Sohar and Salalah ports, with 331 usable responses obtained, representing an 88 percent response rate. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that SCI-CI and SCI-SI have significant direct and indirect positive effects on POP, and they directly influence SCM practices. SCM practices, in turn, significantly enhance POP. Notably, SCM practices partially mediate the relationship between SCI-CI and SCI-SI with POP. These findings underscore the strategic importance of external integration and SCM practices as internal resources for improving port performance. This research provides valuable insights for decision-makers and policymakers in optimizing port operations.

Smallholder paprika farmers in Zimbabwe contribute to local economies and food security but face supply chain challenges like limited market access and poor infrastructure which lead to post harvest losses and unpredictable prices. To survive, these farmers must adopt sustainable value networks to reduce operational costs and improve performance. This study sought to establish the effect of sustainable value networks on the operational performance of smallholder paprika farming in Zimbabwe. This study, using a positivist research philosophy and a quantitative approach, surveyed 288 smallholder paprika farmers in Zimbabwe. Exploratory factor analysis and partial least squares structural equation modelling were used to validate the constructs and test the hypothesised relationships. Results demonstrate a moderate level of implementation of value networks in smallholder paprika farming characterised by successes and challenges. The findings illustrated resource sharing among smallholder farmers, facilitated by initiatives, such as recycled seed exchanges and financial support through village savings and loan associations. However, results show that challenges persist, particularly with market access and financial support. Results indicate that there is a significant awareness and implementation of green supply chain management practices among smallholder paprika farmers even though they do not have access to resources and live in rural areas. The findings demonstrate that value networks significantly influence the adoption of green supply chain management practices, which in turn positively impact operational performance, environmental performance, and social performance. Green supply chain management practices were found to mediate the relationship between value networks and environmental performance, social performance, and operational performance, underlining the critical role of sustainable practices in enhancing performance outcomes. While environmental performance showed a positive effect on operational performance, the direct influence of social performance on operational performance was found to be statistically insignificant, suggesting the need for further exploration of the factors linking social benefits to operational efficiency. The research contributes to both theory and practice by presenting a sustainable value network model for smallholder paprika farmers, integrating value network, green supply chain management practices and environmental performance to enhance operational performance. Practical implications include policy recommendations to strengthen collaboration between smallholder farmers and other stakeholdersand address power imbalances with intermediaries. Future research should extend the study to other agricultural sectors and incorporate more diverse stakeholder perspectives to validate and generalise the proposed sustainable value network model.