This study investigates the influence of technological innovation, sustainability culture, and top management support on project management performance in Saudi Arabia's manufacturing sector. Employing a cross-sectional quantitative research design, data was collected from 293 employees using a convenience sampling technique. Structural Equation Modeling (SEM) with SmartPLS software was utilized for data analysis to examine the proposed relationships between the constructs. The findings indicate that technological innovation has a significant positive effect on both sustainability culture and top management support. Furthermore, both sustainability culture and top management support were found to positively influence project management performance. The results underscore the critical role of technological advancements in fostering a culture of sustainability and obtaining strong leadership support, which in turn contributes to enhanced project outcomes. This study offers important insights for the manufacturing sector in Saudi Arabia, particularly as it aligns with the country’s Vision 2030 objectives, aiming to diversify its economy through innovation, sustainability, and enhanced project performance.

Projects play a crucial role in the success and development of industries, organizations and businesses, hence making project management an important practice which needs to be up to date with new trends and modern technology such as artificial intelligence (AI). With the advent of artificial intelligence there have been a number of studies aimed to design and introduce new ways and means of utilizing this phenomenon into project management. This research aims to find AI methods, tools, approaches, models and frameworks for each of the project management knowledge domains introduced by PMboK. The methodology followed the PRISMA guidelines for systematic literature reviews to collect, screen and analyze the literature to find relevant studies. The findings presented bibliographic data on the topic and current trends, frequently used AI methods and project management techniques and tools which benefit from these AI methods under each project management domain.

The significance of project scheduling and sequencing has increased considerably in recent years, driven by the rising customer demand for highly personalized solutions. Companies have to consider multiple criteria while executing multiple projects simultaneously to meet the customer demands. Therefore, this study focuses on the multi-objective multi-project scheduling and sequencing problem (MP-SSP). A simulation-based mathematical model is developed and integrated with a multi-objective genetic algorithm. The objectives of this model are to minimize the project execution cost, project completion time and project lateness simultaneously while maximizing the resource utilization in the stochastic environment. Goal attainment programming is introduced in the simulation integrated multi-objective genetic algorithm (SIHMO-GA) to increase the effectiveness of the algorithm. Further, response surface methodology (RSM) has been used to find the optimum parameters of the proposed SIHMO-GA. The effectiveness of the proposed SIHMO-GA is evaluated through a real-world case study by comparing it with simulation-optimization approaches, namely the multi-objective genetic algorithm (MOGA) and goal attainment programming. Gap analysis indicates that the SIHMO-GA provides best trade off values of the above-mentioned conflicting objectives under a stochastic environment. This study supports practical scheduling and sequencing of multiple projects in a stochastic environment by generating solutions that maximize profit, enhance resource utilization, and ensure customer satisfaction through timely project delivery.

Global competition requires the company's appropriate rules and policies. The company is trying to build strong, firm resilience to maintain sustainability. The research aims to analyze the influence of management configuration decisions on company resilience through the integration of resource configuration, operational flexibility, and collaboration in the supply chain. This study was conducted on 462 manufacturing companies in Indonesia that were experiencing changes in operational systems and global competitive pressures. Data was collected through questionnaires and analyzed using the Partial Least Square (PLS) method. The research results show that management configuration decisions influence resource configuration and supply chain collaboration integration but do not directly influence operational flexibility. Resource configuration is proven to increase operational flexibility and supply chain collaboration integration, which in turn strengthens company resilience. Operational flexibility also plays an important role in supporting the integration of supply chain collaboration and company resilience in the face of external disruptions. This research provides a theoretical contribution to the development of a tough and practical supply chain strategy for management in building an organization that is adaptive to disruption.

The global disruption of 2020-2022 forced organizations to reassess their resilience strategies, with executives agreeing to prioritize project management, data analytics, and problem-solving among the top 10 skills for their workforce. In this context, the role of the Project Management Office (PMO) in building resilience and achieving project success has been critically examined. While PMOs are assumed to enhance governance and structured guidance, it remains unclear whether their presence significantly influences resilience-building during disruptions. This study investigates this issue by conducting multi-group analysis (MGA) using data from 170 practicing project managers worldwide who navigated the global disruption. The study examines how Data Analytics Capability (DACP) and Project Management Maturity (PMMM) impact Project Resilience (PRES) and Project Success (PSUC), employing Partial Least Squares Structural Equation Modeling (PLS-SEM) for empirical validation. Results indicate that both DACP and PMMM significantly enhance resilience, with DACP demonstrating a comparatively stronger effect. Project resilience, in turn, has a robust positive impact on project success, highlighting its strategic value beyond mere survival. Notably, while multi-group analysis shows no statistically significant differences between PMO-supported and non-PMO-supported projects, this does not undermine the importance of PMMM and PMO support. Instead, it reinforces that structured governance and maturity practices are considered baseline expectations in high-reliability industries, rather than distinct differentiators of project resilience.

This study examines the relationships between circular economy capability, green supply chain, green process innovation, and sustainable performance in the manufacturing sector of Saudi Arabia. As the country transitions toward its Vision 2030 goals, which emphasize sustainability and economic diversification, the manufacturing sector plays a critical role in adopting circular economy principles and green practices to reduce environmental impact and enhance resource efficiency. Using a cross-sectional research design, data were collected from managerial-level employees through a structured questionnaire. Data analysis was conducted using structural equation modeling (SEM) to examine the hypothesized relationships. The findings reveal that circular economy capability significantly drives green supply chain and green process innovation, which in turn enhance sustainable performance. The study also identifies green supply chain and green process innovation as critical mediators in the relationship between circular economy capability and sustainable performance. The results highlight the importance of integrating circular economy principles with green practices to achieve sustainability goals. It provides actionable insights for organizations to enhance their sustainability efforts, such as investing in resource efficiency, adopting green supply chain practices, and fostering process innovation.

The present study examined the perception of users towards the role of artificial intelligence (AI) in improving personal learning profile (PLP), personal learning network (PLN) and personal learning environment (PLE). Additionally, the impact of PLP, PLN and PLE on perceived ease of use, perceived effectiveness and perceived usefulness in improving the general attitude and satisfaction of users in their intention to use project management was examined. Results showed the impact of PLE on perceived ease of use and perceived usefulness, significant impact of PLP on perceived effectiveness, and impact of student pressure on intention to use project management. Data were obtained from professionals and students with experience in the use of project management modules. Notably, the obtained data were fully based on the perceptions of the respondents, resulting in potential self-perception bias. Perceptions of users towards PLP, PLN and PLE were integrated into the technology acceptance model framework of this study, to understand their impact on the general attitude and satisfaction of learners. Using AI can enhance learner attitude and satisfaction while creating more engaging e-learning, proving the crucial role of AI in forming the right environment through learner profile match.

This study explores the relationship between supply chain practices and sustainability efforts, with the understanding that logistics strategies can have profound impacts on economic growth, society, and environmental conservation. The primary objective of the research is to identify ways in which organizations can improve their supply chains to achieve more favorable sustainability outcomes by examining the relationship between the supply chain and sustainability performance. Data was collected through a questionnaire designed and distributed to 43 companies in the basic materials sector in the Kingdom of Saudi Arabia. The questionnaire data were analyzed using SPSS and Smartpls. The results indicate that supply chains in the basic materials sector in the Kingdom are operating effectively and positively, impacting the promotion of economic, social, and environmental sustainability. The insights from this study can help advance the understanding of how supply chains can drive sustainability improvements while developing a more robust economic framework for sustainability and resource management for stakeholders.

The hotel industry in Jordan plays a crucial role in stimulating economic expansion by attracting tourists and creating job prospects. The industry can benefit from the use of Artificial Intelligence (AI) to improve sustainability through the promotion of green innovation, efficient resource utilization, and reduction of environmental harm. Hence, this study designs a model to enhance the environmental, economic, and social sustainability in the Jordanian hotels Sector. The study aimed to examine the impact of the AI Capabilities (tangible, intangible and human) on social, economic, and environmental sustainability with the mediating effect of the green innovation. The population of this study is all employees in 19 eco-friendly hotels in Jordan, they were 18,850 distributed over four Jordanian regions (Amman, Aqaba, Dead Sea and Petra). A total of 377 questionnaires distributed to respondents using stratified sampling. The study used SEM with SMART-PLS 4 to analyze the data collected. The measurement model applied to analyze the reliability and reliability of the model, the path coefficient in the structural equation model used to test the study hypotheses. The results of this study supported most of the study’s hypotheses, as it supported the impact of tangible and human capabilities on the sustainability, while the study did not find any direct impact of the intangible capabilities on the sustainability in the hotel sector in Jordan. The results show significant direct impact of the three AI capabilities; tangible, intangible and human on the green innovation, also the study found significant impact of the green innovation on the sustainability. The study confirms the three mediation hypotheses of the green innovation on the impact of the AI capabilities on the sustainability in the Jordanian hotel sector. The study provides important implications to the managers in the Jordanian hotel sector to enhance their environmental, economic and social sustainability by improving AI capabilities and innovation.

In the era of the digital world, supply chain systems and processes have changed in an unprecedented manner. Digital technologies and AI related applications have impacted the way in which supply chains are operated compared to traditional and linear supply chains in the previous era. Supply chains in this era are able to be agile, resilient and smarter. Some of the drawbacks of linear supply chains are addressed in the modern supply chains with the application of digital technologies. The future of supply chains now lies in focusing on achieving sustainability by leveraging the advantages provided by the technologies. Sustainability has gained increased focus both in the academic discipline as well as among industry practitioners especially after the development of Sustainable Development Goals (SDGs) by the United Nations. However, most of this development is limited to large scale enterprises and there is a need to improve it in Small and Medium Enterprises (SMEs) and Startups. In Particular, startups require a lot of support in developing their ecosystem in the initial days of their existence. Two major practices of supply chain are found to impact the entrepreneurial ecosystem of startups. The first one is smart supply chain related practices and the second one is sustainable supply chain related practices. This article focuses on the influence of these practices on the entrepreneurial ecosystem of startups. Both these supply chain practices are found to positively influence the development of the entrepreneurial ecosystem. An empirical survey was conducted using a structured questionnaire as a survey instrument in 85 pharmaceutical companies in India. The director of SMEs of Telangana state government was used to qualify the startups and SMEs based on different criteria. A total of 220 responses were received from these companies. Convenient cluster sampling technique was used to select the sample size. The responses were analysed using regression and ANOVA through SPSS. It is found that the smart and sustainable supply chain practices can foster the development of the entrepreneurial ecosystem of startups. The outcomes of this study provide high value-addition to researchers, academicians, students, policy makers, budding entrepreneurs and startup owners and employees.