Environmental, social, and governance (ESG) practices have become central to capital-market decision-making and corporate accountability, yet evidence from emerging and transitioning markets remains fragmented and uneven. This study presents a PRISMA-compliant systematic literature review (SLR) of ESG practices in the Kingdom of Saudi Arabia (KSA), synthesizing Scopus-indexed scholarship to clarify dominant research streams, measurement approaches, institutional drivers, and outcome pathways. Using a transparent Scopus search strategy (TITLE-ABS-KEY) covering 2010–2026 and pre-specified eligibility criteria, the review applies a structured screening workflow, quality appraisal rubric, and codebook-driven data extraction. The resulting corpus is mapped through descriptive and light bibliometric profiling and then synthesized via thematic analysis. The synthesis yields six interrelated themes: (1) institutional and regulatory drivers; (2) corporate governance mechanisms; (3) ESG measurement and disclosure quality; (4) ESG and firm outcomes; (5) sectoral heterogeneity; and (6) implementation constraints. Across themes, the evidence suggests that Saudi ESG scholarship is rapidly growing but remains disclosure-centric and method-dependent. Positive ESG, outcome associations are frequently reported; yet, findings vary with measurement choice, endogeneity handling, sample selection, and time-window length, indicating the need to distinguish ESG "reporting outputs" from ESG "organizational practices". To integrate these insights, the study develops a conceptual framework linking institutional context to firm-level enablers, ESG practices, and outcomes, moderated by sector intensity, firm size and visibility, governance strength, ownership type, and assurance quality, and mediated by capability, disclosure quality, and credibility. The review advances theory and practice by offering a replicable SLR protocol, an integrative model to reconcile mixed findings, and a Saudi-specific research agenda emphasizing causal policy evaluation, materiality-sensitive measurement, assurance and data governance, and richer qualitative process evidence.

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.

Systematic literature review in supply chain and decision support system, in general, have been rapidly performed during the last decade. However, the studies on the epistemological progress of decision support system related to the supply chain are still lacking. This study intended to provide comprehensive information on the trends, methodologies and the applications on different sectors and platforms used by scientists for building their decision support systems in supply chain. We used different keywords to collect the raw data based on articles published in well-known journals in the world to select the eligible studies which furthermore assembled. The data were processed by using bibliometric tool in VOSviewer and Microsoft Excel. The results of this systematic review give some key learning of the trends on the use of decision support system on smoothing the flow of supply chain and the logistic performance in the last decade and also provide a background for future research related to the fields.

This survey aims to provide a coherent and bibliometric overview of the theories and constructs employed in smart homes acceptance and adoption literature. To achieve the study aims, we conducted a systematic search for every article related to the SH concept, services and applications, user acceptance and adoption, and integrated IoT home appliances and devices, in 10 major library databases, namely, IEEE Digital Library, ACM Digital Library, Association for Information Systems (AIS), Elsevier, Emerald, Taylor and Francis, Wiley InterScience, Springer, Inderscience, and Hindawi. These databases contain literature focusing on smart home adoption using IoT technology. 40 research articles of journal and peer-reviewed conferences were found relating to our research objective, presented and distributed chronologically, by publisher, country, theory and model, key construct, and with full bibliometrics for each article. Additionally, this survey includes a word cloud and a taxonomy of the entire factors used to understand users’ acceptance and adoption of smart homes in different contexts and applications. This study has many advantages in covering the current research gap in the literature and also the researchers identify theoretical and practical research implications, research limitations, and recommendations for improving the acceptance and usage of smart homes literature.

Supply chain disruptions pose significant challenges to global economic stability, necessitating advanced predictive tools for effective risk management. As Machine Learning (ML) offers promising solutions for enhancing resiliency, this study investigates its applications in supply chain management. Utilizing a systematic literature review, we examined recent research to identify effective ML models and techniques, focusing on both supervised and unsupervised learning. Our analysis covered various industries to understand the adaptability and effectiveness of these models in mitigating supply chain risks. The results highlight the growing implementation of ML in anticipating disruptions, with supervised learning demonstrating superior predictive precision under specific conditions. At the same time, unsupervised approaches offer valuable insights in data-scarce scenarios. Context-specific data surfaced as crucial in model accuracy, underscoring the need for tailored approaches. This study concludes that integrating ML with current supply chain systems can significantly enhance operational resilience, advocating for continued exploration of novel data sources and interdisciplinary collaborative efforts.

Portfolio management has become a strategic activity in response to increasing organizational complexity, requiring models capable of reconciling multiple criteria, limited resources, and uncertainty. In this context, Portfolio Decision Analysis (PDA) has emerged as a well-established field within Operations Research dedicated to supporting such decision-making processes. This study presents a systematic literature review conducted in accordance with the PRISMA protocol, aiming to analyze the evolution of mathematical models applied to portfolio management. The search resulted in a final sample of 152 articles published between 1994 and 2025. The findings reveal a significant increase in publications from 2014 onward, with applications predominantly concentrated in project management and new product development (NPD), and a predominance of financial and technical-operational decision criteria. The results also indicate a methodological transition from classical optimization techniques to hybrid approaches that integrate multicriteria decision-making methods, fuzzy logic, mathematical programming, and, more recently, machine learning. The main research gaps identified include the need for broader empirical validation, the expansion of applications to different contexts, and the development of new hybrid approaches. This study provides an integrated overview of the methodological evolution of Portfolio Decision Analysis, offering a valuable reference for future research.

This study aims to analyze how the application of Scrum improves project development in companies and positively impacts the organizational environment. The research was conducted through a review of scientific articles published between 2013 and 2024, allowing the identification of the main motivators, challenges, and benefits of the agile methodology in organizations. The results show that Scrum improves internal communication, increases delivery efficiency, and enhances the quality of products and processes, delivering value to stakeholders. Furthermore, the analysis revealed that implementing the methodology depends on factors such as organizational culture, team training, and the team's understanding of agility. As a limitation, the study identified temporal variation in the articles: their distribution over the years was not homogeneous, and there was no in-depth quantitative evaluation to accurately measure the impacts of applying Scrum. Based on these conclusions, it is suggested that future research explores the integration of Scrum with other agile methodologies, conduct a comparative analysis with traditional methods, examine its application in specific sectors, and assess its impact on organizational culture.

Projects are essential mediums for businesses, organizations and industries to reach their strategic goal, however managing multiple projects simultaneously is a complex task. There have been a number of studies conducted on developing and introducing frameworks and models to help optimize project portfolio management. This study aims to identify these frameworks and analyze them to gain an understanding of the core components of these frameworks and to introduce the recent models and approaches used to optimize project portfolios. The methodology followed the guidelines of systematic literature reviews, by identifying a substantial number of papers, screening them and analyzing them until the research goals were met. The findings included 46 of relevant articles proposing models in project portfolio context, the data of the articles were recorded and presented and the models proposed were analyzed. Bibliographical findings were provided and by analyzing the models their core components were identified and using the core components a figure presenting the key components was illustrated.

This research paper is a systematic literature review (SLR) based on a theory-grounded analysis of supply chain resilience (SCR) concerning, in particular, resilience capabilities and resilience capability logics. In accordance with PRISMA 2020, 45 peer-reviewed journal articles found in Scopus were identified, screened and analysed. In addition to classifying the resilience capabilities, this review takes a dynamic capabilities and contingency approach to theorize the role resilience capabilities play as interacting logics through the phases of disruption (pre-disruption, during disruption, and post-disruption). The results suggest 7 central resilience capabilities (flexibility, redundancy, agility, collaboration, visibility, robustness and adaptability) that are grouped into three higher-order capability logics (buffering, adaptive and transformational). It has systematic trade-offs between efficiency and resilience, is characterized by a pre-eminent focal-firm bias, and limited longitudinal and multi-tier research are revealed in the review. This study lays these contributions on process-oriented resilience capability logic framework and sequencing of capability, shifting the capability enumeration to a capability configuration and sequencing to enhance theory development in supply chain resilience. Theoretically based coding scheme drew on the dynamic capabilities theory (sensing, seizing, reconfiguring) and contingency logic in order to discover underlying capability mechanisms.