Up to 78% of oil and gas megaprojects fail to deliver on initial commitments, yet maintenance readiness is routinely deferred until after construction decisions are already irreversible. This study investigates the previously unexamined link between maintenance readiness and interface management across the capital-project life cycle through a systematic literature review combined with a 30-year longitudinal analysis of 941 anonymised interface lessons-learned records spanning 21 countries. Root causes were classified using ISO 14224 of 2016, and predictive models were validated through stratified coding (Cohen's κ = 0.81) and five-fold cross-validation. The analysis revealed that five root causes drove 81.2% of all failures, including slow interface identification (18.5%), uncertain technical definitions (17.4%), weak governance (17.4%), unclear scope and ownership (16.3%), and cross-discipline communication breakdowns (11.6%), with key effects being performance impact (41.8%) and schedule delay (25.7%), while 33.0% of records were classified as high or critical risk and approximately 40.3% originated in Front-End Engineering Design. The Interface Criticality Index (ICI) correlated strongly with high-risk classification (r = 0.83), and the Random Forest classifier achieved ROC-AUC = 0.848 and PR-AUC = 0.850 respectively. The findings conclude that maintenance readiness should be established as a distinct, front-end-loaded discipline rather than a post-handover activity, as embedding readiness controls upstream converts lessons-learned records from retrospective artefacts into proactive design constraints, enabling early intervention before costly lock-in.

This systematic review synthesizes 40 peer-reviewed studies to examine the role of inclusive finance and institutional governance in FinTech adoption in Pakistan's banking sector. While user-centric behavioural models are the dominant narrative in global discourse, this review highlights the underappreciated role of institutional trust, regulatory design, and structural inclusion in the scalability of FinTech ecosystems. The paper identifies four thematic clusters using PRISMA and TCCM frameworks, namely, user-level drivers, governance enablers, financial inclusion mechanisms, and integrated adoption frameworks. Findings suggest that inclusion should be reimagined not just as an objective but also as a prerequisite to digital finance, and quality of governance directly moderates trust and adoption behaviour. The study presents an original conceptual framework that links ecosystem-level conditions and adoption dynamics, highlighting the need for multi-level theoretical approaches that incorporate behavioural, institutional, and infrastructural dimensions. Implications are drawn for policymakers, FinTech innovators, and financial regulators who are looking for inclusive, trusted, and sustainable digital finance solutions in emerging markets.

This scientometric study provides a comprehensive analysis of the intellectual landscape surrounding Retrieval-Augmented Generation (RAG), a transformative paradigm that addresses fundamental limitations of Large Language Models by grounding generative outputs in external knowledge sources. Analyzing 200 highly cited Scopus-indexed publications from 2006 to 2026, this study employs bibliometric techniques including citation analysis, co-authorship network analysis, keyword co-occurrence analysis, and thematic evolution mapping to characterize the field's development trajectory, intellectual structure, and emerging frontiers. The findings reveal exponential growth in RAG research since 2020, with a 215.7% compound annual growth rate between 2020 and 2023, accelerating dramatically after 2024. The United States and China emerge as dominant contributors, supported by a collaborative “Triple Helix” network linking academia, industry, and government. Thematic analysis identifies six major research clusters: foundational RAG architectures, biomedical and healthcare applications, educational applications, legal and compliance applications, industrial and manufacturing applications, and multimodal RAG systems. Evaluation practices reveal fragmentation with over 40 distinct metrics employed across studies. Key challenges identified include hallucination mitigation, computational efficiency, retrieval quality optimization, and privacy preservation. Emerging directions include agentic RAG systems, graph-based retrieval, iterative retrieval-generation synergy, and personalized RAG applications. This study contributes a data-driven roadmap for navigating this rapidly evolving field and identifies priority areas for future investigation.

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.

Generative artificial intelligence (GenAI) has reshaped organizational work since the release of ChatGPT in late 2022, yet research on the cultural dimensions of its adoption remains fragmented. This study synthesizes the emerging field through a bibliometric review combining performance analysis, science mapping, and the Theory–Context–Characteristics–Methodology (TCCM) framework. A Scopus search executed on 5 May 2026 yielded 257 records, refined to 128 by automatic filters, and reduced to 69 articles after PRISMA 2020 screening. Analysis employed bibliometrix (R 4.3.0) and four VOSviewer 1.6.20 networks. The field exhibits an annual growth rate of 114.07%, with publications rising from one in 2022 to 36 in 2025 and 21 in early 2026 across 65 sources and 23 countries (China leads with 12 articles), anchored by Ng et al. (2025), Jo and Bang (2023), and Lee and Park (2024). The keyword map yields four thematic clusters; the co-citation map reveals three intellectual sub-structures (Technology Acceptance, Methodological and Applied GenAI, and GenAI in Management). Both layers converge on a technology-acceptance dominant, methodologically homogeneous, and culturally thin configuration: classical culture references (Schein, Cameron, and Quinn) and dedicated AI-ethics works do not reach the co-citation core. To redress this asymmetry, we propose an integrative framework: Leadership Vision → AI Literacy and Trust → Psychological Safety → Ethical Climate → GenAI Adoption Culture → Productivity, Innovation, and Well-being. The agenda outlines five priorities, including cross-cultural instruments, longitudinal multi-wave designs, and emerging-market studies foregrounding Indonesia and ASEAN.