This paper aims to identify the major research concepts studied in the literature of sustainability in construction projects. Two bibliometric analysis tools—(a) BibExcel and (b) Gephi, were used to analyze the bibliometrics indices of papers and visualize their interrelations as a network, respectively. Therefore, a research focus parallelship network (RFPN) analysis and keyword co-occurrence network (KCON) analysis were performed to uncover the primary research themes. The RFPN analysis clustered the studies into three major categories of evaluating sustainability, project management for sustainability, and drivers of sustainable construction. The KCON analysis revealed that while each paper had a different focus, the underlying concept of all clusters was sustainability, construction, and project management. We found that while ‘sustainability’ was the leading keyword in the first cluster, i.e., evaluating sustainability, it was the second top keyword with the eigenvector centrality of over 0.94 in the other two clusters. We also found that the concept of sustainability should be included in construction projects from the early stages of design and feasibility studies and must be monitored throughout the project life. This review showed that previous researchers used a variety of statistical and mathematical techniques such as structural equation modelling and fuzzy decision-making methods to study sustainability in construction projects. Using an integrated approach to identifying the research gaps in this area, this paper provides researchers with insights on how to frame new research to study sustainability in construction projects.

This study systematically evaluates the literature of supply chain resilience by integrating bibliometric and network analysis techniques. This analysis is conducted following the dynamic capability, as a theoretical lens, to understand and analyse supply chain resilience. We extracted and curated 294 peer‐reviewed articles from leading academic databases, Web of Science and Scopus, covering the period from January 2000 to 2024. We applied two complementary network analysis methods, a keyword co‐occurrence network (KCON) to analyse the frequency and interrelationships among key terms, and a research focus parallelship network (RFPN) to map the connectivity among research streams. These analytical approaches revealed three distinct research clusters, optimisation for supply chain resilience, technology adoption for supply chain resilience, and resilience strategies against disruptions and risk management. By linking emerging trends in digital transformation with traditional risk management practices, this study offers a new perspective for both academic inquiry and managerial practice. Practically, integrating digital technologies, especially digital twin and machine learning, into risk management processes offers a strategic roadmap for real-time monitoring and decision-making in supply chain resilience. Furthermore, this study identifies significant research gaps, including an exploration of micro‐level dynamics within organisations, and the development of advanced simulation techniques and robust approaches to manage uncertainties.

Purpose: Despite the potential of artificial intelligence (AI) systems to increase revenue, reduce costs and enhance performance, their adoption by organisations has fallen short of expectations, leading to unsuccessful implementations. This paper aims to identify and elucidate the factors influencing AI adoption at both the organisational and individual levels. Developing a conceptual model, it contributes to understanding the underlying individual, social, technological, organisational and environmental factors and guides future research in this area. Design/methodology/approach: The authors have conducted a systematic literature review to synthesise the literature on the determinants of AI adoption. In total, 90 papers published in the field of AI adoption in the organisational context were reviewed to identify a set of factors influencing AI adoption. Findings: This study categorised the factors influencing AI system adoption into individual, social, organisational, environmental and technological factors. Firm-level factors were found to impact employee behaviour towards AI systems. Further research is needed to understand the effects of these factors on employee perceptions, emotions and behaviours towards new AI systems. These findings led to the proposal of a theory-based model illustrating the relationships between these factors, challenging the assumption of independence between adoption influencers at both the firm and employee levels. Originality/value: This study is one of the first to synthesise current knowledge on determinants of AI adoption, serving as a theoretical foundation for further research in this emerging field. The adoption model developed integrates key factors from both the firm and individual levels, offering a holistic view of the interconnectedness of various AI adoption factors. This approach challenges the assumption that factors at the firm and individual levels operate independently. Through this study, information systems researchers and practitioners gain a deeper understanding of AI adoption, enhancing their insight into its potential impacts.