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.

The European Journal of Information Systems provides a distinctive European perspective on the theory and practice of information systems for a global audience. We encourage first rate research articles by academics, but also case studies and reflective articles by practitioners. We provide a critical view on technology, development, implementation, strategy, management and policy.

Buy‐now‐pay‐later (BNPL) in Australia is a rapidly growing payment innovation. Regulators and consumer groups have expressed concerns at the financial risks posed by BNPL. As BNPL is not regulated under consumer credit law, financial regulator and consumer groups have recommended that BNPL users adopt a range of responsible financial behaviours for their financial wellbeing. This study, using a survey of BNPL users and structural equation modelling, shows a link between most of these recommended financially responsible behaviours and financial wellbeing and that the financial behaviours of younger users (aged under 25) place them at greater risk of reduced financial wellbeing.

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.

The contribution of market structure to the success of an electronic marketplace (e-marketplace) is an issue that has not received a great deal of attention in the literature. Although, researchers have given some attention to the antecedents of successful e-marketplace participation by organizations,there has been relatively little attention given to the e-marketplace structures. In this paper, the issue of e-marketplace strategy and its relationship withe-marketplace structure is examined. A classification of e-marketplace strategies is presented. The market structures of intermediary, hierarchy, consortium,and large group ownership structures are mapped onto this classification using four case studies to explain the relationship between structure and strategy. Each structural model has a range of implications for market participants. These include the economic and service benefits from intermediaries, economic benefits, and the potential to deliver improved levels of service for hierarchies,the relational benefits from the strong network ethos of consortia, and large group ownership structures which have potential for regional community or industry sector development.