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Systematic literature review of the factors of information sharing that enable circular procurement in the construction industry
Full text linkConstruction is a fundamental sector in most economies, and its high consumption of physical resources presents major opportunities for applying circular economy principles to minimise waste and improve sustainability. Procurement functions at the intersection of construction supply chains, managing design, purchasing, and material sourcing activities. Effective information sharing both within and between organisations is essential for embedding circular economy principles into procurement processes. This paper adopts a systematic literature review approach to identify and synthesise research on information sharing in circular procurement. Descriptive analysis outlines the research trends, theoretical and methodological orientations, and geographical distribution of studies, while synthesis categorises barriers and enablers of information sharing across micro, meso, and macro-organisational levels. Additionally, circular economy-related information and knowledge management activities, such as creation, dissemination, and application, are mapped against stages of the strategic procurement process and the Royal Institute of British Architects Plan of Work, strengthening the study’s relevance to construction practice. By integrating fragmented yet expanding bodies of literature, the paper provides a comprehensive understanding of how circular economy information and knowledge management intersect with procurement in construction. It offers a construction-specific framework that embeds circularity throughout the procurement lifecycle, with support from the Resource-Based View and Institutional theory and also identifies key organisational enablers and barriers. The review also highlights limited empirical and theoretical integration in current research and recommends future studies that apply behavioural and organisational theories to explore knowledge-sharing dynamics, examine policy impacts, and assess the role of technology and skills development in advancing circular procurement
The Impact of Attribute Agenda-Setting on Brand-Customer Engagement: Evidence from Chinese ERKE’s Douyin
Full text linkActive consumer engagement on social media platforms can effectively enhance brand awareness, loyalty, and word-of-mouth. While numerous studies have examined the impact of content and media characteristics on consumer engagement (CE), the specific effect of different brand attributes on CE on Douyin remains underexplored. To address this research gap, this study applies attribute agenda-setting theory to explore how brand attributes drive consumer engagement on Douyin. The study used survey data from the Chinese sportswear brand ERKE’s customer community on Douyin. The partial least squares structural equation modeling (PLS-SEM) was employed to examine how media use, attribute agenda-setting, and the need for orientation (NFO) contribute to brand communication effectiveness on Douyin. Findings showed that all direct impacts were statistically significant, but no differences between structural models for different NFO groups were found. The study highlighted the role of attribute agenda-setting in brand communication, showing that emphasizing substantive attributes stimulates primer CE while emphasizing affective attributes fosters deeper CE. This work extends agenda-setting theory and offers insights for branded content management, suggesting a balance of substantive and affective attributes to enhance CE
Analysis and comparison of mixed diazonium generated films, using different surface modification methods
No full textThe formation of organic films on electrode surfaces can be performed using several methods. One of these methods is the electrochemical reduction of diazonium salts, where a diazonium cation is electrochemically reduced at an electrode surface, resulting in a phenyl radical forming a covalent bond with the electrode interface. This method has also been applied to the creation of mixed films. The formation of mixed films, consisting of a specific composition of two components from different diazonium precursors are important for many applications. The composition of mixed films is difficult to control due to the inherent reduction potential of the specific diazonium precursors heavily influencing films composition, often resulting in the more easily reduced diazonium dominating the electrode surface. Herein, describes the investigations of diazonium derived mixed films and the relative surface concentrations of each component in mixed films prepared using different surface modification techniques. Mixed films containing nitrophenyl and anthraquinone were prepared using both electrochemical and spontaneous grafting methods and were analysed using cyclic voltammetry, where it was discovered that both mixed films contained more nitrophenyl in the film, despite both diazonium cations having similar reduction potentials. Comparison of the nitrophenyl/anthraquinone mixed films revealed that spontaneous grafting led to a much higher relative surface concentration of nitrophenyl than in electrochemically prepared mixed films. Mixed films containing nitrophenyl/bromophenyl and nitrophenyl/iodophenyl were prepared in both acetonitrile and 0.1 M H2SO4 using both electrochemical and spontaneous grafting methods. These mixed films were analysed using cyclic voltammetry and XPS to obtain relative surface concentration ratios of the components in the mixed films. It was found that the grafting method drastically effects the relative surface concentration of each film component. Spontaneously prepared mixed films had a relative surface concentration ratio that indicated that nitrophenyl dominated the surface more than in the electrochemically prepared mixed films, which was attributed to the spike in the open circuit potential that occurs once the electrode is immersed into the diazonium grafting solutions. Mixed films were also prepared using a protection/deprotection method where tetra alkyl ammonium cations were ionically paired with deprotonated carboxyl groups present on the terminal position of a diazonium before the surface was modified, where steric hinderance between the alkyl chains left gaps in the film. The bulky tetra alkyl ammonium cations were removed after washing the modified electrode in a competitive medium, leaving empty spaces in the organic film on the electrode surface, where a mixed film was prepared by grafting nitrophenyl into the gaps. Analysis using XPS demonstrated that the ion pairs would remain on the surface until rinsed with a competitive medium. Cyclic voltammetry revealed that using different tetra alkyl ammonium cations with different length alkyl chains allowed for the size of the gaps in the film to be controlled
Analysing roadwork zone incursions and accidents using a sustainable GenAI system
Full text linkRoadwork zone incursions, which refer to unauthorized or unintended entries into restricted work areas, present significant safety hazards and often lead to road accidents. While these incidents account for a fraction of total crashes, their potential severity makes them a critical concern for road safety management. Analysing incursion data and accident records is essential for identifying risk factors, understanding patterns, and improving prevention strategies. However, the data spans structured, semi-structured, and unstructured formats, growing in complexity over time, making traditional analysis methods unsustainable and difficult to scale. These methods also fail to account for the relational and hierarchical dependencies within data and lack natural language (NL) interaction, limiting user engagement and the ability to derive insights. A sustainable solution must be flexible, scalable, and adaptable to diverse data formats, with intuitive interactions for long-term efficiency. Recent generative artificial intelligence (GenAI) advancements offer solutions for analysing multi-format data by incorporating relational dependencies. However, standalone large language models (LLMs) struggle with integrating private accident datasets. To address this challenge, this study proposes a GenAI-powered information system (IS) that leverages LLMs and retrieval-augmented generation (RAG) for context-aware accident analysis through NL interactions. The system analyses real-world incursion data and associated accidents, identifying key risk factors and emerging patterns. Evaluated using the Llama3.2 and DeepSeek-R1 models, the system exhibits scalability, efficiency, and sustainability in roadwork safety management, contributing to improved risk mitigation and accident prevention
Best practice in obstetric and midwifery management of twin pregnancy in the antenatal period
No full textThe National Institute for Health and Care Excellence (NICE) guidance sets out key recommendations to enhance effective antenatal care, and prevent adverse maternal and perinatal outcomes (Gibson et al 2020). This paper will review the main complications associated with a twin pregnancy, and the midwifery and obstetric management required to mitigate the consequences of adverse outcomes for mother and bab
Aligning Value and Practice: Reimagining Inclusion in Contemporary Higher Education
Full text linkIn contemporary higher education, inclusion is widely invoked as a core institutional value, yet its practical enactment remains uneven and often symbolic. This paper interrogates the tension between inclusion as an espoused value and inclusion as a demonstrable professional competence. While universities foreground inclusion in mission statements and strategic plans, such commitments frequently operate as ‘non‐performative’ gestures that proclaim change without redistributing power or transforming structural conditions. By distinguishing values from competences, the paper reframes inclusion not only as a moral value but as a set of intentional pedagogical, curricular, and organisational practices that can be developed, evidenced, and held accountable. Drawing on research in inclusive pedagogy, co‐creation, and the political economy of higher education, the analysis exposes how marketised logics can commodify inclusion while simultaneously obscuring its uneven and inequitable implementation. The persistent values‐practice gap is shown to be more than an implementation failure: it is a structural feature of systems that benefit from ambiguity, reputational gain, and minimal disruption. The paper argues for ‘embedded, contested competence’, in which inclusion becomes a shared responsibility supported by institutional alignment, resourcing, accountability, and critical reflexivity. Bridging the gap requires not only technical refinement but a renewed commitment to the value of transforming the conditions under which inclusion is realised
Understanding the barriers UK midwives face in leading research: A critical discussion paper
Full text linkMidwives have a pivotal role in supporting excellence in care through research. However, in the UK, very few midwives lead research. It has been estimated that <0.1 % of clinical midwives are employed in clinical academic careers, where they pursue a joint role working clinically and conducting research, and only 12 % of midwives working in academia hold a PhD. Increasing midwifery-led research and building research capacity for midwives has long been on the UK national agenda. Due to their experience, midwives can address clinically important questions and research solutions to problems that they have observed in their practice. However, midwives face many barriers to commencing and sustaining a career where they engage in midwife-led research. This critical discussion paper aims to explore and understand the factors acting as barriers to UK midwives embarking on and maintaining a career leading research. This paper presents a critical discussion of the barriers for UK-based midwives to pursue research careers based on four main key points. The paper draws from UK-based reports and international contemporary literature. Four key barriers were identified from the literature: Lack of opportunities, awareness and knowledge of research among midwives; Lack of role models and mentorship; Lack of NHS and Approved Education Institution (AEI) organisational support; Lack of a clear pathway of progression. Significant barriers exist for UK midwives to engage in midwife-led research, which must be addressed to enable midwife-led research capacity to be improved and establish a workforce of leading midwifery researchers. Key recommendations have been identified to improve the accessibility of conducting midwife-led research. [Abstract copyright: Copyright © 2025. Published by Elsevier Ltd.
Qualitative system dynamics approach to modelling cost escalation determinants in Nigerian public sector projects
No full textPurpose Cost escalation has been identified as one of the most significant challenges and recurring issues in the delivery of social and economic infrastructure in the Nigerian public sector. Like many other countries in the Global South, the Nigerian public sector projects suffer from poor cost performance. Thus, this study aims to analyse the dynamics of construction cost escalation determinants and produced a conceptual system dynamic model that captures the intricate interactions among these determinants. Design/methodology/approach This study includes a literature review and qualitative approach using semi-structured interviews. Data were obtained from 17 stakeholders involved in the delivery of Nigeria public sector projects, including Project managers, Architects, Civil engineer, Quantity surveyors and Construction managers. The data were analysed using a coding framework informed by case study approach, the principles of thematic analysis and saliency analysis framework, enabling the modelling of intricate interactions among various determinants of cost escalation. Findings The findings revealed a complex and dynamic interaction among the determinants, indicating that an isolated solution to cost escalation, particularly in developing countries like Nigeria, is unlikely. Instead, a holistic, system-oriented approach that considers the interplay of multiple determinants is necessary for effective cost escalation management. One of the determinants identified was the lack of technical competence among professionals involved in the delivery of public sector construction projects. In addition, the analysis of the causal loop diagram revealed intervention points in the project delivery process to counter cost escalation. This study postulates that a key intervention strategy involves enhancing the competencies of construction professionals through synergistic collaboration between various stakeholders: public sector entities, professional bodies, statutory regulatory bodies and policymakers. Research limitations/implications As the findings are rooted in Nigerian public construction projects, their relevance to privately financed projects might be limited. Also, the study focuses solely on the construction stage, without extending to the post-construction phase. Furthermore, the themes and their causal links were derived from viewpoints of experts from the Nigerian context. Future studies could enhance the external validity of the research results by examining their applicability in other contexts and by integrating the later phases of public sector construction projects, such as facilities management operation, to offer a more holistic view of the problem. Originality/value This study presents a collaborative and holistic approach to address cost escalation in Nigerian public sector projects by modelling the complex interactions among various determinants influencing cost escalation within a dynamic framework and identifying critical intervention points in the project delivery process
AI in Project Management: Machine Learning Application for Construction Scheduling
Full text linkThis study investigates the integration of Artificial Intelligence (AI) and Machine Learning (ML) into construction project scheduling to address inefficiencies in traditional methods. While conventional tools like Primavera and MS Project rely heavily on manual input, existing AI/ML research often neglects critical scheduling components such as activity relationships, critical path analysis, and user-friendly interfaces. This research aims to bridge these gaps by developing an ML-driven scheduling tool that automates activity sequencing, duration prediction, and critical path identification, while incorporating a practical interface for industry adoption.The study employs a multi-methods approach, integrating both quantitative and qualitative data gathered from a survey of construction professionals with a design science framework. Drawing on these insights, a Random Forest Regressor model was developed and trained using real-world power sector project data specifically overhead transmission line (OHTL) and substation construction, with its performance evaluated through R² and MAE metrics. Additionally, a prototype interface was constructed using Streamlit to assess usability and facilitate practical integration. Crucially, external validation by five highly experienced industry experts (>15 years relevant roles) confirmed its significant speed advantage (80% found it 'Much faster'). Experts positively rated its user-friendliness and ease of schedule generation, with 80% considering it for actual projects as a valuable initial planning tool.Preliminary findings, based on historical power sector data showed that the developed model demonstrates high predictive accuracy (R² values of 0.91 for successor, 0.93 for predecessor; MAE < 2.2 days). It autonomously determines activity sequences, predicts durations, and identifies the critical path, significantly enhancing planning efficiency and reducing errors. A user-friendly Streamlit interface enables dynamic schedule generation in under 30 seconds, with options to adjust duration constraints and export to Excel and XER with minimal manual intervention. Theoretically, this study contributes a novel, integrated Machine Learning framework that, unlike prior fragmented research, holistically automates the end-to-end scheduling process. Practically, it delivers a validated software artifact that demonstrates significant efficiency gains reducing schedule generation time from days to seconds and provides new empirical insights into the factors governing AI adoption in construction project management
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