Lukumon O. Oyedele’s research while affiliated with University of the West of England, Bristol and other places

Big data analytics (BDA) has been introduced in the past few years in most industries as a factor capable of revolutionizing their operations by offering significant efficiency opportunities and benefits. To compete in this digital age, businesses must adopt a client-centric service model, founded on data delivering continuous value and achieving optimal performance, whilst also upgrading their own decision-making and reporting processes. This article aims to explore how UK FM organizations are currently capitalizing on BDA to drive innovation and ‘added value’ in their operations. The objective is to shed light on the initial BDA adoption efforts within the UK’s FM sector, particularly capturing the benefits experienced by FM organizations in relation to customer value and improved decision-making processes. Drawing upon exploratory sequential research including a qualitative stage with 12 semi-structured interviews and an industry-wide questionnaire survey with 52 responses, a novel fifteen-variable model for BDA outcomes was developed. Exploratory Factor Analysis (EFA) and a Higher-Order model using Partial Least Square Structural modelling (PLS-SEM) were used to validate the scale. The EFA output generated three dimensions with 14 items. The dimensions included Improved client value, FM business operations added value, and Improved efficiency added value. Furthermore, the results of PLS-SEM confirmed the validity of the scale items and the reflective–formative measurement model. The findings suggest that the contemporary digitization trend offers the FM service the unique opportunity to develop a smarter, client-centric strategy resulting in more personalized services and stronger customer relationships. Furthermore, efficient resource management and planning powered by analytics and data-driven insights emerge as a key driver for competitive differentiation in the field. As one of the first studies to develop and validate scale items measuring specific dimensions of BDA adoption outcomes, the study makes significant contributions to the literature.

Purpose-Despite an enormous body of literature on conflict management, intra-group conflicts visa -vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams. Design/methodology/approach-This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology. Findings-Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power Big data adoption and project team conflict The authors acknowledge and express their sincere gratitude to the Engineering and Physical Sciences Research Council (EPSRC-EP/S031480/1) for providing financial support for this study. Since submission of this article, the following author(s) have updated their affiliations: Hakeem A Owolabi is at the Greater Manchester Business School conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other. Research limitations/implications-The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations. Practical implications-The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team. Social implications-The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams. Originality/value-The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

The study presents a sophisticated hybrid machine learning methodology tailored for predicting energy loads in occupied buildings. Leveraging eight pivotal input features-building compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution-we elucidate the intricate relationships between building characteristics and their corresponding heating load (HL) and cooling load (CL). We meticulously analyze these features across 12 diverse structural forms, each emblematic of unique architectural designs and building materials. Using a dataset encompassing 768 buildings, we demonstrate the prowess of our proposed models. Among the algorithms we employed, the extreme gradient boosting algorithm stands out, registering impressive accuracy metrics (HL: RSQ = 0.9986, RMSE = 0.3797, MAE = 0.2467 and MAPE = 1.1812; CL: RSQ = 0.9938, RMSE = 0.7578, MAE = 0.4546 and MAPE = 1.6365). We further integrate SHAP analysis, revealing that relative compactness positively influences both HL and CL the most, closely followed by surface area and glazing area. By merging an explainable extreme gradient boosting algorithm with a Bayesian-based metaheuristic optimization technique, we ensure both high predictive accuracy and interpretability. This study holds profound implications for enhancing building energy efficiency, curbing waste, and championing the shift to sustainable energy sources, aligning seamlessly with SDG 7. J o u r n a l P r e-p r o o f Highlights • Tree-based machine learning models were utilized to analyze residential energy loads. • The XGBoost model proved highly accurate, predicting heating and cooling loads. • Bayesian-based metaheuristic optimization helped to tune algorithm and hyperparameter. • SHAP offered detailed explanations of energy load predictions via design parameters. • Top prediction influencers were relative compactness, surface, and glazing areas. Graphical Abstract J o u r n a l P r e-p r o o f 1.

Big data analytics (BDA) has been introduced in the past few years in most industries as a factor capable of revolutionizing their operations by offering significant efficiency opportunities and benefits. To compete in this digital age, businesses must adopt a client centric service model, founded on data delivering continuous value, achieving optimal performance whilst also upgrading their own decision making and reporting processes. This study focuses on value outcomes (i.e. the end results of the implementation process) associated with the BDA adoption in the Facilities Management (FM) sector in United Kingdom (UK). Drawing upon qualitative case-study findings and an industry-wide questionnaire survey, a novel fifteen-variable model for BDA outcomes was developed and validated. This paper further uses the Confirmatory Factor Analysis (CFA) to establish the relationships between the variables and reveal the model’s principal dimensions. The identified themes focus on improved client experiences and efficient resource management and planning. In the current dynamic market environment, the findings of this study will help FM organisations to formulate effective data-driven strategies and client facing business models.

The idea of developing a system that can converse and understand human languages has been around since the 1200s. With the advancement in artificial intelligence (AI), Conversational AI came of age in 2010 with the launch of Apple's Siri. Conversational AI systems leveraged Natural Language Processing (NLP) to understand and converse with humans via speech and text. These systems have been deployed in sectors such as aviation, tourism, and healthcare. However, the application of Conversational AI in the architecture engineering and construction (AEC) industry is lagging, and little is known about the state of research on Conversational AI. Thus, this study presents a systematic review of Conversational AI in the AEC industry to provide insights into the current development and conducted a Focus Group Discussion to highlight challenges and validate areas of opportunities. The findings reveal that Conversational AI applications hold immense benefits for the AEC industry , but it is currently underexplored. The major challenges for the under exploration were highlighted and discusses for intervention. Lastly, opportunities and future research directions of Conversational AI are projected and validated which would improve the productivity and efficiency of the industry. This study presents the status quo of a fast-emerging research area and serves as the first attempt in the AEC field. Its findings would provide insights into the new field which be of benefit to researchers and stakeholders in the AEC industry.

The reinforcement and imitation learning paradigms have the potential to revolutionise robotics. Many successful developments have been reported in literature; however, these approaches have not been explored widely in robotics for construction. The objective of this paper is to consolidate, structure, and summarise research knowledge at the intersection of robotics, reinforcement learning, and construction. A two-strand approach to literature review was employed. A bottom-up approach to analyse in detail a selected number of relevant publications, and a top-down approach in which a large number of papers were analysed to identify common relevant themes and research trends. This study found that research on robotics for construction has not increased significantly since the 1980s, in terms of number of publications. Also, robotics for construction lacks the development of dedicated systems, which limits their effectiveness. Moreover, unlike manufacturing, construction's unstructured and dynamic characteristics are a major challenge for reinforcement and imitation learning approaches. This paper provides a very useful starting point to understating research on robotics for construction by (i) identifying the strengths and limitations of the reinforcement and imitation learning approaches, and (ii) by contextualising the construction robotics problem; both of which will aid to kick-start research on the subject or boost existing research efforts.

The advent of digital technologies has brought substantial improvements in various domains. This article provides a comprehensive review of research emphasizing AI-enabled IoT applications in poultry health and welfare management. This study focused on poultry welfare since modern poultry management is confronted with issues relating to standardized parameters for welfare assessment and robust monitoring systems, particularly for broilers' health and disease outbreak prevention. Evidence has shown that modern digital technologies have high possibilities for intelligent automation of current and future poultry management operations to facilitate high-quality and low-cost poultry production. Therefore, this study presents a systematic review of the current state-of-the-art AI-enabled IoT systems and their recent advances in developing intelligent systems in this domain. Also, the study provides an overview of the critical applications of identified digital technologies in poultry welfare management. Lastly, the study discusses the challenges and opportunities of AI and IoT in poultry farming.