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This study aims to explore an emerging collection of smart building technologies, known as smart workplace solutions (SWS), in the context of facilities management (FM).

This study is based on semi-structured interviews with facility managers in Finland, Norway and Sweden who have deployed SWSs in their organizations. SWS features, based on empirical data from a previous study, were also used to further analyse the interviews.

It analyses the benefits that SWSs bring from the facility management point of view. It is clear that the impetus for change and for deploying SWS in the context of FM is primarily driven by cost savings related to reductions in office space.

This research has been conducted with a focus on office buildings only. However, other building types can learn from the benefits that facility managers receive in the area of user-centred smart buildings.

SWSs are often seen as employee experience solutions that are only related to “soft” elements such as collaboration, innovation and learning. Understanding the FM business case can help make a more practical case for their deployment.

SWSs are an emerging area, and this study has collected data from facility managers who use them daily.

This paper explores the patterns of the current needs of users' social characteristics in post occupancy evaluation (POE) associated with the environmental performance of green buildings using systematic literature review (SLR). This paper aims to establish a conceptual nexus between environmental performance mandates and the current needs of the users' social characteristics.

This paper adopts a SLR approach designed using Preferred Reporting Items for Systematic Reviews and Meta-Analyses for 21 articles that were selected as qualitative synthesis in this study. The search parameter for the selected articles in this review was limited to publications in three databases, Scopus, Web of Science and Emerald, between January 2016 and January 2023, with the help of qualitative software ATLAS.ti 9© in the presentation of the network codes. The initial literature search has retrieved 99 papers which sequentially excluded 42 papers due to exclusion criteria, and the researcher was left with 57 papers. Out of 57, 14 papers were then removed due to duplication of records found in the Scopus and Web of Science databases, and 43 articles were further screened for qualitative synthesis. A thorough critical appraisal was applied to ensure that only selected papers were included, consensus was achieved among the authors and 22 papers were excluded. The qualitative synthesis has finalized 21 studies, and they are selected as confirmative findings.

Using network codes presentation of ATLAS.ti 9©, the result shows that the social characteristics are influenced by the evaluated building category and the users' category – the stakeholders (owners, designers) and the occupants. New-fangled elements in environmental performance mandates are legitimacy and accessibility. The users’ social characteristics are derived from the category of users, where the stakeholders (designers, owners) are relatively a novel benchmark in meeting the POE objectives towards environmental performance. The least attention on the users’ social characteristics based on the findings shows that image, experiential (conjoint), happiness, interactive behaviour, morale and values are depicted as the social current needs in the environmental performance using POE. However, all stakeholders and the building occupants’ social characteristics must have a confirmative relation to the performance mandates, especially for newly performance mandates elements: legitimacy and accessibility.

The research limits the literature search between the recent January 2016 and January 2023 in Scopus, Web of Science and Emerald databases. Limiting the year of publication to the recent years is important to select and rank relevant scientific papers which encompass the reviewed subject. Other limitations include the selection of papers focusing on the POE approach and environmental performance as the main subject of evaluation. Other evaluation purposes that are not related to environmental objectives are excluded in this study.

The characteristics of the social elements become a challenging subject in meeting the environmental performance needs as they lean more towards intangible elements. The novelty of the findings is drawn from the new pattern and current needs of users' social characteristics in POE for environmental performance.

Many buildings fail to meet user expectations, causing a performance gap. Pre-occupancy evaluation (PrOE) is believed to have the potential to close the gap. It enables designers to obtain end-user feedback in the design phase and improve the design for better performance. However, PrOE implementation faces challenges due to still maturing knowledgebase. This study aims to understand the state-of-the-art knowledge of PrOE, thereby identifying future research needs to advance the domain.

A systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework was conducted. A thorough search in five databases and Google Scholar retrieved 90 articles, with 30 selected for systematic review after eliminating duplicates and irrelevant articles. Bibliometric analyses were performed using VOSviewer and Biblioshiny on the article metadata, and thematic analyses were conducted on their contents.

PrOE is a vehicle for engaging building end-users in the design phase to address the credibility gap caused by the discrepancies between the expected and actual performance of buildings. PrOE has gained limited applications in healthcare, residential, office and educational building design for two broad purposes: design management and marketing. Using virtual reality technologies for PrOE has demonstrated significant benefits. Yet, the PrOE domain needs to mature in multiple perspectives to serve its intended purpose effectively.

This study identifies four knowledge gaps for future research to advance the PrOE domain: (1) developing a holistic PrOE framework, integrating comprehensive performance evaluation criteria, useable at different stages of the design phase and multi-criteria decision algorithms, (2) developing a mixed reality tool, embodying the holistic PrOE framework, (3) formulating a PrOE framework for adaptive reuse of buildings and (4) managing uncertainties in user requirements during the lifecycle in PrOE decisions.

The purpose of this study is to identify and evaluate the primary constraints that quantity surveying firms in Nigeria encounter while integrating indoor environmental quality (IEQ) principles into building designs.

The study used a quantitative approach by administering a well-structured questionnaire to 114 quantity surveyors. The collected data were analyzed using methods such as frequencies, percentages, mean item scores, Kruskal–Wallis test and exploratory factor analysis.

The top five ranked constraints were limited access to funding or financing options, limited availability of green materials, limited availability of insurance for sustainable buildings, limited availability of sustainable design resources and limited diversity and inclusivity within the design profession. Based on the factor analysis, the study identified six clusters of constraints: structural-related constraints, technical-related constraints, financial-related constraints, capacity-related constraints, legal-related constraints and culture-related constraints.

This study has several practical implications for quantity surveying firms, policymakers and industry stakeholders involved in building design and construction in Nigeria. The findings of this study can also inform future research on the integration of IEQ principles into building designs.

By identifying and structuring the clusters of constraints faced by quantity surveying firms in Nigeria when implementing IEQ principles, this study provides a novel approach to understanding the challenges associated with IEQ implementation in the building sector. This understanding can guide policymakers, industry stakeholders and quantity surveying firms in developing effective strategies to overcome these constraints and promote IEQ principles in building design and construction.

The Heating, Ventilation and Air Conditioning (HVAC) system is a significant energy consumer in built environments, and the building energy consumption could be minimised by optimising HVAC controls. Hence, this paper aims to investigate the applicability of Variable Refrigerant Flow (VRF) air conditioning systems for optimising the indoor comfort of buildings in Sri Lanka.

To address the research aim, the quantitative approach following the survey research strategy was deployed. Data collected through questionnaires were analysed using descriptive statistical tools, including Mean Rating (MR), Relative Important Index (RII) and Standard Deviation (SD).

The findings revealed that VRF systems are popularly used in Sri Lankan apartment buildings. Furthermore, energy efficiency and comfort were recognised as the most significant top-ranked benefits, while ventilation issues and initial cost were recognised as significant challenges. Moreover, the allocation of trained technicians and provision of proper ventilation through a Dedicated Outdoor Air System (DOAS) were highlighted as applicable mitigation strategies for the identified challenges in VRFs.

The study recommends VRF systems as a suitable technology to ensure energy efficiency, reduce GHG emissions and achieve climate performance within the built environment. The opportunities for adopting VRF systems for developing countries could be explored based on the research findings. The identified challenges would assist the design engineers and facilities professionals to devise suitable strategies to mitigate issues of VRF systems in developing countries.

This research provides empirical proof of the energy efficiency and comfort aspects of VRFs. The study has explored and recommended VRF technology as a beneficial application to overcome the persistent energy crisis in developing countries.

Suning Group launched Suning.com when its chain stores were developing at the highest speed, realizing the transformation to an Internet retailer. Suning continued to follow the growth strategy of “Technological transformation and Smart Services”, and was renamed Suning Commerce Co. Ltd. It launched a business model of “e-commerce + stores + retail service providers”. Riding on the brand new O2O business model, Suning is thinking and practicing from simple donation to actual implementation, from constructing public welfare network to extending CSR ecosystem in a bid to advance towards deeper and more extensive Internet economy, and to create greater social value.

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

This study aimed to address a gap in subcontractor management by focusing on previously unexplored complexities surrounding subcontractor management in developing countries. While past studies concentrated on selection and relationships, this study delved into how effective subcontractor management impacts project success.

This study used the Bayesian Network analysis approach, through a meticulously developed questionnaire survey refined through a piloting stage involving experienced industry professionals. The survey was ultimately distributed among participants based in Accra, Ghana, resulting in a response rate of approximately 63%.

The research identified diverse components contributing to subcontractor disruptions, highlighted the necessity of a clear regulatory framework, emphasized the impact of financial and leadership assessments on performance, and underscored the crucial role of main contractors in Integrated Project and Labour Cost Management with Subcontractor Oversight and Coordination.

Previous studies have not considered the challenges subcontractors face in projects. This investigation bridges this gap from multiple perspectives, using Bayesian network analysis to enhance subcontractor management, thereby contributing to the successful completion of construction projects.

This study aims to formulate a user-friendly pre-design model that could be a decision support tool for green wall systems to assist designers in selecting an optimal green wall system aligned with specified performance criteria while concurrently addressing project requirements linked to social and economic parameters. This approach seeks to enhance overall project satisfaction for the designer and the owner.

A correlation between the green wall context and design requirements and its performance on the buildings have been defined by considering its social and economic parameters, which represented the owner preferences to ensure the most satisfaction from installation as it achieves the required performance that is defined by the designer such as maximizing thermal insulation, improving indoor air quality, reducing the needed heating and cooling loads, etc. and also to achieve the satisfaction in social and economic requirements defined by the owner such as system installation cost, system maintenance cost, adding beauty value, etc.

The research developed an easy pre-design model to be a tool for green wall system decision-making for the most suitable system, which contains three main steps: the first one is defining the required performance of the green wall (designer requirements), the second step is limiting the context of the project which is made by designer and the owner requirements and finally the third step is choosing the system components that ensures achieving the requirements of both owners and designer, related to the building and climate context.

The added value lies in developing a green wall decision-making tool, essentially a pre-design model. This model considers the correlation between the project’s context, encompassing climate and building conditions. It provides a structured approach for decision-making in the early stages of green wall design. It offers valuable insights into the optimal choices related to system type, installation methods and plant characteristics. This enhanced decision-making tool contributes to more informed and efficient design processes, considering each project’s specific needs and conditions.

In the context of a developing country, Indian buildings need further research to channelize energy needs optimally to reduce energy wastage, thereby reducing carbon emissions. Also, reduction in smart devices’ costs with sequential advancements in Information and Communication Technology have resulted in an environment where model predictive control (MPC) strategies can be easily implemented. This study aims to propose certain preemptive measures to minimize the energy costs, while ensuring the thermal comfort for occupants, resulting in better greener solutions for building structures.

A simulation-based multi-input multi-output MPC strategy has been proposed. A dual objective function involving optimized energy consumption with acceptable thermal comfort has been achieved through simultaneous control of indoor temperature, humidity and illumination using various control variables. A regression-based lighting model and seasonal auto-regressive moving average with exogenous inputs (SARMAX) based temperature and humidity models have been chosen as predictor models along with four different control levels incorporated.

The mathematical approach in this study maintains an optimum tradeoff between energy cost savings and satisfactory occupants’ comfort levels. The proposed control mechanism establishes the relationships of output variables with respect to control and disturbance variables. The SARMAX and regression-based predictor models are found to be the best fit models in terms of accuracy, stability and superior performance. By adopting the proposed methodology, significant energy savings can be accomplished during certain hours of the day.

This study has been done on a specific corporate entity and future analysis can be done on other corporate or residential buildings and in other geographical settings within India. Inclusion of sensitivity analysis and non-linear predictor models is another area of future scope.

This study presents a dynamic MPC strategy, using five disturbance variables which further improves the overall performance and accuracy. In contrast to previous studies on MPC, SARMAX model has been used in this study, which is a novel contribution to the theoretical literature. Four levels of control zones: pre-cooling, strict, mild and loose zones have been used in the calculations to keep the Predictive Mean Vote index within acceptable threshold limits.