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This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces.

The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor.

All 46 factors were assessed as “Very Important” through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are “AI ability to learn from patient data”, “insufficient data privacy measures for patients”, “availability of technical support and maintenance services”, “physicians’ acceptance of AI in healthcare”, “reliability and uptime of AI systems” and “ability to reduce medical errors”.

Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices.

This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders’ comprehension of the factors challenging the integration of AI technologies.

The purpose of this study is to explore the increased uptake of Artificial Intelligence (AI) technology by Facility Management (FM) organizations for enhanced operational efficiency and competitive advantage. While AI adoption in FM has been widely reported, limited attempts have been made to assess its impact on the social well-being of FM employees. To contribute towards addressing this gap, this study established the essential employee social well-being factors mostly impacted by the adoption of AI in South African FM organizations.

A four-stage design comprising a comprehensive review of literature, expert interviews, questionnaire census and focus group discussion sessions was used to elicit data from a sample of participants drawn from 22 South African FM organizations. The data was analyzed using a combination of content analysis, relative importance index and interpretative structural modeling for various data sets toward achieving the study’s objectives.

Sixteen employee social well-being factors, classified under job satisfaction, social relationship and knowledge development categories, respectively, were identified as being impacted by AI adoption in FM organizations. Furthermore, it was established that job security, job autonomy and professional status, which belong to the job satisfaction social well-being factor category, were deemed by FM employees as being mostly impacted by AI adoption.

The enhanced understanding of the impact of AI adoption on FM employees’ social well-being factors will contribute to the development of a collaborative intelligence framework for managing AI adoption in FM organizations toward engendering optimal AI–FM employee relationships for improved productivity.

Besides being one of the foremost studies to investigate the impact of AI adoption on FM employees’ social well-being, this study introduces a hierarchical framework of understanding employee social well-being factors based on multi-stakeholder perspectives.

Many parameters influence the success of sustainable projects (SPs) in the architecture–engineering–construction. One of these important parameters is the project delivery attributes (PDA), which are influenced by the project delivery system (PDS) while affecting the selection of it. This study aims to evaluate the significance of PDAs in influencing both the performance and success of SPs in Turkey, where the interest in SPs is high.

The impact of PDAs was determined by applying the two-round Delphi method with a semistructured interview involving the main stakeholders of a construction project, like owners, designers, contractors and consultants who played active roles in SPs, as well as academics to theoretically evaluate the issue. The significance of PDAs was assessed using the relative importance index, and the results were validated using the interrater agreement analysis.

The study identified key PDAs impacting SPs as owner character, commitment and motivation; simulation and energy modelling; and timing of stakeholders/early involvement.

The investigation of the significance of the PDAs is a lesser-studied context. Therefore, a research framework that enables an effective set of methods for solving the sectoral problems of PDAs that have impacts on SPs has been proposed. The framework is expected to open new opportunities for the generation and regulation of the PDSs for SPs. The findings will provide valuable insights to project stakeholders, particularly owners, local authorities and policymakers to assess which PDAs have a greater impact on sustainability performance when setting PDSs in SPs in other developing countries.

In the Global North, affordable housing has evolved and thrived, and it is now gaining traction in the Global South, where governments have been vocal supporters of the concept. Therefore, this paper aims to investigate the important criteria for selecting affordable housing units in Ghana.

A quantitative research approach was used, and a survey was administered to the residents. The data was analysed using both descriptive and inferential statistics. The relative importance index technique was used to rank the important criteria, and the EFA technique was used to create a taxonomy system for the criteria.

The hierarchical ranking of the most significant criteria for selecting affordable housing includes community safety, waste management and access to good-quality education. Furthermore, the important criteria for selecting affordable housing are classified into two groups, namely, “sustainability criteria” and “housing demand and supply and social service provision”.

This study has implications for the real estate industry and construction stakeholders, as this will inform decision-making in terms of the design of affordable housing and the suitability of the location for the development.

These findings provide a baseline to support potential homeowners and tenants in their quest to select affordable housing. Furthermore, these findings will aid future longitudinal research into the indicators or criteria for selecting suitable locations for the development of low- and middle-income housing.

Facilities management (FM) organizations are pivotal in enhancing the resilience of buildings against climate change impacts. While existing research delves into the adoption of digital technologies by FM organizations, there exists a gap regarding the specific utilization of artificial intelligence (AI) to address climate challenges. This study aims to investigate the drivers and barriers influencing the adoption and utilization of AI by South African FM organizations in mitigating climate change challenges.

This study focuses on South Africa, a developing nation grappling with climate change’s ramifications on its infrastructure. Through a combination of systematic literature review and an online questionnaire survey, data was collected from representatives of 85 professionally registered FM organizations in South Africa. Analysis methods employed include content analysis, Relative Importance Index (RII), and Total Interpretative Structural Modeling (TISM).

The findings reveal that regulatory compliance and a responsible supply chain serve as critical drivers for AI adoption among South African FM organizations. Conversely, policy constraints and South Africa’s energy crisis emerge as major barriers to AI adoption in combating climate change challenges within the FM sector.

This study contributes to existing knowledge by bridging the gap in understanding how AI technologies are utilized by FM organizations to address climate challenges, particularly in the context of a developing nation like South Africa. The research findings aim to inform policymakers on fostering a conducive environment for FM organizations to harness AI in fostering climate resilience in built assets.

Due to the lack of consensus on influential variables for real estate appraisal, which varies from one country to another based on the national preferences and customs of each country, this study aims to identify the most influential variables affecting condominium apartment real estate appraisal within the context of Al Bireh city, Palestine.

The methodology adopts a cross-sectional quantitative approach, entailing the administration of an online questionnaire survey to 103 buyers and appraisers. The questionnaire aims to evaluate 32 variables concerning their impact on condominium real estate appraisal. Out of these, 25 are derived from three specific previous studies, and the remaining 7 are identified through various studies or by the authors, taking into account the local context and the geopolitical situation in Palestine. Respondents assign scores to these variables on a five-point Likert scale, ranging from 1 to 5, where 1 indicates less influence and 5 signifies the most influence. Variables with an arithmetic mean score exceeding 4 are deemed the most influential.

The findings underscore 16 and 17 influential variables as perceived by buyers and appraisers, respectively. Notably, 13 variables are common, including aspects such as parking, elevator, neighborhood, floor apartments, finishing quality, construction material, condition, building apartments, area, open sides, building floors, colonies and age.

The primary constraint of this study is its dependence on insights solely from buyers and appraisers, disregarding input from other stakeholders like investors or developers. The questionnaire lacks vital respondent characteristics, such as gender and occupation, impeding the analysis of variable dependence on participant attributes. Although some additional influential variables are suggested through the responses of an open-ended question, the questionnaire is not repeated, leaving their influence unassessed. This study's focus on Al Bireh city limits the opportunity for result comparison with other cities, diminishing its generalizability.

The implications of this research are twofold: to provide stakeholders with a checklist for variables influencing apartment price value and to guide data collection related to the real estate appraisal sector, facilitating its use as input in advanced appraisal methods such as artificial intelligence with a view to improving overall performance. Obtaining an informed, mature and accurate appraisal has direct economic, business and financial impacts at the level of policymakers and individuals.

To the best of the authors' knowledge, this study is the inaugural endeavor in Palestine focusing on identifying pivotal factors influencing condominium apartment appraisal values. This study concludes by presenting a checklist comprising the most influential variables, offering utility to various stakeholders, including buyers, appraisers and developers. In addition, the questionnaire incorporates an open-ended question, soliciting respondents' input on additional variables they believe impact the appraisal process.

The smart sustainable cities (SSC) concept has a wide acknowledgement amongst governments and societies that deal with emerging technology and help in developing better urban communities. However, the fact that citizens' participation (CP) is not adherent to the current policies and governance often boosts their aspirations of decision-making to become smart cities. This paper aims to identify SSC variables and, more importantly, rank, categorise and discuss the factors towards implementing SSC by engaging, empowering and enabling citizens to participate in the urban development of SSC.

A comprehensive literature review identified 38 factors in the CP process. Those factors were used to design an online questionnaire administered to the respondents. A total of 164 valid responses were collected. A two-stage statistical analysis was adopted. First, the Relative Importance Index (RII) was used to rank and prioritise the importance of the factors that affect the current policies and agenda. Second, factor analysis was utilised to categorise and group those factors.

This study founds four significant factors that help in implanting SSC: “knowledge of smart sustainable cities”, “awareness of smart sustainable cities”, “willingness of the citizens to participate” and “opinion on the current agenda of the government's role”.

This study has a few limitations which can be considered in future studies. First, the response rate of the participant is relatively low (163), so sampling a larger segment will support the broader perception of the citizens.

The outcome of this paper underlines the need for the successful implementation of smart cities by adopting CP in the process of impacting policies and governance. Particularly, it identifies factors that help cities and policymakers in engaging CP in developing new policies and revising existing policies for promoting SSC.

There is a need to investigate the most critical factors that influence CP for implementing SSC. These factors have not been adequately examined in extant literature.

Human activities in recent years with the excessive emission of greenhouse gases have had a negative impact on increasing global temperatures. In this regard, one of the best ways to control it is to move toward sustainability with more use of renewable energy. Therefore, this study aims to assess the indicators of sustainable energy, explore benefits of sustainable energy and evaluate strategies to enhance energy sustainability in line with Sustainable Development Goal (SDG) 7.

Quantitative research strategy was adopted. Questionnaires were developed and administered through convenience and snowball sampling techniques to professionals in the energy sector. Data collected was validated by using Cronbach’s alpha coefficient and discriminant validity, whereas objectives were analyzed by using the relative importance index, mean score ranking and Kruskal–Wallis test.

From the findings, the significant indicators of sustainable energy were as follows: reduction in greenhouse gas emissions from energy production, use of renewable energies and policies on proper utilization of energy resources. Furthermore, a reduction in greenhouse gas emissions, less harm caused to the environment and an increase in the economic and social development process were the major benefits of sustainable energy. Finally, the findings of the study revealed that a strong and accounted policy program, adopting sustainable energy indicators and strategic communication are the significant strategies needed to be put in place to enhance energy sustainability.

The study serves as a reminder to policymakers of the crucial role they have to play in enhancing energy sustainability by putting in place suitable policy programs and methods.

The originality of this study is that it is arguably a pioneering study in Ghana and contributes to the body of knowledge on energy sustainability.

Construction accidents cause as much harm in Bangladeshi construction as it does globally. This study examines the primary causes of accidents and undertakes an impact assessment of neglecting safety protocols in construction projects in Bangladesh, funded publicly, privately and through a Public-Private Partnership (PPP).

Research was initiated with a comprehensive questionnaire from experts, sourcing data in Bangladesh's construction sector. Data analysis utilized Cronbach's alpha, relative important index and a fishbone diagram for causal visualization.

The study identified the three major causes of safety negligence as “Poor safety culture (RII = 0.857),” “Top management's inattention (RII = 0.825)” and “Lack of personal care (RII = 0.825).” Effects: “Rising project expenses (RII = 0.88),” “Increased medical costs (RII = 0.87)” and “Worker compensation expenses (RII = 0.87).” The study also used the Ishikawa-Fishbone and effect-flow diagrams to highlight accident causes/effects and compare their primary causes in PPP, public and private projects.

Research on construction safety in Bangladesh has mainly focused on identifying factors within specific construction sectors. Since the rules and regulations vary across these three sectors, different health and safety hazards may arise. As a result, this research fills a critical gap by providing a comparative study that examines the causes and impacts of different project types in the Bangladeshi construction industry. By pinpointing the result, this research aims to enhance the safety and well-being of the construction workers sector-wise, thereby contributing to the industry's sustainable growth.

The COVID-19 pandemic was a shock to the food supply chain without modern precedent. Challenges in production, manufacturing, distribution and retailing led to the highest rates of food price inflation in the US since the 1970s. The major goal of this paper is to describe statistically the impact of the pandemic of food price inflation and volatility in the US and to discuss implications for industry and for policymakers.

We use Bureau of Labor Statistics data to investigate food prices in the US, 2020–2021. We apply 16 statistical approaches to measure price changes and volatility and three regression approaches to measure counterfactuals of food prices, had the pandemic not occurred.

Food price inflation and volatility increased substantially during the early months of the pandemic, with a great deal of heterogeneity across food products and geographic regions. Food price inflation was most pronounced for meats, and contrary to expectations, highest in the western US Forecasting approaches demonstrate that grocery prices were about 7% higher than they would have been without the pandemic as of the end of 2021.

The research on COVID-19 and the food system remains in its nascent stage. As findings on food loss and waste, employment and wages, food insecurity and more proliferate, it is vital to understand how food prices were connected to these phenomena and affected. We also motivate several ideas for future work.