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This paper aims to examine the housing market responses to two outbreaks of respiratory diseases in Hong Kong during the Information Era – the 2003 SARS and COVID-19 outbreaks.

The authors first investigate the aggregate housing price changes during SARS and COVID-19. Next, the authors conduct a battery of univariate analyses pertaining to the relationship between district-level housing price movements and geographic and demographic patterns during the pandemic periods. Finally, to shed light on the housing price dynamics at the micro level, the authors conduct an estate-level analysis with the data of 234 residential estates from 2003 to 2020, focusing on the impacts of SARS and COVID-19 on the idiosyncratic volatility of residential estates.

Overall, SARS and COVID-19 outbreaks are negatively associated with housing prices. However, unlike SARS, the impact of COVID-19 on housing prices was moderate and transient. The geographic imbalances of the epidemic-induced underperformance are observed at the district and estate levels. Finally, the estate-level analysis presented in this paper indicates that the average idiosyncratic volatility of residential estates is 1.5% higher during the SARS period but 3.7% lower during the COVID-19 period. Lower volatility during COVID-19 is likely explained by household learning from the SARS period.

Regulators and investors could resort to efficient information disclosure to attenuate idiosyncratic volatility's adverse impact on housing market returns.

To the best of the authors’ knowledge, the authors are among the first to examine housing market responses to the 2003 SARS and COVID-19 outbreaks using the Hong Kong housing market as a laboratory.

Social presence (SP), which refers to individuals’ perception of others being engaged as “real people” in the same situation, is a crucial component in technology-rich learning environments (TREs). This study aims to identify major learning design, antecedents and outcomes of SP within TREs, and identify common findings from the past two decades.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses review principles and a qualitative analysis of selected articles, a final review of 72 studies that met inclusion criteria was obtained. Key information, including education level, discipline, sample size, study type and measurements, was extracted and studies were further analyzed and synthesized based on design features and learning modes.

The study identifies five crucial factors for instructional design to foster SP in TREs: technology affordances, multimedia features, social factors, instructional principles, learner characteristics and learning management systems. The authors compare two learning modes across three dimensions and identify popular technologies used in studies related to SP over the past two decades. Practical recommendations are provided for educators and educational technology developers to enhance SP within technology-rich learning environments.

This research contributes to the discourse on online learning and computer-supported communication, particularly in the post-COVID-19 era. By examining factors influencing SP and providing implications for instruction and educational technology development, this study offers evidence-based support to educators for engaging learners and fostering authentic learning experiences through adaptive selection of educational technologies.

This study aims to understand the impact of the Covid-19 pandemic disruptive event on delivery of the built environment degree apprentice programme in higher education in the UK and identify the key strategies to minimise the effect.

A qualitative approach was used to collect and analyse data from a sample set of built environment degree apprenticeship stakeholders. Semi-structured interviews were conducted with 17 key stakeholders to collate emerging themes on their perceptions of the impacts of the pandemic and strategies to adopted to minimise it.

The investigation reveals that the core impacts of Covid-19 on the apprentices training programme are lack of access to the site, furlough, limited access to off the job training, limited interaction with tutors and peers, too much time on the screen, limited pastoral care and lack of contact with a mentor. The census from the research participants is that despite the development and gain with the various virtual platform used during pandemic physical meetings with their mentor remain pivotal to the built environment apprentices learning and training.

The results provide relevant stakeholders and actors supporting degree apprentices training programmes (training providers and employers, among others) with the information needed to improve the delivery of built environment degree apprenticeship training programmes during a disruptive event Covid-19. The study identifies various strategies to minimise the impact of disruptive events on the apprentices training, including technology, regular meeting with mentors online, and personal and pastoral care.

The study is the first to document the impact of the Covid-19 pandemic on degree apprenticeship programs in the built environment. This study provides an in-depth understanding of how these programs have been affected and offers potential solutions to reduce or mitigate potential damage. The research will inform future policy decisions related to degree apprenticeship programs in the built environment.

Data from different countries suggest a higher prevalence of anxiety, depression and suicides among manual and trade workers in the construction industry than in the general population. The present review examines the causes and effects of poor mental health and the effectiveness of interventions to improve manual and trade workers' mental health in the construction industry. It also identifies gaps in research and makes several suggestions for practice and future research.

A systematic literature review was conducted to examine and consolidate evidence reported in 54 relevant journal articles published between 2010 and 2021 on the mental health of manual and trade workers.

Three major themes emerged in the review of the 54 journal articles: causes of poor mental health, effects of poor mental health and interventions to improve mental health. The leading causes of poor mental health among construction manual and trade workers are poor work-life balance, high job demand, poor cultural norms and mental health stigma, chronic bodily pain, lack of social support, workplace injustice and job insecurity. The prominent effects of poor mental health are suicidality, drug and alcohol addiction, poor workplace safety and poor work performance. Moreover, the study found that some of the strategies recently implemented in the construction industry to improve mental health are deemed ineffective, or their effectiveness remains inconclusive.

The review's scope is limited to research on manual and trade workers, and it did not investigate the mental health of construction professionals and construction management students.

The review provides valuable insights into the causes and effects of poor mental health among manual and trade workers and the effectiveness of mental health interventions in the construction industry.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

Request for information (RFI) documents play a pivotal role in seeking clarifications in construction projects. However, perceived as inevitable “non-value adding” tasks, they harbour risks like schedule delays and increased project costs, underlining the importance of strategic RFI management in construction projects. Despite this, a lack of literature dissecting RFI processes impedes a full understanding of their intricacies and impacts. This study aims to bridge the gap through a comprehensive literature review, delving into RFI intricacies and implications, while emphasising the necessity for strategic RFI management to prevent project risks.

This research study systematically reviews RFI-related papers published between 2000 and 2023. Accordingly, the review discusses key themes related to RFI management, yielding best practices for industry stakeholders and highlighting research directions and gaps in the body of knowledge.

Present RFI management platforms exhibit deficiencies and lack analytics essential for streamlined RFI processing. Complications arise in building information modelling (BIM)-enabled projects due to software disparities and interoperability hurdles. The existing body of knowledge heavily relies on manual content analysis, an impractical approach for the construction industry. The proposed research direction involves automated comprehension of unstructured RFI content using advanced text mining and natural language processing techniques, with the potential to greatly elevate the efficiency of RFI processing.

The study extends the RFI literature by providing novel insights into the problemetisation with the RFI process, offering a holistic understanding and best practices to minimise adverse effects. Additionally, the paper synthesises RFI processes in traditional and BIM-enabled project settings, maps a causal-loop diagram to identify associated issues and summarises approaches for extracting knowledge from the unstructured content of RFIs. The outcomes of this review stand to offer invaluable insights to both industry practitioners and researchers, enabling and promoting the refinement of RFI processes within the construction domain.

Racial code-switching is an impression management behavior for people to blend into social and professional situations by adhering to norms outside their own. Drawing on the identity threat perspective, this study aims to examine the harmful effects of racial code-switching on employee psychological depression and hospitality industry turnover intentions.

The current study used a two-wave time-lagged survey of 286 restaurant frontline employees. Participants were asked to rate their racial code-switching, identity threat and shame in the first survey. Participants reported their depression and industry turnover intention in the second survey one week later.

The results showed that employees that engaged in racial code-switching had higher intentions to leave the hospitality industry via the sequential mediating roles of identity threat, shame and depression.

The findings provide practical implications on how hospitality practitioners can foster employee authenticity and tenure by evaluating impression management strategies. This paper provides a discussion, suggestions and future research directions on how to take sustainable actions toward diversity, equity, inclusion, justice and belonging.

Although racial code-switching is a common behavioral strategy for whites and people of color, research on racial code-switching in the hospitality industry is limited. This study is among the first to examine racial code-switching’s health and career consequences.