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Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.

The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.

Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.

The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.

There is a renowned interest in adaptability as an important principle for achieving circularity in the built environment. Circular building adaptability (CBA) could enable long-term building utilisation and flexible use of space with limited material flows. This paper identifies and analyses design strategies facilitating CBA to propose a framework for enhancing the implementation of the concept.

Interviews were conducted with professionals experienced in circular building design to explore the questions “How do currently applied design strategies enable CBA?” and “How can CBA be implemented through a conceptual design framework?”. The interviews encircled multi-residential building examples to identify currently applied circular design strategies. The interviews were analysed through qualitative content analysis using CBA determinants as a coding framework.

The results show that all ten CBA determinants are supported by design strategies applied in current circular building design. However, some determinants are more supported than others, and design strategies are often employed without explicitly considering adaptability. The design strategies that enable adaptability offer long-term solutions requiring large-scale modifications rather than facilitating low-impact adaptation by dwelling occupants. The proposed conceptual design framework could aid architects in resolving these issues and implementing CBA in their circular building design.

This paper’s contribution to CBA is threefold. It demonstrates design strategies facilitating CBA, proposes a conceptual design framework to apply the concept and identifies the need for a more comprehensive application of available adaptability strategies.

Unlocking the potential of Big Data Analytics (BDA) has proven to be a transformative factor for the Architecture, Engineering and Construction (AEC) industry. This has prompted researchers to focus attention on BDA in the AEC industry (BDA-in-AECI) in recent years, leading to a proliferation of relevant research. However, an in-depth exploration of the literature on BDA-in-AECI remains scarce. As a result, this study seeks to systematically explore the state-of-the-art review on BDA-in-AECI and identify research trends and gaps in knowledge to guide future research.

This state-of-the-art review was conducted using a mixed-method systematic review. Relevant publications were retrieved from Scopus and then subjected to inclusion and exclusion criteria. A quantitative bibliometric analysis was conducted using VOSviewer software and Gephi to reveal the status quo of research in the domain. A further qualitative analysis was performed on carefully screened articles. Based on this mixed-method systematic review, knowledge gaps were identified and future research agendas of BDA-in-AECI were proposed.

The results show that BDA has been adopted to support AEC decision-making, safety and risk assessment, structural health monitoring, damage detection, waste management, project management and facilities management. BDA also plays a major role in achieving construction 4.0 and Industry 4.0. The study further revealed that data mining, cloud computing, predictive analytics, machine learning and artificial intelligence methods, such as deep learning, natural language processing and computer vision, are the key methods used for BDA-in-AECI. Moreover, several data acquisition platforms and technologies were identified, including building information modeling, Internet of Things (IoT), social networking and blockchain. Further studies are needed to examine the synergies between BDA and AI, BDA and Digital twin and BDA and blockchain in the AEC industry.

The study contributes to the BDA-in-AECI body of knowledge by providing a comprehensive scope of understanding and revealing areas for future research directions beneficial to the stakeholders in the AEC industry.

This chapter explores genderwashing in the context of exclusive talent management (ETM) and defensive diversity management (DDM). It makes the counter intuitive argument that ETM is a misnomer in that it privileges maintenance of an organizational hierarchy based on social identity over the development of talent. Further, DDM is a genderwashing tool, enabling organizations to fend off criticism through symbolic diversity, equity, and inclusion (DEI) initiatives while enacting discourses that legitimate structures, practices, and norms that produce a status hierarchy based on social identities. A genderwashing perspective reveals this contradiction and spotlights the uncomfortable reality of workplace inequalities. It also shows that operating within boundaries set by the status quo renders DDM ineffective in removing the real career impediments faced by women and members of minoritized groups (MMG). A transformative diversity management (TDM) approach is needed to confront these realities and enable organizations to support the career aspirations of women and MMG.

This study aims to examine the relationship between cultural intelligence and organisational commitment and its effect on learning goal orientation and turnover intention within the expatriate society of the United Arab Emirates (UAE).

A survey instrument was developed to collect data from 173 non-management expatriates employed by multinational corporations located in Dubai, UAE. SmartPLS bootstrap software was used to analyse the path coefficients and test the research hypotheses.

The results demonstrate that cultural intelligence enhances both learning goal orientation and turnover intention of expatriates. Moreover, organisational commitment partially mediates the relationship between cultural intelligence and turnover intention/learning goal orientation.

This study contributes by advancing extant knowledge with regard to cultural intelligence and organisational commitment effects on turnover intention and learning goal orientation of expatriates within a context of high cultural heterogeneity.

Over the past two decades, the study of team resilience has evolved from focusing primarily on team performance to recognizing its importance in various aspects of team functioning, including psychological health, teamwork, and overall Well-Being. This evolution underscores the need for a broader, more inclusive understanding of team resilience, advocating for a shift from a narrow performance-centric view to a holistic perspective that encompasses the multifaceted impact of resilience on teams.

In advocating for this holistic perspective, this chapter reviews the extant literature, highlighting that resilience is not merely about sustaining performance but also about fostering a supportive, adaptive, and psychologically safe environment for team members. Significant areas for further exploration, including the nuanced nature of adversities teams face, the processes underpinning resilient behaviors, and the broad spectrum of outcomes resilience can influence beyond task performance are also discussed.

The chapter serves as a call to action for a more inclusive examination of how resilience manifests and benefits teams in organizational settings. The proposed shift in perspective aims to deepen understanding of team resilience, promoting strategies for building resilient teams that thrive not only in performance but in all aspects of their functioning.

Body weight has a long history of functioning as a symbol of one’s beauty, social status, morality, discipline, and health. It has also been a standard inflicted much more intensely on women than men. While US culture has long idealized thinness for women, even at risky extremes, there is growing evidence that weight standards are broadening. Larger bodies are becoming more visible and accepted, while desire for and approval of a thin ideal has diminished. However, the continued widespread prevalence of anti-fat attitudes and stigma leaves uncertainty about just how much weight standards are changing. This study used an online survey (n = 320) to directly compare evaluations of thin, fat, and average size women through measures of negative stereotypes, prejudicial attitudes, and perceptions about quality of life. Results indicated that, as hypothesized, thin women were perceived less favorably than average weight women. However, fat women were perceived less favorably than both average and thin women. Men were harsher than women in their evaluations of only fat women. Additionally, participants being underweight or overweight did not produce an ingroup bias in their evaluations of underweight and overweight targets, respectively. That is, participants did not rate their own group more favorably, with the exception of overweight participants having lower prejudice toward overweight targets. These findings add to the emerging evidence that women’s weight standards are in transition, marked by an increasingly negative perception of thin women, though not necessarily growing positivity toward fat women. This evidence further points toward the need for more extensive research on attitudes of people across the entire weight spectrum.

This study examines the effects of an inclusive psychological climate on leader inclusion, workgroup inclusion, and employee outcomes (trust in organization and organizational identification). Leader inclusion and workgroup inclusion are explored as both direct and serial mediators in the psychological climate to outcome relationships.

Data from 336 employees in 55 teams were collected at two time points from an educational media company in China.

Results from multi-level modeling suggest that, for employees, the inclusive psychological climate to trust relationship has both direct and indirect effects, including a serially occurring indirect effect through leader inclusion and workgroup inclusion. For the inclusive psychological climate to organizational identification relationship, there were only indirect effects, including a serially occurring indirect effect through both leader inclusion and workgroup inclusion.

These results suggest the value of an inclusive psychological climate for setting the stage for more localized inclusion experiences through the leader and the workgroup. These inclusionary work environments promote social exchange as shown by employer trust and social identification with the organization.

This study examines the combined and serial effects of an inclusive psychological climate, leader inclusion, and workgroup inclusion on outcomes that represent a deep connection with the organization (organizational trust and organizational identification).

Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.

To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.

We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.

The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.

With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.

This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.

Emerging from the COVID-19 pandemic, the Philippines has undergone the “new normal” transition, creating a strategic recovery effort to reinvigorate the industry. In tourism, these transitions aim to safeguard employees' and guests' health and safety, ensure continuity of business operations, boost tourism confidence leading to satisfaction, and establish a resilient and sustainable tourism industry in the postpandemic era. Hence, this chapter employs a system thinking leveraging a causal loop diagram (CLD) to construct a comprehensive roadmap for Philippine tourism's postpandemic resurgence through the system thinking lens. The CLD visually illustrates the inter-related factors influencing the recovery process, encompassing collaborative engagements, innovations, economic revitalization, and health and safety protocols. By analyzing the causal relationships among these variables, this chapter explicates the dynamic and interconnected nature of the postpandemic recovery leading to the recovery of the Philippine tourism industry, especially in the context of thinking small. Through this chapter, thinking small could involve a shift toward localized solutions and community-focused initiatives that allow them to foster local economies, build resilience, and create a more inclusive and sustainable postpandemic recovery.