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This chapter examines the influence of external public borrowing resources on economic progress in Tunisia. The study focuses on two stages: First, the influence is studied in a direct sense and then in an indirect sense, i.e., through a transmission channel of this influence. By applying the autoregressive distributed technique with staggered lags (ARDL), over a period ranging from 1986 to 2019, the results showed that the influence of external borrowing resources on growth seems to be unfavorable in the short term but positive in the long term, hence the importance of the empirical technique chosen. Second, three interaction variables were tested, namely total government expenditure, government investment expenditure, and the real effective exchange rate. The results obtained call for better attention to the channels identified to maximize the positive influence of external public debt on the country's economic progress.

This study aims to examine the learning experiences of indigenous West Papuan students studying accounting in Indonesia during the COVID-19 pandemic.

A qualitative approach was taken with 25 indigenous accounting students at five universities in the region who shared testimonies of their online learning experiences. The interview data collected were analysed using initial and selective coding and then interpreted under several themes.

The paper shows the personal, faculty and external challenges in indigenous students’ learning activities during university closures because of COVID-19. The interrelated challenges included students’ struggles to adapt their learning habits when using various online applications, difficulties in understanding how the faculty managed lectures, tutorials and evaluations without adequate access to learning materials, the lack of a learning infrastructure, issues with equipment, and obtaining internet data credits. Students’ economic struggles and health issues exacerbated these challenges. While enduring various struggles and being frustrated about their future, all students expected a change in offline learning policies by the government to lessen their strict physical distancing.

The findings can inform the importance of integrating accounting students’ learning challenges and needs into curriculum development.

This study highlights the learning challenges of indigenous accounting students during the COVID-19 pandemic and how approaches to online learning need to consider the experience of these students.

This paper develops a decision-analysis model to prioritize and select the site to establish a new hospital over different indicators such as cost, market conditions, environmental factors, government factors, locations and demographics. In this way, an integrated model is proposed under the intuitionistic fuzzy information (IFI), the standard deviation (SD), the rank-sum (RS) and the measurement of alternatives and ranking using the compromise solution (MARCOS) approach for ranking hospital sites (HSs).

The IF-SD-RS model is presented to obtain the combined weight with the objective and subjective weights of diverse sub-criteria and indicators for ranking sites to establish the hospital. The IF-MARCOS model is discussed to prioritize the various sites to establish the hospital over several crucial indicators and sub-criteria.

The authors implement the developed model on a case study of HSs assessment for the construction of new hospital. In this regard, inclusive set of 6 key indicators and 18 sub-criteria are considered for the evaluation of HSs. This study distinguished that HS (h₂) with combined utility function 0.737 achieves highest rank compared to the other three sites for the given information. Sensitivity analysis is discussed with different parameter values of sub-criteria to examine how changes in weight parameter ratings of the sub-criteria affect the prioritization of the options. Finally, comparative discussion is made with the diverse extant models to show the reasonability of the developed method.

This study aims to develop an original hybrid weighting tool called the IF-SD-RS model with the integration of IF-SD and IF-RS approaches to find the indicators' weights for prioritizing HSs. The developed integrated weighting model provides objective weight by IF-SD and subjective weight with the IF-RS model. The model presented in the paper deals with a consistent multi-attribute decision analysis (MADA) concerning the relations between indicators and sub-criteria for choosing the appropriate options using the developed IF-SD-RS-MARCOS model.

Amid rapid technological progress, the construction industry is embracing Construction 4.0, redefining work practices through emerging technologies. However, the implications of Construction 4.0 technologies to enhancing well-being are still poorly understood. Particularly, the challenge lies in selecting technologies that critically contribute to well-being enhancement. Therefore, this study aims to evaluate the implications of Construction 4.0 technologies to enhancing well-being.

A list of Construction 4.0 technologies was identified from a national strategic plan on Construction 4.0, using Malaysia as a case study. Fourteen construction industry experts were selected to evaluate the implications of Construction 4.0 technologies on well-being using fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The expert judgment was measured using linguistic variables that were transformed into fuzzy values. Then, the collected data was analyzed using the following analyses: fuzzy TOPSIS, Pareto, normalization, sensitivity, ranking performance and correlation.

Six Construction 4.0 technologies are critical to enhancing well-being: cloud & real-time collaboration, big data & predictive analytics, Internet of Things, building information modeling, autonomous construction and augmented reality & virtualization. In addition, artificial intelligence and advanced building materials are recommended to be implemented simultaneously as a very strong correlation exists between them.

The novelty of this study lies in a comprehensive understanding of the implications of Construction 4.0 technologies to enhancing well-being. The findings can assist researchers, industry practitioners and policymakers in making well-informed decisions to select Construction 4.0 technologies when targeting the enhancement of the overall well-being of the local construction industry.

This study aims to analyze the impact of corporate governance on firms’ external financing decisions in the Middle East and North Africa (MENA) region.

The authors analyzed a unique set of panel data comprising 2,425 nonfinancial firms whose shares are traded on stock exchanges in countries in the MENA region. The authors fitted an ordinary least squares model to estimate the regression coefficients. The authors performed a sensitivity analysis using alternative measures of the critical variables and an endogeneity analysis using instrumental variable methods with plausible external instruments.

The results revealed that corporate governance characteristics of firms are strongly associated with their degree of leverage. They also showed that macrofinancial conditions, financial regulations, corporate governance enforcement and social conditions mitigate the impact of corporate governance on firms’ financing decisions.

A larger sample size will further improve the results; however, this is difficult and depends on the extent to which increasing disclosure practices allow more corporate information to reach international databases.

This study provides new evidence on the role of corporate governance on firms’ financing decisions and documents the essential mitigating role of institutions, alerting managers to consider them.

This study is a novel attempt. Based on information from different data sources, this study explored the predictive power of corporate governance, ownership structures and other firm-specific characteristics in explaining corporate leverage in MENA countries. Overall, the analysis provides new evidence of the association between corporate governance and capital structure in the MENA region, highlighting the critical role of institutions.

This study aims to examine the literature from the Web of Science database published on board committees between 2002 and 2023 and outline the quantitative summary, journey of board committees’ research and suggest future research directions.

This study examines bibliometric-content analysis combined with a systematic literature review of articles on board committees to document the summary of the field. The authors used co-citation, co-occurrence and cluster analysis under bibliometric-content analysis to present the field summary.

Board committee composition, such as their gender, independence and expertise, as well as factors affecting corporate governance, such as reporting quality, earnings management and board monitoring, all have a significant impact on board committee literature. The field is getting growing attention from authors, journals and countries. Nevertheless, there is a need for further exploration in areas like expertise, member age and tenure, the economic crisis and the nomination and remuneration committee, which have not yet received sufficient attention.

This paper has both theoretical and practical contributions. From a theoretical perspective, this study substantiates the prevalence of agency theory within board committee literature, reinforcing the foundational role of agency theory in shaping discussions about board committees. On practical ground, the comprehensive overview of board committee literature offers scholars a road map for navigating this field and directing their future research journey. The identification of research gaps in certain areas serves as a catalyst for scholars to explore untapped dimensions, enabling them to strengthen the essence of the committees’ performance.

Policymakers are developing national strategic plans to encourage organizations to adopt Construction 4.0 technologies. However, organizations often adopt the recommended technologies without aligning with organizational vision. Furthermore, there is no prioritization on which Construction 4.0 technology should be adopted, including the impact of the technologies on different criteria such as safety and health. Therefore, this study aims to evaluate Construction 4.0 technologies listed in a national strategic plan that targets the enhancement of safety and health.

A list of Construction 4.0 technologies from a national strategic plan is evaluated using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Then, the data are analyzed using reliability, fuzzy TOPSIS, normalization, Pareto, sensitivity, ranking and correlation analyses.

The analyses identified six Construction 4.0 technologies that are critical in enhancing safety and health: Internet of Things, autonomous construction, big data and predictive analytics, artificial Intelligence, building information modeling and augmented reality and virtualization. In addition, six pairs of Construction 4.0 technologies illustrate strong relationships.

This study contributes to the existing body of knowledge by ranking a list of Construction 4.0 technologies in a national strategic plan that targets the enhancement of safety and health. Decision-makers can use the study findings to prioritize the technologies during the adoption process. Also, to the best of the authors’ knowledge, this study is the first to evaluate the impact of Construction 4.0 technologies listed in a national strategic plan on a specific criterion.

This article explores the impacts of the changing land-use on urban heat island (UHI) in an urban transformation zone in Ankara (Türkiye). Identifying a characteristic rural landscape until the 1950s, the study area experienced a drastic land-use change by razing the fertile landscape of the city and replacing it with a sealed surface. Development of the squatter houses after the 1960s and, subsequently, the implementation of a new housing morphology have introduced new sceneries, scales and surface conditions that make the study area a noteworthy case to analyze.

Regarding the drastic spatio-temporal change of the study area, this research assesses the impacts of the changing land-use on UHI based on three periods. Using 1957, 1991 and 2021 aerial imaginaries and maps, it analyzes the temperature alteration caused by the changing land-use. To do so, different surface types, green patterns and built-up areas have been modeled using Ankara climatic data and transferred to ENVI-Met to calculate the Universal Thermal Climate Index (UTCI) values.

The calculation has been developed over a transect covering an area of 40 m × 170 m, which includes diversity in terms of architecture, landscape and open space elements. To encourage future design strategies, the research findings deliberate into three extents that discuss the lacking climate knowledge in the ongoing urban transformation projects: impervious surface ratio and regional albedo variation, changing aspect ratio and temperature variation at the pedestrian level.

Urban transformation projects, being countrywide operations in Türkiye, need to cover climate-informed design strategies. Herein, the article underlines the critical position of design decisions in forming a climate-informed urban environment. Dwelling on a typical model of housing transformation in Türkiye, the research could trigger climate-informed urban development strategies in the country.

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 aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the scattering of literature on this topic, given the challenge and opportunity for the educational and academic community.

This review highlights the enormous social influence of COVID-19 by mapping the extensive yet distinct and fragmented literature in AI and academic integrity fields. Based on 163 publications from the Web of Science, this paper offers a framework summarising the balance between AI and academic integrity.

With the rapid advancement of technology, AI tools have exponentially developed that threaten to destroy students' academic integrity in higher education. Despite this significant interest, there is a dearth of academic literature on how AI can help in academic integrity. Therefore, this paper distinguishes two significant thematical patterns: academic integrity and negative predictors of academic integrity.

This study also presents several contributions by showing that tools associated with AI can act as detectors of students who plagiarise. That is, they can be useful in identifying students with fraudulent behaviour. Therefore, it will require a combined effort of public, private academic and educational institutions and the society with affordable policies.

This study proposes a new, innovative framework summarising the balance between AI and academic integrity.