Search results

This study aims to investigate the association between various demographic factors and housing affordability in Saudi Arabia.

A questionnaire survey of households was undertaken, and responses were analysed using chi-square analysis and logistic regression.

The study found that gender and job rank were only significantly related to housing value, but not to housing type, type of tenancy or number of bedrooms. Age, level of income, nationality, household size and job sector had significant associations with housing type, type of tenancy, number of bedrooms and housing value. However, the study did not find a significant relationship between the education level of the head of the household and any housing characteristics. The findings from the logistic regressions indicated that the level of income odds ratio (OR = 25.634), and the value of housing (OR = 0.037) were the two most significant predictors of access to affordable housing, both with levels of significance of p < 0.001.

Even though this study has provided valuable findings and contributions to the literature and policymakers, certain limitations must also be highlighted. First, the study focused only on four housing characteristics: housing type, housing tenancy, number of bedrooms and housing value. It did not consider other housing characteristics, such as housing age and housing conditions, which also affect the affordability of housing. Second, the method adopted for this study has a limitation in terms of its sampling technique, namely, snowball sampling, which relies on each participant to recommend others based on their judgement and recommendation. Third, the sample size for this study was small. As a result, the generalization of these findings to Kingdom of Saudi Arabia (KSA) will be limited.

The current study’s findings will help decision makers in the housing sector to implement a housing delivery strategy that responds to escalating demand. It also contributes to the emerging body of literature by clarifying the relationships and influence between demographic factors and accessibility to affordable housing. In addition, the findings of this study support KSA’s ambitious Vision 2030, through which the government seeks to increase the rate of homeownership. The implications of the findings of this study also extend to help housing policymakers in similar developing countries where the delivery of affordable housing is a challenge.

The study is relevant because it investigates the relationships of demographic factors and housing affordability in one of the three agglomerations in the country. It can thus provide insight into the increasing demand for housing, which can help the Saudi Government to design and implement a housing delivery strategy and can support KSA’s ambitious Vision 2030, which targets increased homeownership. It can also improve our knowledge on the emergent body of literature on the effect of demographic characteristics on the affordability of housing in the country, and in similar developing countries where the delivery of affordable housing is a challenge.

This study aims to explore and analyze the disparities in the distribution of housing types and characteristics among households in Saudi Arabia, taking into consideration the regional perspective.

This study uses quantitative data obtained from the General Authority for Statistics, specifically from the Saudi 2022 Statistical Census. The data were analyzed using descriptive statistics (percentages) as well as inferential statistics, including correlation analysis (Pearson correlation) and t-tests.

The study found a distinct preference among Saudis for villas, with 85.3% choosing this housing type, while only 14.7% of non-Saudis opted for villas. The statistical analysis confirmed the significance of housing type for Saudi citizens (t = 2.561, p = 0.037), while non-Saudis did not show a statistically significant preference (t = 1.703, p = 0.132). The Pearson correlation results revealed a moderate positive correlation (r = 0.641, p = 0.009) between regional landmass and the number of houses, and a very strong positive relationship (r = 0.984) between population and the number of houses across the 13 regions. As expected, with increasing population, there was a significant increase in the number of houses (p = 0.001).

This study fills a research gap by investigating regional disparities in housing characteristics in Saudi Arabia. The findings are valuable for policymakers, housing developers and the housing market in understanding these disparities. The insights from this research can inform decision-making to promote equitable access to housing types and foster social inclusivity in the housing sector.

In construction projects, underpayments can be recognised as one of the significant drawbacks that impact the success of a project. Research into underpayments is considered ambiguous and provides a limited reflection of the issue, which makes it complicated to trace how it originates in the first place. This study aims to examine the causes that lead to underpayments and develop a holistic synthesis of underpayments for subcontractors in the lifecycle of a construction project.

An open-ended and closed-ended questionnaire was used to collect the data using purposeful sampling with 28 construction stakeholders who ranged from main contractors, subcontractors and others (Small medium enterprises SMEs, Consultancies, Clients etc.). Data collected was analysed to trace drivers and the impact of underpayment and suggested mitigation strategies to be identified whilst viewing the perspectives of a main contractor and subcontractor.

The findings show that the most prominent driver for underpayments is variation disputes followed by cash flow. The research also suggests mitigation strategies such as collaborative working, more robust budget control and early identification of risks as potential remedies to overcome the underpayment issue. The research concludes with a framework that elicits the complexity underlying underpayments for subcontractors in construction projects.

The research evolves the understanding that underpayment is a complex phenomenon, relying heavily on the data/information exchange mechanism between the main contractor and subcontractors. This research provokes the need to understand underpayment further so it can be mitigated.

Previous studies have established to a great extent that regulatory frameworks and, in particular, procurement approaches – that are common in a particular context – have a major impact on the success of building information modelling (BIM) implementation in construction projects. Despite the close links between these two concepts, research on the effect of procurement approaches on BIM implementation is scarce. To address this gap, this paper aims to investigate the barriers that affect BIM implementation through the lens of procurement approaches.

A mixed-method approach was adopted using a questionnaire survey (n = 116) and interviews with key stakeholders (n = 12) in Jordan. The outcomes of the quantitative parts were augmented with findings from interviews.

It was revealed that the deployment of unfavourable construction procurement approaches represents a major hurdle towards BIM implementation. Though essential for enhancing BIM implementation, it is revealed that a fundamental change from the common design-bid-build (DBB) to more collaborative procurement approaches remains infeasible in view of the realities that govern the construction industry.

It was revealed the deployment of unfavourable construction procurement approaches represents a major hurdle towards BIM implementation. Though essential for enhancing BIM implementation, it is revealed that a fundamental change from the common DBB to more collaborative procurement approaches remains infeasible given the realities that govern the construction industry.

As the first of its kind, a set of recommendations for establishing supportive, workable procurement that does not deviate significantly from common procedures and practices is presented. Rather than advocating a shift to procurement approaches that are aligned with BIM, the findings offer novel insight into the necessity of developing a framework within the boundaries of the current and widely adopted procurement approaches to address the identified construction procurement issues and facilitate BIM implementation.

The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

Although the overall impacts of innovation and innovative practices have been emphasized in recent years, the effect on the growth of firms in Nigeria have not been proven. Therefore, this study aims to investigate the effect of innovative practices on the growth of quantity surveying firms (QSFs) in Nigeria.

The study adopted the quantitative correlational research design in which a well-structured questionnaire was used to collect data from QSFs in South-West, Nigeria. The data were analyzed using descriptive statistics and multiple regression analysis to investigate the effect of innovative practices on the growth of QSFs.

The study reveals that there is a significant increase in the growth indices used for assessing QSFs, while all the innovation variables were found to be reliable. Based on the result of multiple regression analysis, the relationships were identified as follows: quantity surveying (QS) software influenced the size growth of QSFs; QS software and services affected client growth and profit growth; and all innovation practices impacted asset growth of QSFs.

Although the use of software tools has been found to negatively affect the size of QSFs and other growth indices, there is need for them to embrace innovative software applications for more quality service delivery. In addition, QSFs should formulate strategic objectives that will guide them in taking informed decisions for diversification.

The outcome of this study provides information and direction for innovation practices required to bring about the growth of QSFs.

While the COVID-19 pandemic has impacted the construction industry, it is still unclear from prior studies about adequately positioning the quality assurance (QA) for the post-pandemic era and future pandemics, especially cross-border construction logistics and supply chain (Cb-CLSC). Thus, this study aims to develop a managerial framework to position the QA of Cb-CLSC during pandemics and post-pandemics by taking lessons from how COVID-19 has impacted the existing QA systems and has been managed successfully.

This is achieved pragmatically through an embedded mixed-method design involving a literature review, survey and interview from experts within the Hong Kong SAR–Mainland China links, typically known as the world’s factory. The design is further integrated with the partial least squares structural equation modelling (PLS-SEM) approach.

The study revealed 10 critical managerial practices (MPs) to position the QA to be adequate for the post-pandemic and during future pandemics, with the top three including “strict observance of government regulations (MP1)”, “planning ahead the period of quality assurance with the quarantine days in host countries (MP6)” and “modification of contract to cater for uncertainties (MP4)”. This attained a relatively good percentage agreement of 53% between the industry and academia. However, the top four MPs regarded as very effective include “implementing digital collaborative inspections with subcontractors and trades (MP8)”, “implementing a digital centralized document and issue management system (MP7)”, “strict observance to government regulations, including vaccination of workers, social distancing, use of prescribed nose masks, etc. (MP1)” and “planning ahead the period of quality assurance with the quarantine days in host countries (MP6)”. Two underlying components of the MPs were revealed as policy-process (PP)-related practices and people-technology-process (PTP)-related practices, and these can be modelled into a managerial framework capable of effectively positioning the QA to be adequate during pandemics through to the post-pandemic era.

The findings of this study depicted significant theoretical and practical contributions to the proactive management of QA activities during pandemics through to the post-pandemic era. It could empower organisations to pay attention to smartly and innovatively balancing people, processes, pandemic policy and technology to inform decisions to effectively position the QA for the post-pandemic era and survive the risks of future pandemics.

The study contributes to the body of knowledge in that it develops a managerial framework to position the QA of Cb-CLSC during pandemics and post-pandemics by taking lessons from how COVID-19 has impacted the existing QA systems and has been managed successfully. It is original research with invaluable primary data in the form of surveys and interviews from experts within the Hong Kong SAR–Mainland China links, typically known as the world’s factory.

This paper evaluates the factors influencing the utilization of the Internet of Things (IoT) for sustainable facilities management (SFM) practices in Saudi Arabia.

A mixed approach, combining a literature review, pilot-testing and questionnaire survey, was adopted to evaluate the factors. Twenty-seven factors were identified and grouped into four groups: technical, business and organizational, operational and security and privacy. The questionnaire was distributed to 30 facilities managers and 30 IoT specialists, totaling 60 practitioners, to determine the effect index of each factor. The practitioners' consensus on the ranking of the factors was then determined.

The study identifies the top-ranking factors as: “Difficulty in ensuring data security and protection,” “Difficulty in ensuring data privacy and confidentiality” and “Limited awareness and understanding of IoT benefits and capabilities.” These factors highlight the challenges to successful IoT implementation in the FM sector. The FM sector could benefit from utilizing IoT while maintaining the security, privacy and effectiveness of building operations by successfully addressing these concerns. A high level of consensus on the ranking of the factors was observed between facilities managers and IoT specialists. This was substantiated by a Spearman’s rank correlation coefficient of 0.79.

This study enriches the literature by combining practical insights from facilities managers with technical expertise from IoT specialists on the factors impacting IoT implementation in the Saudi Arabian FM sector. Beyond academic contributions, it provides practical insights for industry professionals, fostering a culture of knowledge-sharing and guiding future research in this field.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

The COVID-19 outbreak reached a critical stage when it became imperative for public health systems to act decisively and design potential behavioral operational strategies aimed at containing the pandemic. Isolation through social distancing played a key role in achieving this objective. This research study examines the factors affecting the intention of individuals toward social distancing in India.

A correlation study was conducted on residents from across Indian states (N = 499). Online questionnaires were floated, consisting of health belief model and theory of planned behavior model, with respect to social distancing behavior initially. Finally, structural equation modeling was used to test the hypotheses.

The results show that perceived susceptibility (PS), facilitating conditions (FC) and subjective norms are the major predictors of attitude toward social distancing, with the effect size of 0.277, 0.132 and 0.551, respectively. The result also confirms that the attitude toward social distancing, perceived usefulness of social distancing and subjective norms significantly predict the Intention of individuals to use social distancing with the effect size of 0.355, 0.197 and 0.385, respectively. The nonsignificant association of PS with social distancing intention (IN) (H1b) is rendering the fact that attitude (AT) mediates the relationship between PS and IN; similarly, the nonsignificant association of FC with IN (H5) renders the fact that AT mediates the relationship between FC and IN.

The results of the study are helpful to policymakers to handle operations management of nudges like social distancing.

The research is one of its kind that explores the behavioral aspects of handling social nudges through FC.