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.

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.

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.

This study examined factors influencing the organisational capabilities of the public sector for building information modelling (BIM) implementation in construction projects with a view to enhancing the performance of public sector projects.

The study adopted a quantitative descriptive analysis that was based on primary data. In total, 198 valid questionnaires obtained from construction professionals within the public sector provided primary quantitative data for the assessment. The respondents provided the responses on the factors which were identified through an in-depth synthesis of literature relating to organisational capabilities of the public sector. Data collected were analysed using descriptive and inferential statistics.

The findings established that the potential of the public sector to deploy BIM in construction projects is greatly influenced by varying degree of organisational capability attributes with bureaucratic culture (mean score, MS = 3.37), structural complexity (MS = 3.17), lack of skilled and trained staff (MS = 3.12), personnel stability (MS = 3.11), staff cooperation (MS = 3.09) and political constraint (MS = 3.07) ranked highest. Through factor analysis, these and other highly influential factors were grouped into eight components, namely management-related, policy-related, technical-related, attitude-related, work structure-related, work ethic-related, decision-related and feedback-related factors. This grouping reflects the various components of organisational capability attributes which the public sector needs to efficiently develop to benefit from project management paradigm introduced by BIM.

This study provided information for improving specific capability attributes with respect to human and technical resources as well as other soft infrastructure to support BIM implementation on building projects by the public sector client. The study also serves as a guide for understanding BIM implementation by the public sector in similar socio-political and economic contexts.

This assessment indicates various degrees by which the organisational attributes of public sector have influenced the attributes' capability to implement BIM on construction projects. Thus, findings provide information on areas of improvement for better implementation of BIM by the public sector in project delivery.

This study aims to assess the essential elements of internal organisational capability that influence the cybersecurity effectiveness of a construction firm. An extended McKinsey 7S model is used to analyse the relationship between a construction firm's cybersecurity effectiveness and nine internal capability elements: shared values, strategy, structure, systems, staff, style, skills, relationships with third parties and regulatory compliance.

Based on a quantitative research strategy, this study collected data through a cross-sectional survey of professionals working in the construction sector in the United Kingdom (UK). The collected data was analysed using descriptive and inferential statistical methods.

The findings underlined systems, regulatory compliance, staff and third-party relationships as the most significant elements of internal organisational capability influencing a construction firm's cybersecurity effectiveness, organised in order of importance.

Future research possibilities are proposed including the extension of the proposed diagnostic model to consider additional external factors, examining it under varying industrial relationship conditions and developing a dynamic framework that helps improve cybersecurity capability levels while overseeing execution outcomes to ensure success.

The extended McKinsey 7S model can be used as a diagnostic tool to assess the organisation's internal capabilities and evaluate the effectiveness of implemented changes. This can provide specific ways for construction firms to enhance their cybersecurity effectiveness.

This study contributes to the field of cybersecurity in the construction industry by empirically assessing the effectiveness of cybersecurity in UK construction firms using an extended McKinsey 7S model. The study highlights the importance of two additional elements, third-party relationships and construction firm regulatory compliance, which were overlooked in the original McKinsey 7S model. By utilising this model, the study develops a concise research model of essential elements of internal organisational capability that influence cybersecurity effectiveness in construction firms.

This study explores the significance and implementation priorities for Digital Product Passports (DPP) in the context of building renovation projects. It aims to reveal bottlenecks and how a data-driven workflow bridges the DPP understanding/implementation gap, facilitating the transition towards practices aligned with the EU Green Deal goals.

A mixed-methods embedded design was employed for a real-case study exploration. Desk research and field observations ground the two-level analysis combining project documentation, namely the Bill of Quantities (BoQ), with different criteria in digitalisation and sustainability, such as economic ratio, 3D modelling, waste management, hazards, energy performance and facility management. All results were interpreted from the DPP lens.

The analysis revealed a system for identifying building products representing a significant part of the renovation budget. About 11 priority DPPs were found. Some are crucial for both the deconstruction and construction phases, highlighting the need for an incremental and strategic approach to DPP implementation.

The study is limited to a single case study. Constraints are minimised given the sample's archetype representativeness. The outcomes introduce the need for strategic thinking for incremental DPP implementation. Future research will explore additional criteria and cases.

The research has resulted in a classification framework for DPPs' significance and priority, which is provided with case results. The outcome of the framework provides views on concept alignment to make the implementation in construction more straightforward. Its practical use can be replicated in other projects, emphasizing the importance of data structure and management for the circular economy.

The purpose of this paper was to study the possibility of using smartphone roughness measurements for developing pavement roughness regression models as a function of pavement age, traffic loading and traffic volume variables. Also, the effects of patching and pavement distresses on pavement roughness were investigated. The work focused on establishing pavement roughness prediction models and applying these models to pavement management systems (PMS) to help decision-makers choose the best maintenance and rehabilitation (M&R) options by using cost-effective methods.

Signal processing techniques including filtering and processing techniques were used to obtain the International Roughness Index (IRI) from raw acceleration data collected from smartphone accelerometer sensors. The obtained IRI values were inputted as a dependent variable in analytical regression models as well as several independent variables with proper transformations.

According to the study results, several regression models were developed with a big variation in the coefficients of determination (R²). However, the best models included pavement age, accumulated traffic volume (∑TV) and construction quality factor (CQF) with R² equal to 0.63. It was also found that the effects of pavement distresses and patching was significant at a-level < 0.05. The patching effect on pavement roughness was found higher than the effect of other pavement distresses.

The presented results and methods in this paper could be used in the future predictions of pavement roughness and help the decision-makers to estimate M&R needs. The work focused on establishing IRI prediction models and applying these models to the PMS to help decision-makers choose the best M & R options.

To develop sound pavement roughness models, it is essential to collect roughness data using automated procedures. However, applying these procedures in developing countries faces several difficulties such as the high price and operation costs of roughness equipment and lack of technical experience. The advantage of using IRI values taken from smartphones is that the roughness evaluation survey may be expanded to cover the full road network at a cheaper cost than with automated instruments. Therefore, if the roughness survey covers more roads, the prediction model’s accuracy will be improved.