Evaluating the Drivers of Cloud Data Management Usage in the South African Construction Industry †
1
cidb Centre for Excellence & Sustainable Human Settlement and Construction Research Centre, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa
2
Department of Quantity Surveying and Construction Management, Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9300, South Africa
3
Department of Industrial Systems Engineering, University of Regina, 3737 Wascana Pkwy, Regina, SK S4S 0A2, Canada
*
Author to whom correspondence should be addressed.
†
Presented at the 1st International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP-2024), Regina, Canada, 27–29 June 2024.
Eng. Proc. 2024, 76(1), 39; https://doi.org/10.3390/engproc2024076039
Published: 24 October 2024
(This article belongs to the Proceedings of 1st International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP-2024))
Abstract
This study explores the drivers of cloud data management (CDM) usage in the South African built environment. A quantitative research approach was adopted for this study through the use of a well-structured questionnaire distributed to construction professionals and IT experts in Gauteng province, South Africa. Principal component analysis (PCA) and descriptive statistics procedures were used to analyse the retrieved data. The findings revealed that the most significant drivers for using CDM services are sustainable data management, improved project life cycle management, and technological advancement. This study concluded that technological progress and sustainability in the construction industry drive CDM usage.
1. Introduction
The construction industry is characterized as a highly fragmented project-based industry due to the sector having various ranges of professions [1]. This causes fragmentation in project management of the construction projects as project information is shared among the construction team. As a result, collaboration becomes difficult as the design is often separated from production. Consequently, the industry’s productivity will decrease since there will be a gap in communication and collaboration among the construction team [1]. Cloud computing can improve the collaboration and information flow amongst different construction stakeholders, thereby overcoming the fragmentation of working practices [2]. Hence, cloud computing can help streamline construction project data management, resulting in reduced project costs and duration [3].
With cloud computing mainly dealing with the management of data, the term cloud data management is often used. Cloud data management involves the comprehensive process of managing, storing, and processing data within the cloud infrastructure for the benefit of archiving data and ensuring data longevity and accessibility [4]. The manual data management of a typical construction project for monitoring the progress of the project is ineffective [5,6]. Hence, cloud data management services are suggested for data management in order to ensure efficiency in the sector. Therefore, the rationale for this study is to highlight the factors that can influence the usage of cloud data management by construction professionals and companies in South Africa (SA). Thus, the research objective of this study is to determine the drivers of cloud data management usage in the South African built environment.
2. The Drivers of Cloud Data Management Usage in the SACI
2.1. Effective Collaboration and Communication
Cloud computing can improve the collaboration and information flow among construction stakeholders, thereby overcoming the fragmentation of working practices [3]. Cloud data management promotes collaboration and communication among construction stakeholders in the construction industry, where they are able to share data, such as project information, amongst each other.
2.2. Safe Cloud Data Management Platform and Remote Data Accessibility
Cloud data management services need to facilitate a safe cloud data management platform coupled with remote data accessibility without restrictions. Data security in cloud computing revolves around ensuring the confidentiality, usability, and integrity of data stored and processed in the cloud environment [2].
2.3. Scalability of the Cloud Service and Low IT Infrastructure Expenditure for Users
The need for scalability is also a driver of cloud data management usage in the construction industry. Cloud data management services offer a flexible and scalable solution to meet the growing demands of dynamic and intricate construction companies. The users can adjust the resource usage from the virtual machines (VMs) in the cloud computing infrastructure physical host of the data stores, ensuring the scalability of the cloud services [7]. This will allow sudden and gradual increases in processing, storage, and bandwidth in the cloud computing infrastructure. Cloud computing achieves cost-saving features through virtualization. Furthermore, refs. [7,8] point out that with VMs, you have one physical server with high processing power hosting several virtual servers (Stores), which allows for efficient storage of data, resulting in low IT capital and operational expenditures.
2.4. Effective Project Management and Manageability of Storage
Cloud data management also improves efficiency by reducing the time and effort required to manage data, enabling stakeholders to focus on other critical tasks. The exchange of project information in the cloud can notably enhance a project’s quality, cost, and performance, which, therefore, fulfils the client’s requirements [9]. Cloud data management services allow for the manageability of storage through the pay-per-use model, which charges users for the duration they use the cloud services, especially SaaS applications [10].
2.5. Data Portability of Cloud Applications and Service Quality and Efficiency
The need for interoperability between different cloud applications drives portability as it is the ability to transfer workloads and data between cloud providers in situations where applications cannot be moved across different platforms, which may lead to vendor lock-in [11]. Moreover, construction stakeholders can access the data remotely on any computer device on the cloud, and the quality of their services can improve as they can act proactively on any issues of the project that arise [12].
2.6. Disaster Recovery and Availability, and Reliable Data Storage and Accessible Infrastructure
Disaster recovery is all about the measures put in place by the Cloud Service Providers (CSPs) in case of a need to recover stolen or damaged technology infrastructure. Ref. [13] postulates that CSPs should offer disaster recovery services and high availability requirements through their data centres. Cloud computing services should have 24 h reliability and allow employees to contact the provider instead of in-house IT staff, with files always available even during downtime, and transfer the responsibility for data security to the provider, who offers more secure and automated disaster recovery services [14].
2.7. Technology Advancement and Decreased Reliance on Labor
Technology advancement enables organizations, including SMEs, to stay competitive and enhance productivity through the adoption of the latest cloud computing technologies, thereby facilitating efficient activity execution and leveraging cloud data management services in the construction sector [15]. Facing challenges like rising costs and complex projects, the construction industry can reduce its dependence on manual labor and improve productivity by adopting technological solutions, specifically cloud data management services, which streamline data administration and enhance efficiency [15,16].
2.8. Competitiveness, Improved Project Life Cycle Management, and Sustainable Data Management
Organizations can adopt cloud computing to enhance competitiveness, optimize resources, and stay ahead of rivals by forming strong partnerships and responding to competitive pressures [15,17,18]. Adopting cloud computing in the fragmented construction industry can streamline processes, connect stakeholders, and facilitate real-time data access, driving digital project management adoption [15]. Digitalizing construction paperwork via cloud computing enhances sustainability, reduces paper dependence, and promotes real-time access, driving cloud data management adoption [15].
3. Research Methodology
The research methodology combines the techniques and methods used to investigate and test a particular theory [19]. This study adopted a quantitative research approach. This study was conducted in the Gauteng province of South Africa. The province was chosen because the researcher was familiar with the province and it was easy to access the target area. The research participants were selected from the five municipalities in the province: the City of Ekhuruleni Metropolitan, the City of Johannesburg Metropolitan, the City of Tshwane Metropolitan, Sedibeng, and West Rand District. A sample size of 104 research participants was achieved out of approximately 33,206 registered construction professionals in the Gauteng province in the SACI according to the 2022–2023 councils annual reports [20,21,22,23]. The first section of the questionnaire obtained the biographical information of the research participants, while the second section obtained the ranking of the drivers of cloud data management usage in the SACI from the respondents’ view using the agreement likert scale. The data analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 29 to give the variables’ mean item score, standard deviation, and exploratory factor analysis.
4. Results and Discussion
4.1. Research Participants’ Biographic Information
The respondents’ educational background, occupation, years of experience, number of construction projects they have worked on, and type of construction organizations they are a member of were among the biographical information gathered. In total, 47.1% (49) of the participants held an honours degree, 23.1% (24) possessed a bachelor’s degree, and 22.1% (23) possessed a master’s degree. Respondents holding a diploma made up 4.8% of the sample (5), while those holding a PhD made up 2.9% (3). This indicates that all research participants involved in the study possess sufficient education to offer guidance on the drivers of cloud data management usage.
4.2. Descriptive Results of the Drivers of Cloud Data Management Usage in the SACI
Table 1 displays a synopsis of the results of the drivers’ mean ranking analysis. A non-parametric test, the Kruskal–Wallis H-test, was conducted since the sample size is small and to determine the differing views of construction professionals about the significant drivers of cloud data management usage in the South African built environment. The Kruskal–Wallis H-test generates a chi-sq. value and a significant p-value, where the p-value can be less than or more than 0.05; if the p-value is less than 0.05, construction professionals’ views differ significantly in their awareness of the drivers, and if the p-value is greater than 0.05, their views do not significantly differ [24]. Therefore, all drivers except for ‘Data portability of cloud applications’ exceed the 0.05 p-value, indicating that professionals’ views do not differ significantly. The top five drivers were ‘sustainable data management’, with MIS = 4.44 and Asymptotic (Asymp.) Sig. = 0.495; ‘improved project life cycle management’, with MIS = 4.42 and Asymp. Sig. = 0.360; ‘technological advancement’, with MIS = 4.35 and Asymp. Sig. = 0.503; and ‘effective collaboration’ and ‘effective communication’, both with MIS = 4.27 and Asymp. Sig. = 0.903 and 0.900, respectively.
4.3. Factor Analysis Results of the Drivers of Cloud Data Management Usage in the SACI
Factor analysis was utilized to ascertain the correlations between the variables prior to rearranging the detected drivers into more logical sections that reflect relationships among various groups of related factors. Two tests were used to determine if the acquired data was suitable for factor analysis: the Kaiser–Meyer–Olkin (KMO) measure of sample adequacy and the Bartlett’s test of sphericity (BTS). A KMO score of 0.894 was found for this investigation, indicating that 89.4% of the data collected was suitable for factor analysis. Table 2 displays that the BTS in this investigation yielded a high chi-squared value of 1022.549 at 53 degrees of freedom and reached statistical significance. This demonstrates that factor analysis is legitimate, and the correlation matrix is not an identity matrix. Factor analysis was carried out using the principal component analysis (PCA) extraction method for dimensionality reduction, followed by direct oblimin rotation with Kaiser normalization to elucidate the factor structure. This technique was mainly used to quantify the drivers influencing the usage of cloud data management in the SA-built environment [25]. Also, Table 2 shows that every factor loading was greater than 0.30, indicating that every variable was taken into account for this investigation. Two rotated components with eigenvalues greater than 1 and a cumulative variance of 54.82% are displayed in the PCA findings. For the sake of clarity and readability, the names of the two components were chosen to represent the characteristics of the variables [26].
4.4. Discussion
4.4.1. Cluster 1: Operational Efficiency and Resilience Drivers
A total of eleven drivers were loaded onto this cluster, and they are ‘Effective communication’ (94.8%), ‘Effective collaboration’ (79.0%), ‘Safe cloud data management platform’ (76.6%), ‘Low IT infrastructure expenditure for users’ (72.4%), ‘Scalability of the cloud service’ (64.1%), ‘Data portability of cloud applications’ (63.1%), ‘Remote data accessibility’ (60.0%), ‘Quality of cloud data management services’ (53.9%), ‘Accessible data even during downtime’ (49.7%), ‘Adequate disaster recovery measures’ (44.2%), and ‘Effective project management’ (42.4%). This component recorded 46.6% as the total variance, and these variables relate to the factors that will drive overall operational efficiency and reliance of construction projects when cloud data management services are used. A study conducted by Ref. [15] indicated that utilizing cloud computing (CC) enables seamless workflow integration and collaboration among stakeholders throughout the project life cycle, including the design, construction, operation, and maintenance phases.
4.4.2. Cluster 2: Strategic and Technological Drivers
The seven extracted drivers for factor 2 were ‘Decreased reliance on manual labor’ (85.6%), ‘Sustainable data management’ (77.9%), ‘Competitiveness of construction organizations’ (74.8%), ‘Improved project life cycle management’ (70.4%), ‘Technological advancement’ (60.6%), ‘Manageability of storage’ (46.7%), and ‘The need for a reliable data storage service’ (46.2%). This cluster enumerated 8.2% as the total variance, and these factors relate to the strategic benefits of using cloud services in improving efficiency in organizations and technological drivers for the adoption of cloud data management services. Studies by Refs. [15,16] argue that facing challenges like rising costs and complex projects, the construction industry can reduce its dependence on manual labor and improve productivity by adopting technological solutions, specifically cloud data management services, which streamline data administration and enhance efficiency.
4.4.3. Implications of the Study
The findings offer practical implications to construction professionals, industry leaders, and policymakers. For construction professionals contemplating the adoption of cloud data management in their construction organizations, this study provides a blueprint of the adoption process to tailor-make their strategies for successful implementation. For industry leaders and policymakers, this study apprises them of the most impactful drivers like sustainable data management and improved project life cycle management. Therefore, this study backs the wider societal goals of sustainability and efficiency in the industry.
5. Conclusions and Recommendations
The full implementation of cloud data management services in the construction industry can bring about tangible improvements in the sector’s performance. Hence, this research paper assessed driving factors for the usage of cloud data management in the SA built environment through the use of a questionnaire. From the data analysis results, this study revealed two clusters, namely operational efficiency and resilience, and strategic and technological drivers. The research limitation of this study is the primary focus on the drivers of cloud data management usage in the South African construction industry context, with this study being conducted in only one province, omitting the other eight due to time, financial, and logistical constraints. This study also provided empirical evidence on the context of drivers of cloud data management usage in the context of a developing country, bridging the gap in the literature.
Author Contributions
Conceptualization, W.B.M. and O.A.; methodology, W.B.M. and O.A.; software, W.B.M.; validation, O.A., C.A. and S.A.K.; formal analysis, W.B.M. and O.A.; investigation, W.B.M.; resources, C.A. and S.A.K.; data curation, W.B.M.; writing—original draft preparation, W.B.M.; writing—review and editing, W.B.M.; supervision, O.A., C.A. and S.A.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data are contained within the article.
Conflicts of Interest
The authors declare no conflict of interest.
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Table 1.
Mean rankings of the drivers for cloud data management services in the SACI.
| The Drivers of Cloud Data Management Usage in the South African Construction Industry | Mean Item Score (MIS) | Kruskal–Wallis H | Asymp. Sig. | Ranking (R) |
|---|---|---|---|---|
| Sustainable data management | 4.4 | 6.392 | 0.495 | 1 |
| Improved project life cycle management | 4.42 | 7.702 | 0.360 | 2 |
| Technological advancement | 4.35 | 6.323 | 0.503 | 3 |
| Effective collaboration | 4.27 | 2.794 | 0.903 | 4 |
| Effective communication | 4.27 | 2.834 | 0.900 | 4 |
| The need for a reliable data storage service | 4.26 | 3.629 | 0.821 | 6 |
| Adequate disaster recovery measures | 4.25 | 6.376 | 0.497 | 7 |
| Accessible data even during downtime | 4.25 | 4.842 | 0.679 | 7 |
| Safe cloud data management platform | 4.24 | 3.358 | 0.850 | 9 |
| Remote data accessibility | 4.23 | 2.007 | 0.959 | 10 |
| Quality of cloud data management services | 4.18 | 7.546 | 0.374 | 11 |
| Manageability of storage | 4.18 | 5.638 | 0.583 | 11 |
| Competitiveness of construction organizations | 4.16 | 9.018 | 0.251 | 13 |
| Effective project management | 4.13 | 5.117 | 0.646 | 14 |
| Decreased reliance on manual labor | 4.12 | 3.236 | 0.862 | 15 |
| Data portability of cloud applications | 4.06 | 14.314 | 0.046 | 16 |
| Scalability of the cloud service | 3.99 | 8.689 | 0.276 | 17 |
| Low IT infrastructure expenditure for users | 3.95 | 2.730 | 0.909 | 18 |
Table 2.
Summary of factor analysis of the drivers for cloud data management services in the SACI.
| Groupings | Initial Eigenvalues | Extraction Sums of Squared Loadings | ||||
| Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
| 1 | 8.396 | 46.644 | 46.644 | 8.396 | 46.644 | 46.644 |
| 2 | 1.472 | 8.180 | 54.824 | 1.472 | 8.180 | 54.824 |
| Kaiser-Meyer-Olkin Measure of Sampling Adequacy | 0.894 | |||||
| Bartlett’s Test of Sphericity | Approx. Chi-Square | 1022.549 | ||||
| Df | 153 | |||||
| Sig | <0.001 | |||||
| Drivers of Cloud Data Management Usage in the SACI | Component | |||||
| 1 | 2 | |||||
| Cluster 1: Operational Efficiency and Resilience Drivers | - | |||||
| Effective communication | 0.948 | |||||
| Effective collaboration | 0.790 | |||||
| Safe cloud data management platform | 0.766 | |||||
| Low IT infrastructure expenditure for users | 0.724 | |||||
| Scalability of the cloud service | 0.641 | |||||
| Data portability of cloud applications | 0.631 | |||||
| Remote data accessibility | 0.600 | |||||
| Quality of cloud data management services | 0.539 | |||||
| Accessible data even during downtime | 0.497 | |||||
| Adequate disaster recovery measures | 0.442 | |||||
| Effective project management | 0.424 | |||||
| Cluster 2: Strategic and Technological Drivers | - | |||||
| Decreased reliance on manual labor | 0.856 | |||||
| Sustainable data management | 0.779 | |||||
| Competitiveness of construction organizations | 0.748 | |||||
| Improved project life cycle management | 0.704 | |||||
| Technological advancement | 0.606 | |||||
| Manageability of storage | 0.467 | |||||
| The need for a reliable data storage service | 0.462 | |||||
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MDPI and ACS Style
Mpingana, W.B.; Akinradewo, O.; Aigbavboa, C.; Khan, S.A. Evaluating the Drivers of Cloud Data Management Usage in the South African Construction Industry. Eng. Proc. 2024, 76, 39. https://doi.org/10.3390/engproc2024076039
AMA Style
Mpingana WB, Akinradewo O, Aigbavboa C, Khan SA. Evaluating the Drivers of Cloud Data Management Usage in the South African Construction Industry. Engineering Proceedings. 2024; 76(1):39. https://doi.org/10.3390/engproc2024076039
Chicago/Turabian StyleMpingana, Wanda Buhle, Opeoluwa Akinradewo, Clinton Aigbavboa, and Sharfuddin Ahmed Khan. 2024. "Evaluating the Drivers of Cloud Data Management Usage in the South African Construction Industry" Engineering Proceedings 76, no. 1: 39. https://doi.org/10.3390/engproc2024076039
APA StyleMpingana, W. B., Akinradewo, O., Aigbavboa, C., & Khan, S. A. (2024). Evaluating the Drivers of Cloud Data Management Usage in the South African Construction Industry. Engineering Proceedings, 76(1), 39. https://doi.org/10.3390/engproc2024076039
