Examining the effect of data-driven technology adoption factor towards Smart Facilities Management in public sector
Facilities Management (FM) in public sector involves overseeing large-scale, complex infrastructures that generate vast amounts of data especially. The integration of data-driven technologies (DDT), such as the Internet of Things (IoT), Cloud Computing, Big Data Analytics (BDA), and Artificial Intel...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | en |
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Universiti Teknologi MARA, Perlis
2025
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/126936/1/126936.pdf https://ir.uitm.edu.my/id/eprint/126936/ https://journal.uitm.edu.my/ojs/index.php/JI |
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| Summary: | Facilities Management (FM) in public sector involves overseeing large-scale, complex infrastructures that generate vast amounts of data especially. The integration of data-driven technologies (DDT), such as the Internet of Things (IoT), Cloud Computing, Big Data Analytics (BDA), and Artificial Intelligence (AI) could utilize data towards Smart FM practice. However, the adoption of these DDT in government FM practices are not fully realized due to individual and organizational challenges. Consequently, this study aims to investigate the key determinants factors influencing DDT adoption in FM from both individual and organizational perspective. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology-Organization-Environment (TOE) framework, this research employs a deductive approach, using a structured questionnaire survey. A total of 216 responses from Malaysian government FM practitioner were analysed by Structural Equation Modelling (SEM) technique. Findings reveal that performance expectancy, effort expectancy, social influence, facilitating conditions, technology readiness, and organizational support significantly influence DDT adoption towards Smart FM, while environmental factors do not. The results provide valuable insights for policymakers and FM practitioners seeking to enhance data-driven transformation in public sector facilities management. |
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