<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The notion of smart buildings is becoming a global trend. The smart concept is spread not only via buildings but also through cities, transportation, and communication. Many difficulties human beings face can be solved by smart building technology. For example, environmental contamination and resource depletion, such as water and fossil fuels. In Sri Lanka, smart building adoption was at a low level. As a result, the purpose of this research is to assess user comprehension of smart building service preferences and adoption from a Sri Lankan viewpoint. A questionnaire survey is used to obtain data from the community as part of the data collection approach. To achieve the objectives stated above, the data will be analysed using principal component analysis, multiple regression analysis, and Pareto analysis. Results indicated that the majority of Sri Lankans do not grasp smart building technologies. Using principal component analysis, eleven major service preferences were determined. Multiple regression analysis is used to identify the factors that influence service preference. The most essential elements impacting smart building adoption are competency to utilize new technology, preference for smart building attributes, and user satisfaction. This study would be an excellent resource for the future adoption of smart building technologies in Sri Lanka.</p>
</abstract>

The notion of smart buildings is becoming a global trend. The smart concept is spread not only via buildings but also through cities, transportation, and communication. Many difficulties human beings face can be solved by smart building technology. For example, environmental contamination and resource depletion, such as water and fossil fuels. In Sri Lanka, smart building adoption was at a low level. As a result, the purpose of this research is to assess user comprehension of smart building service preferences and adoption from a Sri Lankan viewpoint. A questionnaire survey is used to obtain data from the community as part of the data collection approach. To achieve the objectives stated above, the data will be analysed using principal component analysis, multiple regression analysis, and Pareto analysis. Results indicated that the majority of Sri Lankans do not grasp smart building technologies. Using principal component analysis, eleven major service preferences were determined. Multiple regression analysis is used to identify the factors that influence service preference. The most essential elements impacting smart building adoption are competency to utilize new technology, preference for smart building attributes, and user satisfaction. This study would be an excellent resource for the future adoption of smart building technologies in Sri Lanka.

Factors Influencing the Adoption of Smart Building and Service Preferences in Sri Lanka

Baltic Journal of Real Estate Economics and Construction Management

This work is licensed under the Creative Commons Attribution 4.0 International License.

The notion of smart buildings is becoming a global trend. The smart concept is spread not only via buildings but also through cities, transportation, and...

Factors Influencing the Adoption of Smart Building and Service Preferences in Sri Lanka

Publié en ligne: 10 févr. 2024

Pages: 18 - 35

Reçu: 21 déc. 2023

Accepté: 10 janv. 2024

DOI: https://doi.org/10.2478/bjreecm-2024-0002

Mots clés
building management system, service preferences, smart buildings, Sri Lanka

© 2024 Savindika Weerawardhana et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Factors Influencing the Adoption of Smart Building and Service Preferences in Sri Lanka

Publié en ligne: 10 févr. 2024

Pages: 18 - 35

Reçu: 21 déc. 2023

Accepté: 10 janv. 2024

DOI: https://doi.org/10.2478/bjreecm-2024-0002

Mots clésbuilding management system, service preferences, smart buildings, Sri Lanka

© 2024 Savindika Weerawardhana et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
building management system, service preferences, smart buildings, Sri Lanka