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Digital Twin: A Conceptualization of the Task-Technology Fit for Individual Users in the Building Maintenance Sector

Author(s):



Medium: journal article
Language(s): English
Published in: IOP Conference Series: Earth and Environmental Science, , n. 9, v. 1101
Page(s): 092041
DOI: 10.1088/1755-1315/1101/9/092041
Abstract:

There have been numerous research studies on understanding buildings better and finding ways to improve water and energy consumption while making them more comfortable for occupants. With technologies such as Building Automation Systems, IoT devices, mobile applications and BIM software, more data can be generated and stored for intelligence and more effective facility management. The next big thing is the digital twin concept, where a virtual model is created from the physical building that brings together real-time information from all data capture devices, allows monitoring, historical analysis, predictive analytics, and simulation as well as interaction and intervention to be carried out. Many research studies have covered the characteristics, architecture, and benefits of digital twins and examined the potential implementation issues. However, the literature on evaluating return on investment (ROI) and acceptance of digital twins for the built environment at the individual user level is limited. This study aims to develop a conceptual model to examine the motivational intent and behavioural choice by individual users to utilise the digital twin technology to improve their performance. The objectives of this study are to examine technology acceptance theories and develop a conceptual model that measures the relationship between the tasks involved and the digital twin technology characteristics.

License:

This creative work has been published under the Creative Commons Attribution 3.0 Unported (CC-BY 3.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10780530
  • Published on:
    12/05/2024
  • Last updated on:
    12/05/2024
 
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