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Article
Publication date: 29 April 2021

Samad M.E. Sepasgozar, Mohsen Ghobadi, Sara Shirowzhan, David J. Edwards and Elham Delzendeh

This paper aims to examine the current technology acceptance model (TAM) in the field of mixed reality and digital twin (MRDT) and identify key factors affecting users' intentions…

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Abstract

Purpose

This paper aims to examine the current technology acceptance model (TAM) in the field of mixed reality and digital twin (MRDT) and identify key factors affecting users' intentions to use MRDT. The factors are used as a set of key metrics for proposing a predictive model for virtual, augmented and mixed reality (MR) acceptance by users. This model is called the extended TAM for MRDT adoption in the architecture, engineering, construction and operations (AECO) industry.

Design/methodology/approach

An interpretivist philosophical lens was adopted to conduct an inductive systematic and bibliographical analysis of secondary data contained within published journal articles that focused upon MRDT acceptance modelling. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach to meta-analysis were adopted to ensure all key investigations were included in the final database set. Quantity indicators such as path coefficients, factor ranking, Cronbach’s alpha (a) and chi-square (b) test, coupled with content analysis, were used for examining the database constructed. The database included journal papers from 2010 to 2020.

Findings

The extant literature revealed that the most commonly used constructs of the MRDT–TAM included: subjective norm; social influence; perceived ease of use (PEOU); perceived security; perceived enjoyment; satisfaction; perceived usefulness (PU); attitude; and behavioural intention (BI). Using these identified constructs, the general extended TAM for MRDT in the AECO industry is developed. Other important factors such as “perceived immersion” could be added to the obtained model.

Research limitations/implications

The decision to utilise a new technology is difficult and high risk in the construction project context, due to the complexity of MRDT technologies and dynamic construction environment. The outcome of the decision may affect employee performance, project productivity and on-site safety. The extended acceptance model offers a set of factors that assist managers or practitioners in making effective decisions for utilising any type of MRDT technology.

Practical implications

Several constraints are apparent due to the limited investigation of MRDT evaluation matrices and empirical studies. For example, the research only covers technologies which have been reported in the literature, relating to virtual reality (VR), augmented reality (AR), MR, DT and sensors, so newer technologies may not be included. Moreover, the review process could span a longer time period and thus embrace a fuller spectrum of technology development in these different areas.

Originality/value

The research provides a theoretical model for measuring and evaluating MRDT acceptance at the individual level in the AECO context and signposts future research related to MRDT adoption in the AECO industry, as well as providing managerial guidance for progressive AECO professionals who seek to expand their use of MRDT in the Fourth Industrial Revolution (4IR). A set of key factors affecting MRDT acceptance is identified which will help innovators to improve their technology to achieve a wider acceptance.

Details

Engineering, Construction and Architectural Management, vol. 28 no. 5
Type: Research Article
ISSN: 0969-9988

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Article
Publication date: 1 August 2023

Elham Mahamedi, Martin Wonders, Nima Gerami Seresht, Wai Lok Woo and Mohamad Kassem

The purpose of this paper is to propose a novel data-driven approach for predicting energy performance of buildings that can address the scarcity of quality data, and consider the…

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Abstract

Purpose

The purpose of this paper is to propose a novel data-driven approach for predicting energy performance of buildings that can address the scarcity of quality data, and consider the dynamic nature of building systems.

Design/methodology/approach

This paper proposes a reinforcing machine learning (ML) approach based on transfer learning (TL) to address these challenges. The proposed approach dynamically incorporates the data captured by the building management systems into the model to improve its accuracy.

Findings

It was shown that the proposed approach could improve the accuracy of the energy performance prediction compared to the conventional TL (non-reinforcing) approach by 19 percentage points in mean absolute percentage error.

Research limitations/implications

The case study results confirm the practicality of the proposed approach and show that it outperforms the standard ML approach (with no transferred knowledge) when little data is available.

Originality/value

This approach contributes to the body of knowledge by addressing the limited data availability in the building sector using TL; and accounting for the dynamics of buildings’ energy performance by the reinforcing architecture. The proposed approach is implemented in a case study project based in London, UK.

Details

Construction Innovation , vol. 24 no. 1
Type: Research Article
ISSN: 1471-4175

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