Metrics development and modelling the mixed reality and digital twin adoption in the context of Industry 4.0

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

Research output: Contribution to journalReview articlepeer-review

37 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1355-1376
Number of pages22
JournalEngineering, Construction and Architectural Management
Volume28
Issue number5
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • Construction
  • Design
  • Novel method
  • Process
  • Technology

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Architecture
  • Building and Construction
  • General Business,Management and Accounting

Fingerprint

Dive into the research topics of 'Metrics development and modelling the mixed reality and digital twin adoption in the context of Industry 4.0'. Together they form a unique fingerprint.

Cite this