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Predictors To Increase Safety Technology Adoption In Construction: An Exploratory Factor Analysis for Malaysia

Author(s): ORCID

ORCID
ORCID
ORCID
ORCID
Medium: journal article
Language(s): English
Published in: Journal of Civil Engineering and Management, , n. 2, v. 29
Page(s): 157-170
DOI: 10.3846/jcem.2022.18053
Abstract:

Accidents and injuries continue to be major problems in the construction industry despite persistent endeavours over the years to improve health and safety management. Novel approaches using emerging technologies can enhance construction safety performance. Given their limited adoption and lack of empirical evidence in the context of developing countries, this paper examines the predictors influencing the successful adoption of safety technologies in the construction industry. Using a survey questionnaire involving 133 Malaysian construction professionals, the significance of the predictors is prioritised. A factor analysis is used to reduce the predictors involved into a fewer number of dimensions. The most influential predictors are presented. Four underlying dimensions of the surveyed predictors are uncovered, comprising organisational commitment/technology orientation, supporting technological attributes, personal perception/performance expectancy and government support. The rate of technology implementation to improve safety risk mitigation in construction is still limited. By taking cognisance of the critical influential predictors involved, the adoption level of safety technology could be raised. This paper bridges the identified knowledge gap regarding the dimensionality of safety technology adoption predictors in construction, with findings that shed new light on the factors influencing technology adoption in a developing country to stimulate technological innovations to streamline construction safety.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.3846/jcem.2022.18053.
  • About this
    data sheet
  • Reference-ID
    10712999
  • Published on:
    21/03/2023
  • Last updated on:
    21/03/2023
 
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