What Drives Construction Practitioners’ Acceptance of Intelligent Surveillance Systems? An Extended Technology Acceptance Model
Author(s): |
Ying Lu
Yunxuan Deng |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 18 January 2022, n. 2, v. 12 |
Page(s): | 104 |
DOI: | 10.3390/buildings12020104 |
Abstract: |
With the advent of intelligent construction, the intelligent surveillance system using computer vision technology has emerged as a prominent tool to identify unsafe behaviors on construction sites. At the same time, it is still viewed with suspicion by the construction industry, and its penetration rate remains low. To promote the successful implementation of the intelligent surveillance system, this study applied the technology acceptance model approach and developed an intelligent surveillance system acceptance model (ISSTAM) containing 12 variables from individual, organizational, environmental, and technical perspectives. Questionnaires were distributed to construction industry practitioners, 220 of whom provided valid data. Moreover, a structural equation model (SEM) was established for hypothesis testing. The research results suggest that job relevance, government action, training, and technical support positively and indirectly influence the use intention. Meanwhile, perceived usefulness, perceived ease of use, and cost savings directly and positively affect use intention, while privacy risk is verified to have a negative impact upon use intention. This study can help the government, organizations, and technology developers better apply the intelligent surveillance system to improve safety management levels. |
Copyright: | © 2022 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) 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. |
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data sheet - Reference-ID
10657758 - Published on:
17/02/2022 - Last updated on:
01/06/2022