Exploring the acceptance of PPE by construction workers: An extension of the technology acceptance model with safety management practices and safety consciousness

被引:43
|
作者
Wong, Tom Ka Man [1 ]
Man, Siu Shing [1 ]
Chan, Alan Hoi Shou [1 ]
机构
[1] City Univ Hong Kong, Dept Syst Engn & Engn Management, Kowloon Tong, Hong Kong, Peoples R China
关键词
Construction worker; Personal protective equipment; Safety consciousness; Safety management practice; Technology acceptance model; RISK-ASSESSMENT; TRANSFORMATIONAL LEADERSHIP; PERCEIVED USEFULNESS; USER ACCEPTANCE; FALL; PREVENTION; INFORMATION; BEHAVIOR; HEALTH; INTERVENTION;
D O I
10.1016/j.ssci.2021.105239
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Most construction fatalities are attributable to falls from height, which are originally caused by the non-use of personal protective equipment (PPE). Accordingly, this study aimed to present a research model that integrates the technology acceptance model, safety management practices (including safety-offence points system, safety supervision and safety training) and safety consciousness to explain the PPE acceptance by construction workers. Structural equation modelling and mediation analysis were conducted to investigate the influence of these constructs on the PPE acceptance. Results indicated that the safety management practices were influential in shaping attitude towards using PPE with the mediation of safety consciousness, perceived usefulness (PU) and perceived ease of use (PEOU). PU and PEOU were crucial determinants of the PPE acceptance by construction workers. Following these findings, practical implications for enhancing the use of PPE of construction workers were offered for construction management, PPE designers and concerned parties.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] Investigating mobile wireless technology adoption: An extension of the technology acceptance model
    Kim, Sanghyun
    Garrison, Gary
    INFORMATION SYSTEMS FRONTIERS, 2009, 11 (03) : 323 - 333
  • [22] Exploring Students' Acceptance of Construction Information Technology: The Development of a Comprehensive Technology Acceptance Model for the Design of an Education Program at a Japanese University
    Watanabe, Reeko
    Watanabe, Tsunemi
    Skitmore, Martin
    SUSTAINABILITY, 2023, 15 (24)
  • [23] Extended technology acceptance model to explain the mechanism of modular construction adoption
    Shin, Jiwoong
    Moon, Sungwoo
    Cho, Bong-ho
    Hwang, Sungjoo
    Choi, Byungjoo
    JOURNAL OF CLEANER PRODUCTION, 2022, 342
  • [24] User acceptance of You Tube for procedural learning: An extension of the Technology Acceptance Model
    Lee, Doo Young
    Lehto, Mark R.
    COMPUTERS & EDUCATION, 2013, 61 : 193 - 208
  • [25] Technology Acceptance Model: Extension to Sport Consumption
    Ibrahim, Hafedh
    24TH DAAAM INTERNATIONAL SYMPOSIUM ON INTELLIGENT MANUFACTURING AND AUTOMATION, 2013, 2014, 69 : 1534 - 1540
  • [26] What drives construction workers' acceptance of wearable technologies in the workplace?: Indoor localization and wearable health devices for occupational safety and health
    Choi, Byungjoo
    Hwang, Sungjoo
    Lee, SangHyun
    AUTOMATION IN CONSTRUCTION, 2017, 84 : 31 - 41
  • [27] Exploring Students' Acceptance of E-Learning Through the Development of a Comprehensive Technology Acceptance Model
    Salloum, Said A.
    Alhamad, Ahmad Qasim Mohammad
    Al-Emran, Mostafa
    Monem, Azza Abdel
    Shaalan, Khaled
    IEEE ACCESS, 2019, 7 : 128445 - 128462
  • [28] Development and validation of a technology acceptance model for safety-enhancing, wearable locating systems
    Kwee-Meier, Sonja Th.
    Buetzler, Jennifer E.
    Schlick, Christopher
    BEHAVIOUR & INFORMATION TECHNOLOGY, 2016, 35 (05) : 394 - 409
  • [29] What Drives Construction Practitioners' Acceptance of Intelligent Surveillance Systems? An Extended Technology Acceptance Model
    Lu, Ying
    Deng, Yunxuan
    BUILDINGS, 2022, 12 (02)
  • [30] Applying an enhanced technology acceptance model to knowledge management in agricultural extension services
    Department of Computer Science, University of Agriculture Abeokuta, Ogun State, Nigeria
    Data Science Journal, 2008, 7 : 31 - 46