journal article·2023-01-14Open access

Understanding VR-Based Construction Safety Training Effectiveness: The Role of Telepresence, Risk Perception, and Training Satisfaction

Joon Woo Yoo, Jun Sung Park, Hee Jun Park

Applied Sciences· Volume 13· Issue 2

Abstract

The use of virtual reality as a safety training technology is gaining attention in the construction industry. While current studies focus mainly on the development of VR-based safety training programs, studies focusing on improving its effectiveness is still lacking. Thus, this study aims to understand the psychological process of training transfer and determine the factors that affect VR safety training effectiveness. The study analysed survey data from 248 construction workers who finished construction safety training using VR using PLS-SEM. The results show that the telepresence experienced through the VR and the risk perception of the trainees regarding occupational accidents significantly affect their satisfaction with VR safety training, which affected its effectiveness. Considering that the use of VR in the construction safety training context is still in its early stages, the results of our study, which comprehensively analyses both the technological and psychological aspects of VR safety training, could provide meaningful implications to VR training content developers. Furthermore, the theoretical approach of our study could be implemented in future studies focusing on the topic of training effectiveness.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Virtual Reality Applications and Impacts · Human-Automation Interaction and Safety · Occupational Health and Safety Research

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Industrial Engineering, Yonsei University, Seoul 03722, Republic of Korea

References (64)

Show source-provided references
1. Impact of safety training and interventions on training-transfer: Targeting migrant construction workers10.1080/10803548.2018.1465671 ↗
2. The Korea Ministry of Employment and Labor (MoEL) (2021, February 01). Analysis of Industrial Accidents, (In Korean).
3. Virtual reality simulation for construction safety promotion10.1080/17457300.2013.861853 ↗
4. Factors affecting effectiveness in e-learning: An analysis in production management courses10.1002/cae.20337 ↗
5. Development of a virtual reality training system for live-line workers
6. Conges, A., Evain, A., Benaben, F., Chabiron, O., and Rebiere, S. (2020, January 22–26). Crisis management exercises in virtual reality. Proceedings of the 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), Atlanta, GA, USA.10.1109/VRW50115.2020.00022 ↗
7. A review on virtual reality skill training applications10.3389/frvir.2021.645153 ↗
8. Alzahrani, N.M. (2020). Augmented reality: A systematic review of its benefits and challenges in e-learning contexts. Appl. Sci., 10.10.3390/app10165660 ↗
9. “Untact”: A new customer service strategy in the digital age10.1007/s11628-019-00408-2 ↗
10. Construction safety training using immersive virtual reality10.1080/01446193.2013.828844 ↗
11. Factors affecting training effectiveness in the context of the introduction of new technology—A US case study10.1111/1468-2419.00137 ↗
12. Factors affecting transfer of training in Thailand10.1002/hrdq.1142 ↗
13. Transfer of training: A review and directions for future research10.1111/j.1744-6570.1988.tb00632.x ↗
14. Influence of trainee characteristics, instructional satisfaction, and organizational climate on perceived learning and training transfer10.1002/hrdq.1162 ↗
15. A dynamic model of training transfer10.1016/j.hrmr.2017.11.004 ↗
16. Perceived usefulness, perceived ease of use, and user acceptance of information technology10.2307/249008 ↗
17. Technology Acceptance Model in M-Learning Context: A Systematic Review10.1016/j.compedu.2018.06.008 ↗
18. Riantini, R.E. (2018, January 13–14). Adoption of E-Commerce Online to Offline with Technology Acceptance Model (TAM) Approach. Proceedings of the 2018 4th International Conference on Computer and Information Sciences, Kuala Lumpur, Malaysia.10.1109/ICCOINS.2018.8510613 ↗
19. An empirical study of consumers’ intention to use ride-sharing services: Using an extended technology acceptance model10.1007/s11116-018-9893-4 ↗
20. Technology acceptance model 3 and a research agenda on interventions10.1111/j.1540-5915.2008.00192.x ↗
21. A theoretical extension of the technology acceptance model: Four longitudinal field studies10.1287/mnsc.46.2.186.11926 ↗
22. User acceptance of virtual reality: An extended technology acceptance model10.1080/10447318.2019.1708612 ↗
23. The use and acceptance of ICT by senior citizens: A comparison of technology acceptance model (TAM) for elderly and young adults10.1007/s10209-018-0642-4 ↗
24. Investigating the determinants of construction professionals’ acceptance of web-based training: An extension of the technology acceptance model10.1016/j.autcon.2011.09.016 ↗
25. Defining virtual reality: Dimensions determining telepresence10.1111/j.1460-2466.1992.tb00812.x ↗
26. A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda10.1016/j.compedu.2019.103778 ↗
27. The effects of progressive levels of interactivity and vividness in web marketing sites10.1080/00913367.2001.10673646 ↗
28. The impact of virtual reality (VR) technology on sport spectators’ flow experience and satisfaction10.1016/j.chb.2018.12.040 ↗
29. An adoption model for virtual reality games: The roles of presence and enjoyment10.1016/j.tele.2019.101239 ↗
30. Immersive virtual reality news: A study of user experience and media effects10.1016/j.ijhcs.2020.102576 ↗
31. Exploratory field study of training motivation: Influence of involvement, credibility, and transfer climate
32. The virtual reality hardware acceptance model (VR-HAM): Extending and individuating the technology acceptance model (TAM) for virtual reality hardware10.1016/j.jbusres.2018.10.021 ↗
33. The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education10.1016/j.compedu.2018.09.009 ↗
34. Examining the students’ behavioural intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach10.1016/j.compedu.2017.04.010 ↗
35. Wu, W.C., and Vu, V.H. (2022). Application of Virtual Reality Method in Aircraft Maintenance Service—Taking Dornier 228 as an Example. Appl. Sci., 12.10.3390/app12147283 ↗
36. An empirical investigation on end-users’ acceptance of enterprise systems10.4018/irmj.2004070103 ↗
37. A model of acceptance of augmented-reality interactive technology: The moderating role of cognitive innovativeness10.1007/s10660-014-9163-2 ↗
38. Validation of haptic enabling technology acceptance model (HE-TAM): Integration of IDT and TAM10.1016/j.tele.2014.01.002 ↗
39. Integrating health belief model and technology acceptance model: An investigation of health-related internet use10.2196/jmir.3564 ↗
40. Risk, Trust, and the Interaction of Perceived Ease of Use and Behavioral Control in Predicting Consumers’ Use of Social Media for Transactions10.1016/j.chb.2017.11.010 ↗
41. Patients’ acceptance of smartphone health echnology for chronic disease management: A theoretical model and empirical test10.2196/mhealth.7886 ↗
42. Predicting autonomous and controlled motivation to transfer training10.1111/j.1468-2419.2009.00322.x ↗
43. Decoding Training Effectiveness: The Role of Organisational Factors10.1108/JWL-05-2016-0038 ↗
44. Hyman, L., Lamb, J., and Bulmer, M. (2006, January 24–26). The use of pre-existing survey questions: Implications for data quality. Proceedings of the Q2006 European Conference on Quality in Survey Statistics, Cardiff, Wales, UK.
45. Understanding computer security behavioural intention in the workplace10.1108/ITP-12-2012-0147 ↗
46. Understanding the acceptance of mobile health services: A comparison and integration of alternative models
47. An integrated model of training effectiveness and satisfaction with employee development interventions10.1108/00197851211231487 ↗
48. What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction10.1016/j.compedu.2006.11.007 ↗
49. The use of vivid stimuli to enhance comprehension of the content of product warning messages10.1111/j.1745-6606.1989.tb00247.x ↗
50. Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective10.1016/j.intmar.2017.04.001 ↗
51. Korea Occupational Safety & Health Agency (2020, November 01). Video-Based VR Content—Scaffolding. (In Korean).
52. Response rates and responsiveness for surveys, standards, and the Journal10.5688/aj720243 ↗
53. Hair, J.F., Hult, G.T.M., Ringle, C.M., and Sarstedt, M. (2013). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage Publications.
54. When to use and how to report the results of PLS-SEM10.1108/EBR-11-2018-0203 ↗
55. NDAR: A model federal system for secondary analysis in developmental disabilities research10.1016/B978-0-12-407760-7.00003-7 ↗
56. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates. [2nd ed.].
57. Falk, R.F., and Miller, N.B. (1992). A Primer for Soft Modeling, University of Akron Press.
58. Technology acceptance model in educational context: A systematic literature review10.1111/bjet.12864 ↗
59. Teleoperated mobile robot with two arms: The influence of a human-machine interface, VR training and operator age10.1016/j.ijhcs.2021.102707 ↗
60. The effect of interactivity on learning physical actions in virtual reality10.1080/15213260802285214 ↗
61. Vidal-Balea, A., Blanco-Novoa, O., Fraga-Lamas, P., Vilar-Montesinos, M., and Fernández-Caramés, T.M. (2020). Creating collaborative augmented reality experiences for industry 4.0 training and assistance applications: Performance evaluation in the shipyard of the future. Appl. Sci., 10.10.3390/app10249073 ↗
62. Muñoz-Saavedra, L., Miró-Amarante, L., and Domínguez-Morales, M. (2020). Augmented and virtual reality evolution and future tendency. Appl. Sci., 10.10.3390/app10010322 ↗
63. Miguel-Alonso, I., Rodriguez-Garcia, B., Checa, D., and Bustillo, A. (2023). Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments. Appl. Sci., 13.10.3390/app13010593 ↗
64. Checa, D., Miguel-Alonso, I., and Bustillo, A. (2021). Immersive virtual-reality computer-assembly serious game to enhance autonomous learning. Virtual Real., 1–18.10.1007/s10055-021-00607-1 ↗