Personalized Construction Safety Interventions Considering Cognitive-Related Factors
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 12
Abstract
Eliminating workers’ unsafe behavior is one of the most important goals for onsite management and behavior-based safety programs have been widely used. However, the existing method lacks consideration of the worker’s inner and personal factors causing the decrease in the effectiveness of the measures. This study puts forward a personalization method for safety interventions and aims to examine the effectiveness and practicability of this change in safety interventions to reduce construction workers’ unsafe behaviors. Personalization of safety interventions was achieved through a diagnostic intervention model targeting the construction worker’s cognitive-based competence, psychological needs, and safety motivation with consideration of personality traits. The workers’ data were collected via a questionnaire and smart helmets on a building construction site. The results indicate that addressing these inner factors could achieve a persistent positive effect on construction workers’ safe behaviors. The effect size of improvement on cognitive-based competence is more than that of safety motivation. Improvements in long-term memory retrieval ability, subjective norms, and risk tolerance are the most significant in the cognitive-based competence. Personalized safety interventions and the current safety management are compatible.
Practical Applications
This study identifies two drivers behind the construction worker’s behaviors and contextualizes them into cognitive-based competence and safety motivation. The experiment shows that considering individual internal causes through the diagnosis of the two behavioral drivers is important in enhancing construction workers’ safety behavior. These changes in safety management have been found effective: personality traits are useful in determining the emotional tone for the expression of safety communication; psychological needs help promote safety motivation; improving cognitive-based competence or/and safety motivation can be set as the objectives of safety interventions. The participation of managerial personnel, especially supervisor and site supervisors, can help improve both the safety communication and safety performance. Smart helmets are useful in the personalized safety interventions by recording the worker’s behaviors and delivering safety instructions. The findings guide project managers in designing specific behavioral interventions to reduce construction workers’ unsafe behavior and provide an experimental protocol for the future studies.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
References
Abubakar, A. M., H. Karadal, S. W. Bayighomog, and E. Merdan. 2020. “Workplace injuries, safety climate and behaviors: Application of an artificial neural network.” Int. J. Occup. Saf. Ergon. 26 (4): 651–661. https://doi.org/10.1080/10803548.2018.1454635.
Aroke, O., S. Hasanzadeh, B. Esmaeili, M. D. Dodd, and R. Brock. 2022. “Using worker characteristics, personality, and attentional distribution to predict hazard identification performance: A moderated mediation analysis.” J. Constr. Eng. Manage. 148 (6): 04022033. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002295.
Balian, R. 2004. “Entropy, a protean concept.” Prog. Math. Phys. 38 (Jan): 119. https://doi.org/10.1007/978-3-0348-7932-3_7.
Bhandari, S., R. Hallowell Matthew, W. Alruqi, and R. Salas. 2021. “Modeling the relationship between personal risk tolerance, work-related risk tolerance, and risk-taking behavior of construction workers.” J. Constr. Eng. Manage. 147 (4): 04021016. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002021.
Breaugh, J. 1999. “Further investigation of the work autonomy scales: Two studies.” J. Bus. Psychol. 13 (Sep): 357–373. https://doi.org/10.1023/A:1022926416628.
Cameron, I., and R. Duff. 2007. “A critical review of safety initiatives using goal setting and feedback.” Construct. Manage. Econ. 25 (5): 495–508. https://doi.org/10.1080/01446190701275173.
Chen, G., S. M. Gully, and D. Eden. 2001. “Validation of a new general self-efficacy scale.” Organ. Res. Methods 4 (1): 62–83. https://doi.org/10.1177/109442810141004.
Cheung, C. M., R. P. Zhang, Q. Cui, and S.-C. Hsu. 2021. “The antecedents of safety leadership: The job demands-resources model.” Saf. Sci. 133 (Mar): 104979. https://doi.org/10.1016/j.ssci.2020.104979.
Choi, B., S. Ahn, and S. Lee. 2016. “Role of social norms and social identifications in safety behavior of construction workers. I: Theoretical model of safety behavior under social influence.” J. Constr. Eng. Manage. 143 (5): 04016124. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001271.
Choi, B., and S. Lee. 2016. “Role of social norms and social identifications in safety behavior of construction workers. II: Group analyses for the effects of cultural backgrounds and organizational structures on social influence process.” J. Constr. Eng. Manage. 143 (5): 04016125. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001254.
Choi, B., and S. Lee. 2018. “An empirically based agent-based model of the sociocognitive process of construction workers’ safety behavior.” J. Constr. Eng. Manage. 144 (2): 04017102. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001421.
Cohen, J. 2013. Statistical power analysis for the behavioral sciences. Oxfordshire, UK: Routledge.
Fang, D., Y. Huang, H. Guo, and H. W. Lim. 2020a. “LCB approach for construction safety.” Saf. Sci. 128 (Mar): 104761. https://doi.org/10.1016/j.ssci.2020.104761.
Fang, D., C. Zhao, and M. Zhang. 2016. “A cognitive model of construction workers’ unsafe behaviors.” J. Constr. Eng. Manage. 142 (9): 04016039. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001118.
Fang, W., L. Ding, P. E. D. Love, H. Luo, H. Li, F. Peña-Mora, B. Zhong, and C. Zhou. 2020b. “Computer vision applications in construction safety assurance.” Autom. Constr. 110 (Feb): 103013. https://doi.org/10.1016/j.autcon.2019.103013.
Fang, W., P. E. D. Love, H. Luo, and L. Ding. 2020c. “Computer vision for behaviour-based safety in construction: A review and future directions.” Adv. Eng. Inf. 43 (Jan): 100980. https://doi.org/10.1016/j.aei.2019.100980.
Fogg, B. 2019. Fogg’s behavior model. Stanford, CA: Stanford Univ.
Geller, E. S. 2001. “Behavior-based safety in industry: Realizing the large-scale potential of psychology to promote human welfare.” Appl. Preventive Psychol. 10 (2): 87–105. https://doi.org/10.1017/S0962-1849(02)01002-8.
Goh, Y. M., C. U. Ubeynarayana, K. L. X. Wong, and B. H. W. Guo. 2018. “Factors influencing unsafe behaviors: A supervised learning approach.” Accid. Anal. Prev. 118 (Sep): 77–85. https://doi.org/10.1016/j.aap.2018.06.002.
Hard Hat Workers Dataset. 2022. “Dataset.” Accessed July 30, 2022. https://link.csdn.net/?target=https%3A%2F%2Fpublic.roboflow.com%2Fobject-detection%2Fhard-hat-workers.
He, C. Q., G. S. Jia, B. McCabe, Y. T. Chen, and J. D. Sun. 2019. “Impact of psychological capital on construction worker safety behavior: Communication competence as a mediator.” J. Saf. Res. 71 (Dec): 231–241. https://doi.org/10.1016/j.jsr.2019.09.007.
Hedberg, E. C., and S. Ayers. 2015. “The power of a paired t-test with a covariate.” Social Sci. Res. 50 (Mar): 277–291. https://doi.org/10.1016/j.ssresearch.2014.12.004.
Helmet_Dataset. 2022. “Dataset.” Accessed July 30, 2022. https://www.kaggle.com/datasets/abhishek4273/helmet-dataset.
Hu, Z., W. T. Chan, H. Hu, and F. Xu. 2023. “Cognitive factors underlying unsafe behaviors of construction workers as a tool in safety management: A review.” J. Constr. Eng. Manage. 149 (3): 03123001. https://doi.org/10.1061/JCEMD4.COENG-11820.
Hu, Z., H. Hu, F. Xu, and W. Wang. 2022. “Research on the safety behavior capability of construction workers based on cognitive style.” J. Saf. Environ. 22 (1): 194–200. https://doi.org/10.13637/j.issn.1009-6094.2020.1186.
Jeelani, I., A. Albert, R. Azevedo, and E. J. Jaselskis. 2017. “Development and testing of a personalized hazard-recognition training intervention.” J. Constr. Eng. Manage. 143 (5): 04016120. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001256.
Jeelani, I., K. Han, and A. Albert. 2018. “Scaling personalized safety training using automated feedback generation.” In Proc., Construction Research Congress 2018, 196–206. Reston, VA: ASCE.
Komaki, J., K. D. Barwick, and L. R. Scott. 1978. “A behavioral approach to occupational safety: Pinpointing and reinforcing safe performance in a food manufacturing plant.” J. Appl. Psychol. 63 (4): 434–445. https://doi.org/10.1037/0021-9010.63.4.434.
Lakens, D. 2013. “Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs.” Front. Psychol. 4 (Nov): 863. https://doi.org/10.3389/fpsyg.2013.00863.
Li, G., R. Suo, G. Zhao, C. Gao, L. Fu, F. Shi, J. Dhupia, R. Li, and Y. Cui. 2022. “Real-time detection of kiwifruit flower and bud simultaneously in orchard using YOLOv4 for robotic pollination.” Comput. Electron. Agric. 193 (Feb): 106641. https://doi.org/10.1016/j.compag.2021.106641.
Li, H., M. Lu, S.-C. Hsu, M. Gray, and T. Huang. 2015. “Proactive behavior-based safety management for construction safety improvement.” Saf. Sci. 75 (Jun): 107–117. https://doi.org/10.1016/j.ssci.2015.01.013.
Li, Y., Y. Hu, B. Xia, M. Skitmore, and H. Li. 2018. “Proactive behavior-based system for controlling safety risks in urban highway construction megaprojects.” Autom. Constr. 95 (Nov): 118–128. https://doi.org/10.1016/j.autcon.2018.07.021.
Lingard, H., and S. Rowlinson. 1997. “Behavior-based safety management in Hong Kong’s construction industry.” J. Saf. Res. 28 (4): 243–256. https://doi.org/10.1016/S0022-4375(97)00010-8.
Liu, Q., N. Xu, H. Jiang, S. C. Wang, W. S. Wang, and J. P. Wang. 2020. “Psychological driving mechanism of safety citizenship behaviors of construction workers: Application of the theory of planned behavior and norm activation model.” J. Constr. Eng. Manage. 146 (4): 04020027. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001793.
Mohajeri, M., A. Ardeshir, M. T. Banki, and H. Malekitabar. 2020. “Discovering causality patterns of unsafe behavior leading to fall hazards on construction sites.” J. Constr. Eng. Manage. 22 (15): 3034–3044. https://doi.org/10.1080/15623599.2020.1839704.
Mohajeri, M., A. Ardeshir, and H. Malekitabar. 2022. “Diagnostic intervention program based on construction workers’ internal factors for persistent reduction of unsafe behavior.” Eng. Constr. Archit. Manage. 30 (2): 478–495. https://doi.org/10.1108/ECAM-05-2021-0435.
Newaz, M. T., P. Davis, M. Jefferies, and M. Pillay. 2019. “The psychological contract: A missing link between safety climate and safety behaviour on construction sites.” Saf. Sci. 112 (Feb): 9–17. https://doi.org/10.1016/j.ssci.2018.10.002.
Nykanen, M., K. Salmela-Aro, A. Tolvanen, and J. Vuori. 2019. “Safety self-efficacy and internal locus of control as mediators of safety motivation—Randomized controlled trial (RCT) study.” Saf. Sci. 117 (Aug): 330–338. https://doi.org/10.1016/j.ssci.2019.04.037.
Pandit, B., A. Albert, Y. Patil, and A. J. Al-Bayati. 2019. “Impact of safety climate on hazard recognition and safety risk perception.” Saf. Sci. 113 (Mar): 44–53. https://doi.org/10.1016/j.ssci.2018.11.020.
Russell, B. C., A. Torralba, K. P. Murphy, and W. T. Freeman. 2008. “LabelMe: A database and web-based tool for image annotation.” Int. J. Comput. Vision 77 (1): 157–173. https://doi.org/10.1007/s11263-007-0090-8.
Ryan, R. M., and E. L. Deci. 2000. “Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being.” Am. Psychologist 55 (1): 68. https://doi.org/10.1037/0003-066X.55.1.68.
Safety Helmet Detection. 2022. “Improve work safety by detecting the presence of people and safety helmets.” Accessed July 30, 2022. https://www.kaggle.com/datasets/andrewmvd/hard-hat-detection.
Sawilowsky, S. S. 2009. “New effect size rules of thumb.” J. Mod. Appl. Stat. Methods 8 (2): 597. https://doi.org/10.22237/jmasm/1257035100.
Shengyu, G., L. Hanbin, K. Liulin, J. Xiaoyan, and X. Chaohua. 2015. “Content-based personalized behavior safety training of metro construction workers.” J. Civ. Eng. Manage. 32 (4): 53–58. https://doi.org/10.3969/j.issn.2095-0985.2015.04.009.
SHWD (Safety Helmet Wearing-Dataset). 2022. “Safety helmet (hardhat) wearing detect dataset.” Accessed July 30, 2022. https://github.com/njvisionpower/Safety-Helmet-Wearing-Dataset.
Smith, J. R., and W. R. Louis. 2008. “Do as we say and as we do: The interplay of descriptive and injunctive group norms in the attitude–behaviour relationship.” Br. J. Social Psychol. 47 (4): 647–666. https://doi.org/10.1348/014466607X269748.
Tang, N., H. Hu, F. Xu, and F. Zhu. 2019. “Personalized safety instruction system for construction site based on internet technology.” Saf. Sci. 116 (Jul): 161–169. https://doi.org/10.1016/j.ssci.2019.03.001.
Ting, H.-I., P.-C. Lee, P.-C. Chen, and L.-M. Chang. 2020. “An adjusted behavior-based safety program with the observation by front-line workers for mitigating construction accident rate.” J. Chin. Inst. Eng. 43 (1): 37–46. https://doi.org/10.1080/02533839.2019.1676654.
Wang, D., X. Wang, M. A. Griffin, and Z. Wang. 2020. “Safety stressors, safety-specific trust, and safety citizenship behavior: A contingency perspective.” Accid. Anal. Prev. 142 (Jul): 105572. https://doi.org/10.1016/j.aap.2020.105572.
Wang, J., P. X. W. Zou, and P. P. Li. 2016. “Critical factors and paths influencing construction workers’ safety risk tolerances.” Accid. Anal. Prev. 93 (Aug): 267–279. https://doi.org/10.1016/j.aap.2015.11.027.
Wen Lim, H., N. Li, D. Fang, and C. Wu. 2018. “Impact of safety climate on types of safety motivation and performance: Multigroup invariance analysis.” J. Manage. Eng. 34 (3): 04018002. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000595.
Yu, Y., H. Guo, Q. Ding, H. Li, and M. Skitmore. 2017. “An experimental study of real-time identification of construction workers’ unsafe behaviors.” Autom. Constr. 82 (Oct): 193–206. https://doi.org/10.1016/j.autcon.2017.05.002.
Zhang, J., P. Xiang, R. Zhang, D. Chen, and Y. Ren. 2020. “Mediating effect of risk propensity between personality traits and unsafe behavioral intention of construction workers.” J. Constr. Eng. Manage. 146 (4): 04020023. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001792.
Zhang, M., and D. Fang. 2013. “A continuous behavior-based safety strategy for persistent safety improvement in construction industry.” Autom. Constr. 34 (Sep): 101–107. https://doi.org/10.1016/j.autcon.2012.10.019.
Zhang, P., N. Li, D. Fang, and H. Wu. 2017. “Supervisor-focused behavior-based safety method for the construction industry: Case study in Hong Kong.” J. Constr. Eng. Manage. 143 (7): 05017009. https://doi.org/10.1061/(asce)co.1943-7862.0001294.
Zhao, H., and S. E. Seibert. 2006. “The big five personality dimensions and entrepreneurial status: A meta-analytical review.” J. Appl. Psychol. 91 (2): 259. https://doi.org/10.1037/0021-9010.91.2.259.
Zheng, J., C. Lu, C. Hao, D. Chen, and D. Guo. 2021. “Improving the generalization ability of deep neural networks for cross-domain visual recognition.” IEEE Trans. Cognit. Dev. Syst. 13 (3): 607–620. https://doi.org/10.1109/TCDS.2020.2965166.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 12 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 28, 2023
Accepted: Jul 24, 2023
Published online: Oct 11, 2023
Published in print: Dec 1, 2023
Discussion open until: Mar 11, 2024
ASCE Technical Topics:
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.