Technical Papers
Nov 24, 2017

An Empirically Based Agent-Based Model of the Sociocognitive Process of Construction Workers’ Safety Behavior

Publication: Journal of Construction Engineering and Management
Volume 144, Issue 2

Abstract

Workers’ unsafe behaviors have a substantial impact on construction safety. To regulate workers’ unsafe behavior, construction practitioners have mainly used formal controls (e.g., penalties). However, the formal approaches may not be effective at eliciting desired behavioral changes in improving safety behavior. Therefore, recently, researchers have paid more attention to how unsafe behaviors are produced. In this regard, cognitive models of safety behavior and empirical evidence on social influence have been suggested. However, there is a noticeable paucity of research investigating the mechanism behind the link between cognitive process, social influence, site risk, and safety behavior. In this paper, an empirically based agent-based model that incorporates theoretical and empirical findings of the sociocognitive process of workers’ safety behaviors is developed. The model is used to conduct experiments examining how the sociocognitive process interacts with safety management interventions (i.e., strictness and frequency of management feedback, and project identification) and influences workers’ safety behaviors across different site risk conditions (i.e., low-, modest-, and high-risk conditions). The results demonstrated that all three interventions contribute to decreasing the incident rate. Also, the interaction effects of the interventions in different site risk conditions were found using the parameter sweeping. The results indicated that (1) promoting workers’ project identification would be an effective strategy in the modest-risk site condition; (2) other interventions should be combined after achieving the medium strictness of management feedback in the high-risk site condition; and (3) other interventions would not be effective without very strict management feedback in the low-risk site condition. This paper contributes to the body of knowledge on construction safety by extending the authors’ understanding of the role of sociocognitive process and its interaction with the environment in shaping workers’ safety behaviors. Additionally, the experiment results are expected to lay a strong foundation for developing effective safety management interventions in the construction projects.

Get full access to this article

View all available purchase options and get full access to this article.

Acknowledgments

The authors thank Professor Patrick Grim from Center for the Study of Complex Systems at the University of Michigan for his valuable and constructive suggestions during the validation process. The first author also wishes to acknowledge financial support by the University of Michigan from a Rackham Predoctoral Fellowship. All opinions and findings in this paper are those of the authors and do not necessarily represent those of the University of Michigan.

References

Ahn, S., and Lee, S. (2015). “Methodology for creating empirically supported agent-based simulation with survey data for studying group behavior of construction workers.” J. Constr. Eng. Manage., 04014065.
Ahn, S., Lee, S., and Steel, R. (2013). “Effects of workers’ social learning: Focusing on absence behavior.” J. Constr. Eng. Manage., 1015–1025.
Andersen, L. P., Karlsen, I. L., Kines, P., Joensson, T., and Nielsen, K. J. (2015). “Social identity in the construction industry: Implications for safety perception and behaviour.” Constr. Manage. Econ., 33(8), 640–652.
Anderson, K., and Lee, S. (2016). “An empirically grounded model for simulating normative energy use feedback interventions.” Appl. Energy, 173(1), 272–282.
Axtell, R. L., and Epstein, J. M. (1994). “Agent-based modeling: Understanding our creations.” Bull. Santa Fe Institute, 9(2), 28–32.
Bagozzi, R. P., and Lee, K.-H. (2002). “Multiple routes for social influence: The role of compliance, internalization, and social identity.” Social Psychol. Q., 65(3), 226–247.
Balci, O. (1998). “Verification, validation, and testing.” Handbook of simulation, J. Banks, ed., Wiley, New York.
Bartels, J., Pruyn, A., De Jong, M., and Joustra, I. (2007). “Multiple organizational identification levels and the impact of perceived external prestige and communication climate.” J. Organizational Behav., 28(2), 173–190.
Bergami, M., and Bagozzi, R. P. (2000). “Self-categorization, affective commitment and group self-esteem as distinct aspects of social identity in the organization.” Br. J. Social Psychol., 39(4), 555–577.
Beus, J. M., Payne, S. C., Bergman, M. E., and Arthur, W., Jr. (2010). “Safety climate and injuries: An examination of theoretical and empirical relationships.” J. Appl. Psychol., 95(4), 713–727.
Bonabeau, E. (2002). “Agent-based modeling: Methods and techniques for simulating human systems.” Proc. Nat. Acad. Sci., 99(S3), 7280–7287.
Brondino, M., Silva, S. A., and Pasini, M. (2012). “Multilevel approach to organizational and group safety climate and safety performance: Co-workers as the missing link.” Saf. Sci., 50(9), 1847–1856.
Bruch, E., and Atwell, J. (2015). “Agent-based models in empirical social research.” Sociol. Methods Res., 44(2), 186–221.
Carmeli, A. (2005). “Perceived external prestige, affective commitment, and citizenship behaviors.” Organization Stud., 26(3), 443–464.
Chi, S., Han, S., and Kim, D. (2013). “Relationship between unsafe working conditions and workers’ behavior and impact of working conditions on injury severity in US construction industry.” J. Constr. Eng. Manage., 826–838.
Choe, S., and Leite, F. (2017). “Assessing safety risk among different construction trades: Quantitative approach.” J. Constr. Eng. Manage., 04016133.
Choi, B., Ahn, S., and Lee, S. (2017a). “Construction workers’ group norms and personal standards regarding safety behavior: Social identity theory perspective.” J. Manage. Eng., 04017001.
Choi, B., Ahn, S., and Lee, S. (2017b). “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., 04016124.
Choi, B., and Lee, S. (2017a). “Modeling the effect of a socio-psychological process on construction workers’ safety behavior.” Proc., Int. Workshop on Computing in Civil Engineering, ASCE, Reston, VA.
Choi, B., and Lee, S. (2017b). “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., 04016125.
Choudhry, R. M., and Fang, D. (2008). “Why operatives engage in unsafe work behavior: Investigating factors on construction sites.” Saf. Sci., 46(4), 566–584.
Dutton, J. E., Dukerich, J. M., and Harquail, C. V. (1994). “Organizational images and member identification.” Administrative Sci. Q., 39(2), 239–263.
Epstein, J. M. (1999). “Agent-based computational models and generative social science.” Complexity, 4(5), 41–60.
Fang, D., Chen, Y., and Wong, L. (2006). “Safety climate in construction industry: A case study in Hong Kong.” J. Constr. Eng. Manage., 573–584.
Fang, D., Wu, C., and Wu, H. (2015). “Impact of the supervisor on worker safety behavior in construction projects.” J. Manage. Eng., 04015001.
Fang, D., and Wu, H. (2013). “Development of a Safety Culture Interaction (SCI) model for construction projects.” Saf. Sci., 57(0), 138–149.
Fang, D., Zhao, C., and Zhang, M. (2016). “A cognitive model of construction workers’ unsafe behaviors.” J. Constr. Eng. Manage., 04016039.
Fugas, C. S., Meliá, J. L., and Silva, S. A. (2011). “The ‘is’ and the ‘ought’: How do perceived social norms influence safety behaviors at work?” J. Occup. Health Psychol., 16(1), 67–79.
Fugas, C. S., Silva, S. A., and Meliá, J. L. (2012). “Another look at safety climate and safety behavior: Deepening the cognitive and social mediator mechanisms.” Acc. Anal. Prev., 45, 468–477.
Gilbert, N. (2008). Agent-based models, Sage, Thousand Oaks, CA.
Glendon, A. I., and Walker, B. L. (2013). “Can anti-speeding messages based on protection motivation theory influence reported speeding intentions?” Acc. Anal. Prev., 57, 67–79.
Goh, Y. M., and Binte Sa’adon, N. F. (2015). “Cognitive factors influencing safety behavior at height: A multimethod exploratory study.” J. Constr. Eng. Manage., 04015003.
Grimm, V., et al. (2006). “A standard protocol for describing individual-based and agent-based models.” Ecol. Modell., 198(1–2), 115–126.
Grimm, V., Berger, U., Deangelis, D. L., Polhill, J. G., Giske, J., and Railsback, S. F. (2010). “The odd protocol: A review and first update.” Ecol. Modell., 221(23), 2760–2768.
Gundlach, G. T., Achrol, R. S., and Mentzer, J. T. (1995). “The structure of commitment in exchange.” J. Marketing, 59(1), 78–92.
Hallowell, M. (2010). “Safety risk perception in construction companies in the Pacific northwest of the USA.” Constr. Manage. Econ., 28(4), 403–413.
Haslam, R. A., et al. (2005). “Contributing factors in construction accidents.” Appl. Ergon., 36(4), 401–415.
He, H., and Brown, A. D. (2013). “Organizational identity and organizational identification: A review of the literature and suggestions for future research.” Group Organization Manage., 38(1), 3–35.
Heinrich, H. W., Petersen, D., and Roos, N. (1950). Industrial accident prevention, McGraw-Hill, New York.
Hinze, J. (2006). Construction safety, Pearson Education, Upper Saddle River, NJ.
Hogg, M. A., and Smith, J. R. (2010). “Attitudes in social context: A social identity perspective.” Eur. Rev. Social Psychol., 18(1), 89–131.
Hogg, M. A., and Terry, D. I. (2000). “Social identity and self-categorization processes in organizational contexts.” Acad. Manage. Rev., 25(1), 121–140.
Janssen, M. A., and Ostrom, E. (2006). “Empirically based, agent-based models.” Ecol. Soc., 11(2), 37.
Jebelli, H., Ahn, C. R., and Stentz, T. L. (2016). “Comprehensive fall-risk assessment of construction workers using inertial measurement units: Validation of the gait-stability metric to assess the fall risk of iron workers.” J. Comput. Civ. Eng., 04015034.
Ji, M., You, X., Lan, J., and Yang, S. (2011). “The impact of risk tolerance, risk perception and hazardous attitude on safety operation among airline pilots in China.” Saf. Sci., 49(10), 1412–1420.
Jiang, L., Yu, G., Li, Y., and Li, F. (2010). “Perceived colleagues’ safety knowledge/behavior and safety performance: Safety climate as a moderator in a multilevel study.” Acc. Anal. Prev., 42(5), 1468–1476.
Jiang, Z., Fang, D., and Zhang, M. (2015). “Understanding the causation of construction workers’ unsafe behaviors based on system dynamics modeling.” J. Manage. Eng., 04014099.
Kim, H., Lee, H., Park, M., and Choi, B. (2013). “Automated information retrieval for hazard identification in construction sites.” ASCE Int. Workshop on Computing in Civil Engineering, ASCE, Reston, VA, 897–904.
Klügl, F. (2008). “A validation methodology for agent-based simulations.” Proc., 2008 ACM Symp. on Applied computing, Association for Computing Machinery, New York, 39–43.
Law, A. M. (2013). Simulation modeling and analysis, McGraw-Hill Education, New York.
Lingard, H. C., Cooke, T., and Blismas, N. (2010). “Safety climate in conditions of construction subcontracting: A multi-level analysis.” Constr. Manage. Econ., 28(8), 813–825.
Macy, M. W., and Willer, R. (2002). “From factors to actors: Computational sociology and agent-based modeling.” Ann. Rev. Sociol., 28(1), 143–166.
Meliá, J. L., Mearns, K., Silva, S. A., and Lima, M. L. (2008). “Safety climate responses and the perceived risk of accidents in the construction industry.” Saf. Sci., 46(6), 949–958.
Mitropoulos, P., Cupido, G., and Namboodiri, M. (2009). “Cognitive approach to construction safety: Task demand-capability model.” J. Constr. Eng. Manage., 881–889.
Nakra, R. (2006). “Relationship between communication satisfaction and organizational identification: An empirical study.” Vision: J. Bus. Perspect., 10(2), 41–51.
Neal, A., Griffin, M. A., and Hart, P. M. (2000). “The impact of organizational climate on safety climate and individual behavior.” Saf. Sci., 34(1–3), 99–109.
Ormerod, P., and Rosewell, B. (2009). “Validation and verification of agent-based models in the social sciences.” Epistemological aspects of computer simulation in the social sciences, F. Squazzoni, ed., Springer, Berlin.
Peters, K., Haslam, S. A., Ryan, M. K., and Fonseca, M. (2013). “Working with subgroup identities to build organizational identification and support for organizational strategy: A test of the aspire model.” Group Organization Manage., 38(1), 128–144.
Rasmussen, J. (1986). Information processing and human-machine interaction: An approach to cognitive engineering, Elsevier Science, New York.
Reason, J. (1990). Human error, Cambridge University Press, Cambridge, U.K.
Riketta, M. (2005). “Organizational identification: A meta-analysis.” J. Vocational Behav., 66(2), 358–384.
Rodríguez-Garzón, I., Lucas-Ruiz, V., Martínez-Fiestas, M., and Delgado-Padial, A. (2015). “Association between perceived risk and training in the construction industry.” J. Constr. Eng. Manage., 04014095.
Rogers, R. W. (2010). “A protection motivation theory of fear appeals and attitude change1.” J. Psychol., 91(1), 93–114.
Rosenstock, I. M. (1974). “Historical origins of the health belief model.” Health Educ. Monogr., 2(4), 328–335.
Sa, J., Seo, D.-C., and Choi, S. D. (2009). “Comparison of risk factors for falls from height between commercial and residential roofers.” J. Saf. Res., 40(1), 1–6.
Salminen, S., and Tallberg, T. (1996). “Human errors in fatal and serious occupational accidents in Finland.” Ergonomics, 39(7), 980–988.
Sargent, R. G. (2000). “Verification, validation, and accreditation: Verification, validation, and accreditation of simulation models.” Proc., 32nd Conf. on Winter simulation, Society for Computer Simulation International, San Diego, 50–59.
Seo, D.-C. (2005). “An explicative model of unsafe work behavior.” Saf. Sci., 43(3), 187–211.
Shin, M., Lee, H.-S., Park, M., Moon, M., and Han, S. (2014). “A system dynamics approach for modeling construction workers’ safety attitudes and behaviors.” Acc. Anal. Prev., 68, 95–105.
Siu, O.-L., Phillips, D. R., and Leung, T.-W. (2004). “Safety climate and safety performance among construction workers in Hong Kong: The role of psychological strains as mediators.” Acc. Anal. Prev., 36(3), 359–366.
Sluss, D. M., Klimchak, M., and Holmes, J. J. (2008). “Perceived organizational support as a mediator between relational exchange and organizational identification.” J. Vocational Behav., 73(3), 457–464.
Smith, E. R., and Conrey, F. R. (2007). “Agent-based modeling: A new approach for theory building in social psychology.” Personality Social Psychol. Rev., 11(1), 87–104.
Suraji, A., Duff, A. R., and Peckitt, S. J. (2001). “Development of causal model of construction accident causation.” J. Constr. Eng. Manage., 337–344.
Terry, D. J., and Hogg, M. A. (1996). “Group norms and the attitude-behavior relationship: A role for group identification.” Personality Social Psychol. Bull., 22(8), 776–793.
Tixier, A., Hallowell, M., Albert, A., Van Boven, L., and Kleiner, B. (2014). “Psychological antecedents of risk-taking behavior in construction.” J. Constr. Eng. Manage., 04014052.
Törner, M., and Pousette, A. (2009). “Safety in construction—A comprehensive description of the characteristics of high safety standards in construction work, from the combined perspective of supervisors and experienced workers.” J. Saf. Res., 40(6), 399–409.
UKHSE (U.K. Health and Safety Executive). (2002). “Strategies to promote safe behavior as part of a health and safety management system.”, Liverpool, U.K.
USBLS (U.S. Bureau of Labor Statistics). (2015). “Table 1. Employment by major industry sector, 2004, 2014, and projected 2024.”, Washington, DC.
USBLS (U.S. Bureau of Labor Statistics). (2016a). “Chart 3. Number and rate of fatal occupational injuries by industry sector, 2014.”, Washington, DC.
USBLS (U.S. Bureau of Labor Statistics). (2016b). “Table 1. Incident rates of nonfatal occupational injuries and illnesses by case type and ownership, selected industries, 2015.”, Washington, DC.
USBLS (U.S. Bureau of Labor Statistics). (2016c). “Table 2. Numbers of nonfatal occupational injuries and illnesses by case type and ownership, selected industries, 2015.”, Washington, DC.
Walumbwa, F. O., Avolio, B. J., and Zhu, W. (2008). “How transformational leadership weaves its influence on individual job performance: The role of identification and efficacy beliefs.” Personnel Psychol., 61(4), 793–825.
Walumbwa, F. O., Mayer, D. M., Wang, P., Wang, H., Workman, K., and Christensen, A. L. (2011). “Linking ethical leadership to employee performance: The roles of leader-member exchange, self-efficacy, and organizational identification.” Organizational Behav. Hum. Dec. Processes, 115(2), 204–213.
Wang, J., Zou, P. X. W., and Li, P. P. (2016). “Critical factors and paths influencing construction workers’ safety risk tolerances.” Acc. Anal. Prev., 93, 267–279.
Weidman, J., Dickerson, D., and Koebel, C. (2016). “Effective intervention strategy to improve worker readiness to adopt ventilated tools.” J. Constr. Eng. Manage., 04016028.
Wickens, C. D. (1984). Engineering psychology and human performance, Merrill, Columbus, OH.
Wilde, G. J. S. (1982). “The theory of risk homeostasis: Implications for safety and health.” Risk Anal., 2(4), 209–225.
Zeigler, B. P., Kim, T. G., and Praehofer, H. (2000). Theory of modeling and simulation: Integrating discrete event and continuous complex dynamic systems, Academic Press, San Diego.
Zhang, M., and Fang, D. (2013). “A cognitive analysis of why Chinese scaffolders do not use safety harnesses in construction.” Constr. Manage. Econ., 31(3), 207–222.
Zhang, T., and Nuttall, W. J. (2011). “Evaluating government’s policies on promoting smart metering diffusion in retail electricity markets via agent-based simulation.” J. Prod. Innov. Manage., 28(2), 169–186.
Zohar, D. (1980). “Safety climate in industrial organizations: Theoretical and applied implications.” J. Appl. Psychol., 65(1), 96–102.

Information & Authors

Information

Published In

Go to Journal of Construction Engineering and Management
Journal of Construction Engineering and Management
Volume 144Issue 2February 2018

History

Received: Apr 3, 2017
Accepted: Jul 19, 2017
Published online: Nov 24, 2017
Published in print: Feb 1, 2018
Discussion open until: Apr 24, 2018

Permissions

Request permissions for this article.

Authors

Affiliations

Byungjoo Choi, S.M.ASCE [email protected]
Ph.D. Candidate, Dept. of Civil and Environmental Engineering, Univ. of Michigan, 2350 Hayward St., G.G Brown Bldg., Ann Arbor, MI 48109. E-mail: [email protected]
SangHyun Lee, M.ASCE [email protected]
Associate Professor, Dept. of Civil and Environmental Engineering, Univ. of Michigan, 2350 Hayward St., G.G Brown Bldg., Ann Arbor, MI 48109 (corresponding author). E-mail: [email protected]

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.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share