Answers are grounded in this paper and cite their sources.
Construction workers’ attitudes and behaviors are one of the most important factors of a construction project’s performance. As the attention paid to the impact of social influences and norms on worker behavior grows, agent-based modeling and simulation (ABMS) emerges as a research tool for studying workers’ group behavior. With ABMS, researchers can uncover the underlying process of group behavior emerging from individuals’ interactions in an organization. However, validating agent-based simulation with real data is the greatest challenge in using ABMS for organizational behavior research. With this background in mind, the objective of this paper is to propose a methodology for creating an empirically supported agent-based model for studying workers’ behavior influenced by social norms. The proposed methodology suggests that empirical data collected by a questionnaire can be used for ABMS in three steps: (1) testing the agent behavior rules used in an agent-based model (i.e., testing the modeling assumptions), (2) demonstrating the model behavior’s qualitative agreement with real workers’ behavior (i.e., testing the simulation results against real data in a qualitative manner), and (3) creating a specific agent-based model with the model parameters that correspond to a specific empirical case. A specific agent-based model created in this way can then be seen as a scenario generator that corresponds to a specific reality and can be used to answer what if questions. Therefore, the model can be used to develop policies/interventions to improve workers’ behavior in a given situation. The proposed methodology is illustrated by a study on construction workers’ absenteeism that was conducted by the authors. This paper contributes to the body of knowledge of workforce management in construction; the proposed methodology provides a means of simulating workers’ group behavior and developing policies/interventions to improve worker behavior at the group level in construction.
No takes yet. Share an insight, caveat, or question.
Ahn et al. (2014) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: