@article{jocs-stress-simulation-copy-1379464220,
author = "Mu{\~n}oz L{\'o}pez, Sergio and Iglesias, Carlos A.",
abstract = "Workplace stress has a significant impact on productivity, since keeping workers’ stress on an adequate level results a key factor for companies to increase their performance. While a high stress level may conduct to anxiety or absenteeism, a low level may also have undesirable consequences, such as lack of motivation. To identify and understand all the elements which interfere on workers’ stress results a key factor in order to improve workers’ performance. However, the complexity of human behavior increases the difficulty of recognizing the influence of these stressors and finding a way to regulate workers’ stress. This paper proposes the use of agent-based simulation techniques for addressing the challenge of analyzing workers’ behavior and stress regulation policies. The main contributions of the paper are: (i) the definition of a stress model that takes into account work and ambient conditions to calculate the stress and the productivity of workers; (ii) the implementation of this model in an agent-based simulation system, enabling the analysis of workplace stress and productivity for different stress regulation policies; (iii) the analysis of four different stress regulation policies; and (iv) the validation of the model with a sensitivity analysis and with its application to a living lab.",
comments = "JCR 2019 Q1 2.644, SJR 2020 Q1 0.7, Scopus 2020 Q1 8.2",
doi = "https://doi.org/10.1016/j.jocs.2021.101326",
journal = "Journal of Computational Science",
keywords = "agent based social simulation;stress;stress regulation",
month = "February",
title = "{A}n {A}gent {B}ased {S}imulation {S}ystem for {A}nalyzing {S}tress {R}egulation {P}olicies at the {W}orkplace",
url = "https://authors.elsevier.com/sd/article/S1877-7503(21)00024-7",
year = "2021",
}