--

12 (1) 2022

Simulation modeling - An effective method in doing business and management research


Author - Affiliation:
Nguyen Thi Bich Tram - Ho Chi Minh City Open University , Vietnam
Corresponding author: Nguyen Thi Bich Tram - tram.ntb@ou.edu.vn
Submitted: 04-06-2021
Accepted: 24-08-2021
Published: 22-02-2022

Abstract
Although simulation modeling is a popular method in doing scientific research for many disciplines, namely decision science, optimization, information technology. Social science disciplines, particularly business and management, still receive little attention in applying this method. The paper introduces simulation modeling as an effective method in doing business and management research using Dimensions database and comparative analysis to explore (1) suitability and contribution in general disciplines as well as business and management, (2) advantages and disadvantages, (3) common types of models, and (4) effective software that researchers could use for specific research problems. The findings of this study confirm that simulation modeling is an effective method in doing business and management research thanks to its abilities in dynamics reflecting the real world, abstracted illustration, and naturally accommodating more plausible behavioral assumptions. However, modelers need to be aware of computational possibilities, the model’s complexity, and validity which might impact the simulated research results. The objective of this study is a calling for more simulation-based research to tackle complicated study problems related to business organizations with advanced technology and powerful tools listed in the paper. Thus, modelers are served well by this work as a reference for basic knowledge related to simulation modeling in business and management research.

Keywords
business and management research methods; dimensions database; simulation modeling

Full Text:
PDF

Cite this paper as:

Nguyen, T. T. B. (2022). Simulation modeling - An effective method in doing business and management research. Ho Chi Minh City Open University Journal of Science – Economics and Business Administration, 12(1), 108-124. doi:10.46223/HCMCOUJS.econ.en.12.1.1916.2022


References

Adams, J., Draux, H., Jones, P., Osipov, I., Porter, S., & Szomszor, M. (2018). Dimensions - A collaborative approach to enhancing research discovery. Retrieved July 21, 2021, from https://www.dimensions.ai/resources/a-collaborative-approach-to-enhancing-research-discovery/


Axelrod, R. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. ;Princeton, NJ: Princeton University Press.


Bode, C., Herzog, C., Hook, D., & Mcgrath, R. (2019). A collaborative approach to creating a modern infrastructure for data describing research: Where we are and where we want to take it. Retrieved July 22, 2021, from 10.6084/m9.figshare.5783094


Borshchev, A. (2013). The big book of simulation modeling - AnyLogic simulation software. Retrieved July 20, 2021, from Anylogic North America website: http://www.anylogic.com/big-book-of-simulation-modeling


Borshchev, A., & Filippov, A. (2004). From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. International Conference of The System Dynamics Society, 22, 25-29. ;


Bresciani, S., Ciampi, F., Meli, F., & Ferraris, A. (2021). Using big data for co-innovation processes: Mapping the field of data-driven innovation, proposing theoretical developments and providing a research agenda. International Journal of Information Management, 60, Article 102347. doi:10.1016/j.ijinfomgt.2021.102347


Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: SAGE.


Esser, F., & Vliegenthart, R. (2017). Comparative research methods. In The international encyclopedia of communication research methods (pp. 1-22). Hoboken, NJ: Wiley. ;


Gnanapragasam, S. N., Hodson, A., Smith, L. E., Greenberg, N., Rubin, G. J., & Wessely, S. (2021). COVID-19 survey burden for healthcare workers: Literature review and audit. Public Health. ;Advance online publication. doi:10.1016/j.puhe.2021.05.006


Guan, D., Wang, D., Hallegatte, S., Davis, S. J., Huo, J., Li, S., … Gong, P. (2020). Global supply-chain effects of COVID-19 control measures. Nature Human Behaviour, 4(6), 577-587. doi:10.1038/s41562-020-0896-8


Hantrais, L. (2008). International comparative research: Theory, methods and practice. London, UK: Macmillan International Higher Education.


Harrison, J. R., Lin, Z., Carroll, G. R., & Carley, K. M. (2007). Simulation modeling in organizational and management research. Academy of Management Review, 32(4), 1229-1245. doi:10.5465/amr.2007.26586485


Hook, D. W., Porter, S. J., & Herzog, C. (2018). Dimensions: Building context for search and evaluation. Frontiers in Research Metrics and Analytics, 3, 1-11. doi:10.3389/frma.2018.00023


Jiang, Y., & Wen, J. (2020). Effects of COVID-19 on hotel marketing and management: A perspective article. International Journal of Contemporary Hospitality Management, 32(8), 2563-2573. doi:10.1108/IJCHM-03-2020-0237


Jobber, D., & Lancaster, G. (2017). Selling and sales management (8th ed.). London, UK: Pearson Education, Inc.


Levinthal, D. A., & Marengo, L. (2016). Simulation modelling and business strategy research. In M. Augier & D. J. Teece (Eds.), The palgrave encyclopedia of strategic management (pp. 1-5). London, UK: Palgrave Macmillan.


Maina, J., & Mwangangi, P. (2020). A critical review of simulation applications in supply chain management. Journal of Logistics Management, ;2020(1), 1-6. doi:10.5923/j.logistics.20200901.01


Mills, M., van de Bunt, G. G., & de Bruijn, J. (2006). Comparative research. International Sociology, 21(5), 619-631. doi:10.1177/0268580906067833


Otto, C., Willner, S. N., Wenz, L., Frieler, K., & Levermann, A. (2017). Modeling loss-propagation in the global supply network: The dynamic agent-based model acclimate. Journal of Economic Dynamics and Control, 83, 232-269. doi:10.1016/j.jedc.2017.08.001


Singh, S., Kumar, R., Panchal, R., Manoj, & Tiwari, M. K. (2021). Impact of COVID-19 on logistics systems and disruptions in food supply chain. International Journal of Production Research, 59(7), 1993-2008. doi:10.1080/00207543.2020.1792000


Sinha, D., Bagodi, V., & Dey, D. (2020). The supply chain disruption framework post COVID-19: A system dynamics model. Foreign Trade Review, 55(4), 511-534. doi:10.1177/0015732520947904


Sterman, J. D. (2000). System dynamics: Systems thinking and modeling for a complex world. Cambridge, MA: Massachusetts Institute of Technology.


Thierry, C., Bel, G., & Thomas, A. (2010). The role of modeling and simulation in supply chain management. SCS M&S Magazine, 1(2010), 1-8.


Vasudevan, K., & Devikar, A. (2011). Selecting simualtion abstraction levels in simulation models of complex manufacturing systems. Proceedings of the 2011 Winter Simulation Conference (WSC), 66, 2268-2277. doi:10.1109/WSC.2011.6147938



Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.