Computer Simulation Methodology in Business Dynamics Research

Main Article Content

Chaiwat Baimai

Abstract

          Nowadays, managing business deals largely with high degree of complex environment. From an academic viewpoint, understanding such complexity is somewhat limited using traditional methodological approaches such as causal models and structural equation modeling. This article introduces a relatively well-developed technique in natural science, namely computer simulation, as an alternative to business dynamics researchers. The major advantage of simulation is that organizational interactions and changing economic landscape can be visually simulated. Thus, this method helps explain complex and dynamic phenomena, i.e., disruption. Although computer simulation has long been a useful method applied in various disciplines, conducting business dynamics research using this technique has received less attention. This might be due to a lack of systematic knowledge gathering related to this subject. This article aims to fill this gap in order to take advantage of simulation by using computer for operations research.

Article Details

Section
บทความวิชาการ (Review Article)

References

Asawaphum, S. (2016). Systematic theory thinking and educational administration. Buabandit Journal of Educational Administration, 16(2), 1-12.

Baril, C., Gascon, V., Miller, J., & Cote, N. (2016). Use of a discrete-event simulation in a Kaizen event: a case study in healthcare. European Journal of Operational Research, 249(1), 327-339.

Bertalanffy, L. (1968). General system theory: foundations, development, applications. New York: George Braziller.

Bollen, K., & Long, S. (1993). Testing structural equation models. Newbury Park, California: Sage Publications.

Cannon, W. (1929). Organization for physiological homeostasis. Physiological Reviews, 9(3), 399-431.

Capaldo, A., & Giannoccaro, I. (2015). Interdependence and network-level trust in supply chain networks: a computational study. Industrial Marketing Management 44(1), 180-195.

Chan, N., & Wong, H. (2006). Simulation techniques in financial risk management. New Jersey: Wiley.

Chandler, G., DeTienne, D., McKelvie, A., & Mumford, T. (2011). Causation and effectuation processes: a validation study. Journal of Business Venturing, 26(3), 375-390.

Csaszar, F. (2018). A note on how NK landscapes work. Journal of Organization Design, 7(15), 1-6. Retrieved January 9, 2019, from https://doi.org/10.1186/s41469-018-0039-0

Davis, J., Eisenhardt, K., & Bingham, C. (2007). Complexity theory, market dynamism, and the strategy of simple rules. Working paper, Stanford Technology Ventures Program, Stanford, CA: Stanford University.

Demil, B., & Lecocq, X. (2010). Business model evolution: in search of dynamic consistency. Long Range Planning, 43(2), 227-246.

Ganco, M., & Agarwal, R. (2009). Performance differentials between diversifying entrants and entrepreneurial start-ups: a complexity approach. Academy of Management Review, 34(2), 228-252.

Ganco, M., & Hoetker, G. (2009). NK modeling methodology in the strategy literature: bounded search on a rugged landscape, in Donald D. Bergh, David J. Ketchen (ed.) Research Methodology in Strategy and Management (Volume 5), Emerald Group Publishing Limited, 237-268.

Hair, J., Hult, G., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling. Thousand Oaks, CA: Sage Publications.

Hannan, M., & Freeman, J. (1977). The population ecology of organizations. American Journal of Sociology, 82(5), 929-964.

Heizer, J., Render, B., & Munson, C. (2017). Operations management: sustainability and supply chain management (12th ed.). Essex, UK: Pearson Education.

Henderson, L. (1913). The fitness of the environment: an inquiry into the biological significance of the properties of matter. New York: Macmillan.

Jain, A., & Kogut, B. (2014). Memory and organizational evolvability in a neutral landscape. Organization Science, 25(2), 479-493.

Kauffman, S. (1987). Towards a general theory of adaptive walks on rugged landscapes. Journal of Theoretical Biology, 128(1), 11-45.

Kauffman, S. (1993). The origins of order: self-organization and selection in evolution. Oxford: Oxford University Press.

Kauffman, S. (1995). At home in the universe. Oxford: Oxford University Press.

Keyhani, M., Lévesque, M., & Madhok, A. (2013). Toward a theory of entrepreneurial rents: a simulation of the market process. Strategic Management Journal, 36(1), 76-96.

Khraisha, T. (2020). Complex economic problems and fitness landscapes: assessment and methodological perspectives. Structural Change and Economic Dynamics, 52, 390-407.

Kirzner, I. (1973). Competition and entrepreneurship. Chicago, IL: The University of Chicago Press.

Kirzner, I. (1997). Entrepreneurial discovery and the competitive market process: an Austrian approach. Journal of Economic Literature, 35(1), 60-85.

Leenawong, N., & Maneechai, S. (2008). Mathematical model and simulation for multiple complex systems. University of the Thai Chamber of Commerce Journal, 28(1), 48-65.

Limphaibool, W., Chaisuwan, C., & Buranapin, S. (2019). A critical incident analysis from experiences of executives on organizational resilience. Chulalongkorn Business Review, 41(4), 87-114.

Levinthal, D. (1997). Adaptation on rugged landscapes. Management Science, 43(7), 934-950.

Lukhnovskii, I., & Golovko, M. (1980). Statistical theory of classical equilibrium systems. Moscow: Kiev, Izdatel'stvo Naukova Dumka.

Mae Fah Luang University. (2019). The evolution of production management. Retrieved January 11, 2020, from http://phalit-thai.tripod.com/boywww/eew/chapter1_2.htm

Pepper, S. (1972). Systems philosophy as a world hypothesis. Philosophy and Phenomenological Research, 32(4), 548-553.

Rahmandad, H., & Sterman, J. (2012). Reporting guidelines for simulation-based research in social sciences. System Dynamics Review, 28(4), 396-411.

Rivkin, J. (2001). Reproducing knowledge: replication without imitation at moderate complexity. Organization Science, 12(3), 274-293.

Roundy, P., Bradshaw, M., & Brockman, B. (2018). The emergence of entrepreneurial ecosystems: a complex adaptive systems approach. Journal of Business Research, 86, 1-10.

Santhitiwanich, A. (2014). Epistemological status of social science. Kasetsart Journal of Social Sciences, 35(3), 472-487.

Sarasvathy, S. (2001). Causation and effectuation: toward a theoretical shift from economic inevitability to entrepreneurial contingency. Academy of Management Review, 26(2), 243-263.

Sastry, M. (1997). Problems and paradoxes in a model of punctuated organizational change. Administrative Science Quarterly, 42(2), 237-275.

Schumpeter, J. (1934). The theory of economic development. New York: Oxford University Press.

Solow, D., Burnetas, A., Tsai, M., & Greenspan, N. (1999). Understanding and attenuating the complexity catastrophe in Kaufman’s NK model of genome evolution. Complexity, 5(1), 1-21.

Solow, D., Vairaktarakis, G., Piderit, S., & Tsai, M. (2002). Managerial insights into the effects of interactions among members of a team. Management Science, 48(8), 1060-1073.

Waehama, W. (2018). Comparison of the effectiveness of work integrated learning and computer simulation teaching method for theoretical subjects in hospitality industry curriculum. Journal of Management Sciences, 35(1), 51-74.

Welter, C., & Kim, S. (2018). Effectuation under risk and uncertainty. Journal of Business Venturing, 33(1), 100-116.

Yanpiboon, T., Popaitoon, S., & Songsrirote, N. (2019). Network ties, entrepreneurial orientation, and innovation of new firms in Thai agricultural business sector. Journal of Business Administration, 42(164), 44-60.