Agent-based modeling of the impact of advertising on the regional economic cluster lifecycle

Volume 3, Issue 4
Pages: 203—211

V. A. Shamis
— Siberian State Automobile and Highway University (Omsk, Russian Federation)
O. M. Kulikova — Siberian State Automobile and Highway University (Omsk, Russian Federation)
S. Y. Neiman — Omsk State Technical University (Omsk, Russian Federation)
E. V. Usacheva — Omsk State Medical University (Omsk, Russian Federation)

Download full text

The aim of the study is the development and testing of an algorithm for modeling the impact of advertising on various stages of the life cycle of economic clusters. It is assumed, that the life cycle of the cluster consists of the stages: a diffuse group, a hidden cluster, an evolving cluster, a mature cluster, a collapsing cluster. Using the agent-based simulation methods, hierarchical clustering and chaos theory, the following results were obtained: a conceptual model of the behavior of cluster members for cluster formation processes at each stage of the cluster life cycle and an imitation model of the influence of advertising on the life cycle of the economic cluster; the patterns of various stages of the life cycle of the economic cluster and the functioning of the cluster without influence and under the influence of advertising were revealed. Advertising reduces the time at the stages of the associated life cycle of the cluster, increases the stage of maturity of the cluster. Companies that do not comply with the principles of clustering are under the influence of advertising and promotional activities. Such enterprises most often arise in the cluster at the stages of its formation.

Keywords: economic clusters, cluster life cycle stages, advertising and promotion, simulation and modeling, computational experiment

DOI:  https://doi.org/10.15826/recon.2017.3.3.023

References

  1. Livi C., Jeannerat H., 2015. Born to be Sold: Start-ups as Products and New Territorial Life Cycles of Industrialization. European Planning Studies, 23 (10), 1953-1974.
  2. Arbia G., Espa G., Quah D., 2008. A class of spatial econometric methods in the empirical analysis of clusters of firms in the space. Empirical Economics, 34 (1), 81-103.
  3. Banasick S., Lin G., Hanham R., 2009, Deviance residual moran’s I test and its application to spatial clusters of small manufacturing firms in Japan. International Regional Science Review, 32(1), 3-18.
  4. Chincarini L., Asherie N., 2008. An analytical model for the formation of economic clusters. Regional Science and Urban Economics. 38 (3), 252-270.
  5. Dilaver O., Bleda M., Uyarra E., 2014. Entrepreneurship and the emergence of industrial clusters. Complexity, 19(6), 14-29.
  6. Popp A., Wilson J., 2007. Life cycles, contingency, and agency: Growth, development, and change in English industrial districts and clusters. Environment and Planning A, 39 (12), 2975-2992.
  7. Tsai B.-H., Li Y., 2009. Cluster evolution of IC industry from Taiwan to China. Technological Forecasting and Social Change, 76(8), 1092-1104.
  8. Yanling, L., Ma, F., 2009. Game analysis of knowledge spillover in industrial cluster. In: Proceedings-International Conference on Management and Service Science. MASS 2009, 5305509.
  9. Iammarinoa S., McCann P., 2006. The structure and evolution of industrial clusters: Transactions, technology and knowledge spillovers. Research Policy, 35 (7), 1018-1036.
  10. Manescu G., Kifor C.-V., 2015. Developing a collaborative model specific to the field of defence based on the life cycle of a cluster. In: International conference knowledge-based organization, 21 (1), 243-247.
  11. Sondereggera P., Täube F., 2010. Cluster life cycle and diaspora effects: Evidence from the Indian IT cluster in Bangalore. Journal of International Management, 16 (4), 383-397.
  12. Menzel M.-P., Fornahl D., 2010. Cluster life cycles-dimensions and rationales of cluster evolution. Industrial and Corporate Change, 19 (1), 205-238.
  13. Valdaliso J.M., Elola A., Franco S., 2016. Do clusters follow the industry life cycle? Diversity of cluster evolution in old industrial regions. Competitiveness review, 26 (1), 66-86.
  14. Kasabov E., 2016. Modelling life-science clusters in terms of resources and capabilities. European planning studies, 24 (10), 1884-1912.
  15. Haiying Yu., Minghui J., Chengzhang L., 2016. Chaos theory perspective for industry clusters development. Modern Physics Letters B, 30 (8), 112-128.
  16. Vertakova Yu., Grechenyub O., Grechenyuk A., 2016. Identification of clustered points of growth by analyzing the innovation development of industry. Procedia Economics and Finance, 39, 147-155.
  17. Zeng Y., Xiao R., 2014. Modelling of cluster supply network with cascading failure spread and its vulnerability analysis. International Journal of Production Research, 52 (23), 6938-6953.
  18. Boush G., Shamis V., Kulikova O., Neiman S., 2016. Markov Processes in Modeling Life Cycle of Economic Clusters. In: Supplementary Proceedings of the 9th International Conference on Discrete Optimization and Operations Research and Scientific School (DOOR 2016). Vladivostok, Russia. Vol. 1623., pp. 545-557.
  19. Funk T., 2013. Advertising and Promotion. Advanced Social Media Marketing. Apress, Berkeley, CA.
  20. Boush G.D., Kulikova O.M., Shelkov I.K., 2016. Agent modelling of cluster formation processes in regional economic systems. R-Economy. 2 (1), 89-101.
  21. Murtagh F., Legendre P., 2014. Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? Journal of Classification, 31 (3), 274-295.