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Tytuł artykułu

Dynamics of innovation diffusion with two step decision process

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper discusses the dynamics of innovation diffusion among heterogeneous consumers. We assume that customers’ decision making process is divided into two steps: testing the innovation and later potential adopting. Such a model setup is designed to imitate the mobile applications market. An innovation provider, to some extent, can control the innovation diffusion by two parameters: product quality and marketing activity. Using the multi-agent approach we identify factors influencing the saturation level and the speed of innovation adaptation in the artificial population. The results show that the expected level of innovation adoption among customer’s friends and relative product quality and marketing campaign intensity are crucial factors explaining them. It has to be stressed that the product quality is more important for innovation saturation level and marketing campaign has bigger influence on the speed of diffusion. The topology of social network between customers is found important, but within investigated parameter range it has lover impact on innovation diffusion dynamics than the above mentioned factors.
Rocznik
Strony
39--53
Opis fizyczny
Bibliogr. 12 poz., rys., tab.
Twórcy
autor
  • Szkoła Główna Handlowa, al. Niepodległości 162, 02-554 Warszawa
autor
  • Szkoła Główna Handlowa, al. Niepodległości 162, 02-554 Warszawa
Bibliografia
  • [1] Albert R., Barabási A.-L., Statistical mechanics of complex networks, Reviews of Modern Physics, 74, 2002, 47-97.
  • [2] Bass F. M., A new product growth for model consumer durables, Management Science, 15, 1969, 215-227.
  • [3] Breiman L., Random Forests, Machine Learning, 45, 2001, 5-32.
  • [4] Delre S. A., Jager W., Janssen M. A., Diffusion dynamics in small-world networks with heterogeneous consumers, Computational & Mathematical Organization Theory (2007) 13, 185-202.
  • [5] Granovetter M., Threshold models of collective behaviour, American journal of sociology, 83, 1978, 1420-1443.
  • [6] Granovetter M., Soong R., Threshold models of interpersonal effects in consumer demand, Journal of Economic Behaviour & Organization, 7, 1986, 83-99.
  • [7] McCullen N. J., Rucklidge A. M., Bale C. S. E., Foxon T. J., Gale W. F., Multiparameter Models of Innovation Diffusion on Complex Networks, SIAM Journal of Applied Dynamical Systems, 12, 2013, 515-532.
  • [8] Nan N., Zmud R., Yetgin E., A complex adaptive systems perspective of innovation diffusion: an integrated theory and validated virtual laboratory, Computational and Mathematical Organization Theory, Springer, May 2013, DOI: 10.1007/s10588-013-9159-9
  • [9] Newman M. E. J., The Structure and Function of Complex Networks, Society for Industrial and Applied Mathematics Review, 45, 2, 2003, 167-256.
  • [10] Rogers, E. M., Diffusion of innovations (3rd ed.), Free Press, New York, 1983.
  • [11] Watts D.J., Strogatz S.H., Collective dynamics of ”small-world” networks. Nature, 393, 1998, 40-442.
  • [12] Wejnert B., Integrating Models of Diffusion of Innovations: A Conceptual Framework, Annual Review of Sociology, 28, 2002, 297-306.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3fd36a43-86d4-4d82-8f96-d90f3b99baf8
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