Skip to main content

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 523))

  • 517 Accesses

Abstract

In this paper, the dynamics of the consumer preference change, affected by a word-of-mouth communication (w-o-m), is investigated. Is seems to be interesting, how the network structure parameters (number of contacts, diversity of groups, possibility to change the source of information) influence the consumer behavior on the duopoly market. In the model presented in this article, a two-dimensional cellular automaton (CA) is used which enables to simulate the number of informal contacts, by adoption of variety of different neighborhood radii and also different neighborhood shapes. Although, one type of networks is examined in this article (CA type model), different network structure parameters are obtained by changing the above neighborhood parameters together with varied population densities and an agent movement possibility. The results indicate that the network structure parameters, are of particular importance in the processes of preferences’ change.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gotts, N.M., Polhill, J.G., Law, A.N.R.: Agent-based simulation in the study of social dilemmas. Artif. Intell. Rev. 19, 3–92 (2003)

    Article  MATH  Google Scholar 

  2. Macy, M.W., Willer, R.: From factors to actors: computational sociology and agentbased modelling. Annu. Rev. Sociol. 28, 143–166 (2002)

    Article  Google Scholar 

  3. Gilbert, N., Jager, W., Deffuant, G., Adjali, I.: Complexities in markets: introduction to the special issue. J. Bus. Res. 60, 813–815 (2007)

    Article  Google Scholar 

  4. Moldovan, S., Goldenberg, J.: Cellular automata modeling of resistance to innovations: effects and solutions. Technol. Forecast. Soc. Change 71, 425–442 (2004)

    Article  Google Scholar 

  5. Wei, Y., Ying, S., Fana, Y., Wang, B.: The cellular automaton model of investment behavior in the stock market. Phys. A 325, 507–516 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  6. Frels, J., Heisler, D., Reggia, J., Schuetze, H.: A cellular automata model of competition in technology markets with network externalities. In: Sunderam, V.S., et al. (eds.) ICCS 2005, LNCS 3515, 378–385. Springer-Verlag, Berlin, Heidelberg (2005)

    Google Scholar 

  7. Morone, P., Taylor, R.: Knowledge diffusion dynamics and network properties of face-to-face interactions. J. Evol. Econ. 14, 327–351 (2004)

    Article  Google Scholar 

  8. Goldenberg, J., Libai, B., Muller, E.: Riding the Saddie: how cross-market communications can create a major slump in sales. J. Mark. 66, 1–16 (2002)

    Article  Google Scholar 

  9. Goldenberg, J., Libai, B., Muller, E.: The chilling effects of network externalities. Int. J. Res. Mark. 27, 4–15 (2010)

    Article  Google Scholar 

  10. Goldenberg, J., Libai, B., Muller, E.: Using complex systems analysis to advance marketing theory development: modeling heterogeneity effects on new product growth through stochastic cellular automata. Acad. Market. Sci. Rev. 9, 20 pages (2001)

    Google Scholar 

  11. Goldenberg, J., Efroni, S.: Using cellular automata modeling of the emergence of innovation. Technol. Forecast. Soc. Change 68(3), 293–308 (2001)

    Article  Google Scholar 

  12. Kiesling, E., Günther, M., Stummer, C., Wakolbinger, L.M.: Agent-based simulation of innovation diffusion: a review. Central Eur. J. Oper. Res. 20(2), 183–230 (2012)

    Article  Google Scholar 

  13. Teraji, S.: Herd behavior and the quality of opinions. J. Socio-Econ. 32, 661–673 (2003)

    Article  Google Scholar 

  14. Brass, D.J.: Being in the right place—a structural analysis of individual influence in an organization. Adm. Sci. Q. 29, 518–539 (1984)

    Article  Google Scholar 

  15. Brown, J.J., Reingen, P.H.: Social ties and word-of-mouth referral behavior. J. Consum. Res. 14, 350–362 (1987)

    Article  Google Scholar 

  16. Hennig-Thurau, T., Walsh, G.: Electronic word-of-mouth: motives for and consequences of reading consumer articulations on the Internet. Int. J. Electron. Commer. 8(2), 51–74 (2004)

    Google Scholar 

  17. Steffes, E.M., Burgee, L.E.: Social ties and online word of mouth. Internet Res. 19, 42–59 (2009)

    Article  Google Scholar 

  18. Goldsmith, R.E., Horowitz, D.: Measuring motivations for online opinion seeking. J. Interact. Advertising 6(2), 1–16 (2006)

    Article  Google Scholar 

  19. Burt, R.S.: Secondhand brokerage: evidence on the importance of local structure for managers, bankers, and analysts. Acad. Manag. J. 50(1), 119–148 (2007)

    Article  MathSciNet  Google Scholar 

  20. Fleming, L., Mingo, S., Chen, D.: Collaborative brokerage, generative creativity, and creative success. Adm. Sci. Q. 52, 443–475 (2007)

    Google Scholar 

  21. Granovetter, M.: The impact of social structure on economic outcomes. J. Econ. Perspect. 19(1), 33–50 (2005)

    Article  Google Scholar 

  22. Alkemade, F., Castaldi, C.: Strategies for the diffusion of innovations on social networks. Comput. Econ. 25(1), 3–23 (2005)

    Article  MATH  Google Scholar 

  23. Bohlmann, J.D., Calantone, R.J., Zhao, M.: The effects of market network heterogeneity on innovation diffusion: an agent-based modeling approach. J. Prod. Innov. Manage. 27, 741–760 (2010)

    Article  Google Scholar 

  24. Bouzdine-Chameeva, T., Galam, S.: Word-of-mouth versus experts and reputation in the individual dynamics of wine purchasing. Adv. Complex Syst. 14, 871–885 (2011)

    Article  Google Scholar 

  25. Wolfram, S.: Statistical mechanics of cellular automata. Rev. Mod. Phys. 55, 601–644 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  26. Wolfram, S.: Universality and complexity in cellular automata. Phys. D 10, 1–35 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  27. Wolfram, S.: A New Kind of Science. Wolfram Media, Inc. (2002)

    Google Scholar 

  28. Sarkar, P.: A brief history of cellular automata. ACM Comput. Surv. 32(1), 80–107 (2000)

    Article  Google Scholar 

  29. Zimbres, R.A., Oliveira, P.P.B.: Dynamics of quality perception in a social network: a cellular automaton based model in aesthetics services. Electron. Notes Theor. Comput. Sci. 252, 157–180 (2009)

    Article  MathSciNet  Google Scholar 

  30. Becker, G.S.: A note on restaurant pricing and other examples of social influences on price. J. Polit. Econ. 99, 1109–1116 (1991)

    Article  Google Scholar 

  31. Yu, W., Helbing, D.: Game theoretical interactions of moving agents. In: Hoekstra, A., Kroc, J., Sloot, P. (eds.) Simulating Complex Systems by Cellular Automata. Springer-Verlag, Berlin, Heidelberg (2010)

    Google Scholar 

  32. Garber, T., Goldenberg, J., Libai, B., Muller, E.: From density to destiny: using spatial dimension of sales data for early prediction of new product success. Mark. Sci. 23(3), 419–428 (2004)

    Article  Google Scholar 

  33. Ma, F., Chao, G.: Research on communication products diffusion in china using cellular automata. Int. Bus. Res. 4(2), 147–152 (2011)

    Article  Google Scholar 

  34. Kowalska-Styczeń, A., Sznajd-Weron, K.: Access to information in word of mouth marketing within a cellular automata model. Adv. Complex Syst. 15(1250080) 17 pages (2012). doi:10.1142/S0219525912500804

  35. Davis, J.P., Bingham, C.B.: Developing theory through simulation methods. Acad. Manage. Rev. 32(2), 480–499 (2007)

    Article  Google Scholar 

  36. Mullen, B., Copper, C.: The relation between group cohesiveness and performance: an integration. Psychol. Bull. 115(2), 210–227 (1994)

    Article  Google Scholar 

  37. Mason, W.A., Conrey, F.R., Smith, E.R.: Situating social influence processes: dynamic, multidirectional flows of influence within social networks. Pers. Soc. Psychol. Rev. 11(3), 279–300 (2007)

    Article  Google Scholar 

Download references

Acknowledgments

The work was partially financially supported by the Polish National Science Center grant no. UMO-2014/15/B/HS4/04433.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Agnieszka Kowalska-Styczeń .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kowalska-Styczeń, A. (2017). The Impact of Structure Network Parameters on Consumers Behavior: A Cellular Automata Model. In: Świątek, J., Wilimowska, Z., Borzemski, L., Grzech, A. (eds) Information Systems Architecture and Technology: Proceedings of 37th International Conference on Information Systems Architecture and Technology – ISAT 2016 – Part III. Advances in Intelligent Systems and Computing, vol 523. Springer, Cham. https://doi.org/10.1007/978-3-319-46589-0_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46589-0_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46588-3

  • Online ISBN: 978-3-319-46589-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics