Advertisement

Diffusion of Reverse Innovations across Markets: An Agent-Based Model

  • Pável Reyes-Mercado
Chapter

Abstract

This chapter uses an Agent-Based Model (ABM) to perform simulated experiments on adoption of a reverse innovation under the influence of paid advertising messages and word of mouth. The main objective is analyze the adoption and diffusion of a reverse innovation across a developing country—the country in which a reverse innovation is first launched—to then analyze its further diffusion in a developed country. While most of literature on diffusion and adoption of innovation is focused on aggregated transversal studies, this chapter utilizes an ABM to analyze the phenomenon at the micro market level, that is, observable consumer decisions. As secondary data on sales of reverse innovations is almost unavailable, this study relies on an experimental design with different levels of paid advertising messages and word of mouth, network densities, and market sizes. Results show that a reverse innovation diffuses faster in a developing country and reaches a larger market segment than the diffusion in a developed country. Managers are called to enact an ambidextrous marketing strategy across countries and to expect differentiated diffusion patterns.

References

  1. Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.CrossRefGoogle Scholar
  2. Bass, F. M., Krishnan, T. V., & Jain, D. C. (1994). Why the Bass model fits without decision variables. Marketing Science, 13(3), 203–223.CrossRefGoogle Scholar
  3. Bloomberg. (2017). Living in the dark: 240 million Indians have no electricity. Bloomberg News Website. Retrieved January 12, 2018, from https://www.bloomberg.com/news/features/2017-01-24/living-in-the-dark-240-million-indians-have-no-electricity.
  4. Christensen, C. (1997). The innovator’s dilemma: When new technologies cause great firms to fail. Boston, MA: Harvard Business Review Press.Google Scholar
  5. Christensen, C. M., Raynor, M. E., & McDonald, R. (2015). What is disruptive innovation. Harvard Business Review, 93(12), 44–53.Google Scholar
  6. Delre, S. A., Jager, W., Bijmolt, T. H., & Janssen, M. A. (2010). Will it spread or not? The effects of social influences and network topology on innovation diffusion. Journal of Product Innovation Management, 27(2), 267–282.CrossRefGoogle Scholar
  7. Hang, C. C., Chen, J., & Subramian, A. M. (2010). Developing disruptive products for emerging economies: Lessons from Asian cases. Research-Technology Management, 53(4), 21–26.CrossRefGoogle Scholar
  8. Hossain, M., Simula, H., & Halme, M. (2016). Can frugal go global? Diffusion patterns of frugal innovations. Technology in Society, 46(1), 132–139.CrossRefGoogle Scholar
  9. Immelt, J. R., Govindarajan, V., & Trimble, C. (2009). How GE is disrupting itself. Harvard Business Review, 87(10), 56–65.Google Scholar
  10. Kapoor, K., Dwivedi, Y., & Williams, M. (2014). Innovation adoption attributes: A review and synthesis of research findings. European Journal of Innovation Management, 17(3), 327–348.CrossRefGoogle Scholar
  11. Kiesling, E., Günther, M., Stummer, C., & Wakolbinger, L. M. (2012). Agent-based simulation of innovation diffusion: A review. Central European Journal of Operations Research, 20(2), 183–230.CrossRefGoogle Scholar
  12. Koryak, O., Lockett, A., Hayton, J., Nicolaou, N., & Mole, K. (2018). Disentangling the antecedents of ambidexterity: Exploration and exploitation. Research Policy, 47(2), 413–427.CrossRefGoogle Scholar
  13. Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151–162.CrossRefGoogle Scholar
  14. Nunes, P. F., & Breene, T. S. (2011). Jumping the S-Curve. How to beat the growth cycle, get on top, and stay there. Boston, MA: Harvard Business Review Press.Google Scholar
  15. Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: A critical review and research directions. International Journal of Research in Marketing, 27(2), 91–106.CrossRefGoogle Scholar
  16. Prahalad, C. K., & Mashelkar, R. A. (2010). Innovation’s holy grail. Harvard Business Review, 88(7-8), 132–141.Google Scholar
  17. Rand, W., & Rust, R. T. (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing, 28(3), 181–193.CrossRefGoogle Scholar
  18. Ray, S., & Ray, P. (2011). Product innovation for the people’s car in an emerging economy. Technovation, 31(5–6), 216–227.CrossRefGoogle Scholar
  19. Rogers, E. M. (2010). Diffusion of innovations. New York: Simon and Schuster.Google Scholar
  20. The Hindu. (2017). India to achieve ‘electricity for all’ by early 2020: IEA. The Hindu Business Website. Retrieved January 12, 2018, from http://www.thehindu.com/business/india-to-achieve-electricity-for-all-by-early-2020s-iea/article21291173.ece.
  21. WIPO. (2013). Chotukool: Keeping things cool with frugal innovation. WIPO Magazine. Retrieved January 12, 2018, from http://www.wipo.int/wipo_magazine/en/2013/06/article_0003.html.
  22. Zeng, M., & Williamson, P. J. (2007). Dragons at your door: How Chinese cost innovation is disrupting global competition. Boston, MA: Harvard Business Review Press.Google Scholar
  23. Zeschky, M., Widenmayer, B., & Gassmann, O. (2011). Frugal innovation in emerging markets. Research-Technology Management, 54(4), 38–45.CrossRefGoogle Scholar
  24. Zimmermann, A., Raisch, S., & Birkinshaw, J. (2015). How is ambidexterity initiated? The emergent charter definition process. Organization Science, 26(4), 1119–1139.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2019

Authors and Affiliations

  • Pável Reyes-Mercado
    • 1
  1. 1.Anahuac UniversityMexico CityMexico

Personalised recommendations