Annals of Operations Research

, Volume 244, Issue 2, pp 647–676 | Cite as

Loyal customer bases as innovation disincentives for duopolistic firms using strategic signaling and Bayesian analysis

  • Madjid Tavana
  • Debora Di Caprio
  • Francisco J. Santos-Arteaga
Original Paper

Abstract

In this paper we model the strategic behavior of firms competing in duopolistic environments with a loyal customer base and formalize their decision to delay the introduction of the most technologically developed product available. The proposed model extends and complements the partial approaches studied in the economic, management and operations research literatures. The former emphasizes the role of the strategic knowledge spillovers that may take place among competing firms because of their incentives to introduce technologically superior products while assuming the acceptance of such products by customers as given. The second defines its technology acceptance model based on the demand side of the economic system without considering the resulting strategic interactions that arise among the firms. The latter addresses the effect that signals about a new technology have on the information acquisition behavior of decision makers (DMs) but does not consider the capacity of DMs to account for several product characteristics and their interaction when acquiring information. Using a duopolistic innovation game model we illustrate how the existence of loyal customer bases allows for higher expected payoffs when generating monopolized markets but decreases the incentives of firms to introduce the most technologically developed product available. The signaling equilibria of the game are determined by demand-based factors and the incentives of customers to acquire information on the existing products in the market. Among the main implications of our model is also the fact that the availability of decision support systems that can be used by DMs through their information acquisition processes would improve the quality of the technology being introduced in the market and increase the firms’ probability of success.

Keywords

Multi-attribute sequential search Customer loyalty Strategic signaling Technological evolution Product introduction 

References

  1. Abdolvand, N., Albadvi, A., & Aghdasi, M. (2015). Performance management using a value-based customer-centered model. International Journal of Production Research. doi:10.1080/00207543.2015.1026613.
  2. Anderson, R. E., & Srinivasan, S. S. (2003). E-satisfaction and e-loyalty: A contingency framework. Psychology & Marketing, 20, 123–138.CrossRefGoogle Scholar
  3. Arruda-Filho, E. J. M., Cabusas, J. A., & Dholakia, N. (2010). Social behavior and brand devotion among iPhone innovators. International Journal of Information Management, 30, 475–480.CrossRefGoogle Scholar
  4. Arruda-Filho, E. J. M., & Lennon, M. M. (2011). How iPhone innovators changed their consumption in iDay2: Hedonic post or brand devotion. International Journal of Information Management, 31, 524–532.CrossRefGoogle Scholar
  5. Aytac, B., & Wu, S. D. (2013). Characterization of demand for short life-cycle technology products. Annals of Operations Research, 203, 255–277.CrossRefGoogle Scholar
  6. Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8, 244–254.Google Scholar
  7. Bai, C., & Sarkis, J. (2014). Supplier development investment strategies: A game theoretic evaluation. Annals of Operations Research. doi:10.1007/s10479-014-1737-9.
  8. Baumol, W. J. (2010). The microtheory of innovative entrepreneurship. Princeton: Princeton University Press.Google Scholar
  9. Belk, R., Ger, G., & Askegaard, S. (2003). The fire of desire: A multisited inquiry into consumer passion. Journal of Consumer Research, 30, 326–351.CrossRefGoogle Scholar
  10. Bohlmann, J. D., Golder, P. N., & Mitra, D. (2002). Deconstructing the pioneer’s advantage: Examining vintage effects and consumer valuations of quality and variety. Management Science, 48, 1175–1195.CrossRefGoogle Scholar
  11. Burnham, T., & Mahajan, V. (2003). Consumer switching costs: Typology, antecedents, and consequences. Journal the Academy of Science, 32, 109–126.CrossRefGoogle Scholar
  12. Christensen, C. M. (1997). The innovator’s dilemma: When new technologies cause great firms to fail. Boston, Massachusetts: Harvard Business School Press.Google Scholar
  13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.CrossRefGoogle Scholar
  14. Di Caprio, D., Santos Arteaga, F. J., & Tavana, M. (2014). The optimal sequential information acquisition structure: A rational utility-maximizing perspective. Applied Mathematical Modelling, 38, 3419–3435.CrossRefGoogle Scholar
  15. Feick, L., Lee, J., & Lee, J. (2001). The impact of switching costs on the customer satisfaction-loyalty link: Mobile phone service in France. Journal of Services Marketing, 15, 35–48.CrossRefGoogle Scholar
  16. Ferrell, O. C., & Hartline, M. (2012). Marketing Strategy (6th ed.). Nashville: South-Western College Pub.Google Scholar
  17. Hanusch, H., & Pyka, A. (Eds.). (2007). Elgar companion to neo-schumpeterian economics. Cheltenham, UK: Edward Elgar.Google Scholar
  18. Hartung, P. H., & Fisher, J. L. (1965). Brand switching and mathematical programming in market expansion. Management Science, 11, B-231–B-243.CrossRefGoogle Scholar
  19. Hendricks, K. B., & Singhal, V. R. (2008). The effect of product introduction delays on operating performance. Management Science, 54, 878–892.CrossRefGoogle Scholar
  20. Herbon, A. (2014). Dynamic pricing vs. acquiring information on consumers’ heterogeneous sensitivity to product freshness. International Journal of Production Research, 52, 918–933.CrossRefGoogle Scholar
  21. Jones, M., Mothersbaugh, D., & Beatty, S. (2000). Switching barriers and repurchase intention in services. Journal of Retailing, 76, 259–274.CrossRefGoogle Scholar
  22. King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43, 740–755.CrossRefGoogle Scholar
  23. Laksana, K., & Hartman, J. C. (2010). Planning product design refreshes with service contract and competition considerations. International Journal of Production Economics, 126, 189–203.CrossRefGoogle Scholar
  24. Lee, Y., Kozar, K. A., & Larsen, K. R. T. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems, 12, 752–780.Google Scholar
  25. Lee, M., Lee, C., & Wu, C. (2009). Brand image strategy affects brand equity after M&A. European Journal of Marketing, 45, 1091–1111.CrossRefGoogle Scholar
  26. Li, Y., & Jin, Y. H. (2009). Racing to market leadership: Product launch and upgrade decisions. International Journal of Production Economics, 119, 284–297.CrossRefGoogle Scholar
  27. Liu, Y., Li, H., Peng, G., Lv, B., & Zhang, C. (2015). Online purchaser segmentation and promotion strategy selection: Evidence from Chinese e-commerce market. Annals of Operations Research, 233, 263–279.CrossRefGoogle Scholar
  28. Malerba, F., Nelson, R., Orsenigo, L., & Winter, S. (2007). Demand, innovation, and the dynamics of market structure: The role of experimental users and diverse preferences. Journal of Evolutionary Economics, 17, 371–399.CrossRefGoogle Scholar
  29. Mas-Colell, A., Whinston, M. D., & Green, J. R. (1995). Microeconomic Theory. New York: Oxford University Press.Google Scholar
  30. Mittal, B., & Lee, M. (1989). A causal model of consumer involvement. Journal of Economic Psychology, 10, 363–389.CrossRefGoogle Scholar
  31. Nelson, R. R., & Winter, S. G. (1985). An evolutionary theory of economic change. Cambridge: Belknap Press.Google Scholar
  32. Netessine, S., & Tang, C. S. (2009). Consumer-driven demand and operations management models. New York: Springer.Google Scholar
  33. Premkumar, G., & Bhattacherjee, A. (2008). Explaining information technology usage: A test of competing models. Omega, 36, 64–75.CrossRefGoogle Scholar
  34. Qiasi, R., Baqeri-Dehnavi, M., Minaei-Bidgoli, B., & Amooee, G. (2012). Developing a model for measuring customer’s loyalty and value with RFM technique and clustering algorithms. The Journal of Mathematics and Computer Science, 4, 172–181.Google Scholar
  35. Roberts, F. S. (2008). Computer science and decision theory. Annals of Operations Research, 163, 209–253.CrossRefGoogle Scholar
  36. Salem Khalifa, A. (2004). Customer value: A review of recent literature and an integrative configuration. Management Decision, 42, 645–666.CrossRefGoogle Scholar
  37. Santouridis, I., & Trivellas, P. (2010). Investigating the impact of service quality and customer satisfaction on customer loyalty in mobile telephony in Greece. TQM Journal, 22, 330–343.CrossRefGoogle Scholar
  38. Simon, H. A. (1997). Administrative behavior. New York: The Free Press.Google Scholar
  39. Smith, J. E., & Ulu, C. (2012). Technology adoption with uncertain future costs and quality. Operations Research, 60, 262–274.CrossRefGoogle Scholar
  40. Su, M., & Rao, V. R. (2011). Timing decisions of new product preannouncement and launch with competition. International Journal of Production Economics, 129, 51–64.CrossRefGoogle Scholar
  41. Tavana, M. (2004). A subjective assessment of alternative mission architectures for the human exploration of Mars at NASA using multicriteria decision making. Computers & Operations Research, 31, 1147–1164.CrossRefGoogle Scholar
  42. Tavana, M., Di Caprio, D., & Santos-Arteaga, F. J. (2014). An optimal information acquisition model for competitive advantage in complex multiperspective environments. Applied Mathematics and Computation, 240, 175–199.CrossRefGoogle Scholar
  43. Tellis, G. J., Yin, E., & Niraj, R. (2009). Does quality win? Network effects versus quality in high-tech markets. Journal of Marketing Research, 46, 135–149.CrossRefGoogle Scholar
  44. Von Riesen, D., & Herndon, N. (2011). Consumer involvement with the product and the nature of brand loyalty. Journal of Marketing Channels, 18, 327–352.CrossRefGoogle Scholar
  45. Wang, Y. S., Tang, T. I. J., & Tang, T. D. (2001). An instrument for measuring customer satisfaction toward web sites that market digital products and services. Journal of Electronic Commerce Research, 2, 89–102.Google Scholar
  46. Yang, B., Burns, N. D., & Backhouse, C. J. (2004). Management of uncertainty through postponement. International Journal of Production Research, 42, 1049–1064.CrossRefGoogle Scholar
  47. Yenipazarli, A. (2015). A road map to new product success: Warranty, advertisement and price. Annals of Operations Research, 226, 669–694.CrossRefGoogle Scholar
  48. Zhang, D., & Cooper, W. L. (2008). Managing clearance sales in the presence of strategic customers. Production and Operations Management, 17, 416–431.CrossRefGoogle Scholar
  49. Zhou, E., Zhang, J., Gou, Q., & Liang, L. (2015). A two period pricing model for new fashion style launching strategy. International Journal of Production Economics, 160, 144–156.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Madjid Tavana
    • 1
    • 2
  • Debora Di Caprio
    • 3
    • 4
  • Francisco J. Santos-Arteaga
    • 5
    • 6
  1. 1.Business Systems and Analytics Department, Distinguished Chair of Business AnalyticsLa Salle UniversityPhiladelphiaUSA
  2. 2.Business Information Systems Department, Faculty of Business Administration and EconomicsUniversity of PaderbornPaderbornGermany
  3. 3.Department of Mathematics and StatisticsYork UniversityTorontoCanada
  4. 4.Polo Tecnologico IISS G. GalileiBolzanoItaly
  5. 5.School of Economics and ManagementFree University of BolzanoBolzanoItaly
  6. 6.Departamento de Economía Aplicada II Facultad de EconómicasUniversidad Complutense de MadridPozueloSpain

Personalised recommendations