Marketing Letters

, Volume 28, Issue 1, pp 85–97 | Cite as

How often should a firm modify its products? A Bayesian analysis of automobile modification cycles

  • Goksel Yalcinkaya
  • Tevfik Aktekin
  • Sengun Yeniyurt
  • Setiadi Umar


In this paper, we develop and estimate a series of Bayesian generalized gamma family and mixture family models of product modification timing that take into account both firm- and industry-specific effects that are time varying. Our models are capable of capturing non-standard modification behavior such as multi-modality and non-monotonicity in the hazard rates motivated by real data. Additionally, we explore the existence of latent groups with respect to product modification timing strategies. The models are estimated using Markov chain Monte Carlo (MCMC) methods using a panel data from the automotive industry that covers 50 years and contains 6598 car model-year observations for 683 car models. The results reveal the non-monotonic modification behavior over time and the existence of three latent groups. Larger product portfolios and higher industry product proliferation lengthen the modification time. Brand and competitive modification dynamism increases the frequency of major modifications.


Product modification Bayesian inference MCMC estimation Finite mixtures Generalized gamma models New product development 


  1. Bayus, B. L., Jain, S., & Rao, A. G. (1997). Too little, too early: introduction timing and new product performance in the personal digital assistant industry. Journal of Marketing Research, 34(1), 50–63.CrossRefGoogle Scholar
  2. Bayus, B. L., & Putsis, W. P. (1999). Product proliferation: an empirical analysis of product line determinants and market outcomes. Marketing Science, 18(2), 137–153.CrossRefGoogle Scholar
  3. Bowman, D., & Gatignon, H. (1995). Determinants of competitor response time to a new product introduction. Journal of Marketing Research, 32(1), 42–53.CrossRefGoogle Scholar
  4. Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S., & Zhao, L. (2005). Statistical analysis of a telephone call center: a queueing-science perspective. Journal of the American Statistical Association, 100(469), 36–50.CrossRefGoogle Scholar
  5. Bucklin, R. E., Siddarth, S., & Silva-Risso, J. M. (2008). Distribution intensity and new car choice. Journal of Marketing Research, 45(4), 473–486.CrossRefGoogle Scholar
  6. Chandy, R., & Tellis, G. J. (1998). Organizing for radical product innovation. Journal of Marketing Research, 35(4), 474–487.CrossRefGoogle Scholar
  7. Chib, S., & Greenberg, E. (1995). Understanding the metropolis-hastings algorithm. The American Statistician, 49(4), 327–335.Google Scholar
  8. Dadpay, A., Soofi, E. S., & Soyer, R. (2007). Information measures for generalized Gamma family. Journal of Econometrics, 138, 568–585.CrossRefGoogle Scholar
  9. Dekimpe, M. G., & Hanssens, D. M. (1999). Sustained spending and persistent response: a new look at long-term marketing profitability. Journal of Marketing Research, 36(4), 397–412.CrossRefGoogle Scholar
  10. Diebolt, J., & Robert, C. P. (1994). Estimation of finite mixture distributions through Bayesian sampling. Journal of the Royal Statistical Society, Series B, 56(2), 363–375.Google Scholar
  11. Druehl, C. T., Schmidt, G. M., & Souza, G. C. (2009). The optimal pace of product updates. European Journal of Operational Research, 192(2), 621–633.CrossRefGoogle Scholar
  12. Gelfand, A. E. (1996). Model determination using sampling-based methods. Markov Chain Monte Carlo in Practice. W. R. Gilks, S. Richardson and D. J. Spiegelhalter, Chapman & Hall/CRC Interdisciplinary StatisticsGoogle Scholar
  13. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D, Chapman & Hall/CRC Interdisciplinary Statistics. B. (2003). Bayesian data analysis.Google Scholar
  14. Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (1996). Markov chain monte carlo in practice. Chapman & Hall/CRC Interdisciplinary Statistics.Google Scholar
  15. Hahn, E. D., & Doh, J. P. (2006). Using Bayesian methods in strategy research: an extension of Hansen et al. Strategic Management Journal, 27(8), 783–798.CrossRefGoogle Scholar
  16. Hansen, M. H., Perry, L. T., & Reese, C. S. (2004). A Bayesian operationalization of the resource-based view. Strategic Management Journal, 25(13), 1279–1295.CrossRefGoogle Scholar
  17. Huber, J., & Train, K. (2001). On the similarity of classical and bayesian estimates of individual mean partworths. Marketing Letters, 12(3), 259–269.CrossRefGoogle Scholar
  18. Jen, L., Chou, C.-H., & Allenby, G. M. (2003). A bayesian approach to modeling purchase frequency. Marketing Letters, 14(1), 5–20.CrossRefGoogle Scholar
  19. Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.CrossRefGoogle Scholar
  20. Liechty, J. C., Fong, D. K. H., Huizingh, E. K. R. E., & De Bruyn, A. (2008). Hierarchical bayesian conjoint models incorporating measurement uncertainty. Marketing Letters, 19(2), 141–155.CrossRefGoogle Scholar
  21. Ma, S., Tan, H., & Shu, F. (2015). When is the best time to reactivate your inactive customers? Marketing Letters, 26(1), 81–98.CrossRefGoogle Scholar
  22. Mainkar, A. V., Lubatkin, M., & Schulze, W. (2006). Toward a product-proliferation theory of entry barriers. Academy of Management Review, 31, 1062–1075.CrossRefGoogle Scholar
  23. Orbach, Y., & Fruchter, G. (2014). Predicting product life cycle patterns. Marketing Letters, 25(1), 37–52.CrossRefGoogle Scholar
  24. Padmanabhan, V., Rajiv, S., & Srinivasan, K. (1997). New products, upgrades, and new releases: a rationale for sequential product introduction. Journal of Marketing Research, 34(4), 456–472.CrossRefGoogle Scholar
  25. Rossi, P. E., & Allenby, G. M. (2003). Bayesian statistics and marketing. Marketing Science, 22(3), 304–328.CrossRefGoogle Scholar
  26. Schmidt, J. B., & Calantone, R. J. (2002). Escalation of commitment during new product development. Journal of the Academy of Marketing Science, 30(2), 103–118.CrossRefGoogle Scholar
  27. Shankar, V. (1999). New product introduction and incumbent response strategies: their interrelationship and the role of multimarket contact. Journal of Marketing Research, 36(3), 327–344.CrossRefGoogle Scholar
  28. Singpurwalla, N. (2006). Reliability and risk a bayesian perspective. West Sussex: John Wiley and Sons, Ltd.Google Scholar
  29. Slotegraaf, R. J., & Inman, J. J. (2004). Longitudinal shifts in the drivers of satisfaction with product quality: the role of attribute resolvability. Journal of Marketing Research, 41(3), 269–280.CrossRefGoogle Scholar
  30. Soyer, R., & Xu, F. (2002). Assessment of mortgage default risk via bayesian reliability models. Applied Stochastic Models in Business and Industry, 30(3), 308–330.Google Scholar
  31. Stacey, E. W. (1962). A generalization of the gamma distribution. The Annals of Mathematical Statistics, 33(3), 1187–1192.CrossRefGoogle Scholar
  32. Urban, G. L., & Hauser, J. R. (1993). Design and marketing of new products. Englewood Cliffs: Prentice-Hall.Google Scholar
  33. Waarts, E., & Wierenga, B. (2000). Explaining competitors’ reactions to new product introductions: the roles of event characteristics, managerial interpretation, and competitive context. Marketing Letters, 11(1), 67–79.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Goksel Yalcinkaya
    • 1
  • Tevfik Aktekin
    • 2
  • Sengun Yeniyurt
    • 3
  • Setiadi Umar
    • 3
  1. 1.Department of Marketing, Peter T. Paul College of Business and EconomicsUniversity of New HampshireDurhamUSA
  2. 2.Department of Decision Sciences, Peter T. Paul College of Business and EconomicsUniversity of New HampshireDurhamUSA
  3. 3.Department of SCM and Marketing Sciences, Rutgers Business SchoolRutgers UniversityNewarkUSA

Personalised recommendations