Annals of Operations Research

, Volume 263, Issue 1–2, pp 209–230 | Cite as

Merchant selection and pricing strategy for a platform firm in the online group buying market

  • Tomohiro Ando
Data Mining and Analytics


The online group-buying market is characterized by intense competition between brokers, called platform firms, which function as intermediaries between merchants and consumers. In an environment of intense competition, merchant selection and pricing strategies are critical for platform firms. This paper employs business analytics to support strategy formulation for these firms by forecasting market demand and analyzing competitive environments. We apply the proposed decision framework, which relies on business analytics, to a study of the online group-buying market in Japan.


Business analytics Disequilibrium Endogeneity Online group buying 



We are grateful to the Guest Editor, two anonymous referees for their helpful suggestions and comments. We also appreciate the constructive comments of participants at the 2014 INFORMS Workshop on Data Mining and Analytics. We have benefited from fruitful discussion with Anand Bodapati and Chen Hsiao. This research was partially supported by the Japan Center for Economic Research and a Grant-in-Aid for Young Scientists (B) from the Japan Society for the Promotion of Science.


  1. Amemiya, T. (1974). The nonlinear two-stage least-squares estimator. Journal of Econometrics, 2, 105–110.CrossRefGoogle Scholar
  2. Amemiya, T. (1985). Advanced Econometrics. Cambridge, MA: Harvard University Press.Google Scholar
  3. Ando, T. (2014a). High-dimensional data analysis: Statistical modeling and model averaging with R. Tokyo: Asakura Publishing. (in Japanese).Google Scholar
  4. Ando, T. (2014b). A statistical analysis of the online group-buying market. In Proceedings of the 2014 INFORMS workshop on data mining and analytics. San Francisco, CA.Google Scholar
  5. Atanasova, C. V., & Wilson, N. (2004). Disequilibrium in the UK corporate loan market. Journal of Banking and Finance, 28, 595–614.CrossRefGoogle Scholar
  6. Berry, S. (1994). Estimating discrete-choice models of product differentiation. RAND Journal of Economics, 25, 242–262.CrossRefGoogle Scholar
  7. Berry, S., Levinsohn, J., & Pakes, A. (1995). Automobile prices in market equilibrium. Econometrica, 63, 841–890.CrossRefGoogle Scholar
  8. Besanko, D., Gupta, S., & Jain, D. (1998). Logit demand estimation under competitive pricing behavior: An equilibrium framework. Management Science, 44, 1533–1547.CrossRefGoogle Scholar
  9. Cao, P., Li, J., & Yan, H. (2012). Optimal dynamic pricing of inventories with stochastic demand and discounted criterion. European Journal of Operational Research, 217, 580–588.CrossRefGoogle Scholar
  10. Chang, H.-J., Teng, J.-T., Ouyang, L.-Y., & Dye, C.-Y. (2006). Retailer’s optimal pricing and lot-sizing policies for deteriorating items with partial backlogging. European Journal of Operational Research, 168, 51–64.CrossRefGoogle Scholar
  11. Chintagunta, P. (2001). Endogeneity and heterogeneity in a probit demand model: Estimation using aggregate data. Marketing Science, 20, 442–456.CrossRefGoogle Scholar
  12. Chintagunta, P., Dube, J. P., & Goh, K. Y. (2005). Beyond the endogeneity bias: The effect of unmeasured brand characteristics on household-level brand choice models. Management Science, 51, 832–849.CrossRefGoogle Scholar
  13. Davis, P. (2006). Spatial competition in retail markets: Movie theaters. RAND Journal of Economics, 37, 964–982.CrossRefGoogle Scholar
  14. Fair, R. C., & Jaffee, D. M. (1972). Methods of estimation for markets in disequilibrium. Econometrica, 40, 497–514.CrossRefGoogle Scholar
  15. Fair, R. C., & Kelejian, H. H. (1974). Methods of estimation for markets in disequilibrium: A further study. Econometrica, 42, 177–190.CrossRefGoogle Scholar
  16. Hartley, M. J. (1976). The estimation of markets in disequilibrium: The fixed supply case. International Economic Review, 17, 687–699.CrossRefGoogle Scholar
  17. Hsu, M.-H., Chang, C.-M., Chu, K. K., & Lee, Y.-J. (2014). Determinants of repurchase intention in online group-buying: The perspectives of DeLone & McLean IS success model and trust. Computers in Human Behavior, 36, 234–245.CrossRefGoogle Scholar
  18. Hsu, M.-H., Chang, C.-M., & Chuang, L.-W. (2015). Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan. International Journal of Information Management, 35, 45–56.CrossRefGoogle Scholar
  19. Hurlin, C., & Kierzenkowski, R. (2007). Credit market disequilibrium in Poland: Can we find what we expect? Non-stationarity and the short-side rule. Economic Systems, 31, 157–183.CrossRefGoogle Scholar
  20. Jiang, R., Manchanda, P., & Rossi, P. E. (2009). Bayesian analysis of random coefficient logit models using aggregate data. Journal of Econometrics, 149, 136–148.CrossRefGoogle Scholar
  21. Khouja, M., & Robbins, S. S. (2005). Optimal pricing and quantity of products with two offerings. European Journal of Operational Research, 163, 530–544.CrossRefGoogle Scholar
  22. Khouja, M., & Smith, M. A. (2007). Optimal pricing for information goods with piracy and saturation effect. European Journal of Operational Research, 176, 482–497.CrossRefGoogle Scholar
  23. Liao, S.-H., Chu, P. H., Chen, Y.-J., & Chang, C.-C. (2012). Mining customer knowledge for exploring online group buying behavior. Expert Systems with Applications, 39, 3708–3716.CrossRefGoogle Scholar
  24. Meir, R., Lub, T., Tennenholtz, M., & Boutilier, C. (2015). On the value of using group discounts under price competition. Artificial Intelligence, 216, 163–178.CrossRefGoogle Scholar
  25. Nevo, A. (2000). Mergers with differentiated products: The case of the ready-to-eat cereal industry. RAND Journal of Economics, 31, 395–421.CrossRefGoogle Scholar
  26. Nevo, A. (2001). Measuring market power in the ready-to-eat cereal industry. Econometrica, 69, 307–342.CrossRefGoogle Scholar
  27. Parsons, A., Ballantine, P. W., Ali, A., & Grey, H. (2014). Deal is on! Why people buy from daily deal websites. Journal of Retailing and Consumer Services, 21, 37–42.CrossRefGoogle Scholar
  28. Phillips, R., Simsek, A. S., & van Ryzin, G. (2015). The effectiveness of field price discretion: Empirical evidence from auto lending. Management Science, 61, 1741–1759.CrossRefGoogle Scholar
  29. Riddel, M. (2004). Housing-market disequilibrium: An examination of housing-market price and stock dynamics 1967–1998. Journal of Housing Economics, 13, 120–135.CrossRefGoogle Scholar
  30. Sudhir, K. (2001). Competitive pricing behavior in the auto market: A structural analysis. Marketing Science, 20, 42–60.CrossRefGoogle Scholar
  31. Velupillai, K. V. (2006). A disequilibrium macrodynamic model of fluctuations. Journal of Macroeconomics, 28, 752–767.CrossRefGoogle Scholar
  32. Xing, W., Wang, S., & Liu, L. (2012). Optimal ordering and pricing strategies in the presence of a B2B spot market. European Journal of Operational Research, 216, 87–98.CrossRefGoogle Scholar
  33. Zhang, Z., Zhang, Z., Wang, F., Law, R., & Li, D. (2013). Factors influencing the effectiveness of online group buying in the restaurant industry. International Journal of Hospitality Management, 35, 237–245.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Melbourne Business SchoolUniversity of MelbourneCarltonAustralia

Personalised recommendations