IDEAS home Printed from https://ideas.repec.org/r/inm/ormksc/v17y1998i1p29-44.html
   My bibliography  Save this item

A Hierarchical Bayes Model of Primary and Secondary Demand

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Zhu, Siying & Cai, Yutong & Wang, Mengtong & Wang, Hua & Meng, Qiang, 2023. "How will China–Singapore International Land–Sea Trade Corridor affect route choice behaviour? A discrete choice model," Transport Policy, Elsevier, vol. 144(C), pages 11-22.
  2. Harikesh Nair & Jean-Pierre Dubé & Pradeep Chintagunta, 2005. "Accounting for Primary and Secondary Demand Effects with Aggregate Data," Marketing Science, INFORMS, vol. 24(3), pages 444-460, November.
  3. Nitin Mehta, 2007. "Investigating Consumers' Purchase Incidence and Brand Choice Decisions Across Multiple Product Categories: A Theoretical and Empirical Analysis," Marketing Science, INFORMS, vol. 26(2), pages 196-217, 03-04.
  4. Andrews, Rick L. & Currim, Imran S., 2009. "Multi-stage purchase decision models: Accommodating response heterogeneity, common demand shocks, and endogeneity using disaggregate data," International Journal of Research in Marketing, Elsevier, vol. 26(3), pages 197-206.
  5. Jaehwan Kim & Greg M. Allenby & Peter E. Rossi, 2002. "Modeling Consumer Demand for Variety," Marketing Science, INFORMS, vol. 21(3), pages 229-250, December.
  6. Charles Cunningham & Ken Deal & Yvonne Chen, 2010. "Adaptive Choice-Based Conjoint Analysis," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 3(4), pages 257-273, December.
  7. Pradeep K. Chintagunta & Harikesh S. Nair, 2011. "Structural Workshop Paper --Discrete-Choice Models of Consumer Demand in Marketing," Marketing Science, INFORMS, vol. 30(6), pages 977-996, November.
  8. von Haefen, Roger H., 2003. "Incorporating observed choice into the construction of welfare measures from random utility models," Journal of Environmental Economics and Management, Elsevier, vol. 45(2), pages 145-165, March.
  9. Olivier Toubia & Duncan I. Simester & John R. Hauser & Ely Dahan, 2003. "Fast Polyhedral Adaptive Conjoint Estimation," Marketing Science, INFORMS, vol. 22(3), pages 273-303.
  10. Puneet Manchanda & Asim Ansari & Sunil Gupta, 1999. "The “Shopping Basket”: A Model for Multicategory Purchase Incidence Decisions," Marketing Science, INFORMS, vol. 18(2), pages 95-114.
  11. Zhang Qin & Seetharaman P.B. & Narasimhan Chakravarthi, 2005. "Modeling Selectivity in Households' Purchase Quantity Outcomes: A Count Data Approach," Review of Marketing Science, De Gruyter, vol. 3(1), pages 1-21, July.
  12. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
  13. Victor Martínez‐de‐Albéniz & Arnau Planas & Stefano Nasini, 2020. "Using Clickstream Data to Improve Flash Sales Effectiveness," Production and Operations Management, Production and Operations Management Society, vol. 29(11), pages 2508-2531, November.
  14. Bhat, Chandra R. & Srinivasan, Sivaramakrishnan & Sen, Sudeshna, 2006. "A joint model for the perfect and imperfect substitute goods case: Application to activity time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 40(10), pages 827-850, December.
  15. Nitin Mehta & Xinlei (Jack) Chen & Om Narasimhan, 2010. "Examining Demand Elasticities in Hanemann's Framework: A Theoretical and Empirical Analysis," Marketing Science, INFORMS, vol. 29(3), pages 422-437, 05-06.
  16. Jung-Kyu Jung & Jae Young Choi, 2022. "Choice and allocation characteristics of faculty time in Korea: effects of tenure, research performance, and external shock," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2847-2869, May.
  17. Arthur Hsu & Ronald T. Wilcox, 2000. "Stochastic Prediction in Multinomial Logit Models," Management Science, INFORMS, vol. 46(8), pages 1137-1144, August.
  18. Francisco C. Pereira, 2019. "Rethinking travel behavior modeling representations through embeddings," Papers 1909.00154, arXiv.org.
  19. Wei‐Lin Wang & Demetrios Vakratsas, 2021. "The Dual Impact of Product Line Length on Consumer Choice," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3054-3072, September.
  20. Natter, Martin & Feurstein, Markus, 2002. "Real world performance of choice-based conjoint models," European Journal of Operational Research, Elsevier, vol. 137(2), pages 448-458, March.
  21. Jie Zhang & Lakshman Krishnamurthi, 2004. "Customizing Promotions in Online Stores," Marketing Science, INFORMS, vol. 23(4), pages 561-578, June.
  22. Kopalle, Praveen K. & Kannan, P.K. & Boldt, Lin Bao & Arora, Neeraj, 2012. "The impact of household level heterogeneity in reference price effects on optimal retailer pricing policies," Journal of Retailing, Elsevier, vol. 88(1), pages 102-114.
  23. Zhang, Qin & Seetharaman, P.B. & Narasimhan, Chakravarthi, 2012. "The Indirect Impact of Price Deals on Households’ Purchase Decisions Through the Formation of Expected Future Prices," Journal of Retailing, Elsevier, vol. 88(1), pages 88-101.
  24. Lynd Bacon & Peter Lenk, 2012. "Augmenting discrete-choice data to identify common preference scales for inter-subject analyses," Quantitative Marketing and Economics (QME), Springer, vol. 10(4), pages 453-474, December.
  25. Kwangpil Chang & S. Siddarth & Charles B. Weinberg, 1999. "The Impact of Heterogeneity in Purchase Timing and Price Responsiveness on Estimates of Sticker Shock Effects," Marketing Science, INFORMS, vol. 18(2), pages 178-192.
  26. Neeraj Arora & Ty Henderson, 2007. "Embedded Premium Promotion: Why It Works and How to Make It More Effective," Marketing Science, INFORMS, vol. 26(4), pages 514-531, 07-08.
  27. Yu, Jie & Goos, Peter & Vandebroek, Martina, 2011. "Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 378-388.
  28. Lieven, Theo, 2015. "Policy measures to promote electric mobility – A global perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 78-93.
  29. Greg M. Allenby & Thomas S. Shively & Sha Yang & Mark J. Garratt, 2004. "A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts," Marketing Science, INFORMS, vol. 23(1), pages 95-108, June.
  30. Theodoros Evgeniou & Constantinos Boussios & Giorgos Zacharia, 2005. "Generalized Robust Conjoint Estimation," Marketing Science, INFORMS, vol. 24(3), pages 415-429, May.
  31. Nadarajah, Saralees & Kotz, Samuel, 2009. "Models for purchase frequency," European Journal of Operational Research, Elsevier, vol. 192(3), pages 1014-1026, February.
  32. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
  33. Revelt, David & Train, Kenneth, 2000. "Customer-Specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier," Department of Economics, Working Paper Series qt1900p96t, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
  34. Kim, Chul & Jun, Duk Bin & Park, Sungho, 2018. "Capturing flexible correlations in multiple-discrete choice outcomes using copulas," International Journal of Research in Marketing, Elsevier, vol. 35(1), pages 34-59.
  35. Braun, Alexander & Schmeiser, Hato & Schreiber, Florian, 2016. "On consumer preferences and the willingness to pay for term life insurance," European Journal of Operational Research, Elsevier, vol. 253(3), pages 761-776.
  36. Eisen-Hecht, Jonathan I. & Kramer, Randall A. & Huber, Joel, 2004. "A Hierarchical Bayes Approach To Modeling Choice Data: A Study Of Wetland Restoration Programs," 2004 Annual meeting, August 1-4, Denver, CO 20253, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  37. Debabrata Talukdar & K. Sudhir & Andrew Ainslie, 2002. "Investigating New Product Diffusion Across Products and Countries," Marketing Science, INFORMS, vol. 21(1), pages 97-114, February.
  38. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
  39. Benoit Playe & Chloé-Agathe Azencott & Véronique Stoven, 2018. "Efficient multi-task chemogenomics for drug specificity prediction," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-34, October.
  40. Jui-Sheng Chou & Dinh-Nhat Truong & Yonatan Che, 2020. "Optimized multi-output machine learning system for engineering informatics in assessing natural hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 101(3), pages 727-754, April.
  41. Crabbe, M. & Vandebroek, M., 2012. "Improving the efficiency of individualized designs for the mixed logit choice model by including covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 2059-2072.
  42. González-Benito, Óscar, 2004. "Random effects choice models: seeking latent predisposition segments in the context of retail store format selection," Omega, Elsevier, vol. 32(2), pages 167-177, April.
  43. Marco Giarratana & Alessandra Perri, 2014. "Product and Marketing Actions in a Competitive Scenario," Working Papers 30, Venice School of Management - Department of Management, Università Ca' Foscari Venezia.
  44. Thorsten Teichert, 2001. "Nutzenermittlung in wahlbasierter Conjoint-Analyse: Ein Vergleich von Latent-Class- und hierarchischem Bayes-Verfahren," Schmalenbach Journal of Business Research, Springer, vol. 53(8), pages 798-822, December.
  45. Khandker Habib & Eric Miller, 2008. "Modelling daily activity program generation considering within-day and day-to-day dynamics in activity-travel behaviour," Transportation, Springer, vol. 35(4), pages 467-484, July.
  46. Franke, Melanie & Nadler, Claudia, 2019. "Energy efficiency in the German residential housing market: Its influence on tenants and owners," Energy Policy, Elsevier, vol. 128(C), pages 879-890.
  47. Sanghak Lee & Greg M. Allenby, 2014. "Modeling Indivisible Demand," Marketing Science, INFORMS, vol. 33(3), pages 364-381, May.
  48. Bhat, Chandra R. & Sen, Sudeshna, 2006. "Household vehicle type holdings and usage: an application of the multiple discrete-continuous extreme value (MDCEV) model," Transportation Research Part B: Methodological, Elsevier, vol. 40(1), pages 35-53, January.
  49. Yalcinkaya, Goksel & Aktekin, Tevfik & Yeniyurt, Sengun, 2020. "Out with the old: A Bayesian approach to estimating product modification rates," Journal of Business Research, Elsevier, vol. 118(C), pages 141-149.
  50. Johnson, Joseph & Tellis, Gerard J. & Ip, Edward H., 2013. "To Whom, When, and How Much to Discount? A Constrained Optimization of Customized Temporal Discounts," Journal of Retailing, Elsevier, vol. 89(4), pages 361-373.
  51. Fernando Bernstein & Gregory A. DeCroix, 2015. "Advance Demand Information in a Multiproduct System," Manufacturing & Service Operations Management, INFORMS, vol. 17(1), pages 52-65, February.
  52. Bhat, Chandra R., 2008. "The multiple discrete-continuous extreme value (MDCEV) model: Role of utility function parameters, identification considerations, and model extensions," Transportation Research Part B: Methodological, Elsevier, vol. 42(3), pages 274-303, March.
  53. Anocha Aribarg & Neeraj Arora & Moon Young Kang, 2010. "Predicting Joint Choice Using Individual Data," Marketing Science, INFORMS, vol. 29(1), pages 139-157, 01-02.
  54. Jack (Xinlei) Chen & Om Narasimhan & George John & Tirtha Dhar, 2010. "An Empirical Investigation of Private Label Supply by National Label Producers," Marketing Science, INFORMS, vol. 29(4), pages 738-755, 07-08.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.