Modeling consideration sets and brand choice using artificial neural networks

European Journal of Operational Research

Volumn 154, Issue 1, 2004, Pages 206-217

  Vroomen, Björn ^a   Franses, Philip Hans ^b   Van Nierop, Erjen ^b,c  

^a ERASMUS UNIVERSITY ROTTERDAM   (Netherlands)

^b ERASMUS UNIVERSITY ROTTERDAM   (Netherlands)

^c CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Artificial neural network; Brand choice; Consideration set

Indexed keywords

DATA STRUCTURES; DECISION MAKING; MARKETING; OPERATIONS RESEARCH; REGRESSION ANALYSIS;

BRAND CHOICE; CONSIDERATION SET;

NEURAL NETWORKS;

EID: 0344874736     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0377-2217(02)00673-2     Document Type: Article

References (34)

1. Agrawal D., Schorling C. Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model. Journal of Retailing. 72(4):1996;383-407.
2. Andrews R.L., Manrai A.K. Simulation experiments in choice simplification: The effects of task and context on forecasting performance. Journal of Marketing Research. 35:1998;198-209.
3. Andrews R.L., Srinivasan T.C. Studying consideration effects in empirical choice models using scanner panel data. Journal of Marketing Research. 32(1):1995;30-41.
4. Bishop C.M. Neural Networks for Pattern Recognition. 1995;Oxford University Press, Oxford.
5. Bronnenberg B.J., Vanhonacker W.R. Limited choice sets, local price response and implied measures of price competition. Journal of Marketing Research. 33:1996;163-173.
6. Chiang J., Chib S., Narasimhan C. Markov chain monte carlo and models of consideration set and parameter heterogeneity. Journal of Econometrics. 89:1999;23-248.
7. Chintagunta P.K., Prasad A.R. An empirical investigation of the dynamic "mcfadden model" of purchase timing and brand choice: Implications for market structure. Journal of Business and Economic Statistics. 16:1998;2-11.
8. Daganzo C. Multinomial Probit: The Theory and its Applications to Demand Forecasting. 1979;Academic Press, New York.
9. Dasgupta C.G., Dispensa G.S., Ghose S. Comparing the predictive performance of a neural network model with some traditional market response models. International Journal of Forecasting. 10:1994;235-244.
10. Doornik, J.A., 1999. Object-Oriented Matrix Programming Using Ox, third ed. Timberlake Consultants Press and Oxford, London, Available from: .
11. Fotheringham A.S. Consumer store choice and choice set definition. Marketing Science. 7(3):1988;299-310.
12. Franses P.H., Draisma G. Recognizing changing seasonal patterns using artificial neural networks. Journal of Econometrics. 81:1997;273-280.
13. Franses P.H., van Dijk D. Nonlinear Time Series Models in Empirical Finance. 2000;Cambridge University Press, Cambridge.
14. Guadagni P.M., Little J.D.C. A logit model of brand choice calibrated on scanner data. Marketing Science. 2:1983;203-238.
15. Hausman J., Wise D. A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogenous preferences. Econometrica. 45:1978;319-339.
16. Heimel J.P., Hruschka H., Natter M., Taudes A. Konnexionistische kaufakt- und markenwahlmodelle. Zeitschrift für Betriebswirtschaftliche Forschung. 50:1998;596-613.
17. Horowitz J.L., Louviere J.J. What is the role of consideration sets in choice modelling? International Journal of Research in Marketing. 12:1995;39-54.
18. Hruschka H. Determining market response functions by neural network modeling: A comparison to econometric techniques. European Journal of Operational Research. 66:1993;27-35.
19. Hruschka H., Fettes W., Probst M., Mies C. A flexible brand choice model based on neural net methodology. A comparison to the linear utility multinomial logit model and its latent class extension. OR Spectrum. 24:2002;127-143.
20. Hu M.Y., Shanker M., Hung M.S. Estimation of posterior probabilities of consumer situational choices with neural network classifiers. International Journal of Research in Marketing. 16:1999;307-317.
21. Kuan C.M., White H. Artificial neural networks: An econometric perspective (with discussion). Econometric Reviews. 13:1994;1-91.
22. Kumar A., Rao V.R., Soni H. An empirical comparison of neural network and logistic regression models. Marketing Letters. 6:1995;251-263.
23. Lattin J.M., Bucklin E. Reference effects of price and promotion on brand choice behavior. Journal of Marketing Research. 26:1989;299-310.
24. Manrai A.K., Andrews R.L. Two-stage discrete choice models for scanner panel data: An assessment of process and assumptions. European Journal of Operational Research. 111:1998;193-215.
25. McFadden D. Conditional logit analysis of qualitative choice behavior. Zarembka P. Frontiers in Econometrics. 1973;105-142 Academic Press, New York.
26. Mitchell T.M. Machine Learning. 1997;McGraw-Hill, Singapore.
27. Roberts J.H., Lattin J.M. Development and testing of a model of consideration set composition. Journal of Marketing Research. 28:1991;429-440.
28. Roberts J.H., Lattin J.M. Consideration: Review of research and prospects for future insights. Journal of Marketing Research. 34:1997;406-410.
29. Shocker A.D., Ben-Akiva M., Boccara B., Nedungadi P. Consideration set influences on consumer decision-making and choice: Issues, models and suggestions. Marketing Letters. 2:1991;181-197.
30. Swanson N.R., White H. A model-selection approach to assessing the information in the term structure using linear models and artificial neural networks. Journal of Business and Economic Statistics. 13:1995;265-275.
31. Vellido A., Lisboa P.J.G., Vaughan J. Neural networks in business: A survey of applications (1992-1998). Expert Systems with Applications. 17:1999;51-70.
32. West P.M., Brockett P.L., Golden L.L. A comparative analysis of neural networks and statistical methods for predicting consumer choice. Marketing Science. 16(4):1997;370-391.
33. Zapranis A., Refenes A. Principles of neural model indentification, selection and adequacy. With applications to financial econometrics perspectives in neural computing. 1999;Springer-Verlag, London.
34. Zhang G., Hu M.Y., Patuwo B.E., Indro D.C. Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European Journal of Operational Research. 116:1999;16-32.