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A Comparative Analysis of Neural Networks and Statistical Methods for Predicting Consumer Choice

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  1. Shao, Wei & Lye, Ashley & Rundle-Thiele, Sharyn, 2009. "Different strokes for different folks: A method to accommodate decision -making heterogeneity," Journal of Retailing and Consumer Services, Elsevier, vol. 16(6), pages 495-501.
  2. Hruschka, Harald & Fettes, Werner & Probst, Markus, 2004. "An empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications," European Journal of Operational Research, Elsevier, vol. 159(1), pages 166-180, November.
  3. Wittaya Pornpatcharapong & Chuvej Chansa-ngavej & Supachok Wiriyacosol & Chanchai Bunchapattanasakda, 2011. "The Use of Artificial Neural Networks to Prioritize Impact Factors Affecting Thai Rural Village Development," Journal of Social and Development Sciences, AMH International, vol. 2(2), pages 89-93.
  4. Hu, Michael Y. & Tsoukalas, Christos, 2003. "Explaining consumer choice through neural networks: The stacked generalization approach," European Journal of Operational Research, Elsevier, vol. 146(3), pages 650-660, May.
  5. Dapeng Cui & David Curry, 2005. "Prediction in Marketing Using the Support Vector Machine," Marketing Science, INFORMS, vol. 24(4), pages 595-615, January.
  6. Vroomen, Bjorn & Hans Franses, Philip & van Nierop, Erjen, 2004. "Modeling consideration sets and brand choice using artificial neural networks," European Journal of Operational Research, Elsevier, vol. 154(1), pages 206-217, April.
  7. Grant Samkin & Annika Schneider, 2008. "Adding scientific rigour to qualitative data analysis: an illustrative example," Qualitative Research in Accounting & Management, Emerald Group Publishing Limited, vol. 5(3), pages 207-238, October.
  8. Maytal Saar-Tsechansky & Foster Provost, 2007. "Decision-Centric Active Learning of Binary-Outcome Models," Information Systems Research, INFORMS, vol. 18(1), pages 4-22, March.
  9. Qi, Min & Yang, Sha, 2003. "Forecasting consumer credit card adoption: what can we learn about the utility function?," International Journal of Forecasting, Elsevier, vol. 19(1), pages 71-85.
  10. Crone, Sven F. & Lessmann, Stefan & Stahlbock, Robert, 2006. "The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing," European Journal of Operational Research, Elsevier, vol. 173(3), pages 781-800, September.
  11. Marco Vriens & Nathan Bosch & Chad Vidden & Jason Talwar, 2022. "Prediction and profitability in market segmentation typing tools," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(4), pages 360-389, December.
  12. José Joaquín Del Pozo-Antúnez & Antonio Ariza-Montes & Francisco Fernández-Navarro & Horacio Molina-Sánchez, 2018. "Effect of a Job Demand-Control-Social Support Model on Accounting Professionals’ Health Perception," IJERPH, MDPI, vol. 15(11), pages 1-16, November.
  13. Gianni Di Pillo & Vittorio Latorre & Stefano Lucidi & Enrico Procacci, 2013. "An application of learning machines to sales forecasting under promotions," DIAG Technical Reports 2013-04, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
  14. Sparke, K. & Menrad, K., 2008. "Dimensionen der Verbraucherresonanz bei der Neuproduktbeurteilung von Lebensmitteln," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 43, March.
  15. Luiz Moutinho & Paulo Rita & Shuliang Li, 2006. "Strategic diagnostics and management decision making: a hybrid knowledge‐based approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 14(3), pages 129-155, July.
  16. Saiful Anwar & A.M Hasan Ali, 2018. "ANNs-BASED EARLY WARNING SYSTEM FOR INDONESIAN ISLAMIC BANKS," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 20(3), pages 1-18, January.
  17. Naman Shukla & Kartik Yellepeddi, 2021. "Negotiating Networks in Oligopoly Markets for Price-Sensitive Products," Papers 2110.13303, arXiv.org.
  18. Makoto Abe & Yasemin Boztug & Lutz Hildebrandt, 2004. "Investigating the competitive assumption of Multinomial Logit models of brand choice by nonparametric modeling," Computational Statistics, Springer, vol. 19(4), pages 635-657, December.
  19. Venkatesh Shankar & Sohil Parsana, 2022. "An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1324-1350, November.
  20. Phillips, Paul & Zigan, Krystin & Santos Silva, Maria Manuela & Schegg, Roland, 2015. "The interactive effects of online reviews on the determinants of Swiss hotel performance: A neural network analysis," Tourism Management, Elsevier, vol. 50(C), pages 130-141.
  21. Geng Cui & Man Leung Wong & Hon-Kwong Lui, 2006. "Machine Learning for Direct Marketing Response Models: Bayesian Networks with Evolutionary Programming," Management Science, INFORMS, vol. 52(4), pages 597-612, April.
  22. Sifringer, Brian & Lurkin, Virginie & Alahi, Alexandre, 2020. "Enhancing discrete choice models with representation learning," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 236-261.
  23. Han, Yafei & Pereira, Francisco Camara & Ben-Akiva, Moshe & Zegras, Christopher, 2022. "A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 166-186.
  24. Armstrong, J. Scott & Brodie, Roderick J., 1999. "Forecasting for Marketing," MPRA Paper 81690, University Library of Munich, Germany.
  25. Moutinho, Ricardo & Au-Yong-Oliveira, Manuel & Coelho, Arnaldo & Manso, José Pires, 2015. "Beyond the “Innovation's Black-Box”: Translating R&D outlays into employment and economic growth," Socio-Economic Planning Sciences, Elsevier, vol. 50(C), pages 45-58.
  26. G. Di Pillo & V. Latorre & S. Lucidi & E. Procacci, 2016. "An application of support vector machines to sales forecasting under promotions," 4OR, Springer, vol. 14(3), pages 309-325, September.
  27. Potharst, R. & van Rijthoven, M. & van Wezel, M.C., 2005. "Modeling brand choice using boosted and stacked neural networks," Econometric Institute Research Papers EI 2005-05, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  28. Steven M. Ramsey & Jason S. Bergtold, 2021. "Examining Inferences from Neural Network Estimators of Binary Choice Processes: Marginal Effects, and Willingness-to-Pay," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1137-1165, December.
  29. Bergtold, Jason S. & Ramsey, Steven M., 2015. "Neural Network Estimators of Binary Choice Processes: Estimation, Marginal Effects and WTP," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205649, Agricultural and Applied Economics Association.
  30. Yafei Han & Francisco Camara Pereira & Moshe Ben-Akiva & Christopher Zegras, 2020. "A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability," Papers 2002.00922, arXiv.org, revised Jul 2022.
  31. Fabio Luis Marques dos Santos & Paolo Tecchio & Fulvio Ardente & Ferenc Pekár, 2021. "User Automotive Powertrain-Type Choice Model and Analysis Using Neural Networks," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
  32. Fish, Kelly E. & Johnson, John D. & Dorsey, Robert E. & Blodgett, Jeffery G., 2004. "Using an artificial neural network trained with a genetic algorithm to model brand share," Journal of Business Research, Elsevier, vol. 57(1), pages 79-85, January.
  33. Bergtold, Jason S. & Taylor, Daniel B. & Bosch, Darrell J., 2003. "Networking Your Way to a Better Prediction: Effectively Modeling Contingent Valuation Survey Data," 2003 Annual meeting, July 27-30, Montreal, Canada 22152, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  34. Bioch, J.C. & Groenen, P.J.F. & Nalbantov, G.I., 2005. "Solving and interpreting binary classification problems in marketing with SVMs," Econometric Institute Research Papers EI 2005-46, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  35. Sparke, Kai & Menrad, Klaus, 2007. "DIMENSIONEN DER VERBRAUCHERRESONANZ BEI DER NEUPRODUKTBEURTEILUNG VON LEBENSMITTELN (German)," 47th Annual Conference, Weihenstephan, Germany, September 26-28, 2007 7611, German Association of Agricultural Economists (GEWISOLA).
  36. Li, Xi & Shi, Mengze & Wang, Xin (Shane), 2019. "Video mining: Measuring visual information using automatic methods," International Journal of Research in Marketing, Elsevier, vol. 36(2), pages 216-231.
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