Accounting for Discrepancies Between Online and Offline Product Evaluations

References

• Abu-Mostafa YS, Magdon-Ismail M, Lin H-T (2012) Learning from Data, vol. 4 (AMLBook, New York).Google Scholar
• Allison PD, Christakis NA (1994) Logit models for sets of ranked items. Sociol. Methodology 24(1994):199–228.Crossref, Google Scholar
• Beggs S, Cardell S, Hausman J (1981) Assessing the potential demand for electric cars. J. Econometrics 17(1):1–19.Crossref, Google Scholar
• Ben-Akiva M, Bradley M, Morikawa T, Benjamin J, Novak T, Oppewal H, Rao V (1994) Combining revealed and stated preferences data. Marketing Lett. 5(4):335–349.Crossref, Google Scholar
• Bettman JR, Frances Luce M, Payne JW (1998) Constructive consumer choice processes. J. Consumer Res. 25(3):187–217.Crossref, Google Scholar
• Bhat CR, Castelar S (2002) A unified mixed logit framework for modeling revealed and stated preferences: Formulation and application to congestion pricing analysis in the San Francisco Bay area. Transportation Res. Part B: Methodological 36(7):593–616.Crossref, Google Scholar
• Brownstone D, Bunch DS, Train K (2000) Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles. Transportation Res. Part B: Methodological 34(5):315–338.Crossref, Google Scholar
• Chapman RG, Staelin R (1982) Exploiting rank ordered choice set data within the stochastic utility model. J. Marketing Res. 29(3):288–301.Crossref, Google Scholar
• Ding M (2007) An incentive-aligned mechanism for conjoint analysis. J. Marketing Res. 44(2):214–223.Crossref, Google Scholar
• Ding M, Hauser JR, Dong S, Dzyabura D, Yang Z, Su C, Gaskin SP (2011) Unstructured direct elicitation of decision rules. J. Marketing Res. 48(1):116–127.Crossref, Google Scholar
• Dzyabura D, Hauser JR (2011) Active machine learning for consideration heuristics. Marketing Sci. 30(5):801–819.Link, Google Scholar
• Dzyabura D, Jagabathula S (2018) Offline assortment optimization in the presence of an online channel. Management Sci. 64(6):2767–2786.Link, Google Scholar
• Dzyabura D, El Kihal S, Ibragimov M (2018) Leveraging the power of images in predicting product return rates. Working paper, New York University, New York.Google Scholar
• Feinberg FM, Kinnear T, Taylor JR (2012) Modern Marketing Research: Concepts, Methods, and Cases (Cengage Learning, Mason, OH).Google Scholar
• Feit EM, Beltramo MA, Fred Feinberg M (2010) Reality check: Combining choice experiments with market data to estimate the importance of product attributes. Management Sci. 56(5):785–800.Link, Google Scholar
• Gilbride TJ, Lenk PJ, Brazell JD (2008) Market share constraints and the loss function in choice-based conjoint analysis. Marketing Sci. 27(6):995–1011.Link, Google Scholar
• Green PE, Rao VR (1971) Conjoint measurement for quantifying judgmental data. J. Marketing Res. 8(3):355–363.Crossref, Google Scholar
• Hauser JR, Toubia O, Evgeniou T, Befurt R, Dzyabura D (2010) Disjunctions of conjunctions, cognitive simplicity, and consideration sets. J. Marketing Res. 47(3):485–496.Crossref, Google Scholar
• Hausman JA, Ruud PA (1987) Specifying and testing econometric models for rank-ordered data. J. Econometrics 34(1):83–104.Crossref, Google Scholar
• Higgins ET (1996) Knowledge activation: Accessibility, applicability, and salience. Higgins ET, Kruglanski AW eds. Social Psychology: Handbook of Basic Principles (Guilford Press, New York), 133–168.Google Scholar
• Huber J, Zwerina K (1996) The importance of utility balance in efficient choice designs. J. Marketing Res. 33(3):307–317.Crossref, Google Scholar
• Kohli R, Sukumar R (1990) Heuristics for product-line design using conjoint analysis. Management Sci. 36(12):1464–1478.Link, Google Scholar
• Kök AG, Fisher ML, Vaidyanathan R (2015) Assortment planning: Review of literature and industry practice. Retail Supply Chain Management (Springer, Boston), 175–236.Crossref, Google Scholar
• Kuhfeld WF, Tobias RD, Garratt M (1994) Efficient experimental design with marketing research applications. J. Marketing Res. 31(4):545–557.Crossref, Google Scholar
• Lenk PJ, DeSarbo WS, Green PE, Young MR (1996) Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. 15(2):173–191.Link, Google Scholar
• Luo L, Kannan PK, Ratchford BT (2008) Incorporating subjective characteristics in product design and evaluations. J. Marketing Res. 45(2):182–194.Crossref, Google Scholar
• Netzer O, Toubia O, Bradlow ET, Dahan E, Evgeniou T, Feinberg FM, Feit EM (2008) Beyond conjoint analysis: Advances in preference measurement. Marketing Lett. 19(3–4):337–354.Crossref, Google Scholar
• Orme B, Johnson R (2006) External effect adjustments in conjoint analysis. Proc. Sawtooth Software Conf. (Sawtooth Software, Sequim, WA), 183–210.Google Scholar
• Peck J, Childers TL (2003) To have and to hold: The influence of haptic information on product judgments. J. Marketing 67(2):35–48.Crossref, Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, et al.. (2011) Scikit-learn: Machine learning in Python. J. Machine Learn. Res. 12(February):2825–2830.Google Scholar
• PricewaterhouseCoopers (2015) Physical store beats online as preferred purchase destination for U.S. shoppers, according to PwC. Press release (February 9), PricewaterhouseCoopers LLP, New York. http://www.prnewswire.com/news-releases/physical-store-beats-online-as-preferred-purchase-destination-for-us-shoppers-according-to-pwc-300032566.html.Google Scholar
• Punj GN, Staelin R (1978) The choice process for graduate business schools. J. Marketing Res. 15(4):588–598.Crossref, Google Scholar
• Rossi PE, Allenby GM, McCulloch R (2012) Bayesian Statistics and Marketing (John Wiley & Sons, Chichester, UK).Google Scholar
• She J, MacDonald EF (2013) Trigger features on prototypes increase preference for sustainability. ASME 2013 Internat. Design Engrg. Tech. Conf. Comput. Inform. Engrg. Conf., vol. 5 (American Society of Mechanical Engineers, New York), V005T06A043–V005T06A054.Crossref, Google Scholar
• Srinivasan V, Lovejoy WS, Beach D (1997) Integrated product design for marketability and manufacturing. J. Marketing Res. 34(1):154–163.Crossref, Google Scholar
• Swait J, Andrews RL (2003) Enriching scanner panel models with choice experiments. Marketing Sci. 22(4):442–460.Link, Google Scholar
• Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, et al.. (2008) Top 10 algorithms in data mining. Knowledge Inform. Systems 14(1):1–37.Crossref, Google Scholar
• U.S. Census Bureau (2017) Quarterly retail e-commerce sales, 2nd quarter. U.S. Census Bureau News (August 17), https://www.census.gov/retail/mrts/www/data/pdf/ec_current.pdf.Google Scholar