The Strategic Implications of Scale in Choice-Based Conjoint Analysis

Published Online:https://doi.org/10.1287/mksc.2019.1178

References

  • Aksoy-Pierson M, Allon G, Federgruen A (2013) Price competition under mixed multinomial logit demand functions. Management Sci. 59(8):1817–1835.LinkGoogle Scholar
  • Allenby GM, Brazell J, Howell JR, Rossi PE (2014) Economic valuation of product features. Quant. Marketing Econom. 12(4):421–456.CrossrefGoogle Scholar
  • Anderson SP, de Palma A, Thisse JF (1999) Discrete Choice Theory of Product Differentiation (MIT Press, Cambridge, MA).Google Scholar
  • Bass FM (1974) The theory of stochastic preference and brand switching. J. Marketing Res. 11(1):1–20.CrossrefGoogle Scholar
  • Beck MJ, Rose JM, Hensher DA (1993) Consistently inconsistent: The role of certainty, acceptability and scale in choice. Transportation Res. Part E: Logist. Transportation Rev. 56(September):81–93.CrossrefGoogle Scholar
  • Cameron L, Cragg M, McFadden DL (2013) The role of conjoint surveys in reasonable royalty cases. Law360 (October 16), https://www.law360.com/articles/475390/the-role-of-conjoint-surveys-in-reasonable-royalty-cases.Google Scholar
  • Caplin A, Nalebuff B (1991) Aggregation and imperfect competition: On the existence of equilibrium. Econometrica 59(1):25–59.CrossrefGoogle Scholar
  • Choi SC, DeSarbo WS (1994) A conjoint simulation model incorporating short-run price competition. J. Product Innovation Management 11(5):451–459.CrossrefGoogle Scholar
  • Choi SC, DeSarbo WS, Harker PT (1990) Product positioning under price competition. Management Sci. 36(2):175–199.LinkGoogle Scholar
  • Dahan E, Hauser JR (2002) The virtual customer. J. Product Innovation Management 19(5):332–354.CrossrefGoogle Scholar
  • Dahan E, Srinivasan V (2000) The predictive power of Internet-based product concept testing using visual depiction and animation. J. Product Innovation Management 17(2):99–109.CrossrefGoogle Scholar
  • de Palma A, Ginsburgh V, Thisse JF (1987) On existence of location equilibria in the 3-firm Hotelling problem. J. Indust. Econom. 36(2):245–252.CrossrefGoogle Scholar
  • de Palma A, Ginsburgh V, Papageorgiou YY, Thisse JF (1985) The principle of minimum differentiation holds under sufficient heterogeneity. Econometrica 53(4):767–781.CrossrefGoogle Scholar
  • Ding M (2007) An incentive-aligned mechanism for conjoint analysis. J. Marketing Res. 54(May):214–223.CrossrefGoogle Scholar
  • Ding M, Grewal R, Liechty J (2005) Incentive-aligned conjoint analysis. J. Marketing Res. 42(February):67–82.CrossrefGoogle Scholar
  • Ding M, Hauser JR, Dong S, Dzyabura D, Yang Z, Su C, Gaskin S (2011) Unstructured direct elicitation of decision rules. J. Marketing Res. 48(February):116–127.CrossrefGoogle Scholar
  • Dzyabura D, Jagabathula S, Muller E (2019) Accounting for discrepancies between online and offline shopping behavior. Marketing Sci. 38(1):88–106.Google Scholar
  • Economides N (1986) Minimal and maximal product differentiation in Hotelling’s duopoly. Econom. Lett. 21(1):67–71.CrossrefGoogle Scholar
  • Evgeniou T, Boussios C, Zacharia G (2005) Generalized robust conjoint estimation. Marketing Sci. 24(3):415–429.LinkGoogle Scholar
  • Fiebig DG, Keane MP, Louviere J, Wasi N (2010) The generalized multinomial logit model: Accounting for scale and coefficient heterogeneity. Marketing Sci. 29(3):393–421.LinkGoogle 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.LinkGoogle Scholar
  • Hauser JR (1978) Testing the accuracy, usefulness, and significance of probabilistic models: An information theoretic approach. Oper. Res. 26(3):406–421.LinkGoogle Scholar
  • Hotelling H (1929) Stability in competition. Econom. J. 39(153):41–57.Google Scholar
  • Huber J, Orme BK, Miller R (1999) Dealing with product similarity in conjoint simulations. Sawtooth Software: Research Paper Series, https://www.sawtoothsoftware.com/download/techpap/prodsim.pdf.Google Scholar
  • Irmen A, Thisse JF (1998) Competition in multi-characteristics spaces: Hotelling was almost right. J. Econom. Theory 78(1):76–102.CrossrefGoogle Scholar
  • Keeney RL, Raiffa H (1976) Decisions with Multiple Objectives: Preferences and Value Tradeoffs (John Wiley & Sons, New York).Google Scholar
  • Koh L (2012) Order granting in part and denying in part motions to exclude certain expert opinions: Public redacted version. Apple, Inc. v. Samsung Elecs. Co., Case No. 12-CV-00630-LHK (N.D. Cal. Feb. 25, 2014).Google Scholar
  • Luo L, Kannan PK, Ratchford BT (2007) New product development under channel acceptance. Marketing Sci. 26(2):149–163.LinkGoogle Scholar
  • Manski CF (1977) The structure of random utility models. Theory Decision 8(3):229–254.CrossrefGoogle Scholar
  • McFadden DL (2014) Testimony of Daniel L. McFadden in the matter of determination of rates and terms for digital performance in sound recordings and ephemeral recordings (WEB IV). Before the Copyright Royalty Board Library of Congress, Washington DC, Docket No. 14-CRB-0001-WR, October 6. Google Scholar
  • Meissner M, Oppewal H, Huber J (2016) How many options? behavioral responses to two vs. five alternatives per choice. Proc. Sawtooth Software Conf.: September 2016 (Sawtooth Software, Park City, UT), 19–36.Google Scholar
  • Mintz H (2012) Apple wins $1 billion victory over Samsung. San Jose Mercury News (August 24), https://www.mercurynews.com/2012/08/24/2012-apple-wins-1-billion-victory-over-samsung/.Google Scholar
  • Moorthy KS (1988) Product and price competition in a duopoly. Marketing Sci. 7(2):141–168.LinkGoogle Scholar
  • Ofek E, Srinivasan V (2002) How much does the market value an improvement in a product attribute? Marketing Sci. 21(4):398–411.LinkGoogle Scholar
  • Orme B (2014) Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research, 3rd ed. (Research Publishers LLC, Manhattan Beach, CA).Google Scholar
  • Orme B (2017) Personal email to the authors, July 25, 2017.Google Scholar
  • Orme B, Chrzan K (2017) Becoming an Expert in Conjoint Analysis: Choice Modelling for Pros (Sawtooth Software, Orem, UT).Google Scholar
  • Pancreas J, Wang X, Dey DK (2016) Investigating the impact of customer stochasticity on firm price discrimination strategies using a new Bayesian mixture scale heterogeneity model. Marketing Lett. 27(3):537–552.CrossrefGoogle Scholar
  • Reisinger D (2017) Here’s how popular Apple Watch was last quarter. Fortune (February 8), http://fortune.com/2017/02/08/apple-watch-2016-sales/.Google Scholar
  • Rhee BD, de Palma A, Fornell C, Thisse JF (1992) Restoring the principle of minimum differentiation in product positioning. J. Econom. Management Strategy 1(3):475–505.CrossrefGoogle Scholar
  • Salisbury LC, Feinberg FM (2010) Alleviating the constant stochastic variance assumption in decision research: Theory, measurement, and experimental test. Marketing Sci. 29(1):1–17.LinkGoogle Scholar
  • Sawtooth Software (2015) Report on conjoint analysis usage among Sawtooth Software customers. Accessed May 9, 2019, http://www.sawtoothsoftware.com/download/Conjoint_Report_2015.pdf.Google Scholar
  • Sawtooth Software (2016) Results of Sawtooth Software user survey. Accessed May 9, 2019, https://www.sawtoothsoftware.com/about-us/news-and-events/news/1693-results-of-2016-sawtooth-software-user-survey.Google Scholar
  • Sawtooth Software (2019) Randomized first choice settings. Accessed May 9, 2019, https://www.sawtoothsoftware.com/help/lighthouse-studio/manual/randomizedfirstchoicesettingswindow.html.Google Scholar
  • Sonnier G, Ainslie A, Otter T (2007) Heterogeneity distributions of willingness-to-pay in choice models. Quant. Marketing Econom. 5(3):313–331.CrossrefGoogle Scholar
  • Swait J, Louviere J (1993) The role of the scale parameter in the estimation and comparison of multinomial logit models. J. Marketing Res. 30(3):305–314.CrossrefGoogle Scholar
  • Thurstone LL (1927) A law of comparative judgment. Psych. Rev. 34(4):273–286.CrossrefGoogle Scholar
  • Toubia O, Hauser JR, Simester DI (2004), Polyhedral methods for adaptive choice-based conjoint analysis, J. Marketing Res. 41(1): 116–131.Google Scholar
  • Train KE (2009) Discrete Choice Methods with Simulation (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Ulrich KT, Eppinger SD (2004) Product Design and Development, 3rd ed. (McGraw-Hill, New York).Google Scholar
  • Urban GL, Hauser JR (1993) Design and Marketing of New Products, 2nd ed. (Prentice-Hall, Englewood Cliffs, NJ).Google Scholar
  • Urban GL, Hauser JR (2004) “Listening-in” to find and explore new combinations of customer needs. J. Marketing 68(April):72–87.CrossrefGoogle Scholar
  • Vriens M, Loosschilder GH, Rosbergen E, Wittink DR (1998) Verbal vs. realistic pictorial representations in conjoint analysis with design attributes. J. Product Innovation Management 15(5):455–467.CrossrefGoogle Scholar
  • Whyte RM (2005) Order granting in part and denying in part Olin’s motion to exclude the testimony of John Kilpatrick. Palmisano et al. v. Olin Corporation et al., Case No. C-03-01607 RMW (N.D. Cal. July 5, 2005).Google Scholar
  • Wlömert N, Eggers F (2016) Predicting new service adoption with conjoint analysis: External validity of BDM-based incentive-aligned and dual-response choice designs. Marketing Lett. 27(1):195–210.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.