Analyzing Moment-to-Moment Data Using a Bayesian Functional Linear Model: Application to TV Show Pilot Testing

Published Online:https://doi.org/10.1287/mksc.2013.0835

References

  • Aaker DA, Stayman DM, Hagerty MR (1986) Warmth in advertising: Measurement, impact, and sequence effects. J. Consumer Res. 12(4):365–381.CrossrefGoogle Scholar
  • Albert JH, Chib S (1993) Bayesian analysis of binary and polychotomous response data. J. Amer. Statist. Assoc. 88(422):669–679.CrossrefGoogle Scholar
  • Ariely D (1998) Combining experiences over time: The effects of duration, intensity changes and on-line measurements on retrospective pain evaluations. J. Behav. Decision Making 11(1):19–45.CrossrefGoogle Scholar
  • Ariely D, Zauberman G (2000) On the making of an experience: The effects of breaking and combining experiences in their overall evaluation. J. Behav. Decision Making 13(2):219–232.CrossrefGoogle Scholar
  • Baumgartner H, Sujan M, Padgett D (1997) Patterns of affective reactions to advertisements: The integration of moment-to-moment responses into overall judgement. J. Marketing Res. 34(2):219–232.CrossrefGoogle Scholar
  • Blacker IR (1998) The Elements of Screenwriting (Macmillan, New York).Google Scholar
  • Cardot H, Ferraty F, Sarda P (1999) Functional linear model. Statist. Prob. Lett. 45(1):11–22.CrossrefGoogle Scholar
  • Cardot H, Ferraty F, Sarda P (2003) Spline estimators for the functional linear model. Statistica Sinica 13:571–591.Google Scholar
  • Casella G, Berger RL (1992) Statistical Inference, 2nd ed. (Duxbury, Pacific Grove, CA).Google Scholar
  • Choi J, Hui SK, Bell DR (2010) Spatiotemporal analysis of imitation behavior across new buyers at an online grocery retailer. J. Marketing Res. 47(1):75–89.CrossrefGoogle Scholar
  • Corduneanu C (1991) Integral Equations and Applications (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Correll J, Spencer SJ, Zanna MP (2004) An affirmed self and an open mind: Self-affirmation and sensitivity to argument strength. J. Experiment. Soc. Psych. 40(3):350–356.CrossrefGoogle Scholar
  • Dialsmith (2013) Essentials of moment-to-moment research. Report, Dialsmith, Beaverton, OR. http://dialsmith.com/_docs/Dialsmith-Essentials-of-MtM-eBook-070813.pdf.Google Scholar
  • Evgeniou T, Pontil M, Toubia O (2007) A convex optimization approach to modeling consumer heterogeneity in conjoint estimation. Marketing Sci. 26(6):805–818.LinkGoogle Scholar
  • Foutz NZ, Jank W (2010) Prerelease demand forecasting for motion pictures using functional shape analysis of virtual stock markets. Marketing Sci. 29(3):568–579.LinkGoogle Scholar
  • Fredrickson BL, Kahneman D (1993) Duration neglect in retrospective evaluations of affective episodes. J. Personality Soc. Psych. 65(1):45–55.CrossrefGoogle Scholar
  • Gelman A, Carlin JB, Stern HS, Rubin DB (2003) Bayesian Data Analysis, 2nd ed. (Chapman & Hall/CRC Press, Boca Raton, FL).Google Scholar
  • Greene WH (2011) Econometric Analysis, 7th ed. (Prentice Hall, Upper Saddle River, NJ).Google Scholar
  • Hastie T, Mallows C (1993) [A statistical view of some chemometrics regression tools]: Discussion. Technometrics 35(2):140–143.Google Scholar
  • Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. (Springer, New York).CrossrefGoogle Scholar
  • Hays S, Shen H, Huang JZ (2012) Functional dynamic factor models with application to yield curve forecasting. Ann. Appl. Statist. 6(3):870–894.CrossrefGoogle Scholar
  • Hughes GD (1992) Realtime response measures redefine advertising wearout. J. Advertising Res. 32(3):61–77.Google Scholar
  • James GM, Wang J, Zhu J (2009) Functional linear regression that's interpretable. Ann. Statist. 37(5A):2083–2108.CrossrefGoogle Scholar
  • Jank W, Shmueli G (2006) Functional data analysis in electronic commerce research. Statist. Sci. 21(2):155–166.CrossrefGoogle Scholar
  • Johnson SB (2005) Everything Bad Is Good for You: How Today's Popular Culture Is Actually Making Us Smarter (Riverhead, New York).Google Scholar
  • Kahneman D, Fredrickson B, Schreiber C, Redelmeier D (1993) When more pain is preferred to less: Adding a better end. Psych. Sci. 4(6):401–405.CrossrefGoogle Scholar
  • Kass RE, Raftery AE (1995) Bayes factor. J. Amer. Statist. Assoc. 90(430):773–795.CrossrefGoogle Scholar
  • Kirk R, Shrill D (2011) A digital agora: Citizen participation in the 2008 presidential debates. Amer. Behav. Scientist 55(3):325–347.CrossrefGoogle Scholar
  • Lewis B, Lewis D, Cumming G (1995) Frequent measurement of chronic pain: An electronic diary and empirical findings. Pain 60(3):341–347.CrossrefGoogle Scholar
  • Madrigal R, Bee C (2005) Suspense as an experience of mixed emotions: Feelings of hope and fear while watching suspenseful commercials. Adv. Consumer Res. 32:561–567.Google Scholar
  • Miron-Shatz T (2009) Evaluating multiepisode events: Boundary conditions for the peak-end rule. Emotion 9(2):206–213.CrossrefGoogle Scholar
  • Montgomery NV, Unnava HR (2009) Temporal sequence effects: A memory framework. J. Consumer Res. 36(1):83–92.CrossrefGoogle Scholar
  • Nelson LD, Meyvis T, Galak J (2009) Enhancing the television viewing experience through commercial interruptions. J. Consumer Res. 36(August):160–172.CrossrefGoogle Scholar
  • Pham MT, Cohen JB, Pracejus JW, Hughes GD (2001) Affect monitoring and the primacy of feelings in judgment. J. Consumer Res. 28(September):167–188.CrossrefGoogle Scholar
  • Polsfuss M, Hess M (1991) Liking through moment-to-moment evaluation: Identifying key selling segments in advertising. Holman RH, Solomon MR, eds. Advances in Consumer Research, Vol. 18 (Assoication of Consumer Research, Provo, UT), 540–544.Google Scholar
  • Ramanathan S, McGill AL (2007) Consuming with others: Social influence on moment-to-moment and retrospective evaluations of an experience. J. Consumer Res. 34(4):506–524.CrossrefGoogle Scholar
  • Ramsay JO, Li X (1998) Curve registration. J. Roy. Statist. Soc. Ser. B 60(2):351–363.CrossrefGoogle Scholar
  • Ramsay JO, Silverman BW (2002) Applied Functional Data Analysis: Methods and Case Studies (Springer, New York).CrossrefGoogle Scholar
  • Ramsay JO, Silverman BW (2005) Functional Data Analysis, 2nd ed. (Springer, New York).CrossrefGoogle Scholar
  • Redelmeier DA, Kahneman D (1996) Patients' memories of painful medical treatments: Real-time and retrospective evaluations of two minimally invasive procedures. Pain 66(1):3–8.CrossrefGoogle Scholar
  • Redelmeier DA, Katz J, Kahneman D (2003) Memories of colonoscopy: A randomized trial. Pain 104(1–2):187–194.CrossrefGoogle Scholar
  • Reithinger F, Jank W, Tutz G, Shumeli G (2008) Smoothing sparse and unevenly sampled curves using semiparametric mixed models: An application to online auctions. J. Roy. Statist. Soc. Ser. C 57(2):127–148.CrossrefGoogle Scholar
  • Robinson E, Blissett J, Higgs S (2011) Peak and end effects on remembered enjoyment in low and high restrained eaters. Appetite 57(1):207–212.CrossrefGoogle Scholar
  • Rossi PE, Allenby GM, McCulloch R (2005) Bayesian Statistics and Marketing (John Wiley & Sons, Chichester, UK).CrossrefGoogle Scholar
  • Schreiber CA, Kahneman D (2000) Determinants of the remembered utility of aversive sounds. J. Experiment. Psych.: General 129(1):27–42.CrossrefGoogle Scholar
  • Sood A, James GM, Tellis GJ (2009) Functional regression: A new model for predicting market penetration of new products. Marketing Sci. 28(1):36–51.LinkGoogle Scholar
  • Teixeira T, Wedel M, Pieters R (2012) Emotion-induced engagement in Internet video advertisements. J. Marketing Res. 49(2):144–159.CrossrefGoogle Scholar
  • Thorson E, Friestad M (1989) The effects of emotion on episodic memory for television commercials. Cafferata P, Tybout A, eds. Cognitive and Affective Responses to Advertising (Lexington Books, Lexington, MA), 305–325.Google Scholar
  • Todorov A, Mandisodza AN, Goren A, Hall CC (2005) Inferences of competence from faces predict election outcomes. Science 308(5728):1623–1626.CrossrefGoogle Scholar
  • Tsay C-J (2013) Sight over sound in the judgment of music performance. Proc. Natl. Acad. Sci. USA 110(36):14580–14585.CrossrefGoogle Scholar
  • Vanden Abeele P, MacLachlan DI (1994) Process tracing of emotional responses to TV ads: Revisiting the warmth monitor. J. Consumer Res. 20(4):586–600.CrossrefGoogle Scholar
  • Varey C, Kahneman D (1992) Experiences extended across time: Evaluation of moments and episodes. J. Behav. Decision Making 5(3):169–185.CrossrefGoogle Scholar
  • Wang S, Jank W, Shmueli G, Smith P (2008) Explaining and forecasting online auction prices and their dynamics using functional data analysis. J. Bus. Econom. Statist. 26(2):144–160.CrossrefGoogle Scholar
  • West M, Harrison J (1997) Bayesian Forecasting and Dynamic Models, 2nd ed. (Springer, New York).Google Scholar
  • Wirtz D, Kruger J, Scollon CN, Diener E (2003) The role of predicted on-line, and remembered experience in future choice. Psych. Sci. 14(5):520–524.CrossrefGoogle Scholar
  • Woltman Elpers JLCM, Mukherjee A, Hoyer WD (2004) Humor in television advertising: A moment-to-moment analysis. J. Consumer Res. 31(3):592–598.CrossrefGoogle Scholar
  • Woltman Elpers JLCM, Wedel M, Pieters R (2003) Why do consumers stop viewing television commericals? Two experiments on the influence of moment-to-moment entertainment and information value. J. Marketing Res. 40(4):437–453.CrossrefGoogle Scholar
  • Zhou L, Huang J, Martinez JG, Maity A, Baladandayuthapani V, Carroll RJ (2010) Reduced rank mixed effects models for spatially correlated hierarchical functional data. J. Amer. Statist. Assoc. 105(489):390–400.CrossrefGoogle Scholar
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