Relaxation & Monetary Valuation

1. The Relaxed Consumer & The Emotional OracleMichel Tuan PhamColumbia UniversityK U Leuven 2009 Marketing Camp

2. Relaxation & Monetary Valuation Michel Tuan Pham Columbia University Iris W. Hung National University of Singapore Gerald J. Gorn University of Hong Kong

3. A distinct physiological, emotional, and mental state, that is hedonically pleasant, and is characterized by low physiological arousal and low tension, feelings of calmness and peacefulness, and a lack of worry and preoccupation. Relaxation

6. Research Questions • What are the effects of relaxation on consumer judgment and decision making? • Is there something unique about states of relaxation, beside being pleasant states? • Need to compare to less-relaxed affective states that is equally pleasant

7. Main Manipulation 10 min Relaxation DVD Nature scenes Soft music Relaxation instructions (e.g., breathing) 10 min TV documentary World Expo in Japan Scenes of robots Relaxed Less-Relaxed but equally pleasant

8. Pretest

9. Imagine you want to buy a new digital camera and that the camera that you want is the one depicted below. You find out that this camera is available brand new with free shipping on eBay, the popular auction site where people buy and sell goods among themselves (ebay.com.hk). Therefore, you may be able to obtain the camera you want (free of shipping) by biding for it on eBay. 1. What would be the maximum bid (offer) that you would be willing to make to get this camera on eBay? My maximum offer would be ________ dollars. 2. How much do you think this camera is really worth? It is really worth _______ dollars

10. Study 2

11. How much is the crystalline picture frame worth? The crystalline picture frame is worth A. $100-500 B. $500-1000 C. $1000-1500 D. $1500-2000 E. $2000-2500 How much is the vacuum cleaner worth? The vacuum cleaner is worth A. $1500-2000 B. $2000-2500 C. $2500-3000 D. $3000-3500 E. $3500-4000

12. Study 1 * * * * * *

13. Pretest 2

14. Study 1b (Music Manipulation) * * * * * * * *

15. Product Attribute Ratings

16. Study 3 (N = 159) 1. Monetary Valuations (Bid, Worth) 1. Specific Ratings (Ease of use, Features, Looks, Convenience) 2. Monetary Valuations (Bid, Worth) 2. Specific Ratings (Ease of use, Features, Looks, Convenience)

17. Study 3 (N = 159)

18. Meat is an example of ___ Burger is an example of ___ Singer is an example of ___ Computer is an example of ___ Magazine is an example of ___ Dress is an example of ____ Chair is an example of ____ Taxis is an example of ____ Fruit is an example of ____ Beer is an example of ____ … … An example of meat is ___ An example of burger is ___ An example of singer is ___ An example of computer is ___ An example of magazine is ___ An example of dress is ____ An example of chair is ____ An example of taxis is ____ An example of fruit is ____ An example of beer is ____ … … Construal-Level Priming Concrete Construal Priming Abstract Construal Priming Fujita, Trope, Liberman, Levin-Sagi (2006)

19. Study 4 (N = 199)

20. Study 4 (N = 199) Estimated market Price (75.2% MRSP)

21. Not at all 1 2 3 4 5 6 7 Very much Abstract Valuation Thinking When you were thinking about the bid, to what extent did you think about “why you might get this camera”? When you made the bid, to what extent did you think about “capturing moments, objects or faces” with it? Concrete Valuation Thinking When you were thinking about the bid, to what extent did you think about ““how useful each specific feature of the camera was (e.g. number of pixel, zoom, LCD display, shutter speed, image format, flash etc)”? When you made the bid, to what extent did you think about “how to take good pictures with it”?

22. Study 5 (N = 120)

23. Conclusions • Important to study effects of relaxation on consumer judgments and decisions • Relaxation increases monetary valuation even compared to equally-pleasant, less-relaxing state • Effect is inflation of value by relaxed individuals (rather than deflation of value by less-relaxed individuals) • Because relaxation triggers more abstract representations of product’s value • Being relaxed need not always be better in judgments and decisions • Could explain why luxury products often marketed in relaxing environments

24. The Emotional Oracle Michel Tuan Pham Columbia University Leonard Lee Columbia University Andrew T. Stephen Insead Annual Meeting of the Society for Judgment and Decision Making Boston, November 21-23, 2009

25. Research Question • Should you trust or not trust your feelings in judgments and decisions? • Trust-of-Feelings Manipulation (TFM) as Method for Studying Reliance on Affect in JDM • Avnet, Pham, & Stephen (2004-???)

26. Perceived ease of retrieval Perceived difficulty of retrieval Higher momentary trust of feelings Lower momentary trust of feelings Higher reliance on feelings Lower reliance on feelings Trust-of-Feelings Manipulation (TFM)(Avnet, Pham, & Stephen 2004-?, after Schwarz et al. 1991) High Trust Low Trust 2 instances of successful reliance on feelings 10 instances of successful reliance on feelings

27. Avnet, Pham, & Stephen-Exp 2

28. Avnet, Pham, & Stephen-Exp 3

29. Other TFM Results • Does not affect task confidence • Avnet, Pham, & Stephen-Exp 5 & Exp 6 • Does not affect mood • Stephen & Pham (2008) • Avnet, Pham, & Stephen-Exp 7 • Does not affect risk or risk preference • Avnet, Pham, & Stephen-Exp 8 • Does not affect self-awareness • Avnet, Pham, & Stephen-Exp 5

30. The Ultimatum Game Possible Offer Strategies Outcome Responder [$19,$1] Proposer:$15 Responder:$5 Accepts Pie X = $20 P [$15,$5] Proposer Responder Responder (1-P) Proposer:$0 Responder:$0 Rejects [$12,$8] Responder

31. Trust of Feelings and Earnings in Ultimatum Game & Variants Stephen & Pham (2008), Psych. Sc. Average % endowment won

32. Stephen & Pham (2008, Psych.Sc.) Low trust Low trust High trust High trust

33. Low Trust High Trust

34. The Emotional Oracle Michel Tuan Pham Columbia University Leonard Lee Columbia University Andrew T. Stephen Insead Annual Meeting of the Society for Judgment and Decision Making Boston, November 21-23, 2009

35. Overview of Studies

36. Study 1: Movie Box Office (N = 66) Study conducted on October 1 & 2, 2008 Movies in national release on Oct 3, 2008 Participants complete TFM Then ranked movies in order of predicted first-weekend box office revenues

37. Rank-Order Correlation(actual vs. predicted box office success) 0.61 High trust Low trust -0.79 1 0.8 Most accurate 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 ** p = .05 -1 Least accurate Tobit regression; LS means

38. Study 2: Movie Box Office (N = 42) Replication of Study 1 Ran on December 10-11, 2008; movies in national release on December 12, 2008 Same procedure as Study 1

39. Rank-Order Correlation (actual vs. predicted box office success) 0.52 High trust 0.21 Low trust 0.8 0.7 Most accurate 0.6 0.5 0.4 0.3 0.2 0.1 ** p < .01 0 Tobit regression; LS means Least accurate

40. Discussion • Higher trust in feelings increases ability to predict future relative success (popularity) of movies • Two separate studies with two sets of movies • Does it extend to more consequential targets and longer prediction horizon?

41. Study 3: Democratic Nomination Representative national sample of registered voters (N = 229) Conducted during Feb 15-17, 2008 Polls were very close; 52% of delegates already pledged Clinton conceded on June 7, 2008; Obama officially nominated at DNC in August 2008 TFM, then predicted who would be the nominee

42. Percent Correct (Obama will be the nominee) 80 * 74.6 ** 71.9 * 67.5 63.9 59.1 50 ** 14.3 * p < .10 ** p < .05 90 Low TF High TF 80 70 60 50 40 30 20 10 0 Full sample

43. Discussion • High trust in feelings also improves predictions about more consequential targets with a longer time horizon • Could effect be due to some peculiarity of TFM?

44. Study 4: American Idol Sample of regular American Idol viewers, N = 104 Ran over 20 hrs between final performance episode (May 19, 2009) and winner announcement (May 20, 2009) Predict who will win  filler task  measures (1-5) “I trust my feelings when making predictions” “I rely on logic and reasoning when predicting the future”

45. Percent Correct(Kris Allen will win)

46. Logistic Regression Correct (1) vs. incorrect (0) Measures (1-5) “I trust my feelings when making predictions” “I rely on logic and reasoning when predicting the future” Positive effect of trusting feelings(χ2 = 3.96, p < .05) No effect of logic/reason(χ2 = 1.61, p = .20)

47. Discussion • Effect holds even when trust of feelings is measured, rather than manipulated • Not driven by peculiarity of TFM • Effects also holds for predictions of outcomes that are surprising • Is this limited to outcomes driven by popularity?

48. Study 5: Dow Jones Index N = 119 business undergrads Procedure TFM Background info on DJI (e.g., historical levels) Predict the closing level of the DJI one week from today DV = | actual value – predicted value |

49. Absolute Prediction Error(points away from actual value) 796 574 569 458 Low trust Low trust High trust High trust 900 Less accurate 800 700 600 500 400 300 200 100 0 Econ majors Non-econ majors More accurate Main effect TF p = .04; main effect major p = .05; interaction n.s.

50. Recap… Trust in feelings improves ability to predict future outcomes Wide range of domains Including consequential domains Including outcomes that are not popularity-based Whether trust in feelings is manipulated or simply measured Why? Is it empathy / social attunement? Does effect hold for predictions of outcomes that are totally beyond human control?