Transcript The Relaxed Consumer & The Emotional Oracle Michel Tuan Pham Columbia University K U Leuven 2009 Marketing Camp.

The Relaxed Consumer
& The Emotional Oracle
Michel Tuan Pham
Columbia University
K U Leuven 2009 Marketing Camp
Relaxation &
Monetary Valuation
Michel Tuan Pham
Columbia University
Iris W. Hung
National University of Singapore
Gerald J. Gorn
University of Hong Kong
Relaxation
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.
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
Main Manipulation
10 min
Relaxation DVD
10 min
TV documentary
Nature scenes
Soft music
Relaxation instructions
(e.g., breathing)
World Expo in Japan
Scenes of robots
Relaxed
Less-Relaxed but
equally pleasant
Pretest
Feelings of
Pleasantness
Less Relaxed (World
Expo documentary)
Relaxed (Relaxation
DVD)
Feelings of
Relaxation
1
2
3
4
5
6
Subje ctiv e Inte nsity of Fe e lings
7
8
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.
Main Features
Sensor Size
½.5 inch
Resolution
5 mega pixels
Zoom
3X Optical; 4X Digital
LCD Monitor
2.7 inch TFT; 640 x 240
pixels
Image Format
JPEG
Self-Timer
2 sec. / 10 sec.
Recording Media
SD Card, MMC
Power
Lithium-ion Battery
Dimension (WxHxD)
88.5 x 57 x 20.5mm
Weight
112g (camera body only)
Suggested Retail
Price
$2,700
Shipping Fee
Free shipping
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
Study 2
Relaxed (n = 26)
Less Relaxed (n = 25)
$2,700
$2,600
HK Dollars
$2,500
$2,400
$2,300
$2,200
$2,100
$2,000
Bid
Worth
Monetary Valuation
How much is the crystalline picture frame worth?
How much is the vacuum cleaner worth?
The crystalline picture frame is worth
A. $100-500
B. $500-1000
C. $1000-1500
D. $1500-2000
E. $2000-2500
The vacuum cleaner is worth
A. $1500-2000
B. $2000-2500
C. $2500-3000
D. $3000-3500
E. $3500-4000
Study 1
Relaxed (n = 21)
Less Relaxed (n = 24)
5
*
Estimated Price Bracket
4
*
*
3
*
2
*
*
1
Backpack
Crystal
Tulip
Digital
Gauge for
Car Tires
LCD
Monitor
Magazine
Rack
Paper
Shredder
Picture
Frame
Scarf
Tennis
Racquet
Vacuum
Cleaner
Pretest 2
Feelings of Pleasantness
Less Relaxed (Kenny G)
Relaxed (Shawntana)
Feelings of Relaxation
1
2
3
4
5
6
7
rg
y
ip
tc
l
ub
in
k
ke
ts
dr
ni
gh
E
ne
ti c
sh
ne
pi
ng
be
r
ju
m
rk
e
er
pa
ge
em
O
*
W
at
B
un
m
e
re
am
-c
tle
ic
e
*
K
et
e
C
ak
*
ym
da
e
es
hi
p
ip
s
High relaxation
G
S
un
ch
is
e
4
C
he
o
be
rs
at
m
em
P
ot
rt
ch
ai
r
so
C
ru
sa
ge
as
le
S
pa
nd
re
ca
3
in
o
a
ah
iti
m
*
C
as
M
T
A
ro
Study 1b (Music Manipulation)
Low relaxation
5
*
*
*
*
2
1
Product Attribute Ratings
Not easy to use
1
2
3
4
5
6
7
Very easy to use
Has poor features
1
2
3
4
5
6
7
Has good features
Not nice looking
1
2
3
4
5
6
7
Nice looking
Not convenient to use
1
2
3
4
5
6
7
Convenient to use
Study 3 (N = 159)
Main Features
Main Features
Sensor Size
½.5 inch
Sensor Size
½.5 inch
Resolution
5 mega pixels
Resolution
5 mega pixels
Zoom
3X Optical; 4X Digital
Zoom
3X Optical; 4X Digital
LCD Monitor
2.7 inch TFT; 640 x 240
pixels
LCD Monitor
2.7 inch TFT; 640 x 240
pixels
Image Format
JPEG
Image Format
JPEG
Self-Timer
2 sec. / 10 sec.
Self-Timer
2 sec. / 10 sec.
Recording Media
SD Card, MMC
Recording Media
SD Card, MMC
Power
Lithium-ion Battery
Power
Lithium-ion Battery
Dimension (WxHxD)
88.5 x 57 x 20.5mm
Dimension (WxHxD)
88.5 x 57 x 20.5mm
Weight
112g (camera body only)
Weight
112g (camera body only)
Suggested Retail
Price
$2,700
Suggested Retail
Price
$2,700
Shipping Fee
Free shipping
Shipping Fee
Free shipping
1. Monetary Valuations
(Bid, Worth)
2. Specific Ratings
(Ease of use, Features, Looks, Convenience)
1. Specific Ratings
(Ease of use, Features, Looks, Convenience)
2. Monetary Valuations
(Bid, Worth)
Study 3 (N = 159)
Relaxed
Less Relaxed
$2,600
$2,500
$2,400
Bid
$2,300
$2,200
$2,100
$2,000
$1,900
Monetary Valuation First
Attribute Ratings First
Construal-Level Priming
Abstract Construal Priming
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
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 ____
…
…
Concrete Construal Priming
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
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 ____
…
…
Fujita, Trope, Liberman, Levin-Sagi (2006)
Study 4 (N = 199)
Relaxed
Less Relaxed
$2,600
$2,500
$2,400
Bid
$2,300
$2,200
$2,100
$2,000
$1,900
$1,800
Abstract Construal Prime
Concrete Construal Prime
Study 4 (N = 199)
Relaxed
Less Relaxed
$2,600
$2,500
$2,400
Bid
$2,300
Estimated
market
Price
(75.2% MRSP)
$2,200
$2,100
$2,000
$1,900
$1,800
Abstract Construal Prime
Concrete Construal Prime
Abstract Valuation Thinking
When you were thinking about the bid, to what extent did you think about
“why you might get this camera”?
Very
Not at all
1 2 3 4 5 6 7
much
When you made the bid, to what extent did you think about “capturing
moments, objects or faces” with it?
Not at all 1
2
3
4
5
6
7
Very much
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)”?
Not at all
1 2 3 4 5 6 7 Very much
When you made the bid, to what extent did you think about “how to take good
pictures with it”?
Not at all 1
2
3
4
5
6
7
Very much
Study 5 (N = 120)
Relaxed
Less Relaxed
6
5.5
Extent of Thinking
5
4.5
4
3.5
3
Abstract Valuation Thinking
Concrete Valuation Thinking
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
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
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-???)
Trust-of-Feelings Manipulation (TFM)
(Avnet, Pham, & Stephen 2004-?, after Schwarz et al. 1991)
High Trust
2 instances of
successful reliance
on feelings
Low Trust
10 instances of
successful reliance
on feelings
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
Avnet, Pham, & Stephen-Exp 2
Attitude
toward
7.0
reading
Unpleasant soundtrack
6.5
5.97
6.0
Pleasant soundtrack
5.67
5.69
5.5
5.0
4.5
4.33
4.0
3.5
3.0
2.5
High trust of feelings
(easy to generate 2
examples)
Low trust of feelings
(difficult to generate
10 examples)
Avnet, Pham, & Stephen-Exp 3
Negative mood
Positive mood
6.0
Book Evaluation
5.5
5.13
5.0
4.58
4.63
4.5
4.0
3.5
3.0
2.88
2.5
2.0
1.5
High trust of feelings
(easy to generate 2
examples)
Low trust of feelings
(difficult to generate 10
examples)
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
The Ultimatum Game
Possible Offer
Strategies
Outcome
Responder
[$19,$1]
Accepts
Pie X = $20
Proposer
[$15,$5]
P
Responder
(1-P)
[$12,$8]
Proposer:$15
Responder:$5
Rejects
Responder
Proposer:$0
Responder:$0
Trust of Feelings and Earnings in
Ultimatum Game & Variants
Stephen & Pham (2008), Psych. Sc.
Average % endowment won
Experiment
High trust
Low trust
Standard Ultimatum Game
43%
36%
Counteroffer Game
39%
28%
Dictator Game
73%
56%
Stephen & Pham (2008, Psych.Sc.)
60
Proportion of Pie Offered (%)
55
50
45
40
48.1
47.9
Low
trust
42.6
Low
trust
42.0
35
30
25
High
trust
High
trust
20
Offer Size for $5 Pie
Offer Size for $15 Pie
Low Trust
High Trust
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
Overview of Studies
Prediction
Context
Studies Manipulation
Measure
Prediction
Horizon
Movies:
Box office success
1
2
✓
✓
3-4 days
Elections:
2008 Democratic
nominee
3
✓
6 months
TV contest:
2009 American Idol
winner
4
Finance:
Dow Jones Index
5
✓
1 week
Weather
6
7
✓
2 days
✓
✓
20 hours
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 firstweekend box office revenues
Rank-Order Correlation
(actual vs. predicted box office success)
Most
accurate
1
0.8
0.61
0.6
0.4
High
trust
0.2
0
-0.2
-0.4
Low
trust
-0.6
Least
accurate
-0.8
-0.79
** p = .05
-1
Tobit regression; LS means
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
Rank-Order Correlation
(actual vs. predicted box office success)
0.8
Most
accurate
0.7
0.6
0.52
0.5
0.4
High
trust
0.3
0.21
0.2
0.1
Least
accurate
0
Low
trust
** p < .01
Tobit regression; LS means
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?
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
Percent Correct
(Obama will be the nominee)
* p < .10
** p < .05
90
80
80
70
71.9
63.9
*
Low TF
*
High TF
74.6
**
67.5
59.1
60
50
**
50
40
30
20
14.3
10
0
Full sample
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?
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”
Percent Correct
(Kris Allen will win)
70%
60%
50%
50.0%
40%
37.7%
30%
29.2%
20%
12.5%
10%
0%
0.0%
Completely false
Completely true
"I trust my feelings when making predictions"
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)
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?
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 |
Absolute Prediction Error
(points away from actual value)
Less
accurate
900
796
800
700
600
574
569
458
500
Low
trust
400
300
200
Low
trust
High
trust
High
trust
100
More
accurate
0
Econ majors
Non-econ majors
Main effect TF p = .04; main effect major p = .05; interaction n.s.
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?
Study 6: The Weather
 Online panel of people who said they had not
looked at weather forecast recently, N = 52
 Did the TFM, then predicted weather conditions
“two days from today” for their home zip code
 One of six possible weather conditions:
 Sunny/fine/clear
 Partially sunny with some clouds
 Cloudy/overcast
 Rain
 Thunderstorms
 Windy
Percent Correct
50
47.1
45
40
“Same as
today”
guess
35
30
27.8
High
trust
25
20
15
10
Random
guess
Low
trust
5
0
** p = .02
Study 7: The Weather…again!
 Online panel of people who said they
had not looked at weather forecast
recently (N = 116)
 Predicted weather conditions as in
Study 6
Study 7: The Weather…again!
 Online panel of people who said they
had not looked at weather forecast
recently, N = 116
 Predicted weather conditions as in
Study 6
 Prediction  filler task  measures (15)
 “I trust my feelings when making predictions”
 “I rely on logic and reasoning when predicting the
future”
Percent Correct
60%
50%
40%
38.5%
34.9%
30%
20%
16.7%
Random
“Same
guessas
today”
guess
18.5%
10%
0%
0.0%
Completely false
Completely true
"I trust my feelings when making predictions"
Conclusions
 Trust in feelings improves ability to predict
future outcomes
 Variety of outcomes: movie successes, election
results, singing competition winners, stock
market movements, and weather
 Regardless of whether trust in feelings is
manipulated of simply measured
 Why?
 Social attunement explanation possible but not
sufficient
 Feelings help summarize large amounts of rich tacit
knowledge about situations
Architecture of Affective System
 More ordinal (less interval) in computation of
value (Pham, Toubia, & Lin, in prog.)
 More literal: More dependent on target
identification and categorization
 Ultimatum game (Stephen & Pham, 2008, Psych. Sc.)
 Compromise effect (Pham & Parker, in prog.)
 More present-oriented (Chang & Pham, in prog.)
 Flexibly engaged (“On-off switch”)
 Experiential vs. instrumental motives (Pham 1998)
 Present vs. future or past (Chang & Pham)
 Promotion vs. prevention (Pham & Avnet, 2004,
2008)
 Weighs environment cues in more ecological
fashion