the excessive choice effect meets the market: experiments ......trey malone outline the excessive...

The Excessive Choice Effect Meets the Market: Experiments on Craft Beer Choice

Trey MaloneDecember 2, 2016

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Outline


Motivation → Study 1 → Study 2 → Discussion

Q & A

December 2, 2016 Motivation | Study 1 | Study 2 | Discussion 2

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What is the Excessive Choice Effect (ECE)?

Contrary to neoclassical utility theory, an excessive number of choices might lead to increased levels of regret and paralysis

E(U)

Number of options


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Antecedents

A large literature has provided inconclusive evidence on the existence of an ECE

Scheibehenne et al. (2010): Mixed results from 50 experiments (N ≈ 5,000)

Example: Scheibehenne (2008) unable to replicate the famous jam study


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Antecedents

More interesting is “why” and “so what”?

Regret minimization (Irons and Hepburn 2007)

Information asymmetry (Kamenica 2008)

Consumer heterogeneity (Schwartz et al. 2002)

Search costs and uncertainty (Norwood 2006)


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Search costs: it’s easier to find the red ball when N=6 than when N=24

Search Costs and Uncertainty


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Uncertainty: it’s even harder to choose a ball when your preference for red is not very strong



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Imagine buying beer at

(1) a liquor store or (2) a gas station

Key point: store has an incentive to keep the right number of options stocked



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Research Questions

How does the ECE vary with consumers?

Do sellers encourage purchase by altering the choice architecture for buyers?

Can reduced search costs mitigate the ECE?


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Value-Added

Context and consumer heterogeneity play an integral part in the existence of an ECE

I empirically test the ECE’s potential causes

Reducing uncertainty and the cost of search can minimize ECE

By providing buyer-relevant information, sellers alter choice architecture to maximize sales


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Why Craft Beer?

Fulfills the preconditions of an ECE

“Sufficiently” large number of options

No beer listed is highly dominant

Vaguely defined preferences

Beer is an experience good

Quality can only be determined after purchase


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Why Craft Beer?

Proliferation of food options

Craft brewing has growing implications for agricultural producers


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Map of Breweries in the Continental United States, July 2015


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General Hypotheses

Increasing the number of beers at a bar will induce the ECE for some participants

Targeted information might reduce search costs and uncertainty, thereby reducing the probability of no purchase

Beer Advocate scores

Prominently listed “Special”


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Survey Experiment (1) Field Experiment (2)

Pros Cons Pros Cons

• Individualized data

• Stated preferences

• Revealedpreferences

• Loss of Control

• Control • Context

• No individualspecific data

• Within subjects

(more power)

• Between subjects

Two Studies


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ECE varies with:

1. Beer Advocate scores

2. Prominently listed “Special”

3. Novelty-seeking

(Manning, Bearden and Madden 1995)

Study 1: Hypotheses


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Study 1 Data: Consumption Frequency (N=1,697)

Beer Frequency

Craft BeerFrequency

Wine/LiquorFrequency

Never Less than Once a Month

Once a Month 2-3 Times a Month

Once a Week 2-3 Times a Week

Daily


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Study 1: Experimental Design


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5 Beers

Control

Special

Beer Advocate Scores

18 Beers

Control

Special



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Study 1 Results: Heat Maps


142 of 1,697 participants (8.4%) selected a beer that was

revealed dominated in their earlier choice

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Study 1 Results: Logit(Prob. of None)

Variable

Standard Logit with

Clustered Standard

Errors

Random Effects

Logit

Random Effects

Logit with

Novelty-seeking

Interactions

Intercept -0.414* (0.058) -0.447* (0.081) 1.245* (0.287)

Specials -0.671* (0.120) -0.746* (0.115) -5.370* (0.424)

Scores -1.557* (0.129) -1.709* (0.128) -4.643* (0.447)

Number of options -0.036* (0.003) -0.040* (0.006) -0.011 (0.021)

Novelty-seeking --- --- -0.505* (0.084)

# of options x Specials 0.030* (0.007) 0.034* (0.009) 0.302* (0.032)

# of options x Scores 0.076* (0.008) 0.083* (0.009) 0.255* (0.033)

# of options x Novelty-seeking --- --- -0.010 (0.006)

Specials x Novelty-seeking --- --- 1.381* (0.120)

Scores x Novelty-seeking --- --- 0.895* (0.129)

# of options x Specials x

Novelty-seeking

--- --- -0.080* (0.009)

# of options x Scores x

Novelty-seeking

--- --- -0.053* (0.010)

Generalized Chi-square N/A 6905.53 7139.76

Number of choices 10182 10182 10182


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Study 1 Results: P 𝑦𝑖 = 0


Probability of not selecting a beer in the menu experiment(Note: upward sloping line indicates presence of an ECE)

0

0.2

0.4

0.6

0.8

1

N=5 N=18

Control Special Advocate Scores

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Study 1 Results: P( 𝑦𝑖 = 0 |CNS)

High Novelty-Seeking (CNS=5)Low Novelty-Seeking (CNS=1)

Probability of not selecting a beer in the menu experiment(Note: upward sloping line indicates presence of an ECE)


N=5 N=18

Control Special Advocate Scores

0

0.2

0.4

0.6

0.8

1

N=5 N=18

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Study 1: Implications

The ECE does not affect everyone equally

Sellers likely make decisions based on their desired type of buyer

Novelty-seeking buyers likely have lower concern for uncertainty


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In a field context where consumers are not likely to be novelty-seeking in their beer preferences, an ECE will exist

If the ECE does exist, targeted information will reduce its consequences by lowering search costs

Study 2: Hypotheses


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Field experiment conducted at a wine bar in Stillwater, OK

September to October, 2014

Two datasets collected to improve accuracy

Restaurant sales system

Server-collected receipts

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6 Beers

Control

Special


Control

12 Beers

Control

Special



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Study 2 Results: Odds Ratios


TreatmentNumber of Options Odds Ratio - effect of

number of optionsN=6 N=12

Control 16.86% 13.64% 0.959

[0.907, 1.014]b

Special 20.14% 13.25% 0.920

[0.867, 0.976]

Beer Advocate Scores 19.93% 20.44% 1.005

[0.958, 1.055]

Odds Ratio - effects of special and scores

Control to Special 1.244

[0.922, 1.678]

0.968

[0.661, 1.417]

Control to Scores 1.227

[0.929, 1.620]

1.627

[1.154, 2.295]

Beer Sales as a Percentage of All Drink Sales in Field Experiment

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Study 2 Results: P 𝑦𝑖 = 0

Results for Experiment 2

0.75

0.80

0.85

0.90

0.95

N=6 N=12Number of Menu Options

ControlSpecial

BA Scores


Probability of not selecting a beer in the field experiment(Note: upward sloping line indicates presence of an ECE)

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Study 2: Implications

In this context, the ECE existed

Simply providing a prominently listed “Special” does not appear to reduce the ECE

Providing third-party quality information (i.e. Beer Advocate scores) eliminated the ECE


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Discussion: Value-Added

Market context and consumer heterogeneity matter

Reducing uncertainty and cost of search can minimize ECE

By providing buyer-relevant information, sellers alter choice architecture to maximize sales


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Discussion: Key Applied Contribution

To overcome the ECE, know your target market

If your consumers prefer novelty, give them novelty

“the trend for 2016 is not a trend, rather it’s diversity”– Bart Watson, Brewers’ Association

In some contexts, it might be helpful to “nudge” consumers via quality identification


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Discussion: Related Future Research

Are there benefits to categorization?Manipulate menu categories at a pub

Do “mavens” (i.e. bartenders or friends) reduce search costs?

Measure changes in sales/satisfaction when bartender makes recommendations

Veblen effects?Invent a beer, and ask bar patrons if they are more (less) likely to buy it at varying prices


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Q&A



Trey MaloneOctober 17, 2016

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Study 1 Data: Description

Gender Ethnicity Education Age IncomeMale 54.15% White/

Caucasian79.08% Less than High

School1.00% 21 to 24

years7.54% Less than

$29,99915.67%

Female 45.85% African American

7.37% High School/GED 15.62% 25 to 34 years

25.22% $30,000-$59,999

31.17%

Hispanic 7.25% Some College 21.98% 35 to 44 years

16.85% $60,000-$89,999

23.63%

Asian 4.77% 2-year College Degree

11.61% 45 to 54 years

20.39% $90,000-$119,999

12.73%

Native American

0.24% 4-year College Degree

32.41% 55 to 64 years

17.86% $120,000-$149,999

8.66%

Pacific Islander

0.35% Masters Degree 13.73% 65 to 74 years

10.67% $150,000-$179,999

3.36%

Other 0.94% Doctoral Degree 1.36% 75 to 84 years

1.18% More than $180,000

4.77%

Professional Degree (JD, MD)

2.30% 85 years or over

0.29%


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AppendixConsumer Novelty-Seeking Scale


CNS scale statement Mean

I often seek out information about new product and brands.3.427

(1.316)

I like to go to places where I will be exposed to information about new products and

brands.

3.456

(1.252)

I like magazines that introduce new brands.3.104

(1.551)

I frequently look for new products and services.3.433

(1.310)

I seek out situations in which I will be exposed to new and different sources of product

information.

3.354

(1.259)

I am continually seeking new product experiences3.426

(1.339)

When I go shopping, I find myself spending very little time checking out new products

and brands.

2.853

(1.554)

I take advantage of the first available opportunity to find out about new and different

products

3.282

(1.334)

Novelty Seeking Score3.292

(0.198)

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Study 1 Results: Odds Ratios


TreatmentNumber of Options Odds Ratio – effect of number

of optionsN=5 N=18

Control 64.44% 74.34% 1.037

[1.030, 1.043]b

Special 75.28% 76.62% 1.006

[0.992, 1.020]

Beer Advocate Scores 72.46% 77.80% 1.022

[1.016, 1.028]

Odds Ratio - effect of specials and scores

Control to Special 1.680

[1.421, 1.987]

1.131

[1.067, 1.200]

Control to Scores 1.452

[1.343, 1.571]

1.209

[1.129, 1.296]

Percentage of Times Beer Was Chosen in Survey-Based Choice Experiment

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Study 1 Data: Identification as a craft beer drinker

Not at all like me12%

Not much like me14%

Somewhat like me27%

Quite a lot like me24%

Just like me23%


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AppendixBeer options for the field experiment


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AppendixZanotti’s menu with Beer Advocate scores


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Embedded Short Trap Question (Above) and Long Trap Question (Below)


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No FeedbackEmbedded Short

Trap QuestionLong Trap Question

Number of participants 1000 1017% incorrect 20.0 37.2

With FeedbackEmbedded Short

Trap QuestionLong Trap Question

Number of participants 559 591% incorrect 21.8 25.4% revised correctly 9.5 16.6

Effectiveness of Trap Questions at Identifying Inattentive Respondents


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WTP for…

Short Trap Question Long Trap Question

Correct

Respondents

Incorrect

Respondents

Correct

Respondents

Incorrect

RespondentsHamburger $4.16

[$3.98, $4.34]

$5.32

[$4.53, $6.11]

$3.77

[$3.60, $3.93]

$5.91

[$5.14, $6.68]

Steak $7.23

[$6.86, $7.60]

$10.46

[$5.88, $15.04]

$6.11

[$5.77, $6.45]

$12.07

[$8.55, $15.58]

Pork Chop $3.85

[$3.64, $4.05]

$5.08

[$2.68, $7.47]

$3.33

[$3.16, $3.50]

$5.80

[$4.44, $7.16]

Deli Ham $2.41

[$2.21, $2.63]

$3.81

[$2.05, $5.58]

$1.91

[$1.73, $2.08]

$4.62

[$3.34, $5.89]

Chicken Breast $5.43

[$5.18, $5.68]

$7.77

[$5.81, $9.74]

$4.56

[$4.36, $4.75]

$6.98

[$6.2, $7.76]

Chicken Wings $2.14

[$1.92, $2.36]

$2.57

[$0.40, $4.75]

$1.64

[$1.47, $1.80]

$4.57

[$3.25, $5.89]

Beans and Rice $2.76

[$2.41, $3.11]

$1.91

[$0.75, $3.08]

$1.94

[$1.66, $2.22]

$3.27

[$2.42, $4.12]

Pasta $3.67

[$3.40, $3.94]

$2.40

[-$1.35, $6.14]

$2.63

[$2.36, $2.91]

$3.87

[$3.18, $4.56]

Comparison of Willingness-to-Pay (WTP) Estimates for Participants who Correctly and Incorrectly Responded to the Trap Question


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WTP for…

Short Trap Question Long Trap Question

Correct

Respondents

Incorrect

RespondentsCorrect

Respondents

Incorrect

RespondentsMiller Lite $5.70

[$5.00, 6.40]

$10.31

[$5.70, $14.90]$3.75

[$3.33, $4.17]

$7.17

[$4.93, $9.41]

Corona$7.85

[$6.91, $8.80]

$13.93

[$-8.54, $36.41]$5.95

[$5.51, $6.40]

$30.13

[$-967.89,

$1028.15]

Samuel

Adams$10.09

[$8.71, 11.48]

$9.35

[$5.52, $13.15]$6.44

[$5.94, $6.94]

$18.64

[$-159.7, $196.98]

Budweiser $7.98

[$6.33, $9.62]

$8.05

[$5.32, $10.76]$4.32

[$3.99, $4.66]

$12.03

[$-13.99, $38.05]

Oskar Blues $3.31

[$2.75, $3.86]

$2.75

[$-85.43, 90.82]$1.64

[$0.85, $2.44]

$2.91

[$-5.01, $10.84]

Marshall$5.17

[$4.54, $5.79]

$0.83

[$-49.28, 50.97]$3.04

[$2.58, $3.51]

$5.70

[$-114.08, $125.48]

Comparison of Willingness-to-Pay (WTP) Estimates for Participants who Correctly and Incorrectly Responded to the Trap Questions with Feedback


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Per Capita Consumption (2011, in gallons)


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Self Distribution Legislation (2014)


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State Excise Taxes (2012, $ per barrel)

Source: Brewers’ Almanac


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Parameter Estimates for Total Number of Breweries per County

Parameter Poisson ZIP ZINB

Intercept -0.189 (0.420) -1.477* (0.426) -2.001* (0.654)

Political contributions 0.003* (0.001) 0.005* (0.001) 0.012* (0.002)

Beer taxes per barrel 0.007* (0.003) 0.002 (0.003) 0.003 (0.006)

Availability score 0.295* (0.064) 0.331* (0.067) 0.336* (0.100)

Self-distribution 0.228* (0.062) 0.205* (0.067) 0.289* (0.093)

Growler legislation 0.056 (0.035) -0.013 (0.037) 0.164* (0.067)

Per capita beer consumption 0.025* (0.007) 0.012 (0.007) 0.015 (0.011)

Median household income -0.249* (0.016) -0.068* (0.015) -0.124* (0.035)

High Creative class 0.758* (0.051) 1.056* (0.054) 0.909* (0.087)

Percent of persons with a college degree0.051* (0.002) 0.039* (0.002) 0.064* (0.005)

Rural Urban Continuum code -0.312* (0.010)

High amenities 0.219* (0.053)

δ 0.741* (0.050)

Regional fixed effects? Yes Yes Yes

Religion effects? Yes Yes Yes

Logit link function

Intercept -3.051* (0.250) -7.343* (1.010)

Rural Urban Continuum code 0.480* (0.037) 0.917* (0.121)

High amenities -1.022* (0.201) -1.463* (0.452)

AIC 8008 8732 6952

Number of

observations in

each regression

= 3,108

Total number of

breweries

= 4,400


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Brewing Up Entrepreneurship: Government Intervention in Craft Beer

0

0.5

1

1.5

2

2.5

3

3.5

Banned Legal

Total Brewpub Micro

Expected Number of Breweries, Microbreweries, and

Brewpubs within a County when the State Legalizes

On-Premises Growler Sales

0

0.5

1

1.5

2

2.5

3

3.5

Banned Legal

Total Brewpub Micro

Expected Number of Breweries, Microbreweries, and

Brewpubs within a County when the State Legalizes

Self-Distribution


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If You Brew it, Will They Come? Factor Analysis

Miller Lite

Corona

Sam Adams

Budweiser

Oskar BluesMarshall

0

0.5

1

0 0.5 1

Cla

ssic

Novel

Factor Analysis of Taste Perceptions

Miller Lite

Corona

Sam Adams

Budwesier

Oskar Blues

Marshall

-0.5

0

0.5

1

0 0.5 1

Nic

he

Established

Factor Analysis of Beer Familiarity


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Parameter Estimates for Brand Equity Models

ParameterNo Equity

Effects

Brand Equity Main

Effects

Brand Equity with

Interactions

Price -0.266* (0.007) -0.360* (0.009) -0.066 (0.139)

Taste 1.384* (0.018) 1.729* (0.168)

Familiarity 0.648* (0.024) 0.960* (0.235)

Price x Familiarity -0.093 (0.054)

Price x Taste -0.097 (0.039)

Familiarity x Taste -0.110 (0.063)

Price x Familiarity x Taste 0.032* (0.015)

Miller Lite 1.414* (0.048) -4.460* (0.087) -5.485* (0.597)

Budweiser 1.595* (0.047) -4.588* (0.088) -5.613* (0.597)

Corona 2.023* (0.045) -4.348* (0.087) -5.375* (0.596)

Samuel Adams Lager 2.177* (0.045) -4.328* (0.088) -5.355* (0.596)

Oskar Blues Pilsner 0.511* (0.056) -4.408* (0.081) -5.439* (0.595)

Marshall Pilsner 1.049* (0.050) -4.030* (0.078) -5.058* (0.595)

-2 Log likelihood 48045 33864 33855

AIC 48058.7 33882.1 33881.2

Number of observations 13576 13576 13576

Number of participants 1697 1697 1697


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Own Elasticities for Price, Taste, and Familiarity

Own-Price Own-Familiarity Own-Taste

Miller Lite -0.879

[-0.92, -0.839]a

2.741

[2.673, 2.81]

1.580

[1.466, 1.695]

Budweiser -0.826

[-0.864, -0.788]

2.719

[2.651, 2.787]

1.583

[1.468, 1.697]

Corona -1.815

[-1.898, -1.731]

2.946

[2.872, 3.019]

1.927

[1.787, 2.067]

Sam Adams -1.767

[-1.848, -1.685]

2.714

[2.646, 2.782]

1.951

[1.81, 2.093]

Oskar Blues -2.097

[-2.194, -2.001]

1.819

[1.773, 1.864]

1.835

[1.702, 1.968]

Marshall -2.053

[-2.147, -1.958]

1.765

[1.72, 1.809]

1.867

[1.731, 2.002]


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