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This is to certify that the

dissertation entitled

Use Now, Pay Later: An Examination of the
Influence of Advertising Financing Claims on
Time-Related Behaviors and Perceived
Affordability of Purchase

presented by

Patrice Marie Katrak

has been accepted towards fulﬁllment
of the requirements for

Ph.D. Mass Media

degree in

 

 

Major professor

ﬁmﬁWaﬁn Bead

November 3, 2000
Date

 

\

MS U is an Afﬁrmative Action/Equal Opportunity Institution 0-12771

 

LIBRARY
Michigan State
._ Liniversity

 

 

I. -‘.

 

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DATE DUE

DATE DUE

DATE DUE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1100 mm.“

 

 

USE NOW, PAY LATER: AN EXAMINATION OF THE INFLUENCE OF
ADVERTISING FINANCING CLAIMS ON TIME-RELATED
BEHAVIORS AND PERCEIVED AFFORDABILITY OF PURCHASE

By

Patrice Marie Katrak

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Mass Media Ph.D. Program
College of Communications Arts and Sciences

2000

 

[SE NO“ -
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ABSTRACT

USE NOW, PAY LATER: AN EXAMINATION OF THE INFLUENCE OF
ADVERTISING FINANCING CLAIMS ON TIME-RELATED BEHAVIORS
AND PERCEIVED AFFORDABILITY OF PURCHASE

By

Patrice Marie Katrak

The purpose of this experiment was to test the inﬂuence of a class of
advertising claims that are widely used, but virtually never researched. These
are familiar claims—“No Money Down! No interest! No Payment Until 2001,”
“Three Easy Payments of $24.99,” and “Only $199 per month.” These claims are
labeled ﬁnancing claims and deﬁned as statements providing information about
the availability of payment options for speciﬁc products or credit in general.
Such claims may motivate people to buy products they cannot afford, possibly

resulting in purchase decisions with ﬁnancially detrimental consequences.

Literature on affordability, time inconsistency, and time and outcome
valuation points to reasons these claims may be effective. By including
information about the ability to have products in the present while deferring all
or part of the payment into the future, ﬁnancing claims may make products seem
affordable. They may induce impatience by causing consumers to adapt to the
notion of owning products they may not have considered buying before due to
their expense. Financing claims may motivate these changes in most consumers,
but appear most likely to affect those possessing characteristics such as

impatience, present-mindedness, or a tendency toward compulsive consumption.

The
claims in pr
student snh;
hehaxiors
purchase in:
responses re
inexpensive
mosr posiriw.
mate) were
present-one:

lacked such

 

 

 

The experiment examined the influence of three levels of ﬁnancing
claims in print ads for two product types (automobiles and credit cards) on 224
student subjects’ attitudes, affordability perceptions, and purchase-related
behaviors. The only signiﬁcant differences by treatment group involved
purchase intent. However, while not signiﬁcant in most cases, the pattern of
responses to product-speciﬁc dependent variables indicated that the most
inexpensive payment levels (lower than those currently advertised) were received
most positively and those middle-level claims (most similar to the ones currently
made) were received least positively. Subjects who were more impatient,
present-oriented, and compulsive did not respond differently than those who
lacked such qualities. There was no conclusive support showing that ﬁnancing
claims increase affordability perceptions or lead to consumer impatience overall
or among subjects possessing a variety of individual difference characteristics.
By calling attention to these claims and providing research suggestions, this

research may lead to more deﬁnitive work on this important issue.

Copyright by
PATRICE MARIE KATRAK
2000

Dedicated to

My Loving Parents,

Bonnie and Bill Scheffler

My Beloved Husband,
Kerﬁ

and

My Precious Children,
Kade and Callahan

 

The:
they proxidI
Many than}.
rhejoy ofri
haieenrohl
Dhsmtanor
masunng‘.
runhning 5'
hrshming
nmesofne;
Todd Simoril
Unhersnies
Preliminary

 

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generousl} .
ml teachers
Florided 513
Delanmem
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LaSIi's

 

 

ACKNOWLEDGEMENTS

There are many people I wish to express my gratitude to for the assistance
they provided to me throughout this dissertation and the program as a whole.
Many thanks go to my Committee Chairperson, Dr. Nora Rifon. You showed me
the joy of research and without you, your friendship and mentoring I would not
have enrolled in this program nor would I be completing it. Thank you to my
Dissertation Director, Dr. Bruce Vanden Bergh, for your steady guidance and
reassuring words which helped me make my way through this work. To my
remaining committee members, Dr. Bonnie Reece and Dr. Steve Lacy, thank you
for sharing your insights and constructive comments and for helping me in my
times of need. My appreciation also goes to former committee Members, Dr.
Todd Simon and Dr. Mary Jane Sheffet, who have moved on to other
universities, for their help with planning my coursework and conducting my
preliminary research.

Thanks also to others on campus who provided me assistance and support.
A special thank you to Dr. Linda Cowles who took an interest in my research and
generously offered to produce the ads used in this experiment. My thanks to all
my teachers for the knowledge they shared and also to Dr. Randy Fotiu who
provided statistical counsel for this dissertation. Also, thank you to the
Department ofﬁce staff and to my colleagues who helped make this experience
an enjoyable one.

Lastly, and most especially, 1 thank my family who lovingly, patiently
supported me through it all. Heartfelt thanks to my parents, Bonnie and Bill
Scheffler, who sacriﬁced in so many ways to make my college education
possible. Thanks for believing in me, thanks for all the special trips and meals
you made for me, and thanks most of all for being there to help care for my twins
so I could work on this. My deepest thanks to the love of my life, Kerﬁ, whom I
met at the end of my ﬁrst semester in this program and whom I married a year
later. You have been my rock through every class, every late night, every
frustration, and even through the times when I questioned whether I would ﬁnish
this program. You’ve held my hand, been my sounding board, motivated me to
go on, and even helped me format all those tables! I am blessed to have you in
my life. Finally, thanks to my wonderful children who graciously played quietly
with Grandma, Grandpa and Daddy while Mommy studied and understood that
play time with Mommy would begin again in earnest when Mommy finished this.
That time has come. Let’s play.

vi

llST OF T

llST OF T

 

Chapter I

Introductit‘

Problem i
Financing
PUTPOSG C

Signrtrcaé

Chapterz

Review of ti

CONSUme.’
Time Into
AdVertisirt
Time and l
.ifinrdabi.
Adl‘emsin
Smelling

Chaliter 3

RtSearch .‘l
Hj‘ipmhegef
Melit-0d.
Research
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epender

 

TABLE OF CONTENTS

LIST OF TABLES ......................................................................................... 1:
LIST OF FIGURES .................................................................................... xvi
Chapter 1
Introduction .................................................................................................. 1
Problem Statement ....................................................................................... 1
Financing Claims ......................................................................................... 3
Purpose of the Study .................................................................................... 6
Signiﬁcance of the Study .............................................................................. 8
Chapter 2
Review of the Literature ............................................................................. 12
Consumer Decision Making ........................................................................ 12
Time Inconsistency .................................................................................... l3
Advertising’s Role in Creating Impatience .................................................. 2]
Time and Outcome Valuation ..................................................................... 23
Affordability ............................................................................................. 27
Advertising Financing Claims ..................................................................... 29
Susceptibility ............................................................................................. 30
Chapter 3
Research Method ........................................................................................ 36
Hypotheses ................................................................................................ 36
Method ...................................................................................................... 39
Research Design ...................................................................................... 39
Treatments .............................................................................................. 41
Sample Selection ..................................................................................... 46
Independent Variables ............................................................................. 49
Dependent Variables ............................................................................... 52
Pretest ....................................................................................................... 55
Experiment Administration Procedures and Sample Size .............................. 58
Data Analysis ............................................................................................ 59
Chapter 4
Results ........................................................................................................ 62
Manipulation Checks .................................................................................. 62
Auto Ads ................................................................................................ 62
Credit Card Ads ...................................................................................... 65

vii

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Scale Reliabilities ...................................................................................... 66

Descriptive Statistics .................................................................................. 68
Auto Dependent Variables ..................................................................... .. 68
Credit Card Dependent Variables ............................................................. 69
Reasons for Payment Method Selection .................................................... 70
Installment Plans ..................................................................................... 73
Credit Card Attitudes and Use .................................................................. 74
Individual Difference Variables ............................................................... 75
Financial Knowledge ............................................................................... 76
Financial Experience ............................................................................... 78
Subject Proﬁle ........................................................................................ 79

Hypotheses ................................................................................................ 81
Pretest-Posttest Change in Purchase Intent for a Product in General ........... 81

Auto .................................................................................................... 81
Credit Card .......................................................................................... 83
Pretest-Posttest Change in Timeframe of Purchase for a Product in General 85
Auto .................................................................................................... 86
Credit Card .......................................................................................... 87
Attitude toward the Ad ............................................................................ 88
Auto .................................................................................................... 88
Credit Card .......................................................................................... 89
Attitude toward the Advertised Product .................................................... 89
Auto .................................................................................................... 90
Credit Card .......................................................................................... 90
Purchase Intentions for the Advertised Product ......................................... 91
Auto .................................................................................................... 91
Credit Card .......................................................................................... 92
Affordability Perceptions for the Advertised Product ................................ 93
Auto .................................................................................................... 93
Credit Card .......................................................................................... 94
Anticipated Timeframe for Purchase of the Advertised Product .................. 95
Auto .................................................................................................... 95
Credit Card .......................................................................................... 97
Anticipated Method of Payment for the Advertised Product ....................... 98
Auto .................................................................................................... 99
Credit Card ........................................................................................ 100
Level of Financing Claim ...................................................................... 101
Auto .................................................................................................. 101
Credit Card ........................................................................................ 102
Individual Difference Variables ............................................................. 103
Impatience ......................................................................................... 104
Time Orientation ................................................................................ 106
Compulsive Consumption ................................................................... 107
Other Individual Difference Variables ................................................. 109
Age ................................................................................................ 110
Gender ............................................................................................ 114

viii

 

Chapter 5

Discussion

Summary
PTOduct

Class Standing ................................................................................. 1 18

Parents’ Income ............................................................................... 122

Parental Financial Support ............................................................... 125

Personal Income .............................................................................. 128

Credit Card Attitude ........................................................................ 131

Credit Card Control ......................................................................... 136

Credit Card Spending ....................................................................... 138

Credit Card Usage ........................................................................... I41

Borrowing ....................................................................................... 144

Auto Related Financial Knowledge ................................................... 148

Credit Card Related Financial Knowledge ......................................... 150

Overall Impact of Individual Difference Variables ................................ 155
Chapter 5

Discussion ................................................................................................. 157

Summary of Results ................................................................................. 157

Products in General ............................................................................... 157

Advertised Products .............................................................................. 158

Susceptible Subjects ............................................................................. 160

Implications and Explanation for Findings ................................................. 162

Directions for Future Research ................................................................. 168

APPENDIX A—EXPERIMENTAL BOOKLET ............ ’ ............................. 171

APPENDIX B—TREATMENT ADS .......................................................... 188

APPENDIX C—CORRELATION TABLE ................................................. 197

REFERENCES .......................................................................................... 216

ix

Table3 1
Table 3 2
Table 3.3
Table 3 4
Table 4 l
Financ.
Table 42 l
offina
Tablei 3
Table 44
Table 4 5,
Table 46
Table 4.7.
Table 4 8.
Table 4.9
Table 4.10
and Net
Table 4, 1)
Tablet 1:
Treatme
Tablet 13
Changei
Table 414
Tablet 15.
Trearme
1.30184 16
GTQU
Table 4 18.
Ire
Table 44:23
W 4 20
.Group _
Table 4 2
Table 4 22

 

 

LIST OF TABLES

Table 3.1. Independent Variables and Variables Names .................................. 51
Table 3.2. Dependent Variables and Variable Names ..................................... 54
Table 3.3. Automobile Payment Levels ......................................................... 57
Table 3.4. Credit Card Payment Levels ......................................................... 57
Table 4.1. Auto Ads Manipulation Check: Ease of Payment by Levels of

Financing ............................................................................................... 64
Table 4.2. Credit Card Ads Manipulation Check: Ease of Payment by Levels

of Financing ........................................................................................... 66
Table 4.3. Scale Reliabilities ........................................................................ 67
Table 4.4. Descriptive Statistics for Auto Dependent Variables ...................... 69
Table 4.5. Descriptive Statistics for Credit Card Dependent Variables ............ 70
Table 4.6. Auto Payment Method by Reason for Payment ............................... 72
Table 4.7. Credit Card Payment Method by Reason for Payment ..................... 73
Table 4.8. Descriptive Statistics for Individual Difference Scales ................... 76
Table 4.9. Subject Proﬁle ............................................................................. 80
Table 4.10. Pre-Post Change in Auto Purchase Intent (Number of Changes

and Net Point Change) ............................................................................. 82
Table 4.11. Mean Pre-Post Change in Auto Purchase Intent ............................ 82
Table 4.12. ANOVA—Pre-Post Change in Auto Purchase Intent by

Treatment Group ..................................................................................... 83
Table 4.13. Pre-Post Change in Credit Card Acquisition Intent (Number of

Changes and Net Point Change) ............................................................... 84
Table 4.14. Mean Pre-Post Change in Credit Card Acquisition Intent ............. 84
Table 4.15. ANOVA—Pre-Post Change in Credit Card Acquisition Intent by

Treatment Group ..................................................................................... 85
Table 4.16. t-test for Pre-Post Change in Credit Card Acquisition Intent ......... 85
Table 4.17. Pre-Post Change in Timeframe of Auto Purchase by Treatment

Group ..................................................................................................... 86
Table 4.18. Pre-Post Change in Timeframe of Credit Card Acquisition by

Treatment Group ..................................................................................... 87
Table 4.19. Mean Attitude toward the Cavalier Z24 Ad .................................. 88
Table 4.20. ANOVA—Attitude toward the Cavalier 224 Ad by Treatment

Group ..................................................................................................... 88
Table 4.21. Mean Attitude toward the CapitalOne Visa Ad ............................. 89
Table 4.22. ANOVA—Attitude toward the CapitalOne Visa Ad by Treatment

Group ..................................................................................................... 89
Table 4.23. Mean Attitude toward the Cavalier Z24 ....................................... 90
Table 4.24. ANOVA—Attitude toward the Cavalier Z24 by Treatment Group .90
Table 4.25. Mean Attitude toward CapitalOne Visa ........................................ 91
Table 4.26. ANOVA—Attitude toward the CapitalOne Visa Ad by Treatment

Group ..................................................................................................... 91

Table 4.27. Mean Purchase Intent for the Cavalier 224 by Treatment Group....92

 

Table 425
Group
Table 42‘-
Tahle4 3!.
Group F
Table4 .r T
Table 4.32
Treatrr.
Table433 L
Table 4.3:.
by Tree
Table 4 35
Table 4 Sb
Table 4.37
Table 4.38
Table 4 39
Table 4 4(.
Claim
Table 4 41
Table 4 4:
Variabh
Table 4 43‘
CaPiral(
Table-4 44 '
Credit C
Table 4 45
Depend:
Tabie4 46
Depend.
Table4 47
Depend:
'u re 4 48
“Purch
Table 4 49

Dl‘feTEn
Table 4 50

 

Tab?

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ahle4. 31
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Tabr ePEnde

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Tat} alle

354

Tafti '
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Table 4.28.

ANOVA—Purchase Intent for the Cavalier Z24 by Treatment

Group ..................................................................................................... 92
Table 4.29. Mean Acquisition Intent for the CapitalOne Visa ......................... 93
Table 4.30. ANOVA—Acquisition Intent for CapitalOne Visa by Treatment

Group ..................................................................................................... 93
Table 4.31. Mean Perceived Affordability of the Cavalier Z24 ........................ 94
Table 4.32. ANOVA—Perceived Affordability of the Cavalier 224 by

Treatment Group ..................................................................................... 94
Table 4.33. Mean Perceived Affordability for the CapitalOne Visa ................. 95
Table 4.34. ANOVA—Perceived Affordability for the CapitalOne Visa Ad

by Treatment Group ................................................................................ 95
Table 4.35. Timeframe of Purchase of the Cavalier Z24 by Treatment Group ..96
Table 4.36. Timeframe of CapitalOne Acquisition by Treatment Group ........... 98
Table 4.37. Payment Method for Cavalier by Treatment Group ....................... 99
Table 4.38. Payment Method for CapitalOne Purchases by Treatment Group. 100
Table 4.39. Auto Ads—Dependent Variables by Level of Financing Claim... 102
Table 4.40. Credit Card Ads—Dependent Variables by Level of Financing

Claim ................................................................................................... 103
Table 4.41. MANOVA Results—Impatience and Auto Dependent Variables. 104
Table 4.42. MANOVA Results—Impatience and Credit Card Dependent

Variables .............................................................................................. 105
Table 4.43. Analysis of Covariance—Impatience and Acquisition Intent for

CapitalOne Visa .................................................................................... 105
Table 4.44. Analysis of Covariance—Impatience and Purchase Intent for

Credit Cards (General) .......................................................................... 106
Table 4.45. MANOVA Results—Time Orientation and Automotive

Dependent Variables ............................................................................. 107
Table 4.46. MANOVA Results—Time Orientation and Credit Card

Dependent Variables ............................................................................. 107
Table 4.47. MANOVA Results—Compulsive Consumption and Auto

Dependent Variables ............................................................................. 108
Table 4.48. Interaction—Compulsive Consumption and Pre-Post Difference

in Purchase Intent for Auto (General) ..................................................... 108
Table 4.49. Parallel Slopes—Compulsive Consumption and Pre-Post

Difference in Purchase Intent for Auto (General) .................................... 109
Table 4.50. Correlation—Compulsive Consumption and Pre-Post Difference

in Purchase Intent for Auto (General) by Treatment Group ...................... 109
Table 4.51. MANOVA Results—Compulsive Consumption and Credit Card

Dependent Variables ............................................................................. 109
Table 4.52. MANOVA Results—Age and Auto Dependent Variables ............ 110
Table 4.53. Analysis of Covariance—Age and Attitude toward the

Cavalier Z24 ......................................................................................... 111
Table 4.54. Interaction—Age and Affordability of the Cavalier 224 ............. 111
Table 4.55. Parallel Slopes—Age and Affordability of the Cavalier 224 ....... 112
Table 4.56. Correlation between Age and Affordability of the Cavalier Z24

by Treatment Group .............................................................................. 112

xi

Table4 5
Table 4 5
Capir
Table4 5
Table 4 b
Acqu
Table 4 6
Table-l 6
Table 4 6
Table4 6
Casa‘
Table 4 e
Table 4 e
Cara
Table 4 c
Cava
Table 4 (
Varia
Table 4 (
Varia
Table 4 j
Cara
Table 4 j
CaVa
Table 4 7.
Cam
Table 4 f
Varia
Table .1 j
the C
Table,‘ '1
CaDir
Table 4 j

Table 4.57. MANOVA Results—Age and Credit Card Dependent Variables .. 1 12
Table 4.58. Analysis of Covariance—Age and Attitude toward the

CapitalOne Ad ...................................................................................... 113
Table 4.59. Analysis of Covariance—Age and Attitude toward CapitalOne 113
Table 4.60. Analysis of Covariance—Age and Credit Card (General)

Acquisition Intent ................................................................................. 1 14
Table 4.61. MANOVA Results—Gender and Auto Dependent Variables ....... 115
Table 4.62. Interaction—Gender and Attitude toward the Cavalier Z24 ......... 115

Table 4.63. Parallel Slopes—Gender and Attitude toward the Cavalier 224 116
Table 4.64. Correlation between Gender and Attitude toward the

Cavalier Z24 by Treatment Group .......................................................... 1 16
Table 4.65. Interaction—Gender and Purchase Intent for the Cavalier Z24 117
Table 4.66. Parallel Slopes—Gender and Purchase Intent for the

Cavalier Z24 ......................................................................................... 1 17
Table 4.67. Correlation between Gender and Purchase Intent for the

Cavalier 224 by Treatment Group .......................................................... 117
Table 4.68. MANOVA Results—Gender and Credit Card Dependent

Variables .............................................................................................. 1 17
Table 4.69. MANOVA Results—Class Standing and Auto Dependent

Variables .............................................................................................. 118
Table 4.70. Interaction—Class Standing and Affordability of the

Cavalier Z24 ......................................................................................... 1 19
Table 4.71. Parallel Slopes—Class Standing and Affordability ofthe

Cavalier Z24 ......................................................................................... 1 19
Table 4.72. Correlation between Class Standing and Affordability of the

Cavalier 224 by Treatment Group .......................................................... 119
Table 4.73. MANOVA Results—Class Standing and Credit Card Dependent

Variables .............................................................................................. 120
Table 4.74. Analysis of Covariance—Class Standing and Attitude toward

the CapitalOne Ad ................................................................................. 120
Table 4.75. Interaction—Class Standing and Perceived Affordability of

CapitalOne ........................................................................................... 121
Table 4.76. Parallel Slopes—Class Standing and Perceived Affordability of

CapitalOne ........................................................................................... 121
Table 4.77. Correlation between Class Standing and Perceived Affordability

of CapitalOne ....................................................................................... 121
Table 4.78. MANOVA Results—Parents’ Income and Auto Dependent

Variables .............................................................................................. 122
Table 4.79. Analysis of Covariance—Parents’ Income and Perceived

Affordability of the Cavalier Z24 ........................................................... 123
Table 4.80. Analysis of Covariance—Parents’ Income and Purchase Intent

for Auto (General) ................................................................................. 123
Table 4.81. Analysis of Covariance—Parents’ Income and Pre-Post

Difference in Purchase Intent for Auto (General) .................................... 124
Table 4.82. MANOVA Results—Parents’ Income and Credit Card

Dependent Variables ............................................................................. 124

xii

Table 4 83
for Cre
Table4 84
Variabl
Table 4 85
Differe
Table 4 Sb
Depend
Table4 87
the Cap
Table4 88
for a Cr
Table4 80
Variabli
Table 490
Afforda
Table 491
Denend
Table4 93,
Afforda
Table 4.93,
Variable
Table 494}
Oflhe C
Table 4 95
ATTOTdai
Ainrda':
Table 4.97
Depends
Table4 98.

,CaPiIalo
T 'Capirajo
“Milos- l
the Cap):
“101

.Capltalo
Table4 102 I

 

Tab

 

Table 4.83. Analysis of Covariance—Parents’ Income and Purchase Intent

for Credit Card (General) ....................................................................... 125
Table 4.84. MANOVA Results—Parental Support and Auto Dependent

Variables .............................................................................................. 126
Table 4.85. Analysis of Covariance—Parental Support and Pre-Post

Difference in Purchase Intent for Auto (General) .................................... 126
Table 4.86. MANOVA Results—Parental Support and Credit Card

Dependent Variables ............................................................................. 127
Table 4.87. Analysis of Covariance—Parental Support and Attitude toward

the CapitalOne Ad ................................................................................. 127
Table 4.88. Analysis of Covariance—Parental Support and Purchase Intent

for a Credit Card (General) .................................................................... 128
Table 4.89. MANOVA Results—Personal Income and Auto Dependent

Variables .............................................................................................. 129
Table 4.90. Analysis of Covariance—Personal Income and Perceived

Affordability of the Cavalier Z24 ........................................................... 129
Table 4.91. MANOVA Results—Personal Income and Credit Card

Dependent Variables ............................................................................. 130
Table 4.92. Analysis of Covariance—Personal Income and Perceived

Affordability of the CapitalOne .............................................................. 130
Table 4.93. MANOVA Results—Credit Card Attitude and Auto Dependent

Variables .............................................................................................. 131
Table 4.94. Interaction—Credit Card Attitude and Perceived Affordability

of the Cavalier Z24 ............................................................................... 132
Table 4.95. Parallel Slopes—Credit Card Attitude and Perceived

Affordability of the Cavalier Z24 ........................................................... 132
Table 4.96. Correlation between Credit Card Attitude and Perceived

Affordability of the Cavalier Z24 by Treatment Group ............................ 132
Table 4.97. MANOVA Results—Credit Card Attitude and Credit Card

Dependent Variables ............................................................................. 133
Table 4.98. Interaction—Credit Card Attitude and Attitude toward the

CapitalOne Visa Ad ............................................................................... 133
Table 4.99. Parallel Slopes—Credit Card Attitude and Attitude toward the

CapitalOne Ad ...................................................................................... 134
Table 4.100. Correlation between Credit Card Attitude and Attitude toward

the CapitalOne Ad ................................................................................. 134
Table 4.101. Interaction—Credit Card Attitude and Attitude toward

CapitalOne Visa .................................................................................... 134
Table 4.102. Parallel Slopes—Credit Card Attitude and Attitude toward

CapitalOne Visa .................................................................................... 135
Table 4.103. Correlation between Credit Card Attitude and Attitude toward

CapitalOne Visa by Treatment Group ..................................................... 135
Table 4.104. Analysis of Covariance—Credit Card Attitude and

Affordability of the CapitalOne Card ...................................................... 136
Table 4.105. MANOVA Results—Credit Card Control and Auto Dependent

Variable ................................................................................................ 136

xiii

Table4 1
Iowa:
Table 41
Deper.
Table4 l
Affor‘
Table4 Tl.
Deper.
Table4 l L
Deper.
Table4 1‘,
Capita
Table4 1'.

  
    
  

Table 4.106. Analysis of Covariance—Credit Card Control and Attitude

toward the Cavalier 224 Ad ................................................................... 137
Table 4.107. MANOVA Results—Credit Card Control and Credit Card

Dependent Variables ............................................................................. 137
Table 4.108. Analysis of Covariance—Credit Card Control and

Affordability of the CapitalOne Card ...................................................... 138
Table 4.109. MANOVA Results—Credit Card Spending and Auto

Dependent Variables ............................................................................. 139
Table 4.110. MANOVA Results—Credit Card Spending and Credit Card

Dependent Variables ............................................................................. 139
Table 4.111. Interaction—Credit Card Spending and Attitude toward the

CapitalOne Visa .................................................................................... 140
Table 4.112. Parallel Slopes—Credit Card Spending and Attitude toward

the CapitalOne Visa .............................................................................. 140
Table 4.113. Correlation between Credit Card Spending and Attitude

toward CapitalOne by Treatment Group .................................................. 140
Table 4.114. MANOVA Results—Credit Card Usage and Auto Dependent

Variables .............................................................................................. 141
Table 4.115. MANOVA Results—Credit Card Usage and Credit Card

Dependent Variables ............................................................................. 141
Table 4.116. MANOVA Results—Number of Credit Cards and Auto

Dependent Variables ............................................................................. 142
Table 4.117 Analysis of Covariance—Number of Credit Cards and

Affordability of the Cavalier .................................................................. 143
Table 4.118. Analysis of Covariance—Number of Credit Cards and

Purchase Intent for Auto (General) ......................................................... 143
Table 4.119. MANOVA Results—Number of Credit Cards and Credit Card

Dependent Variables ............................................................................. 144

Table 4.120. MANOVA Results—Borrowing and Auto Dependent Variables .. 145
Table 4.121. Interaction—Borrowing and Purchase Intent for the

Cavalier Z24 ......................................................................................... 145
Table 4.122. Parallel Slopes—Borrowing and Purchase Intent for the

Cavalier Z24 ......................................................................................... 146
Table 4.123. Correlation between Borrowing and Purchase Intent for the

Cavalier Z24 by Treatment Group .......................................................... 146
Table 4.124. Analysis of Covariance—Borrowing and Purchase Intent for

Auto (General) ...................................................................................... 146
Table 4.125. Analysis of Covariance—Borrowing and Pre-Post Difference

in Purchase Intent for Auto (General) ..................................................... 147
Table 4.126. MANOVA Results—Borrowing and Credit Card Dependent

Variables .............................................................................................. 147
Table 4.127. MANOVA Results—Dollars Off in Auto Loan Payment

Estimate and Auto Dependent Variables ................................................. 148
Table 4.128. MANOVA Results—Years Off in Time of Auto Loan Payment

Estimate and Auto Dependent Variables ................................................. 149

Table 4.129. Interaction—Years Off in Time of Auto Loan Payment

xiv

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Estimate and Affordability of the Cavalier 224 ....................................... 149
Table 4.130. Parallel Slopes—Years Off in Time of Auto Loan Payment

Estimate and Affordability of the Cavalier 224 ....................................... 150
Table 4.131. Correlation between Years Off in Time of Auto Loan Payment

Estimate and Perceived Affordability of the Cavalier Z24 by Treatment

Group ................................................................................................... 150
Table 4.132. MANOVA Results—Dollars Off in Credit Card Interest

Estimate and Credit Card Dependent Variables ....................................... 150
Table 4.133. MANOVA Results—Years Offin Time of Credit Card

Payment Estimate and Credit Card Dependent Variables .......................... 151
Table 4.134. Interaction—Years Off in Time of Credit Card Payment

Estimate and Attitude toward the CapitalOne Ad ..................................... 152
Table 4.135. Parallel Slopes—Years Off in Time of Credit Card Payment

Estimate and Attitude toward the CapitalOne Ad ..................................... 152

Table 4.136. Correlation between Years Off in Time of Credit Card Payment
Estimate and Attitude toward the CapitalOne Ad by Treatment Group ...... 152
Table 4.137. Interaction—Years Off in Time of Credit Card Payment

Estimate and Attitude toward CapitalOne ............................................... 153
Table 4.138. Parallel Slopes—Years Off in Time of Credit Card Payment

Estimate and Attitude toward CapitalOne ............................................... 153
Table 4.139. Correlation between Years Off in Time of Credit Card

Payment Estimate and Attitude toward CapitalOne .................................. 154
Table 4.140. Analysis of Covariance—Years Off in Time of Credit Card

Payment Estimate and Acquisition Intent for CapitalOne ......................... 154

XV

Figure 2 1

Figure 2 2

J

Figure 2 3

Figure 3 l

 

LIST OF FIGURES

Figure 2.1. A Hypothetical Value Function ..................................................... 19
Figure 2.2. Adaptation and the Value of Consumption ..................................... 20
Figure 2.3. The Time and Outcome Valuation Model ...................................... 24
Figure 3.1. Experimental Design ................................................................... 4O

xvi

CHAPTER 1

INTRODUCTION

Problem Statement

Questions regarding advertising’s role in influencing consumer decision
making have sparked debate and research. Although some contend that
advertising can play a positive role in helping people make rational, efﬁcient
choices in the marketplace by providing information (Stigler 1961; Abernathy
and Franke 1996), advertising has also been criticized for containing little useful
information and encouraging people to waste their scarce resources buying non-
necessities (Pollay 1986). It has been said by the late Canadian economics
professor and comedy writer Stephen Leacock that advertising is “the science of
arresting human intelligence long enough to get money from it” (Morgenstern
1992, p. 46). Rotzoll, Haefner, and Sandage (1990) present a thorough, yet
concise discussion of these opposing views of the role of advertising. General
questions regarding the value or role of advertising in assisting or undermining
rational decision making will most likely remain unresolved because the vast
differences in ads (products categories, advertising appeals, target audiences,
etc.) dictate that research examine smaller sub-sets of advertising that have
common elements.

One such area ripe for exploration is advertising that encourages
consumers to buy products that they cannot afford to purchase outright. This

type of advertising does more than encourage consumers to suspend their

intelligent
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intelligence long enough to separate them from the money they currently have.
It tells them to “buy” a product now and pay with money they will have in the

future, thus separating them from money before they even possess it.

Some advertisements that encourage consumers to spend more than they
have directly tout a particular product while others offer credit which could be
used to purchase a variety of products/services. The type of product (broadly
deﬁned to include credit) influences the type of claims made. Claims stating
“Buy Now! No Money Down! No Interest! No Payment for One Full Year (or
until 2000)!” are a ﬁxture of furniture, carpeting, appliance, and home-
improvement ads. Rent-to-own companies make a variety of these and other
products available to consumers. Exercise equipment ads, infomercials, and
even cosmetic surgery and legal service advertising often contain references to
payment or installment plans. Similarly, automotive advertising frequently
contains information about monthly payments which correspond with a variety of
low interest loans or leasing options, enabling consumers to “swing” driving new
vehicles even if they cannot afford to purchase them. Indeed, over the past two
decades, advertising credit has been a common tactic used in the marketing of

cars (Olney 1991).

Similar claims are made by other companies offering credit. A frenzy of
advertisements that could only go unnoticed by home-owners if “they’re
completely unplugged from the world and lack a mailbox” is now encouraging
consumers to re-mortgage their homes to consolidate their debt (perhaps a wise
ﬁnancial move), and get extra money (actually go deeper in debt) for a new pool,

a vacation, or cash for any reason (Molvig 1999). Then there are ads for credit

cards the."
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cards themselves which make claims about large credit lines, balance transfers,
cash advances, very low minimum payments, and low introductory or teaser
interest rates. Americans were bombarded with some four billion unsolicited
direct mail offers for credit cards in 1998 alone (Cambor 1999). The products
are numerous, the claims slightly different, but the message is the same—the
product is within the consumer’s grasp even if it is not within the consumer’s
means. Advertisements including claims that consumers should "buy" products
even if they cannot afford to purchase them are commonplace, but our
understanding of the inﬂuence of such claims is limited as virtually no research

to date has addressed the issue.

Financing Claims

It is the inﬂuence of claims such as these that this research seeks to
address. Financing claims are deﬁned as advertising statements that provide
information about the availability of credit or payment options for a speciﬁc
product or credit in general. Financing claims inform people of the opportunity
to possess products without paying the complete cost of the products prior to
possessing them. Such claims may make products seem more immediately
affordable. These claims may encourage people to possess products as soon as
they desire them and pay for the products at a later point in time, rather than
saving money until they can actually buy them outright. By encouraging
consumers to take the product (a gain) now and defer payment (a loss) until a
later time, such claims take advantage of human nature to engage in more risk-
taking behaviors when negative outcomes are delayed (a key component of the

time and outcome valuation). Further, ﬁnancing advertising may induce

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impatience to possess products that would otherwise be considered unaffordable.
As a result of seeing these claims consumers may engage in time-inconsistent
behavior in which they forgo their long-term goals to satisfy short-term desires.
Ads containing ﬁnancing claims seem to entice consumers with the notion of
possessing products that are beyond their current means by making them appear
affordable, encourage impatience in purchasing in general, and hold the potential

to motivate irrational and potentially detrimental consumer decision making.

People vary widely in their orientations toward time-related decisions and
spending. As Ainslie (1975) describes, many who study philosophy, economics,
sociology and psychology have examined the issue of why people when
presented with two choices often choose the poorer of the two. Their

explanations for “maladaptive behavior” fall into the following three categories.

1. In seeming to obey impulses, people do not knowingly choose the
poorer alternative but have not really learned the consequences of their
behavior. Socrates said something like this. Those who hold this kind
of theory prescribe education or “insight” as the cure for
impulsiveness.

2. In obeying impulses, people know the consequences of their behavior
but are impelled by some lower principle (the devil, repetition
compulsion, classical conditioning) to act without regard for
differential reward. Those who hold this kind of theory prescribe
some means of exorcising the lower principle, such as abreaction or
desensitization.

3. In obeying impulses, people know the consequences of their behavior, but
their valuation of the consequences is innately distorted so that imminent
consequences have a greater weight than remote ones. Those who hold this
kind of theory prescribe devices that serve to commit future behavior to
courses decided on well in advance. (pp. 463-464)

The explanations and some of the terms used to describe time-inconsistent

decisions such as myopic and maladaptive behaviors as well as the behaviors

 

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themselves (spendthriftness, delinquency, and drug abuse) reﬂect the fact that
many scientists look down upon those who make decisions that favor the present
and neglect the future. Indeed, many consumers seem to share this view and talk
openly about their frustration with their own inability to resist temptation and
how it results in problems such as obesity or indebtedness. “The consensus of
social scientists, and apparently of consumers themselves, is that time
inconsistent preferences are not as legitimate as their more farsighted

counterpart” (Hoch and Loewenstein 1991, p. 493).

However, the preference of economists and others for delaying
gratiﬁcation does not discount the fact that many people choose to enjoy the
moment, buy impulsively, and make choices others would judge not to be in their
best long-term interest, and those people are perfectly happy doing so. Many
people enjoy shopping and spending, and some buy quickly or impulsively
without engaging in much pre-purchase evaluation; many consumers’ practices
do not conform to rational models proposed by many economists (Carsky,
Dickinson and Smith 1995). Strotz (1956) believed that prioritizing the present

and discounting the future is innately human.

To one who would say that to discount the future for remoteness at all
seems to him foolish and irrational, I should reply that he is one who
received very strong training as a child which went beyond simply
teaching him the strategy of consistent planning and effected such a
change in his tastes that he now ﬁnds it unnatural to discount the future on
this account. Moralizing against discounting the future in this way has, of
course, found its way into the prominent literature on this subject. (p. 178)

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Although Strotz expressed concerns about the potential negative impact of what
he called myopic decision making, he understood the naturalness of such
decisions and seemed to reject the notion that the state should take over such
decisions (as some other economists ofthe time suggested). Although time-
inconsistent choices can result in problems such as bankruptcy, they should be
considered just as valid as those made by people who plan and save and live
totally within their means. Our society is based on the idea of the people being

sovereign.

Like impatience and time-inconsistent decisions, advertising claims that
may encourage such behaviors should not be automatically condemned. The
inﬂuence of such advertising claims is currently unknown. Some people may
learn about low interest ﬁnancing options through advertising and use them to
their beneﬁt (allow them to enjoy the present or even free up money for higher
yielding investments). However, others may be motivated by “Buy now! Pay
later!” claims to take on more debt than they can bear. Or, such advertising
claims may have no effect at all. Such advertising needs to be examined so its

impact can be better understood.

Purpose of the Study

The purpose of this research is to explore the impact of advertising
ﬁnancing claims to determine the nature of their effects and whether they have
more inﬂuence with certain consumers. Speciﬁcally, it addresses the following

research questions.

 

 

 

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1. What inﬂuence does advertising of ﬁnancing claims for a speciﬁc product have on
purchase intentions for products within the product category?

2. What inﬂuence does advertising of ﬁnancing claims for a product have on purchase
intent and other related constructs for that product in particular?

3. Are some people more inﬂuenced by ﬁnancing claims than others?

This chapter identiﬁes this purpose, provides an introduction to and a
deﬁnition of ﬁnancing claims, and includes a discussion of the signiﬁcance of
the research. Chapter 2 provides a framework for understanding the possible
impact of such advertising by describing literature that may explain consumers’
responses to ﬁnancing claims. This review includes research focussing on
advertising’s inﬂuence on rational consumer decision making, literature related
to consumers’ time-inconsistent choices and their impatience, information about
advertising’s possible inﬂuence on consumer impatience and irrational decision
making, theories and research related to time and outcome valuation, research on
the impact of affordability perceptions, and articles that suggest individual
difference characteristics which may cause people to be more readily inﬂuenced
by advertising that includes ﬁnancing claims. With this literature review as a
basis, Chapter 3 presents hypotheses about the inﬂuence of ﬁnancing claims and
describes the method used for conducting a two-part experiment examining
consumers’ reactions to ﬁnancing claims. Chapter 4 provides an extensive
review of the results, and Chapter 5 presents a summary and discussion of the

results as well as directions for future research.

 

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Significance of the Study

Exploring the inﬂuence of advertising that contains ﬁnancing claims has
many beneﬁts. Although some authors have suggested that advertising may
inﬂuence consumers’ time-related decision making (Strotz 1956; Hoch and
Loewenstein 1991), only one has examined such advertising. Olney (1991), who
wrote a book examining the extraordinary expansion in consumer durable goods
in the 19205, originally envisioned telling the story of how credit was created to
market cars and other durable goods, but became convinced that this was not the
case. The book included the results of a content analysis of ads in Ladies Home
Journal from 1901 to 1941 showing that ﬁnancing claims were included in ads
for some products (such as electric appliances) but only rarely, and in particular
only three automobile ads made any claims about ﬁnancing. Although Ford
offered payment plans, the following ad made it clear that Henry Ford did not
approve of credit: “Despite confusion, in the minds of many, of extravagance
with progress, a vast majority [of the American people] cling to the old-
fashioned idea ofliving within their incomes. From these came and are coming
the millions of Ford owners” (Olney 1991, p. 160). Overall, Olney concludes,
“If manufacturers were offering credit as a means to market their products, they
certainly were not making it a part of their advertising strategy!” (p. 160). This

is not the case today—ﬁnancing claims are a common element in advertising.

This research is the ﬁrst to experimentally test the inﬂuence of ﬁnancing
claims. It shows how advertising ﬁnancing claims can ﬁt into our existing
knowledge about related areas such as time-inconsistent preferences, consumer

impatience, time and outcome valuations, consumer perceptions of affordability,

 

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consumer time preferences, and rational consumer decision making. In their
work on time preference, Olson and Bailey (1981) described the area as one of
“great practical and theoretical importance” deserving of more serious attention
and examination (p. 24). Mowen (1992) and Amyx and Mowen (1995)
speciﬁcally called for more research addressing the inﬂuence of individual
difference factors. This examination of ﬁnancing claims answers that call and
provides a greater understanding of time—related behaviors and factors that
inﬂuence them. Speciﬁcally, it advances our theoretical understanding by
showing what role advertising (in this case ﬁnancing claim advertising) plays in
inﬂuencing consumers’ time-inconsistent preferences, impatience, affordability
perceptions, and time and outcome valuation—something that researchers have

speculated on but never researched.

This research has practical implications for advertising practitioners as
well. It is beneﬁcial to know whether the time and space spent on these “call to
action” appeals that discuss low and no interest loans and the like is well spent
or wasted. Mowen and Mowen (1991) suggest that such promotions may result
in more sales in the short-term, but they agree with analysts that in the long-run
promotions may be harmful. In the furniture industry, ﬁnancing claims are also
common and their effectiveness has come under question. Kaufman (2000)
reported, “they [furniture merchants] made money not so much by selling sofas
and chairs but by offering credit to low-income people to buy merchandise and
then making a proﬁt on the terms of the loan” (p. 1). James 0th, a buyer for Cal
TV Furniture and Appliances, indicated in a recent article that retailers have
created a situation where they are forced to offer zero-percent ﬁnancing since

everyone else is doing it and that the deals are overemphasized and have lost

 

 

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their luster (Mattioni 1996). Is this true? Are consumers receptive to ﬁnancing
claims that make them feel the previously thought unaffordable is within their
reach? Are they oblivious to such claims perhaps since they assume that credit
terms are always available? Or, are consumers disapproving, particularly if they
are debt averse? It is also valuable for practitioners to know whether consumers
respond most favorably to ads that tout the lowest payments or the longest
deferment of payment or to shorter, yet somewhat higher payments. Despite the
apparent importance of price, advertising pricing claims in general have not
received much attention from researchers. Insight into questions such as these

paves the way for the development of more effective advertising.

This research should also be of interest to consumer advocates who are
concerned that advertising and direct mail solicitations, particularly for credit
cards, are already too effective at hooking “vulnerable consumers” on easy credit
and pushing them into personal bankruptcy (Faber and O’Guinn 1988; Lucas
1992). This is particularly true with respect to credit card advertising to college
students. New ﬁndings show that two thirds of students carry a credit card
balance—just over $2,000 on average—and that one ﬁfth of those have a balance
exceeding $10,000 (Mannix 1999). This combined with rising bankruptcy among
the young and reports of suicide resulting from anxiety over debt led a bill to be
presented in the Senate in December of 1999 (Roberts 1998; “Senate Kills
Credit” 1999). Had the bill passed (it was defeated by a 59 to 38 vote), credit
card companies would have been prohibited from issuing a card to those under
the age of 21 without parental consent or demonstrated ability to repay.
Criticism has also been directed at another industry where ﬁnancing claims are

common; no-equity loans are controversial because of concerns for ‘huge losses

10

 

 

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for lenders and devastating evictions of borrowers” (Clark 1988). The Federal
Reserve Board began holding hearings in July to investigate predatory lending

practices in the industry (Sorohan 2000).

Problems due to debt are disconcerting. For every dollar of income,
consumers spend $1.10 (“Credit Card Debt” 1996). Personal bankruptcies in the
United States reached an all time high (1.1 million or one percent of all
households) in 1996, prompting congressional hearings into the need for
bankruptcy system reform ("And Deeper into Debt" 1997). Losses due to
bankruptcy ($10.4 billion in 1995 for bank cards alone) trickle down and
increase costs, interest, and fees for everyone (Gutner 1996). The results of this
research may dispel fears or lend legitimacy to concerns about the negative

economic inﬂuence of advertising.

Although exploring the issue ofthe existence of vulnerable consumers or
developing a proﬁle of them is beyond the scope of this research, the ﬁndings of
the study may nonetheless shed some light on this interesting issue. For
example, are those who are younger, less educated, less well-off, less ﬁnancially
astute, more impatient, compulsive, and present-oriented more likely to be
inﬂuenced by ﬁnancing claims? A better understanding of the inﬂuence of
ﬁnancing claims on all kinds of consumers has the potential to result in a variety
of beneﬁts to advertisers, researchers, consumer advocates, consumers

themselves, and possibly regulators.

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CHAPTER 2
REVIEW OF THE LITERATURE

Consumer Decision Making

The rational choice model is the traditional and still widely used paradigm
employed to describe consumer decision making. According to such a model,
consumers carefully, dispassionately gather and weigh information about product
attributes, weigh costs with beneﬁts, use decision rules, and make purchase
decisions in a rational, utilitarian-like manner (Bettman I979). Advertising’s
role in the process is to supply potential consumers with information, thus
reducing their search time and making their purchasing decisions more efﬁcient

(Stigler 1961; Rotzoll, Haefner, and Sandage 1990).

While maintaining an appreciation for the inﬂuence of the rational choice
model in adding to our understanding of many consumer purchases, one must
also be aware that not all purchase decisions are consistent with the model’s
assumptions. Consumers are not purely rational; they are also emotional.
Hirschman and Holbrook (1982) for example have described how consumption
can be hedonic—involving multi-sensory, fantasy, and emotive elements—and
even addictive when hedonic needs are taken to an extreme (Hirschman 1992).
Advertising appeals can be emotional as well as rational. In the case of ads
making ﬁnancing claims, it appears that ads are designed to appeal less to the

rational and more to the hedonic side of consumers.

12

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Time Inconsistency

Hoch and Loewenstein (1991) draw together these literature bases that
discuss rational and emotional consumer behaviors by presenting an economic-
psychological explanation of how consumers attempt to use self-control when
faced with time-inconsistent choices. Time-inconsistent choices are transient
alterations from long-term goals that would not have been made if they had been
considered in an objective manner (Stigler and Becker 1977). The notion of
time-inconstancy has been applied to help provide insight into a broad array of
areas requiring self-restraint from macroeconomic matters such as savings
behavior (Thaler and Shefrin 1981) to psychological concerns such as addictions
of all kinds (from those that come easily to mind such as drugs and eating but
also to work, over-spending, compulsive buying, and even computer hacking)
(Herrnstein and Prelec 1992). Controlling time-inconsistent preferences involves
the use of self-control tactics to delay gratiﬁcation. However, consumers are
often impatient. As a whole body of literature on impulsive buying suggests,
consumers sometimes have trouble controlling their urges, particularly their urge

to buy (Rook 1987).

The element of time is taken into account by economists in their
explanations of decision making of all kinds including ones faced by consumers.
Just as corporations use economic principles to guide them in decisions
regarding capital investments, consumers can too, particularly when making
purchase decisions for durable goods such as automobiles by comparing the
present value of future beneﬁts with the current costs. The purchase decision

should be based on calculating the net present value; the purchase should be

13

 

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made ifthe present value of the beneﬁt exceeds the cost. Such calculations take
into account a discount rate—the opportunity cost of money. For example, “If
the consumer already has $10,000 (the cost of the auto) and does not need a loan,
the correct discount rate is the return that could be earned by investing the
money in another asset, say, a savings account or a government bond. On the
other hand, if the consumer is in debt, the discount rate would be the borrowing
rate that he or she is already paying” (Pindyck and Rubinfeld 1989, p. 542).
According to such calculations, it is logical, even ﬁscally wise, to obtain
ﬁnancing for the vehicle if a very low rate is available (such as those advertised
in ﬁnancing claims for new automobiles) to free up capital for higher yielding
investments. Logical consumer decision making guided by principles such as
these are expected to result in decisions that are in the consumer’s long-term best
interest, although risks involved in decisions cannot always be fully anticipated

and accounted for.

Inherent in decisions about the future is this element of risk. “Consumer
behavior involves risk in the sense that any action ofa consumer will produce
consequences which he cannot anticipate with anything approximating certainty,
and some of which at least are likely to be unpleasant” (Bauer 1960). As
originally deﬁned by Bauer and researched by others (see Havlena and DeSarbo
1991 for a more recent review), perceived risk has the following two key
components: 1) the consequences (with focus often on negative outcomes) and
2) uncertainty or the likelihood of the consequences. Concern arises when
consumers lured by immediate beneﬁts stray from logical processes of decision

making and engage in risky behaviors like gambling, charging purchases, or even

14

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smoking where there is a likelihood of substantial long-term negative

consequences. Researchers seek to understand these time-inconsistent decisions.

For centuries, social scientists have attempted to explain how time
inﬂuences decisions and why people make time-inconsistent decisions—
“engaging in behavior that would have been rejected if contemplated in advance
and that may be regretted after the fact” (Hoch and Loewenstein 1991). Dating
as far back as the 18305, economists such as Rae, Senior, and Jervons provided
detailed discussions of intertemporal choices to explain economic issues (such as
allocation of wealth and capital) and primarily attributed time-inconsistent
decisions to present mindedness and emotional or hedonic factors. Near the end
of the nineteenth century, discussions of intertemporal choice were dominated by
Bohm-Bawerk and Fisher’s descriptions ofa cognitive process oftradeoffs
between present and future utility where time-inconsistent decisions resulted
from shortcomings in decision makers’ visions of the future. In the early part of
the twentieth century, writings about time inconsistency turned away from
psychological explanations and more toward graphically and mathematically
depicting impatience or time preference. As the century progressed, researchers
once again began to integrate psychological and economic factors to provide a

more complete understanding of the causes of time-inconsistent decision making.

The most popular explanation for why people make decisions that are
inconsistent with long-term goals (choosing the smaller of two alternative
rewards because it is available sooner) is that ofimpatience. Ainslie (1975)
wrote that “the question of impulsiveness is one of the oldest on record—it is,

after all, the subject of the story of Adam and Eve” and countless other

15

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philosophical ponderings and scientiﬁc observations. Ainslie, like his
predecessor Strotz (1956), explained that impatience results from discounting.
Discounting means that people are “disproportionately attracted to immediately
available rewards” (Hoch and Loewenstein 1991, p. 494) or that “utility
experienced later is generally assumed to carry less weight than that experienced
earlier” (Loewenstein 1988, p. 201). If rewards are sufﬁciently delayed in time
people are able to make rational choices between them, but when one reward is
more immediate (smoking versus good health) people are disproportionately
attracted to it. Strotz (1956) discusses the humanness and commonality of

overvaluing more proximate satisfaction as compared to more distant outcomes.

Such a function suggests that individuals who precommit their future
actions or who naively resolve now what they “will do” in the future,
commonly do not schedule the beginning of austerity until a later date.
How familiar the sentence that begins, “I resolve, starting next . . .” It
seems very human for a person who decides that he ought to increase his
savings plan to start next month, after satisfying some current desires; or
for one to decide to quit smoking or drinking after the week-end, or to say
that “the next one is the last one.”

It has been customary for the United States Army to offer voluntary
enlistees a furlough starting with the date of enlistment. This practice is
not needed to enable a man to put his affairs in order—he can do that ﬁrst
and then enlist—but it does serve as an enticement to those who want the
paternalism (“security”) of the army, but do not want it right now. The
many schemes for instalment (his correct alternative spelling) buying
(notably of used automobiles in the US.) which require “no down
payment and nothing due for two months” are evidence of the
effectiveness of enticements of this kind. Indeed, all purchases on credit
can be viewed as precommitments that often (although not always)
exchange future costs for present pleasure. (p.177)

Strotz continues on to indicate that he suspects that most people are

“born” with a discounting function and that children are taught the “virtue” of

16

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frugality as part of character building. He adds that occasionally people have
lapses (fail to plan appropriately and behave consistently as they have been
taught) and these lapses take the form of “splurges, hinges, and extravagances”
(p. 178). Those who do this often are called spendthrifts and those who do not
are thrifty. Refer to Loewenstein and Elster (1992) for a thorough historical
review of discounting. Due to its elegance, the principle of discounting was
widely embraced by researchers for explaining intertemporal decisions although
it has been criticized for not providing more detailed description of how

discounting occurs.

In their theoretical piece, Hoch and Loewenstein (1991) incorporate the
inﬂuence of time into a model that provides more explanatory power than the
discounting model. They propose that time-inconsistent preferences are do to
sudden increases in desire resulting from a shift in the consumer’s reference
point. In earlier work, Loewenstein (1988), wrote that the reference point
reﬂects the fact that “rather than integrating delayed consumption with existing
consumption plans it appears that people often represent future consumption
plans as gains or losses or as deviations from some standard, such as a past level
of consumption, or the consumption of another person or group” (p. 200). He
demonstrated that in choices between immediate and delayed consumption,
framing [positioning the consumption in terms of a speed-up (determined by ﬁrst
asking how much subjects are willing to pay, then how much more they will
spend to speed up the consumption), a delay premium (what they would pay to
have immediate possession minus the smallest amount they would take to accept
a delay), or simply the present value of consumption at two points in time]

inﬂuenced the reference point employed by subjects, and the reference point

17

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inﬂuenced their choice. Predictions based on a model employing a reference
point differ from traditional discounting models which would be unaffected by

such framing.

According to Hoch and Loewenstein’s model, once the reference point has
changed (and the consumer wants the product), the consumer attaches positive
feelings to possessing the product, and negative feelings to not possessing the
product or object in question which results in feelings of deprivation. In some
cases the desire may be transient and may decrease over time as the consumer
becomes involved or distracted by other activities. But, in most cases desire is
not ﬂeeting; feelings of deprivation will linger and often intensify over time. To
visually depict this changing of reference points and the changes in deprivation
associated with shifts in reference points, Hoch and Loewenstein (1991) use a

framework borrowed from prospect theory (Kahneman and Tversky 1979).

Prospect theory itself was proposed by Kahneman and Tversky (1979) as
an explanation of how people make decisions under risk. According to prospect
theory, values are assigned to individual losses and gains and each multiplied by
a decision weight and then all are summed to form an overall value, which is
used to make a decision or choice. Based on a series of hypothetical choice
problems in which subjects had to choose between certain dollar amounts with
various probabilities of winning, the authors proposed that “the value function is
(i) deﬁned on deviations from the reference point; (ii) generally concave for
gains and convex for losses; (iii) steeper for losses than gains. The S-shaped

value function is depicted in Figure 2.1.

18

 

 

Reprinted u
T

 

 

Figure 2.1. A Hypothetical Value Function

VALUE

 

LOSSES GAINS

 

Reprinted with Permission: From “Prospect Theory: An Analysis of Decision
Under Risk,” by Daniel Kahneman and Amos Tversky,” 1979, Econometrica,
47(2), p. 219, Copyrighted by The Econometric Society.

In Hoch and Loewenstein’s (1991) model they use the value function to
depict the inﬂuence of changing reference points on the consumer’s level of
satisfaction. Figure 2.2 shows the value function which represents satisfaction
level as a ﬁinction of purchase or nonpurchase relative to the reference point
which is located where the value function intersects the x axis. The value
function itself is steeper for losses than gains. The lighter line depicts a
consumer who would beneﬁt from owning a product, but who has not adapted to
the idea of owning it. The line is drawn through the origin to show that
nonpurchase results in zero utility and purchase would result in D1 utility. The

bold line depicts the case of the consumer who had partially adapted to owning

19

the product (the reference point has changed). In this case the value function has
moved to the right to show that the consumer is at a state between nonpurchase
and purchase. D2 shows the larger amount of utility (also referred to as desire)
which results for this person due to “the utility for consumption coupled with the
relief ensuing from not feeling deprived” (Hoch and Loewenstein 1991, p. 495).
As the value functions and the difference between D2 and D1 show, “the
consumer who partially adapts has already had a taste and may dread the pain of

having to forgo further tastes” (Hoch and Loewenstein 1991, p. 495).

Figure 2.2. Adaptation and the Value of Consumption

Satisfaction

    
 
   

 

no adaptation

 

adaptation
. V
no ' U
purchase

 

  

./

 

t t
purchase / D

 

 

Reprinted with Permission: From “Time-inconsistent Preferences and Consumer
Self-Control,” by Stephen J. Hoch and George F. Loewenstein, 1991, Journal of
Consumer Research, 17 (March), p. 494, Copyrighted by the University of
Chicago Press.

20

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In summary, the reference point shifts as the consumer mentally adapts to
the notion of possessing the product prior to purchase and then the desire to
purchase increases. Quickly the consumer becomes impatient to possess the
product. The consumer may give in and buy the product, or may use a variety of
self-control strategies in order to resist the urge to buy a product at odds with his

or her long-term goals.

Such strategies take two forms: I) desire reduction tactics such as
avoidance of situations which increase desire, postponing the choice, or
substitution 2) willpower-based strategies which may include precommitment,
bundling of costs, assessment of economic and psychological costs (guilt and
regret), or appealing to a higher authority or religious doctrine (Hoch and
Loewenstein 1991). Consumers are caught in a struggle for self-control between
the visceral, emotional, impulse-driven desire for gratiﬁcation and the patient,
logical, rational, mindful willpower-based strategies for achieving long-term

goals.
Advertising’s Role in Creating Impatience

According to Hoch and Loewenstein (1991), physical proximity, temporal
PTOXTmity, and social comparison are all factors that may cause consumers to
adjust their reference points and become impatient. Advertising is described as
utiliZi“g each of these mechanisms. Advertising increases physical proximity by

ShW’ing the product in use and vividly simulating the product usage experience

(MacInnis and Price 1987; Wells 1987). Advertising increases temporal

21

 

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proximity by including references to toll-free numbers 24-hours a day for
immediate ordering, and quick delivery. Finally, advertising increases
impatience by creating social comparison since it attempts to get consumers to
identify with prototypical product users (Peter and Olson 1987). Hoch and
Loewenstein (1991) state that advertising not only creates impatience, but it also
contains messages to counter-act willpower and desire-reduction strategies by
containing claims that unbundle costs (as in the cases of credit cards focussing
on low minimum monthly payments) and offer counter-arguments to allay guilt
(as in the case of an ad stating that buying a Mercedes-Benz S-class car is not
self-indulgent but self-preserving due to its safety record). Advertising claims

contain statements that may increase impatience and combat self-control.

If advertising in general contains claims that could lead to consumer
impatience as Hoch and Loewenstein suggest, then advertising with claims of a
“buy now pay later” nature certainly holds the potential to make some consumers
iInpatient to purchase products. Advertising with ﬁnancing claims shows the
product. Even nontangible products like credit are made tangible as in the case
Of sample checks for home equity loans. Further, ﬁnancing ads often show
Pr°t°tYpical product users with the product, frequently mention a direct way to
order, and speciﬁcally tell consumers that they do not have to wait until they can
afford the product to possess it. The advertising may create a change in
consumer-3’ reference points (making them want a product they did not desire
before), Spark impatience to own the product, and further help discount
consumers’ desires to use the self-control needed to make a rational choices

COHSiStent with long-term goals by suggesting that there is no reason to wait for

the desired product, not even a ﬁnancial one (lack of money).

22

 

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Time and Outcome Valuation

Just as the literature on time-inconsistency provides insight into how
consumers may be affected by ﬁnancing claims, so too does the literature on
time and outcome valuation. Both incorporate the concept of discounting and
borrow from prospect theory. Time and outcome valuation (TOV) is the
theoretical explanation proposed by Mowen and Mowen (1991) for the effects of
advancing and delaying outcomes. They propose that time systematically

inﬂuences how individuals assess the worth or the goodness or badness of

outcomes.

Mowen and Mowen (1991) illustrate this is in three dimensions in Figure
2.3. In the diagram, the x axis represents the actual value/cost of the stimulus
(or decision), the y axis depicts the psychological value of the stimulus, and the
third axis shows time from present to future. In the present, the value curve is
the same as those for prospect theory and the model of time-inconsistency as it
rePresents the heavier weighting of losses or negative outcomes as opposed to
gains or positive outcomes. However, as time progresses, the gains and losses
(to an eVen greater degree) are discounted so the plane ﬂattens and approaches
the X aXis. At a future point, the discounted gains are valued more than the
discounted losses. Visually the time and outcome valuation diagram depicts this

l“ﬂuencc of time on the value of outcomes.

23

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Figure 2.3. The Time and Outcome Valuation Model

     
     

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Psydtologicol Value

RePrinted with Permission: From “Time and Outcome Valuations: Implications
for Marketing Decision Making,” by John C. Mowen and Maryanne M. Mowen,

1991, Journal of Marketing, 55 (October), p. 56, Copyrighted by the American
Marketing Association.

The model is based on several assumptions, some of which are depicted in

Figure 2.3. The assumptions are listed below.

A1:
A2:
A3:
A4:
A5:

The psychological reaction to outcomes is described by the
decreasing marginal valuation of gains and losses.

The net valuation of an outcome is expressed as the sum of the
valuations of gains and losses associated with that outcome.

For outcomes that occur in the present, losses are psychologically
valued (i.e., weighted) relatively more heavily than gains.
Outcomes are discounted in time with losses discounted faster than
gains.

Gains and losses may occur at different points in time.

24

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A6: The loss or postponement ofa gain is framed as a loss. The
avoidance or postponement of a loss is framed as a gain. (Mowen and
Mowen 1991, pp. 55-56)

The third and forth assumptions indicate that negative outcomes occurring
in the present are psychologically weighted more heavily than positive ones and
that over time negative outcomes are discounted more quickly than positive
outcomes. So, if losses and gains occur in the present consumers will be more
cautious about taking on risk. However, if the same outcomes occur in the
future, the difference in the discounting of negative and positive outcomes
causes positive claims to be weighted more heavily and an optimistic attitude to
prevail. Other key assumptions (that is not depicted visually by the model) are
that gains and losses resulting from a decision can occur at different points in

time and that framing can play a key role in the perception of an outcome.

TOV predicts increased risk-taking tendencies when outcomes are
delayed. This effect was empirically supported by Mowen (1992). In two small
studies employing hypothetical choice models similar to those used by
Kahneman and Tversky (1979) which also included a range of time periods for
attaining the losses and gains, Mowen found support for the TOV model’s
suggestion that when all things are equal, when outcomes are proximate risk
aVCTSiOn results, but when outcomes are distant risk seeking behaviors increase.
While the aggregate results in the second study support this ﬁnding, individuals
had a bl’Oad range of responses with a few actually inﬂating the value of gains

0%" time. Mowen suggested that research explore individual differences that

may 8account for different assessments of gains and losses over time.

25

Other research on time seems consistent with the basic premises of the
theory, especially an article by Amyx and Mowen (1995). With TOV as a basis,
the authors predicted that a person’s time orientation (present or future oriented)
would moderate the effectiveness of a personal selling approach that involves
either delaying or advancing the payments for and receipt of a new car (negative
and positive outcomes). While the predictions that a future time-oriented person
would prefer delaying a gain were not supported, the authors did ﬁnd that
present time-oriented people compared to future time-oriented people did prefer

delayed payment to immediate payment.

The TOV model helps shed light on ﬁnancing claims. Speciﬁcally,
ﬁnancing claims ﬁt into an element of decision phenomena Mowen and Mowen
(1991) call Gain Now—Loss in the Future. They explain how sometimes
situational factors cause the positive outcomes of a decision to happen quickly
and the negative outcomes to occur later. When this happens, the differential
discounting of gains and losses results in a powerful urge to make a decision
“”1401! allows them to experience the positive outcomes even though it will result
in negative future outcomes. Platt (1973) labeled this the individual trap.
MOWen and Mowen (1991) suggest that such traps are exempliﬁed by behaviors
in drug addiction, air pollution, and even managerial decisions to use rebates and
sales Promotion by auto dealers (they may boost sales in the short-term, but in
the long—term they may result in costly decreases in brand loyalty and increases
in bargain hunting). Financing claims in advertising take advantage of these

tendencies by separating the gain and loss in time, encouraging people to
c(“‘Sidcr the immediate gain while at the same time minimizing the future loss by

delaying part or all of the payment into the future. Buy now. Pay later. Take

26

the gain (the new furniture, clothes, stereo, pool, or car) now. Delay the loss
(the payment for the furniture; the higher level of debt for ﬁnancing the clothes,
stereo, vacation, or pool; the ultimately higher cost for the vehicle). When
applied to ﬁnancing claims, TOV suggests that the often-used advertising

strategy should be effective.
Affordability

Financing claims may play a role in increasing the perceived affordability
of products making such claims. The construct of affordability has been
virtually absent from academic research examining the determinants of purchase.
The theory of reasoned action by Fishbein and Ajzen (1975) has provided the
primary framework for examining such behaviors. According to the theory of
reasoned action, the best predictor of behavior is behavioral intent, and
behavioral intent in turn is predicted by subjective norms and attitude toward the
behavior. However, when the behavior in question is a purchase it stands to
reason that economic factors must play a role. While meta-analysis shows that
the theory of reasoned action predicts consumer intent and behaviors very well
across actions (even those for which the model was not originally intended), it
has been shown to be a better predictor of behaviors than goals where major
obstacles to goal attainment may exist (such as lack of money for the purchase of
expensive items such as vehicles) (Sheppard, Hartwick, and Warshaw 1988).
When attitude and behavioral intent are combined with other factors such as

prior behavior, investment, other behavioral data, and ﬁnancial, economic, and

27

demographic variables as is often the case, prediction of purchase improves
(Brady 1986; McQuarrie and Langmeyer 1987; and Koslowsky, Kluger, and

Yinon 1988; Morwitz and Schmittlein 1992).

Still, affordability is a critical factor in purchase behavior that has not
received adequate attention in marketing research. Notani (1997) has shown that
perception of affordability affects purchase intent and actual purchase. In an
experiment involving student subjects, Notani (1997) found that affordability
perceptions improved the prediction of purchase intent above and beyond the
commonly used variables of attitude and subjective norms for products in general
(spending $10-25 to celebrate completion of mid-terms and spending $25-50 to
celebrate Valentine’s Day) and prediction of actual purchase for more expensive

products (spending $25-50 to celebrate Valentine’s Day).

Notani (1997) describes affordability perceptions as “a psychological
manifestation of an economic variable” (p. 528) and chose to use this variable as
Opposed to an objective measure of income or discretionary income.
Discretionary income (total income minus essential expenditures) provides a
better measure of a consumer’s ability to buy than actual income. However,
consumers have difﬁculty determining their discretionary income (Ferber 1962),
essential expenses are very subjective (Katona 1980), and easy credit has made
Spending less dependent on actual income and more dependent on perceptions of
affordability (Tobin 1972). Notani (1997) speciﬁcally addressed the inﬂuence of

credit claims on perceptions of affordability in the following statement.

28

If perceptions of affordability can be successfully manipulated to make a
product appear affordable, there is the possibility of converting a non-
purchase into a purchase. In fact, instalment (Notani’s correct alternative
spelling) plans, and even credit cards have the effect of making products
seem more affordable. The popularity of such devices shows that
perceptions of affordability play an important role in facilitating purchase.

(p. 543)

Perceived affordability is believed to be a better predictor of purchase
than objective measures of income since the availability of credit allows
consumers to buy products even when their income alone does not allow them to
do so and because consumers with identical incomes may hold vastly different
views of what is affordable, making them differentially prone to debt. Financing
claims certainly appear to hold the potential to make products seem more
affordable to consumers, thus examining the influence of these claims on
perceived affordability, as well as on the more frequently measured variables of
attitude and purchase intent provides a better understanding of the impact of

these claims.

Advertising Financing Claims

In summary, ﬁnancing claims may make products seem more affordable to
consumers. Speciﬁcally, they achieve this by informing consumers that it is
possible to possess a product in the present, while deferring all or part of the
payment into the future. Such claims take advantage of discounting principles
that suggest losses are discounted more rapidly than gains over time, resulting in
more risk-taking behaviors when outcomes are delayed. Financing advertising
may assist in changing consumers’ reference points, inducing impatience to

possess the product, and even counter self-control strategies. As a result,

29

consumers may “buy” products by making or deferring payments over a year, a
few years or even longer rather than put off purchases to future dates when the

products could be paid for in full.

Susceptibility

An investigation of the inﬂuence of ﬁnancing claims would seem
incomplete if it did not address the question of whether certain types of
consumers are more likely to be influenced by such claims. Are certain people
better targets for ﬁnancing claims (from the perspective of the advertiser) or
more susceptible to such claims (from the perspective of the consumer
advocate)? Many marketing and advertising researchers have expressed concern
about vulnerable, susceptible or disadvantaged populations (the young, the
elderly, the under-privileged) and a few have begun to test whether factors such
as age inﬂuence vulnerability (Andreasen 1993; Lee and Soberonn-Ferrer 1997).
Much of the research in the area has focussed on identifying and describing
vulnerable or disadvantaged populations. In an article addressing researching
sensitive topics among vulnerable populations in an issue of Journal of Public
Policy and Marketing devoted to this topic, Ronald Paul Hill wrote, “The issue
of who belongs to a vulnerable population is illustrated aptly by authors of the
articles in this issue” (1995, p. 143). It is illustrated well by examples of
vulnerable populations ranging from the homeless to children to minorities to

addicts to the physically challenged and others in that volume and elsewhere.

Little research however has deﬁned vulnerability. From a legal

perspective, the courts have used the term “unusually susceptible” to identify

30

“small groups of consumers who have idiosyncratic reactions to products that are
otherwise harmless when used by most people” and have expanded susceptible to
include those with varying levels of physical competency, mental competency,
and level of sophistication (Morgan, Schuler, and Stoltman 1995, p. 267). As,
Ringold (1996) indicates “a deﬁnition of vulnerability is somewhat illusive” (p.
97) but, the above orientation is reﬂected in her writing that “vulnerability, at
least in the context of commercial information, implies that the targeted groups
exhibit a diminished capacity to understand the role of advertising, product
effects, or both” (p. 97). She captures the concept of vulnerability best when
she describes at risk groups as “more prone than others in society to marketer’s
inﬂuence” (p. 90). This is similar to the deﬁnition of consumer susceptibility to
salesperson’s inﬂuence which is “the proneness to be affected by the attitude,
opinions, and behavior(s) ofa salesperson, and to allow him/her to impact one’s
shopping and buying behavior(s)” (Goff, Bellenger, and Stojack 1994).

Susceptibility or vulnerability encompasses the notion of being easily inﬂuenced.

This research does not attempt the vast, controversial job of developing a
comprehensive proﬁle of the easily inﬂuenced, rather it seeks a more modest
goal of determining whether people with speciﬁc characteristics (individual
difference factors) are more receptive to ﬁnancing advertising claims. In his
work, Mowen calls attention to the importance of individual differences in
understanding how time inﬂuences the assessment of losses and gains, suggests
that compulsive consumption may be important to the process, and even
examines the inﬂuence of time orientation on such evaluations (Mowen 1992;

Amyx and Mowen 1995). At issue here is whether people who possess

31

individual difference factors such as these are more inﬂuenced by ﬁnancing

claims than those who lack such characteristics.

Receptivity to such claims may be inﬂuenced by several factors. From an
economics perspective, Fisher (1961) suggested that several factors such as age,
income, marital status, and personal characteristics including foresight and self-
control all affected a person’s rate of impatience. Older, married individuals
with higher income and more foresight and self-control were less likely to be
impatient. Research showing characteristics of those with no debt, mild debt,
and severe debt conﬁrms the inﬂuence of these demographic variables (Lea,
Webley and Levine 1992). Other factors such as a person’s attitude toward debt
or use of credit may themselves be related to acceptance of ﬁnancing claims

(Lea, Webley and Levine 1992; Godwin 1997).

The characteristic of impatience, itself, would appear from this as well as
nearly all the literature on discounting to increase susceptibility to ﬁnancing
claims. Impatience can be described as a restlessness or eagerness to complete
things in a hurry and an intolerance for delay (Price 1982). A person who is
impatient in general should be more receptive to advertising claims suggesting

that there is no reason to wait.

Similarly, the preceding literature suggests a person’s time preference or
orientation (future or present) inﬂuences receptiveness to ﬁnancing claims.
Time orientation is deﬁned as, “a multidimensional, stable, individual difference
characteristic that consists of one’s capacity to anticipate and see the future more

clearly” (Amyx and Mowen 1995). In a personal selling context, time-

32

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orientation has been shown to moderate the effect of postponing or expediting
payment for a car on purchase intent (i.e., present time-oriented consumers
preferred delayed payments while future-oriented consumers opted for immediate

payments) (Amyx and Mowen 1995).

Finally, a person’s tendency toward compulsive consumption may also
inﬂuence responses to ﬁnancing claims. Compulsive consumers have been
deﬁned by Faber and O’Guinn (1988) as “people who are impulsively driven to
consume, cannot control this behavior, and seem to buy in order to escape from
other problems” (p. 99). Edwards (1992) deﬁned compulsive consumption as “a
chronic, abnormal form of shopping and spending characterized, in the extreme,
by an overpowering, uncontrollable, and repetitive urge to buy, with disregard
for the consequences” (p. 54). Further description of compulsive consumption is

provided by d’Astous (1990).

Compulsive buying is characterized by an incontrollable urge to buy
which is impelled by a psychological tension arising from internal factors
and which is accompanied by a feeling of relief along with the frustration
caused by the addictive nature of the behavior. The compulsive buyer’s
motives are not primarily linked to the possession of good and services,
they ﬁnd their source (self) in the very act of buying. (p. 16)

In a pilot study by Faber, O’Guinn, and Krych (1987) most respondents credited
factors other than advertising with their buying urges, however the authors
contend that advertising and other media encourage those with self-esteem
problems to resolve them through product consumption. The authors elaborated

on a type of advertising they ﬁnd particularly troublesome.

33

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Advertising for one particular service may play an important role in
compulsive consumption and credit abuse. This is advertising for bank
cards. Up until recently, almost all ofthese ads appealed to desire for
status and belief that, “you can have it all.” Compulsive consumers are
particularly sensitive to these appeals. (p. 135)

Even among normal consumers, compulsive buying tendencies have been
found to correlate negatively with age and self-esteem, and positively with
irrational credit card usage and with susceptibility to social inﬂuence (d’Astous
1990). Further in a very small study (N=30), compulsive consumers have been
found to signiﬁcantly differ from normal consumers in their evaluation of
advertising giving more favorable marks to ads that contained copy (consisting
of no more than a phrase) focussed on social desirability/image rather than on
product quality (d’Astous and Bellemare 1989). These ﬁndings provide an
indication that consumers who have a greater tendency toward compulsive
consumption may respond more favorably to ads containing ﬁnancing claims

than those who are less compulsive.

Tendency toward compulsive consumption, along with a number of
individual difference variables are examined in this study to determine if these
characteristics are associated with more positive responses to ﬁnancing claims.
By including information about the possibility of having a product in the present,
while deferring all or part of the payment into the future, ﬁnancing claims may
make the product seem more affordable and induce impatience (allow the
consumer to adapt to the notion of owning a product they may not have
considered before because they were not able to purchase it outright). By taking
advantage of discounting principles, such claims may lead to risk-taking

behaviors such as purchases on credit. In addition to motivating consumers to

34

 

change
control
claims
possess

Iooard

change their reference points, ﬁnancing advertising may even counter self-
control strategies they employ to avoid time-inconsistent purchases. Financing
claims may motivate these changes in most consumers or only in some
possessing characteristics such as impatience, present-mindedness, or a tendency

toward compulsive consumption.

35

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CHAPTER 3
RESEARCH METHOD

Hypotheses

The goal of this research is to examine the inﬂuence of advertising
ﬁnancing claims to ascertain whether these claims that encourage people to take
their gains in the present and losses in the future are effective at changing
consumers’ reference points and making them impatient to possess products, in
Hoch and Loewenstein’s (1991) terminology, and at making products appear
more affordable. Hoch and Loewenstein’s concepts of changing reference points
has not been deﬁned or tested in previous research. However, insight can be
gained into this process they describe in which the consumer begins to like and
want the product (positioning it as the new reference point) and then quickly
becomes impatient to possess it, by employing conventional terms of attitude
toward the brand and purchase intent (which measure the liking and wanting
aspects of the changing reference point) and expected timeframe of purchase (in
a pretest-posttest measure to address the eagerness and impatience issue). An
additional and common measure, attitude toward the ad, is included to provide a
more complete picture of how the inclusion of ﬁnancing claims inﬂuences
consumers’ feelings toward the ads. Affordability perceptions are also included

as they improve prediction of purchase intent over and above attitude.

Since this is the ﬁrst work to explore ﬁnancing claims, the primary

purpose is to determine the fundamental effectiveness (do they work or don’t

36

 

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payo

defer

presel

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they) of some commonly used claims. Additionally, it is valuable to learn
whether all claims work equally well? Are claims that defer more of the
payment into the future more effective (a linear effect), or do people desire to
defer payment for some optimal period of time but not beyond that (a curvilinear
effect)? Finally, do all people respond the same way to these claims? Since
certain ﬁnancing claims may be effective with some people and not with others,
those traits which might inﬂuence receptivity to ﬁnancing claims (impatience,
present-mindedness, and tendency toward compulsive consumption) were
measured. The selection of treatments and measures was intended to aid in the
design of a straightforward study free of confounds resulting from using
unconventional measures while still enabling the experimental examination ofa
previously unexplored class of advertising claims with its most closely related
theoretical underpinnings in an area of economics where experimental studies
involving advertising are non-existent. Speciﬁcally, this research addressees the

following hypotheses.

Hypothesis 1: Exposure to advertising ﬁnancing claims for a speciﬁc product
signiﬁcantly increases consumers’ intentions to make a
purchase from that product category (creates an intent to buy a
product that would be consistent with a change in reference
points).

Hypothesis 2: Exposure to advertising ﬁnancing claims for a speciﬁc product
signiﬁcantly advances the anticipated timeframe for a purchase
from the product category (creates an eagerness to buy a
product that would be consistent with a change in reference
points).

Hypothesis 3: Inclusion of ﬁnancing claims in advertising signiﬁcantly

improves consumers’ attitudes toward the ad (creates a liking
for the advertisement).

37

Hypothesis 4:

Hypothesis 5:

Hypothesis 6:

Hypothesis 7:

Hypothesis 8:

Hypothesis 9:

Hypothesis 10:

A)

B)

C)

Inclusion of ﬁnancing claims in advertising signiﬁcantly
improves consumers’ attitudes toward the advertised product
(creates a liking for the product that would be consistent with a
change in reference points).

Inclusion of ﬁnancing claims in advertising signiﬁcantly
increases consumers’ intentions to purchase the advertised
product (creates an desire for or intent to buy the product that
would be consistent with a change in reference points).

Inclusion of ﬁnancing claims in advertising signiﬁcantly
increases consumers’ affordability perceptions of the product

(makes the product appear to the potential consumer to be more
affordable).

Inclusion of ﬁnancing claims in advertising signiﬁcantly
advances consumers’ expected time of purchase of the
advertised product (creates an eagerness or impatience to
possess the product).

Inclusion of ﬁnancing claims in advertising signiﬁcantly affects
consumers’ anticipated method of payment for the advertised
product (creates greater likelihood of deferring payment).

The advertised level of the ﬁnancing claim (the expense of
payments with their respective lengths of credit period)
signiﬁcantly affects attitude and purchase-related variables.

Some consumers are more inﬂuenced by ﬁnancing claims than
others.

Impatient people are signiﬁcantly more likely to be inﬂuenced
by advertising that includes ﬁnancing claims than patient
people.

Present-minded people are signiﬁcantly more likely to be
inﬂuenced by advertising that includes ﬁnancing claims than
future-minded people.

People with a greater tendency toward compulsive consumption
are signiﬁcantly more likely to be inﬂuenced by advertising that
includes ﬁnancing claims than consumers with a lower tendency
toward compulsive consumption.

38

 

 

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i558:

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D) Other individual difference variables (such as age, gender, race,
education, income, ﬁnancial knowledge, and attitude toward
credit) signiﬁcantly affect responsiveness to ﬁnancing claims.

Method

RESEARCH DESIGN

The inﬂuence of advertising ﬁnancing claims was examined using an
experimental design that included a pretest and a posttest of two variables for
each product class as well as repeated measures. A pretest-posttest element was
included in the design for two key variables (timeframe of and purchase intent
for the product categories in general) so that changes or shifts due to ad exposure
could be determined. Two common classes of ﬁnancing claims were examined
in an effort to provide a better understanding of the inﬂuence of various
ﬁnancing claims; three levels of ﬁnancing claims were used for each class to
assess the inﬂuence of varying ﬁnancing lengths. Figure 3.1 depicts the
experimental design. Each subject completed a pretest measure of purchase
intent (which included a timeframe) for both product categories, was exposed to
one of three levels of a treatment ad (for example, a moderate strength
automotive ad), answered several questions related to the dependent variables
including a repetition of the purchase intent questions, was exposed to the
control ad for the other type of claims being examined (credit card control ad),
answered several questions related to the dependent measures, and ﬁnally

answered individual difference and demographic questions and completed a

39

.'{.\;"v'
JLdi‘zol

Where:

01

02

O3

04

Xal-a3
Xbl-b3
Xa4
Xb4

Figure 3.1. Experimental Design

Part 1
01 Xal Oz Xb4 O3 O4
0; Xaz 02 Xb4 03 O4
01 Xa3 Oz Xb4 03 04

Part 2
R 01 Xbl 03 Xa4 02 O4
01 sz 03 Xa4 02 O4
01 Xb3 O3 Xa4 Oz 04

Pretest measure of purchase and timeframe measures for automobiles
and credit cards

Repeated measures of the dependent variables for the automobile
claims

Repeated measures of the dependent variables for the credit card
claims

Posttest measures of individual difference and demographic variables
as well as a manipulation check

Three levels of treatments for the automobile ﬁnancing claims
Three levels of treatments for the credit card ﬁnancing claims
The control advertisement for the automobile ﬁnancing claims

The control advertisement for the credit card ﬁnancing claims

Notational system in Figure 3.1 adapted from Campbell and Stanley (1963).

40

It"

 

(IQ

$8.

manipulation check. The order of presentation of the control and treatment ads

was randomized and the subjects were randomly assigned to conditions.

The nature of laboratory experiments and particular features of the design
employed place limitations upon this study. Laboratory experiments have many
advantages including the ability to control many factors which threaten internal
validity as well as facilitation of research in a timely and cost-effective manner.
Unfortunately, external validity can be limited by the artiﬁcial nature of the
conditions and because subjects are often not representative of the population in
general (Campbell and Stanley 1963). A design including pretest-posttest and
repeated measures was chosen as it allowed for measurement of shifts (changes
in reference points which are central to this research) and enabled a larger
sample (by allowing one group to control for the other), however the use of
pretest-posttest and repeated measures increases the likelihood of problems
including testing effects, interaction effects between the treatments and
measurements (both which may increase sensitization) as well as fatigue
(Campbell and Stanley 1963). These threats were minimized by attempting to
make the ads as realistic as possible, limiting the number of pretest questions (to
only include product category purchase intent) and when possible limiting the
number and length of posttest measures. These limitations included, this

experimental design should still effectively address the proposed hypotheses.

TREATMENTS

As stated in the introduction, numerous types of products make ﬁnancing

claims that are similar, yet slightly different in nature. Two common classes of

41

 

ﬁnancing claims, one involving a speciﬁc product and another involving credit
which can be used to buy an array of products and services, were examined to
provide a better understanding of how ﬁnancing claims in general inﬂuence
consumer decision making. These classes of claims along with the products

which frequently make the claims are described below.

First, there are special low or no interest payment plans for products.
Many products (particularly those advertised through infomercials such as
exercise equipment) make claims informing consumers of the possibility of
purchasing products in installments over time. This unbundling of price makes
the product affordable to consumers who do not have the cash to purchase the
product, although they often end up paying an overall higher price for the
product than if they had purchased the product outright. Such claims may also
induce consumers into buying more than they can really afford. Automobiles,
the most widely owned US asset (with 85+% of households owning one or more),
frequently contain ﬁnancing claims of this nature (Aizcorbe and Starr-McCluer
1997). Indeed, low interest loans and leasing deals are used to make increasingly
expensive vehicles more affordable (Seraﬁn and Johnson 1995). Since
automobiles are such a commonly owned and heavily advertised product for
which ﬁnancing claims are frequently made, claims containing information about
low interest loans for an automobile were examined in the ﬁrst part of the

experiment.

Second, there are credit claims themselves which offer low interest rates
and low minimum payments for transfers, cash advances and purchases. These

are the ﬁnancing claims made in advertising for credit cards. Credit card

42

"VJ

H1

Cigﬁ;

‘1

advertising has come under increasing criticism in recent years for using
effective advertising practices to hook vulnerable and basically responsible
consumers on easy credit leading them deep into debt and even bankruptcy
(Lucas 1992; Stark 1997). According to Akin Toffey, VP of marketing for
AT&T Universal Card Services: “An argument can be made that the entire
industry can be faulted for providing ﬂexible credit to those who cannot handle
it. And an argument can be made that people try to live up to the grandiose
lifestyles portrayed in credit card ads when they ﬁnancially cannot” (Lucas 1992,
p. 32). Some have complained that the same industry that has dumped 100
million credit cards on the public in the past six years is now scaling back rebate
programs, devastating debtors with penalty interest rates of 28%, and punishing
those who pay their balances in full with yearly fees (Lucas 1992, Stark 1997).
Credit card reforms (including a bill calling for better disclosure of credit card
terms on the outside of envelopes and in television and radio ads) have even been
introduced into Congress (Lucas 1992, Stark 1997). Due to the criticism of
credit card advertising and its common use of ﬁnancing claims, credit card
advertising which includes statements about low minimum payments was the

focus of the second part of the experiment.

Three treatment ads were created for each of the above categories of
ﬁnancing claims described above, as were two corresponding control ads (which
lack ﬁnancing claims, but were virtually the same in all other respects). As
ﬁnancing claims vary with some offering deferral of payment or credit for only a
short time and others credit over a longer period, a range of claims were
examined. Over the past decade, the trend has been for consumers to take on

credit for longer periods (with 48-month auto loans recently losing their

43

 

P31
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my
and 1
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Mu

popularity to 60-month loans and 4 and 5% minimum payments for credit cards
being replaced by what was once considered very low 2% payments) (Haynes
1998-1999). Accordingly, a range of claims that includes an extension of credit
beyond what was common at the time of the administration of the experiment
was included to determine if consumers would be even more receptive to more

extreme ads which offer credit over longer time periods.

Level of ﬁnancing claim extremity was varied by creating three levels of
claims: Mild or the most expensive payments [offering credit over a shorter
period of time than is normally used (48 months for auto ﬁnancing, 3% minimum
payments for credit cards)], moderate or mid-level payments [offering credit at a
level and over a period of time that is typically offered and Utilized by
consumers (60 months for auto ﬁnancing, 2% minimum payments for credit
cards)], strong or the least expensive payment [offering credit for a period that is
longer than normally provided (84-months for auto ﬁnancing, 1% minimum
payments for credit cards)]. Payments were kept as realistic as possible, using
the same calculation techniques and pricing as in the industry. Each subject was
exposed to two print ads. A treatment ad from one type of ﬁnancing claims
(automobile loan claims for example) was paired with a control ad for the other

type of ﬁnancing claim (credit card ﬁnancing claims).

Print ads rather than broadcast ads were used as print is a common
medium for industries making ﬁnancing claims in general, with home furnishings
and mortgage companies doing the majority of their advertising in print
(Competitive Media Reporting 1996, Management Foresight 1997). Print is

popular with the industries making the ﬁnancing claims to be analyzed in this

44

 

 

\C)

research as well. The big three automotive companies (General Motors, Daimler
Chrysler, and Ford) represented three of the top ﬁve rankings in 10 media
advertising spending (with 30% or more of their spending going toward print
media) (Competitive Media Reporting 1996). Further, the automotive local
dealers associations were leaders in spending on newspaper advertising, holding
three of the top ﬁve positions and six of the top 15, with most spending greater
than 80% of their advertising dollars in newspapers. Finally, while the major
credit card companies spend the majority oftheir 10 media advertising dollars on
broadcast versus print medium, a considerable part of their marketing dollars is
spent on direct mail as our mailboxes will attest (Competitive Media Reporting
1996, Cambor 1997). Thus, print advertisements were used to examine the
inﬂuence of ﬁnancing claims due to the frequent use of print advertising by
companies making these claims, and because it is easier and more cost efﬁcient

to work with print advertisements.

The print ads used were realistic-looking, four-color, full-page ads with
the key experimental claims integrated into the ad in a natural manner.
Accordingly, images in this dissertation are presented in color. The color ads in
appear in Appendix B. Products already on the market were selected to enable
the creation of realistic ads. A Cavalier Z24 was used for the auto ad because
General Motors has speciﬁcally marketed Chevrolet and this particular vehicle to
college campuses and because the customer demographics for this model most
closely match a college population, the intended experimental subjects (1998
Chevrolet Car Product Guide 1997, Halliday 1997, and Weinstein 1988).
CapitalOne Visa was selected for the credit card ads primarily because an

introductory or basic rather than a gold or platinum card was desired, CapitalOne

45

.h
‘ 2"? *_

 

 

11.7

(1”

was not as widely held as many cards (so students would be less likely to already
hold it), and because of the practical need for a card with clear graphics that

would scan and reproduce well.
SAMPLE SELECTION

The personal, sensitive nature of the topic of ﬁnancing and related areas
such as personal debt were important considerations in selecting the sample.
High rates of refusal to answer questions on sensitive subjects (particularly over
the telephone) have been documented; this presents a problem because the
validity of the data depends on the respondents’ willingness to comply with the
request to be interviewed and to supply full and accurate data to the interviewer
(Hornick, Zaig and Shadmon 1991). The same concerns held for this study as
well. Personal assurances of the purely academic nature of the research (versus
marketing/sales) and of anonymity were desired as it was believed they would
result in easier recruitment of subjects and more complete and honest answers.
Stronger assurance of conﬁdentiality have been shown to improve response to
questions regarding sensitive subjects (Singer, Von Thurn, and Miller 1995). For
this reason, the experiment was administered to a purposive sample of students at
a major mid-western university where cooperation and participation in the study
could be more easily attained. Industry literature and the nature of the products
to be examined indicated that a college student population was appropriate for

examining these claims.

Many articles (from the popular press to industry magazines to academic

papers) have discussed the ethics and commonality of credit card companies

46

targeting college students (Penner 1982; “American Express” 1989; Schwartz

1989; Stewart 1989; Duffy 1990; Himmelfarb 1992; Lucas 1992; Kutner 1993;
Kara, Kaynak, and Kucukemiroglu 1994; McEldowney 1994; O’Connell 1994;
Murdy and Rush 1995; Susswein 1995; Frances 1996; Curlo and Strudler 1997,

Edgar 1998).

Speciﬁcally, Lucas (1992) reported that consumerists have criticized

. .ujnc

credit card issuers for targeting vulnerable college students and lowering their
credit standards to capture the potentially lucrative, loyal market who currently
has no credit history and little current income; “the ripeness of the college
market which has $35 billion in annual disposable income, is evidenced by the
fact that 30% of college freshmen and 75% of seniors have a credit card,
according to MasterCard data” (Lucas 1992 p. 34). Susswein (1995) indicated
that, “most of the nation’s major credit card issuers are aggressively marketing
unsecured credit to students (in their own name) through advertising, direct mail
promotions and on-campus recruitment, with giveaways” and strategies to make
credit appear more affordable with ﬂiers touting minimum payments as low as 2-
3% (compared to normal rates of 4-5% at that time) which “put more [money] in
your budget” (pp. 21 and 22). Susswein also reported that the marketing efforts
seem to be paying off since MasterCard estimates that the number of students
using their credit card has increased at 20% per year, and that 1994 industry
estimates indicated that credit card issuers earn $16.5 million a year on student
cardholders, with over $10 million of that coming from interest income.
Unfortunately, Susswein also reported that credit counselors have seen a 10%

increase in college-aged people seeking their services. Since college students

47

are the target of credit card companies, it was believed they would be appropriate

subjects for this study.

Likewise, college students are often singled out by automakers in
campaigns to convince them to buy their ﬁrst new car. Ford, General Motors and
Daimler Chrysler all have cash-incentive buying programs for college graduates
which they advertise in college newspapers and other media, by direct mail, on
the Web, and through campus special events (Halliday 1997). Toyota recently
introduced a Tempo Direct Marketing award-winning incentive program which is
believed to have resulted in the sale of21,178 vehicles in 26 weeks to college
students and recent graduates (35 times the goal) (Halliday 1997; “Toyota
Campaign” 1997). And for more than a decade Honda has used campus
publications to reach college students who are likely to buy a Honda (80% of all
Honda Civic CRX buyers are under 25 and have attended college) (Weinstein
1998). “Many students aren’t waiting till they graduate to buy cars. Full-time
college students in the US are buying more new vehicles than they used to,
according to ID Powers & Associates” (Halliday 1997, p. 34). Such students
accounted for 2.27% of all sales in 1995 (Halliday 1997). Automakers believe
that college students are at a point in their lives where they are developing habits
and brand loyalty (Weinstein 1988); “By the time people reach age 23, they
know what kind of car they want to buy” (Halliday 1997). A sample of college
students was used to provide insight into whether ﬁnancing claims inﬂuence
their purchase—related behaviors including payment for products such as

vehicles.

48

Gaining the cooperation of a random sample of adults for questions about
such personal and sensitive issues as their ﬁnances would have been difﬁcult.
Nonprobability sampling techniques are acceptable particularly when the goal of
the research is to investigate the various relationships among variables (Wimmer
and Dominick 1987). This selection technique (purposive sampling) should

improve the internal validity of this study of a sensitive subject.
INDEPENDENT VARIABLES

In addition to the two types of ﬁnancing claims (each having three levels)
which served as the treatments, several independent variables were measured.
See Table 3.1 for a list of independent variables and a list of variable names used
in the correlation table in Appendix C. The construct of impatience level was
measured using Spence, Helmreich and Pred’s (1987) scale consisting of ﬁve
questions with ﬁve-point scale between two anchors (i.e., When you have to wait
in line such as at a restaurant, the movies, or the post ofﬁce, how do you usually
feel? Accept calmly to feel very impatient and refuse to stay long). Although its
alphas of .67 for men and .63 for women are only in the marginally acceptable
range, the items in the scale appeared to have face validity, the scale is the only
measure of the construct mentioned in the literature, and frequent references to it

never mention its relative low alphas.

A related construct, time preference (future and present mindedness), was
measured using the 7-item 5-point scale employed by Amyx and Mowen (1995)
which was developed to explore the moderating affect of time orientation on

salesmanship claims about time of payment and purchase intent; the scale was

49

 

preferable to other time orientation scales for this research since it has a slightly
higher alpha than most others at .66 and because it was designed to tap present
and future mindedness regarding consumption behaviors whereas many other
scales focus on the past or on areas of the multidimensional concept of time
orientation that are unrelated to this research. While scales with higher alphas
are desirable, the low alphas for the above scales reﬂect that research in these
areas is in its infancy; alphas of greater than .60 are considered acceptable for
introductory work in an area (Nunnally 1967). These somewhat low alphas are
perhaps a less critical concern for this experiment as the scales are addressing a

secondary rather than primary research question.

Compulsive consumption tendency was initially measured using Faber and
O’Guinn’s (1992) 7-item 5-point scale anchored by very often to never. In

Faber’s research, it yielded an impressive alpha of .95.

Other variables were measured as well. A four-factor credit card
consumption scale was included to provide insight into credit card attitudes and
use. Ericson and Cole (1998) obtained acceptable reliabilities; the reported
alphas were as follows: .82 for the spend factor, .78 for usage, .81 for control,
and .71 for attitude. A question used by Ericson and Cole (1998) to classify
users as convenience or ﬁnance users was also included. A general question
about attitude toward debt and an index used to assess attitudes toward
borrowing money for speciﬁc purposes (car, educational expenses, living
expenses, vacation, fur coat/jewelry) was used by Godwin (1997) to measure

attitude toward debt in 1983 and again in 1989 and was included in this study.

50

Table 3.1. Independent Variables and Variables Names

 

Independent Variables

Variable Names

 

E' . Cl' .! Ads

 

 

 

 

 

 

 

 

 

 

 

$183/mo. auto treatment DUM183
$246/mo. auto treatment DUM246
$317/mo. auto treatment DUM317
Auto control DUMACON
Financing Claims in Credit Card Ads
$50/ mo. credit card treatment DUM50
$100/mo. credit card treatment DUMIOO
$151/mo. credit card treatment DUM151
Credit card control DUMCCON
Individual Difference Characteristics
Impatience IMPATNCE
Time orientation TIME
Compulsive consumption COMPUL
Credit Card Attitudes/Use
Credit card attitude CCATT
Credit card control CCCONTRL
Credit card spending CCSPEND
Credit card usage CCUSE
Pay credit cards in full CCFULL
Pay more than minimum but less than hill on credit cards CCMUCH
Pay minimum payment only on credit card balances CCMIN
No credit card balance to pay NOBALNCE
Approval of borrowing BORROW
Number of credit cards CARDS
Financial Knowle_d_ge
Dollars off in estimate of auto interest payment amount ADOLLOFF
Years off in estimate of length of auto payment AYRSOFF
Dollars off in estimate of credit card interest payment VDOLLOFF
Years off in estimate of length of credit card payment VYRSOFF
We
Freshman level student FRESHMAN
Sophomore level student SOPHMORE
Junior level student JUNIOR
Senior level student SENIOR

 

51

 

Emplayment Stataa

 

 

 

 

Employed all year EMPFULL

Employed part of year only EMPPART

Not employed NOTEMP

E. . I H I I I Ex . e

Personal income MYINCOME

Parental income PINCOME

Percentage of parental ﬁnancial support PSUPPORT

Taken out an educational loan LOANEDUC

Bought a vehicle BUYCAR
Mums

Age AGE

Gender GENDER

 

Financial knowledge questions tailored to each of the content areas (automotive
loans and credit card interest) were included to assess each subject’s
understanding of the monetary impact of purchase (i.e., additional years and
money to pay off amount borrowed). Additional questions were asked about the
number of credit cards a person has, whether the subject has ﬁnanced a car, or
bought any product or service on credit. Finally, the following demographic
variables were included in the questionnaire: age, gender, ethnic background,
marital status, major, class standing, employment, personal income, parental

income, and percent of parental ﬁnancial support.

DEPENDENT VARIABLES

Table 3.2 lists the dependent variables used in this study. The following
dependent measures were employed in the experiments: purchase intent and
timeframe of purchase for a product in the product category, namely a new

automobile or new credit card (pretest and posttest of these measures); and

52

posttest only measures (to avoid sensitization to claims) for attitude toward the
ad; attitude toward the advertised product; purchase intent for the advertised
product; expected timeframe of purchase for the advertised product; anticipated
method of payment for the advertised product; and affordability perceptions for
the advertised product. The scale used to measure attitude toward the ad was
previously used by Yi (1990) with an alpha coefﬁcient of .85; it has four 7-point
bipolar items with anchors good-bad, interesting-uninteresting, like-dislike, and
irritating-not irritating. Muehling, Laczniak, and Stoltman’s (1991) scale was
used to measure attitude toward the product. It has three 7-point bipolar items
with anchors good-bad, favorable-unfavorable, and negative-positive, and they
attained an alpha of .93 with the scale. Purchase intent for the advertised
product was measured using the scale employed by Yi (1990) which had three 7-
point bipolar items anchored by likely-unlikely, possible-impossible, probable-
improbable; the alpha was .89. The same scale was used to assess intent to buy a
product from the product category in a pretest and posttest. Finally, to assess
whether the ad made the subject more eager or impatient to purchase a product
within the product category, the subject was asked on the pretest and posttest to
indicate the timeframe in which they intended to purchase from the product
category for a range of times (0-6 months, 7-12 months, 13-24 months, >24
months, never) (Morwitz and Schmittlein 1992). The same scale was used to

assess eagerness to purchase the advertised product on the posttest.

Anticipated method of payment was also measured for the advertised
product speciﬁcally. With respect to the automotive claims, the multiple choice
question included options with leasing and ﬁnancing over selected periods of

time or paying the total cost up-front. In the case of credit cards, the question

53

asked how balances on the card resulting from purchases would be paid and

offered options ranging from less than minimum payment to paid off in full.

Subjects wrote the reasons for their payment method selections.

Table 3.2. Dependent Variables and Variable Names

 

Dependent Variables

Variable Names

 

Am

 

 

 

Pretest auto purchase intent (general) PREAPS
Pretest timeframe of auto purchase (general) PREAP
Attitude toward the auto ad AADA
Attitude toward the Cavalier Z24 ATTZ
Purchase intent for the Cavalier Z24 PIZS
Timeframe of purchase for the Cavalier 224 P12
Perceived affordability of the Cavalier 224 AFFZ
Intended method of payment for the Cavalier 224 HOWPAYA
Posttest auto purchase intent (general) PIAS
Posttest timeframe of auto purchase (general) PIA
Pre-post difference in auto purchase intent (general) DIFAPIS
Pre-post difference in timeframe of auto purchase (general) DIFAPI
Qrgdit Card

Pretest credit card acquisition intent (general) PRECPIS
Pretest timeframe of credit card acquisition (general) PRECPI
Attitude toward the credit card ad AADCO
Attitude toward the CapitalOne card ATTV
Acquisition intent for the CapitalOne card PIVS
Timeframe of acquisition for the CapitalOne card PIV
Perceived affordability of the CapitalOne card AF FV
Intended method of payment for the CapitalOne purchases HOWPAYC
Posttest credit card acquisition intent (general) PICCS
Posttest timeframe of credit card acquisition (general) PICC
Pre-post difference in credit card acquisition intent (general) DIFCPIS
Pre-post difference in timeframe of credit card acquisition (general) DIFCPI

 

54

Affordability was measured by an adaptation of Notani’s (1997) three
items “if I wanted to I would easily afford to (buy a Cavalier Z24 or obtain and
use a CapitalOne Visa card), “For me to (buy a Cavalier Z24 or obtain and use a
CapitalOne Visa card) is” and “My personal income permits me to easily (buy a
Cavalier Z24 or obtain and use a CapitalOne Visa card)” with 7-point scales
anchored by “likely/unlikely,” “easy/difﬁcult,” and “strongly agree/strongly
disagree.” Alphas of .86 and .80 were attained in his study. Following the
collection of data for all other dependent and independent variables, a
manipulation check was conducted to ensure that the subjects exposed to the

ﬁnancing claims actually took notice of them.
Pretest

Prior to the collection of the data, a full-scale pretest of the experiment
was carried out with 31 students in an attempt to identify any potential problems
with the treatment ads (such as insufﬁcient differences in the claims), the control
ads, the scales and questions, and the procedures involved in the administration
of the experiments. Analysis of the pretest results pointed to three areas of
concern. First, the reliability of Faber and O’Guinn’s (1992) compulsive
consumption scale was unacceptable (alpha .54) and considerably lower than had
been previously reported (.95). Some of the seven items in the scale were
negatively correlated. Deleting items did not considerably improve the
reliability. Careful examination of the items themselves indicated that a few of
the items could have been problematic for student subjects (questions regarding
writing checks and making minimum payments on credit cards) due to their

limited ﬁnancial experience. Second, analysis revealed no signiﬁcant

55

differences among the credit card conditions and only a few signiﬁcant/near
signiﬁcant differences for the automobile ads (purchase intent, timeframe of
purchase, and affordability of the Cavalier). In some cases the closeness of
responses across the treatment groups indicated that the manipulation of the
levels of ﬁnancing claims may not have been working as intended. Third, in the
questions designed to assess ﬁnancial knowledge about the claims made in the
ads, there appeared to be a response bias toward the most expensive interest

amounts and the longest payment period options.

To address these concerns, a second pretest was conducted with 29
students. To ensure the low, medium, and high ﬁnancing manipulations were set
at appropriate levels, the products and ﬁnancing conditions were described and
subjects were asked to write in a dollar amount for “easy to pay,” “manageable,”
and “more than I could afford.” The pretest also contained two compulsive
consumption scales. The Faber and O’Guinn scale was re-tested to ensure the
previous results were not spurious. The pretest also included eleven items from
Valence, d’Astous and Fortier’s Compulsive Buying Scale (1988; d’Astous 1990)
which had been used to investigate compulsive consumption in a “normal”
population and yielded acceptable alphas (.88 and .84 respectively). Finally, to
avoid response bias the ﬁnancial knowledge questions were reworded to change

them from true/false to ﬁll in the blank.

Analysis of results indicated that changes were appropriate. While the
range of responses to what was easy, manageable, and difﬁcult to pay for both
the Cavalier and CapitalOne Visa ads overlapped, the measures of central

tendency pointed toward the selection of new levels of ﬁnancing with greater

56

difference between the levels and an overall increase in the credit card payments
and decrease in automotive payments. Adjustments were made to the credit
balance and auto price to attain levels of payments closer to those suggested
through the second pretest while still keeping as close as possible to real-world
prices and calculations of payments. Refer to Tables 3.3 and 3.4 for a
breakdown of payments used in the ﬁrst pretest, those attained in the second

pretest, and the ﬁnal payment levels utilized in collection of data for the

 

 

experiment.
Table 3.3. Automobile Payment Levels
Original
Pretest Target Final
Mean Median Mode Range Levels Levels Levels
Easy $179.14 $180 $200 $80-300 $250 $175 $183
Manageable $251.38 $250 $2 50 $110-500 $288 $2 50 $246
More $413.14 $301* $300 $170-1,000 $348 $325 $317

 

*only one subject answered $301 which was the 50th percentile point, but many
subjects provided answers at the preceding and following values ($300 and $350)

Table 3.4. Credit Card Payment Levels

 

 

Original
Pretest Target Final
Mean Median Mode Range Levels Levels Levels
Easy $61.90 $50 $50 $5-200 $25.21 $50 $50.41
Manageable $129.48 $75* $50 $25-500 $50.41 $100 $100.83
More $276.90 $150 $100 $50-2,500 $75.62 $150 $151.24

 

*only one subject answered $75 which was the 50th percentile point, but several
subjects provided answers at the following value ($100)

57

As for the compulsive consumption scales, the Faber and O’Guinn scale
(1992) again yielded an extremely low alpha (.50) while the Valence, d’Astous
and Fortier (1988) scale produced an alpha of .91 and showed a wide range of
responses. In the ﬁnal experimental booklet, the Faber and O’Guinn scale was
replaced by the Valence, d’Astous and Fortier scale. With regard to the ﬁnancial
knowledge questions, the ﬁll in the blank style of question appeared to work
well. Almost all subjects wrote in answers, the results were comparable in
accuracy to the previously used format for the auto case and improved for the
credit card case, and the estimates appeared to be true reﬂections of the subjects’
beliefs versus response bias. Accordingly, the ﬁll in the blank style questions
were used to assess question-speciﬁc ﬁnancial knowledge. Based on the
pretesting, appropriate adjustments were made to correct identiﬁed areas of

concern prior to the collection of data

Experiment Administration Procedures and Sample Size

The data was collected through a self-administered experimental booklet.
Subjects were given the experimental booklets, informed that their consent was
voluntary (in accordance with policies of the University Committee on Research
Involving Human Subjects), told that the research was for academic purposes
only, assured of their anonymity, instructed in how to answer certain question
types (for example, semantic differential questions), asked to complete the
booklet (included as Appendix A; ﬁnal ads are included as Appendix B), and
informed to return the booklets face down to a box (to help provide further

assurances of anonymity).

58

The experiment was administered in classroom settings to a total of 232
students at a large mid-western university. Subjects were randomly assigned to
one of six treatment conditions through the randomized order of experimental
packet distribution. Eight booklets were excluded due extensive incompleteness
of responses provided by students and in a few cases apparent attempts by the
students to thwart the research. With those subjects removed, the total number
of subjects was 224, with 37 students each in the least and most expensive
payment conditions for the auto, 38 in the middle auto condition, 37 subjects
each in the least expensive and the middle credit card conditions, and 38 in the

most expensive credit card payment condition.

Data Analysis

Prior to analyzing the data to address the hypotheses, univariate and
comparative analyses were conducted to examine abnormalities in the data (such
as limited variance, co-variance, differences in standard deviations, and
skewness) that may affect the study. The data were plotted and examined for

outliers and non-linear relationships.

In some cases potential problems were identiﬁed. Most notably, for
timeframe of purchase questions for the Cavalier and CapitalOne visa, most
subjects did not intend to buy at all so there was a lack of variance on this
measure; descriptive counts and tables rather than statistical tests had to be used
to describe these variables as well as a few others that had small cell sizes. The
SPSS Guide to Data Analysis states that the chi-square test should not be used “if

more than 20% of the cells have expected values less than 5 or if the expected

59

 

value of any cell is less than 1” (Norusis 1988, p. 244). Additionally, ﬁve ofthe
variables (age, personal income, and dollars off in estimates of auto interest
payments, as well as dollars and number of years off in estimates for credit card
payments) were skewed and contained extreme outliers (two for age, three for
personal income, and one each for the other variables) which were excluded from
analyses of the means and other statistical tests involving those variables. Even
with the removal of the most extreme outliers, in some cases the variables
remained skewed (particularly those estimates about the amount of interest paid
on credit card debt and auto loans). However, although data should be
distributed normally, ANOVA is sufﬁciently robust to withstand the degree of
abnormality in these variables (Norusis 1988; Fotiu 2000). In fact, the SPSS
data analysis guide speciﬁcally states, “In practice, analysis of variance gives
good results even if the normality assumption doesn’t quite hold” (Norusis 1988,

p. 263.)

Tests were also run to ensure that the randomized distribution of
experimental booklets resulted in treatment groups free of sample bias.
ANOVAS were run with 15 of the key independent variables to ensure that the
groups were equalized in those characteristics. Analysis for the initial six groups
(one for each experimental condition) revealed no signiﬁcant differences
between the groups. This was again the case for credit cards when the conditions
were condensed to four conditions (three treatment and one control group) which
were used to analyze the hypotheses; there were no signiﬁcant differences.
However, for the four auto conditions (three treatment and one control group)
ANOVA showed that the groups were not equal for one variable, credit card

attitudes [F (3, 207) = 3.048, p = .03]; all post-hoc tests showed signiﬁcant

60

 

differences between least expensive and the middle treatment condition (which
had a mean of 14.97 compared to 11.89 for the least expensive, 12.94 for the
most expensive, and 13.31 for the control). Aside from this variable,

randomization was effective at equalizing the groups (Kerlinger 1986).

Additionally, reliability tests of the scales were assessed and are reported
in Chapter 4. Frequencies and measures of central tendency were used to
describe the subjects and their answers to the questions related to the dependent
and independent variables. Finally, the hypotheses were addressed. Hypothesis
1 was examined by using one way analysis of variance (and t-tests as the means
of the treatment groups were similar to each other and different from the control)
to compare the differences between the pretest and posttest scored in the
treatment and control groups. Hypothesis 2 was examined through cross-tabs.
Hypotheses 3-6 were addressed by using analysis of variance to determine
whether there were signiﬁcant differences between the treatment groups and the
control group for attitude toward the ad, attitude toward the product, purchase
intent for the speciﬁc product, and affordability of the advertised product.
Hypotheses 7 and 8 regarding the intended timeframe of purchase and preferred
method of payment are addressed using cross-tabs and chi-square when cell sizes
are sufﬁcient. Hypothesis 9 speciﬁcally addressing the inﬂuence of the various
levels is addressed by presenting the previously reported analysis in a summary
table to make trends more apparent. Hypotheses 10 A-D are addressed through

the use of MANOVA.

61

CHAPTER 4
RESULTS

This chapter describes the results of two main types of analyses. First,
descriptive statistics are presented for responses contained in the experimental
booklet including the manipulation checks, the dependent variables, and the
independent variables (including a subject proﬁle). Second, the bulk of the
chapter consists of results which address the ten hypotheses regarding the overall
inﬂuence of ﬁnancing claims on attitudes, affordability perceptions, and
intended purchase-related behaviors for the products and how people with

different characteristics respond to such claims.

Manipulation Checks

For both the auto and the credit card cases, two types of manipulation
checks were conducted. One was to ensure that the subjects saw the
manipulation of the ﬁnancing claim. The other one was to determine whether it

was perceived to be at the intended level of ease of payment.

AUTO ADS
Subjects noticed the key auto ﬁnancing manipulations. Most (78.7%) of

the subjects in the treatment condition appropriately recalled seeing ﬁnancing

claims in the ad while signiﬁcantly fewer (38.9%) in the control condition

62

reported seeing the claims although they were not present [)f7 (1, N = 216) =
35.33, p < .001]. Subjects were also asked about other items so as not to draw
attention solely to ﬁnancing claims. Subjects in the control group had
signiﬁcantly higher recall of the price of the vehicle than the subjects in the
treatment condition (94.4% to 72.2%) which is not surprising since the price was
more prominently displayed in the control [2'2 (1, N = 216) = 19.20, p < .001].
Finally, with regard to remembering a piece of information that appeared in
neither ad (a dealer name) there was no signiﬁcant difference [12(1, N = 216) =
.473, p > .05]. Overall, subjects in the treatment groups took notice of the key

claim.

The second part of the manipulation check conﬁrmed that the levels of
treatments were perceived differently. Chi-square tests reveal signiﬁcant
differences between the levels oftreatment, although they were not clear-cut [of7
(6, N = 213) = 12.91, p < .05]. Table 4.1 shows subjects in the most inexpensive
treatment level ($183/mo.) considered the car as easiest to pay for with 21.6%
rating the car as easy to pay for, 48.6% as a manageable amount, and 29.7% as
more difﬁcult to pay for. The control group more than the other two treatment
groups rated the car as easy to pay for. The middle treatment group ($246/mo.)
came in next in terms of ease of payment. Lastly, those in the most expensive
treatment group ($317/mo.) rated the ad as most expensive with 9.1% saying it
was easy to pay for, 27.3% manageable, and the majority 63.6% indicating it was

difﬁcult to pay for.

63

Table 4.1. Auto Ads Manipulation Check: Ease of Payment by Levels of Financing

 

 

A Manageable More than You
Auto Ad Easy to Pay Amount Could Afford
$183/mo. 8 18 11
* (21.6%) (48.6%) (29.7%)
$246/mo. 3 21 13
** (8.1%) (56.8%) (3 5.1%)
$318/mo. 3 9 21
* (9.1%) (27.3%) (63.6%)
**
***
Control 19 46 41
*** (17.9%) (43.4%) (38.7%)

 

* treatments that were signiﬁcantly different from each other

The ratings of ease of payment are ordinal in nature and so computing
overall means is not strictly appropriate, but the means give a simpliﬁed picture
that is consistent with the cross-tabulation. The means are 2.08 for the $183/mo.
group, 2.21 for the control, 2.27 for the $246/mo. group, and 2.55 for the

$317/mo. group.

Chi-square tests comparing individual levels against each other conﬁrmed

signiﬁcant differences between the following levels:

0 The most inexpensive treatment level ($183/mo.) and the most expensive
treatment ($318/mo.) [f (2, N = 70) = 8.20, p < .05]

o The medium treatment ($246/mo.) and the most expensive [12 (2, N = 70) =
6.48, p < .05]

o The control group and the most expensive treatment group [X (2, N = 139) =
6.41, p < .05]

64

However, there was not a signiﬁcant difference between the least expensive and
the medium level [X (2, N = 74) = 2.67, p > .05], the medium level and the

control [12 (2, N = 143) = 2.85, p > .05], and the least expensive and the control
[12(2, N = 143) = .97, p > .05]. Overall, the manipulation check conﬁrmed that

the levels were being perceived somewhat as intended, but not entirely so.

CREDIT CARD ADS

Just as for the auto treatments, the credit card manipulations were
checked. Again, subjects noticed the key manipulations. A full 90% of the
subjects in the treatment conditions in comparison to only 35% in the control
recalled seeing minimum payment information in the ad [3’7 (1, N = 212) = 56.47,
p < .001]. There was no difference between the control and treatment on recall
of credit limit information (which appeared in each ad) [[7 (l, N = 212) = .22, p
> .05] and cash back information (which did not appear in either) [12 (1, N =
210) = .08, p > .05]; 68% of treatment and 66% of control recalled credit limit
information and 10.5% and 9.3% inaccurately recalled seeing rebate or cash back
information. Subjects in the treatment conditions clearly noticed the ﬁnancing

claims.

However, even though the treatment levels were based on students’
assessments of what was easy to pay, manageable, and more difﬁcult to pay for,
the treatment levels did not produce the expected effect. The manipulation check
showed there were no signiﬁcant differences between any of the levels [12 (6, N
= 210) = 4.17, p > .05]. Overall, subjects rated obtaining and using the

advertised credit card as quite easy with 36.2% saying it would be easy to pay

65

for, 50.0% saying it would be a manageable amount, and only 13.8% indicating it
more difﬁcult to pay for. As shown in Table 4.2, though the differences were
not signiﬁcant, the most inexpensive payment condition ($50/mo.) was
considered slightly less expensive than the rest with only 5.9% ranking it as
more than they could afford, compared to 13.6% for the middle treatment
($100/mo), 15.5% for the control, and 16.7% for the most expensive payment

($151/mo.).

Table 4.2. Credit Card Ads Manipulation Check: Ease of Payment by Levels of

 

 

Financing
A Manageable More than You

Credit Card Ad Eagy to Pay Amount Could Afford
$50.41/mo. 16 16 2

(47.1%) (47.1%) (5.9%)
$100.83/mo. 14 18 5

(37.8%) (48.6%) (13.5%)
$151.24/mo. 10 20 6

(27.8%) (55.6%) (16.7%)
Control 36 51 16

(35.0%) (49.5%) (15.5%)

 

Again, while it is not strictly proper to add ordinal data, the means show

the subjects’ ranking of the treatments on ease of payment and the closeness of

the levels. The means are as follows: 1.59 for $50/mo, 1.76 for $100/mo, 1.81

for the control and 1.89 for $151/mo.

Scale Reliabilities

Overall, scale reliabilities were as good or better than anticipated. The

reliabilities obtained in this study are reported in Table 4.3, along with

reliabilities reported in previous research and described in Chapter 3. All scales
included all the items as shown in the experimental booklet, except two. For the
seven-item time orientation scale, one item regarding the use of express mail was
dropped as the scale reliability was higher without it. Likewise, one item (fear
of being perceived of as irrational) was removed from the 11-item compulsive

consumption scale.

Table 4.3. Scale Reliabilities

 

 

Obtained Previously

Scales Reliability Reported Reliability
Pretest Auto Purchase Intent .9609 .89
Pretest Credit Card Acquisition Intent .9578 .89
Attitude Toward the Cavalier 224 Ad .8727 .85
Attitude Toward the Cavalier 224 .9189 .93
Purchase Intent for the Cavalier 224 .8967 .89
Affordability of the Cavalier 224 .8726 .86
Posttest Auto Purchase Intent .9651 .89
Attitude Toward the CapitalOne Visa Ad .9067 .85
Attitude Toward the CapitalOne Visa .9486 .93
Acquisition Intent for the CapitalOne Visa .8962 .89
Affordability of the CapitalOne Visa .8429 .86
Posttest Acquisition Intent for Credit Cards .9629 .89
Time Orientation .7938 .66
Compulsive Consumption .8869 .88
Impatience .7177 .67 (men) .63 (women)
Credit Card Usage .7648 .78
Credit Card Spending .8025 .82
Credit Card Control .7962 .71
Credit Card Attitude .7507 .71

 

67

Descriptive Statistics

AUTO DEPENDENT VARIABLES

Table 4.4, Descriptive Statistics for Auto Dependent Variables, shows the
mean purchase intent was 15.63 on a scale of 3 to 21. Many subjects (29.4%)
rated their purchase intent at the highest level (21). However, when asked about
the timeframe of their purchase, the majority of the subjects (59.4%) indicated
that they intended to buy an automobile but not within the next 24 months. Only
5.5% indicated they would buy within 6 months, 7.8% in 7 to 12 months, 16.4%

in 13 to 24 months, and 11.9% did not plan to buy an automobile at all.

Turning to posttest measures, attitudes toward the ad (mean 18.83) and the
Cavalier Z24 (mean 15.48) were fairly positive. Purchase intent measures for the
Cavalier were very low, with a mean of 7.65 on a scale of 3 to 21. In response to
the timeframe for purchase of the Cavalier question, 92.4% indicated that they
did not intent to buy a Cavalier Z24 at any time. Perceived affordability of the
Cavalier 224 was somewhat low with a mean of 9.22 on a scale ranging from 3
to 21. Posttest purchase intent for auto in general improved slightly to 16.36 as
did timeframe of purchase measures. Payment method for purchase of the
Cavalier was mixed; 10.8% would buy outright, 8.1% procure a 1-year lease,
26.1% procure a 2-year lease, 15.8% procure a 3-year lease, 24.8% procure a 48-

month loan, 9.9% procure a 60-month loan, and 4.5% procure a 82-month loan.

68

Table 4.4. Descriptive Statistics for Auto Dependent Variables

 

 

Standard
Variable Mean Mode Range Deviation
Pretest Auto Purchase Intent 15.63 21 3 to 21 5.48
[3 (low) to 21 (high)]
Attitude toward the Cavalier 18.83 16 4 to 24 5.23
Ad [4 (-) to 28 (+)]
Attitude toward the Cavalier 15.48 18 3 to 21 3.75
224 [3 (-) to 21 (+)]
Purchase Intent for the 7.65 3 3 to 21 4.64
Cavalier [3 (low) to 21 (high)]
Affordability of the Cavalier 9.22 8 3 to 21 5.48
[3 (low) to 21 (high)]
Posttest Auto Purchase Intent 16.36 21 3 to 21 5.40

[3 (low) to 21 (high)]

 

CREDIT CARD DEPENDENT VARIABLES

In Table 4.5, Descriptive Statistics for Credit Card Dependent Variables,
the mean pretest acquisition intent for a credit card was 14.48 on a scale from a
low of3 to a high of21 (with 24.1% rating acquisition rate at 21). Pretest
expected timeframe for credit card acquisition was spread across the board with
16% intending to get a card in 6 months, 14.2% in 7 to 12 months, 18.3% in 13

to 24 months, 27.9% in greater than 24 months, and 23.7% not at all.

In posttest measures, attitude toward the ad (mean 17.55) and attitude
toward CapitalOne (mean 13.04) were moderate. Acquisition intent for
CapitalOne was very negative with a mean of 7.75. Additionally, when asked to
estimate timeframe of CapitalOne acquisition , 82.6% indicated that they did not

intend to get a CapitalOne card. The card was perceived to be fairly affordable,

69

although not overwhelmingly so (mean 14.67). Posttest timeframe of acquisition
measures for a credit card in general became slightly more negative (with 2 to
3% point drops in intent to obtain during the earlier timeframes and increases in
the latest timeframe and not obtain categories). However, the actual acquisition
intent scale mean was slightly more positive as Table 4.5 shows. The expected
method of payment for purchases made with a CapitalOne card were as follows:
56.7% would pay balance off in full, 32.6% would pay less than the full and
more than the minimum, 10.3% would pay the minimum, and .4% would pay less

than the minimum.

Table 4.5. Descriptive Statistics for Credit Card Dependent Variables

 

 

Standard
Variable Mean Mode Range Deviation
Pretest Credit Card Acquisition 14.48 21 3 to 21 6.24
Intent [3 (low) to 21 (high)]
Attitude toward the CapitalOne 17.55 16 4 to 28 5.78
Ad [4 (-) to 28 (+)]
Attitude toward CapitalOne [3 13.04 12 3 to 21 4.72
(-) to 21 (+)]
Acquisition Intent for 7.75 3 3 to 21 4.61
CapitalOne [3 (low) to 21
(high)]
Affordability of CapitalOne [3 14.67 21 3 to 21 5.19
(low) to 21 (high)]
Posttest Credit Card Acquisition 14.87 21 3 to 21 6.36

Intent [3 (low) to 21 (high)]

 

REASONS FOR PAYMENT METHOD SELECTION

Subjects were asked to write in their reasons for making their payment

selection for both the auto and the credit card cases. The primary reason for

70

soliciting information about the reasons for payment selection was to determine
whether subjects might be using credit offers to free up money for higher-
yielding investments (i.e., selecting an 84-month loan so that they could invest
capital not spent on the auto purchase). In no case did a subject indicate that this

was the case. However, analysis of the reasons for payment provides insight.

The most common reason for selecting a payment method for the Cavalier
was ﬁnancial constraints. This reason was provided by 9.4% of subjects.
Additional reasons included the following: Allows for a change if desired by
8.0%, so I can get a new car every year/few years by 7.6%, family inﬂuence by
6.7%, like leasing by 5.8%, to pay it off sooner by 5.4%, more affordable
monthly payment by 5.4%, lack money to buy outright by 4.9%, don’t like

leasing by 4.9%, and income matches this payment by 4.9%.

Financial reasons (such as ﬁnancial constrains or payment matching
income) were the most common, accounting for 36.2% of reasons cited. Reasons
related to timing (i.e., so I can get a new car every year, to pay it off sooner,
good time to have car, car falls apart after this time) and preference (i.e., prefer
to own, like leasing, don’t like leasing, done it before and liked it) were also
common, accounting for 28.0% and 27.1% respectively. Social inﬂuence (by
family and peers) was the least common reason, accounting for only 8.7% of the
reasons provided. As Table 4.6 shows, for those buying outright, the majority
named a ﬁnancial reason for payment method. For those who chose a lease,
timing reasons were most common, with many also naming a ﬁnancial reason or
a preference. Finally, for those choosing a loan, ﬁnancial reasons were the most

common followed by preferences. Reasons did not vary much across the three

71

lease periods, but among the loan periods ﬁnancial reasons became more
common as the length of the loan increased. Speciﬁcally, for the 48-month loan
32.1% named ﬁnancial reasons compared to 54.5% for the 60-month loan, and

100.0% for the 84-month loan.

Table 4.6. Auto Payment Method by Reason for Payment

 

 

Payment Social

Method Financial Timing Preference Influence Total

Buy outright 17 2 0 4 23
73.9% 8.7% 0.0% 17.4%

Lease 23 47 28 13 1 1 1
20.7% 42.3% 25.2% 11.7%

Loan 39 1 1 33 2 85
45.9% 12.9% 38.8% 2.4%

 

In the case of credit cards, the most common explanation provided for
selecting a payment method for purchases made with a CapitalOne card was
avoiding payment for interest by 21.9% of those giving a reason for their
decision. The next most common answer, by 17.4% of subjects, was to avoid
debt/spending money they do not have. Other common responses were tight
budget/ﬁnancial constraints by 8.5%, build credit history by 6.7%, less to worry
about/keep track of by 6.7%, don’t have money to pay in full by 5.8%, and pay

more than minimum to keep debt from building up by 5.8%.

Explanations provided ﬁt into the following categories: Credit savvy (i.e.,
avoiding interest/penalty charges and keeping debt from building) by 32.7%,
credit adverse (i.e., do not like to owe money and credit cards were evil) by

21.4%, ﬁnancial by 20.9%, convenience by 12.7%, build credit line/history by

72

10.0%, and parental inﬂuence by 2.3%. As Table 4.7 shows, the most common
reasons for payment for those planning to pay for Capital One charges in full was
credit savvy, closely followed by credit adverse, and then convenience. Those
planning to pay more than the minimum, but less than in full were split their
reasons evenly between credit savvy and ﬁnancial. For those planning to make
the minimum payment only, ﬁnancial reasons were by far the most common
reason, followed distantly by convenience, and with only one person indicating
credit savvy and none citing a credit adverse reason. Only one subject planned
to pay less than the minimum payment, and that person cited a ﬁnancial reason

(ﬁnancial constraints) as the reason.

Table 4.7. Credit Card Payment Method by Reason for Payment

 

 

Payment Credit Credit Build Parental

Method Savvy Adverse Financial Convenience Credit Influence Total

Full 46 41 3 22 10 4 126
36.5% 32.5% 2.4% 17.5% 7.9% 3.2%

More 25 6 25 3 1 1 1 71

than Min. 35.2% 8.5% 35.2% 4.2% 15.5% 1.4%

Minimum 1 0 17 3 1 0 22
4.5% 0.0% 77.3% 13.6% 4.5% 0.0%

Less 0 0 1 0 0 0 1

than Min. 0.0% 0.0% 100.0% 0.0% 0.0% 0.0%

 

INSTALLMENT PLANS

When asked whether they felt buying things on an installment plan was

good, good in some ways and bad in others, or bad, the majority (83.0%) felt that

73

buying things on installments was good in some ways and bad in others. When
asked to speciﬁcally approve or disapprove of buying ﬁve items on installments,
most subjects felt that it was okay to use installments to buy two (29.4%) or
three (45.1%) items. Not surprisingly, most subjects approved of borrowing
money for educational purposes (96.6%), living expenses (75.4%), and buying an
auto (78.9%), but not for buying a fur/jewelry (3.6%) or taking a vacation

(20.3%).

CREDIT CARD ATTITUDES AND USE

Subjects were asked several questions about their use of and feelings
toward credit cards. Subjects had a mean of 1.92 credit cards. Speciﬁcally,
27.8% did not have a credit card, 39.0% had one card, 13.8% two cards, 8.1%
had three, and the remaining 12.7% had anywhere from 4 to 18 cards. Ten
subjects reported having 10 or more credit cards. When subjects were asked how
they pay for purchases made on their credit cards, 32.6% reported paying in full
(convenience rather than ﬁnance users), 27.7% some but not all in full, 7.1% the
minimum payment, 4.9% have credit cards but do not use them, and 26.3% do
not have a credit card. The slight difference in those reporting not to have a
credit card on these questions may be due to some previously having a card and

reporting about their payments based on past use.

Additionally, subjects responded to a four-factor credit card consumption
scale. With regard to attitude toward credit cards, subjects showed they had
moderate attitudes toward credit cards with a mean score of 13.29 on a scale
ranging from 3 (dislike credit cards) to 21 (like credit cards). When asked

whether credit cards help them maintain ﬁnancial control, subjects also

74

responded toward the middle ofthe range with a mean score of 12.97 on the
scale from 3 (hurt control) to 21 (help control). The same pattern held true for
questions regarding whether subjects spent more when they used credit cards.
The mean was 19.46 on a scale ranging from 5 (spend more with credit cards) to
35 (do not spend more). Subjects responded to questions about their use of
credit cards, with a mean of 16.86 indicating that most did not use their cards
such that they maxed out their credit limit [3 (exhaust limit) to 21 (do not
exhaust limit)]. Table 4.8 presents the descriptive information for these scales as

well as the following individual difference scales.

INDIVIDUAL DIFFERENCE VARIABLES

For the three main scales addressing individual differences (time
orientation, compulsive consumption and impatience), thedistribution of
responses was spread fairly normally across the scale range with the mean
responses being toward the middle of the scales. The mean score for time
orientation was 17.23, indicating that most subjects rated themselves somewhere
between present-minded and future-minded. For impatience, the mean score was
14.29. Again on average subjects rated themselves to be neither very impatient
nor very patient. The same pattern held true for the compulsive consumption
scale, with the mean score being 28.10 on a scale ranging from 10 for not

compulsive to 50 for the highest degree of compulsive consumption.

It should also be noted that compulsive consumption and time orientation
were highly correlated with each other (r = .690, p > .01). Additionally,
impatience was positively correlated with compulsive consumption (r =.253, p >

.01) and not related to time orientation. As expected these variables tended to be

75

related to each other. These correlations along with those for all variables in the

experiment are listed in the correlation table in Appendix C.

Table 4.8. Descriptive Statistics for Individual Difference Scales

 

Standard
Variable Mean Mode Range Deviation

Credit Card Attitude [3 13.29 17 3 to 21 4.44
(dislike credit cards) to 21
(like credit cards)]

Credit Card Control [3 (hurt 12.97 12 3 to 21 4.36
control) to 18 (help control)]

Credit Card Spending from 19.46 19 5 to 35 7.66.
[5 (spend more with credit

cards) to 30 (do not spend

more)]

Credit Card Use [3 (exhaust 16.86 21 3 to 21 4.33
limit) to 21 (do not exhaust

limit) ,

Impatience [5 (patient) to 25 14.29 16 5 to 25 3.58
(impatient)

Time Orientation [6 (ﬁiture- 17.23 17 6 to 30 5.20
minded) to 30 (present-
minded)]

Compulsive Consumption 28.10 31 11 to 50 8.83
[10 (not compulsive) to 50
(compulsive)]

 

FINANCIAL KNOWLEDGE

Subjects were asked two questions regarding their ﬁnancial knowledge
about the ﬁnancing conditions in the ads they were exposed to. One was about

the amount of interest and the other was about time period to pay off debt.

76

With regard to automobile ﬁnancing, subjects were asked to estimate the
amount of interest only to be paid on a standard 60-month loan with 0.9%
interest (a low rate that was prevalent at the time of data collection) for a
$15,000 vehicle. The mean response was $3,229.13. This is signiﬁcantly more
than the actual interest amount of $345. The answers ranged from a low of
$13.50 to a high of $35,000 (an extreme outlier excluded from the calculation of
the mean). The most common response was $1,350 by 12.5% of subjects,
followed by $1,500 by 6.4%, and $150 by 5.7%. Eighteen responses were
$10,000 or more. With regard to the time to pay off a 60-month loan, surprising
only 54.8% correctly reported that a 60-month loan takes 5 years to pay off.
When subjects were wrong in their time estimates, they were evenly split
between under and over estimates. Scores ranged from 0.25 years to 25 years.
Many subjects skipped these questions; 22.3% did not report an answer on the
interest portion and 21.0% left the years portion blank. The questions were
toward the end of the experimental book (which was longer than the booklet the

questions were pretested in) and fatigue may have been a factor.

For the credit card ﬁnancing question, subjects were asked to estimate
how much they would pay in interest and how long it would take before they
paid off the $5,000 balance in the ad with 9.9% annual interest and a typical
monthly payment of 2%. Interest would add up to $2,994 and it would take 16 ‘/2
years to pay off. Due to some high estimates (there exists no ceiling on the top
end as opposed to the bottom), the mean estimate for interest paid was $5,396.77
with answers ranging from $0 to $495,000 (an extreme outlier which was
excluded from calculation of the mean). The most common answer was $500

(for 11.6% of respondents). Roughly two thirds (65.2%) reported less than the

77

actual amount, 2.3% were accurate with $3,000, and the remainder overshot
(some by a very long margin). The mean number of years to pay off the loan
was 8.56 years, with answers ranging from .25 to 100 (an extreme outlier which
was excluded in the calculation of the mean). Many subjects left these questions,
which followed the auto ﬁnancing questions, blank (41.1% for the interest and

39.7% for the years).

FINANCIAL EXPERIENCE

Most subjects were fairly limited in their ﬁnancial experience. When
subjects were asked about their experience with loans, ﬁnancing, and debt, the
area subjects had the most experience with was educational loans; 37.9% had
taken out an educational loan. About one fourth (26.9%) reported having bought
a car, but only 1 1.6% indicated they had taken out a loan while another 16.0%
said that they leased. With regard to other matters related to loans and buying
things on time, 8.2% reported ﬁnancing furniture/carpeting, 1.4% renting to own
furniture/carpeting, and only 1.8% buying a house. More subjects (16.4%)
reported making payments for other things (such as computers, organizational
dues, and stereo and exercise equipment). However, this also includes about one
third of the respondents who wrote in auto or credit card payments here as well.
Additionally, 3.7% reported having taken out some other type of loan (computer
or motorcycle). Very few reported problems with debt; 1.8% sought
advice/assistance in getting out of debt, 1.4% took out a loan to consolidate debt,

and zero reported declaring bankruptcy.

78

SUBJECT PROFILE

Table 4.9 summarizes demographic information about the sample.
Speciﬁcally, the mean age of subjects was 19.89 (when two older students were
excluded). Females (59.3%) out numbered males (40.7%). Nearly all subjects
were single (95.5%). Most (84.2%) were Caucasian. In terms of education level,
96% of subjects indicated they had completed some college (but held no degree),
1.8% reported having Associates degrees, and 2.2% reported already having a
Bachelors (although a few may have been prematurely reporting the degree they
were working toward as already earned). Subjects were fairly well distributed in
terms of class standing; 32.6% were freshman, 18.8% were sophomores, 29.1%
were juniors, and 18.8% were seniors, .4% were graduate students (one subject),
and .4% did not report class standing. Roughly one quarter of the subjects
indicated that their major was advertising. Other common majors were

telecommunication, packaging, and no preference.

Nearly 90% of students reported that they were employed, with about 42%
each working all year or in the summers only. Nearly three fourths of the
students reported their annual personal income to be below $10,000. However, it
appears that the majority of students come from fairly well off families as the
most commonly reported parental income was above $100,000 (for 26.8% of the
students) and the majority of those reporting indicating that their parents’ income
exceeded $80,000. The mean percentage of ﬁnancial support provided to
students by their parents was 67.1%, with scores ranging from 0 to 100% as

listed in Table 4.9.

79

Table 4.9. Subject Proﬁle

 

 

 

 

 

 

 

 

 

 

 

 

Percent Percent
Variable (N=224) Frequency Variable (N=224) Frequency
Age Race
17 .9% 2 African 5.4% 12
18 23.2% 52 Asian 5.8% 13
19 17.4% 39 Caucasian 83.0% 186
20 20.1% 45 Hispanic 3.1% 7
21 16.1% 36 Other 1.3% 3
22 6.7% 15 Missing 1.3% 3
23 3.1% 7
24 and up 4.4% 9
Missing 8.5% 19
Gender Marital Status
Female 57.1% 128 Single 95.5% 214
Male 39.3% 88 Married 1.8% 4
Missing 3.6% 8 Missing 2.7% 6
Class Standing Major
Freshman 32.6% 73 Advertising 26.8% 60
Sophomore 18.8% 42 Telecom. 13.8% 3 1
Junior 29.0% 65 Packaging 11.2% 25
Senior 18.8% 42 No Preference 10.7% 24
Graduate Student .4% 1 Others 32.7% 68
Missing .4% 1 Missing 7.1% 16
Parental Support Employed
100% 25.4% 57 All Year 42.9% 96
90% 9.8% 22 School Year Only 2.7% 6
80% 7.6% 17 Summer Only 42.0% 94
75% 5.8% 13 Not Employed 7.1% 16
50% 1 1.6% 26 Missing 5.4% 12
0% 9.8% 22
All others 26.9% 60
Missing 3.1% 7
Personal Income Parents‘ Income
$0 - $10,000 72.3% 162 $0 - $10,000 3.1% 7
$10,001- $20,000 8.5% 19 $10,001- $20,000 1.8% 4
$20,001-$30,000 1.8% 4 $20,001-$30,000 4.0% 9
$30,001- $40,000 4.5% 10 $30.001- $40,000 7.1% 16
$40.001-$50,000 1.8% 4 $40,001-$50,000 8.0% 18
$50,001- $60,000 .4% 1 $50,001- $60,000 4.5% 10
$60,001—$70,000 .4% 1 $60,001-$70,000 7. 1% 16
$70,001- $80,000 .9% 2 $70,001- $80,000 9.4% 21
$80,001-$90,000 0.0% 0 $80,001-$90,000 8.5% 19
$90,001- $100,000 .4% 1 $90,001- $100,000 11.6% 26
Above $100,000 .9% 2 Above $100,000 26.8% 60
Missing 8.0% 18 Missing 8.0% 18

 

 

Hypotheses

PRETEST-POSTTEST CHANGE IN PURCHASE INTENT FOR A
PRODUCT IN GENERAL

H1. Exposure to advertising financing claims for a specific product
significantly increases consumers’ intention to make a purchase from
that product category (creates an intent to buy a product that would
be consistent with a change in reference points).

Auto

Overall, 95 of the 218 subjects (43.6%) had no change in purchase intent
after exposure to the Cavalier ad. Of those who changed their opinion, 77 had an
increase in purchase intent and 46 had a decrease. By treatment level, 35.1% of
those in the least expensive condition, 45.9% ofthose in the middle condition,
40.0% of those in the most expensive condition, and 46.8% of those in the

control indicted no change.

Table 4.10 shows the number of subjects in each condition who changed
their purchase intent rating and the total number of points change within each
condition. When only looking at the number of subjects with positive verses
negative change (not the net amount) the control group and the least expensive
group had a two to one positive to negative change compared to near equal split
in the medium and most expensive payment groups. However, when the amount
of positive/negative change is considered, only the control group and the

moderate level condition had considerable better positives than negatives.

81

Table 4.10. Pre-Post Change in Auto Purchase Intent (Number of Changes and Net

 

 

Point Change)
Treatment Group Positive Change No Change Negative Change

73183/mo. 16 13 8
(61 points) (57 points)

$246/mo. 9 17 9
(53 points) (29 points)

$317/mo. l 1 14 11
(33 points) (26 points)

Control 40 51 18
(210 points) (80 points)

Total 77 95 46
(357 points) (192 points)

 

——f

However, as seen in Table 4.12, ANOVA revealed no signiﬁcant
difference between the treatments. Since the treatment levels are somewhat
similar to each other (with mean changes of .1 1, .65, and .20 for the most
inexpensive through the most expensive conditions) and lower than the control
(mean change of 1.12), the treatments were grouped and a t-test was run. The
higher the number for mean change, the more purchase intent increased after ad
exposure. The t-test indicated there was no signiﬁcant difference between the
two groups [I (216) = -l.31, p > .05]. Exposure to ﬁnancing claims did not

increase purchase intent for autos in general.

Table 4.11. Mean Pre-Post Change in Auto Purchase Intent

 

$183/mo. $246/mo. $317/mo. Control
. 1 1 .65 .20 1. 12

 

 

82

Table 4.12. ANOVA—Pre-Post Change in Auto Purchase Intent by Treatment

 

 

Group
rSource of Sum of
Variance Squares df Mean Square F F Probability
rBetween Groups 40.882 3 13.627 .672 .570
Within Groups 4341.050 214 20.285
’Total 4381.931 217

f

 

Credit Card

Overall, 91 ofthe 215 subjects (42.3%) had no change in acquisition
intent after exposure to the CapitalOne Visa ad. Of those who changed their
opinion, 76 had an increase in acquisition intent and 48 had a decrease. By
treatment level, 47.2% of those in the least expensive condition, 37.1% of those

in the middle condition, 22.2% of those in the most expensive condition, and

49. l % of those in the control indicated no change.

Table 4.13 shows the number of subjects in each condition who changed
their acquisition intent rating and the total number of points change within each
c0ndition. When only looking at the number of subjects who changed their
acquisition intent rating, the least expensive group had the greatest positive
Versus negative change with 84.2% of those changing opinions being in a
POSitive direction compared to 63.6% for the middle condition, 643% for the
m0 St expensive condition, and 50.0% for the control. When the amount of
POSitive/negative change is considered, only the control group had greater

negative change than positive, whereas the ﬁnancing treatment groups all had

considerably more positive change.

83

Table 4.13. Pre-Post Change in Credit Card Acquisition Intent (Number of
Changes and Net Point Change)

 

’f

 

Treatment Group Positive Change No Change Negative Change
iSSOImo. 16 17 3
(61 points) (16 points)
$100/mo. 14 13 8
(77 points) (27 points)
$151/mo. 18 8 10
(104 points) (60 points)
Control 28 53 27
(105 points) (147 points)
Total 77 95 46
(347 points) (250 points)

 

———

Tables 4.14 and 4.15 present the mean amount of change for each
condition and show that the ANOVA approached signiﬁcance. There was an
increase in acquisition intent in the treatment conditions (with mean change in
the groups ranging from 1.22 to 1.42) and a decrease in acquisition intent in the
control group (mean change of—.39). Of the post hoc tests, only LSD revealed
Signiﬁcant or near signiﬁcant differences between the groups—all being between
the control and the treatment groups (Control and Least Expensive: Mean
Difference —1.64, p = .07; Control and Medium Treatment: Mean Difference

“1.82, p = .05; Control and Most Expensive Mean: Difference -1.61, p = .08).

Table 4.14. Mean Pre-Post Change in Credit Card Acquisition Intent

 

$50/mo. $100/mo. $151/mo. Control
1.25 1.42 1.22 -.39

 

 

84

Table 4.15. ANOVA—Pre-Post Change in Credit Card Acquisition Intent by
Treatment Group

 

 

Source of Sum of

Variance Squares df Mean Square F F Probability
Between Groups 154.027 3 51.342 2.275 .081
Within Groups 4761.210 211 22.565

Total 4915.237 214

 

As the mean change was so close in the treatment conditions, they were
grouped and a t-test was run. The t-test conﬁrmed the difference between the
two groups (Table 4.16). Exposure to ﬁnancing claims for a speciﬁc product
signiﬁcantly increased subjects’ intention to make a purchase from that product

category for credit cards.

Table 4.16. t-test for Pre-Post Change in Credit Card Acquisition Intent

 

 

Treatments Mean Mean Difference t df Sig. (2-tailed)
Combined Treatment 1.2991 -1.6880 4.2948 213 .010
Groups

Control Group -.3889

 

PRETEST-POSTTEST CHANGE IN TIMEFRAME OF PURCHASE FOR A
PRODUCT IN GENERAL

H2. Exposure to advertising financing claims for a specific product
significantly advances the anticipated timeframe for purchase from the
product category (creates an eagerness to buy a product that would be
consistent with a change in reference points).

85

Auto

The majority of subjects in all conditions (83.1%) did not change their

anticipated time of auto purchase. When subjects did change their minds they

were almost equally divided between advancing and delaying auto purchase,

except in the most expensive payment condition ($317/mo.) where 5 delayed, 1

advanced, and 29 remained the same. Although relying on chi-square results

would be inappropriate due to the high number of cells with small cell sizes, it is

apparent that the obtained counts are very close to the expected. There appears

to be no real difference in change of timeframe for purchase by treatment group.

Financing claims do not advance the timeframe of purchase for autos in general.

Table 4.17. Pre-Post Change in Timeframe of Auto Purchase by Treatment Group

 

 

 

 

 

Timeframe of Auto Purchase $183/mo. $246/mo. $317/mo. Control Total
Accelerated
Count 2 2 1 11 16
Expected Count 2.7 2.7 2.6 8.0 16
% within Auto Ads 5.4% 5.4% 2.9% 10.0% 7.3%
Unchanged
Count 31 33 29 89 182
Expected Count 30.7 30.7 29.1 91.4 182
% within Auto Ads 83.8% 89.2% 82.9% 80.9% 83.1%
Delayed
Count 4 2 5 10 21
Expected Count 3.5 3.5 3.4 10.5 21
% within Auto Ads 10.8% 5.4% 14.3% 9.1% 9.6%
Total
gCount 37 37 35 110 219

 

Credit Card

The majority of subjects in all conditions (71.1%) did not change their

anticipated time of acquisition of a credit card. Of those who did change their

timeframe, 21 advanced their anticipated acquisition time and 41 delayed it. In

each of the conditions, those delaying were slightly greater in number than those

advancing. As fewer than 20% of the cells have less than ﬁve cases, chi-square

analysis is acceptable (Norusisl988). The chi-square results [12 (6, N = 219) =

.82, p > .05] and examination of the obtained counts (shown in Table 4.18)

indicate there is no signiﬁcant difference in change of timeframe for credit card

acquisition by treatment group. Financing claims do not advance timeframe of

purchase for credit cards in general.

Table 4.18. Pre-Post Change in Timeframe of Credit Card Acquisition by
Treatment Group

 

Timeframe of Credit Card

 

 

 

 

Acquisition $50/mo. $100/mo. $151/mo. Control Total
Accelerated
Count 2 3 5 11 21
Exoeotod Count 3.5 3.5 3.5 10.5 21
% within Credit Card Ads 5.4% 8.3% 13.5% 10.1% 9.6%
Unchanged
Count 29 28 24 76 157
Expected Count 26.5 25.87 26.5 78.1 157
% within Credit Card Ads 78.4% 77.8% 64.9% 69.7% 71.7%
Delayed
Count 6 5 8 22 41
Expected Count 6.9 6.7 6.9 20.5 41
__ % within Credit Card Ads 16.2% 13.9% 21 .6% 20.2% 18.7%
Total
gCount 37 36 37 109 219

 

ATTITUDE TOWARD THE AD

H3. Inclusion of financing claims in advertising significantly improves
consumers’ attitudes toward the ad (creates a liking for the
advertisement).

Auto

The means for attitude toward the Cavalier 224 ad are very similar in all
conditions. On a scale of 4 (dislike) to 28 (like), the means in all conditions
were close to 19. The means and ANOVA results (Tables 4.19 and 4.20) do not
support the above hypotheses that inclusion of ﬁnancing claims improves

attitude toward the ad.

Table 4.19. Mean Attitude toward the Cavalier 224 Ad

 

$183/mo. $246/mo. $317/mo. Control
19.57 18.42 19.00 18.66

 

 

Table 4.20. ANOVA—Attitude toward the Cavalier Z24 Ad by Treatment Group

 

 

Source of Sum of

Variance Squares df Mean Square F F Probability
Between Groups 30.758 3 10.253 .371 .774
Within Groups 4341.050 214 20.285

Total 4381.931 217

 

88

Credit Card

The means for attitude toward the CapitalOne Visa ad are also very
similar across all treatment groups. On a scale of4 (dislike) to 28 (like), the
means in all conditions were close to 17. The means and ANOVA results
(Tables 4.21 and 4.22) do not support the above hypothesis that inclusion of

ﬁnancing claims improves attitude toward the ad.

Table 4.21. Mean Attitude toward the CapitalOne Visa Ad

 

$50/mo. $100/mo. $151/mo. Control
17.86 17.62 16.39 17.83

 

 

Table 4.22. ANOVA—Attitude toward the CapitalOne Visa Ad by Treatment

 

 

Group
Source of Sum of
Variance Squares df Mean Square F F Probability
Between Groups 63.016 3 21.005 .626 .599
Within Groups 7311.835 218 33.541
Total 7374.851 221

 

ATTITUDE TOWARD THE ADVERTISED PRODUCT

H4. Inclusion of financing claims in advertising significantly improves
consumers’ attitudes toward the advertised product (creates a liking
for the product that would be consistent with a change in reference
points).

89

Auto

The means for attitude toward the Cavalier 224 are very similar across all
conditions. On a scale of4 (dislike) to 28 (like) the means in all conditions were
in the range of 15 to 16. ANOVA conﬁrms that there were no signiﬁcant
differences across the groups. The means and ANOVA results (Tables 4.23 and
4.24) do not support the hypothesis that inclusion of ﬁnancing claims improves

attitude toward the Cavalier Z24.

Table 4.23. Mean Attitude toward the Cavalier Z24

 

$183/mo. $246/mo. $317/mo. Control
16.41 15.08 15.95 15.16

 

 

Table 4.24. ANOVA—Attitude toward the Cavalier Z24 by Treatment Group

 

 

Source of Sum of

Variance Squares df Mean Square F F Probability
Between Groups 57.247 3 19.082 1.360 .256
Within Groups 3086.681 220 14.030

Total 3 143.929 223

 

Credit Card

The means for attitude toward CapitalOne Visa are fairly similar across
the treatment groups, ranging from 12 to nearly 14. The scale itself ranges from
4 (dislike) to 28 (like). The ANOVA results revealed no signiﬁcant differences

across the groups. The means and ANOVA results (Table 4.25 and 4.26) do not

90

support the hypothesis that inclusion of ﬁnancing claims improves attitude

toward CapitalOne Visa.

Table 4.25. Mean Attitude toward CapitalOne Visa

 

$50/mo. $100/mo. $151/mo. Control
13.89 11.97 12.66 13.23

 

 

Table 4.26. ANOVA—Attitude toward the CapitalOne Visa Ad by Treatment

 

 

Group
Source of Sum of
Variance Squares df Mean Square F F Probability
Between Groups 78.657 3 26.219 1.179 .318
Within Groups 4891.057 220 22.232
Total 4969.714 223

 

PURCHASE INTENTIONS FOR THE ADVERTISED PRODUCT

H5. Inclusion of financing claims in advertising significantly increases
consumers’ intentions to purchase the advertised product (creates a
desire for or intent to buy the product that would be consistent with a
change in reference points).

Auto

The means for purchase intent for the Cavalier Z24 are somewhat
different across the treatment groups. On a scale of 3 (not likely to purchase) to
21 (likely to purchase) the means ranged from a low of just below 7 in the

control group to just above 9 in the most inexpensive treatment group (Table

91

4.27). Interestingly, purchase intent in the most expensive condition was nearly
as high as in the least expensive. The ANOVA results (Table 4.28) shows that
the means are indeed different. The means and ANOVA results support the
hypothesis that inclusion of ﬁnancing claims increases purchase intent for the
speciﬁc product, namely the Cavalier Z24. Post hoc analysis conﬁrmed
signiﬁcant differences between the least expensive condition and the control as
well as the most expensive condition and the control (LSD mean differences
2.19, p = .013 and mean difference —1.78, p = .042 respectively). Financing
claims (at the least and most expensive levels) were associated with increased

purchase intent for the Cavalier.

Table 4.27. Mean Purchase Intent for the Cavalier 224 by Treatment Group

 

$183/mo. $246/mo. $317/mo. Control
9.03 7.76 8.62 6.84

 

 

Table 4.28. ANOVA—Purchase Intent for the Cavalier Z24 by Treatment Group

 

 

Source of Sum of

Variance Squares df Mean Square F F Probability
Between Groups 178.787 3 59.596 2.836 .039
Within Groups 4601.625 219 21.012

Total 4780.413 222

 

Credit Card

The means for acquisition intent for the CapitalOne Visa card also appear
somewhat different, ranging from 6.76 in the middle treatment condition to 8.78

in the least expensive treatment condition (Table 4.29). The scale itself ranges

92

from 3 (unlikely to acquire) to 21 (likely to acquire). However, ANOVA reveals
no signiﬁcant difference between the groups (Table 4.30). The hypothesis that
inclusion of ﬁnancing claims increases acquisition intent was not true for the

CapitalOne Visa card.

Table 4.29. Mean Acquisition Intent for the CapitalOne Visa

 

$50/mo. $100/mo. $151/mo. Control
8.78 6.76 7.18 7.92

 

 

Table 4.30. ANOVA—Acquisition Intent for CapitalOne Visa by Treatment Group

 

 

Source of Sum of

Variance Squares df Mean Square F F Probability
Between Groups 91.427 3 30.476 1.441 .232
Within Groups 4653.068 220 21.150

Total 4744.496 223

 

AFFORDABILITY PERCEPTIONS FOR THE ADVERTISED PRODUCT

H6. Inclusion of financing claims in advertising significantly increases
consumers’ affordability perception of the product (makes the
product appear to the potential consumer to be more affordable).

Auto

The means for affordability of the Cavalier 224 vary slightly from a low

of 8.32 for the most expensive condition to a high of 9.82 for the control

93

(Table 4.31). They all tend to fall near the low end of the affordability scale
which ranges from 3 (not affordable) to 21 (affordable). The ANOVA results
shown in Table 4.32 indicate that means do not vary signiﬁcantly from each

other. These results do not support the hypothesis that inclusion of ﬁnancing

claims increases affordability perceptions of the Cavalier Z24.

Table 4.31. Mean Perceived Affordability of the Cavalier Z24

 

$183/mo. $246/mo. $317/mo. Control
8.84 8.71 8.32 9.82

 

 

Table 4.32. ANOVA—Perceived Affordability of the Cavalier 224 by Treatment

 

 

Group
Source of Sum of
Variance Squares df Mean Square . F F Probability
Between Groups 85.460 3 28.487 .949 .418
Within Groups 6601.379 220 30.006
Total 6686.839 223

 

Credit Card

The means for perceived affordability of the CapitalOne Visa card also
appear somewhat different, ranging from 13.16 in the middle treatment condition
to 15.57 in the least expensive treatment condition (Table 4.33). The scale itself
ranges from 3 (not affordable) to 21 (affordable). However, while the ANOVA
results are closer to signiﬁcance than for many of the other dependent variables,

they still fail to reveal signiﬁcant differences between the groups (Table 4.34).

94

The hypothesis that ﬁnancing claims increase affordability perceptions for the

CapitalOne Visa card cannot be supported.

Table 4.33. Mean Perceived Affordability for the CapitalOne Visa

 

$50/mo. $100/mo. $151/mo. Control
15.57 13.16 14.26 15.00

 

 

Table 4.34. ANOVA—Perceived Affordability for the CapitalOne Visa Ad by
Treatment Group

 

 

Source of Sum of

Variance Squares df Mean Square F F Probability
Between Groups 132.412 3 44.137 1.652 .178
Within Groups 5877.477 220 26.716

Total 6009.88 223

 

ANTICIPATED TIMEFRAME FOR PURCHASE OF THE ADVERTISED
PRODUCT

H7. Inclusion of financing claims in advertising significantly advances
consumers’ expected time of purchase of the advertised product
(creates an eagerness or impatience to possess the product).

Auto

Overall, very few of the subjects (17 or 7 .6%) indicated that they had any
intention to purchase the Cavalier 224. Of those intending to purchase, the
majority (11) expected to purchase sometime, but not within 24 months. Four of
those subjects were in the middle treatment condition, three were in the most

expensive, and two each were in the least expensive condition and the control.

95

Two subjects (one each from the least expensive treatment and the control)
expected to purchase in 13 to 24 months. Three subjects (two in the least
expensive group and one in the control) planned to purchase in 7 t012 months.

Finally, one subject (in the control) planned to purchase within 6 months.

There were so few subjects who intended to purchase a Cavalier that even
when the timeframes are condensed into purchase and no purchase there are too
many cells with small sizes to run chi-square analysis. However, in Table 4.36
the obtained responses can be compared to those that would be expected in order
to show that the response is roughly the same across conditions. Intent to
purchase is very slightly higher in the least expensive treatment condition,
followed by the middle, the most expensive, and ﬁnally, it is the lowest in the
control. However, the differences are so small that even had a larger sample
been taken, it is unlikely that the hypothesis regarding ﬁnancing claims
advancing timeframe of purchase would have been supported. The claims do not

appear to affect timeframe of purchase.

Table 4.35. Timeframe of Purchase of the Cavalier Z24 by Treatment Group

 

Timeframe of Auto Purchase $183/mo. $246/mo. $317/mo. Control Total

 

 

 

Purchasing
Count 5 4 3 5 17
Expected Count 2.8 2.9 2.8 8.5 17
% within Auto Ads 13.5% 10.5% 8.1% 4.5% 7.6%

Not Purchasing
Count 32 34 34 106 206
Expected Count 34.2 35.1 34.2 102.5 206
% within AUtO Ads 86.5% 89.5% 91.9% 95.5% 92.4%

Total

¥Count 37 38 37 1 1 1 223

 

Credit Card

Slightly more subjects (39 in all or 17.4%) intended to acquire CapitalOne
Visa than buy a Cavalier. Table 4.36 presents that distribution of responses to
the timeframe of acquisition of a CapitalOne card across the treatment groups.
As the table shows, 24 subjects intended to obtain a card sometime beyond 24
months and 9 intended to obtain one within 6 months. Another four subjects
intended to obtain a CapitalOne card within 7 to 12 months and two subjects in
13 to 24 months. So many cells were empty or extremely small that no pattern of

response by treatment group was apparent.

Since many cells had low cell counts the timeframes were combined to
form two groups, those intending to acquire a CapitalOne card (labeled
Acquisition All Combined) and those intending not to obtain one. Those people
in the most inexpensive treatment condition had the lowest overall acquisition
intent (10.8%), followed by the control (17.0%), the most expensive treatment
(21.1%), and the middle condition (21 .6%). As the expected cell size was over
ﬁve in all the cells, it was valid to run a chi-square test. The chi-square analysis
revealed that there were no differences in purchase intent by treatment group [12
(3, N = 224) = 1.9, p > .05]. Overall, there is no support for the hypothesis that
ﬁnancing claims advanced the timeframe of purchase for the CapitalOne Visa

card.

97

Table 4.36. Timeframe of CapitalOne Acquisition by Treatment Group

 

Timeframe of Credit Card

 

 

 

 

 

 

 

Acquisition $50/mo. $100/mo. $151/mo. Control Total
Acquire within 6 months

Count 2 1 1 5 9

Expected Count 1.5 1.5 1.5 4.5 9.0

% within Credit Card Ads 5.4% 2.7% 2.7% 4.5% 4.0%
Acquire within 7-12 months

Count 0 1 1 2 4

Expected Count .7 .7 .7 2.0 4.0

% within Credit Card Ads 0% 2.7% 2.6% 1.8% 1.8%
Acquire within 13-24 months

Count 0 0 0 2 2

Expected Count .3 .3 .3 1.0 2.0

% within Credit Card Ads 0% 0% 0% 1.8% 9%
Acquire beyond 24 months

Count 2 6 6 10 24

Expected Count 4.0 4.0 4.0 12.0 24.0

% within Credit Card Ads 5.4% 16.2% 15.8% 8.9% 10.7%
Acquisition (all combined)

Count 4 8 8 19 39

Expected Count 6.4 6.4 6.4 19.5 39.0

% within Credit Card Ads 10.8% 21.6% 21.1% 17.0% 17.4%
Not Acquire

Count 33 29 30 93 185

Expected Count 30.6 30.6 31.4 92.5 185.0

% within Credit Card Ads 89.2% 78.4% 78.9% 92.5% 82.6%
Total

Count 37 37 38 1 12 224

 

ANTICIPATED METHOD OF PAYMENT FOR THE ADVERTISED

PRODUCT

H8. Inclusion of financing claims in advertising significantly affects

consumers’ anticipated method of payment for the advertised product
(creates greater likelihood of deferring payment).

Auto

Table 4.37 presents the distribution of responses to the method of payment

question. Examination of responses reveals few differences by treatment group.

Overall, leasing accounted for 50.0% of the chosen payment methods, taking out

a loan 39.2%, and buying outright 10.8%. It was expected that those in ﬁnancing

treatment conditions, particularly those in the least expensive treatment group

who were exposed to the longest loan period, may be more likely to select longer

loan or even longer lease periods, rather than buying the car outright. The

percentage of subjects choosing to buy the car outright was constant across

Table 4.37. Payment Method for Cavalier by Treatment Group

 

 

 

 

 

 

 

 

 

Method of Payment $183/mo. $246/mo. $318/mo. Control Total
l-Year Lease 2 4 3 9 18
5.4% 10.5% 8.1% 8.2% 8.1%
2-Yoar Lease 11 9 11 27 58
29.7% 23.7% 29.7% 24.5% 26.1%
3-Year Lease 8 3 1 23 35
21.6% 7.9% 2.7% 20.9% 15.8%
48-Month Loan 8 11 12 24 55
21.6% 28.9% 32.4% 21.8% 24.8%
60-Month Loan 3 2 5 12 22
8.1% 5.3% 13.5% 10.9% 9.9%
82-Month Loan 1 4 1 4 10
2.7% 10.5% 2.7% 3.6% 4.5%
Buy Outright 4 5 4 11 24
10.8% 13.2% 10.8% 10.0% 10.8%
Total 37 38 37 110 222

 

99

conditions. Few people in any condition chose the longest loan period. The

greatest differences by treatment group appeared in the 3-year lease option;

21.8% of the control group subjects and 21.6% of those in the least expensive

condition selected a 3-year lease versus only 7.9% in the middle condition and

2.7% of those in the most expensive condition. Those last two groups opted

more for loan options. Overall, it is unlikely that ﬁnancing claims inﬂuenced

method of payment for the Cavalier as there are few differences between groups.

Credit Card

As shown in Table 4.38, responses to the payment method for purchases

made with the CapitalOne Visa card do not seem to vary much by treatment

group. The most expensive treatment condition did have a slightly higher

percentage of subjects choosing to pay the minimum payment only. However,

these differences appear to be very slight. Overall, ﬁnancing claims did not

inﬂuence the method of payment.

Table 4.38. Payment Method for CapitalOne Purchases by Treatment Group

 

 

 

 

 

 

Method of Payment $50/mo. $100/mo. $151/mo. Control Total
Pay Balance Off in Full 21 22 17 67 127
56.8% 59.5% 44.7% 59.8% 56.7%
Pay Less than Full Balance, but 14 12 15 32 73
M0” than the Minimum 37.8% 32.4% 39.5% 28.6% 32.6%
Pay Minimum Payment 12 2 6 12 23
10.7% 5.4% 15.8% 10.7% 10.3%
Pay Less than the Minimum 1 0 0 1 1
Payment .9% 0.0% 0.0% .4% .4%
Total 37 37 38 1 12 222

 

LEVEL OF FINANCING CLAIM

H9. The advertised level of the financing claim (the expense of payments
with their respective lengths of credit period) significantly affects
attitude and purchase-related variables.

Overall, the impact of ﬁnancing claims in general and the speciﬁc levels
of the claims did not have much signiﬁcant impact on the dependent variables as
is apparent from the preceding results which provided breakdowns by treatment
group (level of claim) in order to give an indication of the inﬂuence of ﬁnancing
versus no ﬁnancing claims. Here, the focus is on providing an examination of

the speciﬁc inﬂuence ofthe levels of the ﬁnancing treatments.

Auto

An examination of the means for the main dependent variables (Table
4.39) reveals some trends by treatment level. Speciﬁcally, those in the least
expensive treatment level rated highest on ease of payment in the manipulation
check (as described in Table 4.1), attitude toward the ad, attitude toward the Z24,
purchase intent for the Z24, and timeframe of Cavalier purchase (although not
for affordability and pre-post difference in purchase intent) for an auto in
general. The most inexpensive level was fairly consistently received as the best.
Ironically, those in the most expensive condition often gave higher ratings than
the moderate level or the control for product speciﬁc questions. However, while
there is some indication of a pattern of response by treatment group, the effect of
the levels was only signiﬁcant in the case of one dependent variable, purchase

intent for the Cavalier 224.

101

Table 4.39. Auto Ads—Dependent Variables by Level of Financing Claim

 

Variable $183/mo. $246/mo. $317/mo.

Control

F

F Probability

 

Attitude toward 19.57 18.42 19.00
the ad (3-21)

Attitude toward 16.41 15.08 15 .95
the Cavalier Z24
(4-28)

Purchase Intent 9.03 7 .76 8.62
for the Cavalier ,.,
224 (3-21)

**
Affordability of 8.84 8.71 8.32

the Cavalier 224
(3 -2 1)

Posttest Purchase 15.1 1 17.24 16.08
Intent for Auto
(General) (3-21)

Change in .1 1 .65 .20
Purchase Intent
for any Auto

18.66

15.48

6.8

**

9.82

16.57

1.12

.371

1.360

2.836

.949

.552

.672

.774

.256

.039

.418

.648

.570

 

* levels signiﬁcantly different from each other

Credit Card

In the case of credit cards, there was not even a signiﬁcant difference

between the treatment levels on ratings of how easy the credit card was to pay

for (the manipulation check). On that question (as is shown in Table 4.2), the

most inexpensive condition was rated the easiest to pay (although not

signiﬁcantly so). Additionally, on many of the dependent variables (attitude

toward the ad, attitude toward CapitalOne, purchase intent for CapitalOne, and

affordability of CapitalOne), the least expensive condition received the most

102

favorable ratings. However, in only one case, was there a signiﬁcant impact of
the ﬁnancing claims (for all treatment levels combined versus the control) on a
dependent variable (increasing intent to acquire a credit card). The middle
ﬁnancing condition often rated the poorest for the CapitalOne speciﬁc dependent
variables. There is some indication that the level of ﬁnancing claims inﬂuenced

(although not signiﬁcantly so) responses to the dependent variables.

Table 4.40. Credit Card Ade—Dependent Variables by Level of Financing Claim

 

Variable $50/mo. $100/mo. $151/mo. Control F F Probability

Attitude toward 17.86 17.62 16.39 17.83 .626 .599
the ad (3-21)

Attitude toward 13.89 11.97 12.66 13.23 1.179 .318
the CapitalOne

Visa

(4-28)

Acquisition Intent 8.78 6.76 7.14 7.92 1.441 .232

For the CapitalOne
Visa (3 -2 1)

Affordability of 15.57 13.16 14.26 15.00 1.652 .178
the CapitalOne
Visa (3-21)

Posttest 14.64 16.51 14.0 14.76 1.100 .350
Acquisition of

Credit Card

(General) (3 -2 1)

Change in 1.25 1.43 1.22 -.39 2.275 .081
Acquisition Intent

f°r any Cm!“ Combined t

ca“! Mean 1.29 -2.617 .010

 

INDIVIDUAL DIFFERENCE VARIABLES

H10. Some consumers are more favorably influenced by financing claims
than others.

103

Impatience

H10A. Impatient people are significantly more likely to be favorably
influenced by advertising that includes financing claims than
patient people.

Overall, it appears that impatient people do not react differently to ads
containing ﬁnancing claims than people who are more patient. The following
MANOVA results show that impatience did not have any signiﬁcant effects on
response to the ﬁnancing claims in the auto experiments. For credit cards,
impatience was a covariate for acquisition intent for CapitalOne and credit cards
in general with the relationship in the opposite direction of what might be
expected (the more impatient, the lower the acquisition intent). However, when
this was factored in there still were no signiﬁcant differences across the groups

for the dependent variables.

Table 4.41. MANOVA Results—Impatience and Auto Dependent Variables

 

 

Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS NS NA
Purchase Intent for the Cavalier 224 NS NS NA
Perceived Affordability of the Cavalier 224 NS NS NA
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Difference for Purchase Intent for Auto NS NS NA

(General)

 

104

Table 4.42. MANOVA Results——Impatience and Credit Card Dependent Variables

 

 

Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa S NS NA
Perceived Aﬂ‘ordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) S NS NA
Pre-Post Diﬂ‘erence for Acquisition Intent for Credit NS NS NA

Card (General)

 

Acquisition Intent for CapitalOne Visa. Table 4.43 shows impatience
had a signiﬁcant relationship with acquisition intent for the CapitalOne card.
Even when the relationship was taken into account in the analysis of covariance,
there were no signiﬁcant differences between the treatments groups and purchase
intent for the card. The correlation between impatience and purchase intent for
CapitalOne across all treatment conditions was signiﬁcant (r = -.143, p < .05).
(Refer to Appendix C for a listing of all correlation coefﬁcients.) Overall then,

the more impatient the lower the purchase intent for CapitalOne.

Table 4.43. Analysis of Covariance—Impatience and Acquisition Intent for

 

 

CapitalOne Visa
Source of Variance SS 4f MS F-Ratio F-Probability
Within Cells 4566.74 219 20.85
Regression (Impatience) 86.33 1 86.33 4.14 .043
Credit Card Ads 80.18 3 26.73 1.28 .282

 

105

Acquisition Intent for Credit Cards (General). Similarly impatience had
a signiﬁcant relationship with purchase intent for credit cards in general and
factoring impatience in as a covariate did not result in signiﬁcant differences
between the treatment groups for acquisition intent for credit cards. There was a
signiﬁcant correlation between impatience and acquisition intent for credit cards
(the more impatient the lower the acquisition intent for credit cards in general)

(r = -.150, p < .05).

Table 4.44. Analysis of Covariance—Impatience and Purchase Intent for Credit

 

 

 

Cards (General)
Source of Variance SS df MS F-Ratio F-Probability
Within Cells 8682.80 218 39.83
Regression (Impatience) 171.02 1 171.02 4.29 .039
Credit Card Ads 101.70 3 33.90 .85 .467
Time Orientation
HlOB. Present-minded people are significantly more likely to be

influenced by advertising that includes financing claims than
future-minded people.

Tables 4.45 and 4.46 show that time orientation had no signiﬁcant
inﬂuence on subjects’ reaction to ﬁnancing claims. There was no evidence to
indicate that present-minded people for example respond more positively to the
most inexpensive claims or to ﬁnancing claims in general. Time orientation did

not act as a covariate or interact with the treatment conditions in any way.

106

Table 4.45. MANOVA Results—Time Orientation and Automotive Dependent

 

 

Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS NS NA
Purchase Intent for the Cavalier 224 NS NS NA
Perceived Affordability of the Cavalier 224 NS NS NA
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Difference for Purchase Intent for Auto (General) NS NS NA

 

Table 4.46. MAN OVA Results—Time Orientation and Credit Card Dependent

 

 

 

Variables
Analysis of

Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Aﬂ‘ordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Diﬂ‘erence for Acquisition Intent for Credit Card NS NS NA
(General)
Compulsive Consumption
H10C. People with a greater tendency toward compulsive consumption

are significantly more likely to be influenced by advertising that
includes financing claims than consumers with a lower tendency
toward compulsive consumption.

Tables 4.47 and 4.51 show that compulsive consumption has little
inﬂuence on subjects’ reactions to ﬁnancing claims. There was very little
evidence to indicate that compulsive people, for example, respond more

positively to ﬁnancing claims. For only one variable in the auto ad experiments

107

and no variables in the credit card experiments, did compulsive consumption

interact with the treatment conditions in anyway.

Table 4.47. MANOVA Results-—Compulsive Consumption and Auto Dependent

 

 

Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier 224 NS NS NA
Purchase Intent for the Cavalier 224 NS NS NA
Perceived Aﬂ‘ordability of the Cavalier Z24 NS NS NA
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Difference for Purchase Intent for Auto (General) NS S NS

 

Pre-Post Difference in Purchase Intent for an Auto (General). Table
4.48 shows compulsive consumption did have a signiﬁcant interaction with the
auto treatment groups. In the most expensive treatment condition, those who
were more compulsive had more positive change in purchase intent. This was
the only condition where there was a signiﬁcant correlation between compulsive
consumption and pre-post difference in purchase intent for an auto. The test for

parallel slopes nearly showed signiﬁcant differences among the groups.

Table 4.48. Interaction—Compulsive Consumption and Pre-Post Difference in
Purchase Intent for Auto (General)

 

 

Source of Variance SS df MS F-Ratio F-Probability

Within + Residual 4157.22 210 19.80

Compulsiveness 15.47 1 15.47 .78 .378

Auto Ads 146.18 3 48.73 2.46 .064
ﬂto Ads by Compulsiveness 182.66 3 60.89 3.08 .029

 

108

Table 4.49. Parallel Slopes—Compulsive Consumption and Pre-Post Difference in
Purchase Intent for Auto (General)

 

 

Source of Variance SS 4f MS F-Ratio F—Probability
Within + Residual 4157.22 210 19.80

Compulsiveness within 183.83 4 45.96 2.32 .058
Auto Ads

Auto Ads 146.18 3 48.73 2.46 .064

 

Table 4.50. Correlation—Compulsive Consumption and Pre-Post Difference in
Purchase Intent for Auto (General) by Treatment Group

 

 

$183/mo. $246/mo. $317/mo. Control
Pearson Correlation -.083 .153 .513 -.153
Signiﬁcance (2-tailed) .626 .365 .002 .113
N 37 37 35 109

 

Table 4.51. MANOVA Results—Compulsive Consumption and Credit Card

 

 

Dependent Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Affordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Diﬂ‘erence for Acquisition Intent for Credit Card NS NS NA

(General)

 

Other Individual Difference Variables

H10D. Other individual difference variables (such as age, gender,
education, income, credit card attitudes/use, and ﬁnancial
knowledge) significantly affect responsiveness to financing
claims.

109

Age, more than many other individual difference variables, had
relationships with many (ﬁve) of the dependent variables according to the
MANOVA results (Tables 4.52 and 4.57). However, although it acted as a
covariate with four of the dependent variables, only two of these relationships
were corroborated by signiﬁcant correlations. In both cases, age was negatively
associated with attitudes toward the ad and product. In only one case there was a
signiﬁcant interaction between age and the dependent variable (affordability of
CapitalOne); age was positively associated with affordability in the middle level
and control conditions. So, while age was correlated with some of the dependent

variables, in most cases it did not inﬂuence responsiveness to ﬁnancing claims.

Table 4.52. MANOVA Results—Age and Auto Dependent Variables

 

 

Analysis of

Dependent Variable Covariance Interaction Parallel Slings
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 S NS NA
Purchase Intent for the Cavalier Z24 NS NS NA
Perceived Aﬂ‘ordability of the Cavalier Z24 NS S S
Posttest Purchase Intent for Auto (general) NS NS NA
Pre-Post Difference for Purchase Intent for Auto (general) NS NS NA

 

Attitude toward the Cavalier 224. The interaction effect was not
signiﬁcant. Table 4.53 shows that age and attitude toward the Cavalier had a
signiﬁcant relationship. However, when this was accounted for there still were

no signiﬁcant differences between the treatment groups in attitude toward the

110

Cavalier. Age was signiﬁcantly and negatively correlated with attitude toward

the Cavalier (r = -.178, p < .05).

Table 4.53. Analysis of Covariance—Age and Attitude toward the Cavalier 224

 

 

Source of Variance SS 4f MS F-Ratio F-Probability
Within Cells 2767.61 198 13.98

Regression (Age) 86.07 1 86.07 6.16 .014
Auto Ads 43.68 3 14.56 1.04 .375

 

Affordability of the Cavalier Z24. Tables 4.54 through 4.56 show age
had a signiﬁcant interaction with the treatment conditions. The relationship
between age and perceived affordability of the Cavalier was not the same in all
of the treatment conditions. Interestingly, there were fairly strong positive
correlations between age and perceived affordability of the Cavalier in two of the
treatment groups—the middle condition and the control. So in these groups, the
older the more affordable the car. However, there was no relationship in the
other two conditions. The parallel slopes test indicated that the slopes of the

groups were signiﬁcantly different.

Table 4.54. Interaction—Age and Affordability of the Cavalier Z24

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 5378.86 195 27.58

Age 230.60 1 230.60 8.36 .004
Auto Ads 256.18 3 85.39 3.10 .028
Auto Ads by Age 261.12 3 87.04 3.16 .026

 

lll

 

III)
(a

'h

atti;
had
Illls

no,

Table 4.55. Parallel Slopes——Age and Affordability of the Cavalier 224

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 5378.86 195 27.58

Age within Auto Ads 7.2.66 4 175.66 6.37 .000
Auto Ads 256.18 3 85.39 3.10 .028

 

Table 4.56. Correlation between Age and Affordability of the Cavalier ZZ4 by
Treatment Group

 

$183/mo. $246/mo. $317/mo. Control

Pearson Correlation -.O47 .539 .065 .350
Signiﬁcance (2-tailed) .790 .001 .716 .000
N 35 33 34 101

 

Table 4.57. MANOVA Results—Age and Credit Card Dependent Variables

 

 

Analysis of ,
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad S NS NA
Attitude toward the CapitalOne Visa S NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Affordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) 5 NS NA
Pre-Post Diﬂ’erence for Acquisition Intent for Credit NS NS NA

Card (General)

 

Attitude toward the CapitalOne Ad. The interaction between age and
attitude toward the CapitalOne ad was not signiﬁcant. As Table 4.58 shows, age
had a signiﬁcant relationship with attitude toward the CapitalOne ad, but when
this relationship was taken into account analysis of covariance showed no

differences in attitude toward the CapitalOne ad among the treatment groups.

112

10

b1
11c

Tl

501
“11
Reg
Cre

Indeed, age was signiﬁcantly correlated with attitude toward the CapitalOne ad

(the older, the more negative the attitude) (r = -.201, p < .05).

Table 4.58. Analysis of Covariance——Age and Attitude toward the CapitalOne Ad

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within Cells 6348.75 196 32.39

Regression (Age) 327.45 1 327.45 10.11 .002
Credit Card Ads 68.23 3 22.74 .70 .552

 

Attitude toward CapitalOne. The interaction between age and attitude
toward the CapitalOne approached but did not reach signiﬁcance. According to
Table 4.59, age had a signiﬁcant relationship with attitude toward CapitalOne,
but when this relationship was taken into account analysis of covariance showed
no signiﬁcant differences in attitude toward the ads among the treatment groups.
The correlation between age and attitude toward CapitalOne was not signiﬁcant

(r = -.145, p > .05).

Table 4.59. Analysis of Covariance—Age and Attitude toward CapitalOne

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within Cells 4361.90 196 22.03

Regression (Age) 171.53 1 171.53 7.79 .006
Credit Card Ads 106.74 3 35.58 1.62 .187

 

113

 

there
inten
signi

COI’IE

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an,

Gean

 

Acquisition Intent for Credit Cards (General). According to Table 4.60,
there was a signiﬁcant relationship between age and credit card acquisition
intent, but when that relationship was taken into account there were no
signiﬁcant differences between the groups with regard to acquisition intent. The

correlation between age and credit card acquisition intent was not signiﬁcant

(r = -.O75, p > .05).

Table 4.60. Analysis of Covariance—Age and Credit Card (General) Acquisition

 

 

Intent
Source of Variance SS df MS F-Ratio F-Probability
Within 8080.66 197 41.02
Age 164.22 1 164.22 4.00 .047
Credit Card Ads 137.53 3 45.84 1.12 .343

 

Gender

Gender had some impact on the response to ﬁnancing claims for the auto
ads, but none for the credit cards (Tables 4.61 and 4.68). For two dependent
variables (attitude toward and purchase intent for the Cavalier) women had more
positive responses in two of the treatment conditions—the least expensive and
the control—than men did. There was no relationship in the other conditions.
However, the parallel slopes test was only signiﬁcant for purchase intent.

Gender had some impact on response to ﬁnancing claims.

114

Table 4.61. MANOVA Results—Gender and Auto Dependent Variables

 

 

Analysis of

Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS S NS
Purchase Intent for the Cavalier 224 NS S S
Perceived Affordability of the Cavalier Z24 NS NS NA
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Difference for Purchase Intent for Auto NS NS NA

(General)

 

Attitude toward the Cavalier 224. As Tables 4.62 through 4.64 show,
gender had a signiﬁcant interaction with the treatment conditions. The
relationship between gender and attitude toward the Cavalier did not appear to be
the same in all of the treatment conditions. Interestingly, there were negative
correlations between gender and attitude toward the Cavalier in two of the
treatment groups—the least expensive condition and the control. So in these
groups, women had more positive attitudes toward the Cavalier. There were no
signiﬁcant correlations in the other two conditions. However, the parallel slopes
test only approached signiﬁcance indicating the differences were not quite strong

enough to show that the slopes in the groups were signiﬁcantly different.

Table 4.62. Interaction—Gender and Attitude toward the Cavalier 224

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 2783.32 208 13.38

Gender 23.31 1 23.31 1.74 .188
Auto Ads 81.78 3 27.26 2.04 .110
Auto Ads by Gender 125.85 3 41.95 3.13 .026

 

115

Table 4.63. Parallel Slopes—Gender and Attitude toward the Cavalier Z24

 

 

Source of Variance SS 4f MS F-Ratio F-Probability
Within + Residual 2783.32 208 13.38

Gender within Auto Ads 195.63 4 48.91 3.65 .007
Auto Ads 81.78 3 27.26 2.04 .110

 

Table 4.64. Correlation between Gender and Attitude toward the Cavalier 224 by
Treatment Group

 

$183/mo. $246/mo. $317/mo. Control

Pearson Correlation -.400 .088 .169 -.262
Signiﬁcance (2-tailed) .016 .608 .3 17 .006
N 36 36 37 107

 

Purchase Intent for the Cavalier Z24. As Tables 4.65 through 4.67 show,
gender had a signiﬁcant interaction with the treatment conditions. The
relationship between gender and purchase intent for the Cavalier did not appear
to be the same in all of the treatment conditions. Again, there were negative
correlations between gender and purchase intent for the Cavalier in two of the
treatment groups—the least expensive condition and the control. So in these
treatment conditions, women had higher purchase intent for the Cavalier Z24
than men did. The correlations between gender and purchase intent were not
signiﬁcant in the other two conditions. The parallel slopes test showed that the

differences were signiﬁcant.

116

Table 4.65. Interaction—Gender and Purchase Intent for the Cavalier 224

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 4027.91 207 19.46

Gender 116.56 1 116.59 5.99 .015
Auto Ads 189.07 3 63.02 3.24 .023
Auto Ads by Gender 162.66 3 54.22 2.79 .042

 

Table 4.66. Parallel Slopes—Gender and Purchase Intent for the Cavalier Z24

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 4027.91 207 19.46

Gender within Auto Ads 287.69 4 71.92 3.70 .006
Auto Ads 189.07 3 63.02 3.24 .023

 

Table 4.67. Correlation between Gender and Purchase Intent for the Cavalier 224

 

by Treatment Group
$183/mo. $246/mo. $317/mo. Control
Pearson Correlation -.447 -.076 .060 -.201
Signiﬁcance (2-tailed) .006 .660 .723 .039
N 36 36 37 106

 

Table 4.68. MANOVA Results—Gender and Credit Card Dependent Variables

 

 

Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Aﬂordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Difference for Acquisition Intent for Credit NS NS NA

Card (General)

 

117

Class Standing

Class standing had some impact on response to ﬁnancing claims, but the
impact appears to be limited to perceptions of affordability (Tables 4.69 and
4.73). Class standing had signiﬁcant correlations with affordability in two
conditions (middle and control) for the Cavalier and one condition (middle) for
the CapitalOne card. In these conditions, perceived affordability increased with
class standing. Signiﬁcant correlations did not occur in the other conditions.
The differences across the groups on affordability were signiﬁcant for the

Cavalier but not for CapitalOne.

Table 4.69. MANOVA Results—Class Standing and Auto Dependent Variables

 

 

Analysis of

Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS NS NA
Purchase Intent for the Cavalier Z24 NS NS NA
Perceived Aﬂ'ordability of the Cavalier 224 NS S S
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Difference for Purchase Intent for Auto NS NS NA

(General)

 

Affordability of the Cavalier 224. As Tables 4.70 through 4.72 show,
class standing had a signiﬁcant interaction with the treatment conditions. The
relationship between class standing and affordability of the Cavalier was not the
same in all of the treatment conditions. There were positive correlations between

class standing and affordability of the Cavalier in two of the treatment groups——

118

the middle condition and the control. So as might be expected, in these groups
the higher the class standing the more affordable the car. There were no
signiﬁcant correlations in the other two conditions. The parallel slopes test

showed that the slopes of the treatment conditions were not parallel.

Table 4.70. Interaction—Class Standing and Affordability of the Cavalier 224

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 5737.82 215 26.69

Class Standing 279.89 1 279.89 10.49 .001
Auto Ads 241.04 3 80.35 3.01 .031
Auto Ads by Class Standing 293.58 3 97.86 3.67 .013

 

Table 4.71. Parallel Slopes—Class Standing and Affordability of the Cavalier Z24

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 5737.82 215 26.69

Class Standing within Auto 840.11 4 210.03 7.87 .000

Ads ‘

Auto Ads 241.04 3 80.35 3.01 .031

 

Table 4.72. Correlation between Class Standing and Affordability of the Cavalier

 

 

224 by Treatment Group
$183/mo. $246/mo. $317/mo. Control
Pearson Correlation -.095 .587 .166 .339
Signiﬁcance (2-tailed) .578 .000 .495 .000
N 37 38 37 1 12

 

119

Table 4.73. MANOVA Results—Class Standing and Credit Card Dependent

 

 

Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad S NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Aﬁ‘ordability of the CapitalOne Visa NS S NS
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Diﬁ‘erence for Acquisition Intent for Credit NS NS NA

Card (General)

 

Attitude toward the CapitalOne Ad. There was no interaction between
class standing and attitude toward the CapitalOne ad. Table 4.74 shows that
class standing and attitude toward the CapitalOne ad had a signiﬁcant
relationship. However, when this was accounted for there still were no
signiﬁcant differences between the treatment groups in attitude toward the
CapitalOne ad. Class standing was signiﬁcantly and negatively correlated with

attitude toward the Cavalier (r = -.173, p < .05).

Table 4.74. Analysis of Covariance—Class Standing and Attitude toward the

 

 

CapitalOne Ad
Source of Variance SS df MS F-Ratio F-Probability
Within Cells 7177.33 216 32.95
Regression (Age) 188.69 1 188.69 5.73 .018
Credit Card Ads 72.66 3 24.22 .74 .532

 

Perceived Affordability of CapitalOne Visa. As Tables 4.75 through 4.77
show, there was a signiﬁcant interaction between class standing and affordability

of CapitalOne. There was a strong positive relationship between class standing

120

and perceived affordability of CapitolOne in the middle treatment condition, but
no signiﬁcant relationships in any of the other conditions. However, the test of

parallel slopes only approached signiﬁcance.

Table 4.75. Interaction—Class Standing and Perceived Affordability of CapitalOne

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 5371.93 215 24.99

Class Standing 50.53 1 50.53 2.02 .156
Credit Card Ads 151.99 3 50.66 2.03 .111
Credit Card Ads by Class 242.39 3 80.80 3.23 .023
Standing

 

Table 4.76. Parallel Slopes—-Class Standing and Perceived Affordability of

 

 

CapitalOne
Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 5371.93 215 24.99
Class Standing within 452.82 4 113.21 4.53 .002
Credit Card Ads
Credit Card Ads 151.99 3 50.66 2.03 .111

 

Table 4.77. Correlation between Class Standing and Perceived Affordability of

 

 

CapitalOne
$50/mo. $100/mo. $151/mo. Control
Pearson Correlation .106 .594 .179 .045
Signiﬁcance (2-tailed) .532 .000 .289 .636
N 37 38 37 111

 

121

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corr

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Parents’ Income

Parents’ income had relationships with four of the dependent variables
according to the MANOVA results (Tables 4.78 and 4.82). However, it acted as
a covariate in all cases. In three of the cases, parents’ income was positively
associated with perceptions of affordability and increases in purchase intent for
the products in general. In one case however, it was negatively associated with
pre-post change in purchase intent (for autos). So, while parents’ income was
correlated with some of the dependent variables, it did not inﬂuence

responsiveness to ﬁnancing claims.

Table 4.78. MANOVA Results—Parents’ Income and Auto Dependent Variables

 

 

Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS NS NA
Purchase Intent for the Cavalier Z24 NS NS NA
Perceived Aﬂordability of the Cavalier 224 S NS NA
Posttest Purchase Intent for Auto (General) S NS NA
Pre-Post Diﬁ‘erence for Purchase Intent for Auto S NS NA

(General)

 

Perceived Affordability of the Cavalier 224. There was no interaction
between parents’ income and perceived affordability of the Cavalier. Table 4.79
shows that parents’ income and affordability of the Cavalier had a signiﬁcant
relationship. However, when this was accounted for there still were no

signiﬁcant differences between the treatment groups in affordability of the

122

Cavalier. Parents’ income was signiﬁcantly and positively correlated with

perceived affordability ofthe Cavalier (r = .209, p < .01).

Table 4.79. Analysis of Covariance—Parents’ Income and Perceived Affordability

 

 

of the Cavalier Z24
Source of Variance SS 4f MS F-Ratio F-Probability
Within Cells 6008.94 201 29.90
Regression (Parents’ 267.23 1 267.23 8.94 .003
Income)
Auto Ads 44.23 3 14.74 .49 .687

 

Purchase Intent for Auto (General). As in the previous case, there was
no interaction between parents’ income and purchase intent for an auto in
general. Table 4.80 shows that parents’ income and purchase intent for an auto
had a signiﬁcant relationship. However, when this was accounted for, the F
value for purchase intent improved but still did not reach signiﬁcance. Parents’
income was signiﬁcantly and positively correlated with purchase intent for an

auto (general) (r = .210, p < .01).

Table 4.80. Analysis of Covariance—Parents’ Income and Purchase Intent for Auto

 

 

(General)
Source of Variance SS df MS F-Ratio F-Prohability
Within Cells 5482.37 201 27.28
Regression (Parents’ 264.23 1 264.23 9.69 .002
Income)
Auto Ads 152.26 3 50.75 1.86 .137

 

123

Pre-Post Difference in Purchase Intent for Auto (General). Once again,
there was no interaction between parents’ income and pre-post difference in
purchase intent for an auto in general. Table 4.81 shows that parents’ income
and pre—post purchase intent for an auto in general had a signiﬁcant relationship.
When this was accounted for, the differences between the treatment groups in
pre-post purchase intent for an auto approached signiﬁcance. Parents’ income
was signiﬁcantly and negatively correlated with pre-post purchase difference in
purchase intent for an auto (the greater the income, the more negative the change

in purchase intent (r = -.160, p < .05).

Table 4.81. Analysis of Covariance—Parents’ Income and Pre-Post Difference in
Purchase Intent for Auto (General)

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within Cells 3907.42 196 19.94

Regression 105.41 1 105.41 5.29 .023
(Parents’ Income)

Auto Ads 63.88 3 21.29 2.11 .080

 

Table 4.82. MANOVA Results—Parents’ Income and Credit Card Dependent

 

 

Variables
Analysis of'
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Affordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) S NS NA
Pre-Post Diﬂ‘erence for Acquisition Intent for Credit NS NS NA

Card (General)

 

124

 

Pa.

and
the,

Clair

Purchase Intent for Credit Card (General). As in the above auto cases,
there was no interaction between parents’ income and purchase intent for a credit
card in general. Table 4.83 shows that parents’ income and purchase intent for a
credit card had a signiﬁcant relationship. However, when this was accounted
for, there still were no signiﬁcant differences between the treatment groups for
purchase intent for a credit card. Parents’ income was signiﬁcantly and
positively correlated with purchase intent for a credit card in general (r = .156, p

< .05).

Table 4.83. Analysis of Covariance—Parents’ Income and Purchase Intent for

 

 

Credit Card (General)
Source of Variance SS 4" MS F-Ratio F—Probability
Within Cells 7869.57 200 39.35
Regression (Parents’ 240.35 1 240.35 p 6.11 .014
Income)
Credit Card Ads 126.30 3 42.10 1.07 .363

 

Parental Financial Support

Parental support had relationships with three of the dependent variables
according to the MANOVA results (Tables 4.84 and 4.87). In all cases, it acted
as a covariate. It was negatively correlated with pre-post auto purchase intent
and positively correlated with attitude toward the CapitalOne ad and purchase
intent for credit cards in general. Parental support was correlated with a few of
the dependent variables, but it did not influence responsiveness to ﬁnancing

claims.

125

 

 

 

 

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suppc
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p051 ‘

 

Table 4.84. MAN OVA Results—Parental Support and Auto Dependent Variables

 

 

Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad N S NS NA
Attitude toward the Cavalier 224 NS NS NA
Purchase Intent for the Cavalier 224 NS NS NA
Perceived Affordability of the Cavalier 224 NS NS NA
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Difference for Purchase Intent for Auto S NS NA

(General)

 

Pre-Post Difference in Purchase Intent for Auto (General). There was
no interaction between parental support and pre-post difference in purchase
intent for an auto in general. Table 4.85 shows that parental support and pre-
post difference in auto purchase intent had a signiﬁcant relationship. However,
when this was accounted for, there still were no signiﬁcant differences between
the treatment groups for pre-post difference in auto purchase intent. Parental
support was signiﬁcantly and negatively correlated with pre-post auto purchase
intent (the greater the percentage of parental support, the more negative the pre-

post difference in purchase intent (r = -.149, p < .05).

Table 4.85. Analysis of Covariance—Parental Support and Pre-Post Difference in
Purchase Intent for Auto (General)

 

 

Source of Variance SS 4f MS F-Ratio F-Probability
Within Cells 4185.13 206 20.32

Regression (Parental 87.80 1 87.80 4.32 .039
Support)

Auto Ads 35.49 3 11.83 .58 .627

 

126

 

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Table 4.86. MAN OVA Results—Parental Support and Credit Card Dependent

 

 

Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad S NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Affordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) S NS NA
Pre-Post Difference for Acquisition Intent for Credit NS NS NA

Card (General)

 

Attitude toward the CapitalOne Ad. There was no interaction between
parental support and attitude toward the CapitalOne ad. Table 4.87 shows that
parental support and attitude toward the CapitalOne ad had a signiﬁcant
relationship. However, when this was accounted for, there still were no
signiﬁcant differences between the treatment groups for attitude toward the
CapitalOne ad. Parental support was signiﬁcantly and positively correlated with
attitude toward the CapitalOne ad (the greater the percentage of parental support,

the more positive the attitude toward the CapitalOne ad) (r = .160, p < .05).

Table 4.87. Analysis of' Covariance—Parental Support and Attitude toward the

 

 

CapitalOne Ad
Source of' Variance SS df MS F-Ratio F-Probability
Within Cells 6946.19 210 33.08
Regression (Parental 192.21 1 192.21 5.81 .017
Support)
Credit Card Ads 82.96 3 27.65 .84 .475

 

127

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corr

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ECCO

 

Cap,
affg

Clai

 

Acquisition Intent for Credit Card (General). There was no interaction
between parental support and acquisition intent for a credit card in general.
Table 4.88 shows that parental support and acquisition intent for a credit card in
general had a signiﬁcant relationship. However, when this was accounted for,
there still were no signiﬁcant differences between the treatment groups for
acquisition intent for a credit card in general. There was a signiﬁcant positive
correlation between percentage of parental support and acquisition intent for a

credit card in general (r = .169, p < .05).

Table 4.88. Analysis of Covariance—Parental Support and Purchase Intent for a

 

 

Credit Card (General)
Source of Variance SS df MS F-Ratio F-Probahility
Within Cells 8170.70 211 38.72
Regression (Parental 292.03 1 292.03 p 7.54 .007
Support)
Credit Card Ads 210.79 3 70.26 1.81 .146

 

Personal Income

Personal income had relationships with two of the dependent variables
according to the MANOVA results (Tables 4.89 and 4.91). It acted as a
covariate in both cases. In those cases, personal income was positively
associated with perceptions of affordability for both the Cavalier and
CapitalOne. In these two cases personal income was correlated with
affordability perceptions, but it did not inﬂuence responsiveness to ﬁnancing

claims.

128

 

 

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Shox
relar
Sign
Cay;

Perc

 

 

 

 

 

Table 4.89. MANOVA Results—Personal Income and Auto Dependent Variables

 

 

Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS NS NA
Purchase Intent for the Cavalier Z24 NS NS NA
Perceived Affordability of the Cavalier Z24 S NS NA
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Difference for Purchase Intent for Auto NS NS NA

(General)

 

Perceived Affordability of the Cavalier 224. There was no interaction

between personal income and perceived affordability of the Cavalier. Table 4.90

shows that personal income and affordability of the Cavalier had a signiﬁcant

relationship. However, when this was accounted for, there still were no

signiﬁcant differences between the treatment groups in affordability of the

Cavalier. Personal income was signiﬁcantly and positively correlated with

perceived affordability of the Cavalier (r = .324, p < .001).

Table 4.90. Analysis of Covariance—Personal Income and Perceived Affordability

 

 

of the Cavalier Z24
Source of Variance SS df MS F-Ratio F-Probability
Within Cells 4933.9 198 24.92
Regression (Personal 121.45 1 121.45 4.87 .028
Income)
Auto Ads 135.98 3 45.33 1.82 .145

 

129

Table 4.91. MANOVA Results—Personal Income and Credit Card Dependent

 

 

Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Affordability of the CapitalOne Visa S NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Difference for Acquisition Intent for Credit NS NS NA

Card (General)

 

Perceived Affordability of CapitalOne. There was no interaction between
personal income and perceived affordability of CapitalOne. Table 4.92 shows
that personal income and affordability of the CapitalOne had a signiﬁcant
relationship. However, when this was accounted for, there still were no
signiﬁcant differences between the treatment groups in affordability of the
CapitalOne. Personal income was signiﬁcantly and positively correlated with

perceived affordability of CapitalOne (r = .169, p < .05).

Table 4.92. Analysis of Covariance—Personal Income and Perceived Affordability

 

 

of the CapitalOne
Source of Variance SS df MS F-Ratio F-Probability
Within Cells 5153.44 198 26.03
Regression (Personal 809.53 1 809.53 31.10 .000
Income)
Credit Card Ads 101.04 3 33.68 1.29 .278

 

130

 

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mc
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a ff):

 

the.

Credit Card Attitude

Credit card attitude inﬂuenced response to ﬁnancing claims for some
variables. In three cases, credit card attitude was positively correlated with the
dependent variables (perceived affordability of the Cavalier, attitude toward the
CapitalOne ad, and attitude toward CapitalOne) in the middle condition but had
no signiﬁcant correlations in the other conditions. It should be noted that
random assignment did not result in equalized groups for this variable. The
mean for credit card attitude was highest in the middle condition. Attitudes were
most positive in this condition. In one other case, credit card attitude acted as a
covariate. In some cases, credit card attitude impacted response to ﬁnancing

claims, but these should be interpreted with caution.

Table 4.93. MANOVA Results—Credit Card Attitude and Auto Dependent

 

 

Variables
Analysis of

Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS NS NA
Purchase Intent for the Cavalier 224 NS NS NA
Perceived Affordability of the Cavalier Z24 NS S S
Posttest Purchase Intent for Auto (general) NS NS NA
Pre-Post Diﬁ'erence for Purchase Intent for Auto NS NS NA

(general)

 

Perceived Affordability of the Cavalier 224. Tables 4.94 through 4.97
show there was a signiﬁcant interaction between credit card attitude and
affordability of the Cavalier in the four groups. The parallel slopes test showed

there were signiﬁcant differences between the treatment groups. Speciﬁcally,

131

there was a strong positive relationship between credit card attitude and
affordability of the Cavalier Z24 in the middle treatment condition, but no

signiﬁcant relationships in any of the other conditions.

Table 4.94. Interaction—Credit Card Attitude and Perceived Affordability of the

 

 

Cavalier Z24
Source of Variance SS a MS F—Ratio F-Probability
Within + Residual 6070.16 207 29.32
Credit Card Attitude 146.32 1 146.32 4.99 .027
Auto Ads 367.30 3 122.43 4.18 .007
Auto Ads by Credit Card 314.02 3 104.67 3.57 .015

Attitude

 

Table 4.95. Parallel Slopes—Credit Card Attitude and Perceived Affordability of

 

 

the Cavalier 224
Source of Variance SS df MS F-Ratio F—Probability
Within +Residual 6070.16 207 29.32
Credit Card Attitude within 339.86 4 84.96 2.90 .023
Auto Ads
Auto Ads 367.30 3 122.43 4.18 .007

 

Table 4.96. Correlation between Credit Card Attitude and Perceived Affordability
of the Cavalier 224 by Treatment Group

 

$183/mo. $246/mo. $317/mo. Control

 

Pearson Correlation .143 .457 .143 -.115
Signiﬁcance (2-tailed) .407 .004 .427 .234
N 36 37 33 112

 

132

 

 

 

the
101k
on]
Strc

Ca;

Tel;

 

 

Table 4.97. MANOVA Results—Credit Card Attitude and Credit Card Dependent

 

 

Variables
Analysis of

Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS S NS
Attitude toward the CapitalOne Visa NS S S
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Affordability of the CapitalOne Visa S NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Difference for Acquisition Intent for Credit NS NS NA

Card (General)

 

Attitude toward the CapitalOne Ad. Tables 4.98 through 4.100 show
there was a signiﬁcant interaction between credit card attitude and attitude
toward the CapitalOne ad in the four groups. However, the parallel slopes test
only approached signiﬁcance. The conditions were not parallel. There was a
strong positive relationship between credit card attitude and attitude toward the
CapitalOne Visa ad in the middle treatment condition, but no signiﬁcant

relationships in any of the other conditions.

Table 4.98. Interaction—Credit Card Attitude and Attitude toward the CapitalOne

 

 

Visa Ad
Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 6795.56 205 33.15
Credit Card Attitude 46.65 1 46.65 1.41 .237
Credit Card Ads 214.07 3 71.36 2.15 .095
Credit Card Ads by Credit 287.96 3 95.99 2.90 .036

Card Attitude

 

133

 

 

 

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(Ta;
“’21:

t0\k

 

rel;

 

Table 4.99. Parallel Slopes—Credit Card Attitude and Attitude toward the

 

 

CapitalOne Ad
Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 6795.56 205 33.15
Credit Card Attitude within 343.85 4 85.96 2.59 .038
Credit Card Ads
Credit Card Ads 214.07 3 71.36 2.15 .095

 

Table 4.100. Correlation between Credit Card Attitude and Attitude toward the

 

 

CapitalOne Ad
$50/mo. $100/mo. SlSl/mo. Control
Pearson Correlation .063 .435 -.240 .079
Signiﬁcance (2-tailed) .721 .008 .146 .421
N 34 36 38 105

 

Attitude toward CapitalOne. Tables 4.101 thrOugh 4.103 show there was
a signiﬁcant interaction between credit card attitude and attitude toward
CapitalOne in the four groups. The conditions were not parallel. Again, there
was a strong positive relationship between credit card attitude and attitude
toward CapitalOne Visa in the middle treatment condition, but no signiﬁcant

relationships in any of the other conditions.

Table 4.101. Interaction—Credit Card Attitude and Attitude toward CapitalOne

 

 

Visa
Source of Variance SS df MS F-Ratio F—Probability
Within + Residual 4543.66 207 21.95
Credit Card Attitude 97.28 1 97.28 4.43 .036
Credit Card Ads 261.09 3 87.03 3.96 .009
Credit Card Ads by Credit 225.79 3 75.26 3.43 .018

Card Attitude

 

134

Table 4.102. Parallel Slopes—Credit Card Attitude and Attitude toward

 

 

CapitalOne Visa
Source of Variance SS (1] MS F-Ratio F-Probability
Within + Residual 4543 .66 207 21.95
Credit Card Attitude within 282.36 4 70.59 3.22 .014
Credit Card Ads
Credit Card Ads 261.09 3 87.03 3.96 .009

 

Table 4.103. Correlation between Credit Card Attitude and Attitude toward
CapitalOne Visa by Treatment Group

 

$50/mo. $100/mo. 151/mo. Control

 

Pearson Correlation .095 .489 -.057 .009
Signiﬁcance (2-tailed) .589 .002 .734 .926
N 37 37 38 106

 

Perceived Affordability of CapitalOne. The interaction between credit
card attitude and perceived affordability of the CapitalOne Card was not
signiﬁcant. According to Table 4.104, credit card attitude had a signiﬁcant
relationship with affordability of the CapitalOne card but when this relationship
was taken into account analysis of covariance showed no signiﬁcant differences
in perceived affordability of the CapitalOne card across the treatment groups.
There was a signiﬁcant positive correlation between credit card attitude and

perceived affordability of CapitalOne (r = .204, p < .01)

135

Table 4.104. Analysis of Covariance—Credit Card Attitude and Affordability of the

 

 

 

CapitalOne Card
Source of' Variance SS df MS F-Ratio F-Probability
Within Cells 5308.90 210 25.28
Regression (Credit Card 225.31 1 225.31 8.91 .003
Attitude)
Credit Card Ads 127.40 3 42.47 1.68 .172
Credit Card Control

As Tables 4.105 and 4.107 show, credit card control had signiﬁcant
relationships with only two variables. In both cases, credit card control acted as
a covariate. People who felt credit cards added to their sense of ﬁnancial control
gave more favorable ratings to the Cavalier ad and had higher affordability
ratings for CapitalOne. So, in a few cases credit card control acted as a covariate,
but in general it had little effect and did not impact the response to ﬁnancing

claims.

Table 4.105. MANOVA Results—Credit Card Control and Auto Dependent

 

 

Variable
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad S NS NA
Attitude toward the Cavalier 224 NS NS NA
Purchase Intent for the Cavalier Z24 NS NS NA
Perceived Aﬁ'ordability of the Cavalier Z24 NS NS NS
Posttest Purchase Intent for Auto (General) NS NS NA
Pre—Post Difference for Purchase Intent for Auto NS NS NA

(General)

 

136

Attitude toward the Cavalier Ad. The interaction between credit card
control and attitude toward the Cavalier ad was not signiﬁcant. According to
Table 4.106, credit card control had a signiﬁcant relationship with attitude
toward the Cavalier ad, but when this relationship was taken into account
analysis of covariance showed no signiﬁcant differences in attitude toward the
Cavalier ad across the treatment groups. Credit card control had a signiﬁcant,
negative correlation with attitude toward the Cavalier ad (those who feel credit
cards add to their sense of ﬁnancial control had more negative attitudes toward

the Cavalier ad) (r = -. 143, p < .05).

Table 4.106. Analysis of Covariance—Credit Card Control and Attitude toward the

 

 

Cavalier 224 Ad
Source of Variance SS df MS F-Ratio F-Probability
Within Cells 5704.99 209 27.30 '
Regression (Credit Card 126.31 1 126.31 4.63 .033
Control)
Auto Ads 29.93 3 9.98 1.37 .778

 

Table 4.107. MANOVA Results—Credit Card Control and Credit Card Dependent

 

 

Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Affordability of the CapitalOne Visa S NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Difference for Acquisition Intent for Credit NS NS NA

Card (General)

 

137

Perceived Affordability of the CapitalOne Visa Card. The interaction
between credit card control and affordability of the CapitalOne card was not
signiﬁcant. According to Table 4.108, credit card control had a signiﬁcant
relationship with perceived affordability of the CapitalOne card, but when this
relationship was taken into account analysis of covariance showed no signiﬁcant
differences in affordability of the CapitalOne card across the treatment groups.
Credit card control had a signiﬁcant negative correlation with affordability of
CapitalOne (the more people feel credit cards add to their ﬁnancial control, the

lower their perceived affordability ratings for CapitalOne) (r = -.186, p < .01).

Table 4.108. Analysis of Covariance—Credit Card Control and Affordability of the

 

 

 

CapitalOne Card
Source of Variance SS 4f MS F-Ratio F-Probability
Within Cells 5336.09 209 25.58 '
Regression (Credit Card 158.50 1 158.50 6.21 .013
Control)
Credit Card Ads 82.65 3 27.55 1.08 .359
Credit Card Spending

Overall, credit card spending had little impact on response to ﬁnancing
claims (Tables 4.109 and 4.110). In one case, it had a negative correlation with
attitude toward CapitalOne in the control group, but not in the other treatment
groups. So, in the control group subjects who felt that they do not spend more
when they use credit cards than they otherwise would also tended to have more
positive attitudes toward CapitalOne Visa. In all other cases, credit card

spending had no impact on response to ﬁnancing claims. The parallel slopes test

138

approached signiﬁcance. The impact of credit card spending on ﬁnancing claims

was extremely limited.

Table 4.109. MANOVA Results—Credit Card Spending and Auto Dependent

 

 

Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS NS NA
Purchase Intent for the Cavalier Z24 NS NS NA
Perceived Affordability of the Cavalier 224 NS NS NA
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Difference for Purchase Intent for Auto NS NS NA

(General)

 

Table 4.110. MANOVA Results—Credit Card Spending and Credit Card

 

 

Dependent Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS S NS
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Affordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Diﬂ‘erence for Acquisition Intent for Credit NS NS NA

Card (General)

 

Attitude toward CapitalOne Visa. Tables 4.111 to 4.113 show there was a
signiﬁcant interaction between credit card spending and attitude toward
CapitalOne in the four groups. The parallel slopes test approached signiﬁcance
showing there may not have been a parallel relationship between credit card

spending and attitude toward CapitalOne in each of the conditions. The only

139

signiﬁcant correlation was a negative correlation between credit card spending
and attitude toward CapitalOne in the control group. In the control group,
subjects who felt that they do not spend more when using credit cards than they

otherwise would tended to have more negative attitudes toward CapitalOne Visa.

Table 4.1 11. Interaction—Credit Card Spending and Attitude toward the

 

 

CapitalOne Visa
Source of Variance SS df MS F-Ratio F—Probability
Within + Residual 4600.48 206 22.33
Credit Card Spending .00 1 .00 .00 .993
Credit Card Ads 155.86 3 51.95 2.33 .076
Credit Card Ads by Credit 183.19 3 61.06 2.73 .045

Card Spending

 

Table 4.112. Parallel Slopes—Credit Card Spending and Attitude toward the

 

 

CapitalOne Visa
Source of Variance SS 4" MS F-Ratio F-Probability
Within + Residual 4600.48 206 22.33
Credit Card Spending 223.73 4 55.93 2.50 .043
within Credit Card Ads
Credit Card Ads 155.86 3 51.95 2.33 .076

 

Table 4.113. Correlation between Credit Card Spending and Attitude toward
CapitalOne by Treatment Group

 

$183/mo. $246/mo. $317/mo. Control

Pearson Correlation .176 -. 164 .227 -.249
Signiﬁcance (2-tai1ed) .305 .338 .184 .010
N 36 36 36 106

 

140

Credit Card Usage

Overall, credit card usage (using credit cards such that the credit limit is

not exhausted) had no impact on response to ﬁnancing claims and it was not a

covariate in any case.

Table 4.114. MAN OVA Results—Credit Card Usage and Auto Dependent

 

 

Variables
Analysis of
Dependent Variable Covariance Interaction Parallel 81m
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS NS NA
Purchase Intent for the Cavalier Z24 NS NS NA
Perceived Affordability of the Cavalier Z24 NS NS NA
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Difference for Purchase Intent for Auto NS NS NA

(General)

 

Table 4.115. MAN OVA Results—Credit Card Usage and Credit Card Dependent

 

 

Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Affordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Diﬁ‘erence for Acquisition Intent for Credit NS NS NA

Card (General)

 

141

Number of Credit Cards

Overall as shown in Tables 4.116 and 4.119, number of credit cards had
little impact on response to ﬁnancing claims. In two cases, it acted as a
covariate. Number of credit cards was positively correlated with perceived
affordability of the Cavalier and purchase intent for autos in general. Number of
credit cards did not affect the credit card dependent variables in any way and

overall did not impact response to ﬁnancing claims.

Table 4.116. MANOVA Results—N umber of Credit Cards and Auto Dependent

 

 

Variables
Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier 224 NS NS NA
Purchase Intent for the Cavalier 224 NS NS NA
Perceived Affordability of the Cavalier Z24 S NS NA
Posttest Purchase Intent for Auto (General) S . NS NA
Pre-Post Diﬂ‘erence for Purchase Intent for Auto (General) NS NS NA

 

Perceived Affordability of the Cavalier 224. The interaction between
number of credit cards and affordability of the Cavalier Z24 was not signiﬁcant.
According to Table 4.117, number of credit cards had a signiﬁcant relationship
with affordability of the Cavalier, but when this relationship was taken into
account analysis of covariance showed no differences in affordability of the
Cavalier across the treatment groups. There was a signiﬁcant positive
relationship between the number of credit cards and the perceived affordability
of the Cavalier (the more credit cards, the greater the perceived affordability)

(r = .267,p < .001).

142

Table 4.117. Analysis of Covariance—Number of Credit Cards and Affordability of
the Cavalier

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within Cells 6139.75 218 28.16

Regression (Number of 461.55 1 461.55 16.39 .000
Credit Cards)

Credit Card Ads 68.76 3 22.92 .81 .487

 

Purchase Intent for Auto (General). The interaction between number of
credit cards and purchase intent for an auto in general was not signiﬁcant. As
indicated in Table 4.118, number of credit cards had a signiﬁcant relationship
with purchase intent for auto in general, but when this relationship was taken
into account analysis of covariance showed no signiﬁcant differences in purchase
intent for auto in general across the treatment groups. There was a signiﬁcant
positive relationship between the number of credit cards and purchase intent for
auto in general (the more credit cards, the greater the purchase intent) (r = .132,

p < .05).

Table 4.118. Analysis of Covariance—N umber of Credit Cards and Purchase Intent

 

 

for Auto (General)
Source of Variance SS 4f MS F-Ratio F-Probability
Within Cells 6156.96 218 28.24
Regression (Number of 11 1.98 1 111.98 3.96 .048
Credit Cards)
Auto Ads 117.57 3 39.19 1.39 .247

 

143

Table 4.119. MANOVA Results—N umber of Credit Cards and Credit Card

 

 

 

Dependent Variables
Analysis of

Dependent Variable Covariance Interaction Parallel Slgpes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Aﬂ‘ordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Difference for Acquisition Intent for Credit NS NS NA
Card (General)

Borrowing

Overall as shown in Tables 4.120 and 4.126, borrowing approval had little
impact on the inﬂuence of ﬁnancing claims on the dependent variables.
Borrowing approval had no signiﬁcant effect on the credit card dependent
variables at all. It did act as a covariate for two of the auto dependent variables.
Additionally, it had an interaction with intent to purchase a Cavalier (signiﬁcant
positive correlations between borrowing and purchase intent for the least and
most expensive conditions only), but when that interaction was accounted for
there still were no signiﬁcant differences between the treatment groups on
purchase intent for the Cavalier. Overall, borrowing approval had little impact

on how people responded to ﬁnancing claims.

144

Table 4.120. MAN OVA Results——Borrowing and Auto Dependent Variables

 

Analysis of Interaction Parallel Slopes

 

Dependent Variable Covariance

Attitude toward the Ad NS NS NA
Attitude toward the Cavalier 224 NS NS NA
Purchase Intent for the Cavalier 224 NS NS NA
Perceived Aﬁordability of the Cavalier Z24 NS S NS
Posttest Purchase Intent for Auto (General) S NS NA
Pre-Post Difference for Purchase Intent for Auto S NS NA
(General)

 

Purchase Intent for the Cavalier Z24. Tables 4.121 through 4.123 show
borrowing approval had a signiﬁcant interaction with the treatment conditions.
The relationship between borrowing approval and purchase intent for the
Cavalier was not the same in all of the treatment conditions. There were
signiﬁcant positive correlations between borrowing approval and purchase intent
for the Cavalier in two of the treatment groups—the least and most expensive
conditions, but no signiﬁcant correlations for the other two conditions. In these
groups, the higher the approval of borrowing the higher the purchase intent.
However, the parallel slopes test did not conﬁrm that the slopes of the treatment

conditions were not parallel.

Table 4.121. Interaction—Borrowing and Purchase Intent for the Cavalier 224

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 3908.85 212 18.44

Borrowing 264.15 1 264.15 14.33 .000
Auto Ads 93.71 3 31.24 1.69 .169
Auto Ads by Borrowing 223.96 3 74.65 4.05 .008

 

145

Table 4.122. Parallel Slopes—Borrowing and Purchase Intent for the Cavalier Z24

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 3908.85 212 18.44

Borrowing within Auto Ads 337.93 4 84.48 4.58 .001
Auto Ads 93.71 3 31.24 1.69 .169

 

Table 4.123. Correlation between Borrowing and Purchase Intent for the Cavalier

 

 

224 by Treatment Group
$183/mo. $246/mo. $317/mo. Control Ii
Borrowing and Purchase .398 .203 .352 -.031 3
Intent for the Cavalier 224 J
Signiﬁcance (2-tailed) .016 .222 .003 .746
N 36 38 37 109

 

Purchase Intent for Auto (General). The interaction between borrowing
and purchase intent for auto (general) was not signiﬁcant. Table 4.124 shows
borrowing approval had a signiﬁcant relationship with purchase intent for auto
(general), but when this relationship was taken into account analysis of
covariance showed no differences in affordability of the Cavalier across the
treatment groups. There was a signiﬁcant positive relationship between
borrowing approval and purchase intent for auto (general) (the greater the

approval for borrowing, the greater the purchase intent) (r = .198, p < .05).

Table 4.124. Analysis of Covariance—Borrowing and Purchase Intent for Auto

 

 

(General)
Source of Variance SS df MS F-Ratio F-Probability
Within Cells 5996.85 216 27.76
Regression (Borrowing) 247.79 1 247.79 8.93 .003
Auto Ads 94.35 3 31.45 1.13 .337

 

146

Pre-Post Difference in Purchase Intent for Auto (General). The
interaction between borrowing and pre-post difference in purchase intent for an
auto in general was not signiﬁcant. As shown in Table 4.125, borrowing
approval had a signiﬁcant relationship with pre-post difference in purchase
intent for auto in general. When this relationship was taken into account,
analysis of covariance showed no differences in pre-post difference in purchase
intent for auto in general across the treatment groups. Additionally, the
correlation between borrowing approval and pre-post difference in purchase

intent for auto in general was not signiﬁcant (r = .071, p > .05).

Table 4.125. Analysis of Covariance—Borrowing and Pre-Post Difference in
Purchase Intent for Auto (General)

 

 

Source of Variance SS 4" MS F-Ratio F-Probability
Within Cells 129.14 211 .61

Regression (Borrowing) 3.17 1 3.17 5.18 .024
Auto Ads 1.44 3 .48 .79 .503

 

Table 4.126. MANOVA Results——Borrowing and Credit Card Dependent Variables

 

 

Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Aﬂordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Difference for Acquisition Intent for Credit Card NS NS NA

(General)

147

Auto Related Financial Knowledge

Overall as shown in Table 4.127, auto-related ﬁnancial knowledge as
measured by the number of dollars off in auto interest estimate had no impact on

response to ﬁnancing claims and it was not a covariate in any case.

Table 4.127. MAN OVA Results—Dollars Off in Auto Loan Payment Estimate and
Auto Dependent Variables

 

 

Analysis of
Dgpendent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS NS NA
Purchase Intent for the Cavalier 224 NS NS NA
Perceived Aﬁordability of the Cavalier 224 NS NS NA
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Diﬂ‘erence for Purchase Intent for Auto NS NS NA

(General)

 

Overall, auto-related ﬁnancial knowledge as measured by the number of
years off in time of auto interest estimate had little impact on response to
ﬁnancing claims. As shown in Tables 1.128 through 1.131, it affected only
perceived affordability of the Cavalier. For the least and most expensive
conditions, the number of dollars off in auto interest estimate was negatively
correlated with affordability of the Cavalier. Overall, its impact on ﬁnancing

claims was limited and it was not a covariate in any case.

148

Table 4.128. MAN OVA Results—-Years Off in Time of Auto Loan Payment
Estimate and Auto Dependent Variables

 

 

Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the Cavalier Z24 NS NS NA
Purchase Intent for the Cavalier Z24 NS NS NA
Perceived Affordability of the Cavalier Z24 NS S NS
Posttest Purchase Intent for Auto (General) NS NS NA
Pre-Post Difference for Purchase Intent for Auto (General) NS NS NA

 

Perceived Affordability of the Cavalier Z24. As Tables 4.129 through
4.131 show, there was a signiﬁcant interaction between years off in time of auto
loan payment estimate and affordability of the Cavalier in the four groups.
Speciﬁcally, there was a negative correlation between years off in time of auto
loan payment estimate and affordability of the Cavalier 224 in the least and most
expensive treatment condition [the more the estimate is off (over), the lower the
affordability], but no signiﬁcant relationships in the other conditions. The
parallel slopes test did not conﬁrm that there was a signiﬁcant difference

between the treatment groups.

Table 4.129. Interaction—Years Off in Time of Auto Loan Payment Estimate and
Affordability of the Cavalier Z24

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 5069.09 169 29.99

Years 011‘ in Time of Auto 260.92 1 260.92 8.70 .004
Loan Payment Estimate

Auto Ads 83.74 3 27.91 .93 .427
Auto Ads by Years Off in 251.82 3 83.94 2.80 .042
Time of Auto Loan

Payment Estimate

 

149

Table 4.130. Parallel Slopes—Years Off in Time of Auto Loan Payment Estimate
and Affordability of the Cavalier Z24

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within +Residual 5069.09 169 29.99

Years Offin Time of Auto 308.58 4 77.15 2.57 .040
Loan Payment Estimate

within Auto Ads

Auto Ads 83.74 3 27.91 .93 .427

 

Table 4.131. Correlation between Years Off in Time of Auto Loan Payment
Estimate and Perceived Affordability of the Cavalier 224 by Treatment Group

 

$183/mo. $246/mo. $317/mo. Control

Pearson Correlation -.470 -.147 -.420 -.025
Signiﬁcance (2-tailed) .010 .464 .037 .811
N 29 27 25 96

 

Credit Card Related Financial Knowledge

Overall as Table 4.132 shows, credit card related ﬁnancial knowledge as
measured by the number of dollars off in credit card interest estimate had no

impact on response to ﬁnancing claims and it was not a covariate in any case.

Table 4.132. MANOVA Results—Dollars Off in Credit Card Interest Estimate and
Credit Card Dependent Variables

 

 

Analysis of
Dependent Variable Covariance Interaction Parallel Slopes
Attitude toward the Ad NS NS NA
Attitude toward the CapitalOne Visa NS NS NA
Acquisition Intent for the CapitalOne Visa NS NS NA
Perceived Affordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Difference for Acquisition Intent for Credit NS NS NA

Card (General)

 

150

Overall as Table 4.133 shows, credit card related ﬁnancial knowledge as
measured by the number of years off in time of credit card interest estimate had
some impact on response to ﬁnancing claims. In two cases, the number of years
offin time of credit card interest estimate was signiﬁcantly and negatively
correlated with attitude toward the CapitalOne ad and attitude toward CapitalOne
in the least expensive treatment conditions. It was also a covariate in acquisition

intent of CapitalOne.

Table 4.133. MANOVA Results—Years Off in Time of Credit Card Payment
Estimate and Credit Card Dependent Variables

 

Analysis of Interaction Parallel Slopes

 

Dependent Variable Covariance

Attitude toward the Ad NS S S
Attitude toward the CapitalOne Visa NS S NS
Acquisition Intent for the CapitalOne Visa S NS NA
Perceived Affordability of the CapitalOne Visa NS NS NA
Posttest Acquisition Intent for Credit Card (General) NS NS NA
Pre-Post Diﬂ‘erence for Acquisition Intent for Credit NS NS NA
Card (General)

 

Attitude toward the CapitalOne Ad. As Tables 4.134 through 4.137 show,
there was a signiﬁcant interaction between years off in time of credit card
payment estimate and attitude toward the CapitalOne ad in the four groups.
Speciﬁcally, there were negative correlations between years off in‘time of credit
card payment estimate and attitude toward the CapitalOne ad in all the
conditions, but the correlation was only signiﬁcant for the least expensive
treatment condition [the more the estimate was off (over), the more negative the
attitude]. The parallel slopes test conﬁrmed that there was a signiﬁcant

difference between the treatment groups.

151

Table 4.134. Interaction—Years Off in Time of Credit Card Payment Estimate and
Attitude toward the CapitalOne Ad

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 4128.78 120 34.41

Years OE in Time of Credit 462.74 1 462.74 13.45 .000
Card Payment Estimate

Credit Card Ads 297.68 3 99.23 2.88 .039
Credit Card by Time of 333.44 3 111.15 3.23 .025
Credit Card Payment Est.

 

Table 4.135. Parallel Slopes—Years Off in Time of Credit Card Payment Estimate
and Attitude toward the CapitalOne Ad

 

 

Source of Variance SS df MS F-Ratio F-Probability
Within + Residual 4128.78 120 34.41

Years Oﬁ‘ in Time of Credit 536.11 4 134.03 3.90 .005
Card Payment Estimate

within Credit Card Ads

Credit Card Ads 297.68 3 99.23 2.88 .039

 

Table 4.136. Correlation between Years Off in Time of Credit Card Payment
Estimate and Attitude toward the CapitalOne Ad by Treatment Group

 

$50/mo. $100/mo. $15 l/mo. Control

 

Pearson Correlation -.512 -.364 -.329 -.004
Signiﬁcance (2-tailed) .018 .074 .125 .978
N 21 25 23 59

 

Attitude toward CapitalOne. As Tables 4.137 through 4.138 show, there
was a signiﬁcant interaction between years off in time of credit card payment

estimate and attitude toward CapitalOne in the four groups. Speciﬁcally, there

152

were signiﬁcant negative correlations between years off in time of credit card
payment estimate and attitude toward the CapitalOne in the least expensive
condition and a near signiﬁcant negative correlation in the middle treatment
condition [the more the estimate was off (over) the more negative the attitude
toward CapitalOne], but no signiﬁcant relationships in the other conditions. The
parallel slopes test does not conﬁrm that there was a signiﬁcant difference

between the treatment groups.

Table 4.137. Interaction—Years Off in Time of Credit Card Payment Estimate and
Attitude toward CapitalOne

 

 

Source of Variance SS df MS F-Ratio F—Probability
Within +Residual 2809.94 122 23.03

Years Off in Time of Credit 278.22 1 278.22 12.08 .001
Card Payment Estimate

Credit Card Ads 138.65 3 46.22 . 2.01 .117
Credit Card by Time of 240.15 3 80.05 3.48 .018
Credit Card Payment Est.

 

Table 4.138. Parallel Slopes—Years Off in Time of Credit Card Payment Estimate
and Attitude toward CapitalOne

 

 

Source of Variance SS :1] MS F-Ratio F-Probability
Within +Residual 2809.94 122 23.03

Years Off in Time of Credit 368.60 4 92.15 4.00 .004
Card Payment Estimate

within Credit Card Ads

Credit Card Ads 138.65 3 46.22 2.01 .117

 

153

Table 4.139. Correlation between Years Off in Time of Credit Card Payment
Estimate and Attitude toward CapitalOne

 

$50/mo. $100/mo. SlSlmo. Control

 

Pearson Correlation -.572 -.382 -. 176 .010
Signiﬁcance (2-tailed) .005 .060 .421 .939
N 22 25 23 60

 

Acquisition Intent for CapitalOne Visa. The interaction between years
off in time of credit card payment estimate and acquisition intent for the Cavalier
was not signiﬁcant. According to Table 4.140, years off in time of credit card
payment estimate had a signiﬁcant relationship with acquisition intent for
CapitalOne, but when this relationship was taken into account analysis of
covariance showed no signiﬁcant differences in acquisition intent for the
CapitalOne across the treatment groups. There was a signiﬁcant negative
relationship between years off in time of credit card payment estimate and
acquisition intent for the CapitalOne (the more years over in the estimate, the

lower the purchase intent for CapitalOne) (r = -.305, p < .01).

Table 4.140. Analysis of Covariance—Years Off in Time of Credit Card Payment
Estimate and Acquisition Intent for CapitalOne

 

 

Source of Variance SS 4] MS F-Ratio F-Probability
Within Cells 2884.78 125 23.08

Regression (Years Oﬁ" in 92.34 1 92.34 4.00 .048
Time of Credit Card

Payment Estimate)

Credit Card Ads 75.78 3 25.26 1.09 .121

 

154

Overall Impact of Individual Difference Variables

Overall, there was very little support for Hypotheses 10 that some people
(because of individual difference factors) are more responsive to ﬁnancing
claims or certain levels of ﬁnancing claims. In 23 of 204 MANOVAs, individual
difference variables served as covariates. Parents’ income and percentage of
parental support accounted for the most covariate relationships with four and

three respectively.

Interaction, showing differences in treatment levels by individual
difference variables, were very few in number. In only 15 cases out of 204
MANOVAs, did an individual difference variable have a signiﬁcant interaction
with the treatment conditions. Furthermore, in only six of those cases did the
parallel slopes test conﬁrm that the relationship in the c0nditions was not the

same.

When individual difference variables did interact with the treatment
conditions, they were most likely to be for the dependent variables of
affordability followed by attitude toward the ad and product. Credit card attitude
had the most impact of any of the individual difference variables on response to
ﬁnancing claims with two of the three signiﬁcant interactions also having
signiﬁcant results in the parallel slopes test. For the middle condition only
credit card attitude was positively correlated with affordability of the Cavalier
and attitude toward the CapitalOne ad and CapitalOne itself. However, random
assignment did not equalize credit card attitude across the groups (the middle

condition had signiﬁcantly more positive attitudes). The inﬂuence of credit card

155

attitude should be considered carefully in light of this difference. Gender, class
standing, age, credit card spending, borrowing approval and years off in the
credit card estimate all also had some interaction effects. The interaction effects
overall were so few in number that they showed no convincing evidence of

individual difference variables affecting reaction to ﬁnancing claims.

156

CHAPTER 5
DISCUSSION

Summary of Results

This study was conducted to determine whether advertising ﬁnancing
claims inﬂuence consumers’ impatience to purchase and perceptions of
affordability as Hoch and Loewenstein’s work on time-inconsistent decisions,
Mowen and Mowen’s time and outcome valuation model, Notani’s work on
affordability perceptions and the common use of the claims suggest. The
preceding results section is comprised of a rather expansive analysis addressing
ten main hypotheses regarding the inﬂuence of these ﬁnancing claims on
attitudes, affordability perceptions, and purchase-related behaviors. In order to
bring all of this together, this discussion of the conclusions will be organized

around the following main questions the ten hypotheses addressed:

1. What inﬂuence does advertising of ﬁnancing claims for a speciﬁc product have on
purchase intentions for products within the product category?

2. What inﬂuence does advertising of financing claims for a product have on purchase
intent and other related constructs for that product in particular?

3. Are some people more inﬂuenced by ﬁnancing claims than others?

PRODUCTS IN GENERAL

The inﬂuence of ﬁnancing claims for a speciﬁc product on purchase of

products in the product category is mixed. For the automobile ads, exposure to

157

the control condition appeared to have a more positive effect on shifts in
purchase intent than did exposure to any of the treatment conditions. These
differences approached but did not reach signiﬁcance. Whereas for the credit
cards, exposure to the treatment conditions led to an increase in purchase intent
and exposure to the control resulted in a decrease in purchase intent. In the
credit card case the difference was signiﬁcant. Exposure to the claims appeared
to have opposite effects on purchase intent for each of the products. Perhaps, the
payments for the Cavalier were high enough and over a long enough time period
to make students reconsider auto purchase in general. For the credit card
condition, the payments were low enough and ﬂexible enough (they ultimately
can charge as little as they like) that they made credit card acquisition more
appealing. In neither case did exposure to ﬁnancing claims have an inﬂuence on
timeframe of purchase. Overall, these types of ﬁnancing claims for these
subjects, did not have the expected effect of advancing timeframe of purchase
and the evidence is mixed as to whether exposure to ﬁnancing claims affected
purchase intent with it signiﬁcantly increasing it for credit cards and decreasing

it (but not signiﬁcantly so) for autos.

ADVERTISED PRODUCTS

One might expect that if ﬁnancing claims for a speciﬁc product had a hint
of an effect on purchase intent for products in general, their effect on the
advertised product would be more pronounced. This was clearly not the case.
For both the automotive and the credit card experiments, the presence of
ﬁnancing claims had no signiﬁcant impact on attitudes toward the ad, attitudes

toward the product, and perceived affordability of the product. Additionally,

158

they did not appear to have much impact on timeframe of purchase or the method

of payment.

The only area where signiﬁcant effects were found was on purchase intent
and that was for the automotive case only. Those in the least and most expensive
treatment conditions had signiﬁcantly higher purchase intent than those in the
control condition. These results for purchase intent for the Cavalier run contrary
to the near signiﬁcant results for difference in purchase intent for auto in general
where response to the control was more positive than that for the treatments. It
could be that for this particular car payments made the car seem more appealing,
but as indicted by the low purchase intent measures many may have envisioned
themselves buying a more expensive car. The payments for the Cavalier may
have made the subjects more aware of what payments might be for the car they
desired and thus produced negative shifts in purchase intent for an auto in
general. For credit cards, those exposed to ﬁnancing claims did not have
signiﬁcantly higher purchase intent for CapitalOne in particular, even though

they did for credit cards in general.

While there was only one case where the ﬁnancing claims produced a
signiﬁcant effect and short of signiﬁcance caution must be exercised when
interpreting results, there does appear to be a pattern of results for responses to
ﬁnancing claims by treatment group. For the product speciﬁc dependent
variables, the most inexpensive treatment condition (the one with the lowest
monthly payment over the longest period) was rated the most positively of all the
conditions. This was true for three of the four Cavalier dependent variables; the

only exception was for the case of affordability where the mean for the least

159

expensive payment ranked second behind the control. The least expensive
payment condition rated highest for all the CapitalOne speciﬁc dependent
variables. Those in the least expensive payment condition had the most positive
attitudes toward the ad and product as well as the highest affordability ratings
and purchase intent. Additionally, in two of the four auto speciﬁc dependent
variables and three of the four for the credit card speciﬁc dependent variables,
the middle level treatment condition (those most like what consumers are
normally exposed to) rated the lowest. Again, while the ﬁndings are not
signiﬁcant in the majority of cases and it must be remembered that the inﬂuence
only occurred with two speciﬁc types of ﬁnancing claims and with student
subjects, it is interesting to note that ﬁnancing claims that had the lowest
payment levels (lower than most currently advertised) were received the most
positively and those middle-level claims (most similar to the ones currently

made) were received the least positively.

SUSCEPTIBLE SUBJECTS

While the results revealed few clear signiﬁcant effects of ﬁnancing claims
for the subjects as a whole, there still remains the question of whether some
subjects were more inﬂuenced by ﬁnancing claims than others. As described in
Chapter 2, literature as well as common sense suggested that impatient and
present-minded people as well as those with a tendency toward compulsive
consumption may be more inﬂuenced by ﬁnancing claims, particularly the most
inexpensive appearing claims, compared to those who lack such characteristics.
Additionally, a number of other individual difference factors that were thought to

have the potential to inﬂuence response to ﬁnancing claims (age, gender,

160

education, income, credit card attitudes/use, and ﬁnancial knowledge) were

measured and analyzed.

The three individual difference factors that were hypothesized to have an
inﬂuence on response to ﬁnancing claims did not have the predicted effect.
Actually, time orientation had no signiﬁcant effect on the response to ﬁnancing
claims and had no relationships with the dependent variables in the MANOVAS.
Likewise, impatience did not have a signiﬁcant effect on response to ﬁnancing
claims. It did, however, serve as a covariate with two dependent variables,
acquisition intent for CapitalOne and posttest acquisition intent for credit cards
in general. Impatience was signiﬁcantly and negatively correlated with
acquisition intent in both cases. It may be that impatient people do not intend to
obtain a credit card because they already have all the credit cards they need.
Compulsive consumption also had little inﬂuence on response to ﬁnancing
claims. In the most expensive treatment condition only was there a signiﬁcant
relationship (a positive one) between compulsive consumption and pre-post
difference in purchase intent for auto (general), but the parallel slopes test did
not conﬁrm that the relationship was signiﬁcantly different for that condition.
Overall, subjects who were more impatient, present-oriented, and compulsive did
not respond to the treatment conditions differently than those who were not—

they did not appear to be susceptible to the claims.

Many other variables were examined to determine if they impacted
response to ﬁnancing claims. Speciﬁcally the variables were age, gender, class
standing, three income-related variables (personal income, parents’ income, and

percentage of parental support), several credit card related variables (including a

161

four-factor credit card consumption scale, number of credit cards, and a
borrowing approval scale), as well as measures of speciﬁc ﬁnancial knowledge.
Two factors (ethnic background and marital status) were not examined due to
limited variance on these measures. Overall, these factors had more inﬂuence
than the ones described above, but the inﬂuence was far from consistent. As
described in Chapter 4, with all the individual difference variables combined in
only 23 of 204 MANOVAS did the individual difference factor serve as a
covariate, in only 15 was there an interaction, and in only six of those cases were
the treatment conditions shown to be signiﬁcantly different from each other.
Parents’ income and percentage of parental support accounted for the most
covariate relationships and credit card attitude for the most interactions.
However, overall there were so few interaction effects even for these variables
that the individual difference variables tested here did not effect reaction to

ﬁnancing claims in any meaningful way.

Implications and Explanation for Findings

Overall, there were so few signiﬁcant ﬁndings that it appears for this
study that ﬁnancing claims did not have much inﬂuence on consumers’
impatience to purchase as the literature on affordability, time-inconsistent
decisions, and time and outcome valuation model, and the common use of the
claims suggest. Prior to data collection and analysis, it was expected that the
results, although they represent only a few speciﬁc claims and their impact on a
speciﬁc audience, could have led to implications such as the following. First,
advertisers (those who currently make some use of ﬁnancing claims and those

who offer somewhat expensive products but no ﬁnancing) would have more

162

reason to use ﬁnancing claims in the future and better information about which
claims (most probably those that provide the lowest monthly payments over the
longest periods of time) are most likely to appeal to consumers. Second,
consumerists who are concerned about the potential inﬂuence of such advertising
claims could have used this as a partial basis for pushing advertisers to
voluntarily or through some regulatory pressure be more conservative with use of
ﬁnancing claims or to provide more information or educational programs to
ensure audiences (particularly ones perceived to be vulnerable) understand the
ﬁnancial implications related to purchasing products on payment plans or using
the credit. In fact, this seems likely given the recent legislative action. Third,
academics, having gained increased awareness of how advertising ﬁnancing
claims by including messages about taking gains in the present and losses in the
future can inﬂuence consumer impatience, perceptions of affordability, attitudes
toward the product, and purchase intent, may be motivated to do additional
research in the area. This simply is not the case—the results were not

signiﬁcant.

The lack of clearly signiﬁcant results should give us pause for thought.
Are these claims really ineffective—should advertisers discontinue using them?
Should consumerists be put at ease—are consumers less susceptible than critics
of advertising thought? Should academics turn their attention in another

direction? The answer to the above questions is probably not. At least, not yet.

These claims may not be all that effective. It may be that the ﬁnancing
claims in auto and credit card advertising have become so commonplace that

consumers do not take notice of them. While some leaders in the furniture and

163

appliance industries still feel the ﬁnancing claims they use are effective, some
concur with the opinion of one industry executive who said, “It’s (0% ﬁnancing)
lost some of the glimmer it once had, it’s too wide spread, I offer extended
ﬁnancing on my own credit card, and consumers just aren’t as impressed as they
once were” (Mattioni 1996, p. 53). This insensitivity to the claims, combined
with the vast differences in students’ ﬁnancial condition and the attempt to keep
ﬁnancing claims realistic, may have contributed to the lack of signiﬁcant
differences between levels in the manipulation for the credit cards. This too
would be consistent with the pattern of results (although not signiﬁcant) of the
most inexpensive claims (those below what is routinely being made) receiving
the most positive ratings and the middle condition (those claims currently made)
often receiving the poorest rating, even lower than the most expensive claim
group. A perceptual threshold may come into play such that ﬁnancing claims
that are different from those subjects are accustomed to (the least and most
expensive) may have made more of an impression than those so familiar that they

merit little response.

Subjects may be skeptical of advertising and cautious of credit to the
extent that ﬁnancing claims do not affect them or they may be resistant to
revealing that the ads have any effect on them. Reasons provided for selected
payment methods for the Cavalier and particularly the CapitalOne card by
students in this research frequently reﬂected students concerns about credit and
debt. The author of a 1998 industry article reﬂected his belief that students are

credit savvy.

164

Given their sturdy work ethic and maturity, today’s students are
disinclined to take on more debt than they can manage. As a result, they
are very wary of credit, making for cautious use of credit cards. On
average students charge only $101 a month . . . More than half (55
percent) pay in full each month. And of those who don’t pay in full the
average balance is $504 . . . Asked about the most important credit card
features, over 77 percent named a low interest rate, while 73 percent said
no annual fee. (Newton 1998, p. 17)

Today’s college students may be wary of credit, but more recent research
has indicated that credit presents more problems for them than Newton’s article

describes. Results of several studies have prompted many articles about students

7.

and credit cards, and many enlightening facts have been reported. One study
indicated that only “44% of students understand the word ‘budget,’ 34%
comprehend the concept of buying on credit, and a mere eight percent have a
knowledge of compound interest” (“Are College Students Ready” 2000, p. 9). In
another study, reasons cited by students for obtaining credit cards included the

following:

to build a credit history (52%), for emergency purposes (45%), to stretch
their buying power (31%), because they liked the idea of buying goods
and services now and paying for them later (18%), and because they
received a gift or bonus (14%) (Toloken 1999, p. 77).

Over half (59%) of students pay off credit card balances in full, but those
students at four-year colleges who do not pay in full carry an average of $2,226
in revolving credit (with one in ﬁve carrying more than $10,000) (Fargo 1999;
Mannix 1999; “Are College Students Ready” 2000). Getting a credit card has
been described as a right of passage for college students who come onto
campuses where credit card applications are “ubiquitous” (Fargo 1999, p. 104;

“Are College Students Ready” 2000). This has led a few colleges to eliminate

165

credit card solicitation on campus (Toloken 1999). However, like most
universities (80%), the university where this research was conducted ﬁnancially
beneﬁts from allowing the promotion of “afﬁnity” cards on campus (Vickers

1999; “Credit Cards Given” 1999; “Are College Students Ready” 2000).

In spite of concerns about student indebtedness and the onslaught of
advertising they are exposed to, students in this experiment did not appear to be
the vulnerable, susceptible market that Lucas (1992), Susswein (1995), and
others described them to be. The students, perhaps because of their wariness of
credit and advertising, appeared to be largely uninﬂuenced by the ﬁnancing
claims. Furthermore, even those students—the impatient, present-minded,
compulsive, younger ones—who would be expected to be more affected by
ﬁnancing claims were not. More people in general appear to be demonstrating
wariness of debt as well. A new nationwide trend to resist credit card
solicitation (as reﬂected in a drop from 2.8% acceptance of unsolicited credit
card offers to 1%) has been described in the press and credited with helping
reduce historically high personal bankruptcy rates by 8% from 1998 to 1999 and
causing lenders to scale back credit card solicitations from 3.5 billion in 1998 to

3.1 billion in 1999 (Gordon 2000).

If lack of signiﬁcant ﬁndings were due to students being wary or savvy
about credit and advertising (and these qualities are indeed shared by other
consumers), then perhaps advertisers should rethink their use of such claims and
consumerists should rest a little easier, but the reason for the results is unclear.
It may be that the subjects were inﬂuenced but hesitant to indicate that they were

because of their skepticism or awareness of prevailing concerns about credit

166

cards. The pattern of results and the few signiﬁcant ﬁndings (which were found
for the purchase intent variables only) may be considered to be consistent with
this. Ringold (1996) contended that consumers believe advertising is
informative and useful, but at the same time are skeptical of advertising since

they recognize its persuasive role.

Or, it may be that such claims are so pervasive, so insidious that
consumers are not cognizant of their inﬂuence but that their actions (tendency to
buy things on credit) has already been changed by the claims and the climate
they have created. Results from the manipulation checks showing that over one-
third of subjects in the control conditions inaccurately recalled seeing ﬁnancing
claims when they were not present is consistent with this reasoning. Extension
of claims beyond the current types/levels could lead consumers to almost
unknowingly take on credit over longer and longer periods. Indeed, over the
period of this research advertisers for automobiles in particular have begun
touting longer loan periods with their lower payments, weekly payment prices,
and “zero down, zero interest, and zero payments until 2001 .” Credit card
companies have been making no interest claims as well. It appears the breath

and prevalence of ﬁnancing claims being utilized continues to increase.

Perhaps, different results would have been obtained had the experiment
been conducted in more natural manner (with more repetition of the claims, with
an audience having more ﬁnancial experience and being more in the market to
buy, with actual purchase and payment plans as the measures). Without a clearer
understanding of why the ﬁnancing claims did not have the predicted effects on

impatience to purchase, it would be imprudent for advertisers to discontinue use

167

of the claims (if they feel they have been effective), too soon for consumerists to
trust that consumers are safe, and premature for researchers to turn away from an

exciting, still potentially fruitful area of research.

Directions for Future Research

Additional research is needed to determine more conclusively whether
ﬁnancing claims have any impact on consumers’ perceptions of affordability,
purchases and timeframe of purchases. Had additional resources been available,
it would have been preferable to conduct this experiment in a more natural
setting, with more diverse subjects, a broader range of claims and products, more
repetition of the claims (made in broadcast as well as print), and with controls
for repeated measures. In the future, research should investigate the inﬂuence of
ﬁnancing claims in such a manner. Ideally, such research should be done in a
more real-world fashion where subjects, who are closer in time to purchasing, are
exposed multiple times to ﬁnancing claims, and given real opportunities to buy
and make decisions about ﬁnancing. Advertisers, who have the ﬁnancial
resources and the mechanisms for monitoring actual purchases, are in the perfect

position to conduct such ﬁeld experiments.

Academic researchers also have an interest in determining the nature of
the inﬂuence of ﬁnancing claims on attitudes, perceptions of affordability, intent
to purchase, and timing of purchase. Research into claims types and individual
difference factors that mediate the inﬂuence of claims helps us gain a better
understanding of how and why advertising works. In addition to experimental

research which speciﬁcally examines the inﬂuence of ﬁnancing claims, such as

168

that described above, there is need for research into particular areas. Content
analysis could be used to document the types and prevalence of ﬁnancing claims.
In depth interviews and focus groups of college students may shed light on the
reasons why ﬁnancing claims did not produce the expected effects in this
particular experiment by examining issues such as credit wariness or
pervasiveness of claims leading to assumptions about the availability of credit
irregardless of the presence of claims. Additionally, future research could focus
speciﬁcally on those who have had problems with spending or credit (which
were few in number in this research) to determine whether ﬁnancing claims have

a greater impact on them.

Another issue which could be explored is why in pretests of this study
Faber and O’Guinn’s (1992) Compulsive Buying Scale delivered such low
reliability and only marginally acceptable results for Roberts (1998) while
Mowen and Spears (1999) reported no problems with an older more lengthy
version of the scale when using it to measure compulsive consumption among
college students; both scales contained items regarding credit card use and check
writing that should have been problematic for a college sample with limited
ﬁnancial experience. On a broader basis, future research could incorporate the
model of core, situational and surface level traits utilized in the above article and
described more fully by Mowen (2000) to identify additional factors that may be
related to response to ﬁnancing claim as well as other claim types. Drawing
connections between vast numbers of individual difference variables (or surface
traits like compulsive consumption) and higher level traits (such as Mowen’s

eight elemental traits including contentiousness and need for arousal) may

169

organize and unify research into individual difference variables and their effect

on responses to advertising and marketing efforts.

This research has called attention to the commonality of a broad array of
advertising claims that are widely used but have almost never been researched.
The claims range from, “Money Down! No Interest! No Payment for Until
2001!” to “Three easy installment of $19.99” to “We’ll get you into a new
automobile for as little as $199 per month” to “Pay off bills and get extra cash

’3

for any reason. The claims all encourage consumers to “buy” products even if
they cannot afford to purchase them outright. The links between these ﬁnancing
claims and time-inconsistent decisions, time and outcome valuation, and
affordability perception have been described and areas for future research
identiﬁed. While this experiment on two types of these claims with a student

sample failed to support the inﬂuence of ﬁnancing claims, it has opened the

gateway for further examination of this important issue.

170

APPENDIX A—EXPERIMENTAL BOOKLET

171

Dissertation Booklet

Thank you for agreeing to participate in this study. This experiment is being conducted
to gain a better understanding of people’s impressions of automotive and credit card
advertising claims. The information you provide will be used only for academic
purposes. The booklet will take about 30 minutes to complete. The information you
provide will remain completely anonymous. You cannot be identiﬁed by or matched to
your responses. Your participation in this survey is entirely voluntary and you may
choose not to participate, however your cooperation is greatly appreciated. You indicate
your voluntary agreement to participate by completing and returning this questionnaire.

Please contact Patrice Katrak with any questions or concerns at (517) 355-2314.

172

Do you plan to purchase a brand new automobile in the future?

Yes, in the next 6 months

Yes, in 7 to 12 months

Yes, in 13 to 24 months

Yes, sometime, but not within 24 months
No, will not purchase one

What is the likelihood that you will purchase a brand new automobile?

 

 

 

 

likely : : : : : : unlikely
possible : : : : : : impossible
probable : : : : : : improbable

 

 

Do you plan to acquire and use a new credit card in the future?

Yes, in the next 6 months

Yes, in 7 to 12 months

Yes, in 13 to 24 months

Yes, sometime, but not within 24 months
No, will not purchase one

What is the likelihood that you will acquire and use a new credit card?

 

 

 

 

likely : : : : : : unlikely
possible : : : : : : impossible
probable : : : : : : improbable

 

 

173

Treatment or Control Ad (Appendix B) was inserted here.

174

WITHOUT TURNING BACK, PLEASE ANSWER THE FOLLOWING
QUESTIONS ABOUT THE ADVERTISEMENT ON THE PREVIOUS PAGE.

5a.

What are your overall feelings toward the advertisement you just read?

 

 

 

 

 

 

good : : : : : : bad
interesting : : : : : : uninteresting
like : : : : : : dislike
irritating : : : : : : not irritating

 

 

What is your overall attitude toward the Chevrolet Cavalier 224 described in
the ad?

 

 

 

good : : : : : : bad
favorable : : : : : : unfavorable
negative : : : : : : positive

 

 

Do you plan to purchase a new Chevrolet Cavalier Z24 in the future?

Yes, in the next 6 months

Yes, in 7 to 12 months

Yes, in 13 to 24 months

Yes, sometime, but not within 24 months
No, will not purchase one

What is the likelihood that you will purchase a new Chevrolet Cavalier 224?

 

 

 

 

likely : : : : : : unlikely
possible : : : : : : impossible
probable : : : : : : improbable

 

 

If I wanted to, I could easily afford to purchase a new Chevrolet Cavalier
224.
likely : : : : : : unlikely

 

 

For me to purchase a new Chevrolet Cavalier Z24 rs
easy . . . . : : difﬁcult

 

My personal income permits me to easily purchase a Chevrolet Cavalier Z24.
strongly agree : : . . . : strongly disagree

 

 

175

Do you plan to purchase a brand new automobile (of any type) in the future?

Yes, in the next 6 months

Yes, in 7 to 12 months

Yes, in 13 to 24 months

Yes, sometime, but not within 24 months
No, will not purchase one

What is the likelihood that you will purchase a brand new automobile (of any
$19.9)?

 

 

 

 

 

likely : : : : : : unlikely
possible : : : : : : impossible
probable : : : : : : improbable

 

 

 

If you were to purchase a new Chevrolet Cavalier Z24, what would be the
primary method you would use to pay for it?

Buy it outright with cash or check
Lease it by signing a 1-year agreement
Lease it by signing a 2-year agreement
Lease it by signing a 3-year agreement
Buy it by taking out a 48-month loan
Buy it by taking out a 60-month loan
Buy it by taking out a 84-month loan

Please explain why you chose the payment method you selected on the
previous question:

 

 

 

 

176

Treatment or Control Ad (Appendix B) was inserted here.

177

WITHOUT TURNING BACK, PLEASE ANSWER THE FOLLOWING
QUESTIONS ABOUT THE ADVERTISEMENT ON THE PREVIOUS PAGE.

5a.

What are your overall feelings toward the advertisement you just read?

 

 

 

 

 

 

 

 

 

good : : : : : : bad
interesting : : : : : : uninteresting
like : : : : : : dislike
irritating : : : : : : not irritating

 

 

 

What is your overall attitude toward the CapitolOne Visa card described in
the ad?

 

 

good : : : : : : bad
favorable : : : : : : unfavorable
negative : : : : : : positive

 

Do you plan to acquire and use a new CapitalOne Visa card in the future?

Yes, in the next 6 months

Yes, in 7 to 12 months
Yes, in 13 to 24 months
Yes, sometime, but not within 24 months
No, will not purchase one
What is the likelihood that you will acquire and use a new CapitalOne Visa
card?

 

 

 

 

 

 

likely : : : : : : unlikely
possible : : : : : : impossible
probable : : : : : : improbable

 

 

 

If I wanted to, I could easily afford to acquire and use a new CapitolOne Visa
card.
likely : : : : : : unlikely

 

 

 

For me to acquire and use a new CapitalOne Visa card is
easy : : : : : : difﬁcult

 

 

 

My personal income permits me to easily acquire and use a new CapitalOne
Visa card.
strongly agree : : : : : : strongly disagree

 

 

178

Do you plan to acquire and use purchase a new credit card of an e in
the future?

Yes, in the next 6 months

Yes, in 7 to 12 months

Yes, in 13 to 24 months

Yes, sometime, but not within 24 months
No, will not purchase one

What is the likelihood that you will acquire and use a new credit card (of any
true)?

 

 

 

 

likely : : : : : : unlikely
possible : : : : : : impossible
probable : : : : : : improbable

 

 

If you were to acquire and use a new CapitalOne Visa card, what would be
the primary method you would use to pay for items you purchased with it
each month?

Pay the balance off in ﬁrll

Pay less than the ﬁrll balance, but more than the minimum payment
Pay the minimum payment

Pay less than the minimum payment

Please explain why you chose the payment method you selected on the
previous question:

 

 

 

 

179

THE FOLLOWING QUESTIONS DO NOT RELATE DIRECTLY TO THE ADS
YOU SAW. THEY ARE DESCRIPTIVE OF YOU, YOUR FEELINGS AND
BEHAVIORS.

1. Please circle the number that most closely represents your purchasing
behavior and feelings.

NEITHER
STRONGLY SOMCEWHAT AGREE NOR SOMEWHAT STRONGLY
AGREE AGREE DISAGREE DISAGREE DISAGREE

a. If I really want to buy
something, I frequently
make the purchase
quickly and think about
the consequences later .............. 1 2 3 4 5

b. I tend to spend money
as soon as I earn it .................. 1 2 3 4 5

c. I am the type of person
who likes to slowly
save up money in order
to make large purchases ........... l 2 3 4 5

d. I enjoy shopping and
buying on impulse .................. 1 2 3 4 5

e. I tend to think about

alternatives a great
deal before I buy things ............ 1 2 3 4 5

f. I always pay oﬂ‘
my entire credit
card bill each month ................. 1 2 3 4 5

g. If I have purchased
something mail order, I
like to have the company
express mail it, so I
will get it more quickly ............. 1 2 3 4 5

180

. Please circle the number that most closely represents your behavior and feelings.

NEITHER
STRONGLY SOMEVVHAT AGREE NOR SOMEW’HAT STRONGLY
AGREE AGREE DISAGREE DISAGREE DISAGREE
. When I have money, I
cannot help but spend

part or the whole of it ............... 1 2 3 4 5

. For me, shopping is a way
of facing the stress of my
daily life and of relaxing ........... 1 2 3 4 5

. There are times when I
have a strong urge to buy
(clothing, books, etc.) ............... l 2 3 4 5

. There are some things that

I buy that I do not show

to anybody for fear of being

perceived as irrational in

my buying behavior

(“a foolish expense”) ................ 1 2 3 4 5

. As soon as I enter a

shopping center, I have

an irresistible urge to go

into a shop to buy something 1 2 3 4 5

. I have oﬁen bought a

product that I did not need,

while knowing that I have

very little money left ................ 1 2 3 4 5

. I am impulsive in
my buying behavior .................. l 2 3 4 5

. I sometimes feel that

something inside pushed
me to go shopping .................. 1 2 3 4 5

181

i.

NElTlIER
STRONGLY SOMEVVHAT AGREE NOR SOMEVVIIAT STRONGLY
AGREE AGREE DISAGREE DISAGREE DISAGREE

At times, 1 have felt

somewhat guilty after

buying a product, because

it seemed unreasonable ............... l 2 3 4 5

I often have an

unexplainable urge, a

sudden and spontaneous

desire, to go and buy

something in a store ................... 1 2 3 4 5

. I am a spendthriﬁ .................... l 2 3 4 5

. I an one of those people

who often respond to
direct mail offers
(e. g. books, tapes/CD5) .............. 1 2 3 4 5

. Please place an X on the line that most closely corresponds to your feelings.

When a person is talking and takes too long to come to the point, how often
do you feel like hurrying the person along?

very frequently : : : : almost never

 

 

Typically, how easily do you get irritated?

extremely easily : : : : not easily at all

 

 

 

Do you tend to do most things in a hurry?

deﬁnitely true : : : : not true at all

 

 

 

How is your "temper" these days?

very hard to control : : : : I seldom get angry

 

 

 

When you have to wait in line such as at a restaurant, the movies, or the post
office, how do you usually feel?

accept calmly : : : : feel very impatient and
reﬁrse to stay long

 

182

4. Do you think it is a good idea or a bad idea for people to buy things on the
installment plan?

goodidea

good in some ways, bad in some ways
bad idea

5. Is it all right for someone like yourself to borrow money for

APPROVE DISAPPROVE
car
educational expenses
living expenses
vacation/trip
fur coat or jewelry

9.0-.057!»

6. In a typical month, how do you tend to pay the balance(s) (the total amount
shown on your bill) on your credit card(s)? (Check one.)

I pay my credit card balance(s) in ﬁill every month.

Every month I pay no more than the minimum payment due on by credit
card(s).

Every month I pay as much as I can on my credit card balances; some are
paid in hill and other(s) a balance remains.

I hold at least one credit card, but I never use it (them), so I do not have a
balance to pay off.

I do not have a credit card, so I do not have a balance to pay off.

183

Please circle the number for each statement that best represents your feelings
and about use of credit cards.

srnonch STRONGLY
AGREE msxcrusr:

When I shop with credit

card(s) I tend to make

unplanned purchases ............................. l 2 3 4 5 6 7

I have been told that I spent

beyond my credit limit on

my credit cards ...................................... 1 2 3 4 5 6 7

Because I have a credit card,

I felt more in control of

my ﬁnancial ability ............................... 1 2 3 4 5 6 7

It is unwise to use any credit card .......... l 2 3 4 5 6 7

Without a credit card, my

spending habits would not

be different ........................................... l 2 3 4 5 6 7

I completely exhaust the

credit limit on my credit cards ............... 1 2 3 4 5 6 7

I feel a sense of ﬁnancial control

with a credit card .................................. 1 2 3 4 5 6 7

Credit cards should be used only

in case of emergency ............................. 1 2 3 4 5 6 7

I end up buying more when 1 shop

with a credit card(s) compared to

when I shop with cash or checks ........... l 2 3 4 5 6 7

Because of the way I use my

credit card I always have enough

credit available on it .............................. l 2 3 4 5 6 7

Credit cards add to my general

ﬁnancial well being ............................... l 2 3 4 5 6 7

I dislike all credit/charge cards .............. l 2 3 4 5 6 7

It is easy for me to overspend when

I shop with a credit card ........................ 1 2 3 4 5 6 7

IfI did not have a credit card 1

would probably spend less .................... l 2 3 4 5 6 7

184

8. Assume that you took out a sixty month loan at 0.9% interest to buy a $15,000
automobile.

A Approximately how much would you end up paying in INTEREST ONLY over the
life of the loan (please ﬁll in a speciﬁc dollar amount)? $

 

B. How many years would it take to pay off the loan? years

 

)9

Assume that you charged $5,000 on a credit card with a 9.9% annual interest
rate and a $20 payment minimum and made only the 2% minimum payment
each month and made no additional purchases.

 

A. Approximately how much would you end up paying in INTEREST ONLY by the
time the credit card balance was paid off completely (please ﬁll in a speciﬁc dollar
amount)? $

B. How many years would it take to pay off the total balance charged? years

 

10. Indicate with an X which activities apply to you.

bought a car
leased a car
took out a loan for a car
took out a loan for educational expenses
took out a loan to buy a house
ﬁnanced furniture, carpeting, or appliances
rented to own furniture or appliances
took out a loan to consolidate debt
sought assistance in getting out of debt
declared bankruptcy
paid for something on installments (please specify)

 

 

took out any other type of loan (please specify)

185

Please fill in your age:
Gender: Female Male
Marital Status: Single Married Widowed Divorced

Ethnic Background: African Asian Caucasian Hispanic
Other (please specify)

 

Highest Level of Education Completed: (circle one)

Less than high school

High school graduate

Some college

College graduate: AAS BA BS Masters JD PHD Nﬂ) DO

Current Class Standing: Freshman Sophomore Junior Senior Graduate Student
Other (specify) Your Major:

 

 

Employed: All Year During School Year Only During Summer Only Not Employed

Income: Present Personal Parental Household
Household Income Income When You Were
(incl. spouse's if appl.) In High School

$0 - $10,000

$10,001- $20,000
$20,001 - $30,000
$30,001 - $40,000
$40,001 - $50,000
$50,001 - $60,000
$60,001 - $70,000
$70,001 - $80,000
$80,001 - $90,000
$90,001 - $100,000
above $100,001

Size of your parents household when you were in high school:
Your current number of dependents including yourself:

Please fill in the percentage of financial support (for college, room and board,
expenses, etc. combined) your parents provide to you (0% to 100%):

Please fill in the number of credit cards you have:

186

WITHOUT TURNING BACK, PLEASE ANSWER THE FOLLOWING
QUESTIONS WHICH RELATE TO THE ADVERTISEMENTS YOU READ
EARLIER.

1.

.4}:

Thinking back to the ad you saw for the Chevrolet Cavalier 224, please
indicate with an X whether you remember noticing that the ad contained any
of the following (mark all that apply):

The name of a dealership
The price of the vehicle
Financing rates for the vehicle

Thinking back to the ad you saw for the Chevrolet Cavalier Z24, please
indicate with an X whether the vehicle would be (mark only one answer)

easy for you to pay for
a manageable amount to pay
more than you could afford

Thinking back to the ad you saw for the Visa card, please indicate with an X
whether you remember noticing that the ad contained any of the following
(mark all that apply):

The beginning credit limit
Cash back awards/rebates
The minimum payment amount/percentage

Thinking back to the ad you saw for the CapitalOne Visa card, please
indicate with an X whether the purchases made with it would be (mark only
one answer)

easy to pay for

a manageable amount to pay
more than you could afford

187

APPENDIX B—TREATMENT ADS

188

Automobile Least Expensive Payment Advertisement

OI

 

The true measure of success isn't the title on a
business card or the size 'of the income. It' s know-

In:mkﬁ:rlm wiur WIWIS

you are wherever mat may be. If you' re driving a
Chevrolet Cavalier 224, that’ s going to be just
about anywhere. With plenty of head and leg room.
and air conditioning standard. just about any
joumey's sure to be comfortable. 111e sporty 2.4
llter engine is newly reﬁned to be smoother and

.m__4_._._x

access is the quality of the journey

3%“ "’1‘"

quicker than ever. With 150 hp and Sequential Fuel
Injection. the Chevrolet Cavalier 224 will get you
wherever you want to go. The best news is that
the Cavalier 724 is now more affordable to own
than ever. Prices start at $14,971. With low 0.9%]
84 month financing, you'll be on the road with the
wind in your hair for just $183 per month. Now.
we call that success.

\IJ/P‘J

v

Genuine Chevrolet:a *

The Cars More Americans Trust.

IarlrI-rn MI

189

Automobile Middle Level Payment Advertisement

OI

   

access is the quality of the journey.

 

The true measure of success isn't the title on a
business card or the size of the income. It's know-

you are wherever that may be. If you‘ re driving a
Chevrolet Cavalier 224, that's going to be just
about anywhere. With plenty of tread and leg room.
and air conditioning standard, just about any
joumey'a sure to be comfortable. The sporty 2.4
liter engine is newly refined to be smoother and

"summer.

 

quicker than ever. With 150 hp and Sequential Fuel
Injection, the Chevrolet Cavalier 224 will get you
wherever you want to go. The best news is that
the Cavalier 224 is now more affordable to own
than ever. Prices start at $14,971. With low 0.9%]
60 month ﬁnancing. you'll be on the road with the
wind in your hair for just $246 per month. Now.
we call that success.

Genuine Chevrolet3 *9

The Cars More Americans Trust.

lam. .n MAI

190

Automobile Most Expensive Payment Advertisement

Cl

 

The true measure of success isn't the title on a
business card orthe slzeofthe Income. it'sknow-
you are wherever that may be. If you' re driving a
Chevrolet Cavalier 224. that's going to be just
about anywhere. With plenty of head and leg room.
and air conditioning standard. just about any
joumey'a sure to be comfortable. The sporty 2.4
liter engine is newly reﬁned to be smoother and

locating-huh

access is the quality of the Journey

quickerthanever.With 150hpandSequentlal Fuel
injection, the Chevrolet Cavalier 724 will get you
wherever you want to go. The best news is that
the Cavalier Z24 is now more affordable to own
than ever. Prices start at $14,971 . With low 0.9%]
48 month ﬁnancing. you'll be on the road with the
wind in your hair for just $317 per month. Now,
we call that success.

Genuine Chevrolet
The Cars More American: Trust

Hus-il- .-. Ana-hl

191

iii

 

Automobile Control Advertisement

I

The true measure of success isn‘t the
title on a business card or the size of the income.
lt'a knowing who you are and being comfort-
able with where you are wherever that may be.
if you’re driving a Chevrolet Cavalier 24, that's
going to be just about anywhere. With plenty of
head and leg room, and air conditioning stan-
dard, just about any joumey's sure to be corn-
fortable. The sporty 2.4 liter engine is newly

Immm

access is the quality of the journey.

refined to be smoother and quicker than ever.
With 150 hp and Sequential Fuel Injection. the
Chevrolet Cavalier 724 will get you wherever you
want to go. The best news is that the Cavalier
224 is now more affordable to own than ever.
Prices start at $14,971. See your local Chevrolet
dealer today and you'll be on the road with the
wind in your hair at a price you're comfortable
with. How, we call that success.

Genuine Chevroleta ﬂ

The Cars More Americans Trust.

Ina-il- ..n AIM-I

192

Credit Card Least Expensive Payment Advertisement

Y. ‘
:- °
When you’re just starting out. therex not much room In the budget for frills. it‘s
having the basrcs that really count—the basic wardrobe, your ﬁrst computer. the
books and supplies you need for school. something to sit in and something to
sleep on. And 01 course. something to tote it all around in. Thats about all it takes.
But when you need it. you REALLY need it and you need it NOW That: whereyour
essential CapitalOne VISA card comes in. You need a credit card that under-
stands you Thats why CapitalOne VISA offers a low We minimum payment.
On full beginning credit limit of $5,000, that: only 850.41 per month. Apply now and
your new CapitalOne VISA will help you get the start you need.

Tuition 82,000.00
Computer I, $00.00
Iaelrpaek 50.00
Textbooks 000.00
Jeans 60.00
Jacket I I 0.00
Miss. 300.00

3 5,000.00

You pav just

0
per month

ilj,."“"V I
‘i

"in
"i" ""li‘ 'nu'v -

sew.

 

193

Credit Card Middle Level Payment Advertisement

 

When you're just starting out there: not much room in the budget for frills. Its
having the basics that really count—the basuc wardrobe, your ﬁrst computer. the
books and supplies you need for school, something to sit in and something to
sleep on. And ol course. something to tote it all around In. Thats about all it takes.
But when you need it, you REALLY need it and you need it NOW Thats where your
essential CapitalOne VISA card comes in You need a credit card that under-
stands you man why CapitalOne VISA offers a low 2% minimum payment. On
a full beginning credit limit of $5.000, thats only $100.03 per month. Apply now and
your new CapitalOne VISA will help you get the start you need.

 
 
 
 
 
 
 
 
 
 
  
 
 
  
  
  
   
 
 

Tuition $1,000.00
Computer 1,600.00

 

Iatllpaelr 50.00
Textbooks 000.00
Jeans 60.00
Jacket I l0.00

Mlle. 380.00
$5,000.00

You pav iusl

 

 

m'ii.

....
'Hi'r “‘11.-

194

Credit Card Most Expensive Payment Advertisement

   

l .
summm

When you‘re just starting out, there! not much room in the budget for frills Its ]
haying the basucs that really count—the DaSK wardrobe, your ﬁrst computer, the ‘
books and supplies you need for school, something to sit In and something to sleep
on. And of course. something to tote it all around in. Thats about all it takes But
when you need it you REALLY need It and you need it NOW. That: where your
essential CapitalOnz VISA card comes in You need a credit card that under»
stands you That: why CapitalOne VISA offers a low 391: mun-mum payment On
a lull beginning credit limit of 35,000, that: only $151.24 per month Apply now and
your new CapitalOne VISA wm help you get the start you need

..4_.._..; - -n~

  
 
 
 
 
 
  
  
 
 
  
   
 
   
 
   

-_— _..__._

Tuition $1,600.00
Computer 1,600.00

 

Iadtpadt 50.00 l
Tclthookl 400.00 +
Jeans 60.00 ‘

Jam 1 10.00
Misc. 300.00
$5,000.00

\(vu pm iusr

151.

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'47"

    

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Pt'i munrh

 

C apih I ()1 16’

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u. may. apiulnm l.

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Member mic

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195

Credit Card Control Advertisement

 

When you re Just starting out. there's not much room in the budget for frills
It‘s havmg the basucs that really count—the basic wardrobe, your first com-

 
 
 
 
 
 
 
  
   
 
 
   
  
  
  
  

puter, the books and supplies you need for school. something to SI! in and
something to sleep on And of course. something to tote it all around in
That‘s about all it takes But when you need it. you REALLY need It and you
need it Now That's where your essential CapitalOne VISA card comes In.
You need a credit card that understands you That's why CapitalOne VISA
offers you a beginning credit limit of $5,000 Apply now and your new
CapitalOne VISA Will help you get the start you need.

' Tuition $1,400.00

 

Computer 1,600.00
Iaekpaek 50.00
Textbooks £00.00
Jeans 60.00
Jacket 1 10.00
Misc. 300.00

.000.00

  
   

 

 

 

94.. .nnui lot. 112 APIl u
imam“. Muir-“spa
to thank (ApiulUM u
u nursed mdemtl
Membtr mlC
JMMN

    
   
    
 

 

196

APPENDIX C—CORRELATION TABLE

197

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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