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Faculty of Education and Economic Studies Department of Business and Economics
Customer Perceived Value of Credit Card
Rewards
- A study on Canadian Consumers
Lisa Smedley
2013
Thesis, C-level, 15 credits
Business Administration
Bachelor’s thesis in Business Administration
Bachelor of Business Administration
Supervisor: Jonas Kågström
Examiner: Lars-Torsten Eriksson
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Abstract
Title: Customer Perceived Value of Credit Card Rewards
- A study on Canadian Consumers
Level: Final assignment for Bachelor’s Degree in Business
Administration
Author: Lisa Smedley
Supervisor: Jonas Kågström
Date: 2013 - January
Aim: The aim of this study is to investigate what influences
Customer Perceived Value; where Canadian consumers’ preferences
lie in terms of rewards in the Canadian credit card industry.
Method: After researching previous studies and determining what
constructs have been utilized prior on similar research topics, I
implement a quantitative, and to some extend iterative, research
approach. Through survey research, I investigate Canadian
consumer preferences through a survey sample of 124 Canadian
consumers in Calgary, Alberta, Canada.
Result & Conclusions: One finding in the study indicates that
utilitarian benefits, which provide financial gain for the card holder,
are perceived by respondents as the most valuable reward. Another
finding is that inexperienced credit card holders see significantly
greater value in symbolic benefits than experienced card holders do.
The present study does not support the theory that customer
involvement influences the customer’s perception of rewards.
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Suggestions for future research: More extensive research is
needed on the subject of whether Canadian consumers’ perceived
value of rewards is influenced by their level of involvement in their
credit card. Also, studies involving additional factors that could
possibly determine a consumer’s perception of rewards, such as
income and ethnicity should be investigated for a more well-
rounded understanding of customer preferences.
Contribution of the thesis: The present study contributes with new
findings that can be of substantial significance for Canadian
financial institutions as it provides insight into what credit card
rewards Canadian consumers perceive as being valuable to them.
Key words: Rewards programs, credit cards, customer loyalty,
perceived customer value, timing of reward, type of reward,
dimension of benefit, utilitarian, hedonic, symbolic
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Acknowledgements
First, I would like to thank my Supervisor Jonas Kågström, for his
contagious optimism, ambition and passion for research. With
invaluable insight he has guided me through composing this thesis at
long-distance from Gävle, Sweden.
I would also like to thank Jenni M. Karl for helping me to gain access
to the Brain and Behaviour class at the University of Lethbridge.
Thank you, also, to everyone in Calgary who participated in my
survey investigation.
2012-12-31
Lethbridge, Canada
Lisa Smedley
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Table of Contents
1 Introduction ....................................................................................... 8
1.1 The effects of credit card rewards on businesses and consumers ............... 8
1.2 Customer rewards as firms’ marketing tools and profit boosters .............. 10
1.3 Research question ...................................................................................... 12
1.4 Purpose ....................................................................................................... 12
2 Theory and Literature overview ................................................... 13
2.1 Literature outline ........................................................................................ 13
2.2 Customer Perceived Value ......................................................................... 15
2.3 Potential constructs .................................................................................... 19
2.3.1 Involvement ......................................................................................... 19
2.3.2 Type of reward .................................................................................... 23
2.3.3 Timing of reward ................................................................................. 25
2.3.4 Target of attitude ................................................................................. 28
2.3.5 Dimension of benefit ........................................................................... 29
2.4 Summary of theory .................................................................................... 31
2.5 The four constructs selected for use in the current study and my first
model of their expected correlation with each other ....................................... 33
2.5.1 A first model of the correlation between the chosen constructs for
Customer Perceived Value ........................................................................... 36
3 Methodology .................................................................................... 37
3.1 Ontological and epistemological considerations ....................................... 37
3.2 Research methods for the present study .................................................... 38
3.2.1 Approach ............................................................................................. 38
3.2.2 Building the theoretical foundation ..................................................... 40
3.2.3 Data collection and respondent selection ............................................ 41
3.3.1 Pearson Correlation ............................................................................. 44
3.3.2 Factor Analysis .................................................................................... 44
3.4 Reliability, validity and generalizability .................................................... 46
3.5 Possible methodology errors ...................................................................... 48
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3.5.1 Errors in quantitative research ............................................................. 48
3.5.2 Errors in survey research ..................................................................... 48
4 Empirical findings .......................................................................... 50
4.1 Findings from the survey investigation ..................................................... 51
4.1.1 Level of Involvement: High versus low .............................................. 52
4.1.2 Timing of rewards: Immediate versus delayed ................................... 54
4.1.3 Type of rewards: Direct versus indirect .............................................. 58
4.1.4 Dimension of benefit: Hedonic, Utilitarian and symbolic benefits .... 61
4.2 Additional comments about compilation of survey results ....................... 66
5 Analysis ............................................................................................ 69
5.1 Pearson’s Correlation between constructs ................................................. 69
5.1.1 Level of Involvement .......................................................................... 69
5.1.2 Type: Direct versus Indirect rewards .................................................. 71
5.1.3 Timing: Immediate versus Delayed rewards ....................................... 71
5.1.4 Dimension of benefits: Utilitarian, Hedonic and Symbolic ................ 72
5.1.5 Additional, strong, correlations between constructs ........................... 74
5.2 Factor Analysis .......................................................................................... 75
5.2.1 Component 1 ....................................................................................... 77
5.2.2 Component 2 ....................................................................................... 77
5.2.3 Component 3 ....................................................................................... 78
5.2.4 Component 4 ....................................................................................... 79
5.2.5 Component 5 ....................................................................................... 79
5.3 A revised model of Customer Perceived Value 80
6 Conclusions ...................................................................................... 81
6.1 Managerial Implications…………………………………………………82
7 References ........................................................................................ 84
8 Appendix .......................................................................................... 87
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Table of figures
Figure 1. Increase in Loyalty Program research……………………….….…...13
Figure 2. Map of references; Rewards programs……………………………...14
Figure 3. Map of references; Means of payment……………………………....15
Figure 4. Compilation of Zeithaml’s definition of Customer Perceived
value…………………………………………………………………………16
Figure 5. Zeithaml’s definition of Customer Perceived Value
(as cited in Ravald & Grönroos, 1996, p. 21)…………………..……………16
Figure 6. Ravald & Grönroos’ definition of Customer Perceived Value
(1996, p. 23)…………………………………………………………………16
Figure 7. Potential constructs for the present study. Displays formerly used
means of categorization of rewards………………………………………….19
Figure 8. Types of Reward schemes (Dowling & Uncles, 1997, p. 12)……….26
Figure 9. Perceived Benefits of Loyalty Programs.
(Mimouni-Chaabane & Volle, 2010, p. 33)…………………………………29
Figure 10. The four constructs selected for use in the present study………….33
Figure 11. A first model of the correlation between the chosen constructs
for Customer Perceived Value……………………………………………….36
Figure 12. Deductive approach (Bryman & Bell, 2003, p. 12)………………..39
Figure 13. Inductive approach (Bryman & Bell, 2003, p. 12)…………………39
Figure 14. KMO and Bartlett's Test………………….………………………..45
Figure 15. Bryman and Bell’s four sources of error in social survey research
(2003, p. 110)………………………………………………………………..49
Figure 16. Each respondent’s age in relation to their level of experience
of holding at least one credit card……………………………………………51
Figure 17. Question 1………………………………………………………….52
Figure 18. Responses to level of involvement in relation to each
respondent’s age……………………………………………………………..53
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Figure 19. Male and female responses to level of involvement……………….53
Figure 20. Question 2………………………………………………………….54
Figure 21. Question 4………………………………………………………….55
Figure 22. Question 3………………………………………………………….56
Figure 23. Question 5………………………………………………………….57
Figure 24. Question 6………………………………………………………….58
Figure 25. Question 7………………………………………………………….58
Figure 26. Question 8………………………………………………………….59
Figure 27. Question 9………………………………………………………….60
Figure 28. Question 10………………………………………………………...61
Figure 29. Question 14………………………………………………………...62
Figure 30. Question 17………………………………………………………...62
Figure 31. Question 11………………………………………………………...63
Figure 32. Question 13………………………………………………………...63
Figure 33. Question 16………………………………………………………...64
Figure 34. Question 12………………………………………………………...64
Figure 35. Question 15………………………………………………………...65
Figure 36. Question 18………………………………………………………...65
Figure 37. Differences in answers between men and women…………………66
Figure 38. Similarities in answers between men and women…………………67
Figure 39. Answers to question 12 in relation to age………………………….67
Figure 40. Similarities in responses between ages…………………………….68
Figure 41. Rotated Component Matrix………………………………………...76
Figure 42. A revised model of Customer Perceived Value……………………80
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1 Introduction In this chapter, the effects that credit card rewards have on the
economy, businesses and consumers will be discussed, as well as
reasons as to why businesses decide to implement them. Research
questions and the purpose of the present study will also be presented
here.
1.1 The effects of credit card rewards on
businesses and consumers Loyalty programs have two general aims: to increase sales revenues
and to maintain existing customers (Uncles, Dowling, & Hammond,
2003, pp. 294–295). Uncles et al. claim that the foundation for loyalty
programs’ popularity lies in the idea that business’ profits can be
increased by accomplishing either one of these two aims.
Stereotypically, loyalty programs offer customers financial and social
rewards to reinforce purchasing behavior (Uncles et al., 2003, p. 294)
and rewards used in the credit card industry are no different: credit
card firms adopt rewards programs to promote consumer usage of
their credit card.
Ching & Hayashi (2010, p. 1783) found that consumers who’s
primary method of payment is credit and debit card would all reduce
their usage of the card if their payment card rewards were removed.
They also found that consumers were more enticed to use their
rewards payment card for larger transactions. Carbó-Valverde and
Liñares-Zegarra's study also suggested that removing rewards from
consumers’ payment cards can make consumers reduce their payment
card usage (2011, p. 3276), and thus retrogress to start using cash as
their primary payment method. Additionally, earlier studies performed
individually, by both Feinberg and Soman, have shown that
consumers are more prone to use credit cards to pay for more durable
products in relation to products with a shorter shelf-life (as cited by
Carbó-Valverde & Liñares-Zegarra 2011, p. 3276). Furthermore,
studies performed by Bolton, Kannan, & Bramlett (2000, p. 106) have
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shown that consumers who are members of credit card rewards
programs tend to overlook negative experiences with their card
issuing financial institutions, as well as resulting in them paying less
attention to competing firms’ offers (2000, p. 105). Wirtz, Mattila, &
Lwin, (2007, p. 332) suggest that because consumers rarely have a
psychological attachment to their credit card issuer, presenting credit
card holders with a desirable rewards program is probable to increase
customer loyalty.
Maritz Canada’s 2012 report on Customer loyalty programs showed
that 92% of Canadians are members of at least one loyalty program of
some kind. The same study showed that 31% of all Canadians, 49 %
of them being high income Canadians, would switch credit card
providers if it wasn’t for the loyalty program their card issuer offers
(2012, p. 2-4). In 2006, the annual revenue for MasterCard and Visa
credit cards was estimated at $30 billion USD, solely from
interchange fees. Of those fees, rewards were estimated to account for
44% of the total amount (Ching & Hayashi, 2010, p. 1775).
However, credit card rewards are not only affecting credit card
firms’ economy. Using payment cards as a primary payment method,
instead of cash or cheques, can possibly reduce the overall cost of our
world economy and at the same time increase overall sales (Ching &
Hayashi, 2010, p. 1773). The transition from cash to card payments
has thus become one of the main ambitions of both financial planners
and financial institutions’ today (Carbó-Valverde & Liñares-Zegarra,
2011, p. 3286).
Research has shown that one of the positive effects that credit card
rewards programs have on consumers’ method of payment is that it
encourages, and increases, the total number of transactions made each
day in relation to paper based payment (Simon, Smith & West, 2010,
p. 1771; Carbó-Valverde & Liñares-Zegarra, 2011, p. 3286). Another
positive outcome of credit card rewards is that being rewarded for
using payment cards appears to increase consumers’ spending overall
(Ching & Hayashi, 2010, p. 1773). Both of these effects can thus be
interpreted as important not only in a business economy perspective
but for long-term national and international economy planning as well.
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Although the subject of credit card rewards programs can be viewed
as an international development issue, the present study will focus on
credit card rewards from a general business perspective, in an attempt
to investigate consumer’s perceived value of credit card rewards.
1.2 Customer rewards as firms’ marketing
tools and profit boosters Rewards as customer marketing tools have been used in the credit
card industry for more than 25 years (Ching & Hayashi, 2010, p.
1774) but loyalty programs in general have been around for much
longer. The original loyalty programs in fact started at least as early as
the in 1950’s (Davis, 1959, p. 141) and consisted of trading stamps.
The program was referred to as the “gold stamp” program (Shugan,
2005, p. 188), enabling families to receive quantity discounts by
collecting stamps at the time of purchase which could later be traded
in for goods. In 1958, approximately two thirds of American families
belonged to at least one such program (Shugan, 2005, p. 186). With
the increasing popularity of rewards programs, it did not take long for
curious researchers to take on the task of determining their proclaimed
efficiency. According to Davis (1959, p. 141), several articles had
already been written on the subject of loyalty programs by the time he
published his own in the late 1950’s. The stamps, however,
disappeared when new owners took over the stores that had first
implemented them. The new stores offered lower prices overall and
the trading stamp loyalty programs disappeared.
During the 1970’s researchers in Europe found that merchants in
B2B-businesses who established close professional bonds with their
customers had more loyal customers who gave their supplier a larger
share of wallet, i.e. a large portion of their spending (Dowling &
Uncles, 1997, p. 3). This spiked a strong interest in marketers and
would increasingly be the target of future customer loyalty research.
More current studies have shown that customers who are satisfied
with the reward program they are participating in are willing to give
the company a larger share of wallet than those customers who are not
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satisfied with their rewards program (Demoulin & Zidda, 2008, p. 11).
Shugan (2005, p. 191) states that: “True loyalty programs invest now
for the future and trust rather than demand trust.” But further claims
that instead of creating assets by following these rules, many loyalty
programs create long-term liabilities for the company.
Dowling and Uncles note six general reasons as to why businesses
adopt loyalty programs: Maintaining sales, profits and margins;
adding value for existing customers; increasing existing customers’
cross-product sales; separating the brand from competitors’; and lastly
preventing competitors from presenting the brand’s customers with
other comparable service or product loyalty proposals (Dowling &
Uncles, 1997, pp. 4–5). Additionally, they suggest that loyalty
programs can help the brand stand out to customers as well as aid
them in standing strong in a market place that is nowadays highly
influences by loyalty rewards. The author’s refer to this as the ‘Me-too
pressure’ (Uncles et al., 2003, p. 310).
Customer loyalty programs discriminate against non-loyal
customers and because it is believed that non-loyal customers
generally burden the brand with higher average service costs, this can
be viewed as a positive side effect. Loyalty programs can funnel out
those customers and thus aid the company in being more profitable
with existing and loyal customers (Shugan, 2005, p. 191). The idea of
targeting loyalty programs to a company’s most valuable customers is
supported by Yi & Jeon (2003, p. 231). They suggest that by
discouraging customers who do not add as much value to the
company, the program turns into a self-improving tool for the brand.
While the reasons as to why credit card companies decide to
implement reward programs might to some extent differ between
firms it is likely that adoption of rewards programs is, partly, a result
of the firms’ battles against competitive parity in a fiercely
competitive industry (Dowling & Uncles, 1997, p. 5; Agarwal,
Chakravorti, & Lunn, 2010, p. 19).
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1.3 Research question As mentioned earlier, several studies have shown that a rewards
program that is perceived as valuable to the customer will not only
increase their loyalty to the firm but will increase their overall
spending habits. It is thus clear that credit card rewards affect
consumers’ purchasing decisions, but do consumers settle for just any
kind of reward or do they have a strong preference for one certain type
of reward? And when do they want to receive that reward,
immediately at the time of their purchase or later on?
Mimouni-Chaabane & Volle (2010, p. 32) noted that the majority of
existing research focuses on how the businesses’ finances benefit from
such a program. In other words, a gap had been left for the question of
customer perceived benefits. But in order to achieve an increase in
profits, credit card firms must offer their customers rewards that those
customers perceive as valuable.
This study will thus, from a business economics perspective,
concentrate on questions such as how do buyers value rewards in the
credit card industry? What types of rewards are most generally
preferred among consumers in the credit card industry? What factors
affects consumer preferences for credit card rewards? Is there a
distinct difference in preferences between male and female
cardholders? Is there a difference in preferences in relation to
cardholders’ ages?
1.4 Purpose The purpose of this study is to investigate what credit card rewards
Canadian consumers perceive as being valuable to them. By utilizing
a business researching method I will be describing, analyzing and
comparing today’s consumers’ perceived value of credit card rewards
through a survey investigation. I hope to be able to conclude whether
credit card firms should incorporate drastic changes into their rewards
programs to catch consumers’ interests, increase their loyalty to the
firm and thus increase firm profit.
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2 Theory and Literature
overview In this chapter, the theory used for the empirical study will be
presented. The chapter begins with a summarizing literature review,
followed by the concept of customer perceived value. Constructs used
in prior research to determine perceived customer value of loyalty
programs will then be presented, as well as the constructs that have
been selected for use in the current study.
2.1 Literature outline The amount of available research on the subject of loyalty programs
has increased immensely over the last three decades. A search on ISI
Web of Science provides several hundreds of articles on the subject.
Figure 1. Increase in Loyalty Program research (ISI)
The vast majority of prior research on loyalty programs has been
written from firms’ financial perspectives and thus entails instructions
on how to avoid negative financial implications during and post
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implementation. There is, however, some research encompassing
consumer preference and bias towards credit card rewards. In order to
determine what components in loyalty programs that are most often
preferred by consumers, authors have organized rewards into different
categories. The following text entails a short presentation of some of
these categories.
Dowling and Uncles (1997) and Yi and Jeon (2003) categorized
rewards into type of reward and timing of reward. To these categories,
they also added the construct of customer involvement as a
determining factor. Other authors, such as Keh and Lee (2006) who
performed similar research, chose to exclude the construct of
involvement completely. Kristof de Wulf et al. (2003) divided rewards
into hard versus soft benefits in their research, taking consumer inputs
and outputs into consideration. To provide some insight into how
perplexing research on the subject of customer rewards is the first
schedule below presents some of the authors referenced in the present
study and how they reference each other in their respective research
on the subject of investigating the relationship between consumers and
rewards programs.
Figure 2. Map of references; Rewards programs
A collection of other articles have been written on the subject of
what promotes consumer’s means of payment, and whether receiving
Do customer loyalty
programs really work?
Dowling, Uncles
Behavioral learning
theory: It's
relevance to
marketing and
promotions.
Rothschild, Gaidis
Measuring consumer
involvement profiles.
Kapferer, Laurent
The valkue concept and
relationship marketing.
Ravald, Grönroos
Consumer perceptions of
price, quality and value.
Zeithaml
Zaichkowsky:
1985, 1986, 1994
Effects of loyalty
programs on value
perception, program
loyalty and brand
loyalty. Yi, Jeon
What drives consumer
participation to loyalty
programs? De Wulf et al.
Do rewards programs
build loyalty for
services? Keh, Lee
Perceived benefits of
loyalty programs
Mimouni-Chabaane,
Volle
Self-control for the
righteous. Kivetz,
Simonson
Implications of loyalty
program membership
and service experience
for customer retention
and value. Bolton et al.
Customer satisfaction
with services.
McDougal, Levesque
Credit where credit is
due. Parahoo
Modelling the relationship between perceived
value, satisfactionand repurchasing intentions
Patterson, Spreng
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rewards affects that choice. The second schedule below presents some
insight into how the authors’, whose research is referenced in the
present study, connects and refers to one another.
Figure 3. Map of references; Means of payment
Throughout the process of finding and reading articles on the topic
of credit card rewards, all whilst critically examining their research
methods and results, a selection of constructs and determining factors
have been compiled for use in the present study. A presentation of all
these constructs will follow in the coming chapters. However, first, a
discussion about what the constructs will be leading up to in the study:
Customer Perceived Value. A selection of researchers’ opinions on,
and previously created definitions of, Customer Perceived Value will
thus be presented forthwith.
2.2 Customer Perceived Value Customer perceived value is defined differently by different authors. It
is thus difficult to come by a consistent definition of the term but
according to Dowling and Uncles it is the customer’s perceived value
that creates price insensitivity, not brand loyalty (1997, p. 14).
However, depending on the context in which it is being studied, value
can take on different meanings: Patterson and Spreng noted that in an
How effective are
rewards programs in
promoting payment
card usage? Empirical
evidence. Carbo -
Valverde
Payment card
rewards
programs and
consumer
payment choice.
Ching, Hayashi
Price incentives
and consumer
payment
behavior. Simon,
Smith, West
Debit or
credit?
Zinman
Why use debit
instead of credit?
consumer choice
in a trillion-dollar
market. Zinman
The economics of
credit cards, debit
cards and ATMs:
A survey and
some new
evidence.
An empirical
analysis of
payment card
usage. Rysman
How do you pay?
The role of incentives
at the point-of-sale.
Arango et al.
Why do banks
reward their
customers to use
their credit cards?
Agarwal et al. The failure of
competition
in the credit
card market.
Ausubel
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economic context, value can be synonymic with function or
desirability, whilst in a marketing context it is most often defined from
the consumer’s perspective (1997, p. 416).
Zeithaml (1988, p. 14) defined perceived value as: “The
consumer’s overall assessment of the utility of a product based on
perceptions of what is received and what is given”:
Figure 4. My compilation of Zeithaml’s definition of Customer Perceived value
Zeithaml’s definition is similar to that of Kent B. Monroe, which
suggests that customer perceived value is the difference between
consumer benefits and consumer sacrifice. Here, benefits represent the
physical attributes of the product or service, and sacrifice the financial
cost.
Figure 5. Zeithaml’s definition of Customer Perceived Value (as cited in Ravald &
Grönroos, 1996, p. 21)
Ravald and Grönroos suggest that differences in consumers’
perceived value is dependent on the consumer’s personal values,
preferences, needs and on their personal financial situation. According
to the authors, establishing what value the customer is requesting must
be the firm’s first aim in delivering customer satisfactory value (1996,
p. 22). Furthermore, the relationship between the firm and customer
must be taken into account when calculating perceived value, stating
that the components of the episode (the core product and the firm’s
surrounding services) alone, is not enough. Grönroos and Ravald thus
provide a model that differs from Monroe’s.
Figure 6. Ravald & Grönroos’ definition of Customer Perceived Value (1996, p. 23)
Total Episode Value = Episode Benefits + Relationship Benefits
Episode Sacrifice + Relationship Sacrifice
Customer-perceived value = Perceived Benefits
Perceived Sacrifice
Customer Perceived Value = Utility of Product (Received – Given)
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Bolton et al. found that members of loyalty programs of financial
services were less sensitive than other customers to perception of both
lower services and price disadvantages of their company (2000, p.
105). However, the authors also note that for a program to have a
long-term positive effect on its customers their experiences with the
programs must be mostly pleasant (2000, p. 96).
McDougall and Levesque found that perceived value, together with
service quality, was the most important driver in determining
customer satisfaction (2000, p. 407). The authors conducted a study in
the purpose of investigating the connection between core service
quality (consisting of perceived value and relational service quality),
customer satisfaction and future intentions. They based their theory on
the idea that customer satisfaction is a direct result of customer
perceived value, and that level of satisfaction is what determines
consumers’ future intentions.
Customer Perceived Value Customer Satisfaction Future Intentions
Figure 6. My compilation of McDougall & Levesque’s definition of Customer Perceive
value (2000, p. 395)
Summary
Each customer’s perceived value of a product or service is believed to
influence their loyalty to a brand (Dowling & Uncles, 1997, p. 14).
However, the definition of what customer perceived value is differs
between researchers. Zeithaml believed perceived value was the
consumer’s overall impression of the product based on what had been
received and what had been given. Kent B Monroe, similar to
Zeithaml, thought of customer perceived value as the difference
between the physical attributes of the product or service and the
financial cost of the purchase. Ravald and Grönroos created a model
that was based on the belief that customer perceived value is
dependent on the consumer’s personal values, preferences, needs and
on their personal financial situation.
Customer Perceived Value Customer Satisfaction Future Intentions
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The definition of customer perceived value that will be employed in
the current study is that of McDougall and Levesque, where customer
perceived value is the connection between core service quality,
customer satisfaction and future intentions. The theory that a
customer’s satisfaction with his or her credit card rewards will predict
whether they will be loyal to their credit card firm in the future is thus
employed throughout the present study.
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2.3 Potential constructs Many constructs have been developed to determine wherein the
optimal key to customer loyalty lies and according to Della Porta and
Keating, the more widespread a concept is, the less informative it
turns out (2008, p. 92). Here, five of the constructs used in prior
research of customer rewards will be presented in their most
concentrated form, four of which will be selected for use in the
present study.
Figure 7. Potential constructs for the present study. Displays formerly used means of
categorization of rewards.
2.3.1 Involvement Customer involvement is generally measured through high versus low
levels of involvement, reflecting the level of interest that the customer
has in selecting and knowing their brand or product (Dowling &
Uncles, 1997, p. 16; Yi & Jeon, 2003, p. 234). A customer with high a
Type of
Reward:
*Direct vs.
Indirect
*Advertised
vs.
Unexpected
*Hard vs.
Soft
*Primary vs.
Secondary
Timing of
Reward:
*Immediate
vs. Delayed
Target of
Attitude:
*Deal vs.
Brand loyalty
Involvement:
*High vs.
Low
Dimension
of Benefit:
*Utilitarian,
Hedonic or
Symbolic
Customer Perceived Value
20
level of involvement knows their brand and what it has to offer very
well. High involvement customers are well educated about their
brand’s loyalty program, observant towards promotions and will thus
fully benefit from all that is offered to them by their brand (Parahoo,
2012, p. 5). Parahoo noted that customer involvement plays a highly
important role in determining consumer behavior to the credit card
industry (2012, p. 5) and suggested that there is a great need for credit
card firms to concentrate on increasing levels of involvement in their
customers (2012, p. 14).
Customer Involvement Profile
Laurent and Kapferer developed CIP, the Customer Involvement
Profile in 1985. It states that the five facets of involvement are (1985,
p. 43):
1. The consumer’s perceived significance of the item
2. The perceived risk linked to purchasing the item
3. The sign/symbolic value of the item ascribed by the consumer
4. The Hedonic value of, or the consumers’ emotional appeal for,
the item
5. The perceived undesirable outcome of making a poor purchasing
decision
According to the authors, not only would the Customer
Involvement Profile create a better understanding for involvement
dynamics but it could be used to segment the market: instead of just
measuring high versus low levels of involvement, customers could
show high involvement on some facets and low on others (Laurent &
Kapferer, 1985, p. 52).
Personal Involvement Inventory
Judith Lynne Zaichkowsky disapproved of the CIP model, claiming it
suggested that involvement could be measured as a stable state (1994,
p. 59). Zaichkowsky also found that the construct of involvement had
been measured in several different fields: advertisement, products and
purchases, by authors using different measures and thus providing
results with major spread (1985, p. 341). Zaichkowsky’s own
21
objective was thus to develop a measure that would include all aspects
of involvement and allow researchers in all divergent fields to use one
reliable method. In 1985, Zaichkowsky introduced a context-free 20
item scale, measuring the motivational state of involvement, calling it
the PII: Personal Involvement Inventory (1994, p. 59). It was based on
this three factor conceptualization of involvement:
1. Personal: interests, values and needs
2. Physical: differentiating traits in an item that appeals to
consumers
3. Situational: factors that momentarily increase consumers’ interest
in the item
Each individually, two or all three of these factors together were
claimed to show the level of involvement with stimulus for an item.
The author (1985, p. 342) concluded that high involvement is
generally a result of a consumer’s personal relevance to an item.
One year after the personal involvement inventory was released
there was still no clear definition of involvement (Zaichkowsky, 1986,
p. 4). However, Zaichkowsky could confirm that involvement as a
construct is a natural motivator and that when humans are involved
they comprehend magnitude and listen and behave in a different
manner than when uninvolved (1985, p. 12). In 1996, Zaichkowsky
published a new article, defending, revising and reducing the PII after
it had received criticism saying that it did in fact not provide equal
validity for different fields of involvement study (1994, p. 59)
High versus low involvement
Grahame R. Dowling and Mark Uncles studied involvement for
product types and involvement for customer types (1997, p. 10). In
their study, products and brands could have a high or low involvement
status. As examples of low involvement brands the authors mentioned
gas stations and every-day-food brands, whilst for high-involvement
products they referred to the car manufacturer General Motors.
Typically, low involvement brands, or “me-too” brands as Dowling
and Uncles referred to them, should have less extensive rewards
programs attached to them because low involvement products are
often bought out of consumer habit. High involvement products, on
22
the other hand, should offer more extensive incentives (Dowling &
Uncles, 1997, p. 16) as it is expected to take more effort to entice the
customer to choose a certain brand over competitors when the
customer finds the purchase to be highly important to them.
Furthermore, Dowling & Uncles suggest that there are two
decisions for the buyer to make at the time of any purchase. One is the
category decision, which the authors exemplify as deciding whether to
take the bus or plane to a destination, and the other is brand decision;
what air transport service company should I fly with? For high-
involvement purchases the authors believed that consumers are highly
involved in both decisions, whilst for low involvement purchases the
involvement level for both decisions is low, although slightly higher
for the category decision (1997, p. 16).
Youjae Yi and Hoseong Jeon’s study also shows that involvement
effects how customers react to rewards programs (2003, p. 229). Their
study was drawn on that of Dowling and Uncles’ with the exception of
adding the construct of time frame for rewards. As far as the construct
of involvement goes, Yi and Jeon’s study suggests that involvement
moderates the effect of both type of reward as well as that of timing
(2003, p. 237). For high involvement situations direct rewards proved
to be more efficient for consumers to build loyalty to a brand than
indirect rewards. In the low involvement situation, the type of reward
did not impact the relationship between consumer and brand but
timing of reward did: immediate rewards proved to be more effective
for low involvement customers than delayed rewards (Yi & Jeon,
2003, p. 229). In high involvement situations, direct rewards were a
greater success than indirect rewards but timing of rewards made no
difference in perceived value of the loyalty program (Yi & Jeon, 2003,
p. 236).
Summary
For many years, the construct of involvement was undefined but
researchers were still aware that it could act as a determining factor in
how consumers perceived loyalty programs. Zaichkowsky (1985)
divided consumer involvement up into several possible levels while
Laurent and Kapferer (1985) created the five facets to establish
23
consumers’ levels of involvement. Other, more recent, studies
conducted by authors such as Parahoo (2012), Yi and Jeon (2003) and,
although not as recent, Dowling and Uncles (1997) measure
consumers’ levels of involvement as either high or low. However,
they all agree that the consumer’s level of involvement affects their
perceived value of, and satisfaction with, a product or service.
2.3.2 Type of reward
Primary versus secondary reinforces
Michael L. Rothschild and William C. Gaidis divided reward-types
into primary (core products) and secondary (coupons and tokens)
reinforcers. Rothschild and Gaidis noted that primary reinforcers were
initially much more powerful than secondary but that over time
consumers noticed that the secondary reinforcers could be converted
for primary ones and thus developed a relatively better liking for the
secondary reinforcers (1981, p. 73).
Direct versus indirect rewards
Dowling and Uncles’ version of the construct is to some extent
comparable to the promotional strategy categorization created by
Rothschild and Gaidis. Here, the construct consists of direct rewards,
which are directly linked to the brand or product bought; and indirect
rewards which are not in any way connected to what the object or
service was or where it was purchased (1997, p. 10). Dowling and
Uncles suggested that direct rewards should be more efficient in
building customer loyalty than indirect rewards are because they
encourage the value proposition of the product. However, because the
authors do not provide readers with any empirical research
information, it is unclear what their conclusions are based on.
Yi and Jeon (2003, p. 234) concur with Dowling & Uncles in that
direct rewards are better suited to enhance loyalty marketing. Yi and
Jeon furthermore claim that direct rewards are prone to be given more
attention by the customer because they are linked to the product or
24
service that the customer found important enough to purchase. Their
study also showed that consumer value perception of direct rewards
programs surpassed that of programs with indirect rewards for
consumers who were highly involved in a purchase (2003, p. 239). Yi
and Jeon thus claimed that perceived customer value of rewards can
be lessened if an indirect reward is given to consumers with high
involvement for the product or service. However, for consumers who
had low levels of involvement in their purchase there was no
difference in perceived value between direct and indirect types of
rewards.
Hard versus soft rewards
Kristof De Wulf et al. utilized the categorization of hard benefits: i.e.
pricing or gifts, versus soft benefits: consisting of additionally
provided product information, together with the construct of timing of
rewards (Wulf, Odekerken-Schroder, Canniere, & Van Oppen, 2003,
pp. 75-76). Attempting to prove that consumers prefer to receive both
soft and hard benefits immediately, the authors asked 2000 Belgian
consumers to answer 16 questions on their preferences for incentive
programs (Wulf et al., 2003, p. 77). The results of the study showed
that consumers preferred hard, immediate benefits over all other
combinations of benefits and soft benefits were only found valuable in
combination with hard benefits. Moreover, results showed that
consumers found cost of participation and program benefits to be of
most significance in determining their participation in a program
(Wulf et al., 2003, p. 78).
Advertised versus unexpected rewards
Patrali Chatterjee performed laboratory experiments on 391 students
to investigate how consumers interpret advertised rewards versus
unexpected rewards (2007, p. 63). Chatterjee found that those who
received unexpected coupons were more satisfied with their overall
purchasing experience than those who received advertised coupons.
However, those who had received unexpected coupons experienced a
higher perception of retailer injustice. The author suggested this was
25
because the consumers would perceive handing out unadvertised
coupons to be a manipulative move performed by the retailer. Another
of Patterjee’s findings was that the perceived value of the coupon was
lower when received unexpectedly. The authors explained this to be
the result of consumers feeling that they had already missed the
opportunity to use their unexpected coupon because their purchase
had already been completed. Furthermore, value denomination was
considered to be most significant when the coupon did not state a
specific future start date (2007, p. 65).
Summary
Several authors have attempted to categorize rewards by separating
and dividing them into Types. Rothschild and Gaidis divided them up
into primary (core products) and secondary (coupons and tokens)
reinforcers. Dowling and Uncles and Yi and Jeon divided rewards up
into direct: directly linked to the product or service bought, and
indirect: rewards with no link to the product or service bought. Kristof
de Wulf et al. separated rewards by categorizing them as either hard:
gifts or pricing, or soft: additional product information, whilst
Chatterjee categorized rewards into whether the rewards were
expected by the consumer or not, and referring to them as advertised
or unadvertised.
2.3.3 Timing of reward
Immediate versus Delayed Timing of rewards, also referred to as ‘timing of redemption’, is a
construct used to separate rewards’ effect on consumers depending on
at what point in time the consumer receives the reward (Rothschild &
Gaidis, 1981, p. 73; Wulf et al., 2003, p. 75; Yi & Jeon, 2003, p. 230).
The construct of timing of rewards is divided into immediate rewards,
which are rewards received upon every visit or at every purchase, and
delayed rewards, which are received upon every other-, third-, tenth
visit or with accumulation of points (Dowling & Uncles, 1997, p. 12;
Yi & Jeon, 2003, p. 230).
26
Rothschild and Gaidis found that immediate rewards were almost
always preferable to delayed rewards. They suggested that delayed
rewards would not necessarily reinforce the consumer’s desired
behavior but rather their most recent behavior. A delayed reward
which the consumer receives through mail will, according to the
authors, reinforce the behavior of opening their mail box rather than
their previous purchasing behavior (Rothschild and Gaidis, 1981, p.
73).
Dowling and Uncles, too, suggested that immediate rewards be used
over delayed rewards, stating that psychology research had shown that
delayed rewards had a less motivational effect than immediate rewards
(Dowling & Uncles, 1997, p. 11). The authors created a matrix
showing the connection between direct, indirect, immediate and
delayed rewards. Their conclusion was that delayed, indirect rewards
were the least efficient for both consumers and firms in attempting to
build customer loyalty but was surprisingly still the most often used
form of reward program.
Timing of Rewards
Immediate Delayed
Directly supports
The Product’s
Value Proposition
Type of Reward
Other, Indirect
Types of Reward
Figure 8. Types of Reward schemes (Dowling & Uncles, 1997, p. 12)
Yi and Jeon performed experiments where the immediate reward
was a scratch-and-win lottery ticket with a 10% chance of winning,
and the delayed reward was given at every 10th
visit (2003, p. 235).
Their results showed that under low involvement conditions,
1 Retailer/Brand 2 Airline Frequent
Manufacturer Flyer Clubs,
Promotions Coupons & Tokens
(Price Promotions) (The GM Card)
3 Competitions 4 Multi-Product
& Frequent-Buyer Clubs
Lotteries (Fly Buys)
(Instant Scratches)
27
immediate rewards proved to make a greater impact on customers than
delayed rewards (2003, p. 237).
Hean Tat Keh and Yih Hwai Lee conducted experiments of how
type of reward, timing of reward and satisfaction from service
interacted in a bank and a restaurant setting (Keh & Lee, 2006, p.
130). Their results differed slightly from prior studies in that when a
customer was satisfied with the overall service experience, direct
delayed rewards were most efficient in enhancing customer loyalty
(Keh & Lee, 2006, p. 133). They suggested that only when customers
are dissatisfied with the service experience are direct immediate
rewards most efficient.
Summary
The construct of timing of rewards is divided into immediate and
delayed rewards. Immediate rewards being those that the consumer
receives upon their first visit or purchase and delayed are those that
the consumer must wait to receive. Rothschild and Gaidis concluded,
from their study, that immediate rewards were almost always
preferred by consumers over delayed rewards. Dowling and Uncles
believed that immediate rewards were of greater worth in creating
customer loyalty whilst Yi and Jeon determined that preference to
either immediate or delayed rewards depended on the consumers’
levels of involvement. Keh and Lee stated that consumers’ preferences
depended on their levels of satisfaction with a service.
28
2.3.4 Target of attitude
Deal versus brand loyalty
Target of attitude, or deal versus brand loyalty, determines what area
of interest has captured the consumer’s attention (Yi & Jeon, 2003, p.
233). Some consumers will be interested in a brand because they are
enticed with the brand and genuinely like it. Other consumers’ main
focus will be the loyalty program in and of itself because the
consumers enjoy shopping for deals and not for specific brands. The
target attitude theory consists of categorizing consumers into brand
loyal versus deal, or program, loyal. Brand loyal consumers are more
likely than program loyal consumers to stay loyal to the brand even
after the loyalty program has ended. High involvement consumers are
thus more likely to be brand loyal simply because they have put more
effort into knowing their brand and already perceive a greater value
from the brand or product itself than from the rewards that come with
it. Yi and Jeon’s study suggested that when consumers perceive that a
rewards program is valuable to them, what initially started as program
loyalty for low-involvement products can result in a long-term brand
loyalty (2003, p. 238).
Summary
The construct of Target of attitude deals with where a consumer’s
interest lies. If the consumer’s main interest and loyalty is in the actual
brand and what it stands for, then that consumer is brand loyal. If the
consumer is only willing to buy a product or service because of the
rewards that come with the purchase, then that customer is program
loyal. According to Yi and Jeon a customer’s Target of attitude is, just
like type and timing of rewards, dependent on their level of
involvement.
29
2.3.5 Dimension of benefit
Utilitarian, hedonic or symbolic
Aida Mimouni-Chaabane and Pierre Volle's study was performed on
French members of loyalty programs, investigating preferences for
reward benefits (2010, p. 32). Utilitarian, hedonic and symbolic
dimensions of perceived benefits were studied. In rewards programs,
utilitarian benefits are associated with financial gain and convenience,
hedonic benefits are those of entertainment and exploration, whilst
symbolic represent a feeling of community and a sense of belonging
to an exclusive group (2010, p. 33). The authors found that the
perceived benefits and motivations were diverse among consumers
(2010, p. 36) and thus consequently suggested that both monetary as
well as non-monetary incentives should be integrated into all loyalty
programs.
Figure 9. Perceived Benefits of Loyalty Programs (Mimouni-Chaabane & Volle, 2010, p.
33)
Kivetz and Simonson (2002, p. 203) conducted multiple studies on
the differences in preferences of rewards among consumers. The
participants were a total of 5700 passengers, between the ages of 18
and 80, in an airport. All participants were asked to rate non-cash and
Dimensions of Sub dimensions of Definition:
Benefits: Benefits:
Utilitarian Monetary savings: To spend less and save more money
Convenience: To reduce choice and save time and effort
Hedonic Exploration: To discover and try new products sold by
the company
Entertainment: To enjoy collecting and redeeming points
Symbolic Recognition: To have a special status, to feel
distinguished and be treated better
Social: To belong to a group that shares the same
values
30
cash lottery prices on five-point scales according to sense of utility
(practicality and necessity) and hedonic (pleasure and luxury), some
of the studies included a difference in timing of rewards as well. Their
results showed that hedonic luxury rewards are generally more
effective than utilitarian cash-rewards (2002, p. 212). They suggested
that the reason for this was that cash-rewards would likely be spent on
necessities, making consumers decline utilitarian rewards and instead
pre-commit to hedonic luxuries as rewards (2002, p. 209).
Summary
Research on Dimension of benefit aims to investigate what aspects of
their lives consumers prefer to be rewarded in. When research is
focused on rewards programs, utilitarian benefits are associated with
financial gain and convenience, hedonic benefits are those of
entertainment and exploration and symbolic benefits represent a
feeling of community and a sense of belonging to an exclusive group.
Mimouni-Chabane and Volle found that preferences were diverse
among consumers because motivation for rewards differed. Kivetz
and Simonson, on the other hand, found that hedonic rewards were
preferred by most consumers and believed that was because
consumers felt obligated to spend utilitarian benefits (monetary
rewards) on necessities before luxury.
31
2.4 Summary of theory Each customer’s perceived value of a product or service is believed to
influence their loyalty to a brand (Dowling & Uncles, 1997, p. 14).
Although four different definitions of customer perceived value have
been presented (Zeithaml, Monroe, Ravald and Grönroos, and
McDougall and Levesque), the definition that will be employed in the
current study is that of McDougall and Levesque. They presented the
theory that customer perceived value is the connection between core
service quality (consisting of perceived value and relational service
quality), customer satisfaction and future intentions. The theory that a
customer’s satisfaction with his or her credit card rewards will predict
whether they will be loyal to their credit card firm in the future is thus
employed throughout the present study. The present study is created to
determine what rewards are preferred by Canadian consumers and
what influences their preferences. A high preference for a specific
reward will thus be recognized as one that the customer perceives as
being of high value to them. Perceptions of rewards and preferences
for rewards will be utilized interchangeably in the present study.
Empirical emphasis will be placed on those rewards which Canadian
consumers find are of the highest perceived value to them because
they are theoretically expected to result in product loyalty.
In the theory chapter, five constructs that have previously been used
in research on customer reward programs in general and on reward
programs in the credit card industry, are presented. The first of these
five constructs is that of Involvement, measuring the consumer’s level
of interest in a product. The second construct presented in the theory
chapter is the construct of Type of rewards which, in past research has
divided rewards into primary and secondary, direct and indirect and
advertised and unadvertised. The construct of Timing of rewards is the
third construct presented and divides rewards into immediate and
delayed depending on whether the consumer must wait for their
reward or not. The fourth construct is that of Target of attitude which
deals with where a consumer’s interest lies; in the brand or in the
rewards program. All three of the latter mentioned constructs; Type
and Timing of rewards and Target of attitude have been argued to be
greatly influenced by a customer’s levels of involvement in a product.
32
The fifth, and final, construct presented in the theory chapter is that of
Dimension of benefits which aims to investigate what aspects of their
lives consumers prefer to be rewarded in: the utilitarian, hedonic or
symbolic dimension.
In the next chapter, I will explain my reasoning in including only
four of these five presented construct in the study. I will also explain
how, and in line with which theories, each construct will be utilized.
33
2.5 The four constructs selected for use in the
current study and my first model of their
expected correlation with each other
Figure 10. The four constructs selected for use in the present study
Construct 1: Involvement. High versus low
Dowling and Uncles, Yi and Jeon and Parahoo believe that a
customers’ level of involvement is a determining factor in their
perceived value of rewards. The construct of involvement will thus be
regarded as an important component in the analysis of the survey
sample in the present study. One area of focus in the study will be put
on investigating whether involvement is a determining factor in
perceived value of credit card rewards too. To be able to measure a
possible relationship between involvement and the other constructs,
the correlation between respondents’ levels of involvement with each
of their answers to the other constructs will be compared.
Construct 2: Timing of reward. Immediate versus Delayed
Timing of rewards will be measured as immediate versus delayed
rewards because it is a categorization of rewards that is commonly
Type of Reward:
Direct vs. Indirect
Timing of
Reward:
Immediate vs.
Delayed
Involvement:
High vs. Low
Dimension of
Benefit: Utilitarian,
Hedonic, Symbolic
Customer Perceived Value
34
reoccurring in previous research about rewards programs as well as
applicable in investigating credit card rewards. Immediate rewards in
the present study are rewards that can be redeemed instantly upon use
of the credit card, whilst delayed rewards will be represented by any
rewards that cannot be redeemed at the time of credit card use but for
which the consumer must wait.
Construct 3: Type of reward. Direct versus Indirect
Type of reward is another construct that is commonly reoccurring in
previous research and applicable to research on credit card rewards. In
investigating types of rewards, those that will be utilized in the current
study are direct versus indirect rewards, in line with Dowling and
Uncles’ and Yi and Jeon’s research. Direct rewards will be
represented by those that are strongly connected to the credit card in
and of itself, i.e. concern credit card fees and/or credit card debt.
Indirect rewards are all kinds of rewards not directly linked to the
credit card, for example department store vouchers or movie tickets.
Construct 4: Dimension of benefit. Hedonic, Utilitarian and
Symbolic
In order to provide Canadian credit card firms with information about
where Canadian consumers’ interests lie, the construct of dimension
of benefit will also be included in the study. To investigate in what
dimension, or area, of consumers’ lives in which they prefer to be
rewarded, the construct investigates consumers preferences for
utilitarian (necessity, financial gain), hedonic (pleasure) and symbolic
(feeling part of a group) benefits.
Difference between direct rewards and utilitarian benefits in
the study
Utilitarian benefits are often symbolized by items of financial gain.
Because the object of this study is credit card rewards, a clear
distinction must be made between direct rewards and utilitarian
benefits. In order for the two constructs not to overlap, Utilitarian
35
benefits (representing necessities) will be associated with items of
financial gain such as vouchers and coupons for Groceries and cash.
Direct rewards, however, will be strongly connected solely to rewards
of financial gain that are in relation to the actual credit card such as
CC fees and CC debt.
Construct not included in the empirical study: Target of
attitude
As mentioned earlier, the construct ‘target of attitude’ determines
whether the consumer is interested in a product or brand because of
the product or brand itself or if their interest has been spiked strictly
from the appealing idea of the rewards that come with it. Because
credit card rewards are not a temporary or time sensitive factor but are
instead a highly permanent part of the card, target of attitude is not
regarded significant in the present study. The object of this study is
not to recognize what any other factors of credit cards are preferred by
consumers but for that of its customer rewards.
36
2.5.1 A first model of the correlation between the
chosen constructs for Customer Perceived Value
The model presented below represents the relationships that I expect
to find in the empirical findings of the present study. The model is
based entirely on the previous research mentioned in the theory
chapter: Each respondent’s level of involvement is expected to
influence their preference for all other constructs. Furthermore, the
consumer’s overall opinion of the factors contained in each of the
selected constructs is expected to conclude what their perceived value
of each reward is and thus, to some extent, predict their future
intentions of loyalty to their credit card provider.
Type of Reward:
Direct vs. Indirect
Timing of
Reward:
Immediate vs.
Delayed
Involvement:
High vs. Low
Dimension of
Benefit: Utilitarian,
Hedonic, Symbolic
Customer Perceived Value of
Rewards
Figure 11. A first model of the correlation between the chosen constructs for
Customer Perceived Value
37
3 Methodology In this methodology chapter, epistemology and ontology is discussed,
and execution of collecting prior research and survey data for the
current study is presented. Furthermore, my choice of researching
method, the study’s generalizability as well as attempts to increase
both validity and reliability in the study are explained.
3.1 Ontological and epistemological
considerations In order for others to accurately interpret my empirical findings, I
must give insight into how my research and findings should be
interpreted. It is thus necessary that I share what my own
interpretations of what reality and knowledge is. Bryman et al. refer to
this as double and third level interpretations (2012, p. 10).
Discussions about ontology can concern whether a social reality
exists or not: objectivists believe that it does exist whilst
constructionists do not (Bryman et al. 2012, p. 11). Instead,
constructionists believe that reality consists only of individual
interpretations. I do not fully agree with either of these standpoints but
rather take a soft constructionist position (Bryman et al. 2012, p. 11),
agreeing that the existence of a social reality is possible but that our
individual perceptions and opinions are not necessarily completely
dependent on it, or at all times connected with it.
Epistemology is concerned with what knowledge is. Those who
take a positivistic stance make clear distinctions between theory and
research and support the development of new theories from research
which has not taken pre-existing ideas into consideration. Their
research often includes both deductive and inductive elements but
believe that prior to referring to a theory as ‘knowledge’ it must be
thoroughly tested. Others take on an interpretivistic stance, which
means that they focus on understanding peoples’ individual reasoning
behind their actions, interpreting their ever-changing human
behaviors. In interpretivism, actions are perceived as being based on
38
individually perceived meanings in the social environment (Bryman et
al. 2012, pp. 8-9).
In the attempt to generalize how Canadian consumers perceive
credit card rewards, and using both inductive and deductive
approaches in my research, it could be argued that I have taken a
positivistic stance. However, I also take pre-existing ideas into
consideration in my research and believe that each individual has
diverse motivational factors which make one perceive value
differently than another. That said I also believe that the concept of a
majority vote exists and that although individuals have different
perceptions of one and the same reward, consistently there is a
majority of consumers who all perceive rewards in a similar manner.
The difficulty in recognizing whether said majority is mostly the
same, consisting of the same individuals or not, will be investigated
through my statistical analysis.
In this study, I thus attempt to analyze what the perceptions of that
majority is in order to aid credit card companies in learning in what
and where to focus their efforts in building high value rewards
programs. Consequently I do not only intend to interpret my own
interpretations of this study, but the respondents’ interpretations too.
3.2 Research methods for the present study The aim of the current study is to acquire information about what
credit card rewards the Canadian population generally prefers. Using a
quantitative research strategy (Bryman, 1997, s. 20), the current study
is carried out through a survey investigation administered to a fitting
population segment: a random selection of Canadian consumers in
Calgary, Alberta.
3.2.1 Approach
A quantitative research strategy is one in which statistical
measurements of the empirical findings are used as the basis for the
analysis (Bryman et al. 2012, p.13). Although many quantitative
studies are constructed alongside the author’s own hypothesis,
39
Bryman and Bell argue that hypotheses do not have to be part of a
quantitative study but are rather more occurring in studies that are
carried out through an experimental research approach (2003, p. 68).
The current study thus entails research questions rather than
hypotheses.
The study follows a deductive research theory approach as it was
started by researching previous theories on the subject of rewards and
loyalty programs which lead me towards a survey investigation and
my own empirical findings. In a deductive research strategy, theories
are empirically tested in order to be either confirmed or rejected and
revised (Bryman et al. 2012, p. 8). The theory that stands to be tested
in the present study is that a consumer’s level of involvement in their
credit card influences their perceptions of type and timing of credit
card rewards, and their preference for dimension of the benefits. The
constructs are thus expected to collectively determine what the
consumers’ overall perceived value of credit card rewards are.
Figure 12. Deductive approach (Bryman & Bell, 2003, p. 12)
However, the study also has an inductive segment to it as I am
forced to revise my initial theory after analyzing my empirical
findings. A new theory, or generalization, is thus created as my
findings contradict some of those of previous research, leading me to
draw new conclusions about what influences customer perceived
value.
Figure 13. Inductive approach (Bryman & Bell, 2003, p. 12)
According to Bryman et al. it is not possible to only keep to one of
the two strategies: deductive or inductive, but instead researchers
move back and forth between the two strategies throughout all
practically implemented studies, the present study being no exception.
Deductive approach: Theory Observations/findings
Inductive approach: Observations/findings Theory
40
The two research strategies combined is referred to as iterative
research (2012, p. 9).
3.2.2 Building the theoretical foundation
By searching key words such as “loyalty programs”, “consumer +
rewards” and “rewards + credit” on academic search engines such as
Google Scholar, Scopus, ISI Web of Science and Emerald a good
foundation of previous research to build the present study on was
created. To keep constructs used in the different studies separate they
were listed in a structured word document (appendix 9). Additionally,
reference maps were created in an Excel spreadsheet to simplify an
overview of what authors referenced each other (see 2.1 Literature
overview).
Working this way enabled easy re-tracking to look at what had been
read earlier in order to discuss observations between results of
previous studies, the authors’ conclusions and their personal opinions.
Whilst each article was read, parts that had been noted to be of
potential future use were highlighted, notes were also scribbled in the
margins whenever one article was contradicting, or agreeing with,
another.
Through creation of the questionnaire, I focused my research on
what survey questions other authors, who had written about similar
topics, had utilized. Another area that I concentrated my research on
was previous authors’ methodology chapters in preparation for the
process of data collection with high levels of validity. Survey
questions that were considered fitting for the previous study were
listed, some of them edited, and lastly compiled into a finished
questionnaire.
The majority of authors who had performed similar research
utilized a 7-point likert scale in their survey research. However, in an
attempt to increase precision in respondents’ answers, I chose to
utilize a visual analogue scale (VAS) in the present study. The VAS-
scale consists of a ten centimeter, straight line anchored by
continuums such as “Unappealing to me” on one end and “Highly
appealing to me” on the other. Each respondent is subjected to a
question or statement and then asked to put an X where in the linear
41
spectrum that they believe they fit. Each answer is then measured with
a ruler, resulting in a high precision answer between 1 and 100, as
opposed to those answers entered in to a 7-point likert scale where
there are only 7 options for the respondent to choose between.
3.2.3 Data collection and respondent selection
Pinsonneault and Kraemer claim that even the most different types of
survey research have three common characteristics. The first
characteristic is the most basic purpose of the survey research:
gathering quantitative information from a population segment which
represents the target population of the study, to investigate e.g.
relationships between variables. The second characteristic is the
manner in which survey research data is collected: confronting a
sample group made up of individuals, and/or organizations, and
asking them controlled, predetermined questions. The answers
collected from the sample group are what will later be the basis of the
survey research analysis. The last characteristic is that the sample
group is of sufficient size and represents the target population well
enough to be generalizable for a larger population segment than was
employed in the actual study (1993, pp. 5–6). All three of these
features are fulfilled in the current study.
After having had my questionnaire for the current study approved
by my Supervisor, Jonas Kågström, I decided to visit a larger city to
collect a random sample of respondents, consisting of Canadian
consumers. The city that was decided to be satisfactory for this
purpose was Calgary, Alberta. Calgary’s population in April of this
year was, according to a 2012 civic census, just over 1, 120 000
persons (www.calgary.ca) The first location in which an attempt to
find respondents was made was the CrossIron Mills mall. Because of
unawareness of their mall policy only a small number of respondents
were collected here before being asked to approach the Administration
Office to apply for permission to conduct research in the area.
The second choice of a location for survey administering was thus
based on the ambition of finding an area that would not be subject to
any such policy. Steven Avenue Walk located in the downtown area
of Calgary was selected for a second attempt to find respondents.
42
Steven Avenue Walk is a pedestrian-only street, directly linked to
the main train tracks in the city of Calgary, with a large part of the
city’s population passing through each day. The avenue offers many
different types of stores, a small market and live music. One Tuesday
morning had already been spent at Cross Iron Mills mall and two half
days were spent at Steven Avenue mall: a Friday and the following
Saturday, all three days within the same week of each other. By
utilizing Sullivan’s systematic sampling technique in order to retrieve
a truly random sample, every 5th
person passing by was approached
(Sullivan, 1994, p. 1297). In total, 184 persons were asked to
participate in the research and 130 of them agreed to complete the
questionnaire. This resulted in a response rate of 70,65%, however six
of the respondents had not entered their answers in a measurable
manner and could thus not be included in the study. The final survey
sample thus consisted of 124 respondents.
According to Pinsonneault and Kraemer, the precision of a study
substantially increases with a sample of 100-200 responses. The
survey sample selected for the study must represent the population
that is of focus in the study at the specific point in time when the
survey was administered (1993, p. 10). The successfully collected
survey sample was thus regarded to be of satisfactory size and type.
In an initial attempt to compare the preferences of the current
generation of Canadian consumers’ with that of the next generation of
consumers (i.e. today’s students) a second survey sample was
collected.
The second survey sample was found at the University of
Lethbridge and administered to students in the “Brain and Behaviour”
class. There, permission was given to come in to the class on a
Tuesday morning to present the research and its purpose and to ask
students to participate. To raise the response rate in the class,
everyone who completed the survey were offered the option of
participating in a draw for five gift cards to a popular Canadian fast
food restaurant; Tim Hortons. According to their professor, the class
was supposed to have around 140 students in attendance but
attendance was lower than usual and only about 100 students were in
the class on the day that I had been given permission to. However,
43
only 60 responses, out of which 59 could be used in the study, were
given. Response rate for the student sample thus ended up at an
unsatisfactory 60 %.
To collect more answers from students, University of Lethbridge’s
staff informed me about their University research pool. However, to
utilize the pool, Human social ethics clearance of the study and its
purpose was needed. Applying for such clearance included filling out
extensive but mandatory paperwork explaining the research and then
waiting to get approval by the researching pool faculty.
Administration of the survey would take place in a classroom at the
University, rented by the author, in the hope that students would chose
to fill out the questionnaire. Because of a limited timeline to finish the
research, and uncertainty in response frequency, their offer was
refused.
In the end, the student sample ended up being much too small and
focus of analysis was instead put solely on the on the random sample
of consumers from Calgary. However, because the study would now
consist of an analysis of only the one sample, more time could be
invested in performing a more in-depth graph- and statistical analysis.
3.3 Tools for statistic measurement and
interpretation of findings Because of the limited timeline involved in conducting this study, I
was unable to require enough knowledge about the statistics program
SPSS to correctly enter my empirical results into it myself. Instead, I
measured, organized and entered all my answers to the survey into an
excel spread sheet after which my Supervisor, Jonas Kågström,
entered the answers into the program. Jonas then supplied me with a
print-out of the automated findings. However, I emphasize that Jonas
did not take any part in interpreting the results but simply instructed
me in what is being measured in Pearson’s correlation and what the
program looks for in a rotated component matrix. All interpretation
and all analysis for this study have been performed by me, the author,
only. To aid me in my analysis, I have utilized The SSPS survival
44
manual written by Julie Pallant. In the text that follows, I will explain
how these statistical tools work.
3.3.1 Pearson’s Correlation
The empirical findings will be presented through interpretation of a
Correlation Matrix created through SPSS, in combination with graphs
and tables that I have created in Windows Excel. Pallant writes that
correlations are different from causality in that correlations do not
reveal whether one factor caused the other. In the two questions that
are highly correlated with each other it is thus, with only the
correlation at hand, not possible to determine whether a high rating of
question A prompted a high rating for question B, or if B prompted
high ratings for question A. Nor is it possible to tell whether there was
a third question influencing both answers (2005 p. 124). Only those
correlations that have a significance at the 0,01 level (indicated with
**) and at the 0,05 level (indicated with *) have been included in the
analysis. The Correlation matrix which was created in SPSS from my
survey sample and that I have used for interpretation in the empirical
study can be found in appendix 3.
3.3.2 Factor Analysis
To analyze the empirical data further all answers to the survey were
run through a factor analysis in the statistical program SPSS, using a
Rotated Component Matrix. This process allowed me to recognize
what additional factors the respondents had in common in their
answers.
KMO
The Kaiser-Mayer-Olkin (KMO) value and Bartlett’s test measure the
adequacy of a random survey sample. The KMO value is measured on
a scale from 0 to 1. 1 is not a realistically possible result but the value
must be higher than 0,6 for the sample to be considered viable for
analysis (Pallant, 2005 p. 183). My sample of 124 respondents
obtained a very strong Kaiser-Mayer-Olkin value of 0,805. Bartlett’s
45
test should be presented as significant (at a value of 0,05 or smaller),
which the testing of my survey sample does, showing that it supports
factorability of the correlation matrix. Below is the KMO value and
Bartlett’s test for my survey investigation.
Kaiser-Meyer-Olkin Measure of
Sampling Adequacy. ,805
Bartlett's Test of
Sphericity
Approx.
Chi-Square
987,3
19
df 153
Sig. ,000
Figure 14. KMO and Bartlett's Test
Total Variance Explained
Total Variance Explained is a way to group data into components to
find patterns and explain results. For the present study, this meant that
SPSS organized the original 18 questions in the survey into five
components that all had an Eigenvalue of 1 or more (Pallant, 2005, p.
192). Together these five components represent 67,77% of the results
from the survey. The components each contribute to the results to
different degrees portrayed through their individual percentage
portions with component 1 being the highest, in this study at 32,388%
and component 5 being the lowest, in this case at 5,944%. The
reasoning behind converting the 18 questions into five components is
that it aids the analysis process by telling me that all questions in the
same component have something in common. It is, however, up to me
to understand and determine what that common factor could be.
Rotated Component Matrix
The five highlighted components in the Total Variance Explained
table are, as mentioned earlier, also organized in a Rotated Component
Matrix which can be found in the analysis chapter of this thesis. The
Rotated Component Matrix clearly states what questions belong in
each of the five components. The components and the questions that
they each entail are what will be the core discussion in the analysis
46
chapter. To make the Rotated Component Matrix easier for readers to
interpret, I have created a color scheme for each construct which is
continually used throughout both the empirical findings chapter as
well as throughout the analysis.
3.4 Reliability, validity and generalizability Reliability concerns whether the results of the study can be replicated
by performing another, comparable study and is an area that is of
especially large concern in quantitative research (Bryman & Bell,
2003, p. 33). The theoretical ground of this study is based on
previously published scientific articles written on similar topics, their
results and their findings. The articles referenced in the present study
have all received several quotations, some as many as over a
thousand, indicating that they can be regarded reliable sources. The
questions for the present study’s survey were all collected from these
highly referenced articles.
To increase reliability, data collection was carried out in a manner
similar to that of the data collection of reliable previous research. The
survey sample was collected by two persons: the author and one
helper, administering the questionnaires to Canadian consumers in
Calgary, Alberta. The first questionnaires were administered by both
administers together to ensure that both were communicating the same
instructions to each respondent. To reach a higher level of reliability,
all 124 surveys and each response to each question was controlled and
measured by the author only.
Della Porta and Keating state that validity is one of research’s most
vulnerable points and that neither accuracy of observations, level of
comparability or how replicable the research is, if it does not have
strong validity the study will have collapsed (2008, p.282). Internal
validity concerns the level of generalizability, or lack of bias, in the
study sample while external validity represents the level to which the
findings of the study can be generalized, outside of its sample context
(Bryman & Bell, 2003, p. 33). To increase validity in the current
study, a random sample of consumers was utilized as respondents for
the survey research. In an attempt to increase reliability, the 7-point
47
likert scale that was utilized in a majority of the previous research
material was exchanged with a Visual Analogue Scale for higher
precision in answers in the present study. The VAS scale has
previously proved to provide both high reliability and high validity
(Lingjærde & Regine Føreland, 1998, p. 392).
The results of a survey can only be generalized to the population in
which, and in the location where, the survey took place (Bryman &
Bell, 2003, p. 109; Della Porta & Keating, 2008, p. 92). According to
Della Porta and Keating the only sampling that can be generalized and
still be true is a probability sample, namely: the random sample (2008,
p. 244), indicating that generalizability is fairly high in the present
study. My survey sample of 124 respondents cannot reliably be
viewed as representative of the entire Canadian population but does
fulfill its purpose in contributing to Canadian credit card rewards
research.
Pinsonneault and Kraemer believe that authors should be more
careful when generalizing their findings and focus on strengthening
the relationship between the respondents and the population that is
under analysis. By better defining what population is the desired
subject of analysis and then ensuring that the survey sample accurately
represents that population, the results of the research will be stronger
(1993, p. 28). The target population for analysis in the present study
was Canadian consumers. Although the survey sample is too small to
be generalizable for the entire Canadian population, generalizability
was increased by the fact that respondents were asked to fill out their
resident status so that it was possible to know how many were actually
Canadian. Out of the final sample of 124 respondents, only 5 persons
were neither a Citizen nor a Permanent resident.
48
3.5 Possible methodology errors
3.5.1 Errors in quantitative research
According to Bryman and Bell (2003, p. 87), two reasons as to why
research in general is not always carried out in an ideal practice are
that, 1: Teachers of research methodology cannot possibly cover all
eventualities that may occur during the researching process and some
inaccuracy is thus likely to take place in many research papers. 2:
good practice of researching methods is not always followed by
researchers, not necessarily because of the researchers’ incompetence
but rather because of matters such as time, cost and feasibility.
Some of the critique that has been directed towards quantitative
research (Bryman & Bell, 2003, p. 86) is that:
It is not customized to the social world but expects measures
that are created for the natural world to apply to social
interactions.
It does not take into account that respondents do not all
comprehend and interpret questions in the same way.
It often ignores that the respondent may not have the
knowledge necessary to correctly answer the questionnaire.
3.5.2 Errors in survey research
Bryman and Bell consider four areas of error in social survey research
(2003, p. 110-111). The first is the Sampling error, which constitutes
that it is highly unlikely that the researcher will succeed in collecting a
completely representative sample. The second is the Sampling-related
errors which are related with the actions associated with the sampling
procedure and external validity of research results. The authors
provide examples of such errors: inaccurate sampling frames and non-
response. The data-collection error is the third area and is represented
by inadequate or weak language in the questionnaire. The fourth area;
the data processing error, is that of incorrect coding of questions and
mistakes in managing the collected data.
49
Figure 15. Bryman and Bell’s four sources of error in social survey research (2003, p.
110)
In order to avoid as many of these errors as possible I have tested
my random survey sample by running it through KMO and Bartlett’s
test. Non-response and inaccurately filled out questionnaires were not
included in the final sample of 124 responses and to aid consumers in
understanding the language in the questionnaire, my assistant and I
were present, ready and available to answers any of their questions
during their participation in the survey. Finally, the survey data was
measured and organized by me, the author only, to decrease risk of
possible interpretation bias.
Error
Sampling
error
Sampling-
related error
Data collection
error
Data processing
error
50
4 Empirical findings In this chapter, the most basic findings from the survey investigation
will be presented, in preparation for a more thorough examination in
the Analysis chapter. Here, in the Empirical findings chapter,
respondents’ answers to each question will be presented through
distribution histograms, standard deviations and means. I have
created all graph, tables and histograms in this chapter in Windows
Excel 2010.
A random sample of Canadian consumers was collected in the city of
Calgary, Alberta, Canada. By visiting the Cross Iron Mills mall and
Steven Avenue, 124 responses to the questionnaire were gathered, of
whom 96% were Canadian citizens, 56 percent were women and 44
percent were men. 97,6 percent of respondents had between two and
fifty years of experience of holding at least one credit card. The
average amount of experience among experienced respondents was
18,6 years, showing that the majority of respondents had substantial
experience of holding credit cards. All questions used in the
questionnaire were either quoted from, or based on, prior studies
performed on similar topics, as were the continuums verbage
anchoring the questions: “unappealing to me” etc. The order of the
questions in the survey was also carefully considered: To highlight the
difference between the two options presented within each construct
and allow respondents to compare them to each other, questions about
timing and type of rewards were clumped together. To aid
comprehension of the survey results, I have organized the
respondents’ answers into frequency histograms, rounding them off to
fit into 0, 10, 20 30 and so on up to 100. Answers to each question
will thus be presented by portraying the frequency with which the
respondents distributed their answers from 0 to 100 on the VAS-scale.
From this point on, all questions within the same construct will be
presented in the same color: Involvement will be presented in the color
dark blue; Timing of rewards will be presented in green; Type of
reward in red; and Dimension of benefits in purple color.
51
4.1 Findings from the survey investigation This first graph presents each respondent’s age in relation to their
level of experience of holding at least one credit card. The blue line
represents age and has been organized from 18-74+. Each
respondent’s age has been paired with their level of experience, which
is represented by the blue area located directly underneath the line.
Figure 16. Each respondent’s age in relation to their level of experience with holding at
least one credit card.
Although there are a couple of exceptions, as was expected the
graph shows that the older the respondent is the more experience they
generally have with holding at least one credit card.
0
10
20
30
40
50
60
70
80
90
1 6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
10
1
10
6
11
1
11
6
12
1
Experience
Age group
52
4.1.1 Level of Involvement: High versus low
Question 1: “I researched and carefully considered my options before
choosing my credit card.”
Figure 17. Question 1
This question was created to measure each respondent’s level of
involvement in choosing their credit card. Because prior research in
rewards programs has shown that involvement does affect a person’s
bias to type and timing of rewards, this question was placed first in the
questionnaire. The results show that the majority of respondents found
that this statement at least somewhat applied to them, signaling that
many had a medium to high level of involvement in their choice of
credit card. The outcome of this question is highly satisfactory as it
allows for more extensive analysis about the relationship between
perceived value in each construct and the consumers’ level of
involvement.
In the graph below, each respondent’s level of involvement has
been measured in relation to their age group. The purpose of this
graph is to investigate whether there is a connection between the age
of the respondent and their level of involvement. The graph shows that
there generally is no connection. However, more of the very youngest
respondents (18-25) had a higher level of involvement whilst all the
other age groups showed a more varying result. This could be an
indication that more effort needs to be directed towards the younger
population in order to earn their business. But, it could also be a result
of the younger population having gone through the process of
0
5
10
15
20
25
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q1
Mean=53,6 Std. Dev=33,29
Question 1 Frequency %
0 Unappealing 0 0 10 22 18 20 12 10 30 5 4 40 6 5 50 7 6 60 7 6 70 15 12 80 16 13 90 17 14 100 Highly appealing 17 14
53
choosing their credit card more recently and thus have a better
memory of the experience.
Figure 18. Responses to level of involvement in relation to each respondent’s age
This next graph (below) shows the similarities between male and
female respondents in level of involvement. The results show that men
in the survey sample generally had a slightly higher level of
involvement in their choice of credit card than women in the sample
did. It also shows that more women than men participated in the
study: Out of the entire random sample of 124 respondents, 55 were
men and 69 were women.
Figure 19. Male and female responses to level of involvement
0
20
40
60
80
100
120
1 61
11
62
12
63
13
64
14
65
15
66
16
67
17
68
18
69
19
61
01
10
61
11
11
61
21
Answer to Q1
Age group
0
20
40
60
80
100
120
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67
Q1 Men
Q1 Women
54
4.1.2 Timing of rewards: Immediate versus delayed
Immediate rewards
Question 2: “A 1% discount on my current purchase”
Figure 20. Question 2
This immediate reward was one that the majority of respondents
perceived as one of low value and the average response among
respondents was 43. The 1% discount was created as a comparison to
the question of a delayed reward of 5% (in Q3). In relation to other
rewards mentioned in the survey, question 2 has a percentage rebate
amount stated in it, which must to be taken into account when
analyzing the result. In other words, respondents could have partly had
the amount of the rebate in mind (1%) when they stated their
perceived value of the reward, skewing their answers and this study’s
investigation of perceived value of timing of rewards.
0
5
10
15
20
25
30
35
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q2
Mean=43,0 Std. Dev=31,63
Question 2 Frequency %
0 Unappealing 0 0 10 33 27 20 11 9 30 6 5 40 12 10 50 9 7 60 10 8 70 11 9 80 10 8 90 11 9 100 Highly appealing 11 9
55
Question 4: “I collect reward points and I find out how many points I have
collected immediately after my purchase”
Figure 21. Question 4
This immediate reward also received a low rating by a majority of
the respondents: only 29 percent of respondents gave it a rating of
>70. Just like in question 2, question 4 was created as a comparison to
another question (to Q5). It must thus again be taken into account that
some respondents may have answered this question with idea of
collecting points in mind rather than only the timing of those points. It
was not at all unusual that respondents’ commented on the idea of
point systems at the time of data collection. Comments such as “I just
love collecting points” were often stated. This is something that could
have had an influence on respondents’ answers to question 4.
0
10
20
30
40
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q4
Mean=39,4 Std. Dev=32,78
Question 4 Frequency %
0 Unappealing 0 0 10 35 28 20 14 11 30 13 10 40 11 9 50 9 7 60 6 5 70 10 8 80 4 3 90 7 6 100 Highly appealing 15 12
56
Delayed rewards
Question 3: “A 5% discount on a purchase of the same value as in question
#2 on my fifth visit”
Figure 22. Question 3
Although there is a small spike at 70 and 100, the majority of
respondents found this delayed discount unappealing. Interestingly,
the result of this 5 percent delayed reward is very similar to that of the
immediate 1 percent reward mentioned earlier (Q2). The average
response for the immediate version of this reward (question 2) was 43,
and the average for question 3 is 44.
0
5
10
15
20
25
30
35
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q3
Mean=44,4 Std. Dev=33,62
Question 3 Frequency %
0 Unappealing 1 1 10 31 25 20 13 10 30 9 7 40 9 7 50 7 6 60 7 6 70 13 10 80 9 7 90 9 7 100 Highly appealing 16 13
57
Question 5: “I collect reward points and at the end of every month I find out
how many points I have collected”
Figure 23. Question 5
In question 5, 44 percent of respondents gave the delayed reward of
finding out how many points have been collected at the end of the
month a low rating of <30. However, as with question 4, many other
respondents found the delayed points reward highly appealing and 36
percent gave it a rating of >70. The remaining 20 percent of
respondents gave it a fairly equal spread of 30-60.
0
10
20
30
40
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q5
Mean=44,7 Std. Dev=35,10
Question 5 Frequency %
0 Unappealing 1 1 10 31 25 20 16 13 30 6 5 40 10 8 50 11 9 60 5 4 70 5 4 80 7 6 90 12 10 100 Highly appealing 20 16
58
4.1.3 Type of rewards: Direct versus indirect
Direct rewards Question 6: “For every $100 spent on the credit card, a rebate of $1 is credited
to my credit card debt”
Figure 24. Question 6
This question portrays an interesting result: As portrayed in the
histogram respondents’ perception of this reward varied greatly. The
average response to this question is 50,6. 37 percent of respondents
gave this direct reward a rating of <30, and 44 percent of respondents
gave it a rating of >70.
Question 7: “For every $100 spent on the credit card, my credit card fee is
lowered by $1”
Figure 25. Question 7
0
5
10
15
20
25
30
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q6
Mean=50,6 Std. Dev=33,53
0
5
10
15
20
25
30
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q7
Mean=48,5 Std. Dev=34,52
Question 6 Frequency %
0 Unappealing 0 0 10 26 21 20 7 6 30 12 10 40 6 5 50 8 6 60 11 9 70 7 6 80 17 14 90 11 9 100 Highly appealing 19 15
Question 7 Frequency %
0 Unappealing 1 1 10 28 23 20 8 6 30 14 11 40 4 3 50 9 7 60 8 6 70 6 5 80 15 12 90 14 11 100 Highly appealing 17 14
59
The results for question 7 are similar to those of question 6 in that
perceptions varied: many consider this particular reward to be very
unappealing while many others found this reward highly appealing.
The average response to question 7 is 48, 5. 41 percent of respondents
rated it <30 and 42 percent rated it >70. The results imply that
perceived value for direct rewards in the credit card industry highly
varies with individuals.
Indirect rewards Question 8: “For every $100 spent on my credit card, I get a $1 shopping
voucher at select department stores”
Figure 26. Question 8
The majority of respondents found the indirect reward of vouchers
in department stores (Q8) unappealing. 52 percent of respondents gave
it a <30 rating and only 22 percent rated it at 70 or over. Interestingly,
the histogram for question 9 which also measures customer perceived
value for indirect rewards looks very similar to that of question 8.
Because a monetary value is included in both of these questions, it
must be taken into account that consumers may have had the amount
of the voucher in mind when answering both of them.
0
10
20
30
40
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q8
Mean=36,5 Std. Dev=29,98
Question 8 Frequency %
0 Unappealing 1 1 10 34 27 20 17 14 30 12 10 40 12 10 50 12 10 60 8 6 70 6 5 80 8 6 90 4 3 100 Highly appealing 10 8
60
Question 9: “For every $100 spent on my credit card, I get a $1 voucher at
select restaurants”
Figure 27. Question 9
Although there is a spread in responses in question 9 and some
respondents found this indirect reward medium-highly appealing, the
vast majority found it unappealing. A whole 62 percent gave it a
rating of <30 while only 21 percent rated it >70. Answers to this
question are also quite similar to those of question 8: again I believe it
is fair to make the assumption that respondents found these questions
to be quite similar to each other.
0
10
20
30
40
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q9
Mean=33,4 Std. Dev= 30,04 Question 9 Frequency %
0 Unappealing 0 0 10 36 29 20 24 19 30 17 14 40 6 5 50 8 6 60 7 6 70 5 4 80 6 5 90 6 5 100 Highly appealing 9 7
61
4.1.4 Dimension of benefit: Hedonic, Utilitarian and
symbolic benefits
Hedonic benefits Question 10: “A $5 coupon for gourmet foods”
Figure 28. Question 10
To receive a coupon for gourmet foods was rated unappealing (<30)
by the majority (approximately 57 percent) of the respondents.
However there are also spikes at 70 and 100: In total only 27 percent
of respondents gave it 70 or higher. To get more clarity in who rated
this reward higher, we must look at its correlation with questions
representing other constructs in the Analysis chapter.
0
10
20
30
40
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q10
Mean=37,4 Std. Dev=31,7
Question 10 Frequency %
0 Unappealing 0 0 10 35 28 20 17 14 30 19 15 40 4 3 50 7 6 60 8 6 70 12 10 80 5 4 90 4 3 100 Highly appealing 13 10
62
Question 14: “I discover new products”
Figure 29. Question 14
Discovering new products was rated unappealing by the vast
majority (72 percent) of respondents, only 17 percent of all
respondents gave it a >70 rating.
Question 17: “I try new products”
Figure 30. Question 17
Similar to the result of question 14, trying new products was not
something that was perceived as valuable to many respondents.
Although the amount of ratings towards “unappealing” are not as
extreme in Q17 as they are in Q14, 57 percent of respondents gave the
reward of trying new products a rating of 30 or less.
0
10
20
30
40
50
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q14
Mean=31,8 Std. Dev=27,98
0
10
20
30
40
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q17
Mean=34,2 Std. Dev=29,93
Question 14 Frequency %
0 Unappealing 0 0 10 42 34 20 19 15 30 11 9 40 8 6 50 10 8 60 13 10 70 6 5 80 7 6 90 3 2 100 Highly appealing 5 4
Question 17 Frequency %
0 Unappealing 0 0 10 38 31 20 22 18 30 10 8 40 8 6 50 12 10 60 10 8 70 4 3 80 6 5 90 4 3 100 Highly appealing 10 8
63
Utilitarian benefits Question 11: “A $5 coupon for groceries”
Figure 31. Question 11
Receiving a $5 coupon for groceries had a large spread in
respondents’ perceived value. Although the tallest spike is at 10, with
18 percent of responses, strong spikes were also found at both 70 (14
percent) and 100 (15 percent). Overall, slightly more respondents
found this reward more appealing than not with an average response
of 51%.
Question 13: “I shop at a lower financial cost”
Figure 32. Question 13
Shopping at a lower financial cost was considered one of the most
valuable rewards offered in the survey with an average response at
64,2. As much as 62 percent of respondents gave this reward a 70<
rating and only 20 percent rated it <30.
0
5
10
15
20
25
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q11
Mean=51,2 Std. Dev=32,16
0
5
10
15
20
25
30
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q13
Mean=64,2 Std. Dev=30,55
Question 14 Frequency %
0 Unappealing 0 0 10 22 18 20 9 7 30 12 10 40 4 3 50 9 7 60 12 10 70 17 14 80 13 10 90 8 6 100 Highly appealing 18 15
Question 13 Frequency %
0 Unappealing 0 0 10 9 7 20 9 7 30 7 6 40 5 4 50 10 8 60 7 6 70 9 7 80 21 17 90 20 16 100 Highly appealing 27 22
64
Question 16: “I save money”
Figure 33. Question 16
Question 16 had, by far, the highest amount of ratings of ‘highly
appealing’ out of all of the rewards mentioned in the survey. With 78
percent of respondents giving the reward of saving money a 70+
rating, it can be considered a reward that is generally perceived as
being of very high value to many Canadian consumers.
Symbolic benefits Question 12: “A $5 voucher for a membership in a customer club”
Figure 34. Question 12
Receiving a voucher for as membership club was regarded one of
the rewards in the survey with the least appeal. With an average
response of only 24, 78 percent rated it 30 or lower.
0
10
20
30
40
50
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q16
Mean=74,5 Std. Dev=26,44
0
10
20
30
40
50
60
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q12
Mean=24,0 Std. Dev=25,13
Question 16 Frequency %
0 Unappealing 0 0 10 5 4 20 5 4 30 3 2 40 4 3 50 6 5 60 5 4 70 7 6 80 17 14 90 32 26 100 Highly appealing 40 32
Question 12 Frequency %
0 Unappealing 0 0 10 50 40 20 18 15 30 29 23 40 7 6 50 6 5 60 3 2 70 1 1 80 1 1 90 1 1 100 Highly appealing 8 6
65
Question 15: “I receive better treatment than other customers”
Figure 35. Question 15
Although there is some spread in perceived value for being
rewarded with better treatment than other customers, many of the
respondents (43 percent) gave it a low rating of <30. Some
consumers’ comments about this reward at the time of data collection
were “I don’t like it when they treat me differently” and “I want to be
left alone when I am shopping”.
Question 18: “I belong to a community of people who share the same values”
Figure 36. Question 18
Just like Q12 and Q15, this symbolic reward received one of lowest
ratings out of all the rewards in the survey. The average response for
question 18 is higher than for any other symbolic reward in the
survey, although overall symbolic rewards were by far given the
lowest ratings of perceived value in the survey. Belonging to a
0
5
10
15
20
25
30
35
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q15
Mean=42,5 Std. Dev=31,49
0
10
20
30
40
0 10 20 30 40 50 60 70 80 90 100
Distribution histogram for Q18
Mean=37,9 Std. Dev=31,96
Question 15 Frequency %
0 Unappealing 0 0 10 33 27 20 11 9 30 9 7 40 6 5 50 14 11 60 11 9 70 10 8 80 13 10 90 5 4 100 Highly appealing 12 10
Question 18 Frequency %
0 Unappealing 0 0 10 37 30 20 14 11 30 16 13 40 5 4 50 12 10 60 8 6 70 7 6 80 6 5 90 7 6 100 Highly appealing 12 10
66
community of likeminded people got a <30 rating from 54 percent of
respondents whilst a >70 rating was only given by 27 percent.
4.2 Additional comments about compilation of
survey results The distribution histograms for question 2 and 4 shows that the two
options are similarly unpopular, signaling that preferences for
immediate rewards are likely not perceived as the most valuable
rewards among credit card holders. It must however be mentioned that
both questions could be affected by the rewards that were offered (a
discount and points) rather than the actual timing of those rewards.
Although, because question 4 was rated lower than question 5, a
question which only offers a delayed version of Q4, results indicate
that credit card holders do prefer delayed rewards even though the
reward received is, relatively, no greater when received later.
With few exceptions, throughout the entire survey results showed
that there was no obvious difference in perceived value between
women and men or between respondents’ age groups. The only
questions that showed any difference between males and females were
question 3 and question 18. Men had a slightly stronger preference to
the delayed, 5 percent discount (Q3) and women had a slightly higher
preference for belonging to a community of likeminded people (Q18).
Figure 37. Differences in answers between men and women
All other graphs displayed results very similar to those of questions
9 (indirect type) and 14 (hedonic dimension of benefit). These are
0
20
40
60
80
100
120
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Q3 Men Q3 Women
0
20
40
60
80
100
120
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Q18 Men Q18 Women
67
typical examples of what the remaining graphs looked like, showing
no differences in perceived value in credit card rewards between
women and men.
Figure 38. Similarities in answers between men and women
Only one of the questions showed any difference in perceived value
of rewards in relation to respondent’s ages. The question of receiving
a $5 voucher for a membership in a customer club was not found
appealing for any respondents over the age of 57 (Q12: hedonic
dimension of benefit), although results for a higher perceived value of
this reward were also very scattered among remaining age groups.
Figure 39. Answers to question 12 in relation to age
0
20
40
60
80
100
120
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Q9 Men Q9 Women
0
20
40
60
80
100
120
1 6 11 16 21 26 31 36 41 46 51 56 61 66
Q14 Men Q14 Women
0
20
40
60
80
100
120
1
10
19
28
37
46
55
64
73
82
91
10
0
10
9
11
8
Answer to Q12 Age group
68
No other graphs created to investigate possible links between
perceived value of rewards and respondents’ ages showed any obvious
patterns. The two graphs, for Q2 (immediate timing) and
Q11(utilitarian dimension of benefit), below portray typical examples
of this.
Figure 40. Similarities in responses between ages
0
20
40
60
80
100
120
1
11
21
31
41
51
61
71
81
91
10
1
11
1
12
1
Answer to Q2 Age group
0
20
40
60
80
100
120
1
10
19
28
37
46
55
64
73
82
91
10
0
10
9
11
8
Answer to Q11 Age group
69
5 Analysis In this chapter, findings from the empirical findings will be analyzed
and compared to previous research. Using Pearson’s Correlation and
a factor analysis, I reflect on how the rewards in the survey were
perceived by the respondents as well as what affects customer
perceived value.
5.1 Pearson’s Correlation between constructs The highest correlation of all in the present study is that between age
and the consumers’ experience with credit cards, with a statistically
significant value of 0,889. This shows that the older the respondent
the more experience they have with holding at least one credit card.
5.1.1 Level of Involvement
In expecting involvement to have a significant influence on the
constructs of type and timing as that is what previous research has
shown, it was surprising to find that in the current study the opposite
was true. Contradictory to previous research, involvement did not
have noticeably strong correlations with any other constructs in the
study. Correlations between involvement and Q5 (delayed points)
were stronger than any other correlations with involvement, at a
relatively weak value of 0,372. Other statistically significant
correlations with involvement were those of shopping at a lower
financial cost (Q13), at a value of 0,248 and the hedonic reward of
discovering new products (Q14) at 0,234. Again, these correlations are
too weak to allow me to draw any further conclusions.
A counter argument to the low correlation would be that credit
cards are low involvement products. However, Dowling and Uncles
suggest that there are high involvement products and low involvement
products. They refer to low involvement products as those that are
purchased out of the consumer’s own habitual patterns (1997, p. 10).
Because it takes extensive paper work, collecting information about
the consumer’s prior credit history and a waiting period applies before
70
one can retrieve their credit card from their credit card firm or bank, it
would thus seem wrongful to assume that credit cards are easily
attainable and that they can fall into the low involvement product
category. Furthermore, because the distribution histogram clearly
shows that a number of respondents did show high levels of
involvement in their credit card, these findings were not a result of
lack of involvement in respondents.
According to Dowling and Uncles there are two decisions made by
the consumer in each purchasing decision: the category and the brand
decision. I expect that these two categories translate to the credit card
industry and that the consumer will have made their category decision
already when they decide to make a payment with their credit card
rather than with cash or cheque. The decision that is most interesting
of the two, in the present study, is thus the brand decision. In the
present study, the brand decision is expected to correctly translate into
the consumer’s choice of credit card firm. Dowling and Uncles argue
that a high involvement consumer will show high involvement in both
the category and the brand decision (1997, p. 16), in other words they
would have high levels of involvement in their choice of payment and
in their choice of credit card firm too.
Previous studies have shown that involvement affects the
customer’s perceived value in a service or product (Yi & Jeon, 2003,
p. 229; Parahoo, 2012, p. 13). In Yi and Jeon’s study, it was
concluded that a consumer’s level of involvement did affect the
consumer’s perceived value in both type of reward as well as in timing
of reward. A high level of involvement made the consumer more
sensitive to being rewarded with the accurate type of reward: direct
rewards, whilst low involvement consumers’ perceived the timing of
the reward to be more crucial to determine its value and preferred
immediate rewards.
In summary, the findings in the present study do not support those
of previous research about the influence of involvement on a
customer’s perceived value of rewards. If these findings study would
have followed Dowling and Uncles’, Yi and Jeon’s and Parahoo’s
predictions about the consumer’s level of involvement, the present
71
study would have shown much higher correlations between
involvement and direct rewards and immediate rewards.
5.1.2 Type: Direct versus Indirect rewards
Yi and Jeon propose that direct rewards are more beneficial in regards
to building customer loyalty than indirect rewards because they
support the service or product in question (2003, p. 234). In the
present study, questions on direct rewards, both those that reduced the
credit card holder’s debt (Q6) and their fees (Q7), had a fairly strong
correlation of 0,446.
Interestingly, indirect rewards for vouchers for department stores
(Q8) and indirect rewards for restaurants (Q9) also had a high
correlation, of 0.584. The correlations between indirect and direct
rewards, however, were noticeably lower (Q6 to Q8: 0,376 and Q7 to
Q9: 0,359). This could mean that respondents either prefer to receive
direct rewards that are linked to their credit card finances or indirect
rewards not connected to their credit card at all. One and the same
individual did thus generally not perceive an equally high value for
direct and indirect rewards in the survey.
As mentioned earlier, Yi and Jeon suggested that consumers with
high levels of involvement showed tendencies of a higher perceived
value from direct rewards than from indirect rewards whilst low level
involvement consumers showed no preference. In the present study,
however, both direct and indirect rewards showed statistically
insignificant results in correlation with involvement, meaning that no
further interpretations can be made.
5.1.3 Timing: Immediate versus Delayed rewards
The correlation between immediate and delayed discount rewards was
very strong and portrayed an interesting finding: The correlation
between the immediate discount (Q2) and the delayed discount (Q3)
was a statistically significant high value of 0,634, indicating that
timing of the reward made very little difference in perceived value of
discount rewards in the present study.
72
The same pattern is clear with immediate and delayed points as
rewards. The correlation between immediate points (Q4) and delayed
points (Q5) was a strong value of 0,575. The respondents who took
part in the survey were thus interested in either receiving rewards in
the form of discounts or interested in collecting points, but not both.
The timing of said rewards: immediate or delayed did thus not seem to
have any effect on the respondents in this study.
5.1.4 Dimension of benefits: Utilitarian, Hedonic and
Symbolic Agarwal et al.'s research showed that cash-back rewards
‘significantly’ (2010, p. 18) increased both spending and debt
accumulation on consumer’s credit cards. In the same study, the
authors could determine that cash-back rewards programs are a
fiscally efficient tool for banks to increase return as the average card
holder only redeemed a fragment of their prior spending in cash.
Cash-back rewards were thus crowned the best revenue generator for
credit card firms when consumers had not used their payment card at
all before the reward program was implemented.
In the present study, all questions about utilitarian rewards had high
correlations with each other. Shopping at a lower financial cost (Q13)
and saving money (Q16) were both, according to the distribution
histograms, perceived as high value rewards. The two questions also
had a very strong correlation value of 0,564 with each other,
indicating that a reduction in cost is a reward that the respondents
generally found appealing in relation to the other rewards in the
survey.
However, the highest correlation between any two questions from
different constructs was that between question 10 and 11 where
consumers rated $5 coupons for groceries and gourmet foods. It had
been my prediction that most consumers who were interested in
utilitarian benefits would choose the reward consisting of groceries
but the statistical results instead showed that consumers generally
found gourmet foods to be a just as attractive as a reward with a
correlation value of 0,715. This finding contradicts that of Simonson
73
and Kivetz, who concluded that hedonic rewards were considered
more appealing than utiliarian because the hedonic rewards would
allow consumers to commit to guilt-free luxury (2002, p. 212).
Except for the high correlation found between the hedonic reward
of gourmet foods and the utilitarian reward of groceries, discovering
new products (Q14) and trying new products (Q17) also had a very
strong correlation: 0,725. The latter finding could be a result of the
two rewards being quite similar to each other. Q17, unlike Q14,
measures not only the consumers’ interest in new products but also
their willingness to purchase them as they would otherwise be unable
to try them. The strong correlation could thus both mean that the
respondents would be prepared to spend money to try new products
because new products had a strong enough appeal, or that respondents
assumed they would have to spend money in order to discover new
products as well.
To some extent, the findings from the present study also contradicts
Mimouni-Chaabane and Volle’s findings which concluded that
perceived value of dimension of benefits differed largely among
consumers (Mimouni-Chaabane & Volle, 2010, p. 36). In the present
study, consumers instead tended to perceive rewards as being of high
value depending on what the reward consisted of, rather than
depending on what dimension of their life the reward was focused on.
Preferences were thus not fluctuating over all three dimensions but
were rather heavily weighted towards utilitarian benefits and hedonic
benefits, whilst symbolic benefits were not perceived as nearly as
valuable.
Interestingly, the correlation between number of years of experience
of holding at least one credit card and a $5 voucher for a customer
club (Q12) had a very strong negative value of -0,201. It thus appears
that the less experienced the credit card holder is, the more interested
they are in symbolic rewards and a feeling of belonging. Reversely, it
also means that the more experienced the card holder is, the less
interested in symbolic rewards they are. As mentioned earlier, the
correlation between age and experience is very strong, which means
that the result of question 12 and experience could be related to age as
well, although the statistical analysis for those particular questions
74
(age and Q12) were insignificant. However, the finding that
inexperienced credit card holders are more interested in symbolic
rewards than those with many years of experience is very interesting.
It could potentially be telling credit card firms that they need to focus
more attention in form of symbolic rewards towards new credit card
holders.
5.1.5 Additional, strong, correlations between constructs
Correlations for question 9, the indirect reward of a voucher for select
restaurants also had a high correlation with the other food-related
rewards such as a voucher for groceries (Q11) as well as the hedonic
reward of a voucher for gourmet foods (Q10). The strongest of these
correlations was that between groceries and gourmet foods (0,715) but
the rewards gourmet foods and a restaurant voucher rewards had a
correlation of 0,595 and that between groceries and a restaurant
voucher was 0,497. This indicated that consumers were either
interested in food-related rewards or they were not. In other words,
there was little indecisiveness with food-related rewards among the
respondents in the survey sample.
Other, very strong, correlations were found between gourmet foods
(Q10: hedonic) and a voucher for a customer club (Q12: symbolic),
these two questions had a correlation of 0, 615. A reward consisting of
a voucher for department stores (Q8) had a correlation of 0,672 with
that of a voucher for groceries (Q11) and a correlation of 0,605 with
that of a voucher for gourmet foods. These results are assumed to have
to do with the value of the vouchers, since Q10, Q11 and Q12 are all
rewards that were suggested at an individual value of $5. I will discuss
these correlations further in the factor analysis, next.
75
5.2 Factor Analysis For further reflection upon the survey findings, a factor analysis of the
consumers’ answers was carried through. The factor analysis divided
the 18 questions in the survey into five components, or factors. All the
questions that are included in the same component have something in
common with each other. In a perfect world where theory is consistent
with practice, each component would entail only one construct but
because reality rarely follows theoretical rules, some components will
consist of two or more constructs. Here, I will analyze one component
at a time in order to gain more clarity in how consumers perceive
credit card rewards. The color scheme is the same as earlier in the
thesis: The construct of Type of rewards is presented in red;
Dimension of benefits in purple; Timing of rewards in green; and
Involvement in blue.
76
Component
1 2 3 4 5
Q6: Direct to debt ,766 ,195
Q13:Utilitarian
financial cost ,760 ,307
Q16:Utilitarian:
saving money ,704 ,200
Q2:Immediate
discount ,676 ,227 ,153 ,345
Q7: Direct to fee ,600 ,316
Q3:Delayed
discount
,595 ,190 ,172 ,250 ,561
Q10:Hedonic
gourmet foods ,116 ,827 ,240 ,114
Q8:Indirect dep.
stores ,295 ,819
Q11:Utilitarian
groceries ,346 ,774
Q9:Indirect
restaurant ,206 ,694 ,165 ,281
Q12:Symbolic
customer club ,649
,458 ,165
Q17:Hedonic
trying new
products
,190 ,843
Q14:Hedonic:
discover products ,133 ,800 ,185
-
,164
Q18:Symbolic
belonging to
community
,212 ,193 ,647
Q15:Symbolic
better treatment ,116 ,523 ,474 ,252
Q5:Delayed points ,162
,841 -
,188
Q4:Immediate
points ,107 ,163 ,824
Q1: Involvement ,267
-
,129 ,159
,331 -,738
Figure 41. Rotated Component Matrix
Extraction Method: Principal Component Analysis. Rotation Method: Varimax
with Kaiser Normalization. A rotation converged in 7 iterations.
77
5.2.1 Component 1
Component one has the strongest effect on the survey results out of all
five components, it accounts for 32,39 percent. This first component
consists of both direct rewards: reductions in consumer debt and in
credit card fees, both discount rewards: immediate and delayed
discounts of 1% and 5% each, and the two utilitarian rewards that had
to do with decreased spending: saving money and shopping at a lower
financial cost. What this tells me is that out of all the different
rewards in the survey, the rewards with the highest perceived value
were those that provided consumers with financial benefits. It did not
matter to respondents what the timing of their discount was, it did not
matter where their reduction in credit card cost went: towards fees or
debt, not did it matter whether they were saving their money or
shopping at a lower financial cost.
All of these rewards that provided consumers with financial gain
were rated high on the VAS-scale and were thus perceived as
important and of high value to the consumers. This finding adds to
Agarwal et al.’s theory about rewards promoting financial gain
generating the most revenue for the brand (2010, p. 18). By
implementing rewards that promote financial gain, both credit card
firms and their customers will likely be more satisfied and thus
increase their perceived value of the reward program.
5.2.2 Component 2
The second component includes all food-related rewards and the
concept of receiving vouchers as a reward. A voucher for groceries, a
voucher for gourmet foods and a voucher for select restaurants are all
included in component number two. However, this component also
includes a voucher for select department stores and a voucher for a
membership in a customer club.
There is a substantial decrease from component one to component
two as to how much the components account for the results of the
survey. The first component accounted for 32,4% whilst component
two only accounts for 11,9%. However, judging by what rewards the
second component includes, there is quite a clear pattern in
78
preferences. All rewards in this component consist of vouchers and
many consist of food-related items. Although some of the vouchers
are useful in completely different settings, i.e. a voucher for gourmet
foods and a voucher for select department stores, it must not be
forgotten that all of the vouchers suggested in the survey were of
about the same value ranging from $1-$5. In other words, the
similarity of said rewards could have had an impact on the consumers’
perceived value of them.
However, component two consists of two constructs: dimension of
benefits and the indirect type of rewards. There is a clear explanation
for that; Indirect rewards cannot possibly be perceived by respondents
as a category that is separate from the others. They are constructed to
measure a consumers perceived value of rewards that have nothing at
all to do with their credit card and in that sense, indirect rewards are,
to the consumer, similar to those rewards which fall under the
construct of dimension of benefits. To the consumer, an indirect
reward in the survey is just another reward that is completely
unrelated to any aspect of their credit card such as their credit card
debt or credit card fees. In the case of the present study those
unrelated rewards are represented by vouchers for restaurants and
department stores.
5.2.3 Component 3
The third component consists of parts from only one construct:
dimension of benefits. The two hedonic rewards that have to do with
trying and discovering new products are situated in this component,
together with the two symbolic rewards of belonging to a community
of likeminded people and receiving better treatment than other
customers. This third component accounts for 10,6 percent of the
results, in other words only 1,3 percent less than component two
which means it does pull some weight to the results of the survey but
not a crucial amount. All suggested rewards in this component were
given a very low rating by the respondents. It does, however, show
that there is a distinction in how consumers perceive rewards that have
to do with new products and being part of a social community: they
79
are not perceived to be as valuable as the constructs in component 1
and 2.
5.2.4 Component 4
The fourth component consists solely of two rewards: the only two
that had to do with collecting points: immediate points and delayed
points. This indicates that collecting points was something that the
respondents in the survey sample perceived to be different or special,
in terms of their overall comprehension of all the rewards in the
survey. This fourth component accounts for only 6,9% of the results
from the survey. Although collecting points is highly common in
rewards programs in Canada today, it was not, neither immediately
nor with some delay, a reward that was perceived as highly valuable
among consumers in the survey sample.
The finding that respondents perceived point collection rewards to
be rather unappealing, and that timing made no difference in their
perceived value of the reward contradicts findings in similar previous
research which has shown that immediate rewards are preferable to
delayed rewards. Yi and Jeon’s study showed that there was a
difference between a customer’s perceived value of immediate and
delayed rewards when that customer’s level of involvement was high
and when it was low. They claimed that under low involvement,
customers perceived immediate rewards to be of greater value than
delayed rewards (2003, p. 237). Because there is no such pattern (no
high correlation between involvement and timing) in the present
study, I suggest that additional, more recent research is necessary to
conclude whether such a connection is present in Canada today.
5.2.5 Component 5
Involvement is alone in component number five, and accounts for
only 5,9 percent of the results. This shows that the consumers’ levels
of involvement had little impact on the results from the survey overall.
Additionally, the fact that it shows a negative value (-0,738) indicates
that the consumers in this study did not show the same connections
between high involvement and high perceived value in certain rewards
80
that other respondents have shown in previous research. Determining
whether this has to do with Canadian consumers regarding credit card
rewards differently than consumers in other countries, where similar
studies have been carried out, or to do with the fact that some of the
previous studies are at least ten years of age and views on credit cards
overall have changed, will require further research.
5.3 A revised model of Customer Perceived
Value The model below portrays the survey sample’s perceived value of
rewards. It was highly influenced by the rewards that most clearly
offered them financial gain. The survey sample’s level of involvement
was not a determining factor but delayed discounts (as opposed to
points), direct rewards and utilitarian benefits were given the highest
ratings out of all the rewards offered in the survey.
Increased
Financial Gain
Type of Reward:
Direct
Timing of
Reward:
Delayed
(Discounts)
Dimension of
Benefit:
Utilitarian
High Customer Perceived Value of
Rewards
Figure 41. A revised model of Customer Perceived Value
81
6 Conclusions In this chapter, the most significant findings from the study will be
presented. Conclusions that are drawn here are concentrated around
my research questions and the purpose of the study.
This study was aimed at determining what factors influence Canadian
consumers’ perceived value of credit card rewards. Four constructs
were used in the study: Involvement, Type and Timing of rewards,
and Dimension of benefits. For each question, a numeric table was
created to see where most consumers found themselves fitting into the
VAS-scale. The table portrayed response frequencies and percentages,
along with a frequency histogram visually portraying the answer
frequency to each question. For a more in-depth analysis, a correlation
matrix and factor analysis were performed to discover additional
connections in respondents’ perceived value of the rewards that were
suggested to them in the survey.
The rewards that were, by far, rated most valuable by consumers in
the survey sample were those that supplied credit card holders with
obvious financial gain: The utilitarian rewards of saving money and
shopping at a lower financial cost. The direct rewards suggested in the
survey also offered rewards of obvious financial gain and were given
higher ratings than indirect rewards which offered vouchers for use in
association with additional future spending. Also, when investigating
timing of rewards, it became clear that respondents rated discounts
more appealing than collecting points, regardless of the reward’s
timing. Being offered a discount may also be related with obvious
financial gain whilst points are usually associated with spending more
money to attain points over time. The idea of collecting points is that
over time they culminate in a discount but in order to retrieve that
discount the consumer must first spend more money and point
collection is often a slow process. Not until the consumer redeems
enough points will they get access to a discount. The survey sample
rated the delayed discount as more valuable than the immediate
discount, indicating that they would not mind waiting for a discount
82
that was, in relation to the immediate discount, of the same size but
appeared larger at point of redemption.
It was surprising to find that collecting points was regarded
unappealing to so many respondents since points collection is a widely
used form of rewards systems in Canada. At the time of data
collection, those who did give additional comments about their view
on rewards often communicated that collecting points was something
they enjoyed doing. However, the respondents who did enjoy
collecting points also expressed that they rarely used their points but
found more pleasure in collecting than in redeeming points.
Another, very prominent finding between two very different
constructs was that the less experience the credit card holder had, the
more interested they generally were in symbolic rewards of customer
clubs. This is a significant finding in that it communicates to credit
card firms that efforts of symbolic rewards should be aimed towards
new credit card holders.
As mentioned in the theory chapter, according to prior research the
construct of Involvement highly influences Customer Perceived Value
so it was a surprise to find that this was not the case in the present
study. Although the respondents’ levels of involvement in their credit
card were often rated a medium to high, the correlations matrix
showed that there was no clear connection between involvement and
other constructs. However, one conclusion that can be drawn from the
findings in terms of the construct of involvement is that younger
respondents generally showed higher levels of involvement than the
older respondents did. Another finding is that men in the survey
sample generally had a slightly higher level of involvement in their
credit card than women in the sample did. Because of insignificant
statistical values regarding correlations between involvement and the
remaining constructs in the correlation matrix, as well as its placement
in the Rotated Component Matrix, additional conclusions cannot be
drawn from the construct of involvement in the present study.
The last of my research questions was if there were any differences
between men and women’s preferences for credit card rewards and
whether there were any age related differences. The present study
83
shows that there were no distinct differences in preferences for credit
card rewards between men and women in the survey sample.
6.1 Managerial implications The Canadian credit card industry is highly affected by fierce
competition between credit card suppliers. Credit card firms not only
have to entice new customers but have to assure that their current
customers are brand loyal in order to contain profitability. One credit
card rarely differs from another in its most basic functions and thus
one of the few ways in which credit card suppliers can differentiate
themselves is to offer credit card rewards that are of high value to
their customers.
The findings in this study imply that in order for firms to reach
customer loyalty and thus firm profitability, some changes should be
made to the rewards that are being offered to credit card holders today.
The finding that is perhaps the most important one of all in the present
study is that customers’ highest perception of value lies in receiving
rewards that provide them with financial gain: more than anything,
credit card holders want to know that they are saving money by using
their credit card. Currently, a vast majority of Canadian credit card
rewards consist of point collection. The survey sample, however,
clearly stated that with few exceptions, little interest is found in
redeeming points and instead they preferred the traditional percentage
discounts as rewards. This indicates that in order to reach increased
firm profitability and customer loyalty, credit card issuers must revise
their rewards systems; point collection rewards are outdated and no
longer of much interest to the majority of consumers.
Because there were no obvious differences between men and
women and few differences between age groups, I believe more
extensive research needs to be conducted to investigate possible
influences of more particular factors. Studies involving questions of
consumers’ annual income and ethnicity could perhaps show clearer
patterns of customer perceived value of rewards.
84
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87
8 Appendix
Appendix 1. Questionnaire
What credit cards rewards do you perceive as valuable?
I am a: Male Female Student Yes No Canadian: Citizen Permanent resident Other
I have ……. years of experience of holding at least one credit card Please circle your age group: 18-25 26-33 34-41 42-49 50-57 58-65 66-73 74+
Please mark the line with a small X where in the spectrum you fit: Example:
1. I will/did research and carefully consider(ed) my options before choosing my credit card (Involvement, Aurifeille et al. 2001. Edited)
Does not apply to me completely applies to me
Please rate your preference for the following credit card rewards:
2. A 1% discount on my current purchase (Immediate, Yi, Jeon) Unappealing to me Highly appealing to me
3. A 5% discount on a purchase of the same value as in question #2 on my fifth visit (Delayed, Yi, Jeon)
Unappealing to me Highly appealing to me
4. I collect reward points and I find out how many points I have collected immediately after my purchase (Immediate, Rothschild & Gaidis.
Edited)
Unappealing to me Highly appealing to me
88
5. I collect reward points and at the end of every month I find out how many points I have collected (Delayed, Rothschild and Gaidis,
Edited)
Unappealing to me Highly appealing to me
6. For every $100 spent on the credit card, a rebate of $1 is credited to my credit card debt (Direct, Keh and Lee, edited)
Unappealing to me Highly appealing to me
7. For every $100 spent on the credit card, my credit card fee is lowered by $1 (Direct, Keh and Lee, edited)
Unappealing to me Highly appealing to me
8. For every $100 spent on my credit card, I get a $1 shopping voucher at select department stores (Indirect, Keh and Lee, edited)
Unappealing to me Highly appealing to me
9. For every $100 spent on my credit card, I get a $1 voucher at select restaurants (Indirect, Keh and Lee, edited)
Unappealing to me Highly appealing to me
10. A $5 coupon for gourmet foods (Hedonic, Mattila, 2010)
Unappealing to me Highly appealing to me
11. A $5 coupon for groceries (Utilitarian, Mattila, 2010)
Unappealing to me Highly appealing to me
12. A $5 voucher for a membership in a customer club (Symbolic, Mattila, 2010)
Unappealing to me Highly appealing to me
89
The credit card rewards program that would be optimal for me ensures that:
13. I shop at a lower financial cost (Utilitarian, Mimouni, Volle 2010)
Unimportant to me Very important to me
14. I discover new products (Hedonic, Mimouni, Volle 2010)
Unimportant to me Very important to me
15. I receive better treatment than other customers (Symbolic, Mimouni, Volle 2010)
Unimportant to me Very important to me
16. I save money (Utilitarian, Mimouni, Volle 2010)
Unimportant to me Very important to me
17. I try new products (Hedonic, Mimouni, Volle 2010)
Unimportant to me Very important to me
18. I belong to a community of people who share the same values (Symbolic, Mimouni, Volle 2010)
Unimportant to me Very important to me
Thank You for your participation!
90
Appendix 2. Compilation of Respondents’ Answers
RespondentSex (m=0, w=1)Status (Citizen=1, PR=2, Oth=3)Age group (18-25=1, 26-33=2, 34-41=3, 42-49=4, 50-57=5, 58-65=6, 66-73=7, 74+=8) Years of holding credit cardQ1: InvolvementQ2: Immediate discountQ3: Delayed discountQ4: Immediate pointsQ5: Delayed pointsQ6: Direct to debtQ7: Direct to feeQ8: Indirect dep storesQ9: Indirect restaurantQ10: Hedonic gourmet foodsQ11: Utilitarian groceriesQ12: Symbolic customer clubQ13: Utilitarian financial costQ14: Hedonic: discover productsQ15: Symbolic better treatmentQ16: Utilitarian: saving moneyQ17: Hedonic trying new productsQ18: Symbolic belonging to community
1 1 1 2 10 5 93 93 94 92 92 90 93 95 95 96 4 95 5 60 97 4 55
2 1 2 5 30 65 78 92 34 94 95 96 96 95 96 95 96 98 98 3 98 95 94
3 2 1 4 20 90 50 98 96 98 98 5 98 4 98 98 97 97 97 97 97 97 97
4 2 1 2 5 52 98 97 96 97 97 99 46 95 90 96 97 97 2 98 98 9 98
5 1 1 3 15 78 75 86 97 90 75 28 8 7 6 6 7 79 59 76 98 56 23
6 1 1 2 7 2 3 98 98 98 3 5 5 98 3 4 4 5 5 96 97 4 3
7 2 1 6 35 97 3 11 13 13 3 14 3 2 2 24 5 89 25 5 97 6 6
8 2 1 1 2 63 86 86 6 6 55 74 78 19 21 40 8 84 41 82 96 15 27
9 2 1 4 25 50 7 6 75 5 92 7 3 3 2 3 3 94 54 3 95 72 46
10 1 1 1 8 80 82 83 3 3 82 97 3 2 2 3 2 98 3 56 97 3 3
11 2 1 5 30 100 1 1 1 100 100 100 100 1 1 100 1 100 1 1 100 1 1
12 2 1 3 20 15 81 94 7 8 9 10 5 6 8 7 8 90 14 52 53 54 47
13 2 1 3 20 47 10 8 8 10 7 6 5 5 23 25 24 61 73 73 56 72 68
14 2 1 2 2 92 4 6 6 6 72 12 9 8 8 9 5 80 78 78 82 84 56
15 1 1 5 30 80 10 10 80 80 10 8 10 10 60 60 10 60 60 80 80 60 10
16 2 1 2 4 84 7 19 63 53 90 87 18 23 21 68 17 82 17 83 85 19 50
17 1 1 3 5 17 18 18 55 24 27 34 38 23 22 26 28 25 38 34 31 39 35
18 2 1 3 20 92 3 5 5 48 45 45 45 18 18 63 11 47 7 5 78 3 10
19 2 1 5 32 65 50 68 87 80 24 49 14 12 17 20 22 69 14 48 90 21 48
20 2 1 3 20 65 40 50 60 34 27 53 53 30 22 68 24 40 28 32 48 37 18
21 1 1 5 30 59 4 6 8 8 25 41 36 16 27 27 24 15 16 30 46 25 28
22 2 1 6 40 50 5 98 95 4 98 97 100 100 98 100 3 100 50 52 97 95 43
23 1 1 4 20 19 8 26 61 15 19 22 20 25 28 26 26 59 54 10 68 15 16
24 1 1 3 20 62 9 6 86 85 6 64 1 5 6 3 3 5 10 5 1 3 5
25 2 1 3 2 12 44 7 30 1 1 64 14 3 5 1 1 5 38 9 60 23 7
26 1 1 5 30 98 65 69 100 100 100 100 46 48 70 70 52 100 100 100 76 97 97
27 1 1 2 10 100 100 1 1 95 1 3 96 100 3 2 98 98 1 95 97 98 1
28 2 1 1 5 33 74 54 5 1 64 47 15 29 48 25 25 82 15 20 83 8 19
29 1 1 2 9 3 97 95 3 4 98 98 98 95 95 95 100 98 1 5 95 97 97
30 1 1 5 30 27 70 70 13 10 47 13 20 15 10 11 11 95 7 5 95 3 5
31 1 1 5 30 100 3 1 3 98 1 1 1 1 1 1 1 100 43 100 47 1 1
32 2 2 4 18 16 22 5 15 24 52 24 12 10 14 15 18 48 7 28 58 17 20
33 1 3 4 20 78 71 84 95 80 65 79 80 90 92 91 61 83 55 55 80 20 51
34 2 1 8 50 34 63 68 67 10 78 83 15 40 55 78 34 80 73 75 78 49 70
35 2 1 8 50 26 20 71 15 41 89 12 49 15 5 53 1 89 10 11 85 20 82
36 1 1 1 7 81 9 7 6 5 82 5 42 55 5 53 5 48 3 2 93 3 24
37 1 1 5 40 7 35 72 5 18 86 8 45 83 23 90 20 25 54 68 29 47 18
38 2 1 6 30 65 24 21 17 79 71 48 25 1 1 1 1 1 1 42 41 1 1
39 2 1 4 30 70 15 28 28 64 12 29 10 5 4 49 5 48 6 5 5 7 54
91
40 2 1 2 9 14 34 70 88 86 10 6 92 15 90 94 96 12 9 55 98 7 7
41 2 1 6 30 86 87 75 58 93 80 83 70 17 22 41 26 89 60 63 90 40 25
42 2 1 1 4 90 33 66 83 83 74 78 15 79 74 78 16 89 68 72 95 100 28
43 1 1 2 10 76 33 9 7 34 33 74 14 30 31 48 32 80 80 15 79 77 80
44 2 1 3 14 77 82 80 58 100 83 3 35 30 53 74 27 78 19 37 89 30 38
45 1 1 5 35 42 93 95 49 45 97 55 5 7 9 92 10 90 43 49 97 14 16
46 1 1 4 0 23 18 10 100 33 26 27 43 25 100 100 100 100 100 100 40 100 100
47 2 1 4 20 72 52 19 49 45 58 28 24 54 62 83 25 30 44 65 85 24 69
48 2 1 3 15 28 32 30 29 16 17 16 17 19 19 17 17 16 16 16 14 16 16
49 2 1 5 30 59 14 12 24 24 26 41 30 14 22 24 21 27 31 24 20 20 23
50 1 1 5 10 32 48 70 33 40 36 85 60 69 71 70 50 78 20 63 87 46 72
51 1 1 3 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
52 2 1 5 20 42 45 36 27 42 39 41 40 12 27 50 20 37 39 36 35 37 35
53 1 1 1 3 70 67 82 15 19 78 88 80 78 77 76 46 84 85 55 89 55 87
54 1 1 2 5 74 13 10 8 5 45 69 10 12 12 12 5 96 5 32 86 12 10
55 2 1 2 10 40 22 43 33 33 42 59 30 26 50 34 25 73 38 38 75 28 64
56 1 1 4 25 55 62 80 44 82 75 76 68 68 28 78 26 82 30 63 88 30 50
57 2 1 2 15 65 18 20 38 90 21 22 38 24 22 21 22 63 18 50 83 12 19
58 2 1 3 12 13 32 37 48 56 60 7 10 20 20 20 21 77 12 75 76 15 22
59 2 1 2 12 80 82 48 66 97 5 54 32 28 27 76 28 90 7 51 94 10 50
60 1 1 4 25 88 53 10 24 58 54 98 50 54 60 60 55 53 88 7 96 42 7
61 1 1 2 10 17 81 5 5 5 7 6 82 88 82 82 10 10 10 83 84 10 11
62 2 1 2 7 5 52 98 52 84 100 100 18 72 6 58 3 72 14 14 81 42 52
63 1 1 3 8 51 60 32 85 88 14 85 51 27 14 62 26 83 50 51 83 30 29
64 1 1 4 25 98 97 5 93 96 98 96 97 5 98 97 7 94 5 5 97 6 7
65 2 1 5 30 84 6 3 5 60 52 40 50 53 76 76 50 78 78 53 78 78 90
66 1 1 2 8 63 62 60 30 30 74 79 27 34 59 73 27 78 12 24 76 7 9
67 1 1 3 5 1 67 67 7 10 92 52 6 12 50 85 1 75 7 7 78 8 3
68 1 3 5 25 80 27 25 20 19 17 29 21 20 27 27 30 48 16 27 81 20 23
69 1 1 5 38 95 8 19 80 82 3 8 12 15 18 19 18 24 62 92 17 83 13
70 2 1 6 40 7 7 7 33 7 7 40 5 10 10 10 10 12 11 5 33 9 18
71 2 1 2 10 68 53 62 20 20 57 78 40 34 68 88 32 80 35 50 87 50 73
72 1 1 3 18 12 7 6 41 42 6 5 7 6 8 7 8 12 13 10 87 5 7
73 2 1 2 10 72 12 15 44 48 60 27 26 17 93 82 38 47 21 68 87 70 70
74 2 1 3 25 36 75 74 34 66 83 82 80 50 53 80 50 75 48 50 98 50 51
75 2 1 5 30 9 51 51 64 89 3 5 3 5 8 18 20 97 63 45 27 81 97
76 2 1 1 0 94 58 80 35 50 56 65 49 48 56 69 51 59 51 47 78 67 56
77 2 1 5 35 1 75 75 15 12 79 80 72 73 73 100 15 69 64 20 97 54 27
78 2 1 6 35 82 7 48 79 45 3 7 8 9 13 48 4 53 59 23 88 48 85
79 2 1 6 35 77 7 53 90 94 90 90 10 9 11 10 10 93 10 93 93 14 97
80 1 1 4 25 69 12 17 6 13 22 24 8 64 57 8 3 12 11 5 42 7 1
81 2 1 3 18 70 23 1 26 35 34 92 65 82 37 48 23 57 66 82 97 58 89
92
82 2 1 1 5 56 83 35 12 16 76 74 65 48 62 86 40 90 6 1 85 5 95
83 1 2 1 5 73 67 67 50 51 84 24 52 48 65 66 27 82 28 49 68 51 44
84 2 1 1 3 58 11 15 97 94 94 7 7 6 7 6 5 97 52 97 100 52 50
85 1 1 2 10 68 71 55 26 25 75 37 47 34 42 59 30 53 54 76 91 70 30
86 2 2 1 3 33 34 47 10 10 61 14 38 17 20 40 41 40 23 18 70 35 25
87 1 1 4 20 82 90 68 12 74 92 97 10 12 13 13 9 95 8 7 96 8 92
88 2 3 2 8 6 9 24 5 7 30 6 54 28 93 93 20 43 7 5 75 3 5
89 1 3 2 11 3 1 25 1 1 28 98 26 45 69 68 1 66 1 50 65 1 3
90 2 1 5 33 98 98 3 3 3 98 1 1 45 3 28 1 45 5 3 98 2 44
91 2 1 1 4 7 66 90 37 19 48 89 86 86 90 84 82 93 3 97 93 55 91
92 2 1 1 7 98 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
93 1 1 2 8 20 51 93 5 5 31 63 8 7 5 33 5 15 28 7 73 43 89
94 1 3 2 9 89 34 72 16 32 67 79 24 74 65 66 19 78 54 74 72 72 70
95 1 1 2 13 70 8 13 6 32 59 78 26 29 27 26 27 25 26 70 26 26 27
96 2 1 2 10 18 38 36 37 34 18 22 35 42 30 63 42 36 70 48 63 57 56
97 1 1 2 8 6 18 33 5 5 46 52 55 57 48 76 10 29 14 13 83 18 79
98 2 1 2 10 82 51 61 32 49 59 30 14 25 15 55 8 78 7 80 82 20 82
99 2 2 2 2 97 65 48 93 5 98 97 61 11 10 96 3 96 4 4 94 5 3
100 2 1 2 8 86 93 92 93 64 80 96 79 83 62 61 37 90 52 63 94 65 14
101 1 1 3 8 41 5 1 2 3 35 52 52 9 67 54 25 62 5 5 54 19 3
102 1 1 2 10 91 91 95 64 90 90 84 21 17 12 44 7 89 87 85 100 85 9
103 2 1 5 45 7 97 100 45 100 28 100 62 96 97 100 26 87 48 50 100 35 5
104 1 1 5 35 91 28 40 89 95 8 84 32 14 22 50 16 39 25 24 87 11 80
105 2 1 6 40 42 39 40 3 11 62 13 46 51 66 69 7 97 17 43 85 17 77
106 2 1 5 20 82 48 48 66 96 48 69 19 26 13 69 16 78 77 22 90 71 4
107 2 1 5 25 5 75 15 16 16 71 73 75 66 12 68 8 7 5 5 50 5 5
108 1 2 3 2 100 85 100 100 83 100 82 86 80 100 78 77 100 72 73 100 33 35
109 1 1 3 20 81 71 22 53 20 74 75 20 24 24 25 24 70 34 63 75 20 22
110 1 1 4 20 3 55 58 5 36 5 5 7 1 42 43 3 95 3 56 18 3 3
111 2 1 3 12 96 7 7 97 97 3 3 2 4 6 5 5 5 5 8 10 10 5
112 1 1 3 15 90 92 90 25 10 10 100 5 5 7 10 5 95 7 75 100 5 5
113 2 1 2 10 78 50 87 70 14 48 49 95 100 97 100 100 48 97 97 96 97 98
114 2 1 4 30 82 46 10 30 78 69 84 28 12 10 51 13 79 6 12 78 6 10
115 1 2 1 5 16 70 70 24 64 86 25 31 53 65 66 22 66 16 17 66 15 70
116 2 1 4 7 1 7 10 50 48 9 12 8 9 46 76 13 17 10 10 97 7 1
117 2 1 7 46 26 10 22 63 7 16 8 10 8 8 7 10 11 46 16 13 44 10
118 2 1 2 11 89 80 97 10 97 98 5 90 7 10 97 9 95 10 10 97 98 3
119 2 1 5 20 5 5 0 5 0 3 0 0 2 2 2 3 48 9 7 90 3 45
120 2 1 5 30 5 83 85 28 25 75 47 80 63 64 65 30 74 22 78 81 16 16
121 1 1 4 20 72 48 34 70 68 61 72 35 32 35 55 34 66 42 47 68 49 48
122 1 1 2 6 8 5 3 13 13 5 10 14 7 38 60 28 89 33 10 82 34 18
123 2 1 5 37 78 7 12 34 75 29 59 55 32 19 58 20 76 29 28 82 29 30
124 1 1 5 35 6 39 60 3 7 76 77 10 20 3 77 3 76 9 70 81 42 40
93
Appendix 3. Pearson Correlation Matrix (SPSS)
Sex (m=0, w=1)Status (Citizen=1, PR=2, Oth=3)Age group (18-25=1, 26-33=2, 34-41=3, 42-49=4, 50-57=5, 58-65=6, 66-73=7, 74+=8)Years of holding credit cardQ1: InvolvementQ2: Immediate discountQ3: Delayed discountQ4: Immediate pointsQ5: Delayed pointsQ6: Direct to debtQ7: Direct to feeQ8: Indirect dep storesQ9: Indirect restaurantQ10: Hedonic gourmet foodsQ11: Utilitarian groceriesQ12: Symbolic customer clubQ13: Utilitarian financial costQ14: Hedonic: discover productsQ15: Symbolic better treatmentQ16: Utilitarian: saving moneyQ17: Hedonic trying new productsQ18: Symbolic belonging to community
Sex (m=0, w=1)Pearson Correlation1 -0,162 0,112 0,14 0,014 -0,103 -0,036 0,055 0,02 0,028 -0,13 0,037 -0,113 -0,081 0,046 -0,037 -0,023 -0,028 -0,052 0,038 0,028 0,116
Sig. (2-tailed) 0,072 0,217 0,122 0,88 0,253 0,688 0,545 0,829 0,758 0,149 0,682 0,213 0,37 0,611 0,685 0,8 0,758 0,569 0,673 0,756 0,199
N 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124
Status (Citizen=1, PR=2, Oth=3)Pearson Correlation 1 -0,117 -0,14 0,001 -0,015 0,072 -0,039 -0,078 0,071 0,063 0,1 0,157 ,248(**) 0,156 0,099 0,044 -0,017 -0,063 0,022 -0,059 -0,028
Sig. (2-tailed) 0,197 0,122 0,993 0,866 0,426 0,667 0,39 0,436 0,488 0,27 0,082 0,006 0,084 0,273 0,628 0,851 0,487 0,805 0,512 0,754
N 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124
Age group (18-25=1, 26-33=2, 34-41=3, 42-49=4, 50-57=5, 58-65=6, 66-73=7, 74+=8)Pearson Correlation 1 ,889(**) -0,039 -0,163 -0,103 0,099 0,089 -0,067 -0,056 -0,091 -0,165 -0,142 -0,063 -0,155 -0,028 0,136 -0,102 -0,134 -0,032 0,001
Sig. (2-tailed) 0 0,666 0,07 0,256 0,272 0,326 0,462 0,535 0,315 0,067 0,115 0,487 0,085 0,753 0,131 0,258 0,137 0,725 0,992
N 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124
Years of holding credit cardPearson Correlation 1 0,029 -0,103 -0,042 0,032 0,114 -0,023 -0,031 -0,057 -0,084 -0,15 -0,04 -,201(*) -0,006 0,105 -0,087 -0,099 -0,023 -0,006
Sig. (2-tailed) 0,75 0,253 0,642 0,728 0,207 0,8 0,736 0,528 0,354 0,097 0,658 0,025 0,948 0,247 0,338 0,273 0,801 0,946
N 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124
Q1: InvolvementPearson Correlation 1 0,051 -0,098 ,203(*) ,372(**) ,192(*) 0,17 0,051 -0,106 -0,096 -0,049 0,05 ,248(**) ,234(**) 0,165 ,178(*) ,180(*) 0,034
Sig. (2-tailed) 0,577 0,278 0,024 0 0,033 0,059 0,575 0,241 0,291 0,591 0,579 0,005 0,009 0,067 0,048 0,046 0,706
N 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124
Q2: Immediate discountPearson Correlation 1 ,634(**) 0,084 ,178(*) ,453(**) ,400(**) ,403(**) ,368(**) ,277(**) ,341(**) ,294(**) ,438(**) 0,043 0,17 ,419(**) 0,114 0,115
Sig. (2-tailed) 0 0,351 0,048 0 0 0 0 0,002 0 0,001 0 0,638 0,059 0 0,208 0,204
N 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124
Q3: Delayed discountPearson Correlation 1 ,264(**) ,208(*) ,454(**) ,366(**) ,337(**) ,412(**) ,329(**) ,393(**) ,273(**) ,422(**) 0,162 ,309(**) ,392(**) ,262(**) ,278(**)
Sig. (2-tailed) 0,003 0,02 0 0 0 0 0 0 0,002 0 0,072 0 0 0,003 0,002
N 124 124 124 124 124 124 124 124 124 124 124 124 124 124 124
Q4: Immediate pointsPearson Correlation 1 ,575(**) 0,115 0,151 0,097 0,092 ,207(*) 0,156 ,217(*) 0,132 ,326(**) ,348(**) 0,142 ,218(*) 0,142
Sig. (2-tailed) 0 0,204 0,094 0,286 0,31 0,021 0,084 0,015 0,144 0 0 0,117 0,015 0,117
N 124 124 124 124 124 124 124 124 124 124 124 124 124 124
Q5: Delayed pointsPearson Correlation 1 0,131 0,133 0,174 0,069 0,052 0,133 ,183(*) ,244(**) 0,166 ,288(**) ,181(*) 0,148 0,061
Sig. (2-tailed) 0,146 0,14 0,054 0,446 0,569 0,141 0,042 0,006 0,066 0,001 0,044 0,1 0,503
N 124 124 124 124 124 124 124 124 124 124 124 124 124
Q6: Direct to debtPearson Correlation 1 ,446(**) ,376(**) ,284(**) ,259(**) ,448(**) 0,121 ,497(**) 0,121 0,1 ,469(**) ,196(*) ,254(**)
Sig. (2-tailed) 0 0 0,001 0,004 0 0,179 0 0,182 0,271 0 0,029 0,004
N 124 124 124 124 124 124 124 124 124 124 124 124
Q7: Direct to feePearson Correlation 1 ,362(**) ,359(**) ,288(**) ,382(**) 0,16 ,370(**) 0,11 0,083 ,343(**) 0,061 ,193(*)
Sig. (2-tailed) 0 0 0,001 0 0,075 0 0,226 0,36 0 0,501 0,032
N 124 124 124 124 124 124 124 124 124 124 124
Q8: Indirect dep storesPearson Correlation 1 ,584(**) ,605(**) ,672(**) ,559(**) ,260(**) 0,145 0,132 ,373(**) ,270(**) 0,175
Sig. (2-tailed) 0 0 0 0 0,004 0,108 0,144 0 0,002 0,052
N 124 124 124 124 124 124 124 124 124 124
Q9: Indirect restaurantPearson Correlation 1 ,595(**) ,497(**) ,467(**) 0,136 ,189(*) ,284(**) ,316(**) ,290(**) ,280(**)
94
Sig. (2-tailed) 0 0 0 0,131 0,036 0,001 0 0,001 0,002
N 124 124 124 124 124 124 124 124 124
Q10: Hedonic gourmet foodsPearson Correlation 1 ,715(**) ,615(**) ,239(**) ,286(**) ,243(**) ,275(**) ,299(**) ,339(**)
Sig. (2-tailed) 0 0 0,007 0,001 0,007 0,002 0,001 0
N 124 124 124 124 124 124 124 124
Q11: Utilitarian groceriesPearson Correlation 1 ,432(**) ,319(**) ,207(*) 0,101 ,394(**) ,254(**) ,289(**)
Sig. (2-tailed) 0 0 0,021 0,263 0 0,004 0,001
N 124 124 124 124 124 124 124
Q12: Symbolic customer clubPearson Correlation 1 ,217(*) ,373(**) ,328(**) ,209(*) ,472(**) ,432(**)
Sig. (2-tailed) 0,016 0 0 0,02 0 0
N 124 124 124 124 124 124
Q13: Utilitarian financial costPearson Correlation 1 ,207(*) ,293(**) ,564(**) ,299(**) ,307(**)
Sig. (2-tailed) 0,021 0,001 0 0,001 0,001
N 124 124 124 124 124
Q14: Hedonic: discover productsPearson Correlation 1 ,376(**) 0,115 ,725(**) ,353(**)
Sig. (2-tailed) 0 0,202 0 0
N 124 124 124 124
Q15: Symbolic better treatmentPearson Correlation 1 ,195(*) ,388(**) ,261(**)
Sig. (2-tailed) 0,03 0 0,003
N 124 124 124
Q16: Utilitarian: saving moneyPearson Correlation 1 0,151 ,209(*)
Sig. (2-tailed) 0,095 0,02
N 124 124
Q17: Hedonic trying new productsPearson Correlation 1 ,429(**)
Sig. (2-tailed) 0
N 124
Q18: Symbolic belonging to communityPearson Correlation 1
Sig. (2-tailed)
N
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
95
Appendix 4. Descriptive Statistics (SPSS)
Mean Std. Deviation N
Sex (m=1, w=2) 1,5565 ,49882 124
Status (Citizen=1, PR=2,
Oth=3) 1,1371 ,44784 124
Age group (18-25=1, 26-33=2,
34-41=3, 42-49=4, 50-57=5,
58-65=6, 66-73=7, 74+=8) 3,3871 1,64643 124
Years of holding credit card 18,0484 12,45862 124
Q1: Involvement 53,5645 33,28669 124
Q2: Immediate discount 43,0403 31,63213 124
Q3: Delayed discount 44,4355 33,61722 124
Q4: Immediate points 39,4435 32,77598 124
Q5: Delayed points 44,6774 35,09697 124
Q6: Direct to debt 50,5726 33,53052 124
Q7: Direct to fee 48,4919 34,52264 124
Q8: Indirect dep stores 36,5323 29,97707 124
Q9: Indirect restaurant 33,4274 30,03769 124
Q10: Hedonic gourmet
foods 37,0403 31,68606 124
Q11: Utilitarian groceries 51,2016 32,15764 124
Q12: Symbolic customer
club 24,0403 25,12680 124
Q13: Utilitarian financial
cost 64,2339 30,55027 124
Q14: Hedonic: discover
products 31,7823 27,97257 124
Q15: Symbolic better
treatment 42,5484 31,49432 124
Q16: Utilitarian: saving
money 74,5484 26,43918 124
Q17: Hedonic trying new
products 34,1774 29,92702 124
Q18: Symbolic belonging
to community 37,9435 31,96322 124
96
Appendix 5. Total Variance Explained (SPSS)
Co
mp
on
ent Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of
Variance
Cumulativ
e % Total
% of
Variance
Cumula
tive % Total
% of
Variance
Cumula
tive %
1 5,830 32,388 32,388 5,830 32,388 32,388 3,260 18,110 18,110
2 2,150 11,946 44,335 2,150 11,946 44,335 3,247 18,038 36,147
3 1,903 10,574 54,908 1,903 10,574 54,908 2,539 14,108 50,255
4 1,245 6,917 61,825 1,245 6,917 61,825 1,912 10,620 60,875
5 1,070 5,944 67,769 1,070 5,944 67,769 1,241 6,894 67,769
6 ,786 4,368 72,138
7 ,733 4,072 76,210
8 ,679 3,773 79,983
9 ,637 3,540 83,523
10 ,514 2,856 86,379
11 ,486 2,701 89,080
12 ,417 2,319 91,398
13 ,346 1,921 93,319
14 ,307 1,708 95,027
15 ,285 1,583 96,611
16 ,240 1,335 97,946
17 ,204 1,132 99,078
18 ,166 ,922 100,000 Extraction Method: Principal Component Analysis.
97
Appendix 6. Rotated Component Matrix (SPSS)
Component
1 2 3 4 5
Q6: Direct to debt ,766 ,195
Q13:Utilitarian
financial cost ,760 ,307
Q16:Utilitarian:
saving money ,704 ,200
Q2:Immediate
discount ,676 ,227 ,153 ,345
Q7: Direct to fee ,600 ,316
Q3:Delayed
discount ,595 ,190 ,172 ,250 ,561
Q10:Hedonic
gourmet foods ,116 ,827 ,240 ,114
Q8:Indirect dep
stores ,295 ,819
Q11:Utilitarian
groceries ,346 ,774
Q9:Indirect
restaurant ,206 ,694 ,165 ,281
Q12:Symbolic
customer club ,649 ,458 ,165
Q17:Hedonic
trying new
products
,190 ,843
Q14:Hedonic:
discover products ,133 ,800 ,185
-
,164
Q18:Symbolic
belonging to
community
,212 ,193 ,647
Q15:Symbolic
better treatment ,116 ,523 ,474 ,252
Q5:Delayed points ,162 ,841
-
,188
Q4:Immediate
points ,107 ,163 ,824
Q1: Involvement ,267
-
,129 ,159 ,331
-
,738
Extraction Method: Principal Component Analysis. Rotation Method: Varimax
with Kaiser Normalization. A Rotation converged in 7 iterations.
98
Appendix 7. Scree Plot (SPSS)
Appendix 8. Graph showing increase in research in loyalty programs
Timeline:
Loyalty Program
1986-01-01 - 1990-01-01 ca 1
1990-01-01 - 1995-01-01 ca 6
1995-01-01 - 2000-01-01 ca 12
2000-01-01 - 2005-01-01 ca 44
2005-01-01 - 2010-01-01 ca 115
2010-01-01 - 2012-10-01 ca 85
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Component Number
0
1
2
3
4
5
6
Eig
enva
lue
Scree Plot
99
Appendix 9. Overview of the process of selecting constructs for the preset study
Construct Content Explanation Arguments for Arguments against Authors
using/menti
oning
construct
Timing of
reward/rede
mption:
Immediate
vs Delayed
Rewards
Preferred timing of
received rewards:
Immediate=Received upon
every visit. Delayed=
Received upon every X-
nth visit
Easy to understand, fits well
with what I want to study, well
used by authors who have
studied loyalty programs
Has been concluded
already that Delayed
rewards are preferred
only if they are of
higher value than the
immediate rewards.
Keh & Lee
(2006),
Dowling &
Uncles (1997)
Yi & Jeon
(2003)
Dimension
of benefits:
Utilitarian,
Hedonic,
Symbolic
Value
Preferred goods or services
in L-P’s.
Utilitarian= Financial
advantages. Hedonic=
experimental, emotional,
personally gratifying.
Symbolic= Personal
expression, self-esteem,
social approval.
Divides different types of
rewards into helpful, easy-to-
understand categories which in
turn would give
result/conclusion body. “It is
perceived brand value, not
brand loyalty which drives price
insensitivity.” Dowling &
Uncles
Prior studies have
shown that consumers
are strongly divided in
their opinions. One
category is not generally
preferred over another.
Could result in vague
conclusions.
Mimouni-
Chabane &
Volle (2008)
Yi & Jeon
(2003)
Type of
Rewards:
Direct vs
Indirect
Rewards
Direct= Supports the
product’s value
proposition.
Indirect=Refers to
incentives that are not
relevant to a given
product.
Very commonly mentioned in
studies about LP’s. Often used
in combination with
Immediate/Delayed in matrix
form.
Examples of Direct
rewards would be very
limited in the credit card
industry; cash back or
lowered card fees?
Canadians hardly pay
fees in relation to their
rewards as is.
Dowling &
Uncles (1997),
Keh & Lee
(2006)
Yi & Jeon
(2003)
Target of
Attitude:
Brand vs
Program
Loyalty
Brand loyalty: Customer is
loyal to brand and its
products (good for
business). Program
loyalty: Customer is loyal
Term is mentioned often in prior
loyalty program/customer
loyalty research.
Whether my test group
is attracted to the
rewards or is brand loyal
has little meaning since
credit card rewards are
Dowling &
Uncles (1997)
100
to good rewards programs
and will lose interest in
brand once program has
ended.
not temporary programs
but an unchanging part
of the card.
Involvement: High vs
Low
Reflects how interested
customers are in knowing
their brand/product well
Very often referred to in prior
research. Is said to have great
impact on preferred target
attitude
Whether my test group
knows their own credit
card rewards well is
insignificant in my
study. They will all have
equal knowledge of the
fictional rewards.
S. K Parahoo
(2010).
Dowling &
Uncles (1997)
Yi & Jeon
(2003)
Promotional
Strategy:
Primary vs
Secondary
Primary= Core product or
service. Secondary=
Coupon or tokens that
need to be converted
Could fit well into study as it
would work as a separator for
different rewards programs, just
like timing of rewards does.
Not sure if it would
perhaps be excessive.
Have only seen method
mentioned in two
articles, never used.
Does anyone really want
coupons rather than a
core product? And if so,
isn’t that covered in
Utilitarian vs hedonic
value? Is said to be
“conceptually
consistent” with the
Direct vs Indirect
rewards. (Yi & Yeon)
Keh & Lee
(2006)
Yi & Jeon
(2003)
