Framing effects on evaluation of coupon offers: assessing the ...

<strong>Framing</strong> <strong>effects</strong> on evaluation of coupon offers: assessing the performance
of alternative face value frames *
1. Introduction
Coupons are an effective sales promotions tool for businesses of almost every size and type
(Berdnarz & Bergiel, 2001). In recent years, they have become the most heavily utilized
promotion vehicle in the retail industry because of their ability to directly influence buying
habits (Ben –Zion et al, 1999; Neslin & Shoemaker, 1983; Papatla & Krishnamurthi, 1996),
subsequent purchases (Bawa & Shoemaker, 1987), behavior of non-redeemers (Bawa&
Shoemaker, 1989) and store traffic and store profits (Venkatesan & Farris, 2012; Walters &
MacKenzie, 1988). Marketing managers strive to improve the effectiveness of coupon
campaigns. The way a coupon’s face value is expressed, known as coupon face value framing,
can serve this purpose in a smart and completely inexpensive way, but it remains a neglected
issue in coupon design (Yin & Dubinsky, 2004). The current study tries to fill this void in the
literature by examining the impact of three alternative, yet totally equivalent on a total cost
basis, coupon face value presentations (i.e., % off, € off, Reduced Price) on cognitions,
coupon evaluations and redemption intention.
2. Literature Review
2.1 Coupon Literature Review
Coupons are certificates that provide the consumer with an incentive, monetary or nonmonetary,
to buy a product (Schultz et al, 1998). Coupon distribution has risen rapidly in the
last forty years (Nielsen Clearing House [NCH], 1998) and this popularity has triggered a
strong academic interest (Dhar & Raju, 1998). A main stream of coupon research includes
investigations into the determinants of coupon redemption rate. Coupon face value was
identified as its most critical determinant (Chakraborty & Cole, 1991; Krishna & Shoemaker,
1992; Kumar &Swaminathan 2005; Leone &Shrinivasan, 1996; Mittal, 1994; Neslin&
Clarke, 1987; Raghubir, 1998, 2004; Ramasway & Srinivasan, 1998; Reibstein & Traver,
1982; Venkatesan & Farris, 2012; Ward & Davis, 1978). Very little is currently known about
how different face value statement types (coupon face value frames) influence coupon
attractiveness, coupon evaluations and coupon redemption (Varadarajan, 1984; Yin &
Dubinsky, 2004). The fact that coupon offers are becoming extremely diverse (Ranghubir,
2004; Suri et al, 2004) and the empirical findings from the discount framing literature,
presented in the next section, are suggestive of the important role of coupon framing in
purchase behaviour.
2.2. <strong>Framing</strong> LiteratureReview
The term framing refers to individuals responding differently to the same decision problem
(Frisch, 1993; Li, Shun, & Wang, 2007). Thaler (1980), based on prospect theory (Kahneman
& Tversky, 1979), found that the structure of the problem (framing) may affect consumers’
perceptions and choice. Since then, the framing of alternatives was found to affect choices in
many and diverse domains, such as health precautionary behavior adoption
(Chandran&Menon, 2004; Maheswaran & Meyers-Levy, 1990; Meyerowitch & Chaiken,
1987), credit card usage (Ganzach & Karsahi, 1995), product and service evaluation (Fortin &
Greenlee, 1998; Kwong & Wong, 2006; Levin &Gaeth, 1988), industrial buying decision-
* Acknowledgements: This research has been co-financed by the European Union (European Social Fund -
ESF) and national funds through the Operational Program ‘Education and Lifelong Learning’ of the
National Strategic Reference Framework (NSRF) - Research Funding Program: ‘Heracleitus II: Investing
in knowledge society through the European Social Fund’.

making (Puto 1987; Qualls & Puto, 1989), donations to non-profit organizations (Smith &
Berger, 1995), and new or innovative product adoption (Grewal et al, 1994).
The application of framing to discount promotions has been the focus of several studies
(Berkowitz & Walton, 1980; Das, 1992; Della Bitta et al, 1981; Del Veccio et al, 2007;
Gendall et al, 2006; Gourville,1998; Kim & Kramer, 2006; Lichtenstein et al, 1991; Liefeld &
Heslop, 1985; Ramanathan & Dhar, 2010), which addressed the presentation of the savings of
promotional offers, also known as deal semantics. Coupon framing literature on the other
hand appears quite scanty. Diamond (1992) and Diamond and Sanyal (1990) studied different
coupon face value frames but in the former case only in an effort to discover the latent
decision rules used and in the latter for a combined offer between spaghetti sauce and rice
soup. More recently, Kramer and Kim (2007) proved that processing fluency enhanced
evaluations for gain-framed face values (don’t pay 20 % of the final price) and not novelty
<strong>effects</strong>.
The present study considers framing <strong>effects</strong> of three common alternative monetary face
value frames, “€ off”, “% off” and “reduced price”. It is based but also considerably extends
the work of Chen, Monroe, and Lou (1998). These researchers relied on the findings that
consumers tend to process expected price reductions relatively rather than in absolute
monetary amounts (Grewal & Marmorstein, 1994) and that the psychological pleasure derived
from a discount depends not only on the absolute amount of the monetary savings but also on
the price level of the promoted product (Grewal et, 1998; Heath et al, 1995) and proposed that
for high-price products consumers will perceive a price reduction framed in monetary terms
as more significant than the same price reduction framed in percentage terms and the exact
opposite for a low price product. Their hypothesis was supported only for the perceived
savings and not for purchase intention, probably because of the small incentive (10%).
Gendall et al (2006) reached similar conclusions in a replication study. The present study
attempts to resolve this issue, by testing this hypothesis for a medium and for a high discount
size, both capable to overcome the problem.
A third frame, the “reduced price” one, is also tested along its two other counterparts. This
frame has a different logic since it stresses the money left to pay for the product and not the
one saved. The consumer has to perform a mental accounting (Thaler, 1985) to realize the
savings, making unavoidably miscalculations, or use an assimilation-contrast process
(Monroe, 1979) and never calculate the savings. Either of these, will make this frame to
underperform relative to the “€ off” frame. Primary dependent variables of the study are three
interrelated antecedents of behavior (Fishbein & Ajzen, 1975): perceptions (transaction value
which is stipulated in terms of perceptions of savings), deal evaluations (attitude towards the
offer) and redemption intention. Thus we state the following hypothesis:
H1.: For high price products the ordering of the three face value frames according to their
impact on transaction value, attitude towards the offer and redemption intention, will be
from the highest to the lowest: “€ off”, “reduced price” and “% off”. For the low price
product the order will be: “% off”, “€ off” and “reduced price”.
The discount level has also been found to significantly influence buying behavior (Gupta
& Cooper, 1992). In addition, if the discount level is maintained in a reasonably high level it
is also expected that it will not dilute the framing <strong>effects</strong> because of low processing (Grewal
et al, 1996) or credibility issues (Gupta & Cooper, 1992). Thus:
H2.: The discount level will significantly influence all the dependent variables but it will
not affect the observed framing <strong>effects</strong>.
The interrelationships among perceptions, attitudes and intentions and their traits as
antecedents of actual behavior are well established theoretically and validated empirically in
consumer research (Bagozzi, 1982; Fishbein & Ajzen, 1975). It is expected that the same
pattern will appear in this coupon face value framing setting. More specifically:

H3.: The formation of redemption intention from the perceived transaction value will be
mediated by the existing attitudes towards the offer.
The conceptual model formed by the hypotheses is presented in the Appendix (Figure 1).
3. Methodology
3.1 Pretests,Method & Participants
Before performing the main experiment, two pretests were conducted. Firstly, five high
priced product categories and 5 low priced ones were presented in alphabetical order to 30
respondents asking them to report their familiarity and involvement with them, their price
level perception and the probability to purchase them in the near future. Paired-t comparisons
revealed that a laptop was perceived as the most familiar, involving, likely to purchase high
priced product of all and a USB memory stick was the low priced one with the same
traits.Then, an internet and market search revealed that the best seller laptop during the period
of the study was a Toshiba Tecra with median market price 1099 €, while the 4 GB Transcend
USB with median retail price 8.99 € was the best seller USB. So, these two branded products
were included in the study. Brand names were used because of the need to make the whole
task more realistic and to overcome the frequent criticism that when price and price discount
are the only information available, subjects naturally overemphasize their importance
(Gendall et al, 2006). Secondly, 30 different student subjects verified that the 25 % discount
was considered mediumand the 40 % one as plausibly high for these high-tech products.
After conducting the pretests, a 3 (€ off, % off, reduced price) X 2 (Laptop, USB) X 2
(25%, 40 % discount) full factorial between subjects experimental design was applied. Two
hundred forty seven business students from two large Business Schools were randomly
assigned in the experimental conditions resulting in at least 20 observations per cell. All
participants were volunteers. Subjects were administered a booklet consisted of a distraction
task where they indicated their brand attitudes towards various high-tech brands, including the
chosen ones, then there was an introduction, a cover story, the stimulus (simulated real
coupon) and a questionnaire. The entire process took approximately 10-15 minutes to
complete. Thus, participants’ fatigue was avoided and the reliability and validity of the
provided responses was increased. Neither institution nor class session exerted any significant
effect on any of the dependent variables (p >.05) and as a result the data from each session
and institution could be pooled. From respondents’ answers on an open–ended question, it
was obvious that no one surmised the real purpose of the study. So, none of them was
excluded from the analyses because of courtesy bias and self-generated validity (Sawyer,
1975). Age and gender had no significant <strong>effects</strong> on any of the dependent variables (p > .05),
validating once more the notion that simple individual demographics are poor predictors of
coupon behavior (Bawa, Srinivasan, &Srivastava, 1997; Mittal, 1994). As a result, these
variables were excluded from the subsequent analyses.
3.2 Measures
Redemption intention was measured by using a semantic differential scale anchored “Not
at all Likely” and “Very Likely”. Attitude towards the coupon offer was measured by
employing a 3-item semantic differential scale adapted from Lichtenstein, Burton and Karson
(1991) (α = .95). Transaction value was measured by utilizing a 4-item scale adapted from
Yadav and Monroe (1993) (α = .92). Moreover, because Kim and Kramer (2006) had found
that novel discount presentations have more favorable perceived savings and purchase
likelihood than conventional ones, respondents assessed the novelty of the coupon face value
frame they saw by employing the 3-item bipolar scale devised by the previous authors (α =
.84). Coupon proneness was measured by the 8-item Likert style construct developed by
Lichtenstein et al (1990) (α = .87) and price consciousness by utilizing the 5-item scale of
Lichtestein et al (1993)(α = .78).Brand attitudes were captured by using a 3-item bipolar scale

adapted from (Muehling & Laczniak, 1988)(α = .97). All scales had nine points and were
highly reliable according to Nunnally (1967).
4. Results
4.1 Manipulation checks
In the case of the framing manipulation, 86 % of those exposed to the % off frame
indicated that the coupon incentive was a percentage price reduction, 93 % of those exposed
to the € off frame reported that the coupon incentive was a euro price reduction and finally, 76
% of those seeing the reduced priceframe indicated that the incentive was a reduced retail
price (χ 2 (4, 247) = 303.99, p < .001). Also, the laptop was considered as significantly high
priced than the USB stick (Mlaptop= 6.3, Musb = 3.3, t (240.076) = -15.14, p

egressions that provide the linkages to the meditational model were applied and the
necessary regression coefficientsalong with their standard errors were included in theSobel
test (Sobel, 1982). The test was highly significant (Sobel test statistic=5.45, p< .001)
providing supportto H3. The derived results are discussed in the next session.
5. Discussion and Managerial Implications
The results of the experiment reveal that the “reduced price” frame can be considered as a
peer of the “€ off” frame both for high and low priced products. In the former case they
outperform the “% off”frame while in the latter the opposite happens. This unexpectedly good
performance of the “reduced price” frame may be attributed to the overestimation of the
difficulty that the mental accounting would impose on the calculation of the savings. Probably
consumers are more trained to do these calculations nowadays since they are inundated by
offers or business students more capable working with numbers. The chosen retail prices and
discount sizes may have also facilitated this phenomenon. As regards the promotion depth, it
does not distort the framing <strong>effects</strong> in the case of medium and credibly high sizes while it
improves promotional effectiveness. The significant effect of product only in redemption
intention is another indication that respondents clearly discriminated the content of the
dependent variables (the lack of high inter-correlations was a first sign). Even if a promotion
is attractive, you can more easily be temptedto buy a new or extra low-priced and handy
product than a high-priced one that may loom like an expensive luxury. Finally, the study also
shed light on the influence that face value frames exert on the antecedents of behavior. It
seems that transaction value influences attitude towards the offer, which in turn has an impact
on redemption intention.
From the above findings, significant managerial implications can be derived. Managers can
smartly and inexpensively improve their promotional effectiveness by taking the price level of
the promoted product and the presentation of the face value into consideration each time they
design coupon campaigns. These factors influence all the stages in the evaluation process
when the discount sizes are medium or plausibly high. Coupon face values expressed in
percentage terms are more effective for low-priced products while a reduced price
presentation or a monetary one appear more suitable for high-priced goods. For the discount
size, the notion that everyone wants (plausibly) more still holds.
6. Limitations and Future Research
The sample and the study’s environment may secure the purity of the observed causal
<strong>effects</strong> but may hurt external validity. It would be desirable to investigate such framing <strong>effects</strong>
in a field setting. Moreover, the framing <strong>effects</strong> were validated measuring the impact on the
antecedents of behavior that correlate with it but do not fully predict it. Thus, an important
extension is to use actual data from consumer purchases. Finally, an extension one might
consider is to explore the boundary conditions of the observed framing <strong>effects</strong> by occupying
other promotion depths, product categories (outside the high tech spectrum) and product price
levels.

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8. Appendix
Figure 1: The conceptual model of the research study.
Table 1: Means and t-test results that prove the non-existing confounds
Familiarity t a Sig. Involvement t a Sig.
Price
Believability
t a Sig.
Offer
Believability
Laptop 5.48 6.20 6.34 6.49
1.06 .289
-.07 .942
1.49 .138
USB 5.20
6.22
6.02
6.11
a. All t values mentioned have 245df
Table 2: The Multivariate Tests Results
t a Sig.
1.88 .061
Effect
Pillai’s
Value
F
Hypothesis
df
Error df Sig. η 2 Observed
Power a
Frame .004 .161 6.000 468.000 .987 .002 .091
Promotion Depth .033 2.659 3.000 233.000 .049 .033 .645
Product Price Level .169 15.791 3.000 233.000 .000 .169 1.000
Frame*Promotion Depth .045 1,777 6.000 468.000 .102 .022 .670
Frame *Product .082 3.342 6.000 468.000 .003 .041 .938
Depth*Product .005 .420 3.000 233.000 .739 .005 .133
Frame *Product*Depth .010 .378 6.000 468.000 .893 .005 .160
a
Computed using alpha = .05

Overall
Frame
Table 3: Univariate Tests (Between-Subjects Effects) Results
Effect
Promotion Depth
Product Price Level
Frame*Depth
Frame *Product
Depth*Product
Frame * Product*Depth
Dependent
Variable
Redemption
Intention b
Sum of
Squares
df
Mean
Square
F Sig. η 2
Observed
Power a
189.769 11 17.252 4.725 .000 .181 1.000
Attitude c 48.787 11 4.435 1.844 .048 .079 .867
Transaction
58.555 11 5.323 2.157 .018 .092 .923
Value d
Redemption
Intention
1.114 2 .557 .153 .859 .001 .073
Attitude .257 2 .129 .053 .948 .000 .058
Transaction
Value
.061 2 .030 .012 .988 .000 .052
Redemption
Intention
15.423 1 15.423 4.224 .041 .018 .535
Attitude 12.002 1 12.002 4.989 .026 .021 .604
Transaction
Value
16.065 1 16.065 6.509 .011 .027 .719
Redemption
Intention
92.594 1 92.594 25.359 .000 .097 .999
Attitude .089 1 .089 .037 .847 .000 .054
Transaction
Value
2.095 1 2.095 .849 .358 .004 .151
Redemption
Intention
11.762 2 5.881 1.611 .202 .014 .339
Attitude 1.625 2 .813 .338 .714 .003 .104
Transaction
Value
2.571 2 1.286 .521 .595 .004 .135
Redemption
56.306 2 28.153 7.710 .001 .062 .947
Intention b
Attitude c 34.284 2 17.142 7.125 .001 .057 .929
Transaction
34.438 2 17.219 6.976 .001 .056 .924
Value d
Redemption
Intention b
2.584 1 2.584 .708 .401 .003 .133
Attitude c .027 1 .027 .011 .915 .000 .051
Transaction
.049 1 .049 .020 .888 .000 .052
Value d
Redemption
Intention b
3.744 2 1.872 .513 .600 .004 .134
Attitude c .066 2 .033 .014 .986 .000 .052
Transaction
2.908 2 1.454 0.589 .556 .005 .147
Value d
a Computed using alpha = .05
b R 2 = .181 (Adjusted R 2 = .143)
c R 2 = .079 (Adjusted R 2 = .036)
d R 2 = .092 (Adjusted R 2 = .049)

Figures 2, 3 and 4: The effect of face value presentation on the dependent variables as a
function of the price level of the product category.
Table 4: The means for each dependent variable by face value presentation and product
price level for the 25 % discount.
Product Price
Level
Low (USB)
High (Laptop)
Face Value
Presentation
Redemption
Intention
Attitudes Transaction Value
€ off 6.10 5.62 5.66
% off 7.40 6.75 6.50
Reduced Price 5.80 5.87 5.48
€ off 5.85 6.20 6.56
% off 4.41 5.61 5.38

Reduced Price 5.75 6.25 6.31
Table 5: The means for each dependent variable by face value presentation and product
price level for the 40 % discount.
Product Price
Level
Low (USB)
High (Laptop)
Face Value
Presentation
Redemption
Intention
Attitudes Transaction Value
€ off 7.05 6.27 6.16
% off 7.77 6.92 6.81
Reduced Price 6.57 6.30 6.28
€ off 6.52 6.81 6.56
% off 5.65 5.90 5.76
Reduced Price 6.17 6.73 6.61