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The Journal of Inquiry 2015 14, Issue 1, 41-58
http:www.uvu.edu/woodbury/jbi/articles
ISSN 2155-4072

The Effect of Online External Reference Price

on Perceived Price, Store Image, and Risk

By MOON YOUNG KANG∗ AND KWON JUNG

Previous research has shown that external reference prices provided by price
comparison sites are known to increase both sellers’ price competition and
buyers’ price sensitivity. However, there is no clear answer regarding the
different impacts of various competition patterns, which are caused by the advent
of competitors within price comparison sites, with respect to consumers’
perceptions of price, store image, and risk. Our objective in this research is to
investigate the effect of the external reference price within price comparison sites,
which is determined by competitors’ offering price, on perceived price, store
image, and risk. In this research, we investigated whether perceived price, store
image, and risk differ according to 1) store name (a known vs. unknown store);
2) brand name (a known vs. unknown brand); and 3) product category (look-and-
feel vs. non-look-and-feel). Our results demonstrate that the effect of online
external reference prices is significant on store image for an unknown store,
regardless of product category. In addition, the effect of online external reference
prices is significant on the price and risk perceptions for look-and-feel products,
but not for non-look-and-feel products when the focal mall is an unknown store.
However, the interaction effect on price perception disappears when the focal
mall is a known store.

Keywords: Online Shopping; External Reference Price; Price Perception; Store
Image Perception; Risk Perception; Store Name

JEL Classification: M31

I. Introduction

One of the biggest differences between online and offline shopping environments is the
degree to which consumers compare prices. In online shopping environments, price comparison
sites are widespread (Häubl and Trifts, 2000; Iyer and Pazgal, 2003; Pan, Ratchford, and
Shankar, 2004). The presence of price comparison sites lowers consumers’ search costs
(Brynjolfsson and Smith, 2000). While online shopping has become a general trend, online
retailers have a much harder time than ever finding a homerun strategy to defend themselves
from cutthroat competition involving information on competitors’ prices from price comparison
sites, which function as external reference prices. Pretend for a moment that you own and manage
an online shopping mall. When a famous competitor sells the same product online, what would
be its impact on your customers? To be more specific, is the impact the same whether your store

∗
Moon Young Kang, Ph.D.: Korea Advanced Institute of Science and Technology (KAIST), 85 Hoegiro,

Dongdaemun-gu, Seoul 130-722, Korea. Email: [email protected]. Phone: 82-2-958-3328,
Fax: 82-2-958-3328, Kwon Jung, Ph.D.: Professor, KDI School of Public Policy and Management, 263 Namsejong-ro,
Sejong 339-007 Korea. Email: [email protected]. Phone: 82-44-550-1036.

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ON PERCEIVED PRICE, STORE IMAGE, AND RISK

is as famous as the competitor or not? What if the location of the competitor is lower (i.e.,
showing a higher price within the price comparison sites than your price) or higher (i.e., showing
a lower price within the price comparison sites than yours)? What if there is more than one
famous competitor entering the online market with the same product that you carry in your online
store?

Our objective in this research is to investigate the effect of the external reference price
within price comparison sites, which is determined by competitors’ offering price, on perceived
price, store image, and risk. Many previous studies have examined the impact of external
reference prices provided by price comparison sites in online purchasing behavior and price
sensitivity. External reference prices provided by price comparison sites are known to increase
both sellers’ price competition and buyers’ price sensitivity (Bakos, 1997; Degeratu,
Rangaswamy, and Wu, 2000; Iyer and Pazgal, 2003; Shankar, Rangaswamy, and Pusateri, 1999).
However, there is no clear answer regarding the different impacts of various competition
patterns, which are caused by the advent of the competitors within price comparison sites, with
respect to consumers’ perceptions of price, store image, and risk. More specifically, we first
investigate whether perceived price, store image, and risk differ according to 1) store name (a
known vs. unknown store); 2) brand name (a known vs. unknown brand); and 3) product category
(look-and-feel vs. non-look-and-feel). Then, under the significant conditions of the above-
mentioned considerations (store brand, product brand, and category), we examine whether
perceived price, store image, and risk differ by external reference price.

II. Conceptual Background and Hypotheses Development

A. Cue Utilization Theory

Consumers use various cues to infer product quality (Olson, 1973). Cue utilization theory

provides an attractive framework through which to assess consumer perceptions of stores,
brands, and products. These cues can be classified into extrinsic and intrinsic cues. While
intrinsic cues are directly related to the nature and performance of physical products (e.g.,
ingredients, taste, smell, texture, and technical specifications), extrinsic cues are not related to
product performance (Olson, 1972). When consumers make quality evaluations, they rely on
extrinsic cues such as price (Leavitt, 1954), packaging (McDaniel and Baker, 1977), store name
(Wheatley, Chiu, and Goldman, 1981), brand name (Allison and Uhl, 1964), and color (Peterson,
1977). A review of the literature suggests that consumers tend to use both intrinsic and extrinsic
cues when evaluating products (Simonson, 1989; Szybillo and Jacoby, 1974).

B. Perceived Price

When making a purchase decision, consumers evaluate the price of a product based on

some standard, which is known as a reference price (Emery, 1970; Monroe, 1973). Previous
studies have proposed that internal reference prices rely on memory from prior purchases
(Kalwani and Yim, 1992; Winer, 1986). As the reference price accumulates based on the average
market price, fair price, or normal price compared to the actual price, it can serve as a point of
comparison to judge whether the given price is acceptable, fair, high, or low (Grewal et al., 1998;
Mayhew and Winer, 1992; Monroe, 1990). As the internal reference is represented as a region
rather than a point estimate, there exists latitude of price acceptance (Kalyanaram and Little,

43 JOURNAL OF BUSINESS INQUIRY 2015

1994; Monroe and Venkatesan, 1969; Sherif, Taub, and Hovland, 1958). This latitude is referred
to as the acceptable price range with the identification of upper and lower limits (Monroe and
Venkatesan, 1969). Kalyanaram and Little (1994) found the relationship between an internal
reference price and an acceptable price range: consumers with higher average reference prices
have wider acceptable price ranges. According to cue utilization theory, which conceptualizes
products as an array of extrinsic and intrinsic cues serving as quality indicators, store name and
brand name have an effect on product quality as extrinsic cues (Dodds, Monroe, and Grewal,
1991; Rao and Monroe, 1989). Rao and Monroe (1989) found a statistically significant positive
relationship between price and quality perceptions. Thus, price perceptions can be explained by
store name and brand name. Since uncertainty magnifies the impact of memory on setting
internal reference prices (Monroe, 1971), uncertainty involved with the store and brand can
influence consumers’ price perceptions. Thus, we hypothesize the following:
H1-1: Online buyers have different (a) internal reference prices; and (b) acceptable price ranges
by store name.
H1-2: Online buyers have different (a) internal reference prices; and (b) acceptable price ranges
by brand name.

As an online shopping mall is a virtual environment, products in the online environment
can be categorized as either sensory or non-sensory, depending on the product attributes
(Degeratu, Rangaswamy, and Wu, 2000). In addition, de Figueiredo (2000) proposed the degree
of easiness in evaluating a product on the Web, from non-look-and-feel to look-and-feel goods.
This study adopts de Figueiredo (2000)’s classification of product categories: non-look-and-feel
vs. look-and-feel. Non-look-and-feel goods that have fewer sensory attributes (e.g., commodity
and quasi-commodity products such as computers) have characteristics similar to information-
oriented products that consumers often pursue in order to meet utilitarian goals. On the contrary,
look-and-feel goods (e.g., clothing) are similar to emotion-oriented products with hedonic goals.
As price is one of the representative non-look-and-feel product attributes, compared to look-and-
feel products, consumers’ decisions on non-look-and-feel type products involve their cognition
more than their affection or emotion. Based on this relationship, consumers’ sensitivity on price
perceptions may vary by product category. Therefore, we hypothesize the following:
H1-3: Online buyers have different (a) internal reference prices; and (b) acceptable price ranges
by product category.

C. Perceived Store Image

As competition in the market is more and more accelerated, store image becomes an
important component in the consumer’s decision-making process (Nevin and Houston, 1980),
and many stores try to alter their image in order to remain competitive (Grewal et al., 1998). In
traditional offline settings, store image includes the physical environment of the store, service
quality, and merchandise quality (Boulding et al., 1993; Zeithaml, Berry, and Parasuraman,
1996). In online environments, the competition is more severe, the entry barrier to the market is
much lower, and consumers have easy access to information on stores so that the role of
perceived store image may be more important. As in offline stores, the more positive the store
name, the more positive the consumer’s perceived store image is (Grewal et al., 1998; Keaveney
and Hunt, 1992). Likewise, there exists the positive effect of brand name on product quality
perceptions (Richardson, Dick, and Jain, 1994), which is part of the store image. According to
cue utilization theory, brand name and store name are frequently used by consumers as a

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composite of information (Olson, 1976). Zimmer and Golden (1988) found that consumers use
store names to describe a prototypical store (e.g., “Like Sears”), which is a form of the category-
based processing perspective of store image suggested by Keaveney and Hunt (1992). In
addition, Jacoby and Mazursky (1984) found that retailers with an unfavorable image could
improve that image by carrying brands with a more favorable image. Thus, we hypothesize the
following:
H2-1. Online buyers form different store image perceptions by store name.
H2-2. Online buyers form different store image perceptions by brand name.

D. Perceived Risk

Perceived risk refers to consumers’ perceptions of the uncertainty and concomitant adverse
consequences of buying a product (Dowling and Staelin, 1994). Compared to the offline
shopping environment, where consumers check and receive the product at the point of sale, the
online shopping environment does not satisfy this condition, so that the overall perceived risk in
the online shopping environment is greater. As consumers tend to perceive an online store with
a good reputation as being more trustworthy and credible than one with a poor reputation, an
online store’s reputation can foster perceived risks such as financial, performance, and privacy
risk (Chen and Dubinsky, 2003). In addition, as the product quality increases, uncertainty
associated with the store, such as performance risk, decreases (Chen and Dubinsky, 2003;
Sweeney, Soutar, and Johnson, 1999). Moreover, de Figueiredo (2000) explained that the degree
of easiness in evaluating a product on the Web (from non-look-and-feel to look-and-feel goods)
affects the consumer’s information searching process. For look-and-feel type products, the
purchasing process is more involved with sensory information than for non-look-and-feel type
products. Since online shopping sites do not provide as much sensory information as offline
shopping (such as touching, feeling, trying on, or seeing actual products in person), the perceived
risk from look-and-feel type products is higher than that from non-look-and-feel type products.
Thus, we hypothesize the following:
H3-1. Online buyers have different perceived risk by store name.
H3-2. Online buyers have different perceived risk by brand name.
H3-3. Online buyers have different perceived risk by product category.

E. External Reference Price

Kalyanaram and Winer (1995) divided reference prices into two types: internal and
external. While internal reference prices rely on one’s memory from prior purchases (Kalwani
and Yim, 1992; Winer, 1986), external reference prices are provided in the purchase
environment, such as the price tags of competing products on the shelf. According to Grewal,
Marmorstein, and Sharma (1996), when consumers are in a store, a within-store comparison
results in greater perceptions of value than a between-store comparison, whereas a between-store
comparison is more effective than a within-store comparison when consumers are at home.
Compared with traditional offline stores, the conditions of online stores with external reference
prices are similar to those when consumers are at home so that between-store comparisons will
be more effective.

If the focal mall is a well-known store, the advent of a famous competitor is not a significant
threat, and it does not intensify the competition, since the store name enhances buyers’

45 JOURNAL OF BUSINESS INQUIRY 2015

perceptions on the perceived price, product evaluation, and store image so that the buyers do not
perceive the differences (Dodds, 1991). On the other hand, when consumers shop at an unknown
store, if a famous competitor’s price is shown as an external reference price (ERP), there may be
damage done to the image of the unknown store, as consumers can easily perceive the differences
in the store name (Dodds, Monroe, and Grewal, 1991; Rao and Monroe, 1989). For price and
risk perceptions, the effect would be minimal for non-look-and-feel products, since they are
information-oriented products to meet consumers’ utilitarian goals based on intrinsic cues.
However, the effect of external reference prices may be significant on consumers’ decisions
about look-and-feel type products, as these products involve more extrinsic cues (de Figueiredo,
2000). Thus, we propose our hypotheses as the following:
H4-1: When online buyers shop at an unknown mall (a known mall), the effect of external
reference prices (ERP) on price perceptions differs (does not differ) by product category.
H4-2: When online buyers shop at an unknown mall (a known mall), the effect of external
reference prices (ERP) on store image differs (does not differ), regardless of product category.
H4-3: When online buyers shop at an unknown mall (a known mall), the effect of external
reference prices (ERP) on risk perceptions differs (does not differ) by product category.

III. Experiments

A. Study 1: The Effects of Store Name, Brand Name, and Product Category

This study uses a 2 (store name: known vs. unknown) x 2 (brand name: known vs.
unknown) x 2 (product category: look-and-feel vs. non-look-and-feel) factorial design to test the
proposed hypotheses. Store name and brand name are between-subject factors and product
category is manipulated as a within-subject factor. We hired a professional market research
agency in Korea to conduct this experiment by using its online panel members. This study used
experimental materials developed to reflect a hypothetical online shopping situation posted on
the website of the research agency. A total 160 subjects participated in this study. We screened
out subjects who did not have online purchasing experience within the past three months, and
we had an equal quota for gender. Table 1 summarizes the key demographics and online
shopping experiences of the subjects: they are relatively young (51.2% are in their 20s) and have
fairly good online shopping experience.

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Table 1: Subject Profiles on Key Demographics and Internet Buying Experiences (Study 1)

Demographics Percentage Internet buying experiences Percentage
Gender Male 40.0% Internet shopping period Less than 1yr 1.9%

Female 60.0% 1~2 yrs 5.6%
Age Less than 20 16.3% 2~3 yrs 13.1%

21~25 30.6% 3~4 yrs 19.4%
26~30 20.6% 4+ yrs 60.0%
31~35 16.3% Internet shopping

frequencies during past 3
months

1~3 29.4%
More than 35 16.2% 4~6 31.3%

Income Less than 1m 39.3% 7~9 16.8%
1m~1.99m 26.3% 10+ 22.5%
2m~2.99m 16.8% Purchase experience of

digital products
Yes 70.0%

3m~3.99m 10.6% No 30.0%
More than 4m 6.9% Purchase experience of

clothing products
Yes 89.4%

No 10.6%

To manipulate the known and unknown shopping malls, we used the well-known CJ mall

for the known shopping mall and made up a fictitious shopping mall for the unknown shopping
mall. For the two product categories, notebook computers and jeans were used for the non-look-
and-feel and the look-and-feel categories, respectively. We selected these two categories after
evaluating the easiness of quality judgments in the online shopping context among the most
frequently purchased product categories reported by Ernst & Young (2001). For each product
category, two brands were chosen: Samsung Sense and a fictitious brand for notebook
computers, and Levi’s and a fictitious brand for jeans.

When subjects click on the experiment link to participate, they are randomly assigned to
four sites (2 store names x 2 brand names). Then, the subjects are asked to assume a situation in
which they need a product for their personal use, and they decide to purchase it online. For this
study, we prepare web pages almost like a real shipping environment. In the shopping mall, we
provide the product and price information of the target items, where the price of the target item
is set at the middle of the five price levels provided. All subjects then move to the questionnaire
pages for the measurement of price, store image, and risk perceptions. At this stage, they are not
allowed to go back to the product information. Except for the store and brand name, all other
aspects, including web design and price, are identical. Each subject completes the tasks for both
product categories, and the order of the product category is counterbalanced.

Two price perceptions are measured. Internal Reference Price (IRP) is measured by the
mean value of three price estimations on the average market price and fair price, as used by
Grewal et al. (1998). Acceptable Price Range (APR) is the gap between the maximum acceptable
price and the minimum acceptable price (Lichtenstein, Bloch, and Black, 1988; Lii and Lee,
2005; Monroe, 1971) and is calculated from the subjects’ responses on the maximum and
minimum acceptable price estimations. Because of the difference in the price level between the
product categories, we convert the price measure to a percentage deviation from the target price
of each product category to make the comparison between the product categories comparable
(Simonin and Ruth, 1995). The perception measures of store image and risk are measured using
a seven-point Likert scale, based on the items used by Grewal et al. (1998) and Jarvenpaa and
Tractinsky (1999). As reported in Table 2, Cronbach’s alphas of all variables are well above the
reliability standard value of 0.7.

47 JOURNAL OF BUSINESS INQUIRY 2015

Table 2: Scale Items of Dependent Measures

Items Reliability
Notebook Jeans

Internal reference price (Grewal et al., 1998) 0.77 0.77
The normal price of the product would be _______.
The average market price of the product would be _______.
_______ would be the fair price of the product.

Maximum acceptable price – –
I am willing to pay a maximum amount of _______ to buy this product.

Minimum acceptable price – –
I think I have to pay a minimum amount of _______ to buy this product.

Perceived store image (Grewal et al., 1998) 0.89 0.91
Provide accurate product information
Provide good overall service
Provide helpful service
Carry high-quality merchandise

Perceived risk (Jarvenpaa et al., 1999) 0.89 0.90
How would you characterize the decision of whether to buy a product from this
web retailer? (significant opportunity / significant risk)
How would you characterize the decision of whether to buy a product from this
web retailer? (very positive situation / very negative situation)
How would you characterize the decision of whether to buy a product from this
web retailer? (very high potential for gain / very high potential for loss)

B. Study 2: The Effects of Online External Reference Prices

This study uses a 2 (store name: unknown vs. known) x 2 (product category: look-and-feel

vs. non-look-and-feel) x 4 (ERP: non vs. above vs. below vs. above & below) factorial design to
test the proposed hypotheses. We manipulate store name and product category similarly to what
we have done in Study 1. Using the same online panel as in Study 1, a total of 320 subjects
participated in this study. The sample profiles are very similar to those from Study 1. Details
about the sample profile are summarized in Table 3.

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Table 3: Subject Profiles on Key Demographics and Internet Buying Experiences (Study 2)

Demographics Percentage Internet buying experiences Percentage
Gender Male 46.9% Internet shopping period Less than 1yr 1.8%

Female 53.1% 1~2 yrs 6.3%
Age Less than 20 10.3% 2~3 yrs 19.1%

21~25 24.7% 3~4 yrs 17.5%
26~30 18.4% 4+ yrs 55.3%
31~35 16.6% Internet shopping

frequencies during past 3
months

1~3 30.3%
More than 35 30.0% 4~6 30.0%

Income Less than 1m 30.6% 7~9 13.4%
1m~1.99m 24.7% 10+ 26.3%
2m~2.99m 23.1% Purchase experience of

digital products
Yes 69.1%

3m~3.99m 10.9% No 30.9%

More than 4m 10.7%
Purchase experience of
clothing products

Yes 85.6%
No 14.4%

As in Study 1, the price of the focal mall is set at the middle of the five price levels

provided. While all of the external prices are unknown in Study 1, some stores in Study 2 can
be famous, based on the given conditions of none, above (lower price), below (higher price),
and above & below (lower and higher price). As the effect of store name was significant in
Study 1, we divide our analysis into the case when the focal mall is unknown and known.

IV. Results

A. Study 1: The Effects of Store Name, Brand Name, and Product Category

Table 4 shows a summary of the descriptive statistics on the dependent measures. To test
the predicted effects as a multivariate level (perceived price, store image, and risk), a MANOVA
test is conducted first using SPSS. As shown in Table 5, MANOVA results show significant
main effects of product category and store name, providing supporting evidence for H1-1, H2-
1, H3-1, H1-3, and H3-3 at the multivariate level. The effects are further investigated using
univariate analysis for significant effects at the multivariate level. Table 6 summarizes the
univariate ANOVA results for all four dependent variables. First, the results of the main effects
show that the effect of product category is significant for the internal reference price and the
acceptable price range (F=16.30, p<.01), thereby supporting H1-3 (a) and H1-3 (b), but failing to support H3-3. The univariate ANOVA results of store name show significant effects on the internal reference price (F=7.99, p<.01), store image (F=19.69, p<.01), and risk (F=22.93, p<.01), thereby supporting H1-1 (a), H2-1, and H3-1, but failing to support H 1-1 (b). However, the effects of brand name are insignificant on price, store image, and risk perceptions, thereby failing to support H1-2 (a), H1-2(b), H2-2, and H3-2. Based on Study 1, we can conclude that 1) the internal reference price is different by store name (the internal reference is lower for customers using known stores); 2) the internal reference price is different by product category (the internal reference price is lower for the look- and-feel product); 3) the acceptable price range is different by product category (the acceptable price range is wider for the look-and-feel type product); 4) the perceived store image is different 49 JOURNAL OF BUSINESS INQUIRY 2015 by store name (store image is better for the known store); and 5) the perceived risk is different by store name (the perceived risk is smaller for the known store). Table 4: Means and Standard Deviations of Perception Measures of Price, Store Image, and Risk Notebook Jeans Store Brand Total Store Brand Total Unknown Known Unknown Known Unknown Known Unknown Known Price perceptions Internal reference price (IRP) -0.031 -0.063 -0.057 -0.036 -0.047 -0.060 -0.135 -0.120 -0.075 -0.098 (0.098) (0.098) (0.103) (0.094) (0.099) (0.167) (0.192) (0.165) (0.198) (0.183) Acceptable price range (APR) 0.084 0.077 0.078 0.083 0.081 0.125 0.136 0.130 0.131 0.131 (0.092) (0.073) (0.061) (0.101) (0.083) (0.116) (0.097) (0.098) (0.116) (0.107) Store image perception 3.894 4.678 4.450 4.122 4.286 3.881 4.519 4.238 4.163 4.200 (0.949) (1.193) (1.167) (1.103) (1.144) (1.010) (1.281) (1.272) (1.116) (1.193) Risk perception 3.496 2.617 2.975 3.138 3.056 3.613 2.721 3.179 3.154 3.167 (1.260) (1.188) (1.328) (1.270) (1.298) (1.432) (1.247) (1.481) (1.347) (1.411) * Price measure was converted to a percentage deviation from the actual price. It was calculated as: (price- estimate - actual price of the target product)/actual price of the target product. * Scale measures represent average responses from a 7-point Likert scale. Table 5: MANOVA Results Source Pillai's trace Wilks' lambda Hotelling's trace Main effects Product category (Cat) .273** .727** .376** Store name (SN) .209** .791* .265** Brand name (BN) .033 .967 .034 2-way interactions Cat x SN .031 .969 .032 Cat x BN .029 .971 .030 SN x BN .008 .992 .008 3-way interactions Cat x SN x BN .006 .994 .006 Note: *:p<.05, **:p<.01. VOL. 14[1] KANG AND JUNG: THE EFFECT OF ONLINE EXTERNAL REFERENCE PRICE 50 ON PERCEIVED PRICE, STORE IMAGE, AND RISK Table 6: Univariate ANOVA Results Source df F value Internal reference price Acceptable price range Store image perception Risk perception Main effects Product category (Cat) 1 16.301** 32.559** 1.370 1.630 Store name (SN) 1 7.989** .024 19.690** 22.934** Brand name (BN) 1 2.979 .052 1.583 .138 2-way interactions Cat x SN 1 2.917 .945 1.000 .005 Cat x BN 1 .885 .055 2.972 1.175 SN x BN 1 .296 .064 .687 .257 3-way interactions Cat x SN x BN 1 .100 .430 .330 .005 Note: *:p<.05, **:p<.01. A. Study 2: The Effects of Online External Reference Prices Tables 7 and 8 show the summary of the descriptive statistics on the dependent measures when the focal mall is known and unknown, respectively. Table 7. Means and Standard Deviations of Perception Measures of Price, Store Image, and Risk in Known Focal Mall Notebook Jeans ERP by famous competitor No Above Below Above & below Total No Above Below Above & below Total Price perceptions Internal reference price (IRP) -0.045 -0.033 -0.029 -0.051 -0.04 -0.127 -0.092 -0.155 -0.113 -0.121 -0.094 -0.092 -0.07 -0.085 -0.086 -0.124 -0.144 -0.181 -0.159 -0.154 Acceptable price range (APR) 0.075 0.082 0.091 0.066 0.078 0.102 0.136 0.136 0.133 0.127 -0.077 -0.067 -0.057 -0.046 -0.063 -0.093 -0.115 -0.098 -0.112 -0.105 Store image perception 4.781 4.638 4.613 4.294 4.581 4.506 4.4 4.531 4.381 4.455 -1.011 -1.138 -0.65 -0.868 -0.943 -1.072 -1.174 -0.946 -0.845 -1.009 Risk perception 3.05 2.542 2.725 3.025 2.835 2.975 3.158 3.058 3.142 3.083 -1.19 -1.265 -1.247 -1.092 -1.208 -1.266 -1.408 -1.325 -1.164 -1.284 * Price measure was converted to a percentage deviation from the actual price. It was of the target product. * Scale measures represent average responses from a 7-point Likert scale. 51 JOURNAL OF BUSINESS INQUIRY 2015 Table 8: Means and Standard Deviations of Perception Measures of Price, Store Image, and Risk in Unknown Focal Mall …