Electronic Business: Concepts, Methodologies, Tools, and Applications (4-Volumes) P161 pdf

1534
Nibbling, Sniping, and the Role of Uncertainty in Second-Price, Hard-Close Internet Auctions
0.596, the number of bids is 0.660, the proportion
of bids is 0.025, and the proportion of bidders is
0.042.
The empirical distributions for each of our
variables in Table 1b are analogous to those for
the coin auctions summarized in Table 1a. Com-
parisons of the mean and median values indicate
that a number of variables are likely to be non-
normally distributed. As a result, it is necessary
to employ nonparametric analysis to control for
this possibility.
Comparing Tables 1a and 1b, we note differ-
ences in nibbling activity across the two types
of auctions. When evaluated at the sample mean,
the automobile auctions have a higher number
of bids and bidders, nibbles and nibblers, and
a higher proportion of bids that are nibbles and
bidders that nibble. With the exception of bids
that are nibbles, these differences also hold true
at the sample median as well. These statistics
lend support to hypothesis H
1
, as the automobile
auctions (which exhibit more uncertainty) have
both higher numbers and intensities of nibbling.
However, it remains to be seen from the subse-
quent hypothesis tests whether these differences
DUHLQIDFWVWDWLVWLFDOO\VLJQL¿FDQW
We can also make inferences about sniping

activity across the two auction formats. The au-
tomobile auctions exhibit lower mean and median
values for both our sniping activity and intensity
variables. The latter implies that, when we control
for size of the auction, there is less sniping and
more nibbling in the more uncertain market. One
possible explanation is that these auctions also
exhibit more (and more intense) nibbling, which
may lead to higher bid prices, and drive some of
the potential snipers (who may not have extremely-
high reservation values) out of the market before
they have a chance to snipe.
Our hypothesis tests are contained in Tables 2
through 4. Table 2 contains the Mann-Whitney test
used to address hypotheses 1 and 2, while Tables
DQGFRQWDLQWKHFRUUHODWLRQFRHI¿FLHQWDQDO\VLV
used to address hypotheses 3 and 4.
The results in Table 2 provide clear evidence
that the graded coin and automobile auction data
show different nibbling behavior for all of our
measures (p < 0.01), except the proportion of
ELGVWKDWDUHQLEEOHV6SHFL¿FDOO\WKHDXWRPR-
ELOH DXFWLRQ GDWD FRQWDLQHG VLJQL¿FDQWO\ PRUH
nibblers, nibbles, and portion of bidders who
nibble. The automobile auctions also exhibited
Variable M-W Test Z-Stat Prob.
Number of Bids 192.500 -6.372 0.000
Number of Bidders 332.000 -5.189 0.000
Number of Nibblers 344.500 -5.083 0.000
Number of Nibbles 275.000 -5.671 0.000

Number of Bids per Bidder 345.000 -5.085 0.000
Proportion of Bidders who Nibble 578.500 -3.090 0.002
Proportion of Bids that are Nibbles 909.000 -0.265 0.791
Number of Bids in Last Minute 796.000 -1.342 0.180
Number of Bidders in Last Minute 827.500 -1.055 0.292
Proportion of Bids in Last Minute 647.000 -2.704 0.007
Proportion of Bidders in Last Minute 651.000 -2.667 0.008
Table 2. Nonparametric (Mann-Whitney u-test) hypothesis tests
1535
Nibbling, Sniping, and the Role of Uncertainty in Second-Price, Hard-Close Internet Auctions
VLJQL¿FDQWO\PRUHSDUWLFLSDWLRQDVHYLGHQFHGE\
WKH VLJQL¿FDQWO\KLJKHUQXPEHURI ELGGHUV DQG
bids per auction.
$VDFRUROODU\WRWKHVH¿QGLQJV7DEOHDOVR
provides evidence about differences in sniping
activity across the two product categories. With
regard to the total amount of sniping, the samples
DUHQRWVWDWLVWLFDOO\VLJQL¿FDQWO\GLIIHUHQWEXWDUH
different in the proportion of bids and bidders
in the last minute (p < 0.01). This supports our
earlier argument that the amount and intensity of
sniping differs within the same auction format.
The auctions whose product has a more certain
YDOXH FHUWL¿HG FRLQV H[KLELWHG PRUH VQLSLQJ
behavior.
Tables 3 and 4 present our analysis of hypoth-
eses 3 and 4. Analysis of the coin auction data in
7DEOHLQGLFDWHWKDWWKHUHLVQRVLJQL¿FDQWFRU-
relation (p < 0.05) between the amount of sniping
and the amount or intensity of nibbling in the coin

GDWD+RZHYHUWKHUHLVDVLJQL¿FDQWFRUUHODWLRQ
between the intensity of sniping behavior and both
W KH D PR X Q W D QG L Q W HQ VL W \ RI Q LE E O L Q J  6 S HF L ¿F DO O\
the intensity of sniping behavior (when measured
as both the proportion of bids in the last minute
of the auction as well as the proportion of bidders
Number of Bids in Last
Minute
Number of Bidders in Last
Minute
Spearman Spearman
Variable Correlation Prob. Correlation Prob.
Number of Bids 0.072 0.330 0.085 0.301
Number of Bidders 0.139 0.197 0.168 0.151
Number of Nibblers 0.078 0.317 0.109 0.253
Number of Nibbles 0.101 0.269 0.121 0.229
Number of Bids per Bidder -0.061 0.354 -0.151 0.176
Proportion of Bidders who Nibble -0.197 0.111 -0.180 0.133
Proportion of Bids that are Nibbles 0.149 0.180 0.177 0.138
Proportion of Bids in
Last Minute
Proportion of Bidders in
Last Minute
Spearman Spearman
Variable Correlation Prob. Correlation Prob.
Number of Bids -0.299 0.031 -0.286 0.037
Number of Bidders -0.228 0.079 -0.261 0.052
Number of Nibblers -0.281 0.040 -0.314 0.024
Number of Nibbles -0.271 0.045 -0.284 0.038
Number of Bids per Bidder -0.250 0.060 -0.225 0.082

Proportion of Bidders who Nibble -0.468 0.001 -0.491 0.001
Proportion of Bids that are Nibbles -0.168 0.151 -0.204 0.103
Table 3. Nonparametric (Spearman) correlations for graded coin auctions
Note: Probability values apply to a one-tailed test
1536
Nibbling, Sniping, and the Role of Uncertainty in Second-Price, Hard-Close Internet Auctions
who snipe) is negatively correlated (p < 0.05) with
the number of nibbles, the number of nibblers,
and the portion of bids that are nibbles.
In direct contrast, the results for the automo-
ELOHDXFWLRQVGHPRQVWUDWHDVLJQL¿FDQWSRVLWLYH
correlation between the amount of sniping and
the amount of nibbling, as well as the intensity
RIVQLSLQJDQGWKHLQWHQVLW\RIQLEEOLQJ6SHFL¿-
cally, the amount of sniping, as measured by the
number of sniping bids and the number of snip-
HUVLVVLJQL¿FDQWO\DQGSRVLWLYHO\FRUUHODWHGS
< 0.05) with the number of nibbles, the number
of nibblers, and the proportion of bids that are
nibbles. The intensity of sniping, as measured by
the proportion of bids in the last minute and the
proportion of bidders who snipe in the last minute,
LVVLJQL¿FDQWO\DQGSRVLWLYHO\DVVRFLDWHGZLWKWKH
number of nibbles, the number of nibblers, and
the proportion of bids that are nibbles.
When the results contained in Tables 3 and 4
are taken together, our results extend and clarify
Ockenfels and Roth’s (2002) assertion that par-
Number of Bids in Last
Minute

Number of Bidders in Last
Minute
Spearman Spearman
Variable
Correla-
tion
Prob. Correlation Prob.
Number of Bids 0.486 0.001 0.481 0.001
Number of Bidders 0.528 0.000 0.536 0.000
Number of Nibblers 0.509 0.000 0.507 0.000
Number of Nibbles 0.568 0.000 0.574 0.000
Number of Bids per Bidder -0.156 0.148 -0.167 0.132
Proportion of Bidders who Nibble 0.218 0.070 0.183 0.109
Proportion of Bids that are Nibbles 0.438 0.001 0.457 0.001
Proportion of Bids in
Last Minute
Proportion of Bidders in
Last Minute
Spearman Spearman
Variable
Correla-
tion
Prob. Correlation Prob.
Number of Bids 0.336 0.011 0.357 0.007
Number of Bidders 0.420 0.002 0.374 0.005
Number of Nibblers 0.410 0.002 0.358 0.007
Number of Nibbles 0.437 0.001 0.442 0.001
Number of Bids per Bidder -0.220 0.069 -0.134 0.184
Proportion of Bidders who Nibble 0.178 0.116 0.159 0.143
Proportion of Bids that are Nibbles 0.407 0.003 0.365 0.006

Table 4. Nonparametric (Spearman) correlations for automobile auction
Note: Probability values apply to a one-tail test
1537
Nibbling, Sniping, and the Role of Uncertainty in Second-Price, Hard-Close Internet Auctions
ticipants resort to sniping more frequently in
circumstances of higher uncertainty in an effort
to avoid the bidding wars that may occur when
several participants nibble in that same auction.
In auctions with less uncertainty regarding the
value of the good which is being auctioned, the
number of nibblers and the intensity of their nib-
bling have a negative impact on sniping. This is
consistent with the idea that when the true value
of the good has little uncertainty, it is optimal to
conceal your reservation price.
CONCLUSION
We randomly selected items from completed
auctions on eBay for two types of goods: one that
H[KLELWVDVLJQL¿FDQWGHJUHHRIXQFHUWDLQW \DERXW
the product’s value (used cars); and one where
WKHUHLVVLJQL¿FDQWO\OHVVXQFHUWDLQW\DERXWWKH
SURGXFW¶VYDOXHFHUWL¿HGFRLQV8VLQJQRQSDUD-
metric (Mann-Whitney) analysis of variance and
Spearman correlation analysis, we test hypotheses
based on bidder behavior in the same auction
format, but across different product groups. Our
results indicate that auctions with a high amount
RIXQFHUWDLQYDOXHH[KLELWVLJQL¿FDQWO\PRUH
QLEEOLQJ 6SHFL¿FDOO\ PRUH QLEEOLQJ RFFXUUHG
both in terms of the number (and proportion) of

participants who use a nibbling strategy, as well
a s t h e n u m b e r (a n d p r o p o r t i o n) o f i n c r e m e nt a l b i d s
that are submitted over the course of the auction.
Auction participants were also more prone to en-
gage in sniping in auctions with more uncertain
value. Moreover, sniping occurred most often in
high-risk auctions when several other participants
attempted nibbling strategies. This is consistent
ZLWKWKH¿QGLQJVRI2FNHQIHOVDQG5RWK
which suggest that many bidders use sniping to
avoid getting into bidding wars. For lower-risk auc-
tions, where the value of the good is known with
PRUHFHUWDLQW\ZH¿QGWKDWDXFWLRQSDUWLFLSDQWV
are less prone to nibble, and thereby withhold
information concerning their reservation price,
engaging in sniping behavior.
It is also interesting to note that, even though
participants did not have reliable information about
other participants’ reservation prices, coin auc-
tion participants’ behavior deviated only slightly
from that predicted in conventional second-bid,
hard-close auctions. Our analysis indicates that
WKHUHPD\EHOLWWOHEHQH¿WIURPREVHUYLQJRWKHUV¶
bidding behavior in this circumstance. However,
in car auctions, where there was a large amount
of uncertainty concerning the good being pur-
chased, auction participants’ behavior deviated
VLJQL¿FDQWO\IURPWKDWSUHGLFWHGIRUWUDGLWLRQDO
second-bid, hard-close auctions. Our results are
consistent with the idea that bidders engage in

nibbling and sniping to overcome a lack of infor-
mation concerning the valuation of the product.
In this case, the lack of reliable knowledge con-
cerning the reservation price of other participants
may have induced alternative bidding strategies
(nibbling and sniping) in an attempt to overcome
LQIRUPDWLRQGH¿FLHQFLHV
:KLOH RXU ¿QGLQJV SURYLGH DQ LQWHUHVWLQJ
FODUL¿FDWLRQRI,QWHUQHWDXFWLRQWKHRU\RXUVWXG\
is preliminary in nature and could be extended in
a number of ways. First, the spectrum of goods
included in the study could be expanded in or-
der to give a clearer picture of how uncertainty
LQÀXHQFHVRQOLQHELGGLQJEHKDYLRU,QDGGLWLRQ
LW ZRXOG EH LQWHUHVWLQJ WR VWXG\ WKH UDPL¿FD-
tions of other online auction rules, such as those
employed by Amazon.com, on the behavior of
auction participants.
Another potential limitation of our study is the
use of Spearman correlations. While correlations
are a parsimonious technique, an alternative might
be the use of maximum likelihood-based regres-
sion techniques. In this case, many other factors
would be needed to control for omitted variables.
If important control variables are not included,
our results would be biased and inconsistent. Most
of the control variables needed to perform this
1538
Nibbling, Sniping, and the Role of Uncertainty in Second-Price, Hard-Close Internet Auctions
type of analysis are not available through e-Bay

or other online auction providers. We leave these
topics for future research.
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This work was previously published in the International Journal of E-Business Research, edited by I. Lee, Volume 4, Issue 1,
pp. 69-81, copyright 2008 by IGI Publishing (an imprint of IGI Global).
1540
Copyright © 2009, IGI Global, distributing in print or electronic forms without written permission of IGI Global is prohibited.
Chapter 5.10
The Driving Forces of
Customer Loyalty:
A Study of Internet Service Providers
in Hong Kong
T. C. E. Cheng
The Hong Kong Polytechnic University, Hong Kong

L. C. F. Lai
The Hong Kong Polytechnic University, Hong Kong
A. C. L. Yeung
The Hong Kong Polytechnic University, Hong Kong
ABSTRACT
In this study we examine the driving forces of
customer loyalty in the broadband market in
Hong Kong. We developed and empirically tested
a model to examine the antecedents of customer
loyalty towards Internet service providers (ISPs)
in Hong Kong. Structural equation modeling
(SEM) was used to evaluate the proposed model.
A total of 737 valid returns were obtained through
a questionnaire survey. The results show that
customer satisfaction, switching cost, and price
perception are antecedents that lead directly to
customer loyalty, with customer satisfaction exert-
LQJWKHJUHDWHVWLQÀXHQFH$OWKRXJKZHIRXQGWKDW
VHUYLFHTXDOLW\VLJQL¿FDQWO\LQÀXHQFHVFXVWRPHU
satisfaction, which in turn leads to customer loy-
DOW \ZHGLGQRW¿QGDGLUHFWUHODWLRQVKLSEHWZHHQ
service quality and customer loyalty. Our results
also reveal that corporate image is not related to
customer loyalty. Our empirical investigation
suggests that investing huge resources in building
corporate image can indeed be a risky strategy
for ISPs.
INTRODUCTION
'XHWRDUHFHQWVLJQL¿FDQWVXUJHLQWKHQXPEHU
of ISPs, the broadband market in Hong Kong has

1541
The Driving Forces of Customer Loyalty
EHFRPH YHU\ FURZGHG OHDGLQJ WR ¿HUFH SULFH
competition, which has eventually resulted in
the elimination of many ISPs from the market.
From 2001 to 2006, the number of ISPs in Hong
Kong dropped from 258 to 181. As the broadband
market matures, the focus of ISPs has shifted
from customer acquisition to customer retention.
In March 2006, there were around 2.6 million
Internet users, including both broadband and
narrowband users, representing a 39% penetra-
tion rate in Hong Kong. About 64% of these users
DFFHVVWKURXJKWKHEURDGEDQG,QWHUQHW2I¿FHRI
the Telecommunications Authority, 2006). These
¿JXUHVHVWDEOLVK+RQJ.RQJDVRQHRIWKHPRVW
,QWHUQHWFRQQHFWHGFLWLHVLQWKH$VLDQ3DFL¿F
region.
7KHVLJQL¿FDQFHRIFXVWRPHUOR\DOW\FDQQRW
be overemphasized because it relates closely
to the continued survival, as well as the future
growth, of companies. For a company to maintain
DVWDEOHSUR¿WOHYHOZKHQWKHPDUNHWUHDFKHVWKH
saturation point, a defensive strategy aiming at
retaining existing customers is more important
than an offensive one, which targets at expanding
the size of the overall market by inducing potential
customers to subscribe to its services (Ahmad &
Buttle, 2002; Fornell, 1992).
Previous studies on customer loyalty focused

on customer satisfaction and switching barriers
(Dick & Basu, 1994; Gerpott, Rams, & Schindler,
2001; Lee & Cunningham, 2001). These studies
have found that customers experiencing a high
level of satisfaction are likely to remain with
their existing service providers and maintain
their service subscriptions. Switching barriers,
on the other hand, play a moderating role in the
relationship between customer satisfaction and
customer loyalty (Colgate & Lang, 2001; Lee
& Cunningham, 2001). Researchers in this area
have further elaborated on the linkages between
price factors and perceived value (e.g., Grewal,
Monroe, & Krishnan, 1998), as well as between
price and customer loyalty (e.g., Voss, Parasura-
man, & Grewal, 1998). In addition, the marketing
literature supports the general notion that pricing
factors affect the price perceptions of custom-
ers, which in turn contribute to customer loyalty
(Reichheld, 1996).
By using SEM, this study empirically ana-
lyzes whether customer satisfaction, switching
cost, price perception, and corporate image are
antecedents of customer loyalty in the context of
the ISP market in Hong Kong. We also seek to
identify elements of service quality as anteced-
ents of satisfaction, and their levels of impact
on satisfaction, and to ascertain whether service
quality is a direct antecedent of customer loyalty.
We examine the degree to which switching cost

and price perception account for the variations in
the strength of consumer loyalty to ISPs. Finally,
we test if corporate image has any impact on
customers’ loyalty to their present ISPs.
THEORETICAL BACKGROUND AND
HYPOTHESIS DEVELOPMENT
Customer loyalty is a purchase behavior, which,
unlike customer satisfaction, is an attitude (Grif-
¿Q&XVWRPHUOR\DOW\LVFRQFHUQHGZLWKWKH
likelihood of a customer returning, making busi-
ness referrals, providing strong word of mouth, as
well as offering references and publicity (Bowen
& Shoemaker, 1998). Loyal customers are less
likely to switch to competitors in view of a given
price inducement, and they make more purchases
compared to less loyal customers (Baldinger &
Rubinson, 1996). Although most research on loy-
alty has focused on frequently purchased package
goods (i.e., brand loyalty), the loyalty concept is
also important for industrial goods (i.e., vendor
loyalty), services (i.e., service loyalty), and retail
establishments (i.e., store loyalty) (Dick & Basu,
1994). As evidenced in the previous discussions,
customer loyalty has been generally described as
occurring when customers repeatedly purchase
goods or services over time, have word of mouth,
and make referrals to other customers.
1542
The Driving Forces of Customer Loyalty
Antecedents of Customer Loyalty

One of the major factors found to affect customer
loyalty is customer satisfaction. Halstead, Hart-
man, and Schmidt (1994) considered customer
satisfaction as an affective response that focuses
on product performance against some prepurchase
standard during or after consumption. Mano and
O l i ve r (19 93) r ef e r r e d t o s a t i s f a c t i o n a s a n a t t it u d e
or evaluative judgment varying along the hedonic
continuum focusing on the product, which is evalu-
DWHGDIWHUFRQVXPSWLRQ)RUQHOOLGHQWL¿HG
satisfaction as an overall evaluation based on
the total purchase and consumption experience
of the target product, or service performance
compared with prepurchase expectations over
time. Oliver (1997, 1999) regarded satisfaction as
DIXO¿OOPHQWUHVSRQVHRUMXGJPHQWRQDSURGXFW
or service, which is evaluated for one-time or
ongoing consumption.
6HUYLFHTXDOLW\FDQEHGH¿QHGDVWKHUHVXOW
of the comparison between a customer’s expec-
tations on a service and their perception of the
way the service has been delivered (Gronroos,
1984; Lehtinen & Lehtinen, 1982; Lewis &
Booms, 1983; Parasuraman, Zeithaml, & Berry,
1985, 1988, 1994). Perceived service quality is
usually measured by two dimensions, namely
process quality and output quality. Parasuraman
et al. (1985, 1988, 1994) developed the 22-item
SERVQUAL instrument, which has been widely
used to measure service quality in many indus-

tries, such as banking (Mukherjee & Nath, 2005),
health care (Choi, Lee, Kim, & Lee, 2005), and
airport service (Fodness & Murray, 2007). The
SERVQUAL instrument assesses the overall
service quality by comparing service expectation
DQGDFWXDOSHUIRUPDQFHLQWHUPVRI¿YHJHQHULF
dimensions, namely, tangibles, reliability, respon-
siveness, assurance, and empathy.
When consumers switch service providers,
they will incur various costs ranging from the
time spent in gathering information about po-
WHQWLDODOWHUQDWLYHVWRWKHEHQH¿WVIRUIHLWHGGXH
to termination of the existing service. Patterson
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perception of the magnitude of the additional cost
incurred to terminate a relationship and to secure
DQDOWHUQDWLYHRQH6HOQHVGH¿QHGVZLWFKLQJ
FRVWDVWKHWHFK Q LFDO¿QDQFLDODQGSV\FKRORJLFDO
IDFWRUVWKDWPDNHLWGLI¿FXOWRUH[SHQVLYHIRUD
customer to change brands.
&RUSRUDWHLPDJHLVGH¿QHGDVWKHRYHUDOOLP-
pression about a company formed on the minds of
the public (Barich & Kotler, 1991; Dichter, 1985;
Kotler, 1982). It relates to the different physical
and behavioral attributes of a company, such as
business name, logo, corporate values, tradition,
ideology, and the impression of quality communi-
cated by a customer to a potential customer (i.e.,
word of mouth). The building of corporate image
is a lengthy process. The sensory process starts

with ideas, feelings, and previous experience with
a company that are retrieved from memory and
transformed into a mental image (Yuille & Catch-
pole, 1977). Past studies have suggested that a host
of factors, including advertising, public relations,
physical image, word of mouth, and customer’s
actual experience with the goods and services,
LQÀXHQFHWKHFRUSRUDWHLPDJHRIDFRPSDQ\LQ
the mind of a customer (Normann, 1991).
Researchers (e.g., Slater, 1997) and consul-
tants (e.g., Gale, 1994) have recommended that
companies should adjust their strategies to retain
customers in order to achieve superior customer
value delivery as customer value incorporates both
WKHFRVWVDQGEHQH¿WVRIVWD\LQJZLWKDFRPSDQ\
As such, customers’ perceived value is considered
as a strong driver of customer retention. Neverthe-
less, some important questions about the role of
price in services have remained unanswered. One
is whether price perception has a direct effect on
overall customer loyalty. If so, it is essential for
companies to actively manage their customers’
price perceptions because of their impact on value
perceptions. Another question is about the forma-
1543
The Driving Forces of Customer Loyalty
tion of price perception in services. Answers to
these questions can help clarify the measurement
and management of price perception.
Conceptual Model and Hypotheses

We propose a conceptual model that theorizes the
relationships among consumer loyalty, service
quality, customer satisfaction, switching cost, and
corporate image as shown in Figure 1. In what
follows, we justify the postulated relationships
in the model and formulate several hypotheses
to test the model.
Service Quality and Customer
Satisfaction
Service quality researchers refer to satisfaction
DVDWUDQVDFWLRQVSHFL¿FHYDOXDWLRQDQGWRTXDO-
ity as an overall evaluation based on a whole set
of cumulative evaluations. Parasuraman et al.
(1994) recommended examining service quality
and satisfaction, and their causal link, from both
WUDQVDFWLRQVSHFL¿FDQGJOREDOSHUVSHFWLYHV,QWKH
context of the ISP business, which mainly hinges
on the ongoing relationship between a customer
DQGWKHLUVHUYLFHSURYLGHUWKHFXPXODWLYHVSHFL¿F
perspective is more suitable to view this ongo-
ing relationship. Moreover, service quality is
usually considered as an antecedent of customer
satisfaction in the ISP business. Therefore, we
hypothesize that
H1: Perceived service quality is positively related
to customer satisfaction.
Customer Satisfaction and Customer
Loyalty
The marketing literature suggests that customer
OR\DOW\ FDQ EH GH¿QHG LQ WZR GLVWLQFW ZD\V

QDPHO\WKH³EHKDYLRUDODSSURDFK´DQGWKH³DW-
titude approach” (Jacoby & Kyner, 1973). From
the behavioral perspective, customer loyalty is
LGHQWL¿HGDVWKHDFWXDOUHSXUFKDVHEHKDYLRURID
customer (Cunningham, 1961). In contrast, the
Corporate
Image
Service
Quality
Switching
Cost
Customer
Loyalty
Customer
Satisfaction
H1
H4
H2
H3
H5
H6
Price
Perception
H7
Corporate
Image
Service
Quality
Switching
Cost

Customer
Loyalty
Customer
Satisfaction
H1
H4
H2
H3
H5
H6
Price
Perception
H7
Figure 1. Theoretical framework