1
THE INFLUENCE OF INERTIA ON REPURCHASE INTENTION OF
HYPERMARKET CUSTOMERS
Ming-Chun Tsai1, Jia-Chi Lin
2*
1 Department of Business Administration, Chung Hua University, No 707,
Sec2, Wu Fu Rd, Hsin Chu ,300 ,Taiwan, Republic of China
e-mail: [email protected], Tel:886-3-5186579 2 Ph.D. Program of Technology Management, Chung Hua University No 707,
Sec 2, Wu Fu Rd, Hsin Chu ,300 ,Taiwan, Republic of China
e-mail:[email protected],Tel:886-3-6582548 2*Jia-Chi Lin is the corresponding author of this manuscript
Abstract
Purpose: The purpose of this study was to explore the impact of inertia on consumer repurchase intention and
to explore the key factors that influence inertia. This study combined satisfaction, switching barriers,
competitor attraction and inertia in order to establish a repurchase intention regression model.
Design/methodology/approach: Data were collected using self-administered questionnaires, which were
designed by the researchers who referred to the relevant literature. The questionnaires focused on consumers
from the top three hypermarkets in north Taiwan; namely, Carrefour, RT-mart and Amart. A total of 1,080
questionnaires were distributed and there were 848 valid returns, with a return rate of 77%. This study applied
t test, ANOVA analysis and linear regression analysis in order to explore the relationships among satisfaction,
repurchase intention and inertia.
Findings: The results showed that inertia has a direct effect on the relationships between switching barriers,
competitor attraction, satisfaction and repurchase intention. Moreover, inertia does not have significant
moderating effect on satisfaction and repurchase intention. The study also identified that convenience,
reasonable price and consumption frequency influence inertia both significantly and directly.
Originality/value: Inertia does not reduce the effect of satisfaction on repurchase intention for hypermarket
services; however, inertia can improve the effect of switching barriers to repurchase intention in hypermarket
services, as well as act as a barrier to competitor attraction to repurchase intention in the moderating
relationship model. In addition, hypermarket operators ought to identify the important factors that affect
inertia.
Keywords: Inertia, Satisfaction, Repurchase Intention, Hypermarket
Paper type: Research paper
1. Introduction
Kotler (2003) pointed out that the cost of maintaining a regular customer is far lower than
the cost of developing a new customer. Reducing customer defection and increasing
repurchase intention can help reduce enterprise costs and increase enterprise profit.
Repurchase intention refers to the intention to make repeat purchases in consumer behavior;
it belongs to behavior loyalty, and may not represent emotional loyalty (Oliver, 1999). Past
studies on customer loyalty have been based on customer satisfaction; however, repurchase
intention is not entirely resulted from customer satisfaction. Behavior loyalty may be a
manifestation of inertia. Colgate and Danaher (2000) indicated that inertia is a part of
human nature. Customers may not have a strong motivation to seek alternative solutions
when they are accustomed to a specific thing. This behavior can be a cause of repurchase
intention. Huang and Yu (1999) defined inertia as a situation in which a consumer
passively repurchases the same brand in a relatively non-conscious manner without thought.
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This non-conscious pattern of retention is different from loyalty (Huang and Yu, 1999).
In recent researches related to inertia, focus has been placed on the inertia of financial
customers. White and Yanamandram (2004) explored the reasons and consequences of
inertia in financial services. Yanamandram and White (2010) considered switching costs as
an antecedent to inertia and calculative commitment in financial services. They found that
switching costs are the main factor of affecting inertia. On the other hand, Liu, Wu and
Huang (2007) studied the relationships among inertia, switching barriers, competitor
attraction, satisfaction, and repurchase intention in financial services. They indicated that
relationship inertia can improve the effect of switching barriers, reduce the effect of
satisfaction, and increase the possibility of customer retention. However, inertia cannot
reduce effect of attraction from other competitors. This indicates that inertia has a moderate
effect on the relationships between switching barriers and satisfaction in financial services.
In addition to financial services, Lee and Neale (2012) explored the interactions and
consequences of inertia and switching costs in mobile phone services. They found that
switching costs deterred switching and engendered negative word-of-mouth, but only with
low-inertia customers. With high-inertia customers, retention and word-of-mouth behaviors
depended on whether the inertia stemmed from satisfaction or indifference. Aside from
customer loyalty, these studies have shown that inertia is an important factor of affecting
customer repurchase.
The retail industry, including the hypermarket industry, occupies a pivotal position in
the global economy. Furthermore, the hypermarket industry belongs to the service industry,
and the key to sustainable management of the service industry is continuous consumption
from consumers. If an enterprise can maintain the consumers’ repurchase intention, it will
be able to ensure business continuity (Blodgett, Wakefield and Barnes 1995; Colgate
Stewart and Kinsella 1996; Hallowell, 1996; Rust Zahorik and Keiningham, 1995). In past
researches about hypermarket marketing, Chang and Tu (2005) explored the relationships
among store image, customer satisfaction and customer loyalty in Taiwan’s hypermarkets;
Olavarrieta, Hidalgo, Manzur and Farías (2012) studied the determinants of in-store price
knowledge for packaged products in a Chilean hypermarket; and Stancu and Madalin and
Meghisan (2012) discovered the main consumption habits of customers at Auchan
hypermarket in Romania. In these researches, they mainly focus on the price, store image,
customer satisfaction and customer loyalty. However, Hidalgo, Manzur, Olavarrieta and
Farías (2008) pointed out that inertia is an important impact factor on repurchase intention
for low-involvement shopping behavior. Shopping in a hypermarket is considered to be
low-involvement shopping behavior. In other words, the decision-making processes
involved in shopping at a hypermarket do not take a long time, and customers do not need
to exert a lot of effort on it (Solomon, 1994). Inertia has an influence on the level of
consumer repurchase intention at hypermarkets. Therefore, this study proposed that inertia
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may play an important role in hypermarket services.
Grönroos (1984) divided service quality in the service industry into two types. The
first type is technical quality, which emphasizes the quality of service offered to customers,
and the second type is functional quality, which focuses on quality in the delivery of
service. Dabholkar, Thorpe and Rentz(1996) developed a Retail Quality Scale, which
suggests that hypermarkets are a combination of service and products; thus, the service
quality of hypermarkets belongs to functional quality. As information technology and the
internet continue to prevail, financial services have changed from being based on human to
human interaction to a form of technology-based banking (Bitner ,Brown and Meuter 2000).
Thus, financial services belong to the technical quality dimension. In addition, Kang and
James (2004) defined functional quality as a process quality in evaluation that belongs to
low-involvement shopping behavior, whereas technical quality was defined as an outcome
quality that belongs to high-involvement shopping behavior. In past studies, Liu, et al.
(2007) pointed that there are relationships among inertia, switching barriers, competitor
attraction, satisfaction, and repurchase intention; however, the results were based only on
financial services. Hypermarket services and financial services are different two types of
services, and the relationships among inertia, switching barriers, competitor attraction,
satisfaction, and repurchase intention in hypermarket services are worth further verification
and exploration. On the other hand, how to find the main factors affecting inertia is also an
important issue in discussing inertia. Hence this study had two stages. In the first stage, this
study explored the relationships among inertia, switching barriers, competitor attraction,
satisfaction, and repurchase intention in hypermarket services. In the second stage,
consumption frequency, price and convenience were used to discuss major factors affecting
the inertia of hypermarket customers, so as to integrate the moderating effect of inertia and
the major factors affecting inertia. At last, this study compared the differences of the results
between hypermarket services and other services, in the hopes of providing a reference for
hypermarket managers in regards to service marketing management.
2. Theoretical Framework and Hypotheses
2.1 Repurchase Intention
Kotler (2003) noted that the cost of retaining old customers is much lower than the costs
associated with developing relationships with new customers. Reichheld and Sasser (1990)
also pointed out that decreasing the turnover rate of customers will efficiently increase
profit margins. Given this, increasing repurchase intention is beneficial in terms of both the
costs incurred and profitability of the company. After receiving a service from a company,
the customer experiences either a feeling of satisfaction or displeasure and this also affects
the customer’s future repurchasing behavior. Furthermore, given that consumers are
pleased in different circumstances and on various levels, they will also experience different
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levels of satisfaction. If consumers are satisfied, then their repurchasing intention will be
very high. (Kim, Park and Jeong, 2004) Lee and Neale (2012) investigated mobile phone
services, and explored the relationship between switching barriers and repurchase intention,
using inertia as a moderate variable. The results of this study found that switching cost was
included as a switching barrier and indeed impacted on consumers repurchase intention.
This was especially prevalent in high inertia consumers, but the result was not significant.
Ranaweera and Prabhu (2003) pointed out that satisfaction, switching barriers and trust
directly influence consumer retention. Danesh, Nasab, and Ling (2012) confirmed that
customer satisfaction, customer trust and switching barriers are positively related to overall
customer retention in the Malaysian retail market. Thus through examining the relevant
literature, the purpose of this study was set to explore the relationships between switching
barriers, satisfaction, competitor attraction and repurchase intention.
2.2 Satisfaction
Hansemark and Albinsson (2004) define satisfaction as an overall attitude towards a service
provider. Zeithaml and Bitner (2000) argued that customer satisfaction refers to a
consumer’s fulfillment response, and is an emotional reaction to the gap between what the
customers expect and what they get. If the customer is satisfied, then this increases
beneficial behavioral intention regarding the service provider (Chen, Liu, Sheu & Yang,
2012). Much of the literature shows satisfaction to be a significant factor related to
repurchase intention (Day, Denton & Hickner, 1988; Kotler, 1994; Cronin & Taylor, 1992;
Patterson, Johnson & Spreng, 1997; Mittal & Kamakura, 2001). In addition,
Athanassopoulos (2000) discussed satisfaction as an antecedent of customer retention and
pointed out that product innovation, service, price, convenience and business profile are all
important dimensions of customer satisfaction. Currently, the impact of customer
satisfaction on retention plays a more complicated role than it did in the past (Mittal and
Kamakura, 2001; Oliver, 1999). Therefore, in this study hypothesis 1 evaluates the
relationship between customer satisfaction and retention.
H1: Satisfaction has a positive effect on repurchase intention.
2.3 Switching barrier
Shopping in a hypermarket belongs to a kind of lower switching barrier behavior. When
consumers do not find what they are looking for in the original brand and the cost becomes
too high, they tend to switch to another alternative. In this situation, switching barriers play
an important role. In other words, increasing the switching barriers can solve this problem
by retaining old consumers and keeping them accustomed to the company.
Switching barriers are defined as ‘the perceived economic and psychic costs
associated with changing from one alternative to another’(Jones, Mothersbaugh, & Beatty,
2002). Switching barriers include the time, effort, and financial costs incurred by
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consumers when attempting to become familiar with an alternative. On the basis of the
cost-benefit model of behavior, we recommend that consumers will only engage in actions
if the perceived benefits are higher than the perceived costs. Much of the literature shows
that switching barriers have both direct and indirect effects on purchasing retention
(Gremler and Brown, 1996; Bansal and Taylor, 1999; Lee, Lee, and Feick, 2001). Fornell
(1992) examined the switching barriers that exist when customers feel that it is too difficult
to change from their original service provider and found that a number of factors affect
switching barriers, such as switching costs, transaction costs, learning costs, loyalty
discounts, consumers’ hobbies, morale costs, cognitive abilities, and psychological and
financial risks. Recall, that shopping in a hypermarket is a low-involved shopping behavior.
These companies need to pay more attention to retaining customers, such as providing high
quality products and services to meet the needs of consumers. According to the previous
literature, companies with a lower degree of switching barriers tend to lose their consumers,
which is not beneficial to the company. On the other hand, companies with a higher degree
of switching barrier tend to retain consumers and experience higher repurchase intention.
Thus hypothesis 2 was proposed as follows:
H2: Switching barriers has a positive effect on repurchase intention.
2.4 Competitor attraction
In the modern consumer-oriented business environment, delivering advantages to
consumers is indispensable to maintaining buying behavior. It is difficult to retain
consumers when they notice that other companies are more attractive and provide more
benefits. Bansal, Irving and Taylor (2004) pointed out that customer commitment has a
close relationship with whether customers will switch from their original service provider
or not. Meyer and Natalie (1997) suggested that continuance commitment may have a high
correlation with a lack of available alternatives. Jones, Mothersbaugh, and Beatty(2000)
argued that when consumers have less choice or think that their current vendors are good,
they will remain with their original service provider, which means that they are less
inclined towards the attractions of other vendors and maintain their original relationship.
However, when this is not the case other companies are seen as attractive and consumers
will turn to them. Given this, hypothesis 3 was proposed as follows:
H3: Competitor attraction has a negative effect on repurchase intention.
2.5 Inertia
Research in marketing and consumer behavior considers inertia to be a mover of behavioral
loyalty (Yanamandram and White, 2006). Assael (1998) pointed that inertia refers to a kind
of constant consuming model; consumers tend to remain with the same brand not because
they seriously take other brands into consideration but because they get used to it. This is
also consistent with the work of Gefen (2003). Inertia is regarded as a non-conscious model
of retention, consisting of passive repeat patronage without true loyalty and an
unwillingness to spend effort.
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Colgate and Lang (2001) discovered that when consumers are in a high state of inertia,
their continuous consuming behaviors are based on this inertia and not on their being
satisfied with the service they receive. When consumers experience inertia, they do not
have strong motivations to look for alternatives. Inertia, then, plays an important role in the
structure of repurchase intention. Bolton, Lemon, and Verhoef (2004) pointed out that the
length, width, and depth of inertia means that consumers are retained, engage in cross
buying behavior, and tend to increase the frequency of consuming. Hidalgo, et al. (2008)
pointed out that consumers make decisions according to inertia when they choose products
in relation to low-differentiated brands or low consumer-involved products. Shopping in a
hypermarket is a kind of low consumer-involved behavior. Thus, in this study hypothesis 4
was proposed as follows:
H4: Inertia has a positive effect on repurchase intention.
2.6 The Moderating effect of Inertia
Colgate and Lang (2001) discovered that when consumers are in a high state of inertia,
continued buying behavior will occur despite their being dissatisfied with the quality of the
service they receive. Inertia reduces the relationship between consumers’ satisfaction and
repurchase intention. Liu, et al. (2007) demonstrated that inertia can reduce the effect of
consumer satisfaction on repurchase intention in financial services. Therefore in this study,
hypothesis 5 was proposed as follows:
H5: Inertia can reduce the effect of consumer satisfaction on repurchase intention.
Ping (1993) pointed out that when consumers need to pay higher amounts of switching
costs, they tend to remain loyal to the original company. As switching barriers are
promoted, relationships are retained and repurchase intention increase. Thus when
consumers experience inertia and higher switching costs, consumers believe that switching
companies will cause them to lose a lot, and so they prefer to stay with and support the
original company. In other words, the likelihood that they will stay with the same company
is high. On the other hand, Liu, et al. (2007) found that inertia can improve the effect of
consumers’ switching barriers to repurchase intention in financial services. Therefore in
this study, hypothesis 6 was proposed as follows:
H6: Inertia can enhance the effect of consumers’ switching barriers to repurchase
intention.
Wilson (1995) argues that dependence of buyers and sellers will be lower when there
are a large number of high-quality trading partners in an environment. When competitor
attraction is low, consumers will not end their current relationships. However, if
competitors in the market raise more favorable conditions this will affect consumer
repurchase intention. Liu, et al. (2007) proposed the hypothesis that if inertia truly exists
and upgrades will cause consumers to stop looking for alternative vendors, the attraction of
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competitors will be weak. However, in an empirical study of financial services, Liu, et al.
(2007) demonstrated that inertia cannot reduce the effect of attractions from other
manufacturers. Hypermarket shopping is a form of low-involvement shopping behavior. If
consumers have high inertia, they will tend to stay with their original service provider,
which may reduce the effect of competitor attraction on repurchase intention. Therefore in
this study, hypothesis 7 was proposed as follows:
H7: Inertia can reduce the effect of competitor attraction on repurchase intention.
2.7 The Impact of Inertia
Estalami, Martín-Consuegra., Molina & Esteban (2007) pointed out that price fairness is an
antecedent of customer satisfaction and loyalty. The lower the price the competitor
provides, the higher the likelihood that the consumer will switch. Fornell, Robinson &
Wernerfelt (1985) pointed out that reasonable prices affect inertia. Voss, Parasuraman and
Dhruv (1998) maintained that orientation to price fluctuations, especially in relation to
service industries that have demand-oriented pricing, likely results in a combination of
price-performance and price-satisfaction. Stancua et al. (2012) found that “cheap” is a key
consumption driver for consumers. Thus, in this study hypothesis 8 was proposed as
follows:
H8: Reasonable price has a positive effect on inertia
Companies had to become more convenient for users during the service process in
order to retain customers (Reichheld & Schefter 2000). Chang and Tu (2005) discovered
that store image, including facilities, store service, store activities and convenience
sufficiently predicted customer satisfaction. Wang, Fang and Yun (2006) argued
convenience reinforces the customary behavior of customers, and so contributes to
generating inertia. Given this, hypothesis 9 was proposed as follows:
H9: Convenience has a positive effect on inertia
Sonmez and Graefe (1998) found that frequency of consumption is the best tool to
use in predicting future consumers’ behavior. Frequent behavior in the past and a high
interest creates a habitual tendency to continue behavior. Therefore, consumers who are
more frequent shoppers, and thereby have a higher frequency of interaction with their
service provider, are more prone to inertia, and that the stronger the inertia, the stronger the
repurchase intention. Therefore, in this study hypothesis 10 was proposed as follows:
H10: Frequency has a positive effect on inertia
3. Methodology
3.1Reasearch model
Based on the literature review, this study designed its research framework as shown in
Figure 1.
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H7 H2
H10 H5
H4
H8 H1
H9 H3
H6
Phase2 Phase1
Figure 1. Research Framework
3.2 Research variables and measurement
According to the purposes of the research, we modified the questionnaire according to past
literature and expert advice. The dimensions constructed (as shown in Table 1) include
repurchase intention, satisfaction, inertia, competitor attraction and switching barriers.
Finally there were 27 variables in the questionnaire. A seven-point Likert scale was used to
test the questionnaire, with responses ranging from “strongly disagree” to “strongly agree”.
Switching barriers
Frequency
Reasonable Inertia Satisfaction Repurchase Intention
Convenient
Competitor attraction
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Table 1. Operational Definitions of Variables
Constructs Definitions Items Source
Convenience Convenience
Hours convenience
Beatty &
Smith(1987)
Parking convenience
Location convenience
Merchandising convenience
Checkout convenience
Promotion information
convenience
Reasonable Reasonable Reasonable price
Voss et al.(1998) Satisfactory price
Frequency Ef Consumption
frequency
Average frequency of
consumption per month
Sonmez and
Graefe(1998)
Switching barriers
Switching cost
Not accustomed to another
hypermarket
Jones et al.(2000)
Concern with the original
preferential discount
Economic burden, (e.g. additional
parking fees, extra oil, etc.)
Spending a lot of energy, time and
effort
Human
relationships
Confidence that the hypermarket
currently offers me the best price
Gwinner et
al.(1998),Sweeney
and Soutar(2001) Emotional connection
Relationship
investment
Provides a reliable selection of
products and services Wulf, et al.(2001)
Competitor attraction Comparison
with competitor
Other hypermarkets to choose
Bansel et al.(1999)
Other hypermarkets provide a
more satisfactory service
Other hypermarkets provide lower
prices
Other hypermarkets were nearby
Inertia Customary
Familiarity with this hypermarket
Gremler(1995),
Genfen ( 2003)
The ‘first thought’ hypermarket
Have become accustomed to this
hypermarket
Customer
satisfaction Satisfaction
Very satisfied with the shopping
experience Day(1977)
To shop here was a wise decision
Repurchase intention Repurchase
intention continuous shopping Kotler( 1997)
This study designed the questionnaire according to the operational definitions of the
variables, and performed CFA with the collected data. The CFA results showed low factor
loadings for “other hypermarkets to choose” and “other hypermarkets were nearby” (less
than 0.41); therefore, these factors were removed in the final questionnaire.
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3.3 Data collection
This research targeted the branches of the top three hypermarket companies in Taiwan:
Carrefour, Amart and Rt-mart. 120 questionnaires were distributed to each hypermarket.
The data collection was based on stratified sampling in North Taiwan from July 1 to
August 31, 2013. The questionnaires were distributed at the main entrances of the
hypermarkets, evenly on weekdays and weekends. A brief summary of the research
objectives was given to all the interviewees who volunteered to participate in the research.
All of the interviewees were periodical shoppers who bought basic grocery and household
necessities at the hypermarket. All questionnaires were conducted anonymously, and gifts
were given after the interviewees had finished participating. A total of 1080 questionnaires
were distributed, and 848 valid samples were collected, with a return rate of 77%. Based on
the suggestions by Hair, Anderson, Tatham, & Black (1998), the number of samples is
adequate if it is five times the number of variables. The sample number in this study was
therefore adequate.
3.4 Statistical methods
The research first applied SPSS 17.0 to process the descriptive statistic analysis, reliability
analysis and related analysis on the effective questionnaires in order to understand the
structure of the sample, and the internal consistency and relation between various variables.
Second, this research evaluated the properties of measurement scales for convergent
validity and discriminate validity, and constructed composite reliability by Confirmatory
Factor Analysis (CFA) using a maximum likelihood to estimate parameters. The study
applied LISREL 8.70 software as its analysis tool. Finally, this study applied regression
analysis in order to explore the interaction and moderating effect between variables.
4. Results
4.1 Profile of Respondents
This study targeted consumers of hypermarket in North Taiwan as subjects and
successfully collected 848 valid questionnaires. Subsequently, we applied a frequency
distribution table to identify the sample features of this study. The sample structure
attributes are shown in Table 2. As shown in Table 2, there were more female than male
consumers (60.8%), and most of the subjects were between 21 and 30 years old (33%). In
addition, most of the subjects were married with children (45%), had a college degree
(71%), were engaged in a commercial activity (48.6%), and had a monthly salary of 20,000
to 50,000 NT (57.8%). The structure of the sample is similar to that in the hypermarket
research conducted by Beristaina, Zorrilla (2011), and Watchravesringkan & Punyapiroje
(2011).
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Table 2. Profile of Respondents (n=848)
Background variable Frequency Percentage
Gender
Male 332 39.2
Female 516 60.8
Age
Below 20 years old 51 6
21-30 years old 280 33
31-40 years old 234 27.6
41-50 years old 173 20.4
Above 50 years old 109 12.8
Marriage
Single 372 43.9
Married with no children 94 11.1
Married with children 382 45
Education
Below senior high and vocational school 243 28.6
Above College 605 71.3
Occupation
Industry 103 12.1
Commerce 412 48.6
Military and Government 60 7.1
Student 102 12
Housewife/Retired 101 11.9
Other 70 8.3
Salary per month
Below 20,000 NT 189 22.3
20,001-50,000 NT 498 58.7
50,001-80,000 NT 119 14
Above 80,000 NT 42 5
Frequency Consumption/per month
Below 2 times 381 44.9
3-4 times 358 42.2
Above 5 times 109 12.8
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4.2 Reliability and validity analysis
There are 5 variables in this study, which come from the structural questionnaire. This
study applied confirmatory factor analysis to analyze the data. There are four latent factors,
which are Switching Barriers (SB), Competitor attraction (CP), Inertia (IN), Satisfaction
(SA) and Repurchase intention (RP). There are three dimensions, including t1, t2 and t3 in
latent TT variables, cp1, cp2 and cp3 in latent CP variables, in1, in2 and in3 in latent IN
variables, sa1, and sa2 in latent SA variables, and finally rp in the latent RP variable.
In order to evaluate how well the hypothesized model fits the data from the sample,
we used the following 7 indexes: the chi-square test, goodness fit index (GFI), adjusted
goodness fit index (AGFI), the normed fit index (NFI), comparative fit index (CFI), the
root mean square residual (RMR) and the root mean square error of approximation
(RMSEA). The Lisrel 8.7 version of the statistical software package was applied. The
χ2-test assesses the fit of the model by comparing the obtained sample correlation matrix
with the correlation matrix estimate under the model. However, the χ2-test is extremely
sensitive to sample size. The sample size of this study is large, hence χ2/d.f. didn’t attain the
judgment point. The RMSEA reflects the extent to which the model fit approximates a
reasonable fit and indicates an acceptable model fit with values<0.1, The CFI and NFI
indicate that the model’s complexity is acceptable with model fit with values >0.9, and
good with values>0.95.
The results contain various indicators determining the actual value of the research
analysis values. However the χ2-test was sensitive to the sample size. Given this it is easier
to reject the null hypothesis when the sample size is big enough. The valid sample of this
study was 848, with an χ2-test score of 9.928, which exceeds the suggested threshold of 5.
Except for the GFI, which was slightly lower, and the RMR, which was slightly higher,
than the proposed judgment value, the rest of the goodness-of-fit values for the indicators
fall within the recommended scope of the judgment value, indicating the confirmatory
analysis model is a good fit.
In terms of reliability and validity, Table 3 shows that the composite reliability (CR)
for all dimensions of hypermarket consumer shopping behavior scales was more than 0.7,
respectively. In general, the measurement scales used in this study were found to be reliable.
The average variance extracted (AVE) for all dimensions were more than 0.45, and all
exceeded the benchmark of 0.70 for convergent validity (Fornell and Larcker, 1981).
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Table 3. Measurement Scales and Properties
Latent variable Observed variable Mean
(S.D.)
Factor
Loading
Construct
reliability AVE
Switching
Barrier (SB)
Switching cost
(sb1)
4.62
(1.12) 0.64
0.70 0.45 Human relationship
(sb2)
4.54
(1.41) 0.47
Relationship
investment (sb3)
4.55
(1.00) 0.85
Competitor
attraction (CA)
Other
hypermarkets
provide a more
satisfactory service
(ca1)
4.41
(1.14) 0.89
0.81 0.68
Other
hypermarkets
provide lower
prices (ca2)
4.43
(1.128) 0.75
Inertia (IN)
I am familiar with
this hypermarket
(in1)
5.32
(1.12) 0.87
0.90 0.76
This is my
‘first-thought’
hypermarket (in2)
5.29
(1.13) 0.90
I have become
accustomed to this
hypermarket (in3)
5.13
(1.08) 0.84
Satisfaction
(SA)
I am very satisfied
with this shopping
experience (sa1)
5.10
(1.04) 0.88
0.84 0.72
To shop here was a
wise decision (sa2)
4.88
(1.12) 0.82
Repurchase
intention (RP)
I will continue to
shop here
5.27
(1.045) 1 1 1
4.3 Descriptive Analysis
This study used a seven-point scale. As shown in Table 3, the average values of switching
barrier and competitor attraction were 4.29~4.62, and those for inertia, satisfaction and
repurchase intention were 4.88~5.32. The variance of the variables was distributed between
1.00 and 1.14. This revealed that the research samples had a certain difference. Furthermore,
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the switching barriers, which consisted of switching costs, human relationships and degree
of relationship investment were slightly higher than ordinary (median=4). As to competitor
attraction, which contained “other hypermarkets provided a more satisfactory service” and
“other hypermarkets provided lower prices” both were higher than ordinary. As to inertia,
which included “I am familiar with this hypermarket”, “This is my ‘first-thought’
hypermarket” and “I have become accustomed to this hypermarket”, these again were all
higher than 5. Obviously, most of the visible consumers had developed a habitual routine
with their familiar hypermarkets in their daily lives. Finally, in terms of satisfaction and
repurchase intention, these degrees were all higher than 5. Thus, the probability that the
consumers return to shop in the original hypermarket was high.
4.4 Chi-Square Difference Analysis
This study applied the Chi-Square difference test in order to evaluate the properties of
measurement scales for discriminate validity, and assumed the coefficients of the pair wise
latent variables to be 1. The results show that statistical chi-square had a significant
difference (∆χ2>3.84, p<0.05). In other words, the coefficients of pair wise variables are
not equal to 1 (the results of the test are shown in Table 4). All of the variables were valid
in the model of the structure.
Table 4. Discriminate Validity: Chi-Square Difference
△χ2
Switching
Barriers
Competitor
Attraction
Inertia Satisfaction Repurchase
Intention
Switching
Barriers
Competitor
Attraction 131.34*
Inertia 81.91* 145.92*
Satisfaction 56.21* 126.00* 11.04*
Repurchase
Intention 90.86* 150.00* 8.61* 6.30*
4.5 Analysis of the difference between samples with different characteristics
This research probed into the difference between latent variables of hypermarket consumers
with different attributes (such as gender, age, marital status, educational level, occupation,
monthly salary) using a t test and a one-way analysis of variance (ANOVA) as the criterion
for exploring consumer repurchase intention in the hypermarket/retailing industry.
Furthermore, this study applied the LSD test to evaluate the difference between the
sub-group. According to the analytical results shown in appendix, the means of different
constructs according to gender did not have a significant difference (p>0.05). In terms of
age, older consumers tended to show a higher degree in relation to shopping frequency,
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reasonable price, convenience, inertia and satisfaction. In particular, customers aged 40 or
above had a higher degree of recognition than others. Regarding marital status, consumers
who were married had a significantly higher degree in relation to shopping frequency,
convenience, inertia, satisfaction, competitor attraction and switching barriers than those
who were unmarried. In particular, the married customers with children had a high degree
of recognition. As to educational level, consumers with a lower education level have
significantly higher degrees in relation to shopping frequency, competitor attraction and
switching barrier than those with a higher level of education level. In terms of occupation,
housewife/retired and military/government have significantly higher degrees in relation to
shopping frequency, convenience, inertia, repurchase intention, satisfaction and competitor
attraction, than others. Finally, in relation to monthly salary, persons with a higher monthly
salary have a significantly higher degree in relation to convenience and inertia, and have a
significantly lower degree in relation to competitor attraction. In particular, the customers
with salaries of 50,000~80,000 NTD had a high degree of recognition.
4.6. Regression analysis
The study built a regression model by applying regression analysis. The regression model
considered switching barriers, satisfaction, competitor attraction and inertia to be the
independent variables and repurchase intention to be the dependent variable. The main
purpose of the study was to explore the key factor that affects repurchase intention.
However, the results show that the Durbin Watson value was 1.973, between the suggested
period (1.5~2.5), which indicates that the data is suitable to perform a regression analysis.
Furthermore, the results of the regression analysis, as presented in Table 5, show an F value
of 384.01, an R2 value of 0.648 and an adjR
2 value of 0.646. The regression model has high
explanatory power and can be explained from the perspective of the standard coefficient,
which shows that satisfaction and inertia had significantly positive impacts on repurchase
intention. Hypothesis 1 and 4 were supported. On the other hand, switching barriers did not
have a significantly positive impact on repurchase intention, and competitor attraction did
not have a significantly negative impact on repurchase intention. That is, hypothesis 2 and
3 were not supported. The regression model equation is expressed as follows:
y=α +β1x1+β2 x2+β3 x3+β4 x4
y: Repurchase Intention (RP), x1: Satisfaction (SA), x2: Switching Barriers (SB),x3:
Competitor Attraction (CA), x4: Inertia (IN)
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Table 5. Regression analysis
Coefficient
of non std.
Coefficient
of std. T F R
2 AdjR
2 DW
Constant 1.023 7.032*** 384.01*** 0.648 0.646 1.81
x1 0.476 0.458 13.16***
x2 -0.067 -0.061 -2.28**
x3 -0.028 -0.028 -1.31
x4 0.440 0.428 12.84***
**: p<0.05; ***: p < 0.01
Further research explored the moderating effect of inertia and performed a regression
analysis in order to establish a regression model. The regression model used the variables
of switching barrier, satisfaction, competitor attraction and interactions with inertia in order
to explain the repurchase intention of hypermarket consumers. The results of the regression
analysis, as shown in Table 6, show an F value of 248.987, an R2 value of 0.639 and an
adjR2
value of 0.639. The regression model has high explanatory power and can be
explained from the perspective of the standard coefficient, which means that each
dependent variable has significant explanatory power in relation to repurchase intention.
The regression model considered repurchase intention to be a dependent variable and
switching barrier, satisfaction, and competitor attraction to be independent variables and
considered the interactions between inertia and switching barrier, inertia and satisfaction
and inertia and competitor attraction, respectively. The results showed that hypotheses 6
and 7 were supported, and that hypothesis 5 was not supported. The regression model
equation was expressed as follows:
y=α +β1x1+β2 x2+β3 x3+β4 x1 x4+β5 x2 x4+β6 x3x4
y: Repurchase Intention (RP), x1: Satisfaction (SA), x2: Switching Barriers (SB), x3:
Competitor Attraction (CA), x4: Inertia (IN)
Table 6. Regression analysis of the moderating effects of inertia
Coefficient
of non std.
Coefficient
of std. T F R
2 AdjR
2
Constant 3.366 16.551*** 248.987*** 0.642 0.639
x1 0.478 0.460 3.937***
x2 -0.417 -0.382 -3.069***
x3 -0.200 -0.198 -2.088**
x1 x4 -0.002 -0.017 0.087
x2 x4 0.064 0.512 2.636***
x3 x4 0.030 0.222 1.814*
*: p<0.1; **: p<0.05; ***: p < 0.01
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Comparing Table 5 and Table 6, both tables showed that satisfaction had a significant
impact on repurchase intention, but the interaction of inertia and satisfaction did not have a
significant impact on repurchase intention. The moderating effect of inertia on other
variables is illustrated in figure 2. According to figure 2(a), inertia did not reduce the effect
of satisfaction on repurchase intention. That is, inertia did not reduce the effect of
satisfaction on repurchase intention in hypermarket services. Comparing Table 5 and Table
6, both tables showed that switching barriers did not have a significantly positive impact on
repurchase intention, but the interaction of inertia and switching barriers had a significantly
positive impact on repurchase intention. According to figure 2(b), inertia could improve the
effect of switching barriers to repurchase intention in hypermarket services. Comparing
Table 5 and Table 6, both tables showed that competitor attraction had a negative impact on
repurchase intention, but the interaction of inertia and switching barriers had a significantly
positive impact on repurchase intention. According to figure 2(c), inertia could act as a
barrier to the effect of competitor attraction on repurchase intention in hypermarket
services. The results showed the importance of the role played by inertia in the relationship,
and suggested that enterprises should further identify the important factors affecting
relationships with inertia. Therefore, hypotheses 6 and 7 were supported.
RPP RPP RPP
high inertia
low inertia high inertia high inertia
low inertia low inertia
SA SB CA
(a) (b) (c)
Fig. 2. Inertia moderating effect diagram
The study next applied a general linear regression analysis in order to establish the
relationship between the inertial influences in consumer regression models. The regression
model considered convenience, reasonable price and consumption frequency to be the
independent variables and inertia to be the dependent variable. Moreover, the results show
that the Durbin Watson value was 1.95, between the suggested period (1.5~2.5), which
indicates that the data is suitable for the performance of a regression analysis. Furthermore,
the results of the regression analysis, as presented in Table 7, show an F value of 107.12,
and an R2 value of 0.527. The regression model has significant explanatory power and
inertia can be significantly explained by each dependent variable from the standard
coefficient of the regression analysis. Hence, hypotheses 11, 12 and 13 were supported. The
regression model equation is expressed as follows:
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y=α +β1x1+β2 x2+β3 x3
y: Inertia (IN), x1: Reasonable (RR), x2: Convenience (CC), x3: Frequency (FF)
Table 7. Inertia regression analysis
Coefficient
of non std.
Coefficient
of std.
T F R2 DW VIF
Constant 1.21 5.08*** 107.12*** 0.53 1.95
x1 0.32 0.28 7.94*** 1.40
x2 0.39 0.28 8.16*** 1.40
x3 0.2 1.31 4.40*** 1.03
***: p < 0.01
5. Discussion and Conclusion
This study integrated to study the moderating effect of inertia and the major factors
affecting inertia. The results showed that the means of switching barrier and competitor
attraction were not high for hypermarket service. This revealed that, compared to other
services, hypermarket services have relatively lower switching barriers and competitor
attraction. Shopping in a hypermarket belongs to low-involvement shopping behavior, thus,
customers may feel inconvenienced if hypermarkets have higher switching barriers. This
study found that switching barriers had a negative effect on repurchase intention, but
competitor attraction did not have a significantly negative effect on repurchase intention in
the relationship model. The results indicated that customers may have negative feelings if
hypermarkets simply improve the effect of switching barriers, and their repurchase
intention could be lowered. The result was consistent with the research of Lee and Neale
(2012), who found that switching costs deterred switching and engendered negative
word-of-mouth. In addition, hypermarkets had little attraction on competitors, and
competitor attraction did not have a significantly negative effect on repurchase intention.
Furthermore, inertia could improve the effect of switching barriers to repurchase intention
in hypermarket services, and inertia could act as a barrier to the effect of competitor
attraction on repurchase intention in the moderating relationship model. The finding is
different from Liu et al. (2007), who suggested that inertia can improve the effect of
switching barriers to repurchase intention but cannot moderate the effect of competitors’
attractions.
In addition, in terms of satisfaction, the research results showed that the customers of
hypermarkets felt satisfied (a satisfaction score of five in the seven-point scale).
Satisfaction and inertia had positive effects on repurchase intention. However, inertia did
not reduce the effect of satisfaction on repurchase intention in hypermarket services. This
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showed that there were differences between hypermarket services and financial services.
That is, consumer satisfaction and inertia are important factors in hypermarket service, but
they do not represent interaction. This finding is also different from Liu et al.(2007), who
suggested that unlike in hypermarket services, inertia can reduce the effect of satisfaction
on repurchase intention in financial services.
Moreover, this research further established a regression model that considered
convenience, reasonable pricing and consumption frequency as independent variables and
inertia as a dependent variable to find the major factors affecting inertia. The explanatory
power of the whole regression model was up to 53% and is significant, which means that
the model has good explanatory power and that convenience, reasonable pricing and
consumption frequency had a significant and positive impact on inertia. In the past
researches, Yanamandram and White (2010) explored the roles of inertia in financial
services and found that switching costs are an antecedent to inertia. Thus, the original
service providers can increase customer retention if customers feel the pressure of
switching costs. Inertia can be fostered gradually. On the other hand, Lee and Neale (2012)
found in mobile phone service that switching costs deterred switching and engendered
negative word-of-mouth, but only with low-inertia customers. With high-inertia customers,
retention and word-of-mouth behaviors depended on whether the inertia stemmed from
satisfaction or indifference. However, this study showed that convenience had a significant
and positive impact on inertia, but switching barriers had a negative effect on repurchase
intention. This revealed that hypermarket services were different from the findings of
financial services and mobile phone services.
From the findings of this study, we can conclude that inertia plays a key role in
hypermarket consumer behavior. In practice, most consumption in the hypermarket
involved daily necessities and had a lower proportion of total consumption expenditure, so
the decision-making process becomes simple. Given this, the consumption model used here
differs from others, such as luxury goods, and so repurchase intention was not simply
influenced by corporate image, satisfaction, switching barrier and competitor attractions.
For this reason, hypermarket operators, in addition to strengthening customer satisfaction,
also need to know how to reinforce inertia in terms of customer relationships, an
underexplored topic in the marketing management field. For the purpose of what was
mentioned above, this study conducted further research and found that three factors
including “convenience”, “reasonable price” and “consumption frequency” are crucial
factors that impact on inertia. Therefore, the study suggests that hypermarket operators
should upgrade convenience for their consumers (such as product furnishings guidelines,
convenient checkouts, convenient parking spaces and enhanced online shopping, etc.),
provide reasonable prices (such as parity with competitors, price transparency, and the
establishment of APP Search Service) and should hold regular promotional activities in
order to enhance consumer's “consumption frequency”, so that consumers gradually enter
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into an orbit of inertia. If this is achieved then the hypermarket reduces the risk of
consumers switching to other hypermarkets, and enables consumers to continue spending in
order to achieve business continuity targets.
Next, the findings showed that elderly customers, married customers, housewives,
retired customers and military/government customers had significantly higher degrees in
relation to inertia, competitor attractions, satisfaction and repurchase intention, as well as
factors affecting inertia, including shopping frequency, convenience, and reasonable price.
Hypermarket management should focus on these featured groups and learn their needs for
convenience and price fairness, so as to improve customer inertia and increase repurchase
intention.
Overall, this study investigated the role played by inertia in the hypermarket industry
and obtained valuable research findings that are able to provide hypermarket managers with
references in relation to management practices. However, the main limitation of this study
was only taking hypermarket consumers in Northern Taiwan as the research sample; the
proposed scope of follow-up studies should therefore be extended. Furthermore, any
continuation of this study should investigate the relationship between inertia in other
low-involvement industries as well as the role of consumer behavior in order to provide the
service industry with further references related to marketing services.
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Appendix
Analysis of the mean difference of consumers with different attributes
Att
rib
ute
s
Cat
ego
ry
Fre
quen
cy
Rea
son
able
Conv
enie
nce
Iner
tia
Rep
urc
has
e
Inte
nti
on
Sat
isfa
ctio
n
Co
mp
etit
or
attr
acti
on
Sw
itch
ing
Bar
rier
Gender Male 1.73 4.94 5.4 5.2 5.22 5.00 4.55 4.58
Female 1.7 4.95 5.5 5.3 5.31 4.97 4.62 4.57
t value 0.53 0.88 0.93 0.15 0.25 0.74 0.31 0.79
Age Below 20 years old(A) 1.45 4.7 5.04 4.92 5.04 4.8 4.44 4.5
21~30 years old(B) 1.59 4.89 5.34 5.11 5.2 4.87 4.53 4.52
31~40 years old(C) 1.72 4.89 5.47 5.25 5.26 4.94 4.74 4.52
41~50 years old (D) 1.82 5.09 5.68 5.49 5.38 5.13 4.66 4.67
Above 50 years old(E) 1.91 5.07 5.62 5.5 5.43 5.21 4.59 4.71
F value 6.56*** 2.6* 9.66*** 7.40*** 2.06 3.68** 1.98 1.65
Post hoc test LSD test C,D,E>A,B D,E>A,B,C D,E>A,B,C - D,E>A,B,C - -
Marital status Single(A) 1.55 4.88 5.3 0.99 5.14 4.83 4.45 4.43
Married with no
children(B)
1.78 4.95 5.48 0.87 5.3 4.95 4.69 4.53
Married with children
(C)
1.86 5.01 5.62 1.25 5.39 5.14 4.71 4.73
F value 16.03*** 1.74 15.25*** 20.15*** 5.38 8.62*** 8.52*** 10.38***
Post hoc test LSD test B,C>A - C,B>A C>A,B - C>A,B B,C>A C>A,B
Educational
Level
Below senior high and
vocational school
1.86 4.92 5.4 5.34 5.29 5.04 4.82 4.74
Above College 1.65 4.95 5.45 5.23 5.27 4.96 4.5 4.51
t value 3.26*** -0.39 0.49 1.38 0.32 1.15 4.61*** 3.23***
Occupation
Industry(A) 1.74 4.84 5.46 5.09 5.03 4.74 4.57 4.49
Commerce(B) 1.68 4.96 5.51 5.29 5.33 5.0 4.62 4.56
Military and
Government(C)
1.87 5.08 5.57 5.56 5.42 5.25 4.46 4.81
Student(D) 1.53 4.75 4.99 4.97 5.04 4.86 4.34 4.58
Housewife/
Retired(E)
1.95 5.1 5.56 5.53 5.56 5.19 4.8 4.67
Other(F) 1.65 4.97 5.6 5.18 5.13 4.92 4.66 4.45
F value 3.75*** 1.74 8.5*** 5.3*** 4.37**
*
3.37*** 3.12*** 1.46*
Post hoc test LSD test C,E>A,B,D
,F
- A,B,C,E,F>
D
C,E>A,B,D
,F
C,E>A,
B,D
B,C,E>A,D
,F
B,E,F>A,
C,D
-
Salary per
month
Below 20,000 NT(A) 1.64 4.87 5.25 5.1 5.17 4.91 4.5 4.56
20,001-50,000 NT(B) 1.73 4.93 5.49 5.27 5.29 4.98 4.66 4.59
50,000-80,000 NT(C) 1.7 5.05 5.62 5.43 5.31 5.03 4.59 4.53
Above 80,000 NT(D) 1.9 5.13 5.6 5.44 5.45 4.98 4.23 4.57
F value 1.45 1.35 6.62*** 3.47*** 1.13 1.03 3.99*** 0.13
Post hoc test LSD test - - C,D>A,B C,D>A,B - - B,C>A,D -
Note: *:p<0.05,**: p<0.01,***:p<0.00
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