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1 THE INFLUENCE OF INERTIA ON REPURCHASE INTENTION OF HYPERMARKET CUSTOMERS Ming-Chun Tsai 1 , 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. Ming-Chun Tsai, Jia-Chi Lin, Int. Eco. Res, 2017, V8i2, 41 – 67 ISSN:2229-6158 IJER – March - April 2017 Available online @www.ijeronline.com 41

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Page 1: THE INFLUENCE OF INERTIA ON REPURCHASE INTENTION OF ... · Repurchase intention refers to the intention to make repeat purchases in consumer behavior; ... customer repurchase. The

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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