THE INFLUENCE OF PERCEIVED VALUE ON ... 44 volume/4Ching-Lin Huang.pdfrepurchase intention with...

International Journal of Information Technology and Business Management 29

th November 2015. Vol.43 No.1

© 2012 - 2015 JITBM & ARF. All rights reserved

ISSN 2304-0777 www.jitbm.com

THE INFLUENCE OF PERCEIVED VALUE ON REPURCHASE

INTENTION: A LEADING 3C RETAILER IN TAIWAN AS AN

EXAMPLE

Ching-Lin Huang, Kao Yuan University, Taiwan

Abstract

The main research purpose of this study is to validate the influence of perceived value on

repurchase intention with customer satisfaction as the mediating variable. A survey is conducted on the

front-desk employees of different branches of a leading 3C retailer in Taiwan via stratified random

sampling. Structural equation modelling (SEM) is employed to measure the model fit of the overall

model with the structural model and the measurement model. This study then conducts the Sobel test on

the coefficient path of latent variables (non-observable) by using maximum likelihood estimation

(MLE), in order to verify whether the direct effects, mediating effects and total effects are statistically

significant. The research findings suggest that perceived value has significant and direct effects on

repurchase intention. Customer satisfaction also serves a certain degree of mediating effects. The

research findings can provide a reference to the strategic planning of the management of the leading

3C retailer concerned so as to enhance the repurchase intention of customers.

Keywords: perceived value; customer satisfaction; repurchase intention

INTRODUCTION

According to the Internet news on April 10,

2015, elifemall, TKEC and Tatung are all

planning to join the bandwagon of e-Commerce

by combining bricks and mortars with virtual

world, in response to the rapid growth of the

online shopping market.

The channel strength of the 3C retailers in

Taiwan is the result of hardworking employees

who process thousands of orders every day. The

industry is well-recognized by the public and

investors for its down-to-earth culture,

profitability and operational performances.

This study examines whether perceived

value and customer satisfaction are an important

factor to the improvement of repurchase

intention. In other words, the greater the product

quality, the higher the level of customer

satisfaction and repurchase intention and the

higher willingness it is to recommend others for

collective buying【1】.It is essential for any

company to meet with customer needs and

ensure customer satisfaction. Many studies

indicate that high levels of customer satisfaction

boosts repurchase intention and hence, the top

line and bottom line of a company【2】.In the





knowledge economy, perceived value and

customer satisfaction enhance the repurchase

intention of customers and lead to competitive

advantage. This study seeks to validate and

understand the influence of perceived value on

repurchase intention by referring to customer

satisfaction as the mediating variable. By

conducting a case study on a leading 3C retailer

in Taiwan, this study sets out the following

research purposes:

(1) Validation and understanding of

whether the perceived value of the

customer of the leading 3C retailer in

Taiwan has positive influence on their

repurchase intention;


whether the perceived value of the

customer of the leading 3C retailer in

Taiwan has positive influence on their

satisfaction levels; and


whether high levels of customer

satisfaction with the leading 3C

retailer in Taiwan have positive

influence on the repurchase intention

of customers.

LITERATURE REVIEW

This study intends to interview the

front-desk employees at different branches of a

leading 3C retailer in Taiwan in order to verify

the impact of perceived values on repurchase

intention of customers, with customer

satisfaction as the mediating variable. Below is a

description of the academic theories and relevant

studies.

Definitions and Constructs of Perceived

Values

Definitions of perceived values

This study defines the perceived values as

the perceptions and emotions that consumers

hold toward products and prices. Perceived

values depend on subjective and individualistic

assessments. This conceptual definition is based

on the literature below.

Scholars have come up with various

definitions of perceived values. Woodruff 【3】

believed that perceived values are the perceived

preferences for product attributes and attribute

effectiveness. Lovelock【 4】 suggested that

perceived values are the customer evaluation on

the perceived costs and benefits. Ryu, Han &

Kim【5】argued that perceived values are the

general evaluation of the net value of products or

services on the basis of the assessments by

customers on the pros and cons. Chang 【6】

posited that perceived values are a subjective

feeling after consumers have evaluated all the

costs and benefits. Tsai【7】thought that perceived

values are the overall and subjective evaluations

by consumers regarding their costs and benefits

for the advertised products. Hsu 【8】indicated

that perceived values are the ratio of total values

(benefits) to overall costs, as well as the inner

feelings and valuations that customers develop.

Constructs of Perceived Values

This study refers to the measurement of

perceived values developed by Yu【9】 by





classifying perceived values into (1) emotional

value: the pleasure in purchasing the product or

receiving the service; (2) quality function value:

the evaluation of the product quality and

performance; and (3) price function value: the

assessment of whether the product lowers

short-term and long-term costs. Below is the list

of references referred to by this study in the

measurement of perceived values.

According to the conceptualization theory

by Monroe and Krishnan【10】, perceived values

are the indicators developed by customers by

comparing the perceived benefit and perceived

sacrifice in product quality. If perceived quality

outweighs the perceived sacrifice, the customers

develop a positive perceived value and become

more willing to purchase. Zeithaml 【 11】

modified the conceptualization model developed

by Monroe and Krishnan【10】 and develops a

perceived value model by categorizing perceived

values into three levels, i.e. lower-level attributes,

perceived lower-value attributes and higher-level

attributes. Sheth, Newman & Gross 【12】

developed a comprehensive framework that

measures consumers’ values of products or

brands with five constructs, i.e. functional value;

social value; emotional value; epistemic value

and conditional value. Parasuraman & Grewal

【13】divided perceived values into four aspects,

i.e. acquired value, transactional value,

utilization value and residual value. Sweeney &

Soutar【 14】 developed a perceived value

measurement with four constructs, i.e. quality

functional value; price functional value;

emotional value and social value. Petrick【15】

classified perceived values into five constructs,

i.e. behavioral price; monetary price; emotional

response; quality and reputation. Yu【9】defined

perceived values with three constructs, i.e.

emotional value; quality functional value and

price functional value.

Definitions and Constructs of Repurchase

Intention

Definitions of repurchase intention

This study defines repurchase intention as

the behavioural intention for repeated purchases

by customers. This conceptual definition is

based on the literature review as follows:

Crosby, Evans & Cowles【16】believed that

if customers find the service provided to be

trustworthy and satisfactory, they will continue

to transaction with the company.

Hellier, Geursen, Carr & Rickard【17】

suggested that customers evaluate the current

and possible situations in the future in order to

determine whether to repurchase the same

product or service from the same company.

Guo【18】contended that the repurchase

intention is the feel-good factor consumers

experience with the services provided.

Chen【19】posited that repurchase intention

is the degree of willingness consumers have to

accept the same product or service.

Lin【20】indicated that repurchase intention

refers to the willingness to consume or purchase

specific products or services in the future.

Lin【21】 defined repurchase intention as

the willingness to return to the same shop after





consumers have received the service for the first

time.

Yin【22】believed that repurchase intention

is the likelihood of purchasing the same product

or service after the use of the same product or

services.

Constructs of repurchase intention

There is extensive literature examining

repurchase intention. This study extends the two

constructs developed by Kotler【23】as the

sub-constructs for repurchase intention. These

two sub-constructs are (1) repeated purchases by

customers; (2) recommendation to others by

customers. The sub-constructs in this study are

constructed based on the literature review as

follows:

Janes and Sasser 【24】categorized and

measured the repurchase intention with three

indicators: (1) primary behavior: consumers’

loyalty is measured with the trading information

with the company and with an analysis of the

recent purchases and actions (such as the timing

of the most recent purchase, purchase frequency

and quantity); (2) secondary behavior: whether

consumers are willing to openly recommend or

introduce the product or service and broadcast

the message; (3) repurchase intention: this refers

to whether consumers, if asked at any time, are

willing to repurchase specific products or

services in the future. Kotler 【23】measured

repurchase intention with two constructs,

repeated purchases by consumers and

recommendations to others. Meanwhile,

Gronholdt, Martensen & Kristensen【 25】

employed three measures for repurchase

intention: (1) intent to repurchase; (2) primary

behavior: No. of purchases, frequency, value and

quantity of purchases; and (3) secondary

behavior: the willingness of customers to refer

customers, make recommendations and spread

the words. Yu【 9】 thought that repurchase

intention can be a standalone construct【26】.

Definitions and Constructs of Customer

satisfaction

Definitions of customer satisfaction

This study defines customer satisfaction as

the variance between the expected standards and

the actual standards of the products or services.

This conceptual definition is based on the

literature review as follows:

Cardozo【27】was the first scholar that came

up with the concept of customer satisfaction.

Customer satisfaction occurs when the perceived

returns from the product purchase turns out to be

greater than the costs paid. It encourages

repurchase behavior. Howard & Sheth【28】

applied the concept of satisfaction into consumer

theories. They believe that customer satisfaction

is the perception by customers regarding

whether the sacrifice they have made (e.g. time

and money) for the product purchase is

worthwhile. Fornell【29】suggested that customer

satisfaction is an overall feeling. It is an attitude

toward consumption, reflected by the like or

dislike they feel about a service or product.

Anderson & Sullivan【30】indicated that the

measurement for customer satisfaction is based

on the gap between their expected experience





and their actual experience. In other words, the

degree of customer satisfaction depends on the

difference between their expected standards and

actual performance of the product or service.

Lovelock 【 31 】 contended that customer

satisfaction is based on a comparison of

customer expectation for the product or service

and the utility provided by the product or service.

It is an important driver for customer loyalty and

there is a positive correlation between customer

satisfaction and customer loyalty. Szymanski &

Henard【32】indicated that customer satisfaction

is subject to factors such as expectations,

disconfirmations, performances and emotions. In

fact, fairness is also an important determinant.

Yeung, Ging & Ennew【 33】 believed that

customer satisfaction is a validation for customer

expectations and it has almost been

conceptualized as a threshold for customer

satisfaction for services. Wang, Lo & Yang【34】

argued that customer satisfaction is an emotional

status. The levels of customer satisfaction are

influenced by product effectiveness.

Constructs of customer satisfaction

This study refers to Fornell【29】in the

measurement of customer satisfaction by

dividing customer satisfaction into five

sub-constructs: (1) ex-ante expectations before

purchase; (2) perceived performances post

purchase, a comparison of the price level and

quality standards; (3) degree of satisfaction:

overall satisfaction and the gap from

expectations; (4) complaints: No. of complaints

to sales personnel and managers; and (5)

customer loyalty: price tolerance and repurchase

intention. The sub-constructs of customer

satisfaction in this study are based on the

literature review as follows:

Czepiel, Roserberg & Akerele 【 35 】

classified customer satisfaction into three levels:

system satisfaction; enterprise satisfaction and

product/service satisfaction.

Oliver and Desarbo【36】indicated that there

are five theories concerning customer

satisfaction. They are expectation theories,

disconfirmation theories, equity theories,

attribution theories and performance theories.

Fornell【29】classified customer satisfaction

into five sub-constructs, i.e. before-purchase

expectations; perceived performances after

purchase; satisfaction levels; complaints;

customer loyalty.

Oliver【37】believed that it is necessary to

increase the weighting on the attributes that

customers value in order to accurately reflect

and measure the overall level of customer

satisfaction. Zeithaml & Bitner【38】suggested

that satisfaction is subject to the influence of

product quality, service quality, prices, scenarios

and personal factors. In fact, service quality and

customer satisfaction can be collectively

described as overall service levels in their own

right. Service quality is one of the factors

contributing to customer satisfaction and

satisfaction is a wider-reaching concept than

service quality.

Yu【9】 stated that the measurement of

customer satisfaction may be conducted with





two approaches: (1) overall assessments: based

on a single construct; (2) multiple items:

satisfaction measured with the performance of

different attributes.

Perceived Values and Customer Satisfaction

The relationship between perceived values

and customer satisfaction is evidenced with the

following literature.

Hallowell 【 39 】 argued that customer

satisfaction is the acceptance of perceived values.

Fornell, Michael, Eugene, Jsesung, and Barbara

【40】also indicate that perceived values are an

antecedent variable of customer satisfaction.

Patterson & Spreng【41】examined the

relationship among service quality, values,

satisfaction and repurchase intention in four

service industries. The results indicate that

perceived values are indeed the antecedent

variable of customer satisfaction. These two

factors are positively correlated.

Yin【22】contended that perceived values

have positive and significant influence on

customer satisfaction. The higher the perceived

values from customers, the higher the level of

their satisfaction is.

Whilst the abovementioned literature

examines different industries or companies of

varying scales, the perspectives are similar. This

study hence develops the following hypothesis:

Hypothesis 1(H1): The perceived value toward a

leading 3C retailer in Taiwan has

positive and significant influence on

customer satisfaction.

Customer Satisfaction and Repurchase

Intention

Lan【42】believed that customer satisfaction

exhibits positive influence on repurchase

intention. Wang【43】indicated that customer

satisfaction boasts significant influence on

repurchase intention. The higher the customer

satisfaction, the stronger the repurchase intention

is. Fan【44】suggested that customer satisfaction

has a direct impact on repurchase intention.

Wang 【45】concluded that customer satisfaction

has positive and significant influence on

repurchase intention. To sum up the

abovementioned literature, this study develops

the following hypothesis:

Hypothesis 2(H2): The customer satisfaction

toward a leading 3C retailer in Taiwan has

positive and significant influence on repurchase

intention.

Perceived Value and Repurchase Intention

There is limited literature concerning the

relationship between perceived value and

repurchase intention.

Wang【1】 noted that perceived values have

positive and significant influence on customer

satisfaction and repurchase intention. The greater

product quality, the higher customer satisfaction,

the stronger repurchase intention and the more

willingness there is to recommend others for

group buying.

Although the abovementioned literature

addresses different industries or scales, the

opinions are similar. This study hence develops

the following hypothesis:

Hypothesis 3 (H3): The perceived value toward a





leading 3C retailer in Taiwan has positive and

significant influence on repurchase intention.

Research Structure

Based on the above research motives,

purposes and literature review, this study

presents its hypotheses and constructs the

research structure as illustrated in Figure 1.

RESEARCH METHODOLOGY

Research Subjects and Questionnaire Design

This study issues the questionnaires to the

front-desk employees in a leading 3C retailer in

Taiwan with stratified random sampling on the

basis of the number of shops in respective cities

and counties. To enhance content validity and

reliability of the questionnaire, this study

conducted a pilot test of the draft questionnaire

by consulting with experts and eliminated or

modified the inappropriate questions before a

post-test. A total of 1,000 questionnaires were

released and the effective and recovered

questionnaires were 202, at an effective recovery

rate of 20.20%. Table 1 shows the structure of

the questionnaire and the number of questions

concerning the constructs (latent variables) and

sub-constructs (measurable variables).

Processing and Measurement System for

Questionnaire Data

To validate the research structure, this study

applies structural equation modelling (SEM) to

conduct a confirmatory factor analysis (CFA) on

the structure. The questionnaire is segmented

into three parts that deal with three latent

variables, i.e. perceived value, customer

satisfaction and repurchase intention. Each latent

variable contains observable variables (or

explicit variables) and each of these observable

(explicit) variables is accompanied with a

number of questions in the survey. The

questionnaire data is processed and collated into

Repurchase intention H3

H1 H2

Expectation

before purchase Complaints

Satisfaction

levels

Perceived

performance after purchase

Customer

loyalty

Perceived value

Quality functional

value

Emotional

value

Price functional

value

Customer satisfaction

Figure 1: Research Structure

Repeated purchases

Recommendation to others





a data file. The construction of the measurement

system in the model is designed according to the

itemized measurements in the questionnaire.

However, this study employs dual measurement

techniques in order to facilitate the processing by

computer software 【 46】 . The number of

questions and references for the individual

implicit variables and explicit variables in this

study are shown in Table 1.

Table 1 Questionnaire Structure

Main Constructs Sub-Construct No. of

questions References

Perceived Value (PV)

Price functional value 3

This study; Yu【9】 Emotional value 3

Quality functional value 3

Customer

Satisfaction (CS)

Expectations before purchase 2

This study;

Fornell【29】

Perceived performance after purchase 2

Satisfaction levels 2

Complaints 2

Customer loyalty 2

Repurchase Intention

(RI)

Repeated purchase 3 This study; Kotler

【23】 Recommendation to others 3

Linear Structural Model

Confirmatory factor analysis (CFA) is a

technique of exploratory factor analysis. This

paper conducts a pairwise CFA on the three

constructs (i.e. perceived value, customer

satisfaction and repurchase intention). A

structural equation mode (SEM) contains a

structural model and a measurement model, to

effectively resolve the cause-and-effect

relationship between latent variables. In addition,

this paper intends to validate the research model

in three aspects, i.e. the compliance of the

overall model fit with goodness-of-fit criteria;

the fit of the measurement model and the fit of

the structural model.

Common Method Variance (CMV) Test

The CFA results in Table 2 indicate that the

questionnaire in this paper does not suffer from

the problems associated with common method

variance.

Table 2 CMV Test Results

Model χ2 DF Δχ2 ΔDF P





Single factor 1326.3 97 885.2 99 0.000

Multiple factors 441.1 196

Source: This Study

RESEARCH ANALYSIS AND

FINDINGS

Goodness-of-Fit Test

This paper designs the model structure

based on the literature review and the sample

factor analysis above. According to Hair,

Anderson, Tatham & Black 【 47 】 , the

measurement of the overall model fit can be

classified into three categories, i.e. measures of

absolute fit, incremental fit measures and

parsimonious fit measures. The goodness-of-fit

test results are shown in Table 3【48】.

Table 3 Goodness-of-Fit Test Results

Fit Indicators Criteria Results in this study

Measures of Absolute Fit

GFI >.9 .914

AGFI >.8 .902

RMR <.05 .011

Incremental Fit Measures NFI >.9 .913

CFI >.9 .911

Parsimonious Fit Measures PNFI >.5 .692

PGFI >.5 .681

Source: This Study & Chen et al【48】

Measurement System of the Model

Fornell and Larcker【49】indicated that the

factor loading for individual factors of latent

variables (i.e. main constructs) and explicit

variables (i.e. sub-constructs) measures the

strength of linear correlation between the

explicit variable and latent variable concerning

respective factors. The closer the factor loading

is to 1, the more the measurable variable (i.e.

sub-construct variable) is able to measure the

main construct. Table 3 shows that all the factor

loadings of sub-constructs are greater than 0.7,

indicating great reliability. In other words, all the

sub-constructs (i.e. explicit variables) in the

measurement system of the model are able to

appropriately measure the corresponding

constructs (i.e. latent variables). Meanwhile, the

average variance extracted (AVE) is the





calculated value for the explanatory power of

latent variables over the variances of the

measured terms. A high AVE indicates high

reliability and convergent validity of latent

variables. Usually the AVE value should exceed

0.5, i.e. the explained variables greater than

measurement errors of the construct concerned.

All the AVE values in this study are larger than

0.5, indicating high reliability and convergent

validity of all the latent variables (or implicit

variables) (Table 4 and Figure 2).

Table 4: Robustness of Measurement System

Latent Variable Measurable Variable Factor Loading Cronbach’s α Average Variance

Extracted

Perceived value

(PV)

Price functional value (PFV) .83 .82 .62

Emotional value (EV) .84 .83 .63

Quality functional value (QFV) .85 .83 .63

Customer

satisfaction

(CS)

Expectation before purchase (EPP) .83 .82 .62

Perceived value after purchase (PPA) .82 .81 .61

Satisfaction level (DCS) .83 .82 .62

Complaints (C) .82 .81 .62

Customer loyalty (DCL) .83 .82 .63

Repurchase

intention (RI)

Repeated purchase (RP) .84 .83 .63

Recommendation to others (ROP) .82 .81 .61

Source: This Study & Fornell et al【49】

Coefficient of Determination

Coefficients of determination are also

known as squared multiple correlations. They

represent the level of explanatory power of

independent variables on dependent variables. In

other words, the R2 values shown in Table 5

indicate that the implicit independent variables

have adequate explanatory power over on the

respective implicit dependent variables.

Table 5 Path Coefficients of Determination

Coefficients of Determination R2

Perceived value (PV)→ Customer satisfaction (CS) 0.59

Customer satisfaction (CS)→ Repurchase intention (RI) 0.57

Perceived value (PV)→ Repurchase intention (RI) 0.52

Source: This Study





Path Coefficients for Latent Variables

After the model has passed the

goodness-of-fit tests, this paper standardized the

coefficients and estimated the C.R. values for all

the latent variables (Table 6). Figure 2 illustrates

the path analysis.

Table 6 Estimated Parameters for Latent Variables

Estimate S.E. C.R. P value

Perceived Value (PV)→ Customer Satisfaction

(CS) .593 .086 6.895 ***

Customer Satisfaction (CS)→ Repurchase

Intention (RI) .572 .083 6.892 ***

Perceived Value (PV)→ Repurchase Intention

(RI) .521 .081 6.432 ***

Note: *** C.R. values statistically significant (α=0.001) or P-value<0.001

Source: This Study

Correlation Analysis

The results indicate that all the correlations

between factors are significant. The relationships

between research variables (Table 7) are as

follows: (1) PV is positively correlated to RI; (2)

PV is positively related to CS; (3) CS is

positively related to RI.

Table 7 Correlation matrix

Constructs (PFV) (EV) (QFV) (EPP) (PPA) (DCS) (C) (DCL) (RP) (ROP)

(PFV) 1.000

(EV) .631*** 1.000

(QFV) .781*** .552*** 1.000

(EPP) .472** .652*** .533*** 1.000

(PPA) .533*** .653*** .573*** -.332*** 1.000

(DCS) .782*** .681*** .732*** .281 .751*** 1.000

(C) -.512*** -.781*** -.593*** .232 -.651*** -.783*** 1.000

(DCL) .523*** .533*** .554*** .263 .462*** .693*** -.583*** 1.000

(RP) .533*** .511*** .513*** .228 .563*** .683*** -.521*** .881*** 1.000

(ROP) .532*** .509*** .504*** .227 .554*** .681*** -.513*** .871*** .831*** 1.000

***denotes P<0.001; Source: This Study

The standardized results path of SEM analysis

By using the AMOS software, this study

generates an illustration of the standardized path

analysis shown below.





Based on Figure 2, this paper derives the

following results:

(1) The perceived value toward a leading 3C

retailer in Taiwan has positive and significant

influence on customer satisfaction. The

standardized coefficient is 0.593 and hence

H1 is supported.

(2) The customer satisfaction toward a leading

3C retailer in Taiwan has positive and


The standardized coefficient is 0.571 and

hence H2 is supported.

(3) The perceived value toward a leading 3C

retailer in Taiwan has positive and significant

influence on repurchase intention. The

standardized coefficient is 0.522 and hence

H3 is supported.

Path Effects Analysis and Test in Structural

Model

This study uses Bayesian estimations to

analyze and test the path coefficients between

latent variables (non-observable) in the structural

mode. The Sobel test is conducted with the

maximum likelihood estimation (MLE) by

referring to customer satisfaction as the

mediating variable to examine whether the direct

effects, mediating effects and total effects are

statistically significant. According to the results

λx31=.85

λx21=.84

λx11=.83

λy11=.83

I2=.57

I1=.59

D=.52

λy71=.82

λy61=.84 e9

e10 ROP

RP

1

PFV

EV

QFV

e1

e2

e3

ε2

e8

λy31=.83

λy21=.82

λy41=.82

DCL

EPP

PPA

C

DCS

e4

e5

e6

e7

λy51=.83

CS

ε1

2

PV

RI

Figure 2 Standardized SEM Results





shown in Tables 8~9,

(1) The path coefficient from perceived

value (PV) to customer satisfaction (CS)

is I1=0.593, with a 95% confidence

level of (0.507, 0.679). This one-order

indirect utility is significant.

(2) The path coefficient from customer

satisfaction (CS) to repurchase intention

(RI) is I2=0.571, with a 95% confidence

level of (0.488, 0.654). This two-order

indirect utility is also significant.

(3) The path coefficient from perceived

value (PV) to repurchase intention (RI)

is D=0.522, with a 95% confidence

level of (0.441, 0.603). The direct

utility is significant.

Table 8 Bayesian Estimations

Table 9 Numeric Estimands

Numeric Estimands Mean S.D. 95% Lower bound 95% Upper bound

One-order indirect utility I1 0.593 0.086 .507 .679

Two-order indirect utility I2 0.571 0.083 .488 .654

Direct utility D 0.522 0.081 .441 .603

Indirect utility (I1* I2) 0.339 0.063 0.276 0.402

Total utility (D+ I1* I2) 0.861 0.083 0.778 0.944

Total indirect utility as % of

total utility 0.394 0.041 0.353 0.435

This paper draws the following conclusions

based on the results shown in Table 9:

(1) The total indirect utility is estimated with

I1* I2 (0.340) at a 95% confidence level of

(0.276, 0.402). Total indirect utility accounts

for 39.4% of the total utility. Therefore,

indirect utility is statistically significant.

(2) The significance of indirect utility and direct

utility implies that customer satisfaction

boasts, to a degree, mediating effects in this

model.

CONCLUSIONS AND

RECOMMENDATIONS

Based on the above analysis and results,

this paper draws the following conclusions,

presents the research limitations and makes

suggestions to follow-up studies.

Conclusions

This paper conducts a survey on the

Regression weights Mean S.D. 95% Lower

bound

95% Upper

bound Name

Perceived value (PV)→ Customer

satisfaction (CS) .593 .086 .507 .679 I1

Customer satisfaction (CS)→

Repurchase intention (RI) .571 .083 .488 .654 I2

Perceived value (PV)→ Repurchase

intention (RI) .522 .081 .441 .603 D





front-desk employees at a leading 3C retailer in

Taiwan and develops a structural equation model

(SEM) to validate the influence of perceived

value on repurchase intention with customer

satisfaction as a mediating variable. The research

conclusions are detailed as follows:

(1) Influence of perceived value on customer

satisfaction

The research findings support Hypothesis 1

that perceived value has positive and significant

influence on customer satisfaction. This is

consistent with Hallowell 【39】, Fornell et al

【40】, Patterson et al【41】and Yin【22】.

(2) Influence of customer satisfaction on

repurchase intention


that customer satisfaction has positive and


This is consistent with Lan 【42】, Wang【43】,

Fan【44】and Wang【45】.

(3) Influence of perceived value on repurchase

intention


that perceived value has positive and significant

influence on repurchase intention. This is

consistent with Wang 【1】.

The above three conclusions indicate a

good model fit in this paper. In addition,

customer satisfaction serves a degree of

mediating effect. This conclusion is somewhat

similar with Baron and Kenny【50】that complete

mediating effects weaken or obscure the

relationship between independent variables and

dependent variables after the addition of a

mediating variable.

Research Limitations

This paper strives to complete each stage of

research procedures in a robust manner despite

of limited resources. The adoption of stratified

random sampling on population is a research

limitation as it may have caused a low effective

recovery rate of the issued questionnaires.

Suggestions to Follow-Up Studies

The relationship among perceived value,

customer satisfaction and repurchase intention is

universal and goes beyond the leading 3C

retailer in Taiwan examined in this study.

Meanwhile, the author has comes up with

slightly different definitions and measurements

of perceived value, customer satisfaction and

repurchase intention. This study only samples

the front-desk employees at a leading 3C retailer

in Taiwan. Follow-up studies may explore the

influence of perceived value on repurchase

intention in different industries with a larger

sample or simply compare and contrast the

results across industries.

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