Journal Research in Peace, Gender and Development (JRPGD) Vol. 4(5) pp. 94-103, August, 2014
DOI: http:/dx.doi.org/10.14303/jrpgd.2014.037
Available online http://www. interesjournals.org/ JRPGD
Copyright © 2014 International Research Journals
Full Length Research Paper
A mediation of perceived customer value between
purchase intention and perceived benefit/perceived
sacrifice for online book shopping in Iran
Zahra Kehtarpour Fariman
Islamic Azad University, Bojnourd Branch, Bojnourd, Iran.
E-mail: mirvaisi@yahoo.com
Abstract
The interest in use of internet in buying product has increased rapidly in recent years. In Iran some
people see online buying as something necessary to reach competitiveness but others claim online
buying to be merely a management fad. The main objective of this study that for the first time done in
IRAN is to first find out what factors influence purchase intention and then the role of perceived
customer value as a meditating variable in the online book service in Iran. Our sample was the
students of university in Khorasan-e Razavi and Khorasan-e Shomali by using SPSS & Lisrel 5.8 tools
for analyses sampling. Required data were prepared questionnaire by Norazah, 2011. The results
show that three hypothesis& relations between perceived playfulness were accepted by perceived
customer value, perceived ease of use by perceived customer value were rejected.
Keywords: Perceived Usefulness, Perceived Playfulness, Perceived Price, Perceived Ease of Use,
Perceived Customer Value, Purchase Intention, online book story.
INTRODUCTION
The growth of the internet over recent years refers to
major changes in business relationships and the
processes
of
communications
and
transmitting
information between organization and their customers.
The impact of the internet includes changing conventional
logics of certain activities and business. Thus, book
stories are one part of the activities that is most affected
by the development of the new medium. In fact, the
appearance of the new digital stories involvesector and
these changes will foresee ably increase in the future.
Also internet notable changes in this technology are
rapidly changing the way and the aims of electronic
exchange and buying. This article focuses on an
explanation of factors influencing purchase intention of
early adopter towards books online stories in Iran.
Despite the interest of this topic, there is a concern about
literature in this direction. Indeed, there is no currently
overall model to identify the main factors influence
perceived customer value and purchase intention on the
Internet book stories buying in Iran. This approach enable
us to fully exploit the enormous opportunities for
analyzing and studying the factors people, with this
increasingly population, using digital tools for buying
book, conclusively, a significant number of questions
related to this area were appeared. More specifically, this
article has three goals:
1.
To identify the main factor which influences
perceived customer value,but also has an impact on
purchasing intention in this population;
2.
To analyze the possible influence of internet book
stories in Iran.
3.
Offering the practical and managerial implications
according to hypothesize.
Literature Review
Perceived usefulness PU: According to technology
accepted model(TAM), PU is the degree to which an
individual believes that using a particular system would
enhance his or her job performance (Al-Gahtani, 2001;
Davis,1993; Mathwick et al., 2001). Another study by Tan
Fariman 95
and Teo (2000) indicated that PU isan important factor in
determining the adaptation of innovations. As observed
bBhattacherjee (2002), a person’s willingness to transact
with a particular system, isalready considered as PU
(Norazah, 2011).
Perceived playfulness: Moon and Kim (2001)
extended TAM to the web and defined three
interdependentdimensions of perceived playfulness:
concentration, curiosity, and enjoyment(Moon and Kim,
2001, p. 219). These represent “the extent to which the
individual”:
(1) perceives that his or her attention is focused on the
interaction with the WWW;
(2) is curious about the interaction; and
(3) finds the interaction intrinsically enjoyable or
interesting.
Atkinson and Kydd (1997) found thatplayfulness is
positively associated with the web for both entertainment
andcoursework purposes. Atkinson and Kydd (1997) and
Cheung et al. (2000) have alsoillustrated the importance
of
playfulness
as
the
dominant
intrinsic
motivatorunderlying hedonic systems.Based on earlier
research on playfulness, subscribers who fall into a
playful stateduring their interaction with a will find their
attention and curiosity arousedand will become further
enticed to continue the use of these services for
sakingpleasure and enjoyment (Moon and Kim, 2001).
Lin et al.’s (2005) study hassupported the positive
relationships
between
playfulness
and
users’
behavioralintention in the context of web sites.
Perceived price: From the customer perspective, price is
the amount which needs to be paid for the consumer to
obtain the product (Hawkins et al., 1983). Or the price
that the consumer must give up or sacrifice to obtain a
product (Zeithaml, 1998). Varki and Colgate (2001) also
once pointed out that price is the part that requires
payment or sacrifice to obtain the product (Cheng et al.,
2011). Xia et al., 2004 also proposed that price
comparison can be explicit or implicit.
Perceived ease of useRogers (1962) theorized that PEU
demonstrates the degree to which invention isseen as not
being too difficult to understand, learn or operate. PEU in
TAM has beendefined as the extent to which a person
believes that using a certain technology will befree of
effort (Davis, 1989). PEU has been demonstrated in
previous studies to influencebehavior, either directly or
indirectly via PU. However, previous researchers have
notfound conclusive evidence about whether the
construct in TAM would have asignificant influence on the
PU of technology (Norazah, 2011). In fact, Brown (2002)
discussed that PU is not a major influence on usage per
se but moreon the external variables, which would be
more likely to affect PEU.
However, Perceived customer value: Value could be
increased by improving the most important benefits.
Furthermore, perceived value could be enhanced by
decreasing privacy risk. Although, providing better value
in terms of the proposed benefits, combined with lower
risk, would improved consumers’ willingness to pay
(Norazah, 2011).Even though several definitions of
perceived value exist, a well-established and most
universally accepted one is ‘‘theconsumer’s overall
assessment of the utility of a product based on
perceptions of what isreceived and what is given’’
(Zeithaml, 1988, p. 14). In other words, perceived value
involvesa trade-off between what the customer gets (e.g.
quality, benefits, utilities) and what theygive up (e.g.
prices, sacrifices, time) to buy and consume a product.
Two basically different perspectives of perceived value
are
identifiable.
First,
theuni-dimensional
conceptualization of perceived value is based on the
‘‘give versus get’’ or‘‘benefits versus sacrifices’’ trade-off
concept. Secondly, as suggested by Sweeney and
Soutar (2001), a wider conceptualization of perceived
value is desirable in order to capture its complex and
multidimensional nature (Mayr and Zins, 2012).
Purchase intention: Consumer purchase intentions have
been modeled from a wide range of perspectives(Wang
et al., 2012). Those consumers with positive purchase
intentions directed towards foreignproducts get this from
perceived quality and emotional value of consuming
suchofferings (Kumar et al., 2009). Despite this, we
suspect that many Indonesianconsumers will simply
refrain from using foreign products due to them who
believed theywill be perceived, in their social groups
among others, as being unpatriotic citizens. From that
vantage, the literatureindicates that it is important for
banks to understand which variables are likely
tomoderate purchase intentions within the high
Indonesian context society. Lee andGreen (1991) apply
Fishburn’s (1968) behavioral intentions model within
across-cultural context to reveal personal attitudes and
social norms amongindividualistic and collectivist cultures
impact purchase intentions differently.Clearly, the diverse
nature of Indonesian societal values (Irawan, 2007)
implies muchvariance in consumption patterns for foreign
banking products so knowing whichsocio-cultural
variables
impact
purchase
intentions
are
tantamount.These studies have added value to our
understanding of the wide variety offactors leading to
choice behavior but have not been applied within the
Indonesiancontext which is particularly important due to
the wide variance in values and beliefs aswell as the high
context nature of that society. From those two
perspectives alone ourview is that purchase intentions
within Indonesia will be influenced by a number ofemotive
based variables which directly linked to socio-cultural
values held by Indonesiansociety. Thus, drawing upon
the earlier work of Ajzen (1991) we similarly expect that
thecertain salient beliefs will reflect through these
variables and can be used to predict theactual purchase
actions of Indonesian consumers. We base this upon the
overarchingrationale of UT, namely that it helps to explain
the utility consumers obtain from their purchases by
96 J. Res. Peace Gend. Dev.
helping to account for consumer choices in terms of
instrumental,functional, and cognitive benefits derived. In
this regard, typically, bank consumers areable to
maximize utility through a wide range of functional
benefits their products offerand include among others,
access to credit facilities, cheaper funds, higher interest
indeposit accounts and/or the plethora of investment
products available.
Hypotheses and conceptual model
On the basis of the literature review and conceptual
framework
presented
previously,
the
following
hypotheses are proposed for testing in the empirical
study of Iranian online books stories that follows.
H1: Perceived Value is positively related to purchase
intentions towards online book stories.
H2: Perceived Usefulness is positively related to
perceived customer value in online book stories.
H3: Perceived Playfulness is positively related to
perceived customer value in online book stories.
H4: Perceived Price is positively related to perceived
customer value in online book stories.
H5: Perceived Ease of Use is positively related to
perceived customer in online book stories.
Perceived
Usefulness
Perceived
Playfulness
Perceived Price
Perceived Ease
of Use
H2
H3
Perceived
Customer Value
H1
Purchase
Intention
H4
H5
METHODOLOGY
Research method: The research method used for this
research in terms of objective has been applied, in terms
of path has been descriptive (correlation), as for time
cross-sectional and in terms of conducting or data
gathering it has been done via questionnaires (field) and
in terms of the in-depth level, it is wide range conducted
as per the casual model and analysis of variancecovariance matrix (ANOVA-ANCOVA statistical testing).
Location (Statistical population) and time of research:
The statistic group for this research has been students of
university in Khorasan-e Razavi and Khorasan-e
Shomaliprovinces of Iran and a number of 160 individuals
were chosen as the sample. The concerned group has
been selected as it has gotten the highest interaction with
the internet. The initial studies and the period of
conducting this research has been in a time span of 5
months.
Sampling method and determining the samples size:
Considering the features of the current research, the
sampling method applied for this research has been the
probable type, conducted in two phases. In the first
phase the simple random sampling has been used to
choose the university and in phase two the single- stage
cluster sampling has been used in factor analysis; the
data from the statistic group and the number of samples
are from special significance. Generally, the strength or
weaknesses of the factor analysis results which have
directedthe relation with the quantity of samples in that
2000 samples is very good, 400-500 is good and 300
samples is average and finally 100 samples is considered
weak (Lee, 2007). Bringing into attention the time and the
costs limits as well as the above introduction, the number
of samplesstudents of university in Khorasan-e Razavi
and Khorasan-e Shomaliin the three provinces has been
150 items.
Methods and instruments applied for data gathering:
Produce considering the fact that the procedures for
conducting this research includes both quantitative &
qualitative parts, In each part it has been essential to
utilize various means and methods;that is the reason why
semi-structured interviews with students of university in
Khorasan-e Razavi & Khorasan-e Shomalihave been
used in the qualitative part of the research. In the
quantitative part of the research, the most significant
means for data gathering has been the questionnaires.
The questionnaires used for the research have been
prepared with regard to the theoretical aspects and the
results gained from the qualitative part procedures of the
research and its objectives, variables and the information
required for the hypothesis testing of the research. The
first section of the questionnaire concerns the
demographic information of the respondents and has
gotten questions about the details of the respondents
including age, sex, qualification and type of services
received from the bank, and eventually the type of
internet using in sampling. In the second section of the
questionnaire, there were items relating to measuring
each of the latent variables.
In devising questions for the main part of the
questionnaire the Likert-type scale from 1-5 options
which is of the ordinal scale types has been used and
includes questions that respondent may choose one of
the five optionsas per his own discrete provided as per
the Likert-type scale (1 = “total disagreement”; 5 = “total
agreement”). In order to measure the research structure
the scales used by Norozadeh, 2011which have been
utilized in devising some of the questions in the
questionnaire.
Validity of the measurement instrument: In order to verify
the initial validity of the questionnaire, the content validity
method, the opinions of university professors and the
experts as well as standard questionnaires has been
used. In the structural equations model (SEM) used for
determining the validity confirmatory factor analysis
Fariman 97
(CFA) and average variance extracted (AVE) have been
used for construct validity and discriminant validity
respectively (Byrne, 2008). In this research the said
parameters have been used for validity determination of
the questionnaires.
Reliability of the measurement instruments: The
conventional method used for improving and evaluating
the measurement scales is estimating the reliability using
Cranach's coefficient alpha. The composite reliability
method is used in the structural equations model in order
to measure the reliability both of which have been used in
order to determine the reliability of the constructs of this
study (Byrne, 2008).
Data analysis and interpretationIn order to describe the
data, the frequency distribution tables have been used in
this research and at the data inference stage the
structural equations models (SEM) have been used
which deal with the analysis of variance-covariance
matrix (ANOVA-ANCOVA statistical testing). For this
reason, exploratory factor analysis (EFA),confirmatory
factor analysis (CFA), path analysis as well as the
goodness of fit tests analysis of model power using
LISREL 8.54 & SPSS 18.0 software's have been used.
Structural equations model: To test this research study’s
model, we have used data analysis with the help of SEM.
Modeling of structural equations means creating a
statistical model for the study of linear relations between
latent (unviewed) variables and evident (viewed or
observed) variables. In other words, structural equation
modeling is a powerful statistical tool that combines a
measurement model (affirmative factor analysis) and the
structural model (regression of path analysis) into one
statistical synchronic test (Vermunt and Magidson, 2005).
Exploratory factor analysis (EFA): Exploratory factor
analysis (EFA) can be applied to explore a domain for
finding the dimensions or the principal constructs of a
domain. This method is used when the relations between
the observed and latent factors are unidentified or nonabsolute and is looking for the least factors to explain the
covariation of the observed variables; therefore, where
the data is complex and the most significant variables of
the specified domain is unidentified, it could be stated
that the exploratory factor analysis is considered to be an
ideal method (Lee, 2007).
Confirmatory factor analysis (CFA): In this method using
the factor analysis the hypothesis testing could be carried
out and based on the previous studies and/or based on
the theory in question factor loadings could be presumed
for the variables. Then for fitting the target matrix
loadings as accurately as possible, confirmatory factor
analysis is conducted. In addition, the fitness degree
could be measured. This analysis is one of the best
statistical methods used for exploring the relations
between the latent and the observed variables and it
presents the measuring model or confirmatory factor
analysis (Byrne, 2008).
Structural model (Path analysis): The purpose of path
analysis is simultaneous study of characteristics or the
latent variables to conduct researches on each other.
Path analysis or a diagram starts with the cause and
effect process which the researcher specifies enabling
the him/her to test the witnessed casual procedures and
to explore the significance of diverse paths in impressing.
The researcher forms the regression model based on
theory and by path analysis determines whether the
model is valid or not and shows the redesigning of the
model in order to correlate with real data (Byrne, 2008).
In the current research, upon getting assured of the
precision of measuring both the main and minor latent
variables and after removing the indicators and
meaningless minor variables from the data processing
process, in the framework of path analysis, the model or
the research theoretical framework has been tested.
Fitting testsGoodness of fit is a determining model of the
level the variance-covariance data supporting structural
equation model. There are numerous indexes for
evaluating the model such as:
2
1)
Chi-square (χ ): totally the level of significance for
2
χ should be more than 0.05 so that the model fitness with
the data is verified (Lee, 2007).
2)
Proportion of χ2to the degree of freedom (/df),
proportion of/dflower than 3 and also lower than 5
indicates the level of goodness of fit with the data
(Byrne,2001).
3)
Root mean square residual (RMR) the required
level for this index to specify the fit of the model with the
data is close to zero.
4)
Standardized root mean square residual (SRMR)
the required level for this index to specify the fit of the
model with the data is close to zero.
5)
Goodness-of-fit index (GFI) the required level for
this index to specify the fit of the model with the data is
0.9 and above.
6)
Normed fit index (NFI) the required level for this
index to specify the fit of the model with the data is 0.9
and above.
7)
Non-normed fit index (NNFI) the required level for
this index to specify the fit of the model with the data is
0.9 and above.
8)
Incremental fit index (IFI) required level for this
index to specify the fit of the model with the data is 0.9
and above.
9)
Comparative fit index (CFI) required level for this
index to specify the fit of the model with the data is 0.9
and above.
10)
Root mean square error of approximation
(RMSEA) required level for this index to specify the fit of
the model with the data is 0.8 and less (Byrne, 2008).
Analysis of model power
In order to specify the adequacy of the sample size of the
study in measuring the model parameters with regard to
98 J. Res. Peace Gend. Dev.
Table 1. Demographic characteristics and usage patterns of respondents
Measure
Sex
Age
Qualifications
Item
Male
Female
18-24
25-34
35-44
45-54
55-64
Higher than 65
Non-responded
Total
Below high school certificate
High school certificate
Associate’s degree
Bachelor’s degree
Master’s degree
Ph D & higher
Non-responded
Total
Frequency
96
43
8
66
52
38
11
4
2
160
2
33
41
32
9
8
4
160
Percentage
74.7
25.1
5.5
39.8
27.8
19.7
5.1
2.2
0.4
100
0.4
35
22.8
27.9
8.1
5.9
0.8
100
Table 2. The results of Skewness and Kurtosis test
Latent Variables
Perceived Usefulness
Perceived Playfulness
Perceived Price
Perceived Ease of Use
Purchase Intention
the weakness of LISREL in this index, the power
coefficient has been used. Today this coefficient is used
to examine the validity of the results of the statistic
methods and it is highly used for the structural equations
model. In structural equations model gaining the
minimum coefficient of 0.7 and more indicates the
adequacy of the sample‘s size of the study and
appropriate control of type one and type two errors
(Murphy et al., 2008).
ANALYSES AND RESULTS
Descriptive statistics: The current statistics have been
used to describe the demographic characteristics of the
respondents and the status of the latent variables
transmittal.
Demographic characteristics of respondents: Learning
about the sample demographic characteristics is useful in
that it helps the general characteristics of the group to be
reviewed and its general characteristics be known to
other scholars and they can be used to correlate the
χ2
3.145
1.648
3.032
3.681
3.032
Significance
0.514
0.328
0.187
0.135
0.193
result to other communities, and or they could be used in
designing the future researches for other communities.
This information has been shown in table 1.
Examining the normal status of variables distribution:
The Skrewness and Kurtosis test has been used In order
to show that the latent variables of this research have got
the normal status of distribution .In this test the null
hypothesis is the normality of distribution. Since all levels
of significance are above 0.05 therefore the null
hypothesis indicating the distribution is normal has been
accepted. So the normal conditions of the latent variables
in question for the estimation of the unidentified
parameters shall be reliable. The result received from the
skrewness and Kurtosis test regarding examining the
normal distribution of latent variables of the research
have been shown in table 2.
Inferential statistics
This statistics have been utilized for inference over the
characteristics of the statics group.
Fariman 99
Table III. The results of measurement model (CFA) for latent variables
Constructs
Perceived Usefulness
Perceived Playfulness
Perceived Price
Perceived Ease of Use
Purchase Intention
Perceived Customer Value
Measurement Item
Q3
Q4
Q5
Q6
Q7
Q8
Q9
Q10
Q11
Q12
Q13
Q14
Q15
Q16
Q17
Q18
Q19
Q20
Q21
Q22
Q23
Q24
Q25
Q26
Q27
Q28
Measurement model for the latent variables of the
research (confirmatory factor analysis): In the structural
equations model, first the construct validity is to be
studied to find out if the selected items (observed
variables) have got the required precision for measuring
the constructs (latent variables) needed, in so doing the
confirmatory factor analysis is used In case the factor
loading of each item and its construct with a t-value is
above 1.96, then that item has got the required precision
for measurement (Byrne, 2008). Considering the root
mean square error of approximation (RMSEA) for the first
model is more than 0.08,so in order to use these
constructs in designing of the structural model research
and hypothesis testing is needed in order that some
improvements takes place in the measurement model. As
it is required in the methodology of structural equations
model, the researcher should correct and improve the
2
stages using the significance of chi-square (χ ). for this
2
reason, the D which judges from the reduction in chisquare and its significance difference, has been used and
as far as the improved model causes significance
difference due to chi-square compared to its previous
stages the improvement of the model should be
Factor Loadings
0.76
0.71
0.75
0.88
0.72
0.83
0.79
0.74
0.75
0.78
0.71
0.88
0.89
0.85
0.80
0.84
0.82
0.70
0.68
0.71
0.75
0.68
0.80
0.91
0.82
0.77
p-value
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
continued so that the estimated parameters in the final
and improved model turns statistically reliable and could
be used for the adjustability of the items with the
constructs of the study (Byrne, 2001). In the current
research, the measurement model or the confirmatory
factor analysis and the results have been shown in table
3. The results which are the estimated parameters in the
final improvement model signifies what the researcher
has been trying to test via the questionnaire has been
achieved through this instrument. The confirmation of the
measurement model indicates that the relations between
the latent and observed variables in the model are
reliable.
Fit the measurement model for the latent variables of
the research: In order to assess the measurement model
there are several fit indexes. In the current research the
following
indexed
have
been
used:χ2/df,
RMR,SRMR,GFI,NFI,NNFI,IFI,CFI and the valuable
index RMSEA for evaluating the measurement model and
the latent variables and the result have been shown in
table 4, all the fit indexes of the table have achieved the
required quantity. Therefore the data of this research
has got a suitable fit with the factor construct and the
100 J. Res. Peace Gend. Dev.
Table 4. The fitting indexes of measurement model for latent variables
Constructs
Independent variables
Indexes
2
χ /df
RMR
SRMR
GFI
NFI
NNFI
IFI
CFI
RMSEA
χ2/df
RMR
SRMR
GFI
NFI
NNFI
IFI
CFI
RMSEA
χ2/df
RMR
SRMR
GFI
NFI
NNFI
IFI
CFI
RMSEA
Dependent variables
Moderator variables
research foundation signifying the correspondence of the
questions with the theoretic constructs and the results
gained from the data correspond with the above model.
Power analysis, the measurement model for latent
variables of the research: In order to assess the power of
the measurement model for latent variables with regard
to the small size of samples the power analysis method
has been used in this research and the results indicate
that in the present size and based on the quantities
estimated the complete quantity of one has been
achieved, so the measurement model of the latent
variables of the research are reliable and the findings are
interpretable. The power analysis diagram of
measurement model for the latent variables of the
research has been shown in figure 2. In summary, we
claim that the model fit the data reasonably well.
Hypotheses
Result
H1
supported
H2
not supported
H3
supported
H4
not supported
H5
supported4/69
Note: Significant at; p<0.05)
Table 3
T-value
3/92
-/14
4/25
0/44
4/99
Mean
3/56
4/501
3/91
3/948
53
DF
53
53
53
53
Accepted Values
Less Than Five
Close to zero
Close to zero
90% and above
90% and above
90% and above
90% and above
90% and above
0.080% and less
Less than five
Close to zero
Close to zero
90% and above
90% and above
90% and above
90% and above
90% and above
0.080% and less
Less than five
Close to zero
Close to zero
90% and above
90% and above
90% and above
90% and above
90% and above
0.080% and less
Values
4.22
0.16
0.07
0.92
0.97
0.97
0.97
0.97
0.080
0.00
0.18
0.10
0.91
0.95
0.95
0.95
0.95
0.001
2.20
0.18
0.10
0.91
0.95
0.95
0.95
0.95
0.049
Managerial implications
In the new medium involves enormous advantages for
book services in terms of both supply and demand. Due
to these advantages, there is a proliferation of electronic
books and these are among the service most rapidly
sought by Internet users.We draw upon the literature and
present a conceptual model outlining socio-culturalfactors
likely to impact the purchase intentions of using Internet
for buying book. Admittedly, our model draws upon a
number of well-established constructswithin the
marketing literature. The findings above suggest that the
Norazah 2011 is a valid model, whichcan be used to
predict the intention of buying online book in Iranian
students. This proposition is based on the regression
analysis, which registered a high R2 value of
approximately 81 per cent. PEU was found to havea
positive impact on the PU of the purchase intention. In
general, the findings support that PEU was also found to
have a positive influence on the intention to buy online
book. As it noted in the literature, an application viewed
by people to be easier to use ismore likely to be
accepted. However, PU was the more influential driver
identified in thestudy for predicting the intention to have
customer value. A system that is high in PU iswhat the
user believes that will reduce his or her task ambiguities
Fariman 101
and eventually increases work-related performance. A
recent study by Liu et al. (2005) concluded that PEU was
a significant predictor of PU. Moreover, PEU was found
tomediate some of the impact of shared beliefs on PU.
Furthermore, the correlationsbetween the predictor and
criterion variables are highly correlated. This result
ismainly due to the characteristics of the respondents,
given that the subjects of thisstudy are knowledgeable
about the online buying book story. Simply put, a shared
belief in a particular system willfurther lead to PEU and
PU. When a system is perceived to be easy to use, the
PU willalso increase. In addition, the user’s experience
with technology and the fit betweentask and technology,
could have further contributed to PEU. As suggested
byVenkatesh (2000), other factors including perceived
enjoyment and objective usabilityof a technology might
also play an influential role in the process. Concerning
the test ofPU as a mediator between PEU and intention
to use the system, the findings revealedthat PU partially
mediated the effects of PEU.The research model
expounded here that holds promise for assisting other
researchersand practitioners to develop understandings
about how shared beliefs improve the PUand ease of use
of abuying online book.
Limitations and Future Research
Like any other empirical study, the study reported here,
includes various limitations. The main limitations include
that, numerous questionnaires sent to our population
weren’t returned. Other limitations included sparse
information provided by some respondents about various
services.
Future research should make several extensions of
the current study. We suggest using this model in the
other service industries including hospitality, restaurants
and any other industry in which employees have direct
contact with customers. Next, researchers suggest that
others studies should use this model in two service
industries to compare them together, similar to the study
of Norazah 2011.
ACKNOWLEDGMENT
The authors would thank to the students of different
university of Azad and Ferdusi branch for honest
participating in this research. We thank various
department of this university.
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Appendix 1: Measurement of Constructs
Perceived Usefulness;
* PU1 I can decide betternow which book stories I want to buy than past.
* PU2 I can acquire book stories information more easily through the online book stories web site.
* PU3 The online book stories web sites provided a variety of book stories.
* PU4 Overall, I find online book stories web sites is useful.
Perceived Playfulness:
* PL1 I enjoy the course of using online book stories.
* PL2 Listening to online book stories make me feel pleasant.
* PL3 While listening to online book stories, I feel exciting.
* PL4 Overall, I found online book stories are interesting.
Perceived Price:
* PR1 The price for online book stories is a lot of money to spend.
* PR2 The price for online book stories is much more than I expected.
* PR3 What I would expected to pay for online book stories is high.
* PR4 In general, I realized listening online book stories would cost me a lot of money.
Perceived Ease of Use:
* PE1 My interaction with online book stories web site is clear and understands.
* PE2 Learning how to use online book stories would be easy for me.
PE3 It would be easy for me to become skillful at listening to online book stories.
PE4 In general, I Found online book stories web site is accessable.*
Perceived Value:
* PV1 The online book story is valuable for me.
* PV2 I would consider that online book stories to be a good value.
* PV3 The online book stories service is considered to be a good purchase.
Purchase Intention:
* PI1 The likelihood that I would pay for online book stories is high.
* PI2 My willingness to buy online book stories is very high.
* PI3 In general, I would consider purchasing online book stories.