t includes issues such as
company size, good reputation, willingness to custo-
mize and interactions with online customers.
2.2. Company’s Reputation
When customers have no experience with a special e-
vendor, reputation may be the key to absorb customers.
From other people’s word-of-mouth, customers can form
positive experience towards the company. This can di-
minish the perception to risk and uncertainty online and
help to increase customers to engender willingness to
depend on the e-vendor [10]. In a study on online trust
conducted by H. Y. Ha [15] was found that reputation is
a critical component of trust.
Company’s image and reputation have been often
found to be crucial enablers of virtual interactions and
transactions by decreasing the transaction risk as well as
reducing consumer anxiety. High levels of brand aware-
ness and good reputation reduces the online consumers’
demands for integrity or credibility credentials [14]. Ac-
cording to M. Turilli et al. [16], “reputation is widely
recognised as one of the main criteria used to assess the
trustworthiness of a potential trustee. For this reason, an
agent can trust another agent only by means of online
interactions”.
In online markets there is the opportunity to trade with
a larger, fluctuating set of partners. But this means less
reliance on long-term relationships. Many providers, such
as eBay, Amazon, and Yahoo, in order to promote the
exchange of information on the credibility of individual
traders have instituted online reputation systems, known
as “feed-back” systems, to provide the kind of word of
mouth available in traditionally online markets. In fact,
the advantage of online feedback systems over traditional
word of mouth in that penetrating information online
does not require personal contacts and in that feedback
information from even large numbers of buyers can eas-
ily be collected and processed [17].
2.3. Perceived Company’s Reputation and
Online Trust
Y. H. Chen and S. Barnes in their paper tested the hy-
pothesis “Perceived good reputation of the company is
positively related to online initial trust in e-commerce”
and found a positive correlation between them [18]. F. S.
Djahantighi and E. Fakar [12] in their research paper
tested the same hypothesis and contrary to the findings of
Copyright © 2012 SciRes. JSSM
Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust
on Intention for Online Transactions
367
Y. H. Chen and S. Barnes [18], found no correlation. The
first research is conducted in the context of Taiwanese
online bookstores and the second study was about Cus-
tomer’s Trends for Reservation Foreign Hotels via Inter-
net.
The process of building a positive firm reputation is
not easy to address since it is expensive and time-con-
suming and requires a great deal of consistent relation-
ship-enhancing behaviour from the vendor’s part towards
its consumers. This process can be undermined very eas-
ily and any positive endeavour outweighed by a few
mistakes by the firm. A company that acts in a consistent
way concerning the creation of a positive reputation, es-
pecially when it has been established, has motive to con-
tinue doing this, and as people will consider reputation to
be a credible variable upon which to assess trust in the
company [4]. An individual tends to accept easily the ge-
nerally opinion about the reputation of a company and to
use it to form its own personal opinion regarding trust in
that company. If several other people have the belief that
a company has a certain degree of integrity, honesty and
fairness, then a possible customer is likely to assume
those qualities as well and use them to determine the ex-
tent to which can trust the company [4].
Consumer trust can increase significantly when a firm
is perceived to have a good reputation. Perceived repute-
tion is “the degree in which people believe in the com-
pany’s honesty and concern towards its customers” [4].
Many studies showed that there is a positive relation-
ship between perceived reputation and online trust. S. L.
Jarvenpaa and N. Tractinsky [10] in their research con-
firm that perceived reputation is positively related to on-
line initial trust and to online on-going trust. D. H. Mc-
Knight et al. [8] found that perceived reputation had a
positive impact on trusting beliefs in the company as well
as on trusting intentions toward the company for new
consumers. Perceived reputation is positively related to
trust, especially initial trust, in the company [4]. Given
that the literature provides stronger evidence for the posi-
tive relationship between perceived company’s repute-
tion and online trust, the following hypothesis is proposed:
H1. There is a positive relationship between perceived
companys reputation and online (initial and on-going)
trust in the company.
2.4. Virtual Customer Purchase Intention
Understanding the mechanisms of online shopping and
the behaviour of the virtual customer is a priority issue
for practitioners competing in the fast expansion e-mar-
ketplace. This topic is also increasingly drawing the at-
tention of researchers. Many online companies still do
not understand completely the needs and behaviour of
the online customer and continue to struggle with the
issue of effective selling products online [14]. A good
deal of research endeavour is focused on constructing
models of online shopping and decision-making process.
A new step which is fundamental in online buying has
been added to the online shopping process: the step of
building trust or confidence [14].
Pavlou [19] defines purchase intention “as the situa-
tion which manifests itself when a customer is willing
and intends to become involved in online transactions”.
From the online trust literature the most commonly
identified consequences of trust are purchase intention
and perceived risk. According to B. Ganguly et al., “pur-
chase intention is concerned with the likelihood to pur-
chase products online” [5]. Purchase intention is the last
consistency of a number of cues for the e-commerce con-
sumer. The more the vendor is capable of evoking the
consumer’s trust the more willing is the consumer to
purchase from an online store [5]. Several studies [20,21]
have shown that increase in consumer trust on the online
seller increases purchase intention [5].
A number of studies have focused on the factors that
influence customer decision making and behaviour in the
environment of web-shopping. Some other findings are
unique to the web environment. The web site layout de-
sign as well as the information content are significant in
order to arouse initial customers’ interest to further ex-
plore a web site [22]. In addition, matching channel cha-
racteristics and retail information disclose for customer
shopping orientation is also significant factor. Perceived
risk, perceived usefulness and ease of use, pervious adop-
tion, perceived financial benefits and internet use would
affect web-shopping adoption and also web-purchase in-
tentions and decisions [22].
F. S. Djahantighi and E. Fakar (2010) [12] elaborated
two factors affecting purchase intention for online trans-
actions which are online trust and familiarity with online
transactions. The existence of trust increases customers’
beliefs that e-vendors will not participate in opportunistic
behaviour. Besides, prior purchasing experiences are po-
sitively related to purchase intentions in e-commerce [18,
22].
According to C. M. Chiu et al. [23], the factors that are
related to the purchase intention are perceived ease of use,
perceived usefulness, website enjoyment and online trust.
2.5. The Impact of Online Trust on Purchase
Intention
It has been demonstrated that online customers’ purchase
intention is positively affected by trusting beliefs [8,9]. Y.
H. Chen and S. Barnes [19] stated that online initial trust
has a positive impact on purchase intention. Trust would
affect customer’s intention to purchase a product from an
online vendor. S. Grabner-Krauter and E. A. Kaluscha [3]
Copyright © 2012 SciRes. JSSM
Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust
on Intention for Online Transactions
368
agree that lack of trust is one of the most frequently men-
tioned reasons for customers not purchasing from online
vendors. They also reported that trust in the e-commerce
retailer influences customers’ perceived risk of the tran-
saction, the usefulness of the Web-site and perceived
ease of use as well as the customers’ intention to transact.
Therefore, it is concluded, according to the above, that
the level of trust and the intention for online transactions
are positively related, and the following hypothesis is
proposed:
H2. There is a positive relationship between the level
of trust an individual has for an online business and the
intention for online transactions.
2.6. Online Trust and the Greek E-Commerce
Context
According to D. Maditinos and K. Theodoridis [24] there
is low internet and technology infusion, as well as lim-
ited online market in Greece. According to N. K. Mal-
hotra and J. D. McCort [25], important cultural differ-
ences between different countries extend to the e-com-
merce context. L. Chai and P. Pavlou [19] elaborated the
case of cultural differences and other factors influence
the electronic commerce adoption. They have endorsed
that there is an important cultural dimension which is
“uncertainty avoidance” and refers to how much people
feel threatened by ambiguity. It is supported that Greece’s
score is the highest for any country measured [19]. Be-
sides, L. Chai and P. Pavlou state that “this distinct cul-
tural dimension is suggested to moderate consumers’ in-
tentions to adopt e-commerce”. There is a moderating ef-
fect of uncertainty in the intention to purchase on-line.
Since in Greece people feel importantly threatened by
ambiguity, online trust is of high significance in the
Greek context. “Countries with high uncertainty avoid-
ance, such as Greece, dislike uncertain situations and
prefer to act only under known conditions” [19]. A com-
mon mistake is to assume that all customer behaviour is
similar. Managers of online shopping companies should
modify their approaches, depending on the culture they
are targeting [19]. When managers attempt to penetrate
in the Greek market, they should focus on creating and
fostering a safe online transactions image [19]. Accord-
ing to D. Maditinos and K. Theodoridis [24], the security
perception is positively related to e-commerce customer
satisfaction which is related to the intention of a con-
sumer to repurchase through internet. Therefore it should
be a priority to create a strong company’s local identity
and presence in the local country [19].
3. Research Methodology
Primary data for the research were collected by struc-
tured questionnaire. The questionnaire included close-
end questions, it counted on a five-point Likert scale
from “1-Strongly Disagree” to “5-Strongly Agree” and it
was based on the literature review. The sample consisted
of users that are familiar with the Web and especially
with the social networks (Facebook). Because the re-
search took place in Greece, the questionnaire before up-
loading on Facebook, was translated in Greek with great
attention so as not to lose the meaning of the questions.
The questionnaire was anonymous and all the questions
were based on previous research and theories after the
study of several articles. In addition, the questions should
be targeted in order to help in the research and to its
better results. The distribution became online, uploading
the questionnaire on Facebook in pages-groups and re-
mained uploading for two weeks targeting in the partici-
pation of at least 200 users. Before the distribution of the
questionnaire, was transacted a pretest in order to ascer-
tain possible problems in the completion of the ques-
tionnaire as well to examine its structure and its under-
standing from the participants. The pretest involved 10
users.
The data were recorded electronically in a data base.
The questions that use Likert scale analysed as quantita-
tive (5-points scale). The aim was to investigate if the
answers/attitudes (dependent variables) are affected by
the demographics factors (independent variables). The
quantitative methods (t-test and ANOVA test) were used
in order to investigate if there are any differences in the
answers/attitude between the categories of the categorical
variables (demographics factors). For example, males
and females. T-test is used when the categorical-indepen-
dent variable has only two categories (i.e. the gender:
male-female) while ANOVA is used when the categori-
cal variable has more than three categories (i.e. educa-
tional level, age groups) [26].
For the statistical analysis of the data was used SPSS
15.0 software, because it is one of the most broadly used
and reliable software.
In order to examine the reliability of the answers two
couples of similar and two couples of contradictive ques-
tions had been created. Those who had more than two
mistaken couples of questions were considered that had
completed the questionnaire without particular attention
and did not take part in the analysis. In this point it has to
be mentioned that was not found any questionnaire with
more than two mistaken couples of questions. Besides,
users that had completed less than 50 per cent of the
questions were rejected from the analysis because this
kind of questionnaires did not give reliable answers and
for this reason could not be examined the hypotheses of
the research.
Copyright © 2012 SciRes. JSSM
Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust
on Intention for Online Transactions
369
4. Discussion
In order to explore the relation between perceived com-
pany’s reputation and online trust, and between online
trust and intention for online transaction, was used cor-
relation analysis. Correlation analysis is a technique for
investigating the relationship between two quantitative,
continuous variables [26]. In order to test the relationship
between these couples of variables was used Pearson’s
correlation coefficient (r).
Table 1 shows that the correlation between perceived
company’s reputation and online trust is r = 0.351 and
this implies that there is a moderate positive correlation.
This denotes that an increase of perceived company’s
reputation involves an increase of online trust (score). As
for the association between online trust and intention for
online transactions, the correlation coefficient is equal to
0.225. This degree expresses a low positive correlation
between the two variables. This indicates that an in-
crease of trust leads to a low increase of intention for
online transactions. However, is worth mentioning that
both of the values of coefficient correlation are statisti-
cally significant (both P-value < 0.001). This signifi-
cance is due to high number of observations of the
sample (N = 206).
where Pearson correlation refers to the value of Pear-
son coefficient correlation, Sig.(2-tailed) refers to P-va-
lue and N is the number of observations.
Based on the values of Pearson correlation coefficient
(r) was calculated the coefficient of determination (R2 or
r square) as R2 = r2. R2 expresses the proportion of vari-
ance of the dependent variable (Y) which is explained by
the independent variable (X) [26].
According to the previous results, studying the R
square between perceived company’s reputation (inde-
pendent variable) and online trust (dependent variable) it
is concluded that R2 = 0.123. This denotes that 12.3% of
the online trust variation is explained by the perceived
company’s reputation. About the R square between
Table 1. Correlations table.
Reputation TrustIntention
Reputation
Pearson Correlation
Sig. (2-tailed)
N
1
206
0.351**
0.000
206
0.306**
0.000
206
Trust
Pearson Correlation
Sig. (2-tailed)
N
0.351**
0.000
206
1
206
0.225**
0.001
206
Intention
Pearson Correlation
Sig. (2-tailed)
N
1
206
0.351**
0.000
206
0.306**
0.000
206
**Correlation is significant at the 0.01 (2-tailed).
online trust (independent variable) and intention for on-
line transactions (dependent variable) it is concluded that
R2 = 0.051. This indicates that online trust explains 5.1%
of the intention for online transactions variability.
Multivariate statistical analysis was applied to explore
the research hypotheses and to assess the influence of
other (independent) variables on the dependent variables.
Variables taking into account, also, the demographic cha-
racteristics. Regression analysis is used either to predict
the value of a quantitative dependent variable, based on
the value of at least one independent variable and to ex-
plain the impact of changes in an independent variable on
the dependent variable or to find out which factors (in-
dependent variables) and how these factors effect on the
dependent variable [26]. In this study, multiple linear
regression analysis was performed using the stepwise
method in order to test the research hypotheses taking
into account, also, the demographic characteristics.
4.1. Regression Analysis between Online Trust
and Perceived Company’s Reputation
The execution of the multiple linear regression model in
which the demographic characteristics were included as
well, were created the results below. In this model trust
was considered as dependent variable while perceived
company’s reputation as a predictor (independent vari-
able). Looking at the Table 2, specifically in model 2
(final model), the coefficient of perceived company’s
reputation β is equal to 0.381 (P-value < 0.001 < 0.05)
and of Age_Dummy2, β is equal to –0.323 (P-value =
0.007 < 0.05). Both of these predictors are statistically
significant for online trust. This means that these vari-
ables have an impact on the levels of online trust.
Specifically, increasing the perceived company’s
reputation score by one, online trust’s score is increased
by 0.381 having the rest factors stable. About Age_Dum-
my2, the coefficient β = –0.323 indicates that online trust
decreases significantly more in people who are older than
35 years (by 0.323) compared to those who are 17 - 24
years old. Therefore, it is concluded that there is a posi-
tive relationship between perceived company’s reputa-
tion and online trust and a negative relationship between
age and online trust.
ANOVA table (Table 3) denotes that our model (mo-
del 2) is overall statistically significant as it has a P-value
< 0.001.
From Ta ble 4 (model 2), it can be seen that R2
adjusted =
0.145. This implies that our regression model presents
14.5% of online trust variation. Therefore, these ex-
planatory variables can explain a small part of online
trust. Based on the above findings, Hypothesis 1 is sup-
ported as presented in Table 4.
Copyright © 2012 SciRes. JSSM
Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust
on Intention for Online Transactions
Copyright © 2012 SciRes. JSSM
370
Table 2. Regression coefficients for perceived company’s reputation and online trust.
Unstandardized
Coefficients
Standardized
Coefficients 95% Confidence Interval for B
Model
B Std. Error Beta
T Sig.
Lower Bound Upper Bound
1 (Constant)
Reputation
2.676
0.351
0.223
0.066
3.51
11.998
5.336
0.000
0.000
2.236
0.221
3.115
0.481
2
(Constant)
Reputation
Age_Dummy2
2.645
0.381
–0.323
0.220
0.066
0.119
0.380
–0.178
12.031
5.794
–2.710
0.000
0.000
0.007
2.212
0.251
–0.558
3.079
0.510
–0.088
a. Dependent variable:Trust where B (or β) is the value of coefficient, t is the value of t-test and Sig.: represents the P-value.
Table 3. ANOVA for perceived company’s reputation and
online trust.
Model Sum of
Squares df Mean Square F Sig.
1
Regression
Residual
Total
13.789
98.298
112.088
1
203
204
13.789
0.484 28.4770.000a
2
Regression
Residual
Total
17.237
94.851
112.088
2
202
204
8.618
0.470 18.354 0.000b
aPredictors: (Constant), Reputation, bPredictors: (Constant), Reputation,
Age_Dummy2.
Table 4. Model summary.
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1
2
0.351a
0.392b
0.123
0.154
0.119
0.145
0.6959
0.6852
aPredictors: (Constant), Reputation, bPredictors: (Constant), Reputation,
Age_Dummy2.
4.2. Regression Analysis between Intention for
Online Transactions and Online Trust
In this section analysis of the intention for online trans-
actions was considered as dependent variable and online
trust as explanatory variable. Conducting a multiple lin-
ear regression analysis in which the demographic vari-
ables were inserted in the model as well, resulting in a
regression model (model 4 in Table 5) in which are in-
cluded the predictors online trust (β = 0.225, P-value =
0.001 < 0.05), Prof_Dummy2 (β = 0.701, P-value <
0.001 < 0.05), Educ_Dummy2 (β = 0.347, P-value=0.006
< 0.05) and Prof_Dummy1 (β = 0.528, P-value = 0.007 <
0.05) are statistically significant factors for intention for
online transactions. Looking at their P-values it is con-
cluded that all of these variables are statistically signifi-
cant factors for intention for online transactions.
Interpreting the results below someone can say that
increasing the online trust by one then intention for on-
line transactions is increased by 0.225 having the rest
factors stable. About Prof_Dummy2, this denotes that
employed people have increased intention for online
transactions compared to unemployed people. The con-
clusion is the same when you compare students to unem-
ployed people. Still, from the education dummy variable
(Educ_Dummy2) it is concluded that people who have
acquired master or PhD have increased intention for
online transactions compared to those that have gradu-
ated from senior high school.
Below, ANOVA Table 6 presents that the model
(model 4) is overall statistically significant as it has a
P-value < 0.001.
Finally, examining the proportion of intention for
online transactions variance, which is interpreted in Ta-
ble 7 (model 4), by looking at R2
adjusted it can be seen that
this model explains 13.4% of intention for online trans-
actions variability. Therefore, these explanatory variables
can explain a small part of intention for online transac-
tions. Based on the above findings, Hypothesis 2 is sup-
ported.
5. Conclusions
Perceived company’s reputation, online trust and inten-
tion for online transactions are three of the issues for
which a large number of researchers are working on
these in the academic community. Trust is very essential
and has been called key to e-commerce and therefore
building trust is even more vital.
The contribution of this paper, concerns the study of
the relationship between perceived company’s reputation
and online trust and between online trust and intention
for online transactions according to the beliefs and opin-
ions of the sample. These conclusions can contribute
greatly in understanding consumer behaviour for e-com-
merce services and therefore to help improving the of-
fered services.
Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust
on Intention for Online Transactions
371
Table 5. Regression Coefficient for online trust and intention for online transactions.
Unstandardized
Coefficients
Standardized
Coefficients 95% Confidence Interval for B
Model
B Std. Error Beta
T Sig.
Lower Bound Upper Bound
1 (Constant)
Trust
2.479
0.240
0.280
0.072
0.229
8.851
3.354
0.000
0.001
1.927
0.099
3.031
0.382
2
(Constant)
Trust
Prof_Dummy2
2.280
0.239
0.322
0.283
0.070
0.108
0.228
0.201
8.067
3.404
2.993
0.000
0.001
0.003
1.723
0.101
0.110
2.837
0.378
0.534
3
(Constant)
Trust
Prof_Dummy2
Educ_Dummy2
2.250
0.232
0.307
0.308
0.279
0.070
0.106
0.125
0.221
0.191
0.163
8.053
3.342
2.886
2.454
0.000
0.001
0.004
0.015
1.699
0.095
0.097
0.060
2.802
0.369
0.517
0.555
4
(Constant)
Trust
Prof_Dummy2
Educ_Dummy2
Prof_Dummy1
1.875
0.225
0.701
0.347
0.528
0.308
0.068
0.178
0.124
0.193
0.225
0.437
0.183
0.305
6.095
3.289
3.935
2.790
2.733
0.000
0.001
0.000
0.006
0.007
1.268
0.090
0.350
0.102
0.147
2.481
0.360
1.053
0.592
0.909
aDependent Variable: Intention.
Table 6. ANOVA for online trust and intention for online
transactions.
Model Sum of
Squares df Mean
Square F Sig.
1
Regression
Residual
Total
6.477
116.895
123.372
1
203
204
6.477
0.576 11.249 0.001a
2
Regression
Residual
Total
11.440
111.932
123.372
2
202
204
5.720
0.554 10.3280.000b
3
Regression
Residual
Total
14.697
108.676
123.372
3
201
204
4.899
0.541 9.061 0.000c
4
Regression
Residual
Total
18.610
104.762
123.372
4
200
204
4.652
0.524 8.882 0.000d
aPredictors: (Constant), Trust; bPredictors: (Constant), Trust, Prof_Dummy2;
cPredictors: (Constant), Trust, Prof_Dummy2, Educ_Dummy2; dPredictors:
(Constant), Trust, Prof_Dummy2, Educ_Dummy2, Prof_Dummy1.
Table 7. Model summary.
Model R R Square
Adjusted R
Square
Std. Error of
the Estimate
1
2
3
4
0.229a
0.305b
0.345c
0.388d
0.053
0.093
0.119
0.151
0.048
0.084
0.106
0.134
0.7588
0.7444
0.7353
0.7237
aPredictors: (Constant), Trust; bPredictors: (Constant), Trust, Prof_Dummy2;
cPredictors: (Constant), Trust, Prof_Dummy2, Educ_Dummy2; dPredictors:
(Constant), Trust, Prof_Dummy2, Educ_Dummy2, Prof_Dummy1.
However, this study has a number of limitations such
as: the customer sample was taken from Greece and
therefore that study cannot describe consumer behaviour
in a global market, and since the used questionnaire was
distributed through Facebook it excludes all those that
are not Facebook members.
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