Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust on Intention for Online Transactions

doi:10.4236/jssm.2012.54043

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Journal of Service Science and Management, 2012, 5, 365-372

http://dx.doi.org/10.4236/jssm.2012.54043 Published Online December 2012 (http://www.SciRP.org/journal/jssm)

365

Online Trust: The Influence of Perceived Company’s

Reputation on Consumers’ Trust and the Effects of Trust

on Intention for Online Transactions

Andromachi Broutsou, Panos Fitsilis

Project Management Department, TEI of Larissa, Larissa, Greece.

Email: [email protected]

Received August 4th, 2012; revised September 13th, 2012; accepted September 23rd, 2012

ABSTRACT

Trust is a basic ingredient in the creation, the evolvement and the conservation of a long-term relationship between sup-

pliers and buyers. It is also a key differentiator in defining the success or failure of many e-business companies, in order

to endorse the importance of online trust. In this paper we present a study on online trust in the B2C context. More spe-

cifically, we focus on the issues of perceived company’s reputation, online trust and intention for online transactions.

The aim of this study is to examine if there is a positive relationship between perceived company’s reputation and on-

line trust, and between online trust and consumer’s intention for online transactions.

Keywords: Online Trust; Perceived Company’s Reputation; Intention for Online Transactions

1. Introduction

In recent years electronic commerce is growing rapidly

despite the dismal economy [1]. Also, e-commerce has

highly contributed to the enhancing of economic growth,

reduction of barriers of market entry, improvement of ef-

ficiency and effectiveness, reduction of costs and reten-

tion of lean inventories. Among customer’s benefits of

using the Internet are convenience, vast amounts of in-

formation, huge variety of products and services, and

time savings [2]. Online shopping has a unique feature of

uncertainty, anonymity, potential opportunism and lack

of. On-line customers are required to share personal data

(such as telephone number, e-mail address), financial in-

formation (such as credit card numbers), and face the risk

of product and services not matching the website de-

scription or the risk of damage during the delivery proc-

ess control [3]. In addition there seem to be little assur-

ance of proper use of the personal information by the re-

tailer [3]. According to Koufaris and Hampton-Sosa “Lack

of trust in online companies is the primary reason why

many web users do not shop online” [4].

Trust has been defined in different ways by various

authors and is a multi-dimensional construct [5]. Τrust is

the “willingness to rely on another party and to take ac-

tion in circumstances where such actions make one vul-

nerable to the other party” [6]. Trust is a basic ingredient

in the creation, the evolvement and the conservation of a

long-term relationship between suppliers and buyers. It

also exists, when there is confidence, from the one party,

for an exchange partner’s credibility and integrity [6]. In

the new business paradigm of online marketplaces, trust

has been dealt with as a critical enabling factor for busi-

ness-to-consumer (B2C) relationships [7] and to over-

come many challenges. One of these challenges is how to

build online trust to allay risks that associated with online

transactions [1]. Perceived reputation influence user’s

trusting beliefs and trusting intentions towards a web-

based vendor.

In turn, trusting beliefs, perceived web risk and trust-

ing intention influence customer intentions to engage in

the following behaviours:

 Share information with the vendor;

 Follow vendor advice; and

 Purchase from the web site [8].

The present study is going to focus on online trust

since there the literature is limited in this subject. The

purpose is to address the issues of perceived company’s

reputation, online trust and intention for online transac-

tions. Therefore, the main objectives of the research are

the following:

 To determine the extent to which the perceived repu-

tation influence initial online trust as well as the on-

going online trust.

 To identify the importance of online trust on the con-

sumers decision to buy through the web in the Greek

cultural, behavioural, economical and demographical

Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust

on Intention for Online Transactions

366

context.

 To determine the level of online trust and possible

problems/issues emerge in the Greek context.

 To use the findings in order to make recommenda-

tions on the development of online trust in Greece.

The remaining of the paper is organised as follows:

Section 2 presents the relevant literature, Section 3 pre-

sents the research methodology, while the results are

presented in Section 4. The paper closes with the conclu-

sions.

2. Literature Review

2.1. Online Trust

The importance of the nature of trust, its antecedents and

consequents, is widely recognised. Sociologists, organ-

isational behaviour scientists, psychologists, economists,

political scientists and anthropologists have contributed

to the wide body of knowledge which exists on this sub-

ject [9].

Similarly, online trust is of paramount importance

since trust is crucial for the growth of e-commerce [10].

It is argued that “in the virtual world, the issue of trust

gets magnified” and this highlights the significance of

online trust [11]. F. S. Djahantighi and E. Fakar [12],

stated that “online trust is one of the key obstacles to

vendors succeeding on the internet medium. A lack of

trust is likely to discourage online consumers from par-

ticipating in e-commerce”.

The lack of e-trust (online trust) is likely to deter any

purchase over the internet [13]. E. Constantinides [14]

supports that online trust is one of the factors that is fre-

quently associated with the failure or success of online

ventures and its multi-dimensional character makes it a

complicated issue and he presents the most important

trust elements which are: “Transaction Security”, “Cus-

tomer Data Abuse”, “Customer Data Safety”, “Uncer-

tainty Reducing Elements” and “Guarantees Return Poli-

cies”.

F. S. Djahantighi and E. Fakar [12] elaborated the fac-

tors that affect online trust and consequently consumer’s

intention to make an online purchase. These determinants

of online trust are:

 Perceived Usefulness: It refers to the belief that a par-

ticular system would enhance effectiveness and job

performance.

 Perceived Ease of Use: It is the belief that a particular

system would be free from strenuous effort. Modern

technology (Internet) enhances ease of use and trig-

gers positive purchase behaviours, when making on-

line transactions.

 Perceived Enjoyment of Technology: Useful and eas-

ily understood information on web sites lifts the de-

gree of online trust, and influences positively pur-

chase intention.

 Perceived Privacy and Security: Perceived privacy is

the dissemination of information related to online

transactions or behaviours.

 Company Competency: It 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

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

company’s 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]

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.

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)

206

0.351**

0.000

206

0.306**

0.000

206

Trust

Pearson Correlation

Sig. (2-tailed)

0.351**

0.000

206

0.225**

0.001

206

Intention

Pearson Correlation

Sig. (2-tailed)

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.

Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust

on Intention for Online Transactions

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

2.236

0.221

3.115

0.481

(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

0.007

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.

Regression

Residual

Total

13.789

98.298

112.088

203

204

13.789

0.484 28.4770.000a

Regression

Residual

Total

17.237

94.851

112.088

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

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

(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

(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

(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.

Regression

Residual

Total

6.477

116.895

123.372

203

204

6.477

0.576 11.249 0.001a

Regression

Residual

Total

11.440

111.932

123.372

202

204

5.720

0.554 10.3280.000b

Regression

Residual

Total

14.697

108.676

123.372

201

204

4.899

0.541 9.061 0.000c

Regression

Residual

Total

18.610

104.762

123.372

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

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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