A TRUST EVALUATION MODEL OF INTERNET

BANKING CUSTOMERS

Xiaoyan Wang

1,2

and Sulin Pang

Department of Accountancy & Institute of Finance Engineering

School of Management Jinan University, Guangzhou, China

Business Administration Department, Guangdong University of Finance, Guangzhou,China

Keywords： Internet banking, Trust, Trust degree, Trust evaluation model.

Abstract： It is a hot topics that how to measure the trust degree of the Internet banking customers. The major factors

on impacting the customers’ trust are their personal trust propensity and the recommendation trust before

the customer uses it. But the customer's own experience is the major factor after they have used it. In this

paper，we establish a two-stage Internet banking customer trust evaluation model. Using this model, the

bank managers can measure the trust degree of the customers in the different periods. We hope the model

can provide a strong support for the bank managers’ decision.

1 INTRODUCTION

The issue of trust has been paid all the while by

multidisciplinary such as psychology, sociology,

economics and other subjects concern. Psychologists

emphasize the internal nature of the trust ，and they

consider trust as inherent personality traits and a

kind of human expectations, beliefs, confidence,

varying with different individuals. Sociologists

stressed the externalities generated by trust that the

trust is a simplified mechanism as the product of

social mechanism and cultural norms. And trust is a

social phenomenon based on legal (laws and

regulations) or ethical (social and cultural norms)

and a kind of believe that other people can look

forward to cooperative behavior. While economists

argued that, trust is a result of the individual’s

rational choice when the benefit of trust is greater

than that of no confidence. Trust was discussed by

different disciplines from different perspectives, in

the resulting of different understanding, thus the

definition of trust has been unable to unify. Until

1995, Mayer et al (Mayer et al., 1995) advanced the

notion of trust currently widely accepted based on a

comprehensive understanding of various disciplines,

that one side always thought that the other part will

act in accordance with his manner regardless of his

monitoring and control abilities, thus he is willing to

put his own in the state of risk.

Along with the appearance of network

transaction methods, the issues network trust have

caused computer science concerns and made certain

achievements, especially in the area of trust model

research. Marsh (Marsh, 1994) (1994) was one of the

earliest scholars who carried out trust formalistic

research on the basis of sociology. He divided trust

into three types: basic trust, general trust and

situational trust, and proposed a calculable trust

model under the distributed artificial intelligence

environment. In the model, Marsh defined basic trust

as an individual tendency of trust dereferencing in

the interval [-1,1], and he stressed that the basic trust

varies with different individuals because of different

individual tendency. Beth (Beth et al, 1994) (1994)

introduced the concept of experience and used the

method to express the measure of the probability of

trust. Trust is defined in the interval [0,1]. The model

defines two kinds of trust relations: direct trust and

recommendation trust. Beth also gave the first

formula of recommendation trust worthiness: namely,

is the trust worthiness derived from a single path,

and the comprehensive recommendation trust

worthiness is simple average of these single-degree

recommendation trust worthiness. Abdul-Rahman et

al (Abdul-Rahman, 2000) (2000) considered no

practical significance about continuous quantitative

trust, they divided trust according to semantic

variables as fully trust, great trust, trust, a little trust,

not trust, not trust at all , and the discrete values

119

Wang X. and Pang S.

A TRUST EVALUATION MODEL OF INTERNET BANKING CUSTOMERS.

DOI: 10.5220/0003268601190124

In Proceedings of the Twelfth International Conference on Informatics and Semiotics in Organisations (ICISO 2010), page

ISBN: 978-989-8425-26-3

are 4,3,2,1,0, -1. Tang et al (Wen and Zhong, 2003)

(2003) believe that trust is essentially a faith-based,

with subjective and ambiguous character, he draw

fuzzy set theory into trust management studies and

used the grade of membership to describe the

ambiguity of trust. They defined the trust vectors as

measurement mechanism of trust, and adopted

method of fuzzy comprehensive evaluation to

measure trust. But the model denied the random of

trust, and considered the ambiguity as the unique

characteristics of trust. Song et al (Song and Hwang,

2005) (2005), proposed the dynamic trust model

based on fuzzy logic under network environment in

account with the dynamic nature of trust. In their

model, they not only took into account the evidence

production of dynamic trust value, but also took into

account historical factors, and got the final trust

value through these two weighted average. But this

model did not take into account the factor of time.

Wang Liang et al (Liang and Dan, 2008) (2008)

introduced time attenuation function into their trust

model, and pointed out that the attenuation

coefficient values may be dependent on the user’s

specific strategy. Ma Li et al (Li and Weimin, 2009)

(2009) also pointed out that the trust worthiness is

relevant to time and will decay over time continuity.

They defined this feature of decline property of time.

As a virtual trading method, the auguries of

internet banking look gloomy. The lack of customer

s’ trust on internet banking is one of the most

important reasons of its development restrict

[9,10,11]. Therefore, the issue of customers trust on

internet banking is paid more and more attention at

home and abroad. At present, most scholars consider

internet banking as an information system, they

assumed that trust is one of the factors that affected

customers’ use of internet banking, and through the

use of the technology acceptance model proposed by

Davis (Davis, 1993), they used structural equation

model and empirical methods to test assumptions

reasonable. The results show that: Customers trust

indeed has a positive correlation with their intention

[13,14,15,16,17]. However, whether the customer

choose to use internet banking, it’s closely related to

the trust worthiness, and only it exceeded the

threshold value of the customers, will the customer

use it. But at this stage it is short of research work

specifically on quantitative aspects of customers

trust on internet banking. Therefore, this article tries

to build trust evaluation model about internet

banking customer based on the above-mentioned

research. Through the use of this model, the

managers can detect and manage the customers’ trust

to develop a more reasonable measure to increase the

customers’ trust, so as to promote the healthy

development of internet banking.

This paper is organized as follow: section II

defines the important conceptions used in setting

session; Section III establishes the trust evaluation

model on the stage of before the use of internet

banking and the after phase when the customers have

used; and finally a conclusion should be drawn.

2 DEFINITION

In order to research conveniently, it is needed to

explain several key concepts:

(1) Basic trust

In general, before the customers use internet

banking, it has a trust value, this trust worthiness is

the most primitive trust of the individual on others or

things, known as basic trust.

(2) Recommendation trust

Recommendation Trust is established according

to the recommendation of other entities to a

relationship of trust, but not conducted from the two

entities’ direct deal. And the trust worthiness

between them is based on the results of the

assessment from other entities.

(3) Direct trust

Direct trust is also known as the direct

experience or knowledge-based trust, it generates in

the process of direct contact of a trusted party with

trust party. Trust worthiness will increase along with

their experience and the results would change with

constantly revised.

(4) Trust worthiness

The size of the trust can be quantified, and

usually expressed by trust worthiness. Also it is

known as trust level of or trust value. It can use the

fuzzy variables, such as "trust", "no trust", etc. It can

also use the real numbers or probability in [0,1]. In

this paper it is defined in the interval of [0,1].

3 CUSTOMER TRUST ON

INTERNET BANKING

The formation and evolution of customers trust on

internet banking are dynamic process. With the

increasing of the time of transaction and the level of

transaction satisfaction, their mutual trust worthiness

will be in progressive development of infancy to

maturity (Corritore et al., 2003). This dynamic is

specifically manifested in two aspects: first, the

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customer trust on internetbanking over changes over

time. At different times, in different scenarios,

customers trust on internet banking will not be on

the same level. For example, the customer trust is

relatively low because of his unfamiliarity with

internet banking when at the first time. But as the

time of transactions and their own experience

increasing continuously, the trust will increase.

Second, factors influencing on trust are at different

stages. In the initial stage of the formation of trust

relationships, customers are unable to conduct and

predict a comprehensive risk assessment because of

unfamiliarity. At this stage, the factors of customer

trust mainly depends on the basic trust and others

recommendation trust. And when the customer

makes trust decisions after using internet banking,

the above two factors will be gradually weakened.

At this moment, the major factor comes from in the

process of the direct interaction and accumulated

experience and knowledge. Customers adjust their

trust based on the experience each time they use

internet banking. If they are able to achieve the

desired effect, then the trust may increase. On the

contrary, the trust will diminish, even disappear (See

Figure 1).



Trust

source

Time

Pre‐occupancy

Trusteddecision

Afteruse

Recommendation

trust

Directtrust

Basictrust

Figure 1: The Dynamic Process of Customer Trust on

Internet banking.

In general, consumers are called potential

customers before they really use internet banking

transactions. The potential customers will become

real customers once they have used internet banking

transactions. Therefore, then, we establish the

customer trust evaluation model divided into two

kinds of cases: first, trust evaluation model of

potential customers; second, evaluation model of the

reality customers.

3.1 The Evaluation Model of Potential

Customers

3.1.1 Basic Trust

Before customers use the internet banking

transactions, actually they have had an initial trust

already, the trust called initial trust is the basic trust

Marsh has explained, and the basic trust is related to

the tendency of individuals. Trust tendency is

considered general trust and basic position as a

whole of the main body for other persons or things.

In theory, such a general trust can impact on the trust

judge on any specific person or thing. Because they

are all different in their main growth experience and

personality traits, as well as different cultural

backgrounds, so trust will vary accordingly. The

higher the customers trust tendency, the larger the

trust worthiness he has. On the contrary, the initial

trust worthiness is lower. Now, we use a

mathematical model to describe the basic trust of

potential customers.

Suppose that

is basic trust of customer

at the moment

t , basic trust is different for different

customer, therefore:

，

[]

0,1

∈

(1)

The trust tendency of customer

is expressed

. Because each person's trust tendency is

stable, so

is a fixed constant. If the values of

is closer to 0, it is indicated that the initial

customer trust is low; if it’s closer to 1, it is

indicated that the initial customer trust is high.

In practical applications, we can use rating scales

to measure customer trust tendency and determine

the value of

. Rating scale is the most commonly

used psychometric instruments, and the semantic

quantifier is an important part of rating scales

evaluate the amount of psychological applying the

level of natural language. Cicchetti (1985) studies

show that: In the rating scale, the use of seven

quantifiers is the most suitable (Cicchetti and

Showalter, 1985), because if the semantics of

quantifiers rating too few, the sensitivity of scale

will be significantly reduced. On the contrary the

grading will be too much to distinguish the subjects’

areas so that the evaluation results would be affected.

According to the thoughts of Cicchetti et al, we use

seven semantic quantifier to measure the customer's

trust, that is "fully believe," "trust," " a little trust, "

"uncertain," "somewhat do not trust," "do not trust,"

"do not trust at all". The quantitative approach is to

A TRUST EVALUATION MODEL OF INTERNET BANKING CUSTOMERS

121

convert the semantic quantifier to equidistant values,

if "fully trust" means 1, and then "do not trust at all

"means 0.

3.1.2 Recommendation Trust

Trust has transmission characteristics. When the

potential customers are short of understanding of

internet banking or relevant information to judge the

trust, they rely on the recommendation information

of the third parties, such as customer's word of

mouth and the recommendation of some trust

assessment agencies. For example, customer A who

has never used internet banking can consult

customer B who has had such transactions, and

customer B would supply some trust-related

information to customer A, then Customer A will

build his trust on the internet banking through

customer B. See figure 2 below:



customer

A

Internet

banking

customer

B

Figure 2: Trust transmission diagram.

Suppose at the moment t , the customer

has

received n recommendation trust worthiness from n

recommended persons (or advertising media,

third-party of network trust evaluation certification

agencies),

is expressed the recommendation

trust worthiness on the internet banking generated by

others’ recommendation. Then,

()

vtr

⋅

∑

(2)

[0,1]

tr ∈

[0,1]

v ∈

1, 2, 3, ,

n= "

In the formula above,

tr expresses the trust

worthiness that person

recommends to

customer

v represents the customers

focus

of the recommended information. If the value of

v is closer to 1, it is indicated that the

recommendation of the information has great the

impact on customer

.On the contrary,

v is closer

to 0, it indicates the recommendation of the

information has little the impact on customer

When applied, customers focus can still be

measured by rating scale too. Semantic quantifier

can use the seven levels as" take too much count," "

take much count," " take count," "generally" and

"give a little attention," " give little attention, "" not

give attention at all". If the value of "take too much

count" means 1, "not give attention at all" means 0.

3.1.3 Comprehensive Evaluation of the

Potential Customers Trust

As noted above, the potential customers trust

depends largely on the basic trust and

recommendation trust. Customers will adjust their

own trust based on their basic trust on the internet

banking and the recommendation trust. Suppose that

the basic trust of a potential customer on internet

banking is relatively high, but the recommendation

trust from other people is low, so his trust worthiness

on the internet banking may be reduced. On the other

hand, his basic trust has just started in relatively low

level, but the recommendation trust from other

people get relatively high, his trust will improve.

Therefore, the comprehensive assessment of the trust

should make a synthesis of basic trust and

recommendation trust, and thus get the overall trust

on internet banking. So, how to merge two of them

is the key to the establishment of the model. In order

to study conveniently, we consider the potential

customers as the recommending one by themselves,

so that the basic trust and recommendation trust can

be liable to the same treatment. Hereafter, we use

weighted average method to the synthesis of the

value of potential customers trust on internet

banking.

Suppose that

Trust

represents the trust of

potential customer

at the moment of t ,

represents the basic trust of customer

at the

moment of

t , and

represents the

recommendation trust of customer

. Then,

ttt

Trust w TP w TR

⋅+⋅

(3)

In the above formula,

1ww+=,

w ,

w respectively represents the trust tendency

and the share weight of recommendation trust from

others in the overall trust.

3.2 Trust Evaluation Model of Real

Customers

The real customers have already used internet

banking, so their trust worthiness are associated with

their actual experience. In general, if the customer

has used the internet banking repeatedly, then the

ICISO 2010 - International Conference on Informatics and Semiotics in Organisations

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customer trust is not only associated with the current

transaction, but also associated with his previous

evaluation. According to their actual experience,

customers will continue to adjust and update their

trust on the internet banking. In normal conditions,

satisfaction with the results of collaboration is basis

of trust relationship, and satisfaction largely

determines the level of trust. So the customers trust

can be measured by their satisfaction with the results

of collaboration. If the customer is satisfied with the

internet banking, his trust on the internet banking

will increase, otherwise that will be reduced. We

describe the different satisfaction by discrete scale

and natural language at the same time, as shown in

Table 3.

The following, we will describe direct trust of

the real customers by mathematical method:

Assume that customer

has used internet

banking for

n times, the variable

indicated

that satisfaction of customer

at the

i time

transaction,

T represents the direct trust of

customer

after the

n transaction. Then after

the customer used internet banking for

n times, the

calculation of his direct trust is divided into two

situations:

①when

1n = ,

TD td=

(4)

When customer uses internet banking the first

time, his trust is right the first time usage evaluation

because of his short of accumulation about relevant

experience.

②when

1n > ,

(1 )

wtd

TD td

αα

−

=+−

−

∑

(5)

Among them,

wtd n

−

∑

indicates average

value of the trust of customer's history, and

w (

w≤≤

) represents the time weighting factor.

and

−

respectively represents the average

value of customer's history and the share of current

trust in the updated trust value. Generally speaking,

if the history of trading is farther away from current,

then the smaller

will be, on the other hand, the

greater

will be.

The time weighting factors

12 1

,,,

ww w

−

represent the degree of customer focus at moment.

Scientific determination of weight vectors is the key

to get a reasonable result of evaluation. Time weight

vector can be determined according to different

criteria. Next, we use entropy method to determine

the value of

12 1

,,,

ww w

−

" . Entropy is called

average amount of information in information theory,

which is a measure of information. The entropy

value greater, the smaller amount of information

contained.

First, introduce the definition of entropy

time weighting vector and time-degree

−

=−

∑

(6)

−

−−

−

∑

(7)

The entropy

of time weighting vector reflect

different level of information contained included in

the weights.

reflects the degree of emphasis on

the timing (See Table 1). When

is closer to 0, it

is indicated that the more attention paid to the more

recent data by the customer, reflecting the idea of

time decaying.

Secondly, fix

w . In the situation of a given

in advance, now we solve the following linear

programming problem:

max ln

1, [0,1]

1, 2, , 1

−

−−

−

=∈

=−

∑

(8)

Table 1: The Reference Table of Time-degree Scale.

Illustration

0.1 Take highly account of recent data

0.3 Take relatively account of recent data

0.5 Equally in every stage

0.7 Take more account of long-dated data

0.9 Take highly account of long-dated data

0.2 、 0.4 、

0.6、0.8

Corresponds to the middle of the above two

adjacent data to determine

For example, when 0.1

= ， 6n = ，

(0.0029,0.0086,0.0255,0.0755, 0.2238,0.6637)w

A TRUST EVALUATION MODEL OF INTERNET BANKING CUSTOMERS

123

4 CONCLUSIONS

This paper established a two-stage model of

customers trust on internet banking including stage

of Pre-occupancy and after usage. The model has

certain maneuverability so that bank managers can

use it to measure customers trust at different time, so

as to support strong data for bank management

decision-making.

ACKNOWLEDGEMENTS

Supported by the National Natural Science

Foundation (70871055); the New Century Talents

plan of Ministry of Education of China

(NCET-08-0615); the Key Programs of Science and

Technology Department of Guangdong Province

(2010); the Key Programs of Science and

Technology of Guangzhou.

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