Transfer From Offline Trust to Key Online Perceptions:

An Empirical Study

Kun Chang Lee,* In Won Kang** and D. Harrison McKnight***

*Professor of MISSchool of Business Administration

Sungkyunkwan UniversitySeoul 110-745, Korea

leekc@skku.ac.kr

**Assistant ProfessorSchool of Economics & International Trade

Kyunghee UniversitySeoul 130-701, Korea

iwkang@khu.ac.kr

***Assistant ProfessorEli Broad College of Business

Michigan State UniversityEast Lansing, MI 48824-1121

mcknight@bus.msu.edu

Forthcoming IEEE-TEM

Index Terms: Trust, trust transfer, structural assurance, online banking, online trust, marketing channel, flow, website satisfaction, extent of use, coherence, cognitive overhead.

Professor McKnight has served as a corresponding author. Inquiries about this study can be directed to any author.

Transfer From Offline Trust to Key Online Perceptions: An Empirical Study

Abstract- Research has provided little evidence that trust in an offline bank can

encourage adoption of the bank’s online business. Yet more and more brick-and-

mortar banks and other businesses are investing in online websites that

supposedly “leverage” positive consumer impressions of their offline business. The

main purpose of this study is to test empirically whether or not trust in an offline

bank transfers (i.e., influences) perceptions about that company’s online bank. In

order to do so, we analyze how trust in an offline bank influences four perceptions

about its online banking counterpart (flow, structural assurance, perceived website

satisfaction and perceived extent of future use). The study tests the hypothesized

influence of offline trust using a sample of 199 South Korean consumers

responding about offline and online banking. Results show that offline trust

influences all four online perceptions, just as proposed. These effects were

especially prominent among respondents new to online banking. Thus, offline-to-

online transfer should be considered when designing strategies for online channels.

This study fills a key research gap by examining transfer of cognitive beliefs from

an offline to an online setting.

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I. INTRODUCTION

Trust is a central construct in the study of commercial transactions, both in information

systems (IS) and such reference disciplines as marketing, sociology, and organizational

behavior [27, 31, 73, 75, 95]. Trust plays an important role in both offline and online

commercial transactions [106]. Several IS studies on the role of trust in electronic

commerce (e-commerce) have been conducted in recent years [5, 33, 34, 36, 43, 50, 58,

71, 80, 81, 96, 103]. Especially, with the advent of online marketplaces on the Internet as a

new business paradigm, trust has been dealt with as a crucial enabling factor for business-

to-consumer relationships [33, 50] as well for business-to-business marketplaces [80].

A number of trust constructs including institution-based trust [70, 81, 108] have been

defined [34, 72]. Interpersonal or inter-party trust definitions largely fall into two categories:

Willingness to become vulnerable to another [67, 71], such as “a willingness to rely on an

exchange partner in whom one has confidence” [74].

Positive perceptions or beliefs about the attributes of another. This includes the

perception of “confidence in the exchange partner’s reliability and integrity” [75], and the

perceived credibility (honesty) and benevolence of a particular target [30, 56]. Several

have listed integrity, benevolence, and ability as attributes [8, 34, 71]. In buyer-seller

relational exchanges, which are the most popular forms of transactions between persons

or companies, two dimensions of trust are perhaps most pertinent. The first,

credibility/honesty, is the expectation that words or written statements can be relied upon

or depended upon [61]. The second, benevolence, is the extent to which one partner is

genuinely interested in the other’s welfare and is motivated to seek mutual gain [57].

Trust is important in relational exchanges because it allows partners to transcend short-run

inequities or risks to concentrate on long-term profits or gains [73]. Personal relational

exchanges are typified by qualities such as intensity, interaction frequency, relationship

duration, and future relational expectations [48]. These qualities inherent in offline

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relational exchange facilitate trust building. By contrast, trust is more difficult to create

online because parties do not typically have intense, face-to-face contact that enables trust

to be built through tangible cues. One can enter a bank, for example, and learn to trust the

bank tellers through repeated personal interactions that provide interpersonal cues. One

can also become secure quite rapidly with the bank institution itself by entering the bank

and observing the look-and-feel of the building, its furnishings, and the banking procedures

—physical cues on which to base trust [71]. By contrast, the online bank has no such

physical cues for trust building, although its website provides certain online cues.

One way to solve this trust building problem is to transfer trust from a known brick-and-

mortar entity to positive perceptions in the unknown online entity. We postulate that trust

gained through experience with an offline company positively influences key customer

perceptions of the same company’s online division, such as website satisfaction or

intention to use its site. Stewart [96], who called this phenomenon “trust transfer,” applied it

specifically to business-to-consumer e-commerce in terms of buying computers online.

The results revealed that websites that had a picture and street address of their land-

based store generated a higher consumer intention to buy from the store. Stewart [96] did

not use specific trust-related perceptions about the offline store to predict online

perceptions, a gap this paper addresses. Today’s Internet-oriented world has made it even

more important for marketers to study the interplay between offline and online channels

when analyzing customer behavior. Those who do so are rewarded with a level of

predictive power that affords them a distinct advantage over their competitors. Potentially,

this study contributes by testing an instance of the effects of offline bank trust on four

specific consumer perceptions regarding the bank’s online counterpart.

II. LITERATURE REVIEW AND RESEARCH MODEL

A. Trust Transfer Process—Four Types

Narrowly defined, trust transfer means transfer of trust from one domain, such as offline, to

another, such as online. We define trust transfer more broadly to include the influence of

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trust in one domain on attitudes and perceptions in another domain. For example,

consumer trust of an offline bank can affect perceptions about the same firm’s online bank.

Figure 1 depicts several types of trust transfer. The Type 1 TTP shows how trust (or other

perceptions) can be transferred from offline to offline channels. For example, the trust that

customers perceive in an offline company, like General Electric (GE), tends to extend

easily to favorable perceptions about all other GE products or services. Trust for a certain

company means consumer brand knowledge which relates to the cognitive representation

of the brand [83]. Consumer brand knowledge can be defined in terms of the personal

meaning about a brand stored in consumer memory. Therefore, if customers possess a

sound memory about an offline company’s brand, then such memory affects consumer

purchase behavior about the product and/or service offered by the same or similar offline

brand.

Type 2Type 1

Offline

Online

Type 4 Type 3

Offline

Offline

Online Online

Online

Offline

Figure 1. Trust Transfer Process (TTP)

In the mid-1990s, many firms began performing marketing activities both offline and online

(Type 2). As the Internet has proven an ever more effective global communication

medium, marketing strategies have shifted from offline to online channels. One key issue

has been whether consumer trust, something offline firms had worked for years to

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accumulate, would translate into favorable perceptions of online products. Examples both

support and cast doubt on such trust transfer. Offline-turned-online channels such as

Barnes & Noble Company (http://www.barnesandnoble.com) and Samsung Electronics

Corporation (http://www.sec.co.kr) have maintained offline trust and brand loyalty online as

well as offline. On the other hand, FTD (Florists’ Trans world Delivery) and Hallmark have

had trouble leveraging their offline business into online successes [15].

Although many pure online companies have fizzled out, some continue to succeed (e.g.,

Dell Computer). Continuing questions about Dell’s survival prospects are based on

whether the online trust it has built thus far will remain stable in the future, when stronger

competition is expected. It follows, then (Type 3), that if existing online companies wish to

expand their online products and service offerings, they will have to maintain or increase

their level of consumer trust. Stewart [96] suggests that, in online B2C, trust transfers from

trusted websites to unfamiliar websites based on hyperlinks between them.

There are rare cases of Type 4 trust transfer, in which customers’ trust in online channels

move to offline channels. In South Korea, which has a very high Internet adoption rate due

to its highly advanced Internet infrastructure, a number of successful online companies

have expanded their business into offline channels. In the home shopping industry, two

companies, CJ Home Shopping (www.cjmall.com) and GS Home Shopping

(www.gseshop.co.kr), have successfully opened offline shops, taking advantage of their

online reputation. MISSHA (www.beautynet.co.kr), a quality-based and low-priced

cosmetics company that originally started as a pure online company and accumulated a

solid reputation by innovating a process of developing new cosmetic products on the basis

of online polls, is recently receiving a great deal of attention from the Korean cosmetics

industry and mass media, due to its fast and successful expansion into offline markets.

In Figure 1, TTP is categorized as either intra-channel (Types 1 and 3) or inter-channel

(Types 2 and 4). Regardless of type, TTP indicates that trust amassed over time in one

channel (online or offline) may influence the evaluation of a product or service in the same

channel or in another channel.

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A customer’s evaluation of a new brand is based on the schema developed from

experience and knowledge of it [66]. Customers evaluate products and services in order to

reduce transaction risks [12], and it has been established that customers use their

experience to determine their purchase intentions [86]. The consumer schema remains in

memory and is converted into trust in the vendor. Knowledge of TTP can help marketing

strategists enhance consumer loyalty and expand it into different channels.

This study fills a research gap by focusing on Type 2 (Offline-to-Online) TTP, which has

received almost no attention. Offline to online TTP is a pervasive phenomenon, since most

businesses and individual customers are already involved in some form of e-commerce or

Internet business. Type 1 (Offline-to-Offline) TTP has already been studied by numerous

researchers [14, 54, 82]. Once an offline brand (or company) has succeeded in acquiring a

certain level of consumer trust, it can extend easily into another offline business under the

same brand category. By contrast, real cases of Type 3 (Online-to-Online) and Type 4

(Online-to-Offline) TTP are relatively rare, although Stewart [96] has addressed Type 3.

B. Offline Trust and Online Mental Models

Figure 2 represents the general interaction of offline trust with a mental model of online

banking. We begin with this general model before proceeding to the specific research

model. A mental model is the representation of objects and semantic relations that a user

constructs when he or she is processing new information [38]. Customers who view

hyperdocuments on an online banking website must be able to form quick and clear

mental models before attempting to perform online banking transactions.

Coherence positively influences the ability to form mental models, while cognitive

overhead inhibits it. By definition, a document is coherent if a customer can construct a

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mental model from it that approximates reality [52]. A website that combines design factors

to create a user-friendly website design improves customer coherence [63].

Trust

from Offline Banking

Antecedents

- Flow

- Structural Assurance

Outcomes

- Perceived Website Satisfaction

- Perceived Extent of Use

Mental Model in Online Banking

Level of Coherence & Cognitive Overhead

Figure 2. Relationships between Offline Trust and Online Customers’ Mental Model

Note: Dotted line box indicates constructs not in this study

Cognitive overhead, on the other hand, is the additional effort and concentration users

need to perform one or more complex tasks at once [17]. Cognitive overhead is influenced

by orientation, navigation and interface design [101]. For example, multimedia overkill in a

Web interface (e.g., excessive graphics, color, animation, sound) can cause excessive

cognitive overhead because the capacity for human information processing is limited. We

thus argue that various website design factors affect both coherence and cognitive

overhead.

Based on Thuring et al. [101], this study argues that flow and structural assurance affect

perceived website satisfaction and extent of use through coherence and cognitive

overhead. While this study does not measure coherence and cognitive overhead, it argues

that flow and structural assurance partly influence the Figure 2 outcomes by their effects

on coherence and cognitive overhead. Flow means an optimal experience with a task [20].

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Structural assurance means the belief that situational factors like contracts, regulations

and guarantees support the likelihood of success [71]. Using both coherence/cognitive

overhead and their causes (flow and structural assurance) in the model seemed like

overkill in terms of model complexity. Also, because it is viable to test a subset of a larger

theoretical model [99], we chose to test a model that only included flow and structural

assurance.

C. Offline Trust and Flow

Arguments are now made for why trust will transfer in general, and then reasons for why

trust will transfer to online flow. Trust in an offline business should transfer to perceptions

about an online business because of brand knowledge. Since brand knowledge is the

cognitive representation of a particular brand [83], it is also defined as the personal

meaning that is stored in consumer memory: descriptive and evaluative brand-related

information [54]. Brand knowledge involves a synthesis of multiple factors such as

awareness, attributes, benefits, images, thoughts, feelings, attitudes, and experiences

[54]. Among those factors, trustworthiness perceptions about the brand are implicitly

included in attitude, which is believed to transfer to the other entity (refer to Figure 2 in

[54]). Thus, consumer trust in a certain brand will transfer to products and services related

to that brand, regardless of channel.

The concept of flow involves the optimized processing of experiences that occur when

customers navigate or browse the Internet. Online interactivity between companies and

their customers is carried out through flow [44, 101]. Flow helps explain certain aspects of

human-computer interaction [20].

Figure 3 shows the research model that will now be justified. A common measure of flow is

the level of intrinsic enjoyment of an activity, not unlike the emotional response of pleasure

in environmental psychology [55]. In an online environment, flow is based on three

aspects: shopping enjoyment, perceived control, and concentration/attention. This study,

accordingly, defines flow using these three aspects. Just as shopping enjoyment is

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important in offline markets, it is important online, where it can have a significant impact on

customers’ attitudes and intentions about online shopping [49, 55]. Internet shopping does

not always provide the emotionally fulfilling experience of conventional shopping because

it is mostly limited to two-dimensional text and pictures [55].

Figure 3. Research Model

One aspect of online shopping enjoyment is the perception that the website has interesting

content. The second aspect of flow, perceived system user control (i.e., not being

controlled by the system), concerns the information environment on the Internet. A high

level of consumer control should be guaranteed by companies that wish their customers to

conduct online transactions [6, 44]. The third aspect of flow, concentration/attention, is not

always easy to maintain on a website. In an online banking situation, customers do not

engage with tellers personally. Such face-to-face interaction can be very engaging. Online

interaction can also be engaging [20]. However, online concentration/attention may be

discouraged by limited time and lack of information processing resources [86], lack of a

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real space where people can meet each other face to face [55], and other distractions,

such as email, pop-ups, and instant messaging.

Trust in an offline bank probably reduces user hesitance to give attention to the system,

because without trust, the perceived risk of using the system would interfere. Trust

therefore obviates uncertainty or concern and facilitates the development of flow. It is hard

for the user to experience flow when the mind is bogged down in uncertainty or risk. That

is, flow means a feeling of total involvement [20]. It is hard to feel total involvement using

the online system of a bank one does not trust. Trust has long been seen as a way to

address uncertainty [64]. Trust can act as a substitute for control because one feels more

in control and less uncertain with a person one trusts [64]. Offline bank trust would thus

positively influence flow in terms of perceived control because one who trusts the offline

bank would feel more in control and less uncertain when using the bank’s system.

H1: Trust in an offline bank positively influences perceived user flow with the online bank

system.

D. Offline Trust and Structural Assurance

While Internet customers can access products and services efficiently regardless of

location or time, they have limited means of assessing the characteristics of the products

and services they purchase [22, 49, 87]. They can only view products on-screen. This is

one reason many customers perceive transaction risk when shopping on the Internet [86].

Risky situations result in customer uncertainty: unexpected worries, concerns and

psychological misgivings at the time of purchase or adoption [7, 26, 100].

Structural assurance, a form of institution-based trust particularly key for minimizing

perceived risk in online transactions, means consumer projections of success due to such

safety nets as legal recourse, guarantees and regulations that exist in the context [23, 71].

Trust in the e-vendor results from the security customers feel due to these safety nets [34].

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Online banking requires a high level of security, since private information must be

completely protected against computer hacking attacks or technical failures that would

damage customers’ financial status. Online banking transactions should therefore result in

positive consequences with regard to the amount of money and financial conditions

involved. In this study, structural assurance focuses on situations in which customers

contemplate online banking transactions in a known service class. If a consumer trusts the

offline business, this trust should transfer to beliefs that the firm’s online system is safe

and secure, i.e., to structural assurance beliefs. If an offline bank is trusted, it should also

be perceived as having adequate online safeguards. This is as natural as believing a

trusted retail store will back its product guarantees in a new line of business, just as it

already does for existing lines of business.

H2: Trust in an offline bank positively influences structural assurance of the bank’s online

system.

E. Offline Trust and Perceived Website Satisfaction

User satisfaction with a specific web-based system depends on numerous factors,

including web design [62, 79, 105], content [85], user interface [94], navigation and

information structure [69, 90], and contextual marketing [65].

User satisfaction was initially promoted as a proxy for the success of an information

system. Such systems were evaluated in an attempt to specify their quality from the user’s

point of view [32], and many instruments were developed that successfully measured

different aspects of user experiences and opinions [21].

From a marketing perspective, user satisfaction depends largely on performance. Product

experiences alone do not determine overall satisfaction, however; a large body of research

has shown that the level of performance the customer expects is also important [16, 78],

as is knowledge of outcomes that were not experienced. When people evaluate outcomes

they compare their actual results with the results that might have occurred had they

chosen differently [11, 53]. Similarly, trust development has been depicted as the process

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of setting expectations of another’s behavior and then evaluating whether or not those

actions are confirmed [89]. Trust expectations can also act as cognitive filtering devices by

predisposing one to interpret the other’s behavior as consistent with the original

expectations. For example, Holmes [46] found that trusting marriage partners tended to

block out or reinterpret (positively) actions by their partner that did not match their positive

trusting expectations. Likewise, one who trusts has expectations that will likely be

confirmed in terms of perceived website satisfaction. If one has found an offline bank to be

trustworthy, then one is likely to project positive satisfaction towards its online counterpart.

Thus trust in the offline bank should positively affect perceived satisfaction with the

website.

H3: Trust in an offline bank positively influences perceived online bank website

satisfaction.

F. Offline Trust and Perceived Extent of Use

Extent of use means expected or planned future behavior — the probability that a

customer will translate beliefs and attitudes into user-related actions. Different factors can

influence extent of use in online transactions, including product perceptions, purchasing

experiences, customer service and transaction risk [49].

Online products are perceived through interactions between people and machines, while

products in offline transactions are perceived directly. Thus, online transactions highlight

customer risk [45], since customers are unable to directly experience products and

services. The level of risk that customers perceive in online transactions influences their

level of satisfaction with a website. Extent of use depends on the level of satisfaction with

several factors about the website. So far, research on extent of use in offline and online

transactions has assumed that every extent of use applies only to one channel [8, 103,

106]. However, Ramaswami et al. [88], applying to both offline and online transactions,

suggests two factors influence purchase of online financial products: 1) prior clues a

consumer gains in the offline channel; and 2) use of the online channel for information

search.

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Therefore, consumer online behavior is often determined by prior clues in the offline

channel, which are translated into the mental model in the online channel [88]. If a

customer is satisfied with a product in the offline channel, he or she is more likely to favor

that product in an online channel. When a consumer’s experience with an offline vendor is

satisfactory, the consumer will trust the offline vendor. Trust in the offline vendor should

then transfer into positive attitudes towards the vendor’s online products. Gefen et al. [35]

found that trust in an e-commerce vendor leads to intentions to purchase from that vendor.

Similarly, trust in the offline bank should positively influence extent of intended use of the

bank’s online system.

H4: Trust in an offline bank positively influences perceived extent of use of an online bank.

G. Online to Online Model Relationships

Perceived structural assurance also relates to website satisfaction. Depending upon the

extent of customers’ misgivings regarding the purchasing environment, they may be more

committed to the website (and thus more satisfied), or less committed to it (and thus less

satisfied). It is hard to feel satisfied with a website about which one is concerned in terms

of safeguards. Such concerns may increase cognitive overhead. Hence, lack of structural

assurance may interfere with the development of website satisfaction. Security signs or

logos indicating the existence of website security systems have also been shown to lessen

the psychological burden of online customers [34], which should facilitate website

satisfaction.

H5: Online bank structural assurance positively influences perceived online bank website

satisfaction.

Web satisfaction is dependent on both information quality and system quality [69].

Information quality encompasses web design factors and hypertext document quality.

System quality embraces user interface, navigation, and information structure. To the

extent that a site user experiences flow, the user will feel positive towards the system and

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information quality of the website, both of which affect web satisfaction. The user

experiencing flow will: a) devote attention to the site, b) experience greater coherence, and

c) enjoy using the site because of its good content and design. Therefore, flow will be

positively related to perceived website satisfaction.

H6: Online bank system user flow positively influences perceived online bank website

satisfaction.

Enjoyment of the online shopping experience is an important determinant of customer

loyalty [28, 49]. Concentration, a measure of flow, has also been found to positively

influence the overall experience of computer users and their intention to use a system

again [104]. Flow should improve customers’ perceived coherence, which will lead users to

want to use the system. As flow becomes greater, customers tend to become more

absorbed in the website. Flow is a pleasurable experience, something users desire. Thus,

the pleasure behind flow will increase the likelihood that users will utilize the website

again.

H7: Online bank system user flow positively influences perceived extent of use of an online

banking system.

Meanwhile, structural assurance can influence website extent of use. Because online

banking activity is especially vulnerable to financial risks [13, 86], trustworthy structural

assurance relieves the tension of customers and lowers cognitive overhead, both of which

will improve their perceived extent of use. Structural assurance lowers perceived

transaction risks [34, 72], which allows customers a more active interaction with online

banking websites. Suh and Han [98] found that such structural assurances as

nonrepudiation, privacy protection, and data integrity indirectly influenced both intention to

use a website and actual use through trust in the website.

H8: Online bank structural assurance positively influences perceived extent of use of an

online banking system.

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Feelings of interactivity (indicating flow [44, 55]) will be influenced by the perceived safety

of online banking transactions (indicating structural assurance [34, 72]). One may have

trouble reaching a state of flow unless one feels safe about the online vendor’s website.

Once the online banking environment is considered safe, one can use the website without

concern, making flow more likely.

H9: Online bank structural assurance positively influences online bank system user flow.

Many researchers believe that the extent of use varies with the level of satisfaction with

the technology [8, 19, 23]. It has been well documented that customer satisfaction affects

repeat purchases, product return rates, brand loyalty and the prevalence of word-of-mouth

communications [18]. Satisfaction and trust in websites affects purchasing intentions [8,

19, 23, 103]. Greater satisfaction with a website means that customers favor those

products, leading to increased intent to use.

H10: Perceived online bank website satisfaction positively influences perceived extent of

use of an online banking system.

A boundary condition should be noted for H1-H4: the effects of offline trust will likely be

strongest when consumers have little experience with online banking. After some period of

online bank experience, online banking variables, such as website satisfaction, will play a

more important role than will offline bank trust. This boundary condition will be tested using

two subsets of customers—first-time users and experienced users. Results will be

presented after the hypothesis tests.

III. RESEARCH METHODOLOGY

To examine trust transfer across offline and online banking channels, this study employed

a field study method, using questionnaire techniques to measure each construct in the

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model. Questionnaires enable researchers to obtain the beliefs of prospective website

users, and those beliefs are especially important to trust-related research, in which

perceptions are more important than objective measures [46].

A. Customer Online Banking Studies

Online banking transactions have emerged as an important topic in domestic and

international financial circles [60] because online banking allows financial transactions to

be carried out conveniently in virtual space, without regard for time or geographical

location [103]. Previous studies of online banking have focused on four kinds of research:

(1) general description and success factors [2, 4], (2) analysis of behavioral issues such as

customer adoption [76, 92] and customer attitudes toward the usefulness and willingness

to use online banking [59, 60], (3) regional characteristics [51, 91] and (4) other issues,

such as security [40, 107]. Banks are attracted to online banking because it reduces costs

and can provide a competitive edge [47]. These factors have led to growth in automated

banking such that more than half of banking transactions now take place via telephone,

ATM, or the Internet [47]. Internet banking is expected to grow strongly in the future, such

that banks that do not offer Internet services are likely to lose significant portions of their

customers [100]. Internet banking is growing fast in such Asian countries as South Korea,

Singapore, and Thailand [91].

B. Research Setting

This study used South Korean subjects responding to questions about online banking.

Banking is an appropriate test domain for a trust model because of the potential financial

risk to customers an online bank presents [98] and because of the need for online banks to

keep transactions and sensitive personal/financial information secure. Trust only makes a

difference in an environment of uncertainty and risk, and it is difficult for a consumer to

control or monitor an online vendor in order to eliminate risk [34]. Banking also makes a

good test because its online component is relatively new compared to offline banking and

because of the natural link between offline and online banking. South Korea makes an

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interesting place to test the model because of its rapid adoption of Internet. Besides, South

Koreans are processing about 3 banking transactions out of 10 through the online banking

system, or 30.9 % rate for online banking usage according to the Bank of Korea’s survey

data on September 2005. This indicates that South Koreans are aware of online banking

because most South Korean offline banks encourage customers to use online banking so

that they can focus on more profitable banking services.

C. Instrument Development

A questionnaire was designed based on the research model (Figure 3). Respondents

answered questions based on a five-point Likert-type scale ranging from “strongly

disagree” (=1) to “strongly agree” (=5). This study’s trust measure combines trust in the

bank itself, the bank’s services, and the bank’s tellers together so that the concept of

offline trust is not limited to a single object. (See Table 2 for items.) The first item reflects

the honesty/integrity of the offline bank and the second item reflects the bank’s

benevolence, in terms of how the bank’s services meet the consumer’s needs [25]. The

third item reflects general trustworthiness [84]. Thus, our measures cover two of the three

most commonly researched aspects of trust, benevolence and honesty/integrity, while not

covering competence, the third aspect [67, 71].

Flow is measured by the most applicable online aspects of the flow concept: consumer

enjoyment, concentration/attention, and perceived control [37, 55]. These are key aspects

of flow and follow the theory base on which the hypotheses were developed. They form a

unity in terms of being closely related to the psychological aspects of flow.

Customers who use the Internet for financial transactions have been known to feel

threatened by potential online risks [13, 86], such as possible loss of personal property or

identity. In light of the general information available about security for Internet-based

financial transactions, we adapted three structural assurance items from [71] to measure

perceived safety in the vendor’s bank in the midst of perceived online banking risks.

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Several factors determining website satisfaction have been identified, including web

design [62], content [85], user interface [94], navigation and information structure [69]. This

study adopts three measures representing online banking website satisfaction: information

quality, system quality, and overall satisfaction with a website (as noted in [69]). This study

measures perceived extent of use based on the items in [23]. Among them, two seem

most relevant to perceived extent of use of online banking – intention to reuse, and

frequency of use – in order to apply to an online banking case (see Table 2).

The instruments were translated from English into Korean by bi-lingual (Korean and

English) authors. To assure the translation was correct, the authors had 10 MIS doctoral

students review the translated instruments and suggest adjustments for subtle nuances in

Korean expression. Then, to obtain further translation equivalence, the authors compared

the Korean-translated instruments with the same instruments published in Korean in a top-

tier South Korean journal, to assure they would convey the original English meaning of the

instruments precisely. Translation equivalence from English to Korean language was in

this way accomplished in line with other cross-cultural research [77].

D. Pretest

To test the psychometric properties of the constructs [97], a questionnaire pretest was

given to 68 graduate students enrolled in classes administered by the study authors. The

respondents were volunteers and were not told the study’s objective. Respondents were

asked to visit their bank’s website to gauge the convenience and services it offered. The

convergent validity and unidimensionality of each construct was verified using a principal

component factor analysis for factors with eigenvalues above 1, using a varimax rotation.

Each item loaded on the intended construct and each Cronbach’s alpha exceeded 0.80.

The highest cross-loadings were in the 0.4 range. Since the exploratory factor analysis

results appear acceptable for a pretest, we decided to use these measurements (Table 2).

E. Respondents, Primary Data Analysis and Descriptive Statistics

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Respondents included 325 company workers, housewives and college students. 300 were

selected from the Seoul metropolitan area in South Korea, using a stratified quota

sampling method. Seoul was first divided into two parts-Southern area and Northern area

in order to consider economic status. Then five districts were selected from each group on

a random basis, totaling 10 sampling districts. 30 customers were chosen randomly from

each district, totaling 300 respondents. Another 25 respondents were MBA students

enrolled in the first author’s university. In order to assure the sample was representative,

we tried to obtain 165 males and 160 females. We also targeted 125 in the 20s age group,

110 in the 30s age group, and 90 in the 40s age group in order to cover those most

interested in online banking. The questionnaire was divided into two parts. The first asks

questions about offline banking experience, and the second about online banking

experience. Before respondents answered the questions, they were asked to select one

bank with which they did offline (but not online) business, and to visit its website to check

out its basic banking functions and services. Respondents then answered the

questionnaire. The entire exercise took approximately 20 minutes to complete. Among the

325 distributed questionnaires, 85 were not completed validly, and 41 were not returned,

resulting in 199 valid responses for a 61.2 % valid return rate. The demographic

characteristics of the 199 respondents are shown in Table 1.

IV. EMPIRICAL RESULTS

A. Confirmatory Factor Analysis

The research model has five constructs with interrelated dependence relationships or

causal paths, requiring a structural equation model (SEM) analysis [9, 41]. SEM analysis

requires constructs to be assessed rigorously by confirmatory factor analysis (CFA) [1, 29,

41, 93], to examine convergent and discriminant validity. CFA results were obtained using

software package LISREL 8.30.

(1) Sample Size:

19

Experts consider the minimum sample size for reliable SEM analysis to be from 100 [9], to

150 [3] to over 200 [10]. Because our research model is relatively simple, with only five

constructs, the sample size of 199 is considered adequate.

(2) Item Reliability:

Item reliability denotes the amount of variance in an item due to its underlying construct.

Table 2 shows that t-values for all of the standardized factor loadings of items are

significant at p<.01, indicating item reliability. Additional evidence was found using average

SMC (squared multiple correlation), which denotes the explanatory power of items related

to latent variables. SMC figures in Table 2 lie between .73 and .84, indicating item

reliability.

Table 1. Descriptive Statistics (n=199)

Construct Mean Standard Deviation

Trust in Offline Bank 3.13 .66

Flow 2.77 .95

Structural Assurance 3.05 1.02

Perceived Website Satisfaction 2.83 1.02

Perceived Extent of Use 3.03 .83

DemographicCharacteristics

SexMale Female

107 (54%) 92 (46%)

Job

Company Workers

Housewives College Students

109 (55%) 54 (27%) 36 (18%)

Age20-29 30-39 Over 40

46 (23%) 85 (43%) 68 (34%)

Online Banking Experience

Below 1 month including first-

time user1-12 months Over 1 year

113 (57%) 22 (11%) 64 (32%)

Table 2. CFA Results

20

Measurement Items for Each ConstructItem Reliability

Factor Loading

Std. Errors

Std.Loading t-value

Average SMCa

Cronbach’s alpha

Trust in Offline Bank (adapted from Doney and Cannon 1997 [items 1, 3]; Plank et al. 1999 [item 2])

.79 .91This bank keeps the promises it makes to me. 1.00 - .88 -

This bank’s services meet my needs. 1.06 .05 .94 19.62**

This bank’s teller is trustworthy. .98 .06 .86 16.76**

Flow (adapted from Ghani et al. 1991 [item 1]; Koufaris 2002 [items 2, 3])

.84 .94

During my visit to this online banking website, I found a lot of interesting content.

1.00 - .91 -

During my visit to this online banking website, my attention was focused on online banking activity.

1.02 .05 .92 21.06**

During my visit to this online banking website, I felt in control.

1.02 .05 .93 21.29**

Structural Assurance (adapted from McKnight et al. 2002)

.73 .88

This online banking website has enough safeguards to make me feel comfortable using it for my personal business.

1.00 - .88 -

I feel assured that the legal and technological structures of this online banking website adequately protect me from Internet problems.

.97 .07 .85 15.59**

In general, this online banking website is a robust and safe environment in which to transact business.

.95 .06 .84 15.21**

Perceived Website Satisfaction (adapted from McKinney et al. 2002)

.81 .92

I feel satisfied with the information quality offered by this online banking website.

1.00 - .91 -

I feel satisfied with the system quality by this online banking website.

.98 .05 .89 19.02**

After using this online banking website, I feel very satisfied.

.99 .05 .90 19.78**

Perceived Extent of Use (adapted from Devaraj et al. 2002)

.76 .85I intend to use the services offered by this online banking site again.

1.00 - .90 -

I intend to visit this online banking website as often as possible.

.93 .08 .84 12.13**

** p <0.01, a SMC = Squared Multiple Correlation

(3) Construct Reliability, Convergent Validity, and Average Variance Extracted (AVE):

21

Table 3 shows construct reliability and AVE figures. Reliability is a necessary condition for

evaluating convergent validity. Construct reliability estimates range from .86 to .94, and all

are greater than .70. The AVEs, which should meet a .50 standard, fall between .75

and .84, indicating convergent validity.

Table 3. Correlations, Construct Reliability, and Average Variance Extracted

Intercorrelations between Constructs

FlowStructural Assurance

Perceived Website

Satisfaction

Perceived Extent of

Use

Trust in Offline Bank

Flow 1.00

Structural Assurance .72 1.00

Perceived Website Satisfaction

.73 .79 1.00

Perceived Extent of Use

.58 .56 .52 1.00

Trust in Offline Bank .69 .79 .76 .69 1.00

Construct Reliability (>.70)

.94 .89 .93 .86 .92

Average Variance Extracted (>.50)

.84 .75 .81 .76 .80

(4) Discriminant Validity:

Since intercorrelations between constructs are relatively high (refer to Table 3), common

method bias may exist. In order to detect this, a discriminant validity test was performed in

accordance with [29], one of the more statistically rigorous methods of doing so. In this

test, the squared correlations between two constructs must be lower than the

corresponding AVE. Table 3 shows that the AVE figures, ranging from .75 to .84, all

exceed the squared correlations between the five constructs, the highest of which is .63,

confirming discriminant validity of the proposed constructs. Thus, the five constructs

possess adequate convergent and discriminant validity for further SEM analysis.

22

B. Structural Equation Modeling (SEM) Analysis

The SEM results depicted in Figure 4 show that all the fit indices are successfully met. For

example, value divided by degree of freedom is less than 3, and GFI is over .90, AGFI

over .80, NFI over .90, NNFI over .90, and SRMR below .05 [34, 41]. Other fit indices also

meet the theoretical threshold: CFI=.99, IFI=.99. This model, while very parsimonious,

explains a significant portion of the variance in perceived extent of use (R2 = .51),

perceived website satisfaction (R2 = .71), flow (R2 = .56), and structural assurance (R2

= .63). It can be concluded, then, that the proposed structural model is statistically sound.

The structural model SEM results are as follows (Figure 4). First, offline trust impacts

online banking constructs directly, with a significant effect on flow (.72, t=10.49**),

structural assurance (.57, t=7.18**), perceived website satisfaction (.27, t=2.88**) and

perceived extent of use (.64, t=5.03**). Second, structural assurance influences flow (.32,

t=4.38**) and perceived website satisfaction (.41, t=4.00**), but not extent of use. Third,

flow significantly influences perceived website satisfaction (.27, t=3.52**) and extent of use

(.26, t=2.60**). Finally, perceived satisfaction did not influence extent of use.

Flow

Trust

Perceived Website

Satisfaction

StructuralAssurance

PerceivedExtent of

Use

Offline Banking Online Banking

χ2 =89.042, df=67, GFI=0.94, AGFI=0.90, NFI=0.97, NNFI=0.99 RFI=0.95, IFI=0.99, CFI=0.99, SRMR=0.03, RMSEA=0.04

0.72**

0.32 **

n.s.

0.41 **

0.26 **

0.27 **

0.57 **

0.64 **

n.s.

0.27 **

** p < 0.01 (t>1.96)

Figure 4. Structural Equation Model Results

23

The off-to-on portion of these results can be summarized by stating that in support of H1-

H4, offline trust has a strong and positive influence on the online variables proposed. This

supports the existence of Type 2 (Offline-to-Online) TTP. We may therefore conclude that

Type 2 TTP exists and applies to real world banking e-commerce activities.

The questionnaire respondents include those who had never before used online banking

(first-time users) and those who had (experienced users). While the experienced users

reported based on online banking experience, the first-time users reported more based on

cue-based trust [24, 72], in that they supplemented their offline bank trust with cues from

the online website of the bank. To examine the differences between first-time users and

experienced users, we split the sample into first time (n= 111) and experienced (n=88) and

re-ran the model. Table 4 shows the model results split by first-time user data and

experienced user data. For first-time users, offline trust influences all four online constructs

significantly, whereas for experienced users, offline trust affects only three online

constructs significantly—flow, structural assurance, and perceived extent of use, but not

perceived website satisfaction. This likely means that since cue-based trust is replaced

with experience-based trust over time, the experienced users’ perceived website

satisfaction is not affected by offline trust itself. We also observe from Table 4 that the path

coefficients from offline trust to the online constructs for first-time users are significantly

greater (p<.01) than those for experienced users, except for perceived extent of use. This

finding conforms with theory about cue-based trust and experience-based trust, in that

first-time users are influenced more than experienced users by their trust in the brick-and-

mortar bank to form their site flow, structural assurance, and satisfaction impressions.

Experienced users know how online banking works, so they are less influenced by their

offline bank trust. However, offline trust had a greater effect on perceived extent of use

among experienced users, perhaps because these users have developed the skill set

needed to effectively use the product. Thus, they could more clearly apply their offline trust

to a projected use of the service.1 We project that over time with repeated interactions

using the online banking systems, experience-based trust will play a greater role than cue-

based trust for all the dependent variables.

1 We are indebted to an anonymous reviewer for this insight.

24

Table 4. Empirical results for first-time users and experienced users

First-time user model Experienced user modelIndexes of adjustment of the model χ2=98.152(p=0.007), df=67,

GFI=0.89, AGFI=0.83, NFI=0.92, NNFI=0.96 RFI=0.89, IFI=0.97, CFI=0.97, SRMR=0.04, RMSEA=0.06

χ2=82.983(p=0.090), df=67, GFI=0.89, AGFI=0.83, NFI=0.89, NNFI=0.97 RFI=0.85, IFI=0.98, CFI=0.98, SRMR=0.05, RMSEA=0.04

Flow:Structural Assurance

Perceived Website SatisfactionPerceived Extent of Use

R2

.33

.59

.72

.29

R2

.28

.32

.43

.46

TrustFlow 0.55** 0.34** a

TrustStructural Assurance 0.72** 0.40** a

Trust Perceived Website Satisfaction 0.54** n.s a

Trust Perceived Extent of Use 0.40* 0.58** a

Structural Assurance Flow n.s 0.36** a

Structural Assurance Perceived Website Satisfaction n.s 0.63** a

Structural Assurance Perceived Extent of Use n.s. n.s

Flow Perceived Website Satisfaction 0.25** n.s

Flow Perceived Extent of Use n.s 0.35** a

Perceived Website Satisfaction Perceived Extent of Use n.s n.s

** p <0.01 (t>1.96), * p <0.1 (t>1.282)a: t-tests showed significant (p<.01) differences for these coefficients between first-time and experienced groups, using the formula: t = (PC1-PC2)/[ Spooled x SQRT(1/N1+1/ N2)]; Spooled = SQRT{[( N1-1)/( N1+ N2 -2)] x SE1

2 +[( N2-1)/( N1+ N2 -2)] x SE22}; SE =Standard error of path in structural model; PC =Path Coefficient in

structural model

V. Study Limitations

25

This study captures primarily a cross-sectional view of model constructs. Thus, a

longitudinal study would be helpful. Because of the lack of longitudinal data, causality of

the model is not proven. Reverse linkages or bi-directional linkages among the constructs

are possible over time. For example, online satisfaction should lead to online trust over

time, just as Harris and Goode [42] found about satisfaction and trust in the offline world.

This constitutes a boundary condition for our model, in that the model works best when

consumers are relatively new to online banking, such as those in our sample (see Table

1). Sample size is another limitation, although the size is adequate for the tests conducted.

Note that while model fit degraded somewhat when the sample was split by experience

level, yet the RMSEA remained in an acceptable range. Although our scales displayed

acceptable psychometric properties, the items we used are adapted from (and subsets of)

the items used in other studies. Using alternative items, the results may vary somewhat

from ours. Generalizability of results is another weakness. The study results may be

different if the model were tested in other offline/online domains or in other cultures. For

comparison purposes, it is noteworthy that South Korea is regarded as a rapid adopter of

the Internet. The model results may also differ depending on whether the websites serve

an information-intensive or fulfillment-risk function. Our study’s results do not apply directly

to those website vendors who attract customers through sources other than their offline

business. Since offline trust cannot be used by these vendors to build online trust, they

should build trust through other methods, such as through institutional assurances,

website ease of use, and website design quality that signals to consumers the trustworthy

attributes of the web vendor [34, 70].

VI. Managerial Implications

Based on the empirical results, this study arrives at the following implications. First, the

TTP (trust transfer process) provides a unified view for understanding the effects of offline

trust on online perceptions of flow and structural assurance. The empirical results show

that consumer trust in an offline channel transfers rather easily to positive online channel

perceptions, suggesting that vendors can leverage their offline trust to produce online flow

and structural assurance. Hence, marketing strategies are best organized so that online

26

perceptions like these are considered. For example, marketing materials should

emphasize the good content and user control of the online system (to elevate flow

perceptions) and the safeguards and protections of the system, such as SSL /

sophisticated encryption (to elevate structural asurance). Marketing materials could subtly

connect these positive online system attributes to aspects of the quality offline service the

consumer already receives to increase the offline-to-online transference effect.

Second and related, offline trust is important in triggering positive online outcomes. Figure

4 shows that offline trust can trigger positive perceived website satisfaction and perceived

extent of use. After the dot com bubble burst in 1998, financial analysts understood the

importance of companies maintaining some offline activity. This confirms our proposed

TTP: offline trust enables or facilitates the transfer process across channels. The result

that offline trust positively influences these variables such as perceived website

satisfaction and perceived extent of use of the website indicates that offline trust can be

used as an enabling factor by which an online company that started from an offline

channel can attract customers and make them more loyal to its website. Then an online

company with strong offline trust can build up a high level of trust and reputation among

online customers for a certain period, eventually triggering other types of TTP, for

example, Type 3 (Online-to-Online) and Type 4 (Online-to-Offline).

Since offline trust has the impacts we found, several actions should be taken to increase

offline trust in the bank. a) Banks should use marketing campaigns to try to improve

perceptions about the reputation and size of the firm, because these have proven to

increase trust [25, 50]. b) Banks should improve actual and perceived offline customer

service, which should improve the benevolence and competence aspects of trust. c)

Customer service employees should be trained to be more likable, which has been shown

to influence offline trust [25]. d) Marketing should emphasize the values the bank shares

with customers [75].

Third, the results not supportive of two hypotheses—H8 and H10—also have an important

implication. Although structural assurance and perceived extent of use were significantly

correlated (r = .56**), structural assurance did not predict perceived extent of use. This is

27

largely because more powerful factors (i.e., offline trust and flow) outweighed the effects of

structural assurance. Similarly, perceived website satisfaction correlated with perceived

extent of use (r = .52**), but did not predict it because of the more dominant effects of flow

and offline trust. This may also be because in the initial phase, website satisfaction is

tentative and therefore is not heavily relied on as input for whether or not to use the online

banking site. Offline trust, developed and reinforced over time, was relied on instead.

These findings reinforce how important offline trust is to online outcomes.

Fourth, the questionnaire data confirmed the existence of Type 2 TTP, which was not

studied previously in an explicit manner. Offline trust was undeniably transferred to online

channels, in that it had a significant effect on online consumer perceptions.

Further, though offline trust was empirically proven here to be usually a starting point for

TTP initiator, we believe from Figure 1 that TTP can be triggered not only from offline trust,

but from online customer trust. TTP Types 3 and 4 in TTP are examples. As discussed in

the previous section, among the four cells in TTP, Type 4 is very rare to find in real e-

commerce applications. However, as e-commerce becomes omnipresent in business

world and the Internet-infrastructure is getting more advanced and high-speed, Type 4-

related businesses emerge in the market as a new trend in e-commerce to attract more

customers regardless of offline and online. For example, WASSADA

(http://www.wassada.com) is another typical Type 4-related company based in South

Korea, which started from a pure Internet company selling audio electronic items and

expanded into offline stores, successfully taking advantage of the high level of trust among

online customers. MISSHA (http://www.beautynet.co.kr), mentioned earlier, is a case of

another Type 4 business which is very successful and highly praised in various mass-

media in South Korea. The authors feel that Type 4-related successes will most likely be

found in those societies having high speed and broadband Internet infrastructure, and

showing a mature stage of e-commerce, like South Korea, Hong Kong, etc.

Further implications of the empirical results of this study include the following two issues.

Issue 1: The Role of Offline Trust in Determining a Customer’s Online Behavior

28

As already discussed, offline trust plays a crucial role in determining consumer online

behavior. In the case of online banking, offline trust directly affects four key related online

constructs. The flow customers experience on banking websites is especially influenced by

offline trust, as it has a 0.72 path coefficient. Customers’ website satisfaction increases

when offline trust is greater. Similarly, customers’ perceived extent of use is affected by

offline trust, with a 0.64 path coefficient. The latter is especially striking since neither

structural assurance nor perceived satisfaction affected perceived extent of use. Offline

trust also influences customers’ structural assurance beliefs about the safety and security

of the bank’s online website. Perceived website satisfaction is relatively less affected by

issues of offline trust than other online constructs. As noted in [69], many factors influence

perceived website satisfaction. Offline trust, therefore, is only part of the answer. Likewise,

many factors affect the adoption of Internet banking [39], which indicates a need for much

additional research. Internet marketing strategists have struggled to understand why

various strategies prove effective on some websites and ineffective on others. Obviously,

the transfer of offline trust to online channels should be given more attention by

researchers and marketers before online marketing strategies are developed. Research

should explore factors that facilitate transfer of offline trust to online website adoption.

Issue 2: How to secure trust in man-machine website interactions

One characteristic of the online channel is the tendency for customers to perform many

human-computer interactions (“click-and-see” activities). Websites offer a hypermedia

environment which is made up of text, images, voice, and animation, leading to an

enriched environment for human-machine interactions. Nevertheless, the issue is whether

consumers trust the information they get from the websites. Conventional wisdom is that

the more users understand how the information originates, the more online trust increases

[68]. Since websites provide a rather enriched hypermedia environment for customers,

using the websites would probably secure online trust to some extent. However, we still

need to turn to the importance of offline trust to understand how consumers feel secure

about a website. There have been no studies thus far to clearly argue that offline trust is

the key to predicting online behavior. Although a certain level of human-computer

interaction is necessary to navigate a website, the results of this study suggest that mere

29

management of the websites will not lead to the hoped-for results without the proper

degree of offline trust.

VII. CONCLUDING REMARKS

This study’s results show that trust in an offline bank influences key factors in an online

bank environment. Specifically, this paper contributes by showing that trust in an offline

bank influences structural assurance, flow, consumer satisfaction and extent of use of the

bank’s online system. In an era in which many companies are turning to the Internet as a

way to expand their business, this study indicates that firms can leverage customer trust in

their brick-and-mortar business to provide a similar customer-satisfying product line on the

Internet. The extent to which offline trust affected online perceptions suggests that trust in

the offline business may be a key factor of online business success.

There are further research issues that need to be addressed, including the link between

offline trust in the bank and trust in its online banking system. This study addressed

offline/online banking. Other types of businesses should be studied to see the extent to

which TTP works in other domains besides banking. Also, it is important to understand the

psychological mechanisms by which the offline to online trust transfer process operates. It

is hoped that this study will provide a stepping stone to building more effective marketing

strategies for e-commerce.

ACKNOWLEDGMENT

This paper was supported by Samsung Research Fund, Sungkyunkwan University, 2003.

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