Article on Online Shopping

8/11/2019 Article on Online Shopping

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62 Int. J. Internet Marketing and Advertising, Vol. 1, No. 1, 2004

Copyright 2004 Inderscience Enterprises Ltd.

Understanding online shopping behaviour using atransaction cost economics approach

Thompson S.H. Teo*

Department of Decision Sciences, School of Business,

National University of Singapore, 1 Business Link, Singapore 117592

Fax: (65) 6779-2621 E-mail: [email protected]

*Corresponding author

Pien Wang and Chang Hong Leong

Department of Business Policy, School of Business,

National University of Singapore, 1 Business Link, Singapore 117592

Fax: (65) 6779-5059 E-mail: [email protected]

Abstract: Building upon Transaction Cost Economics (TCE) theory, this paperhypothesises that consumers transaction cost of online shopping is affected bysix antecedents: product uncertainty, behavioural uncertainty, convenience,economic utility, dependability, and asset specificity. In turn, transaction costhas a negative relationship with consumers willingness to buy online. We testthe model using data gathered from the USA and China. The results show thatbehavioural uncertainty and asset specificity are positively related totransaction cost whilst convenience and economic utility are negatively relatedto transaction cost among US consumers and those in China. Dependability is

negatively related to transaction cost among US consumers but not consumersin China. Transaction cost is positively related to willingness to buy onlineamong US consumers and those in China. US consumers perceive less productuncertainty, behavioural uncertainty, asset specificity, dependability, as well asmore convenience and economic utility than consumers in China. Theimplications of the results are discussed.

Keywords: internet; online; shopping; transaction cost; cross-culture; China;USA.

Referenceto this paper should be made as follows: Teo, T.S.H., Wang, P. andLeong, H.C. (2004) Understanding online shopping behaviour using atransaction cost economics approach, Int. J. Internet Marketing andAdvertising, Vol. 1, No. 1, pp.6284.

Biographical notes: Thompson S.H. Teo is an Associate Professor andInformation Systems Area Coordinator in the Department of Decision Sciencesat the National University of Singapore. He received his PhD from theUniversity of Pittsburgh. His current research interests include IS planning andmanagement, IT adoption and diffusion, IT performance measurement andelectronic commerce. He has edited three books and published more than 65papers in a variety of international journals.


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Understanding online shopping behaviour 63

Pien Wang is an Assistant Professor in the Department of Business Policy at

the National University of Singapore Business School. Her current researchinterests include knowledge transfer of MNC parents to foreign subsidiaries,foreign direct investment in China, Sino-foreign joint ventures, and electronic commerce in China. She has published many papers in international refereedjournals.

Chang Hong Leong graduated with BBA (Hons) from the National Universityof Singapore.

1 Introduction

Despite the increasing popularity of e-commerce in recent years, studies [1,2] haveshown that internet users feel that it is difficult to enjoy online shopping. The UCLA

Internet Report [2] revealed that barriers to online shopping include the loss of privacy of

personal data, difficulty in assessment of product, difficulty of returning and exchanging

products, shipping charges and discomfort with seller anonymity. Hoffman, Novak and

Chatterjee [3] suggested that a fundamental lack of faith between most online stores and

consumers has prevented people from shopping online or even providing information to

web providers in exchange for access to information. However, little empirical research

has examined the relationships between facilitators and inhibitors of online shopping and

the perceived transaction cost of consumers. Further, there is also scarce research on the

impact of consumers perceived transaction cost on their willingness to buy online.

As the trend towards globalisation intensifies, firms need to target their products at

markets that span national boundaries. Firms engaging in e-commerce must study andunderstand factors affecting the online purchasing behaviour of consumers of different

countries. However, there is relatively little research investigating the factors affecting

consumers online buying behaviour across nations.

The number of internet users has increased exponentially around the world. By

September 2001, there were 143 million US internet users, which comprised 54% of the

US population. Further, among internet users, 39% were making online purchases and

36% were using the internet to search for product and service information [4].

According to China Internet Network Information Center (CNNIC) [5], China has an

online population of nearly 16.9 million and has the fastest internet user growth rate in

the world (doubling the number of users every six months). Despite the growing internet

penetration in China, e-commerce activity remains low as only 10% of internet users had

completed a transaction online [6]. In order for e-commerce to realise its full potential in

China, several issues need to be addressed e.g., security concerns [7], low credit card

penetration [8], and regulatory and legislative uncertainty [9,10].

Building upon the Transaction Cost Economics theory [1113], the objective of our

study is to examine the antecedents of transaction cost and its impact on consumers

willingness to buy online. In addition, we cross-validate our model across two nations:

the USA and China. The USA is selected for testing our model because it is the country

with the highest internet penetration in the world. China is selected to test the

applicability of our model in an emerging economy. As compared to the USA, the

internet penetration rate in China is lower. However, China has experienced tremendous


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64 T.S.H. Teo, P. Wang and H.C. Leong

growth in the internet scene over the past few years. The replication enables us to assess

the applicability of our model across national borders, and at the same time examinescross-cultural similarities and differences in antecedents of transaction cost.

2 Literature review

Williamson [1113] developed his version of Transaction Cost Economics (TCE) based

on the interplay between the three key dimensions of transaction (i.e., uncertainty, asset

specificity and transaction frequency) and the two main assumptions of human behaviour

(i.e., bounded rationality and opportunism). TCE theoretically explains why a transaction

subject chooses a particular form of transaction instead of others [14,15]. Steinfield and

Whitten [16] extended the TCE literature and suggested that TCE can be used to explain

the attractiveness of web commerce for buyers (including institutional and individualconsumers). Since purchasing from online stores can be considered as a choice between

the internet and traditional stores, it is reasonable to assume that consumers will go with

the channel that has the lower transaction cost [17]. Therefore, TCE becomes a viable

theory for explaining the internet shopping decision of consumers.

In the area of e-commerce, researchers have conducted studies using TCE to explain

firm-level and individual-level issues. Steinfield and Whitten [16] showed that using

transaction cost and competitive advantage approaches, supplemented by perspectives

from research on social networks and trust, it is possible to develop locally sensitive web

strategies for businesses in a given community. Benjamin and Wigand [18] examined

electronic markets and the industry value chain from the perspective of transactions and

transaction costs. They also suggested that transaction cost savings might be achieved

through the use of information technology within the entire market hierarchy and

resulting market or industry value chain. Liang and Huang [17] developed a model (with

two antecedents of transaction costs uncertainty and asset specificity) based on TCE

theory to explain the acquisition decision of consumers. More specifically, their study

examines what products are more suitable for marketing electronically and why. In some

ways, this study extends Liang and Huangs work by examining more antecedents

(six instead of two) of transaction costs and testing the model across two countries, i.e.,

the USA and China. The results would also give an indication of the relative importance

of various antecedents to transaction costs.

2.1 Antecedents of transaction costs

Asset specificity according to Williamson [11], three critical dimensions for

characterising transactions are:

1 asset specificity

2 uncertainty

3 transaction frequency.

Asset specificityrefers to durable investments that are undertaken in support of particular

transactions; the opportunity cost of investment is lower in best alternative uses or by

alternative users [12]. In other words, items that are unspecialised among users pose few


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hazards, since buyers in these circumstances can easily turn to alternative sources and

suppliers can sell output intended for one buyer to other buyers without difficulty.

Uncertainty uncertainty refers to the cost associated with the unexpected outcome

and asymmetry of information [13]. Therefore, a higher level of uncertainty generally

implies a higher transaction cost because both parties in the transaction will spend more

time and effort in monitoring the transaction process.

Transaction frequency transaction frequency refers to the frequency with which

transactions recur. According to Williamson [13], higher levels of transaction frequency

provide an incentive for firms to employ hierarchical governance structures, as it will be

easier for these structures to recover large transactions of a recurring kind. However,

TCE researchers have been largely unsuccessful in confirming the hypothesised positive

relationship between transaction frequency and hierarchical governance [19]. Further,

John and Weitz [20] considered transaction frequency as a dichotomous phenomenon

(distribution between one-shot exchange and recurrent exchange) and control transactionfrequency by examining only recurring exchanges. Consequently, we decided to omit this

variable from our model.

Trust trust has been incorporated into the TCE literature by many researchers

[21,22].As Williamson [13] asserts Some individuals are opportunistic some of the time

and that differential trustworthiness is rarely transparent ex ante. As a consequence,

ex ante screening efforts are made and ex post safeguards are created [p.62]. More

relevantly is differential trustworthiness, i.e., parties differing in their moral character

[23]. If differential trustworthiness is assumed, trust can be hypothesised to be a variable

that is likely to reduce transaction costs.

In e-commerce, online stores depend on an electronic storefront to act on their behalf

and there are fewer assurances for consumers that the online store will stay in business

for some time. In traditional contexts, a physical stores trust has been affected by thesellers willingness to make investment specific to a buying firm such as investments in

physical buildings, facilities, and personnel [24]. Therefore, online retailers face a

situation in which consumer trust might be expected to be inherently low.

Consumers interests Williamson [12] noted that the choice of transaction

governance depended on a number of factors, including asset specificity, parties interest

and uncertainty in the transaction. Wigand [25] postulated that parties interest in the

transaction process could be an important factor in estimating transaction costs arising

from exchange between different parties. The extent to which consumers interests are

satisfied in the transaction will affect their perceived transaction costs and their

acceptance of online buying.

In their study of shopping motives for mail catalogue shopping, Eastlick and Feinberg

[26] defined convenience as the advantages (i.e. saving time and effort, shop anytime)

that buyers enjoy through mail catalogue shopping. Online buying, as an alternative to

physical shopping, offers more convenience to consumers because they can save time and

effort in searching for product information. Other sources of convenience may include

better search engines and applications, extensive product reviews and product samples

(e.g., book chapters and CD audio clips). Smith et al.[27] suggested that retailers who

make it easier to find and evaluate products may be able to charge a price premium to

time sensitive consumers.

In a similar vein, Eastlick and Feinberg [26] defined economic utility as comparison-

shopping for competitive prices and bargains. With the emergence of the internet, online


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stores are able to build their virtual website with advanced information technologies. For

example, most online stores have search engines that allow consumers to do comparison-shopping efficiently and effectively.

3 Research model and hypotheses

The research model (Figure 1) suggests that online shoppers incur two types of

transaction cost:

1 searching costs which refer to the costs incurred by buyers in searching for

information about online products and stores

2 monitoring costs which refer to costs incurred by buyers in ensuring that the terms of

the contract have been met.

Figure 1 The research model

Productuncertainty

Behavioraluncertainty

Convenience

EconomicUtility

AssetSpecificity

TransactionCost

Willingnessto Buy

H2a(+)

H3a(-)

H4a(-)

H6a(+)

H7a -

Dependability

H5a(-)

H1a +


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The factors postulated to affect the perceived transaction cost of US consumers and those

in China include: product uncertainty of online stores, behavioural uncertainty of online

stores, consumers interests, economic utility, trust, and asset specificity.

3.1 Uncertainty

Uncertainty arises from the difficulty in predicting the actions of the other party in the

transaction due to opportunism, bounded rationality, and asymmetry of information

[12,13]. A high level of uncertainty is likely to increase transaction cost because both

parties in the transaction spend more time and effort in searching for products and vendor

related information as well as in monitoring the transaction process. In this study, we

examine two kinds of uncertainty of online buying: product uncertainty [28] and

behavioural uncertainty of online stores [26].

Product uncertainty product uncertainty refers to the difficulties in ascertaining thequality of purchased products. Prior to or upon ordering, consumers are likely to wonder

if purchased products will meet their expectation after purchasing. When consumers shop

physically, they can examine a product and then decide whether they will take it home. In

the case of online shopping, they rely on the quality examination that online stores

conduct for them. The performance uncertainty of products bought online is one of the

consumers major concerns [29]. This product uncertainty increases transaction cost.

Therefore, we postulate a positive relationship between product uncertainty and

transaction cost.

H1a There is a positive relationship between product uncertainty andtransaction cost

Consumers with experiential orientation (need to examine merchandise physically before

purchasing) experience high product uncertainty with online stores because they are

unable to examine online products before purchasing. According to Cheskin Research

[30], mainland Chinese consumers have a higher experiential orientation as compared to

Chinese consumers residing in the USA. Therefore, we hypothesise that US consumers

will perceive lower product uncertainty than consumers in China.

H1b US consumers perceive lower product uncertainty than those in China

Behavioural uncertainty of online stores similar to Stump and Heides [31] definition

of performance ambiguity, behavioural uncertainty of online stores refers to the inherent

difficulties faced by buyers in accurately evaluating the contractual performance of

online stores. Due to the opportunistic inclination of the transacting parties, behavioural

uncertainty arises within the context of the exchange itself [20]. Consumers are worried

about false claims by online stores as well as poor after-sales service. This increases

transaction cost as consumers spend more time searching for suitable stores and

monitoring their transactions. It follows that:


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H2a There is a positive relationship between behavioural uncertainty and

transaction cost

According to CNNIC [32], a significant number of consumers in China feel that quality

of products, after-sales service, and the lack of guarantee by vendors are primary

obstacles to online buying. Conversely, the USA has an efficient IT and logistic

infrastructure system, and most online stores such as Amazon.com, guarantee product

quality and provide after-sales service. More importantly, US consumers can exchange or

return their purchases if they are unsatisfied. Therefore, we postulate that US consumers

will perceive lower behavioural uncertainty than those in China.

H2b US consumers perceive lower behavioural uncertainty than consumers inChina

3.2 Consumers interest

Williamson [12] noted that the choice of transaction governance also depended on

parties interest in the transaction. Wigand [25] suggested that the extent to which

consumers interests are satisfied in the transaction would affect their perceived

transaction cost and their acceptance of electronic channels. In this study, we examine

two types of consumers interest: convenience and economic utility.

Convenience similar to the study by Eastlick and Feinberg [26] on motives for mail

catalogue shopping, we define convenience as the advantages (i.e., saving time and

effort, shop anytime) that buyers enjoy through online buying. Online buying, as an

alternative to physical shopping, offers more convenience to consumers because they can

save time and effort in searching for product information. In addition, consumers can alsobuy products from online stores at any time. Therefore, we hypothesise a negative

relationship between convenience and transaction cost.

H3a There is a negative relationship between convenience and transaction cost

According to the CNNIC [32], consumers in China consider saving time as one of the

main benefits of online shopping because they can search for product information

efficiently and effectively. In a similar vein, Greenfield Online [33] reveals that online

shopping is preferred over in-store shopping by some internet users because of its

convenience and time saving. These findings suggest that consumers who value

convenience are more likely to buy on the web. Therefore, we postulate that there is no

difference in the convenience of online shopping between US consumers and those in

China.

H3b There is no difference in the convenience of online shopping between USconsumers and those in China

Economic utility following the definition of Eastlick and Feinberg [26], economic

utility refers to the capability of online stores in providing comparison-shopping for

competitive prices and bargains. Previous research suggests that the internet increases


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price comparisons and intensifies competition. Since the internet facilitates ease of search

and price comparisons which lowers transaction cost, we hypothesise that:

H4a There is a negative relationship between economic utility and transactioncost

According to the CNNIC [32], lower cost is one of the main reasons why consumers in

China buy online. Wigand [25] suggests that electronic networks (e.g., the EasySabre

airline reservation system) connect different buyers and sellers through the internet and

provide some tools for searching the data. They help buyers to evaluate the offerings of

various suppliers quickly, conveniently and inexpensively. As a result, the number of

alternatives increases and the quality of the alternative ultimately selected improves,

whilst the cost of the selection process decreases. Therefore, we propose that there is no

difference in the economic utility of online shopping among US consumers andconsumers in China.

H4b There is no difference in the economic utility of online shopping betweenUS consumers and those in China

3.3 Trust

Mayer, Davis and Schoorman [34] defined trust as the willingness of a party to be

vulnerable to the actions of another party based on the expectation that the other will

perform a particular action important to the trustor, irrespective of the ability to monitor

or control that other party [p.4]. Jarvenpaa, Tractinsky and Vitale [35] defined trust in

the internet store as an online consumers willingness to rely on the online store and totake action in circumstances where such action makes the former vulnerable to the online

store. Quelch and Klein [36] predicted that trust is an important factor in stimulating

online purchasing in the early stages of internet development. Previous studies [30,37]

found widespread distrust among consumers about internet-based merchants. In this

study, one component of trust, dependability, is examined for its impact on transaction

cost.

Dependability dependability refers to the ability of the seller to provide the buyer

with outcomes that match what the former has said or promised [38].In the context of

online buying, consumers rely on online stores to perform many activities in the

transaction process such as examining product quality and providing after-sale services.

If consumers perceive that online stores are less dependable or not trustworthy, they will

spend more time and effort in monitoring their orders, and their perceived transactioncost will increase. It follows that:

H5a There is a negative relationship between dependability of online stores andtransaction cost

The findings of a study conducted by Cheskin Research [30] revealed that the mainland

Chinese consumers, as compared to the Chinese residing in North America, have less

trust in online stores that have no physical presence. This finding can be explained by the

fact that e-commerce is still in the infancy stage in China and the business-to-consumer


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(B2C) market has not achieved the critical mass large enough to induce high trust and

confidence in online consumers. Therefore, we postulate that US consumers will perceivehigher dependability of online stores than consumers in China.

H5b US consumers perceive higher dependability of online stores thanconsumers in China

3.4 Asset specificity

Asset specificity refers to durable investments that are undertaken in support of particular

transactions; the opportunity cost of investment is lower in best alternative uses or by

alternative users [12]. Our model includes two types of asset specificity: physical asset

specificity and human asset specificity. Physical asset specificity refers to investment in

special equipment such as personal computers and modems for the purpose of onlinepurchasing. Human asset specificity refers to investment in time and effort to accumulate

online purchasing experience [29]. These investments increase transactions costs. It

follows that:

H6a There is a positive relationship between asset specificity and transactioncost

In developed nations, online consumers investment in physical asset specificity is low

because either computers are easily accessible in schools and workplaces, or the

investment in hardware and software required for online purchasing represents a

relatively small proportion of their income or savings. However, consumers in

developing countries such as China still have to invest a significant amount of time andmoney to engage in online transactions. For instance, they pay a high connection fee to

internet service providers and also buy computer equipment that may be costly. They also

have to invest time in human asset specificity because they do not possess relevant

computer and internet skills. In addition, the internet penetration rate and computer usage

in China are relatively low as compared to the USA. Therefore, the majority of

consumers in China may have to invest more money, time and effort to acquire the

necessary computer and internet skills to engage in online buying as compared to US

consumers. It follows that:

H6b US consumers perceive lower asset specificity than those in China

3.5 Transaction cost and willingness to buy online

Firms choose transactions that economise on transaction cost. In the context of online

buying, some consumers adopt online shopping because it reduces the time spent

searching for product information. Subsequently, the perceived transaction cost of online

buying decreases. On the other hand, other consumers refuse to adopt online shopping

because they need to spend more time monitoring online stores to ensure that their orders

are processed as promised. As such, these consumers perceive higher transaction cost of

online shopping. It follows that:


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H7a There is a negative relationship between transaction cost and willingness tobuy

Due to lower product uncertainty, lower behavioural uncertainty, lower asset specificity

and higher dependability, US consumers will perceive less transaction cost than

consumers in China. In addition, US consumers might spend less time searching for

online products and monitoring their online purchases because of better connection speed

and IT infrastructure. It follows that:

H7b US consumers perceive lower transaction cost than those in China

4 Method

4.1 Data collection

We used a method similar to the snowballing sampling technique [39] to reach potential

respondents. E-mails (randomly sampled from various websites) were sent to potential

respondents with a short note inviting them to respond to the survey questionnaire.

Follow-up e-mails were sent to non-respondents to increase the number of responses.

Respondents accessed the introduction page to the online survey from three possible

mirror sites. By answering the question Select the country you are from, respondents in

China and the USA were directed to the different URLs where the former would answer

the Mandarin version of the questionnaire and the latter would answer the English

version. Note that potential respondents are any internet users as we are not targeting a

specific group of users.

4.2 Instrument

The English version of the questionnaire was translated into Mandarin by a research

assistant proficient in both English and Mandarin. The translated Mandarin questionnaire

was further verified by two of the three authors of this paper who are also proficient in

both English and Mandarin.

Pre-testing of the English questionnaire was carried out on 30 random internet users

(19 males and 11 females) to test for the comprehensibility of questionnaire items.

Similarly, the Mandarin questionnaire was pre-tested on ten Chinese MBA students.

Exploratory factor analysis was conducted to ensure the proper loading of indicators into

priori constructs, and to ascertain the need for additional questions to replace those of

relatively low reliability.

In order to attract more respondents to participate in the online survey, a lucky draw

with prizes such as Amazon.coms shopping certificates, web cam, and zip drive was

offered. The questionnaire focused on the respondents attitude towards online buying,

demographics, and internet usage. In answering questions concerning attitudes towards

online buying, the respondents were asked to choose a particular product category as their

frame of reference. With the use of javascript, they were reminded of their frame of

reference at the beginning of each question. Respondents were also alerted about

incomplete responses, if any, when he or she clicks on the submit button.


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All scales employed in the questionnaire were seven-point Likert-type scales

measured on strongly disagree (=1) to strongly agree (=7). Product uncertainty wasassessed by four items measuring the reliability of online products [17]. Behaviour

uncertainty of online stores was measured by four items adapted from Eastlick and

Feinberg [26]. These items measured the extent to which it was difficult for consumers to

return or exchange products purchased online, make after sales inquiry online, and obtain

after sales service. Convenience was measured by a four-item scale that assessed the

extent to which online shopping allowed consumers to search for product information in

least time and at any time. Economic utility was assessed by a four-item scale that

measured the extent to which online shopping made it easier for consumer to do

comparison shopping. The items for behaviour uncertainty, convenience, and economic

utility were adapted from Eastlick and Feinberg [26]. Dependability was measured by

three items assessing the extent to which online stores fulfill the promise made. The three

items were adapted from Swan et al.[38].Asset specificity was measured by four items adapted from Joshi and Stump [40].

These four items were related to time spent on learning internet skills, and money and

time committed to purchasing hardware and software for the purpose of online shopping.

Transaction cost was measured by a 7-item scale related to time spent on searching for

information about online stores, examining online products, and monitoring online stores

for product delivery. The seven items were adapted from Liang and Huang [17],

Dahlstrom [41], and Stump and Heide [31]. Willingness to buy was measured by three

items assessing the likelihood and willingness of consumers to purchase online. These

items were adapted from Dodds, Monroe and Grewal [42].

5 Results

5.1 Demographic profile

In total, 1059 and 1021 people responded to the US and Mandarin versions of the survey

respectively. Respondents who were not natives of the USA and China were eliminated.

In order to prevent bias in the results of the cross-national validation due to age,

education, and occupation heterogeneity of the two samples, we further truncated both

samples such that the age, education and occupation of the two groups were similar. This

resulted in the sample of 658 respondents and 660 respondents for the US and China

groups respectively (Table 1).


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Table 1 Demographic profile

USA China Total

.05

40-49 years old 50 49 99

Age

>=50 years old 37 24 61

High school andbelow

61 74 135

2= 7.723

College 303 278 581 df= 4

MS 249 256 505 p> .05

PhD 41 52 93

Education

Others 4 - 4

Student 331 377 708 2= 5.47

IT-related 46 46 92 df= 2

Occupation

Non IT-related 271 235 506 p> .05

5.2 Data analysis

The measurement and structural models for structural equation modelling were analysed

using Amos 4.0.

Measurement model confirmatory factor analysis (CFA) was used to test themeasurement model in which observed elements defined constructs or latent variables.

We used the US group to establish the construct validity and reliability of the baseline

models scales. The modification indices were used as a guide, substantiated by

theoretical evidence, to obtain a better model fit. A total of 11 items were excluded from

the fitted model. The confirmatory factor model provided acceptable fit to the data

(2= 1307.83, df = 455, p


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Table 2 Coefficients and composite construct reliability for the measurement model

US China

ConstructStandardised

coeff.Critical

ratio ReliabilityStandardised

coeff.Critical

ratio Reliability

Product Uncertainty 0.95 0.89

PU1 0.83a - 0.77a -

PU2 0.89 54.41* 0.78 34.38*

PU3 0.98 45.09* 0.88 28.25*

PU4 0.92 40.92* 0.84 27.26*

Behavioural Uncertainty 0.91 0.85

BU1 0.90a - 0.70a -

BU2 0.80 34.84* 0.78 20.92*

BU3 0.97 49.79* 0.79 21.99*

BU4 0.74 30.44* 0.79 21.10*Convenience 0.93 0.96

CON1 0.85a - 0.91a -

CON2 0.88 60.26* 0.93 69.40*

CON3 0.91 39.59* 0.94 52.95*

CON4 0.89 38.16* 0.90 46.79*

Economic Utility 0.92 0.92

ECU1 0.86a - 0.88a -

ECU2 0.82 33.66* 0.88 39.09*

ECU3 0.90 39.06* 0.88 38.95*

ECU4 0.86 35.99* 0.80 32.55*

Dependability 0.91 0.80

DEP1 0.82a - 0.57a -

DEP2 0.98 37.58* 0.94 16.10*DEP3 0.81 31.70* 0.74 17.44*

Asset Specificity 0.93 0.95

AS3 0.70a - 0.88a -

AS4 0.88 33.75* 0.87 47.83*

AS5 0.96 30.71* 0.95 48.04*

AS6 0.95 30.45* 0.94 47.74*

Transaction Cost 0.94 0.92

TC1 0.76a - 0.73a -

TC2 0.63 27.88* 0.68 27.29*

TC3 0.73 32.93* 0.76 27.80*

TC4 0.91 31.58* 0.86 27.56*

TC5 0.93 32.73* 0.94 30.35*

TC6 0.91 32.25* 0.93 30.07*TC7 0.89 31.29* 0.65 20.65*

Willingness to Buy 0.96 0.96

WB1 0.96a - 0.93a -

WB2 0.96 69.70* 0.95 58.61*

WB3 0.93 61.66* 0.93 54.04*

a Loading is set to 1 to fix construct variance, hence no critical ratio is available

* p< 0.01


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We cross-validated the CFA results of the US group with the China group in order to

ensure that the construct validity and reliability of the models scales were established.

The confirmatory factor analysis indicated acceptable fit to the data (2= 984.39, df =

455, GFI = 0.92, AGFI = 0.90, NFI = 0.96, Normed 2= 2.16, RMSEA=0.04). The chi-

square statistics was significant at p=0.01 level for both groups. However, chi-square

statistics are particularly sensitive to large sample sizes [45,46]. Thus other fit indices

are more indicative of the models fit. These indices were within the recommended range

for both the US and China groups. For GFI and CFI, values of .9 and above indicate a

reasonable fit of the proposed model [46]. The values for RMSEA also suggest

reasonable fit (


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grounds that the chi-square difference statistic is commonly rejected when sample sizes

are large [48]. In addition, the results also showed that change in CFI value was less than0.01 for the nested models whilst the RMSEA values remained constant. This indicated

that factor loadings of the measurement model were invariant across the two national

samples.

Structural model After establishing that the measurement model was the same

across the two groups, SEM was then carried out on the structural model (i.e., Model A)

that allowed both groups (US and China) to be estimated simultaneously.

The group analysis tests whether hypotheses H1aH7a are valid for both groups. As

shown in Table 4, the results indicated that the US and China data set fit acceptably.

Table 4 also show the parameter estimates for the US and China data set.

Table 4 Structural model results

USA China UnconstrainedModel A

ConstrainedModel B

PUTCa -0.077** 0.053 0.074**

BUTCb 0.028** 0.168** 0.217**

COTCc -0.204* -0.27** -0.178**

ECUTCd -0.197** -0.154** -0.009

DETCe -0.423** 0.009 -0.099

ASTCf 0.178** 0.146** -0.334**

TCWBg -0.602** -1.248** -0.698**

2 1307.83 1116.16 2507.18 2577.44

Df 455 455 912 935

P 0 0 0 0

2/ df 2.87 2.45 2.75 2.76

GFI 0.89 0.91 0.90 0.90

AGFI 0.87 0.89 0.87 0.87

NFI 0.94 0.95 0.94 0.94

TLI 0.95 0.97 0.96 0.96

CFI 0.96 0.97 0.96 0.96

RMSEA 0.05 0.05 0.04 0.04

a PUTC indicates product uncertainty impacts on transaction cost

b BUTC indicates behavioural uncertainty impacts on transaction costc COTC indicates convenience impacts on transaction costd ECUTC indicates economic utility impacts on transaction coste DETC indicates dependability impacts on transaction costf ASTC indicates asset specificity impacts on transaction costg TCWB indicates transaction cost impact on willingness to buy

* p< .05, **p< .01


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In addition to the fit measures, the model also accounted for a relatively large proportion

of variance in the dependent variables. The antecedents explained 47% and 69% of the

variance for the US and China samples respectively. In addition, transaction cost

explained 24% and 38% of the variance in willingness to buy for the US and China

samples respectively.

The next step to assess group similarity was to test whether the path coefficients of

the model were the same in both groups [47]. All paths were constrained to equality. The

results of this test are presented in the rightmost column of Table 4. The fit indices were

generally in the recommended range. The difference in the values between Model A and

Model B was 70.26, with 23 degrees of freedom. The comparison of two models

indicated that path differences existed across groups (p < .01).

We also conducted independent sample t-tests to examine differences in perceived

antecedents of transaction cost and transaction cost among US and consumers and those

in China. Table 5 summarises the means, standard deviations and t-tests for each variableacross the two nations.

Table 5 Comparison of means of exogenous and endogenous between US and China groups

Construct Group N Mean Std. Dev. t-value

PU USA 658 4.68 1.68 -7.54**

China 660 5.33 1.45

BU USA 658 4.90 1.32 -7.31**

China 660 5.42 1.30

CO USA 658 4.98 1.83 12.78**

China 660 3.61 2.06

ECU USA 658 4.83 1.84 13.33**

China 660 3.50 1.78

DE USA 658 3.96 1.10 -2.03*

China 660 4.08 1.07

AS USA 658 2.81 1.53 -15.36**

China 660 4.33 2.03

TC USA 658 3.45 1.40 -14.50**

China 660 4.68 1.67

* p< .05, **p< .01

5.3 Hypothesis testing

The relationship between product uncertainty and transaction cost was significant and

negative (in the opposite direction to H1a) for US consumers and not significant for

consumers in China. Therefore, H1a is not supported. US consumers perceived lower

product uncertainty than those in China, lending support to H1b.

The relationship between behavioural uncertainty and transaction cost was

significantly positive for US and consumers and those in China, thereby lending support

for H2a. H2b, which suggested that US consumers exhibit lower behavioural uncertainty

than those in China, was also supported.


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Convenience was found to have a significant negative relationship with transaction

cost for US consumers and consumers in China. Thus H3a was supported. Contrary toH3b, US consumers perceived more convenience than those in China. Hence H3b was

not supported.

The results indicated that economic utility was significantly and negatively related to

transaction cost for US consumers and those in China. Thus H4a was supported. Contrary

to H4b, US consumers perceived higher utility than consumers in China. Hence H4b is

not supported.

The relationship between dependability and transaction cost was significantly

negative for the US sample but not significant for the China sample. Thus H5a is partially

supported. Consumers in China perceived more dependability of online stores than US

consumers. Hence H5b is not supported.

Asset specificity was significantly and positively related to transaction cost for both

sets of consumers. Thus H6a was supported. In addition, US consumers perceive lowerasset specificity than consumers in China, thereby supporting H6b.

The relationship between transaction cost and willingness to buy was significantly

negative for the US and China samples, thereby lending support to H7a. H7b, which

suggested that US consumers perceive lower transaction cost than those in China was

also supported.

6 Discussion

Many researchers [18,49] have used TCE to explain the rise of global electronic markets

and the cost-savings afforded by network-based communication. However, few e.g. [17]

have conducted empirical studies using TCE to explain the acquisition decision of

consumers in e-commerce. As such, one contribution of our paper is the development and

empirical testing of a consumer choice model based on TCE to examine consumer online

buying behaviour.

Although the globalisation of consumer markets is not a recent phenomenon, research

that provides a better understanding of the implications of global markets is still

lacking [50]. Thus we are among the first researchers to examine the applicability of the

TCE model in the online buying behaviour of both US consumers and consumers in

China. We also contribute significantly to the cross-national consumer research by

examining the differences and similarities of antecedents of transaction cost and

perceived transaction cost across the two nations that are at different stages of economic

and technological development.

The results largely support our model and hypotheses in both the US and China

samples. Our results show that behavioural uncertainty and asset specificity are positivelyrelated to transaction cost among both sets of consumers. In contrast, convenience and

economic utility are negatively related to transaction cost among both sets of consumers.

Dependability is negatively related to transaction cost among US consumers but not

those in China. Transaction cost is negatively related to more willingness to buy online

among US consumers and consumers in China. The findings also reveal that US

consumers perceived less product uncertainty, behavioural uncertainty, asset specificity,

dependability, as well as more convenience and economic utility than those in China.

The findings of Cheskin Research [30] revealed that mainland Chinese consumers

have a higher experiential orientation (need to examine merchandise physically before


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purchasing) as compared to Chinese consumers residing in North America. As such,

Mainland Chinese consumers experience high product uncertainty with online stores

because they are unable to examine online products.

Our results indicate that higher behaviour uncertainty increases transaction cost of

both sets of online shoppers. One plausible reason is that consumers are worried that

online stores will not allow for the exchange of products purchased and not provide after-

sales services. Thus consumers have to spend more time in searching for product and

store related information and monitoring online stores to check if their orders are

processed as expected.

Our findings also reveal that consumers in China perceived higher behavioural

uncertainty of online stores than US consumers. This is probably because consumers in

China believe that poor after-sales service and lack of guarantee by the vendor are

primary obstacles to online buying [5]. On the other hand, most US online stores such as

Amazon.com provide after-sales service and guarantee the exchange or return of productpurchased if consumers are dissatisfied. Another possible reason is that US consumers

can easily exchange or return their purchases if they are dissatisfied.

Our results show that more convenience reduces transaction cost of US consumers

and those in China. These findings suggest that convenience provided by online stores

such as powerful search engines, extensive product reviews, and product samples reduce

consumers transaction cost of online buying. Our findings also show that US consumers

perceived more convenience than those in China. One possible explanation is that US

consumers spend less time searching for online products and monitoring their online

purchases because of better IT infrastructure and faster connection speed. Also, a

significant differentiating factor between the USA and China is that most e-retailers

(other than China-based firms) require payment in US dollars. Further, most consumers

in China do not have a credit card. This can present a significant hurdle for them in theform of:

1 currency exchange costs (not just the explicit cost of the exchange, but the

time/labour cost of physically performing the transaction)

2 legal restrictions on movements of US dollars into and out of China.

In addition, the cost in buying overseas products for consumers in China is usually high

due to the high US dollar exchange rate as well as shipping charges involved, thereby

negating the convenience of online shopping.

Our results reveal that economic utility is negatively related to transaction cost of

both sets of consumers. This is probably because internet shopping allows consumers to

search for and compare prices easily, which in turn reduces transaction cost. US

consumers are also found to perceive more economic utility than those in China. Thisdifference can be attributed to the lack of web content for consumers in China. According

to the report by United States Internet Council [51], 78% of all websites are currently in

English, whilst 96% of e-commerce sites are in English. With limited selection of

Chinese online stores, consumers in China are less likely to enjoy the economic utility of

online buying.

In addition, there is a negative relationship between dependability and the perceived

transaction cost in the US sample. However, such a relationship was not found in the

China sample. The result regarding the China sample is puzzling and further studies

involving other components of trust, such as responsibility and honesty [38], are


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20/23


Online stores can improve their trustworthiness by having a privacy policy that is

easy to understand and clearly signposted. They must also be sensitive to the issue of

security as it is a significant obstacle hindering the growth of internet-based commerce.

With the increased reliance of the internet to carry out e-commerce, the capacity for

computer misuse and abuse is also increasing. Therefore, managers of online stores

should offer information about security and privacy issues. This information should be

accurate and easy to understand. It should be clear to consumers when they are giving

any personal information, whether they are in a secure environment. Consumers should

also be provided with information about their legal rights and liabilities for any losses

should a fraudulent transaction occur.

In this study, economic utility is one of the factors that reduces consumers

transaction cost of online buying. However, many studies found that a large majority of

online consumers feel that online stores do not offer a price advantage over traditional

retail stores [2,27,52]. One possible reason is the high shipping and delivery costs [30].Therefore, online stores must display one overall total price to the consumer before the

order is completed, which should include any delivery charges. In addition, value-added

services such as assisting consumers in converting prices into their own currencies should

also be provided. This information will help the consumer considerably when deciding

whether to make a purchase.

In most countries, even in the USA, most consumers are still hampered by low

bandwidth because of dial-up access and high connection fees. In addition, managers of

online stores must ensure that their websites are not graphic intensive and can be loaded

quickly even through low-speed connections. This will help to reduce consumers time

spent searching for product information, thereby increasing convenience and economic

utility whilst reducing transaction cost.

Further, in China, there are limited methods of online payment available for internetusers. Companies should come up with innovative solutions, to cater to the needs of

customers. Eachnet.com, for instance, is tailored for cash-paying Chinese. Once Eachnet

members complete transactions on the internet, they meet face to face to pay and take

delivery of the items, avoiding the hassle of paying online. Companies like 8848.net,

which recently opened Chinas first online supermarket, are developing cash on delivery

(COD) to accommodate Chinese consumers [53].

There are several limitations and directions for future research that this study

facilitates. Firstly, this research is based on an e-mail survey where only respondents with

internet access participated in the study. This is not a serious limitation as we expect

potential online shoppers to have internet access. Extensions of this study in other

settings and using other data collection methods should provide additional evidence to

support and expand our findings.

Secondly, participants who find the incentives given (i.e., prizes for lucky draw)

attractive may be more likely to participate. Participants who fear online shopping may

find Amazon.com shopping vouchers less attractive and hence be less likely to

participate. Therefore, the self-selection bias may limit the generalisability of the

findings. We have tried to mitigate this limitation by offering products (zip drive and web

cam) in addition to Amazons shopping vouchers. Future research can offer other

incentives that would attract both users who love or fear online shopping.

Thirdly, the model presented here accounts for only one third of the total variance of

online buying behaviour. Future research can include other factors (not examined in our


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model) that affect transaction cost of online purchases. In addition, the study can be

replicated across samples from different countries in order to assess the generalisabilityof the model further across different cultures. Fourthly, it is plausible to say that some

relationships exist among the independent variables. However, these relationships are not

within the scope of the study. Further, SEM analysis did not indicate the necessity of

adding new paths. Future research can examine such relationships among independent

variables e.g., between dependability and uncertainty.

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