Understanding consumer online shopping behaviour from the perspective of transaction costs

Table 2.2 Summary of the Findings of the Antecedents of Online TCs 
Dependent 
variables 
Independent variables 
Result 
Studies 
TCs 
Uncertainty 
Positive correlation 
(Liang and Huang 
1998);(Kim and Li 
2009b) 
0 
(Teo and Yu 2005) 
Asset specificity 
Positive correlation 
(Liang and Huang 
1998);(Teo et al. 2004); 
(Yen et al. 2013) 
Product uncertainty 
Negative correlation ( only in 
US, not in China) 
(Teo et al. 2004) 
Negative correlation 
(Yen et al. 2013) 
Behavioural uncertainty
Positive correlation 
(Teo et al. 2004); (Teo 
and Yu 2005) 
Convenience 
Negative correlation 
(Teo et al. 2004) 
Economic utility 
Negative correlation 
(Teo et al. 2004) 
Dependability 
Negative correlation 
(Teo and Yu 2005) 
Negative correlation (only in 
US, not in China) 
(Teo et al. 2004) 
Privacy policy 
0 
(Teo and Yu 2005) 
Trust
0 (as privacy policy was not Sig.) (Teo and Yu 2005) 
Branding uncertainty
0 
(Teo and Yu 2005) 
Performance uncertainty
Positive correlation 
(Teo and Yu 2005) 
Environmental uncertainty 
Positive correlation 
(Teo and Yu 2005) 
Online buying frequency 
Negative correlation 
(Teo and Yu 2005); (Kim 
and Li 2009b); (Yen et al. 
2013) 
Personal security 
Positive correlation 
(Kim and Li 2009b) 

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Past research centres on using TCs to explain consumers’ acceptance of and behavioural 
intention towards online shopping. Prior studies have not applied TCs to examinations of 
actual online purchase behaviour. In other words, there is a dearth of academic inquiry into 
the effects of TCs on consumers’ online purchase behaviour. Indeed, most of the studies in 
online TCs literature focus primarily on consumer acceptance of online shopping, satisfaction, 
and consumer behavioural intention such as willingness to buy, intention to purchase, and 
intention to repurchase, overlooking the possible influences of TCs on consumers’ actual 
online purchase behaviour. It is acknowledged that the internet makes it easy for consumer to 
have access to full information on price and product, but it is difficult for online retailers to 
create differentiation on both their price and product (Reichheld
 et al.
2000, Urban
 et al.
2000, 
Vatanasombut
 et al.
2004). For online marketers, a more practical question is: what drives 
consumers to finally purchase from their online store? In such a competitive business 
environment, it is imperative for them to know what the underlying criteria are for consumers 
choosing an online retailer to make a purchase. Thus, a closer examination of underlying 
mechanism of consumer’s online purchase decision-making is necessary. Taking into account 
the crucial roles of TCs in explaining consumer acceptance and behavioural intention, this 
construct may serve as a useful foundation for understanding actual online purchase 
behaviour. 
Furthermore, within the limited online TCs studies, scholars still focus on the early mentality 
of doing business online which, in essence, is driven by discrete transactions. More 
specifically, most studies concentrates on how internet visitors evaluate their initial 
perceptions of the TCs in an online store based on their transaction experience and how TCs 
impact their perceived shopping value (Wu
 et al.
2014) and initial purchase intention toward 
the online store (Liang and Huang 1998, Teo and Yu 2005). Such studies have provided 

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important insights into mechanisms of initial evaluation of TCs in an online store and have 
helped online vendors reduce a visitor’s perceived TCs and increase consumers’ purchase 
intention. However, with marketing strategies for online vendors switching to retaining 
customers, researchers need to take a new perspective on the study of the TCs – a perspective 
directed toward customer loyalty. The literature on online TCs shows that there is a lack of 
understating on the impacts of TCs on customer loyalty in online shopping context. Thus, on 
this issue, more studies are needed to investigate how TCs perceived by a consumer impacts 
his/her decision to be loyal to an online store after he/she has had some purchase experience 
with the online store. 
Although the significant roles of TCs in influencing consumer behaviour has been noted in 
the literature, to the researcher’s knowledge, no published studies have examined the factors 
that mediate the effects of TCs on their behaviour-related consequences, i.e., online purchase 
behaviour and loyalty. Some researchers believe that customer satisfaction acts as an 
important psychological factor that drives customer perceptions into customer loyalty 
(Caruana
 et al.
2000). Therefore, customer satisfaction can be considered as a mediator 
between customer perceptions and loyalty. However, whether customer satisfaction has a 
mediating effect on the relationships between TCs and online behavioural outcomes has not 
yet been empirically tested. In order to better understand the underlying mechanisms linking 
TCs and online purchase and post-purchase behaviour such as customer loyalty, further 
theoretical and empirical attention needs to be paid to issues pertaining to the mediator 
including customer satisfaction between TCs and online behavioural outcomes.
In addition, previous research is further comfounded by the lack of research into the factors 
that could be moderating the links between TCs and behavioural consequences. Although 

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some studies generated valuable insights into the dynamics of consumer TCs (Kim and Li 
2009b, Wu
 et al.
2014), such studies have overlooked the effects of potential moderating 
variables. An exclusive focus on direct relationships to explain the online consumer 
behaviour may mask divergent consumer reactions to the specific nature of online shopping 
environment and thus inhibit a more comprehensive understanding of consumer evaluations 
of TCs and their subsequent shopping behaviour (Byramjee and Korgaonkar 2013b). 
Consumer behaviour theorists acknowledge that personal traits interact with costs in 
determining consumers’ decision-making (Kleijnen
 et al.
2007). Online shopping indeed 
offers convenience and fun but in the meanwhile involves greater risks and uncertainties than 
the traditional shopping channel (Kim and Lennon 2013), which dramatically increase 
consumers’ risks perceptions toward online shopping (Aghekyan-Simonian
 et al.
2012). 
However, research has demonstrated that consumers differ in their attitude to the risks. 
Consumer’s risk-bearing propensity captures their inherent risk-tendency when facing 
uncertain situations (Kim and Byramjee 2014). Some consumers are risk-taking whereas 
others are risk-averse (Kahneman and Tversky 1979). Consumer’s risk-bearing propensity 
may interact with the TCs in online shopping. Moreover, consumers differ in their online 
shopping orientations (Park
 et al.
2011). Some are convenience-oriented consumers while 
others are recreational-oriented shoppers who treat online shopping as a fun activity, thus 
their perceptions of enjoyment of online shopping may vary even under the same shopping 
conditions (Khare and Rakesh 2011). Therefore, perceived enjoyment may also moderate the 
relationship between TCs and online consumer behaviour. Investigating such moderating 
effects is of importance as it provides a full picture of how TCs work in enhancing online 
purchase behaviour and improving customer loyalty, and helps online vendors more 
effectively allocate resources to achieve desired outcomes.

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Despite the existence of Liang and Huang’s (1998) study emphasizing the roles of the 
product categories in influencing consumer acceptance of online shopping by investigating 
the impacts of five products with different characteristics on consumer acceptance, the 
limitation of the study lies in the representativeness of the products, that is, whether the five 
products can well represent all products. On this issue, there exists a lack of a thorough 
theoretical understanding regarding how the product categories differ in the relationships 
among antecedents, TCs and behavioural consequences in an online setting. Understanding 
the extent to which the effects of antecedent factors on TCs varies by product categories is 
essential in developing a comprehensive knowledge in relation to TC reduction activities. 
This is because, when viewed through a managerial lens, understanding the different effects 
of antecedent factors on TCs across product categories provides managers with guidelines to 
formulating relevant cost-reduction strategies, delivering cost-saving shopping experiences to 
customers, and finally achieving superior performance outcomes within different product 
categories. Therefore, it is of great importance to address the omission regarding the differing 
effects of product categories on the relationship between antecedents and TCs, and between 
TCs and behavioural consequences.
Limitations also exist in the sample selection. Most empirical online TC studies use Internet 
user samples (Liang and Huang 1998, Teo
 et al.
2004, Teo and Yu 2005) and the sample 
sizes are small. For example, only 86 Internet users are gathered by Liang and Huang (1998) 
in their study of consumer acceptance of online shopping. The Internet user sample is not 
considered suitable for the study of online purchase and customer loyalty because Internet 
users may not necessarily have purchased online products and service, and may have 
different perceptions of online shopping from the online shoppers who have online purchase 
experiences. Those who have not purchased products online may be unable to answer the 

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questions concerning the recent online purchase experiences and are very likely to provide 
biased information in order to complete the survey. Also, the results generated by the small 
sample may not be very persuasive as the samll sample is hard to be reprsentative of whole 
populations. One exception is the research conducted by Wu et al. (2014) who use a large-
scale sampling and collect data from real online shoppers. To overcome the sampling 
limitations in the majority of past studies, a large-scale sample randomly selected from real-
world online shoppers needs to be employed to provide more robust empirical evidence to the 
study of the TCs and online purchase and post-purchase behaviour. 
The vast majority of prior studies on TCs are strictly confined to the context of developed 
economies where e-commerce has been widely adopted, such as the USA, Singapore, and 
South Korea. It is believed that the findings yielded from these studies may not be 
generalizable in the context of a developing economy where the development of e-commerce 
is at its early stage (e.g., China) due to the gap in the development of Internet and information 
technology. Although the study by Teo et al. (2004) made a comparison of TC model 
between USA and China, the results did not support several hypothesised relationships in 
China, and the study offered little explanation in the findings. As such, the impacts of TCs on 
their online shopping behaviour within the Chinese context still need a great deal of study 
and clarification.