Understanding consumer online shopping behaviour from the perspective of transaction costs

2.3
 
EMPIRICAL EVIDENCE OF ONLINE CONSUMER BEHAVIOUR 
2.3.1
 
Introduction 
In the last decade, most studies of online consumer behaviour have attempted to identify 
major antecedent factors which play critical roles in determining online shopping behaviour. 
This section reviews the literature related to online shopping in an effort to arrive at a state-
of-the-art understanding of what is known about the antecedents significant to online 
shopping behaviour.
During the literature search, various keywords and their synonyms such as online shopping, 
Internet purchasing, online retailing, online consumer behaviour, and e-commerce, were used 
to search for articles dating back as far as 1994. Each article was reviewed and carefully 
screened to ensure that they were related to online consumer behaviour. A total of 1045 
articles were found relevant to online shopping behaviour. Among these articles, a large 
number of antecedent factors that affect online shopping intention and actual purchase 
behaviour were identified. Chang et al.’s (2005) scheme, which included online consumer 
characteristics, vendor and product characteristics, and perceived characteristics of the web as 
a sales channel, was used and adapted into the categorization of the identified antecedents in 
this study. The three major categories were (1) Consumer Characteristics, (2) Online vendor/ 
store and product characteristics, (3) Perceived channel characteristics. The details of these 
categories are summarized in Appendix A, B, and C, respectively.
2.3.2 Consumer Characteristics 
Consumers and vendors constitute two basic actors of a product exchange system. 
 
Consumers’ online behaviour is expected to be influenced by their own personal 

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characteristics (Meskaran
 et al.
2013). Consumers with different characteristics may react to 
online shopping in different ways; for example, those unfamiliar with computers and the 
internet will not consider online shopping as an active alternative. Empirically, previous 
studies have found that online shopping intention and purchase behaviour are affected by a 
variety of personal characteristics, such as demographics, shopping orientations, 
computer/Internet experience, online shopping experiences, and psychological characteristics, 
as displayed in Appendix A. Among them, demographics were the focus of early studies, 
while psychological perception and online experience (e.g. emotion) have been examined in 
more recent studies. It is not surprising that some consumer characteristics were found to 
have consistent effects across different studies, while others were found to have mixed or 
even contradictory impacts.
 
2.3.2.1 Demographics 
Consumers’ socio-demographic characteristics influence their online shopping behaviour 
(Hansen and Jensen 2009) although the findings of previous studies on demographic 
variables were quite mixed (Hasan 2010). Gender, age, education, and income were found to 
have a positive relationship with online shopping in some studies, but not in all. For example, 
Li et al. (1999), Rodgers and Harris (2003), Seock and Bailey (2007), Stafford et al.(2004), 
and Zayer and Colema (2012) found that males are inclined to buy more online than females. 
Women were reported to have a higher level of web apprehensiveness (i.e., individual’s 
resistance to or fear of the web as a channel for context-free online information seeking and 
communication) (Susskind 2004). Bhatnagar et al. (2000b) noted that men are more likely to 
purchase some products (such as hardware, software, and home electronics) online, but less 
likely to buy others (such as apparel and food) via internet. Young and Stoel (2004) believed 
that women were more likely to use the Internet for information search for apparel products 
than men.

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The effect of age on consumers’ intention to purchase online remains unclear. For example, 
some studies (Donthu and Garcia 1999, Stafford
 et al.
2004, Bhatnagar and Ghose 2004a) 
identified a signficnat positive relationship between consumers’ age and their likelihood to 
purchase products online, whereas others reported a significant negative relationship (Joines