A review of literature on consumers' online purchase intentions

Abstract

Nowadays, the rapid development of the Internet and its effect on daily life has introduced a new consumer profile which is referred to as the 'online consumer'. Such consumers are affected by different factors and they have different purchasing habits with respect to traditional consumers. The main goal of this paper is to depict the factors that have an impact on consumers' online purchase intentions through an in-depth analysis of the relevant literature. After an extensive literature review, 100 relevant articles are identified. The factors influencing consumers' online purchase intentions, which have been examined in these selected articles, are classified according to their similarities, and grouped under relevant categories. The study results reveal that while most of the studies focus on the impact of consumer characteristics, and merchant and product characteristics on online purchase intention, the impact of social media is generally underestimated in the literature. This can be attributed to the fact that this is a recently emerged research area. The originality of our paper stems from highlighting a future research agenda for consumers' online purchase intentions.

Full text

JOURNAL OF CUSTOMER BEHAVIOUR, 2015, Vol. 14, No. 3, pp.215-233
http://dx.doi.org/10.1362/147539215X14441363630837
ISSN1475-3928 print /ISSN1477-6421 online © Westburn Publishers Ltd.
A review of literature on consumers’
online purchase intentions1
Ezgi Akar, Bogazici University, Turkey*
V. Aslihan Nasir, Bogazici University, Turkey
1 A previous version of the paper was published in the Proceedings of the Advances
in Business-Related Scientific Research Conference (ABSRC-2014) that was held
on March 26-28 in Venice, Italy. This paper presents an updated and extended
version of the previous one.
Abstract Nowadays, the rapid development of the Internet and its effect on daily
life has introduced a new consumer profile which is referred to as the ‘online
consumer’. Such consumers are affected by different factors and they have
different purchasing habits with respect to traditional consumers. The main goal
of this paper is to depict the factors that have an impact on consumers’ online
purchase intentions through an in-depth analysis of the relevant literature. After
an extensive literature review, 100 relevant articles are identified. The factors
influencing consumers’ online purchase intentions, which have been examined
in these selected articles, are classified according to their similarities, and
grouped under relevant categories. The study results reveal that while most of
the studies focus on the impact of consumer characteristics, and merchant and
product characteristics on online purchase intention, the impact of social media
is generally underestimated in the literature. This can be attributed to the fact
that this is a recently emerged research area. The originality of our paper stems
from highlighting a future research agenda for consumers’ online purchase
intentions.
Keywords Online consumer, Consumer behaviour, Purchase intention, Online
shopping
*Correspondence details and biographies for the authors are located at the end of the article.
JOURNAL OF
CUSTOMER
BEHAVIOUR

INTRODUCTION
The Internet and the rapid development of technology reveal a new global market
where time and space barriers do not exist (Racolta-Paina & Luca, 2010). Additionally,
these advancements lead to the emergence of a new consumer profile called the ‘online
consumer’ (Racolta-Paina & Luca, 2010). These consumers play an important role
in the e-commerce world and they have different purchasing habits compared with
traditional consumers. Companies must pay attention to online consumers’ needs,
habits, lifestyles and characteristics to satisfy them in a more global, competitive and
dynamic environment. Due to its characteristics (e.g., having neither time nor location
constraints), utilising the web as a sales channel is key to reaching different, wide,
and global markets (Peterson, Balasubramanian, & Bronnenberg, 1997). In order to
satisfy online consumers’ needs and to be an important player in such a global and
competitive market, companies should understand consumers’ characteristics, their
online purchase intentions and online behaviours.
The objectives of this study are listed as follows: (1) identification of the factors
that have an effect on online consumers’ purchase intentions and consumer behaviour
based on past studies in the literature, (2) categorisation of these factors in order to
have a comprehensive framework for consumers’ online purchase intentions, and (3)
detection of further research areas on this topic.
RESEARCH BASED ON LITERATURE
In order to identify relevant studies, an electronic search was conducted and a
number of index databases of academic journals were searched. Then, titles and
abstracts of the studies were reviewed to identify more appropriate and relevant
articles in the field. Databases that were included in our meta-analysis were ABI/
INFORM Complete, Ebscohost, and Emerald. Keywords and phrases used in the
literature review were online shopping, internet shopping, online consumer, online
purchasing behaviour, online buying behaviour, online consumer behaviour, and
e-consumer behaviour. The most relevant 100 articles were identified from 2000 to
December 2014.
The articles are limited to those using an empirical research method, and they
mainly investigated online shopping, purchasing intention, attitude, adoption or use.
Hence, conceptual papers and articles using other types of research methods are out
of the scope of this study.
LITERATURE REVIEW AND CLASSIFICATION OF FACTORS
According to literature analysis results, many independent variables are identified.
While some of the independent variables cited in Table 1 pertain to only one
article, other independent variables appear in multiple articles. All variables are
classified according to their similarities. Some of the main categories (i.e., consumer
characteristics, website characteristics, and characteristics of web-as-a-sales-channel)
in Figure 1 are adapted from the literature (Chang, Cheung, & Lai, 2005). However,
all sub-categories and remaining main categories are developed according to the
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Akar & Nasir Consumers’ online purchase intentions 217
TABLE 1 Summary of the factors that have impact on online purchase intention
Independent
variables Studies Freq. Summary of
findings
Consumer characteristics
Demographic variables
Gender Alreck & Settle, 2002; Boyle & Ruppel, 2006; Calık &
Ersoy, 2008; Chen & Lee, 2005; Clemes et al., 2014;
Doolin et al., 2005; El Ansary & Roushdy, 2013;
Fan & Miao, 2012; Girard & Silverblatt, 2003; Gong
& Maddox, 2011; Koyuncu & Lien, 2003; Lian &
Yen, 2014; Rodgers & Harris, 2003; Saprikis, 2013;
Stafford et al., 2004; Thamizhvanan & Xavier, 2013;
Vaidehi, 2014; Van Slyke et al., 2002; Wang et al., 2010
19 Significant
impact (except
Wang et al.,
2010)
Level of internet
usage
Bhatnagar et al., 2000; Calık & Ersoy, 2008; Cho, 2004;
Citrin et al., 2000; Doolin et al., 2005; El Ansary &
Roushdy, 2013; Gong & Maddox, 2011; Koyuncu
& Lien, 2003; Kuhlmeier & Knight, 2005; Liao &
Cheung, 2001; Nysveen & Pedersen, 2004; Park,
2002; Punj, 2011; Saprikis, 2013; Thamizhvanan &
Xavier, 2013; Van Slyke et al., 2002; Wang et al., 2010
17 Significant
impact (except
Thamizhvanan
& Xavier, 2013;
Van Slyke et
al., 2002)
Purchase
experience
Bhatnagar et al., 2000; Cho, 2004; El Ansary &
Roushdy, 2013; Calık & Ersoy, 2008; Citrin et al.,
2000; Dai et al., 2014; Doolin et al., 2005; Gong &
Maddox, 2011; Koyuncu & Lien, 2003; Kuhlmeier
& Knight, 2005; Liao & Cheung, 2001; Nysveen
& Pedersen, 2004; Park & Jun, 2003; Punj, 2011;
Saprikis, 2013; Van Slyke et al., 2002; Wang et al.,
2010
17 Significant
positive impact
Age Bhatnagar et al., 2000; El Ansary & Roushdy, 2013;
Calık & Ersoy, 2008; Clemes et al., 2014; Doolin et al.,
2005; Gong & Maddox, 2011; Hernandez et al., 2011;
Koyuncu & Lien, 2003; Lian & Yen, 2014; Punj, 2011;
Stafford et al., 2004; Thamizhvanan & Xavier, 2013;
Van Slyke et al., 2002
13 Significant
impact (except
Doolin et
al., 2005;
Thamizhvanan
& Xavier, 2013;
Van Slyke et
al., 2002)
Education El Ansary & Roushdy, 2013; Calık & Ersoy, 2008;
Clemes et al., 2014; Girard & Silverblatt, 2003; Gong
& Maddox, 2011; Koyuncu & Lien, 2003; Punj, 2011;
Saprikis, 2013; Thamizhvanan & Xavier, 2013
9 Significant
impact
Income Calık & Ersoy, 2008; Clemes et al., 2014; Doolin et al.,
2005; Girard & Silverblatt, 2003; Gong & Maddox,
2011; Hernandez et al., 2011; Koyuncu & Lien, 2003;
Punj, 2011; Susskind, 2004
9 Significant
impact
Culture Chau et al., 2002; Koyuncu & Lien, 2003; O’Keefe et al.,
2000; Park & Jun, 2003; Rodgers & Harris, 2003; Shiu
& Dawson, 2002; Stafford et al., 2004
7 Significant
impact (except
Koyuncu &
Lien, 2003)
Occupation Calık & Ersoy, 2008; Chen & Lee, 2005; Clemes et al.,
2014; Girard & Silverblatt, 2003
4 Significant
impact
Marital status Clemes et al., 2014; Gong & Maddox, 2011; Koyuncu &
Lien, 2003
3 Significant
impact (except
Koyuncu &
Lien, 2003)
Cont’d...

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Independent
variables Studies Freq. Summary of
findings
Credit card
usage
Thamizhvanan & Xavier, 2013; Van Slyke et al., 2002 2 Significant
impact
(partially
Thamizhvanan
& Xavier, 2013)
Residential area Doolin et al., 2005 1 Significant
impact
Direct shopping
experience
Doolin et al., 2005 1 Significant
impact
Race Koyuncu & Lien, 2003 1 No significant
impact
Sexual
preference
Koyuncu & Lien, 2003 1 Significant
impact
General variables
Trust Aghdaie et al., 2011; Al-Nasser et al., 2014; Becerra
& Korganonkar, 2011; Bianchi & Andrews, 2012;
Chang & Chen, 2008; El Ansary & Roushdy, 2013;
George, 2002, 2004; Harris & Goode, 2010; Hsu et al.,
2014; Kamtarin, 2012; Leerapong & Mardjo, 2013;
Li et al., 2007; Ling et al., 2010; Ling et al., 2011;
Thamizhvanan & Xavier, 2013; Wu & Lee, 2012; Yoon,
2002
18 Significant
positive
impact (except
Leerapong &
Mardjo, 2013)
Perceived risk Adnan, 2014; Almousa, 2014; Bhatnagar et al., 2000;
Bianchi & Andrews, 2012; Boyle & Ruppel, 2006;
Chang & Chen, 2008; Clemes et al., 2014; Doolin et
al., 2005; Hsu & Bayarsaikhan, 2012; Kim & Lennon,
2010; Kim & Byramjee, 2014; Kuhlmeier & Knight,
2005; Leerapong & Mardjo, 2013; Liao & Cheung,
2001; Pavlou, 2003
15 Significant
negative
impact
Attitude Al-Nasser et al., 2014; Bianchi & Andrews, 2012; Çelik
& Yılmaz, 2011; Chu, 2008; El Ansary & Roushdy,
2013;Hsu & Bayarsaikhan, 2012; Laohapensang,
2009; Limayem et al., 2000; Ling et al., 2011;
Mazaheri et al., 2012; Wang et al., 2007; Yörük,
Dündar, Moga, & Neculita, 2011; Yu & Wu, 2007
13 Significant
positive impact
Subjective
norms
Bonera, 2011; Clemes et al., 2014; Foucault &
Scheufele, 2002; Laohapensang, 2009; Leerapong &
Mardjo, 2013; Limayem et al., 2000; Wang et al., 2007;
Yu & Wu, 2007; Zhang et al., 2006
9 Significant
positive impact
(except Wang
et al., 2007)
Personal
Innovativeness
Boyle & Ruppel, 2006; Citrin et al., 2000; Goldsmith,
2001, 2002; Hsu & Bayarsaikhan, 2012; Limayem et
al., 2000; Sin & Tse, 2002; Wang et al., 2010
8 Significant
positive impact
Satisfaction Cho, 2004; Foucault & Scheufele, 2002; Hackman et al.,
2006; Kim & Lennon, 2010
4 Significant
positive impact
(except Kim
and Lennon,
2010)
Perceived self-
efficacy
Bonera, 2011; Boyle & Ruppel, 2006; Wang et al., 2010 3 Significant
positive impact
Perceived
behavioural
control
Laohapensang, 2009; Wang et al., 2007 2 Significant
positive impact
Emotions Ha & Lennon, 2010; Hackman et al., 2006 2 Significant
positive impact

Akar & Nasir Consumers’ online purchase intentions 219
Independent
variables Studies Freq. Summary of
findings
Perceived price Liao & Cheung, 2001; Mehta & Kumar, 2012 2 Significant
impact
Perceived
compatibility
Chen, Gillenson, & Sherrell, 2002; Leerapong & Mardjo,
2013
2 Significant
impact
Web navigation
ability
Adeline, 2008 1 Significant
positive impact
Involvement Chen & Lee, 2005 1 Significant
positive impact
Cognitive
adoption
Wang et al., 2010 1 No significant
impact
Perceived
observability
Leerapong & Mardjo, 2013 1 Significant
impact
Shopping
orientations
Boyle & Ruppel, 2006; Calık & Ersoy, 2008; Girard
& Silverblatt, 2003; Ling et al., 2010; Park, 2002;
Thamizhvanan & Xavier, 2013; Zhang et al., 2006
7 Significant
impact (except
Thamizhvanan
& Xavier, 2013)
Web as a sales channel
General variables
Service quality El Ansary & Roushdy, 2013; Clemes et al., 2014;
Gatautis et al., 2014; Hackman et al., 2006; Liao &
Cheung, 2001; Tsao & Tseng, 2011
6 Significant
positive impact
After-sale
service quality
Gatautis et al., 2014; Jun & Jaafar, 2011; Koo et al.,
2008
3 Significant
positive impact
(except Jun &
Jaafar, 2011)
Online
advertisement
Goode & Harris, 2007; Kiran et al., 2008; Momtaz et al.,
2011
3 Significant
positive impact
Delivery Aghdaie et al., 2011; Alam & Yasin, 2010 2 No significant
impact
E-word of
mouth
Fan & Miao, 2012; Kamtarin, 2012 2 Significant
positive impact
Payment Aghdaie et al., 2011 1 Significant
impact
Service value Hackman et al., 2006 1 Significant
positive impact
Online
feedbacks
Oncioiu, 2014 1 Significant
positive impact
Auctions Calık & Ersoy, 2008 1 Significant
positive impact
Relative
advantages
Adnan, 2014; Clemes et al., 2014; Hsu & Bayarsaikhan,
2012; Kamtarin, 2012; Leerapong & Mardjo, 2013;
Mehta & Kumar, 2012; Ozen & Engizek, 2014; Park &
Kim, 2008; Punj, 2011; Saprikis, 2013; Vaidehi, 2014;
Yörük et al., 2011
12 Significant
positive impact
(except Adnan,
2014)
Merchant characteristics
Reputation Aghdaie et al., 2011; Goode & Harris, 2007; Ha &
Lennon, 2010
3 Significant
positive impact
Brand Gatautis et al., 2014; Momtaz et al., 2011 2 Significant
positive impact
Perceived
marketing mix
Jun & Jaafar, 2011 1 Significant
positive impact
Cont’d...

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Independent
variables Studies Freq. Summary of
findings
Website characteristics
Web
atmosphere
Adnan, 2014; Aghdaie et al., 2011; Alam & Yasin, 2010;
Chen & Lee, 2005; Clemes et al., 2014; Gatautis et
al., 2014; Goode & Harris, 2007; Koo et al., 2008;
Mazaheri et al., 2012; Zhang et al., 2006
10 Significant
positive impact
(except Adnan,
2014)
Perceived ease
of use
Aghdaie et al., 2011; Bonera, 2011; Chen et al., 2002;
Gatautis et al., 2014; Jun & Jaafar, 2011; Mehta &
Kumar, 2012
6 Significant
positive impact
Perceived
usefulness
Aghdaie et al., 2011; Bonera, 2011; Chen et al., 2002;
Gatautis et al., 2014; Wang et al., 2010
5 Significant
positive impact
Reliability Alam & Yasin, 2010; Gatautis et al., 2014; Goode &
Harris, 2007; Mehta & Kumar, 2012
4 Significant
impact
Information
quality
Aghdaie et al., 2011; Koo et al., 2008 2 Significant
positive impact
Message
framing
Chu, 2008 1 Significant
positive impact
Playfulness Bonera, 2011 1 Significant
positive impact
Avatars Holzwarth et al., 2006 1 Significant
positive impact
Product characteristics
Product type Boyle & Ruppel, 2006; Gatautis et al., 2014; Nagra &
Gopal, 2014; Park, 2002; Vijayasarathy, 2002
5 Significant
impact
Product price Clemes et al., 2014; Gatautis et al., 2014 2 Significant
impact (except
Clemes et al.,
2014)
Product variety Clemes et al., 2014; Koo et al., 2008 1 Significant
positive impact
Product
guarantee
Clemes et al., 2014 1 No significant
impact
Product
knowledge
Gatautis et al., 2014 1 Significant
impact
Social media
Chaturvedi & Gupta, 2014; Forbes & Vespoli, 2013;
Leerapong & Mardjo, 2013; Maoyan et al., 2014;
Vinerean et al., 2013
5 Significant
positive impact
similarities of factors. The main categories are (1) consumer characteristics, (2)
characteristics of the web-as-a-sales-channel, (3) website characteristics (4) merchant
characteristics, (5) social media and (6) product characteristics.
Consumer characteristics
General variables
As shown in Table 1, studies mostly focus on trust, perceived risk, attitude towards
online purchasing, subjective norms, perceived self-efficacy, personal innovativeness,
and satisfaction from online purchasing.
Trust is one of the most important dimensions in the majority of the studies.
Consumers’ trust in vendors or websites (i.e., consumers’ evaluations of websites
or vendor trustworthiness) plays a crucial role in online shopping. Lack of trust has

221
Akar & Nasir Consumers’ online purchase intentions
a negative impact on online purchase intention. Hence, consumers do not prefer
shopping online if they think that a website or vendor is not trustworthy (Aghdaie,
Piraman, & Fathi, 2011; Al-Nasser, Yusoff, Islam, & ALNasser, 2014; Becerra &
Korganonkar, 2011; Bianchi & Andrews, 2012; Chang & Chen, 2008; El Ansary
& Roushdy, 2013; George, 2002, 2004; Harris & Goode, 2010; Kamtarin, 2012;
Li, Kim, & Park, 2007; Ling, Chai, & Piew, 2010; Ling, Piew, Daud, Keoy, &
Hassan, 2011; Thamizhvanan & Xavier, 2013; Yoon, 2002). Conversely, Leerapong
and Mardjo (2013) do not find a relationship between consumers’ online purchase
intentions and either consumers’ trust attitude or trust propensity. Hsu, Chuang and
Hsu (2014) study trust from four different perspectives: website, vendor, auction
initiator, and group members. They find a positive effect of trust on online shopping
intention only for website, vendor, and group members, but not for auction initiator.
Besides, Wu and Lee (2012) focus on trust from a different perspective. They
investigate blog trustworthiness instead of website or vendor trustworthiness. They
state that bloggers have an impact on consumers’ purchase intentions. However, they
do not find a significant impact of blog trustworthiness on online shopping intention.
Perceived risk is another factor that is investigated in the majority of the studies.
Li et al. (2007, p. 272) define perceived risk as “consumer’s perceptions of the
uncertainty and adverse consequences of engaging in an activity”. All of the studies
shown in Table 1 state that perceived risk has a negative impact on consumers’
online purchase intentions (Adnan, 2014; Almousa, 2014; Bhatnagar, Misra, & Rao,
2000; Bianchi & Andrews, 2012; Boyle & Ruppel, 2006; Chang & Chen, 2008;
Clemes, Gan, & Zhang, 2014; Doolin, Dillon, Thompson, & Corner, 2005; Hsu
& Bayarsaikhan, 2012; Kim, J., & Lennon, 2010; Kim, S.H., & Byramjee, 2014;
Kuhlmeier & Knight, 2005; Leerapong & Mardjo, 2013; Liao & Cheung, 2001;
FIGURE 1 Categorisation of framework
Source: Consumer characteristics, characteristics of the web as a sales channel, and website
characteristics are borrowed from Chang et al. (2005, p. 545).
Product
characteristics
Online
purchase
intention
General
variables
Relative
advantages
Characteristics of web as a sales channel
General
variables
Demographic
variables
Shopping
orientations
Consumer characteristics
Merchant
characteristics
Website
characteristics
Social
media

Pavlou, 2003). It implies that if consumers think purchasing is very risky on the
Internet due to security or privacy issues, their online purchases decrease.
Wang, Gu and Aiken (2010, p. 56) describe personal innovativeness as “the
degree to which an individual is receptive to new ideas”. It is stated that personal
innovativeness has a positive impact on online shopping intention (Boyle & Ruppel,
2006; Goldsmith, 2001, 2002; Hsu & Bayarsaikhan, 2012; Limayem, Khalifa, &
Frini, 2000; Sin & Tse, 2002; Wang et al., 2010). However, Boyle and Ruppel (2006)
state that identification of innovative consumers is a very difficult task in practice.
Perceived self-efficacy is yet another dimension, as prior research shows.
According to Wang et al. (2010, p. 56), self-efficacy is referred to as “a consumer’s
self-assessment of his or her capabilities to shop online”. If consumers’ level of self-
assessment is high, their online purchases increase (Bonera, 2011; Boyle & Ruppel,
2006; Wang et al., 2010). Subjective norms are defined as “the rules by which
operates [sic] the subjective motivation of individuals to act consistently with the
views of the individuals’ peer and social group” (Bonera, 2011, p. 826). The majority
of the studies find that the views of social groups or those of other individuals, such
as opinion leaders, affect consumers’ purchase intentions (Bonera 2011; Clemes et
al., 2014; Foucault & Scheufele; 2002; Laohapensang, 2009; Leerapong & Mardjo,
2013; Limayem et al., 2000; Yu & Wu, 2007; Zhang, Prybutok, & Koh, 2006).
On the other hand, Wang, Chen, Chang and Yang (2007) do not find an impact of
subjective norms on online shopping intention.
As stated in Table 1, perceived compatibility, perceived observability, web
navigation ability, involvement, emotions, perceived behavioural control, perceived
price, and cognitive adoption are analysed in several studies (Adeline, 2008; Chen
& Lee, 2005; Hackman, Gundergan, Wang, & Daniel, 2006; Hsu & Bayarsaikhan,
2012; Laohapensang, 2009; Liao & Cheung, 2001; Mazaheri, Richard, & Laroche,
2012; Mehta & Kumar, 2012; Wang et al., 2007; Wang et al., 2010).
Demographic variables
As indicated in Table 1, there are different types of demographic variables that have
an impact on consumers’ online purchase intentions. Variables such as gender, age,
education, income, level of internet usage, culture, and online shopping experience
are investigated in many studies.
According to Table 1, all studies but one indicate that gender has an effect on
online shopping intention. The majority of the studies reveal that men are more
likely to make online purchases than women (Brown, Pope, & Voges, 2003; Doolin
et al., 2005; El Ansary & Roushdy, 2013; Fan & Miao, 2012; Girard & Silverblatt,
2003; Lian & Yen, 2014; Rodgers & Harris, 2003; Thamizhvanan & Xavier, 2013;
Vaidehi, 2014). Conversely, Clemes et al. (2014) find that women tend to shop online
more than men.
Age is also analysed as a dimension in the majority of the studies, which find both
positive and negative impacts of age (Brown et al., 2003; Doolin et al., 2005; El
Ansary & Roushdy, 2013; Fan & Miao, 2012; Girard & Silverblatt, 2003; Lian &
Yen, 2014; Rodgers & Harris, 2003; Thamizhvanan & Xavier, 2013), whereas three
studies indicate that there is no significant relationship between age and consumers’
online purchase intentions (Doolin et al., 2005; Thamizhvanan & Xavier, 2013; Van
Slyke, Comunale, & Belanger, 2002). Clemes et al. (2014) reveal that due to their
past internet experience, younger consumers tend to shop online more than older
consumers.
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Education is one of the key factors among demographic variables. More educated
people are more likely to purchase online (Girard & Silverblatt, 2003; Gong &
Maddox, 2011; Punj, 2011; Thamizhvanan & Xavier, 2013). Level of internet usage
is another dimension found in the literature reviewed. Consumers who have previous
internet experience are more likely to shop online than those who do not have such
experience (Bhatnagar et al., 2000; Calık & Ersoy, 2008; Cho, 2004; Citrin, Sprott,
Silverman, & Stem, 2000; Doolin et al., 2005; El Ansary & Roushdy, 2013; Gong
& Maddox, 2011; Koyuncu & Lien, 2003; Kuhlmeier & Knight, 2005; Liao &
Cheung, 2001; Nysveen & Pedersen, 2004; Park, 2002; Punj, 2011; Saprikis, 2013;
Van Slyke et al., 2002; Wang et al., 2010).
Income is also analysed in the majority of the studies. Consumers who have higher
income levels are more likely to shop online than those who have lower income
levels (Calık & Ersoy, 2008; Doolin et al., 2005; Girard & Silverblatt, 2003; Gong
& Maddox, 2011; Hernandez, Jimenez, & Martin, 2011; Punj, 2011). Conversely,
Clemes et al. (2014) state that consumers who have high income levels do not tend to
shop online because they prefer buying branded products at retail stores in order to
have a nice user experience, and get support and service. It is stated that prior online
purchase experience is positively related to online shopping (Bonera, 2011; Bosnjak,
Galesic, & Tuten, 2007; Huang, 2011; Kuhlmeier & Knight, 2005; Leerapong &
Mardjo, 2013; Ling et al., 2010; Moe & Pader, 2004; Momtaz, Islam, Ariffin, &
Karim, 2011; Park & Jun, 2003; Thamizhvanan & Xavier, 2013; Wang et al., 2010;
Yang & Lester, 2004). Moreover, Dai, Forsythe and Kwon (2014) indicate that online
shopping experience is a positive predictor for both digital and non-digital product
categories.
Culture is yet another indicator among demographic variables. All studies but
one reveal that different cultures use the Internet for different purposes, and the
development of the Internet is different around the globe (Chau, Cole, Massey,
Montoya-Weiss, & O’Keefe, 2002; O’Keefe et al., 2000; Pavlou, 2003; Shiu &
Dawson, 2002; Stafford, Turan, & Raisinghani, 2004). Residential area, sexual
preference, purchase experience, marital status, credit card usage, accessibility, and
race have also been investigated in past studies, and their impacts on online shopping
intention are shown in Table 1 (Chen & Lee, 2005; Doolin et al., 2005; Gong &
Maddox, 2011; Koyuncu & Lien, 2003; Thamizhvanan & Xavier, 2013; Van Slyke,
Lou, Belanger, & Sridhar, 2010).
Shopping orientations
Consumers’ shopping orientations play a crucial role in their online purchase
intention. Ling et al. (2010) find that brand and quality orientation are positively
related to online purchase intention. On the other hand, Thamizhvanan and Xavier
(2013) do not find an impact of brand and quality orientation on online purchase
intention. Hence, previous studies have contained mixed findings about whether
brand and quality orientation influence online purchase intention.
In addition, some studies focus on price consciousness, convenience, recreational
shopping, variety seeking, entertainment orientations, and impulsiveness. It has been
found that (1) consumers who purchase more items on the Internet are more price
sensitive, (2) consumers who are loyal to websites prefer to purchase online more,
and (3) people who spend more time online buy more items (Calık & Ersoy, 2008;
Girard & Silverblatt, 2003; Ling et al., 2010; Park, 2002; Thamizhvanan & Xavier,
2013). Conversely, Brown et al. (2003) indicate that shopping orientations do not
have a direct impact on online purchase intention.
Akar & Nasir Consumers’ online purchase intentions 223

Characteristics of the web as a sales channel
General variables
As shown in Table 1, service quality, advertisements, and e-word of mouth are
analysed in the majority of the studies. Service quality and value have a positive
impact on online purchase intention (Boyle & Ruppel; 2006; Clemes et al., 2014;
El Ansary & Roushdy, 2013; Gatautis, Kazakeviciute, & Tarutis, 2014; Hackman
et al., 2006; Liao & Cheung, 2001; Tsao & Tseng, 2011). The effect of online
advertisements has been found to be a significant determinant on online purchase
intention as well (Goode & Harris, 2007; Kiran, Sharma, & Mittal, 2008; Momtaz
et al., 2011). E-word of mouth has emerged with the rapid advancement of online
shopping. It is available to all online consumers and helps them in making online
shopping decisions. Thus, it has a positive impact on online shopping intention (Fan
& Miao, 2012; Kamtarin, 2012).
After-service quality is analysed by Jun and Jaafar (2011), Koo, Kim and Lee
(2008) and Gatautis et al. (2014). Koo et al. (2008) and Gatautis et al. (2014) find an
impact of after-service quality on online purchase intention, whereas Jun and Jaafar
(2011) find no relationship between them. In addition, payment and delivery issues,
auctions, online feedback, and service value have an impact on online purchase
intention (Aghdaie et al., 2011; Alam & Yasin, 2010; Calık & Ersoy, 2008; Hackman
et al., 2006, Oncioiu, 2014).
Relative advantages
Some of the studies focus on the relative advantages of online shopping. They have
shown that online shopping is faster and more convenient than traditional shopping
and that consumers can (1) find the best product easily, (2) have more product
alternatives, (3) shop anytime, anywhere, (4) place orders easily and save money,
and (5) follow promotional activities (Adnan, 2014; Clemes et al., 2014; Hsu &
Bayarsaikhan, 2012; Kamtarin, 2012; Kiran et al., 2008; Leerapong & Mardjo,
2013; Mehta & Kumar, 2012; Punj, 2011; Saprikis, 2013; Vahidehi, 2014; Vinerean,
Certina, Dumitrescu, & Tichindelean, 2013). In addition, Ozen and Engizek (2014)
focus on hedonic values and find that consumers enjoy online shopping and searching.
Consumers socialise and relax while shopping. On the other hand, Adnan (2014)
does not find a significant relationship between hedonic values and online shopping
intention.
Merchant characteristics
Reputation of the vendor or merchant is investigated in prior research and is found
to have a crucial role (Aghdaie et al., 2011; Goode & Harris, 2007; Jun & Jaafar,
2011). Jun and Jaafar (2011) also focus on marketing mix and find that it has a
positive impact on online shopping. Lastly, Momtaz et al. (2011) investigate brands
and find that brand management has an important role in online shopping.
Website characteristics
As stated in Table 1, the studies mainly focus on information quality, perceived ease
of use, usefulness, web atmosphere, and reliability. There is a positive impact of
information quality, perceived ease of use and usefulness on online purchase intention
(Aghdaie et al., 2011; Bonera, 2011; Gatautis et al., 2014; Jun & Jaafar, 2011; Koo
et al., 2008; Mehta & Kumar, 2012). Web atmosphere consists of structure, design,
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Akar & Nasir Consumers’ online purchase intentions 225
layout, functionalities, image, and aesthetics dimensions. There is a significant
relationship between the atmospheric cues of a website and online purchase intention
(Aghdaie et al., 2011; Alam & Yasin, 2010; Chen & Lee, 2005; Clemes et al., 2014;
Gatautis et al., 2014; Goode & Harris, 2007; Koo et al., 2008; Mazaheri et al.,
2012; Zhang et al., 2006). Conversely, Adnan (2014) does not find a positive impact
of website atmosphere on online shopping intention. Reliability of the website has
an impact on online purchase intention (Alam & Yasin, 2010; Gatautis et al., 2014;
Goode & Harris, 2007; Mehta & Kumar, 2012).
Message framing is investigated by Chu (2008), who found that negative messages
have more impact on consumers than positive ones. Other studies find an impact of
playfulness (e.g., using avatars that are representations of humans in a virtual world)
and perceived ease of use and usefulness on online purchase intention (Bonera, 2011;
Holzwarth, Janiszewski, & Neumann, 2006; Ling et al., 2011).
Social media
Nowadays, social media play a very important role in the rapid development of the
Internet. For this reason, it is added as a new category to the framework demonstrated
in Figure 1. Maoyan, Zhujunxuan and Sangyang (2014, p. 93) define social media
as a “network and technology which [is] used to create hot news by Internet users,
then communicate and disseminate information [to] each other” and social media
marketing as “community marketing which is a kind of Internet marketing model,
it points to achieve marketing objectives by participating in various social media
networks” (p. 93).
Vinerean et al. (2013), Forbes and Vespoli (2013), and Leerapong and Mardjo
(2013) analyse the importance of social media and its effects on online purchasing.
Vinerean et al. (2013) state that social media influence online shopping. Forbes
and Vespoli (2013) indicate that people consider the views of opinion leaders in
social media when they make a purchase and buy both expensive and inexpensive
products according to their recommendations. Accordingly, they advise companies
to encourage their customers to post on social media. Another implication is that
consumers desire information now, so there is a shift from traditional social media
platforms, such as Facebook, to ‘quicker’ social media platforms, such as Twitter.
In addition, Maoyan et al. (2014) study social media marketing and find that it
has an effect on consumers’ online purchase intentions with respect to four factors:
placement, marketing activities, experiential marketing, and interaction. These factors
influence consumers’ inner perception (i.e., perceived value and perceived risk) and
altogether, they affect online purchase intention. In addition to this study, Chaturvedi
and Gupta (2014) also observe the effect of social media on consumers’ purchase
intentions. They conclude that social media is a powerful and low-cost platform for
sales promotions to attract and reach the maximum number of consumers.
Product characteristics
Product type and product assortment are the product characteristics that have an
impact on online purchase intention. When a website is often updated with new
products, product assortment increases, which in turn has a positive impact on online
shopping (Koo et al., 2008). Regarding product type, product tangibility is a crucial
factor in the sense that consumers prefer to buy intangible products from online
stores (Brown et al., 2003; Gatautis et al., 2014; Park, 2002; Vijayasarathy, 2002).
On the other hand, Nagra and Gopal (2014) reveal that consumers buy all types

of goods and services online. In particular, they state that consumers explore the
products offline and look for the best price online. Additionally, Clemes et al. (2014)
investigate product price, guarantee, and variety. Although they find a positive impact
of product variety on online purchase intention, they do not find a significant impact
of either guarantee or price. On the other hand, Gatautis et al. (2014) find an impact
of both product price and product knowledge on online purchase intention.
CONCLUSIONS
Technological developments and proliferation of the Internet introduce a new type
of consumer called an online consumer. It is obvious that online consumers have
various characteristics and behave differently from traditional consumers. In order
to understand online consumer behaviour and online purchase intention, an analysis
of consumer characteristics, environment, and technological trends is necessary.
Hence, our paper evaluates past research on relevant factors that have an impact
on consumers’ online purchase intentions and categorises these factors with respect
to their similarities. Indeed, this study is a roadmap for further studies. Our work
includes both heavily investigated and underestimated dimensions of consumers’
online purchase intentions.
Characteristics of online consumers is one of the topics that attracts much attention
in the literature. The majority of research papers focus on demographic profiles and
technographics of online consumers, particularly gender, age, education, income,
culture, internet usage level, and online purchase experience. It is revealed that males
are more inclined to shop online compared with females. Moreover, studies indicate
that younger people make more online purchases than older people due to their level
of internet usage experience. Past research provides evidence that online purchase
experience and high income also increase the online purchase tendency of consumers.
From the consumers’ perspective, trust, perceived risk, attitude, and personal
innovativeness play vital roles in consumers’ online purchase intentions. Trust and
perceived risk are considered as a challenge in the online environment. If customers
trust vendors or websites and think that the environment is risk-free, they make more
online purchases.
Nowadays, companies treat the Web as a sales channel; and service quality and
after-service quality are among the most important dimensions of web channels in
order to increase the satisfaction level of online consumers. In addition, this online
environment offers many relative advantages to consumers. These relative advantages
attract consumers to more convenient, easier, time-saving, and more enjoyable online
shopping. The majority of the papers in this category focus on these points and
advantages of online purchasing.
Features of a user-friendly website have also received considerable attention in
previous articles. The most heavily examined topic is web atmosphere. A website
should be aesthetic, reliable, responsive, and include informative, good, and rich
content. Product type and product characteristics are also intensely studied in past
papers.
Social media and social media marketing are hot topics in the research agenda
due to the development of Web 2.0. Consumers create, share, and disseminate
information and communicate with each other on digital platforms. Past research
indicates that social media affect consumers’ online purchase intentions in this two-
way online environment.
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Akar & Nasir Consumers’ online purchase intentions 227
Our paper also identifies dimensions which are not frequently investigated in the
context of online purchase intention. In this manner, the paper proposes further
research areas about online shopping for academics:
• Impact of payment type, service value, online feedback, and auctions on online
purchase intention;
• Impact of product variability, guarantee, and knowledge on online purchase
intention;
• Impact of avatars, message framing, and playfulness of websites on online
purchase intention;
• Impact of web navigation ability of consumers, their involvement, cognitive
adoption, and perceived observability on online purchase intention.
Furthermore, social media will continue to be a hot topic and will be investigated at
a tremendous rate within a more digitalised world.
MANAGERIAL IMPLICATIONS
In today’s competitive and digitalised world, companies try to observe and understand
the differences between online consumers and traditional consumers. Companies
concentrate on the needs and wants of online consumers in order to attract and
satisfy these consumers. This study suggests different approaches for managers who
are interested in online consumers.
First of all, managers should be aware of the fact that this new type of consumer
behaves in a distinct way and has various and diverse characteristics compared with
those of a traditional consumer. Depending on the demographic profile of online
consumers, companies should design their marketing campaigns accordingly.
Furthermore, trust is one of the crucial dimensions in the digitalised world.
Consumers want to trust their vendors and websites. In order to satisfy consumers
and earn their loyalty, managers should provide consumers with a risk-free
environment. For this reason, many companies may prefer to cooperate with cyber
security companies to increase their websites’ security. Websites should be designed
well and their content should be clear, attention-grabbing and understandable for
consumers. If both content and design represent the business image well, consumers
begin to trust them as a vendor. Besides, service quality and after-service quality are
very important aspects, particularly in the online environment. Companies should
assure their consumers that quality is guaranteed from the beginning of an online
purchase activity until the end.
Companies should give their consumers explicit product information by using a
more descriptive language, and it is suggested that companies provide consumers
not only textual information about their products, but also images and videos on the
websites.
One of the important implications is that companies should follow technological
trends, update themselves, and be prepared for any changes in the market. Companies
should be aware of the rise of social media. It is clear that consumers share their
opinions on social networks, which are very dynamic platforms. Therefore,
companies should analyse consumers’ activities and conversation patterns on these
social networks and should be prepared to take more responsive steps in order to
satisfy online consumers.

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ABOUT THE AUTHORS AND CORRESPONDENCE
Ezgi Akar is a PhD candidate in the Department of Management Information Systems
at Bogazici University. She received her MA in Management Information Systems
from Bogazici University in 2013. Her research interests include digital marketing,
information systems, data mining, and CRM.
Corresponding author: Ezgi Akar, Bogazici University, Department of Management
Information Systems, Hisar Campus, 34342, Bebek, Istanbul, Turkey.
E ezgi.akar@boun.edu.tr
V. Aslihan Nasir received her Bachelor’s degree in Economics from Istanbul University
and a PhD in Marketing from Bogazici University. In 2004, she joined the Department
of Management Information Systems, Bogazici University as Assistant Professor of
marketing. She is currently a Full Professor at Bogazici University. Her research and
publications focus on consumer behaviour, CRM and e-marketing. She is the author
of several articles and research reports that are published in prominent journals and
conference proceedings.
V. Aslihan Nasir, Bogazici University, Department of Management Information
Systems, Hisar Campus, 34342, Bebek, Istanbul, Turkey.
E aslihan.nasir@boun.edu.tr

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