Mapping Online Buyer Behavior: A Critical Review
of Empirical Studies (2001- 2014)
First Author: ASAD AHMAD
Department of Business Administration
Aligarh Muslim University
Aligarh -202002 (UP) INDIA
Second Author: Dr. Mohammed Naved Khan
Department of Business Administration
Faculty of Management Studies & Research
Aligarh Muslim University, Aligarh-202002 (UP) INDIA
The world is changing tremendously and there has been a high
growth in the technology. The World Wide Web (www) has brought a revolution in
the world. The use of computers has increased in the last few decades and most
of the computer users are familiar with the internet and interaction with
websites. The use of internet has tremendously
increased over the past years and has become a common means for information
transfer, services and trade. The high involvement of people with the internet has
brought out new dimensions in the business world. Online shopping has been
observed to be growing across the world, in particularly those countries where
the infrastructure for marketing facilities is highly developed. Internet is
not restricted to networking platform; it also functions as a borderless
transactional platform for consumers.
consumer behavior is a bit different from that of the physical market where the
consumers can feel and touch the desired product. Elliot and Fowell (2000) suggest that further research is urgently
required to explore the nature of the groups of factors that determine Internet
shopping behavior. The authors through this paper have tried to bring out
results which will be helpful not only to the marketers but also the
Technology driven changes in the world are all encompassing. In
2013 a total of 2.7 billion people worldwide were using internet (International
Telecommunications Union, 2013).
World Wide Internet Users
%age of total worlds’
Source: Adapted from Internet Live Stats, 2014
internet has given this world means where we exchange information and
communicate through a series of interconnected computers. It provides us with
fast and versatile communications capabilities (Belch & Belch, 2004). Internet has turned out to be an interactive media. And
this interactive property of internet has made it powerful in the context of e-commerce.
In a short span of time, internet has proved to be a very powerful platform
that has given us a new path to communicate and do business. The internet, as
no other communication medium has given globalized dimension to the world;
we are just a click away from whatever we need. It has become the universal
source of information for millions of people and has proved to be the most
democratic of all media. Through internet a business can directly reach a very
large market irrespective of its location.
There has been a rapid growth in the internet usage over the
past years and interestingly it has become a common means for transferring
information and trade & services. The worldwide B2C e-commerce sales were
found to be more than 1.2 trillion US dollars in the year 2013 (Statista, 2014).
The high involvement of people with the internet has brought out new dimension
of online shopping in the context of business world. Online shopping has been a
growing phenomenon all over the world, particularly those countries where the infrastructure
for marketing is highly developed. Internet has proved not to be only a
networking media but also a global means of transaction for consumers.
Table 2: World Wide E Commerce Sale
Total sales (billion)
(Source: e Marketer, 2013)
shopping has received considerable attention in the popular press. The growth
in online sales may impact the consumers in reflecting the compelling
advantages of internet shopping. A number of benefits are promised to both the
businesses and the consumers. From business perspective, internet has been
visualized as a unique linkage between consumers and retailers using
proprietary technology. On the other hand consumers can use internet as a valuable
communication medium in facilitating controlled search for up-to-date
information and assist themselves in comparison shopping and decision making (Hoffman,
Novak & Chatterjee, 1996).
In terms of numbers, India
has third largest online population in the world after China and United States.
Currently there are 278 million internet users in India and they have grown by
32% from 2013 when there were around 211 million internet users (IAMAI &
IMRB, 2014). Three-fourth of the internet users in emerging economies like
India is under 35 years of age, a number significantly higher than that for
developed economies. According to Taylor & Cosenza (2002) this sector keeps
on growing as a significant buying group and being consumers they “love to
shop”. The great size of the Y generation is having a profound effect on the
retail industry (Kim and Ammeter, 2008). The earning capability of this
generation is supposed to make them an important consumer group when they
graduate and enter the work force. The long term success of the online
retailers heavily depends on the ‘ample purchasing power’ and ‘technological savy
nature’ of this consumer population (Hanford, 2005).
3: Internet Users in India
Internet Users (Million)
Internet Live Stats 2014
The total number of digital buyers in India during 2013 grew by
around 28% to 24.6 million from 19.2 million in 2012 (e-Marketer 2013). Google
India successfully carried out the 3rd Great Online Shopping
Festival (GOSF) from 10th to 12th of December 2014. According
to google there were 1.4 crore visitors to the site during GOSF. In a report tech2
News Staff reported that 77% of the visitors were
male and around 67% of the visitors were from Metros like Delhi, Bangalore and
Internet has transformed
itself into a vast global market place where we have facilities of exchange of goods
and services. Sinha (2010) found that internet is used for purposes like
searching product features, comparing prices and reviews, selection and order
placement, making payments, which is followed by delivery and sales services.
Forsythe and Shi (2003)
have classified internet users as internet shoppers and internet browsers.
Those who have made online purchases were internet shoppers whereas those who only
searched products online but did not purchase were internet browser. Online
shopping/buying behavior is a process of buying products or services through internet.
This process includes five steps which are similar to those of traditional
shopping behavior (Liang and Lai, 2000). Potential consumers when feels a need
for some product or services, they search for the relative information on the
internet. Li and Zhang (2002) defined online shopping attitude as consumers’
psychological state in terms of transactions over the internet.
Kim & Ammeter (2008) and O’Donnell,
2006 found out that youths are more familiar with the e-commerce and they are
five times faster than the older generation in processing the website
information. The online population of older generations has increased as
compared to the past but young people still dominate the online population, (Jones
and Fox, 2009). There exists a section of the younger generation who do not
feel secure in online purchasing hence they avoid shopping online (Sullivan,
2004). Shareef, U. Kumar & V. Kumar (2008) the main factors responsible
for forming perceptions of trust are disposition towards an attitude of trust, perceived
site security, operational security & local environmental security.
(2009) brought up that in the past decade the study of online buyer behavior
has been one of the vital research agendas in e-commerce. Consumer
behavior in online shopping is different from the consumer behavior in the
physical market where consumer has the freedom to touch and feel the product.
and Entertainment have been found to be important factors in evaluation of a
website (Ducoffe, 1996; Richard, 2005). According to Katerattanakul (2002)
consumers browsed internet for both information as well as enjoyment. A
positive relationship exists between attitude towards online shopping and
online purchase intention Shim, Eastlick, Lotz & Warrington (2001),
Watchravesringkan & Shim (2003) and Kim & Park (2005). Hedonic and
Utilitarian motivations play significant role in affecting online buyers purchase
intention (Wolfinbarger & Gilly, 2001).
Aljukhadar and Senecal
(2011) found that the three segments formed by the online buyers are: first, the
basic communicators (consumers using the internet mainly to communicate via
e-mail), second, the lurking shoppers (online consumers who navigate and shop
heavily),third, the social thrivers (consumers who uses the internet
interactive features to socially interact by means of chatting, blogging, video
streaming, and downloading). Barnes & Guo (2011) formulated
a conceptual model of online purchase behavior. The four factors of their model
were external motivators (perceived value), instinct motivators (perceived
happiness), social factors and consumers’ habits.
Mc Quivery et al.
(2000) found that the youths did not conduct online shopping because of the
concern of credit card security. Other factors which hindered online shopping
are inability to see and touch the product, doubt in smoothness of the process
of online order, concerns about privacy of the shared personal information, and
additional expense of delivery were found to be factors which hindered online
buying (George, 2004; Swinyard & Smith, 2003; Zhou, Dai & Zhang, 2007).
Sitkin, Burt & Camerer (1998) described trust as a psychological state. Online purchase intention also depends on trust of the
consumers on the website (Yoon, 2002). Pavlou in 2003 confirmed that the trust
in the electronic retailer do effect the consumer's intention to transact. The
theory of perceived risk is being used since 1960s in explaining consumer’s
behavior in decision making process. Financial risks, social risks,
psychological risks, physical risks time risks and product performance risks are
the perceived risks whenever consumers transacts online (Boksberger, Bieger
& Laesser (2007); Chang, 2008; Corbitt, Thanasankit & Yi, 2003; Lim,
2003; Mitchell, 2001; Smith and Sivakumar, 2004). Kim et al. (2007) defined
perceived risk as the belief of online buyers about the uncertain negative
outcomes from the online transaction. Cox (1967) proposed financial and
psychological risk in internet shopping. Cunningham (1967) figured out
financial, opportunity, safety, social and psychological loss as the dimensions
of perceived risk. Roselius (1971) added time risk as the sixth important
Word of mouth (WOM)
plays a vital role in online consumer behavior. Lee et al. (2008) craved out
that negative word-of-mouth elicits a conformity effect. Romaniuk
and Sharp (2003) studied that sources like consumer experience, marketing
communications and word of mouth builds Brand image.
Young generation generally
have positive attitudes toward online shopping and the unique purchasing
behavior of this group make them an important consumer group to study (Arnaudovska,
Bankston, Simurkova & Budden, 2010 and Cole, 2011; Xu and Paulins, 2005). Herna´ndez,
Jimenez, & Martin (2011) in their research on experienced e-shoppers
analyzed online buying behavior in the light of individuals’ age, gender and
income (socioeconomic characteristics).
Men and women show
different attitudes toward shopping whether it’s online or in retail stores. Studies
have shown favorable attitudes of men toward both the internet and computers in
general (Bimber, 2000; Jackson, Ervin, Gardner & Schmitt 2001), and they have
been found to be using internet eagerly for many purposes, which also includes
online shopping (Dennis, Morgan, Wright & Jayawardhena, 2010). The
selectivity hypothesis suggests that the main reason of gender differences is
because men are content with the overall message themes or schemas, whereas detailed
elaboration of the message content is looked by women (Meyers-Levy, 1989;
Meyers-Levy and Maheswaran, 1991; Meyers-Levy and Sternthal, 1991).
The research on online buyer behavior
has been conducted in different disciplines like information systems,
marketing, management science, psychology and social psychology, etc. (Hoffman &
Novak, 1996; Koufaris, 2002; Gefen, Rao & Tractinsky, 2003; Pavlou,
2003, 2006; Cheung, Chan & Limayem 2005; Zhou et al, 2007). It has been
observed that customer behavior keeps on changing over the period of time
because of the change in perception from past purchases (Taylor and Todd, 1995;
Yu, Wu, Chiao & Tai, 2005). Motivational forces plays an important role in
the explaining the shopping behavior (Wallendorf & Brucks 1993). It has
been argued that motives for shopping influences the attitude towards a store
as well as the perception of retail store attributes (Morschett, Swoboda, and
Foscht 2005). If we analyze things from consumer’s perspective, we find that online
transactions is psychologically and procedurally different from other
internet-related activities such as exchanging emails, sharing pictures &
videos, chatting or reading newspapers. The need to share personal and
financial information in conducting online transactions raises a concern about
privacy and misuse of personal information among the consumers (Biswas and
online consumer behavior models have generally been derived from the models of
the consumer behavior. The few models which have been used frequently in the
literatures are discussed below.
Acceptance Model (TAM)
TAM is one of the most successful theories for examining technology acceptance
(Lee et al., 2003; Sun and Zhang, 2006). It analyzes user behavior by
establishing two key variables: perceived ease of use (PEOU) and perceived
usefulness (PU). Recently, some studies have extended the TAM by including
other concepts which permit more precise explanations of individuals' behavior.
Most of this research introduces perceptions which act either in a similar way
to PEOU and PU (Childers et al., 2001; Ha and Stoel, 2009) or as intermediaries
between them and the dependent variable (Van DerHeijden and Verhagen, 2004;
Roca et al., 2006).
of Reasoned Action (TRA)
Ajzen and Fishbein in 1980 formulated the theory of reasoned
action after trying to estimate the discrepancy between attitude and behavior.
The theory regards consumer’s behavioral intention as the determinant of
consumer behavior, where behavioral intention is a function of ‘attitude toward
the behavior’ (i.e. the general feeling of favorableness or unfavorableness for
that behavior) and ‘subjective norm (SN)’ (i.e. the perceived opinion of other
people in relation to the behavior in question) (Fishbein and Ajzen, 1975;
of Planned Behavior (TPB)
(Azjen, 1985, 1991) is an extension of the theory of reasoned action (Azjen and
Fishbein, 1980). According to TPB, an individual’s performance of a certain
behavior is determined by his or her intent to perform that behavior. This
theory has been a successful model in a wide range of behavioral disciplines to
empirically predict and understand behavior in a variety of situations (Bansal
& Taylor, 2002; Barnett & Presley, 2004; Kang, Hahn, Fortin, Hyun,
& Eom, 2006; Werner, 2004).
Expectation Confirmation Theory (ECT)
It is a cognitive theory explains post purchase adoption
satisfaction as a function of expectations, perceived performance and
disconfirmation of beliefs (Oliver, 1977, 1980). It considers satisfaction as a
key variable for customers’ continuance intention. According to Giannakos et
al., 2011 a high amount of variance of intention to repurchase is explained by
the satisfaction of consumers. The customer retention is high with higher
levels of customer satisfaction Ittner and Larcker (1998). Anderson and
Srinivasan (2003) added that a less satisfied consumer is hard to be retained.
Cognitive Theory (SCT)
The theory of SCT explains how people acquire and maintain certain
behavioral patterns, while it provides the basis for intervention strategies
(Bandura, 1986). SCT (Bandura, 1986) and TPB (Schiffer and Ajzen, 1985)
included a perceived self efficacy. Bandura defined self efficacy as “people's
judgment of their capabilities to organize and execute courses of action
required to attain designated types of performances”. Experience is considered
as the strongest generator of self-efficacy (Bandura, 1986).
The research models of online consumer behavior are combination of
theories of social psychology and consumer behavior. Decomposed Theory of
Planned Behavior (DTPB) was formulated by Taylor & Todd in the year 1995
for the purpose of analyzing technological behavior. It draws upon theory of
planned behavior (TPB) (Ajzen, 1991) by proposing a decomposition of attitude,
subjective norm, and perceived behavioral control into attitudinal, normative,
and control beliefs. In the year 2003, Venkatesh et al. proposed the unified
theory of acceptance and use of technology (UTAUT) which explains the
intentions of the users to use an information system and subsequent usage
behavior. Pappos et al. (2014) drew a research framework using the constructs
of UTAUT, SCT and ECT models. Shareef et al (2013) used TRA, TPB and DTPB to
work on consumer behavior and trustworthiness. Kotlers’ buyer behavior model
has also been adapted by Iuliana et al. (2012) for the online consumer behavior
with the addition of web experience to the factors affecting consumer behavior.
Kotler (2003) added Web experience as an additional input in the traditional
buying behavior frameworks. Researchers have also used S-O-R model (Kim and
Lennon 2013, Chang and Chen 2008) to work on online consumer behavior. FFF
model (Factors, Filtering Elements and Filtered Buyer Behavior) have been used
by Ujwala and Kumar (2012) to work out a conceptual model on factors affecting
online consumer behavior.
Determining Online Consumer Behavior
online shopping is growing with a rapid pace and it is a bit different from the
traditional shopping where we have the products in front of us and we can touch
and feel before we finally purchase the products. But in online atmosphere we
are deprived of this. A lot of factors have been studied in the literatures but
below are the few important factors which determine the online consumer
trustworthiness in online retailing has been found as one of the most
significant factors leading customers to interact with and purchase from specific
websites (Balasubramanian et al, 2003, Darley WK et al. 2010, Tsikriktsis 2002,
Wolfinbarger et al. 2003). Online purchase is considered as a risky affair and
trust is considered as an essential factor for consumers making decisions on
electronic purchases, it can have a direct or indirect impact on consumer
purchasing intentions (Ribbink et al. 2004, Chang and Chen, 2008, Carlos et al.
2009, Martin and Camarero 2009, Cehn et al 2010, Iuliana et al 2012, Dange and kumar
2012, Arun and Xavier 2013, Shareef et al 2013, Pappas et al 2014, Hsu et al
A variety of atmospheric cue, such as application of colors, illustration of
products, complexity of display which directly or indirectly affect consumers'
cognitive and affective responses in the online transactions (e.g., Eroglu et
al., 2001; Chebat and Michon, 2003; Pons and Laroche, 2007). Different online
consumers have different motivational orientations and they respond to web
aesthetics in different ways (Babin and Darden, 1995; Kaltcheva and Weitz,
2006). The website appeal has emerged as an important cue to attract both the
hedonic and utilitarian online consumers (Page and White 2002, Chang and Chen
2008, Rajgopal 2011, Iuliana et al 2012, Kim and Lennon 2013).
of Use and Usefulness: These two variables of Technology Acceptance Model
(Davis, 1989; Davis et al., 1989) with other two factors named Trust and
Perceived Risk has been used to formulate TAM for e-commerce (Venkatesh and
Bala 2008). These four factors combined together are the online motivators for
consumers to purchase products and services online (Childers et al 2001,
Ribbink et al 2004, Roca et al 2009).
Security/ privacy: It is the overall amount of uncertainty which is
perceived by a consumer when he is involved in a purchase situation (Cox et al.
1964). Egger, 2006 stated that online trust must be there when personal
financial information and personal data is shared while making a purchase
online. The transaction security (Financial Information) and the consumer
privacy (Personal Information) has a significant role in building consumers
trust in an online purchase (Park and Kim, Trocchia and Janda 2003, Xie, Teo
and Wan 2006, Carlos et al 2009, Chen et al 2010, Dange and Kumar 2012,
Sangeeta et al, Shareef et al 2013, Hsu et al 2014, Akhter 2014)
Customer online purchase intention is defined as the construct that gives the
strength of a customer’s intention to purchase online (Salisbury et al., 2001).
A number of studies have brought forward that Purchase intention is a
significant factor in determining online consumer behavior (Angela and Monika
2010, Kim and lennon 2013, Arun and Xavier 2013). Purchase Intention shows a
positive response with higher online trust (Verhagen et al., 2006; McKnight et
al., 2002; Lim et al., 2006; Ling et al., 2010, Ilias et al 2014, Hsu et al
Significant literature on online
consumer behavior exists but majority of this research pertains to developed
economies. However, published research is relatively scant with regard to Indian
consumers. According to Dewan & Kraemer (2000) and Clarke (2001) because of
the economic differences in developed and developing countries findings from
developed countries are not directly transferable to developing countries and
vice versa. With India being the 3rd largest internet
using nation has an immense potential for online shopping. Kenneth et al
(2012), Ujwala & Vinay (2012), Sangeeta et al (2013), and Arun & Xavier
(2013) in their respective studies have discussed online consumer behavior and
their determinants in India. Similarly, the concept of online shopping is in
its initial stage and the constructs of TAM posited by Davis (1989) are relevant
for the Indian consumers.
E-commerce in India is growing with scorching
pace and we now have leading online retailers like Flipkart, Snapdeal, e-Bay, Myntra
etc. According to Technopak, organized retail which is currently $46 billion in
India is expected to expand to $182 billion in 2020 while e-tailing is expected
to expand to $32 billion by 2020 from $2.3 billion now (Reuters, 2014, 18 Nov).
The Indian e-commerce is attracting billions of foreign dollars too: Japan's SoftBank Corp invested $627 million in Snapdeal.
Flipkart.com also raised $1 billion from Singapore sovereign wealth fund GIC,
along with existing investors Tiger Global Management LLC and South
African media company Naspers Ltd. The portal has also attracted funds
from eBay Inc and Indian
billionaire Ratan Tata. After SoftBank’s Masayoshi Son and Amazon’s Jeff Bezos pouring of billions of dollars into
India’s racing e-commerce, Reliance Retail has taken its first steps into the
crowded sector (Forbes Asia). In the Indian Economic Summit the government of
Andhra Pradesh has decided to tie up with Google and Facebook to help the
entrepreneurs and the farmers migrate to trade online (ET Bureau, 2014,
A significant percentage of business is expected to migrate to E-commerce
platforms. Keeping this trend in mind the Indian government is working in
strengthening the cyber security and hence Data Privacy and Protection Act is
actively being considered besides the existing Information technology Act 2008 so
as to address the demands of present times (PWC, 2015).
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