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ONLINE BUYING BEHAVIOUR: A BRIEF REVIEW AND UPDATE

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Abstract

Internet has gained status of as a dynamic commercial platform, more than a rich source of communication. It has intensified the complexities of the simple act of buying. “Google” has become the generic term for “searching information”. Traditional buying by individuals has taken the complex mixture of store, mall, television, internet, mobile- based shopping. Not only developed western-countries but even Asian countries, with poor infrastructure and low internet penetration rates, are equally adopting online buying. Indeed, a simple search combining the terms “online” and “buying” or “ shopping” results into more than 15000 results on any academic database source. A review of selected published work in the area of “online buying” reveals that a wide range of topics have been explored and a rich theoretical framework in the form of different models is inexistent. This paper aims to present a comprehensive framework of the relevant literature available in the field of online buying behavior, in the form of different theories, models and constructs; and research results based on them. Tradition 5-staged model of consumer behavior has different stages- need identification, information search, evaluation of alternatives, buying and post purchase evaluation. Additionally, for online buying behavior the stages involved in online buying can be divided into: attitude formation, intention, adoption and continuation with online buying. Most important factors that influence online buying: attitude, motivation, trust, risk, demographics, website etc. are widely researched and reported. “Internet adoption” is widely used as foundation framework to study “adoption of online buying”. Post adoption or continuation with online buying is the area which still needs substantiate research work. Current state of this emerging field offers the potential to identify areas that need attention for future researchers. Through review of online buying literature available, this paper offers theoretical basis to the academicians, practitioners and web-marketers. In addition, the clear understanding of the online buying behavior can provide the opportunities for designing new capabilities and strategies that would quench online buyers’ thrust on value.
AIMA Journal of Management & Research, May 2015, Volume 9 Issue 2/4, ISSN 0974 497 Copy right© 2015
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Article No. 16
ONLINE BUYING BEHAVIOUR: A BRIEF REVIEW
AND UPDATE
Mamta Chawla
Research Scholar, Department of Business Administration, AMU- Aligarh.
Dr. Mohammad Naved Khan
Associate Professor, Department of Business Administration, AMU- Aligarh.
Dr. Anuja Pandey
Associate Professor, AIMA, New Delhi.
Abstract: Internet has gained status of as a dynamic commercial platform, more than a rich source of
communication. It has intensified the complexities of the simple act of buying. “Google” has become the generic
term for “searching information”. Traditional buying by individuals has taken the complex mixture of store, mall,
television, internet, mobile- based shopping. Not only developed western-countries but even Asian countries, with
poor infrastructure and low internet penetration rates, are equally adopting online buying. Indeed, a simple search
combining the terms “online” and “buying” or shopping” results into more than 15000 results on any academic
database source. A review of selected published work in the area of “online buying” reveals that a wide range of
topics have been explored and a rich theoretical framework in the form of different models is inexistent. This paper
aims to present a comprehensive framework of the relevant literature available in the field of online buying
behavior, in the form of different theories, models and constructs; and research results based on them. Tradition 5-
staged model of consumer behavior has different stages- need identification, information search, evaluation of
alternatives, buying and post purchase evaluation. Additionally, for online buying behavior the stages involved in
online buying can be divided into: attitude formation, intention, adoption and continuation with online buying. Most
important factors that influence online buying: attitude, motivation, trust, risk, demographics, website etc. are widely
researched and reported. “Internet adoption” is widely used as foundation framework to study “adoption of online
buying. Post adoption or continuation with online buying is the area which still needs substantiate research work.
Current state of this emerging field offers the potential to identify areas that need attention for future researchers.
Through review of online buying literature available, this paper offers theoretical basis to the academicians,
practitioners and web-marketers. In addition, the clear understanding of the online buying behavior can provide the
opportunities for designing new capabilities and strategies that would quench online buyers thrust on value.
Key Words: Internet, online buying, attitude, adoption, continuation, literature review
Introduction
With the development of IT and its application in different spheres of business even the
traditional buying is challenged by online marketers. The development and intensification of
competition and expanding list of products available online is indicative of gaining patronage of
online buying. As a result of acceptability of Internet, dynamism in market and consumers
attraction towards online buying, researchers are keen to unearth the currents driving and identify
leading indicators of future success of online buying. Current article provides a summary review
of relevant published work and issues that play an important role in online buying. This article,
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by synthesizing online buying literature, helps to understand online buying behavior and offers
future research priorities in the field.
The rational for holding this secondary research work is to explore and integrate the available
literature on online buying behavior to have a holistic view about this discipline. Further to built
a strong foundation for extending and relating it in Indian context by identifying the research
gaps. So that empirical research can be undertaken for the doctoral research work. The scope is
limited only to the overt behaviors displayed by individual customers while buying online for
personal use. Related branch-fields of study are excluded from current exploration e.g. “online
group buying behavior”, “online impulse buying behavior” etc. Focus of current research is on
theories and; research outcomes based on those theories. Methodological reviews are done in a
limited manner. Purposive sample of pivotal published research work has been selected from
three academic databases available online during last two years: EbescoHost, ProQuest and
Google scholar. All of the foundational theoretical models have been reviewed but only last ten
years pivotal output form empirical research work have been included in this review. Key words
like “online buying”, “internet buying”, “online purchasing behavior”, “online buying behavior”
“internet/e- shopping behavior” and “online/ e- shopping” have been used to retrieve relevant
research articles majorly from different Journals and conference Proceedings. Articles from trade
magazines and consultancy reports have been excluded from this review. “Mendeley 1.13.4” has
been utilized to manage and review the research data for citation and bibliography. Present
review paper has been methodological structured for the academicians and marketing
practitioners. Following section discuss concept of online buying behavior, followed by
discussion of major theories and their constructs, then the next section highlight major research
outcome with relevant constructs reported in previous researches. The last section discusses
current state and possible future direction in the field.
Online Buying Behavior
One of the most research oriented area of marketing discipline is consumer behavior. There are
plethora of quantitative and qualitative studies resulting into a robust set of different theories
available on Buying Behavior(Solomon, Russell-Bennett, & Previte, 2012). Most of the theories
have been adopted from different field of studies e.g. psychology, economics, anthropology to
name a few. Engel, Kottat and Blackwell known as EKB model of consumer decision making is
widely recognized and accepted by scholars.
Online buying or shopping refers to the process of researching and purchasing products or
services over the Internet (Varma & Agarwal, 2014). No. of online buying researchers utilized
the five stages EKB model: Need/problem recognition, Information search, Evaluation of
alternatives, Purchase decision, Post-purchase behavior (Wen Gong & Maddox, 2011). Still,
there is no consensus on the applicability of consumer behaviors models to online buying
scenario. An online transaction can involve three steps: process information retrieval,
information transfer, and product purchase (P. A. Pavlou & Chai, 2002; P. A. Pavlou, 2003; P.
Pavlou & Fygenson, 2006). Whereas, the entire online buying has even been divided into two
stages: first consisting of searching, comparing and selecting, placing an order termed as
ordering stage and second stage is order tracking and keeping or returning termed as order
fulfillment stage (C. Liao, Palvia, & Lin, 2010). Online consumer behavior research articles
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appear in a variety of journals and conference proceedings in the fields of information systems,
marketing, management, and psychology (Chan, Cheung, Kwong, Limayem, & Zhu, 2003).
Before moving to the major findings about different relationship reported, following section
continues the discussion on major foundational theoretical models.
Theoretical Framework
Online Consumer behavior models typically blend both economic and psychological models
with IT adoption models, and used as practical models by marketers usually. Researchers in the
field of marketing have attempted to adopt different classical “attitude-behavior” models to
explain Adoption of online buying. Theory of reasoned action or TRA by Fishbein and Ajzen
(1975), (Fishbein & Ajzen, 2011) and, consequently, theory of planned behavior or TPB (Ajzen,
1991); Innovation Diffusion Theory (IDT) (Rogers, 1962, 1983, 1995- cited in (Kamarulzaman,
2011)) have been most commonly used as theoretical models aiming to determine the impact of
beliefs, attitudes, and social factors on online buying intentions. Research output reported so far
in this field, highlighted that the Theory of Reasoned Action (TRA) and its family theories
including the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB)
are the dominant theories in this area. Expectation-Confirmation Theory (ECT) and Innovation
Diffusion Theory (IDT) have also been repeatedly tested in the study of online consumer
behavior (Cheung, Zhu, Kwong, Chan, & Limayem, 2003). Whereas, few theories such as Social
Cognitive Theory and Motivational theories have been combined with the above mentioned
theories and adopted in a new model for the presenting of online buying behavior.
- Theory of Reasoned Action (TRA)
TRA proposed in 1975 has been still been utilized, highlight “behavioral intentions”, refers to the
willingness of performing a specific action under an established situation, and is determined by
the behavioral attitude and the subjective norms. Also referred as Fishbein’s model (cited in(H.
Zhang, Tian, & Xiao, 2014)). In consumer behavior literature TRA is foundation to understand
and predict buying behavior(Yu & Wu, 2007)(K. K. Z. K. Zhang, Cheung, & Lee, 2014).
- Theory of Planned Behavior (TPB)
An extension of TRA, this theory adds two more constructs to the model of “Attitude towards
Behavior” influencing “behavioral intention” influencing “behavior”. One is “Subjective norms”
defined as perceived social pressure to perform or not to perform the behavior. Other is
“Perceived behavioral control” defined as perception of the ease or difficulty of performing the
behavior of interest (Ajzen, 1991).
- Technology Adoption Model (TAM)
Technology Acceptance Model (TAM) is the most cited (Cha, 2011) model which explains
adoption of Information Technology through adopting Theory of reasoned action (TRA-
Fishbein and Ajzen, 1975). It is specific to information system usage which is dependent upon
six variables namely: “perceived usefulness”, “perceived ease of use”, “attitude towards use”,
“intention to use” and “actual usage” (Davis, 1989).
Where “perceived usefulness” (PU) is the degree to which a person believes that a particular
system would enhance his or her job performance; “perceived ease of use” (PEU) is the degree to
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which a person believes that using a particular system would be free of effort; “attitude towards
use” is the user’s evaluation of the desirability of employing a particular information system
application. Behavioral “intention to use” is a measure of the likelihood a person will employ the
application
Davis (1989) asserted that PU and PEU represent the beliefs that lead to such acceptance.
Empirical tests suggest that TAM predicts intention and use. He found that TAM successfully
predicted use of a word processing package and reported PEU and PU were significantly
correlated with use of an office automation package, a text editor, and two graphics packages. A
limitation of TAM mentioned, that it assumes usage is volitional, that is, there are no barriers
that would prevent an individual from using an IS if he or she chose to do so. Although, there are
many factors preventing a person from using an application such as perceived user resources
(Kieran et al., 2001) and perceived behavior control (Ajzen 2002).
Kim (2012) integrated model TAM with initial trust belief. Other studies examined relative
strengths of the associations between the individual independent variables and online buying
intention clearly indicated that Customer Service, Trust and Reliability can explain much of the
variation in online buying intention (Johar & Awalluddin, 2011). Attempts have been made to
utilize TAM with TPB (Sentosa & Mat, 2012) or by adding more constructs to it.
- Innovation Diffusion Theory (IDT)
Along with above three, this theory proposed by Roger (1962, 1995), has also been widely cited
and adopted to understand adoption of an innovation. Technology adoption speed, amount and
degree depends upon five characteristics of the innovation namely: relative advantage,
compatibility, complexity, divisibility or trialibility, and communicability or observability (T
Hansen, 2005; Turan, 2012). Researchers have utilized this model along with other constructs to
understand online buying intentions (Wen Gong, Maddox, & Stump, 2012; Wen Gong &
Maddox, 2011). Online buying has been considered as “discontinuous innovation” as it includes
technological and buying changes as well (T Hansen, 2005; Torben Hansen, Jensen, & Solgaard,
2004)
Adopted in combination to other theories, to explain intention and adoption of online buying in
different setting e.g. internet banking (Lallmahamood, 2007), online travel purchase, online
grocery buying(Torben Hansen et al., 2004) (AMARO, 2014; N Delafrooz, Paim, & Khatibi,
2011)(Amaro & Duarte, 2015)(H. Y. Lee, Qu, & Kim, 2007)(N Delafrooz et al., 2011)(Eri,
Islam, Daud, & Amir, 2011)(Sinha, 2010)(Ganguly, Dash, & Cyr, 2011)(Narges Delafrooz,
Paim, Haron, & Sidin, 2009)(Ostrowski, 2009)(Choi & Geistfeld, 2004). Some of the refined
models explained even 64% of actions (Sentosa & Mat, 2012).
In a comparative study, TPB model reported to be better fit in a developing country as compare
to extended TAM model (Turan, 2012). Other extensions and revisions based on these four
models have been compared and proposed to predict online buying.
- Motivational Model
Motivation with other psychological factors like perception, learning and attitude is always been
cited as major factors influencing consumer to buy even by Kotler (2000) and Schiffman (2000).
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Different studies explored consumer motives to buy online. A detailed typology (Shrivastava,
2011) classified motives into: Pragmatic motivations (e.g. Convenience, Learning about new
Trends, Ease of use, Comparison), Product motivations (e.g. Availability, variety, quality),
Service excellence motivations (Accessibility, Timely delivery, Reliability, Responsiveness),
Economic motivations (discounts and deals, competitive prices) Hassel reduction motivations
(e.g. transportation, timing, driving and parking), Social motivations (e.g. social influence, peer
pressure, social learning, status and authority), Hedonic motivations (Self gratification, fun-of-
buying , Going through search pages, Sensory stimulations, Impulsive shopping ). Rest named as
exogenous motivations (Prevision online experience, life style, trust). Understanding of online
buying motivation is insufficient to explain the complexities on online buying behavior.
- Social Cognitive Theory
According to SCT, environment, cognition and human behavior are three interactive factors
operating as a triadic reciprocal causation (Bandura, 1986; Wood & Bandura, 1989) cited in
(Chen, Huang, & Hsu, 2010). Concept of self-efficacy has been added to existing models to form
construct of Internet- self-efficacy, proposed to directly influence performing online buy. In
combination to other technology adoption models this theory has been utilized to explore online
buying intention and continuation- intention (Suharno, Astut, Raharjo, & Kertahadi, 2014). But,
mixed findings have been reported (Sarigiannidis & Kesidou, 2009).
- UTAUT Model
Unified theory of acceptance and use of technology (UTAUT) model explains user intentions to
use IS and subsequent behavior. Performance expectancy (PE), effort expectancy (EE), Social
Influence (SI) and facilitating conditions (FC) are 4 direct determinants of usage intention and
behavior which can be moderated by Demographic variables (gender, age), experience and
voluntariness of use of IS. The constructs are very similar to the previous models but have been
named differently. As this theory is based upon earlier eight models to explain usage of IS-
TRA(Theory of Reasoned Action), TAM (Technology Acceptance Model), Motivational Model,
Theory of Planned Behavior, a combined theory of Planned Behavior and TAM, Model of
Personal computer use, DOI(Diffusion of Innovation) and Social cognitive theory (Venkatesh et
al., 2003). Number of researchers applied this model (e.g. Koivumäki et al., 2008; Eckhardt et
al., 2009; Curtis et al., 2010; Verhoeven et al., 2010) to different setting of adoption of
technology, but not all have adopted the full model. Modified UTAUT model is also proposed to
better understand adoption of online buying in developing country (Chiemeke & Evwiekpaefe,
2011). Like with other models this model is partially utilized or only cited as part of available
theoretical framework (Williams, Rana, Dwivedi, & Lal, 2011) with little work support to its
robustness for understanding adoption of online buying.
- ECM-IT Model
Researchers have utilized expectation-confirmation model (Expectation Confirmation Model by
Oliver, 1980) to IT framework in order to explain post-adoption online buying behavior (e.g.
Liao et al., 2010; Chen et al., 2010; Kim et al., 2003; Lee, 2010). As the initial ECM-IT
framework suggest, satisfaction and perceived usefulness are main determinants of consumers’
intention to continue buying online. Claudia, (2012) reported that Expectations Disconfirmation
Theory for IT Use is an adaption of Oliver’s expectations disconfirmation paradigm which
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postulates that potential users of an information system hold some initial expectations regarding
the performance of the IS, expectations that can be either confirmed or disconfirmed after use
(Bhattacherjee, 2001). A positive disconfirmation of initial expectations and a greater perceived
IS performance leads to a greater satisfaction with the IS use (Bhattacherjee et al., 2004). Online
customer service and return policy affect Chinese consumers’ post-purchase evaluation and
satisfaction. Detailed results based on this model have been discussed in the following section.
- Model of intention, adoption and continuance (MIAC)
MIAC is the first online consumer behavior theory that associates intention, adoption and
continuance (Chami, 2013). It combines TRA based theories with ECM model and presented a
comprehensive framework of moving beyond adoption and linked continuance of online buying.
It pointed that adoption and continuance are connected to each other through several mediating
and moderating factors such as trust and satisfaction.
Five independent variables as antecedents, (external environment, demographics, personal
characteristics, vendor/service/product characteristics, and web site quality) and five dependent
variables (attitude toward online shopping, intention to shop online, decision making, online
purchasing, and consumer satisfaction) (Li & Zhang, 2002) combined with ECM. Even if, Cited
extensively (Wen; Gong, Stump, & Maddox, 2013) but the complete model has not been fully
utilized.(Kwon & Chung, 2010; S. Liao & Chung, 2011)
- Other Models
Online Pre-purchase Intentions Model has been proposed and empirically tested in the context of
search goods (Shim, Eastlick, Lotz, & Warrington, 2001), which is based on TPB and Interaction
model. In this “intent to search information online” has been used as predictor of “intention to
buy online”. Contrasting to other established model it excludes adoption of online buying. Due is
to its limited nature it has not been utilized much but has been cited extensively(Thamizhvanan
& Xavier, 2013) (Badrinarayanan, Becerra, Kim, & Madhavaram, 2012)(Bonifield, Cole, &
Schultz, 2010) (J. J. Kim, 2004) (BECERRA, 2006).
MIMIC Model (Bavarsad et al., 2013)- Multiple Indicators Multiple Causes (MIMIC) model
evaluate the effects of customer trust, content quality, transactions quality and the website’s
perceived security on the intention to use e-shopping.
Empirically Studied Dependent Variables
Following section covers major endogenous variables reported in different studies.
- Attitude towards online buying and Intention to buy online
A basic construct of most of psychological theorist is the likely-hood of a particular behavior,
“buying intention” is long been utilized as reliable predictor of “buying behavior”. As mentioned
already, TRA and its family models (most cited one- TAM) have been extensively employed to
predict online buying and future buying intentions (Venkatesh et al., 2003), (Dholakia, Bagozzi,
& Pearo, 2004)(Amaro & Duarte, 2015; Cai, Zhao, & Chi, 2012; Gefen, Karahanna, & Straub,
2003; Hong, Thong, & Tam, 2005; H. Lee, Kim, & Fiore, 2010; Ling, Daud, Piew, Choon, &
Corresponding, 2011; Muthalif, 2014; Rapp, Rapp, & Schillewaert, 2008; Taiwo & Downe,
2013; Tan & Qi, 2009; Tang & T.H., 2011). Consumer intentions to buy online have been
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explained by attitude uniformly in the previous studies (Turan, 2012), but all constructs have not
found universally applicable across all environments. In a Chinese study PEU “Perceived ease of
use” has not been found influencing, whereas PU- “Perceived usefulness” influence online
buying intentions (Wen Gong & Maddox, 2011)Wen; Gong et al., 2013). Subjective norms have
been statistically significant and have reported to have positive influence
. - Adoption of online buying
Investigations, on the segments buying online have been reported extensively. Studying buying
of different products online (Sarigiannidis & Kesidou, 2009) e.g. books, travel, grocery (T
Hansen, 2005), electronics (Bashir, 2013; J. Kim & Forsythe, 2010; Liu, Forsythe, & Black,
2011), e-ticketing (Sulaiman, Ng, & Mohezar, 2008). Characteristics of adopters in terms of age,
gender and other socio-demographical along product category have been examined. Some
reported online buyer to be typically characterized as high income level (T Hansen, 2005).
- Continuation with online Buying Behavior
Available literature of online buying behavior can be clearly divided into two major sets; first set
of studies concentrating acceptance or adoption and second set of studies concerning
continuation-intention, which is still in its infancy stage. Earliest study of online banking
employed Expectation- confirmation theory (Bhattacherjee, 2001). Bhattacherjee (2001)
highlighted application of ECM better than adaptation of SERVQUAL model to the online
buying behavior. ECM is the only framework available which constitute of three constructs
namely: expectation, perceived performance and resulting level of satisfaction (Luo, Ba, &
Zhang, 2012). Against attitude, satisfaction temporally and causally precedes post-purchase
attitude and influence continue-intentions. In contrast to traditional buying, delivery of product is
part of post purchase stage. Delivery time, the delivery of the right product regarding its
attributes and performance is highly associated with post-purchase satisfaction (Jiang and
Rosenbloom, 2005) cited in (Claudia, 2012). Hence return policy has been reported an important
factors in considering transaction quality. Another research, combining ECM and TAM in two
stages of online buying-ordering and fulfillment, reported customers’ satisfaction with the
ordering process and the fulfillment process, and the perceived usefulness of the website
contribute significantly (C. Liao et al., 2010)
By combining TAM and ECM and other construct - trust, utilitarian and hedonic motivation the
constructed model explained as high as 64% of variance in US (Wen, Prybutok, & Xu,
2011).Contrary to it one more extension of ECM by adopting online buying perspective
incorporated both constraint-based and dedication-based relationships in a model (Chang &
Chou, 2011). Dedication-based influences included two constructs, “satisfaction” and “perceived
usefulness”. Constraint-based influences included two constructs, “trust” and “perceived
switching costs”. Also “website effectiveness” and “perception of relationship closeness”
proposed as antecedent to trust. This study reported stronger influence of Constraint-based
influences. Along with satisfaction, trust, perceived-usefulness and “perceived switching costs”
combined to predict continuation of online buying, but only 61% of variance were explained by
the model in China. TAM and ECM combined with SCT also utilized to express continuation
intentions (Chen et al., 2010).
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Other Variables and Major Constructs
Following section covers major predictor and exogenous variables reported in different studies.
- Demographics characteristics
Online buyers have different characteristics with varying motives to buy online, consequently
have been extensively studied, in the context of attitude, behavioral intention and adoption of
online buying with respect to different categories of products and different cultural settings. The
factors what characterize the consumer demographic profile: age, sex, occupation, education,
family status, income, living conditions and life expectancy (Andersone & Gaile-Sarkane, 2009).
Age, education and profession have been reported to have significant impact against other
variables- income, gender and ethnicity. Regarding gender there is no consensus e.g. Chinese
male and female consumers hold similar online shopping intentions (Wen Gong & Maddox,
2011). Same is found even in developed countries. Yet, few reported male more likely to shop
online(Cha, 2011). Interestingly, different online buying motives have been reported for both the
gender. In the same Chinese study age and Perceived risk were not found significantly different,
but income and marital status were found to have influence on online buying intentions. Contrary
to other findings married with children are more likely to buy online as compare to singles or
married with no children. Which is consistently found in other studies as well (Brown, Pope, &
Voges, 2003). Students as online buyers have been studied (Al-Swidi, Behjati, & Shahzad,
2012).
- Trust, Risk and Security
To overcome the inherent limitation of employing different IS-adoption models which have their
foundations in TRA other related psychological theories, construct of trust, risk and security
concerns have been strongly established in the online buying literature. “Online trust” has been
reported to be an integral component of customer purchase intention in the context of both
developed and developing countries (Thamizhvanan & Xavier, 2013). Perceived trust has been
reported as positively influencing intention, adoption and continuation behavior.
Other equally important, extensively studied and found as predictor variables are- risk (having
inverse relation) and privacy & security concerns. Online security concern varies over the
product category bought online(Cha, 2011).
- Social Influences
Subjective norm is defined as ―the perceived social pressure that most people who are
important to him/her think he/she should or should not perform the behavior in question (Ajzen,
1991; Cameron, Ginsburg, Westhoff, & Mendez, 2012; Fishbein & Ajzen, 2011). SN have been
found to be strongly influencing intention to buy online (Turan, 2012) (Cha, 2011).
- Product characteristics
Three major types of product: search, experience, and credence goods (Luo et al., 2012). Search
products are those that can be evaluated from externally provided information. Experience
products, on the other hand, require not only information, but also need to be personally
inspected or tried. Credence products are those that are difficult to assess, even after purchase
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and use (Brown et al., 2003) (Cha, 2011). “Tactility” is ability to examine/ test, in terms of touch
and sight, a product (Alkailani, 2009). Findings for this construct as an has mixed results in
different cultural environments e.g. Chinese are not more concerned about the lack of face-to-
face contact or the inability of them to touch and feel tangible products or credit as against
Americans (Wen Gong et al., 2012). Study comparing online buying intention of “real” vs.
“virtual” items reported different criterion employed for each by online buyer (Cha, 2011). There
is no uniformly accepted standard product classification available (Sarigiannidis & Kesidou,
2009) so far in the context of online buying. For virtual items PEU, PU, enjoyment and security
have not found significant, hence proposed different strategy for both types of items.
- Shopping orientations
Different shopping orientations have been found (Brown et al., 2003) when exploring different
motivations e.g. personalizing shoppers, recreational shoppers, economic shoppers, involved
shoppers and convenience shoppers. “Impulse purchase orientation” has significant impact on
the customer online purchase intention but “quality orientation” and “brand orientation” also has
not impact (Thamizhvanan & Xavier, 2013). But there is no consensus on the relevant
classification of online buyers. Moreover which class dominates the total segment of online
buyers is not identified. Some study reported convenience as the major orientation other
highlighted economy or personalizes. Moreover shopping orientation is not found significant
enough for online buying intentions.
- Website characteristics
Website design along with customer service and pricing have been reported as major “retailer
characteristics” affecting online buyer satisfaction (Luo et al., 2012) (Mishra & Priya Mary
Mathew, 2013). Perceived control over site navigation and product category are primary factors
influencing website quality. Study highlights that “high trust consumers” who spend more and
buy more often online the “return policy” cannot compensate the poor website design (Bonifield
et al., 2010). Website quality influence consumers’ perceptions of product quality, and affect
online purchase intentions (Sun, Chen, & Huang, 2014) and even continuation intentions. Signal
credibility found to strengthen the relationship between website quality and product quality
perceptions for a high quality website.
- Other variables
Internet Proclivity is frequency of internet usage (Alkailani, 2009) has been studied. “Prior
online purchase experience” positively effect on the customer purchase intention (Brown et al.,
2003) even in the context of developing countries (Thamizhvanan & Xavier, 2013). Many other
diverse variables have been reported.
Methodology
Internet users, as representative of population, have been widely accepted in researches. Students
have been found to be most utilized as part of sample, contacted through online and paper based
instruments. Quantitative research approach dominates by employing different statistical tools-
chi-square, t-test, factor analysis, structural modeling, majorly employing different packages
SPSS or AMOS. Majority of the study suffered from common limitation coming out of
employing non-random sampling technique and self- administered, self-reporting. Non-
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representative sample selected on convenience, response bias, specific cultural environment,
inherent limitation of psychological theories employing replica of intention as behavior. Detailed
discussion on methodology is beyond the scope of this paper.
Conclusion
Online buying behavior researchers, majorly explores demographics influence on the buying
intentions and adoption stages. However, there is no systematic interpretation about how the first
time buyer is likely to continue with buying online or would like to intensify or pull more of
existent products available offline. Deductive theory approach has been utilized to identify main
factors influencing different stages of online buying.
Psychological theories are utilized to understand behavior of an individual which is extensively
employed to predict “information system” or “technology” adoption behavior. Further, extending
and applying the same framework to understand “online buying behavior” in business to
consumer (B2C) setting of E-Commerce. The relation between internet as an invention and its
broadening application in business activities can be labeled both as a driver and result of
consumer’s online behavior, which needs exploration. Interestingly, time-saving and
convenience are long been associated with adoption of online mode is contradictory to the
strengthening mall-culture and retail-chains, emergence in even developing countries like India.
The researcher of online buying behavior mainly focuses on the quantitative analysis of
constructing model based on survey, limiting only to intention and adoption stages. Interestingly,
majority of the study utilize students either university or college as representative of online
buyers (Cha, 2011; Wen Gong & Maddox, 2011; Suharno et al., 2014; Turan, 2012). Its
contrasting to the findings that married with children are more likely to buy. What makes an
information-seeker over internet to become buyer over internet has been explored in detail, yet
the supporting factors that encourage online consumer to remain active online needs to be
established. Questions like will online mode of buying is going to dominate (given the rapid rate
of smart phones as a driver-current) other modes of buying like traditional store, mall etc.
remains still un-attempted. Other possibility of disappearance of this mode due to high reliance
on internet services and security threats cannot be completely ruled out. In essence, it is high
time to focus more on continuation and intensification of online buying. Moreover forces that
can intensify buyers spending in absolute amount and over different categories remain
unanswered.
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APPENDIX 1- Different models of OCBB
MODEL
AUTHOR
USAGE
CONSTRUCT
Innovation
Diffusion Theory
(IDT)
Rogers,
(1962)
Adapted to information systems
innovations by Moore and Benbasat
(1991). Five attributes from Rogers’
model and two additional constructs
are identified.
Relative Advantage,
Compatibility, Complexity,
Observability and Trialability.
Theory of
Reasoned Action
(TRA)
Fishbein and
Ajzen, (1975)
To predict behavior by understanding
attitude, intention and behavior.
Attitude, Subjective norm,
Behavioral intention
Theory of Planned
Behavior (TPB)
Ajzen, (1991)
Extension of TRA. Includes one more
variable to determine intention and
behavior.
Attitude, Subjective norm,
Perceived Behavioral Control
Expectation-
Confirmation
Theory (ECT) or
Expectation
disconfirmation
theory (EDT)
Oliver (1977,
1980)
Understanding post purchase
satisfaction determined by
confirmation of Expectation and
Experience
Expectations, Perceived
Performance and Confirmation,
Satisfaction
Technology
Acceptance
Model (TAM)
Davis et al.,
(1989)
Understanding attitude towards IS-
information system adaptation and
predicts Intentions & adoption reject
computers
Perceived Usefulness,
Perceived Ease of Use,
Attitude, Intention to Use,
Actual use, Subjective Norm*
Experience*,Voluntariness*,
Image*
Job-Relevance*, Output
Quality*, Result
Demonstrability*
Technology
Acceptance Model
2 (TAM2)
Venkatesh
and Davis,
(2000)
adapted from TAM and includes more
variables (marked with *)
AIMA Journal of Management & Research, May 2015, Volume 9 Issue 2/4, ISSN 0974 497 Copy right© 2015
AJMR-AIMA
Conceptual
Model-- Adoption
of Internet
Shopping
Citrin et al.,
(2000)
Understanding the shift from general
internet usage to a product purchase
via the internet
Open- processing (more general
innovativeness) and domain-
specific innovativeness
explaining move from general
Internet usage to a product
purchase via the Internet.
Model of Intention,
Adoption and
Continuance
(MIAC)
Cheung et al.,
(2003)
Framework of all three Online
Consumer
Behaviour stages- Intention to
Purchase to Repurchase
Intention, Purchase behavior,
Repurchase,Consumer
Characteristics, Product
Characteristics, Merchant-&-
Intermediaries Characteristics,
Medium Characteristics and
Environment Influence
Unified Theory of
Acceptance and
Use of Technology
Model (UTAUT)
Venkatesh et
al. (2003)
integrates different theories and
models to measure user intention and
usage on technology
Performance Expectancy, Effort
Expectancy, Attitude toward
Using Technology, Social
Influence, Facilitating
Conditions, Self-Efficacy
Anxiety
Consumer Personal
Characteristics
Extended
TAM (CPCETAM)
Bigné-
Alcaniz et al.,
(2008)
Understanding innovators and pre-
purchase information as a trigger for
future online shopping intention
through applying TAM
consumer innovativeness and
online shopping information
dependency , future online
shopping intention
7Cs Model
Rayport and
Jaworski
(2001)
Understanding quality of electronic
commerce Website design from the
online consumers’ perspective.
contents, choice, context,
comfort, convenience, support
of clients and communications
... In general, online consumer behavior has been the subject of several theories, models and constructs. According to a recent review (Chawla, Khan, & Pandey, 2015), the scientific contributions are based on the Theory of Reasoned Action (TRA) by Fishbein and Ajzen (1975), the Technology Adoption Model (TAM) (Davis, 1989;Davis, Bagozzi, & Warshaw, 1989) and the Theory of Planned Behavior (TPB) (Ajzen, 1991). ...
... Research on what drives consumers to purchase travel online is very fragmented. A recent stream of literature on the determinants of consumers' propensity to travel and accommodation online purchasing (Agag & El-Masry, 2016;Amaro & Duarte, 2013;Sahli & Legohérel, 2016;Ukpabi & Karjaluoto, 2017) shows a lot of interdisciplinary approaches (see for instance Chawla et al., 2015;van Deursen & van Dijk, 2015), applied also to tourism and travel purchases (Buhalis & O'Connor, 2005;Garín-Muñoz & Pérez-Amaral, 2011). Among the most relevant determinants of consumers' attitude to purchase online, numerous studies focus on consumers' characteristics, namely sociodemographic features, Internet and computer literacy (Card, Chen, & Cole, 2003;Morrisonn, Jing, O'Leary, & Cai, 2001;Weber & Roehl, 1999). ...
... Very recent studies have further refined the analysis by including factors related to subjective norms, highlighting in particular the importance of communicability and sociability as determinants of the intention to purchase travels online (Chawla et al., 2015). Other authors have demonstrated that attitude towards shopping through the Internet represents a determinant of intention to purchase travel online (Amaro & Duarte, 2015;Bigné, Sanz, Ruiz, & Aldás, 2010;Lee et al., 2007). ...
Article
this paper investigates whether individuals' cultural capital increases the probability of purchasing tourism services through the Internet. The identification of possible direct channels through which tourism firms can address and attract consumers is particularly relevant for an industry in which re-intermediation processes by big online travel agencies have partly dampened the benefits of disintermediation along the supply chain. Through a Heckman-corrected probit run on year 2016 microdata referred to the Italian population, evidence is found that the cultural participation is associated to individuals' higher probability to purchase tourism services online. This adds some new dimensions to the understanding of factors that have a bearing on online purchasing.
... Participants were not satisfied with website, because: there are too much choices, prices are not on the first page, and important information is hidden. Participants assessed registration process as easy, but there were some errors 6 it is possible do registration with incorrect or non-exist email address; if the address already exist, the process how make registration is not clear. To find a product is not so easy: we can use search window and write a keyword, or use toolbar (menu). ...
... It was difficult to find information about products, because the website is cluttered with information. The most difficult was to change the name in the online account 6 finding this option required a lot of time, some It was easy to use the website. 4 ...
... The ongoing study finishes in March 2016. This is a complex study, which brings together a number of methods 6 questionnaire, eye tracking, and GSR. For future work, we firstly would like to complete the analysis with other participants in the experimental study at University in Slovakia and we also would like to run a similar experiment in Thailand, and compare the results. ...
Conference Paper
The main goal of e-commerce is to improve the usability of website, which nowadays brings a competitive advantage. If them. It is so easy to visit the competitor-shop, it is only one click away. This study is an investigation of the role of emotional arousal (anxiety-like emotion) and engagement when completing online purchasing related tasks. We have chosen one of the most popular e-shops in the world - Amazon [1]. Amazon was a role model for e-commerce design for many years. [2] Nielse In 2001, we evaluated the usability of 20 ecommerce sites and Amazon was the clear winner, scoring 65% higher than the average of the other 19 sites. Having the Web's best usability served Amazon well: sales This website was tested, evaluated and then redesigned many times. [3] Despite the fact that many tests were done, we would like to verify the impact to users. There is the potential to find out new usability errors. We examine the relationship, if any, between emotional arousal and engagement during a typical online shopping experience. Our aim is to make the shopping process less stressful and easier for the customers. The study is held in the period of November 2015 till March 2016. Participants are between 18 and 65 years old. They are asked to complete a set of no more than three tasks online. For each exercise the participants read a short set of instructions and complete a small task. The task requires the participants to either register to use a website, to add items to a shopping cart or to change their details. Data are non-invasively gathered, including interaction data between the participant and the website and physiological data measuring emotional arousal (anxiety-like data) and engagement. To collect the physiological data a number of standardized made-for-purpose sensors (GSR) are worn by the participant and eye movement data are gathered using an eye-tracker. The participants are asked to answer few questions on how they feel at this point (anxious or relaxed) at the start and end of every task. If there is a relationship between emotional arousal and engagement during online shopping experience, and if it shows us, that participants are under pressure in some particular tasks, it will mean there are usability errors on website. We can identify the errors, and finally make recommendations to those responsible for their removal. They can improve website usability, make online shopping easier for their customers.
... Subsequently, few scholars have done review of the existing literatures and have reported the various frameworks that have been used over the period, its outcomes and future research directions (Bellini et al., 2017;Chawla et al., 2015;Krishnan, 2015;Luniya, 2015;Mishra, 2015;Singhai and Shekhawat, 2015;Vohra, 2016). Few of the recent studies attempted to describe the current state of research on online impulse buying behaviour, focused on theoretical foundation, research methodology, research context and factors influencing online impulse buying (Abdelsalam et al., 2020;Firdaus et al., 2019;Xiao and Nicholson, 2013;Zulfiqar et al., 2018). ...
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Online impulse buying behaviour is one of the most rapidly growing areas of research in online consumer behaviour study. Since the last two decades, it has achieved significant academic attention and developed publication opportunities. This paper presents bibliometric review of scientific literature in the field of online impulse buying behaviour. For this, 482 articles from Scopus database during 2000-2020 are considered. Three major research streams found and reviewed i.e.: a) online store characteristics; b) modelling of online impulse buying behaviour; c) factors influencing impulsive buying in online setup. Most relevant and influential authors, countries, affiliations, most cited documents, and most used keywords are identified. The paper suggests certain future research agenda in online impulse buying behaviour that have been determined from bibliometric analysis. This study offers practical implications for both academicians and practitioners.
... Subsequently, few scholars have done review of the existing literatures and have reported the various frameworks that have been used over the period, its outcomes and future research directions (Bellini et al., 2017;Chawla et al., 2015;Krishnan, 2015;Luniya, 2015;Mishra, 2015;Singhai and Shekhawat, 2015;Vohra, 2016). Few of the recent studies attempted to describe the current state of research on online impulse buying behaviour, focused on theoretical foundation, research methodology, research context and factors influencing online impulse buying (Abdelsalam et al., 2020;Firdaus et al., 2019;Xiao and Nicholson, 2013;Zulfiqar et al., 2018). ...
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Online impulse buying behaviour is one of the most rapidly growing areas of research in online consumer behaviour study. Since last two decades, it has achieved significant academic attention and developed publication opportunities. This paper presents bibliometric review of scientific literature in the field of online impulse buying behaviour. For this, 482 articles from Scopus database during 2000-2020 are considered. Three major research streams found and reviewed i.e.: a) online store characteristics; b) modelling of online impulse buying behaviour; c) factors influencing impulsive buying in online setup. Most relevant and influential authors, countries, affiliations, most cited documents, and most used keywords identified. The paper suggests certain future research agenda in online impulse buying behaviour that have been determined from bibliometric analysis. This study offers practical implications for both academicians and practioners.
... Diversos estudos realizados pelos autoresDavis, Smith, & Lang (2017), porSingh (2018), porChawla et al., (2015), entre outros. 10 Entende-se por bens não perecíveis, os bens de consistência seca e de fácil armazenamento, tais como massas, cereais, café, sal. ...
Conference Paper
O E-Commerce assume, cada vez mais, um papel relevante na economia portuguesa, na medida em que proporciona melhorias significativas no processo de compra dos consumidores, no sentido em que cria uma maior conveniência e uma experiência diferenciadora no processo de compra. Neste estudo destacamos a importância do E-Commerce no setor alimentar, que permite aos consumidores uma nova forma de efetuar as suas compra de bens alimentares. Este novo método de compra de bens alimentares, intitulado ao longo do estudo como “online groceries”, tem vindo a assumir uma relevância crescente no mercado internacional, contrariamente à, ainda, fraca percentagem de penetração no mercado português.
... In addition, literature concludes that less attention has been paid on online shopping behavior in context of Pakistan (Adnan, 2014;Ahmed, Su, Rafique, Khan, & Jamil, 2017;Ratilla, 2016). Therefore, there is need to study online shopping behavior in the future (Bhatti, Saad, & Gbadebo, 2018;Chawla, Khan, & Pandey, 2015;Kumar & Dange, 2012). ...
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