O BEHAVIOUR A BRIEF REVIEW AND UPDATE

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 *)


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

Documents

O BEHAVIOUR A BRIEF REVIEW AND UPDATE