Online Shopping Behavior Model

: A Literature Review and Proposed Model

Soo Yeon Chung Doctoral candidate of e-marketing

Department of Digital Management Graduate School Korea University

chanbee@korea.ac.kr

Cheol Park Professor of Marketing

Department of Business Administration Korea University

cpark@korea.ac.kr

Abstract-In this study, we conducted extensive reviews of

online shopping literatures and proposed a hierarchy model of

online shopping behavior. We coUected 47 studies and clllssijied

them by variables used. Some critical points were found that

research framework, methodology, and lIIck of cross-cultural

comparison, etc So we developed a cross-cultural model of online

shopping including shopping value, attitudes to online retailer's

attributes and online purchasing based on the integrated V-A-B

modeL

Keywords - Online Shopping, e-Commerce, e-Tailer, Online Shopping Behavior, Online Purchase. Internet Shopping, Consumer behavior Casual Model, Online shopping adoption, V-A-B(Value-attitude-behavior) Model.

1. Introduction

The internet is inherently global in nature, as it is globally accessible and crosses national boundaries to allow consumers around the world to participate equally.

Today's online shopping is becoming increasingly popular. Many consumers regard online shopping as another

shopping channel or pattern. According to Korea national statistics, the total sales of

e-rnarketplace have hit an all times high of 4.3 trillion won in Korea as of September 2008 (NSO, 2008). The total sales of

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e-marketpalce, have reached 28.6 trillion won in 2007 and predicted 32.4 trillion won in 2008 (U.S. Department of Commerce, 2008).

Since 1990's, there have been many online consumer researches however the scope of these published studies is rather broad, and only very few literature review on online shopping behavior. Prior literature provides us with a rich foundation on which to build a research framework for the study of online shopping behavior. So, we have attempted to extensive reviews of online shopping to propose a hierarchy model of online shopping behavior by psychological process.

2. A Collection of Prior Literature

We review the prior literature of online consumer shopping behavior and analyze the theories and the underpinning factors and variables. We conducted an exhaustive and systematic electronic search for referred international major journal articles (European Journal of Marketing, Information System Review, International Journal of Electronic Commerce, International Journal of Retail & Distribution Management, Internet Research, Journal of Business Research, Journal of Computer-mediated Communication, Journal of Electronic Commerce Research, Journal of Interactive Marketing, Journal of International Marketing, Journal of Service Marketing, Psychology & Marketing, etc), in addition to the search for using academic search engines( eg, ProQuest, IEEE, Social Science Citation Index) with keywords such as "online shopping", "Internet shopping" and "online shopping adoption, online shopping intention, online purchasing behavior", in our literature search and analysis.

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Specially, we choose literature that, make use of variables is such as purchasing, purchase intention, re-buying, frequency of buying, shopping adoption in casual model and these models include not only simple casual relationship by independent variables and dependence variables, but also mediated variables and moderate variables that affect on online shopping behavior.

A total of 47 articles in area of online shopping behavior were identified from 1999 to November 2008.

3. Literature Analysis Results

3.1. Historical overview

The result that total 47 articles distinguish by year and author is showed as table 1.

T bIlL' a e lterature A I . RIb nalysls esu ts )y year an d h aut or Author Research Subject Method Result & Feature (Year) Hairong [I'he impact of perceived pemographics (gender, etal. phannel utilities, pncome, education) affect 1999) ~hopping orientations, Regression ~ttitude of online purchase,

~d on the consumer's ~owledge of channels, on-line buying ~hopping Orientation which behaviour jn turn affect online purchase.

Eastlick Profiling Potential otential innovator and & Lotsz Adopters and ron-adopter group 1999) Non-adopters of an Logit ~emberships were perceived

nteractive Electronic regression

pharacteristics 1ihopping Medium Anova innovativeness, risk, prior

~hopping experience) of 'nteractive shopping nnovation.

itrin et. iAdoption of internet ~onsumer's innovativeness I., ~hopping, The Role of Anova ~at factor is a direct and 2000) ~onsurner Regression . ndirect positive effect of

Innovativeness ~nline shopping adoption. i& An Assessment of ~onsumer characteristics and

Zhang Research on Consumer Conceptual pnline store affect attitude 2000) Online Shopping study ~hich in turn affect online

Attitudes and Behavior ~hopping behavior. Lin & owardsan S Quality (information, LU Understanding of the esponse time, accessibility) 2000) Behavioural Intention t~ Correlation ~ffect online shopping

Use a Web Site Path analysis ~ttitude, perceived usefulness ~d ease of use, which in turn liffect online re-buvinl!.

Molesw lBuying Cars Online: Depth ~onsumer have resistant of orth& ~e Adoption of the interview Far purchase by online Suortti ~ebfor Conceptual hopping but positive affect ir 2002) !High-Involvement, f/igh-Cost Purchases study earch stage.

lYoon ifhe Antecedents and Transaction security, web-site 2002) onsequences of Trust properties, search

n Online Purchase Regression functionality and personal Decisions variables affect trust and

attitude, which in turn affect purchase intention.

~hen& ~u1tural Differences in Online experience, reputation, pUbinsk the Online Behavior of Regression quality of goods affect online ~ (2003) Consumers purchase intention. Perceived

risk set antecedents to trust. ~roglu Empirical Testing of a Based on S-O-R model, ~t. aI., Model of Online Store Experiment atmosphere of website affect 2003) Atmospherics and study consumers' attitude and

Shopper Responses SEMs atisfaction of shopping site, which in turn affect adoption.

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Park & iA Cross-cultural Perceived risk and internet ~un pomparison of Internet usage affect online shopping 2003) ~uying behavior focus Anova

pn Effects ofInternet Regression behavior as welI as a moderating effect between

f1sage, perceived risks, r'onsumers of Korea and U.S. lind innovativeness

ark & dentifying Key Factors ~ttribute of online shopping It<im ~ffecting Consumer Regression fbat information satisfaction, 2003) Purchase Behavior in an

pruine Shopping ~enefit, commitment affect

rontext hopping behavior.

echtl !Adoption of Online nnovative attribute, 2003) ~hopping by German Path- ~saction condition, delivery

ProCery Shoppers analysis ponvenience positive effect pgline shopping adoption.

P'cass [Web Retailing Perceived usefulness and ~ ~doption focus on perceived ease of use effect IFrench ~xploring the Nature of f\ifferentialIy by opinion 2003) nternet Users Web PLS eadership, web shopping

~etailing Behaviour Fompatibility, self-efficacy, ~curity, impulsiveness, ~tisfaction and shopping prientation.

~hai& !A Cross-Cultural erceived behavioral control, Pavlov nvestigation of ~ttitude, subject norm affect 2004) ~Iectronic Commerce Regression pnline shopping adoption, but

~doption in Greece and ~ehavioral control non Ine United State ignificantly affect online

hoppinl! adoption in Greece peorge ifhe Theory of Planned ifrustworthiness positively 2004) ~ehavior and Internet ~ffects attitudes and

Purchasing Path- Self-efficacy positively

analysis ~ffects perceived behavioral ontrol which in turn

positively affect purchasing behavior

ayawar Personal Value' OV(List of value) affect fihena nfluence on e-shoppin~ SEMs attitude which in turn affect 2004) ~ttitude and Behaviour earch intention, re-visit,

witchinl! intention. ~ohmo ~ross-Country Using secondary data of26 fx! et. ~mpirical Research on SEMs countries, level of economic ~I., po-line Shopping

PLS condition and trust 2004) ~ehavior ignificantly affect online

hopping behavior. ~onsuw iA Literature Review on Proposed the hierarchy model ~ et. al. ~atDrives based on prior literature. 2004) ~onsumers to Shop Conceptual Proposed moderator

pruine. study determines consumer characteristics, good traits, and online experience.

~hih An Empirical study on erceived ease of use, 2004) rredicting User atisfaction, online service

Acceptance of Regression quality, security, and e-shopping on the Web onnection cost affect attitude

which in turn affect online hopping behavior.

"iore et Hedonic Value from Experiment IT(Image interactivity ~I., mage Interactivity and study echnology) affect value 2005a) Responses Toward an

Online Store focus on which in turn affect purchase

fun and profit SEMs 'ntention and persistent visit.

Fiore et Effect of Image IT(Image interactivity aI. nteractivity Experiment ~chnology) affect hedonic 2005b) Technology on study ~alue (arousal variables,

Consumer Responses Path- Fmotional pleasure), which in Toward the Online analysis ~ affect online purchase Retailer ·ntention.

Grunert onsumer's willingness ~posed the hierarchy model & to Buy Food through the Conceptual ~ased on TPB and consumer Ramus nternet study ~ecision making theory in 2005) prior literature.

HolIowa rhe Role of Cumulative SEMs ","on-heavy user group more

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Online Purchasing Fffective satisfaction by 2005) Experience in Service ~ice recover. Heavy user

Recovery Management ~oup more effective re-buying by satisfaction of recover.

Ftal., ~ural Consumer ~ographics affect 2007) ~doption of Online nnovativeness,

~hopping ~tworthiness, prior Fxperience, which in turn fUrect online shopping

Kim and A Consumer Shopping ~earch intention for product jJehavior. Park hannel Extension . nformation via the online 2005) Model focus on Attitude tore was the strongest

~e ~onsumer Attitude ~onsumer risk averseness is 2007) ~oward Virtual Stores regatively related to

Shift Toward the Online SEMs

predictor of consumer's ~tore flDline purchase intention as

~ell as a mediating variable

~d its Correlates ponsumer attitude, whereas

SEMs ponsumer convenience brientation and the impulse

between predictor variables endency are both positive and purchase intention. related with consumer attitude

~ee& ~ustomer Perceptions Web-site design, reliability, toward virtual store. ~in pf e-service Quality in responsiveness, and trust 2005) pruine Shopping

SEMs affect overall service quality and consumer satisfaction

pupta& lTbe Moderation Effect Based on customers' decision !Kim pfTransaction making model, shows that the 2007) Experience on the effects of p.convenience and

which in turn significantly Decision Calculus in SEMs P. Price change over the related to purchase intentions. On-Line Repurchase transaction experience,

Martine pnIine Shopping, the Different levels of consumers' -Lopez ~tandard Learning . nternet expertise determine

etAI., ~ierarchy, and the predominance of the 2005) ~onsumers' Internet

SEMs central or the peripheral route

~xpertise- An within the information of thei ~merican-Spanish ffective and behavioral ~omparison responses to the medium.

whereas the effects ofP.value and pleasure do not.

~marul Adoption of travel o identify the profile of aman e-shopping in the UK online shoppers and

et. al. antecedents of online 2007) SEMs hopping adoption

Demographic and buying ~oet.al. factors Affecting Web-shopping intention are patterns) for travel services 2005) ntentions to Purchase directly affected by using TAM.

~a the Internet web-search behavior and web Kimet. nfluences of Online SET (sensory-enabling hopping adoption decision, I., Store Perception, echnology) affect attitude,

SEMs and are indirectly affected by 2007) Shopping Enjoyment, p.uesfulness, p.esae of use, web-shopping attitudes, past and Shopping Experiment njoyment, which in turn web-shopping experiences and past experience with the web.

nvolvement on study ~ffect usage, evaluation. onsumer Patronage SEMs ~oposed moderator

Behavior Towards an f!etermines anxiety of Sorce et. Attitude and Age There is no difference Online Retailer echnology and aI., Differences in Online T-test

between old/young consumer 2005) Buying

Regression ~up, but significantly difference in frequency of

nnovativeness. MukheIj Role of Electronic Trus ~vacy and security features e& ·n Online Retailing - A pfthe website along with

goods and sort of goods. Nath re-examination of the ~hares values are key Eastlick Understanding online rrrust, commitment, et. al., B-to-C Relationship: ~utation are positively 2006) An Integrated Model of SEMs related to online purchasing,

2007) pommitment-trust ~tecedents of trust, which in ~eory SEMs urn positively influence

relationship comment. The Privacy Concerns, ~hereas privacy is negatively relationship termination cost

rust, and Commitment ~lated with online purchase. ~s a negative impact on ~ackma A Service Perspective [Behavioral intentions are ret. aI., On Modeling Intentions ~irectly influenced by online

Fonsumer commitment. [Wong f'\ Research on sociality f'\ vatar, as social que,

2006) of on-line Purchasing ~ervice quality, online service Ft. al., pf Retail Web Site Experiment positively influence flow,

PLS ~alue and online service ~atisfaction. Online service

2007) study ~alue (search, utilitarian), and . ntention( search, purchase).

~atisfaction, in turn, is lHausma Ifhe Effect of Web Ifests a wide range of design ~ffected by online service ~alue and quality.

Demang onceptualizing ~ense-making and eot& Fonsumer behavior in Fxploratory potential are Broderic flDline shopping ~stinct constructs,

fmvironments SEMs Fxploratory potential mediates 2007) ~e relationship between

r& ~terface Features on Flements (human element, ~iekpe ~onsumer Online SEMs pomputer element) are linked 2008) rurchase Intentions ~odel using gratification

theory, TAM, flow. ose-Ca lTbe Moderating Role 0 Value (search, affect) affect

"ezudo rser Motivation in Experiment attitude (web-site, brand) Ft. al., nternet Access and study which in turn affect online

~ense-making potential and nvolvement.

2008) ndividuals' Responses SEMs purchase. Proposed moderato toa Website determines motivation.

fiore& ~n Integrative rroposed the hierarchy model

!Kim framework Capturing Conceptual ~ased on C-E-V, C-A-B and

2007) ~xperiential and study S-O-R model in prior

rtilitarian Shopping ~xperience iterature.

~m& Sensory Enabling p. usefulness and forsythe echnology Acceptance p.entertainment value as 2008) Model

SEMs trong predictors of

consumers' attitudes toward using sensory enabling

Pood& pnIine Behavioral e-tailer attributes (reputation, !Harris ntentions: An banner ad, security, trust, 2007) ~mpirical Investigation

Correlation design, contents) affect online

pf Antecedents and Moderate

hopping intention. Switching ~oderators regression

cost mediates the relationship between attribute and

echnologies. l<.im& The Effects of Visual

Experiment Visual, viral stimulus of

Lennon, and Verbal Information study

·nformation significantly 2008) on Attitudes and ffect attitude. But only viral

Purchase Intentions in Anova . nformation turn in affect

Internet Shopping Regression purchase intention.

·ntention. Lennon A Longitudinal Look at SEMs Education, income,

Ha& Antecedents in a SEMs

-sopping quality (trust, Stoel echnology Acceptance p.usefulness, enjoyment),

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in. ulture, Product Type, ndividualism is only culture d Price Influences on . mension to have a

Experiment ignificant effect on purchase study . ntention. Product type and

Anova 'ndividualism by price Regression 'nteraction also have a

ignificant effect, whereas rice does not. ignificant differences exist tween e-shopper and

Anova on-e-shoppers with respect the subscale of thrill and

Based on the analysis of historical overview between literature related online shopping behavior models, we found stream of subject on online shopping behavior research by year.

Since 1999, literature that is relevant to online shopping behavior may be broadly divided into three streams of research.

One stream primarily deals with the main factors influence online shopping adoption. For example, several researchers examined the effects of demographics, web-site attribute, and quality of e-tailer on online purchase or continuous online shopping. Another stream of research may be deals with the motives or value for online purchase involving attitudinal (affective, cognitive, behavioral) antecedents to online purchase. For example, several researchers attempted to find antecedents and consequents of perception and attitude. In recent year, an increasing number of researches on sensory, image interactivity, image stimulus focus on experience economy which is third stream of research approach to regard more important consumers' experience than consumers' perception, motive, satisfaction and attitude. As part of our analysis, we identified some main theories that are used by literature. Most research based on Theory of Reasoned Action (TRA) and its family theories including the Technology Acceptance Model (TAM) and the Theory of Planned behavior (TPB). Many researcher attempts to examine factors influence online shopping adoption using extended TAM.

3.2. Classification of variable groups

We classify variables and factors of literature relevant online shopping behavior by variable group. The result that totals 47 articles classification of variable groups is shown as Figure 1.

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~V'-}-~-'-~---l Cultur€', Purcrlas!? exoeHente II

TYP€' oi GOOdS, COIlSUmif?r Cna"!ttf.'nstl(

Web-silEo reeftE'~IL Rls~ I .. __ .J

Figure 1. Literature Classification by variable groups

3.3. Classification of factors type

We classify variables and factors of literature relevant online shopping behavior into 4 factor types (consumer factors, perception/attitude, Behavior, Web site Attribution). The result that totals 47 articles classification by variables is shown as Table 2.

Table 2. Literature Classification by variables

Consumer FactOfll

Perception

fAlt.ode

Web .... Allributl!

Demographics. Value • Molivation. Knowfedge. : Irmovalivenes.. self-efficacy. Intention 0/ Impulse : buying. Control. Involvemenl. Onlne Experience

I Perceived Risk· Benefit. u.efulne .... easy 01 usc. FklW. , Trust. Alt.ucla. Satisfaction. Affect

, Frequency of visit. Word-of Mouth. Participation. l ny.My

'Service Quality, Desigll. Contents. Navigation. : Communication. SeCldy. PrivacY. Reputation. Delivery. : Enjoyment. Conwnience. Interactivity. Goods. Transaction. Speed

4. Discussion and Limitations of prior literature

Most researches are based on TRA, TAM, TPB. Many researchers regard online shopping as innovative service. Thus we found some variables relevant to TAM from most of the literature. However online shopping is becoming increasingly popular and generalized. In this situation, we need the theory and model that explain online consumer behavior properly for the occasion from traditional consumer behavior theories. Researchers, therefore, should try to explore new theories and frameworks and investigate online consumer behavior from different perspectives and angles.

Although there are international studies on online shopping behavior, only a few studies (c.f. Moon et. aI., 2008; Martinez­Lopez et aI., 2005; Mohmood et. aI., 2004; Chai & Pavlov, 2004; Park & Jun, 2003) examining cultural influences on Internet marketing have been carried out.

None of the studies to our attention have investigated how cultural values are depicted on the online shopper buying behavior of different countries, and how describe a causal

Feb. 15-18, 20091CACT 2009

relationship between shopping value, attitude, and behavior on e-marketplace. Recognizing the importance of processing shopping value, attitude, consumer buying behavior, and lack of cross cultural studies on online shopping.

5. Proposed Model

After reviewing the relevant literature, we propose a conceptual model that comprehensively link online shopping behaviors and its antecedent factors.

We develop a conceptual model called C-V-A-B (culture-value-attitude-behavior) model by synthesizing the identified factors and presenting a holistic view of consumer decision making process on online shopping. Our C-V-A-B model is shown as Figure 2. The conceptual model was developed based on V-A-B (value-attitude-behavior) model (Homer and Kahle, 1988).

This model investigates the psychological process that consumer experience during shopping. More specially, this study is to employ a value-attitude-behavior model in order to investigates the structural relationship between culture, shopping value (hedonic, utilitarian), online shopping site attitudes (evaluation of e-tailor site attribute, evaluation of EC system), consumption behavior (volume and frequency of buying) consumer characteristics (innovativeness, intention of web experience) and Trust Ques (online review, Reputation of seller, vividness of information).

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I - .~

i ..... --IJ< CCnooJmptIcn v ..... ~--I~:""'~ .--

I~of~, .-- E_alEC ._"-- ._.o-I -il'lltlContex1 w, --.-J '-<>""""" -.- cr..-. I

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Figure 2. Theoretical Framework of extended V -A-B Model on online shopping

Homer and Kahle(1988) also verified the causal relationship underlying the value-attitude-behavior hierarchy in the very specific context of natural food shopping by using the structural equation modeling technique. Homer and Kahle(1988) suggested, however, that this hierarchical model should be tested in different situations(Shim and Eastlick, 1998; Homer and Kahle, 1988), even so, to date, none of the studies have examined the relative importance of personal value on the attitudes and behavior in the e-marketplace.

According to Homer and Kahle(1988), previous researchers held that values, explicit or implicit, function as grounds for behavioral decisions in general and consumption behaviors in particular(e.g., Carman, 1977; Williams, 1979).

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Kahle(1980) argued, however, that values have only an indirect effect on consumer behavior through less abstract mediating factors such as domain-specific attitudes. The value-attitude-behavior hierarchy model implies that "the influence should theoretically flow from abstract values to mid-range attitudes to specific behaviors" (Homer and Kahle, 1988).

We will investigate the cross cultural difference between online shopping behavior on the United Kingdom and South Korea. The United Kingdom is regarded as representative of Western culture. South Korea as representative of eastern culture for the purpose of analyzing cultural value compare with western culture.

To analyze the depiction of cultural values on the online shopper buying behavior, we use the value-attitude-behavior hierarchy model by Homer and Kahle (1988).

The reasoning is that consumers live with a traditional core culture encompassing values, attitudes, beliefs, perceptions, and behaviors based on that culture (Cho, et aI., 2005; Hallowell, 1972). An assumption of the study of a cultural group is that the behavioral patterns characteristic of a particular culture, express the shared values and beliefs of that culture (Singh, 2006).

5-1. Cultural Influences on Buying Behavior

Consumer buying behavior interact with Cultural difference criteria of high versus low contexts, perceived risk distance, and individualism-collectivism on e-marketplace.

Previous research studies have shown that most Asian cultures (e.g., Japanese, Chinese, and South Korean) fall at the high· context culture end of the continuum, which means that consumers in these cultures tend to take very little invest in the coded, explicit part of the information message (Cho and Cheon, 2005; Gudykunst and Nishida, 1986).

5-2. consumption Values (Hedonic Value !Utilitarian Value)

Many researchers have classified consumer value(c.f. Rokeach 1973; Kahle, Beatty, & Homer 1986; Sheth, Newman, and Gross 1991), this study examine hedonic value and utilitarian value (c.f. Rintamaki et. aI., 2006; Carpenter et. aI., 2005; Pierre et. aI, 2000; Babin et. aI., 1994).

The purpose of further study is to measure consumer values of online shopping-hedonic and utilitarian values-and to explore the relationships between consumer value of online shopping and consumption behavior.

5-3. Evaluation of e-tailor site Attribute

The evaluation of web-site attribute have a direct effect on consumer buying behavior in the e-marketplace differ from traditional marketplace. Several Researchers suggest web-site attribute dimension (Hairong et. aI., 1999; Li & Zhang, 2000; Yoon, 2002; O'cass & French, 2003; Park & Kim, 2003; George, 2004; Shih, 2004; Lee & Lin, 2005; Martinez-Lopez et. aI., 2005; Good & Harris, 2007; Mukherjee & Nath, 2007; Wong et. aI., 2007; Hausman & Siekpe, 2008; Kim & Lennon,

Feb. 15-18, 20091CACT 2009

2008; Rapp et. ai., 2008; Chau, Au and Tam, 2000; Stevenson et. ai., 2000; Haubl and Trifts, 2000; Lynch and Arely, 2000; Li et aI, 1999, Lohse and Spiller, 1999; Lynch and Arely, 2000). This study classified into three web-site attributes dimensions, structures, contents, operations of web-site.

5-4. Evaluation ofEC system

The Perceived risk that very importance variable for adaptation of online shopping (c.£. van Noort et ai., 2008; van Noort, Kerkhof, and Fennis, 2007; van den Poel & Leunis, 1999; Javenpaa and Todd, 1997; Swaminathan et. ai., 1999; Park and Jun 2003; Pires et. ai., 2004; Corbitt, Thanasankit, and Yi, 2003).

Many marketing practitioners and researchers continue to be interested in perceived risk because it is more powerful in explaining consumer behavior and also because the theory has intuitive appeal and broad application (c.f. Forsythe and shi, 2003; Kimery and McCord, 2002; Javenpaa, Tractinsky, and Vitale, 2000)

5-5. Moderating Effects of Consumer Characters and Trust Ques(Innovativeness, online experience, Information vividness, seller Reputation, Online WOM)

We investigate consumer characteristics and trust ques, which set mediated variables.

Previous research studies have shown that the higher degree of innovativeness (c.f. Kenneth and Yang, 2006; Lee et. ai., 2006; Wang et. ai., 2005; Brian et. ai., 2005; Lu et. ai., 2005; Lassar et. ai., 2005; Ha and Stoel, 2004; Park and Jun, 2003), online experience(Hairong et. ai.,1999; Guta & Kim, 2003; Monsuwe et. aI, 2004; So et. ai., 2005; Lenon et. ai., 2007) and online skill (c.£. Hauble 1999; Lee 2002) are more effect of e-commerce adaptation (Anderson and Srinivasan 2003).

Trust Ques(c.f. van Noort et ai., 2008; Snoj et. ai., 2004; Sweeney and Soutar 2001; Sweeney et. ai., 1999) positively influence online shopping behavior.

5-6. Research Questions

We propose the following research questions; RQl. Is there a cultural difference in the effect of online

shopper buying Behavior on the E-marketplace? RQ2. Are there cultural differences in effect of factors

(influenced of buying behavior) on the E-marketplace? RQ3. Is there a cultural difference in the effect of V-A-B

Model on the e-marketplace?

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