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Sustaining Online Shopping: ModeratingRole of Online Shopping Motives

CHUANLAN LIUSchool of Human Ecology, Louisiana State University, Baton Rouge, Louisiana, USA, and

National Public Livelihood Economy Research Center, Central University of

Finance and Economics, Beijing, China

SANDRA FORSYTHE

Department of Consumer Affairs, Auburn University, Auburn, Alabama, USA

This research tests the effects of Technology Acceptance Model

factors (usefulness, enjoyment, and ease of use) on the use of the

online channel for information search and online purchase in

the post-adoption context. By applying the Prospect Theory and

introducing the concept of motivational approach–avoidance

conflict, this research also examines the moderating role of the sim-

ultaneous online shopping motives of pursuing benefits and avoid-

ing risks in online shopping behaviors. Multi-group comparisons across shoppers with high=low motivational conflict (benefit percei-

vers vs. risk perceivers) show that online shopping motives limit the

predicting power of the Technology Acceptance Model in the

post-adoption context. Theoretical and practical implications are

provided.

KEYWORDS e-tailing, online shopping, post-adoption, Prospect

Theory moderating effects, shopping motivations

INTRODUCTION

The success of the online shopping channel depends more on post-adoptionuse of the channel for purchasing an increasingly wide range of productsthan on initial adoption. However, there is a lack of research exploringpost-adoption online shopping behavior (Shih and Venkatesh 2004). Thisresearch examines whether the drivers behind initial acceptance of the

Address correspondence to Chuanlan Liu, School of Human Ecology, Louisiana State

University, 330 Human Ecology Building, Baton Rouge, LA 70808, USA. E-mail: [email protected]

Journal of Internet Commerce , 9:83–103, 2010Copyright # Taylor & Francis Group, LLCISSN: 1533-2861 print=1553-287X onlineDOI: 10.1080/15332861.2010.503848

83



online shopping channel can explain and predict post-adoption usage in anattempt to answer the following questions. Do online shopping motivesinherited from traditional consumer studies affect post-adoption online shop-ping behavior? Why is it that consumers who perceive a high level of risk

associated with online shopping may still choose to shop online? Recent studiesexamine direct or mediated effects of utilitarian and hedonic shopping motiveson online shopping behavior (e.g., Close and Kukar-Kinney, forthcoming; To,Liao, and Lin 2007); but how consumers’ motives for pursuing benefits andavoiding risks simultaneously shape their use of the online channel has not

yet been fully examined. A better understanding of how such motives worktogether to impact consumers’ online shopping behavior is necessary todevelop an efficient approach to keep consumers shopping online.

This research represents the first attempt to integrate two dominanttheories—the Technology Acceptance Model (TAM) and the Prospect

Theory—to gain a more in-depth understanding of why consumers, eventhose who perceive considerable risk in shopping online, choose to continueshopping online. It is conducted in response to a call for both theory and cor-responding models to be developed in the context of post-adoption(Limayem, Hirt, and Cheung 2007). Based on this theoretical frameworkand empirical findings, it is clear that, in addition to promoting the benefitsof the Web site to retain current online shoppers and encourage more onlinepurchases, e-tailers may find it effective to develop marketing strategies that

will create a sense of loss among shoppers who fail to purchase online.The TAM (Davis 1989) represents a dominant theoretical framework for

studying the adoption of technologies, including the Internet (Venkateshet al. 2003). The TAM has been extended from the workplace to individualacceptance of technology-based innovations (e.g., Bruner and Kumar 2005)and extensively adopted for explaining and predicting online shoppingbehavior. Yet, the degree to which the TAM factors affect post-adoption usagehas not been fully examined (Kollman 2004), and those factors inherited fromthe TAM need to be re-examined in the context of post-adoption use of the online shopping channel to better understand the diffusion of the onlinechannel (Zhou, Dai, and Zhang 2007). Some extant research explores

post-adoption usage, but it focuses on adoption within the workplace, whichmay differ from voluntary adoption, since external pressure in the workplaceimpacts the adopters’ acceptance behavior. However, a consumer’s accept-ance of the online channel is voluntary. Thus, instead of being pushed by external pressure, the consumer’s use decisions are driven by perceived utility derived from the decision or action.

Perceived utility is a trade-off between gains and losses from a decisionor action. Thus, the motives of pursuing benefits and avoiding risks simul-taneously affect the way consumers weigh gains and losses in thedecision-making process. Consumers will respond differently to the per-

ceived benefits and risks associated with shopping online, depending on

84 C. Liu and S. Forsythe



whether their dominant motives for shopping are pursuing benefits oravoiding risks, suggesting that motives for using the online channel may put some boundary conditions on the explanatory power of the TAM. In fact,it has been proposed that shopping motives shape consumers’ attitudes

toward stores and their store choice behavior (Morschett, Swoboda, andFoscht 2005). As such, it is useful to investigate the moderating effects of online shopping motives on continuing usage of the online channel in apost-adoption context.

To this end, this research tests the effects of TAM factors (usefulness,enjoyment, and ease of use) on post-adoption use of the online channelfor information search and online purchase and the moderating effects of online shopping motives (i.e., among benefit seekers and risk avoiders) onpost-adoption use of the online channel. A conceptual model of post-adoption use of the online channel is developed through combining aspects

of the TAM (Davis 1989; Davis, Bagozzi, and Warshaw 1992) and theProspect Theory (Kahneman and Tversky 1979) within the consumerdecision-making process. Based on the conceptual model, a set of researchhypotheses consistent with the research objectives are proposed, and theresearch methodology, results, and discussion are presented.

CONCEPTUAL MODEL AND RESEARCH HYPOTHESES

Figure 1 depicts the proposed conceptual model, specifying the online infor-mation search and online purchase as two types of online channel usage.Usefulness and enjoyment are the two factors directly driving the use of the online channel, instead of being mediated by attitude and ease of useand indirectly influencing post-adoption usage by increasing perceptions

FIGURE 1 Research model and hypotheses. Note . Dashed lines denote moderating effects.

Sustaining Online Shopping 85



of usefulness and enjoyment of using the channel. Online shopping motivesare proposed as moderating variables.

Post-Adoption Usage of the Internet for Shopping

The Internet can facilitate consumer decision-making at each step of the pro-cess (Liu 2007). Use of the Internet for shopping is not a one-time decision,nor need it include all steps of the decision process. Online consumers makeuse of the Internet because of its power as an information search vehicle,even though an online search may not result in an online purchase. Researchhas categorized the use of the online channel into partial versus full usagebased on the number of decision-making steps completed through the chan-nel and has suggested that consumers’ adoption often moves from partialadoption to full adoption (Liu). It has been argued that online pre-purchase

activities (such as information search) and online purchase are distinct andshould be treated separately, since different antecedents may influence eachuse pattern (Moe and Fader 2004; Close and Kukar-Kinney, forthcoming).Intention to use the Internet to search for information is the strongest predic-tor of Internet purchase intention and mediates relationships betweenpurchase intention and other predictors (i.e., attitude toward Internetshopping, perceived behavioral control, and previous Internet purchaseexperiences; Zhou et al. 2007; Shim et al. 2001).

Furthermore, use of the Internet for information search predicts immedi-

ate or future purchase. For instance, Moe and Fader (2004) empirically testedhow different types of pre-purchase visits influence purchase intentionsusing page-to-page click stream data from a given online store. Their empiri-cal findings confirmed that different types of pre-purchase visits contribute toimmediate or future purchase.

H1: More use of the online channel for information search leads to moreonline purchasing.

Usefulness, Enjoyment, Ease of Use, and Online Channel Shopping

Usefulness affects online shopping intention both directly and indirectly through the shopper’s attitude. As a measure of expectation, usefulnessaffects IT users’ satisfaction and, consequently, continued use intention(Xiaoni and Prybutok 2003; Schaupp 2010). Consumers seek value throughthe shopping process; thus, usefulness is a significant criterion for consumersto continue using the shopping channel, since such beliefs reflect shopping

value from an online channel that may not be available from alternativeshopping channels. The online channel has strong usefulness over otherchannels in terms of obtaining product information and alternative product

comparisons (Bhatnagar and Ghose 2004). Purchasing online may also




provide value not available from other channels such as convenience, lessphysical effort, broad product selection, and availability.

H2a and H2b: Usefulness of the online channel for shopping leads to more(a) online information search and (b) online purchasing.

Enjoyment influences the adoption of technology-based innovation(Davis et al. 1992), attitudes toward the Internet shopping channel (Childerset al. 2001), and the use of technology-based self-service (Dabholkar andBagozzi 2002). Both usefulness and enjoyment influence consumer adoptionof Internet devices, with enjoyment contributing more to consumer adoptionthan usefulness (Bruner and Kumar 2005), as consumers pursue bothutilitarian and hedonic value through shopping. For instance, online apparelretailers may provide virtual models for trying on products or options for

customizing a customer’s order, creating a cognitively and aesthetically richshopping environment not readily imitable in the traditional shopping world.Hedonic, fun, relaxing feelings evoked by a Web site impact the shoppingexperience of the customer (Childers et al.; Huang, Lurie, and Mitra 2009).Users who exhibit a high degree of pleasure while shopping spend moretime visiting a retailer and tend to choose the retailer for purchasing (Kim,Fiore, and Lee 2007).

H3a and H3b: Enjoyment from using the online channel leads to more (a)online information search and (b) online purchasing.

The original TAM posited and found that ease of use affects the useful-ness of a system. Studies on technology acceptance in the consumer domainhave also tested this relationship, finding that as consumers believe systemsare easier to use, they perceive these systems to be more useful (Bruner andKumar 2005; Kee-Sook, Jeen-Su, and Heinrichs 2008). Online shoppers enjoy using a channel that is easy to use and have fun performing a given task on asystem that is easier to use than on a system that requires more effort to figureout how to make it work. As the online channel becomes easier to use forinformation search and online purchase, it provides shoppers with a greater

sense of online shopping enjoyment.

H4a and H4b: Ease of use indirectly affects post adoption usage throughits impact on (a) usefulness and (b) enjoyment.

Moderating Role of Online Shopping Motives

Motives refer to the processes initiated by aroused needs that lead to behaviorsto achieve benefits or avoid undesired outcomes (Solomon 2009). Motives

have been classified as utilitarian and hedonic (Hirschman and Holbrook




1982). Sheth (1983) defined shopping motives as ‘‘a customer’s needs and wants related to the choice of outlets at which to shop for a specific productor service class’’ (p. 15) and postulated that consumers match their shoppingmotives against retailer attributes when establishing their shopping prefer-

ences for retail outlets. Consequently, shopping motives have been widely studied to understand consumer patronage behavior and outcomes.Some shopping motives are more utilitarian, while others are more

hedonic. The magnitude of utilitarian and hedonic shopping motives variesacross retail channels chosen (Maher, Marks, and Grimm 1997; Eastlick andFeinberg 1999; Close and Kukar-Kinney, forthcoming). e-Commerce litera-ture documents that both hedonic and utilitarian motives drive visits toonline stores; however, utilitarian motives, including convenience, productselection, product information, prices, and promotion, have a more salienteffect on online buying than hedonic motives such as new experiences, free-

dom, and control (Bridges and Florsheim 2008; Wolfinbarger and Gilly 2001;Zhou et al. 2007).

Research on shopping motivation and patronage outcomes focuses onhow consumers are motivated to approach desired outcomes. However,shopping motives have valence, which means that they can be positive ornegative (Forsythe, Petee, and Kim 2003; Solomon 2009), and consumersmay be motivated to avoid a negative outcome. For instance, while an onlineshopping channel serves consumers’ needs for convenience, broad productselections, and excellent product information, it also activates negativemotives for avoiding financial, privacy, time, and product risks (Forsythe

et al. 2006; Bhatnagar and Ghose 2004). Much research has been devotedto perceived risks associated with online patronage behavior and outcomesand strategies to reduce perceived risk (Cho and Lee 2006). When consumersmake a choice to shop online, both positive motives for pursuing benefitsand negative motives for avoiding risks impact their choice simultaneously,

which means an approach (benefits)–avoidance (risks) conflict exists duringthe channel choice-making process. However, no published research hasexamined how the motivational conflict of approach–avoidance affectsconsumers’ use of the online channel for shopping.

The utility that motivates consumer shopping behavior has traditionally been interpreted as the trade-off between what is given up and what isreceived in return (Dodds and Monroe 1985; Yadav and Monroe 1993), thatis, the perceived net gain or benefit versus the perceived net loss or risk.Consumers strive to maximize the perceived utility of their shopping experi-ence by assessing the tradeoffs between the expected benefits and the per-ceived risks associated with shopping online. When online consumersperceive significantly greater benefits over risks associated with the onlinechannel, they experience less approach–avoidance conflict. When consu-mers perceive benefits but also high risks associated with shopping online,

they experience a high approach–avoidance conflict. Overall, motives of




pursuing benefits and avoiding risks working together lead online consumersto experience different degrees of motivational conflict.

Consumers select retail channels that meet their expectations ondominant shopping motives (Gehrt and Shim 1998; Bhatnagar and Ghose

2004). Furthermore, shopping motives shape consumers’ perceptions of and attitudes toward stores and their store choice behavior (e.g., Morschettet al. 2005). When consumers perceive high benefits and low risk, they experience a low approach–avoidance conflict, and the dominant shop-ping motives are often to pursue benefits. These shoppers tend to havea high expectation of benefits and choose a channel to meet their expec-tations of achieving the desired benefits. The effects of perceived onlineshopping benefits on the decision to shop online may be weakened by consumers’ high expectations of obtaining benefits from shopping online.

When consumers perceive both high risks and high benefits, they experi-

ence a relatively high approach–avoidance conflict. Thus, risk avoidancemay be the dominant motive shaping their channel choice, because con-sumers are more sensitive to losses than to gains when decisions areframed in terms of potential gains. However, sure gains (i.e., benefits)encourage consumers to overcome anticipated losses because of onlineshopping risks. Consequently, the power of perceived benefits of shop-ping online to impact channel choice may be greater among those whohave a high motivational conflict than among those who have a low motiva-tional conflict (but high expectations of online shopping benefits). In addition,decision-making in a high motivational conflict condition requires much

cognitive effort, reinforcing the importance of easy and effective informa-tion search and choice to facilitate the online shopping process (Smith andSivakumar 2004).

An enjoyable shopping process may increase the likelihood of onlinepurchase among shoppers with a low motivational conflict; however, it doesnot ease the anxiety caused by a high motivational conflict. Thus, enjoymentmay have stronger influence upon online information search and purchasefor shoppers with a low motivational conflict than for those with a highmotivational conflict.

Furthermore, time and effort spent online getting information to makea purchase decision—a relatively risk-free use—leads to reduced uncer-tainty about the outcome. Consequently, for those online shoppers whoexperience a high motivational conflict, using the online channel for infor-mation search reduces their perceived risk and the degree of conflictexperienced, increasing the probability of online purchase. Hence, theimpact of online information search on online purchase behavior may differ for shoppers experiencing different degrees of motivational conflict.Thus, motivational conflict between pursuing online shopping benefitsand avoiding online shopping risks may moderate several of the hypothe-

sized relationships.




H5a: Online information search is more strongly related toonline purchase for shoppers with more motivationalconflict than for those with less motivational conflict.

H5b and H5c: Usefulness is more strongly related to (b) online infor-mation search and (c) online purchase for shoppers witha higher motivational conflict than those with less motiva-tional conflict.

H5d and H5e: Enjoyment is more strongly related to (d) online infor-mation search and (e) online purchase for shoppers witha low motivational conflict than those with a high motiva-tional conflict.

METHOD

Data Collection

Data were collected through a commercial online survey service providerbecause of its large panel size, low service price, and incentives for parti-cipants. The selected provider awarded every participant the same numberof e-points to be used to claim online gift cards from e-tailers. Stratified sam-pling (Trost 1986; Lohr 2009), based on U.S. online population demographicsincluding gender, age, and income, was used to obtain a representativenational sample of online shoppers. Fifteen hundred people were identified

from the stratified sampling and were sent invitations by way of e-mail. A total of 789 responses were received, indicating that 53 percent of the samplepopulation responded. Duplicate responses were identified through IPaddresses and time stamps for answering the survey. After eliminating allduplicate and incomplete responses, 598 valid responses were obtained(39.9 percent).

Measures

Research constructs were measured using 7-point multi-item scales(1¼ strongly disagree; 7¼ strongly agree). The measures of usefulness andease of use were adopted from Moore and Benbasat (1991). The measurefor enjoyment was adapted from Childers and colleagues (2001) by changingthe term ‘‘technology-assisted shopping’’ to ‘‘online shopping.’’ Measures of online shopping motives were adopted from Forsythe and colleagues (2006),

with 16 items measuring online shopping benefits and 12 items measuringonline shopping risks. Sustained online shopping was assessed by askingthe frequency of (1) searching for product information and (2) onlinepurchases in the past six months, as well as the intention to conduct these

two shopping activities online in the next six months. In this way, the




measure represents the degree of sustained usage of the online channel by examining the current and intended frequency of each activity in a singleconstruct.

Exploratory factor analysis (EFA) was used to examine the basic struc-

ture of the measures of variables in the proposed research model. Using aprincipal axis extraction method, the measures of usefulness, enjoyment,ease of use, and the two sustained online shopping behaviors (informationsearch and purchase) were analyzed with a varimix rotation (Andersonand Gerbing 1988). The final factor analysis solution, with 13 items measur-ing 5 factors, accounted for approximately 71.3 percent of the total variance.Communalities were between 0.58 and 0.91 (see table 1). Confirmatory factoranalysis (CFA) for the research constructs of usefulness, enjoyment, ease of

TABLE 1 Scale=Item Measurement Properties

Constructs=itemsCronbach’s

alphaEFA itemloading

CFA itemloading

Scale=itemmean

Usefulness 0.84Using the Internet to shop improves the

quality of my shopping0.70 0.67 4.66

Using the Internet to shop helps me toaccomplish shopping tasks more quickly

0.73 0.70 5.29

Using the Internet to shop enhances my effectiveness in shopping

0.71 0.72 5.06

Using the Internet to shop makes it easier to

do my shopping

0.77 0.81 5.53

Enjoyment 0.83Internet shopping is exciting 0.85 0.71 4.97Internet shopping is boringa À0.70 0.66 2.68Internet shopping is enjoyable 0.73 0.82 5.18Internet shopping makes me feel good 0.66 0.77 4.71

Ease of use 0.69Internet shopping doesn’t require a lot of

mental effort0.92 0.97 4.18

Internet shopping is easy to do 0.54 0.41 5.62Internet shopping is clear and

understandable0.58 0.76 5.05

Information search nab

Frequencies of product information searchduring last six months

0.81 0.89 3.76

Intended frequencies with which productinformation would be sought in next sixmonths

0.78 0.89 3.62

Online purchase nab

Frequencies of online purchase during lastsix months

0.91 0.87 2.43

Intended frequencies of online purchase innext six months

0.88 0.96 2.45

aItem was reversed when conducting all statistical analyses.b

Not available.




use, online information search, and online purchase resulted in anacceptable measurement model (goodness of fit index [GFI] ¼ 0.955, com-parative fit index [CFI]¼ 0.965, v2

ð55Þ¼ 181.09, root mean square error of approximation [RMSEA]¼ 0.06).

The 28 online shopping motive items were then subjected to factoranalysis separately from those constructs included in the proposed researchmodel (Dabholkar and Bagozzi 2002), resulting in three dimensions of posi-tive motives (convenience, product information, and shopping experience)and two dimensions of negative motives (channel risks and product risks).The final factor analysis solution, with 23 items measuring 5 factors,accounted for approximately 63.3 percent of the total variance. Communal-ities were between 0.50 and 0.74 (see table 2). CFA with the five dimensionsof shopping motives also resulted in an acceptable measurement model(GFI¼ 0.89, CFI¼ 0.90, v2¼ 820.28, df ¼ 217, RMSEA ¼ 0.068). Table 3

shows correlations between research constructs and extracted variance foreach construct.

TABLE 2 Scale=Item Measurement Properties of Online Shopping Motives

Constructs=itemsCronbach’s

alphaEFA itemloading

CFA itemloading

Scale=itemmean

Convenience 0.88I don’t have to leave home 0.83 0.74 5.93Can save the effort of visiting stores 0.81 0.75 5.66Don’t have to wait to be served 0.77 0.72 5.77Can shop in privacy of home 0.71 0.77 5.97

Access to many brands and retailers 0.70 0.79 5.86Can shop whenever I want 0.66 0.70 6.19

Product Information 0.78Broader selection of products 0.83 0.68 5.34Items from everywhere are available 0.74 0.79 5.56Can get good product information online 0.53 0.73 5.61

Hedonic experience 0.68To try new experience 0.79 0.70 4.46Exciting to receive a package 0.72 0.62 5.15Not embarrassed if you don’t buy 0.69 0.63 5.20

Channel risk 0.85Can’t trust the online company 0.76 0.73 3.82Too complicated to place order 0.73 0.71 3.03Pictures take too long to come up 0.70 0.58 3.73Difficult to find appropriate Web site 0.69 0.66 3.39May purchase something by accident 0.68 0.69 3.07My credit card number may not be secure 0.66 0.65 4.28My personal information may not be kept 0.63 0.62 4.51

Product risk 0.84Can’t try on clothing online 0.85 0.64 5.82Size may be problem with clothes 0.79 0.72 5.40Can’t examine the actual product 0.77 0.81 5.14Inability to touch and feel the item 0.75 0.80 5.18




Characteristics of the Respondents

Respondents were middle-to-upper income adults, with 70 percent younger

than 45 years old (table 4). Women were slightly over-represented, consistent with the proportion of female online shoppers in the United States. Approxi-mately, 15 percent of the respondents had been using Internet for shoppingfor 2 years or less, 34 percent for 2 to 4 years, 28 percent for 4 to 6 years, and24 percent for more than 6 years. Almost two-thirds of the respondentsreported having made an online purchase once a month or more duringthe past six months, and half spent ten or more hours on the Internet at homeevery week. Most respondents (94 percent) used the Internet for shopping at

TABLE 3 Construct Correlations

Usefulness EnjoymentEase of

useInformation

searchOnline

purchase

Usefulness 0.71a

Enjoyment 0.77b 0.76Ease of use 0.77 0.69 0.44Information search 0.34 0.30 0.23 0.87Online purchase 0.42 0.38 0.25 0.51 0.78

aNumbers in diagonal cells (in bold) are variance extracted for construct.b All correlations are significant at level of .001.

TABLE 4 Demographic Profile of the Respondents

Characteristic Percent Characteristic Percent

Age Education19 or under 1.5 Less than high school 0.820–24 13.5 High school graduate or equivalent 16.425–34 28.9 Some college= vocational school 39.135–44 25.8 College graduate 28.845–54 22.4 Some postgraduate study 4.255–64 7.0 Graduate degree 10.765 or over 0.8 Employment status

Gender Full time 47.9Male 33.1 Part time 11.8Female 66.9 Self-employed 8.3

Household annual income Unemployed 4.1Under $30,000 16.5 Homemaker 13.1$30,000–$34,999 13.4 Student 9.9$35,000–$49,000 21.7 Retired 4.9$50,000–$74,999 23.8 Ethnic group$75,000–$99,999 14.4 African-American 3.0$100,000–$124,999 4.6 Asian 5.5$125,000–$149,999 1.3 Hispanic 3.0$150,000 or more 2.5 White 84.3

Other 4.2




home, which provides additional evidence that Internet shopping hasbecome a regular part of everyday life for Americans.

RESULTS

Testing Direct Effects

Structural equation modeling, conducted to test the proposed model(GFI¼ 0.930, CFI¼ 0.944, v2

ð83Þ¼ 330.767, RMSEA ¼ 0.070), demonstrated agood fit; therefore, the research model was accepted. Significant path coeffi-cients supported H1, H2a, H2b, H3a, H4a, and H4b, but not H3b (see table 5).Therefore, usefulness—but not enjoyment—directly affects online purchase.Enjoyment impacts online information search—a strong predictor of onlinepurchase; therefore, enjoyment affects online purchase only indirectly. Ease

of use affects usefulness and enjoyment. The path between informationsearch and online search is greater than the path from usefulness to onlinepurchase. To test whether the difference is statistically significant, a con-strained model was used by making the two path coefficients (b1 vs. b2b)equal. A Chi-Square difference between these two models (v2

ð1Þ¼ 0.203, p > .05) indicated that the effects of using of the online channel for infor-mation search is stronger that the beliefs in usefulness on drivingpost-adoption use of the channel for online purchases.

Testing Moderating EffectsThree indicators of positive motives (benefits) and two indicators of negativemotives (risks) of online shopping were created by averaging factor items foreach dimension of benefits and risks (see table 2). The five indicators werethen subjected to K-mean cluster analysis. Two groups emerged and were

TABLE 5 Summary of Hypotheses Testing Results

Relationship withinproposed research model

Pathcoefficient H5 Testing results

Online purchase Information search 0.42Ã H1 (b1) AcceptedInformation search Usefulness 0.20Ã H2a (b2a) AcceptedOnline purchase Usefulness 0.21Ã H2b (b2b) AcceptedInformation search Enjoyment 0.12ÃÃ H3a (b3a) AcceptedOnline purchase Enjoyment ns H3b (b2b) Not acceptedUsefulness Ease of use 0.85 H4a (b4a) AcceptedEnjoyment Ease of use 0.78 H4b (b4b) Accepted

Mode fit indices v2ð83Þ¼ 330.767, GFI¼ 0.930,

CFI¼ 0.944, RMSEA ¼ 0.070

Note . ns¼not statistically significant.

Ã p < .01; ÃÃ p < .05.




labeled as benefit perceivers and risk perceivers. Table 6 profiles the two shop-ping groups. Both groups perceive benefits (convenience, product infor-mation, and online hedonic experience) and risks (product risk and channel

risk). The benefit perceivers perceive high benefits and low risks of onlineshopping and, consequently, experience a low motivational approach–avoid-ance conflict. Benefit perceivers are the online shoppers who have dominantmotives of pursuing benefits from shopping online, even though they also per-ceive some risk of shopping online. The other group—the risk perceivers— has high risk perceptions, even though they also perceive benefits from shop-ping online. Hence, this group experiences a high motivational approach– avoidance conflict. Overall, benefit perceivers use the online channel for infor-mation search and purchase more than do risk perceivers.

To investigate the moderating effects of online shopping motives, amulti-group analysis was conducted using the two shopper groups with alow=high motivational approach–avoidance conflict obtained from the clus-ter analysis (Hair et al. 2006). The proposed model was then tested across thetwo groups to determine the presence of differences in structural weights(i.e., the moderating effects of shopping motives). As a first step, a con-strained multi-group model (Model 1; base model—no moderating effects)

was estimated, where each structural weight was constrained to be equalacross the two groups. Then, an unconstrained multi-group model (Model2—moderating effects) was estimated in which the structural weights were

estimated for each group (benefit perceivers vs. risk perceivers). Both

TABLE 6 Shopper Groups by Online Shopping Benefits and Risks

Online shopper groups

Clustering

variables

Low conflict(benefit

perceivers)

High conflict(risk

perceivers)

Overall

sample

F

valuea

Pairwise

contrasts

BenefitsConvenience 6.37 5.51 5.90 (0.84)b 208.15 A >BInformation 6.13 4.87 5.44 (1.10) 353.56 A >BHedonic experience 5.43 4.51 4.93 (1.10) 124.04 A >B

RisksChannel risk 2.96 4.24 3.64 (1.10) 300.20 A <BProduct risk 5.13 5.62 5.39 (1.03) 35.59 A <B

Online shopping usageInformation searchb 4.07 3.50 3.76 (1.3) 29.70 A >BOnline purchaseb 2.76 2.14 2.43 (1.2) 37.90 A >BNumber of observations 276 322 598Percentage of observations 47.2% 53.8%

Note . A ¼benefit perceivers; B¼ risk perceivers.adf ¼1.b Means (and standard deviations) of the frequencies of online shopping activities, information search, and

purchasing online during the last six months.




T

A B L E

7

S t r u c t u r a l E q u a t i o n s R e

s u l t s f o r M o d e r a t i n g E f f e c t s

R

e l a t i o n s h i p w i t h i n p r o p o s e d r e s e a r c h m o d e l

M o d e l 2

P

a t h t o

P a t h f r o m

M o d e l 1

B a s e m

o d e l

L o w

c o n f l i c t

( b e n e f i t

p e r c e i v e r s )

H i g h c o n f l i c t

( r i s k

p e r c e i v e r s )

H 5

P a t h

c o m p a r i s o n

T e s t i n g

r e s u l t s

O

n l i n e p u r c h a s e

I n f o

r m a t i o n s e a r c h

0 . 4 2 Ã

0 . 3 9 Ã

0 . 4 1 Ã

H 5 a

D v 2 ð 1 Þ ¼ 0 . 0 5

N o t a c c e p t e d

I n f o r m a t i o n s e a r c h

U s e

f u l n e s s

0 . 1 6 Ã

N s

0 . 3 4 Ã Ã

H 5 b

A c c e p t e d

O


U s e

f u l n e s s

0 . 2 3 Ã

0 . 2 0 Ã

0 . 2 5 Ã

H 5 c

D v 2 ð 1 Þ ¼ 4 . 1 1 Ã Ã

A c c e p t e d

I n f o r m a t i o n s e a r c h

E n j o y m e n t

n s

0 . 3 3 Ã Ã

n s

H 5 d

N o t a c c e p t e d

O


E n j o y m e n t

n s

N s

n s

H 5 e

A c c e p t e d

M

o d e f i t i n d i c e s

v 2 ð 2 0 7 Þ ¼ 6 5 9 . 6 6 9 ,

R M S E A ¼

0 . 0 6 1 ,

C F I ¼ 0 . 8 7 5 , G F I ¼ 0 . 8 7 3

v 2 ð 2 0 0 Þ ¼ 6 2 5 . 0

8 7 ,

R M S E A ¼ 0 . 0

6 0 ,

C F I ¼ 0 . 8 8 1 , G F I ¼ 0 . 8 8 0

D v

2

¼ 3 4 . 5 8 1 , D d f ¼ 7 ,

p ¼ 0 . 0 0

0

N

o t e . n s ¼ n o t s t a t i s t i c a l l y s i g n i f i c a n t .

Ã

S i g n i f i c a n t a t l e v e l . 0 0 1 ; Ã Ã s i g n i f i c a n t

a t l e v e l . 0 5 .

96



models exhibited acceptable levels of model fit (see table 7) and allow forspecific model comparisons. The presence of significant differences betweenModel 1 and Model 2 (Dv2¼ 34.581, Ddf ¼ 7, p ¼ .000) indicated that the setof relationships in the model do differ significantly across the groups repre-

senting online consumers with differing dominant shopping motivations. Thefit of the constrained model (Model 1) was significantly poorer than the freemodel (Model 2).

The next step was to examine each hypothesized relationship to assess whether a path difference could be found across the high and low motiva-tional approach–avoidance conflict groups. The fully constrained multi-group model, which represented the absence of any moderated relationships(i.e., the base model with all relationships equal across the groups), wascompared with an identical multi-group model, where the constraint of equality for the hypothesized relationship being examined was relaxed.

The Chi-Square difference between the two multi-group models was testedto see whether the tested path was significantly different across the twogroups. A significant Chi-Square difference (e.g., p < .05) indicated variancefor that relationship and, thus, moderating effects, because of different domi-nant shopping motivations of pursuing benefits or avoiding risks. The resultsshowed some moderating effects as three of the seven tested paths were sig-nificantly different across the two groups, confirming different effects for theTAM factors on online shopping for high versus low conflict groups (table 7).

As hypothesized, usefulness has more salient effects on information searchand online purchase for the risk perceivers than for the benefit perceivers.

Enjoyment affects online information search, but not online purchase, amongbenefit perceivers; therefore, H5d but not H5e is accepted. Informationsearch consistently affects online purchase for both groups.

DISCUSSION AND IMPLICATIONS

The success of the online channel depends largely on use diffusion amongthe consumers who have adopted the online channel for information search

and=or purchase (Shih and Venkatesh 2004). Thus, research to identify factors that will enhance the likelihood of continued engagement amongcurrent online shoppers is necessary. Even though the TAM has been widely applied to understand initial acceptance of information systems in both the

workplace and in individual adoption contexts, the predicting power of the TAM in a post-adoption context has not been confirmed. Based onempirical testing, the factors predicting initial acceptance may not have thesame predicting power in the context of post-adoption usage.

Furthermore, factors predicting initial acceptance are not equally effec-tive in predicting online search versus purchase behavior. Usefulness is the

only factor predicting both types of post-adoption usage—consistent with




findings from research on initial adoption of the online channel (Bhattacherjeeand Premkumar 2004; e.g., Kee-Sook et al. 2008) that usefulness=relativeadvantage predicts both initial and continued adoption. Enjoyment margin-ally affects use of the channel for information search among benefit percei-

vers but has no effect on search or purchase behaviors of risk perceivers. Thisfinding is consistent with the prediction that enjoyment is not sufficient toease the anxiety caused by high motivational conflict when purchasingonline and with findings (e.g., Kee-Sook et al.) that enjoyment does notdirectly influence online retail Web site usage. The results show that onlineshoppers primarily perceive utilitarian, but not hedonic, benefits as suregains from using the channel, which is consistent with findings by Overby and Lee (2006) that utilitarian value is more strongly related to preferencetoward shopping online stores than is hedonic value.

Given that beliefs about usefulness are directly associated with both

online information search and purchase behavior, it is important that func-tional features of Web sites effectively facilitate the online decision-makingprocess. Making online shopping easy and efficient would appear to be moreimportant than enhancing the enjoyment of shopping the online channel, asease of use consistently affects both usefulness and enjoyment.

Shoppers (benefits perceivers and risks perceivers) exhibit differentdegrees of motivational approach–avoidance conflict when using the onlinechannel, and the factors driving post-adoption use of the channel are not thesame across shoppers with different degrees of motivational conflict. Althoughperceived usefulness impacts channel usage among both benefit perceivers

and risk perceivers, the risk perceivers (with a high motivational conflict) areoften more influenced by the usefulness of the online channel than are thebenefit perceivers (low motivational conflict), as the path between usefulnessand online purchase is more salient for risk perceivers than for benefit percei-

vers (see table 5). The Prospect Theory better explains this phenomenon,because the decision of whether or not to purchase a product online may differ,depending on whether it is framed in terms of gains or losses.

According to the Prospect Theory (Kahneman and Tversky 1979), suregains encourage individuals to overcome risk aversion. Perceived usefulness

(sure gains) encourages risk perceivers to overcome their perceived risks.However, the online search process can turn into a loss of time and effortspent searching if the consumer fails to complete the purchase onlinebecause of perceived risks. In this case, the tendency to avoid time and effortlosses will encourage consumers to overcome their risk perceptions and pur-chase online (Kahneman and Tversky). Therefore, even though risk percei-

vers do perceive high levels of risk, it does not mean they will not continueshopping online. These findings reinforce the importance of enhancingperceived benefits by emphasizing the usefulness of the Web site andprovide empirical evidence to support the Prospect Theory as an effective

perspective to examine online decision-making.




Consumers who perceive greater risks from online shopping tend to useit less than those who are less sensitive to online shopping risks, consistent

with a basic tenant of the Prospect Theory that people tend to respond moresensitively to losses than to gains. The finding that usefulness has more sali-

ent effects on the use of the channel for the risk perceivers than for the bene-fit perceivers also supports the idea that sure gains motivate people toovercome perceived loss. Furthermore, the finding that risk perceivers alsoshop online when the decision is framed in terms of the potential loss (of time and effort spent searching) reinforces the effectiveness of the ProspectTheory in explaining online shopping behaviors.

This research represents the first attempt to integrate two dominanttheories (the TAM and the Prospect Theory) to gain more in-depth under-standing of online consumer behavior. This research shows that the ProspectTheory can be used to explain the online decision-making process and may

inspire more research exploring the application of the Prospect Theory tounderstand the online decision-making process. For instance, consumerscan easily make decisions to use the online channel for the pre-purchasedecision-making steps to enjoy channel benefits as an information search

vehicle without taking risks. However, the decision to purchase online afterthe pre-purchase search is completed may be framed in terms of losses orgains, depending on the reference point the consumer chooses to use. If the consumer uses another retail channel as a reference point, the decision

will be more likely framed in terms of gains, and the consumer must perceiveenough gains from buying online over buying from other channels to com-

plete the on-line purchase. However, if the consumer considers the time andeffort expended to search and make a choice as the decision reference point,then not completing the online transaction means a loss to the consumer.Once the decision-making process is framed in terms of losses, one showsa greater tendency to take risks (to avoid loss of time and effort) and willbe more likely to purchase online (Kahneman and Tversky 1979). Futureresearch may use experimental designs to determine what reference pointconsumers use at each step of the decision-making process.

The findings show that benefit perceivers come to expect even more

benefits from online shopping with respect to both functional and hedonic value as they gain more online shopping experience. Hence, it may beincreasingly challenging for e-tailers to meet this group’s expectations. Inaddition to promoting the benefits of the Web site to retain current onlineshoppers and encourage more online purchases, e-tailers may find it effec-tive to develop marketing strategies that will create a sense of loss amongshoppers who fail to purchase online.

The current research applied the TAM to the post-adoption contextshowing that the TAM may have relatively less power in predictingpost-adoption usage than in predicting initial adoption. Researchers have

called for both theory and corresponding models to be developed in the




context of post-adoption (Limayem et al. 2007) to better understand con-sumer innovation diffusion. Individual characteristics and online shoppingmotives put boundary conditions on the TAM in predicting individual usage,as evidenced by the significant moderating effects of shopping motives on

the proposed relationships in the TAM.The current study has some limitations that suggest interesting opportu-nities for future research. First, there are limitations with respect to datacollection. An online survey was used to collect data from a national sampleof Internet users for this empirical study. However, the survey suffered fromthe problems of self-selection and self-reporting normally associated with apanel sample. In addition, cross-sectional data were collected to study con-tinued usage. A longitudinal study with several waves of representativecross-sectional data or responses from a research panel across time willgenerate more in-depth results.

The current research only includes beliefs as predicting variables. Futureresearch may include Internet usage behaviors. Post-adoption use of theonline channel is dynamic and multi-dimensional (Shih and Venkatesh2004; Liu and Forsythe 2010); however, this research only includes twodimensions of usage—online information search and purchase. In fact,online search behavior itself is complex (Moe and Fader 2004), and futureresearch may specifically focus on understanding search behavior in thepost-adoption context. Here, information search and online purchase aretreated separately since they are two separate decisions and may use differ-ent reference points to frame decisions in terms of gains or losses.

As mentioned above, this research does not include information todecide what reference points consumers are using when they make thechoice to use the online channel to search information and purchase online.Future studies may use the Prospect Theory to examine whether effort andtime spent online for information search may cause consumers’ onlinepurchase decision to be framed in terms of losses (vs. gains).

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