attitude shift toward the online store

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PRACTITIONER PAPER

A consumer shopping channelextension model: attitude shift

toward the online storeJihyun Kim and Jihye Park

Iowa State University, Ames, USA

Abstract

Purpose The purpose of this study is to examine the consumer shopping channel extensionfocusing on attitude shift from offline to online store with a theoretical approach.

Design/methodology/approach Two hundred and sixty two students in a large US midwestern

university participated and provided usable survey responses. Structural equation modeling wasemployed to test hypotheses and the modified theory of planned behavior in the online retailingenvironment.

Findings The results showed that attitude toward the offline store was a significant predictor ofattitude toward the online store. In addition, search intention for product information via the onlinestore was the strongest predictor of consumers purchase intention via the online store as well as amediating variable between predictor variables and purchase intention.

Research limitations/implications The sample of this study was slightly biased by gender andage. Female college-aged consumers were the majority. This demographic group is, however,meaningful to investigate for apparel multichannel retailers due to the strong consumer demand andbuying power.

Originality/value This paper offered a theoretical framework to understand and predict theconsumer shopping behavior in the multichannel retailing setting. In addition, the present paper

contributed to the academia by expanding the theory of planned behavior and online prepurchaseintentions model.

Keywords Attitudes, Internet, Shopping, Consumer behavior

Paper type Research paper

Multi-channel retailing has been recognized as a new key marketing program forretailers. The multi-channel retail format includes not only physical stores andcatalogs, but also online stores, kiosks, and wireless channels. Clark (1997) classifiedtwo dominant multi-channel retailers in the current online market:

(1) click-and-mortars who respond consumer demand through offline and onlinestores (e.g. BestBuy.com, Gap.com, Barnes and Noble.com, Macys.com); and

(2) catalog firms that present their print catalogs on the web (e.g. J.Crew.com,Landsend.com).

According to Gartners (2002) research, approximately 75 percent of retailerrespondents had a multi-channel retailing strategy (MRS) either in place or in planto enhance the firms overall performance. Multi-channel retailers who sold the

The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at

www.emeraldinsight.com/researchregister www.emeraldinsight.com/1361-2026.htm

This research was funded by the College of Family and Consumer Science at Iowa StateUniversity. This paper has been reviewed in the same manner as an academic paper.

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Journal of Fashion Marketing andManagementVol. 9 No. 1, 2005pp. 106-121q Emerald Group Publishing Limited1361-2026DOI 10.1108/13612020510586433


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products across online and traditional channels accounted for more than 67 percent ofonline retailing. More than 50 percent of multi-channel retailers reported positiveoperating margins for online business in 2001 (Shop.org, 2002). Forrester Research(2003a) reported that about half of online customers also purchased offline, and in

opposition, about 20 percent of their offline customers purchased online. Usingmulti-channel approach, online store could enhance their offline sales or vice versa.Those customers who purchased the products in both online and offline, showedgreater loyalty to the retailers.

In 2002, online retail sales increased to $76 billion, 48 percent up compared to theprior year (Forrester Research, 2003b). Online retail sales are expected to continue togrow up to $269 billion by 2005 (Forrester Research, 2002). Online sales accounted for3.6 percent of the total retail sales in 2002 and were expected to reach 4.5 percent in2003. Apparel and accessories ranked as the third best selling product category via theinternet behind travel and computer hardware (U.S. Online Consumer Sales Surge to$53 billion in 2001, 2002). This phenomenon of fast growing e-tailing may encourageoffline retailers to expand their selling channel to the internet using a MRS.

In the past, much of the research on e-commerce has been focusing on the internetsite as a single channel rather than as a channel extension from the traditional retailformat. In addition, the past research has not addressed the impact of consumerattitude toward the traditional retailer on shifting shopping channels. Therefore, thepurpose of this study is to examine the consumer shopping channel extension focusingon attitude shift from offline to online store with a theoretical approach. This researchprimarily adopted the theory of planned behavior (Ajzen, 1985, 1991) to explainconsumer shopping behavior in the context of the multi-shopping channelenvironment. The theory of planned behavior was modified to strengthen theproposed model with current relevant literature in multi-channel retailing. This studyalso investigated the relationships among consumer attitude toward the offline store,

attitude toward the online store, information search intention from the online store,perceived behavioral control via the online store, and purchase intention, focusing onthe multi-channel retailers (click & mortars) who sell apparel products in both offlineand online stores.

Theoretical frameworkThe theory of planned behaviorThe theory of planned behavior (Ajzen, 1985, 1991) posits that attitude toward abehavior, subjective norm, and perceived behavioral control are the antecedents ofintention to perform a behavior. Attitude toward a behavior is referred as anindividuals positive or negative evaluation of a relevant behavior and is composed ofan individuals salient beliefs regarding the perceived consequences of performing

behavior. Subjective norm is a function of normative beliefs, which represents anindividuals perception of whether significant others approve or disapprove of abehavior. The perceived behavioral control, which is an additional variable to thetheory of reasoned action (Fishbein and Ajzen, 1975), makes the theory of plannedbehavior distinct from the original theory. The perceived behavioral control accountsfor an individuals non-volitional aspects of behavior. This explains an individualsperception of ease or difficulty by evaluating whether he/she possesses requisiteresources and opportunities necessary to perform a behavior. Several empirical

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findings (e.g. Ajzen, 1991; Doll and Ajzen, 1992) supported the addition of the perceivedbehavioral control to the traditional attitude-behavior model to be more plausible.

Attitude toward the traditional retailer

Retailer name recognition may have higher impact on online purchase (Degeratu et al.,2000). Park and Stoel (in press) found that familiarity of retailer name influencedfavorable evaluation of the retailers web site and in turn influenced purchase intentiononline. Retailers who were well known in the traditional channel increased consumersconfidence of shopping in their online stores. In addition, past experience with theretailer and the frequency of service use also positively influenced the degree ofsatisfaction with the retailer. Those who had frequent shopping or service experienceswith the retailer were likely to perceive greater satisfaction with the retailer in bothoffline and online shopping settings (Bolton and Drew, 1991; Cadotte et al., 1987;Shankar et al., 2003; Vredenburg and Wee, 1986).

Attitude toward the retailer may influence the attitude toward the retailer-related

promotional activity such as advertising. According to MacKenzie et al. (1986), thosewho were likely to have a positive attitude toward the retailer exhibited a positiveattitude toward the advertisement. This implied that the prior attitude toward theretailer might amplify the effective communications between consumer and theretailer. Similarly, consumer attitude toward the brick-and-mortar (e.g. Gap) can beshifted to the online format of the retailer (e.g. Gap.com) that has both transactionaland communicational functions to enhance the performance of both formats. Balabanisand Reynolds (2001) found the effect of a prior attitude toward the traditional retaileron the attitude toward the online version of the retailer. Customers who had a shoppingexperience with the traditional retailer and gained more knowledge about productquality and service may trust the online store operated by the traditional retailer.In fact, Steinfield et al. (2002) found that click-and-mortar firms relied on their

established brand recognition from the traditional channel in order to build consumertrust for the new retail format. Although several researchers (e.g. Shankar et al., 2003)have addressed the possible linkage between attitude toward the offline store andattitude toward the online store, only one study (Balabanis and Reynolds, 2001)empirically examined the relationship. Even though there is a little empirical researchthat examined the direct effect of attitude toward the offline store on attitude towardthe online store, based on the literature, it is reasonable to expect that the more positiveattitude toward the offline store, the more positive attitude toward the online version ofthe retailer. Therefore, the following hypothesis was developed (Figure 1).

H1. There is a positive relationship between attitude toward the offline store and

attitude toward the online store.

Attitude toward the online storeConsumers attitude toward the internet may be an important determinant for internetuse for product information search. Helander and Khalid (2000) found that a positiveattitude toward e-commerce has a significant influence on shopping from the internet.Klein (1998) proposed that the internet may influence information search behaviorbecause of the greater convenience and accessibility. The positive attitude toward the

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internet can increase information gathering behavior from the internet. Blackwell et al.(2001) provided a similar notion on the positive relationship between attitude andinformation search behavior. When a consumer has a positive attitude toward theretailer, he or she is likely to exhibit greater willingness to search product informationfrom the retailer. Empirical evidences showed that there is a positive effect of attitudetoward the internet purchase on internet search intention (Shim et al., 2001;Watchravesringkan and Shim, 2003). Based on the literature, it is reasonable to expectthat the more positive attitude toward the online store, the greater amount of searchintention for product information via the online store. Therefore, the following

hypothesis was developed (Figure 1).

H2. There is a positive relationship between attitude toward the online store andsearch intention for product information via the online store.

Attitude and purchase intentionThe relationship between attitude and purchase intention toward the traditionalretailer has been intensively investigated in the past (George, 2002). Similarly, in theinternet context, a number of past studies (e.g. Goldsmith and Bridges, 2000; Shim et al.,2001) showed that attitude toward the internet shopping was positively related tointernet shopping intention. The positive attitude toward the internet shoppingsignificantly increased intention to use the internet for purchasing. Using the surveydata from the Graphics, Visualization, and Usability (GVU) center in 1998, George(2002) and Kwon and Lee (2003) reported the positive impact of attitude toward theinternet purchasing on the intent to purchase via the internet. In addition, Kim et al.(2003) found that consumers who had more favorable attitudes toward online shoppinghad greater intention to purchase clothing via the internet. Watchravesringkan andShim (2003) also confirmed a positive causal relationship between attitude towardonline shopping and online purchase intention focusing on apparel. Similarly, Yoh et al.(2003) found that attitude toward the internet apparel shopping influenced apparel

Figure 1.Proposed model predicting

a consumer shoppingchannel extension

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buying intention via the internet. Based on the theoretical and empirical findings in theliterature, the following hypothesis was generated (Figure 1).

H3. There is a positive relationship between attitude toward the online store and

purchase intention via the online store.

Perceived behavioral control via the online storeInternet shopping provides the numerous benefits for consumers such as time savingand search convenience. However, internet shopping may require capability to accessthe internet and other relevant resources (i.e. high speed internet, modem). Accordingto the theory of planned behavior (Ajzen, 1985, 1991), the perceived behavioral controlcan influence actual implementation of a behavior. Individuals who perceive greatereasiness or capability are likely to be more confident in performing a behavior (i.e.purchase via the internet) and thus, actually implement the behavior (i.e. make a

purchase via the online store), compared to those who perceive less easiness. Empiricalevidences also supported the theoretical linkage between perceived behavioral controland purchase intention. Shim et al. (2001) found that perceived behavioral controlpositively influenced information search intention online. Johnson et al. (2003) alsofound that people were likely to use the internet for purchasing products when theyperceived less complexity to use the internet. Those who used the internet for purchasebelieved less difficulty to use and access to the internet, as compared to those who didnot use the internet for purchase. The technology acceptance model (Davis, 1989) alsopresented the similar findings. According to OCass and Fenech (2003), perceived easeof the internet use positively influenced attitude toward the web retail and in turn,adoption of internet shopping. Pavlou (2003) also found that intention to use the

internet for purchasing was determined by perceived ease of the internet use. In fact,internet shoppers reported that internet shopping was easier and more entertaining.Those who shopped from the internet perceived less difficulty for searching theinformation and purchasing the product online, as compared to those who did not shopfrom the internet (Swinyard and Smith, 2003). Goldsmith and Goldsmith (2002) alsofound that consumers who had greater confidence in their ability to shop online weremore likely to purchase products online, as compared to those who had less confidence.Therefore, based on the literature, the following hypothesis was developed (Figure 1).

H4. There is a positive relationship between perceived behavioral control via theonline store and purchase intention via the online store.

Applying the same logic, one who perceives more easiness and confidence in the

internet shopping is more likely to use the internet for searching product information(Shim et al., 2001). The choice model for the internet and other information sourcesdeveloped by Ratchford et al. (2001) presented that the use of specific types of sourcesdepended on skills of using each source and ease of accessing a source. Ability to useand access to the internet influenced use of the internet for information search. Basedon the literature, it is likely that the greater perceived behavioral control via the onlinestore, the greater search intention for product information via the online store.Therefore, the following hypothesis was generated (Figure 1).

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H5. There is a positive relationship between perceived behavioral control via theonline store and online search intention for product information.

Information search intention and purchase intention via the online storeThe relationship between intention to use the internet for information search andintention to use the internet for purchasing was found in the online prepurchaseintentions model developed by Shim et al. (2001). Individuals who had greater intentionto use the internet for information search were likely to have greater intention to usethe internet for purchasing. Kleins (1998) economics of information search modeladdressed that consumers would choose the least costly way for searching andpurchasing the goods and services. Searching and purchasing within one channel (e.g.the internet) may be perceived as more costly than searching and purchasing in themultiple channels. Thus, consumers may choose a single channel to reduce shoppingcost rather than use multiple channels for gathering information and purchasingproducts. Ratchford et al. (2003) also found that consumers were likely to search more

information from the internet when purchasing products online. Purchase intention viathe internet increased as a function of the amount of online search intention for productinformation (Ratchford et al., 2003). Lohse et al. (2000) found that individuals who werelikely to use the internet for product information search had greater purchase intentionfrom the internet. In addition, Rowley (2000) suggested that frequent internet browsingfor information search eventually lead to frequent internet purchase. Similarly, onlinepurchasers were likely to spend more time on the internet, as compared to non-onlinepurchasers. This may indicate that the amount of the internet use for informationsearch influences purchasing behavior online (Swinyard and Smith, 2003). The positiverelationship between internet information search intention and internet purchaseintention was also found for apparel products in the previous studies (Shim et al., 2001;Watchravesringkan and Shim, 2003). Based on the literature, it is reasonable to expect

that people who have greater search intention for product information via the onlinestore are likely to have greater purchase intention via the online store, as compared topeople who have lower search intention for product information via the online store.Thus, the following hypothesis was developed (Figure 1).

H6. There is a positive relationship between online search intention for productinformation and purchase intention via the online store.

MethodSubjectsTwo hundred and sixty two undergraduate students in a large US midwestern

university volunteered to participate in this study. These young adult consumers arelikely to be a great potential in multi-channel retailing for apparel, because they areheavy buyers of clothing, influence other consumers for spending more money forclothing, and make a frequent purchase on the internet and offline stores (Hogg et al.,1998; Silverman, 2000). In addition, in academia, college students are generallyaccepted for theory testing in which the multivariate relationships among constructsare the major interest, rather than the univariate differences (Calder et al., 1981).Respondents received extra course credits as an incentive for participation in the class.

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InstrumentScales for hypotheses. Three of six items (e.g. disagreeable-agreeable,unpleasant-pleasant, and negative-positive) developed by Stayman and Batra (1991)were used to measure attitudes toward the offline store and online store, using a

five-point semantic bipolar scale. The reported reliability in Stayman and Batra (1991)was 0.96. Two items developed by the researchers were used to measure productinformation search intention via the online store. For example, the question, Howlikely is that you will search for apparel product information via this online store? wasasked using a five-point Likert-type scale ranging from 1 (strongly dislikely) to 5(strongly likely). Perceived behavioral control via the online store was assessed usingthree items reported in Ajzen (1991). These items were then, revised to reflect theinternet apparel shopping context (e.g. I am confident to shop from this online storefor apparel products.). To assess purchase intention from the online store, two itemswere developed by the researchers (e.g. I would be willing to buy apparel through thisonline store). A five-point Likert-type scale ranging from 1 (strongly disagree) to 5(strongly agree) was used for perceived behavioral control and purchase intention fromthe online store.

Demographics and shopping behavior. Respondents provided some demographicinformation including age, ethnic background, and sex. Respondents were also askedto select one favorite retailer who operate both offline and online channels and then,answer the questions related to their past shopping experience such as the number ofshopping for apparel purchase via the self-selected traditional retailer, the number ofapparel purchases made in the past 12 months, and the amount of money spent in theself-selected traditional retailer for apparel purchase. Those questions were repeatedfor the online version of the retailer.

Procedure

Respondents completed a self-administered questionnaire for this study. Respondentswere first asked to recall their favorite traditional retailer that also operate online store,based on their past shopping experience in both shopping channels. They were thenasked to identify and place the retailers name in the blank given in the questionnaire.Next, respondents were asked to answer the questions derived from the priorexperience with the retailer that they had chosen.

ResultsPreliminary analysesThe mean age of respondents (n 262) was about 21 years. Approximately, 97 percentwere between the ages of 18-25 years. About 80 percent were female. Thus, our sample

is limited to female college students. This demographic group is, however, meaningfulto investigate for apparel multichannel retailers due to the strong consumer demandand buying power. According to the Youth/Harris Interactive College Explorer study,college students spent about $200 billion per year and an average of $287 a month ondiscretionary items other than tuition, books/school fees, etc. (Harris Interactive, 2002).Female students tended to show higher fashion interest and spend more money onclothing than male students (Han et al., 1991). In addition, about 93 percent of collegestudents accessed the internet (Harris Interactive, 2002).

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The majority of respondents were Caucasian American (85.9 percent), followed byAsian heritage (8.8 percent) and African American (3.1 percent). More than 85 percentof respondents had purchased a product over the internet and more than 71 percentreported their previous purchase experience of apparel on the internet. About

41 percent of respondents reported that they visited the self-selected physical storeto search for clothing information in every few month, 24 percent reportedevery month, and another 21 percent reported once or twice in the past 12 months.About 42 percent of respondents reported that they purchased clothing from theself-selected physical store for two to five times, 21 percent reported six to ten times,and another 21 percent reported more than ten times in the past 12 months.The averaged amount of money that they spent on purchasing during the past12 months was about $200.

Less than half (46.1 percent) reported that they had searched clothing informationfrom the self-selected online store for every few month (28.2 percent) or every month(17.9 percent) in the past 12 months. About a quarter (26.3 percent) reported that theypurchased clothing from the self-selected online store for two to five times and 18.7percent reported they purchased once. Forty four percent reported that they had notmade any purchase for apparel via the online store. About 36 percent spent less than$200 on clothing purchase and 13.7 percent spent from $201 to $500 on clothingpurchase. This is consistent with the previous findings about college students internetpurchase behavior (Shop.org, 2003).

Measurement modelThe conceptual model consists of two exogenous variables (attitude toward the offlinestore and perceived behavioral control via the online store) and three endogenousvariables (attitude toward the online store, information search intention via the onlinestore, and purchase intention via the online store). The six hypotheses in the proposed

model (Figure 1) were tested using the analysis of moment structures (AMOS) version4.0. Correlations among construct measures and descriptive statistics were shown inTable I. All five research constructs were positively correlated with each other(, 0:05).

A confirmatory factor analysis was conducted for the measurement model. Themeasurement model specifies how the observed variables (indicators) relate tounobserved variables (latent constructs) (Kline, 1998). Table II presents the results of

CorrelationsModel constructs Mean SD 1 2 3 4 5

1. Attitude toward purchasing via offline store 4.29 0.82 2. Perceived behavioral control over onlinepurchase 4.05 1.05 0.11* 3. Attitude toward purchasing via onlineversion of the store 3.85 0.98 0.42*** 0.47*** 4. Information search intention via theinternet 3.81 1.11 0.19** 0.50*** 0.53*** 5. Purchasing intention via online store 3.65 1.25 0.13* 0.65*** 0.57*** 0.70***

Notes: *p , 0.05; **p , 0.01; ***p , 0.001

Table I.Descriptive statistics and

correlation matrix ofmodel constructs

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the measurement model, including standardized factor loadings, standard error (SE),t-values, average variance extracted, and squared multiple correlations for eachindicators. The confirmatory factor analysis of the measurement model on multi-itemscales showed that each factor loadings of indicators in each construct were

statistically significant and sufficiently high for structural model testing. The averagevariance extracted offers the information about the amount of variance that iscaptured by the construct in relation to the amount of variance due to measurementerror (p. 45) and is considered as a more conservative measure than constructreliability (Fornell and Larcker, 1981). Fornell and Larcker (1981) suggested that thelatent construct has a reliable measurement structure when the value of averagevariance extracted is over 0.50. In this study, the values for the five research constructsranged from 0.66 to 0.89. These indicated that all five research variables achieved arange of fairly good to very good reliabilities among indicators to measure the latentconstructs. In addition, all squared multiple correlations of indicators in themeasurement model were higher than 0.50, which revealed that the latent constructaccounted for more than half of the explained variance in each indicator. Thus, both theaverage variance extracted and the squared multiple correlations of indicators showedthat the measurement model was reliable and valid to conduct subsequent structuralequation model analysis and to test the proposed hypotheses (Table II).

Structural model: hypotheses testingThe analysis of causal model was conducted using a maximum likelihood estimation,which has been commonly employed in the structural modeling (Hair et al., 1998). Theoverall fit indices for the proposed model revealed a chi-square of 155.38 (df 58;p # 0:001), goodness-of-fit index (GFI) of 0.92, normed fit index (NFI) of 0.95,comparative fit index (CFI) of 0.97, and root mean square error of approximation

(RMSEA) of 0.08. Fit statistics above 0.90 for GFI, NFI, and CFI were used as anindicator of a good model fit to the data (Bagozzi and Yi, 1988; Hair et al., 1998).Following Bagozzi and Yi (1988), the chi-square statistic was not considered a goodindicator for model fit because n was over 200 in this study. Therefore, the indicesindicated that the proposed model fits the data well.

Figure 2 displays the final model with structural path coefficients and t-values foreach relationship as well as squared multiple correlations (R2) for each endogenousconstruct. The results indicated support for all proposed hypotheses, suggesting thedirect effect of:

(1) attitude toward the offline store on attitude toward the online store (g11 0:42;t 6:83; p # 0:001);

(2) attitude toward the online store on information search intention (b21 0:41;t 6:78; p # 0:001);

(3) attitude toward the online store on purchase intention via the online store(b31 0:20; t 3:30; p # 0:001);

(4) perceived behavioral control via the online store on information search intentionvia the online store (g22 0:29; t 4:14; p # 0:001);

(5) perceived behavioral control via the online store on purchase intention via theonline store (g32 0:36; t 5:58; p # 0:001); and

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(6) information search intention via the online store and purchase intention via theonline store (b32 0:45; t 5:99; p # 0:001).

Therefore, H1-H6 were supported.To further examine the effects of attitudes, perceived behavioral control, and

information search intention on purchase intention, the decomposition of direct,indirect, and total effects of predictor variables on endogenous variables was analyzed.The proposed model explained a substantial amount of variance in purchase intentionvia online retailer (57 percent) (Table III). All predictor variables had significant direct

and/or indirect effects. Considering the total effects of all constructs on purchaseintention via online store, perceived behavioral control via the online store exhibitedthe strongest total effect (0.51), followed by the direct and total effect of informationsearch intention via the online store (0.45).

The model also explained the moderate amount of variance in information searchintention via the online store (29 percent). All predictor variables had significant directand indirect effects (Table III). Considering the total effects of all constructs oninformation search intention via the online store, attitude toward the online store

Construct/indicatora

Standardizedfactor loading

(CFA) SE t

Averagevariance

extractedb

Squaredmultiple

correlation

j1 (attitude toward purchasing via theoffline store) 0.85

X1 0.89 0.80X2 0.94 0.042 24.15 0.87X3 0.95 0.042 24.67 0.89

j2 (Perceived behavioral control via theonline purchase) 0.74

X4 0.88 0.69X5 0.95 0.062 15.20 0.90X6 0.80 0.067 17.82 0.64

h1 (attitude toward purchasing via theonline version of the store) 0.89

Y1 0.94 0.88Y2 0.95 0.033 31.35 0.90Y3 0.94 0.033 30.57 0.89

h2 (Information search intention via theonline store) 0.82

Y4 0.88 0.78Y5 0.92 0.074 15.44 0.85

h3 (purchasing intention via the onlinestore) 0.66

Y6 0.90 0.81Y7 0.71 0.107 10.69 0.50

Notes: aMeasurement based on a five-point Likert scale where 1=Strongly Disagree and5=Strongly Agree; baverage variance extracted is considered more conservative way to evaluate the

measurement model and was calculated as suggested by Hair et al. (1998) and Fornell and Larcker(1981)

Table II.Measurement model

results for hypothetical

model with new factorstructures

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exhibited the strongest direct and total effects (0.41) followed by the direct effect ofperceived behavioral control via the online store (0.33).

Discussion and implicationsThe present study provides evidence of consumer shopping channel extensionfocusing on the effect of consumers prior attitude toward the offline retailer on attitudeshift toward the online version of the retailer. As expected, the results exhibited thatconsumers attitude toward the traditional retailer positively predicted attitude towardthe online version of the retailer. This supports the previous literature (Balabanis andReynolds, 2001) that found attitude shift from offline store to online store. This findingcan strengthen the current MRS for click-and-mortar retailers who operate both atraditional channel and a new channel format (e.g. online store), such as Gap, BananaRepublics, and Abercrombie & Fitch. Creating and enhancing consumer attitudetoward the offline store may be the key point that can positively influence attitudetoward the online store. Store image and service consistency between or among

multi-channels also may be beneficial for retailers to enhance consumers attitudetoward the online store.

The present study also implies the importance of creating multiple channels tosatisfy consumers demand and thus, be successful in achieving business goals.Benefits and values of the existing image and/or reputation of the traditional retailstore can be transferred to the online format. The click-and-mortars can perform betterthan click-only retailers, possibly because they already established the stable segmentof target customers and build retailer trust.

Figure 2.Final model predictinga consumer shoppingchannel extension

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The results also showed that positive attitude toward the online store shifted fromattitude toward the offline store increased search intention of product information viathe online store. This implies that online store can serve as not only a transactionalchannel but also an information search channel. Multi-channels using both offline andonline may interact to create the best outcome in supporting each channel. Due to thenature of apparel that needs sensory examination for purchase (e.g. fitting, touching)(McCorkle, 1990), consumers often avoid purchasing directly from the online store andinstead, use the online channel to obtain product and service information (Elliot andFowell, 2000). Positive attitude toward the online store built from attitude toward theoffline store can lead customers to use online store for searching product and serviceinformation and utilize such information to confirm purchase in the offline store.

Co-existence of offline and online stores may create a synergy effect based on theircomplementary and interactive functions to effectively enhance consumer shoppingexperience. The model developed in this study can provide the rationale for theretailers for implementing MRS strategy and also explain the current phenomena ofsuccessful cases in click-and-mortar retailers.

This study revealed that purchase intention online can be explained by attitudetoward the offline store, attitude toward the online store, and online search intention,respectively. Thus, it is important for retailers to build positive attitude toward onlinestore at the outset which also influences online shopping behavior. Role of currentoffline channel may be even more important to increase online sales. Bringing existingcustomers from offline store to online store may be even easier for the retailer who hasachieved positive reputation in the competitive market.

The results showed the strong positive impact of information search intention onpurchase intention via the online store. This suggests the important marketingimplications to not only click-and-mortar retailers but also click retailers. To attractinternet browsers who may become active purchasers, retailers should provide thein-depth, verbal and visual information to assist purchase decisions online. Bothquantity and quality of the information content must be secured. In addition, onlinecustomer service must be provided to ensure customer satisfaction with shoppingexperience.

Information searchintention via the

online storePurchase intention via the

online store

Predictor variables

Indirect

effect

Direct

effect

Total

effect

Indirect

effect

Direct

effect

Total

effect

Attitude toward purchase via theoffline store 0.17 0.17 0.16 0.16Perceived behavioral control via theonline purchase 0.33 0.33 0.15 0.36 0.51Attitude toward purchase via theonline store 0.41 0.41 0.18 0.20 0.38Information search intention via theonline store 0.45 0.45

R2 0.29 0.57

Notes: Standardized path estimates are reported. All path estimates are significant at p , 0:05

Table III.Examining indirect,

direct, and total effects ofpredictor variables on

information searchintention and purchase

intention via onlineretailer

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Due to the lack of the current multi-channel retailing literature, this study addsvaluable empirical findings to the literature and serves as a step stone for the futureresearch. This present study also contributed to extend a theory of planned behavior(Ajzen, 1985, 1991) and an online prepurchase intentions model (Shim et al., 2001) in the

multi-channel retailing setting. Strong support for the relationships among attitude,perceived behavioral control, information search, and purchase intention online werefound. Retailers who operate an online store as well as an offline store may developboth effective search tools online and web site design to reduce perceived difficulty ofsearching information and to enhance perceived behavioral control.

Future research may explore the interactive functions of offline and online channelsfor retailers and possible extensions of current brick-and-mortar retailers toclick-and-mortars using cost and benefit analysis. Moreover, consumer perception ofloyalty and trust to the retailer can be assessed to explain attitude shifts from offlinestore to online store in the future research. Understanding the effect of familiarity withthe retailer or brand on apparel purchase online would be beneficial to the retailer todevelop multi-channel marketing strategy to expand the shopping channel. Thelimitation of this study was the nature of the respondents. Lack of randomness in thesample may reduce external validity of our findings across various populations. Inaddition, our implications may be useful to the multi-channel retailers who targetcollege-aged consumers.

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Bollen, K.A. (1989), Structural Equation and Latent Variables, Wiley, New York, NY.

Stigler, G. (1961), The economics of information, Journal of Political Economy, Vol. 69 No. 3,pp. 213-25.

Talukdar, D. and Lee, M. (2001), A model of consumer choice of the Internet as an informationsource, International Journal of Electronic Commerce, Vol. 5, pp. 7-21.

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