An Online Prepurchase Intentions Model _The Role of Intention to Search

8/12/2019 An Online Prepurchase Intentions Model _The Role of Intention to Search

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An online prepurchase intentions model: The role ofintention to search

Soyeon Shim*, Mary Ann Eastlick, Sherry L. Lotz, Patricia Warrington

Retailing and Consumer Sciences, The University of Arizona, Tucson, AZ, 85721-0033 USA

Received 15 September 2000; accepted 20 May 2001

Best Overall Paper AwardThe Sixth Triennial AMS/ACRA Retailing Conference, 2000

Abstract

In this study, an Online Prepurchase Intentions Model is proposed and empirically tested in the

context of search goods. The focus of this research is to determine whether intent to search the Internet

for product information is a key element for marketing researchers to employ in predicting consumers

Internet purchasing intentions. Data were collected through a mail survey to computer users whoresided in 15 U.S. metropolitan areas. Two-stage structural equation modeling was employed to test

hypotheses. The results show that intention to use the Internet to search for information was not only

the strongest predictor of Internet purchase intention but also mediated relationships between pur-

chasing intention and other predictors (i.e., attitude toward Internet shopping, perceived behavioral

control, and previous Internet purchase experience). Direct and indirect relationships between two

antecedents (attitude toward Internet shopping and previous Internet purchase experience) and Internet

purchase intention were also found. Theoretical and managerial implications are discussed. 2001 by

New York University. All rights reserved.

1. Introduction

Purchasing via the Internet is one of the most rapidly growing forms of shopping, withsales growth rates that outpace buying through traditional retailing (Levy & Weitz, 2001).

Decision made by a panel of Journal of Retailing editorial board members.

* Corresponding author. Retailing and Consumer Sciences, The University of Arizona, P.O. Box 210033,

Tucson, AZ, 85721-0033. Tel.: 1-520-621-7147; fax: 1-520-621-9445.E-mail address: [email protected] (S. Shim).

Pergamon

Journal of Retailing 77 (2001) 397416

0022-4359/01/$ see front matter 2001 by New York University. All rights reserved.

PI I : S0 0 2 2 - 4 3 5 9 ( 0 1 ) 0 0 0 5 1 - 3


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Another goal is to examine the respective roles of consumer attitude and other relevant

variables in predicting both Internet searching and purchasing intentions. In order to achieve

the studys objectives, an Online Prepurchase Intentions Model is developed that integrates

an interaction model of prepurchase consumer information search (Klein, 1998) with the

Theory of Planned Behavior (Ajzen, 1985, 1991). Kleins Interaction Model highlights the

important role of information search in consumers Internet purchase behavior in the context

of goods that differ based on the type of information sought prior to purchase (i.e., search vs.

experience goods).

The model also suggests other variables such as past experience as important antecedents

of search. Ajzens (1985, 1991) Theory of Planned Behavior expands the application of a

traditional attitude-behavioral model by incorporating the concept of perceived behavioral

control, that is, the perception of whether one possesses necessary resources and opportu-nities to perform a behavior as a direct predictor of behavioral intentions (Ajzen, 1985). The

concept of perceived behavioral control is useful where the achievement of behavioral goals

is contingent on external and internal resources, for example, access to computers and

computer skills.

2. Literature review

2.1. The role of information search in interactive consumer behavior

Because the Internet is perceived as a powerful tool for consumer information search,

marketers remain highly interested in understanding the relationship between consumers use

of the Internet for information search and their choice of channel (e.g., brick-and-mortar

stores vs. Internet) for the final purchase. On the theoretical side, due to the potential

importance of search in online behavior, academicians such as Klein (1998) argue that search

processes be made part of interactive consumer models as critical predictors of Internet

consumer behavior.

Kleins (1998) Interaction Model of prepurchase consumer information search employs agoods classification model based on the principles of information economics in which

consumers analyze the relative costs and benefits of an additional search. This model adopts

Stiglers (1961) framework in which search is understood to cease when consumers perceive

that the marginal costs of a subsequent search exceed those of its benefits. Search costs

include perceived time, travel, and access to media as well as monetary factors. Benefitsof

search encompass extent and duration of search and the nature of search sources, for

example, types and numbers of information sources.

Klein (1998) argues that information search facilities on the Internet are particularly useful

for search goods due to the low perceived costs of providing and assessing objective data.Confirming this point, Liang and Huang (1998) indicate that consumers are likely to conduct

transactions in a manner that minimizes transaction costs such as those related to searching

for product information, receiving post-sales services, and so forth. They report that search

goods requiring limited direct examination (e.g., books) were perceived to have lower

399S. Shim et al. / Journal of Retailing 77 (2001) 397416


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acquisition costs on the Internet than experience goods (e.g., shoes) that necessitate directprior inspection or trial.

In addition to product type, Kleins (1998) model predicts that two other broad variablecategories will influence information-seeking behavior. Specifically, certain consumer char-acteristics (i.e., product knowledge, prior experience, attitude toward shopping, and socialinfluence) may affect the degree of Internet search. The second category, media attributes, ischaracterized by such factors as information presentation format, flow, and interactivity.

The contention that information search is a key part of the interactive media process canalso be supported from a perceived risk perspective. In general, the greater the risksconsumers perceive, the more extensive is their information search prior to purchase(Dowling, 1986; Mitchell & Boustani, 1994). Because Internet shopping is a new mode of

shopping involving various and seemingly novel types of perceived risks (Eastlick, 1996),the consumer is likely to place added importance on searching for information when usingthis channel.

2.2. The theory of planned behavior

The Theory of Planned Behavior (Ajzen, 1985, 1991) posits that both attitude toward abehavior andsubjective normare immediate determinants ofintentionto perform a behavior.Attitude toward a behavior is recognized as a persons positive or negative evaluation of a

relevant behavior and is composed of a persons salient beliefs regarding the perceivedoutcomes of performing a behavior. On the other hand, subjective norm, a function ofnormative beliefs, represents a persons perception of whether significant referents approveor disapprove of a behavior. The Theory of Planned Behavior further proposes that intentionto perform a behavior is the proximal cause of such a behavior. Intentions representmotivational components of a behavior, that is, the degree of conscious effort that a personwill exert in order to perform a behavior.

To capture nonvolitional aspects of behavior, the Theory of Planned Behavior incorpo-rates an additional variable not typically associated with traditional attitude-behavioralmodels (e.g., Fishbein & Ajzen, 1975). Specifically, it proposes that perceived behavioral

control, in conjunction with attitude and subjective norm, is a direct predictor of behavioralintention. Perceived behavioral control is the perception of ease or difficulty in performinga behavior. The aspect of ease or difficulty specifically relates to whether or not a personperceives that he/she possesses requisite resources and opportunities necessary to performthe behavior in question. Empirical evidence indicates that the addition of perceived behav-ioral control to the traditional attitude-behavioral model has resulted in meaningful improve-ments in the prediction of intentions (Ajzen, 1991).

2.3. Hypothesis development

In the present study, a Model of Online Prepurchase Intentions was developed to inves-tigate the predictors of intention to use the Internet for both information search and purchasein the context of search goods. This integrated model was derived primarily from theInteraction Model of prepurchase consumer information search (Klein, 1998) and the Theory

400 S. Shim et al. / Journal of Retailing 77 (2001) 397416


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of Planned Behavior (Ajzen, 1985, 1991), as previously described, in addition to otherrelevant literature. Fig. 1 shows the hypotheses that led to the development of the OnlinePrepurchase Intentions Model.

2.4. Internet information search intention as a predictor of internet purchasing intentions

The view that the shopping process is composed of consumers shopping strategies andgoals could explain why information search intentions via the Internet might be an anteced-ent of intention to use the Internet for purchase. It is believed that consumers developshopping strategies, that is, action plans, for performing complex shopping behaviors

(Darden & Dorsch, 1990). Such shopping strategies may comprise single or multiple stepscalled unit acts. Thus, an overall shopping goal is accomplished through the enactment of oneor more interrelated steps. These unit acts are very similar to the concept of implementationintentions, defined as specific plans leading to the enactment of goal intentions, that is, thedecision to perform or not perform the behavior (Gollwitzer, 1993). Individuals who developimplementation intentions have been found to be more likely to act on these intentions(Orbell, Hodgkins, & Sheeran, 1997).

Lichtenstein and Brewer (1980) indicated that plans and/or events are subject to hierar-chical processes in which events occur in sequences. Foss and Bower (1986) describe this

process as goal-subgoal relationships. They argue that goals are typically arranged in ahierarchy of subgoals (p. 95) that can be represented in a goal reduction tree. Given thishierarchical nature of goals, it is logical to assume that the relationship between informationsearch intention and purchasing intention is hierarchical. Thus, the intention to researchproducts and shop online could be construed as subcomponents of one goal. Kleins (1998)

Figure 1. Theoretical Model Predicting Online Purchase Intentions



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H5b

: Purchase experience via the Internet will positively and directly predict intention

to use the Internet for purchase.

3. Method

3.1. Sampling and data collection

The sample employed for the study consisted of 2,000 households with personal computerowners in 15 U.S. metropolitan areas (Seattle, San Francisco, Los Angeles, Denver, Phoenix,Dallas/Ft. Worth, Houston, Minneapolis/St. Paul, Chicago, NY, Washington D.C., Atlanta,

Orlando, New Orleans, and Cleveland). Household names were obtained from a mailing listbrokerage firm. Personal computer owners were selected because of their potential access toInternet shopping. Prior to the survey, the questionnaire was pretested, using a smallconvenience sample, to ensure readability and a logical arrangement of questions as well asto select for the main study product categories that consumers perceive to be search goods.

Data were collected via a mail survey in which a pre-contact postcard was employed,followed by two mailings of a self-administered questionnaire. The precontact postcard wasmailed to each household two weeks before the first mailing of the questionnaire. A 36%response rate was obtained from the survey, including 706 of 1,974 delivered surveys. Of the

706 respondents, only those with access to a computer at home or work were included in theanalysis (n 684).

3.2. Respondents characteristics

Characteristics of respondents were similar to those reported for home computer users byMedia Research, Inc. in that respondents consisted of slightly more males (53%) thanfemales (47%), representing a wide variety of age groups with most in the 3544 age group.Subjects were highly educated, with 58% holding a four-year college degree or higher and30% holding a vocational or two-year college degree. Accordingly, respondents were

employed in predominantly white collar and professional occupations, with about 60%earning total household incomes of $50,000 or more. Almost 76% of respondents werewhite, while 24% represented other ethnic groups (e.g., African American, Asian American,or Hispanic).

3.3. Measures

3.3.1. Intention to use Internet for product information search and purchase

The likelihood that respondents would use the Internet or a store to search for product

information was assessed on a 7-point semantic differential scale (1information searchentirely by store; 7 information search entirely by Internet) for each of three productcategories (books, computer software, and videos). These product categories were deemed assearch products (see Preliminary Data Analysisfor justification of these product categoriesas search products). Independent of where they might eventually buy these products,



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respondents were asked to indicate the likelihood that they would seek information abouteach of the products entirely from a retail store, entirely from the Internet, or from some

combination of both.Internet purchasing intentionwas assessed using the same 7-point scalein a different section of the questionnaire. Respondents were asked to indicate the likelihoodthat they would shop for each of the three products entirely by store, entirely by Internet, orthrough some combination of both.

3.3.2. Attitude toward internet shopping

Attitude toward Internet shopping encompassed 24 general attributes representing variousaspects of shopping such as price, merchandise, security, service, social shopping, andshopping hours. These aspects of shopping were derived from the literature on store choice

(Kopp, Eng & Tigert, 1989; Mazursky & Jacoby, 1986) and electronic nonstore retailing(Eastlick, 1996; Ernst & Young LLP, 1999; Szymanski & Hise, 2000). Attitude wasmeasured using an expectancy-value model in which the subjects evaluation of eachattribute was weighted by his/her belief that Internet shopping would provide that attribute.Subjects were first asked to indicate, on a 7-point Likert scale (1not important at all;7extremely important) how important (ei) each attribute was to them when choosing whereto shop for the three categories of search products. In another section of the questionnaire,subjects were asked to indicate on a similar 7-point Likert scale how likely they felt it wasthat Internet shopping for these search products would provide each attribute (b

i). Each of the

24 attributes was presented in a different order in each section to avoid order effects.

3.3.3. Subjective norm and perceived behavioral control

In assessing subjective norm (i.e., perceived social influence) we again used a 7-pointLikert scale to measure responses to a single statement. Subjects were asked to indicate theimportance of referents (i.e., important friends, family, etc.) approving of their use of theInternet for shopping. Perceived behavioral controlwas assessed by two questions, arrangedon a 7-point Likert scale (1very unlikely; 7very likely), that measured subjects percep-tion of the ease of Internet shopping and their access to the Internet.

3.3.4. Previous internet shopping experienceSubjects used a 7-point ordinal scale to rate the number of purchases they made through

the Internet for each of the three search products in the past 12 months. The scale rangedfrom none (0) to 11 or more (7).

4. Preliminary data analysis

4.1. Verification of search product categories

Special efforts were made to restrict the category of products to that of search products,as Klein (1998) warned that the online search and purchase procedure may differ for searchand experience goods. Therefore, subjects were asked to respond to two statements for eachproduct that assessed the importance of obtaining product information by experiencing the



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product with one of the five senses and obtaining information by assessing factual informa-tion about the product. Perceptions regarding each statement were measured using a 7-point

scale (1not at all important; 7extremely important). Five categories of shopping goods,that is, videos, apparel, books, computer software, and clothing accessories, were providedfor each subjects evaluation. Further examination revealed that three products (computersoftware, books, and videos) were evaluated highest in terms of assessing the products usingfactual information (M 5.49, M 4.27, M 4.05) and lowest in terms of assessing theproducts using sensory experience (M 4.65, M 4.38, M 4.49). Therefore, computersoftware, books, and videos represented the search product category.

4.1.1. Attitude toward internet shopping

Prior to final data analysis, a principal components factor analysis with varimax rotationwas conducted on attitudes toward Internet shopping to determine the structure of attitudeitems. Attitude toward Internet shopping was computed for each attribute, using an expect-ancy-value model (i.e., A e

ibi). In this formula,e

irepresents the importance assigned each

attribute, and bi represents the belief that Internet shopping provides each attribute. Results

Table 1

Exploratory factor analysis on attitude toward Internet shopping

Loading Eigen Value Proportion of

Variance

Explained

Reliability

Transaction Services 7.38 38.9% 0.87Payment security 0.79Privacy 0.76Safety 0.68Product Guarantees 0.66Minimal Cost/Time for Returns 0.64Return Policy 0.62

Convenience 1.87 9.8% 0.85Overall Speed of Process 0.83Ease of Finding What I Want 0.73Time Savings 0.72Instant Ability to Get Items 0.70Freedom from Hassles 0.61

Sensory Experience 1.31 6.9% 0.75Being Around Others 0.77Seeing/Touching Products 0.72Personal Sales Assistance 0.65Fun Place to Visit 0.64

Seeing/Experiencing NewThings

0.54

Merchandise 1.15 6.1% 0.80Comparison Shopping 0.78Variety of Product/Brand Choice 0.77Latest Product Information 0.72



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of the factor analysis procedure revealed four factors with Eigen values of one or higher that

explained 61.7% of the cumulative variation in attitude (see Table 1).

The first factor, transaction services, included six items related to security, product

guarantees, safety, privacy, and service. The second factor, convenience, consisted of five

items indicating overall speed of Internet shopping and freedom from hassles. The third

factor,sensory experience, included items pertaining to the social, personalizing, and rec-

reational experiences of shopping. Merchandise, the fourth factor, was characterized by

recency of product information, comparative shopping opportunities, and variety of mer-

chandise choice.

Of the four factors, transaction service was selected as a surrogate for attitude toward

Internet shopping in further analysis. This was done for a number of reasons. First, the

proportion of variance extracted by the transaction services factor (38.9%) was far greaterthan that extracted by the remaining three factors (below 10% for each factor). This indicates

that the transaction services factor is the most significant contributor to overall attitude

toward Internet shopping. Second, when a Lisrel model was initially run, only the transac-

tion services factor appeared as a significant predictor, which raised a potential issue

regarding multicollinearity among the factors. Indeed, multiple regression analysis confirmed

the presence of multicollinearity among the attitude factors. Third, items representing

transaction services were among reasons commonly cited for breakdowns in consumers

online search and purchase efforts.

Finally, the primary objective of this study was in developing and testing a model ratherthan delineating which aspects of attitude toward Internet were more significant than others

in predicting the search and shopping intention. Therefore, inclusion of the most significant

factor, transaction services, in the model was deemed appropriate in accomplishing this

studys goal, given the limitations presented in analysis. However, exclusion of the other

factors (convenience, merchandise, and sensory experience) from the model should not be

interpreted as an indication that these factors are unimportant in determining consumers

intention to use the Internet. Further discussion is offered in the limitations and future

research section.

5. Results

Lisrel 8.3 was employed to conduct structural equation modeling using a two-stage

analysis. Delineating the patterns of relationships among constructs was the primary focus of

the study; therefore, a correlation matrix was used for estimating the structural model (Hair,

Anderson, Latham & Black, 1995).

We first developed the measurement model, consisting of four exogenous and two

endogenous constructs, by conducting confirmatory factor analysis on multi-item scales (i.e.,attitude toward Internet shopping, perceived behavioral control, previous Internet shopping

experience, intention to use the Internet for information search, and intention to use the

Internet for shopping). Following recommendations by Joreskog and Sorbom (1993), a

conservative error variance was established for the single-item scale (i.e., subjective norm).



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The structural model was then estimated to test the hypotheses. Simple statistics andcorrelations of model constructs are reported in Table 2.

5.1. Measurement model results

Table 3 presents the results of the measurement model, including the standardized factorloadings, standard errors, construct reliabilities, and proportions of variance extracted for

Table 2

Model variables descriptive statistics and correlations

Model Variables Mean Std. Dev. Correlations

1 2 3 4 5 6

1. Attitude toward Internet

Shopping

20.8 9.6

2. Subjective Norm 4.0 1.7 .34 3. Perceived Behavioral Control 4.7 1.6 .31 .35 4. Internet Purchase Experience 1.6 0.9 .27 .34 .29 5. Internet Search Intention 3.3 1.5 .28 .31 .34 .47 6. Internet Shopping Intention 3.0 1.7 .35 .41 .32 .53 0.74

Table 3Measurement model results for hypothetical model with new factor structure

Construct/Indicator Standardized

Factor Loading

SE t Construct Reliability Proportion of

Extracted Variance

1 (Attitude toward Internet shopping)

X1 0.83a 0.86 50.9%

X2 0.80 0.047 20.70*

X3 0.70 0.048 17.61*

X4

0.68 0.045 18.09*X

5 0.65 0.045 17.39*X6 0.59 0.046 15.33*

2 (Perceived Behavioral Control) 0.80 66.4%

X13 0.90a

X14 0.72 0.071 11.21*

3 (Previous Internet Purchase Experience) 0.71 45.0%X

15 0.67a

X16 0.70 0.080 31.22*

X17 0.64 0.077 12.59*1 (Intention to Use Internet for Information

Search)X

18 0.78a 0.79 56.1%

X19 0.66 0.047 18.57*X

20 0.80 0.057 18.30*2 (Intention to Use Internet for Shopping)

X21

0.86a 0.90 75.8%X

22 0.84 0.036 28.22*X23 0.91 0.036 29.91*

Note: First path was set to 1, therefore, no SEs or t values are given; * ps .001



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each construct. Factor loadings of the indicators for each construct were statistically signif-icant and sufficiently high to demonstrate that the indicators and their underlying constructswere acceptable. The reliabilities and variance extracted for each latent variable revealed thatthe measurement model was reliable and valid. Computed using indicator standardized factorloadings and measurement errors (Hair et al., 1995), the extracted reliabilities and varianceranged from 0.71 to 0.90 and 45.0% to 75.8%, respectively.

5.2. Causal equation model results

Results of structural equation modeling obtained for the theoretical model revealed a 2

of 325.49 (df110; p 0.0001), GFI of 0.95, adjusted GFI of 0.92, CFI of 0.97, RMSEA of0.05, and2/dfof 2.96. All relationships proposed by the theoretical model were significantexcept for the path (p 0.10) from subjective norm to intent to use the Internet forinformation search. Modification indices also suggested the addition of a path from attitudetoward Internet shopping to intent to use the Internet for purchasing. Because attitudes aredeemed direct determinants of behavioral intentions (Ajzen, 1985, 1991), potentially includ-ing both search and purchase behavioral intentions, the model was modified to add this path.

The modified structural model indicated an improved fit, producing a GFI of 0.96, an

adjusted GFI of 0.93, CFI of 0.97, and a RMSEA of 0.05. While the 2

of 265.85 (96df) wasstill significant (p 0.0001), an expected result due to the large sample size (Hair et al.,1995), the ratio (2 /df) of 2.77 indicated good model fit. Fig. 2 presents the model andstructural path coefficients for each relationship. These results indicate support of allproposed hypotheses but one.

Figure 2. Final Model Predicting Online Prepurchase Intentions



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Hypothesis 1, predicting a positive relationship between intention to use the Internet forinformation search and intention to use the Internet for purchase was supported. Results revealed

that the path between these two constructs was indeed positive (21 0.70) and significant (p 0.001). The proposed positive relationship between attitude toward Internet shopping and intentto use the Internet for information search (H

2) was also supported (

11 0.13; p 0.01). The

third hypothesis, predicting a positive relationship between subjective norm and intention to usethe Internet for information search was not supported. Because the path between these variableswas significant at onlyp 0.10, it was removed from the final structural model. The remaininghypotheses, however, were supported. Significant (ps 0.001) and positive path coefficientswere observed between perceived behavioral control and intent to use the Internet for informationsearch (H4), Internet purchase experience and intent to use the Internet for information search

(H5a), and Internet purchase experience and intent to use the Internet for purchasing (H5b). Therespective path coefficients providing support for Hypotheses 4, 5a, and 5b were

12 0.20,

13

0.42, and 23 0.17.

Considering the total effects of all constructs on intent to use the Internet for purchasing,intent to use the Internet for information search exhibited the strongest direct impact (21 0.70) followed by the indirect effect of prior Internet purchase experience through intentionto search (13*21 0.29). Prior Internet purchase experience also exhibited a strong directeffect on intention to use the Internet for purchasing (

23 0.17).

Regression analysis was employed to further substantiate the mediating role of intention to use

the Internet for information search. According to Baron and Kenny (1986), a mediating effect isobserved when an independent variable significantly predicts both a mediator and dependentvariable, and when the addition of the mediator significantly contributes more to the explainedvariation in the dependent variable than that contributed by the independent variable. Accord-ingly, intention to search (the mediator) was first regressed on each of the independent constructs(i.e., attitude toward Internet shopping, perceived behavioral control, and previous Internetpurchase experience) using simple regression analysis. Next, intention to use the Internet forpurchasing (the dependent variable) was regressed on each of the independent constructs usingsimple regression analysis. Results showed that each independent variable significantly predictedboth the mediator and the dependent variable (ps 0.0001).

In the last stage, multiple regression analysis was employed to test whether intention tosearch via the Internet explained more variation in intention to purchase via the Internet thaneach of the independent variables did. This was to confirm the mediating role demonstratedby the structural equation model of intention to use the Internet for information search. Table4 shows that the unique variation (i.e., Part R2) in intention to use the Internet for purchaseexplained by the search construct ranged from 54.6% to 55.1%, significantly (ps 0.0001)exceeding the unique contribution made by each independent construct (i.e., attitude, be-havioral control, previous Internet purchase experience).

6. Discussion and conclusions

Through this research, an Online Prepurchase Intentions Model was developed whichintegrated two existing theoretical models. Kleins (1998) Interaction Model suggested the



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proposed relationships between Internet search and purchase intention and the importance ofprior experience in predicting search intention. Ajzens (1985, 1991) Theory of PlannedBehavior served to identify key roles played by attitude and perceived behavioral control,though the role predicted for subjective norm was not confirmed by the studys findings.

6.1. Predictive role played by internet search intention

A central focus of this study was to determine whether intent to search via the Internetmight be employed by retailers as an index to indicate the probability of purchase intent. Assuggested, intent to search via the Internet contributed a substantial portion of the varianceexplained in Internet purchase intent, and mediated relationships between intent to purchaseand several antecedent variables. Theoretically, this evidence confirms the premise thatconsumers develop complex shopping strategies consisting of both search and behavior(Darden & Dorsch, 1990; Klein, 1998), and that they may implement these strategies in ahierarchical fashion in which the ultimate purchase goal (purchasing via the Internet) may bepreceded by subgoals (information search via the Internet) (Lichtenstein & Brewer, 1980).

In the context of Internet shopping, this subgoal-goal approach may be especially applicableto shopping for search goods which are purported to have substantially lower transactioncosts on the Internet than experience goods (Liang & Huang, 1998). Therefore, underparticular product-purchase situations, online retailers should not view search via the Internetand a decision to purchase via the Internet as independent processes.

Results also suggest that, in the context of search goods, an intention to search the Internetfor product information leads to an intention to purchase through the same medium.Therefore, information search and its selected channel should be considered extremelycrucial elements leading to a choice in purchase format. This finding is interesting in light of

the popular press suggesting that consumers are, and will be, using multiple-channel com-binations including stores, catalogs, and Internet. Accordingly, retailers have been encour-aged to employ channel synchronization strategies (Olafson, 2001). While consumers mayview e-tailers that have bricks-and-mortar stores as comparatively less risky choices thanstand-alone e-tailers, the evidence contradicts the argument that, in the context of this

Table 4

Unique variation between Internet purchase intention and Internet search intention controlling for independent

variables

Independent Variable Model

R2df Part R2

Search Intention Independent

Variable

Attitude toward Internet Shopping 57.2% 2 55.1% (t 26.7;

p .0001)

9.49% (t 8.29;

p .0001)Perceived Behavioral Control 55.2% 2 54.6% (t 25.6;

p .0001)

1.34% (t 2.99;

p .0029)Internet Purchase Experience 59.1% 2 54.9% (t 22.5;

p .0001)

4.70% (t 5.76;

p .0001)



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product category, consumersintendto use different channel formats in their search-purchasepatterns.

Theoretically, traditional information economics (Stigler, 1961) may offer explanations.Perhaps consumers intentions to use the same channel for both purchase and search reflectefforts to reduce costs (e.g., time, effort) for the entire search-purchase transaction. In somecases, consumers initial intentions may not be realized because unexpected events arise thatincrease the total transaction cost. For example, consumers may have difficulty in submittingcredit card information or downloading a website in a timely manner. These types ofproblems may prompt consumers to revise their initial intentions because, given the newcircumstances, alternative choices (e.g., traveling to purchase a product at a bricks-and-mortar store) may be perceived as less costly. Because of the theoretical foundations of

choice of format for search and purchase are not thoroughly understood at this time,especially given the emergence of new channels such as the Internet, future research iswarranted in this area.

6.2. The role of attitudes, perceived behavioral control, and past behavior

The study also revealed that intention to use the Internet for product information searchacts as a central mechanism through which consumer characteristics affect higher-orderdecision-making goals. In order of salience, consumer characteristicsincluding previous

Internet purchase experience, perceived behavioral control, and attitude toward Internetshoppingall had indirect effects on intention to use the Internet for purchase. This findingsupports the relationships proposed in Kleins (1998) Interaction Model and lending credi-bility to the possible significance of other variables proposed in the model that were notaddressed by this study (e.g., media attributes such as ability to customize, user control,flow). Future research is warranted in this area.

Of the salient consumer characteristics, the most influential predictor of intention to searchfor information via the Internet was previous Internet purchase experience, a factor relatedto consumers perceived risk. This finding supports other previous attitude-behavior re-searchers (e.g., Bentler & Speckart, 1979, 1981; Sutton & Hallett, 1989) who asserted that

past behavior is a predictor of future behavior. Previous purchase experience also exhibiteda strong, direct effect on intention to use the Internet for shopping, a finding consistent withtraditional attitude-behavior models (Eagly & Chaiken, 1993). These findings also confirmthose of other studies demonstrating the impact of prior similar shopping experiences onbehavior toward innovative nonstore formats (e.g., Eastlick, 1996; Eastlick & Lotz, 1999;Shim & Drake, 1990). Specifically in the context of Internet shopping, Weber and Roehl(1999) demonstrated that previous Internet experience predicted adoption and use of elec-tronic shopping. The importance of previous, similar shopping experiences in predictingfuture information search and, ultimately, shopping format choice, illustrates the importance

of turning existing Internet customers into repeat customers by providing them with satis-fying Internet shopping experiences. Such past experiences may directly and indirectlydecrease consumers perceived risk levels associated with online search and shopping,respectively, leading to future continued Internet use.

A second consumer characteristic predicting product information search intention was per-



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ceived behavioral control. Although all subjects in this study owned personal computers,availability of resources/opportunities to engage in Internet search was significantly related to the

subjects search intention. When studying consumers Internet purchasing behavior, researchersshould take perceived behavioral control into consideration in that Internet shopping does requireskills, opportunities, and resources, and thus does not occur merely because consumers decide toact. Further research is warranted to delineate the exact nature of control factors, for example,skill, time, Internet connections. This finding confirms elements of the Theory of PlannedBehavior (Ajzen, 1991) in that perceived ease or difficulty of performing a behavior is essentialto whether or not the behavior will be carried out. It also contributes to the information searchliterature demonstrating the link between consumer constraints and the number and types ofsearch conducted (e.g., Avery, 1996). Additionally, findings offer support for the theoretical

tenets of the economics of information theory, in which constraints are perceived as costs that arecompared to benefits in the analysis of whether or not to pursue additional searches. From thepractitioners view, the evidence indicates that perceived constraints should be mitigated bycorrective strategies. For example, Internet retailers that offer in-store kiosks as a means forconsumers to learn how to shop via the Internet could, in turn, bolster consumers feelings ofcontrol through enhancing their Internet skill levels.

Consumers attitudes toward Internet shopping were also important in predicting Internetpurchase intentions, both directly and indirectly through Internet search. In this study,attitude toward Internet shopping encompassed specific attributes related to transaction

services (e.g., factors related to conducting secure, safe, and private Internet transactions andobtaining fulfillment services such as product returns and guarantees). This finding isconsistent with the Theory of Planned Behavior (Ajzen, 1985, 1991) that predicts thatattitudes are determinants of behavioral intentions. Additional support for these findings isderived from reports of research firms that perceived risk of service failure due to fulfillment,privacy, and security issues ranks among the 10 most important barriers to Internet adoption(Ernst & Young LLP, 1999). Online retailers efforts to establish a risk-free image withconsumers would seem to be one of the key strategies for attracting consumers to an Internetshopping format. An additional brick and mortar and/or catalogue presence may aid in thisendeavor by offering consumers a recognized brand identity.

Although other consumer characteristics were found to be determinants of search and/orpurchase intentions via the Internet, subjects intentions to search the Internet were not foundto vary by subjective norm influence. This evidence refutes traditional attitude-behavioralmodels (Ajzen, 1985, 1991) and Kleins (1998) Interaction Model but confirms the work ofBearden and Etzel (1982) on reference group influence. Specifically, their research infersweak reference group influence for behaviors that are not readily visible to the public. Assearch, either by bricks-and-mortar store or Internet, can be a somewhat private behavior,others may be inconsequential in the choice of the search channel format. If future researchon this issue confirms this theoretical tenet, Internet retailers may want to reconsider monies

and efforts invested in strategies that utilize reference group pressure as a motivator forencouraging Internet shopping. More effective plans may include strategies that reduceconsumers perceived risk (e.g., guarantees) and/or encourage repetitive interactions withInternet providers in order to develop consumers experience and knowledge of Internetshopping protocols.



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7. Limitations of the study and implications for future research

Several limitations of this study, encompassing the nature of the sample, data collectionprocedures, and the identification of factors related to Internet search and purchasing, shouldbe considered when interpreting the studys results and developing future research to extendand expand its scope. Because the present study was cross-sectional in nature, longitudinaltrends, which would be most helpful in determining patterns with respect to consumerattitudes, behavioral intentions, and so forth, could not be identified. Also, consumers pastbehaviors were collected on a self-report basis. Perhaps future efforts could obtain consum-ers actual behavior through real-time electronic data collection in order to minimize thedisadvantages associated with self-report data.

This study investigated consumers prepurchase intention behaviors. Future studies shouldincorporate the traditional decision-making stages that lead to actual purchase in order tounderstand media attributes, consumer characteristics, product attributes, and search condi-tions contributing to intention-behavior consistencies and inconsistencies. Information pro-viding to a better understanding of situations that lead to site abandonment by potentialInternet purchasers would also be of both theoretical and practical interest.

In addition, the theoretical model may not have incorporated all relevant variables, especiallyother salient Internet attitudes. Other research on Internet shopping identified attitudes related toboth convenience and merchandise as important factors influencing consumers Internet shopping

behavior (Ernst & Young LLP, 2001; Szymanski & Hise, 2000). While this exploratory analysisindicated that positive relationships did exist between convenience and merchandise attitudeconstructs and intention to use the Internet for both search and purchase, multicollinearityproblems between these attitudes and the transaction services attitude prohibited their inclusionin the research model. Further studies should, however, consider an array of attitudes consistentlyrelated to both traditional stores and Internet shopping.

Lastly, future research should explore the types of media attributes and consumer char-acteristics that lead to Internet search and purchase intent for experience products. Afundamental question facing online retailers is whether the antecedents that predict theInternet purchase of search goods are different from those that predict the purchase of

experience goods. Therefore, future research should investigate whether the results revealedby this study can also be applied to experience goods. In light of the importance of factorssuch as brand-name recognition in facilitating Internet purchasing of search goods anddeveloping technologies designed to create realistic virtual experiences (e.g., software thatsimulates actual sensory experiences such as smell and color detection, body-scanningtechnologies, etc.), research should also address the types of virtual experiences that mightsubstitute for direct contact with an experience good.

Acknowledgments

The authors thank the International Council of Shopping Centers Educational Foundationfor their generous financial support of this research. The authors also express their appre-ciation to the Academy of Marketing Science/American Collegiate Retailing Association 6th



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Triennial Conference participants and its anonymous reviewers for selecting this paper for

the conferenceBest Paperaward and to Louis P. Bucklin, Editor of theJournal of Retailing

for his contributions in the development of this article.

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An Online Prepurchase Intentions Model _The Role of Intention to Search

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