Summarizing the articles

Factors Driving Consumer Intention to Shop Online: An Empirical InvestigationCHI ANG AND DHOL AKIAFACT ORS DRIVI NG ONLI NE SHOP PI NG

Kuan-Pin Chiang School of Business, Brooklyn Campus

Long Island University

Ruby Roy Dholakia College of Business Administration

University of Rhode Island

This article examines consumers’ intention to shop online during the information acquisition stage. Specifically, the study incorporates 3 essential variables, which are likely to influence consumer intentions: (a) convenience characteristic of shopping channels, (b) product type characteristics, and (c) perceived price of the product. Results indicate that convenience and product type influenceconsumer intention to engage in online shopping.When consumers per- ceive offline shopping as inconvenient, their intention to shop online is greater. Also, online shopping intention is higher when consumers perceive the product to be search goods than ex- perience goods.

For consumers, shopping does not mean just going to physi- cal stores any more. As an illustration,consider the following two portrayals of shopping experience, which recently ap- peared in the trade press.

I enjoy spending a couple of hours in the shopping mall, lookingat stuff and too often buying stuff….I just don’t think shopping is somethingyou can do sitting in front of computer … For me the essence of shopping is a communal experience that involves physically entering stores and handling prod- ucts and talking to people (Alsop, 1999)

I hate shopping, I hate going to malls, finding a parking place, fighting the holiday crowds, being confronted by floors of merchandise, beating the bushes for bargains, or talking with the salespeople…. I would much rather browse at my convenience, make a decision, and click to order even if I pay the full retail price. I consider paying full retail to be a bargain in return for avoiding the traditionalshoppingexperi- ence. (Reed, 1999).

These cases illustrate a continuum of shopping prefer- ence anchored by online shopping at one end and tradi-

tional shopping at the other. Never before have consumers been able to shop from anywhere at anytime with a few clicks of their fingers. In fact, online shopping, an unfore- seen event only a few years ago, has continued to grow. It is predicted that U.S. business-to-consumer sales over the Internet will grow from an estimated $25 billion in 1999 to $152 billion in 2002, according to Giga Information Group (Pastore, 2000). The rapid growth of online retailing has created a vibrant market space and competition with all other shopping channels. It has challenged traditional retail- ers and is reshaping consumers’ shopping habits. The incli- nation to use a particular shopping channel often depends on various factors such as consumer characteristics as well as situational variables.

The breakdown of time and location constraints funda- mentally distinguishes online shopping from traditional shopping formats (Sheth & Sisodia, 1999). Time and cost of travel are virtually eliminated for consumers who can shop from anywhere at anytime. Specifically, the reduction of search cost has allowed shoppers to engage in compara- tive shopping more efficiently (Alba et al., 1997; Kalakota & Whinston, 1997; Klein, 1998). Based on a recent Internet shopping study by Ernst and Young (1999), the two major reasons for consumers to shop online are increased conve- nience and greater savings. Given the unique characteristics

JOURNAL OF CONSUMER PSYCHOLOGY, 13(1&2), 177–183 Copyright © 2003, Lawrence Erlbaum Associates, Inc.

Requests for reprints should be sent to Kuan-Pin Chiang, Assistant Pro- fessor of Marketing, School of Business, Long Island University, Brooklyn, NY 11201. E-mail: [email protected]

of the Internet, these findings are not surprising. The re- duced cost of search enables consumers to compare prices across online retailers with just a few clicks. Yet, what if consumers perceive shopping offline to be as convenient as shopping online? And, what if the price offered in physical stores is comparable to the online price? Which shopping medium would consumers prefer then? The answer is not clear.

Although online shopping is more convenient and makes price comparisons easier, it is obvious that consumer retail patronage is not limited exclusively to any one particular channel. Because the Internet is a multi-dimensionalchannel aggregating several salient features of other shopping for- mats such as distribution, transaction, and communication, it coexists as a system both complementary to and competing with other conventional retailing channels (Burke, 1997; McGaughey & Mason, 1998; Peterson, Balasubramanian, & Bronnenberg, 1997). In addition to making a product pur- chase decision (transaction), consumers must often decide on a shopping medium (communication),which can best satisfy their information needs and wants. In this competitive con- text, how consumers decide to use a particular shopping channel, particularly the Internet, is important to understand from a managerial point of view.

The article begins with a review of relevant literature. Based on this review, hypotheses are developed regarding factors that influence consumers’ intention to shop online. Hypotheses are tested with data obtained from a survey. The results are presented followed by a discussion of the findings.

BACKGROUND AND HYPOTHESES

Consumers can choose from a variety of different retail chan- nels including traditional retail store, catalog/mail order, home/TV shopping, and the Internet. Each shopping channel is characterized by a different combinationof multiple attrib- utes, which influences consumers’ choice of a retail channel (Claxton & Ritchie, 1979; Hansen & Deutscher, 1977–1978; Oharian & Tashchain, 1992). For the purpose of this study, onlythetraditionalretailstoreand theInternetareconsidered.

The literature suggests that the intention to shop online is influenced by a number of variables including convenience, price, and product categories (Burke, 1997; Peterson et al., 1997). A recent study by Chiang (2001), examining the ef- fects of price, product type, and convenienceon consumer in- tention to shop online, found price and convenience to influ- ence consumers’ intention but reported no main effect for product type.

Peterson et al. (1997) outlined a framework of consumer decision sequences. They argued that the performance and competition of shopping medium are mediated by (a) con- sumer’s choice of communication, transaction, and distribu- tion channels; (b) the product or service offerings being mar-

keted; and (c) the specific sequence of decision followed by consumers in carrying out their purchasing function. In such a case, consumers have the choice of (a) whether to focus on a product/service category or a brand at any stage of the in- formation acquisition process, (b) whether to use the Internet or conventional retail channels for information acquisition, and (c) whether to use the Internet or a conventional retail channel for the final transaction and brand acquisition. They illustrated how the choice of product category or brand could influence the consumer’s acquisition process. When con- sumers begin their acquisition process with a selected brand, the competition among shopping channels is limited because the brand choice is clearly defined and consumers are likely to focus on price information and brand availability in the search process. On the other hand, when consumers are not clear about their brand choice but only have a category in mind, information acquisition process could take place in ei- ther or both the Internet and conventionalshoppingchannels.

In this study, the focus is on consumers’ choice to shop on the Internet and at the physical stores during the information acquisition stage. At this stage, consumers only have a cate- gory inmindbutlackspecificbrandpreferences.Basedonpre- vious discussions, the study incorporates three essential vari- ables influencingconsumers’choiceof shoppingmedium: (a) convenience characteristic of shopping channels, (b) product characteristics, and (c) perceived price of the product.

Convenience

Unlike traditional shopping, the distinct characteristic of on- line shopping is its convenience and it has been found to be the major motive for consumers to shop electronically (Jarvenpaa & Todd, 1997). In their survey of 220 consumers, Jarvenpaa and Todd (1997) found that convenience was the single most salient benefit of online shopping. Similarly, Burke (1998) conducted six focus groups in different regions of the United States and found that convenience was the most frequently cited reason for consumers to engage in online shopping. Burke stated:

shoppers appreciated the ability to visit the virtual store at any hour, and to perform other activities, like exercise, cook- ing and child care while shopping. They could shop even when transportation was unavailable, and avoid crowded parking lots or bad weather. Online shopping eliminated drive time and checkouttime, and allowed shoppersaccess to distant stores. (p. 356)

When consumers perceive offline shopping to be inconve- nient, their intention to shop online is greater and vice versa. Thus, the following hypothesis is offered.

H1: Consumers’ intention to shop online is greater when they perceive shopping offline as inconvenient.

178 CHIANG AND DHOLAKIA

Product Type

Becauseof the introductionof the Internet, scholars in market- ing field have been trying to explore the implications of the Internet on consumer marketing. One notable implication is that “the suitabilityof the Internet for marketing to consumers depends to a large extenton the characteristicsof the products and services being marketed” (Peterson et al., 1997, p. 334). Therefore, it is essential to consider product characteristics and incorporate a product classification into the analysis. The rationale here is that retail formats differ substantiallyin their capability to provide information about product attributes linked to consumption benefits (Alba et al., 1997).

Several classifications of products have been proposed within the domain of online retailing (Alba et al., 1997; Klein, 1998; Peterson et al., 1997). One common way to clas- sify the products is search and experience goods. According to Nelson (1974), a good is defined as a “search good” when full information for dominant product attributes can be known prior to purchase. On the other hand, experience good is defined as when full information on dominant attribute can only be known with direct experience and information search for such attribute is more difficult than direct product experi- ence. In short, a search good can be evaluated by external in- formation obtained prior to purchases whereas experience goods need to be personally experienced.

Because experience goods require personal inspection prior to purchase and such information is often difficult to obtain electronically, it is likely that consumer’s intention to shop online is lower for experience goods than search goods. Furthermore, the reduction of search cost is likely to favor search goods in an online environment. Thus, it is hypothe- sized that consumers’are more likely to shop online when the products are search goods than experience goods.

H2: Consumers’ intention to shop online is greater for search goods compared to experience goods.

Alternately, because online shopping reduces the cost of searching for product information prior to purchase, consum- ers are likely to shop online for search goods than experience goods if they perceive shopping offline as inconvenient. When it is convenient to shop offline, product type may not influence consumers’ intention to shop online. Thus, the fol- lowing interaction effect is hypothesized.

H3: A two-way interaction between perceived conve- nience and product type will exist.

When consumers perceive shopping offline as inconvenient, online shopping intention will be higher for search goods than experience goods. When consumers perceive shopping offline as convenient, online shopping intention will not be affected by product type.

Price

One of the major motives drawing consumers to shop online is the promise of greater savings. In fact, 85% of consumers look for price information when shopping online (Reed, 1999). Online or offline, price is unquestionably one of the most important cues utilized during a consumer’s decision- making process. Price can be defined as the consumer’s per- ceptual representation or subjective perception of the objec- tive price of the product (Jacoby & Olson, 1977). Ziethaml (1982) proposed that consumers encode and interpret actual price in ways that are meaningful to them. Specifically, it has been suggested on the basis of the adaptation level (Helson, 1964) and assimilation–contrast theories (Sherif, Sherif, & Nebergall, 1965) that consumers carry with them adaptation level prices or a latitudeof acceptableprices for a given prod- uct category and judge the actual price of a product to be high, low, or fair in comparison with these internal standards (Gabor & Granger, 1970; Monroe, 1973, 1990). Therefore, it has been concluded that it is the perceived price, not the ac- tual price, of a product that affect consumers’product evalua- tion and choices (Jocoby & Olson, 1977; Zeithaml, 1988). Consequently, it is argued that consumers’ perceived price would influence their choices of shopping channels.

Alba et al. (1997)pointedout that a key difference between online and offline shopping is the ability of online consumers to obtain more information about both price and non-price in- formation as a result of reduced search cost. Because consum- ers are able to obtain more price information online and com- pare across onlineretailers with a few clicks, they are likely to shoponlinewhen the priceof a product is high rather than low. Thus, the following hypothesis is proposed.

H4: Consumers’ intention to shop online is greater when theyexpectthepriceof theproducttobehighthanlow.

METHOD

Preliminary Procedures

To select products for testing, a preliminary list of 56 product categories was developed based on the popularity of online shopping. A questionnaire was constructed to assess percep- tions of the individual products as search or experience goods, the average price and willingness to purchase online.

A conveniencesample of 34 students enrolled in an under- graduate marketing class participated in this preliminary phase. Respondents were requested to indicate their percep- tions about whether the 56 product categories were search or experience goods on a 5-point scale. The following defini- tions were provided:

• A search good is one that full information on “domi- nant” attributes can be known prior to purchase.

FACTORS DRIVING ONLINE SHOPPING 179

• An experiencegood is one that full informationon “dom- inant” attributes cannot be known without direct experi- ence or information search for “dominant” attributes is more costly/difficult than direct product experience.

Based on the distribution of the resulting mean scores, two products were selected to represent search goods (books) and experience goods (perfume). The average price reported for books is $16 and $41 for perfume.

Survey Design

The hypotheseswere tested using a 2 × 2 × 2 factorial design. Two variables were manipulated: price (low, high) and con- venience (convenient, inconvenient) whereas product type was designed as a within-subjects variable. Each participant received a questionnaire varied by price and convenience,for each product, representing search and experience goods. To prevent order effects, the three variables—product type, price, and convenience—were randomly mixed. We ob- served no order bias (all p > .05).

Stimulus

Books and perfume, used in the study, represented two prod- uct types—search and experience goods. The price levels were designed to create price differences so as to facilitate measuring their impact on consumers’ intention to shop on- line, yet at the same time, be realistic. For each product type, two levels of price were calculated by adding and subtracting 20% of the estimated average price, obtained from the pre- liminary tests. For books, a high price of $19.99 and a low price of $12.99 were set. For perfume, a low price of $34.99 and a high price of $49.99 were established. Convenience was operationalized as “driving time to the store” with stores “nearby” as convenient and stores located “more than 30 min away by car” as inconvenient.Although the construct of con- venience has several dimensions (Gehrt, Yale, & Lawson, 1996; Yale & Venkatesh, 1986), it is operationalized only by its time dimension in this study.

Covariates and Dependent Measure

We used regression analysis to explore the effects of possible covariates on consumers’ online shopping intentions, for ex- ample, channel loyalty, time constraint, and price sensitivity, as well as respondents’age, gender, income, residence, prod- uct involvement, experiences with the Internet, and Internet access. However, we found, at least in this study, that none of thesecovariateshadanyeffect onconsumers’intentiontoshop online. These variables were not pursued in further analysis.

Measures for online shopping intention were adapted from Baker, Levy, and Grewal (1992) with Cronbach’s alpha of 0.86. The three 5-point agreement items were: “The likeli- hood that I would search for this product online is high”; “I

would be willing to buy this product online”; “I would be willing to recommend my friends to buy this product online.” After respondents evaluated each profile varied by price, product type, and convenience, they responded to each item ranging from 1 (strongly disagree) to 5 (strongly agree). In this study, reliability analysis showed an alpha of 0.91.

Participants

Respondents were recruited on a train traveling in the North- east region. They were contacted by the researchers and asked to participate in the study. Among those contacted,160 questionnaireswere returned. After initial examinationof the questionnaires, 13 were excluded due to incomplete data, which resulted in 147 usable questionnaires. Demographic characteristics of the sample were: female (59.2%), 22- to 44-years-old, mostly college educated with family income level of $40,000 to $69,000.

Procedure

Eight versions of questionnaires were randomly distributed to respondents on the train. Each participantwas individually contacted by one of the researchers and asked if he or she was willing to participate in the study. The questionnaire con- tained a cover letter explaining the purpose of the study and five sections of questions. Respondents were told that the questionnaire was intended to understand how consumers make their decision to shop on the Internet or in the physical retail stores. The first section of questionnaire included prior online shopping experience, measures of covariates, and in- volvement on books and perfume. Subsequently, they were given two hypotheticalscenarios for each product type. They were told that they were considering buying either a book or perfume. Average price and expected price for each product were given. Within each scenario, two profiles were pre- sented varied by price (high, low) and location of the store (nearby, more than 30 min by car). After each profile, respon- dents were asked to indicate their intention to shop online for that particular product. After providing responses for both products, respondents answered questions relating to manip- ulation checks and respondent characteristics.

RESULTS

Manipulation Checks

Manipulation checks were included for product type, price and convenience of physical shopping to verify that the ex- perimental factors varied, as intended. Respondents were asked to indicate their agreement with statements relating to the manipulated variables (e.g., Compared to the average price, I consider $12.99 to be expensive for a book) on a 5-point scale from 1 (strongly disagree) to 5 (strongly agree). They were also asked to indicate their perception about

180 CHIANG AND DHOLAKIA

whether books and perfume were search or experience goods on a 5-point scale ranging from 1 (search goods) to 5 (experi- ence goods). For each manipulationcheck, one-way analysis of variance (ANOVA) was performed. Results suggest that the manipulations of product type and convenience had the intended effects, F(1, 279) = 5.60, p < .05 and F(1, 292) = 89.21, p < .05. Manipulationof prices for books and perfume are marginally significant, F(1, 144) = 3.51, p = .06 and F(1, 142) = 3.71, p = .06.

Analysis

The hypotheses were examined using ANOVA. Three main effects (product type, price, and convenience)and one 2-way interaction were examined. Table 1 shows means for each treatment condition.

The first comparison examined was the main effect for convenience. H1 states that consumers are likely to shop on- line when they perceive shopping offline as inconvenient. ANOVA results (Table 2) show that there is a main effect of

convenience on consumers’ intention to shop online, sup- porting H1, F(1, 283) = 36.12, p < .05.

The significant main effect of product type supports H2; consumer intention to shop online is greater for books (search goods) than for perfume (experience goods), F(1, 283) = 30.07, p < .05. Hypothesis 3 predicted a two-way in- teraction between product type and convenience. When con- sumers perceive shopping offline as inconvenient, online shopping intention was expected to be greater for search products than experience products. The interaction effect was not significant,F(1, 283) = .041, p > .05. We discuss pos- sible reasons shortly.

Finally, the price main effect was also not significant, so H4, which stated that consumers’ intention to shop online should be greater when they expect the price of the price to be low than high, is also not supported, F(1, 283) = 2.19, p >.05.

DISCUSSION

This study empirically examined the influence of conve- nience, price, and product type on consumers’ intention to shop online.The results of the study indicate that convenience influences consumers’ intention to shop online. When con- sumers perceive shopping offline as inconvenient, they are more likely to shop on the Internet. This finding is consistent with previousstudies (e.g., Chiang,2001). Compared to other shoppingmedium, online shoppingprovides a greater degree of conveniencethatmotivatesbuyersto shoponline.Although this study considered only the time dimension of convenience (driving time to stores), one can infer that other dimensionsof convenience (such as waiting time at checkout lines and crowded shopping environment) would also similarly influ- enceconsumer’s intentionto shoponline.This findingimplies that conventional retailers need to address this disadvantage by making shopping more convenient,especially for increas- ing numbers of time-conscious consumers. Generally, shop- ping in stores has several drawbacks such as “painful to drive to the mall,” difficult to compare products and prices at differ-

FACTORS DRIVING ONLINE SHOPPING 181

TABLE 1 Means Values (Standard Deviation) of Dependent

Measure Across Price, Product Type and Convenience

Convenience of offline shopping

Convenient Inconvenient

Product Type Price M SD M SD

Search (books) Low 2.71 (1.25) 3.38 (1.13)

High 2.87 (1.27) 3.95 (1.11)

Experience (perfume) Low 2.11 (0.98) 2.75 (1.18)

High 2.00 (1.17) 2.98 (1.42)

Note. All ratings were on 5-point scale ranging from 5 (very likely) to 1 (very unlikely).

TABLE 2 Effect of Price, Product Type, and Convenience on Shopping Intention Online

Source SS df MS F Sig.

Product Type 42.95 1 42.45 30.07 .000* Price 3.13 1 3.13 2.19 .140 Convenience 51.60 1 51.60 36.12 .000* Product Type × Convenience .058 1 .058 .041 .840 Price × Convenience 2.50 1 2.5 1.75 .187 Product Type × Price 1.56 1 1.56 1.10 .296 Product Type × Price × Convenience .028 1 .028 .019 .889 Error 404.28 283 1.43 Total 506.18 290

Note. Dependent Variable: Online shopping intention. *p < .05

ent stores, and limited inventory. In dealing with these issues, retailers could combine the speed, convenience and immedi- acy of e-commerce technology, which is gradually changing the way in which conventional retailing works.

The study also reveals that product type influences con- sumers to shop online. For search goods such as books, inten- tion to shop online is higher than intention to shop for experi- ence goods. Because search costs are reduced, search goods have a greater chance of success in this electronic environ- ment. This is consistent with product categories currently purchased by consumers online. This poses challenges for online retailers specializing in experience goods. As domi- nant product attributes cannot be obtained prior to purchase, transforming an experience good into a search good could potentially stimulate online purchases.

The results did not support the influence of price on online shopping intentions.There are several possible explanations. One apparent reason could be the appropriateness of prices used for each product. As stated earlier, the level of prices was set by adding and subtracting 20% of average price esti- mated from the pre-test. Our primary concern was to provide a realistic price range and increase the external validity of this study. Manipulation check shows that the perception of price difference was only marginally significant. Increasing the price range would certainly have improved statistical sig- nificance but would have probably undermined the external validity of the study. Second, it is possible that the average price estimated by students was not ideal for real consumers used in the study. Third, the products chosen in this study are low price items. Thus, the benefit of online comparison— shopping that could provide greater savings—is limited. It is possible that higher priced products would have a greater in- fluence on online shopping intention. In fact, Chiang (2001) using computers as high-priced products, found a significant main effect for the price variable. Finally, it is likely that in a more realistic setting where price differences cannot be too large for competitive purposes, price is not as important a variable influencing online shopping intentions.

CONCLUSION

The availability of online shopping has intensified the com- petitionamong retailers in various shopping channels, partic- ularly between online and conventional retailers. Although e-commerce is growing, conventional retailers are fiercely competing to retain their customers. Because consumers do not concentrate their shopping activities within one particu- lar shopping channel, identifying forces driving consumer choices is essential for retailing strategies. The findings of this study suggest that convenience and product type are two major forces driving consumers to shop on the Internet.

Future research is needed to better understand the influ- ences of price on consumers’ choice of shopping channels. First, it is possible that price may not be the dominatingfactor

for online shopping and may be dependent on product cate- gory. As this study revealed, price does not clearly discriminateamong consumers’choiceof shoppingchannels; there is a need, therefore, to more closely investigate the level of price differences that will affect consumers’ selection of shopping channels. Second, prices considered in this study were relatively low. Future research shoulddirect its attention to high-pricedproduct categories.Finally, the study only con- sidered the effect of locationalconvenience;future research is needed to consider other dimensions of convenience.

ACKNOWDEGMENTS

We would like to express our gratitude to Research Institute of Telecommunicationsand Information Marketing (College of Business Administration, University of Rhode Island) for financial support of this research study. We also thank Dawn Iacobucci and the anonymous reviewers of this article for their helpful comments.

REFERENCES

Alba, Joseph, Lynch, John, Wietz, Barton, Janiszewski, Chris, Lutz, Rich- ard, Sawyer, Alan, & Wood, Stacy. (1997). Interactive home shopping: Consumer, retailer, and manufacturer incentives to participate in elec- tronic marketplaces. Journal of Marketing, 61, 38–53.

Alsop, Stewart. (1999,October 25). Online shopping?Forget it! Just give me a mall. Fortune, 357–358.

Baker, Julie, Levy, Michael, & Grewal, Dhruv. (1992). An experimental ap- proach to making retail store environment decision. Journal of Retailing, 68, 445–460.

Burke, Raymond R. (1998). Do you see what I see? The future of virtual shopping. Journal of the Academy of Marketing Science, 25, 352–360.

Burke, Raymond R. (1997). Real shopping in virtual stores. In Stephen P. Bradley and Richard L. Nolan (Eds.), Sense and respond: Capturing the value in the network era. Boston, MA: Harvard Business School.

Chiang, Kuan-Pin. (2001). Effects of price, product type and convenience on consumer intention to shop online. In Ram Krishnan and Madhu Viswanathan (Eds.), Proceedings of AMA Winter Educators’Conference, Vol. 12, pp. 163–169. Chicago: American Marketing Association.

Claxton, John D., & Ritchie, J. R. Brent. (1979). Consumer prepurchase shopping problems: A focus on the retailing component. Journal of Re- tailing, 55, 24–43.

Ernst & Young (1999). The second annual Internet shopping study. Re- trieved October 18, 2000, from http://ey.com/publicate/con- sumer/pdf/Internetshopp.pdf

Garbor, Andre, & Granger, Clive. (1970). Pricing consciousness of consum- ers. In Bernard Taylor & Gordon Wills (Eds.), Pricing Strategy (pp. 132–151). Princeton, NJ: Brandon/Systems.

Gehrt, Kenneth C., Yale, Laura J., & Lawson, Dianna A. (1996). The conve- nience of catalog shopping: Is there more to it than time? Journal of Direct Marketing, 10, 19–28.

Hansen, Robert A., & Deutscher, Terry. (1977–1978). An empirical investi- gation of attribute importance in retail store selection. Journal of Re- tailing, 53, 59–72.

Helson, Harry. (1964). Adaptation-level theory. New York: Harper & Row. Hof, Robert D. (1999, October 4). A new era of bright hopes and terrible

fears. Business Week, 84–98. Jacoby, Jacob, & Olson, Jerry C. (1977). Consumer response to price: An at-

titudinal information processing perspective. In Yoram Wind & Marshall

182 CHIANG AND DHOLAKIA

Greenberg (Eds.), Moving ahead in attitude research. Chicago: American Marketing Association.

Jarvenpaa, Sirkka L., & Todd, Peter A. (1997). Is there a future for retailing on the Internet? In Robert A. Peterson (Ed.), Electronic marketing and the consumer (pp. 139–154). Thousand Oaks, CA: Sage.

Kalakota, Ravi, & Whinston, Andrew B. (1997). Electronic commerce: A manager’s guide. Boston: Addison-Wesley.

Klein, Lisa R. (1998). Evaluating the potential of interactive media through a new lens: Search versus experience goods. Journal of Business Re- search, 41, 195–203.

McGaughey, Ronald E., & Mason, Kevin H. (1998). The Internet as a mar- keting tool. Journal of Marketing Theory and Practice, 6, 1–11.

Monroe, Kent B. (1973). Buyers subjective perceptions of price. Journal of Marketing Research, 10, 70–80.

Monroe, Kent B. (1990). Pricing: Making profitable decisions. New York: McGraw Hill.

Nelson, Philip J. (1974). Advertising as information. Journal of Political Economy, 82, 729–754.

Ohanian, Roobina, & Tashchian, Armen. (1992). Consumers’ shopping ef- fort and evaluation of store image attributes: The roles of purchasing in- volvement and recreational shopping interest. Journal of Applied Busi- ness Research, 8, 40–49.

Pastore, Michael. (2000). Future of e-tail lies with multi-channel retailer. Retrieved October 13, 2001, from http://cyberatlas.internet.com/mar- kets/retailing/print/0,,6061_417411,00.html

Peterson, Robert A., Balasubramanian, Sridhar, & Bronnenberg, Bart J. (1997). Exploring the implications of the Internet for consumer market- ing. Journal of the Academy of Marketing Science, 25, 329–346.

Reed, Sandy. (1999,October 25). Online shopping?You Bet! Infoworld, 91. Sherif, Carolyn, Sherif, Muzafer, & Nebergall, Roger E. (1965). Attitude

and Attitude Change. Philadelphia: Saunders. Sheth, Jagdish N., & Sisodia, Rajendra S. (1999). Revisiting marketing’s

lawlike generalizations. Journal of Academy of Marketing Science, 27, 71–87.

Yale, Laura, & Alladi, Venkatesh. (1986). Toward the construct of conve- nience in consumer research. Advances in Consumer Research, 13, 403–408.

Ziethaml, Valarie A. (1982). Consumer response to in-store price informa- tion environment. Journal of Consumer Research, 8, 357–369.

Ziethaml, Valarie A. (1988). Consumer perceptions of price, quality and value: A means–end model and synthesis of evidence. Journal of Mar- keting, 52, 2–22.

Accepted by Dawn Iacobucci

FACTORS DRIVING ONLINE SHOPPING 183