Interactive E-Commerce: Promoting Consumer Efficiency or Impulsivity?

Authors

  • Junghyun Kim,

    Corresponding author
    1. A Ph.D. candidate in the Department of Telecommunication, Information Studies and Media at Michigan State University. Her research interests include Internet user behavior and the impact of computer media on individual and collective human behavior.
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  • Robert LaRose

    Corresponding author
    1. A professor in the Department of Telecommunication, Information Studies, and Media at Michigan State University. He holds a Ph.D. in Communication Theory and Research from the University of Southern California. His research examines the uses and effects of the Internet.
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Address: 406 Communication Arts and Sciences, Michigan State University, East Lansing, MI 48824. Tel: (517) 353-3650.

Address: 413 Communication Arts and Sciences, Michigan State University, East Lansing, MI 48824. Tel: (517) 353-6336.

Abstract

Previous research established that online shopping activity might be caused by impulse as much as by rational thinking about the conveniences of e-commerce. Interactive features of ecommerce sites, such as email alerts of special offers and “clickable” product arrays, may stimulate unregulated buying activity by undermining consumer self-regulation, but this connection has not been empirically verified. In this study, structural equation modeling techniques were used to model the relationship of interactive e-commerce features to online buying activity with a sample of 174 college students. Recreational shopping orientation predicted the usage of interactive shopping features thought to promote unregulated purchases, increasing deficient self-regulation, and leading to increased online buying activity. Convenience shopping orientation had a direct impact on buying activity, but it did not influence buying activity through the usage of convenience shopping features. Convenience shopping orientation also contributed to the usage of recreational shopping features that promoted deficient self-regulation. Overall, the model explained fifty percent of the variance in online buying activity.

Introduction

This study examines two orientations toward online shopping behavior. The convenience maximization orientation (Girard, Silverblatt, & Korgaonkar, 2002; Jarvenpaa & Todd, 1997; Li, Kuo, & Russel, 1999; Steinfield & Whitten, 1999) refers to shoppers' attitudes (Holbrook, 1986) toward shopping as a procedure to maximize their individual economic efficiencies; specifically, to minimize their search and transaction costs. Shoppers with a recreational orientation view shopping as a form of recreation and often make impulse buys (Bellenger & Korgaonkar, 1980; Donthu & Garcia, 1999). Shopping orientation plays an important role in shopping venue preferences (Korgaonkar & Smith, 1985). Girard, Korgaonkar and Silverblatt (2003) found that both convenience and recreational orientations were strong predictors of preferences for Internet shopping.

Recently, the recreational shopping orientation has been re-examined in light of the self-regulation mechanism of social cognitive theory (Bandura, 1991). In online shopping environments, some interactive features of e-commerce sites are thought to stimulate recreational and unregulated buying, while others are associated with convenience-oriented and planned buying (LaRose, 2001). However, the theoretical and empirical links among shopping orientations, utilization of online features, and buying activity have yet to be established. The present study proposes and tests a model of the Internet shopping experience, integrating an understanding of the phenomenon of unregulated buying and the differing shopping orientations of online consumers within a social cognitive framework.

Shopping Orientations

Stone (1954) introduced and defined shopping orientation as a rather broad concept, which is a shopping lifestyle or shopper's style encompassing shopping activities, interests and opinions. Other researchers (Darden & Howell, 1987; Gutman & Mills, 1982; Hawkins, Best, & Coney, 1989; Lumpkin, 1985; Shim & Bickle, 1994) defined shopping orientation as a complex and multidimensional phenomenon that has personal (e.g., motives, needs, preferences, economic condition, and social class) and market behavior (e.g., preferences for information sources, patronage behavior, and store attributes) dimensions. Much of the shopping orientation literature has attempted to define distinct segments of shoppers that vary according to their shopping styles.

However, in the present study, shopping orientation is defined as a shopper's attitude toward shopping activity that may vary with the situation rather than an invariant personality trait of the shopper. This definition is based on Holbrook's (1986) definition of a shopping value as a key outcome or expected benefit pursued by shoppers. In social cognitive theory, expected outcome is an important determinant of behavior (Bandura, 1991), in this case shopping behavior. From this perspective, shoppers may possess multiple shopping orientations and apply them as the situation demands. That is, a shopper can make both goal-oriented and unregulated purchases depending on environmental stimuli (e.g., interactive e-commerce features at websites) that he or she is exposed to.

Perhaps the most parsimonious and frequently cited categorization of shopping orientations in the marketing literature is convenience versus recreational orientation (Bellenger, Robertson, & Greenberg, 1977). Convenience orientation stresses the utilitarian value of shopping, as a task-related, rational, deliberate, and efficient activity (Babin, Darden, & Griffin, 1994). Therefore, shoppers with convenience orientations try to minimize their search cost as much as possible to save time or energy for activities other than shopping (Anderson, 1971). For example, utilitarian values might motivate shoppers to view shopping for a Christmas gift online as something that needs to be done out of necessity within relatively short period of time.

On the other hand, in situations where a recreational orientation is present shopping can be a leisure-time activity or a function of nonpurchase motives such as the need for social interaction, diversion from routine activities, sensory stimulation, and (in the offline shopping context) exercise (Bellenger & Korgaonkar, 1980). The hedonic value of recreational orientation results from enjoyment and playfulness rather than from task completion (Holbrook & Hirschman, 1982). Hedonic value is indicated by increased arousal (e.g. excitement caused by bargains), perceived freedom, fantasy fulfillment, and escapism (Hirschman, 1983). Thus, shoppers who pursue hedonic or recreational outcomes from shopping tend to spend more time on shopping, go shopping without plans or product lists, and continue shopping even after purchasing products they planned to buy. Even though recreational orientation enables shoppers to enjoy shopping process rather than specific purchases, there are some occasions when the actual purchase can produce hedonic value such as self-enhancement (Babin, et al., 1994). In these situations, purchases may be driven by need to purchase rather than need for a product (Rook, 1987). Therefore, shopping experiences driven by a recreational orientation lead shoppers to make more unregulated buys than those driven by a convenience orientation (Bellenger & Korgaonkar, 1980).

What is the relationship between convenience orientation (utilitarian value) and recreational orientation (hedonic value)? Even though these two types of expected outcomes are often distinguished as distinct dimensions, they are not necessarily mutually exclusive. For instance, if a shopper finds the product he/she intends to buy at a low price at the first store he/she visits, both utilitarian and recreational expected outcomes could be found in that shopping experience. That is, utilitarian value is present because of the efficiency and easiness of the product acquisition, while hedonic value comes from the excitement caused by the bargain sale (Babin, et al., 1994). Alternatively, shoppers may visit a website with a specific gift purchase in mind, but may be attracted by an on-site shopping recommendation to buy a “fun” gift for themselves on impulse. Utilitarian and hedonic expected outcomes were significantly correlated (r = .16) in Babin et al.'s study (1994), perhaps since both are associated with pleasure and arousal during shopping. This suggests that shopping with a convenience orientation may be accompanied by pleasure or arousal, and does not need to exclude hedonic outcomes.

Online Shopping Orientations

Previous studies about shopping orientations were rooted in traditional retail shopping settings. However, convenience orientations can be directly applied to online shopping environments, since one of the most important benefits of online shopping is that shoppers can save the time and effort needed for door-to-door visits to each store for product or price comparisons (Darian, 1987; Girard, et al., 2002; Jarvenpaa & Todd, 1997; Li, et al., 1999). Indeed, the Internet has been hailed as a perfect channel for convenience-oriented shopping (Donthu & Gilliland, 1996; Steinfield & Whitten, 1999).

However, convenience is not necessarily the most important or only reason for participating in online shopping. Some shoppers might shop online because it is convenient, while others might shop online because it is new and fun. Donthu and Garcia (1999) provided evidence that recreational shopping orientations existed online as well. Brown, Pope and Voges (2003) found that recreational shopping was more important than convenience for online shoppers. Brown et al. also found that online shoppers possessed multiple shopping orientations, combining the pursuit of convenience and recreational outcomes, for example.

LaRose (2001) argued that self-regulation is a key factor affecting the dominance of recreational over rational shopping motives. Self-regulation is an internal system that enables people to set up goals with which to evaluate their behavior (Bandura, 1991). Self-regulation can be either strengthened or weakened by stimuli encountered during the shopping experience, which might change a shopper's shopping orientation as a result. For example, an online consumer might enter Amazon.com with a specific book purchase in mind, but might be prompted to make further, unplanned purchases by the suggestions offered at the website. Such distracted self-regulation is called deficient self-regulation, which is defined as a state in which conscious self-control is relatively diminished. LaRose and Eastin (2002) found that deficient self-regulation was related to the amount of online shopping activity, although they did not establish a direct link between website characteristics and shopping activity. They argued that deficient self-regulation led to unregulated buying behavior, increasing the overall amount of online shopping activity.

The Internet provides an especially conducive environment for deficient self-regulation to arise. For example, people who make unregulated buys do not feel comfortable about letting others know about their purchases and prefer shopping channels in which they have relative anonymity, such as telemarketing or direct mail ordering (Rook & Fisher, 1995). Internet shopping transactions are mediated only by an impersonal computer system and are completed within the privacy of one's home, which perhaps represents the ultimate in anonymous shopping. Similarly, credit cards enable online shoppers to avoid guilty feelings created when spending cash, while 24 hour retailing and money-back guarantees also make it easier for online shoppers to indulge their buying urges (Rook & Fisher, 1995). Advertising models and sales representatives who urge excessive buying (Faber & O'Guinn, 1992) are other enablers of unregulated buying in the offline shopping environment, and their functions may be fulfilled by chats with online sales consultants and other shoppers in the online shopping environment.

Interactive features of e-commerce websites could guide shoppers into either convenience-oriented or recreational shopping (LaRose, 2001). Features promoting convenience-oriented shopping reinforce shoppers' self-regulation while reducing the possibility of unregulated buying. These will be called “convenience shopping features.” A search engine is one such feature (Zhang, 2002), since shoppers could find the products they intended to buy easily, without browsing, using the search engine. Other features help shoppers make the best choice in price as well as quality, such as price comparison features, candid peer reviews, expert reviews, or display of price information.

On the other hand, other features may promote recreational shopping and unregulated buying by disturbing self-regulation. According to social cognitive theory, these interactive features systematically undermine the three subfunctions of self-regulation (LaRose, 2001): self-monitoring, judgmental process, and self-reactive influence. Website features that undermine self-regulation are here called “recreational shopping features.” For example, the excitement generated by e-mail alerts of new products may overwhelm self-monitoring of one's spending behavior, while e-mail alerts about special offers might negate judgmental comparisons with one's budget and counteract self-reactive guilt feelings for exceeding it.

A content analysis of the features of popular e-commerce websites (LaRose, 2001) found that features likely to undermine self-regulation and those likely to bolster it (e.g., providing running tallies of past expenditures) both existed, but the recreational features greatly outnumbered the latter. An analysis of 100 popular e-commerce sites (Kim & LaRose, 2003) found positive relationships between features thought to stimulate unregulated buying (e.g., e-mail alerts of bargain sales or auction closings) and aggregate sales data. However, neither study established the mechanism that could link those features to individual consumer behavior. That is the purpose of the present study.

A Model of Online Buying Activity

Convenience and recreational orientations are hypothesized to be first order independent (exogenous) variables. Since convenience orientation focuses on the purchase of products as the primary goal of shopping activity, there should be a strong direct relationship between convenience shopping orientation and buying activity (Anderson, 1971; 1972; Babin, et al., 1994; Donthu & Gilliland, 1996).

H1: Convenience shopping orientation increases online buying activity.

An indirect effect on buying activity through convenience shopping features might also be expected. A convenience orientation leads Internet shoppers to website features that save their search costs in order to pursue their utilitarian outcomes during shopping. Such features may include search engines, price information, price comparison functions, and product reviews from other shoppers or experts. Here, shoppers will buy products they originally plan to buy, contributing to their total online buying activity.

H2: Convenience shopping orientation increases the usage of convenience shopping features on e-commerce websites.

H3: Usage of convenience shopping features increases online buying activity.

Recreational orientation will not necessarily lead to buying activity since recreational shoppers may derive gratification from browsing and playing the features found at a website rather than making planned purchases. Instead, we propose an indirect path from recreational orientation to buying activity through the usage of recreational shopping features and deficient self-regulation. Shoppers who pursue recreational outcomes in online shopping might enjoy “playing” with features that do not necessarily help them make rational and efficient purchases, which might eventually undermine their self-regulation and ultimately the goal of efficient shopping. Even though recreational shopping does not tend to lead to planned purchases, the hedonic outcome pursued by shoppers could be achieved by unregulated buying (Babin et al., 1994). Therefore, the path between deficient self-regulation and online buying activity means that the purchase behavior mediated by deficient self-regulation has a good chance of being unregulated buying.

H4: Recreational shopping orientation increases usage of recreational shopping features on e-commerce websites.

H5: Usage of recreational shopping features on the e-commerce websites increases deficient self-regulation.

H6: Deficient self-regulation increases online buying activity.

Internet shoppers were found to be both convenience-oriented and recreation-oriented in previous research (Donthu & Garcia, 1999; Donthu & Gilland, 1996). In addition, shoppers who start shopping with convenience orientations might subsequently pursue hedonic outcomes (Beatty & Ferrell, 1998; Lynos & Henderson, 2000), particularly after being exposed to website features that encourage recreational shopping activity (LaRose, 2001). For example, lists of best selling items and lists of new items might be able to reduce the search time and effort of shoppers, but also encourage unregulated buying by making it easy for shoppers to surf products they do not intend to buy. In other words, the positive association between convenience and recreational orientations found offline (Babin et al., 1994) might be replicated online.

H7: Convenience shopping orientation increases the usage of recreational shopping features on e-commerce websites.

Figure 1 is a proposed structural equation model summarizing the causal relationships among consumer shopping orientations, e-commerce website features and online buying activity.

Research Methods

Sampling and Data Collection Procedures

Students in residence at a large Midwestern university were recruited by campus mail to participate in an online survey. The student phone directory was the sampling frame. The first mailing included a cover letter explaining the purpose of the research and the URL of the web survey site along with a unique ID for each respondent. Two follow-up mailings were sent to non-respondents. For half of the sample 25 cents was included as a token of appreciation, and the other half of the respondents were included in a drawing for a fifty-dollar gift certificate. There was no difference in the response rates between the two incentive conditions. The response rate was thirty-six percent, which were 195 respondents out of the 540 mail solicitations that actually reached potential participants.

Operational Measures

Convenience shopping orientation was measured by six items1 that were originally designed for off-line shopping (Li et al., 1999), but modified for online shopping behavior. It was assumed that convenience orientation operates the same in the online and offline world, and indeed these have been demonstrated in past research in the electronic markets sphere (e.g. Girard, Korgaonkar, & Silverblatt, 2003; Li et al., 1999). This measure included both convenience and economic (price consciousness) orientation items, which were originally separate constructs in previous studies (Anderson, 1970; 1971; Bellenger and Korgaonkar, 1980). However, these two were found to be highly correlated, so they were merged into a single measure in this study. Logically, the convenience provided by online shopping is economic - the reduction of cost and time needed to search for the best price and the best product. Convenience shopping orientation items were rated on a 7-point Likert scale, ranging from “Strongly agree,” rated as a 7 to “Strongly disagree,” rated as 1.

Recreational shopping orientation consisted of six items2 including four measures of conventional shopping orientation in traditional retail store environments (Li et al., 1999) and two items measuring pleasure and arousal related to online shopping. Recreational orientation items specific to the online shopping environment were added in recognition of the unique attributes of web shopping (e.g. 24/7 accessibility and interactive features) that may emphasize its hedonic qualities and highlight recreational orientations to a degree not found offline. Thus, web-specific items were deemed an appropriate addition to recreational orientation, but were not needed for convenience orientation. These items were rated on a 7-point Likert scale, ranging from “Strongly agree,” rated as a 7 to “Strongly disagree,” rated as 1.

Usage of convenience shopping features (five items)3 (Kim & LaRose, 2003; LaRose, 2001; Zhang, 2002) described online actions supposed to help shoppers do efficient shopping and increase utilitarian outcomes. For example, usage of search engines helps shoppers find products they want without browsing individual product categories. Price information, price comparison features and product reviews by experts or other shoppers are features that could support shoppers' self-regulation by providing information that is useful for efficient shopping. These items were rated on a 5-point scale, ranging from “Very often,” rated as a 5 to “Never,” rated as 1.

Usage of recreational shopping features was a four-item4 index (Kim & LaRose, 2003; LaRose, 2001; Zhang, 2002). The usage of this group of features is expected to increase arousal, pleasure and excitement of online shopping through the notification of bargains, new products or best selling products. Email alerts of new products and special offers could be divided into two types: Emails issued by websites where shoppers have registered their personal information through an opt-in procedure and unauthorized spam emails. Regardless of the type of emails, once opened by shoppers, the messages within the emails could spur unregulated buying. Thus, these two different types of email alerts were not distinguished in the present study. These items were rated on a 5-point scale, ranging from “Very often,” rated as a 5 to “Never,” rated as 1.

Deficient self-regulation scale5 was replicated from LaRose and Eastin's study (2002). These items were rated on a 5-point scale, ranging from “Very often,” rated as a 5 to “Never,” rated as 1.

Buying activity scale6 consisted of three items measuring the number of products purchased and the amount of money spent on product purchases, based on the previous studies (Cobb & Hoyer, 1986; Kaufman-Scarborough & Lindquist, 2002; Li, et al., 1999).

Table 1 shows the summary statistics, reliabilities, and intercorrelations for six scales used for structural equation modeling.

Data Analysis

The hypothesized model was tested using Bentler's (1989) EQS structural equation program. Out of 195 cases, twenty-one cases with extensive missing data were excluded, so the final sample size used was 174. In the sample of 174, cases with missing values were replaced with EM algorithm in EQS program. All the multi-item scales for six constructs were treated as a single indicator of its corresponding latent factor by taking the mean value of all the items in each construct (Bandalos, 2002). Therefore, random measurement error was corrected by setting the random error variance associated with each construct equal to the product of its variance and the quantity one minus its estimated reliability (Bollen, 1989). The report of data analysis results referred to the guidelines suggested by Raykov, Tomer, and Nesselroade (1991). NFI, NNFI and CFI indices between .90 and .95 provide reliable evidence of acceptable fit, while a value above .95 means good fit of the model (Bentler, 1990; Bollen, 1990).

Results

The data showed acceptable fit to the model with χ2 (6, n=174) = 14.94, p= .02, NFI = .943, NNFI = .910, CFI = .964. All the hypothesized paths in model had expected signs (positive) and were statistically significant except one path: the path from convenience shopping features to online buying activity.

Convenience shopping orientation was a direct causal antecedent to online buying activity (β= .32, p < .001) and the usage of convenience shopping features (β= .53, p < .001). Thus, hypothesis 1 and 2 were supported. However, the indirect path from convenience shopping orientation to online buying activity through the usage of the convenience shopping features was not significant (β= .11, p= .29). As expected in hypothesis 4, recreational shopping orientation was a significant contributor of the usage of recreational shopping features (β= .39, p < .001). The usage of recreational shopping features led to deficient self-regulation (β= .58, p < .001), which led to online buying activity (β= .50, p < .001). Convenience shopping orientation also turned out to contribute to online buying behavior indirectly through the usage of recreational shopping features (β= .20, p < .05) as expected in hypothesis 7.

Convenience shopping orientation and recreational shopping orientation were positively associated (r= .31, p < .01). The correlation between the disturbance terms of convenience shopping features (D3) and recreational shopping features (D4) was statistically significant (r= .58, p < .001).

Predictive power of the model was indicated by the size of R2 of endogenous variables. Twenty eight percent of the variance in the usage of convenience shopping features was explained only by the convenience shopping orientation. Twenty six percent of the variance in the usage of recreational shopping features was explained by recreational and convenience shopping orientation. Then, 33 percent of the variance in deficient self-regulation was explained by the usage of recreational shopping features. Finally, 50 percent of the variance in online buying activity was accounted for deficient self-regulation, convenience shopping orientation and convenience shopping features.

Discussion

While previous researchers examined Internet shoppers' characteristics (Donthu & Garcia, 1999; Donthu & Gilliand, 1996) and others investigated website features (Kim & LaRose, 2003; Lohse & Spiller, 2000), respectively, the present research links these two with online buying activity. Specifically, usage of interactive e-commerce features connects shopping orientations and online buying behavior. This study showed that the interactive features of e-commerce websites play a pivotal role in most online buying activity regardless of one's shopping orientation.

Shoppers with recreational orientations utilize website features in order to purse hedonic outcomes, and the excitement of an online sale or an enticing array of product offerings weakens their self-control, increasing unregulated buying activity. On the other hand, shoppers with utilitarian outcomes in mind tend to be directly led to buying behavior unmediated by the usage of interactive e-commerce features. This may be because consumers with convenience orientations shop with a specific purchase plan in mind, quickly locate the item, and proceed directly to the checkout. That may be also because they have already done their comparison shopping before they arrive at the site from which they planed to make the purchase or have satisfied themselves that the site they regularly use has good deals. Therefore, shoppers with convenience orientations may not be out “window shopping” and may not waste time even on convenience features such as search engines, product reviews, or other convenience shopping features.

This study showed that convenience and recreational shopping orientations are linked to each other. Convenience and recreational shopping orientations were positively correlated, replicating Babin et al.'s study (1994) of offline shopping, which showed that these two orientations might not be mutually exclusive. Convenience orientation also led to the usage of Web features associated with unregulated buying, which is shown by the fact that convenience orientation made a significant contribution to the usage of recreational shopping features in this study. Perhaps shoppers who visit websites with convenience orientations can be drawn into unregulated purchases through interactive features, which, in the end, feed unregulated buying. For example, shoppers who want to make efficient and time saving purchases may use a new product list as an efficient tool to locate the exact product they are planning to buy, reducing their search cost. For shoppers who approach websites with recreational orientations, on the other hand, the new product list could be used as “bait” for unplanned buying. However, this feature also creates a trap for the shoppers who are trying to do efficient shopping in that it undermines self-regulation, leading to unregulated buying. Thus, some of the shoppers who started shopping with convenience orientations may have been led down the path from recreational shopping features to deficient self-regulation, which led to unregulated buying.

Perhaps, as some have suggested (Hoffman & Novak, 1996; Novak, Hoffman, & Duhachek, 2003; Novak, Hoffman, & Yung, 2000), consumers who utilize interactive features enter a seamless sequence of responses, a “flow” state in which their sense of time and reality becomes distorted and their self-control is diminished. In the present framework, flow states might be interpreted as an indication of deficient self-regulation, specifically a failure of the self-monitoring subfunction of self-regulation. Lack of time pressure created by a flow state may also lead to unregulated buying (Beatty & Ferrell, 1998). So, the convenience of online shopping could facilitate unregulated buying proclivities that are present in nearly all consumers (cf. Cobb & Hoyer, 1986), as well rational consideration of minimizing search and transaction costs.

Information overload (Hausman, 2000) could be another explanation for the linkage among convenience orientation, usage of recreational features, and deficient self-regulation. Even though consumers with convenience orientations start Internet browsing to reduce their search costs, they may be overwhelmed by the amount of product information they find online. Thus, there arises the irony that instead of reducing their search costs, shoppers with convenience orientations come to spend much more time searching and comparing product information online than they intended. In frustration, they might rely on impulse-provoking features to obtain closure to the search process before achieving their economic goals.

Overall, the present findings offer a challenge to explanations of the online shopping experience that emphasize economic convenience and the operation of an efficient electronic marketplace. Deficient self-regulation is the antithesis of that explanation. It indicates that online buying could be out of control and is either not being actively self-monitored or not being judged against rational standards of consumer efficiency. Even consumers who approach the Internet with a convenience orientation, who are presumably seeking the efficiencies that convenience maximization perspective speaks of, can have their self-regulation diminished by “playing” certain of the interactive features found at online stores.

Limitations

A college student sample was used for the present study, and college students do not represent all Internet consumers. Specifically, college students spend large amounts of time on the Internet and are inexperienced in controlling credit card debt, and thus may be less able to exercise self-control than older shoppers. Thus, recreational shopping features may have more of an effect on them. Second, the intercorrelations between the recreational and convenience features were rather high. A more detailed list of features might resolve the distinction between the two. For example, the product directory feature could have been separated into one with pictures that might be more appealing to those pursuing recreational orientations, and the other with only text that might be used more by shoppers who pursue convenience orientations. Finally, while structural equation modeling of cross-sectional data can test assumptions about the nature of causal relationships, it cannot establish the direction of causality. Experimental research in which the interactive features of websites are manipulated is called for.

Future Research

First, the effects of interactive features might be examined more clearly by comparing a test site heavily laden with convenience shopping features and with one emphasizing recreational shopping features. Second, to see if online buying activity initiated by a convenience orientation and mediated by convenience shopping features is planned, while online buying activity initiated by a recreational orientation and mediated by recreational shopping features is unregulated, planned purchases should be distinguished from unplanned ones. Third, further research about the shopping orientations of online shoppers is also needed, so that online shopping orientations may be distinguished from offline orientations. Here, convenience orientation used conventional measures of shopping orientation from the offline shopping setting simply by changing the frame of reference of the question to online shopping, while newly invented variables for online shopping environment were added to the recreational orientation measure. Fourth, product type can be another important factor that affects buying behavior of online shoppers. Online shoppers might proceed from different shopping orientations depending upon which type of product they are trying to purchase. For example, convenience orientation was a stronger predictor for purchasing experience goods (e.g. clothing and perfume) on the Internet, while both convenience and recreational orientation had significant influences on purchases of credence goods (e.g. vitamins and water purifiers, Girard, et al., 2003). Finally, time spent on shopping could be used as an outcome variable to distinguish shoppers pursuing convenience outcomes from those pursing recreational ones, because the latter theoretically spend more time on shopping than the former. A previous study found that websites with interactive e-commerce features that support self-regulation had shorter average visits and fewer pages consumed per person compared to websites with recreational features (Kim & LaRose, 2003). However, a study incorporating shopping orientations, online shopping features, and shopping outcomes (duration, planned and unplanned purchases) has yet to be done.

Implications for Marketers and Consumers

With Internet shopping features disturbing their goal-directed and self-regulated shopping, Internet shoppers may be destined to make unregulated buys. Thus, e-commerce marketers might need to pay more attention to nondirected browsing behavior, since this may stimulate the buying activity of convenience shoppers and recreational shoppers alike. In some respects, recreational shopping is not a desirable shopping pattern for e-commerce websites, since shoppers tend to spend longer time browsing, tie up servers, often leave sites without making purchases, and return products that they really do not need. However, recreational aspects of online shopping may also be especially habit forming (cf. LaRose & Eastin, 2002) so that by indulging them, the website proprietor may be able to build consumer loyalty.

The failure to link convenience shopping features to online buying activity calls the value of “usability” testing of e-commerce sites (e.g., Nielsen, 2000) into question. Usability research focuses on minimizing the time consumers spend on completing online tasks. That is desirable if consumers in fact engage in online shopping primarily to minimize their search and transaction costs. However, if shoppers who pursue utilitarian outcomes do not rely on convenience shopping features, the logic of usability testing becomes far less compelling. Indeed, the present study suggests that websites with low usability metrics might stimulate buying activity by engaging consumers and lowering their resistance to unregulated buying.

From the consumer's perspective, interactive e-commerce can either be an economic convenience or a trap for the unwary. Pulled into a site by email alerts, enticed by interactive product displays, and only a click away from the checkout counter, consumers may find their normal self-monitoring overwhelmed. That can result in unregulated purchases of unneeded products and a short-circuiting of the diligent comparison shopping that the Internet promises. Rather than help consumers minimize search costs, the convenience of online shopping could spur further unregulated buying. This is of particular concern for the college students of today who are acquiring the consumer habits of a lifetime. Are they learning to be rational comparison shoppers or are they getting hooked on yet another addictive computer game? We close by urging that consumer education efforts be directed toward helping consumers understand the purpose of interactive ecommerce features and what they can add to the shopping experience–and to the total at the checkout counter.

Footnotes

  • 1

    7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree): How much do you agree or disagree with the following statements about online shopping? It is more convenient than shopping in physical stores, It saves money, It takes less time to buy what you want, You can shop around for the best buy, You can consider a wide selection before making a purchase, You can get a good deal.

  • 2

    7-point Likert scale from 1 (strongly disagree) to 7 (strongly agree): How much do you agree with the following statements? It is enjoyable, It cheers you up, I enjoy shopping, Window-shopping is usually a pleasant experience for me, I like to shop around and look at displays, I never feel bored when I go shopping.

  • 3

    5-point scale from 1 (never) to 5 (very often): How often do you use these when you shop online? Search engine for finding products I want, Product price information, Price comparison with other websites, Product-related articles or reviews written by experts, Reviews written by other shoppers.

  • 4

    5-point scale from 1 (never) to 5 (very often): How often do you use these when you shop online? Email alerts of new products, Email alerts of special offers, List of new items, List of best selling items.

  • 5

    5-point scale from 1 (never) to 5 (very often): How often have you…? Bought a product on the Internet that I did not originally intend to buy, Felt a sudden urge to buy something I saw on the web, Bought something on the Internet I knew I couldn't afford, Bought something on the Web that I really didn't need, Browsed for products I wasn't really serious about buying, Kept buying more and more every time I went shopping on line, Felt anxious to go on line and buy some more, Abandoned an Internet purchase when I reached the check-out counter, Returned something I bought online.

  • 6

    5-point scale from 1 (never) to 5 (very often): How often have you…? Actually bought a product on the InternetOpen-ended question: How many times have you made purchases on the Web in the last 3 months? How much would you say you have purchased online just in the last month? These two items were subjected to log10 (n+1) transforms to minimize the impact of extreme values.

Ancillary