06 zhang.indd

1 downloads 0 Views 128KB Size Report
of consumers may be impulsive buyers (Rook and Fisher. 1995). Obviously ...... and Robert Fisher (1995), “Normative Influences in Impulse. Buying,” Journal of ...

MODELING INFLUENCES ON IMPULSE PURCHASING BEHAVIORS DURING ONLINE MARKETING TRANSACTIONS Xiaoni Zhang, Victor R. Prybutok, and David Strutton The online shopping environment still represents a comparatively new, and largely unexplored, marketing channel. Not surprisingly, little is understood regarding the nature of and influences on impulsive consumer purchases during online marketing exchanges. This paper investigates these and related issues. Associated results tentatively suggest that significant relationships may exist between gender, subjective norms, consumer impulsivity, purchase intention, and actual purchase behavior in online marketing environments. Managerial and theoretical implications are developed and discussed.

Not long ago, few viable shopping options existed through which consumers could avoid congested parking lots or tiresome trips, out-of-stock merchandise, lengthy checkout times, and indifferent retail service levels. At the time, consumers had little choice but to slog ahead reluctantly. Obviously, for increasing numbers of Web-savvy consumers, such constraints no longer apply. More people than ever have shopped online during 2005. In response, e-marketers are increasingly pursuing promotional tactics intended to convert Web viewers into Web customers (i.e., consumer conversion). One approach through which such consumer conversion might logically be initiated entails purposively designing sites in ways that stimulate more impulsive consumer behavior. By the time an e-shopper has reached a site, and began thinking “I really shouldn’t” (purchase that item), truly savvy e-marketing organizations might have already designed its Web site to loosen consumers’ normal levels of self-control (Baumeister 2002), so long as they are delivering appropriate value to said consumers, of course. The promotional tactical thrust recommended here is consistent with traditional marketing definitions of

Xiaoni Zhang (Ph.D., University of North Texas), Assistant Professor of Information Systems, College of Informatics, Northern Kentucky University, Highland Heights, KY, [email protected] Victor R. Prybutok (Ph.D., Drexel University), Regents Professor of Decision Sciences and Director of the Center for Quality and Productivity, College of Business Administration, University of North Texas, Denton, TX, [email protected] David Strutton (Ph.D., University of Mississippi), Professor and Chairperson of the Department of Marketing and Logistics, College of Business Administration, University of North Texas, Denton, TX, [email protected]

impulse purchasing. Such definitions long reasoned that augmented products, rather than consumer-related factors, provide the primary stimulus that induces consumers to purchase impulsively (Stern 1962). Since Stern’s seminal research, the impulse purchasing construct has emerged as a broadly recognized social-psychological trait. It is, for example, broadly understood that most consumers purchase impulsively at least occasionally (Bellman, Lohse, and Johnson 1999; Donthu 1999). Relatedly, certain purchase circumstances are thought to inhibit impulse purchases. Such conditions would typically entail situations in which consumers’ self-control or self-regulatory responses are better able to gain or retain mastery over their naturally arising impulsive tendencies. In contrast, other purchase conditions exist wherein consumers are more likely to yield to their naturally arising buying impulses (Roberson, Shaver, and Lawrence 1991). Online marketers who are better able to discriminate between online consumption conditions that are more (or less) likely to stimulate impulsive shopping behaviors would clearly enjoy a competitive advantages—assuming that those same marketers were also able to act strategically based on their insights. And likely, regardless of how impulsive the typical online shopper actually is, there are few, if any, online retailing site sponsors that would not welcome the opportunity to create a site and a consequent visitation experience that encouraged even more online shopper impulsiveness. Brick-and-mortar stores have long been designed in ways intended to encourage impulse purchasing. In traditional stores, it is not uncommon, for example, to hear public announcements about great deals currently available for only a short while. Not by accident did “blue light” specials emerge as part of the American cultural lexicon. But absent the ability or opportunity to attract the requisite Journal of Marketing Theory and Practice, vol. 15, no. 1 (winter 2007), pp. 79–90. © 2007 M.E. Sharpe, Inc. All rights reserved. ISSN 1069-6679 / 2006 $9.50 + 0.00. DOI 10.2753/MTP1069-6679150106.


Journal of Marketing Theory and Practice

consumer attention (as often eventuates in online retailing settings), stores naturally encounter less opportunity to prompt unplanned purchases (Coley and Burgess 2003; Lee 2002; Weinberg and Gottwald 1982). Studies in this area have explored the effects of gender differences, layout, product categories, consumer emotions and self-control, and cultural effects, among other constructs. Significantly, no studies exist addressing the same constructs’ effects in online shopping settings. Marketers should learn more about how and why impulsive purchasing might be stimulated during online buyer–seller exchanges. To narrow the gap in our current understanding regarding how and why more impulsive purchases might arise—or be stimulated—during online buyer–seller interactions, this study tested the technology acceptance model (TAM). Special attention was allocated toward investigating possible relationships between impulsive consumer behavior and gender in online shopping environments. While several apparently obvious reasons exist suggesting why gender disparities may exist with respect to online impulse purchases, in the context of this research, the most pivotal may derive from another manifest tendency, that being, men tend to not ask for directions. This core male trait follows from other, naturally arising, gender-based differences, including, but not limited to, task orientation, schematic planning, and perceived task-specific self-efficacy—that is, computer technology proficiency. The potential role and implications of these factors are directly and indirectly investigated in this paper.

RESEARCH OBJECTIVE The research objective of this study was to identify and test various factors that may influence impulse purchasing during online transactions. Unlike traditional shopping transactions, online consumers do not need sales associates or cashiers to complete a transaction. In traditional shopping settings, impulse purchases might be triggered by the persuasiveness of a salesperson or sales promotion, the layout or atmosphere of a store, or enticing product arrangements. This is widely understood. And, traditionally, marketers have acted based on such insight in their efforts to “manage” impulse purchasing. But little is known about the identity and nature of factors that may trigger or inhibit impulse purchasing during online transactions. Questions pertaining to whether various Web designs, the manner in which products are presented online, the perceived usefulness of Web sites, or the

dominant subjective norms of prospective shoppers exercise significant effects on impulse purchasing in online settings have not been conclusively answered. Nor have researchers investigated whether gender might discriminate consumers with respect to their impulse purchasing behaviors during online marketing interactions. The specific research objectives of this paper entailed addressing and tentatively answering each question introduced above.

DEVELOPING A THEORETICAL FRAMEWORK Impulse Purchasing Behaviors: Definition and Role Impulsive purchases are akin to unplanned purchasing behaviors. They occur whenever consumers experience a sudden urge to buy something immediately, absent substantive additional evaluation, and act based on the urge (Rook 1987). As a construct, impulse purchasing is thought to capture a relatively enduring consumer trait that produces urges or motivations to buy products or services, absent much regard for actual need. More than two-fifths of consumers may be impulsive buyers (Rook and Fisher 1995). Obviously, certain consumers find impulses more difficult to resist than do others. For such consumers, pleasure, as well as satisfaction, may be associated with impulse purchasing. Researchers from different disciplines have assessed the role that the importance of impulsive behavior plays as an ingredient in the broader mosaic of human behavior. Wolman (1973) was first to frame impulsiveness as a psychological trait generated in response to a stimulus. Decades ago, marketers began investigating the effect that product arrangements or in-store locations might exercise on impulse purchase behaviors (Cox 1964). It has since become standard promotional practice for retailers to arrange certain items—such as candies, snacks, or magazines—near checkout points in efforts to entice consumers into impulse purchases. Impulse purchases generally emanate from purchase scenarios that feature higher emotional activation, less cognitive control, and largely reactive behavior (Weinberg and Gottwald 1982). Impulse purchasers also tend to be more emotionalized than nonpurchasers. Given the ongoing development of the digital economy, and the shopping conveniences being delivered through digitalized exchanges, one might reason that more impulsive individuals may be more prone to shopping online. BizRate (2003), for example, reports that differences exist between men and women with respect to the manner in

Winter 2007 which they shop online for holiday-type purchases. BizRate found that while online, female consumers generally shop in more of a planned fashion, are less impulsive, and buy more ahead of time while online. In contrast, men tend to emerge online in full force two or fewer weeks before the Christmas holidays. Upon arrival, they shop with the goal of quickly (and perhaps impulsively) satisfying their purchasing needs (Shumacher and Morahan-Martin 2001). When prospective customers enter any retail site’s home page, they invariably encounter at least three basic choices. During the transaction, the first choice encountered is whether to use the search engine. The second choice entails deciding whether to choose a featured product. The third relates to whether to use the category links. Often, price may not be the determining factor that prompts impulsive consumer purchases on e-commerce sites. Spool (1999) asserts that well-designed Web sites must also feature more meaningful navigation links. He likewise suggests these navigation links outperform search engines. According to Spool, the biggest driver of impulse purchases follows from the degree to which a site encourages users to navigate category links rather than the site’s search engine. The Web has evolved rapidly. Search engines and consistent Web layout are now standard Web design features. Moreover, certain of these design features might be managed in ways that encourage impulse purchases.

Technology Acceptance Model: Definition, Development, and Modification Executing the research objective associated with this study will require use of a valid model that addresses the online transaction from each side of the buyer–seller dyad. With this requirement in mind, the TAM (Davis 1986) was adopted for use. TAM permits each side of the online buyer–seller dyad to be explicated. Moreover, it is generally recognized that online shoppers may be characterized by a double identity—that is, they are “shoppers” as well as “computer users” (Koufaris 2002). This observation implies that, while attracting or retaining customers is a function that remains primarily within the marketing domain, technology also provides a tool that can assist or impede marketers in their efforts to fulfill the selling task. It follows that to understand why consumers might return to an online store or feel sufficiently at ease or enticed to purchase from that site, researchers must examine shoppers’ interactions with a Web site as both a store and a technology system. TAM permits, and indeed facilitates, this type of consumer examination.


Finally, the decision to employ TAM as a key study metric followed from the general proposition that consumers who accept (and therefore are more likely to use) new technology to shop are more likely to be exposed to reminder, suggestion, and pure impulse stimuli as a result of their exposure to various Web sites on the Internet. This premise is consistent with Mowen’s definition of an impulse purchase involving “an unplanned action that results from (exposure to) a specific stimulus” (2002, ). The efforts of various researchers have attested to the reliability and construct validity of the original TAM (Adam, Nelson, and Todd 1992; Agarwal and Prasad 1991; Igbaria, Guimaraes, and Davis 1995). In this study, one overarching goal was to extend prior applications of the original TAM in ways that generated useful theoretical and practical marketing insights for marketers. The purpose of this extension was to allow the influence of several factors that theoretically should drive various aspects of consumers’ online behavior (i.e., impulsivity, purchase intention, and purchase frequency) during Internet-based marketing exchanges to be addressed empirically. Table 1 identifies the research constructs, summarizes prior research support, and provides working definitions for each TAM construct tested in the current study. Considerable research has been conducted across several disciplines in an effort to understand the dynamics that influence the degree to which consumers adopt and voluntarily use information systems. Throughout these endeavors, TAM has proven consistently useful. TAM features a solid foundation in psychological research, and also captures several clearly relevant technological constructs. Critical aspects of both technology acceptance and social influence theory are rooted in the model. This study proposed that one modification and one extension be introduced to TAM. Specifically, one independent variable—consumer impulsivity—and one mediating variable—gender—was added. This modified TAM should capture user (i.e., consumer) traits, intentions, and behaviors toward a new shopping technology (i.e., the Internet as retail outlet). Each dimension captured in this modified TAM is consistent with traditional consumer decision-making models and the premise that consumer attitudes should influence intentions, while intentions influence behaviors. These additions to TAM will permit this study’s research objective to be pursued. By modifying TAM in this fashion, a platform now exists from which theoretically useful and managerially actionable insights can be developed regarding the precise identity of factors that influence impulse purchasing in online shopping environments.


Journal of Marketing Theory and Practice Table 1 Summary of Constructs Involved in the Study


Prior Research


Actual Purchase

Baumeister (2002)

Intention Impulsive Buying (Impulsivity)

Mathieson (1991) Mowen (2002)

Subjective Norms

Fishbein and Ajzen (1975)

The number of times a person did online transactions during the past six months. Intention to make an online purchase. “An unplanned action that results from (exposure to) a specific stimulus.” “Person’s perception that most people who are important to him think he should or should not perform the behavior in question.”

Hypothesized Relationships Specifically, the model hypothesizes that impulsivity will be positively associated with (or promote) purchase intention. The model also hypothesizes that gender differences will exist regarding the purchase intention, impulsivity, and the actual purchase behaviors of consumers. The modified version of TAM that is evaluated in this paper is depicted in Figure 1. In Figure 1, the gender construct’s oval shape is intended to specify that gender should function as a moderating variable for subjective norms, purchase intention, actual purchase behaviors, and consumer impulsivity. The research model shown in Figure 1 explores the role that gender may play in contributing to differences in consumers’ purchase intention and actual purchase behavior in online settings, as well as gender’s contribution to differences with respect to other psychological and social influence constructs. Some of the relationships presented in our model were tested in prior works (Zhang and Prybutok 2003; Zhang, Prybutok, and Koh 2006) but not within the context of the relationships as posited in Figure 1. For example, the relationship between consumer impulsivity and purchase intention was proposed by Zhang, Prybutok, and Koh’s (2006) but this work extends their finding to include the moderating effect of gender on the relationship between consumer impulsivity and purchase intention. The current work, in contrast, focuses primarily on those relationships that are most pertinent to marketing. In addition, the potential role of gender as moderator variable is explored. The TAM construct identified as “subjective norms” assesses a “person’s perception that most people who are important to him think he should or should not perform the behavior in question” (Fishbein and Ajzen 1975, p. 195). As a measurement construct, subjective norms encompass individuals’ judgments of whether the important people in their lives will approve or disapprove of the behavior in question. People tend to trust the opinions of their families

and friends. Consequently, they tend to value these opinions highly when making consumption decisions. The theory of reasoned action (TRA) attempts to relate individuals’ attitudes and subjective norms to their intentions to act (Fishbein and Ajzen 1975). Subjective norms clearly should influence a person’s behavioral intentions. According to TRA, values held and opinions espoused by individuals’ important referents tend to influence the behaviors of those individuals. TRA has proven useful as a means of explaining diverse decision-making situations (Patry and Pelletier 2001), such as those that will be examined in this study. A significant relationship was also observed to exist between subjective norms and behavioral intentions. In 1989, Davis introduced subjective norms into an expanded version of TAM. Surprisingly, the empirical results failed to support the anticipated positive relationship between subjective norms and behavior intention. Other researchers have since employed subjective norms to investigate various issues relating to behavioral intention (Klein and Hirschheim 1989; Mathieson 1991; Venkatesh and Davis 1996). These studies also failed to confirm the proposition that subjective norms exercise significant effects on behavioral intention. But others (Taylor and Todd 1995) have been able to gather evidence supporting the general premise. In another research setting, Venkatesh and Davis (1996) reported the existence of significant relationships between subjective norms and the perceived usefulness of a new technology. In the same study, a significant relationship between subjective norms and intention to use the new technology in question was also observed. More recently, Venkatesh and Morris (2000) validated this revised version of TAM. In doing so, they focused on the influence of gender differences and subjective norms. Women were influenced more than men by the perceptions of the ease of use and subjective norms with the technology. But the subjective norms effect diminished over time. In general, though, sufficient support exists to integrate the case of subjective norms into a modified version of TAM. At the least, it ap-

Winter 2007


Figure 1 Research Model Tested in This Study

pears the possible role of subjective norms in Web-based settings merits more conclusive attention. This action was taken with respect to the study reported in this paper. In 1999, Karahana, Detmar, and Chervany evaluated technology adoption from two perspectives: individuals’ preadoption and postadoption (continued use) beliefs and attitudes. Users and potential adopters of new information technology differed regarding the self-described determinants of their behavioral intentions, attitudes, and subjective norms. Mixed findings have been reported regarding the relationship between subjective norms and behavioral intentions in the technology adoption literature. Current research suggests that subjective norms may influence technology adoption. Nevertheless, no study has systematically examined this possibility in an e-marketing consumer context. Expanding the TAM model by including the opportunity to assess this potentially important relationship empirically in an online shopping environment therefore seems fitting. Given that various individual characteristics (i.e., consumer attitudes toward online purchasing, consumer impulsivity, and various social perceptions of consumers) may logically influence consumer behavior during online transactions, a decision to expand the psychological grounding of TAM may prove theoretically significant. Moreover, results that follow from such an investigation may prove managerially actionable. In technologically-oriented research, males and females have been observed to differ in both their attitudes toward and perceptions of technology. Pradeep’s (1999) work, for example, suggests that gender may be significantly associated with various aspects of online purchasing behaviors. Males may use the Internet more frequently than females. Or, perhaps, males may make fewer online purchases than females. Because gender—as a moderator variable—may mediate possible relationships between subjective norms and purchase intention, this study proposes that the genders should differ regarding their online impulsivity, online

purchase frequency, and in the degree to which subjective norms influence online purchase intention. Because numerous consumer models (e.g., too many to efficiently cite here) suggest a positive relationship exists between intention and behavior, the model also links purchase intention to purchase behavior. On the basis of the preceding discussion, several hypotheses were tested: Hypothesis 1: During online buyer–seller exchanges, subjective norms will be positively associated with consumers’ purchase intention. Hypothesis 2: During online buyer–seller transactions, impulsivity will be positively associated with consumers’ purchase intention. Hypothesis 3: During online buyer–seller transactions, impulsivity will be positively associated with consumers’ subjective norms. Hypothesis 4: During online buyer–seller exchanges, intention to purchase will be greater for male consumers. Hypothesis 5: During online buyer–seller exchanges, male consumers will exhibit greater impulsivity than female consumers. Hypothesis 6: While online, male consumers will purchase more frequently than female consumers. Hypothesis 7: During online buyer–seller exchanges, the influence of subjective norms on purchase intention will be greater for male consumers.

METHODOLOGY AND ANALYSIS Scale Development To begin scale development, factors thought to be important in electronic shopping transactions were identified.


Journal of Marketing Theory and Practice

Marketing and information systems journals were sourced for this purpose. A survey instrument was constructed to measure “purchase intention” and “purchase frequency,” with each construct adapted from the work of Davis (1989). Each measure was adapted to fit the online shopping context. Purchase frequency was assessed by asking consumers about the number of online transactions a person conducted during the past six months. All items used to measure the other constructs relevant to this research model were also selected from previous research. Again, each was adapted to this study’s Internetbased marketing transactional context. Specifically, the Rook and Fisher scale was used to measure impulsivity. This scale’s items purportedly measure “a consumer’s tendency to buy spontaneously, unreflectively, immediately, and kinetically” (Rook and Fisher 1995, ). To measure subjective norms, Ajzen and Fishbein’s (1980) scale was employed. Subjective norms were adopted from Ajzen and Driver’s (1992) work. Impulsivity was adopted from Rook and Fisher’s (1995) work. Intention was adapted from Mathieson (1991). Cronbach’s alpha is the most widely used internal consistency reliability coefficient. Most researchers suggest that the acceptance level for coefficient alpha should exceed 0.7 (Roberson, Shaver, and Lawrence 1991). Reliability scores for intention, subjective norms, and impulse were 0.86, 0.91, and 0.94, respectively. Each reliability scores exceeded the 0.7 criterion by a substantial margin. Construct validity can be established based on content and discriminant and convergent validity. Review of the relevant literature and subsequent review of the initial draft of the instrument by a group of experts was conducted to establish the content validity of the survey instrument (Churchill 1979; Devillis 1991). In practice, the convergent validity between two measures designed to assess the same construct is supported if the correlation among the constructs is statistically significant and sufficiently large to encourage further examination of validity (Devillis 1991). Discriminant validity can be established by examining the correlations among constructs. Low to moderate correlation between two constructs provides evidence of discriminant validity. All factor loadings were used in this analysis. These loadings were produced using a principal component analysis with varimax rotation. The factor loadings shown in Table 2 indicate that all items loaded on the construct they were intended to measure. On this basis, the constructs of interest appeared to demonstrate adequate convergent and discriminant validity.

Table 2 Factor Loadings

I1 I2 I3 N1 N2 N3 N4 N5 N6 N7 M1 M2 M3 M4 M5 M6 M7 M8




0.06 0.01 0.02 0.03 0.04 0.14 0.15 0.12 0.14 0.08 0.79 0.81 0.88 0.86 0.85 0.78 0.82 0.72

0.66 0.75 0.61 0.25 0.15 0.10 0.09 0.14 0.04 –0.05 0.07 0.12 0.03 0.05 –0.03 0.10 0.05 0.06

0.10 0.11 0.09 0.60 0.76 0.90 0.89 0.87 0.67 0.70 0.00 0.10 0.14 0.13 0.15 0.04 0.04 0.16

Sampling Frame Subjects likely to purchase from the Internet presumably should provide an acceptable sampling frame for a study of online shopping impulsivity, as should consumers who have already purchased through the channel. Following this premise, college students clearly should provide suitable subjects for a study of this nature. Nearly all college students are familiar with the Internet’s shopping environment. Similar rationales have made students popular subjects used in many studies of online behaviors (Gefen, Karahanna, and Straub 2003; McKnight and Chervany 2001–2002). To evaluate consumers’ online shopping behavior, data was collected though a Web-based survey. The survey on the Web was password protected. To answer the survey, students needed a user name and password. Instructors in a college of business announced in class about the survey and handed out instructions. The instructions contained the Web site address, user name, and password. In all, 332 usable responses were collected. Among those responding, there were 163 females (49.1 percent). Internet purchases had been made by 96.1 percent of those sampled. Almost 97 percent of respondents reported they had already shopped online. Male respondents lagged just slightly. More than 95 percent had previously made online purchases. Among those sampled, 81.3 percent worked part or full time.

Winter 2007 More than 48 percent of the respondents spent at least five hours a week surfing the Internet, 30.1 percent spent six to ten hours surfing the Internet weekly; 10 percent spent 11 to 15 hours online, and 5.5 percent spent 16 to 20 hours online. More than 6 percent of the respondents spent over 20 hours online each week. Respondents were asked to report the number of times they had shopped online during the past year. The data revealed that only 7.6 percent of respondents had shopped only once. More than 90 percent had shopped at least five times. These results suggest that the respondents are familiar with online shopping and, therefore, are sufficiently well versed to inveigh knowledgeably about the research propositions developed in this study. On average, this sample suggests that females spend 8.7 hours per week on the Internet, whereas males spent an average of 5.5 hours. Nearly 80 percent of the females work full time, while 83.4 percent males worked.

Procedure The primary research model was based on a widely applied TAM. Consequently, a theoretical basis existed to use confirmatory analysis. LISREL 8.51 was used to assess the model’s fit and analyze the paths showing the relationships between constructs. A two-stage approach was used.

RESULTS LISREL results revealed that the root mean square error of approximation (RMSEA) = 0.053, normed fit index (NFI) = 0.95, nonnormed fit index (NNFI) = 0.95, goodness-of-fit index (GFI) = 0.91, and adjusted goodness-of-fit index (AFGI) = 0.9. The indices indicated that the proposed model fit the data well and support the notion of the overall model fit. The structural relations produced by LISREL are displayed in Table 3. With one exception—that is, the relationship between purchase intention and subjective norms—the coefficients in the equations are statistically significant. This result confirmed all paths proposed in Figure 1. These data suggest consumers’ subjective norms (as a mediator of behavior) are directly related to purchase intentions when they engage in Internet-based marketing transactions (supporting H1). These data also suggest consumers’ impulsivity is positively associated with purchase intention during online marketing exchanges (supporting H2). H3, positing that consumer’s impulsivity will be positively associated with their subjective norms, was also significant at the 0.01 level.


Table 3 Structural Models Constructs


Purchase Intention = Impulsivity =

0.06 Subjective norms + 0. 09 impulsivity* 0.14 Subjective norms**

* significant at the 5 percent level; ** significant at the 1 percent level.

The remaining hypotheses evaluated the influence of online consumers’ gender on their purchase intention, the influence of subjective norms, consumer impulsivity, and frequency of (Internet-based) purchase. To assess whether significant differences existed between females and males with respect to those constructs, multivariate analysis of variance (MANOVA) was used. Hotelling’s trace was significant. H4, H5, and H6 were supported. These results collectively suggest that gender differences exist among this sample with respect to purchase intention, consumer impulsivity, and frequency of purchase. However, H7 (i.e., positing that males will be influenced more heavily by subjective norms) was only marginally supported at the 0.06 significance level. Table 4 summarizes the descriptive information associated with this analysis.

THEORETICAL AND MANAGERIAL IMPLICATIONS Several actionable managerial insights follow from these results. To varying degrees, these insights might apply to any marketer actively engaged in the management of a marketing Web site. Beginning logically, the first insight follows from the fact that these data suggest that male consumers’ “intention to purchase” is significantly greater whenever either males or females engage in Internet-based marketing transactions. Based on this observation alone, one might tentatively infer that males comprise a more attractive target audience for Internet-based marketers. But at this preliminary stage, only one managerial proposition can be offered with acceptable certitude: simply understanding that such gender differences exist at all with respect to online purchasing intention suggests e-marketers should, at the least, consider promoting differently to male and female shoppers. Common marketing sense also suggests that a cohort of individuals reporting a greater “intention to purchase” while online ought to be promoted to differently than a cohort that has reported a lower intention to purchase. The former cohort ought to be persuaded, or perhaps even


Journal of Marketing Theory and Practice Table 4 Descriptive Statistics and MANOVA

Frequency of purchase Purchase Intention Subjective Norms Impulsivity



Standard Deviation

Standard Error Mean

Female Male Female Male Female Male Female Male

2.23 2.61 4.70 5.02 3.68 4.06 3.69 4.19

0.90 1.35 1.45 1.28 1.86 1.94 1.66 1.63

0.07 0.10 0.11 0.10 0.15 0.15 0.13 0.13

motivated, through an approach that facilitates those individuals’ passage to the conclusive node of the consumer decision chain—a decision to actually purchase. However, the appropriateness of this suggestion likely varies greatly in accordance with the product category that is actually in play. A second practically and theoretically significant implication derives from the observation that males were more impulsive online shoppers than females. Given that this difference derives from an enduring and easily discernible human trait, it appears particularly relevant to Internet marketers. First, this observation suggests promotional activities aimed primarily at stimulating more spontaneous/less reflective purchasing activity from female consumers are less likely to meet with success. Another managerial implication actually extends beyond the literal scope of this study’s empirical results. Yet, because of its potential practical significance, it probably still merits mention: presuming that the sales promotional stimuli most likely to trigger impulsive responses among male consumers would often differ substantially from the stimuli likely to trigger analogous responses among females seems reasonable. If so, the possibility of such differences should be accounted for strategically within Internet marketer’s promotional strategies. As a starting point, consideration should be given to the sales promotion strategies that traditional retailers and storefronts have used to successfully stimulate impulsive purchases. Then, as a follow-up, marketers should evaluate which of these strategies might transfer most effectively to Internet-based marketing environments. A third theoretically and practically significant empirical implication derived from the observation that is male and female consumers differed significantly with respect to the frequency of their online purchases. The mean value for males exceeded the mean values associated with female con-

Significance 0.00 0.04 0.06 0.01

sumers by a significant margin. Given the nature and magnitude of the first two significant observations, this finding should not be surprising. In fact, it supports the statistical veracity of the two implications reported above. Practically speaking, this result suggests marketing organizations that offer and promote more male-oriented product categories and product lines online may improve their chance to enhance their sales revenues. Because males purchase online more frequently than females, Internet companies that tailor their products and presentations to male-oriented needs may induce more frequent purchases. As is true of many statistical observations, these results can be spun multiple ways. One prescriptive interpretation is that these results suggest that—other things being equal—male consumers represent a more viable target audience for Internet-based promotional efforts. The rationale behind this conjecture is simple, if not bordering on the Machiavellian: additional Internet-based promotions should be targeted at male consumers because they appear more likely to respond impulsively after exposure to such promotions. Yet the same results could also be interpreted as implying that marketers should more carefully construct and target online sales promotions when they are targeting female consumers shopping in online settings—because females are currently less likely to respond impulsively to such promotions. A fourth managerially relevant implication ensues from the observation that the influence of “subjective norms” on online shopping behaviors failed to significantly discriminate male from female shoppers. Notwithstanding that nonsignificant finding, it is important to note that the magnitude of influence of subjective norms on Internet shopping behavior was comparatively high without regard to gender. Opinion leadership—and the imperative marketers frequently face to engender it in ways that contribute to the appeal or positioning of their offerings—obviously

Winter 2007 remains in play in Internet-based marketing settings. These results thus imply that marketers’ promotional reference to the purchase-related opinions and ideas of significant others—that is, in the eyes of targeted consumers—is likely to prove effective. And, apparently, female and male consumers are equally subject to influence from the opinions of others they view as significant—even if the exposure to that subjective influence is occurring within the confines of an Internet-based marketing transaction. This latest observation should probably not come as a surprise. Fundamental human behaviors—indeed, traits—should rarely change, even if the technologies that facilitate those behaviors are changing. This result simply underscores that Internet marketers must still mind their marketing fundamentals. When promoting offerings online to either gender, marketers still must identify and then reach out specifically to deliver the “right” offering-related messages to the “appropriate” human referents. The appropriate human referents, of course, are those individuals who are willing and able to exercise subjective normative influence, and the consequent opinion leadership, on others who shop online. A positive relationship was revealed to exist between Internet “consumer impulsivity” and “purchase intention.” This implies that more impulsive consumers should tend to make more purchases while online. This result also implies that e-marketers should enact promotional measures intended to stimulate impulsive behaviors among visitors to their Web sites. Clearly, if Internet marketers can elevate the level of impulsiveness among the consumers who visit their Web sites, a higher propensity to purchase should follow among those consumers. The use of sales promotions—which, by definition, offer consumers reasons to buy now—clearly should be incorporated into Internetbased integrated marketing communications. And, apparently, marketing managers might logically operate from an assumption that the same sales promotions that tend to stimulate spur-of-the-moment purchase decisions in traditional retail settings—coupons, contests, sweepstakes and games, samples, cash refunds, and premiums and price packs—should work equally well in Internet-based retailing settings. The observed relationship between consumer impulsiveness and intention to purchase online also suggests online storefronts should design Web sites to encourage impulsive purchases. Certain “design rules” are known to increase the likelihood that visitors will purchase impulsively. One such rule entails reminding online shoppers about similar products and accessories associated with the product they selected during the checkout process.


A positive relationship also existed between “subjective norms” and “consumer impulsivity.” This observation implies that marketers could also stimulate more consumer impulsivity by communicating to site users that “people who are important to them (i.e., presumably idealized referents) “think”/“model” the “impulsive” action is/as appropriate. To market successfully based on this premise, Internet marketers must first identify the reference groups of their targeted segments. The ability to tap into reference groups, and to use those groups (either membership or aspirational), has long been applied by marketers to expose consumers to new behaviors, lifestyles, or ways of consuming. But the possibility that marketers might also use reference groups to influence the attitudes and self-concepts of targeted consumers and create pressures to conform that may affect consumers’ product and brand choices within online settings is truly intriguing. Clearly, the more things change (in this case, due to technologically based changes in the way products are promoted and delivered), the more they stay the same—particularly when the “staying the same” in question relates to fundamental marketing principles. Internet marketers ought to learn more about how subjective norms (and, by extension, reference groups) can be applied to influence consumer impulsivity during online transactions. The prevailing evidence presented above suggests that many conventional marketing strategies can be adapted successfully to online settings. The use of coupons, discounts, or various “giveaways” certainly might be used to shape subjective norms and drive consumer impulsivity. While virtually every aspect of online buyer–seller communities is mediated by technology, the need to create sensory experiences that respond to and promote impulsive behavior through sensory cues has somehow largely been overlooked by most e-tailers. Yet the development of the Internet has driven the emergence of new social traditions values—new subjective norms, if you will. Web designers—as well as Web marketers—should strive to create new subjective norms to improve virtual contact, enhance communication, and facilitate more cohesive interactions with customers and prospects. After all, marketing is rarely just about making a single sale—be it impulsive or otherwise. Instead, marketing is almost always about the relationship.

CONCLUSIONS This study expanded the application of TAM into an online marketing environment. The results of this expansion suggests that TAM could be used to better understand consumer online shopping behavior. The ability to more effectively


Journal of Marketing Theory and Practice

explain and predict consumers’ behavior online is critical for online marketers. Simply stated, online marketers need to learn more about how and who buys what products, as well as why purchases are made at all, in online settings. The possibility exists that TAM can be skillfully applied to provide some of these necessary insights. A solid marketing strategy is an important element that will contribute substantially to the success of any Web business. Companies should invest time and money into designing proper Internet-based marketing strategies. But for that time and money to be invested wisely, questions regarding how potential or actual customers might best be understood, as well as questions regarding the factors that actually drive consumers’ shopping proclivities, must be answered, and answered effectively. These results suggest that e-marketers should consider designing Web site elements in ways that encourage more impulsive consumer behavior. This could be done routinely as part of the firm’s new product development and promotional strategies. Easy-to-use payment options, for example, should represent “musts” to facilitate impulsive purchases on the Web (Nielsen 2001). Current findings (www.freemedia.com) also suggest that when making impulsive purchases, online shoppers are typically more interested in interacting with known or trusted brands. Consumers also appear more inclined to purchase impulsively when encountering genuine or perceived “good deals” on the Web. Anecdotal evidence suggests that the Internet has already emerged as a new medium for impulse spending. If valid, the potential economic consequences of such a trend are significant. But the encouraging profitability consequences of this trend expand further when one considers that impulse purchases have traditionally accounted for a substantial volume of the goods or services sold annually across a wide range of product categories (Hausman 2000).

LIMITATIONS AND FUTURE RESEARCH This study features several limitations. First, the study used only 332 college students as its final set of survey respondents. While numerous studies show that college students are a reasonable proportion of Web shoppers, to make these results more robust and generalizable, this model should be tested using a more general Web population. Second, a Web-based survey was used to collect data. It is important to replicate the current study with a paper-based survey to test the difference between Web-based survey and paper-based survey. In addition, although consumers’ opinions about impulsivity were explored, the Web vendors’ views remains

to be explored. Vendors may have different views about impulsivity. Clearly, an impulse purchase is an unplanned purchase. Certain Web design elements may function as triggers that stimulate consumers’ desire to purchase in an unplanned fashion. However, no current research exists to support this rationale. This condition, of course, begs the need for future research. The intersection of technological advancements and consumer desire has literally forced retailers and consumer goods companies into e-business. Some went willingly, others have been willfully pushed by other forces or individuals. But most will go eventually. And embracing the prospect of online commerce provides opportunities to earn increased market visibility, reach, savings, new revenue streams, and general efficiencies. In most marketing sectors, the need to explore the structure and dynamic movements of Web business opportunities is essential. This study contributes several tentative insights relating to the influence of users’ perceptions and motivations about online shopping. Future work that is suggested as a result of this study include investigation of specific elements on online shopping, assessing how much targeted consumers spend on each purchase rather than how often they actually purchase, and working with Web vendors to determine the effectiveness of Web site design. The practical application and utility of any instrument largely depends upon its robustness. In order for the modified TAM instrument to yield practical values for practitioners, we suggest multigroup testing on the instrument as well as validating it with subjects from different age groups. The subjects used in this study are traditional students with the majority in their twenties. The question of whether the same findings hold true for different subject groups is worthy of further investigation. The question of whether any of these avenues for future research merits consideration has already been answered: given the continuous emergence of online commerce, each surely does.

REFERENCES Ajzen, Icek, and B.L. Driver (1992), “Contingent Value Measurement: On the Nature and Meaning of Willingness to Pay,” Journal of Consumer Psychology, 1 (4), 297–316. ———, and Martin Fishbein (1980), Understanding Attitudes and Predicting Social Behavior, Englewood Cliffs, NJ: Prentice Hall. Angus Reid Group , “Shopping All Over the World,” (available at retrieved from the World Wide Web on March 15, 2001). Baumeister, Roy Frank (2002), “Yielding to Temptation: Self-Control Failure, Impulse Purchasing, and Consumer Behavior,” Journal of Consumer Research, 28 (4), 670–677.

Winter 2007 Bellman, Steven, Gerald L. Lohse, and Eric J. Johnson (1999), “Predictors of Online Buying Behavior,” Communications of the ACM, 42 (12), 32–38. Churchill, Gilbert. A., Jr. (1979), “A Paradigm for Developing Better Measures of Marketing Constructs,” Journal of Marketing Research, 16 (1), 64–73. Coley, Amanda, and Brigitte Burgess (2003), “Gender Differences in Cognitive and Affective Impulse Buying,” Journal of Fashion Marketing & Management, 7 (3), 282–296. Cox, Keith (1964), “The Responsiveness of Food Sales to Shelf Space Changes in Supermarkets,” Journal of Marketing Research, 1 , 63–67. Davis, Fred D. (1986), “Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results,” Ph.D. Dissertation, Massachusetts Institute of Technology, Cambridge. ——— (1989), “Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology.” MIS Quarterly, 14 (3), 319–340. Devillis, Robert F. (1991), Scale Development: Theories and Applications, Newbury Park, CA: Sage. Donthu, Naveen (1999), “The Internet Shopper,” Journal of Advertising Research, 39 (3), 52–59. Eastlick, M.A. (1994), “Gender Differences in MailCatalog Patronage Motives,” Journal of Direct Marketing, 8 (2), 37. Faber, Ronald, and Thomas O’Guinn (1992), “A Clinical Screener for Compulsive Buying,” Journal of Consumer Research, 19 , 459–469. Fetto, John (2003), “Twenty-One, and Counting . . .,” American Demographic, (September), 23–26. Fishbein, Martin, and Icek Ajzen (1975), Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Reading, MA: Addison-Wesley. Gefen, David, Elena Karahanna, and Detmar W. Straub (2003), “Trust and TAM in Online Shopping: An Integrated Model,” MIS Quarterly, 27 (1), 51–90. Greenfield, Donald N. (1999), Virtual Addiction, Oakland, CA: New Harbinger. Hausman, Angela (2000), “A Multi-Method Investigation of Consumer Motivations in Impulse Buying Behavior,” Journal of Consumer Marketing, 17 (5), 403–426. Hedegaard, J. “This Is Your Brain on eBay,” (available at www.worth.com/articles/Z9909F03.html). Klein, Heine K., and Rudy A. Hirschheim (1989), “Legitimation in Information Systems Development: A Social Change Perspective,” Information Technology & People, 5 (1), 29–47. Koufaris, Marios (2002), “Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior,” Information Systems Research, 13 (2), 205–223. Kyle, Bobette , “5 Ways to Encourage Impulse Purchases,” (available at WebSiteMarketingPlan.com). Lee, Julie Anne J. (2002), “The Influence of Culture on Consumer Impulse Buying Behavior,” Journal of Consumer Psychology, 12 (2), 163–177. Lohse, Gerald L., and Peter Spiller (1998), “Electronic Shopping,” Communications of the ACM, 41 (7), 81–87.


Maguire, James (2003), “Case Study: BizRate,” (available at ecommerce.Internet.com/how/biz/article/0,3371,10365_1563011,00.html). Mathieson, Kieran (1991), “Predicting User Intentions: Comparing Technology Acceptance Model with the Theory of Planned Behavior,” Information Systems Research, 2 (3), 173–191. McKnight, Harrison D., and Norman L. Chervany (2001–2002), “What Trust Means in eCommerce Customer Relationships: An Interdisciplinary Conceptual Typology,” International Journal of Electronic Commerce, 6, 2 (Winter), 35–60. Mowen Nielsen, Jacob (2001), (available at useit.mondosearch.com/ cgi bin/MsmGo.exe?grab_id=5386838&EXTRA_ARG=&host_ id=2&page_id=78&query=impulse&hiword=IMPULSE+). Nunnally, Jum C. Psychometric Theory, 2d ed., New York, McGrawHill, 1978. Patry, Alain L., and Luc G. Pelletier (2001), “Extraterrestrial Beliefs and Experiences: An Application of the Theory of Reasoned Action,” Journal of Social Psychology, 141 (2), 199–218. Pradeep, K. Korgaonkar (1999). “A Multivariate Analysis of Web Usage.” Journal of Advertising Research, 39 (2), 53. Roberson, Joseph P., Peter R. Shaver and Steven W. Lawrence (1991), “Criteria for Scale Selection and Evaluation,” in Measures of Personality and Social Psychological Attitudes, John P. Roberson, Phillip R. Shaver, and Lawrence S. Wright, eds., San Diego: Academic Press, 1–15. Rook, Dennis (1987), “The Buying Impulse,” Journal of Consumer Research, 14, 189–199. ———, and Robert Fisher (1995), “Normative Influences in Impulse Buying,” Journal of Consumer Research, 22, 305–313. ———, and Meryl P. Garner (1993), “In the Mood: Affective Elements in Impulse Buying,” in Research in Consumer Behavior, vol. 6, Janeen Costa-Arnold and Russell W. Belk, eds., Greenwich, CT: JAI Press, 1–28, 2 Schumacher, P., and J. Morahan-Martin (2001), “Gender, Internet, and Computer Attitudes and Experiences,” Computers in Human Behavior, 17 , 95–110. Spool, Jared (1999), Web Site Usability, : Harcourt. Stern, Hawkins (1962), “The Significance of Impulse Buying Today,” Journal of Marketing, 26 , 59–62. Van Slyke, Craig, and Christie L. Comunale (2002), “Gender Differences in Perceptions of Web-Based Shopping,” Communications of the ACM, 45 (8), 82–86. Venkatesh, Viswanath, and Fred D. Davis (1996), “A Model of the Antecedents of Perceived Ease of Use: Development and Test,” Decision Sciences, 27 (3), 451–481. ———, and Michael G. Morris (2000), “Why Don’t Men Ever Stop To Ask For Directions? Gender, Social Influence, and Their Role in Technology Acceptance And Usage Behavior,” MIS Quarterly, 24 (1), 115–140. Weinberg, Peter, and Wolfgang Gottwald (1982), “Impulse Consumer Buying as a Result of Emotions,” Journal of Business Research, 10 (1), 43–48. Wolman, Benjamin (1973), Dictionary of Behavioral Science, New York: Van Nostrand Reinhold.


Journal of Marketing Theory and Practice

Zhang, Xiaoni, and Victor Prybutok (2003). “Factors Contributing to Purchase Intentions on the Internet,” Journal of Internet Commerce, 2 (1), 3–18.

———, ———, and Chang Koh (2006). “The Role of Impulsiveness in a TAM-Based Online Purchasing Behavior Model,” Information Resources Management Journal, 19 (2), 54–68.