Profiling Internet Users Based on Their Propensity to Adopt Online Shopping

ABSTRACT - In this study Internet users are profiled based on their propensity to adopt online shopping. Five Internet shopping adopter groups are distinguished by classifying online shoppers into two adopter groups based on their time of adoption and non-adopters into three groups based on their intended use of online shopping. The resulting segments are profiled not only with regard to socio-demographic and webographic characteristics, but also with respect to exploratory buying behavior tendency and extroversion. Our findings point to significant differences between these adopter groups, demonstrating the significance of a more graded hierarchical approach to the adoption of online shopping.

Citation:

Malaika Brengman and Maggie Geuens (2002) ,"Profiling Internet Users Based on Their Propensity to Adopt Online Shopping", in AP - Asia Pacific Advances in Consumer Research Volume 5, eds. Ramizwick and Tu Ping, Valdosta, GA : Association for Consumer Research, Pages: 30-39.

Asia Pacific Advances in Consumer Research Volume 5, 2002      Pages 30-39

PROFILING INTERNET USERS BASED ON THEIR PROPENSITY TO ADOPT ONLINE SHOPPING

Malaika Brengman, Limburgh University Centre, Ghent University, Belgium

Maggie Geuens, Limburgh University Centre, Ghent University, Belgium

ABSTRACT -

In this study Internet users are profiled based on their propensity to adopt online shopping. Five Internet shopping adopter groups are distinguished by classifying online shoppers into two adopter groups based on their time of adoption and non-adopters into three groups based on their intended use of online shopping. The resulting segments are profiled not only with regard to socio-demographic and webographic characteristics, but also with respect to exploratory buying behavior tendency and extroversion. Our findings point to significant differences between these adopter groups, demonstrating the significance of a more graded hierarchical approach to the adoption of online shopping.

INTRODUCTION

Several retail analysts and researchers predict that electronic retailing has the potential to become an important distribution alternative (Alba et al., 1997). The interest in electronic commerce has been fuelled by the potential size of the world-wide online market, resulting from an exponential growth of Internet adoption. According to a recent 36-country study by Taylor Nelson Sofres Interactive (TNSi), some 31% of the total adult population uses the Internet and the number of Internet users worldwide, who have shopped online has increased by 50 percent over the past year (Pastore, 2001). In emerging markets, like Belgium, consumers still seem to hold a rather negative attitude toward online shopping (Geuens et al., 2000), but in the United States 33 percent of all Internet users already shop online. Promising as the figures may appear, the business-to-consumer online market has yet to reach the critical mass to ensure its own future success (Vellido, 2000). Online retailing currently accounts for only 0.2% of the total European retail market (BCG, 2000). Some predict that electronic shopping could account for 15 to 20 percent of the retail market by 2010 (Anonymous, 2000a), whereas others claim that online shops will never gain a substantive share of the market (Anonymous, 2000c). It is clear that consumer reluctance can be a major barrier toward further growth. Citrin et al. (2000) underscore that the future commercial success of the Internet depends on whether current users of the Internet (e.g. those who access information and/or communicate electronically) will also use this medium for product purchase. So far, little is known about consumer attitudes toward adopting electronic shopping and the factors that influence these attitudes (Eastlick and Lotz, 1999). Most empirical studies with regard to online shopping have concentrated on the comparison of Internet shoppers with non-shoppers. Eastlick and Lotz (1999) note that research profiling the early adopter and the following adopter groups is virtually non-existent. However, the group of people who have adopted online shopping and the group that has not yet adopted this way of shopping, may not be homogeneous. Indeed, distinguishing between one adopter and two non-adopter groups, DahlTn (1999) observes significant differences. So do Rangaswamy and Gupta (1999), who differentiate between one non-adopter and two adopter groups. To gain a better understanding in the adoption and diffusion process of the Internet as a new shopping medium, it seems worthwhile to extend the work of DahlTn and Rangaswamy and Gupta (1999). To this end, online shoppers and non-shoppers are further segmented, based respectively on the time of adoption and intentions to adopt in the future. Resulting segments are profiled not only with regard to socio-demographic and webographic characteristics, but also with respect to personality traits as exploratory buying behavior tendency and extroversion.

THE ADOPTION OF INTERNET SHOPPING

As the Internet can be conceived as a new kind of shopping channel, shopping by means of this new retail format can be considered as adopting an innovation (DahlTn, 1999; Eastlick and Lotz, 1999; Rangaswamy and Gupta, 1999; Citrin et al., 2000). Hence, theories on the adoption of innovations could shed light on who currently shops and who will shop online in the near or far future. The traditional binary construct of adoption/non-adoption is giving way to more graded decision structures that include intermediate stages considering the multistage decision process for adopting (Rangaswamy & Gupta, 1999). The diffusion theory has focused considerable attention on the characteristics of innovators and other adopter categories (Gatignon and Robertson, 1985; McDonald and Alpert, 1999). Based on time of adoption commonly the following adopter groups are distinguished (Rogers, 1995): innovatrs (venturesome and willing to try new ideas at some risk), early adopters (opinion leaders, adopt new ideas early, but carefully), early majority (deliberate, adopt before the average person but are rarely leaders), late majority (sceptical, adopt only after the majority) and laggards (tradition bound and suspicious of changes, adopt because the adoption is rooted in tradition) (Wedel and Kamakura, 2000). Consumers can also be categorized as adopters, active rejecters or passive rejecters of an innovation (Engel et al., 1993). Adopters embrace the new product, while rejecters do not. Of the latter, active rejecters make an informed decision not to adopt, whereas passive rejecters really have not taken this into consideration. Following the distinction between adopters and active and passive rejecters proposed by Engel et al (1993), DahlTn (1999) made a distinction between online shoppers, active non-online shoppers and passive non-online shoppers. He defined shoppers as consumers who at some time had purchased something on the Internet. Active non-online shoppers had tried or simulated shopping on the Internet but not actually bought anything. Finally, passive non-online shoppers were defined as those who had neither tried or simulated shopping nor bought anything on the Net. DahlTn (1999) found that the active non-shoppers were somewhere in between shoppers and non-shoppers, as they showed more interest in shopping than passive non-shoppers and could be viewed as prospective shoppers. Rangaswamy and Gupta (1999) categorize Internet users in skeptics, triers and buyers, based upon the number of different product categories in which they had made online purchases within the past six months. Of 17 product categories considered, skeptics had not bought any, triers had bought fewer than three, and buyers had bought three or more.

RESEARCH OBJECTIVES

The purpose of the current study is to profile Internet users based on their propensity to adopt online shopping and to identify differences between the different adopter groups on variables that are deemed to predict the adoption of Internet shopping. In contrast with previous studies, five Internet shopping adopter groups will be distinguished. Online shoppers will be classified into two adopter groups (innovators and early adopters) based on the time of adoption (Rogers, 1995). Non-adopters will be classified into three groups (hesitators, undecided and sceptics) based on their intended use of online shopping in the future (e.g. Eastlick and Lotz, 1999). The five adopter groups will be compared to each other not only with regard to socio-demographic and Internet usage characteristics, but also concerning personality variables.

BACKGROUND AND HYPOTHESIS DEVELOPMENT

Socio-demographic profile

Previous studies show that age is positively related to both online purchase intention and online purchase behaviour (Crisp et al 1997; DahlTn 1999; Donthu and Garcia 1999). Recent findings (Cyber Atlas, June 28, 2001) also demonstrate that while the highest proportion of Internet users worldwide are under 30 years of age, those who are most likely to make an online purchase are between 30 and 40 years old.

Concerning gender, research results are contradictory. Rangaswamy and Gupta (1999) found online buyers more likely to be male, although DahlTn (1999) found a significant over-representation of internet shoppers in the category of female respondents.

Also household coposition seems to matter. Indeed, Crisp et al, (1997) found that household size positively affects intentions toward internet shopping.

The majority of in-home shopper studies described in-home shoppers as above-average in socio-economic status as measured by household income, education, and occupation of the household head (Gillet, 1976; Croft, 1998; Berkowitz, Walton and Walker 1979) The same conclusions seem to hold for the internet shopper (DahlTn 1999; Donthu and Garcia 1999; Rangaswami and Gupta 1999).

Based on these findings we hypothesize the following:

Adopter groups with a higher propensity toward online shopping are more likely

H1: to be older

H2: to be male

H3: to belong to larger families

H4: to have a higher socio-economic status in terms of employment, education and income level

Webographic profile

Heavy users within a product category or those with significant experience in similar product categories have been shown to be more likely to adopt related new products (e.g. Dickerson and Gentry, 1983; Robertson, 1971; Taylor, 1977), because they have acquired the ability or knowledge structure to predict outcomes of closely related products. Likewise, empirical evidence reveals that prior Internet experience positively affects intentions toward Internet shopping (Crisp et al., 1997) and actual Internet shopping behavior (Hoffman and Novak, 1997).

It has been demonstrated that Internet users who have been using the Web for a longer time are more likely to use it for more task-oriented activities, such as shopping (Hammond et al., 1997; Novak et al., 2000). Indeed, DahlTn (1999) found online shoppers on average to have used the Internet for a longer time than non-shoppers. Web shoppers had had access to the Net for 15.9 months, 5.1 months longer than active non-shoppers and 6.1 months longer than passive non-shoppers. Those who actually buy on the web also appear to be more frequent web users (Hoffman, Kalsbeek and Novak, 1996) and to spend more time on the Internet (Rangaswamy and Gupta 1999). Citrin et al. (2000) found that higher levels of Internet usage for purposes other than shopping (such as for communication, education or entertainment) led to increased levels of the use of the Internet for shopping. Furthermore, on the basis of a logistic regression analysis, the following web-usage related variables appear to best predict actual purchases: in decreasing order of relevance "Looking for product information on the net", "Number of months online", "Number of e-mails received per day", "Work on the internet in their offices every week", "Read news online at home every week" (Bellman et al., 1999).

Since Web shoppers appear to be more experienced Internet users, we hypothesize the following:

H5: More experienced Internet users belong to adopter groups with a higher inclination to shop online.

Personal characteristics

Innovaiveness.

The construct of consumer innovativeness is "central to the theory of the diffusion of innovations" (Midgley and Dowling, 1978, p233). Rogers (1995) defines innovativeness as the degree to which a person’s observed time of adoption occurs relatively earlier than that of other people in his/her social system. At a higher level of abstraction innovativeness has been conceptualized as a persisting personal predisposition to innovate and can be considered as a personality construct that is possessed, to a greater or lesser degree, by all individuals, since everyone in the course of their lives, adopts some objects or ideas that are new in the context of their individual experience (e.g. Hirschman, 1980; Midgley and Dowling, 1978; Robertson, 1971). Therefore, people with a higher degree of innovativeness are believed to be more prone to adopt innovations. However, they might not always be among the first to actually adopt an innovation because intervening factors, such as marketing or word-of-mouth communications, can play an important role in the diffusion process. This personal innovativeness characteristic has gained wide acceptance in consumer marketing research in terms of explaining the adoption of new retail formats such as in-home shopping modes (Berkowitz et al., 1979; Reynolds, 1974; Eastlick and Lotz, 1999). DahlTn (1999) argues that web shoppers should also be more innovative than non-shoppers, since they were the first to adopt the new shopping channel. He measured innovativeness by means of three statements on 7-point Likert scales: "I often try new things", "I wait and see others try new things before I try myself" and "I do like everyone else", but cannot find support for the hypothesis that innovativeness affects Web shopping proneness positively (DahlTn, 1999). Since the respondents showed a rather high degree of innovativeness overall, DahlTn claims that this could perhaps be explained by the fact that Web users in general are innovative and that this may hold especially for internet users that participate in electronic surveys. On the other hand, Donthu and Garcia (1999) did find Internet shoppers to be more innovative and more variety seekers than Internet non-shoppers. Rangaswamy and Gupta (1999) measured "innovativeness" using a multi-item scale, with items such as: "I like to fool around with new products even if they turn out to be a waste of time", "Buying a new product that has not yet been proven is usually a waste of time and money" and "I am among the first in my circle of friends to buy a new version of software when it is released". They demonstrate that the impact of consumers’ innovativeness on propensity to purchase online is mediated by perceived online risk and attitude toward online vendors. Using a student sample, Citrin et al. (2000) also found support for the proposition that increases in domain-specific innovativeness result in increases in consumer adoption of the Internet for shopping. However, open-processing innovativeness (a general trait of innovativeness referring to a cognitive style of being open to new experiences), did not influence the use of the internet for online shopping (Citrin et al., 2000). These results underline the importance of considering the consumer’s domain-specific innovativeness when trying to understand and predict a consumer’s propensity to adopt the Internet for shopping. Although innovativeness in this context traditionally has dealt primarily with the disposition to purchase new items (Steenkamp, ter Hofstede & Wedel, 1999), several researchers have distinguished between a purchase and an information component of innovativeness (Baumgartner & Steenkamp, 1996; Manning, Bearden & Madden, 1995; Venkatraman & Price, 1990). As a consequence, two dimensions of exploratory buying behavior can be distinguished, namely exploratory acquisition of products (EAP) and exploratory information seeking (EIS) (Baumgartner and Steenkamp, 1996). The first dimension (EAP) reflects a consumer’s tendency to seek sensory stimulation in product purchase through risky and innovative product choices, and varied and changing purchase and consumption experiences. Consumers who are high on EAP enjoy taking chances in buying unfamiliar products, are willing to try out new and innovative products, value variety in making product choices and change their purchase behavior in an effort to attain stimulating consumption experiences. The second dimension (EIS) reflects a tendency to obtain cognitive stimulation through the acquisition of consumption-relevant knowledge out of curiosity. Consumers who are high on EIS like to go browsing and window shopping, are interested in ads and other promotional materials that provide marketing information, and enjoy talking to other consumers about their purchases and consumption experiences. A scale for the measurement of these two components of innovativeness termed 'Exploratory Buying Behavior Tendencies’ (EBBT) has been developed and validated by Baumgartner and Steenkamp (1996).

Based on these conceptualizations we propose the following hypothesis:

H6: Internet users who are more innovative with regard to their general shopping behavior, are more likely to belong to adopter groups with a higher propensity toward online shopping.

Extroversion.

The psychological concept of extroversion refers to an outgoing, cheery, sociable dimension of personality. Costa & McCrae (1988), for example have described extroverts as people who "have needs for social contact, attention, and fun" (p261). Given this characterization of extroversion, it is not surprising that when a choice has to be made between mediated and interpersonal forms of communication, extroverts are more likely to choose activities that provide direct social contact (Argyle & Lu, 1990). Introverts make a habit of seeking environments where they can exert greater control over sensory inputs. Media exposure such as time devoted to TV viewing, radio listening, pleasure reading, and movie attendance appear to be negatively correlated with extroversion, while a preference for non-mediated activities, especially conversation, is positively related to extroversion (Finn, 1997). As extroverts appear to favor interpersonal rather than mediated sources of gratification, it is suggested by Finn (1997) that the same personality-based preferences are bound to shape an individual’s adoption of new communication technologies as well. Therefore, it can generally be expected that lower levels of extroversion predict greater amounts of mass media use and, more specifically, the use of the Internet since the Internet permits control of sensory inputs as well. Likewise we can expect that mediated shopping activities as home shopping and online shopping are also negatively correlated with extraversion. Alba et al. (1997) pointed out that the Internet is less attractive to consumers who value social interactions, since it allows for very limited interactions relative to other retail formats, such as department stores. This is confirmed by Swaminathan et al. (1999), but among a student sample Balabanis and Reynolds (2001) were not able to detect a significant relation between 'sociability’ and positive affect toward online shopping. With regard to the diffusion of innovations this may have important implications. According to Bass (1969) word-of-mouth communications drive the diffusion process. It has been argued that 'innovators’ adopt early because of their 'innovativeness’, but that the vast majority of consumers rely on interpersonal (word-of-mouth) communications such as recommendations before they decide to adopt (Mc Donald and Alpert, 1999). According to Gatignon and Robertson (1985, p849):"personal influence is the key factor responsible for the speed and shape of the diffusion process". In this context an important question is whether adopter groups with a higher propensity to shop online are more introvert, because that may have a detrimental effect on the diffusion process. Since 'computing innovators’ have been found not to be interested in a great deal of social interaction (Dickerson and Gentry, 1983, p232; Francis et al., 1996), the same can be expeted for online shopping innovators, which may have a negative influence on the speed of diffusion of online shopping among those who have not yet accepted this new technology.

Based on the previous findings we propose the following:

H7: Internet users belonging to adopter groups with a higher propensity toward online shopping are less extrovert.

RESEARCH METHOD

Sample

In March and April of 2000, 356 face-to-face interviews were conducted by means of a structured questionnaire. The questionnaire was not administered by means of the web, because we wanted to reach light as well as heavy Internet users (in contrast to the popular GVU web surveys and several other studies as by Novak et al. (2000) which are based on self selection and do not include novices). Instead a "random walk" procedure throughout the Flemish [Flemish (similar to Dutch) is one of the three official languages spoken in Belgium, besides French and German.] part of Belgium, with door-to-door inquiries, was opted for. Only respondents with Internet access were actually interviewed.

Measures

A first series of questions pertained to respondents’ general Internet usage (where and how long they had access to the web, Internet usage frequency and intensity). Next, some questions were asked with regard to respondents’ online buying behavior and intentions. A further series of questions involved personal characteristics. Shopping innovativeness was measured by means of the 20-item, 'exploratory buying behavior tendency’ scale, which is composed of 2 underlying factors (EIS-exploratory information seeking and EAP-exploratory acquisition of products) (Baumgartner & Steenkamp, 1996). Extraversion has been measured according to the 12-item extroversion dimension of personality of the five-factor NEO Personality Inventory developed by Costa & McCrae (1992). Each statement was presented in 5-point Likert-type format, ranging from B2 'characterizes me not at all’ to +2 'characterizes me extremely well’. Questions were recoded for reverse polarity. Finally, some questions pertaining to socio-demographic characteristics (such as age, gender, household size, number of children and socio-economic status) were included in the questionnaire.

TABLE I

DEMOGRAPHIC AND SOCIOECONOMIC CHARACTERISTICS OF RESPONDENTS (N-321)

RESEARCH RESULTS

Respondent characteristics

After data screening, 321 useable surveys were retained. Respondents’ socio-demographic characteristics (reported in table I) were similar to those of the average Belgian Internet user at the time of investigation: predominantly young, male and with a higher education (cfr. 'Belgian Internet Mapping Study IV’ by Insites and the Belgian Internet Advertising Bureau, May 2000).

As intended, we managed to reach respondents with a varied profile as far as webographic characteristics are concerned. Our sample included novices, as well as experienced users, and light, as well as heavy users (see table II for an overview). Comparable to the Belgian Internet population at that time (Insites, 2000), only 82 (25.5%) of our respondents had Internet shopping experience. While some had shopped more often than others, overall respondents had not much experience with the new shopping channel. The majority indicated to be 'satisfied’ to 'very satisfied’ with their online shopping experience (see table III for more details).

TABLE II

INTERNET USAGE AMONG RESPONDENTS (N=321)

TABLE III

INTERNET SHOPPING AMONG RESPONDENTS WITH ONLINE SHOPPING EXPERIENCE (N-82)

Online shopping adopter groups

Because intentions have been shown to be predictive of actual behavior (Midgley and Dowling, 1993), we conceive the adoption of online shopping as a hierarchical process comprising both intention to adopt and actual adoption (Mittlestaedt et al., 1976). In this regard, we distinguish among five Internet shopping adopter groups. Online shoppers are classified into two adopter groups (innovators and early adopters) based on the time of adoption (Rogers, 1995). Non-adopters are classified into three groups (hesitators, undecided and sceptics) based on their intended use of online shopping in the future (e.g. Eastlick and Lotz, 1999). This way the following five online shopping adopter groups are distinguished:

$ Innovators, which are defined as those who have shopped online for the first time more than one year ago.

$ Early adopters, which are defined as those who have shopped online for the first time less than one year ago.

$ Hesitators, which are defined as those who have not shopped online yet, but are rather to very willing to do so in the future.

$ Undecided, which are defined as those who have not shopped online yet, and responded neutral to the fact whether they intend to do so in the future.

$ Sceptics, which are defined as those who have not shopped online yet, and do not intend to do so in the future.

TABLE

Differences among adopter and potential adopter groups are examined in terms of:

$ Socio-demographic characteristics

$ Webographic characteristics

$ Personal characteristics

Socio-demographic characteristics of online shopping adopter groups

First of all, differences with regard to socio-demographic variables among adopter and potential adopter groups are examined. We refer to table IV for a detailed overview of these findings.

Our first hypothesis (H1) proposed that adopter groups with a higher propensity towards online shopping are older. This was confirmed by our findings (Spearman correlation=.149; p=.008). Innovators seem over-represented in the age-group of 31-40 years old and early adopters seem over-represented among the 2630 year olds. Whereas both adopter groups seem underrepresented among the respondents aged 25 or younger. This is in line with previous findings that indicate that while the typical Internet user may be young, the typical online shopper appears to be older. The second hypothesis (H2), that adopter groups with a higher propensity towards online shopping are more likely to be male, was also confirmed by our findings. Males represented respectively 76%, 73.7%, 58.3%, 49.0% and 51.6% in adopter groups with a declining propensity to shop online (c², p=.021). Hypothesis 3 (H3) predicted that adopter groups with a higher propensity towards online shopping have larger household sizes and more children. However, the opposite appears to be true: adopter groups with a higher propensity towards online shopping tend to have smaller household sizes and less children. The difference appears to be significant between the early adopters and the group of undecided (ANOVA, p<.001). In the group of undecided larger families are clearly over-represented, while singles are almost non-existent in this potential adopter group. Singles appear to be over-represented in the early adopter and innovator categories, what was originally hypothesized, but could not be confirmed by Crisp et al. (1997). Furthermore, it is notable that families without children are over-represented among online shopping adopters. In line with hypothesis 4 (H4), it was expected that adopter groups with a higher propensity toward online shopping have a higher economic status. This contention is supported as far as employment and level of education are concerned: unemployed and lower educated respondents tend to be under-represented among online shopping adopters, and especially among innovators. No significant relation was found with regard to household income, although a cross-tabulation shows that the distribution appears to be skewed in the hypothesized direction.

Webographic characteristics of online shopping adopter groups

We refer to table V for a detailed overview of the findings with regard to differences in Internet-usage among adopter and potential adopter groups.

Hypothesis 5 (H5) suggested that more experienced Internet users belong to adopter groups with a higher inclination to shop online. We found support for this hypothesis in the sense that adopter-groups with a higher propensity to shop online appeared to be more experienced Internet users in terms of the number of years they have been online, how frequently they access the Internet and the time they spend online. Innovators have had Internet access for a longer time and are clearly over-represented among those who access the Internet daily (60% as compared to respectively 44%, 26%, 27% and 26% for adopter groups with a declining inclination to shop online). Especially early adopters seem to spend more time online (on average 6.5 hours a week), while the undecided seem to spend less time on the Internet (on average 2.5 hours a week).

Personal characteristics of online shopping adopter groups

Finally, differences with regard to personal characteristics among adopter and potential adopter groups are examined. We refer to table VI for a detailed overview of these findings.

A first hypothesis in this regard (H6) proposed that Internet users who are more innovative with respect to their general shopping behaviour, are more likely to belong to adopter groups with a higher propensity towards online shopping. For the 'exploratory information seeking’ component of shopping innovativeness, however, no significant relation could be revealed. As far as the 'exploratory acquisition of products’ component of shopping innovativeness is concerned, our findings point to a significant difference in the hypothesized direction between early adopters on the one hand and hesitators and sceptics on the other hand. A one-way analysis of variance with post-hoc Scheffe test reveals that early adopters appear indeed to be more innovative in their purchasing behaviour than hesitators and sceptics (mean scores of respectively .28; -.07 and -.20). The difference between the other adopter groups was not significant.

A last hypothesis (H7) predicted that Internet users belonging to adopter groups with a higher propensity towards online shopping are less extrovert. In contrast to our expectations, the mean scores for extroversion seem to increase for adopter groups with a higher inclination to shop online, with the exception of the innovator group, who scores lowest on extroversion. The difference in extroversion among the different adopter groups is, however, not very significant (ANOVA, p=.058).

DISCUSSION

In this study Internet users were profiled based on their propensity to adopt online shopping. Five Internet shopping adopter groups were distinguished by classifying online shoppers into two adopter groups (innovators and early adopters) based on their time of adoption and non-adopters into three groups (hesitators, undecided and sceptics) based on their intended use of online shopping in the future. Profiling the resulting segments with regard to socio-demographic, webographic and personal characteristics shows that online shoppers and non-online shoppers are no homogeneous groups, demonstrating the significance of a more graded hierarchical approach to the adoption of online shopping. Indeed, significant differences between these adopter groups could be revealed: Internet users belonging to adopter groups with a higher propensity toward online shopping appear in general more likely to be male, to be older, to belong to smaller households with less children and to have a higher socio-economic status. They also appear to have more Internet experience, to be more innovative with respect to their purchase behavior and to be more extrovert. Still, it is recommended to take a closer look at the differences between each of the individual adopter groups, because some notable exceptions can be revealed. We notice, for instance, that innovators are more introvert than the other adopter categories, which may be responsible for the slow take-off of b-to-c e-commerce. The finding that early adopters on the other hand appear more extrovert is promising for e-shopping diffusion. To speed up the diffusion process, online retailers should now focus their attention on targeting hesitators (males and females, aged 21-25, belonging to larger households), who can be viewed as prospective buyers and may therefore be easier to persuade (e.g. DahlTn, 1999).

TABLE IV

SOCIO-DEMOGRAPHIC CHARACTERISTICS OF ONLINE SHOPPING ADOPTER GROUPS

TABLE V

WEBOGRAPHIC CHARACTERISTICS OF ONLINE SHOPPING ADOPTER GROUPS

TABLE VI

PERSONAL CHARACTERISTICS OF ONLINE SHOPPING ADOPTER GROUPS

LIMITATIONS AND SUGGESTIONS FOR FURTHER RESEARCH

Currently, data pertaining to attitudes toward online shopping benefits and drawbacks are being processed. Indeed, it seems intuitively clear that these play an important role in the decision to adopt or not. Results from a study among potential adopters and non-adopters of an interactive teleshopping medium (Eastlick and Lotz, 1999) suggest that strongest predictors of potential innovator and non-adopter group membership are perceived characteristics of the interactive shopping innovation, including relative advantage over other shopping formats and compatibility with lifestyles. We recognize the need to explore this further in an Internet shopping context. Further research could also reveal the most important motivators for e-shopping and whether different adopter groups have different shopping motivations. To study the diffusion of online shopping more thoroughly, longitudinal data would be useful. It would be interesting to find out whether the 'hesitator’ group actually transfers earlier to an 'early majority’ of adopters as compared to the 'undecided’ and 'sceptics’. To operationalize graded diffusion models, that consider a multistage decision process of adoption, more detailed information is necessary about how poential adopters transition from one stage to the next (Rangaswamy & Gupta, 1999). In this light it should be investigated how adopter and potential adopter groups were/are persuaded to adopt e-shopping, i.e. by means of word-of-mouth or mass communication. Eventually, to cyberspace vendors it is essential that potential adopter groups are further profiled with regard to psychographic variables such as web-usage-related lifestyle (cfr. Smith and Swinyard, 2001) and media use, so that prospective e-shopping adopters can be targeted effectively. Finally, it would also be interesting to examine whether online shopping adopter groups in other countries have similar profiles or not.

REFERENCES

Alba, J., J. Lynch, B. Weitz, C. Janiszewski, R. Lutz, A. Sawyer, S. Wood, (1997). "Interactive home shopping: Consumer, retailer and manufacturer incentives to participate in electronic marketplaces", Journal of Marketing, 61, 3, 38B51.

Anonymous, (2000a). "Shopping around the web", The Economist, 26 February http:/www.economist.com/editorial/freeforall/ 20000226/su 7636.html

Anonymous, 2000b. "Distribution dilemmas", The Economist, 26 February http:/www.economist.com/editorial/freeforall/ 20000226/su3412.html

Argyle M. and Lu L., (1990). "The happiness of extraverts.", Personality and Individual Differences, 11, p1011-1017.

Balabanis G. and Reynolds N., (2001). "Factors influencing online shopping affect and e-store benefits sought", in Breivik E., Falkenberg A.W. and Gronhaug K. (eds.), "Rethinking European Marketing" , Proceedings of the 30^th EMAC conference, Bergen, Norway, May 8-11.

Bass F.M., (1969). "A new product growth model for consumer durables", Management Science, vol. 15, n¦1 (January), p215-227.

Baumgartner H. and Steenkamp J.-B. E.M., (1996). "Exploratory consumer buying behavior: conceptualization and measurement", International Journal of Research in Marketing, 13, p121-137.

Bellman S., Lohse G.L. and Johnson E.J., (1999). "Predictors of online buying behaviour", Communications of the ACM, 42 (12), p32-38.

Berkowitz E.N., Walton J.R. and Walker Jr. O.C., (1979), "In-home shopper: the market for innovative distribution systems", Journal of Retailing, 55, 3, p15-33.

Boston Consulting Group, (2000). "The race for on-line richesBe-retailing in Europe", http://www.bcg.com, february.

Citrin A.V., D.E. Sprott, S.N. Silverman and D.E. Stem Jr., (2000). "Adoption of Internet shopping: the role of consumer innovativeness", Industrial Management & Data Systems, 100/7, p294-300.

Crisp C.B., Jarvenpaa S.L. and Todd P.A., (1997). "Individual differences and Internet shopping attitudes and intentions", working paper, University of Texas, Austin, URL: http://ccwf.cc.utexas.edu/crisp/Indiv_Shop.htm

Costa P.T. Jr. and McCrae R.R., (1988). "From catalog to classification: Murray’s needs and the five-factor model", Journal of Personality and Social Psychology, 55, 258-265.

Costa P.T. Jr. and McCrae R.R., (1992). "Four ways five factors are basic", Personality and Individual Differences, 13, 653-665.

Croft M., (1998). "Shopping at your convenience", Marketing Week, 21, 18, p36-37.

DahlTn M., (1999). "Closing in on the Web consumerBA study in Internet shopping", in Bohlin E., Brodin K. Lundgren A. and Thorngren (eds.), "Convergence in Communications and Beyond," chapter 12, Elsevier Science, Amsterdam.

Dickerson M.D. and Gentry J.W.G., (1983). "Characteristics of adopters and non-adopters of home computers", Journal of Consumer Research, 10 (September), p225-235.

Donthu, N. and A. Garcia, (1999). "The internet shopper", Journal of Advertising Research, May-June, p52-58.

Eastlick M.A. and Lotz S., (1999). "Profiling potential adopters and non-adopters of an interactive electronic shopping medium", International Journal of Retail and Distribution Management, vol. 27, 6, p209-223.

Engel, Blackwell and Miniard, (1993). "Consumer Behavior", The Dryden Press.

Finn S., (1997). "Origins of media exposure: Linking personality traits to TV, radio, print, and film use", Communication Research, Sage Publications, Beverly Hills, 24, 5, p507-529.

Francis L.J., Katz Y.J. and Evans T.E., (1996). "The relationship between personality and attitudes towards computers: an investigation among female undergraduate students in Israel", British Journal of Educational Technology, 27 (September), p164-70.

Gatignon H. and Robertson T.S., (1985). "A propositional inventory for new diffusion research", Journal of Consumer Research, 11, March, p849-867.

Geuens M., Brengman M. en S’Jegers R., (2000). "A Consumer’s View on Grocery Shopping in the Future", in Joel R. Evans and Barry Berman (eds.) Retailing 2000:Launching the New Millennium Special Conference Series, Volume IX, Academy of Marketing Science and The American Collegiate Retailing Association, p184-188.

Gillet P.L., (1976). "In-home shoppersBan overview ", Journal of Marketing, 40, 4, p81-88.

Hammond K. McWillian G. and Diaz A.N., (1997). "Fun and work on the Web: differences in attitudes between novices and experienced users", Working paper 97-803, London Business School, Londen.

Hirschman E.C., (1980). "Innovativeness, novelty seeking and consumer creativity", Journal of Consumer Research, 7, December, p283-295.

Hoffman D.L. and T.P. Novak, (1997). "A new marketing paradigm for Electronic Commerce", Information Society, Special Issue on Electronic Commerce, vol. 13, n¦1 (Jan-Mar.), p43-54.

Hoffman and Novak, (1997). "Measuring the Flow Experience among Web Users", Preliminary draft; http://www2000.ogsm.vanderbilt.edu/novak/flow.july.1997/flow.htm.

Hoffman D.L., Kalsbeek W.D. and Novak T.P., (1996). "Internet and Web use in the United States: baselines for commercial development". Special section on "Internet and the home", Communications of the ACM, 39, December, p36-46.

Insites and the Belgian Internet Advertising Bureau, (2000). "Belgian Internet Mapping Study IV"

Manning K.C., Baerden W.O. and Madden T.J., (1995), "Consumer innovativeness and the adoption process", Journal of Consumer Psychology, 4 (4), p329-45.

McDonald H. and Alpert F., (1999). "Are Innovators worth identifying?", in Cadeaux and Uncles (eds.) "Marketing in the third Millenium", Australia-New Zealand Marketing Academy Conference Proceedings, Sydney, UNSW.

Midgley D.F. and Dowling G., (1978). "Innovativeness: The concept and its measurement", Journal of Consumer Research, 4, p229-242.

Midgley D.F. and Dowling G.R., (1993). "A longitudinal study of product form innovation: the interaction between predispositions and social messages", Journal of Consumer Research, 19 (March), p611-625.

Mittlestaedt R.A., Grossbart S.A., Curtis W.W. and DeVere S.P., (1976). "Optimum stimulation level and the adoption decision process", Journal of Consumer Research, vol. 3, September, p84-94.

Novak T.P., Hoffman D.L. and Y.-F. Yung, (2000). "Measuring the customer experience in online environments: a structural modeling approach", Marketing Science, vol. 19, n¦ 1, Winter, p22-42.

Pastore M., (2001). "Number of net users shopping online up 50 percent", Cyber Atlas, June 28^th, URL: http://cyberatlas.internet.com/markets/retailing/article/0,,6061_793821,00.html

Rangaswamy A. and Gupta S., (1999). "Innovation Adoption and Diffusion in the Digital Environment: Some Research Opportunities", eBusiness Research Center Working Paper, 02-1999, Penn State SMEAL & School of Information Sciences and Technology.

Reynolds F.D., (1974). "An analysis of catalog buying behavior", Journal of Marketing, 38 (July), p47-51.

Robertson, (1971). "Innovative behavior and communication", Holt, Rinehart and Winston Inc.

Rogers E.M., (1995). "Diffusion of Innovations", 4the ed., New York: The Free Press.

Smith S.M. and Swinyard W.R., (2001). "The identification of shopping behaviors among Internet users", World Marketing Congress, 28 June-1 July 2001, Cardiff Business School.

Steenkamp J.-B. E. M., ter Hofstede F. and Wedel M., (1999). "A cross-national investigation into the individual and national-cultural antecedents of consumer innovativeness", Journal of Marketing, April, 63, 2, p55-69.

SwaminathanV., Lepkowska-White E, and Rao B., (1999). "Browsers or buyers in cyberspace? An investigation of factors influencing electronic exchange", Journal of Computer-Mediated Communication 5(2). URL: http://www.ascusc.org/jcmc/vol5/issue2/swaminathan.htm.

Taylor J.W., (1977). "A striking characteristic of innovators", Journal of Marketing Research 14 (February), p104-107.

Vellido A., (2000). "A methodology for the characterization of business-to-consumer e-commerce", thesis submitted in partial fulfilment of the requirements of Liverpool John Moores University for the degree of Doctor of Philosophy, September. URL: www.cms.livjm.ac.uk/research/snc/av%5Fthesis.htm

Venkatraman M.P. and Price L.L., (1990). "Differentiating between sensory and cognitive innovativeness: concepts, measurements and implications", Journal of Business Research, 20, June, p293-315.

Wedel M. and Kamakura W., 2000. "Market segmentation: conceptual and methodological foundations", second edition, Kluwer Academic Publishers.

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