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WORKING PAPER
Segmenting Internet shoppers based on their web-usage-related lifestyle: a cross-cultural validation Malaika Brengman 1 Maggie Geuens 2 Bert Weijters 3 Scott M. Smith 4 William R. Swinyard 5
December 2003 2003/205
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Limburgs Universitair Centrum, Ghent University Ghent University, Vlerick Leuven Gent Management School 3 Vlerick Leuven Gent Management School 4,5 Marriott School of Management, Brigham Young University 2
Corresponding author: Maggie Geuens, Ghent University, Department of Marketing, Hoveniersberg 24, 9000 Gent, tel. 32 9 263 35 21, e-mail:
[email protected].
D/2003/7012/43
Segmenting Internet shoppers based on their web-usage-related lifestyle: a cross-cultural validation
Abstract Online surveys in the US and Belgium were conducted to cross-culturally validate the Internet shopper lifestyle scale (Smith and Swinyard, 2001). Special attention was devoted to sample, construct and measurement equivalence. In both countries, the same six basic dimensions were found to underlie the scale: Internet convenience, perceived self-inefficacy, Internet logistics, Internet distrust, Internet offer, and Internet window-shopping. Except from having the same basic meaning and structure in Belgium as in the US, the Web-usage-related-lifestyle scale also led to the same segments in both countries. Four online shopping segments (Tentative Shoppers, Suspicious Learners, Shopping Lovers and Business Users) and four online nonshopping segments (Fearful Browsers, Positive Technology Muddlers, Negative Technology Muddlers and Adventurous Browsers) are profiled with regard to their Web-usage-related lifestyle, themes of Internet Usage, Internet attitude, psychographic and demographic characteristics.
Keywords: Internet segmentation, Internet lifestyle, cross-cultural research, e-shoppers, scale validation
Segmenting Internet shoppers based on their web-usage-related lifestyle: a cross-cultural validation Introduction Recent findings show that despite the prevalent problems of the industry, online shopping is continuing to undergo significant growth worldwide. Responsible for the growth is the growing number of users in emerging markets that are shopping online for the first time, and the increasing confidence in online properties. According to a 36-country - 42,000 interview study by Taylor Nelson Sofres Interactive (TNSi), some 31 percent of the total adult population uses the Internet and Internet shoppers worldwide have increased by 50 percent over the past year (Pastore, 2001). Consequently, retailers can use their Internet presence to reach consumers all around the world (Quelch and Klein, 1996; Jarvenpaa, Tractinsky and Saarinen, 1999). It is therefore vital that vendors in cyberspace understand online consumers (Fram and Grady, 1995). As research on Internet consumer behavior is still very much in its infancy (Hoffman and Novak, 1997; Vellido, 2000), the identification of consumer segments, has been highlighted as one of the important and necessary avenues of research needed in the field of ecommerce (Chang, 1998). To come to a better understanding of the psychology of online shopping, Smith and Swinyard (2001) developed an “Internet shopper lifestyle” measurement instrument that was the basis of their segmentation of US online shoppers and non-shoppers. Although lifestyle research has been said to be particularly interesting in cross-cultural research (Kahle, 1999), a major criticism is that the cross-cultural validity of international life style instruments remains to be demonstrated (Brunsø and Grunert, 1995). Replicating the study of Smith and Swinyard (2001) in Belgium enables us to test for cross-cultural differences in the basic meaning and structure of Internet shopper lifestyle scale. Furthermore, in view of the fact that Belgium can be considered as an emerging market where consumers still hold a rather negative attitude toward online shopping (Geuens, Brengman and S’Jegers, 2000), it is interesting to investigate to what extent similar online segments can be distinguished in Belgium and the US. Web-Usage-Related Lifestyle: The relevance of lifestyle segmentation Following the idea that Market segmentation can give a competitive edge, several demographic and socio-economic based Internet segmentation attempts recently emerged Crisp, Jarvenpaa and Todd (1997), and Donthu and Garcia (1999). However, demographic and socio-economic descriptors have been neither the most effective in developing segments (Wedel and Kamakura, 2000) nor a good predictor of the propensity to buy on-line (Vellido, 2000). Internet-related psychographic characteristics seem more closely related to actual online purchase behavior. Those who actually purchase online appear to have been using the Internet for a longer time (Dahlén, 1999; Novak, Hoffman and Yung, 2000), to be more frequent web users (Hoffman, Kalsbeek and Novak, 1996) and to spend more time on the Internet (Rangaswamy and Gupta 1999). Moreover, using propensity to adopt Internet shopping as a segmentation base, three segments can be discerned: a group that shops online, a group that has tried to shop online but did not succeed, and a group that has never tried and feels sceptical towards online shopping (Dahlén, 1999; Rangaswamy and Gupta, 1999). Brengman and Geuens (2002) distinguish five Internet shopping adopter groups by classifying online shoppers 3
into two groups based on their time of adoption and non-adopters into three groups based on their online shopping intentions. But again, web-based psychographics do not tell the full story. Individuals differ in important ways above and beyond demographics and psychographics. Starting from consumers’ motivations to use the Internet, McDonald (1996) segmented the Internet audience as “Avid Adventurers”, “Fact Collectors”, “Entertainment Seekers”, and “Social Shoppers”. Vellido, Lisboa and Meehan (1999) investigated consumers’ opinion on online purchasing and online vendors which seem to consist of the underlying dimensions “Control and Convenience”, “Trust and Security”, “Affordability”, “Ease of Use”, and “Effort/Responsiveness”. Using these dimensions as a segmentation base discerns seven segments: “Unconvinced”, “Security conscious”, “Undecided”, “Convinced”, “Complexity Avoiders”, “Cost Conscious”, and “Customer Service Wary”. These studies are very valuable, but one can go still further to understand online consumers. Based on the idea that “the more you know and understand about consumers, the more effectively you can communicate and market to them” (Plummer, 1974), the study of people’s values and lifestyles has become a standard tool for both social scientists and marketers around the world. Conceptual and methodological developments aside (Kahle, 1999), lifestyle segmentation instruments have shown value in segmenting markets, especially when combined with more product-specific variables, such as media-usage (Wedel and Kamakura, 2000. Internet shopper lifestyle scale measurement Smith and Swinyard (2001) have developed an instrument that encompasses interests and opinions towards the Internet, as well as web-specific behaviors that increase the likelihood of obtaining relevant online segments. The instrument contains 38 Internet shopping psychographic statements (Internet shopper lifestyle scale), 14 measures of Internet behavior, and 13 themes of Internet usage. This questionnaire was mailed to a probability sample of 4000 US online households, of which 1738 (43.5 percent) replied and e-mailed to 20,000 e-mail addresses, of which 2477 (12.4 percent). A comparative analysis across sample modes showed demographic differences, but no difference in psychographic profiles. Principal components analysis with Varimax rotation revealed six factors underlying the web-usage-related lifestyle scale. Cluster analysis based on these six factors identified four shopper and four non-shopper segments among US online households. Shoppers were defined as household heads with home Internet access who had made an online purchase in the two months preceding the study. Internet shoppers were divided into these segments: shopping lovers (11.1 percent), Internet explorers (8.9 percent), suspicious learners (9.6 percent), and business users (12.4 percent). Internet non-shoppers were similarly identified as: fearful browsers (10.7 percent), shopping avoiders (15.6 percent), technology muddlers (13.6 percent), and fun seekers (12.1 percent). Shopping lovers are competent computer users who frequently buy online and really enjoy doing so. Internet explorers believe Internet shopping is fun and could be considered opinion leaders for online buying. Suspicious learners are not very computer literate, but are open-minded for learning new things and are suspicious of giving their credit card number. Business users don’t often make personal online purchases. They mainly use the Internet for business purposes and look at the Internet in terms of what it can do for their professional life. Fearful browsers are very computer literate and often practice “Internet-windowshopping.” They do not buy online for the moment since they distrust the security on the Internet, dislike shipping charges and are reluctant to buying things without seeing them in person. Shopping avoiders will be difficult to turn into online shoppers since they do not want
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to wait for product delivery and want to see things in person before they buy. Technology muddlers don’t spend much time online, are somewhat computer illiterate and are not interested in increasing their computer knowledge— an uninteresting target for online retailers. Fun seekers value the entertainment of the Internet, but are afraid of buying online. Furthermore, they have a relatively low education and income level leaving them not much spending power. Cross-cultural validation of the web-usage-related lifestyle instrument Collecting data in different cultures with the aim of obtaining comparative results requires that the measurement instrument possesses cross-cultural validity. The purpose of this study is to replicate the study of Smith and Swinyard (2001) in Belgium to determine the crosscultural validity of Smith and Swinyard’s Internet shopper lifestyle scale. In doing so, both conceptual and methodological issues in cross-cultural research are considered. More specifically, sample equivalence, construct equivalence and measurement equivalence are ensured or tested following the guidelines offered by Steenkamp and Ter Hofstede (2001). Afterwards, the instrument is used to segment the US and the Belgian online audience. Sampling equivalence Sampling equivalence is not about obtaining a sample with the same socio-demographic characteristics cross-nationally, nor about drawing a sample representative for the population as a whole, but is about the representativeness of the sample with respect to the relevant target population (Kumar, 2000). The target population in this study is made up of household heads with home Internet and e-mail access. Due to the costs involved in obtaining a sufficiently large representative sample by mail, it was decided not to carry out a mail, but an online e-mail survey. Therefore, both in the US and in Belgium an online sample was drawn in a more or less identical way. The online American sample was obtained through www.postmasterdirect.com. E-mail requests to participate in the survey were sent out to 20,000 representative e-mail users in Spring 2001. The response rate was about 11 percent. In Belgium a random sample of e-mail addresses was also compiled. According to a procedure outlined in Sheehan and Hoy (1999), email addresses were randomly selected from user lists, generated by means of search engines (People Finders), provided by different Belgian Internet Service Providers. An invitation e-mail to visit the site on which the questionnaire was posted was sent out to 11,500 Belgian Internet users in November 2001. 2188 correctly completed questionnaires were returned (response rate of 19.0 percent). In that the purpose of the current study is not to provide a cross-cultural segmentation, but rather to validate a scale and to compare which segments can be detected in the US and in Belgium, the sample sizes of the US and Belgium need not be proportionate to the respective population sizes (Steenkamp and Ter Hofstede, 2001). The characteristics of both the US and the Belgian online samples are described in Table 1. Despite the fact that efforts were taken to obtain representative samples, both the US and Belgian samples turn out to be non-representative of the online populations. Therefore, for subsequent analyses the US data is weighed for age and gender according to the results of Harris Interactive (Anonymous, 2001), while the Belgian data is weighed according to the results of the InSites 2001 telephone survey.
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Table 1. Characteristics of the US and Belgian Samples Gender Male Female Age < 18 19-29 30-44 45-64 +65 Education Lower Higher Hours Online At Home Per Week ≤5 6-10 11-20 > 20
US
Belgium
24.1 % 75.9 %
89.1 % 10.9 %
4.8 % 22.6 % 40.8 % 30.1 % 1.7 %
.5 % 28.6 % 46.8 % 24.2 % .9 %
26.7 % 73.3 %
22.8 % 78.2 %
15.2 % 18.0 % 28.6 % 39.2 %
33.5 % 29.7 % 24.0 % 12.8 %
Construct equivalence In this study construct equivalence mainly deals with the question of whether the Internet shopper lifestyle measurement instrument is equivalent across the US and Belgium. Special attention should be devoted to items that are highly American in content and to dimensions that may not be included in the US scale while they are important for Belgium. With respect to the Internet shopper lifestyle scale, we believe construct equivalence problems are minimal. Items such as “I want to see things in person before I buy” and “I often go to the Internet for product reviews or recommendations” are assumed to have the same meaning in the US and Belgium. Only for the item “I like it that no car is necessary when shopping on the Internet” and for items referring to the price of an Internet purchase, the meaning may be different for Belgians and Americans. In a densely populated country such as Belgium, the distance to the nearest supermarket or shopping mall is on average very small, but parking troubles may make people hesitant of taking their car. In the US, it is quite the opposite, parking usually is no problem but distances are on average much larger. Concerning price, we have to take into account that purchases on the Internet typically are relatively more expensive in Belgium than in the US. The dimensions of the Internet shopper lifestyle scale are expected to cover the main interests and opinions of Belgian Internet users. A recent qualitative Belgian study in which eight focus groups were organized with regard to the “ideal future store”, revealed that to Belgians, the dimensions that mattered included convenience of the Internet (not needing a car, time-saving), the costs involved, the delivery problems, the physical experience and the security threat of insecure payments. However, no aspects were found that are not covered by the 38 item web-usage-related lifestyle scale (Geuens et al., 2000). To conclude, the construct equivalence of the web-usage-related lifestyle scale is assumed moderate to high.
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Measure equivalence Measure equivalence pertains to whether the variables and items used in the questionnaire are comparable across the US and Belgium. For a distinction between calibration, translation, and score equivalence see Steenkamp and Ter Hofstede (2001). CALIBRATION EQUIVALENCE. Calibration equivalence deals with the question of whether monetary units, measures of weight, distance, volume, etcetera are equivalent across the US and Belgium. The US questionnaire was composed of the web-usage-related lifestyle scale, computer usage items, themes of Internet usage, Internet shopping and Internet spending questions, computer literacy items, demographics and psychographics. Some of the demographic questions or variables involved posed a problem since they are classified in a different way in both countries. Therefore, these variables were rescaled to broad categories. The education measure, for example, was revised to correspond to the norms of the two countries, where higher education for the US means that people have a higher degree than high school while for Belgium it indicates a higher degree than secondary school. Similarly, income was classified to better represent the median income of the respective countries. Spending amounts in dollars were translated in euros. Type of housing (apartment, condominium, mobile/trailer, single unit home) and zip-code were omitted in the Belgian study since they were less relevant or not applicable. TRANSLATION EQUIVALENCE. Translation equivalence pertains to verbatim and meaning equivalence. The US questionnaire was translated both in French and Dutch. A second person backtranslated the questionnaire and adaptations were made where necessary. Afterwards, the questionnaire was pretested and no problems were encountered. SCORE EQUIVALENCE. Score equivalence can be defined as the equivalence of the observed scores on the measures. Lack of score equivalence may be due to cross-national differences, (1) in response styles, or (2) in responses to specific items/questions. Response styles refer to a tendency to respond in a systematic manner to questionnaire items on some other criterion than what the items were designed to measure (Paulhus, 1991). Baumgartner and Steenkamp (2001) outline for five forms of stylistic responses (acquiescence and disacquiescence, extreme response style, midpoint responding, and noncontingent responding), as well as how to measure them and how to correct for these response style biases by regressing the raw summated scale scores on response bias indices. In both samples, our regression analyses revealed significant effects on each of the five response styles. On average the Belgian respondents scored higher on the acquiescence response style (t(4118)=-4.493), while US respondents scored higher on the disacquiescence response style (t(4305)=7.541), extreme response style (t(4128)=8.332), midpoint response style (t(3895)=13.333), and response range (t(4119)=3.299). Therefore, in subsequent analyses “corrected” instead of “observed” scores were used. But most importantly, lack of score equivalence may further arise from cross-national differences. We are especially interested in the question of whether Belgians react differently to the scale than Americans. Multigroup confirmatory factor analysis was used to test this notion, since there is general agreement that this procedure is the most powerful approach to testing for cross-national measurement invariance. As a first step, exploratory factor analysis was carried out on the US sample, followed by confirmatory factor analysis. Afterwards, multigroup confirmatory factor analysis was employed on both the American and Belgian sample.
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Results A Varimax rotated principal components analysis extracted seven factors that explained 56 percent of the variance. A six-factor solution was identified based on the eigenvalues, screeplot, the interpretability of the factors, and the coefficient alpha’s of the separate factors. Internet Convenience, composed of the items: “for me, shopping in stores is a hassle” (reversed scaled), “I think Internet shopping would avoid the hassle of local shopping”, “I would like not having to leave home when shopping”, “I like it that no car is necessary when shopping on the Internet”, and “I like having products delivered to me at home” (Alpha = .87), Perceived Self-Inefficacy, with items: “I’d have a hard time searching the Internet to find what I need”, “I don’t think Internet stores carry things I want”, “I find the Internet ordering process is hard to understand and use”, and “I don’t know much about using the Internet” (Alpha = .79), Internet Logistics, pointing to the items: “I dislike the delivery problems & backorders of Internet buying”, “I want to see things in person before I buy”, “I don’t like having to wait for products to arrive in the mail”, “it would be a real hassle to return merchandise bought online”, and “it’s hard to judge the quality of merchandise on the Internet” (Alpha = .75), Internet distrust, reflecting the items: “I don’t want to give out my credit card number to a computer” and “I worry about my credit card number being stolen on the Internet” (Pearson r = .65, p < .001), Internet offer, identified by the items: “Local stores have better prices and promotions than Internet stores”, “I think Internet shopping offers better quality than local stores”, and “I think Internet shopping offers better selection than local stores” (reverse scored) (Alpha = .79), and Internet Window Shopping, composed of the items: “I often go to the Internet to preview products” and “I often go to the Internet for product reviews or recommendations” (Pearson r = .68, p < .001). These dimensions resemble the ones distinguished by Smith and Swinyard (2001), and several of them correspond with the ones previously found by Vellido et al. (1999): our Internet Convenience and Internet Logistics factors boil down in their Control and Convenience dimension, our Perceived Self-Efficacy resembles their Ease of Use factor, and our Internet Distrust factor corresponds with their Trust and Security factor. Our Internet Offer and Internet Window Shopping factors, however, do not appear in the Vellido et al. (1999) study, while their Affordability and Effort/Customer Service cannot be revealed in our data. Only 21 of the 38 original web-usage-related lifestyle items were retained. Confirmatory factor analysis using AMOS indicated an acceptable fit for the six-factor structure (GFI = .924, AFGI = .899, TLI = .897, CFI = .914, RMSEA = .066). If the same pattern of factor loadings can be found in the Belgian sample, the basic meaning and structure of the web-usage-related lifestyle scale is said to be equivalent in the US and Belgium. The fit of the multigroup confirmatory factor analysis model was satisfactory (GFI = .927, AGFI = .903, TLI = .887, CFI = .906, RMSEA = .046) indicating that the same factors underlie the web-usage-related lifestyle scale in Belgium as in the US. Factor loadings for both countries are shown in figure 1.
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Figure 1. PCA result for the American and Belgian samples American sample
Belgian sample
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Online Segments in the US and Belgium As is common in segmentation studies, we clustered respondents on the factors found in the previous analysis, a practice also referred to as the tandem approach (Vellido et al., 1999; Steenkamp and Ter Hofstede, 2001). The cluster analysis was done separately for Internet shoppers and Internet non-shoppers, for each of the American group and Belgian group. Several segments resemble the ones detected in Smith and Swinyard’s previous study. Furthermore, the segment profiles are very similar for the US and Belgium. Table 2 and 3 show the profiles of the eight with respect to their scores on the Web-usage-related lifestyle dimensions, as well as on computer usage themes, psychographics, demographics, and attitude towards the Internet. Table 2. Profiling the different Internet shopper segments p Web-usage-lifestyle F1: Internet convenience F2: Perceived self-inefficacy F3: Internet logistics F4: Internet distrust F5: Internet offer F6: Internet window shopping Internet usage Shopping 1 Fun 2 Information 3 Business 4 Email Webographics Online spendings last 2 months Hours per week online % gifts bought online % gifts bought in real stores Computer literacy 5 Internet literacy 6 Demographics Age Education Personal income Attitude towards the Internet Excited to explore websites Love Internet shopping The web contributes to my life Scale :
-- very low
- low
Tentative Shoppers US B
Suspicious learners US B
Shopping lovers US B
Business users US B
US
B
