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A typology of online shoppers based on shopping motivations
Journal of Business Research 57 (2004) 748 – 757
A typology of online shoppers based on shopping motivations Andrew J. Rohma,*, Vanitha Swaminathanb,1 a
b
Department of Marketing, Northeastern University, Boston, MA 02115, USA Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA
Abstract This paper develops a typology based upon motivations for shopping online. An analysis of these motives, including online convenience, physical store orientation (e.g., immediate possession and social contact), information use in planning and shopping, and variety seeking in the online shopping context, suggests the existence of four shopping types. These four types are labeled convenience shoppers, variety seekers, balanced buyers, and store-oriented shoppers. The convenience shopper is more motivated by convenience. The variety seeker is substantially more motivated by variety seeking across retail alternatives and product types and brands than any other shopping type. Balanced buyers are moderately motivated by convenience and variety seeking. The store-oriented shoppers are more motivated by physical store orientation (e.g., the desire for immediate possession of goods and social interaction). Shopping types are profiled in terms of background variables and the propensity to shop online. The results are contrasted with a matched sample of off-line shoppers. Implications of this typology for theory and practice are discussed. D 2002 Elsevier Inc. All rights reserved.
1. Introduction Revenues from online retailing continue to grow. A recent Forrester Research report forecast that online retail sales will reach US$269 billion in 2005, from US$45 billion in 2000 (Dykema, 2000). The growth of online shopping has generated considerable interest among academic researchers. In particular, researchers have begun examining the impact of online shopping environments on consumer choice (Swaminathan et al., 1999), the role of Internet shopping as a channel of distribution (Alba et al., 1997), factors influencing shopping online (Swaminathan et al., 1999), and the impact of online shopping on price sensitivity (Shankar et al., 1999). Given the significant growth in online retailing, the online retailer needs to understand the particular reasons why consumers choose to shop online. This need is particularly relevant for the increasingly competitive online grocery retail market, in which numerous national and regional firms compete among themselves as well as bricks-and-mortar stores within a relatively static market. * Corresponding author. Tel.: +1-413-545-5665; fax: +1-413-5453858. E-mail addresses: [email protected] (A.J. Rohm), [email protected] (V. Swaminathan). 1 Tel.: + 1-413-545-5665. 0148-2963/$ – see front matter D 2002 Elsevier Inc. All rights reserved. doi:10.1016/S0148-2963(02)00351-X
The objective of this research is to develop a typology of online shoppers based on shopping motives. While there is a rich tradition of shopping typologies developed for store or catalog settings (Stone, 1954; Stephenson and Willett, 1969; Darden and Ashton, 1975; Williams et al., 1978; Bellenger and Korgaonkar, 1980; Westbrook and Black, 1985; Gehrt and Shim, 1998), there is a paucity of research examining typologies in the online context. This research makes an important contribution to the current literature by extending our knowledge of consumer typologies to the online channel. From a managerial perspective, online shopping typologies or classification schemes provide the basis for understanding and targeting different groups of consumers. Given that online retailing has tremendous growth, a typology specific to this channel will enable us to identify distinct segments of consumers, thereby enabling retailers to effectively tailor their offerings to these customer types. The shopping typology developed here is based on the grocery-shopping context. The grocery-shopping context is an effective one in which to study consumers and their shopping motivations for various reasons. First, previous research (e.g., Darden and Ashton, 1975; Williams et al., 1978) examines shopping motivations in the grocery context. Therefore, this context allows us to contrast results obtained in this study with previous research findings. Second, the purchase cycle for groceries is frequent and a wide array of goods. Third, although numerous online
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grocery retailers have struggled to reach profitability, the potential for growth in the online replenishment channel remains significant. Anderson Consulting (Buss, 1999) predicts that by the year 2007 almost 20 million people will buy their groceries and other household goods online, compared with fewer than 200,000 currently. Based upon Bunn (1993), we employ a five-step procedure for empirical typology development. The resulting cluster solution supports and extends current shopping typologies by differentiating between online grocery consumer types. In order to gain a broader understanding of shopping motives across retail settings, we conducted a parallel study of grocery shoppers in the offline, or bricksand-mortar, setting. A cluster analysis of offline shoppers reveals a unique to the bricks-and-mortar setting. The paper is structured as follows. First, we review the literature on shopping motives. Second, we discuss the sampling frame and data collection procedures. Third, we present analyses and results. Fourth, we discuss the implications of this research as well as future research directions.
2. Conceptual background Past shopping typologies have primarily been based on consumer motives for shopping (e.g., Tauber, 1972; Bellenger and Korgaonkar, 1980; Westbrook and Black, 1985). Motivation theory (e.g., McGuire, 1974)—which suggests that human motives, whether cognitive or affective, are
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primarily geared towards individual gratification and satisfaction—provides the theoretical basis for examining the underlying reasons for why people shop. Consumers may be motivated by the ability to implicitly derive a certain set of utilities by patronizing a given type of shopping setting (Sarkar et al., 1996). These utilities may include location (place utility), expanded store hours and quick, efficient checkout (time utility), and an efficient inventory and distribution system that enables consumers immediate possession (possession utility) of the goods purchased. The motivations that underlie extant shopping typologies are summarized in Table 1. As can be seen in Table 1, several motives may be used to classify the online shopper: shopping convenience, including time savings (e.g., Bellenger and Korgaonkar, 1980; Darden and Ashton, 1975; Eastlick and Feinberg, 1999; Stephenson and Willett, 1969; Westbrook and Black, 1985; Williams et al., 1978); information seeking (e.g., Bellenger and Korgaonkar, 1980), social interaction gained from shopping (e.g., Bellenger and Korgaonkar, 1980; Westbrook and Black, 1985), and shopping as a recreational experience itself (e.g., Bellenger and Korgaonkar, 1980; Gehrt and Shim, 1998). Additionally, the literature suggests that the tendency to seek variety (e.g., Raju, 1980; McAlister and Pessemier, 1982; Menon and Kahn, 1995) and the desirability of immediate possession (e.g., Alba et al., 1997) may also be motives for shopping. These six motives, that help to classify the online shopper, are examined in greater detail next.
Table 1 Review of the shopping typology literature Author(s)
Gehrt and Shim (1998) Westbrook and Black (1985)
Bellenger and Korgaonkar (1980) Williams et al. (1978) Darden and Ashton (1975)
Darden and Reynolds (1971) Stephenson and Willett (1969) Stone (1954)
Shopping context
mail-order catalogs urban retail department stores mall and shopping center retailers mall-based shopping centers nonmall retail grocery stores supermarkets large and small urban stores consumer products (e.g., apparel, shoes) department stores
Sample and data collection
catalog shoppers French surveys written adult female shoppers 203 structured personal interviews adult shoppers 324 questionnaires intercept 298 grocery shoppers interviews personal middle-class suburban 116housewives personal interviews and written surveys 167 middle to upper class suburban housewives surveys written actual store patronage and buying behavior depth interviews of female shoppers
Primary shopping motives Overall convenience/ time savings
The shopping experience
B
B
B
B
B
B
B
B
Information seeking
B
B
B
B B
B
B
Social interaction
B
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2.1. Shopping convenience Numerous shopping motive studies (Stephenson and Willett, 1969; Darden and Ashton, 1975; Williams et al., 1978; Bellenger and Korgaonkar, 1980; Eastlick and Feinberg, 1999) have identified convenience as a distinct motive for store choice in the offline setting. Bellenger and Korgaonkar (1980) characterized the convenience shopper as selecting stores based upon time or effort savings. Recent research (Swaminathan et al., 1999) suggests that convenience is an important factor, particularly because location becomes irrelevant in the online shopping context. The online shopper may be motivated by the convenience of placing orders online at home or at the office any time of day. Consistent with past research regarding time and effort savings (Bellenger and Korgaonkar, 1980; Eastlick and Feinberg, 1999), we consider time and effort savings as a part of the overall shopping convenience construct. 2.2. Information seeking Bellenger and Korgaonkar (1980) propose that the ability to seek and gather information in a retail setting is a shopping motive in the offline context. Online shopping offers an infrastructure by which the consumer is able to search, compare, and access information much more easily and at deeper levels than within the bricks-and-mortar retail structure (Alba et al., 1997; Lynch and Ariely, 2000). This concept of information as adding value to the retail experience is supported by Hoffman and Novak (1996), who suggest that the Internet offers not only a wide variety of information, it offers the capability to deliver specific information tailored to the needs of the consumer. 2.3. Immediate possession Sheth (1983) and Shaw (1994) discuss the utility derived from the possession of goods or services. Certain consumers will demand instantaneous delivery of products or services and will patronize those retailers able to provide immediate possession. In an analysis of competition between direct marketers and conventional retailers, Balasubramanian (1998) suggests that direct marketers can reduce consumer resistance to catalog or Internet purchases by reducing delivery time. For these reasons, consumers motivated by immediate possession may choose to shop within a conventional retail store format rather than in the online context. 2.4. Social interaction The concept of retail social interaction as a source of shopping motivation stems from work by Tauber (1972) positing that numerous social motives help to influence shopping behavior. These motives include social interaction, reference group affiliation, and communicating with others having similar interests. Alba et al. (1997) suggest that
desire for social interaction plays a role in determining the choice of retail format, e.g., the store, catalog, or online setting. Past research suggests that consumers motivated by social interaction may choose to shop within a conventional retail store format as opposed to the online context. 2.5. The retail shopping experience The retail shopping experience is often considered a shopping motive unto itself (e.g., Bellenger and Korgaonkar, 1980; Dawson et al., 1990; Bendapudi and Berry, 1997). The recreational shopper has been defined in the literature as one who enjoys shopping as a leisure-based activity, spends more time per shopping trip on average, considers store de´cor an important patronage decision, and is more impulsive, e.g., tends to make unplanned purchases (Bellenger and Korgaonkar, 1980). Tauber (1972) identified a variety of psychosocial needs related to shopping behavior, one of which was that certain shoppers received sensory stimulation from the retail environment. According to Bellenger and Korgaonkar (1980), this type of shopper is motivated by the process and enjoyment of the shopping experience itself, independent of product-specific or other task-directed objectives. Online retailers, in general, may find it difficult to replicate the sensory effects and product-trial experiences available to the consumer in a physical store setting. Therefore, similar to the catalog setting, online retailers may find it more challenging to attract recreational shoppers who may be less predisposed to shopping online. 2.6. Variety seeking Although variety-seeking research is limited in the online setting, previous research has suggested that variety-seeking or varied behavior stems from intrapersonal or interpersonal motives (McAlister and Pessemier, 1982). Consumer behavior research (Raju, 1980; Menon and Kahn, 1995) has linked variety seeking to the presence of an ideal level of stimulation (e.g., an intrapersonal motive for novelty, complexity, or change), whereby a consumer’s optimal stimulation level determines their degree of exploratory and variety-seeking behavior in situations such as shopping. The ability to comparison shop may increase variety-seeking behavior in the online context; therefore, variety seeking is likely to be a significant motive in the online context. In summary, a typology based upon these items will thus capture the mix of motives influencing the various types of online consumers. The sampling frame, data collection procedure, and construct measures are described next.
3. Method This section describes a study undertaken to better understand the online shopper. It also describes an associ-
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ated study of offline shopping motives conducted in the grocery context in order to compare online and offline shopping motives and consumer types. 3.1. Online sample The research sample employed in this study consists of both active and lapsed customers of an online grocery retailer. Although the respondents are limited to a groceryshopping context, the random sample employed in this study includes consumers across various purchase frequencies. Further, the scale items examine general shopping motivations and utilities and thus apply to general shopping contexts across retail channels. These items seek to help better illustrate customer motivations for online shopping in general, as well as within the online grocery setting. Appendix A lists these items. The unit of analysis in this study is the individual online grocery consumer. A random sample of 1000 potential respondents was drawn from the customer database of an online grocery retailer based in the northeast United States. To ensure appropriate response rates, a twostep process was followed. First, prenotification cards introducing the study and emphasizing the importance of the respondent’s involvement preceded the mailing of the written surveys by one week. Second, the surveys were mailed with a cover letter thanking the consumer for their participation along with preaddressed and postage-paid envelopes. This step also included an incentive in the form of credit towards their next grocery order placed with the focal online grocer upon return of the completed survey. Of the 1000 mailed surveys, a total of 429 responses were received from online shoppers. Of these, 17 were eliminated based on incomplete responses (i.e., several responses had missing values). This resulted in a usable sample of 412 responses. Eleven percent of this usable online sample (n = 46) were considered lapsed customers since they had not shopped with the focal online grocery retailer during their last 10 shopping trips. 3.2. Offline sample To rule out the possibility that the specific characteristics of the sample were responsible for the results obtained, and to gain a better understanding of motivations such as time savings and recreation in the offline context, another survey of a matched sample of grocery customers within the bricks-and-mortar setting was undertaken. This survey was mailed to approximately 350 grocery customers. Respondents were randomly chosen from a mailing list representing the zip codes from which the original online respondents were drawn. One hundred and three completed questionnaires were returned from this mailing, resulting in a 29% response rate. In order to ensure that the respondents were comparable to the online sample, the respond-
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ents were matched in terms of age, education, and income. Chi-square tests indicated no significant differences among age, income, and education distributions between the offline and online samples at the 5% significance level. Table 2 outlines the online and offline respondent demographics by gender, age, education, income, and household size, as well as the propensity to shop online within the online sample. In order to test for nonresponse bias, the early and late responses were contrasted in terms of demographic variables and responses to the key variables of interest. None of the differences in terms of demographic variables and responses to key variables of interest emerged significant. Therefore, the responses were pooled.
Table 2 Respondent demographics online and offline shoppers Demographic profile
Percentage of sample Online shoppers
Offline shoppers
Gender Female Male
72 28
75 25
Age Less than 30 years old Between 30 and 49 years 50 years and over
27 63 10
19 59 21
Education Less than high school graduate High school graduate or equivalent Some college, no degree College graduate Postgraduate
0 2 7 33 58
3 5 16 41 35
Income Less than US$15,000 US$15,000 – 29,999 US$30,000 – 49,999 US$50,000 – 74,999 US$75,000 – 99,999 US$100,000 + Do not know
3 5 13 19 15 35 10
4 9 15 14 10 36 12
Household size (including all adults and children) 1 person 19 2 persons 37 3 persons 20 4 – 5 persons 21 6 or more persons 2
31 34 19 16 0
Propensity to shop for groceries online High propensitya 31 Moderate propensityb 34 35 Low Propensityc a 70% or more of respondent’s online grocery shopping is with focal online retailer within last 10 shopping trips. b Between 30% and 60% of online grocery shopping is with focal online retailer within last 10 shopping trips. c 20% or less of online grocery shopping is with focal online retailer within last 10 shopping trips.
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3.3. Measures Measures for the online study were developed following standard scale development procedures (Churchill, 1979). Multi-item scales were generated based upon previous measures, a review of the relevant literature, and preliminary interviews. The written survey contained 31 items on a seven-point Likert scale anchored by strongly agree and strongly disagree. These items examined shopping motives that were considered salient to the online context, including convenience, information seeking, immediate possession, social interaction, the retail shopping experience, and variety seeking. The measures for this study were developed as follows. 3.3.1. Shopping convenience Overall shopping convenience is defined as time and effort savings in shopping. The scale for shopping convenience was developed based on Hawes and Lumpkin (1984) and Gehrt and Shim (1998). Five items were generated that were intended to tap into general aspects of convenience, including time savings and effort reduction associated with shopping over the Internet. 3.3.2. Information seeking Information seeking is defined as searching, comparing, and accessing information in a shopping context. Three information-seeking items were selected, based on Arora (1985), to measure information seeking. 3.3.3. Immediate possession Immediate possession refers to the instantaneous delivery of products or services. In the absence of existing scales to measure immediate possession, four immediate possession items were generated that tap into the desire for immediate possession versus delayed delivery. 3.3.4. Social interaction Social interaction refers to consumers’ desire to seek out social contacts in retail and service settings. A three-item social interaction scale was developed based upon previous work by Hawes and Lumpkin (1984) and Westbrook and Black (1985). 3.3.5. Retail shopping experience Retail shopping experience refers to the enjoyment of shopping as a leisure-based activity and taps into aspects of the enjoyment of shopping for its own sake. Three scale items based on Bellenger and Korgaonkar (1980) and Stephenson and Willett (1969) were used to measure retail shopping experience. 3.3.6. Variety seeking Variety seeking is defined as the need for varied behavior or the need to vary choices of stores, brands, or products.
Five variety-seeking items were generated based on previous work by Raju (1980). The psychometric properties of the final measures were assessed using exploratory factor analysis with varimax rotation, coefficient alpha, and adjusted item-to-total correlations. Scale items were evaluated for possible deletion based upon standard procedures (Hair et al., 1995). First, scale items with loadings less than .30 (Hair et al., 1995), significant mixed factor loadings, and communality indices less than .40 were deleted. Second, scale items that increased adjusted item-to-total correlations when removed were also deleted. The revised factor solution was derived based upon the remaining items. The resulting factor analysis yielded four underlying dimensions of shopping motives: overall convenience, physical store orientation, information use in the planning and shopping task, and variety seeking. Appendix A lists the scale items, factor loadings, Cronbach’s alpha, and item –total correlations for each of the four factors. Each factor contained between four and five items. The resulting scale scores were determined by taking the average of the individual scale items. For the offline study, multi-item measures were used to represent the six shopping motives identified in the earlier online study. These motives, including convenience, information-use in planning and shopping, immediate possession, retail shopping experience, social interaction, and variety seeking, were captured using measures adapted from the online survey to the offline shopping context. For instance, the survey item ‘‘I find the Internet provides me with a lot of information about products and services’’ was adapted for the offline setting to ‘‘In general, I find stores provide me with a lot of information about products.’’
4. Results 4.1. Online grocery shopping types Based upon the set of measurement items, factor analysis, and resulting scale scores, subsequent cluster analysis identified a four-group typology of online grocery shopping types. Initial cluster analysis employed Ward’s minimum variance method to obtain a hierarchical cluster solution. No outlying observations were found, supporting the sample’s representativeness. Examination of the dendogram and cubic cluster criterion plots as well as the pseudo-F statistic (Johnson, 1998) suggested a four-to-five-cluster solution. Based upon the relationships found between cluster solutions and the background variables (described more fully in the next section) as well as the supporting literature, a four-cluster solution was found the most meaningful and interpretable. Consistent with previous research (e.g., Bunn and Clopton, 1993), a variation of Punj and Stewart’s (1983) crossvalidation procedure was followed to check the reliability of the four-cluster solution. This is primarily used in order to
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ensure that the sample size used in the study is not reduced during cross-validation. Means and standard deviations for each cluster and pairwise contrasts of shopping motives across clusters are reported in Table 3. The four factors, consisting of overall convenience ( F = 73.60, P < .01), physical store orientation ( F = 5.92, P < .05), information use in planning and shopping ( F = 10.42, P < .01), and variety seeking ( F = 299.46, P < .001), differed significantly across clusters. An analysis of the four clusters reveals the following four shopping types: The convenience shopper (Cluster 1). This group, which comprises 11% of the online grocery sample, is the smallest online shopping classification. This shopping type is motivated more than the other three types by the prospects of overall online shopping convenience. This segment also exhibits less of a physical store orientation (e.g., is motivated less by the prospect of immediate possession of goods or services purchased and social interaction) as well as less variety-seeking behavior across retail channels. The variety seeker (Cluster 2). The largest group in the sample (41%) is moderately motivated by online shopping convenience, yet substantially more so by variety seeking across retail alternatives and product types and brands. The variety seeker exhibits scores close to the mean on physical store orientation as well as a tendency to plan purchases and shopping trips. The balanced buyer (Cluster 3). This online shopping type (33% of the sample) is similar to the variety seeker in his or her desire for convenience and lowest in his or her tendency to plan the shopping task or seek information. The balanced buyer is moderately motivated by the desire to seek variety and exhibits a score near the mean in terms of physical store orientation. The store-oriented shopper (Cluster 4). This online shopping type (15% of the sample) is characterized by the lowest level of online shopping convenience. He or she rates highest overall on physical store orientation (i.e., desire for immediate possession of goods and social interaction),
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below the mean for tendency to plan purchases, and relatively low for variety-seeking behavior. A significantly greater percentage of store-oriented shoppers, measured by the question ‘‘How long have you been shopping for products and services over the Internet,’’ were found to have shopped online less frequently as compared to the other three shopping types. Deeper distinctions among clusters are made in the following sections, yet several initial observations can be made. First, as seen in Table 3, post hoc pairwise contrasts using Duncan’s multiple-range procedure suggest that Cluster 1 (convenience shoppers) differs significantly from Cluster 2 (variety seekers), and Cluster 4 (store-oriented shoppers) with regard to overall online shopping convenience. Convenience shoppers and store-oriented shoppers differ across overall online shopping convenience, physical store orientation, use of information in planning and shopping, and variety seeking. Except for variety seeking, variety seekers, and balanced buyers follow similar patterns, differing only in intensity. Multiple comparisons indicate that the store-oriented shopper differs from the other three shopping types across the information use and planning motive. The propensity to grocery shop online was examined across shopping types as well (see Table 3). A comparison of propensity to shop online for groceries revealed differences across the four shopping types ( P < .05). The storeoriented shoppers (as can be seen in Table 4) also have the lowest online purchase frequencies across shopping types for all of the specific product categories examined. These results that compare online shopping propensities across cluster types enhance the validity of the four online shopping types and proposed typology. 4.2. Relationship between background variables and online shopping types A more thorough understanding of the online consumer can be gained by relating the background variables to the
Table 3 Underlying shopping motives: means and standard deviations by online shopping type Shopping motive
Factor 1: overall convenience Factor 2: physical store orientation Factor 3: information use in planning and shopping Factor 4: variety seeking Propensity to shop online*** Number of observations Percentage of observations
Online shopping type
Total sample
F value * ( P)
Pairwise contrasts**
Convenience shopper
Variety seeker
Balanced buyer
Store-oriented shopper
6.02 (0.91) 4.96 (0.85) 4.53 (0.65)
5.21 (0.93) 4.64 (0.81) 4.61 (0.79)
5.78 (0.85) 5.06 (0.81) 4.38 (0.80)
3.38 (0.95) 5.60 (0.75) 3.59 (0.68)
5.52 (0.83) 4.92 (1.12) 4.46 (0.82)
73.60 ( P < .01) 5.92 ( P < .05) 10.42 ( P < .01)
A > B,D; C>B,D D>A,B,C A,B,C>D
3.58 (0.39) 6.61 (0.51) 45 11
5.34 (0.53) 5.87 (0.86) 169 41
4.59 (0.38) 6.81 (0.70) 136 33
3.88 (0.95) 3.59 (0.91) 62 15
4.56 (1.91) 4.45 (0.23) 412 100
299.46 ( P < .01) 52.73 ( P < .01)
B>A,C,D; C>A,D; D>A A>B,D; C>B,D
A = convenience shopper; B = variety seeker; C = balanced buyer; D = store-oriented shopper. * df = 3. ** Duncan’s post hoc multiple-range test (a=.05). *** Means (and standard deviations) of propensity to shop online, based upon the percentage of times a respondent had shopped at the focal online retailer during their last 10 shopping trips.
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Table 4 Product types purchased online by online shopping type Product type purchased online base (n = 412)
Books/magazines Clothing Toys Music CDs Computer hardware Computer software Travel Home electronics/appliances Flowers Financial services
Number of respondents that purchased within online shopping typea Convenience shoppers
Variety seekers
Balanced buyers
Store-oriented shoppers
29 16 9 16 15 19 22 8 13 12
91 51 20 59 30 77 96 18 38 38
77 44 18 40 42 41 58 12 22 27
11 12 3 11 6 3 25 3 6 11
Chi-square* *
P
32.91 4.38 5.87 6.92 16.50 35.83 8.18 5.17 8.33 1.55
< .01 NS NS NS < .01 < .01 .05 NS .05 NS
a
What types of products or services have you purchased online during the past 6 months? * * df = 3.
four online shopping types illustrated previously. Several variables (e.g., age, household size, per capita income, gender, and product type) that potentially influence retail shopping and online purchasing behavior are examined. The results of these background variables compared across the four clusters are reported next. First, univariate ANOVA and chi-square tests revealed no differences in age, income, and household size across the four online shopping types. This suggests a homogeneous online shopping sample with respect to demographics. Second, the percentage of members within each of the four shopping types having bought certain product types was examined. The results reported in Table 4 indicate significant differences in purchase behavior across clusters for the following product classes: books and magazines (c2 = 32.91, P < .01), computer hardware (c2 = 16.50, P < .01), computer software (c2 = 35.83, P < .01), travel (c2 = 8.18, P=.05), and flowers (c2 = 8.33, P=.05). Variety seekers and balanced buyers exhibit purchase frequencies similar to those of the convenience shopper in several product classes (e.g., books and magazines, music CDs, computer software, travel, and flowers). This suggests that although the convenience shopper might be more motivated by the convenience of shopping online, the variety seeker and balanced buyer may constitute relatively active online shopping types as well. 4.3. Offline grocery shopping types The procedure for selecting factors and determining cluster membership was similar to that of the online sample. The analysis for the bricks-and-mortar consumers identified three distinct clusters of offline shoppers: the time-conscious shopper, the functional shopper, and the recreational shopper. These clusters represent 20%, 32%, and 48% of the offline sample, respectively. Four offline shopping motives (factors), consisting of offline or physical store orientation ( F = 16.95, P < .01), shopping adventure and experience ( F = 223.32, P < .01), impulse shopping ( F = 9.26, P < .01),
and time savings ( F = 12.77, P < .01), differed significantly across clusters.
5. Discussion The empirical findings suggest that there are distinct online grocery shopping types. These shopping types are named convenience shoppers, variety seekers, balanced buyers, and store-oriented shoppers. The mean scores of underlying shopping dimensions across clusters directionally support the convenience shoppers as being most motivated by convenience, a key factor influencing the growth of online shopping. Conversely, these directional results also support the profile of store-oriented shoppers as being more motivated by offline store characteristics such as immediate possession and social contact. Variety seekers are characterized by those who seek variety in retail alternatives or products and brands. Balanced buyers exhibit scores relatively close to the mean for all four shopping dimensions except for a lower propensity to plan purchases, therefore suggesting a segment that makes more impulsive purchases online. Chi-square tests of differences in purchase frequencies across the clusters indicate that significant differences exist for the following product classes: books and magazines, computer hardware, computer software, travel services, and flowers (see Table 4). The convenience shopper exhibits the highest purchase frequency for the majority of these product classes, excluding only computer software, in which variety seekers exhibited the highest purchase frequency. This suggests that the variety seeker, although noted for his or her variety-seeking behavior and tendency to seek out alternative retail types, is an important consumer type for the online retailer to target due to this shopping type’s online purchasing activity. Further, the store-oriented shopper exhibits the lowest purchase frequency for all of the product classes, suggesting this group might be less of an immediate priority to the online retailer.
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This research enhances our understanding of shopping motives that are salient to the online context. Similar to previous typologies (e.g., Bellenger and Korgaonkar, 1980) conducted in traditional shopping contexts, this study identified overall shopping convenience as a motive for shopping online, particularly among convenience shoppers. Additionally, as in previous typologies (e.g., Bellenger and Korgaonkar, 1980; Stephenson and Willett, 1969), the desire for social interaction was identified as a shopping motive, particularly among store-oriented shoppers. These similarities suggest that certain underlying motives for shopping, such as desire for convenience and social interaction, have not changed due to the online context. Unlike previous shopping typologies, variety seeking was identified as an online shopping motive for a certain consumer type. This finding suggests that variety-seeking behavior is an important construct, particularly as emerging shopping channels, such as the Internet, offer the consumer more choice and ease of access. Unexpectedly, this research failed to support two factors commonly attributed to reasons why consumers shop online: (1) time savings and (2) recreation and enjoyment. Scale items measuring these two constructs were dropped from the exploratory factor analysis for poor or mixed loadings. Perhaps the notion of time savings was subsumed within the overall shopping convenience construct. Additionally, it is possible that the use of the Internet for online shopping appeals to more functional as opposed to recreational shoppers. One implication of this research is that some of the underlying motivations such as convenience remain important in both online and offline settings. Time savings and recreation and enjoyment, which emerged as key motivations in previous research in offline settings, did not appear to be significant within the context of this online study. One possible explanation maybe that while the Internet saves time during the purchasing of goods, and eliminates the time needed to travel to the physical store, it also increases the time taken to receive goods. Therefore, time savings may not be perceived as a significant advantage while shopping online. The analysis for the offline sample identified three distinct clusters of offline shoppers: time-conscious, functional, and recreational shoppers. The results from this matched offline sample suggest that time savings, functional shopping, and shopping as recreation are significant factors in the bricks-and-mortar context. These results approximate those of previous offline shopping typologies (e.g., Bellenger and Korgaonkar, 1980) that identified two distinct shopping types: the recreational and the economic shopper. The significance of the time-savings factor in the offline results is particularly interesting. This is in contrast to our findings regarding time savings in the online context. However, because offline shopping is generally thought to be more time-consuming than in the online context, time savings becomes a significant differentiator in the offline
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context. In addition, comparing the offline and online results, variety seeking and convenience were found to be significant factors in the online but not the offline setting. The offline results discussed here add validity to the online results in that the factors of recreational shopping and time savings in the offline setting and convenience and variety seeking in the online setting seem to differentiate the two shopping contexts. 5.1. Theoretical implications This study represents an important first step in extending the general shopping literature to the context of online shopping. Variety seeking and convenience were found to be significant motivating factors in the online shopping context. Time savings and recreational motives were significant motives in the offline but not the online context, suggesting that these factors may serve to differentiate the shopping types across these channels. By illustrating shopping motives that are salient to the online setting (e.g., convenience and variety seeking) versus the offline context (e.g., time savings and recreational motives), we begin to build a theory of online shopping motivation and behavior. This study also contributes to our current knowledge of marketing on the Internet. In a broad sense, previous work in Internet marketing has examined a wide array of consumer, retailer, and producer phenomenon within various types of shopping channels, including interactive home shopping (e.g., Alba et al., 1997), the application of network theory (Iacobucci, 1998), the concept of flow in helping to describe consumer navigation behavior (Hoffman and Novak, 1996), consumer perception of risk in Internet shopping (Swaminathan et al., 1999), and personal privacy issues related to online shopping (Rohm and Milne, 1998). However, to the authors’ knowledge, scant research that investigates differences across consumers on the basis of online shopping motives exists. 5.2. Managerial implications Retail and marketing managers may benefit from the results reported here. The findings suggest that consumers who are motivated by convenience are likely to shop online for specific types of products and services, e.g., books and magazines and travel. An online retailer seeking to market explicitly to this segment may want to develop strategic alliances with retailers specializing in these product or service areas. The convenience shopper, balanced buyer, and variety seeker exhibit a high propensity to shop in various product classes. Given that the balanced buyer and variety seeker exhibit greater propensity to seek variety in their shopping, it is possible that these groups are attracted by the Internet’s convenience, as well as its search and comparison capabilities that enable consumers to seek alternatives based upon various attributes, such as price. In addition, these groups are moderately motivated by immediate possession, suggesting that
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Appendix A. Factor analysis of shopping motives and utilities Scale items
Factor loadings
1. Internet ordering convenience 2. The Internet is a convenient way of shopping 3. The Internet is often frustrating a 4. I save a lot of time by shopping on the Internet 5. Shopping over the Internet is a pleasant experience 6. I would rather buy at store than wait for delivery 7. I like to shop where people know me 8. While shopping on the Internet, I miss the experience of interacting with people 9. I like browsing for the social experience 10. I like to have a great deal of information before I buy 11. I always compare prices 12. I carefully plan my purchases 13. I buy things I had not planned to purchase 14. I am cautious in trying new products a 15. I enjoy exploring alternative stores 16. Investigating new stores is generally a waste of time a 17. I like to try new products and brands for fun 18. I like to buy the same brand a a
Physical store characteristics
Information use in planning and shopping
Variety seeking
.74 .84
.16 .31
.10 .01
.06 .03
.71 .71
.52 .78
.05 .05
.19 .12
.09 .03
.57 .74
.81
.02
.06
.01
.72
.37
.57
.21
.09
.64
.08 .18
.68 .82
.12 .01
.09 .04
.47 .50
.03 .03
.75 .29
.15 .65
.09 .20
.43 .34
.14 .16 .05 .12 .05 .12
.07 .01 .17 .11 .17 .09
.64 .68 .41 .29 .27 .13
.11 .04 .26 .42 .70 .70
.23 .34 .33 .47 .50 .45
.05 .22
.07 .20
.07 .06
.65 .50
.40 .22
Item is reverse coded. The cumulative variance is explained by four factors: 53%. Scale items are anchored by 1 = strongly disagree and 7 = strongly agree.
Coefficient alpha
.80
.71
.52
.60
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Overall convenience
Item-to-total correlation
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their demand for digital products such as music CDs and software is likely to be relatively high. On the other hand, the store-oriented shopper is highly motivated by immediate possession. Marketers may need to examine ways in which they can enhance the ability to deliver within a shorter period, e.g., same-day delivery, so that a greater proportion of segments such as store-oriented shoppers can be attracted to online shopping. 5.3. Future research Some limitations of this research, that also provide a basis for future research, should be noted. Online shopping is a relatively new phenomenon. The results presented in this study are based on a sample of consumers who may be perceived as early adopters and innovators in the context of online shopping. One potential limitation is that the characteristics of the sample may change once more consumers begin shopping online. Another limitation is that respondents were customers of a single online retailer within a single industry. Future research should focus on extending these findings to other industries. Future research in this area should also examine how shopping typologies might vary with bricks-and-clicks shoppers, e.g., people who shop for specific items in both the online and online settings.
Acknowledgements The authors thank George Milne, Rajdeep Grewal, Easwar Iyer, Thomas Brashear, and Marc Weinberger for their constructive comments on earlier versions of this paper.
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A typology of online shoppers based on shopping motivations Andrew J. Rohma,*, Vanitha Swaminathanb,1 a
b
Department of Marketing, Northeastern University, Boston, MA 02115, USA Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA
Abstract This paper develops a typology based upon motivations for shopping online. An analysis of these motives, including online convenience, physical store orientation (e.g., immediate possession and social contact), information use in planning and shopping, and variety seeking in the online shopping context, suggests the existence of four shopping types. These four types are labeled convenience shoppers, variety seekers, balanced buyers, and store-oriented shoppers. The convenience shopper is more motivated by convenience. The variety seeker is substantially more motivated by variety seeking across retail alternatives and product types and brands than any other shopping type. Balanced buyers are moderately motivated by convenience and variety seeking. The store-oriented shoppers are more motivated by physical store orientation (e.g., the desire for immediate possession of goods and social interaction). Shopping types are profiled in terms of background variables and the propensity to shop online. The results are contrasted with a matched sample of off-line shoppers. Implications of this typology for theory and practice are discussed. D 2002 Elsevier Inc. All rights reserved.
1. Introduction Revenues from online retailing continue to grow. A recent Forrester Research report forecast that online retail sales will reach US$269 billion in 2005, from US$45 billion in 2000 (Dykema, 2000). The growth of online shopping has generated considerable interest among academic researchers. In particular, researchers have begun examining the impact of online shopping environments on consumer choice (Swaminathan et al., 1999), the role of Internet shopping as a channel of distribution (Alba et al., 1997), factors influencing shopping online (Swaminathan et al., 1999), and the impact of online shopping on price sensitivity (Shankar et al., 1999). Given the significant growth in online retailing, the online retailer needs to understand the particular reasons why consumers choose to shop online. This need is particularly relevant for the increasingly competitive online grocery retail market, in which numerous national and regional firms compete among themselves as well as bricks-and-mortar stores within a relatively static market. * Corresponding author. Tel.: +1-413-545-5665; fax: +1-413-5453858. E-mail addresses: [email protected] (A.J. Rohm), [email protected] (V. Swaminathan). 1 Tel.: + 1-413-545-5665. 0148-2963/$ – see front matter D 2002 Elsevier Inc. All rights reserved. doi:10.1016/S0148-2963(02)00351-X
The objective of this research is to develop a typology of online shoppers based on shopping motives. While there is a rich tradition of shopping typologies developed for store or catalog settings (Stone, 1954; Stephenson and Willett, 1969; Darden and Ashton, 1975; Williams et al., 1978; Bellenger and Korgaonkar, 1980; Westbrook and Black, 1985; Gehrt and Shim, 1998), there is a paucity of research examining typologies in the online context. This research makes an important contribution to the current literature by extending our knowledge of consumer typologies to the online channel. From a managerial perspective, online shopping typologies or classification schemes provide the basis for understanding and targeting different groups of consumers. Given that online retailing has tremendous growth, a typology specific to this channel will enable us to identify distinct segments of consumers, thereby enabling retailers to effectively tailor their offerings to these customer types. The shopping typology developed here is based on the grocery-shopping context. The grocery-shopping context is an effective one in which to study consumers and their shopping motivations for various reasons. First, previous research (e.g., Darden and Ashton, 1975; Williams et al., 1978) examines shopping motivations in the grocery context. Therefore, this context allows us to contrast results obtained in this study with previous research findings. Second, the purchase cycle for groceries is frequent and a wide array of goods. Third, although numerous online
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grocery retailers have struggled to reach profitability, the potential for growth in the online replenishment channel remains significant. Anderson Consulting (Buss, 1999) predicts that by the year 2007 almost 20 million people will buy their groceries and other household goods online, compared with fewer than 200,000 currently. Based upon Bunn (1993), we employ a five-step procedure for empirical typology development. The resulting cluster solution supports and extends current shopping typologies by differentiating between online grocery consumer types. In order to gain a broader understanding of shopping motives across retail settings, we conducted a parallel study of grocery shoppers in the offline, or bricksand-mortar, setting. A cluster analysis of offline shoppers reveals a unique to the bricks-and-mortar setting. The paper is structured as follows. First, we review the literature on shopping motives. Second, we discuss the sampling frame and data collection procedures. Third, we present analyses and results. Fourth, we discuss the implications of this research as well as future research directions.
2. Conceptual background Past shopping typologies have primarily been based on consumer motives for shopping (e.g., Tauber, 1972; Bellenger and Korgaonkar, 1980; Westbrook and Black, 1985). Motivation theory (e.g., McGuire, 1974)—which suggests that human motives, whether cognitive or affective, are
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primarily geared towards individual gratification and satisfaction—provides the theoretical basis for examining the underlying reasons for why people shop. Consumers may be motivated by the ability to implicitly derive a certain set of utilities by patronizing a given type of shopping setting (Sarkar et al., 1996). These utilities may include location (place utility), expanded store hours and quick, efficient checkout (time utility), and an efficient inventory and distribution system that enables consumers immediate possession (possession utility) of the goods purchased. The motivations that underlie extant shopping typologies are summarized in Table 1. As can be seen in Table 1, several motives may be used to classify the online shopper: shopping convenience, including time savings (e.g., Bellenger and Korgaonkar, 1980; Darden and Ashton, 1975; Eastlick and Feinberg, 1999; Stephenson and Willett, 1969; Westbrook and Black, 1985; Williams et al., 1978); information seeking (e.g., Bellenger and Korgaonkar, 1980), social interaction gained from shopping (e.g., Bellenger and Korgaonkar, 1980; Westbrook and Black, 1985), and shopping as a recreational experience itself (e.g., Bellenger and Korgaonkar, 1980; Gehrt and Shim, 1998). Additionally, the literature suggests that the tendency to seek variety (e.g., Raju, 1980; McAlister and Pessemier, 1982; Menon and Kahn, 1995) and the desirability of immediate possession (e.g., Alba et al., 1997) may also be motives for shopping. These six motives, that help to classify the online shopper, are examined in greater detail next.
Table 1 Review of the shopping typology literature Author(s)
Gehrt and Shim (1998) Westbrook and Black (1985)
Bellenger and Korgaonkar (1980) Williams et al. (1978) Darden and Ashton (1975)
Darden and Reynolds (1971) Stephenson and Willett (1969) Stone (1954)
Shopping context
mail-order catalogs urban retail department stores mall and shopping center retailers mall-based shopping centers nonmall retail grocery stores supermarkets large and small urban stores consumer products (e.g., apparel, shoes) department stores
Sample and data collection
catalog shoppers French surveys written adult female shoppers 203 structured personal interviews adult shoppers 324 questionnaires intercept 298 grocery shoppers interviews personal middle-class suburban 116housewives personal interviews and written surveys 167 middle to upper class suburban housewives surveys written actual store patronage and buying behavior depth interviews of female shoppers
Primary shopping motives Overall convenience/ time savings
The shopping experience
B
B
B
B
B
B
B
B
Information seeking
B
B
B
B B
B
B
Social interaction
B
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2.1. Shopping convenience Numerous shopping motive studies (Stephenson and Willett, 1969; Darden and Ashton, 1975; Williams et al., 1978; Bellenger and Korgaonkar, 1980; Eastlick and Feinberg, 1999) have identified convenience as a distinct motive for store choice in the offline setting. Bellenger and Korgaonkar (1980) characterized the convenience shopper as selecting stores based upon time or effort savings. Recent research (Swaminathan et al., 1999) suggests that convenience is an important factor, particularly because location becomes irrelevant in the online shopping context. The online shopper may be motivated by the convenience of placing orders online at home or at the office any time of day. Consistent with past research regarding time and effort savings (Bellenger and Korgaonkar, 1980; Eastlick and Feinberg, 1999), we consider time and effort savings as a part of the overall shopping convenience construct. 2.2. Information seeking Bellenger and Korgaonkar (1980) propose that the ability to seek and gather information in a retail setting is a shopping motive in the offline context. Online shopping offers an infrastructure by which the consumer is able to search, compare, and access information much more easily and at deeper levels than within the bricks-and-mortar retail structure (Alba et al., 1997; Lynch and Ariely, 2000). This concept of information as adding value to the retail experience is supported by Hoffman and Novak (1996), who suggest that the Internet offers not only a wide variety of information, it offers the capability to deliver specific information tailored to the needs of the consumer. 2.3. Immediate possession Sheth (1983) and Shaw (1994) discuss the utility derived from the possession of goods or services. Certain consumers will demand instantaneous delivery of products or services and will patronize those retailers able to provide immediate possession. In an analysis of competition between direct marketers and conventional retailers, Balasubramanian (1998) suggests that direct marketers can reduce consumer resistance to catalog or Internet purchases by reducing delivery time. For these reasons, consumers motivated by immediate possession may choose to shop within a conventional retail store format rather than in the online context. 2.4. Social interaction The concept of retail social interaction as a source of shopping motivation stems from work by Tauber (1972) positing that numerous social motives help to influence shopping behavior. These motives include social interaction, reference group affiliation, and communicating with others having similar interests. Alba et al. (1997) suggest that
desire for social interaction plays a role in determining the choice of retail format, e.g., the store, catalog, or online setting. Past research suggests that consumers motivated by social interaction may choose to shop within a conventional retail store format as opposed to the online context. 2.5. The retail shopping experience The retail shopping experience is often considered a shopping motive unto itself (e.g., Bellenger and Korgaonkar, 1980; Dawson et al., 1990; Bendapudi and Berry, 1997). The recreational shopper has been defined in the literature as one who enjoys shopping as a leisure-based activity, spends more time per shopping trip on average, considers store de´cor an important patronage decision, and is more impulsive, e.g., tends to make unplanned purchases (Bellenger and Korgaonkar, 1980). Tauber (1972) identified a variety of psychosocial needs related to shopping behavior, one of which was that certain shoppers received sensory stimulation from the retail environment. According to Bellenger and Korgaonkar (1980), this type of shopper is motivated by the process and enjoyment of the shopping experience itself, independent of product-specific or other task-directed objectives. Online retailers, in general, may find it difficult to replicate the sensory effects and product-trial experiences available to the consumer in a physical store setting. Therefore, similar to the catalog setting, online retailers may find it more challenging to attract recreational shoppers who may be less predisposed to shopping online. 2.6. Variety seeking Although variety-seeking research is limited in the online setting, previous research has suggested that variety-seeking or varied behavior stems from intrapersonal or interpersonal motives (McAlister and Pessemier, 1982). Consumer behavior research (Raju, 1980; Menon and Kahn, 1995) has linked variety seeking to the presence of an ideal level of stimulation (e.g., an intrapersonal motive for novelty, complexity, or change), whereby a consumer’s optimal stimulation level determines their degree of exploratory and variety-seeking behavior in situations such as shopping. The ability to comparison shop may increase variety-seeking behavior in the online context; therefore, variety seeking is likely to be a significant motive in the online context. In summary, a typology based upon these items will thus capture the mix of motives influencing the various types of online consumers. The sampling frame, data collection procedure, and construct measures are described next.
3. Method This section describes a study undertaken to better understand the online shopper. It also describes an associ-
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ated study of offline shopping motives conducted in the grocery context in order to compare online and offline shopping motives and consumer types. 3.1. Online sample The research sample employed in this study consists of both active and lapsed customers of an online grocery retailer. Although the respondents are limited to a groceryshopping context, the random sample employed in this study includes consumers across various purchase frequencies. Further, the scale items examine general shopping motivations and utilities and thus apply to general shopping contexts across retail channels. These items seek to help better illustrate customer motivations for online shopping in general, as well as within the online grocery setting. Appendix A lists these items. The unit of analysis in this study is the individual online grocery consumer. A random sample of 1000 potential respondents was drawn from the customer database of an online grocery retailer based in the northeast United States. To ensure appropriate response rates, a twostep process was followed. First, prenotification cards introducing the study and emphasizing the importance of the respondent’s involvement preceded the mailing of the written surveys by one week. Second, the surveys were mailed with a cover letter thanking the consumer for their participation along with preaddressed and postage-paid envelopes. This step also included an incentive in the form of credit towards their next grocery order placed with the focal online grocer upon return of the completed survey. Of the 1000 mailed surveys, a total of 429 responses were received from online shoppers. Of these, 17 were eliminated based on incomplete responses (i.e., several responses had missing values). This resulted in a usable sample of 412 responses. Eleven percent of this usable online sample (n = 46) were considered lapsed customers since they had not shopped with the focal online grocery retailer during their last 10 shopping trips. 3.2. Offline sample To rule out the possibility that the specific characteristics of the sample were responsible for the results obtained, and to gain a better understanding of motivations such as time savings and recreation in the offline context, another survey of a matched sample of grocery customers within the bricks-and-mortar setting was undertaken. This survey was mailed to approximately 350 grocery customers. Respondents were randomly chosen from a mailing list representing the zip codes from which the original online respondents were drawn. One hundred and three completed questionnaires were returned from this mailing, resulting in a 29% response rate. In order to ensure that the respondents were comparable to the online sample, the respond-
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ents were matched in terms of age, education, and income. Chi-square tests indicated no significant differences among age, income, and education distributions between the offline and online samples at the 5% significance level. Table 2 outlines the online and offline respondent demographics by gender, age, education, income, and household size, as well as the propensity to shop online within the online sample. In order to test for nonresponse bias, the early and late responses were contrasted in terms of demographic variables and responses to the key variables of interest. None of the differences in terms of demographic variables and responses to key variables of interest emerged significant. Therefore, the responses were pooled.
Table 2 Respondent demographics online and offline shoppers Demographic profile
Percentage of sample Online shoppers
Offline shoppers
Gender Female Male
72 28
75 25
Age Less than 30 years old Between 30 and 49 years 50 years and over
27 63 10
19 59 21
Education Less than high school graduate High school graduate or equivalent Some college, no degree College graduate Postgraduate
0 2 7 33 58
3 5 16 41 35
Income Less than US$15,000 US$15,000 – 29,999 US$30,000 – 49,999 US$50,000 – 74,999 US$75,000 – 99,999 US$100,000 + Do not know
3 5 13 19 15 35 10
4 9 15 14 10 36 12
Household size (including all adults and children) 1 person 19 2 persons 37 3 persons 20 4 – 5 persons 21 6 or more persons 2
31 34 19 16 0
Propensity to shop for groceries online High propensitya 31 Moderate propensityb 34 35 Low Propensityc a 70% or more of respondent’s online grocery shopping is with focal online retailer within last 10 shopping trips. b Between 30% and 60% of online grocery shopping is with focal online retailer within last 10 shopping trips. c 20% or less of online grocery shopping is with focal online retailer within last 10 shopping trips.
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3.3. Measures Measures for the online study were developed following standard scale development procedures (Churchill, 1979). Multi-item scales were generated based upon previous measures, a review of the relevant literature, and preliminary interviews. The written survey contained 31 items on a seven-point Likert scale anchored by strongly agree and strongly disagree. These items examined shopping motives that were considered salient to the online context, including convenience, information seeking, immediate possession, social interaction, the retail shopping experience, and variety seeking. The measures for this study were developed as follows. 3.3.1. Shopping convenience Overall shopping convenience is defined as time and effort savings in shopping. The scale for shopping convenience was developed based on Hawes and Lumpkin (1984) and Gehrt and Shim (1998). Five items were generated that were intended to tap into general aspects of convenience, including time savings and effort reduction associated with shopping over the Internet. 3.3.2. Information seeking Information seeking is defined as searching, comparing, and accessing information in a shopping context. Three information-seeking items were selected, based on Arora (1985), to measure information seeking. 3.3.3. Immediate possession Immediate possession refers to the instantaneous delivery of products or services. In the absence of existing scales to measure immediate possession, four immediate possession items were generated that tap into the desire for immediate possession versus delayed delivery. 3.3.4. Social interaction Social interaction refers to consumers’ desire to seek out social contacts in retail and service settings. A three-item social interaction scale was developed based upon previous work by Hawes and Lumpkin (1984) and Westbrook and Black (1985). 3.3.5. Retail shopping experience Retail shopping experience refers to the enjoyment of shopping as a leisure-based activity and taps into aspects of the enjoyment of shopping for its own sake. Three scale items based on Bellenger and Korgaonkar (1980) and Stephenson and Willett (1969) were used to measure retail shopping experience. 3.3.6. Variety seeking Variety seeking is defined as the need for varied behavior or the need to vary choices of stores, brands, or products.
Five variety-seeking items were generated based on previous work by Raju (1980). The psychometric properties of the final measures were assessed using exploratory factor analysis with varimax rotation, coefficient alpha, and adjusted item-to-total correlations. Scale items were evaluated for possible deletion based upon standard procedures (Hair et al., 1995). First, scale items with loadings less than .30 (Hair et al., 1995), significant mixed factor loadings, and communality indices less than .40 were deleted. Second, scale items that increased adjusted item-to-total correlations when removed were also deleted. The revised factor solution was derived based upon the remaining items. The resulting factor analysis yielded four underlying dimensions of shopping motives: overall convenience, physical store orientation, information use in the planning and shopping task, and variety seeking. Appendix A lists the scale items, factor loadings, Cronbach’s alpha, and item –total correlations for each of the four factors. Each factor contained between four and five items. The resulting scale scores were determined by taking the average of the individual scale items. For the offline study, multi-item measures were used to represent the six shopping motives identified in the earlier online study. These motives, including convenience, information-use in planning and shopping, immediate possession, retail shopping experience, social interaction, and variety seeking, were captured using measures adapted from the online survey to the offline shopping context. For instance, the survey item ‘‘I find the Internet provides me with a lot of information about products and services’’ was adapted for the offline setting to ‘‘In general, I find stores provide me with a lot of information about products.’’
4. Results 4.1. Online grocery shopping types Based upon the set of measurement items, factor analysis, and resulting scale scores, subsequent cluster analysis identified a four-group typology of online grocery shopping types. Initial cluster analysis employed Ward’s minimum variance method to obtain a hierarchical cluster solution. No outlying observations were found, supporting the sample’s representativeness. Examination of the dendogram and cubic cluster criterion plots as well as the pseudo-F statistic (Johnson, 1998) suggested a four-to-five-cluster solution. Based upon the relationships found between cluster solutions and the background variables (described more fully in the next section) as well as the supporting literature, a four-cluster solution was found the most meaningful and interpretable. Consistent with previous research (e.g., Bunn and Clopton, 1993), a variation of Punj and Stewart’s (1983) crossvalidation procedure was followed to check the reliability of the four-cluster solution. This is primarily used in order to
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ensure that the sample size used in the study is not reduced during cross-validation. Means and standard deviations for each cluster and pairwise contrasts of shopping motives across clusters are reported in Table 3. The four factors, consisting of overall convenience ( F = 73.60, P < .01), physical store orientation ( F = 5.92, P < .05), information use in planning and shopping ( F = 10.42, P < .01), and variety seeking ( F = 299.46, P < .001), differed significantly across clusters. An analysis of the four clusters reveals the following four shopping types: The convenience shopper (Cluster 1). This group, which comprises 11% of the online grocery sample, is the smallest online shopping classification. This shopping type is motivated more than the other three types by the prospects of overall online shopping convenience. This segment also exhibits less of a physical store orientation (e.g., is motivated less by the prospect of immediate possession of goods or services purchased and social interaction) as well as less variety-seeking behavior across retail channels. The variety seeker (Cluster 2). The largest group in the sample (41%) is moderately motivated by online shopping convenience, yet substantially more so by variety seeking across retail alternatives and product types and brands. The variety seeker exhibits scores close to the mean on physical store orientation as well as a tendency to plan purchases and shopping trips. The balanced buyer (Cluster 3). This online shopping type (33% of the sample) is similar to the variety seeker in his or her desire for convenience and lowest in his or her tendency to plan the shopping task or seek information. The balanced buyer is moderately motivated by the desire to seek variety and exhibits a score near the mean in terms of physical store orientation. The store-oriented shopper (Cluster 4). This online shopping type (15% of the sample) is characterized by the lowest level of online shopping convenience. He or she rates highest overall on physical store orientation (i.e., desire for immediate possession of goods and social interaction),
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below the mean for tendency to plan purchases, and relatively low for variety-seeking behavior. A significantly greater percentage of store-oriented shoppers, measured by the question ‘‘How long have you been shopping for products and services over the Internet,’’ were found to have shopped online less frequently as compared to the other three shopping types. Deeper distinctions among clusters are made in the following sections, yet several initial observations can be made. First, as seen in Table 3, post hoc pairwise contrasts using Duncan’s multiple-range procedure suggest that Cluster 1 (convenience shoppers) differs significantly from Cluster 2 (variety seekers), and Cluster 4 (store-oriented shoppers) with regard to overall online shopping convenience. Convenience shoppers and store-oriented shoppers differ across overall online shopping convenience, physical store orientation, use of information in planning and shopping, and variety seeking. Except for variety seeking, variety seekers, and balanced buyers follow similar patterns, differing only in intensity. Multiple comparisons indicate that the store-oriented shopper differs from the other three shopping types across the information use and planning motive. The propensity to grocery shop online was examined across shopping types as well (see Table 3). A comparison of propensity to shop online for groceries revealed differences across the four shopping types ( P < .05). The storeoriented shoppers (as can be seen in Table 4) also have the lowest online purchase frequencies across shopping types for all of the specific product categories examined. These results that compare online shopping propensities across cluster types enhance the validity of the four online shopping types and proposed typology. 4.2. Relationship between background variables and online shopping types A more thorough understanding of the online consumer can be gained by relating the background variables to the
Table 3 Underlying shopping motives: means and standard deviations by online shopping type Shopping motive
Factor 1: overall convenience Factor 2: physical store orientation Factor 3: information use in planning and shopping Factor 4: variety seeking Propensity to shop online*** Number of observations Percentage of observations
Online shopping type
Total sample
F value * ( P)
Pairwise contrasts**
Convenience shopper
Variety seeker
Balanced buyer
Store-oriented shopper
6.02 (0.91) 4.96 (0.85) 4.53 (0.65)
5.21 (0.93) 4.64 (0.81) 4.61 (0.79)
5.78 (0.85) 5.06 (0.81) 4.38 (0.80)
3.38 (0.95) 5.60 (0.75) 3.59 (0.68)
5.52 (0.83) 4.92 (1.12) 4.46 (0.82)
73.60 ( P < .01) 5.92 ( P < .05) 10.42 ( P < .01)
A > B,D; C>B,D D>A,B,C A,B,C>D
3.58 (0.39) 6.61 (0.51) 45 11
5.34 (0.53) 5.87 (0.86) 169 41
4.59 (0.38) 6.81 (0.70) 136 33
3.88 (0.95) 3.59 (0.91) 62 15
4.56 (1.91) 4.45 (0.23) 412 100
299.46 ( P < .01) 52.73 ( P < .01)
B>A,C,D; C>A,D; D>A A>B,D; C>B,D
A = convenience shopper; B = variety seeker; C = balanced buyer; D = store-oriented shopper. * df = 3. ** Duncan’s post hoc multiple-range test (a=.05). *** Means (and standard deviations) of propensity to shop online, based upon the percentage of times a respondent had shopped at the focal online retailer during their last 10 shopping trips.
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Table 4 Product types purchased online by online shopping type Product type purchased online base (n = 412)
Books/magazines Clothing Toys Music CDs Computer hardware Computer software Travel Home electronics/appliances Flowers Financial services
Number of respondents that purchased within online shopping typea Convenience shoppers
Variety seekers
Balanced buyers
Store-oriented shoppers
29 16 9 16 15 19 22 8 13 12
91 51 20 59 30 77 96 18 38 38
77 44 18 40 42 41 58 12 22 27
11 12 3 11 6 3 25 3 6 11
Chi-square* *
P
32.91 4.38 5.87 6.92 16.50 35.83 8.18 5.17 8.33 1.55
< .01 NS NS NS < .01 < .01 .05 NS .05 NS
a
What types of products or services have you purchased online during the past 6 months? * * df = 3.
four online shopping types illustrated previously. Several variables (e.g., age, household size, per capita income, gender, and product type) that potentially influence retail shopping and online purchasing behavior are examined. The results of these background variables compared across the four clusters are reported next. First, univariate ANOVA and chi-square tests revealed no differences in age, income, and household size across the four online shopping types. This suggests a homogeneous online shopping sample with respect to demographics. Second, the percentage of members within each of the four shopping types having bought certain product types was examined. The results reported in Table 4 indicate significant differences in purchase behavior across clusters for the following product classes: books and magazines (c2 = 32.91, P < .01), computer hardware (c2 = 16.50, P < .01), computer software (c2 = 35.83, P < .01), travel (c2 = 8.18, P=.05), and flowers (c2 = 8.33, P=.05). Variety seekers and balanced buyers exhibit purchase frequencies similar to those of the convenience shopper in several product classes (e.g., books and magazines, music CDs, computer software, travel, and flowers). This suggests that although the convenience shopper might be more motivated by the convenience of shopping online, the variety seeker and balanced buyer may constitute relatively active online shopping types as well. 4.3. Offline grocery shopping types The procedure for selecting factors and determining cluster membership was similar to that of the online sample. The analysis for the bricks-and-mortar consumers identified three distinct clusters of offline shoppers: the time-conscious shopper, the functional shopper, and the recreational shopper. These clusters represent 20%, 32%, and 48% of the offline sample, respectively. Four offline shopping motives (factors), consisting of offline or physical store orientation ( F = 16.95, P < .01), shopping adventure and experience ( F = 223.32, P < .01), impulse shopping ( F = 9.26, P < .01),
and time savings ( F = 12.77, P < .01), differed significantly across clusters.
5. Discussion The empirical findings suggest that there are distinct online grocery shopping types. These shopping types are named convenience shoppers, variety seekers, balanced buyers, and store-oriented shoppers. The mean scores of underlying shopping dimensions across clusters directionally support the convenience shoppers as being most motivated by convenience, a key factor influencing the growth of online shopping. Conversely, these directional results also support the profile of store-oriented shoppers as being more motivated by offline store characteristics such as immediate possession and social contact. Variety seekers are characterized by those who seek variety in retail alternatives or products and brands. Balanced buyers exhibit scores relatively close to the mean for all four shopping dimensions except for a lower propensity to plan purchases, therefore suggesting a segment that makes more impulsive purchases online. Chi-square tests of differences in purchase frequencies across the clusters indicate that significant differences exist for the following product classes: books and magazines, computer hardware, computer software, travel services, and flowers (see Table 4). The convenience shopper exhibits the highest purchase frequency for the majority of these product classes, excluding only computer software, in which variety seekers exhibited the highest purchase frequency. This suggests that the variety seeker, although noted for his or her variety-seeking behavior and tendency to seek out alternative retail types, is an important consumer type for the online retailer to target due to this shopping type’s online purchasing activity. Further, the store-oriented shopper exhibits the lowest purchase frequency for all of the product classes, suggesting this group might be less of an immediate priority to the online retailer.
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This research enhances our understanding of shopping motives that are salient to the online context. Similar to previous typologies (e.g., Bellenger and Korgaonkar, 1980) conducted in traditional shopping contexts, this study identified overall shopping convenience as a motive for shopping online, particularly among convenience shoppers. Additionally, as in previous typologies (e.g., Bellenger and Korgaonkar, 1980; Stephenson and Willett, 1969), the desire for social interaction was identified as a shopping motive, particularly among store-oriented shoppers. These similarities suggest that certain underlying motives for shopping, such as desire for convenience and social interaction, have not changed due to the online context. Unlike previous shopping typologies, variety seeking was identified as an online shopping motive for a certain consumer type. This finding suggests that variety-seeking behavior is an important construct, particularly as emerging shopping channels, such as the Internet, offer the consumer more choice and ease of access. Unexpectedly, this research failed to support two factors commonly attributed to reasons why consumers shop online: (1) time savings and (2) recreation and enjoyment. Scale items measuring these two constructs were dropped from the exploratory factor analysis for poor or mixed loadings. Perhaps the notion of time savings was subsumed within the overall shopping convenience construct. Additionally, it is possible that the use of the Internet for online shopping appeals to more functional as opposed to recreational shoppers. One implication of this research is that some of the underlying motivations such as convenience remain important in both online and offline settings. Time savings and recreation and enjoyment, which emerged as key motivations in previous research in offline settings, did not appear to be significant within the context of this online study. One possible explanation maybe that while the Internet saves time during the purchasing of goods, and eliminates the time needed to travel to the physical store, it also increases the time taken to receive goods. Therefore, time savings may not be perceived as a significant advantage while shopping online. The analysis for the offline sample identified three distinct clusters of offline shoppers: time-conscious, functional, and recreational shoppers. The results from this matched offline sample suggest that time savings, functional shopping, and shopping as recreation are significant factors in the bricks-and-mortar context. These results approximate those of previous offline shopping typologies (e.g., Bellenger and Korgaonkar, 1980) that identified two distinct shopping types: the recreational and the economic shopper. The significance of the time-savings factor in the offline results is particularly interesting. This is in contrast to our findings regarding time savings in the online context. However, because offline shopping is generally thought to be more time-consuming than in the online context, time savings becomes a significant differentiator in the offline
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context. In addition, comparing the offline and online results, variety seeking and convenience were found to be significant factors in the online but not the offline setting. The offline results discussed here add validity to the online results in that the factors of recreational shopping and time savings in the offline setting and convenience and variety seeking in the online setting seem to differentiate the two shopping contexts. 5.1. Theoretical implications This study represents an important first step in extending the general shopping literature to the context of online shopping. Variety seeking and convenience were found to be significant motivating factors in the online shopping context. Time savings and recreational motives were significant motives in the offline but not the online context, suggesting that these factors may serve to differentiate the shopping types across these channels. By illustrating shopping motives that are salient to the online setting (e.g., convenience and variety seeking) versus the offline context (e.g., time savings and recreational motives), we begin to build a theory of online shopping motivation and behavior. This study also contributes to our current knowledge of marketing on the Internet. In a broad sense, previous work in Internet marketing has examined a wide array of consumer, retailer, and producer phenomenon within various types of shopping channels, including interactive home shopping (e.g., Alba et al., 1997), the application of network theory (Iacobucci, 1998), the concept of flow in helping to describe consumer navigation behavior (Hoffman and Novak, 1996), consumer perception of risk in Internet shopping (Swaminathan et al., 1999), and personal privacy issues related to online shopping (Rohm and Milne, 1998). However, to the authors’ knowledge, scant research that investigates differences across consumers on the basis of online shopping motives exists. 5.2. Managerial implications Retail and marketing managers may benefit from the results reported here. The findings suggest that consumers who are motivated by convenience are likely to shop online for specific types of products and services, e.g., books and magazines and travel. An online retailer seeking to market explicitly to this segment may want to develop strategic alliances with retailers specializing in these product or service areas. The convenience shopper, balanced buyer, and variety seeker exhibit a high propensity to shop in various product classes. Given that the balanced buyer and variety seeker exhibit greater propensity to seek variety in their shopping, it is possible that these groups are attracted by the Internet’s convenience, as well as its search and comparison capabilities that enable consumers to seek alternatives based upon various attributes, such as price. In addition, these groups are moderately motivated by immediate possession, suggesting that
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Appendix A. Factor analysis of shopping motives and utilities Scale items
Factor loadings
1. Internet ordering convenience 2. The Internet is a convenient way of shopping 3. The Internet is often frustrating a 4. I save a lot of time by shopping on the Internet 5. Shopping over the Internet is a pleasant experience 6. I would rather buy at store than wait for delivery 7. I like to shop where people know me 8. While shopping on the Internet, I miss the experience of interacting with people 9. I like browsing for the social experience 10. I like to have a great deal of information before I buy 11. I always compare prices 12. I carefully plan my purchases 13. I buy things I had not planned to purchase 14. I am cautious in trying new products a 15. I enjoy exploring alternative stores 16. Investigating new stores is generally a waste of time a 17. I like to try new products and brands for fun 18. I like to buy the same brand a a
Physical store characteristics
Information use in planning and shopping
Variety seeking
.74 .84
.16 .31
.10 .01
.06 .03
.71 .71
.52 .78
.05 .05
.19 .12
.09 .03
.57 .74
.81
.02
.06
.01
.72
.37
.57
.21
.09
.64
.08 .18
.68 .82
.12 .01
.09 .04
.47 .50
.03 .03
.75 .29
.15 .65
.09 .20
.43 .34
.14 .16 .05 .12 .05 .12
.07 .01 .17 .11 .17 .09
.64 .68 .41 .29 .27 .13
.11 .04 .26 .42 .70 .70
.23 .34 .33 .47 .50 .45
.05 .22
.07 .20
.07 .06
.65 .50
.40 .22
Item is reverse coded. The cumulative variance is explained by four factors: 53%. Scale items are anchored by 1 = strongly disagree and 7 = strongly agree.
Coefficient alpha
.80
.71
.52
.60
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Overall convenience
Item-to-total correlation
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their demand for digital products such as music CDs and software is likely to be relatively high. On the other hand, the store-oriented shopper is highly motivated by immediate possession. Marketers may need to examine ways in which they can enhance the ability to deliver within a shorter period, e.g., same-day delivery, so that a greater proportion of segments such as store-oriented shoppers can be attracted to online shopping. 5.3. Future research Some limitations of this research, that also provide a basis for future research, should be noted. Online shopping is a relatively new phenomenon. The results presented in this study are based on a sample of consumers who may be perceived as early adopters and innovators in the context of online shopping. One potential limitation is that the characteristics of the sample may change once more consumers begin shopping online. Another limitation is that respondents were customers of a single online retailer within a single industry. Future research should focus on extending these findings to other industries. Future research in this area should also examine how shopping typologies might vary with bricks-and-clicks shoppers, e.g., people who shop for specific items in both the online and online settings.
Acknowledgements The authors thank George Milne, Rajdeep Grewal, Easwar Iyer, Thomas Brashear, and Marc Weinberger for their constructive comments on earlier versions of this paper.
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