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Segmenting consumers based on the benefits and risks of Internet shopping
Journal of Business Research 57 (2004) 1352 – 1360
Segmenting consumers based on the benefits and risks of Internet shopping Amit Bhatnagar*, Sanjoy Ghose 1 School of Business Administration, University of Wisconsin—Milwaukee, P.O. Box 742, Milwaukee, WI 53201, USA Received 17 December 2001; accepted 18 December 2002
Abstract An analytical model is developed to examine the role perceived benefits and risks of online shopping play in forming consumer preferences for online shopping. Two dimensions of perceived risk are considered in the model. Product risks follow from the consumers’ inability to examine products online. Security Risks follow from the consumers’ fear that the open Internet network would allow their personal data to be compromised. We segment the sample based on their sensitivity to the benefits and risks of Internet shopping. The segments are profiled based on their demographics. We hypothesize how the two dimensions of risks will vary across the segment characteristics. The model is calibrated on national survey data collected online. The hypotheses were largely supported by the data. D 2003 Elsevier Inc. All rights reserved. Keywords: Perceived risk; Online retailing; Latent segmentation
1. Introduction Researchers such as Hoffman et al. (1995), Alba et al. (1997), and Peterson et al. (1997) have discussed the several benefits that online shopping confers on the consumers. These benefits all provide ‘‘convenience’’ to a degree that is not quite available in traditional shopping channels. Unfortunately, electronic commerce also magnifies the uncertainties that are involved with any purchase process, leading to higher perceived risks. Two types of risks are particularly relevant. First is the consumers’ inability to physically examine the product. This need to physically examine a product would vary with the product category, and therefore, we call this aspect of risk as product risk. Another source of perceived risk arises due to the manner in which transactions are conducted over the Internet. The data is transmitted over open lines, leading to consumer fears that the data may be compromised. We label this kind of risk security risk. Naturally, this category of risk is present whenever one plans to purchase any product online. * Corresponding author. Tel.: +1-414-229-2520; fax: +1-414-2296957. E-mail addresses: [email protected] (A. Bhatnagar), [email protected] (S. Ghose). 1 Tel.: + 1-414-229-4224. 0148-2963/03/$ – see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0148-2963(03)00067-5
We develop an analytical model in this paper to study the influence of perceived benefits and the two kinds of perceived risks on consumer shopping proclivities. We hypothesize that product risk would decrease with age, Internet experience, and proportion of search attributes, and security risk would decrease with education. The model is empirically calibrated on survey data collected online and the results supported the two hypotheses. The structure of the remainder of paper is as follows. In the next section, we discuss the relevant literature, and then we advance our hypotheses. In Section 3, we present our analytical model. Section 4 describes the data and presents the results of our analysis. Finally, we conclude in Section 5 with managerial implications and directions for future research.
2. Problem conceptualization In this section, we draw from two related research traditions in marketing. The first stream consists of papers that have looked at retail patronage issues in general. This stream is theoretical in nature and comprises conceptual models of store patronage behavior. The second stream deals with nonstore retailing. These papers have been empirical in nature and study the determinants of different forms of non-store
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purchasing behavior. This stream has primarily focused on the role of convenience and perceived risk in shaping consumer preferences for non-store retailing formats. Our purpose here is not to do an exhaustive literature review, but rather to look at some past work to develop insights into the research area that we are examining. According to Sheth (1983), consumers have two types of motives while shopping, functional and nonfunctional. Functional motives are related to time, place, and possession needs. Specific examples include features such as one-stop shopping, cost and availability of needed products, and convenience in parking and shopping. These motives are intrinsic to the outlets. Nonfunctional motives are those that are related to various shopping outlets as a result of their association with certain social or emotional values. It may include store image, novelty-seeking, etc. These motives are extrinsic to store outlets. ‘Convenience shoppers’ approach retail store selection from a time- or money-saving point of view and are essentially influenced by functional motives. Compared to them, ‘recreational shoppers’ are motivated by nonfunctional motives and prefer closed malls and department stores for their usual shopping (Bellenger and Korgaonkar, 1980), as opposed to ‘convenience-economic shoppers,’ who prefer non-store shopping (Korgaonkar, 1981). The rationale is that consumers who are high on nonfunctional needs tend to be ‘conspicuous consumers’ and prefer those outlets where they can be ‘seen’ by their friends and colleagues. It is therefore reasonable to assume that Internet store patrons would be convenience-economic shoppers and be motivated chiefly by functional benefits. Therefore, in our analysis, we focus on only functional benefits. Even though consumers perceive the Internet to have a number of functional benefits, it has some clear disadvantages. Consumers perceive a higher level of risk when shopping on the Internet. This is not surprising since studies have consistently shown that consumers perceive higher risks in non-store shopping formats, such as telephone shopping (Cox and Rich, 1964), mail order (Spence et al., 1970), catalog (Reynolds, 1974), direct sales (Peterson et al., 1989), and catalog showroom (Korgaonkar, 1981). In fact, Cox and Rich (1964) found that perceived risk is the most powerful factor differentiating those women who shop by phone from those who do not. We extend this stream of research on the role of perceived risk in nontraditional retailing by studying the latest development in nontraditional retailing, online stores. The concept of ‘perceived risk’ was introduced in marketing by Bauer (1960), and since then, it has been the focus of several studies in consumer behavior. The construct of perceived risk has been conceptualized in a variety of ways (Dowling and Staelin, 1994; Gemunden, 1985; Ingene and Hughes, 1985; Ross, 1975). The most common concept of perceived risk used by researchers defines risk in terms of the consumer’s perception of uncertainty. The uncertainty can be about the outcome or about the adverse consequences of buying a product (or service). These may produce
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anxiety. The amount of risk perceived by an individual in any situation can also be determined by the individual’s demographic characteristics (Taylor, 1974). Perceived risk is known to be multidimensional (Cunningham, 1967). However, our interest in this study is limited to two dimensions of perceived risk. The first dimension of risk is specific to the Internet and arises due to consumers’ fear of their account being hacked by unscrupulous elements. This risk is fostered by media stories regarding security considerations concerning transactions over the Internet are quite common (Minahan, 1997; Oberndorf, 1996). We call this security risk. For a given consumer, this risk is going to be common across product categories. However, the online industry is devoting considerable amounts of energy to mitigating this type of risk (Reinbach, 1996). For example, several task forces have been working to improve the security (McCarthy, 1997); one approach to resolving it would be improved cryptography programs (Adam et al., 1997; Wilder, 1996; Bhimani, 1996). In addition, federal consumer protection laws limit the liability of credit card holders to US$50 (Scheer, 1999); this limit has been in existence for a number of years. However, we would expect only educated consumers to be aware of these issues or to fully comprehend the efforts being made by the government and the industry to protect consumer privacy. Hence, we expect the impact of security risk to be reduced by education. We therefore hypothesize that as education increases, consumers’ perceived security risks would decrease. We call the other dimension of risk, product risk, as this risk perception arises due to issues like the inability to physically inspect products (Spence et al., 1970; Bhatnagar et al., 2000). We also know that product risk is likely to be greater when there is little information about the product category, the consumer has little self-confidence in evaluating brands, there are variations in quality between brands, or when the price is high (Assael, 1992). This indicates that this type of risk would vary across product categories. For instance, Darian (1987) found that while 80% of consumers would not worry about buying linen on the phone, only 7% would not worry about buying kitchen tables and chairs. However, it is also recognized that as consumers get older, they accumulate experiences and build up knowledge capital (Urbany et al., 1989). With increasing years, due to long exposure to many shopping choices, consumer preferences get fixed and they gain more confidence in shopping for the product categories they seek. This reduces the need for the pre-purchase information search as they can substitute their knowledge capital for any lack of information. This knowledge capital should reduce the uncertainty involved with the purchase and consequently the product risk. We therefore hypothesize that product risk will decline with increasing age. We expect this phenomenon to show up in the online shopping context. Cognitive psychologists have shown that experts differ from novices in the amount, content, and organization of their knowledge (Chi et al., 1982). Product risks decrease as
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consumers gain increased knowledge about the available alternatives, and the amount of knowledge regarding alternatives is known to increase with experience (Bettman and Park, 1980). Mervis and Rosch (1981) examined a number of domains (product categories, such as furniture) and found that the number of dimensions (or evaluative criterion) in a domain increased as individuals acquired more experience. The cognitive skill of consumers is directly related to the number of dimensions employed in judgment (Crosby and Taylor, 1981). Therefore, experience increases not only the knowledge base but also the cognitive skills (Mitchell and Dacin, 1996), leading to higher amounts of search efficiency among more experienced consumers. Our third hypothesis is that product risk will decline with increasing Internet-specific experiences. Nelson’s (1970) classification of all products as search or experience is very relevant for determining the level of product risk in a category. Search products are marked by a relatively higher proportion of search attributes, i.e., those attributes that can be evaluated before purchase. Experience attributes are similarly characterized by a relatively higher proportion of experience attributes, i.e., those attributes that can be evaluated only after purchase. In general, consumers will perceive higher product risks in categories that are high on experience attributes. Our fourth hypothesis, therefore is that product risk will decline with increase in the proportion of search attributes. To summarize, we expect that individuals shopping on the Internet would be motivated by functional motives. Thus, their online shopping preferences should be positively affected by the convenience-type benefits offered by the Internet environment. We also expect that both product and security dimensions of perceived risks will negatively affect online shopping preferences. We also believe that product risk would decrease with an increase in age and security risk would decrease with an increase in education. Finally, and perhaps most importantly, we identify consumer segments that differ along the three dimensions of benefits and the two risks. We show that even though perceived risks are high among all the segments, the relative importance of the two risks vary across segments.
3. The model In the present research, we examine consumer shopping behavior for the latest retail format, the Internet. Specifically, we want to build an analytical model to study how consumer perceptions of benefits and risks of buying online affect consumer choice probabilities of using this new retail medium. Consumers consider several factors when choosing a certain retail format for their product purchases. Some of these factors are convenience, variety of selection, quality of products, return policies, and special services—delivery, credit, perceived risks, etc. Different consumers derive dif-
ferent levels of benefits from these factors. Let the aggregate value of total benefits at a cyberstore be Vij for consumer i buying from product category j. This value Vij translates into utility Uij for the household according to some given mathematical expression, which for the present case we assume to be the commonly used negative exponential form (Horsky and Nelson, 1982; Roberts and Urban, 1988), Uij ¼ a bfexpðri Vij Þg
ð1Þ
where a (a > 0) and b are constants and ri is the coefficient of risk aversion of household i. The above formulation of utility function assumes declining marginal utility—a standard assumption in traditional microeconomics theory. Currim and Sarin (1984) conducted an empirical study of how students evaluate job offers and found that the negative exponential model offered a better fit over linear models for 40 out of 43 students. The above formulation is deterministic in nature and assumes that the consumer has perfect knowledge about the different types of benefits. However, realistically, it is impossible to have perfect knowledge, and the consumer has some uncertainty about the different benefits. The level of this uncertainty is likely to be higher for new retailing formats like cyberstores as compared to the more traditional stores. The consumers therefore make decisions under uncertainty and expect to get different levels of valuation Vij with different probabilities. Due to this uncertainty in the value of the benefits, the consumer tends to maximize the expected utility, rather than the utility. We assume that the valuation of the benefits Vij is normally distributed. It can be shown that, under this assumption, the expected utility of the consumer would be given by ð2Þ EbUij c ¼ a b exp ri E½Vij 0:5ri Var½Vij
where, E[Vij] and Var[Vij] are the expected mean and variance of the value of benefit bundle Vij. Here, ri, the coefficient of risk aversion, measures how sensitive the household i’s expected utility is to the mean and variance of the value of benefit bundle. The higher the level of uncertainty in the consumer’s mind, the greater the variance in the value of the bundle. A higher variance lowers the expected utility that a consumer hopes to derive. Since the above function is monotonic in E[Vij] 0.5riVar[Vij], we can also assume that the consumer will patronize the format with high levels of E[Vij] 0.5riVar[Vij]. We can therefore recast Eq. (2) as E½Uij ¼ E½Vij 0:5ri Var½Vij
ð3Þ
The consumers would perceive a large variance (i.e., Var[Vij]) in the aggregate value of the benefits, if they think that shopping at a certain format, such as in online stores, is risky. We can assume that the higher the perceived risk, the higher the variance in the value of the benefits. Since risk is known to be multidimensional, we look at two different
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aspects of risk, product risk and security risk. The former is represented in our model by PRij and the latter by IRi. Since the security risk is constant across product categories, it does not have a j subscript. We capture Var[Vij] in Eq. (3) by the sum of PRij and IRi. Var½Vij ¼
PRij
þ IRi
ð4Þ
We can incorporate Eq. (4) in Eq. (3) to state E½Uij ¼ i Bi 0:5ri ðPRij þ IRi Þ ¼ aij þ i Bi þ i IRi
ð5Þ
where, Bi is benefits of shopping on the Internet. aij ¼ 0:5ri PRij ;
i Bi ¼ E½Vij ;
i ¼ 0:5ri
ð6Þ
When shopping, a number of other unaccounted for variables can also influence a household’s choice decision. These variables can be represented by a household specific error term, ei. Thus, the true expected utility that the consumer obtains by purchasing over the Internet is given by E½U true ij ¼ E½Uij þ ei ¼ aij þ i Bi þ i IRi þ ei
ð7Þ
The consumer would buy on the Internet if the expected utility is greater than zero. We make the standard assumptions that the random components are independently and identically distributed as an extreme value of Type I and arrive at the standard logit formulation. From this, it follows that the probability of consumer i buying from category j over the Internet is given by Pij ¼ PðE½Uijtrue > 0Þ ¼ PðE½Uij þ ei > 0Þ ¼
1 1þ
eðE½Uij Þ
¼
1 1þ
eððaij þi Bi þi IRi ÞÞ
represented by the vector Di. Following Gupta and Chintagunta (1994), we assume that this probability is given by expðgs þ !s Di Þ Pis ¼ P expðgk þ !k Di Þ
3.1. Consumer segmentation The consumers are heterogeneous in their responsiveness to the benefits and the two aspects of perceived risk. Traditionally, heterogeneity has been accounted for by either assuming the response parameters to be randomly distributed across the population or by assuming that there are latent segments. We follow the second approach by assuming that all the consumers are drawn from s segments. The probability Pis that household i belongs to segment s is determined by the demographic map of the individual,
ð9Þ
s
where gs and !s are segment-specific parameters to be estimated from the model. These parameters determine the segment membership for any household. Given these parameters, the households are assigned to that segment where the probability is the highest. In latent class framework, it is assumed that all consumers within a segment are homogeneous in their response parameters. Segments, however, exhibit heterogeneity in their response parameters. The probability P ijs that a household belonging to segment s makes a purchase from category j on the Internet is given by Pijs ¼
1 1 þ eððasj þs Bi þs IRi ÞÞ
ð10Þ
where asj, s, and s are segment-specific response parameters. Eq. (10) is just Eq. (8), with individual specific parameters replaced by segment-specific parameters. The probability of observing a sequence of buy/no buy decisions on the Internet for a household, across the j product categories, conditional on membership in segment s, is given by Y P si ¼ P sij j
Therefore, for household i, the unconditional likelihood function i, is given by X i ¼ P si Pis s
ð8Þ
In the above model, Bi stands for the benefits of Internet shopping, and IRi captures the impact of security risks. The intercept aij would vary across product categories to capture any product-specific differences. Since product risk would also vary across product categories, the intercept would be a proxy for it. This becomes clear after examining the reparametrizations in Eq. (6).
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and the sample likelihood function is Y ¼ i
ð11Þ
i
Maximizing the sample likelihood in Eq. (11) allows us to estimate segment-specific parameters. The parameters of this model are estimated conditional on a prespecified number of latent segments. We initiate the estimation process by first calibrating a single-segment model, then a two-segment model, a three-segment model, and so forth, until the additional parameters required for an additional segment do not lead to any significant improvement in model fit. The model has the most optimum fit to the data when the Bayesian Information Criterion (BIC) is minimized. The BIC is given by BIC = 2 + kln(N), where is the log likelihood, k is the number of parameters, and N is the sample size. For additional support, we also estimate the Akaike Information Criterion (AIC) and Constrained Akaike Information Criterion (CAIC). AIC is estimated as 2 + 3k, and CAIC is calculated as 2 + k[ln(N) + 1].
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These numbers work like the ‘adjusted R2.’ As the number of parameters increases, the likelihood increases. BIC and CAIC penalize over parameterization, with CAIC being more stringent than BIC.
4. Data and analysis The model was estimated on data collected via online surveys conducted nationally. The survey was posted on the Web and therefore administered to the entire population. The survey was run for 4 weeks. Participants were solicited through announcements on Internet-related newsgroups (e.g., comp.infosystems.www.announce, comp.internet.nethappenings, etc.), and on the www-surveying mailing list, and announcements made in the popular media (e.g., newspapers, trade magazines, etc.). The survey had three different sets of data. In the first set, the respondents were asked whether or not they buy online. The question was repeated for a number of product categories like hardware, software, books, etc., and for each category, the respondent had to answer yes or no. These responses were the choice variables in our model. There were a large number of product categories in the sample. Here, we are interested in studying product risks, and therefore, to get a good spread of product risks, we decided to focus on certain experience products and certain search products. Internet has reclassified many traditional search products as experience products and traditional experience products as search products. On the Internet, traditional search products, such as electronics, flowers, magazines, etc. have become experience goods. Unlike a traditional store, in an Internet store, one cannot determine the picture quality of TV, or the audio quality of CD player. Therefore, electronics, which are high on search attributes in a traditional store, become experience products in an Internet store. Similarly, flowers can be tested for freshness, fragrance, etc. in a traditional store but not in an online store. While one can go to a supermarket and flip through a magazine, one cannot do so in an online store. On the other hand, traditional experience products such as software, music, and books have become search products on the Web. Software is considered a search product on the Internet because it is possible to download trial versions of most of the software, allowing consumers to evaluate products before purchase. Similarly, at most of the online music stores, such as CDnow.com, it is possible to download a few songs of each album and evaluate them before purchase. At online bookstores, such as Amazon.com, one cannot read a book before purchase, but one can read comments by other consumers who have read the book. Thus, buyers can have a better understanding of what to expect in that book, making it a search good. Videos in general would be experience in any retailing environment, but at some Internet stores, one can read reviews of video, which makes it a search product at those stores.
The second set of data asked several demographic questions. The demographic profile of the entire sample is presented in Table 1. The respondent population had a very good age spread There were a fair number of respondents from all age segments. The sample had a fair proportion of high-income people, with the largest segment earning between US$50,000– US$74,999. The sample was evenly divided between married and single/divorced consumers. An overwhelming number of consumers had some college education. Men outnumbered the women respondents. Most of the respondents had been on the Internet for at least a year, with a large majority indicating use between 1 and 3 years. Since the survey was administered online, a precondition for responding to the survey was access to the Internet, and therefore, it is not surprising that sample statistics match those of the overall Internet user profile (Clemente, 1998). Other surveys, including one conducted by ABC News, indicate those intending to buy online are likely to earn over US$75,000 and have a college education (www.nua.ie). This matches our sample demographics. In the third set, eight questions quizzed consumers about the benefits and risks of shopping on the Internet. The respondents had to indicate agreement/disagreement on 5point Likert scales. Since the responses were heavily correlated, we used principal component analysis to reduce this set of variables. Two factors emerged with eigenvalues greater than 1.00, and these two factors accounted for 59.24% of the total variation. The first factor accounted Table 1 Descriptive statistics of the sample Variables
Frequency
Variables
Frequency
Age Under 20 21 – 25 26 – 30 31 – 35 35 – 40 41 – 45 46 – 50
53 117 164 131 118 101 117
Education Grammar school High school Two-year school Some college College graduate Master’s degree Doctoral degree/ Professional degree
11 76 38 292 310 173 67
51 – 55 56 – 60 61 – 65 Over 66
84 49 11 22
Income Under US$10,000 US$10,000 – US$19,999 US$20,000 – US$29,999 US$30,000 – US$39,999 US$40,000 – US$49,999 US$50,000 – US$74,999 US$75,000 – US$99,999 Over US$100,000 Marital Single, divorced, separated Married
36 70 100 120 128 241 116 156
419 548
Gender Male Female
595 372
Internet experience < 6 months 6 – 12 months 1 – 3 years 4 – 6 years 7 years or more
29 64 396 312 166
A. Bhatnagar, S. Ghose / Journal of Business Research 57 (2004) 1352–1360 Table 2 Results of factor analysis Internet benefits (B) Providing credit card information through the web is the single most important reason I don’t buy through the web more often. Providing credit card information through the web is no riskier than providing it over the phone to an offline vendor. It is just as safe to use credit cards when making purchases from Internet vendors. Internet vendors offer more useful information about the choices available. It is easier to place orders with Internet vendors. It is easier to cancel orders placed with Internet vendors. Internet vendors offer better prices. It is easier to contact Internet vendors. Percentage of variance explained
Security risk (IR) 0.845
0.819
0.876
0.728
0.714 0.683 0.578 0.747 38.96
20.28
The numbers indicate the factor loadings.
for 38.96% of the variation, and the second factor accounted for 20.28% of the variation. These two factors were then rotated by means of Varimax with Kaiser normalization. The resulting rotated factor matrix is presented in Table 2. The first factor is Internet benefits; all of the variables with high correlations refer to the different benefits of patronizing Internet-based stores. The second factor is security risk; it is correlated with all the variables that deal with the risks of shopping on the Internet. The reliability of the two constructs was checked by calculating the Cronbach’s alpha. The Cronbach’s alpha for the benefits was .7432 and for security risk was .8211. Nunnally (1978) recommends that if the Cronbach’s alpha is higher than .7, then the constructs are internally consistent. Therefore, our two constructs are internally consistent and reliable measures of benefits and security risk. The factor scores for the first factor served as the independent variable B in our model (Eq. (7)). Factor scores for the second factor were the values for the variable IR in our model. Table 3 shows that as the number of segments increases, the log likelihood increases, but the AIC, BIC, and CAIC first decrease and then increase. The model fit criterion, i.e., AIC, BIC, and CAIC are the least for the three-segment solution. We therefore restrict the rest of the analysis to the
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three-segment solution because it offers the best fit to the data. Table 4 presents the values of the risk and benefit response parameters for the three latent segments. A large significant negative value for aj implies that segment members perceive high levels of product risk in product category j. This decreases their propensity to buy online in that product category. For a given segment, modulations in this parameter across product categories show how the product risk varies for this segment across product categories. Similarly, those segments that have higher significant negative values for are influenced more by security risks than segments with lower values of . Consequently, segments with higher negative values of are less likely to buy online. Likewise, by examining the magnitude of significant values, we can determine the influence Internet benefits have on a segment’s probability of shopping online. The perceived Internet benefits had a significant positive effect on the shopping behavior of all three segments of consumers, the smallest effect was for segment 2, where the impact of security risk was not significant. Interestingly, the segment for which consumer choice probabilities were least affected by perceived Internet benefits also saw no effects of security risks on consumer choice. As Table 4 depicts in general segment 1 experiences more product risk than segment 2, which in turn experiences more such risk than segment 3. Therefore, we can label segment 1 as High product risk – High security risk, segment 2 as Moderate product risk – Low security risk, and segment 3 as Low product risk – Moderate security risk. In other words, Internet benefits are not necessarily correlated with product risks. Table 5 displays the demographic parameter values for the consumer segmentation part of our model (Eq. (6)). These estimates explain the effect of demographic variables on segment membership; they also help in testing our hypotheses. The parameters are estimated assuming the third segment to be the base segment. For a given segment, a positive significant value of response parameter for any variable implies that as that variable increases for a consumer, his probability of belonging to that segment increases as compared to segment 3. The first segment had high product risk and high security risk. This segment has the lowest income, lowest mean age, and least Internet experience. Thus, it is not surprising that both the risks are the highest in this segment. The second segment perceives no security risk, but for most of the categories faces a higher product risk than segment 3 and a
Table 3 Segments selected based on AIC, BIC, and CAIC Segments
Likelihood
Number of parameters
Number of respondents
AIC
BIC
CAIC
2 3 4
3756.04 3686.69 3670.62
27 44 61
967 967 967
7593.072 7505.386 7524.250
7697.676 7675.851 7760.576
7724.676 7719.851 7821.576
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Table 4 Response parameters for the three segments Parameter
Product risk (aj) Software Hardware Books Electronics Flowers Magazines Music Video Internet benefit () Security risks () Fractional segment size
Segment 1
Segment 2
Segment 3
High product risk, high security risk
Moderate product risk, low security risk
Low product risk, moderate security risk
0.3341* (0.1883) 0.9712** (0.2468) 0.8961** (0.2491) 3.7022** (0.4368) 3.0426** (0.4988) 2.9230** (0.3655) 1.8081** (0.2099) 3.9228** (0.5437) 0.4376** (0.0750) 0.8340** (0.1193) 0.60
0.8606 (0.5472) 13.2115** (0.5690) 0.9193 (0.6485) 3.5222** (1.2429) 1.3011** (0.3589) 1.2722** (0.5850) 0.2819 (0.9123) 0.6688 (0.7514) 0.2398** (0.1247) 0.0374 (0.3938) 0.15
2.4890* * (0.3915) 1.3327** (0.4516) 1.1028** (0.3251) 0.7928** (0.2047) 1.0127** (0.2211) 0.6715** (0.2524) 0.2620 (0.2764) 0.6816** (0.2558) 0.4022** (0.1086) 0.4515** (0.1240) 0.25
The numbers in brackets are the standard errors. * Significant at .1 level. ** Significant at .05 level.
lower product risk than segment 1. This segment consists of higher number of women, with less Internet experience than segment 3, but more Internet experience than segment 1. Segment 2 is also more educated and younger than segment 3, but less educated and older than segment 1. As one moves from segment 1 to segment 2 and then to segment 3, one observes three clear trends. In general, as product risk declines, the average age and Internet experience of the segment members increase. Therefore, we can conclude that product risk declines with age and Internet experience, which supports our second and third hypotheses. Segment 2 is the most educated and least affected by security risks in their online shopping behavior. While segment 3 is impacted less by security risks than segment 1, there is no difference in their educational level. Therefore, our first hypothesis that security risks would decline with increases in education is only partially supported. Table 5 Parameter estimates and effects of demographic variables on segment membershipa Variable
Intercept Education Income Gender (1 = female, 2 = male) Married (0 = divorced, separated, single, widowed; 1 = married, living with another) Age Number of years on the Internet a
Segment 1
Segment 2
High product risk, high security risk
Moderate product risk, low security risk
4.8780** (1.1762) 0.0305 (0.1127) 0.1485** (0.0655) 0.3789 (0.3828)
4.4246** (1.4455) 0.3139* (0.1848) 0.1598 (0.1051) 2.0813* (0.5401)
0.3214 (0.2904)
0.1190** (0.0480) 0.4072** (0.1568)
Segment 3 is treated as the ‘base’ segment. * Significant at .1 level. ** Significant at .05 level.
Segment 1 perceives higher product risks in categories video, electronics, flowers, and magazines, respectively, and lower product risks in software, books, hardware, and music, respectively. Segment 2 perceives higher product risks in hardware, electronics, flowers, and magazines, and lower product risks in books, music, video, and software, respectively. Segment 3 perceives higher product risks in flowers, electronics, video, and magazines. In general, all segments perceive product risks in electronics, flowers, and magazines, and lower risks in software, books, and music. Therefore, our fourth hypothesis that product risks would be lower in product categories that are high in search attributes is supported by our data analysis. We used Eq. (9), consumer demographics and estimated values of !s as obtained from Table 5, to determine the probability of each household belonging to each one of the three segments. Each household was then assigned to the segment for which it had the highest membership probability. For each segment, we then determined the percentage of population that purchase in a particular product category. These percentages are shown in Table 6. The results show that segment 3, which perceives the lowest levels of product risks, purchases more than other segments in almost all Table 6 Percentage of population that shops in a product category Product category
Segment 1
Segment 2
Segment 3
High product risk, high security risk
Moderate product risk, low security risk
Low product risk, moderate security risk
Software Hardware Books Electronics Flowers Magazines Music Video
37.31 52.45 47.77 10.58 13.92 16.70 32.96 15.70
31.82 45.45 63.64 9.09 4.55 13.64 45.45 13.64
59.57 76.60 59.57 19.15 27.66 17.02 38.30 19.15
0.0094 (0.4278)
0.1120* (0.0685) 0.3862* (0.2174)
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product categories. The only exceptions are books and music in which segment 2 purchases the most. In none of the eight product categories, segment 1 is the biggest buyer. This is what one would expect, as segment 1 perceives highest levels of product and security risks.
5. Managerial implications The future growth of electronic commerce depends largely on how potential customers view the relatively new cyber-retail medium. In the present research, we have used a modeling approach to examine consumers’ perceptions of the benefits and costs of shopping online. We have attempted to capture consumer heterogeneity in our model by uncovering the existence of possible market segments among e-commerce consumers. We calibrated our analytical model on data that had information on consumers’ online purchase behavior for eight different product categories. Our analysis revealed the existence of three distinct segments that differ in terms of the impact of the two types of risks and benefits on their online shopping propensity. From the parameter estimates, it is clear that the shopping behavior of segment 1 was the most affected by their perceptions of security risk. This is also the segment for which Internet benefits had the greatest effect. The pattern, therefore, seems to be that these two effects are positively correlated. The customer group (e.g., segment 1) that is most adversely affected by the perceived risks of the Internet environment is also likely to most appreciate the benefits of the Internet. The managerial implication of this finding is of the utmost importance. Normally, firms would spend little time and effort targeting those consumers who are greatly influenced by risks since marketing expenditures to modify their perceptions would be relatively high. However, because these segments also perceive high levels of benefits, they also have the highest propensity to shop online. If cyber-firms can educate this group about the high level of Internet security on their sites, then the purchasing probabilities of this segment will be enhanced more than for other segments. Given budget constraints for such customer education, managers would do best to focus on such a market segment. It is important to study which risk is important to a segment as the marketing strategy to mitigate these risks would depend on the type of risk. For a security risksensitive segment, communication messages should focus on the steps the company has taken to ensure customer privacy. Consumers may have the option to set up accounts with the company via phone and be allotted a password. Such steps would guarantee consumer loyalty and would not necessitate consumer use of his/her credit card number online. Since this type of risk is related to education, eretailers need to focus on educating consumers. Internet firms have come a long way in developing different encryption devices, but they have done a poor job of
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informing consumers about these features. Hence, it is important to study and modify consumer perceptions, rather than remain satisfied with the firm’s awareness of its existing level of high technology-based security system. To attract a product risk-sensitive segment, companies should advertise liberal return policies. For these consumers, one can also set up catalog showrooms, like Service Merchandise, where they can go and see the product before placing an order online. The store only needs to maintain a showroom, which eliminates extensive warehouse storage costs. The savings can be used to lower the product prices. Dell Computers has recently adopted this strategy. From being a completely online-based store, Dell has started to open traditional retail outlets where consumers can go and ‘play’ with the various models. Many online merchants routinely collect demographic data from Web users. Our findings indicate that it is possible to use these demographics to assign consumers to different segments that differ in their sensitivities to the benefits and the two types of risks. Once the segment membership is determined, the managers would know the relative importance of benefits and risks to the segment members. Managers can then generate strategies that would be designed specifically for the target segments. Such differential strategies will help enhance the attractiveness of this new retailing medium to current and potential customers.
References Adam N, Awerbuch B, Slonim J, Wegner P, Yesha Y. Globalizing business, education, culture through the Internet. Commun ACM 1997;40(February):115 – 21. Alba J, Lynch J, Weitz B, Janiszewski C, Lutz R, Sawyer A, et al. Interactive home shopping: consumer, retailer, and manufacturer incentives to participate in electronic marketplaces. J Mark 1997;61(July):38 – 53. Assael H. Consumer behavior and marketing action. Boston (MA): PWSKent Publishing; 1992. Bauer. Consumer behavior as risk taking. Proc Amer Mark Assoc 1960;December:389 – 98. Bellenger DN, Korgaonkar P. Profiling the recreational shopper. J Retail 1980;56(Fall):77 – 92. Bettman JR, Park CW. Effects of prior knowledge, exposure and the phase of the choice process on consumer decision processes: a protocol analysis. J Consum Res 1980;7(December):234 – 48. Bhatnagar A, Misra S, Rao HR. On risk, convenience and Internet shopping behavior. Commun ACM 2000;43(November):98 – 106. Bhimani A. Securing the commercial Internet. Commun ACM 1996; 39(June):29 – 31. Chi M, Glaser R, Rees E. Expertise in problem solving. In: Sternberg RJ, editor. Adv Psychol Hum Intell, vol. 1. Hillsdale (NJ): Erlbaum; 1982. p. 7 – 76. Clemente P. The state of the net: the new frontier. New York (NY): McGraw-Hill; 1998. Cox D, Rich SU. Perceived risk and consumer decision making—the case of telephone survey. J Mark Res 1964;1(November):32 – 9. Crosby LA, Taylor JR. Effects of consumer information and education on cognition and choice. J Consum Res 1981;8(June):43 – 56. Cunningham SM. The major dimensions of perceived risk. In: Cox DF, editor. Risk taking and information handling in consumer behavior. Cambridge: Harvard Univ. Press; 1967. p. 84 – 6.
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Nelson PJ. Information and consumer behavior. J Polit Econ 1970;78(2): 311 – 29. Nunnally JC. Psychometric theory. New York: McGraw-Hill; 1978. Oberndorf S. Securing the web. Cat Age 1996;13(December):51. Peterson RA, Albaum G, Ridgway NM. Consumers who buy from direct sales companies. J Retail 1989;65(Summer):273 – 85. Peterson RA, Balasubramanian S, Bronneneberg BJ. Exploring the implications of the internet for consumer marketing. Acad Mark Sci 1997; 25(Fall):329 – 46. Reinbach A. Visa taking internet payments global. Bank Syst + Technol 1996;33(December):45. Reynolds FD. An analysis of catalog buying behavior. J Mark 1974;38(July): 47 – 51. Roberts JH, Urban GL. Modeling multiattribute utility risk and belief dynamics. Manage Sci 1988;34(February):167 – 86. Ross I. Perceived risk and consumer behavior: a critical review. In: Schlinger MJ, editor. Adv Consum Res, vol. 2. Urbana (IL): Association for Consumer Research; 1975. p. 1 – 19. Scheer L. Safety net. Forbes 1999;164(6):24. Sheth JN. An integrative theory of patronage preference and behavior. In: Darden WR, Lusch RF, editors. Patronage behavior and retail management. Orlando (FL): Elsevier; 1983. p. 9 – 28. Spence HE, Engel JF, Blackwell RD. Perceived risk in mail order and retail store buying. J Mark Res 1970;7(August):364 – 9. Taylor JW. The role of risk in consumer behavior. J Mark 1974; 39(April): 54 – 60. Urbany JE, Dickson PR, Wilkie WL. Buyer uncertainty and information search. J Consum Res 1989;16(September):208 – 15. Wilder C. E-Commerce gets real. Informationweek 1996;9(December): 14 – 5.
Segmenting consumers based on the benefits and risks of Internet shopping Amit Bhatnagar*, Sanjoy Ghose 1 School of Business Administration, University of Wisconsin—Milwaukee, P.O. Box 742, Milwaukee, WI 53201, USA Received 17 December 2001; accepted 18 December 2002
Abstract An analytical model is developed to examine the role perceived benefits and risks of online shopping play in forming consumer preferences for online shopping. Two dimensions of perceived risk are considered in the model. Product risks follow from the consumers’ inability to examine products online. Security Risks follow from the consumers’ fear that the open Internet network would allow their personal data to be compromised. We segment the sample based on their sensitivity to the benefits and risks of Internet shopping. The segments are profiled based on their demographics. We hypothesize how the two dimensions of risks will vary across the segment characteristics. The model is calibrated on national survey data collected online. The hypotheses were largely supported by the data. D 2003 Elsevier Inc. All rights reserved. Keywords: Perceived risk; Online retailing; Latent segmentation
1. Introduction Researchers such as Hoffman et al. (1995), Alba et al. (1997), and Peterson et al. (1997) have discussed the several benefits that online shopping confers on the consumers. These benefits all provide ‘‘convenience’’ to a degree that is not quite available in traditional shopping channels. Unfortunately, electronic commerce also magnifies the uncertainties that are involved with any purchase process, leading to higher perceived risks. Two types of risks are particularly relevant. First is the consumers’ inability to physically examine the product. This need to physically examine a product would vary with the product category, and therefore, we call this aspect of risk as product risk. Another source of perceived risk arises due to the manner in which transactions are conducted over the Internet. The data is transmitted over open lines, leading to consumer fears that the data may be compromised. We label this kind of risk security risk. Naturally, this category of risk is present whenever one plans to purchase any product online. * Corresponding author. Tel.: +1-414-229-2520; fax: +1-414-2296957. E-mail addresses: [email protected] (A. Bhatnagar), [email protected] (S. Ghose). 1 Tel.: + 1-414-229-4224. 0148-2963/03/$ – see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0148-2963(03)00067-5
We develop an analytical model in this paper to study the influence of perceived benefits and the two kinds of perceived risks on consumer shopping proclivities. We hypothesize that product risk would decrease with age, Internet experience, and proportion of search attributes, and security risk would decrease with education. The model is empirically calibrated on survey data collected online and the results supported the two hypotheses. The structure of the remainder of paper is as follows. In the next section, we discuss the relevant literature, and then we advance our hypotheses. In Section 3, we present our analytical model. Section 4 describes the data and presents the results of our analysis. Finally, we conclude in Section 5 with managerial implications and directions for future research.
2. Problem conceptualization In this section, we draw from two related research traditions in marketing. The first stream consists of papers that have looked at retail patronage issues in general. This stream is theoretical in nature and comprises conceptual models of store patronage behavior. The second stream deals with nonstore retailing. These papers have been empirical in nature and study the determinants of different forms of non-store
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purchasing behavior. This stream has primarily focused on the role of convenience and perceived risk in shaping consumer preferences for non-store retailing formats. Our purpose here is not to do an exhaustive literature review, but rather to look at some past work to develop insights into the research area that we are examining. According to Sheth (1983), consumers have two types of motives while shopping, functional and nonfunctional. Functional motives are related to time, place, and possession needs. Specific examples include features such as one-stop shopping, cost and availability of needed products, and convenience in parking and shopping. These motives are intrinsic to the outlets. Nonfunctional motives are those that are related to various shopping outlets as a result of their association with certain social or emotional values. It may include store image, novelty-seeking, etc. These motives are extrinsic to store outlets. ‘Convenience shoppers’ approach retail store selection from a time- or money-saving point of view and are essentially influenced by functional motives. Compared to them, ‘recreational shoppers’ are motivated by nonfunctional motives and prefer closed malls and department stores for their usual shopping (Bellenger and Korgaonkar, 1980), as opposed to ‘convenience-economic shoppers,’ who prefer non-store shopping (Korgaonkar, 1981). The rationale is that consumers who are high on nonfunctional needs tend to be ‘conspicuous consumers’ and prefer those outlets where they can be ‘seen’ by their friends and colleagues. It is therefore reasonable to assume that Internet store patrons would be convenience-economic shoppers and be motivated chiefly by functional benefits. Therefore, in our analysis, we focus on only functional benefits. Even though consumers perceive the Internet to have a number of functional benefits, it has some clear disadvantages. Consumers perceive a higher level of risk when shopping on the Internet. This is not surprising since studies have consistently shown that consumers perceive higher risks in non-store shopping formats, such as telephone shopping (Cox and Rich, 1964), mail order (Spence et al., 1970), catalog (Reynolds, 1974), direct sales (Peterson et al., 1989), and catalog showroom (Korgaonkar, 1981). In fact, Cox and Rich (1964) found that perceived risk is the most powerful factor differentiating those women who shop by phone from those who do not. We extend this stream of research on the role of perceived risk in nontraditional retailing by studying the latest development in nontraditional retailing, online stores. The concept of ‘perceived risk’ was introduced in marketing by Bauer (1960), and since then, it has been the focus of several studies in consumer behavior. The construct of perceived risk has been conceptualized in a variety of ways (Dowling and Staelin, 1994; Gemunden, 1985; Ingene and Hughes, 1985; Ross, 1975). The most common concept of perceived risk used by researchers defines risk in terms of the consumer’s perception of uncertainty. The uncertainty can be about the outcome or about the adverse consequences of buying a product (or service). These may produce
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anxiety. The amount of risk perceived by an individual in any situation can also be determined by the individual’s demographic characteristics (Taylor, 1974). Perceived risk is known to be multidimensional (Cunningham, 1967). However, our interest in this study is limited to two dimensions of perceived risk. The first dimension of risk is specific to the Internet and arises due to consumers’ fear of their account being hacked by unscrupulous elements. This risk is fostered by media stories regarding security considerations concerning transactions over the Internet are quite common (Minahan, 1997; Oberndorf, 1996). We call this security risk. For a given consumer, this risk is going to be common across product categories. However, the online industry is devoting considerable amounts of energy to mitigating this type of risk (Reinbach, 1996). For example, several task forces have been working to improve the security (McCarthy, 1997); one approach to resolving it would be improved cryptography programs (Adam et al., 1997; Wilder, 1996; Bhimani, 1996). In addition, federal consumer protection laws limit the liability of credit card holders to US$50 (Scheer, 1999); this limit has been in existence for a number of years. However, we would expect only educated consumers to be aware of these issues or to fully comprehend the efforts being made by the government and the industry to protect consumer privacy. Hence, we expect the impact of security risk to be reduced by education. We therefore hypothesize that as education increases, consumers’ perceived security risks would decrease. We call the other dimension of risk, product risk, as this risk perception arises due to issues like the inability to physically inspect products (Spence et al., 1970; Bhatnagar et al., 2000). We also know that product risk is likely to be greater when there is little information about the product category, the consumer has little self-confidence in evaluating brands, there are variations in quality between brands, or when the price is high (Assael, 1992). This indicates that this type of risk would vary across product categories. For instance, Darian (1987) found that while 80% of consumers would not worry about buying linen on the phone, only 7% would not worry about buying kitchen tables and chairs. However, it is also recognized that as consumers get older, they accumulate experiences and build up knowledge capital (Urbany et al., 1989). With increasing years, due to long exposure to many shopping choices, consumer preferences get fixed and they gain more confidence in shopping for the product categories they seek. This reduces the need for the pre-purchase information search as they can substitute their knowledge capital for any lack of information. This knowledge capital should reduce the uncertainty involved with the purchase and consequently the product risk. We therefore hypothesize that product risk will decline with increasing age. We expect this phenomenon to show up in the online shopping context. Cognitive psychologists have shown that experts differ from novices in the amount, content, and organization of their knowledge (Chi et al., 1982). Product risks decrease as
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consumers gain increased knowledge about the available alternatives, and the amount of knowledge regarding alternatives is known to increase with experience (Bettman and Park, 1980). Mervis and Rosch (1981) examined a number of domains (product categories, such as furniture) and found that the number of dimensions (or evaluative criterion) in a domain increased as individuals acquired more experience. The cognitive skill of consumers is directly related to the number of dimensions employed in judgment (Crosby and Taylor, 1981). Therefore, experience increases not only the knowledge base but also the cognitive skills (Mitchell and Dacin, 1996), leading to higher amounts of search efficiency among more experienced consumers. Our third hypothesis is that product risk will decline with increasing Internet-specific experiences. Nelson’s (1970) classification of all products as search or experience is very relevant for determining the level of product risk in a category. Search products are marked by a relatively higher proportion of search attributes, i.e., those attributes that can be evaluated before purchase. Experience attributes are similarly characterized by a relatively higher proportion of experience attributes, i.e., those attributes that can be evaluated only after purchase. In general, consumers will perceive higher product risks in categories that are high on experience attributes. Our fourth hypothesis, therefore is that product risk will decline with increase in the proportion of search attributes. To summarize, we expect that individuals shopping on the Internet would be motivated by functional motives. Thus, their online shopping preferences should be positively affected by the convenience-type benefits offered by the Internet environment. We also expect that both product and security dimensions of perceived risks will negatively affect online shopping preferences. We also believe that product risk would decrease with an increase in age and security risk would decrease with an increase in education. Finally, and perhaps most importantly, we identify consumer segments that differ along the three dimensions of benefits and the two risks. We show that even though perceived risks are high among all the segments, the relative importance of the two risks vary across segments.
3. The model In the present research, we examine consumer shopping behavior for the latest retail format, the Internet. Specifically, we want to build an analytical model to study how consumer perceptions of benefits and risks of buying online affect consumer choice probabilities of using this new retail medium. Consumers consider several factors when choosing a certain retail format for their product purchases. Some of these factors are convenience, variety of selection, quality of products, return policies, and special services—delivery, credit, perceived risks, etc. Different consumers derive dif-
ferent levels of benefits from these factors. Let the aggregate value of total benefits at a cyberstore be Vij for consumer i buying from product category j. This value Vij translates into utility Uij for the household according to some given mathematical expression, which for the present case we assume to be the commonly used negative exponential form (Horsky and Nelson, 1982; Roberts and Urban, 1988), Uij ¼ a bfexpðri Vij Þg
ð1Þ
where a (a > 0) and b are constants and ri is the coefficient of risk aversion of household i. The above formulation of utility function assumes declining marginal utility—a standard assumption in traditional microeconomics theory. Currim and Sarin (1984) conducted an empirical study of how students evaluate job offers and found that the negative exponential model offered a better fit over linear models for 40 out of 43 students. The above formulation is deterministic in nature and assumes that the consumer has perfect knowledge about the different types of benefits. However, realistically, it is impossible to have perfect knowledge, and the consumer has some uncertainty about the different benefits. The level of this uncertainty is likely to be higher for new retailing formats like cyberstores as compared to the more traditional stores. The consumers therefore make decisions under uncertainty and expect to get different levels of valuation Vij with different probabilities. Due to this uncertainty in the value of the benefits, the consumer tends to maximize the expected utility, rather than the utility. We assume that the valuation of the benefits Vij is normally distributed. It can be shown that, under this assumption, the expected utility of the consumer would be given by ð2Þ EbUij c ¼ a b exp ri E½Vij 0:5ri Var½Vij
where, E[Vij] and Var[Vij] are the expected mean and variance of the value of benefit bundle Vij. Here, ri, the coefficient of risk aversion, measures how sensitive the household i’s expected utility is to the mean and variance of the value of benefit bundle. The higher the level of uncertainty in the consumer’s mind, the greater the variance in the value of the bundle. A higher variance lowers the expected utility that a consumer hopes to derive. Since the above function is monotonic in E[Vij] 0.5riVar[Vij], we can also assume that the consumer will patronize the format with high levels of E[Vij] 0.5riVar[Vij]. We can therefore recast Eq. (2) as E½Uij ¼ E½Vij 0:5ri Var½Vij
ð3Þ
The consumers would perceive a large variance (i.e., Var[Vij]) in the aggregate value of the benefits, if they think that shopping at a certain format, such as in online stores, is risky. We can assume that the higher the perceived risk, the higher the variance in the value of the benefits. Since risk is known to be multidimensional, we look at two different
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aspects of risk, product risk and security risk. The former is represented in our model by PRij and the latter by IRi. Since the security risk is constant across product categories, it does not have a j subscript. We capture Var[Vij] in Eq. (3) by the sum of PRij and IRi. Var½Vij ¼
PRij
þ IRi
ð4Þ
We can incorporate Eq. (4) in Eq. (3) to state E½Uij ¼ i Bi 0:5ri ðPRij þ IRi Þ ¼ aij þ i Bi þ i IRi
ð5Þ
where, Bi is benefits of shopping on the Internet. aij ¼ 0:5ri PRij ;
i Bi ¼ E½Vij ;
i ¼ 0:5ri
ð6Þ
When shopping, a number of other unaccounted for variables can also influence a household’s choice decision. These variables can be represented by a household specific error term, ei. Thus, the true expected utility that the consumer obtains by purchasing over the Internet is given by E½U true ij ¼ E½Uij þ ei ¼ aij þ i Bi þ i IRi þ ei
ð7Þ
The consumer would buy on the Internet if the expected utility is greater than zero. We make the standard assumptions that the random components are independently and identically distributed as an extreme value of Type I and arrive at the standard logit formulation. From this, it follows that the probability of consumer i buying from category j over the Internet is given by Pij ¼ PðE½Uijtrue > 0Þ ¼ PðE½Uij þ ei > 0Þ ¼
1 1þ
eðE½Uij Þ
¼
1 1þ
eððaij þi Bi þi IRi ÞÞ
represented by the vector Di. Following Gupta and Chintagunta (1994), we assume that this probability is given by expðgs þ !s Di Þ Pis ¼ P expðgk þ !k Di Þ
3.1. Consumer segmentation The consumers are heterogeneous in their responsiveness to the benefits and the two aspects of perceived risk. Traditionally, heterogeneity has been accounted for by either assuming the response parameters to be randomly distributed across the population or by assuming that there are latent segments. We follow the second approach by assuming that all the consumers are drawn from s segments. The probability Pis that household i belongs to segment s is determined by the demographic map of the individual,
ð9Þ
s
where gs and !s are segment-specific parameters to be estimated from the model. These parameters determine the segment membership for any household. Given these parameters, the households are assigned to that segment where the probability is the highest. In latent class framework, it is assumed that all consumers within a segment are homogeneous in their response parameters. Segments, however, exhibit heterogeneity in their response parameters. The probability P ijs that a household belonging to segment s makes a purchase from category j on the Internet is given by Pijs ¼
1 1 þ eððasj þs Bi þs IRi ÞÞ
ð10Þ
where asj, s, and s are segment-specific response parameters. Eq. (10) is just Eq. (8), with individual specific parameters replaced by segment-specific parameters. The probability of observing a sequence of buy/no buy decisions on the Internet for a household, across the j product categories, conditional on membership in segment s, is given by Y P si ¼ P sij j
Therefore, for household i, the unconditional likelihood function i, is given by X i ¼ P si Pis s
ð8Þ
In the above model, Bi stands for the benefits of Internet shopping, and IRi captures the impact of security risks. The intercept aij would vary across product categories to capture any product-specific differences. Since product risk would also vary across product categories, the intercept would be a proxy for it. This becomes clear after examining the reparametrizations in Eq. (6).
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and the sample likelihood function is Y ¼ i
ð11Þ
i
Maximizing the sample likelihood in Eq. (11) allows us to estimate segment-specific parameters. The parameters of this model are estimated conditional on a prespecified number of latent segments. We initiate the estimation process by first calibrating a single-segment model, then a two-segment model, a three-segment model, and so forth, until the additional parameters required for an additional segment do not lead to any significant improvement in model fit. The model has the most optimum fit to the data when the Bayesian Information Criterion (BIC) is minimized. The BIC is given by BIC = 2 + kln(N), where is the log likelihood, k is the number of parameters, and N is the sample size. For additional support, we also estimate the Akaike Information Criterion (AIC) and Constrained Akaike Information Criterion (CAIC). AIC is estimated as 2 + 3k, and CAIC is calculated as 2 + k[ln(N) + 1].
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These numbers work like the ‘adjusted R2.’ As the number of parameters increases, the likelihood increases. BIC and CAIC penalize over parameterization, with CAIC being more stringent than BIC.
4. Data and analysis The model was estimated on data collected via online surveys conducted nationally. The survey was posted on the Web and therefore administered to the entire population. The survey was run for 4 weeks. Participants were solicited through announcements on Internet-related newsgroups (e.g., comp.infosystems.www.announce, comp.internet.nethappenings, etc.), and on the www-surveying mailing list, and announcements made in the popular media (e.g., newspapers, trade magazines, etc.). The survey had three different sets of data. In the first set, the respondents were asked whether or not they buy online. The question was repeated for a number of product categories like hardware, software, books, etc., and for each category, the respondent had to answer yes or no. These responses were the choice variables in our model. There were a large number of product categories in the sample. Here, we are interested in studying product risks, and therefore, to get a good spread of product risks, we decided to focus on certain experience products and certain search products. Internet has reclassified many traditional search products as experience products and traditional experience products as search products. On the Internet, traditional search products, such as electronics, flowers, magazines, etc. have become experience goods. Unlike a traditional store, in an Internet store, one cannot determine the picture quality of TV, or the audio quality of CD player. Therefore, electronics, which are high on search attributes in a traditional store, become experience products in an Internet store. Similarly, flowers can be tested for freshness, fragrance, etc. in a traditional store but not in an online store. While one can go to a supermarket and flip through a magazine, one cannot do so in an online store. On the other hand, traditional experience products such as software, music, and books have become search products on the Web. Software is considered a search product on the Internet because it is possible to download trial versions of most of the software, allowing consumers to evaluate products before purchase. Similarly, at most of the online music stores, such as CDnow.com, it is possible to download a few songs of each album and evaluate them before purchase. At online bookstores, such as Amazon.com, one cannot read a book before purchase, but one can read comments by other consumers who have read the book. Thus, buyers can have a better understanding of what to expect in that book, making it a search good. Videos in general would be experience in any retailing environment, but at some Internet stores, one can read reviews of video, which makes it a search product at those stores.
The second set of data asked several demographic questions. The demographic profile of the entire sample is presented in Table 1. The respondent population had a very good age spread There were a fair number of respondents from all age segments. The sample had a fair proportion of high-income people, with the largest segment earning between US$50,000– US$74,999. The sample was evenly divided between married and single/divorced consumers. An overwhelming number of consumers had some college education. Men outnumbered the women respondents. Most of the respondents had been on the Internet for at least a year, with a large majority indicating use between 1 and 3 years. Since the survey was administered online, a precondition for responding to the survey was access to the Internet, and therefore, it is not surprising that sample statistics match those of the overall Internet user profile (Clemente, 1998). Other surveys, including one conducted by ABC News, indicate those intending to buy online are likely to earn over US$75,000 and have a college education (www.nua.ie). This matches our sample demographics. In the third set, eight questions quizzed consumers about the benefits and risks of shopping on the Internet. The respondents had to indicate agreement/disagreement on 5point Likert scales. Since the responses were heavily correlated, we used principal component analysis to reduce this set of variables. Two factors emerged with eigenvalues greater than 1.00, and these two factors accounted for 59.24% of the total variation. The first factor accounted Table 1 Descriptive statistics of the sample Variables
Frequency
Variables
Frequency
Age Under 20 21 – 25 26 – 30 31 – 35 35 – 40 41 – 45 46 – 50
53 117 164 131 118 101 117
Education Grammar school High school Two-year school Some college College graduate Master’s degree Doctoral degree/ Professional degree
11 76 38 292 310 173 67
51 – 55 56 – 60 61 – 65 Over 66
84 49 11 22
Income Under US$10,000 US$10,000 – US$19,999 US$20,000 – US$29,999 US$30,000 – US$39,999 US$40,000 – US$49,999 US$50,000 – US$74,999 US$75,000 – US$99,999 Over US$100,000 Marital Single, divorced, separated Married
36 70 100 120 128 241 116 156
419 548
Gender Male Female
595 372
Internet experience < 6 months 6 – 12 months 1 – 3 years 4 – 6 years 7 years or more
29 64 396 312 166
A. Bhatnagar, S. Ghose / Journal of Business Research 57 (2004) 1352–1360 Table 2 Results of factor analysis Internet benefits (B) Providing credit card information through the web is the single most important reason I don’t buy through the web more often. Providing credit card information through the web is no riskier than providing it over the phone to an offline vendor. It is just as safe to use credit cards when making purchases from Internet vendors. Internet vendors offer more useful information about the choices available. It is easier to place orders with Internet vendors. It is easier to cancel orders placed with Internet vendors. Internet vendors offer better prices. It is easier to contact Internet vendors. Percentage of variance explained
Security risk (IR) 0.845
0.819
0.876
0.728
0.714 0.683 0.578 0.747 38.96
20.28
The numbers indicate the factor loadings.
for 38.96% of the variation, and the second factor accounted for 20.28% of the variation. These two factors were then rotated by means of Varimax with Kaiser normalization. The resulting rotated factor matrix is presented in Table 2. The first factor is Internet benefits; all of the variables with high correlations refer to the different benefits of patronizing Internet-based stores. The second factor is security risk; it is correlated with all the variables that deal with the risks of shopping on the Internet. The reliability of the two constructs was checked by calculating the Cronbach’s alpha. The Cronbach’s alpha for the benefits was .7432 and for security risk was .8211. Nunnally (1978) recommends that if the Cronbach’s alpha is higher than .7, then the constructs are internally consistent. Therefore, our two constructs are internally consistent and reliable measures of benefits and security risk. The factor scores for the first factor served as the independent variable B in our model (Eq. (7)). Factor scores for the second factor were the values for the variable IR in our model. Table 3 shows that as the number of segments increases, the log likelihood increases, but the AIC, BIC, and CAIC first decrease and then increase. The model fit criterion, i.e., AIC, BIC, and CAIC are the least for the three-segment solution. We therefore restrict the rest of the analysis to the
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three-segment solution because it offers the best fit to the data. Table 4 presents the values of the risk and benefit response parameters for the three latent segments. A large significant negative value for aj implies that segment members perceive high levels of product risk in product category j. This decreases their propensity to buy online in that product category. For a given segment, modulations in this parameter across product categories show how the product risk varies for this segment across product categories. Similarly, those segments that have higher significant negative values for are influenced more by security risks than segments with lower values of . Consequently, segments with higher negative values of are less likely to buy online. Likewise, by examining the magnitude of significant values, we can determine the influence Internet benefits have on a segment’s probability of shopping online. The perceived Internet benefits had a significant positive effect on the shopping behavior of all three segments of consumers, the smallest effect was for segment 2, where the impact of security risk was not significant. Interestingly, the segment for which consumer choice probabilities were least affected by perceived Internet benefits also saw no effects of security risks on consumer choice. As Table 4 depicts in general segment 1 experiences more product risk than segment 2, which in turn experiences more such risk than segment 3. Therefore, we can label segment 1 as High product risk – High security risk, segment 2 as Moderate product risk – Low security risk, and segment 3 as Low product risk – Moderate security risk. In other words, Internet benefits are not necessarily correlated with product risks. Table 5 displays the demographic parameter values for the consumer segmentation part of our model (Eq. (6)). These estimates explain the effect of demographic variables on segment membership; they also help in testing our hypotheses. The parameters are estimated assuming the third segment to be the base segment. For a given segment, a positive significant value of response parameter for any variable implies that as that variable increases for a consumer, his probability of belonging to that segment increases as compared to segment 3. The first segment had high product risk and high security risk. This segment has the lowest income, lowest mean age, and least Internet experience. Thus, it is not surprising that both the risks are the highest in this segment. The second segment perceives no security risk, but for most of the categories faces a higher product risk than segment 3 and a
Table 3 Segments selected based on AIC, BIC, and CAIC Segments
Likelihood
Number of parameters
Number of respondents
AIC
BIC
CAIC
2 3 4
3756.04 3686.69 3670.62
27 44 61
967 967 967
7593.072 7505.386 7524.250
7697.676 7675.851 7760.576
7724.676 7719.851 7821.576
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Table 4 Response parameters for the three segments Parameter
Product risk (aj) Software Hardware Books Electronics Flowers Magazines Music Video Internet benefit () Security risks () Fractional segment size
Segment 1
Segment 2
Segment 3
High product risk, high security risk
Moderate product risk, low security risk
Low product risk, moderate security risk
0.3341* (0.1883) 0.9712** (0.2468) 0.8961** (0.2491) 3.7022** (0.4368) 3.0426** (0.4988) 2.9230** (0.3655) 1.8081** (0.2099) 3.9228** (0.5437) 0.4376** (0.0750) 0.8340** (0.1193) 0.60
0.8606 (0.5472) 13.2115** (0.5690) 0.9193 (0.6485) 3.5222** (1.2429) 1.3011** (0.3589) 1.2722** (0.5850) 0.2819 (0.9123) 0.6688 (0.7514) 0.2398** (0.1247) 0.0374 (0.3938) 0.15
2.4890* * (0.3915) 1.3327** (0.4516) 1.1028** (0.3251) 0.7928** (0.2047) 1.0127** (0.2211) 0.6715** (0.2524) 0.2620 (0.2764) 0.6816** (0.2558) 0.4022** (0.1086) 0.4515** (0.1240) 0.25
The numbers in brackets are the standard errors. * Significant at .1 level. ** Significant at .05 level.
lower product risk than segment 1. This segment consists of higher number of women, with less Internet experience than segment 3, but more Internet experience than segment 1. Segment 2 is also more educated and younger than segment 3, but less educated and older than segment 1. As one moves from segment 1 to segment 2 and then to segment 3, one observes three clear trends. In general, as product risk declines, the average age and Internet experience of the segment members increase. Therefore, we can conclude that product risk declines with age and Internet experience, which supports our second and third hypotheses. Segment 2 is the most educated and least affected by security risks in their online shopping behavior. While segment 3 is impacted less by security risks than segment 1, there is no difference in their educational level. Therefore, our first hypothesis that security risks would decline with increases in education is only partially supported. Table 5 Parameter estimates and effects of demographic variables on segment membershipa Variable
Intercept Education Income Gender (1 = female, 2 = male) Married (0 = divorced, separated, single, widowed; 1 = married, living with another) Age Number of years on the Internet a
Segment 1
Segment 2
High product risk, high security risk
Moderate product risk, low security risk
4.8780** (1.1762) 0.0305 (0.1127) 0.1485** (0.0655) 0.3789 (0.3828)
4.4246** (1.4455) 0.3139* (0.1848) 0.1598 (0.1051) 2.0813* (0.5401)
0.3214 (0.2904)
0.1190** (0.0480) 0.4072** (0.1568)
Segment 3 is treated as the ‘base’ segment. * Significant at .1 level. ** Significant at .05 level.
Segment 1 perceives higher product risks in categories video, electronics, flowers, and magazines, respectively, and lower product risks in software, books, hardware, and music, respectively. Segment 2 perceives higher product risks in hardware, electronics, flowers, and magazines, and lower product risks in books, music, video, and software, respectively. Segment 3 perceives higher product risks in flowers, electronics, video, and magazines. In general, all segments perceive product risks in electronics, flowers, and magazines, and lower risks in software, books, and music. Therefore, our fourth hypothesis that product risks would be lower in product categories that are high in search attributes is supported by our data analysis. We used Eq. (9), consumer demographics and estimated values of !s as obtained from Table 5, to determine the probability of each household belonging to each one of the three segments. Each household was then assigned to the segment for which it had the highest membership probability. For each segment, we then determined the percentage of population that purchase in a particular product category. These percentages are shown in Table 6. The results show that segment 3, which perceives the lowest levels of product risks, purchases more than other segments in almost all Table 6 Percentage of population that shops in a product category Product category
Segment 1
Segment 2
Segment 3
High product risk, high security risk
Moderate product risk, low security risk
Low product risk, moderate security risk
Software Hardware Books Electronics Flowers Magazines Music Video
37.31 52.45 47.77 10.58 13.92 16.70 32.96 15.70
31.82 45.45 63.64 9.09 4.55 13.64 45.45 13.64
59.57 76.60 59.57 19.15 27.66 17.02 38.30 19.15
0.0094 (0.4278)
0.1120* (0.0685) 0.3862* (0.2174)
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product categories. The only exceptions are books and music in which segment 2 purchases the most. In none of the eight product categories, segment 1 is the biggest buyer. This is what one would expect, as segment 1 perceives highest levels of product and security risks.
5. Managerial implications The future growth of electronic commerce depends largely on how potential customers view the relatively new cyber-retail medium. In the present research, we have used a modeling approach to examine consumers’ perceptions of the benefits and costs of shopping online. We have attempted to capture consumer heterogeneity in our model by uncovering the existence of possible market segments among e-commerce consumers. We calibrated our analytical model on data that had information on consumers’ online purchase behavior for eight different product categories. Our analysis revealed the existence of three distinct segments that differ in terms of the impact of the two types of risks and benefits on their online shopping propensity. From the parameter estimates, it is clear that the shopping behavior of segment 1 was the most affected by their perceptions of security risk. This is also the segment for which Internet benefits had the greatest effect. The pattern, therefore, seems to be that these two effects are positively correlated. The customer group (e.g., segment 1) that is most adversely affected by the perceived risks of the Internet environment is also likely to most appreciate the benefits of the Internet. The managerial implication of this finding is of the utmost importance. Normally, firms would spend little time and effort targeting those consumers who are greatly influenced by risks since marketing expenditures to modify their perceptions would be relatively high. However, because these segments also perceive high levels of benefits, they also have the highest propensity to shop online. If cyber-firms can educate this group about the high level of Internet security on their sites, then the purchasing probabilities of this segment will be enhanced more than for other segments. Given budget constraints for such customer education, managers would do best to focus on such a market segment. It is important to study which risk is important to a segment as the marketing strategy to mitigate these risks would depend on the type of risk. For a security risksensitive segment, communication messages should focus on the steps the company has taken to ensure customer privacy. Consumers may have the option to set up accounts with the company via phone and be allotted a password. Such steps would guarantee consumer loyalty and would not necessitate consumer use of his/her credit card number online. Since this type of risk is related to education, eretailers need to focus on educating consumers. Internet firms have come a long way in developing different encryption devices, but they have done a poor job of
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informing consumers about these features. Hence, it is important to study and modify consumer perceptions, rather than remain satisfied with the firm’s awareness of its existing level of high technology-based security system. To attract a product risk-sensitive segment, companies should advertise liberal return policies. For these consumers, one can also set up catalog showrooms, like Service Merchandise, where they can go and see the product before placing an order online. The store only needs to maintain a showroom, which eliminates extensive warehouse storage costs. The savings can be used to lower the product prices. Dell Computers has recently adopted this strategy. From being a completely online-based store, Dell has started to open traditional retail outlets where consumers can go and ‘play’ with the various models. Many online merchants routinely collect demographic data from Web users. Our findings indicate that it is possible to use these demographics to assign consumers to different segments that differ in their sensitivities to the benefits and the two types of risks. Once the segment membership is determined, the managers would know the relative importance of benefits and risks to the segment members. Managers can then generate strategies that would be designed specifically for the target segments. Such differential strategies will help enhance the attractiveness of this new retailing medium to current and potential customers.
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