The Influence of Product Knowledge on Purchase Venue Choice ...

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The Influence of Product Knowledge on Purchase Venue Choice: Does Knowing More Lead from Bricks to Clicks? Natalia Kolyesnikova, Ph.D.* Assistant Professor, Nutrition, Hospitality, & Retailing, Texas Tech University Associate Director, Texas Wine Marketing Research Institute [email protected]

Debra A. Laverie, Ph.D. Professor, Area of Marketing, Texas Tech University [email protected]

Dale F. Duhan, Ph.D. Professor, Area of Marketing, Texas Tech University [email protected]

This study examines consumer choice of purchase venues. Specifically, the study explores consumer characteristics (product knowledge, product involvement, and age) as they relate to the choice of purchase venue: physical venues (restaurants, bars, grocery stores, or liquor stores) versus virtual venues (mail order or Internet). The results indicate that subjective product knowledge is positively related to the use of virtual purchase venues. Conversely, objective knowledge is positively related to the use of physical purchase venues and negatively related to the use of virtual venues. Product involvement and age are positively related to both subjective and objective knowledge. In addition, both product involvement and age have indirect and positive relationships with purchase-venue choices.

James B. Wilcox, Ph.D. Professor, Area of Marketing, Texas Tech University [email protected]

Tim H. Dodd, Ph.D. Professor, Nutrition, Hospitality, & Retailing, Texas Tech University Director, Texas Wine Marketing Research Institute [email protected]

*Corresponding Author: Natalia Kolyesnikova

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Introduction Markets around the world are offering consumers wider and deeper selections of wine, as well as a greater variety of places and ways to purchase them (i.e., purchase venues). In recent years, this flood of additional choices and the accompanying potential for consumer information overload has been an important area of study in the wine marketing literature (Mora, 2006; Murray & Demick, 2006; Seghieri et al., 2007). The greater variety of purchase venues is reflected in both the number and type of outlets available to consumers. This growth is a result of two broad influences. First, there are more wine consumers who are “pulling” more wine products through existing supply chains. According to the Wine Institute and the Department of Commerce, retail sales of wine in the United States have almost tripled since 1991 (Kesmodel, 2008). As a result, many

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retail stores and restaurants that previously carried limited selections of wine are now offering wider selections to serve these customers (Lynch & Ariely, 2000; Mora, 2006). Second, many local jurisdictions have changed regulations and are allowing sales through supply chains that were previously barred from selling wine, such as restaurants, grocery stores, and mail order or Internet outlets. An example of such a change is the 2005 ruling by the U.S. Supreme Court allowing direct-toconsumer shipments of wine by producers. Previously, only 17 states allowed such shipments, today 34 U.S. states let consumers order directly from out-of-state wineries (O'Connell, 2006). Online and mail order sales of wine are on the rise. In 2005, Internet wine sales in the U.S. market were $340 million (Mark, 2005). In 2007, online wine sales went up 3% compared to the previous year (VinterActive, 2009). Many wineries

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nowadays are taking advantage of the Internet to provide information and to serve low-volume consumer orders. Gebauer and Ginsburg (2001) offered two explanations for this. First, the circumvention of the intermediary parts of the supply chain promises higher margins to the sellers. Second, the Internet is well suited for information dissemination and for the improvement of consumer knowledge, thus lowering the uncertainty associated with the products.

recognized the importance of product knowledge in decision making (Brucks, 1985; Coulter et al., 2005; Zaichkowsky, 1985). However, much less is known about the relationships of these factors to their purchase-venue choices. Additionally, consumer knowledge about the complex product category influences the selection of purchase venue for wine. Knowledge likely relates to shopping in physical or virtual venues.

Purchase Venues

services. In recent years, terms such as “e-tailing” or “bricks and clicks” have been coined to describe the distinction between the experiences of shopping by visiting the retail premises and shopping through catalogs or web sites. This distinction between physical and virtual purchasing venues is as old as the existence of catalog retailers. However, the growing versatility of the Internet has allowed the virtual shopping experience to more closely approximate the physical experience through interactive features such as search engines, online comparison and review services, virtual tours, full motion, and sound videos of products. Indeed, many retailers are retraining their sales personnel and reorienting their physical stores in light of the fact that many consumers are now researching product features and prices on the web before they physically visit the retail store.

A purchase venue is a physical or virtual place where customers may choose to buy their goods and

Another reason for the growth in virtual shopping is the convenience that it offers. Greater convenience

The Proposed Model Consumers choose different retail venues for a host of reasons related to personal preferences and to specific purchase situations (Pan & Zinkhan, 2006). The study reported here examines how wine consumer knowledge and other characteristics influence the choice of different purchase venues. Specifically, the study focused on wine consumers' objective and subjective product knowledge, their age, and their involvement with wine products. The consumer information-processing and decision-making literature has long

Figure 1 presents a proposed model of the relationships among consumer characteristics, consumer knowledge, and purchase-venue choices. The definitions of each construct and its relationships within the model are presented and discussed following.

Figure 1

The Hypothesized Model of Consumer Selection of Wine Purchase Venues

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may result in more frequent shopping (e.g., checking web sites daily for new options) and higher levels of purchase revenue. Therefore, it is not surprising that in 2007 online retail sales in the U.S. market have increased more than 5 times as a percent of total retail sales compared to 2000 (U.S. Census Bureau, 2007). However, virtual buying through catalogs and web sites requires the postponement of consumption. As a result, in some purchase situations virtual venues are not as expedient as physical venues. A number of other influences on the choice of purchase venues have been proposed. Many of these influences have great relevance for the retailing of wine. For instance, Chiang and Dholakia (2003) and Lynch et al. (2001) found that “hightouch” or experience products (those requiring feel, smell, taste, or trial) are more likely to be purchased in “brick & mortar” (physical) venues than through virtual venues. Wines, like food, are quintessential experience goods requiring smell, taste, trial, and so on for judgment and the development of preferences. This aspect of wine is important to consider because in most jurisdictions alcohol regulations restrict exactly the kind of shopping activity that is needed for wine (smell, taste, trial, etc.). With the exception of sampling opportunities at stores that sell solely alcohol, experiential shopping is generally allowed only in restaurants or bars where the sales are typically limited to purchases for immediate consumption at the venue. This problem is compounded by the fact that in these venues selection is typically narrow and prices are generally significantly higher than in other retail venues. It is no surprise that in the U.S. market wine sales for immediate consumption account for only 22.2 percent by case volume (Adams Beverage Group, 2006). Clearly, most wines are sold under circumstances that are incongruous with the nature of the product category.

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Even if wine were not an experience product, the product category is extremely heterogeneous (large numbers of brands, vintages, varieties, origins, etc.). Therefore, consumers are faced with an overwhelming array of information about wine products. For instance, there are over 10,000 different brands of wine available in the U.S. market alone and many of these brands have many vintages and varieties (O'Connell, 2006). This combination of an overwhelming information-processing task and a very limited ability to experience wine products as part of the shopping process often shapes consumers' choices of venue. Some consumers may not consider wine important enough to justify the time and effort to learn much about wines or even shop for them. Those consumers frequently opt out of the product category entirely-in other words, choose other beverages. The nature of the supply chain simply puts them off. Alternatively, other wine shoppers may rely heavily on extrinsic indicators (brands, varieties, price levels, etc.) and/or outside sources of recommendations (salespeople, friends, wine reviews, etc.) as surrogates for experiential shopping. It is only a small portion of wine consumers who have the interest, the knowledge, or the opportunity to shop for wines on the basis of intrinsic product characteristics (smell, taste, trial, etc.). In these circumstances, the physical venues may be in a position to add considerable value to consumers' purchase experiences. For instance, many wine consumers may attach more importance to customer service features, such as helpful and knowledgeable salespeople, ease of returning the product, or even the option of tasting the product if they are purchasing for immediate consumption. They may also seek hedonic shopping experiences that create an attractive atmosphere around the purchase of wine that enhances the perceived value of the products and the experience of purchasing them. Wine tourism serves the demand for such experiences.

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As a result of these aspects of products, consumers, and the decision task, one would expect the greatest portion of wine shoppers to use physical purchase venues. Additionally, one would expect that consumers' actual or perceived knowledge about this complex product category would influence their choice of purchase venue for wine. The following section reviews the types of customer knowledge and how that knowledge may relate to shopping in physical or virtual venues. Consumer Knowledge Many research studies have found that product knowledge plays an important role in consumer decision making (Bettman & Park, 1980; Brucks, 1985; Rao & Monroe, 1988; Sujan, 1985). The concept of consumer knowledge is defined as the extent of experiences and familiarity that one has with a product. It is information that is available for making decisions without external searching (Alba, 1983; Brucks, 1985; Rao & Monroe, 1988; Sujan, 1985). Product knowledge is delineated into subjective and objective knowledge. These two types of knowledge are interrelated but nonetheless distinct components of product knowledge (Raju et al., 1995). Objective knowledge is typically defined as the content and organization of knowledge that consumers hold in their memory. Objective knowledge includes knowledge about products related to terminology, product attributes, uses for the products, and more (Brucks, 1985). The greater the level and accuracy of knowledge held in a person's memory, the greater the level of objective knowledge. Objective knowledge is also viewed as a surrogate for the ability to process product information. We expect that wine consumers with greater levels of objective knowledge are better able to process the wealth and complexity of wine-related information. We propose that the relationship between objective knowledge and both physical and

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virtual venues will be positive. However, we expect that higher levels of objective knowledge are more likely to lead to the use of physical venues than virtual venues. Hypothesis 1: Objective knowledge will be significantly and positively related to virtual purchase venues. Hypothesis 2: Objective knowledge will be significantly and positively related to physical purchase venues. Subjective knowledge is consumers' own assessment of their product knowledge level. Subjective knowledge can be “objective” in the sense that consumers may know enough about the product domain to accurately assess their own knowledge levels. Subjective knowledge can influence decision makers' perception of their ability to process product information (regardless of their actual ability) and their inclination to use various purchase venues. Additionally, subjective knowledge may imply the level of knowledge that the consumer feels is needed for making purchase decisions in that product category. For instance, some consumers may feel that little information is needed for making good decisions in a product category, perhaps relying on extrinsic characteristics such as brand names and prices. In that case, their subjective assessment of their own knowledge level may be high (e.g., “I know all that I need to know to make good decisions.”), even though their objective knowledge level may be low. Indeed, a great deal of marketing effort is devoted to creating exactly that situation through attempts to convince consumers that knowing a particular brand is all that is necessary for making good purchase decisions. Conversely, consumers with moderate levels of objective knowledge may better understand how much they do not know about the product category and therefore they may assess their own knowledge level as low. For these reasons, subjective knowledge is often viewed as essentially a person's self-

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confidence regarding decision making in a particular product category (Brucks, 1985). Consumers with high levels of subjective knowledge are less likely to feel the need for outside personal sources of information (e.g., the advice of sales personnel) and thus may be more comfortable choosing virtual purchase venues. Therefore, we suggest that Hypothesis 3: Subjective knowledge will be significantly and positively related to virtual purchase venues. Similarly, consumers with lower subjective knowledge may be more likely to buy at venues where they can draw on the knowledge or recommendations of salespeople. Hypothesis 4: Subjective knowledge will be significantly and positively related to physical purchase venues. However, customers with either high or low subjective knowledge can often easily access ratings and evaluations of wines (e.g., Wine Spectator, Parker ratings, customers' comments, etc.) through the web or other sources before they choose a purchasing venue. Indeed, use of such impersonal resources has been found to be related to the level of subjective knowledge (Dodd et al., 2005). Also, investing the time and effort to seek knowledge before choosing a shopping venue implies that a consumer has a greater level of involvement in the product category. Consumer Characteristics: Product Involvement Product involvement is defined as the personal relevance of a product category to a consumer (Higie & Feick, 1989). Enduring involvement with a product category is influenced by how the product relates to the consumer's life (Bloch & Richins, 1983). When the product category is important to consumers, there are strong links between the product and the consumers (Celsi et al., 1993). Definitions of product involvement generally include the concepts of

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product importance, the risks included in the product selection decision, and also the symbolic and pleasure value of the product (Laurent & Kapferer, 1985). This implies a dimension of product involvement that has been termed “think/feel” (Vaughn, 1980) or “cognitive/affective” (Zaichkowsky, 1985). Park and Moon (2003) investigated the relationship between involvement and consumer product knowledge and found a “utilitarian/hedonic” aspect of the relationship that was significant in understanding the influence of involvement on consumer knowledge. Their definition of involvement includes product importance, risk importance, a product's symbolic value, and a product's pleasure value. Product involvement considerably influences consumers' cognitive and behavioral responses in the marketplace (Coulter et al., 2005). Studies concerning wine consumption situations have explored a number of relationships among product involvement, innovators, variety seeking, demographic characteristics, experience, and sources of information used by consumers (Dodd, 1998; Kolyesnikova et al., 2007; Lockshin et al., 2001; Quester & Smart, 1998). For instance, the number of information sources used by wine tourists varies based on the level of product involvement, the number of previous winery visits, and attitude (Dodd, 1998). When consumers with a low level of product involvement initially consider purchasing in a product category, they tend to rely on extrinsic factors such as advertisements, price, and recommendations from others, including salespeople in the shopping venue (Celsi et al., 1993). Because over time consumers become more involved with the product, they are more likely to rely on intrinsic cues to make decisions (Beatty & Smith, 1987). Long-term involvement suggests that the product is important in the life of the consumer (Bloch & Richins, 1983). When consumers have enduring involvement, the product

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is related to their sense of self (Celsi et al., 1993). We propose that there is a positive relationship between consumer involvement in wine products and their levels of knowledge about the product category. Hypothesis 5: Product involvement will be significantly and positively related to subjective knowledge. Hypothesis 6: Product involvement will be significantly and positively related to objective knowledge. Consumer Characteristics: Age The idea that age affects people's attitudes and behaviors has been suggested for some time (Beatty & Smith, 1987; Klippel & Sweeny, 1974). Studies have found that as people age, they show greater reluctance to adopt new technologies (Akhter, 2003; Gilly & Zeithaml, 1985; Phillips & Sternthal, 1977), they become more cautious, and they seek greater certainty in their decisions (Botwinick, 1984; Vuori & Holmlund-Rytkonen, 2005). Older consumers exhibit more negative perceptions towards new technologies (Pommer et al., 1980), are less likely to use credit cards (Porter et al., 1979), or automated teller machines (Bednar et al., 1995), and they are less likely to purchase on the Internet (Akhter, 2003). Explanations for these findings include empirical evidence suggesting that the information search behavior of older consumers (55 years of age and older) is different from that of younger consumers (Phillips & Sternthal, 1977; Vuori & HolmlundRytkonen, 2005). Because wine markets are highly fragmented and information search is more complex for consumers than in many product categories, age differences in search behavior are relevant to this research. Agerelated factors such as previous experience with the product category, familiarity with the brands, and information-processing abilities (both in terms of the quantity of information processed and the speed with which it is processed) may influence venue choices.

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When given a novel decision task, younger consumers were found to search for information more intensely than older consumers (Cole & Balasubramanian, 1993). However, older consumers have been found to use their experience to process larger chunks of information (Phillips & Sternthal, 1977). For instance, in a survey of automobile buyers Furse et al. (1984) found that older consumers searched less and were more satisfied with their choices than other age groups. The implied relative lack of product experience of younger consumers may play a role not only in the level of product knowledge but also in the use of technology as an information source, and by extension, the use of different purchase venues. These age differences may have emerged because older consumers are more experienced with the product category than younger consumers. This may also be the case for purchase-venue decisions regarding wine that is, older consumers are likely to have more experience with the product category and therefore feel less need for external information search. Previous research on the use of technology indicates that younger consumers will be more likely to use virtual venues and older consumers will be more likely to use traditional retail outlets. A recent study of U.S. millennial generation's alcoholic beverage attitudes and usage by A. C. Nielsen (2007) reported that most millennials consider themselves to be only slightly knowledgeable about wine. Although specific years vary slightly by source, Strauss and Howe (1992) originally defined millennials as those born in 1982 and approximately the 20 years thereafter. There are approximately 76 million millennials in the United States (Thach & Olsen, 2006). We expect that the influence of age is mediated through product knowledge. Hypothesis 7: Consumer age will be significantly and positively related to subjective knowledge.

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Hypothesis 8: Consumer age will be significantly and positively related to objective knowledge.

Research Method Context The wine market is an appropriate context for this research for two reasons. First, recent changes in both technology and regulations have opened new supply chain opportunities for many wineries and wine sellers. Clearly, the issue of how consumers respond to these new options is of interest to supply chain managers. Second, the idiosyncrasies of most wine supply chains and markets are likely to provide a particularly robust test of the influence of subjective and objective knowledge on how consumers will interact with these new supply chain options. Sample and Data Collection A telephone survey of randomly selected U.S. households was conducted during the summer months 2006 from a telephone bank housed at a large public university in the southwestern United States. Trained interviewers screened individual respondents through a series of questions. Qualified respondents were those who (1) were 21 years of age and older, (2) consumed wine, and (3) had consumed wine within the past 12 months. The 12-month time frame helped screen for respondents' involvement with wine and their frequency of usage. It also included individuals who might be casual drinkers who consume wine during holiday seasons only. The interviewers noted the disposition of all calls, including completed interviews, no answer, answering machines, refusals, language barrier, disconnect, fax numbers, and business numbers. Numbers that resulted in answering machines and no answers were called a maximum of five times. When potential respondents were reached, the callers introduced themselves by giving their name and the name of the university affiliation, providing a statement

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concerning the academic purpose of the research, and a promise that no attempt to sell any goods or services would occur. If the respondents passed the screening questions and agreed to participate, the callers proceeded to a structured script of the interview. On average, the interview lasted for about 10 minutes. A computer-assisted, data entry system prompted callers through the questionnaire. Responses were entered directly into a computer data file. Overall, the interviewers made telephone calls to 14,821 randomly selected households, reaching 5,650 (38%) respondents. This reflects declining response rates resulting from increased use of answering machines, caller identification systems, and the increased magnitude of telephone solicitation (Massey et al., 1997). Of the 5,650 respondents, 923 (16%) were qualified. A report by MKF Research (2000) indicates that 36% of the U.S. population are nondrinkers, 37% are beer and spirits drinkers, 11% are core wine drinkers (consume wine at least once a week), and 16% are marginal drinkers (consume wine occasionally, but not on a weekly basis). Thus, a qualification rate of 16% is similar to the MKF findings. Of the 923 qualified participants, 54% agreed to participate. This good cooperation rate resulted in a final sample of 502 competed interviews.

at restaurants, bars, and grocery stores were obtained by three separate questions. The scores on these questions loaded on a single component when subjected to principal components analysis. Thus, the sum of the three expenditure scores was divided by the reported income category, again to produce a value that was relative to respondents' income. Product knowledge was defined in two forms, objective knowledge and subjective knowledge. Objective knowledge was measured with four questions involving factual information about wine. Each answer was recorded as being correct (1) or incorrect (0) and summed to give a score for objective knowledge that ranged from 0 to 4. The number of objective test questions answered correctly has been used in previous research as one available index of objective knowledge (Raju et al., 1995). The four items used in this current study were adapted from previous studies on wine consumers (Barber, 2008; Dodd et al., 2005). The items for the objective knowledge measurement are listed in Table I.

Subjective knowledge was measured as a reflective construct with three indicators. The scale developed by Park et al. (1994) was utilized. Respondents' self-reported assessment of product knowledge was measured on three items: “How much do you feel you know about wine?”; “Compared to your friends and acquaintances, how much do you feel you know about wine?” and “Compared to a wine expert, how much do you feel you know about wine?” Each item was administered on a 7-point Likert scale anchored between very little (1) and very much (7). For data analysis, scores on the three items were averaged. Consumer characteristics included product involvement and age. Product involvement was operationalized via the Personal Involvement Inventory (PII) scale originally developed by Zaichkowsky (1985). The modified version of PII (Mittal, 1995) was used in this study. The indicators of product involvement were “unimportant/important, means nothing to me/means a lot to me, insignificant/significant, does not matter to me/matters to me,” each assessed by a 7-point bipolar scale.

Table 1

Items for Measure of Objective Knowledge

Measures In this research purchase venues were delineated into (1) physical purchase venues (i.e., restaurants, bars, and grocery stores where buying is done in person) and (2) virtual purchase venues (i.e., mailorder and web site orders). The measures used for each type of venue were operationalized differently. The question regarding expenditures on the web and by mail-order was asked as a single question and the response was divided by the reported income category in order to make expenditure relative to the respondent's income. Expenditures

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Ratings on the four items were averaged to generate the overall mean score for the product involvement measure. A higher number indicated higher product involvement. Consumer age was operationalized as a single item measure-number of years since the respondent was born.

Results Sample Characteristics Of the 502 survey respondents, 33.7% were men and 66.3% were women. This result is consistent with other studies that have reported higher percentages of female wine consumers (Barber et al., 2006; Lesch et al., 1991). The mean respondent age was 52.18 years (SD = 15.03), the modal age was 51 years and the median 52. The age levels ranged from 21 to 87 years old. Respondents had considerably higher levels of education than the general U.S. population. Over 60% of the sample had earned either an undergraduate (34.5%) or graduate degree (27.1%). To compare, the U.S. Census Bureau (2000) reported that less than one-quarter (24.4%) of the U.S. population have a bachelor's degree or more. Only 9% of the total U.S. population has advanced graduate degrees. Participants' income levels were also substantially higher than the general U.S. population, with only

12.4% of the sample earning less than $40,000 as a total annual household income. Almost onethird of the respondents reported that their annual household income exceeded $100,000. For comparison, the median income of the U.S. population in 2005 was $46,326 (U.S. Census Bureau, 2006). Other studies reported similar demographic characteristics of wine consumers (Chaney, 2001; Dodd & Gustafson, 1997). Overall, the sociodemographic background of the sample in this research study mirrored the profile of wine consumers in general (MKF Research, 2000). According to the MKF report on U.S. wine consumer demographics (2000), wine consumers are more likely to be middle-aged (72% of all wine drinkers are older than 40), educated (63% of core wine drinkers have college degrees), and with higher incomes (households with incomes above $75,000 accounted for 28% of all wine sales).

reflective multi-item measures (subjective knowledge and involvement). In the case of summated or single item measuresage, objective knowledge, and the two relative expenditures on wine at shopping venues-no measurement error was allowed. To examine discriminant validity of subjective and objective knowledge, the chi-square difference test was used (Bagozzi & Yi, 1988). The two constructs within the measurement model were first constrained to correlate at 1.0 and then freed to allow the correlation between the constructs to be estimated. The difference in chi-square values between the two models (with one degree of freedom) was 67.8, indicating discriminant validity. The measurement model fit reasonably well, with χ2 = 59.4, df = 32, RMSEA = .052, RMR = .03, GFI = .96, NNFI = .97, and CFI = .99. Table II details the results of the measurement model: standardized loadings and composite reliabilities for the multiitem measures.

Analysis Hypothesized Model Measurement The data analysis followed the twostep approach recommended by Anderson and Gerbing (1988). First, the measurement model was tested. Measurement assessment was conducted using the procedures recommended by Fornell and Larcker (1981) for the

We tested the hypothesized model in Figure 1 with structural equation modeling (LISREL 8.54). Overall, the model fit quite well with χ2 = 74.1, df = 38; RMSEA = .052; RMR = .052; GFI = .95; NNFI = .97; and CFI = .98. The results indicate support (p