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Product category determinants of price knowledge for durable consumer goods
Journal of Retailing 80 (2004) 129–137
Product category determinants of price knowledge for durable consumer goods Hooman Estelami a,∗ , Peter De Maeyer b a
Graduate School of Business, Fordham University, 113 West 60th Street, New York, NY 10023, USA b Singapore Management University 469 Bukid Timah Road, Singapore 259756
Abstract Existing research in pricing has not extensively examined the impact of the product category on consumers’ knowledge of prices, especially for durable goods. In two empirical studies, this paper examines the influence of the product category on consumers’ knowledge of prices for durables. The first study utilizes data from the popular television game show The Price is Right to establish significant cross-category variations in price knowledge, while the second study links these variations to the specific characteristics of each product category. The results extend existing research findings by isolating the impact of product category determinants, such as purchase frequency, advertising exposure, and use of the price–quality cue, on consumers’ knowledge of prices. © 2004 by New York University. Published by Elsevier. All rights reserved. Keywords: Pricing; Consumer behavior; Marketing communications
Introduction Price knowledge is considered to be a fundamental requirement in rational consumer decision making (Bettman, 1979; Monroe, 2003; Nagle & Holden, 2002). In order to make an educated purchase decision, a consumer needs to have adequate information about product prices. However, a growing volume of literature suggests that consumer knowledge and information processing of prices is less than perfect. Research evidence indicates that consumers often utilize simplifying heuristics and cues in evaluating prices and offer value (e.g., Inman, Peter, & Raghubir, 1997; Raghubir, 1998; Urbany, Bearden, Kaicker, & Borrero, 1997), and that consumer price knowledge is disturbingly poor (e.g., Dickson & Sawyer, 1990; Estelami, Lehmann, & Holden, 2002; Krishna, Currim, & Shoemaker, 1991). This research stream indicates that a large proportion of consumers do not know prices for items they regularly purchase, and that their price estimates are often far apart from the products’ actual prices (Estelami & Lehmann, 2001; Monroe & Lee, 1999). While research findings agree on the poor level of consumer knowledge of prices, existing research has largely ignored the impact of the product category on price knowledge.
∗
Corresponding author. Tel.: +1 212 636 6296. E-mail address: [email protected] (H. Estelami).
It is neither known how much price knowledge systematically varies across product categories, nor what factors might feed these variations. This inability to gauge cross-category price knowledge variations has been attributed to several factors. One contributing factor has been the diverse and often incompatible research and data collection methodologies utilized by the various researchers, as noted by the review done by Monroe and Lee (1999). A second factor has been the narrow range of product categories examined by each researcher, and an overwhelming focus on grocery products as the basis of empirical investigation (Conover, 1986; McGoldrick & Marks, 1987). As a result of research design choices made in previous studies, the mass of research findings collected through the years has been primarily based on studies of frequently purchased nondurable consumer goods (Estelami & Lehmann, 2001; Monroe & Lee, 1999). Little is known about consumer price knowledge of durable goods, placing our accumulated understanding of consumer price knowledge in question. Nevertheless, understanding variations in consumer price knowledge across durable product categories is important not only to academics interested in identifying their sources, but also to practitioners whose pricing strategies may depend on the extent of consumer price knowledge (or lack thereof) within the categories they manage. The objective of this paper is therefore to examine the level of consumer price knowledge for durable goods, and
0022-4359/$ – see front matter © 2004 by New York University. Published by Elsevier. All rights reserved. doi:10.1016/j.jretai.2004.04.003
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to identify its determinants. We will first examine the potential role of three product category characteristics on consumer price knowledge, namely: category purchase frequency, price–quality cue utilization within the category, and the category’s advertising exposure. In Study 1, field data from the popular television game show The Price is Right is utilized to estimate consumer price knowledge for dozens of product categories. Study 2 extends the results by linking consumer price knowledge to the above factors, utilizing a consumer survey. The paper concludes with a discussion of implications for pricing research and practice.
Cross-category variations in consumer price knowledge Consumer exposure to market information initiates an information processing sequence, the result of which may be the storage of price information in long-term memory (e.g., Jacoby & Olson, 1977; Monroe & Lee, 1999). Increased exposure to prices is expected to improve the associated memory traces and to help create a richer knowledge base for product prices (Monroe, 2003; Nagle & Holden, 2002). However, since the early studies of Gabor and Granger (1961) on the topic, research has mostly converged on the disturbing fact that consumer price knowledge is relatively poor. Some researchers estimate that as much as about half of all consumers are unaware of the actual prices of items they frequently purchase (e.g., Harrell, Hutt, & Allen, 1976; Helgeson & Beatty, 1987; Le Boutillier, Le Boutillier, & Neslin, 1994). Moreover, price estimates provided by consumers are often found to be significantly different from the products’ actual prices (Dickson & Sawyer, 1990; Monroe & Lee, 1999). A central question in this research stream has been in identifying factors that influence consumer’s knowledge of prices. For example, Gabor and Granger’s (1961) study of homemakers’ knowledge of grocery product prices found that price knowledge varies by social class. Wakefield and Inman (1993) who also examined the impact of shopper demographics on price knowledge, found mixed effects for variables such as gender, age, and income. Other studies have expanded the study variables and methodological approaches used. For example, Krishna et al. (1991) studied not only the accuracy in consumers’ knowledge of regular prices, but also their knowledge of promotional prices. Dickson and Sawyer (1990) examined the length of time consumers spend observing prices and choosing brands within the store, and found no significant relationship between the price-checking time interval and price knowledge. In an experimental setting, Schindler and Wiman (1989) demonstrated that price knowledge might be affected by the format of the price, and that prices with 0 endings (e.g., $200) are more likely to be accurately remembered than those with 9 endings (e.g., $199).
An intriguing observation in this research stream has been a lack of systematic examination of consumer price knowledge across a wide range of product types. One reason for this has been that most studies in this area have been conducted on a narrow range of low-involvement products. For example, in a meta-analysis of price knowledge studies conducted in the past four decades, Estelami and Lehmann (2001) found the overwhelming majority of reported studies to be of frequently purchased consumer nondurables (e.g., bread, milk, soup, butter, detergent). Rarely are studies of services or durable goods undertaken. The rare cases in which other types of products are examined include durable goods such as radios, refrigerators, television sets, and automobiles (Estelami, 1998; Helgeson & Beatty, 1987; Stephens & Moore, 1977) and services such as dry cleaning, photo development, optometry, banking, and tourist attractions (Lawson, Gnoth, & Paulin, 1995; Turley & Cabannis, 1995). Of the total of 279 price knowledge studies reviewed by Estelami and Lehmann (2001), only 11 reported price knowledge levels for durable consumer goods. Considering the large volume of market consumption attributed to consumer durables, the above observations beg one to question the validity of our current understanding of consumer price knowledge. The natural question that emerges relates to the level of consumer price knowledge for durable goods, and what its potential determinants might be. In the following section, we will first develop a conceptual foundation for the impact of three specific product category characteristics – purchase frequency, the use of the price–quality cue, and advertising exposure – on consumer price knowledge. Consumer price knowledge variation across durable categories is then documented, and the relationship with the above factors examined in two empirical studies. Product category purchase frequency Conventional thinking would suggest that product categories that are frequently purchased must be associated with higher levels of consumer price knowledge, than less frequently purchased categories. Purchase experience in a category is likely to result in an increased level of exposure to price information. This increased exposure could in turn result in rehearsal, and eventually the storage of price information in long-term memory (Bettman, 1979; Winer, 1986). Moreover, frequently purchased products are often essential goods that demand constant and repetitive allocation of consumers’ spending budgets (Monroe, 2003; Nagle & Holden, 2002). As a result, consumers may have strong budgetary incentives to educate themselves on prices for products that they frequently purchase. While based on the above discussion, one would speculate that product category purchase frequency should have a positive impact on consumer price knowledge, findings in price knowledge research have been ambiguous on this very basic relationship. For example, while some studies
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have found evidence for cross-category variations in consumer price knowledge (e.g., Progressive Grocer, 1964), the magnitude of these variations have been found to be weak, and in some cases insignificant (e.g., Harrel, Hutt & Allen, 1976; Conover, 1986; Dickson & Sawyer, 1990; Estelami & Lehmann, 2001; Krishna et al., 1991; Wakefield & Inman, 1993). However, the inconclusive results in past studies may be attributed to the fact that most of the research has been conducted on a small selection of nondurable consumer goods (e.g., bread, milk, soda, butter, detergent, etc.) The use of such products, which inspire little consumer involvement in processing of product information, may itself account for the inconclusive results observed. Use of low-involvement, low priced products, most of which have roughly equivalent levels of purchase frequencies may have therefore prevented any underlying relationships between purchase frequency and price knowledge to be empirically revealed. In contrast, for durable consumer goods, the impact of product category purchase frequency on consumer price knowledge is likely to be strong. Durable consumer goods categories often experience noticeably higher levels of price and product differentiation, thereby increasing the importance of price in the decision making process (Monroe, 2003; Nagle & Holden, 2002). Moreover, durable purchases are typically associated with higher levels of consumer involvement than purchases of nondurable goods, motivating increased attention to price information. Under such circumstances, frequent exposure to prices through increased purchase frequencies is likely to increase the likelihood of price information rehearsal and storage in long-term memory, thereby improving consumer memory for prices. It is therefore expected that there would be a positive relationship between purchase frequency and price knowledge. H1. Consumer price knowledge in a category is positively related to purchase frequency within the category. Product category advertising exposure Exposure to brand advertising in a product category is expected to influence consumers’ knowledge base of the category. The multiple-store theory of human memory (Lindsay & Norman, 1972; Shiffrin & Atkinson, 1969) suggests that an increase in the number of dedicated exposures to product advertisements increases the likelihood of rehearsal and elaboration of presented information. This not only facilitates the movement of the communicated information from short- to long-term memory, but also promotes further elaboration on the presented stimulus. Similarly, adaptation level theory (Helson, 1964) suggests that dedicated exposures to product category information can help develop an implicit memory of category-based information, to be used in subsequent decision making (e.g., Winer, 1986). This increased exposure may be a result of general advertising levels in a given category, or a consumer’s own
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selected exposure to advertisements in the category. Moreover, the presentation of price information in advertisements is likely to contribute to consumers’ general knowledge of prices. Increased advertising exposure therefore further facilitates elaboration of price information and may result in the development of more precise knowledge of prices (Jacoby & Olson, 1977; Sawyer, 1975). We may therefore expect that a consumer’s exposure to advertisements in a product category will positively impact the consumers’ price knowledge for that category. H2. Consumer price knowledge in a category is positively related to consumer exposure to advertising in the category. Use of the price–quality cue When information on a given product attribute is missing, consumers often have to utilize other product attributes as cues for making inferences about the missing information (Aizen & Fishbein, 1980; Troutman & Shanteau, 1976). Research in pricing has established that when product quality is unclear, price is used by consumers as more than a simple measure of monetary sacrifice, and is often used as a proxy for product quality (Dodds, Monroe, & Grewal, 1991; Gabor & Granger, 1966; Scitovszky, 1944). For product categories in which this association is strong, high prices infer high levels of perceived quality (Sivakumar & Raj, 1997). This is especially true for quality perceptions of durable goods, since they tend to be technically more complex than nondurables, and therefore their quality is more difficult to determine (Monroe, 2003). Although the association between price and quality may be a true reflection of objective quality variations (Lichtenstein & Burton, 1989), it is also often a result of the inability of the consumer to objectively determine product quality using any source of information other than price itself (Gabor & Granger, 1966; Monroe, 2003). In such circumstances, consumers’ use of price as an indicator of product quality would imply that price information might be of considerably higher diagnostic value than simply a determinant of monetary outlays. This increases the information value of price and promotes additional incentives for consumers to develop a working memory for prices. Therefore, one might expect a positive relationship between consumers’ use of the price–quality cue in a given category and price knowledge. H3. Consumer price knowledge in a category is positively related to consumers’ use of the price–quality cue in that category. It is important to note that the strength of the above relationship may however be influenced by the strength of the link between the product’s perceived quality and its objective quality (Lichtenstein & Burton, 1989), and typically, the relationship is stronger for nondurables than for durables
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(Monroe, 2003). In circumstances where a product’s production cost, and hence its objective value are difficult for the consumer to determine, consumers may be more likely to rely on price as an information source for assessing price quality. In such circumstances, accurate knowledge of price information maybe perceived by consumers as a tool in informed decision making (Dodds et al., 1991; Zeithaml, 1988). This increased attention given to price may subsequently increase price knowledge. In contrast, if the production cost and hence the value of the product is obvious to the consumer, reliance on price as a cue for product quality may be unnecessary, thereby reducing the need for price information as a diagnostic resource in decision making. This would likely result in lower utilization of the price–quality cue.
Study 1
Data collection procedure Data were obtained for showcases featured in the introductory phase of the Price is Right whereby four contestants are randomly selected from the studio audience. The selected contestants are then asked to estimate the price of the product featured on a given showcase. The winning contestant, the one who provides a price closest to the actual retail price of the product without exceeding it, then moves on to subsequent stages of the game. Since for each showcase, a contestant’s stated price might be influenced by the previous contestants’ stated prices, only data for the very first contestant was used. For each showcase, the product category, the first contestant’s stated price and the actual retail price of the item were recorded. The percentage error between the latter two measures provides an estimate of the contestant’s knowledge of prices:
Overview Percentage error = The objective of this study was to estimate the extent of variation in consumer price knowledge levels across an array of durable consumer goods. The traditional approach for such a study would be to conduct interviews with consumers, in which a sample of consumers are presented with products, and asked to estimate their prices (e.g., Dickson & Sawyer, 1990; Le Boutillier et al., 1994; Progressive Grocer, 1964). However, to tap into a large database of such price knowledge measures, data from the popular television game show The Price is Right was used. The Price is Right has been rated as the fourth popular television game show in the United States. The show has been running on network television for over three decades and has been replicated internationally in several countries. In a typical show, four consumers are randomly selected from the studio audience. The selected contestants are then asked to state their price estimates for a given product. The contestant who can best estimate the price, without exceeding the actual price, will advance to subsequent stages of the game, with the opportunity to win valuable prizes. Because of its unique setting, The Price is Right provides an attractive opportunity for studying consumer price knowledge across the large array of consumer durable goods featured on the show. The products featured range from desks and sofas, to home audio systems and pool tables. The array of products displayed on the show reflects the wide spectrum of durable consumer products demanded by the marketplace (Holbrook, 1993). In addition, since the winning contestants in the show may eventually win prizes such as automobiles, boats, and cruises, they tend to be highly motivated to respond to price knowledge questions as accurately as possible. Moreover, the show draws from a wide range of consumer demographics, with contestants representing every region of the United States, enabling a broader examination of the topic than traditional approaches of using convenience samples or student subjects.
|actual price − stated price| actual price
When the error is close to zero, it signifies an accurate price estimate, close to the actual price, and hence an accurate level of price knowledge for the product category. In contrast, when the percentage error is large, consumer price knowledge is likely to be poor. In order to quantify price knowledge levels across categories, a price knowledge score (PKS) was computed for each product category. This was done by computing the proportion of respondents who provided price estimates within 10% (PKS10 ) and 25% (PKS25 ) of the actual price for each category. This approach to quantifying consumer price knowledge is consistent with a long tradition of research in pricing (e.g., Dickson & Sawyer, 1990; Estelami & Lehmann, 2001; Mazumdar & Monroe, 1992; Zeithaml, 1982). PKS therefore can range from a low of 0, to a high of 100 in which case all respondents are able to provide accurate price estimates. The more liberal percentage tolerance used here (10 and 25%) versus other similar studies that typically use a 5% range as the accuracy criterion (e.g., Dickson & Sawyer, 1990), was utilized since the bidding rules governing the Price is Right penalize respondents who provide bids over the actual price, thereby increasing the measurement bias associated with the price estimates obtained. The collected data were obtained by through daily broadcasts of the Price is Right in a 40-week time period spanning January to October 1997. The above procedure produced 201 broadcasts that were subsequently coded by trained observers. This resulted in a total of 1,206 observations, providing price knowledge information on 51 product categories. Results As can be seen in Table 1, a wide array of consumer durable goods was captured through The Price is Right. Across all products, the average price knowledge score
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Table 1 Price knowledge scores across product categories Product category
N
Price knowledge scorea PKS25
PKS10
Art pieces (paintings, sculpture, etc.) Baker’s rack Bar set Barbeque grill and accessories Bed Bedroom furniture Bicycle Boat Camera (35 mm and video) Camping equipment Chandeliers Chest Child/infant accessories Children’s toys Wall/grandfather/CooCoo clock Computer hardware Desk/table Dinner set Dinnerware Encyclopedia Fireplace accessories Fridge Games (e.g., monopoly, chess, etc.) Golf equipment Hardware tools Home exercise equipment
42 18 9 18 5 7 15 26 17 13 51 54 20 22 34 6 31 28 35 8 19 34 13 14 15 42
35.7 22.2 33.3 44.4 60.0 71.4 46.7 42.3 47.1 53.9 33.3 38.9 35.0 36.4 23.5 50.0 41.9 25.0 42.9 12.5 47.4 38.2 30.8 7.1 40.0 40.5
12.5 11.1 0.0 16.7 40.0 42.9 20.0 26.9 23.5 15.4 19.6 11.1 20.0 22.7 5.9 33.3 25.8 0.0 8.6 0.0 26.3 14.7 30.8 7.1 20.0 16.7
Product category
N
Price knowledge score PKS25
PKS10
Jewelry Kitchen equipment/small appliances Lawnmower Luggage Model autos Motorcycle Music instruments Office accessories Outdoor furniture Phone equipment Pool table Rugs Serving cart Sewing machine Sofa Sports gear Stereo equipment Stove/oven Telescope Tennis equipment Television set VCR/DVD player Videos and CDs Washing machine/dryer Watch Otherb
26 24 17 27 13 24 20 8 25 18 17 15 27 11 48 53 22 38 21 8 30 10 12 27 21 48
50.0 50.0 23.5 25.9 53.9 25.0 45.0 37.5 32.0 38.9 29.4 53.3 33.3 9.1 50.0 39.6 54.6 42.1 19.1 25.0 36.7 50.0 33.3 74.1 57.1 35.4
26.9 20.8 11.7 22.2 30.8 4.2 25.0 37.5 12.0 11.1 11.8 33.3 18.5 0.0 31.3 24.5 31.8 1.9 4.8 25.0 26.7 0.0 8.3 25.9 28.6 12.5
a PKS signifies the percentage of respondents who provide price estimates within 25% of the actual price. PKS signifies the percentage of respondents 25 10 who provide price estimates within 10% of the actual price. b Other: all categories with less than five observations.
(PKS25 ) was found to be 39, indicating that on average, 39% of consumers were able to provide price estimates within 25% of the actual price. However, as also shown in Table 1, significant variations in price knowledge scores can be observed across categories. PKS25 ranged from a low of 7 to a high of 74. For example, while nondiscretionary product categories such as kitchen appliances, stoves, sofas, beds, and washing machines exhibit above average PKS levels, other less commonly possessed recreational and discretionary products such as golf equipment, sewing machines, grandfather clocks, and encyclopedias exhibited considerably lower price knowledge scores. Similar results are observed for PKS10 , signifying the percentage of respondents with price estimates within 10% of the actual price. In order to assess the statistical significance of price knowledge score variations across the 51 products, a chi square test was conducted. The observed variations were found to be significant at the p < .05 level for both PKS25 (χ2 = 69.4; Φ = 0.24) and PKS10 (χ2 = 71.3; Φ = 0.24). In general, several patterns are evident in the results. For example, products that are commonly owned by households, such as washing machines, beds, rugs, and bedroom furniture appear to have high price knowledge scores. Not only are these products that are frequently owned by consumers, but also due to their mass consumption, they are
frequently advertised in various forms of media. Moreover, due to the high level of competition in these mass market products, price may be an accurate indication of product quality, thereby increasing the diagnostic value of accurate price knowledge to the consumer. Other household items that are of a discretionary nature, such as outdoor furniture, baker’s racks, and lawnmowers exhibit comparatively lower levels of consumer price knowledge—with PKS10 scores of less that 15. This indicates that less that 15% of consumers can estimate prices for these product categories, within a 10% range of the actual price. However, the lowest levels of price knowledge seem to be exhibited by non-essential and niche-market products. For example, sewing machines, encyclopedias, and bar sets all display PKS10 levels of 0. This indicates that for these products not a single contestant was able to provide accurate price estimates, within 10% of the actual price.
Study 2 Overview In order to further examine the source of the variations observed in Study 1, a detailed measurement of specific
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product category characteristics – purchase frequency, category advertising exposure, and use of the price–quality cue within the category – would be needed. However, common sources of purchase frequency data (e.g., Marketing Fact Book, 2000) and advertising exposure (Simmons Market Research Bureau, 1998) primarily cover nondurable consumer goods such as grocery products and health and beauty supplies, and a complete coverage of the durable consumer goods identified through the Price is Right was unavailable through conventional secondary data sources. As a result, in this study, a complete measurement of these constructs was undertaken through an independent consumer survey. Data collection procedure A consumer survey was carried out to measure purchase frequency, price–quality cue utilization, and advertising exposure. These measures were obtained through a primary consumer survey, as no secondary data sources (e.g., Marketing Fact Book, 2000; Simmons Market Research Bureau) comprehensively cover the large array of products surveyed in this study. An objective measure of price knowledge was obtained by asking respondents to estimate retail prices for specific products presented to them. This approach to measuring price knowledge is consistent with earlier studies on consumer price knowledge (Conover, 1986; Dickson & Sawyer, 1990). A total of 36 products featured on The Price is Right were used. These were: baker’s racks, bar sets, barbeque grills, beds, bicycles, boats, chandeliers, drawer chests, grandfather clocks, dinette sets, encyclopedias, fireplace accessories, golf clubs, guitars, lawn mowers, motor scooters, pool tables, pre-recorded music CDs, sewing machines, snow blower machines, sofas, stereos, stoves, suit cases, trash compactors, treadmill machines, wine serving carts, telescopes, tennis rackets, TV sets, VCRs, wrist watches, refrigerators, and video cameras. For each product, the respondent was presented with the name of the product category and a picture of a typical product. Consumer measures of the three independent and one dependent variable were then obtained through single-item scales. Purchase frequency was measured by: “I’ve purchased or considered purchasing similar products before”. Price–quality cue utilization was measured by: “The higher the price of this type of product, the higher the quality”. Advertising exposure was measured by: “Prices for products like this are often advertised”. This measure of advertising exposure was utilized in order to more specifically gauge consumer exposure to price advertising rather than general advertising exposure. All independent variables were measured on a 1–7 Likert scale ranging from “strongly disagree” (1) to “strongly agree” (7). This was followed by presenting the respondent with a sample description of the product obtained from The Price is Right, and requesting a dollar price estimate. Price knowledge error was then estimated by estimating the percent deviation
between the estimated price provided by the respondent and the actual retail price of the product. Since 36 products were considered excessive for each respondent to evaluate, a total of six products were presented to each individual respondent. This was achieved by randomizing the 36 products, and partitioning the randomized set into six mutually exclusive sets. Each set was then administered to an individual respondent, with every six respondents providing a full set of responses on the products surveyed. The randomization procedure was done separately for each six set of respondents. Respondents were recruited though mall-intercepts and rewarded with their choice of a souvenir key chain. A total of 146 consumers participated in the study. The average age and household income of the sample were 32.5 and $67,000, respectively. The sample was 61% female, and 53% of the respondents were married or living with a spouse. Although this clearly is not a national sample, the respondent demographics closely resemble the underlying population where the survey was conducted. The median household income for the county where the survey was administered was $59,000 and the population is 53% female (Bureau of the Census, 2001). Cell sizes for the 36 products range from 23 to 27. Results Standardized regression estimates were obtained by regressing price knowledge error on the three independent variables—purchase frequency, advertising exposure, and use of the price–quality cue. These are reported in Table 2. As can be seen, the overall regression is significant at the p < .001 level. Purchase frequency has a negative impact on price knowledge error. This is evident by the negative and significant coefficient associated with purchase frequency (p < .01). Similarly, advertising exposure reduces price knowledge error (p < .01). In addition, the impact of the price–quality cue seems to be insignificant (p > .1). In order to further probe the impact of the independent variables on consumer price knowledge, a second regression was conducted in which the price knowledge score (PKS25 ) from Study 1 was used as the dependent variable. The results of this analysis are shown in Table 2. As can Table 2 Regression results for Study 2 (standardized beta estimates) Independent variable
Dependent variable Price knowledge error
Price knowledge score (PKS25 )a
Purchase frequency Advertising exposure Price–quality cue R2
−0.246∗∗∗ −0.217∗∗∗ −0.047 0.00
0.174∗∗∗ 0.280∗∗∗ 0.085∗∗ 0.15
a Represents percentage of respondents in Study 1 with price estimates within 25% of the actual price. ∗∗ p < .05. ∗∗∗ p < .01.
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be seen, the regression is significant (p < .01; R2 = 0.15). Moreover, the relationships between the independent variables and price knowledge are consistent with expectations. Purchase frequency has a positive impact on PKS, indicating that higher purchase frequencies result in more accurate estimations of prices. Similarly, both advertising exposure and the use of the price–quality cue has a positive impact on PKS. Interestingly, of the three independent variables, the price–quality cue exhibits the weakest impact on consumer price knowledge.
Discussion and conclusion Consumer durable goods account for a significant proportion of economic output in the Western world. Nevertheless, despite the large number of studies that have been conducted on consumer price knowledge over the years, very few have examined price knowledge for consumer durables. In this work, we profiled consumer price knowledge for a large array of consumer durable product categories in a field setting. Study 1 results indicate a wide range of price knowledge levels across the spectrum of consumer durable goods. Categories with recreational use were found to exhibit significantly lower levels of consumer price knowledge than categories considered as essential items. Furthermore, the findings of Study 2 indicate that purchase frequency has a positive and significant impact on price knowledge. This is an important finding since past research has primarily focused on frequently purchased consumer nondurables (e.g., butter, bread, milk, detergent, etc.) With little variation in purchase frequency levels, use of such low-involvement product categories may have contributed to the weak or null results of purchase frequency observed in past studies. The results also showed significant effects on price knowledge for consumer exposure to product category advertising. This indicates that in heavily advertised product categories, consumers will eventually develop a rich knowledge base for prices, and one result of advertising may therefore be the educating of the consumer population. The findings of this work are of use to marketing mangers, whose pricing practices may partially depend on an understanding of consumer knowledge of prices. Game-theoretic studies have long established that the optimal pricing strategy for a product may largely depend on how well consumers are aware of product prices and on consumers’ information search behavior (e.g., Bronnenberg & Vanhonacker, 1996; Lancaster & Ratchford, 1990; Ratchford, 1980). These works indicate that in categories where price knowledge is poor, and price search behavior is minimal, significant opportunities may exist for high-margin pricing. In contrast, price-setting flexibility is significantly reduced in product categories where consumer price knowledge is high, resulting in increased competitive pressure placed solely on prices. It is therefore essential for any product manager to not
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only know the average level of consumer price knowledge, but to also empirically gauge the potential existence of market segments, based on price knowledge levels. Results of the empirical work presented here reveal a significant amount of consumer price knowledge variation both across and within categories. Managerial knowledge of the proportion of buyers who possess high versus low levels of knowledge of prices may therefore be a driving factor in choice of pricing strategy as well as associated tactics in the communication of prices and product promotions. Categories in which a large proportion of consumers lack accurate knowledge of prices could provide managers with considerable flexibility in setting prices. On the other hand, a generally high level of consumer price knowledge may indicate a strong need for seeking competitive intelligence on prices, and the utilization of tactics such as price-matching guarantees and comparative price advertising in order to provide consumers with an increased level of confidence in their purchase decisions. It is important to acknowledge however that use of pricing tactics that depend on consumers’ ignorance of prices may have both ethical and legal consequences that should be carefully considered before the implementation of such tactics. Therefore, deceptive use of price information and the potential misuse of poor consumer price knowledge in a given category by sellers may be an important consideration for public policy and consumer protection initiatives. The results of this work expand existing research findings on price knowledge in several ways. While the majority of existing works have focused on nondurable products with high purchase frequencies (e.g., Conover, 1986; Estelami & Lehmann, 2001; Monroe & Lee, 1999) this work has expanded the horizon of current research findings by studying consumer price knowledge for low purchase frequency products. Moreover, in contrast to prior research that has largely ignored product category characteristics that may explain cross-category variations in consumer price knowledge, this work has systematically examined and isolated specific category determinants of price knowledge. The findings reveal that while two factors – namely purchase frequency and advertising exposure within a category – do have a positive impact on consumer price knowledge, use of the price–quality cue, despite it is theoretical appeal, has a significantly weaker effect on consumer price knowledge. While this work is the first large-scale analysis of price knowledge for consumer durables and its underlying drivers, several limitations need to be acknowledged. The use of the unconventional field setting of a television game show, used to obtain price knowledge scores in Study 1, may have created additional sources of variance in the data. Consumers’ arousal and emotion levels, as well as the demographics of the contestants may influence the accuracy of the price knowledge measures provided. It is important to note though that this would most likely influence all product categories equally, and therefore while the overall average of price knowledge measures may be affected by the measurement approach, the relative level of price knowledge scores across
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products is likely to be unaffected. The consumer survey results may have also been limited by the fact that some of the measures were obtained through consumer self-reports. These self-reports may have inherent biases associated with them, such as a social desirability response and inaccuracies resulting from measurement and nonsampling error (Churchill, 1996). Objective historical information – such as purchase frequencies and advertising exposure – could not be obtained on an individual level due to limited availability of such data for consumer durable goods, and absence of reliable secondary data sources. Clearly, this line of research can be expanded in several directions. For example, it would be very useful to expand consumer price knowledge studies beyond the scope of manufactured goods, and into the area of services. In doing so, it would also be useful to determine what factors may affect consumer price knowledge of services, and how the price information search and information acquisition processes might vary between goods and services. Moreover, the role of category characteristics, other than those studied here, on consumer price knowledge remains open to further study. These may include characteristics such as the technological sophistication of the products, its use as a luxury item or as a necessity, and the stage in the product life cycle. In addition, this research stream can be expanded by using measures other than consumers’ explicitly measured price knowledge levels. Use of other measures of price knowledge obtained or inferred through consumer transaction histories may provide further insights on this issue.
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H. Estelami, P. De Maeyer / Journal of Retailing 80 (2004) 129–137 Schindler, Robert M., & Wiman, Alan R. (1989). Effects of odd pricing on price recall. Journal of Business Research, 19(2), 165–177. Scitovszky, Tibor. (1944). Some consequences of the habit of judging quality by price. Review of Economic Studies, 12(Winter), 100–105. Shiffrin, R. M., & Atkinson, R. C. (1969). Storage and retrieval processes in long-term memory. Psychological Review, 76(1), 179–193. Simmons Market Research Bureau (1998). Choices II system. New York: Simmons Inc. Sivakumar, K., & Raj, S. P. (1997). Quality tier competition: How price change influences brand choice and category choice. Journal of Marketing, 61(July), 71–84. Stephens, L. F., & Moore, R. L. (1977). Price accuracy as a consumer skill. Journal of Advertising Research, 15(4), 27–34. Troutman, C. M., & Shanteau, J. (1976). Inferences based on non-diagnostic information. Organizational Behavior and Human Performance, 19(January), 43–55.
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Product category determinants of price knowledge for durable consumer goods Hooman Estelami a,∗ , Peter De Maeyer b a
Graduate School of Business, Fordham University, 113 West 60th Street, New York, NY 10023, USA b Singapore Management University 469 Bukid Timah Road, Singapore 259756
Abstract Existing research in pricing has not extensively examined the impact of the product category on consumers’ knowledge of prices, especially for durable goods. In two empirical studies, this paper examines the influence of the product category on consumers’ knowledge of prices for durables. The first study utilizes data from the popular television game show The Price is Right to establish significant cross-category variations in price knowledge, while the second study links these variations to the specific characteristics of each product category. The results extend existing research findings by isolating the impact of product category determinants, such as purchase frequency, advertising exposure, and use of the price–quality cue, on consumers’ knowledge of prices. © 2004 by New York University. Published by Elsevier. All rights reserved. Keywords: Pricing; Consumer behavior; Marketing communications
Introduction Price knowledge is considered to be a fundamental requirement in rational consumer decision making (Bettman, 1979; Monroe, 2003; Nagle & Holden, 2002). In order to make an educated purchase decision, a consumer needs to have adequate information about product prices. However, a growing volume of literature suggests that consumer knowledge and information processing of prices is less than perfect. Research evidence indicates that consumers often utilize simplifying heuristics and cues in evaluating prices and offer value (e.g., Inman, Peter, & Raghubir, 1997; Raghubir, 1998; Urbany, Bearden, Kaicker, & Borrero, 1997), and that consumer price knowledge is disturbingly poor (e.g., Dickson & Sawyer, 1990; Estelami, Lehmann, & Holden, 2002; Krishna, Currim, & Shoemaker, 1991). This research stream indicates that a large proportion of consumers do not know prices for items they regularly purchase, and that their price estimates are often far apart from the products’ actual prices (Estelami & Lehmann, 2001; Monroe & Lee, 1999). While research findings agree on the poor level of consumer knowledge of prices, existing research has largely ignored the impact of the product category on price knowledge.
∗
Corresponding author. Tel.: +1 212 636 6296. E-mail address: [email protected] (H. Estelami).
It is neither known how much price knowledge systematically varies across product categories, nor what factors might feed these variations. This inability to gauge cross-category price knowledge variations has been attributed to several factors. One contributing factor has been the diverse and often incompatible research and data collection methodologies utilized by the various researchers, as noted by the review done by Monroe and Lee (1999). A second factor has been the narrow range of product categories examined by each researcher, and an overwhelming focus on grocery products as the basis of empirical investigation (Conover, 1986; McGoldrick & Marks, 1987). As a result of research design choices made in previous studies, the mass of research findings collected through the years has been primarily based on studies of frequently purchased nondurable consumer goods (Estelami & Lehmann, 2001; Monroe & Lee, 1999). Little is known about consumer price knowledge of durable goods, placing our accumulated understanding of consumer price knowledge in question. Nevertheless, understanding variations in consumer price knowledge across durable product categories is important not only to academics interested in identifying their sources, but also to practitioners whose pricing strategies may depend on the extent of consumer price knowledge (or lack thereof) within the categories they manage. The objective of this paper is therefore to examine the level of consumer price knowledge for durable goods, and
0022-4359/$ – see front matter © 2004 by New York University. Published by Elsevier. All rights reserved. doi:10.1016/j.jretai.2004.04.003
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to identify its determinants. We will first examine the potential role of three product category characteristics on consumer price knowledge, namely: category purchase frequency, price–quality cue utilization within the category, and the category’s advertising exposure. In Study 1, field data from the popular television game show The Price is Right is utilized to estimate consumer price knowledge for dozens of product categories. Study 2 extends the results by linking consumer price knowledge to the above factors, utilizing a consumer survey. The paper concludes with a discussion of implications for pricing research and practice.
Cross-category variations in consumer price knowledge Consumer exposure to market information initiates an information processing sequence, the result of which may be the storage of price information in long-term memory (e.g., Jacoby & Olson, 1977; Monroe & Lee, 1999). Increased exposure to prices is expected to improve the associated memory traces and to help create a richer knowledge base for product prices (Monroe, 2003; Nagle & Holden, 2002). However, since the early studies of Gabor and Granger (1961) on the topic, research has mostly converged on the disturbing fact that consumer price knowledge is relatively poor. Some researchers estimate that as much as about half of all consumers are unaware of the actual prices of items they frequently purchase (e.g., Harrell, Hutt, & Allen, 1976; Helgeson & Beatty, 1987; Le Boutillier, Le Boutillier, & Neslin, 1994). Moreover, price estimates provided by consumers are often found to be significantly different from the products’ actual prices (Dickson & Sawyer, 1990; Monroe & Lee, 1999). A central question in this research stream has been in identifying factors that influence consumer’s knowledge of prices. For example, Gabor and Granger’s (1961) study of homemakers’ knowledge of grocery product prices found that price knowledge varies by social class. Wakefield and Inman (1993) who also examined the impact of shopper demographics on price knowledge, found mixed effects for variables such as gender, age, and income. Other studies have expanded the study variables and methodological approaches used. For example, Krishna et al. (1991) studied not only the accuracy in consumers’ knowledge of regular prices, but also their knowledge of promotional prices. Dickson and Sawyer (1990) examined the length of time consumers spend observing prices and choosing brands within the store, and found no significant relationship between the price-checking time interval and price knowledge. In an experimental setting, Schindler and Wiman (1989) demonstrated that price knowledge might be affected by the format of the price, and that prices with 0 endings (e.g., $200) are more likely to be accurately remembered than those with 9 endings (e.g., $199).
An intriguing observation in this research stream has been a lack of systematic examination of consumer price knowledge across a wide range of product types. One reason for this has been that most studies in this area have been conducted on a narrow range of low-involvement products. For example, in a meta-analysis of price knowledge studies conducted in the past four decades, Estelami and Lehmann (2001) found the overwhelming majority of reported studies to be of frequently purchased consumer nondurables (e.g., bread, milk, soup, butter, detergent). Rarely are studies of services or durable goods undertaken. The rare cases in which other types of products are examined include durable goods such as radios, refrigerators, television sets, and automobiles (Estelami, 1998; Helgeson & Beatty, 1987; Stephens & Moore, 1977) and services such as dry cleaning, photo development, optometry, banking, and tourist attractions (Lawson, Gnoth, & Paulin, 1995; Turley & Cabannis, 1995). Of the total of 279 price knowledge studies reviewed by Estelami and Lehmann (2001), only 11 reported price knowledge levels for durable consumer goods. Considering the large volume of market consumption attributed to consumer durables, the above observations beg one to question the validity of our current understanding of consumer price knowledge. The natural question that emerges relates to the level of consumer price knowledge for durable goods, and what its potential determinants might be. In the following section, we will first develop a conceptual foundation for the impact of three specific product category characteristics – purchase frequency, the use of the price–quality cue, and advertising exposure – on consumer price knowledge. Consumer price knowledge variation across durable categories is then documented, and the relationship with the above factors examined in two empirical studies. Product category purchase frequency Conventional thinking would suggest that product categories that are frequently purchased must be associated with higher levels of consumer price knowledge, than less frequently purchased categories. Purchase experience in a category is likely to result in an increased level of exposure to price information. This increased exposure could in turn result in rehearsal, and eventually the storage of price information in long-term memory (Bettman, 1979; Winer, 1986). Moreover, frequently purchased products are often essential goods that demand constant and repetitive allocation of consumers’ spending budgets (Monroe, 2003; Nagle & Holden, 2002). As a result, consumers may have strong budgetary incentives to educate themselves on prices for products that they frequently purchase. While based on the above discussion, one would speculate that product category purchase frequency should have a positive impact on consumer price knowledge, findings in price knowledge research have been ambiguous on this very basic relationship. For example, while some studies
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have found evidence for cross-category variations in consumer price knowledge (e.g., Progressive Grocer, 1964), the magnitude of these variations have been found to be weak, and in some cases insignificant (e.g., Harrel, Hutt & Allen, 1976; Conover, 1986; Dickson & Sawyer, 1990; Estelami & Lehmann, 2001; Krishna et al., 1991; Wakefield & Inman, 1993). However, the inconclusive results in past studies may be attributed to the fact that most of the research has been conducted on a small selection of nondurable consumer goods (e.g., bread, milk, soda, butter, detergent, etc.) The use of such products, which inspire little consumer involvement in processing of product information, may itself account for the inconclusive results observed. Use of low-involvement, low priced products, most of which have roughly equivalent levels of purchase frequencies may have therefore prevented any underlying relationships between purchase frequency and price knowledge to be empirically revealed. In contrast, for durable consumer goods, the impact of product category purchase frequency on consumer price knowledge is likely to be strong. Durable consumer goods categories often experience noticeably higher levels of price and product differentiation, thereby increasing the importance of price in the decision making process (Monroe, 2003; Nagle & Holden, 2002). Moreover, durable purchases are typically associated with higher levels of consumer involvement than purchases of nondurable goods, motivating increased attention to price information. Under such circumstances, frequent exposure to prices through increased purchase frequencies is likely to increase the likelihood of price information rehearsal and storage in long-term memory, thereby improving consumer memory for prices. It is therefore expected that there would be a positive relationship between purchase frequency and price knowledge. H1. Consumer price knowledge in a category is positively related to purchase frequency within the category. Product category advertising exposure Exposure to brand advertising in a product category is expected to influence consumers’ knowledge base of the category. The multiple-store theory of human memory (Lindsay & Norman, 1972; Shiffrin & Atkinson, 1969) suggests that an increase in the number of dedicated exposures to product advertisements increases the likelihood of rehearsal and elaboration of presented information. This not only facilitates the movement of the communicated information from short- to long-term memory, but also promotes further elaboration on the presented stimulus. Similarly, adaptation level theory (Helson, 1964) suggests that dedicated exposures to product category information can help develop an implicit memory of category-based information, to be used in subsequent decision making (e.g., Winer, 1986). This increased exposure may be a result of general advertising levels in a given category, or a consumer’s own
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selected exposure to advertisements in the category. Moreover, the presentation of price information in advertisements is likely to contribute to consumers’ general knowledge of prices. Increased advertising exposure therefore further facilitates elaboration of price information and may result in the development of more precise knowledge of prices (Jacoby & Olson, 1977; Sawyer, 1975). We may therefore expect that a consumer’s exposure to advertisements in a product category will positively impact the consumers’ price knowledge for that category. H2. Consumer price knowledge in a category is positively related to consumer exposure to advertising in the category. Use of the price–quality cue When information on a given product attribute is missing, consumers often have to utilize other product attributes as cues for making inferences about the missing information (Aizen & Fishbein, 1980; Troutman & Shanteau, 1976). Research in pricing has established that when product quality is unclear, price is used by consumers as more than a simple measure of monetary sacrifice, and is often used as a proxy for product quality (Dodds, Monroe, & Grewal, 1991; Gabor & Granger, 1966; Scitovszky, 1944). For product categories in which this association is strong, high prices infer high levels of perceived quality (Sivakumar & Raj, 1997). This is especially true for quality perceptions of durable goods, since they tend to be technically more complex than nondurables, and therefore their quality is more difficult to determine (Monroe, 2003). Although the association between price and quality may be a true reflection of objective quality variations (Lichtenstein & Burton, 1989), it is also often a result of the inability of the consumer to objectively determine product quality using any source of information other than price itself (Gabor & Granger, 1966; Monroe, 2003). In such circumstances, consumers’ use of price as an indicator of product quality would imply that price information might be of considerably higher diagnostic value than simply a determinant of monetary outlays. This increases the information value of price and promotes additional incentives for consumers to develop a working memory for prices. Therefore, one might expect a positive relationship between consumers’ use of the price–quality cue in a given category and price knowledge. H3. Consumer price knowledge in a category is positively related to consumers’ use of the price–quality cue in that category. It is important to note that the strength of the above relationship may however be influenced by the strength of the link between the product’s perceived quality and its objective quality (Lichtenstein & Burton, 1989), and typically, the relationship is stronger for nondurables than for durables
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(Monroe, 2003). In circumstances where a product’s production cost, and hence its objective value are difficult for the consumer to determine, consumers may be more likely to rely on price as an information source for assessing price quality. In such circumstances, accurate knowledge of price information maybe perceived by consumers as a tool in informed decision making (Dodds et al., 1991; Zeithaml, 1988). This increased attention given to price may subsequently increase price knowledge. In contrast, if the production cost and hence the value of the product is obvious to the consumer, reliance on price as a cue for product quality may be unnecessary, thereby reducing the need for price information as a diagnostic resource in decision making. This would likely result in lower utilization of the price–quality cue.
Study 1
Data collection procedure Data were obtained for showcases featured in the introductory phase of the Price is Right whereby four contestants are randomly selected from the studio audience. The selected contestants are then asked to estimate the price of the product featured on a given showcase. The winning contestant, the one who provides a price closest to the actual retail price of the product without exceeding it, then moves on to subsequent stages of the game. Since for each showcase, a contestant’s stated price might be influenced by the previous contestants’ stated prices, only data for the very first contestant was used. For each showcase, the product category, the first contestant’s stated price and the actual retail price of the item were recorded. The percentage error between the latter two measures provides an estimate of the contestant’s knowledge of prices:
Overview Percentage error = The objective of this study was to estimate the extent of variation in consumer price knowledge levels across an array of durable consumer goods. The traditional approach for such a study would be to conduct interviews with consumers, in which a sample of consumers are presented with products, and asked to estimate their prices (e.g., Dickson & Sawyer, 1990; Le Boutillier et al., 1994; Progressive Grocer, 1964). However, to tap into a large database of such price knowledge measures, data from the popular television game show The Price is Right was used. The Price is Right has been rated as the fourth popular television game show in the United States. The show has been running on network television for over three decades and has been replicated internationally in several countries. In a typical show, four consumers are randomly selected from the studio audience. The selected contestants are then asked to state their price estimates for a given product. The contestant who can best estimate the price, without exceeding the actual price, will advance to subsequent stages of the game, with the opportunity to win valuable prizes. Because of its unique setting, The Price is Right provides an attractive opportunity for studying consumer price knowledge across the large array of consumer durable goods featured on the show. The products featured range from desks and sofas, to home audio systems and pool tables. The array of products displayed on the show reflects the wide spectrum of durable consumer products demanded by the marketplace (Holbrook, 1993). In addition, since the winning contestants in the show may eventually win prizes such as automobiles, boats, and cruises, they tend to be highly motivated to respond to price knowledge questions as accurately as possible. Moreover, the show draws from a wide range of consumer demographics, with contestants representing every region of the United States, enabling a broader examination of the topic than traditional approaches of using convenience samples or student subjects.
|actual price − stated price| actual price
When the error is close to zero, it signifies an accurate price estimate, close to the actual price, and hence an accurate level of price knowledge for the product category. In contrast, when the percentage error is large, consumer price knowledge is likely to be poor. In order to quantify price knowledge levels across categories, a price knowledge score (PKS) was computed for each product category. This was done by computing the proportion of respondents who provided price estimates within 10% (PKS10 ) and 25% (PKS25 ) of the actual price for each category. This approach to quantifying consumer price knowledge is consistent with a long tradition of research in pricing (e.g., Dickson & Sawyer, 1990; Estelami & Lehmann, 2001; Mazumdar & Monroe, 1992; Zeithaml, 1982). PKS therefore can range from a low of 0, to a high of 100 in which case all respondents are able to provide accurate price estimates. The more liberal percentage tolerance used here (10 and 25%) versus other similar studies that typically use a 5% range as the accuracy criterion (e.g., Dickson & Sawyer, 1990), was utilized since the bidding rules governing the Price is Right penalize respondents who provide bids over the actual price, thereby increasing the measurement bias associated with the price estimates obtained. The collected data were obtained by through daily broadcasts of the Price is Right in a 40-week time period spanning January to October 1997. The above procedure produced 201 broadcasts that were subsequently coded by trained observers. This resulted in a total of 1,206 observations, providing price knowledge information on 51 product categories. Results As can be seen in Table 1, a wide array of consumer durable goods was captured through The Price is Right. Across all products, the average price knowledge score
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Table 1 Price knowledge scores across product categories Product category
N
Price knowledge scorea PKS25
PKS10
Art pieces (paintings, sculpture, etc.) Baker’s rack Bar set Barbeque grill and accessories Bed Bedroom furniture Bicycle Boat Camera (35 mm and video) Camping equipment Chandeliers Chest Child/infant accessories Children’s toys Wall/grandfather/CooCoo clock Computer hardware Desk/table Dinner set Dinnerware Encyclopedia Fireplace accessories Fridge Games (e.g., monopoly, chess, etc.) Golf equipment Hardware tools Home exercise equipment
42 18 9 18 5 7 15 26 17 13 51 54 20 22 34 6 31 28 35 8 19 34 13 14 15 42
35.7 22.2 33.3 44.4 60.0 71.4 46.7 42.3 47.1 53.9 33.3 38.9 35.0 36.4 23.5 50.0 41.9 25.0 42.9 12.5 47.4 38.2 30.8 7.1 40.0 40.5
12.5 11.1 0.0 16.7 40.0 42.9 20.0 26.9 23.5 15.4 19.6 11.1 20.0 22.7 5.9 33.3 25.8 0.0 8.6 0.0 26.3 14.7 30.8 7.1 20.0 16.7
Product category
N
Price knowledge score PKS25
PKS10
Jewelry Kitchen equipment/small appliances Lawnmower Luggage Model autos Motorcycle Music instruments Office accessories Outdoor furniture Phone equipment Pool table Rugs Serving cart Sewing machine Sofa Sports gear Stereo equipment Stove/oven Telescope Tennis equipment Television set VCR/DVD player Videos and CDs Washing machine/dryer Watch Otherb
26 24 17 27 13 24 20 8 25 18 17 15 27 11 48 53 22 38 21 8 30 10 12 27 21 48
50.0 50.0 23.5 25.9 53.9 25.0 45.0 37.5 32.0 38.9 29.4 53.3 33.3 9.1 50.0 39.6 54.6 42.1 19.1 25.0 36.7 50.0 33.3 74.1 57.1 35.4
26.9 20.8 11.7 22.2 30.8 4.2 25.0 37.5 12.0 11.1 11.8 33.3 18.5 0.0 31.3 24.5 31.8 1.9 4.8 25.0 26.7 0.0 8.3 25.9 28.6 12.5
a PKS signifies the percentage of respondents who provide price estimates within 25% of the actual price. PKS signifies the percentage of respondents 25 10 who provide price estimates within 10% of the actual price. b Other: all categories with less than five observations.
(PKS25 ) was found to be 39, indicating that on average, 39% of consumers were able to provide price estimates within 25% of the actual price. However, as also shown in Table 1, significant variations in price knowledge scores can be observed across categories. PKS25 ranged from a low of 7 to a high of 74. For example, while nondiscretionary product categories such as kitchen appliances, stoves, sofas, beds, and washing machines exhibit above average PKS levels, other less commonly possessed recreational and discretionary products such as golf equipment, sewing machines, grandfather clocks, and encyclopedias exhibited considerably lower price knowledge scores. Similar results are observed for PKS10 , signifying the percentage of respondents with price estimates within 10% of the actual price. In order to assess the statistical significance of price knowledge score variations across the 51 products, a chi square test was conducted. The observed variations were found to be significant at the p < .05 level for both PKS25 (χ2 = 69.4; Φ = 0.24) and PKS10 (χ2 = 71.3; Φ = 0.24). In general, several patterns are evident in the results. For example, products that are commonly owned by households, such as washing machines, beds, rugs, and bedroom furniture appear to have high price knowledge scores. Not only are these products that are frequently owned by consumers, but also due to their mass consumption, they are
frequently advertised in various forms of media. Moreover, due to the high level of competition in these mass market products, price may be an accurate indication of product quality, thereby increasing the diagnostic value of accurate price knowledge to the consumer. Other household items that are of a discretionary nature, such as outdoor furniture, baker’s racks, and lawnmowers exhibit comparatively lower levels of consumer price knowledge—with PKS10 scores of less that 15. This indicates that less that 15% of consumers can estimate prices for these product categories, within a 10% range of the actual price. However, the lowest levels of price knowledge seem to be exhibited by non-essential and niche-market products. For example, sewing machines, encyclopedias, and bar sets all display PKS10 levels of 0. This indicates that for these products not a single contestant was able to provide accurate price estimates, within 10% of the actual price.
Study 2 Overview In order to further examine the source of the variations observed in Study 1, a detailed measurement of specific
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product category characteristics – purchase frequency, category advertising exposure, and use of the price–quality cue within the category – would be needed. However, common sources of purchase frequency data (e.g., Marketing Fact Book, 2000) and advertising exposure (Simmons Market Research Bureau, 1998) primarily cover nondurable consumer goods such as grocery products and health and beauty supplies, and a complete coverage of the durable consumer goods identified through the Price is Right was unavailable through conventional secondary data sources. As a result, in this study, a complete measurement of these constructs was undertaken through an independent consumer survey. Data collection procedure A consumer survey was carried out to measure purchase frequency, price–quality cue utilization, and advertising exposure. These measures were obtained through a primary consumer survey, as no secondary data sources (e.g., Marketing Fact Book, 2000; Simmons Market Research Bureau) comprehensively cover the large array of products surveyed in this study. An objective measure of price knowledge was obtained by asking respondents to estimate retail prices for specific products presented to them. This approach to measuring price knowledge is consistent with earlier studies on consumer price knowledge (Conover, 1986; Dickson & Sawyer, 1990). A total of 36 products featured on The Price is Right were used. These were: baker’s racks, bar sets, barbeque grills, beds, bicycles, boats, chandeliers, drawer chests, grandfather clocks, dinette sets, encyclopedias, fireplace accessories, golf clubs, guitars, lawn mowers, motor scooters, pool tables, pre-recorded music CDs, sewing machines, snow blower machines, sofas, stereos, stoves, suit cases, trash compactors, treadmill machines, wine serving carts, telescopes, tennis rackets, TV sets, VCRs, wrist watches, refrigerators, and video cameras. For each product, the respondent was presented with the name of the product category and a picture of a typical product. Consumer measures of the three independent and one dependent variable were then obtained through single-item scales. Purchase frequency was measured by: “I’ve purchased or considered purchasing similar products before”. Price–quality cue utilization was measured by: “The higher the price of this type of product, the higher the quality”. Advertising exposure was measured by: “Prices for products like this are often advertised”. This measure of advertising exposure was utilized in order to more specifically gauge consumer exposure to price advertising rather than general advertising exposure. All independent variables were measured on a 1–7 Likert scale ranging from “strongly disagree” (1) to “strongly agree” (7). This was followed by presenting the respondent with a sample description of the product obtained from The Price is Right, and requesting a dollar price estimate. Price knowledge error was then estimated by estimating the percent deviation
between the estimated price provided by the respondent and the actual retail price of the product. Since 36 products were considered excessive for each respondent to evaluate, a total of six products were presented to each individual respondent. This was achieved by randomizing the 36 products, and partitioning the randomized set into six mutually exclusive sets. Each set was then administered to an individual respondent, with every six respondents providing a full set of responses on the products surveyed. The randomization procedure was done separately for each six set of respondents. Respondents were recruited though mall-intercepts and rewarded with their choice of a souvenir key chain. A total of 146 consumers participated in the study. The average age and household income of the sample were 32.5 and $67,000, respectively. The sample was 61% female, and 53% of the respondents were married or living with a spouse. Although this clearly is not a national sample, the respondent demographics closely resemble the underlying population where the survey was conducted. The median household income for the county where the survey was administered was $59,000 and the population is 53% female (Bureau of the Census, 2001). Cell sizes for the 36 products range from 23 to 27. Results Standardized regression estimates were obtained by regressing price knowledge error on the three independent variables—purchase frequency, advertising exposure, and use of the price–quality cue. These are reported in Table 2. As can be seen, the overall regression is significant at the p < .001 level. Purchase frequency has a negative impact on price knowledge error. This is evident by the negative and significant coefficient associated with purchase frequency (p < .01). Similarly, advertising exposure reduces price knowledge error (p < .01). In addition, the impact of the price–quality cue seems to be insignificant (p > .1). In order to further probe the impact of the independent variables on consumer price knowledge, a second regression was conducted in which the price knowledge score (PKS25 ) from Study 1 was used as the dependent variable. The results of this analysis are shown in Table 2. As can Table 2 Regression results for Study 2 (standardized beta estimates) Independent variable
Dependent variable Price knowledge error
Price knowledge score (PKS25 )a
Purchase frequency Advertising exposure Price–quality cue R2
−0.246∗∗∗ −0.217∗∗∗ −0.047 0.00
0.174∗∗∗ 0.280∗∗∗ 0.085∗∗ 0.15
a Represents percentage of respondents in Study 1 with price estimates within 25% of the actual price. ∗∗ p < .05. ∗∗∗ p < .01.
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be seen, the regression is significant (p < .01; R2 = 0.15). Moreover, the relationships between the independent variables and price knowledge are consistent with expectations. Purchase frequency has a positive impact on PKS, indicating that higher purchase frequencies result in more accurate estimations of prices. Similarly, both advertising exposure and the use of the price–quality cue has a positive impact on PKS. Interestingly, of the three independent variables, the price–quality cue exhibits the weakest impact on consumer price knowledge.
Discussion and conclusion Consumer durable goods account for a significant proportion of economic output in the Western world. Nevertheless, despite the large number of studies that have been conducted on consumer price knowledge over the years, very few have examined price knowledge for consumer durables. In this work, we profiled consumer price knowledge for a large array of consumer durable product categories in a field setting. Study 1 results indicate a wide range of price knowledge levels across the spectrum of consumer durable goods. Categories with recreational use were found to exhibit significantly lower levels of consumer price knowledge than categories considered as essential items. Furthermore, the findings of Study 2 indicate that purchase frequency has a positive and significant impact on price knowledge. This is an important finding since past research has primarily focused on frequently purchased consumer nondurables (e.g., butter, bread, milk, detergent, etc.) With little variation in purchase frequency levels, use of such low-involvement product categories may have contributed to the weak or null results of purchase frequency observed in past studies. The results also showed significant effects on price knowledge for consumer exposure to product category advertising. This indicates that in heavily advertised product categories, consumers will eventually develop a rich knowledge base for prices, and one result of advertising may therefore be the educating of the consumer population. The findings of this work are of use to marketing mangers, whose pricing practices may partially depend on an understanding of consumer knowledge of prices. Game-theoretic studies have long established that the optimal pricing strategy for a product may largely depend on how well consumers are aware of product prices and on consumers’ information search behavior (e.g., Bronnenberg & Vanhonacker, 1996; Lancaster & Ratchford, 1990; Ratchford, 1980). These works indicate that in categories where price knowledge is poor, and price search behavior is minimal, significant opportunities may exist for high-margin pricing. In contrast, price-setting flexibility is significantly reduced in product categories where consumer price knowledge is high, resulting in increased competitive pressure placed solely on prices. It is therefore essential for any product manager to not
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only know the average level of consumer price knowledge, but to also empirically gauge the potential existence of market segments, based on price knowledge levels. Results of the empirical work presented here reveal a significant amount of consumer price knowledge variation both across and within categories. Managerial knowledge of the proportion of buyers who possess high versus low levels of knowledge of prices may therefore be a driving factor in choice of pricing strategy as well as associated tactics in the communication of prices and product promotions. Categories in which a large proportion of consumers lack accurate knowledge of prices could provide managers with considerable flexibility in setting prices. On the other hand, a generally high level of consumer price knowledge may indicate a strong need for seeking competitive intelligence on prices, and the utilization of tactics such as price-matching guarantees and comparative price advertising in order to provide consumers with an increased level of confidence in their purchase decisions. It is important to acknowledge however that use of pricing tactics that depend on consumers’ ignorance of prices may have both ethical and legal consequences that should be carefully considered before the implementation of such tactics. Therefore, deceptive use of price information and the potential misuse of poor consumer price knowledge in a given category by sellers may be an important consideration for public policy and consumer protection initiatives. The results of this work expand existing research findings on price knowledge in several ways. While the majority of existing works have focused on nondurable products with high purchase frequencies (e.g., Conover, 1986; Estelami & Lehmann, 2001; Monroe & Lee, 1999) this work has expanded the horizon of current research findings by studying consumer price knowledge for low purchase frequency products. Moreover, in contrast to prior research that has largely ignored product category characteristics that may explain cross-category variations in consumer price knowledge, this work has systematically examined and isolated specific category determinants of price knowledge. The findings reveal that while two factors – namely purchase frequency and advertising exposure within a category – do have a positive impact on consumer price knowledge, use of the price–quality cue, despite it is theoretical appeal, has a significantly weaker effect on consumer price knowledge. While this work is the first large-scale analysis of price knowledge for consumer durables and its underlying drivers, several limitations need to be acknowledged. The use of the unconventional field setting of a television game show, used to obtain price knowledge scores in Study 1, may have created additional sources of variance in the data. Consumers’ arousal and emotion levels, as well as the demographics of the contestants may influence the accuracy of the price knowledge measures provided. It is important to note though that this would most likely influence all product categories equally, and therefore while the overall average of price knowledge measures may be affected by the measurement approach, the relative level of price knowledge scores across
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products is likely to be unaffected. The consumer survey results may have also been limited by the fact that some of the measures were obtained through consumer self-reports. These self-reports may have inherent biases associated with them, such as a social desirability response and inaccuracies resulting from measurement and nonsampling error (Churchill, 1996). Objective historical information – such as purchase frequencies and advertising exposure – could not be obtained on an individual level due to limited availability of such data for consumer durable goods, and absence of reliable secondary data sources. Clearly, this line of research can be expanded in several directions. For example, it would be very useful to expand consumer price knowledge studies beyond the scope of manufactured goods, and into the area of services. In doing so, it would also be useful to determine what factors may affect consumer price knowledge of services, and how the price information search and information acquisition processes might vary between goods and services. Moreover, the role of category characteristics, other than those studied here, on consumer price knowledge remains open to further study. These may include characteristics such as the technological sophistication of the products, its use as a luxury item or as a necessity, and the stage in the product life cycle. In addition, this research stream can be expanded by using measures other than consumers’ explicitly measured price knowledge levels. Use of other measures of price knowledge obtained or inferred through consumer transaction histories may provide further insights on this issue.
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