Consumer price knowledge in the market for apparel

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Consumer price Consumer price knowledge in the knowledge in the market market for apparel Peter Kenning and Heiner Evanschitzky Department of Retailing and Distribution, Marketing Center Muenster (MCM), University of Muenster, Muenster, Germany

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Verena Vogel School of Marketing, Curtin University of Technology, Australia, and

Dieter Ahlert Department of Retailing and Distribution, Marketing Center Muenster (MCM), University of Muenster, Muenster, Germany Abstract Purpose – The aim of this study is to analyze consumers’ price knowledge in the market for apparels. Design/methodology/approach – After reviewing earlier attempts at assessing the construct, the price estimation error “PEE” was used, a measure based on explicit price knowledge stored in long-term memory, as a valid indicator of price knowledge. Findings – The results, including data from about 1,527 consumers on 66 products from the German apparel market, indicate that price knowledge is relatively low. Originality/value – Although, in the literature, there are several studies on price knowledge in the food industry, little is known about price knowledge in other industry sectors. This is quite surprising since pricing strategy is a concept which is vitally important to all retailers. Therefore, this study is a first contribution to extending the concept of behavioral pricing to the apparel market. Keywords Behaviour, Pricing, Knowledge management, Retail marketing, Clothing Paper type Research paper

Introduction In the international market, since date, apparel quota restrictions have been lifted by the World Trade Organization. Owing to the overproduction, which is common in this industry, deregulation can cause prices to drop substantially, most likely resulting in increased in “price wars” in national markets (Apparel Magazine, 2004). In addition, fashion discounters such as Hennes and Mauritz, with about 800 stores in 14 countries and 206 in Germany alone, have developed a cult among young consumers seeking “designer” wear at bargain-basement prices (Jones and Gallun, 2004). In addition to fashion discounters, department stores are the other major suppliers of fashion/apparel. They are currently struggling against the fashion discounters. In total, annual turnovers in the German apparel market is approximately 57 billion euros. There are two major warehouse chains that earn about 7.5 billion euro, which equals a market share of 12.8 percent (BTE, 2005). This work was supported by grants from the Wilhelm-Lorch-Stiftung, Germany and the German Ministry for Education and Research (BMBF, No. 01HW0163). Moreover, the authors are grateful to Dr Brian Bloch for his comprehensive editing of the English version of the paper.

International Journal of Retail & Distribution Management Vol. 35 No. 2, 2007 pp. 97-119 q Emerald Group Publishing Limited 0959-0552 DOI 10.1108/09590550710728075

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Especially in the German apparel retail market, the marketing-mix instrument of “price” is currently used excessively to attract consumers to a particular product or store (GfK, 2000). Nearly, every advertisement emphasizes price. Over 90 percent of retailers in this market screen the prices of their competitors on a regular basis (Hartmann, 2000). However, information about consumer price knowledge is seldom taken into consideration when deciding on the price of a product. Therefore, it is necessary to develop a focus on consumer price knowledge when developing a new pricing strategy. It is common knowledge that price influences a customer’s buying decision (Monroe, 1973; Alba et al., 1999; Thomas and Morwitz, 2004). But are consumers really aware of the low prices charged by retailers? As a precondition to valuing a price as low, the consumer must have at least a vague idea of the “normal price.” Only if this concept of a normal price (the price a product usually costs in a regular fashion store, when there is no price promotion or end-of-season sale) is present, can consumers determine whether or not the offer is a bargain (Baumgartner, 2003). Price knowledge is a psychological construct that relevant to retailer success, since it influences both a consumer’s buying decision and the sales margin. At the same time, it can help the retailer to exploit consumer “willingness to pay.” There are at least four compelling reasons to examine the apparel market in Germany. Firstly, it is the second largest retail market in Germany, but suffering from a decrease of about 4 percent in 2003 (Statistisches Bundesamt, 2005). Both facts indicate the importance of the marketing instrument “price”. Secondly, investigating price knowledge in this market is useful and relevant, because of the short product-life-cycles of apparel. Thirdly, as Estelami and De Maeyers (2004) point out, the overwhelming majority of reported pricing studies are based on frequently purchased, low-involvement consumer nondurables (e.g. milk, detergent) (Evanschitzky et al., 2004). In contrast, apparel are medium to high-involvement goods, with relatively high price levels and product differentiation. Therefore, prices may play an important role in the decision-making process (Estelami and De Maeyer, 2004). A fourth, more practical reason is the general lack of consumer-based information in this market (Hartmann, 2000). Therefore, it may be useful for practitioners to gain some deeper insights into the level of price knowledge in the market for apparel. Accordingly, this paper commences with a brief overview of the conceptual background of the price-knowledge construct. This is followed by a summary of relevant price-knowledge studies over the past four decades. Three relevant indicators of price-knowledge are introduced and subsequently used to present our current price knowledge study. A discussion of the findings, and especially the fact that estimated prices are generally higher than actual prices, leads to the conclusion that, without losing consumers, sales managers may be able to increase revenue and c.p. profit through moderate price increases. The paper concludes with a brief outline of the limitations of our study and suggests areas for further research. Conceptual background The pricing of products in the German apparel market largely ignores consumer data. In particular, there is a failure to take into consideration the price knowledge and potential willingness to pay of the consumers. Instead, there is a usually cost or

competitor-orientated pricing policy. As Hartmann (2000) points out, only 50 percent of retailers include any information at all about consumer willingness-to-pay in their pricing policy. As a consequence, less than 50 percent of retailers sell more than 70 percent of their products at regular prices. Therefore, it might be useful to gain some insight into market-specific consumer price knowledge as a basis for improving pricing in practice. In order to achieve this objective, it is necessary, first and foremost, to define the construct “price knowledge.” The construct consists of two parts, “specific price knowledge,” which includes accurate, figure-oriented content, and “price feeling.” In terms of the latter, a consumer, for instance, has only vague (ordinal, or nominal) price knowledge expressed as a price judgment such as “expensive” or “inexpensive.” This distinction is made in the work of Monroe and Lee (1999), who distinguish between price knowledge as a part of implicit and explicit memories (for a general analysis see Miyashita (2004)). One possible reason for this might be that there are several memory and learning systems in the human brain (Rolls, 2000). Price knowledge, as part of the explicit memory, can be remembered consciously, while price knowledge as implicit memory, is an unconscious function (Sanfey, 2004). Therefore, it is possible that buyers would be unable to recall a price when asked, although at a sub-conscious level, price knowledge is present, for instance, in the form of an ability to identify whether a price is within a “normal” price range (Monroe and Lee, 1999; Bechara and Damasio, 2005). In our study, we operationalize price knowledge as the ability to keep a price in mind, even when not having recently been confronted with that particular price (e.g. the product has not just been purchased). That price is referred to as the “normal price” of a particular product. Moreover, we operationalize price knowledge as the ability to identify a price band, a range of acceptable prices ranging from an estimated “low” to an estimated “high” price of a certain product. Literature review Price knowledge has been a research object in behavioral pricing theory for more than 40 years. Many studies have focused on different products (e.g. food vs non-food products), places (USA vs other countries), and perspectives (e.g. macro-economic vs socio-demographic determinants). Additionally, these studies have used a range of different measurement methods for the construct. To the best of our knowledge, no study has focused exclusively on investigating consumer price knowledge of apparel. The differences in findings on price knowledge presented in Table I could (among other factors) be the result of socio-, macro-economic, and environmental determinants, product/product category characteristics, and research design characteristics (Estelami et al., 2001; Estelami and Lehmann, 2001; Vanhuele and Dre`ze, 2002). The socio-economic environment can lead diverging findings in methodologically similar price-knowledge studies. Vanhuele and Dre`ze (2002, p. 76) provide evidence of a significant difference between price knowledge in France and the USA. One possible explanation is that French consumers simply pay less attention to prices and, as a result, have a much lower level of price knowledge than their US counterparts. However, in contrast, Estelami et al. (2001, p. 350) found no cross-country variations in price knowledge between American and non-American consumers. However, they note that this may be due to the relatively small number of non-American pricing surveys

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Table I. Survey of selected previous price knowledge research (predominantly food retailing) 1,000 consumers, Finland

1,000 consumers, Finland

1,063 consumers, USA

Re-analysis of three studies

168 shoppers in a super-market, USA

66 adult women, USA

Aalto-Seta¨la¨ and Raijas (2003a)

Aalto-Seta¨la¨ and Raijas (2003b)

Brown (1969, 1971)

Buzas and Marmorstein (1988)

Conover (1986) 1st study

Conover (1986) 2nd study

Sample size, location of survey Subject under investigation

4 product categories (cola, flour, toothpaste, peanut butter)

9 product categories (milk, bread, mayonnaise, cola, margarine, coffee, detergent, orange juice, paper towels)

Analysis of the three data sets

8 grocery products (milk, margarine, coffee, sausages, soft drink, sugar, orange juice, frozen fish) 8 grocery products (milk, margarine, coffee, sausages, soft drink, sugar, orange juice, frozen fish) 80 products

Price knowledge results

Exact price recalls vary between 9.3 percent for peanut butter and 44.4 percent for flour Exact price recalls vary between 13 percent for peanut butter and 42.6 percent for flour (continued)

Six out of the eight differences in the medians are within 5 percent (average 7.9 percent) Long-term memory, explicit Three out of the eight price knowledge differences in the medians are within 5 percent (average 10.4 percent) Long- vs short-term memory: Price level rankings depend not clearly reported, implicit on the geographic area high price knowledge: price knowledge r 2Haverton ¼ 0.98; low price knowledge: r 2SF/St Louis ¼ 0.00 Meta-analysis Price recall accuracy is directly related to retail dealing, brand loyalty, private label penetration, average dollar purchase, and brand concentration ration Price recall accuracy is inversely related to manufacturer couponing, number of items, and purchase cycle Long- and short-term Exact price knowledge over memory, explicit and all products: 51.2 percent implicit price knowledge (from 35.6 percent for milk to 65 percent for bread)

Long-term memory, explicit price knowledge

Measure

Questioning at the point of sale after the purchase if something from the corresponding product category was bought Laboratory setting in which Long- and short-term memory, explicit and store shelves were located Phone survey two days later implicit price knowledge

Analysis of the three data sets

Personal observation and interviews with store managers

Telephone interview in Finland, October 2001 and March 2002

Telephone interview in Finland, October 2001

Method

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Author, year of publication

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Sample size, location of survey 802 consumers, USA

320 consumers, Germany

670 candidates of the quiz show “The Price is Right” USA

Meta analysis of 22 former studies

Meta analysis of 297 former studies


Dickson and Sawyer (1990)

Diller (1988)

Estelami (1998)

Estelami and Lehmann (2001)

Estelami et al. (2001)

Analysis of the data sets


10 product categories (toothpaste, chocolate, sparkling wine, aluminum foil, adhesive plaster, sugar, salt, ketchup, cooking oil, potato chips) 670 products in 29 product categories (e.g. toys, boats, chest, jewelry, home audio, telescopes)

4 product categories (toothpaste, margarine, coffee, cereal)

Subject under investigation



Meta analysis

Meta analysis

Analysis of the data from the Long-term memory, explicit quiz show price knowledge

Phone survey

PAD over all products: 30.9 percent Price knowledge over all products in a 20 percent range varies between 10 percent for pool tables and 57 percent for dishwashers A significant amount of variation in the accuracy of consumers’ price recall is related to research design characteristics such as the presence of financial rewards, respondents’ task size and the price elicitation Economic expansion (as reflected by GDP growth rates), inflation, interest rates and passage of time decrease consumer price knowledge. No significant effects of unemployment rate and country of study on price knowledge were found (continued)

Exact price knowledge over all products: 47.1 percent Price knowledge over all products within a 5 percent range: 55.6 percent Long-term memory, explicit Medium price: 84.7 percent and implicit price knowledge Price ranking stores: 26.2 percent Price ranking brands: 61.7 percent Long- and short-term memory, explicit price knowledge

Questioning at the point of sale after the purchase


Measure

Method


Table I.

Table I. Measure

4 products (jeans, 10-speed bicycles, soap, toothpaste)

Long- and short-term Questionnaire (25 minutes after product presentation), memory, explicit price phone survey two days later knowledge


Interview with the test 383 female consumers, Israel 1 product category (meat) persons at home with 3 products (fresh poultry, fresh beef, imported frozen beef)

Goldman (1977)

260 students, USA


422 female consumers, Great 7 products (tea, coffee, sugar, Interview with the test Britain jam, margarine, flour, cereal) persons at home (cited from McGoldrick and Marks, 1987, p. 66)

Gabor and Granger (1961) (cited from Mu¨ller and Mai (1986, p. 107))

Helgeson and Beatty (1987)


Detergents, dairy products, Questioning at the point of retail foods, shopping basket sale before the purchase

993 consumers, Germany

Evanschitzky et al. (2004)

Study 1: observations of 201 Long-term memory, explicit price knowledge broadcasts Study 2: consumer survey (N ¼ 146)

Method

Two studies in the USA. One Durable consumer goods study quite similar to Estelami (1998)


Estelami and De Maeyer (2004)

Sample size, location of survey

Purchase frequency has a negative impact on price-knowledge error. Similarly advertising exposure reduces price knowledge error. The impact of price-quality cue is insignificant Low price knowledge in Germany (especially for retail brands), high price uncertainty Exact price knowledge over all products: 59 percent (between 79.3 percent for tea and 34.8 percent for cereal) Price knowledge over all products within a 5 percent range: 65 percent (cited from McGoldrick and Marks, 1987, p. 66) Price knowledge over all products within a 5 percent range: 51 percent (between 46 percent for fresh beef and 58.2 percent for poultry) Accurate price recall after 25 minutes: soap 75 percent, toothpaste 58 percent, bicycle 53 percent, jeans 28 percent Accurate recall after 2 days: soap 62 percent, toothpaste 45 percent, bicycle 43 percent, jeans 20 percent (continued)


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400 consumers in a supermarket in NY, USA

1,600 consumers, Finland

235 consumers in New Hampshire, USA

578 female consumers, Germany

623 consumers, USA

90 female consumers, USA

Kujala and Johnson (1993)

Le Boutillier et al. (1994)

Lenzen (1984)

Manning et al. (2003)

Mazumdar and Monroe (1990)

Sample size, location of survey

Krishna et al. (1991)

Author, year of publication Long-term memory, explicit price knowledge

Mail survey and personal interview


Short-term memory, explicit price knowledge

Long- and short-term memory, explicit and implicit price knowledge

Long-term memory, implicit Questionnaire in a widely used metropolitan public rail price knowledge system

4 product categories (orange Experiment juice, canned soup, cereal, pasta)

Unspecified grocery products

Long-term memory, explicit Interview with the test 10 products (toast, persons at home about prices price knowledge margarine, coffee, cheese, jam, butter, eggs, roast pork, of three stores liver sausage, brandy)

2 product categories (coffee and soda)

Not clearly reported

Measure

Method

Fresh produce (e.g. fruits and Mail survey in 1987 vegetables) and fresh meat

9 products (Coke, Pepsi, Minute Maid, 7UP, Sealtest, Dolly Madison, Brawny, Bounty, Ruffles)

Subject under investigation Exact price knowledge over all products (regular price): 15.0 percent Exact price knowledge over all products (special offer): 19.5 percent Price knowledge and search behavior are not directly related. Rather, they each have price importance as a common antecedent Exact price knowledge over all products: 61.3 percent (between 71.3 percent for soda and 45.7 percent for coffee) Exact price knowledge over all products: 0.7 percent Price knowledge over all products within a 5 percent range: 3.5 percent Percentage of lifetime spent in the USA, education and price consciousness had a positive effect on consumers’ price usage knowledge Percentage of household shopping and income had no impact Exact price knowledge over all products: 47.78 percent (between 42.2 percent for soup and orange juice to 53.3 percent for pasta and cereal) (continued)



Table I.

Sample of 59 women from two cities, USA

400 consumers in a supermarket, France

Urbany and Dickson (1991)

Vanhuele and Dre`ze (2002)

59 products

Several thousand shoppers, USA

312 students in Wisconsin, USA

25 products

480 households, USA

Partch and Litwak (1990) (cited from Wakefield and Inman (1993, p. 230)) Progressive Grocer (1964) (cited from McGoldrick and Marks (1987, p. 66))

Stephens and Moore (1975)

1 product category (wine)

383 female consumers, Germany

Mu¨ller and Mai (1986)

18 products (e.g. mayonnaise, paper towels, sugar, soup, tuna, toothpaste, cheese, coffee, orange juice, coke, salad) Eight products (toilet paper, mayonnaise, yogurt with fruit, liquid detergent, granulated sugar, mineral water, milk, toothpaste)

9 products (e.g. milk, bread, ice cream, TV consoles, radio, regular gas, refrigerators, soap)

10 product categories (coffee, beans, fish fingers, cereal, canned soup, flour, sauces, extracts, vegetable oil, digestives)

214 female shoppers in two supermarkets, Great Britain


McGoldrick and Marks (1987)

Table I. Sample size, location of survey

Price recall , 50 percent

Exact price recognition over all products: 2.1 percent Price recognition over all products in a 5 percent range: 21.3 percent (continued)

Price knowledge over all products within a 5 percent range varies between 91 percent (Coca-Cola) and 12 percent (Nestle´ Quick) Long-term memory, explicit High-school students know price knowledge the prices of six out of nine products (e.g. milk, soap, gas) better than grammar-school students. Indeed, the price knowledge for ice cream and milk is lower Long-term memory, explicit Exact price knowledge over and implicit price knowledge all products: 53 percent

Short- and long-term memory, explicit price knowledge

Long-term memory, implicit Survey at the point of sale before the shopping occasion price knowledge of products that are usually bought at this location

Interview with the test persons at home

One-hour self-administered questionnaire

Questioning at the point of sale regardless of whether the items were used or not

Not clearly reported


Exact price knowledge over all products: 28.7 percent (from 18 percent for oil to 39 percent for beans) Price knowledge over all products within a 5 percent range: 54.6 percent (from 20 percent for extracts to 69 percent for fish fingers) Exact price recall: 65.5 percent


Questioning at the point of sale after the purchase if something from the corresponding product category was purchased

Questioning at the point of sale directly after the purchase Phone survey


Measure

Method

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Sample size, location of survey 289 consumers, USA

67 consumers, USA

160 female consumers, USA


Wakefield and Inman (1993)

Wilkinson et al. (1980)

Zeithaml (1982) and Zeithaml and Fuerst (1983)


90 products in 12 categories (e.g. beans, peas, deodorant, soap, dish detergent, ketchup, salad dressing, peanut butter, paper towels)

32 unspecified supermarket items

4 product categories (toothpaste, coffee, margarine, cereal)

Laboratory setting which simulated the conditions of exposure

Personal, inhome interview




Exact price knowledge over all products: 55 percent Price knowledge over all products within a 5 percent range: 8.7 percent Long- vs short-term memory: Price knowledge and specific not clearly reported, implicit self-confidence were positively related price knowledge Short-term memory, explicit Exact price recall error price knowledge between 17.16 and 26.26 percent depending on the age of the consumers

Measure

Method


Table I.

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included in their analysis. Even in different parts of a single country, in this case the USA, price knowledge varies (Brown, 1969; Table I). The reason for this distinction is that respondents in different cities rely on the same price signals, which indeed vary, depending on the number of shopping opportunities (Brown, 1969). As additional considerations, some studies focus on external/macro-economic determinants of price knowledge, such as the effects of inflation, unemployment, GDP growth, and interest rates. Estelami et al. (2001) found a positive relationship between economic growth, inflation rate, and price recall error. It has also been documented how the changeover to the euro has affected consumer price knowledge in Finland (Aalto-Seta¨la¨ and Raijas, 2003b). As indicated earlier, the lack of similarity between products and product categories (e.g. food and non-food) leads to different results. Note that Table I shows studies primarily dealing with grocery products. For example, one survey indicates that in heterogeneous product categories with a larger price range and more product references (e.g. toothpaste), the accuracy of consumer price estimations is lower than in homogenous categories (e.g. sugar) (Vanhuele and Dre`ze, 2002). For instance, if the price range is large, the product group will be associated in the memory with many different prices, and therefore becomes an unreliable cue for remembering any given price. In the first group, the increased complexity of price information can have a negative impact on memory performance (Vanhuele and Dre`ze, 2002). Nevertheless, there are conflicting results with respect to the price knowledge of frequently purchased products. Within the context of grocery shopping, fast-moving grocery products such as coffee, milk, and butter are characterized by a higher level of price knowledge than products purchased less frequently (Estelami, 1998; Le Boutillier et al., 1994; Krishna et al., 1991). In their meta-analysis, Estelami and Lehman (2001) do not find any support for the hypothesis that frequently purchased goods can be described by a higher level of price knowledge than services and consumer durables. They found no evidence of significant variation in price recall accuracy across the three above-mentioned product categories, although, due to a high level of shopping experience in a certain category, consumers may have developed a better memory for relevant price information, e.g. for frequently purchased goods (Estelami, 1998). It has been proven that a significant amount of variation in price knowledge is related to research design characteristics, such as the offer of financial rewards and the price elicitation approach (Estelami and Lehmann, 2001). As shown in Table I, the most common sampling methods are telephone interviews, mail questionnaires, and face-to-face interviews at the point of sale, in a laboratory or in the testperson’s home. These methods influence the accuracy of price knowledge, as evidenced by the results of two studies from Conover (1986, p. 592) which are based on different methods. Interviews administered before shopping, as consumers enter the store, reveals information about price knowledge stored in long-term memory (Evanschitzky et al., 2004). Post-purchase interviewing, on the other hand, reveals what is stored in the short-term memory. This knowledge comes from perceived price information at the point of sale (Vanhuele and Dre`ze, 2002; Monroe and Lee, 1999). The use of different price knowledge definitions is closely connected with the research design, as shown by Monroe and Lee (1999, p. 213). Some researchers (Dickson and Sawyer, 1990; Estelami, 1998; Wakefield and Inman, 1993; Zeithaml, 1984) use the “Percentage Absolute Deviation” (PAD), and others define different levels

of price knowledge (e.g. prices falling within a range, such as ^ 5 percent, see: Goldman (1977) and McGoldrick and Marks (1987)). Another group considers any answer about the price, irrespective of correctness (Goldman, 1977). The different focuses of these various studies complicates the comparability of results (e.g. effects of promotions on price knowledge) (Krishna et al., 1991). Some researchers attempt to confirm hypotheses about socio-demographic aspects of consumers such as age (Zeithaml and Fuerst, 1983; Aalto-Seta¨la¨ and Raijas, 2003a; Stephens and Moore, 1975), income (Estelami and Lehman, 2001; Goldman, 1977) and gender (Estelami and Lehman, 2001; Wakefield and Inman, 1993) while others explore price knowledge in a global consumer context (Conover, 1986; Estelami, 1998; Manning et al., 2003; Urbany and Dickson, 1991). Even studies focusing on the socio-demographic determinants, reveal different results. For example, some surveys show price-knowledge differences depending on age differences (Brown, 1971; Zeithaml and Fuerst, 1983), whereas others do not find such differences (Estelami, 1998; Goldman, 1977; Krishna et al., 1991; McGoldrick and Marks, 1986; Wakefield and Inman, 1993). This could be due to the combined effects of the design characteristics (Estelami and Lehman, 2001). The use of generalization and comparability in studies requires careful consideration, because different methods, content, and definitions can lead to different results. Even in cases where almost identical procedures are used, different results have been found (Table I). By extending the construct of price knowledge of apparels to the German market, we add to the ongoing debate on consumer price knowledge. It is the first such study conducted in Germany. Data collection and methodology For our study, we collected data on the accuracy of apparel shopper price knowledge. The sampling time frame was August 2003. Interviewers were stationed in outlets that are part of a major German warehouse chain, located in nine different cities. The management of the chain selected these nine locations (out of a total of 84) as a representative sample of the store chain as a whole. The investigated department-store chain has more than 27,000 employees and a turnover of approximately 3.8 billion euros. The underlying retail brand is well-known and the positioning of the chain is slightly up-market. The target-consumer is older than 35 years and has an above average income. Presenting the products for which prices should be estimated, the interviewers approached 1,527 consumers directly as they entered the store. Consumers were asked to estimate the price of that particular product. More precisely, we gave the following instructions to the respondents: We have given you a list of products. Please estimate the normal price of each product. Do not think only about the price these products usually cost at [this particular retailer], but also about their prices at other retailers you shop at. Also, for each product, try to remember a particularly low and a particularly high price you have seen in the past.

Following Vanhuele and Dre`ze (2000), no incentives were given to participants in order to avoid attracting price-sensitive shoppers. The method of interviewing shoppers immediately after they enter the store, though contrary to many earlier studies (Conover, 1986; Dickson and Sawyer, 1990; Le Boutillier et al., 1994; McGoldrick and Marks, 1987; Wakefield and Inman, 1993) which requested product prices immediately


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after purchase, avoids measuring price knowledge based on price recall from short-term memory and focuses instead on price knowledge from long-term memory. By doing so, we follow the method of Vanhuele and Dre`ze (2002, p. 74) in measuring long-term price knowledge by asking consumers at the start of the purchasing process[1]. In this way, we maximize the number of contextual cues (e.g. beginning of a purchase in a particular store) to ensure the presence of generally available shopping knowledge and to avoid the bias of in-house surveys such as those described by Urbany and Dickson (1991) or those of surveys in public places (e.g. in the railway network, as with Manning et al. (2003)). Moreover, we are able to avoid measuring consumer capability to store price information in short-term memory, as can be the case when asking immediately after purchase. Price knowledge stored in long-term memory is more likely to affect a buying decision, than the price knowledge stored in short-term memory. Another important issue that must be addressed when selecting a methodology is the conceptual distinction between explicit and implicit memory and the respective price knowledge. Explicit price knowledge involves the conscious retrieval of factual information, whereas implicit price knowledge involves subconscious information storage, which nonetheless influences buying behavior (Estelami and Lehmann, 2001). Explicit price knowledge is usually operationalized by asking the exact price of a certain product, whereas implicit knowledge can be assessed by offering a semantic differential such as “more expensive – less expensive” or “good value – poor value” (Coulter, 2003). In our study, we focus on measuring explicit price knowledge stored in long-term memory. It is not unusual for a consumer to decide whether or not to buy something in a certain store by comparing some price information (e.g. in a brochure or poster) with his or her explicit price knowledge stored in long-term memory. Therefore, we asked shoppers a variety of questions relating to both their price knowledge and their demographics. The sample consisted of 25 percent males and 75 percent females, with an average age of 40 years and an average monthly net income of 1,735 euros (about 2,065 US dollars). The high percentage of women in the sample is not critical, because this figure approximates that of the store’s overall apparel clientele. In addition, most products in our sample were fashion apparel for women. Moreover, many former studies have analyzed price knowledge of women (Table I; Goldman, 1977; Mazumdar and Monroe, 1990) or included a high percentage of women in the sample (Conover, 1986: 80.6 percent females). With respect to age-group differentials and income, the panel was found to be quite representative of the German population (Statistisches Bundesamt, 2005). For our survey, we selected a total of 66 products from four product groups: ladies’ wear, menswear, shoes and underwear[1]. (For details on products and product groups, see Table II.) The consumers were asked to indicate the normal, low, and high price for each product. In this way, we were able to determine customer estimations of the normal price of a particular product and, additionally, a price band, a range of acceptable prices ranging from “low” to “high” for that particular product (Monroe and Lee, 1999). The width of this range can be interpreted as an indicator of price uncertainty (Adam, 1958). We did not use the information about the price band to calculate a mean value of, for example, high price and low price (similarly, Helgeson and Beatty, 1987) as a proxy for “normal price.” We asked shoppers directly to name

Top T-shirt Blouse Corduroys Pullover A Polo-shirt Shirt with stripes Pullover B Comfort-pants Jeans Skirt Summer dress Jacket Coat Leather jacket Leather trousers Trouser suit Blazer Cocktail dress Average Trotteur A Trotteur B Trotteur C Casual shoes Trotteur D Trotteur E High heels

Ladies’ wear, absolute PEE ¼ 27.74 percent

Shoes, absolute PEE ¼ 37.95 percent

Product

Product group

99.07 141.54 167.66 110.27

73.48 110.36 134.70 76.09 42.99 50.22 47.94 42.98 61.23 50.36 61.53

31.07 48.10 52.01 49.33 36.49 41.55 82.09 163.76 152.60

23.10 37.76 39.04 36.69 29.19 31.69 62.34 121.80 116.35

25.43 31.21 25.22 22.58 40.72 29.67 37.31

16.73 17.31 33.12 50.80 42.20 21.97

Estimated normal price (e)

12.04 11.60 22.40 39.46 30.17 16.18

Estimated low price (e)

62.39 76.65 74.30 48.31 87.91 66.44 80.75

121.96 191.86 223.06 143.08

42.01 63.57 64.06 67.16 49.87 56.62 104.93 207.37 189.70

23.14 23.48 50.24 65.86 57.02 31.12

Estimated high price (e)

25.00 25.00 25.00 39.95 49.95 49.95 49.95

79.95 99.95 119.95 89.95

34.95 24.95 49.95 39.95 29.95 39.95 75.00 199.95 159.99

14.95 9.95 39.95 79.95 54.95 14.95

Actual sales price (e) 63.13 67.74 76.65 50.14 61.59 63.16 58.08 50.95 48.54 58.68 52.32 56.46 50.93 51.99 47.93 49.61 53.94 49.40 61.13 56.44 84.17 84.25 98.62 72.61 73.38 76.53 73.60 (continued)

2 11.91 2 73.95 17.09 36.46 23.19 2 46.99 11.10 2 92.79 2 4.12 2 23.47 2 21.85 2 4.01 2 9.45 18.10 4.62 2 23.91 2 41.61 2 39.78 2 22.59 2 71.98 2 100.88 2 91.74 2 7.57 2 22.59 2 0.82 2 23.18

Price band * (percent)

PEE * (percent)


Table II. PEEs for the four product groups (results well rounded)

Table II.

Menswear, absolute PEE ¼ 24.55 percent

Underwear, absolute PEE ¼ 35.18 percent

Trotteur F Trotteur G Average Slip A Slip B Slip C Nightdress A Nightdress B Nightdress C Bra A Bodice A Bra B Slip D Undershirt Slip E Nightdress D Bodice B Bra C Skirt Undergarment Bra D Average T-shirt Polo-shirt Sweatshirt Shirt A Shirt B Slips A Shorts Slacks Jeans

Product

8.83 11.40 25.55 17.85 21.26 6.97 8.18 34.36 31.43

3.73 3.68 7.65 16.93 14.96 15.57 12.58 11.24 12.78 4.40 7.48 5.04 16.86 6.52 16.04 11.00 13.16 19.08

34.15 31.37

Estimated low price (e)

15.04 18.94 39.80 27.05 32.58 11.12 13.91 46.62 45.30

7.07 7.13 11.44 25.37 23.77 22.27 16.93 14.09 18.30 9.27 11.92 7.74 23.12 11.83 21.71 18.24 20.47 25.89

59.31 51.99

Estimated normal price (e)

23.82 29.72 52.69 38.59 46.04 15.25 20.44 65.31 63.66

7.79 7.87 14.02 29.35 27.41 25.79 21.80 15.31 23.16 9.35 12.95 10.68 24.59 12.84 25.72 18.71 24.79 32.66

74.96 74.08

Estimated high price (e)

9.95 9.95 32.95 34.95 34.95 9.95 12.95 39.95 39.95

3.95 4.50 9.95 21.45 21.45 16.95 15.95 10.95 13.95 4.95 7.49 9.95 22.95 7.95 31.45 14.95 18.95 35.95

49.95 49.95

Actual sales price (e)

2 51.17 2 90.37 2 20.79 22.61 6.79 2 11.75 2 7.45 2 16.69 2 13.38

2 97.09 2 74.93 2 14.97 2 18.29 2 10.82 2 31.36 2 6.14 2 28.67 2 31.15 2 87.35 2 59.12 22.21 2 0.72 2 61.45 30.98 2 21.98 2 8.01 27.99

2 18.74 2 4.08

PEE * (percent)

110

Product group

74.82 81.01 79.89 61.81 63.76 58.72 53.69 58.75 49.40 53.66 30.64 57.76 71.97 53.52 71.79 37.28 57.97 46.35 51.93 61.34 52.49 55.16 91.86 89.13 69.36 73.50 73.63 74.54 85.64 62.11 67.79 (continued)

Price band * (percent)

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Product group Cityshirt A Cityshirt B Tie Slips B Nightgown Pullover Sports jacket Suit Coat Trousers Leather jacket Average

Product 23.40 25.34 11.68 9.34 21.01 26.76 71.77 122.74 78.08 39.69 118.30

Estimated low price (e) 32.78 37.82 18.86 12.92 32.27 38.31 103.87 184.25 110.15 58.50 180.60

Estimated normal price (e) 42.87 48.32 29.23 18.59 42.71 50.69 142.09 252.74 130.36 70.63 262.53

Estimated high price (e) 36.95 36.95 14.95 12.95 29.95 24.95 79.95 119.95 169.95 49.95 159.95

Actual sales price (e)

Price band * (percent) 58.74 62.40 85.79 66.19 68.13 61.81 65.77 69.24 50.16 56.09 75.74 70.38

PEE * (percent) 11.29 2 2.36 2 26.14 0.21 2 7.75 2 53.55 2 29.91 2 53.61 35.19 2 17.12 2 12.91


Table II.

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the “low,” “high,” and “normal” prices of certain products. In order to calculate the price estimation error (PEE), we used the direct measure of the normal price estimation of shoppers. The price-knowledge data was calculated at the level of the individual product and then aggregated to the level of the four product group (“ladies’ wear,” “menswear,” “shoes,” and “underwear”) in order to generalize our findings. The accuracy of price estimation was measured by three indicators. Firstly, non-responses were used as a proxy for “no price knowledge.” This measure of price knowledge is simple and counts the percentage of respondents who were unable to estimate prices at all. Nonetheless, it is important to identify persons with no price knowledge at all. If they were “forced” to make estimations, the results could be biased. After excluding the group of non-responses, we calculated the width of the price band as an indicator of price uncertainty. This is calculated by subtracting the estimated high price from the estimated low price and dividing the result by the mean value of the two prices. By so doing, we obtain a percentage-value which is comparable across all products. Apart from these rather simple indicators, we measured consumer price knowledge with a third indicator: the PEE. This indicator is based on the PAD (Dickson and Sawyer, 1990; Estelami, 1998; Mazumdar and Monroe, 1992; Wakefield and Inman, 1993). It is calculated as follows (Evanschitzky et al., 2004; similar, Estelami and De Maeyer, 2004): PEE ¼

Actual price 2 Estimation of normal price Actual price

Because, we asked consumers to indicate the normal price of a particular product, we were able to calculate the error of estimation of the normal price. A greater difference between the estimated normal price and the actual price, results in a higher absolute value of the PEE and, consequently, in a lower level of consumer price knowledge. In addition to the absolute value of the PEE, its algebraic sign can be interpreted as follows: a positive PEE means that the actual sales price is higher than the expected price, and hence, the sales price is underestimated. The converse, a negative PEE, implies an overestimation of sales prices. Results The following results relate to German consumer price knowledge of four product groups. Independent of the product group, it is evident that the price knowledge of German consumers is relatively low, compared to results obtained from other product categories and countries (Estelami and Lehmann, 2001; Estelami et al., 2001). This is reflected by the high percentage of non-responses in the survey. On average, less than 26 percent of the consumers have any idea of the price of a particular product[2]. That figure varies from 14 percent of consumers having at least a vague idea of the price for shoes, to 37 percent for menswear. A test of the two groups (those who failed to estimate prices and those who did not fail) yielded no significant differences with respect to age, gender, income, and education. The width of the price band, as an indicator of price uncertainty, varies from 55.16 to 79.89 percent at the group level. An analysis of variance with price band as the dependent variable and product category as the independent variable, indicates that observed variations are significant ( p , 0.01; F ¼ 19.871). As noted,

we operationalized consumer price knowledge with a modified version of the PAD, the PEE. We calculated this indicator for all products. We then aggregated data of the four product groups. Moreover, Table II shows the price band of all products and the average price band of each group of products. Price knowledge ranges from 2 100.88 to 36.46 percent at the product level. The range of absolute PEE extends from 24.55 to 37.95 percent at the group level. It should be noted that group means do not differ significantly ( p . 0.1; F ¼ 1.087). This finding is consistent with Estelami and Lehmann (2001), who found no significant impact of product category on price recall. Price knowledge is lowest for shoes. For all 66 products, the average absolute PEE is 30.19 percent. In contrast to our results, Estelami and Lehmann (2001) explored 250 price-knowledge studies and found that the average PAD for consumer durables is 22.29 percent and, for frequently purchased goods, 14.0 percent. In our study, the absolute PEE is even higher than for consumer durables (e.g. automobiles, home electronics), which again confirms the conclusion that apparel price knowledge in Germany is relatively low. That is particularly surprising and relevant, since most fashion apparels are medium- to high-involvement products and one would expect higher price knowledge than for consumer durables, for instance. In examining at the PEE (including the algebraic sign), we conclude that consumers estimate prices at too high a level. A PEE with a negative sign indicates that actual prices are lower than estimated prices. Consumers overestimate prices in almost 80 percent of all cases. Looking at individual products, we note that the price knowledge of shoes (“trotteur B”) with a PEE of 2 100.88 percent is the lowest. A pair of cords (“corduroys”) has a PEE of 36.46 percent, which is the maximum positive average deviation of all products. The finding that negative variations from the actual price are greater than the positive, are also obtained for many other products (Table II). The data suggests that the PEE is predominantly negative. Positioning the actual price in the context of the estimated low and high prices, four different cases can be identified (Table III). First of all, it is possible that the actual price is even lower than the expected low price (position 1). This case occurs for 18.18 percent of all products. Earlier studies confirm this overestimation of actual prices (Aalto-Seta¨la¨ and Raijas, 2003a, b; Conover, 1986). An overestimation of prices can occur, even if the actual price is higher than the estimated low price, or, more precisely, if it is located between the low and normal estimated price (position 2) which applies to a total of 60.61 percent of all product estimations. The remaining 21.21 percent (positions 3 and 4) underestimate prices, while only 6.06 percent of the products are more expensive than the estimated high prices (position 4).

Position of actual sales price in relation to estimated price (1) Actual sales price , estimated low price (2) Estimated low price , actual sales price , estimated normal price (3) Estimated normal price , actual sales price , estimated high price (4) Estimated high price , actual sales price

Percentages of actual sales prices which can be situated here (percent)

Estimation error

18.18

Overestimation

60.61

Overestimation

15.15 6.06

Underestimation Underestimation


Table III. Position of the actual price in relation to the estimated low, normal and high prices

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Discussion Generally speaking, the price knowledge of German consumers on apparels is rather low. This is indicated by the results which reveal that more than 26 percent of consumers assert that they have no price knowledge at all. In addition, we discovered high price uncertainty, indicated by a wide price band up to 100 percent at the product level, and about 80 percent at the product-group level. The reason for the relatively low price knowledge can be explained by frequent variation in apparel prices (usually unnoticed by consumers) and a lack of consumer awareness, because of the relatively short life-cycles of apparels. Therefore, it is difficult for consumers to establish a consistent estimation of the price level. What we were not able to test in our study, was the impact of retail image on price perception. Srivastava et al. (2001) observed that consumers are less likely to search actively for prices if price-matching refunds are offered. Such policies are likely to create a strong and favorable price image of a retailer. However, our retailer does not apply such a policy. Therefore, we would not expect that our retailer has such a strong price image that this may cause consumers to have low price knowledge. Relatively low price knowledge is evident with respect to shoes. Possible reasons could be the lower shopping frequency in contrast to the three other groups. A low shopping frequency implies that the consumer is seldom confronted with these products and therefore does not learn much about prices. It can be noted that consumers not only demonstrate low price knowledge, but they also tend to overestimate prices. This fact is expressed by a negative PEE, a finding which indicates that the actual prices are lower than expected. An important implication of these results is that product pricing can be modified. Since, the actual prices in the product groups are generally overestimated, retailers have room for price variation. Moderate price increases would not convince consumers that the product is “expensive,” and they would still buy the product even at a higher price. The discussion shows that the pricing of products should not be based exclusively on neoclassical price theory, but also on consumer price knowledge. By doing so, German retailers, in particular, could improve their low revenue by increasing prices moderately for products with low price knowledge. This seems quite feasible, because, in contrast to other similarly-conceptualized studies that focus on the food industry (Evanschitzky et al., 2004), the PEE in the apparel market is, despite its medium to high-involvement character, rather low. This may be due to substantial price and product-differentiation in the apparel market and the short life-cycles of fashion products (Hartmann, 2000). Differentiation could, therefore, be a major element of future pricing strategies. Limitations of the study and future research Our study generally confirms much of the earlier research on price knowledge. Price knowledge is, in comparison to other branches relatively low and depends on the specific products evaluated (Table I). Accordingly, our understanding of the construct “price knowledge,” which has been tested mainly in the Anglo-American market, has now been extended to the German market without, however, explicitly making a cross-cultural comparison. To the best of our knowledge, no studies have been published in major journals that explicitly compare certain measures of price knowledge in a cross-cultural setting. This is important, since the impact of a

particular culture on price awareness is generally acknowledged (Durvasula et al., 1993; Estelami et al., 2001; Ger and Belk, 1996; Keegan and Schlegelmilch, 2000; Senauer et al., 1993). Moreover, it is crucial for companies that act globally (Boss, Benetton) to find out whether consumer price knowledge varies across countries. This applies especially to retailers which aim to operate more internationally in the future (Hennes & Mauritz, Wal-Mart). Further research is needed to fill this obvious void in cross-cultural price knowledge research. As with all studies, ours suffers from some limitations. Since, the research design follows a majority of earlier studies, some generalization is possible, but not all earlier studies can be included in a meta-analytic procedure. Moreover, we use PEE as a measure of price knowledge and also aggregate it at the product-group level. Our data was collected in August 2003, a year and a half after the introduction of the euro. Owing to this currency change, consumers may not yet be familiar with prices in euros. A change in consumer perceptions of price levels could very well have taken place in the meantime (Aalto-Seta¨la¨ and Raijas, 2003a). This fact could explain the relatively poor price knowledge measured by our survey. The examination of longitudinal data on PEE for the German market is a logical starting-point for further research. Another reason for the relatively poor price knowledge, as evidenced by our study, could be the measurement approach employed. Because we asked for the exact low, normal, and high price, we focused primarily on explicit memory, ignoring information on prices stored in implicit memory. Even though some consumers could not have recalled prices accurately, they could have an adequate general price feeling. For example, they could indicate if the sales price is relatively low or high with respect to other products. Thus, this implicit knowledge can influence behavior even though it often cannot be remembered (Vanhuele, 2002; Monroe and Lee, 1999). In our study, we did, however, not explicitly test if the consumers might at least be able to judge a favorable “value for money ratio” of a product. Thus, this implicit knowledge can influence behavior even though it often cannot be recalled. We, therefore, recommend further research to control for that effect, which would broaden our knowledge of price awareness. Another limitation relates to the product category in which we analyzed price knowledge. Our study tested apparel products. Thus, it is possible that our findings are influenced by product life-cycles or shopping frequency in this sector. There is reason to believe that products characterized by shorter life-cycles (e.g. fashion apparel) may be characterized by even lower price knowledge than products in the food sector, for example. Further research is needed in this respect. Furthermore, we focused only on a single apparel department store chain in order to increase the validity of our comparison of prices remembered, in contrast to actual store prices (as in Vanhuele and Dre`ze’s (2002) survey). Because the price data was collected in large outlets, the selected products have a lower average price level than in small stores. This fact can explain the identified overestimation of sales prices. Differences in price knowledge, depending on the type of store (e.g. department store vs boutique), could be a fruitful avenue for future research. However, we do not believe that there are severe limitations in the generalization or substantive implications of our results. The retailer we analyzed is typical in size and success for the German market. Moreover, we were able to control the most important


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environmental variables, especially sales promotions, during the survey period. Nonetheless, as indicated above, we would recommend replications of this study in different environmental and cultural contexts. In particular, longitudinal data and studies of cross-cultural price knowledge would further improve our understanding of one of the most heavily researched constructs in pricing theory: price knowledge. Notes 1. All participants had to evaluate all products. To control for the effects of relevance and experience with certain products, we constructed two sub-groups of products: menswear and women’s wear. We then constructed two sub-samples, one of men, the other of women. Generally, men have a slightly better price knowledge when evaluating men’s wear and women have slightly lower price knowledge when evaluating ladies wear. Nonetheless, we need to acknowledge that the overall price knowledge is low in both groups. Therefore, we conclude that we can generalize the low price knowledge irrespective of product relevance and experience. We thank one anonymous reviewer for raising that point. 2. In a similar study, Evanschitzky et al. (2004) established that nearly 50 percent of consumers had no price knowledge for some food products. References Aalto-Seta¨la¨, V. and Raijas, A. (2003a), “Actual market prices and consumer price knowledge”, Journal of Product & Brand Management, Vol. 12 No. 3, pp. 180-90. Aalto-Seta¨la¨, V. and Raijas, A. (2003b), “Consumer price knowledge before and after the euro changeover”, International Journal of Consumer Studies, Vol. 12 No. 3, pp. 210-7. Adam, D. (1958), Les re´actions du consommateur devant le prix, Sedes, Paris. Alba, J.W., Mela, C.F., Shimp, T.A. and Urbany, J.E. (1999), “The effect of discount frequency and depth on consumer price judgments”, Journal of Consumer Research, Vol. 21, pp. 219-35. Apparel Magazine (2004), “The results are in”, Apparel Magazine, Vol. 46 No. 2, p. 7. Baumgartner, B. (2003), “Measuring changes in brand choice behavior”, Schmalenbach Business Review, Vol. 55, pp. 242-56. Bechara, A. and Damasio, A.R. (2005), “The somatic marker hypothesis: a neural theory of economic decision”, Games and Economic Behavior, Vol. 52 No. 2, pp. 336-72. Brown, F.E. (1969), “Price image versus price reality”, Journal of Marketing Research, Vol. 6, pp. 185-91. Brown, F.E. (1971), “Who perceives supermarket prices most validly?”, Journal of Marketing Research, Vol. 8, pp. 110-3. BTE (2005), available at: www.bte.de/presseinfo/050823_4.htm (accessed November 22). Buzas, T.E. and Marmorstein, H. (1988), “Consumers’ knowledge of supermarket prices: the effects of manufacturer and retailer promotions”, Advances in Consumer Research, Vol. 15 No. 1, pp. 360-3. Conover, J. (1986), “The accuracy of price knowledge: issues in research methodology”, Advances in Consumer Research, Vol. 13, pp. 589-93. Coulter, K.S. (2003), “The effects of congruent/incongruent magnitude representation on explicit and implicit knowledge of prices”, Journal of Product & Brand Management, Vol. 12 No. 5, pp. 293-304. Dickson, P.R. and Sawyer, A.G. (1990), “The price knowledge and search of supermarket shoppers”, Journal of Marketing, Vol. 54, pp. 42-53.

Diller, H. (1988), “Das Preiswissen von Konsumenten: Neue Ansatzpunkte und empirische Ergebnisse”, Marketing ZfP, Vol. 1, pp. 17-24. Durvasula, S., Lysonski, S. and Andrews, J.C. (1993), “Cross-cultural generalizability of a scale for profiling consumers’ decision making styles”, Journal of Consumer Affairs, Vol. 27 No. 1, pp. 55-71. Estelami, H. (1998), “The price is right . . . or is it? Demographic and category effects on consumer price knowledge”, Journal of Product & Brand Management, Vol. 7 No. 3, pp. 254-66. Estelami, H. and De Maeyer, P. (2004), “Product category determinants of price knowledge for durable consumer goods”, Journal of Retailing, Vol. 80 No. 2, pp. 129-37. Estelami, H. and Lehmann, D.R. (2001), “The impact of research design on consumer price recall accuracy: an integrative review”, Journal of the Academy of Marketing Science, Vol. 29 No. 1, pp. 36-49. Estelami, H., Lehmann, D.R. and Golden, A.C. (2001), “Macro-economic determinants of consumer price knowledge: a meta-analysis of four decades of research”, International Journal of Research in Marketing, Vol. 18 No. 4, pp. 341-55. Evanschitzky, H., Kenning, P. and Vogel, V. (2004), “Consumer price knowledge in the German retail market”, Journal of Product & Brand Management, Vol. 13 No. 6, pp. 390-405. Gabor, A. and Granger, C.W.J. (1961), “On the price consciousness of consumers”, Applied Statistics, Vol. 10, pp. 170-88. Ger, G. and Belk, R.W. (1996), “Cross-cultural differences in materialism”, Journal of Economic Psychology, Vol. 17 No. 1, pp. 55-77. GfK (2000), Wie kauft Deutschland Konsumgu¨ter ein?, GfK, Nu¨rnberg. Goldman, A. (1977), “Consumer knowledge of food prices as an indicator of shopping effectiveness”, Journal of Marketing, Vol. 41, pp. 46-54. Hartmann, M. (2000), “Preismanagement im Bekleidungseinzelhandel - Status Quo und Optimierungsansa¨tze”, in Trommsdorff, V. (Ed.), Handelsforschung 2000/2001, pp. 391-413. Helgeson, J.G. and Beatty, S.G. (1987), “Price expectation and price recall error: an empirical study”, Journal of Consumer Research, Vol. 14, pp. 379-86. Jones, S. and Gallun, A. (2004), “‘Ikea of apparel’ eyes magnificent mile”, Crain’s Chicago Business, Vol. 25 No. 44, p. 1. Keegan, W.J. and Schlegelmilch, B.B. (2000), Global Marketing Management, Prentice-Hall, Englewood Cliffs, NJ. Krishna, A., Currim, I.S. and Shoemaker, R.W. (1991), “Consumer perceptions of promotional activity”, Journal of Marketing, Vol. 55, pp. 4-16. Kujala, J.T. and Johnson, M.D. (1993), “Price knowledge and search behavior for habitual, low involvement food purchases”, Journal of Economic Psychology, Vol. 14 No. 2, pp. 249-65. Le Boutillier, J., Le Boutillier, S. and Neslin, S.A. (1994), “A replication and extension of the Dickson and Sawyer price-awareness study”, Marketing Letters, Vol. 5 No. 1, pp. 31-42. Lenzen, W. (1984), Die Beurteilung von Preisen durch Konsumenten: Eine empirische Studie zur Verarbeitung von Preisinformationen des Lebensmitteleinzelhandels, Frankfurt a.M.. McGoldrick, P.I. and Marks, H.J. (1986), “How many grocery prices do shoppers really know?”, Retail & Distribution Management, Vol. 1, pp. 24-7. McGoldrick, P.I. and Marks, H.J. (1987), “Shoppers awareness of retail grocery prices”, European Journal of Marketing, Vol. 21 No. 3, pp. 63-76.


IJRDM 35,2

118

Manning, K.C., Sprott, D.E. and Miyazaki, A.D. (2003), “Unit price usage knowledge: conceptualization and empirical assessment”, Journal of Business Research, Vol. 56 No. 5, pp. 367-77. Mazumdar, T. and Monroe, K.B. (1990), “The effects of buyers’ intentions to learn price information on price encoding”, Journal of Retailing, Vol. 66 No. 1, pp. 15-32. Mazumdar, T. and Monroe, K.B. (1992), “Effects of inter-store and in-store price comparisons on a price recall accuracy and confidence”, Journal of Retailing, Vol. 68 No. 1, pp. 66-89. Miyashita, Y. (2004), “Cognitive memory: cellular and network machineries and their top-down control”, Science, Vol. 306 No. 5695, pp. 435-40. Monroe, K.B. (1973), “Buyers’ subjective perceptions of price”, Journal of Marketing Research, Vol. 10, pp. 70-80. Monroe, K.B. and Lee, A.Y. (1999), “Remembering versus knowing: issues in buyers processing of price information”, Journal of the Academy of Marketing Science, Vol. 27 No. 2, pp. 207-25. Mu¨ller, S. and Mai, G. (1986), “Kann die Preiskenntnis als Indikator fu¨r die Preisbeachtung wa¨hrend der Kaufentscheidung dienen?”, Jahrbuch der Absatz- und Verbrauchsforschung, Vol. 32 No. 2, pp. 100-15. Partch, K. and Litwak, D. (1990), “Prices: how much do shoppers really know or care”, Supermarket Business, June, pp. 15-21. Progressive Grocer (1964), “How much do customers know about retail prices?”, Progressive Grocer, Vol. 43 No. 2, pp. C104-6. Rolls, E.T. (2000), “Memory systems in the brain”, Annual Review of Psychology, Vol. 51, pp. 599-630. Sanfey, A.G. (2004), “Is knowing always feeling?”, PNAS, Vol. 101 No. 48, pp. 16709-10. Senauer, B.L., Asp, E.R. and Kinsey, J.N. (1993), Food Trends and the Changing Consumer, University of Wisconsin Press, Madison, WI. Srivastava, J., Lurie, N., Mick, D.G. and Boulding, W. (2001), “A consumer perspective on price-matching refund policies: effect on price perceptions and search behavior”, Journal of Consumer Research, Vol. 28 No. 2, pp. 296-307. Statistisches Bundesamt (2005), available at: www.destatis.de (accessed November 22). Stephens, L.F. and Moore, R.L. (1975), “Price accuracy as a consumer skill”, Journal of Advertising Research, Vol. 15 No. 4, pp. 27-34. Thomas, M. and Morwitz, V. (2004), “Effects of framing on magnitude perceptions of price”, Advances in Consumer Research, Vol. 31 No. 1, pp. 454-6. Urbany, J.E. and Dickson, P.E. (1991), “Consumer normal price estimation: market versus personal standards”, Journal of Consumer Research, Vol. 18, pp. 45-51. Vanhuele, M. (2002), “How and why consumers remember price information?”, Advances in Consumer Research, Vol. 29 No. 1, pp. 142-4. Vanhuele, M. and Dre`ze, X. (2000), “Do consumers really know if the price is right? Direct measures of reference price and their implications for retailing”, Les cahiers de recherche, Groupe HEC, Paris. Vanhuele, M. and Dre`ze, X. (2002), “Measuring the price knowledge shoppers bring to the store”, Journal of Marketing, Vol. 66, pp. 72-85. Wakefield, K.L. and Inman, J.J. (1993), “Who are the price vigilantes? An investigation of differentiating characteristics influencing price information processing”, Journal of Retailing, Vol. 69 No. 2, pp. 216-33.

Wilkinson, J.B., Mason, J.B., Paksoy, C.B. and Durand, R.M. (1980), “Confidence and price knowledge: the case of advertised food specials”, Advances in Consumer Research, Vol. 7 No. 1, pp. 772-5. Zeithaml, V.A. (1982), “Consumer response to in-store price information environments”, Journal of Consumer Research, Vol. 8, pp. 357-69. Zeithaml, V.A. (1984), “Issues in conceptualizing and measuring consumer response to price”, Advances in Consumer Research, Vol. 11 No. 1, pp. 612-6. Zeithaml, V.A. and Fuerst, W.I. (1983), “Age differences in response to grocery store price information”, Journal of Consumer Affairs, Vol. 17 No. 2, pp. 402-19. Corresponding author Peter Kenning can be contacted at: [email protected]

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