Consumers’ store-level price knowledge: Why are some consumers more knowledgeable than others?

Journal of Retailing 81 (4, 2005) 319–329

Consumers’ store-level price knowledge: Why are some consumers more knowledgeable than others? Anne W. M¨agi a,1 , Claes-Robert Julander b,∗ a

University of Florida, Marketing Department, P.O. Box 117155, Gainesville, FL 32611, USA b Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden Accepted 28 February 2005

Abstract What consumers know or think they know about stores’ relative price levels is an important research area from both a societal as well as a retail perspective. This study investigates the determinants of objective as well as subjective store-price knowledge. Using structural equation modeling, the effects of price consciousness, income, education, and three forms of price-related experience on the two knowledge dimensions, as well as the relationship between objective and subjective knowledge, are tested. Whereas out-of-store price search had positive effects on both subjective and objective price knowledge, the two other types of experience, number of stores shopped, and length of residence in the market only affected objective price knowledge, indicating that the two knowledge dimensions are determined differently. Furthermore, price consciousness had a larger effect on subjective than on objective knowledge. Finally, subjective and objective store-price knowledge were not significantly related in this study. © 2005 New York University. Published by Elsevier Inc. All rights reserved. Keywords: Consumers; Store-price knowledge; Structural equation modeling

Introduction Consumers’ knowledge about individual prices has justifiably received extensive attention from marketing and consumer researchers (e.g., Dickson & Sawyer 1990; Vanheule & Dr`eze 2002; Wakefield & Inman 1993). An equally important phenomenon should be consumers’ store-level price knowledge, that is, the extent to which their perceptions about competing stores’ relative price levels are accurate. Perceived price level, or price image, has been shown to be an important determinant of store choice. For example, Arnold, Oum, and Tigert (1983) found that price perceptions are the second most important factor after locational convenience in logit models of store choice, and Severin, Louviere, and Finn (2001), who did not include locational convenience in their super∗

Corresponding author. Tel.: +46 8 736 9013. E-mail addresses: [email protected] (A.W. M¨agi), [email protected] (C.-R. Julander). 1 The author is currently a visiting scholar at the University of Florida Marketing Department. Tel.: +352 392 0161x1333.

market models, found that price perceptions were second in importance to merchandise quality. When such perceptions correspond poorly to actual price differences between stores, consumers could end up patronizing stores they might not have chosen with more accurate information. On the other hand, consumers with good store-price knowledge would be in a good position to make store choices that maximize their utility. Given the potential implications for store choice, it is important to investigate factors that explain why some consumers might have better store-price knowledge than others. From a consumer welfare perspective, one would hope that economically disadvantaged households would have comparatively good price knowledge since these households, in particular, would need the ability to distinguish more expensive from less expensive stores. Indeed, in a recent meta-analysis, the relationship between price recall and income was found to be negative (Estelami & Lehmann 2001). However, in an early study on store-price knowledge, income was positively related to perceptual accuracy (Brown 1971), which suggests that income might not have the same effect on store-price

0022-4359/$ – see front matter © 2005 New York University. Published by Elsevier Inc. All rights reserved. doi:10.1016/j.jretai.2005.02.001

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knowledge as on product-price knowledge. One potential explanation for the difference in results could be that more complexity is involved in making store-price assessments than in recalling or recognizing single prices, which could benefit consumers with higher education who in general also would have higher incomes. Store-price knowledge is also interesting to study from a retailer perspective. For individual store managers, it is important to find out the degree to which their overall price strategy is reflected in consumer price perceptions, and whether there are systematic discrepancies in these perceptions. Also, assessing the level of store-price knowledge should enable retailers to better understand the effectiveness of price competition in a particular market. If overall objective store-price knowledge is low, this would indicate that in general consumers are unaware of the pricing strategies employed by retailers and that non-price factors could have a substantial impact on price perceptions. Under such conditions, it would be more difficult to manage a store’s price image. Thus, in general, retailers should be better off when overall price knowledge is good. With the exception of Brown’s work on store-level price perception validity (1969, 1971), no previous study that the authors are aware of has investigated store-price knowledge and its determinants. The purpose of this paper is to address this gap in the research literature. In line with previous research on consumer knowledge (e.g., Park, Mothersbaugh, & Feick 1994), both objective and subjective store-price knowledge will be included in the study, taking into account that what consumers think they know and what they do know are two different phenomena. The following section will present the background for the study and hypotheses concerning the determinants of objective and subjective store-price knowledge, respectively. In the remainder of the paper, results from a study that combines survey data with price data collected in-store will be presented.

Background Consumer knowledge has been an important construct in the consumer behavior and marketing literatures for decades (Alba & Hutchinson 1987, 2000; Brucks 1985; Park et al. 1994). In particular, consumers’ knowledge about different products, brands or product classes has been explored. This stream of research generally distinguishes between objective knowledge, that is, what the consumer actually knows as measured by an objective task, and self-perceived, or subjective knowledge, namely what the consumer thinks she/he knows (Brucks 1985; Park et al. 1994). Studies have shown that subjective and objective knowledge are only associated to a low or moderate degree, indicating that many consumers are poorly calibrated when it comes to their own assessment of their knowledge (Alba & Hutchinson 2000). This also means that self-report measures of knowledge are rarely good indicators of actual knowledge.

Both knowledge dimensions are relevant study objects. As outlined in the introduction, a consumer’s level of objective knowledge affects his or her ability to make optimal product, or store, choices. Subjective consumer knowledge reflects consumer confidence and has been shown to affect information search and other purchase related behaviors (Flynn & Goldsmith 1999). In addition, the goal to better understand the nature of the relationship between the two knowledge dimensions in itself warrants the inclusion of both in any study on consumer knowledge. Price knowledge Consumer price knowledge has primarily been investigated by assessing consumers’ ability to recall prices for specific consumer products that they are in the process of buying (Dickson & Sawyer 1990; Wakefield & Inman 1993). These studies show that many shoppers are unable to recall the price of a product they have just placed in their shopping cart, which has been taken as evidence of poor price knowledge among consumers. Recently, it has been suggested that price recall may only measure one aspect of price knowledge since memory for price information might not be easily recalled, but still might influence consumer purchase decisions (Monroe & Lee 1999). Based on this premise, Vanheule and Dr`eze (2002) also investigate price recognition and dealspotting ability. Although consumers in general tended to be better at these tasks, a large share of respondents did not perform well on any of them. Thus, independent of which aspect of price knowledge is investigated, studies in this area indicate that consumers vary extensively as to their level of price knowledge with a substantial share of consumers exhibiting fairly poor levels of knowledge. Given the variation in price knowledge at the individual product level, it seems reasonable to assume that consumers will also vary extensively in their store-price knowledge. Accurately assessing the price level of a store is, in addition, a much more daunting task. Retailers and grocery retailers, in particular, normally carry several thousands of products. Thus, consumers not only need to process and memorize information about specific prices, but also need to integrate information into an overall assessment of the store’s price level. Furthermore, price-level assessments are relative in nature since prices at other stores need to be taken into account, which increases the amount of information that needs to be processed. Given the complexity of this task, consumers tend to resort to simplifying heuristics that may be misleading (Alba, Broniarczyk, Shimp, & Urbany 1994). Consumers’ knowledge about stores’ relative price levels can be manifested in an ability to assess how much price levels differ between stores, for example, that the prices in store A are on average 5 percent higher than the prices in store B, but 10 percent lower than the prices in store C. However, even less detailed knowledge about store price levels could be useful for the consumer. Specifically, the ability to rank stores according to their price levels could aid the consumer in mak-

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ing choices between stores. Consumers’ ability to accurately rank stores was, in fact, used by Brown (1971) as the indicator of store-price knowledge. In addition, although some consumers might not be able to rank most of the stores in their market area according to price, they might still be able to correctly identify which of two stores is more expensive if the difference between the stores’ price levels is substantial. Overall, since consumers’ knowledge about price levels can be useful even in cases when it is not very detailed, measurements of store-price knowledge should reflect various levels of specificity of knowledge. Determinants of store-price knowledge Based on the consumer knowledge and price recall literatures, this section will discuss potential determinants of objective and subjective store-price knowledge, respectively. The hypothesized model tested in this study is presented in Fig. 1. Park et al. (1994) show that product experience in terms of usage, ownership, and information search affects both objective and subjective knowledge. It is intuitive that consumers need experience with the knowledge domain in order to gain objective knowledge. Memories of product experiences are also highly accessible cues that consumers tend to use when making an assessment of how much they know. Park et al. (1994) find, furthermore, that experience affects subjective knowledge to a higher degree than it affects objective knowledge. Thus, while a product-related experience does not necessarily lead to the correct encoding of veracious information in memory, it is likely to have an impact on subjective knowledge. In the case of store-price knowledge, domain relevant experience would be exposure to price information from

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different stores, both in terms of advertising and visits to competing stores. That is, the more consumers behave in a manner that increases their exposure to prices, the more likely it is that both their objective store-price knowledge and their subjective store-price knowledge are extensive. It has been shown that although most consumers shop in only a handful of stores (Urbany, Dickson, & Sawyer 2000), the number of stores used on a regular basis varies extensively (M¨agi 2003), and we hypothesize that this will be reflected in consumers’ price knowledge. Likewise, consumers vary in whether or not and how often they read grocery stores’ ads and fliers, and this should also affect price knowledge. H1. The number of stores frequented on a regular basis has a positive effect on (a) objective and (b) subjective store-price knowledge. H2. The amount of out-of-store price search has a positive effect on (a) objective and (b) subjective store-price knowledge. Another aspect of experience that could be of importance for store-price knowledge is its duration. The longer the time the consumer has had experience with the stores in the particular market, the better are his or her opportunities to learn about their relative price levels. It is also possible that consumers over time pay less attention to prices thinking that they already have good knowledge about competing stores’ price levels. If that is the case, length of residence will have a stronger effect on subjective knowledge than on objective knowledge. H3. The length of residence in the market has a positive effect on (a) objective and (b) subjective store-price knowledge.

Fig. 1. Hypothesized model.

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Studies on grocery shopping indicate that some consumers are inherently more motivated to comparison shop (M¨agi 2003; Stone 1954; Williams, Painter, & Nicholas 1978). These price conscious consumers should be more likely to shop in more stores, read more store advertising, and thereby become more knowledgeable about stores’ price levels. Wakefield and Inman (1993) find that price recall accuracy is higher for individuals who use price in making brand decisions, which suggests that such an effect would also be plausible on the store price level. Although not including possible mediating effects, Brown (1971) also finds a positive relationship between using price as a basis for store choice, and the ability to rank stores according to price levels. In addition, the perceived importance of comparing prices might have a direct effect on what consumers think they know. Radecki and Jaccard (1995) argue that self-relevance can have an effect on subjective knowledge through dissonance mechanisms, independent of any effects through objective knowledge. That is, if a topic is relevant to the consumer she/he is less likely to acknowledge a low level of knowledge about it. Price-conscious shoppers should find the topic of grocery prices highly relevant and would thus be less likely to admit, even to themselves, that they might have low price knowledge. H4. Price consciousness has positive indirect effects on (a) objective price knowledge and (b) subjective price knowledge mediated by effects on out-of-store price search and number of stores frequented. H5. Price consciousness has a positive direct effect on subjective price knowledge. Income could be argued to be a factor that at least normatively would increase the self-relevance of store prices—lowincome households should have more to gain from being aware of differences in stores’ price levels. In addition, it has been argued that high-income households perceive the alternative cost of time spent on price search as higher (Stiegler 1961; Wakefield & Inman 1993). Income per household member has been shown to negatively affect price search in the grocery market (Urbany, Dickson, & Kalapurakal 1996), although wage rates have been shown to have only a moderate effect on the subjective value of time spent on price search (Marmorstein, Grewal, & Fishe 1992). Income has been shown to have a negative effect on price recall (e.g., Wakefield & Inman 1993), and a recent meta-analysis found a negative relationship between income and price recall accuracy (Estelami & Lehmann 2001). However, Brown (1971) found that store-price knowledge increased with income. Dickson and Sawyer (1990) also found that consumers in a less advantaged housing area exhibited lower price recall ability. This suggests that the nature of the effects of income on price-related knowledge is far from clear, and while a negative effect seems intuitive, it needs to be empirically assessed.

H6. Income has a negative indirect effect on objective price knowledge through direct effects on price consciousness and price search behavior. H7. Income has a negative indirect effect on subjective price knowledge through direct effects on price consciousness and price search behavior. Apart from experience and motivation, ability to process domain-relevant information should also be an important predictor of knowledge. Regarding consumers’ ability to acquire store-price knowledge, it is reasonable to suggest that it would increase with the level of formal education. Capon and Kuhn (1982) show that consumers tend to use more elaborate and accurate reasoning strategies for evaluating deals with increasing education. Based on the argument that education reflects ability to process price information, we hypothesize a direct effect of education on objective store-price knowledge. However, we also assume that education is positively related to income. If hypothesis H6 holds, it is therefore possible that any positive direct effect would to some extent be cancelled out by a negative indirect effect through income. Since this would depend on the magnitude of effects, we do not posit a specific hypothesis regarding the total effect of education on objective price knowledge. H8. Education has a positive direct effect on objective price knowledge. Finally, it seems reasonable to assume that consumers with good actual knowledge would also perceive themselves as more knowledgeable (Radecki & Jaccard 1995). However, research shows that consumers are in general poorly calibrated in their assessments of their own knowledge (Alba & Hutchinson 2000). The correspondence between objective and subjective knowledge could possibly also vary across knowledge domains. For example, Radecki and Jaccard (1995) found a moderate effect in the context of birth control methods, but no significant effect in the context of nutritional contents of foods. No previous study has investigated the relationship between subjective and objective knowledge in the domain of store price levels to indicate the nature of the relationship in context. Therefore, we hypothesize that objective store-price knowledge will have at least some effect on subjective store-price knowledge. H9. Objective store-price knowledge has a positive effect on subjective store-price knowledge.

The study Data collection Two data sets were used for this study: proprietary priceindex data collected from stores in one Swedish market area

A.W. M¨agi, C.-R. Julander / Journal of Retailing 81 (4, 2005) 319–329

and a mail survey sent out to a random sample of residents in the same market. The data for the price index was collected instore in the form of a weighted basket of about 300 common daily commodities. Products were sampled so as to cover all product categories and maximize the item-by-store hit, and their respective prices were weighted by sales volume. The price data were collected from a total of twenty stores ranging from traditional formats in the city center, to superstores and discount grocers. All four main Swedish grocery chains are present in this market. The twenty stores together completely dominate the local market with close to a 90 percent grocery market share. Moreover, one of the dominating chains is a federation of retailers with substantial intra-chain competition. Thus, the level of competition in the market and the variance among competing stores were judged to be sufficient for the investigation. There are about 100,000 inhabitants in this market. The questionnaire was mailed to 944 households in the area; 543 individuals responded to the questionnaire. Respondents with incomplete answers on key variables for the analyses were removed, as were shoppers who indicated that they only did a minor share of the household’s grocery purchases. This resulted in a sample of 462 usable cases. Sixty-four percent of the respondents in the sample were women, and the average age was 47 with a range of 20–83 years. Measures In the literature on product-price knowledge, the ability to recall the exact price of a particular product has been the predominant measure of objective price knowledge (Estelami & Lehmann 2001). A recent study (Vanheule & Dr`eze 2002) also used measures of price recognition that take into account the notion that consumers can be knowledgeable about store prices without being able to recall them. Especially in a grocery retailing context, consumers might not pay much attention to particular prices. However, incidental exposure to prices might still affect implicit memory and be reflected in improved performance in, for example, recognition tasks (Monroe & Lee 1999). In the case of objective store-price knowledge, consumers are not typically exposed to specific pieces of information representing average store prices that could be either recalled or recognized at a later date. Rather, store price perceptions are judgments made by the consumer based on product price information in either explicit or implicit memory as well as other cues. Therefore, methods of measuring price knowledge developed for product prices are not readily applicable for store-price knowledge. In this study, indicators of objective store-price knowledge were based on the respondents’ assessments of the price levels of five of the largest stores in the area. Respondents were asked to indicate the price levels of five dominating stores in the area. Apart from their size, the choice of stores was also based on the variance in price-levels among them. Store A (Chain 1) is a discount grocer with a 10 percent market share, Store B (Chain 2) is a superstore with a 14 percent market

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share, Store C (Chain 3) is a superstore with a 19 percent market share, Store D (Chain 2) is a supermarket with a 5 percent market share, and Store E (Chain 4) is a supermarket with a 2 percent market share. All other stores included in the price survey had market shares of 5 percent or lower. The stores’ relative price levels are reported in the bottom half of Table 2 in the form of percentage point deviations from the market average. To simplify the task, 11-point scales anchored in “approximately 25 percent lower than market average” to “25 percent higher than market average” were used. The question was organized in a matrix form, so the judgments of each store were visually easy to compare. The question resembles the recognition task used in product price studies by taking into account that while consumers might not be able to recall store-price levels, consumers with better objective store-price knowledge should perform better on the assessment task. Since the answers on the objective knowledge questions are of substantive interest in themselves, some results are provided in Table 2. Three indicators of a latent objective price knowledge variable were computed from comparisons of the judgments of the price level of each store, with the price levels estimated from the price data. The first indicator (knowledge of price levels) reflects consumers’ knowledge of the stores’ exact price levels. It was computed by comparing the price level indicated by the respondent to the price-level as measured by the price data. If the respondent chose the correct category (e.g., prices approximately less than 15 percent for Store A, where prices were 14.6 percent below the market average), the respondent received six points. For the next two closest response categories (prices approximately less than 20 and 10 percent, respectively), the respondent received five points, and the points decreased by one with each additional step that the provided response differed from the correct response category. If the respondent did not provide an answer for a particular store they received zero points. With a total of six points per store and five stores, the knowledge of price levels scale had a theoretical range of 0–30. The actual range was 0–29 and the mean score was 14.8 (SD = 7.46). The next two variables both reflect the extent to which respondents were able to rank correctly the stores in terms of price levels. The first variable (overall ranking ability) was constructed by comparing each respondent’s ranking of the five stores to the actual rank order. For this purpose, a Spearman rank-order correlation coefficient was computed for each respondent for the correlation between the five stores’ price levels according to the respondent’s assessment and the stores’ price levels according to the price indices. Due to the substantial number of non-responses on the price-level assessments for some of the stores, missing data were recoded in a manner as to represent the lowest level of knowledge (prices higher than 25 percent for Stores A–D, and prices lower than 25 percent for Store E). Values on this variable ranged from −.94 to .99 (M = .04, SD = .68). The third indicator of objective store-price knowledge captured the respondent’s ability to make correct rankings

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Table 1 Confirmatory factor analysis for multiple-item measures Lambda loadings

Construct reliability

Variance extracted

.85

.66

knowledgea

Subjective I have good knowledge about prices on groceries Compared to most other people, I know less about current prices on groceries (rev. coded) When it comes to grocery prices, I think I know a lot

.88 .71 .85

Objective knowledge Knowledge of price levels Pairwise rankings Overall ranking ability

.82 .97 .94

.93

.88

Out-of-store price searchb I read the ads grocery stores publish in the newspapers I read the fliers that grocery stores mail to me

.88 .84

.84

.71

.82 .64

.82

.60

Price consciousnessa I compare what I get for my money in different stores I choose where to shop based on were I can find what I need at the lowest prices I always compare the prices in the stores that are accessible to me Fit statistics χ2 /df/p-value Root Mean Square Error of Approximation Comparative Fit Index Goodness-of-Fit Index a b

.85

56.6/38/.00 .03 .99 .98

Ten-point scales anchored in strongly disagree–strongly agree. Ten-point scales anchored in never–always.

between pairs of stores (pair-wise ranking). For each pair of stores, the respondent received one point for each correct comparison. With five stores, the theoretical range on this variable is 0–10. The distribution on the variable from no correct comparisons to ten correct comparisons is, in percentages: 19, 12, 9, 7, 4, 6, 8, 6, 16, 9, 3 (the percentages do not add up to 100 due to rounding). The mean for the variable was 4.3 (SD = 3.37). It should be noted that even if the prices were lower in Store C than in Store D, their correct price levels are in the same response category (approximately lower than 5 percent). In order to account for this, the respondent received a correct score for the comparison if either the same response category had been indicated for each store, or a lower price level was indicated for store C. The questionnaire also included three items reflecting subjective price knowledge (Flynn & Goldsmith 1999), three items reflecting consumers’ price consciousness (Lichtenstein, Ridgway, & Netemeyer 1993; M¨agi 2003), and two items measuring price search behavior. Ten-point scales were used for all of the above items. In the questionnaire, the items for the first two scales were intermixed with nonprice-related items not used in this paper in order to reduce the tendency for carry-over effects. A confirmatory factor analysis was performed on the multiple-item scales objective store-price knowledge, subjective store-price knowledge, price consciousness and price search, to assess the reliability of the measures. This analysis yielded satisfactory results (Table 1). The squared correlations between all pairs of constructs were lower than the variance extracted for each

construct, providing an indication of discriminant validity (Fornell & Larcker 1981). Finally, respondents were asked to indicate their monthly disposable household income, including benefits and after tax. In the analysis, household income was divided by number of people in the household to take into account that budget constraints are dependent on total household income relative to household spending levels. Respondents were also asked which stores they had shopped in during the past two months. The number of stores shopped in ranged from one to nine, with an average of 3.6 (SD = 1.63). Length of residence was measured by asking respondents to indicate how long they had lived in the particular city on a four-point scale (less than 1 year, 1–5 years, 6–10 years, and more than 10 years). Level of educational attainment was also measured on a four-point scale. Means, standard deviations and correlations for the raw data are presented in Appendix A. Results Consumers’ store-price knowledge Respondents’ assessments of the price levels of the five stores are presented in Table 2. In the table, the stores are organized according to their price level, with Store A having the lowest price level. As mentioned previously, each store’s price level relative to the market average is reported in the lower part of the table. The category representing the correct answer for each store is marked in bold.

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Table 2 Price perceptions of the main stores in the market Store A

Store B

Store C

Store D

Store E

Percieved deviation from market average Approx. 25 percent lower Approx. 20 percent lower Approx. 15 percent lower Approx. 10 percent lower Approx. 5 percent lower

3 10 20 39 17

1 2 4 20 29

0 1 2 14 21

0 0 1 1 5

0 1 0 2 7

Price level corresponds to market average Approx. 5 percent higher Approx. 10 percent higher Approx. 15 percent higher Approx. 20 percent higher Approx. 25 percent higher

9 2 1 0 0 0

26 9 7 3 1 0

34 11 10 6 1 1

24 21 27 13 5 2

18 19 24 14 9 5

−14.6 −10.1 29

−8.7 −2.6 21

−6.1 0.2 16

−3.1 +6.6 33

+6.8 +7.5 47

Actual deviation from market average Average perceived deviation from market average Non-response (percentage)

Frequencies in percent. The correct response category for each store is marked in bold.

For all stores except Store D, about 20 percent of respondents who provided a price-level assessment gave a correct answer. With a more generous definition of an adequate answer including the next two response categories, we find that between 30 (Store D) and 69 percent (Stores A and C) of the answers are correct. Comparing the average assessed price levels (the second to last row of Table 2) with the actual price levels shows that on average the respondents also generate a correct rank order of the stores according to price level. Taken together, this indicates that although the variance is substantial, there are consumers who seem to have a fairly good idea of the relative price levels of competing stores in this market. However, the high level of non-response has to be noted—it ranges from 16 to 47 percent for the five stores—which implies that a large share of consumers in the market are either unable or unwilling to provide price-level assessments of the main stores in the market. Viewed from a managerial perspective, the results suggest that stores’ relative positioning in terms of price levels are, on average, fairly accurately perceived by consumers. However, Store D constitutes an interesting exception: the store’s perceived price level is clearly biased upward. In fact, it is believed to be almost as expensive as Store E whereas the price data indicates that the difference in price levels between the two stores is almost ten percentage points. One potential explanation for the biased perception of Store D suggested by the retailer providing the data is that Store D had an image as an upscale supermarket that was not congruent with its price level, which was almost as low as that of the two superstores in the market. Data were not available for a formal test of the hypothesis, but the case clearly illustrates the effects of non-price factors on price perceptions. Structural equation model We used maximum likelihood estimation with LISREL 8.52 based on the covariance matrix to test the hypothe-

ses set forth in the paper. The error variances of the single indicators were set at .1sx2 to reflect moderate levels of measurement error (Anderson & Gerbing 1988). The specified model fit the data adequately. However, based on a review of modification indices, an additional path was included in the model. This path linked level of education to price search and its interpretation will be provided below. Standardized estimates, goodness-of-fit indices and explained variance for the key dependent variables are presented in Table 3. The results provide support for hypotheses H1–H3, with the exception of H1b and H3b indicating that the types of experience investigated in this study affect objective price knowledge and in one case, subjective price knowledge. Although the number of stores frequented positively affects objective price knowledge, the effect on subjective knowledge is both insignificant and in the wrong direction. That is, although patronizing several stores does increase actual knowledge, consumers do not seem to realize that this behavior affects their knowledge levels. This suggests that a fair amount of passive learning of prices occurs when consumers shop across stores (Monroe & Lee 1999) that is not reflected in explicit memory and thus not available when consumers assess their own knowledge levels. A similar explanation can be provided for the lack of effect of length of residence on subjective knowledge. Although the length of exposure to the pricing strategies of stores in a particular market increases objective knowledge, much of this might constitute passive learning. If so, that could explain why consumers would not consider length of residence as a cue when assessing their knowledge. Price consciousness is reflected in both a higher number of stores shopped and a higher level of price search. The total indirect effects of price consciousness on objective and subjective store-price knowledge are, respectively, .10 (t = 3.73) and .05 (t = 2.01), which supports H4. The positive effects of price consciousness and number of stores shopped on objective price knowledge contrasts with findings from

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Table 3 Standardized coefficients and fit statistics for the structural model Parameter

t-value

Estimate

Number of stores frequented → subjective knowledge Price search → subjective knowledge Price consciousness → subjective knowledge Length of residence → subjective knowledge Objective knowledge → subjective knowledge Number of stores frequented → objective knowledge Price search → objective knowledge Length of residence → objective knowledge Education → objective knowledge Price consciousness → number of stores frequented Price consciousness → price search Education → price search Income/household size → price search Income/household size → price consciousness Education → income/household size Fit statistics χ2 /df/p-value Root Mean Square Error of Approximation Comparative Fit Index Goodness-of-Fit Index

−.08

−1.88

.14a .66a .06 .07 .17a

2.96 11.55 1.55 1.83 3.58

.13b .10b .22a .24a

2.52 2.02 4.21 4.59

.40a −.25a −.07 −.04 −.03

7.55 −5.08 −1.44 −.78 −.59

126.4/78/.00 .03 .99 .97

Explained variance

Objective knowledge

Subjective knowledge

Squared multiple correlations

.09

.53

a b

Significant at p < .01. Significant at p < .05.

Vanheule and Dr`eze (2002), which showed that consumers who claimed to cross-shop in order to find better prices, did not have better product-level price knowledge as reflected in price recall and recognition. The inconsistency in results may indicate that different mechanisms lead to objective storelevel and product-level price knowledge. Price consciousness also has a significant direct effect on subjective price knowledge (H5). The magnitude of the effect suggests that consumers are prone to use self-relevance as a cue when making assessments of their own knowledge. That is, when making this assessment, the more price conscious they are, the more likely they would be to believe that they are very knowledgeable about prices. It is noteworthy that although directional, the effect of income on price consciousness and price search was not significant, suggesting that income may not be an important determinant of price-related behaviors and in turn of price knowledge (H6 and H7). There was, furthermore, no effect of educational attainment on income. This can, in part, be explained by the fact that income was measured at the household level since the household’s total disposable income is an indicator of household budget constraints. The contribution of the respondents’ individual income levels to total household income might vary greatly, thus concealing any effects of education on income.

Education has the expected positive effect on objective price knowledge (H8). However, the added path in the model signifies that education is negatively related to price search. A possible explanation of this effect could be that shoppers with higher education often have the type of occupation that requires long working hours, and thereby have less time to spend on household chores, such as perusing newspaper ads for grocery stores. Although the negative effect on search to a limited degree cancels out the direct positive effect of education on objective knowledge, the total effect of education on objective knowledge is positive and significant (.19, t = 3.72). Finally, although directional, the effect of objective price knowledge on subjective price knowledge was not significant. The model explained a substantial share of variance in subjective store knowledge whereas the share of explained variance in the objective knowledge variable was fairly modest. Discussion The results show that although the perceptions of the stores’ relative price levels on average are reasonably accurate, objective store-price knowledge varies extensively across consumers in the particular market studied. From a societal perspective it is rather disheartening that many consumers seem to have quite low levels of knowledge about the comparative price levels of the stores they can choose from. However, given the limited amount of research in this area it is premature to conclude that large consumer segments have insufficient levels of store-price knowledge, since we do not know what “sufficient knowledge” implies in the domain of store-price knowledge. From a retailer perspective, the variability in objective store-price knowledge confirms that price perceptions are fairly malleable and to a substantial degree affected by nonprice factors. This suggests that store price perceptions are difficult to manage and that merely changing price levels is not likely to be an effective measure for obtaining a change in a store’s price image. For individual stores, particularly for retailers that like Store D tend to be perceived as significantly more expensive than they are, it is also important to investigate what the particular factors are that affect price image. The results also suggest that consumers in many cases are unaware of retailers’ overall pricing strategies. Although the results were directional, income did not have the expected negative effects on price search or price consciousness and consequently on neither objective nor subjective price knowledge. Apart from factors limited to the specific market surveyed, a possible explanation for the weak effect of income could be that households with low income might adapt their consumption behavior rather than their price search behavior and, therefore, do not exhibit above average levels of price knowledge. Moreover, it could also be argued that price search behavior would not necessarily decrease with increasing income. Shopping-related habits, for example regarding price search, developed early in life could be sustained even though the shopper’s financial situation might

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improve later. Finally, this study assumes that disposable income level (relative to household size) is the main determinant of a household’s spending capacity, but clearly housing costs and other fixed monthly payments would also affect household budget constraints. Education did have the expected positive effect on objective store-price knowledge, which suggests that ability is an important determinant of objective knowledge in this domain. Given the complexity of the task, this should not be surprising. Although education was not related to income in this study, which, in part, could have been due to different measurement levels, it is likely that education is related to household income in markets where income distributions are wide and in general for single or one-parent households. This would imply that consumers who would benefit the most from good price knowledge are least able to acquire it. As suggested earlier, time poverty could be an explanation for why education had a negative effect on price search behavior. Time usage and perceived time poverty could be important determinants of price search and by extension price knowledge, since acquiring and processing price information from several stores is a time-consuming activity. Consumers who are neither willing nor able to spend time on grocery shopping-related tasks would thus potentially have lower price knowledge. Price consciousness, which in itself was not determined by income, did have indirect effects on both knowledge constructs mediated by price-search behavior and store patronage, as well as a direct effect on subjective price knowledge. Perhaps psychological benefits or being a skilled shopper are more important than the money saved by being a vigilant shopper. Finally, this study corroborates previous findings from the knowledge literature by showing that subjective and objective knowledge in the domain of store-price knowledge are not strongly related. The level of miscalibration can potentially be explained by the complexity and ambiguity involved in assessing store price levels (Park et al. 1994); when information gained from experience tends to be ambiguous, the gap between subjective and objective knowledge could be substantial, since extensive exposure to information about the product or service does not necessarily lead to better objective knowledge, whereas the amount of information in memory, no matter how inaccurate, is seen as an indicator of good knowledge by the consumer when making a self-assessment. Although information on product prices in itself is not ambiguous, the sheer amount of price information that needs to be taken into account should make the assessment of store price levels ambiguous. Calibration also requires unambiguous feedback about one’s judgments (Alba & Hutchinson 2000), but in the case of store-price knowledge consumers in general are likely not exposed to such information. With the lack of appropriate feedback, biasing effects such as beliefs about one’s competence (Alba & Hutchinson 2000), in this case the consumer’s self-perception as a price-conscious shopper, have full reign.

327

Further research Store-level price knowledge has to date received scant attention in the marketing and consumer behavior literatures. We hope this study will encourage others to investigate this phenomenon, and would like to suggest a few different directions for further research. A key area for further study is the consequences of storeprice knowledge. In particular, it would be important to investigate whether consumer price knowledge is related to store choice behavior. Moreover, it would also be interesting to investigate the difference in effects between subjective versus objective knowledge in this domain, given that the discrepancy between these two dimensions of knowledge is substantial. Although this study did identify some of the determinants of objective store-price knowledge, the share of explained variance was modest and future studies should look at other possible determinants. One such factor could be motivation to process information. Education, which in this study was used as an indicator of ability to process information, could also be an indicator of motivation to process information. By including measures that tap into information processing motivation, such as need for cognition (Cacioppo, Petty, Feinstein, & Jarvis 1996), future studies will be able to analyze the relative effects of ability and motivation on knowledge acquisition, and also improve the overall explanatory power of models on consumer knowledge. Including other dimensions of price-related experience, such as word-of-mouth, could also improve our understanding of how price knowledge is formed. A broader range of measures of experience would also allow for a comparison of a model in which experience is modeled as a formative latent construct, with a model in which each experience dimension has its separate effect. As mentioned above, it could also be fruitful to include time usage and time poverty in models of consumer price knowledge. Such measures would reflect consumers’ opportunity to acquire price knowledge. Consumers’ price consciousness turned out to be an important predictor of both objective and subjective storeprice knowledge. However, contrary to expectations this variable was not linked to household income. Although this result could in part be specific to this study, the lack of relationship indicates that there are other determinants of price consciousness. For example, a consumer’s price consciousness might in part have been learned in childhood (Moore, Wilkie, & Lutz 2002). Time usage and perceived time availability could potentially also be important determinants given that consumers face a trade-off between saving time and saving money. It is also possible that market-related factors could affect price consciousness; consumers might tend to focus more on price differences and engage in across-store comparisons in markets where discount retailers have a large market share. This study focuses on price knowledge for grocery stores, which we found a natural choice given the importance of gro-

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328

cery spending for the household as well as the frequent nature of grocery shopping. However, future studies should extend the approach taken here to other retail sectors. It would also be interesting to investigate whether objective price knowledge is generally better in retail sectors where consumers shop frequently, or whether consumers tend to be more knowledgeable about competing stores’ price levels for high-ticket items. Finally, this study has looked exclusively on store-level price knowledge. For future studies, it would be relevant to link store-level knowledge to item-level price knowledge. Although it is plausible that consumers with good productlevel price knowledge also have good store-price knowledge, differences between the two knowledge levels could,

1 Subjective knowledge 1. I have good knowledge about prices on groceries 2. Compared to most other people, I know less about current prices on groceries (rev.) 3. When it comes to grocery prices, I think I know a lot Objective knowledge 4. Knowledge of price levels 5. Pairwise rankings 6. Overall ranking ability

2

3

a

Acknowledgements The authors wish to thank Jo Andrea Hoegg, Barton Weitz and the reviewers for helpful comments on this manuscript, and ICA Handlarnas AB, Sweden, for making the data available for research.

Appendix A. Correlations, means and standard deviations for the raw data

5

6

7

8

9

10

11

12

13

14

15

1.000 .623

1.000

.742

.609

1.000

.102

.129

.103

1.000

.104 .106

.138 .128

.102 .102

.800 .782

1.000 .932

1.000

.082

.082

.045

1.000

.039

.102

.076

.733

1.000

.082

.080

.085

.299

.294

1.000

.113

.111

.125

.228

.178

.498

1.000

.029

.036

.041

.329

.295

.681

.580

1.000

.205

.203

.161

.052

.010

.232

.191

.143

Price search 7. I read the ads grocery .321 .282 .315 stores publish in the newspapers 8. I read the fliers that .318 .253 .313 grocery stores mail to me Price consciousness 9. I compare what I get for .564 .399 .526 my money in different stores 10. I choose where to shop .325 .250 .340 based on were I can find what I need at the lowest prices 11. I always compare the .505 .382 .487 prices in the stores that are accessible to me 12. Number of stores .089 .082 .071 frequented 13. Education −.049 −.016 −.055 14. Household −.048 −.055 −.005 income/household size 15. Length of residence .079 .100 .089 Means Standard deviations

4

for example, arise due the differences in store patronage patterns.

5.53 2.50

6.60 2.69

5.84 2.44

1.000

.167 .166 .178 −.224 −.204 −.002 −.064 −.076 .129 1.000 −.061 −.047 −.071 −.074 −.050 −.025 −.065 −.015 −.120 −.026 .073 14.76 7.46

.075 4.27 3.37

.066 .14 .64

.115 5.82 3.17

.110 −.011 6.62 3.17

5.38 2.87

Measured in 1,000 SEK. 1 USD was approximately equal to 10 SEK at the time of data collection.

.070 5.31 3.22

.048 4.34 3.02

.026 −.164 3.55 1.63

2.86 1.07

1.000 .059 17.57a 13.7

1.000 10.54 3.28

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