Overall Store Price Image: the Interactive Influence of Product Consumption Span, Unit Product Price, and Shopping Basket Size

Kalpesh Kaushik Desai, SUNY-Buffalo
Debabrata Talukdar, SUNY-Buffalo
EXTENDED ABSTRACT - In spite of extensive research on pricing, most prior research has focused on consumer price perceptions of individual products. Relatively little research has been conducted to investigate consumers’ price perceptions of entire retail outlets or the overall store price image (OSPI for short).
[ to cite ]:
Kalpesh Kaushik Desai and Debabrata Talukdar (2002) ,"Overall Store Price Image: the Interactive Influence of Product Consumption Span, Unit Product Price, and Shopping Basket Size", in NA - Advances in Consumer Research Volume 29, eds. Susan M. Broniarczyk and Kent Nakamoto, Valdosta, GA : Association for Consumer Research, Pages: 213-215.

Advances in Consumer Research Volume 29, 2002     Pages 213-215

OVERALL STORE PRICE IMAGE: THE INTERACTIVE INFLUENCE OF PRODUCT CONSUMPTION SPAN, UNIT PRODUCT PRICE, AND SHOPPING BASKET SIZE

Kalpesh Kaushik Desai, SUNY-Buffalo

Debabrata Talukdar, SUNY-Buffalo

EXTENDED ABSTRACT -

In spite of extensive research on pricing, most prior research has focused on consumer price perceptions of individual products. Relatively little research has been conducted to investigate consumers’ price perceptions of entire retail outlets or the overall store price image (OSPI for short).

In one of the very few studies on the topic, Alba et al. (1994) pitted the prior OSPIs of two grocery stores against their very different current pricing strategiesCone store (labeled the "frequency store") was less expensive than the other on two-thirds of the items whereas, the competitor store (labeled the "magnitude store") had an advantage on the remaining third of the items by an amount that was, on average, twice as large as its disadvantage on other items. The total basket prices at the two stores were equivalent. Results revealed that under time pressure, subjects judged the stores with frequent, shallow discounts as having a lower OSPI. Further, the frequency information was so dominant that despite the store’s higher prior OSPI, subjects still rated the frequency store as having a lower OSPI.

Alba et al.’s (1994) research helps us understand how consumers’ OSPIs undergo a modification, but it leaves unanswered the question of what factors influence OSPI in the first place. Also, it limits our understanding to the effects of two factorsµthe number of products being sold at lower prices (frequency cue) and the extent of such price reduction (magnitude cue)µon changes in OSPI. However, prior research suggests that the type of products being sold at lower prices is also likely to shape OSPI (Bell and Lattin 1998). Finally, Alba et al. (1994) do not investigate the role of consumer characteristics on OSPI.

Our paper extends prior research by examining the type of products that needs to be offered at lower prices to influence OSPI. Based on two product-related factorsµconsumption span (average time between two consecutive consumption or usage incidences) and unit pricesµwe classify grocery store products into four exhaustive and mutually exclusive product categories and examine if there is any systematic difference in the relationsip between OSPI and category-level pricing of these four categories. We also investigate to what extent this relationship is moderated by shopping basket size, a factor found to have strategic implications for segmentation and target marketing (Julander 1992).

Prior research (Monroe and Lee 1999) suggests that for typical grocery products, consumers may not encode actual prices (which they cannot recall) but instead encode prices in more meaningful terms such as "too high," "a good deal," or "expensive." However, combining these prices to form an OSPI and comparing it with OSPIs of other stores will still be very effortful and complex. Thus, consumers will attempt to simplify this task. Alba et al. (1994) speculated that one way in which consumers can reduce this complexity is through price comparison of selective group of products. Thus, prices of some products are likely to be more salient in influencing OSPI than others. Instead of using the traditional classification of grocery products into produce, meat, beverages, and so on, we divided (the regularly consumed/used) grocery products into four categories based on two intrinsic factors of a product that make the prices of these products salient in influencing OSPIµconsumption span and unit prices. The four categories of products that result from this classification are (i) products with short consumption span and high unit prices (SH), (ii) products with short consumption span and low unit prices (SL), (iii) LH products, and (iv) LL products.

Whereas the meaning of unit price of a product is self-explanatory, consumption span of a product refers to the average time between two consecutive consumption (for consumables) or usage (for durables) incidences of a standard unit of that product by a typical consumer. The typical consumption span for some of the regularly consumed/used products such as snacks and toothpaste is likely to be relatively short (e.g., few days or weeks). These products are labeled as shorter consumption span products. In contrast, the typical consumption span for other (regularly consumed/ used) products such as cloth hangers and light bulbs is likely to be relatively long (e.g., months). We label these types of products as longer consumption span products. Prices of products with higher vs. lower unit prices will have greater influence on OSPI because they are more noticeable. Similarly, prices of shorter vs. longer consumption span products will have greater influence on OSPI because, in a given time period, these products are likely to be purchased more often. Thus, consumers will have more opportunities to be exposed to their prices.

The above grocery product categorization is essential for investigating systematic differences in the saliency of product category prices on the OSPI of consumers, in general. For focused target marketing strategy, grocery store managers would also like to know whether and how such saliency of product category prices is likely to vary across heterogeneous consumers. In this study we use shopping basket size as the individual difference variable. It refers to the average number of different products (e.g., potato chips, milk, etc) a consumer (of a given household size) would purchase on a shopping trip. The greater the number of products purchased per trip, the larger the basket size. The small (large) basket shoppers divide their total consumption needs into many smaller (few larger) baskets and thus buy relatively small (large) percentage of their total grocery needs on a single trip. Irrespective of the nature of motivational force, the behavioral upshot will be that a typical small (vs. large) basket shopper is likely to incur less expenditure per trip and is likely to make many trips over a given time period.

Using the above concepts, we investigate the following two hypotheses: (i) prices of SH products will have the strongest (and positive) influence on OSPI followed by prices of SL, LH, and LL products in that sequence and (ii) as basket size increases, the influence of prices of SH and SL products on OSPI will be mitigated whereas those of LH nd LL products will be accentuated.

In exchange for extra credit, 117 undergraduates from a northeastern university participated in a survey. For the one or two grocery stores where they shop regularly, subjects provided their OSPIs and rated prices of 20 distinct grocery products, five each from the four product categories (SH, SL, LH, LL) that varied on unit prices and consumption span. Asking subjects to rate OSPIs for only grocery stores (and not convenience stores) enabled us to control for store type. Subjects also provided information pertaining to their shopping basket sizes and the items they usually purchase from grocery stores. We used the regression analysis approach (Sharma et al. 1981) to test our hypotheses. Results of manipulation checks and several preliminary analyses established our confidence in the reliability and interpretation of the regression results and established the internal validity of the OSPI and basket size measures. Finally, the estimation results of our regression model provided strong support for both our hypotheses.

The findings help to identify "focal" product categories across distinct consumer segments that grocery stores can price promote to consumers to favorably influence the store’s OSPI. Thus, these findings hold important strategic implications for category management and target marketing that are likely to increase the overall effectiveness of retail promotional strategies.

REFERENCES

Alba, Joseph W.,Wesley Hutchinson, and John G. Lynch (1991), "Memory and Decision Making," in Handbook of Consumer Theory and Research, eds. H. H. Kassarjian and T. S. Robertson, Englewood Cliffs, NJ: Prentice Hall, 1-49.

Alba, Joseph W., Susan M. Broniarczyk, Terence A. Shimp, and Joel E. Urbany (1994), "The Influence of Prior Beliefs, Frequency Cues, and Magnitude Cues on Consumers’ Perceptions of Comparative Price Data," Journal of Consumer Research, 21(September), 219-235.

Anonymous (1993), "Around the World in a Shopping Basket," International Journal of Retail & Distribution Management, 21(2), 39-40.

Arnold, Stephen J., Tae H. Oum, and Douglas J. Tigert (1983), "Determinant Attributes in Retail Patronage: Seasonal, Temporal, Regional, and International Comparisons," Journal of Marketing Research, 20 (May), 149-157.

Barsalou, Lawrence W (1983), "Ad Hoc Categories," Memory and Cognition, 11, 211-227.

Biswas, Abhijit, Elizabeth J. Wilson and Jane W. Licata (1993), "Reference Pricing Studies in Marketing: A Synthesis of Research Results," Journal of Business Research, Vol. 27, 239-256.

Bell, David R. and James M. Lattin (1998), "Shopping Behavior and Consumer Preference for Store Price Format: Why "Large Basket" Shoppers Prefer EDLP," Marketing Science, 17(1), 66-88.

Belch, George E. (1982), "The Effects of Television Commercial Repetition on Cognitive Response and Message Acceptance," Journal of Consumer Research, June, 56-65.

Berelson, Bernard and Gary A. Steiner (1964), Human Behavior: An Inventory of Scientific Findings. New York: Harcourt, Brace, and World.

Blattberg, Robert C., Richard Briesch, and Edward J. Fox (1995), "How Promotions Work," Marketing Science, 14 (Summer), G122-G132.

Briesch, Richard A., Lakshman Krishnamurthi, Tridib Mazumdar, and S. P. Raj (1997), "A Comparative Anaysis of Reference Price Models," Journal of Consumer Research, 24 (September), 202-214.

Buyukkurt, B. Kemal (1986), "Integration of Serially Sampled Price Information: Modeling and Some Findings," Journal of Consumer Research, 13 (December), 357-373.

Cox, Anthony D. and Dena Cox (1990), "Competing on Price: The Role of Retail Price in Shaping Store-Price Image," Journal of Retailing, 66 (Winter), 428-445.

Desai, Kalpesh Kaushik and Wayne D. Hoyer (2000), "Descriptive Characteristics of Memory-Based Consideration Sets: Influence of Usage Occasion Frequency and Usage Location Familiarity," Journal of Consumer Research, December, 309-323.

Dickson, Peter and Alan Sawyer (1990), "The Price Knowledge and Search of Supermarket Shoppers," Journal of Marketing, 54 (July), 42-53.

Hasher, Lynn, and Rose T. Zacks (1979), "Automatic and Effortful Processes in Memory," Journal of Experimental Psychology: General, 108, 356-388.

Hauser, John R. and Glen L. Urban (1977), "A Normative Methodology for Modeling Consumer Response to Innovation," Operations Research, 25 (4), 579-619.

Hawkins, Scott A. and Stephen J. Hoch (1992), "Low Involvement Learning: Memory Without Evaluation," Journal of Consumer Research, 19 (September), 212-225.

Hinrichs, James V. and Laura R. Novick (1982), "Memory for Numbers: Nominal vs. Magnitude Information," Memory and Cognition, 10 (5), 47-486.

Hoch, Stephen J. and John Deighton (1989), "Managing What Consumers Learn from Experience," Journal of Marketing, 53 (April), 1-20.

Hoch, Stephen J., Byung-Do Kim, Alan L. Montgomery, and Peter E. Rossi (1995), "Determinants of Store-Level Price Elasticity," Journal of Marketing Research, 32(1), 17-29.

Hoyer, Wayne D. (1984), "An Examination of Consumer Decision Making for a Common Repeat Purchase Product," Journal of Consumer Research, 11 (December), 822-829.

Julander, Claes-Robert (1992), "Basket Analysis: A New way of Analysing Scanner Data," International Journal of Retail & Distribution Management, 20(7), 10-18.

Lichtenstein, Donald R., Richard G. Netemeyer, and Scot Burton (1995), "Assessing the Domain Specificity of Deal Proneness: A Field Study," Journal of Consumer Research, 22 (December), 314-326.

MacKinnon, J. G. and H. White (1985), "Some Heteroskedasticity Consistent Covariance Matrix Estimators with Improved Finite Sample Properties," Journal of Econometrics, 29, 305-325.

Marmorstein, Howard, Dhruv Grewal, and Raymond P. H. Rishe (1992), "The Value of Time Spent in Comparison Shopping: Survey and Experimental Evidence," Journal of Consumer Research, 19 (June), 52-61.

Monroe, Kent B. (1990), Pricing: Making Profitable Decisions. 2nd ed. New York: McGraw-Hill.

Monroe, Kent B., and Angela Y. Lee (1999), "Remembering Versus Knowing: Issues in Buyers’ Processing of Price Information," Journal of the Academy of Marketing Science, 27 (2), 207-225.

Rajaram, Suparna (1996), "Perceptual Effects on Remembering: Recollective Processes in Picture Recognition Memory," Journal of Experimental Psychology: Learning, Memory, and Cognition, 22 (2): 365-377.

Richardson, Paul S., Alan S. Dick, and Arun K. Jain (1994), "Extrinsic and Intrinsic Cue Effects on Perceptions of Store Brand Quality." Journal of Marketing, 58, (October), 28-36.

Sharma, Subhash, Richard M. Durand, and Oded Gur-Arie (1981), "Identification and Analysis of Moderator Variables," Journal of Marketing Research, 28, (August), 291-300.

Tzelgov, Joseph, Joachim Meyer, and Avishai Henik (1992), "Automatic and Intentional Processing of Numerical Information," Journal of Experimental Psychology: Learning, Memory, and Cognition, 18 (1): 166179.

Urbany, Joel E., William O. Bearden, and Dan C. Weilbaker (1988), "The Effect of Plausible and Exaggerated Reference Prices on Consumer Perceptions and Price Search," Journal of Consumer Research, 15 (June), 95-110.

Wagner, W. F. (1988), Yellow Pages Report, Scotts Valley, California: Mark Publishing.

Wakefield, Kirk L. and Jeffrey J. Inman (1993), "Who are the Price Vigilantes? An Investigation of Differentiating Characteristics Influencing Price Information Processing," Journal of Retailing, Vol. 69 (Summer), p. 216-233.

Walters, Rockney G. and Scott B. MacKenzie (1988), "A Structural Equations Analysis of the Impact of Price Promotions on Store Performance," Journal of Marketing Research, Vol. 25 (February), 51-63.

Winer, Russell S. (1988), "Behavioral Perspectives on Pricing: Buyers’ Subjective Perception of Price Revisited," in Issues in Pricing: Theory and Research, ed. Timothy M. Devinney, Lexington, MA: Lexington Books, 35-57.

Zeithaml, Valerie A. (1988), "Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence," Journal of Marketing, 52 (July), 2-22.

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