Comparing Dynamic Consumer Choice in Real and Computer-Simulated Environments

Journal of Consumer Research, Inc.

Comparing Dynamic Consumer Choice in Real and Computer-Simulated EnvironmentsAuthor(s): Raymond R. Burke, Bari A. Harlam, Barbara E. Kahn and Leonard M. LodishSource: Journal of Consumer Research, Vol. 19, No. 1 (Jun., 1992), pp. 71-82Published by: The University of Chicago PressStable URL: http://www.jstor.org/stable/2489189 .

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Comparing Dynamic Consumer Choice in

Real and Computer-simulated Environments

RAYMOND R. BURKE BARI A. HARLAM BARBARA E. KAHN LEONARD M. LODISH*

Actual supermarket purchases made by consumers over a seven-month period are compared with the choice decisions collected in one sitting in a laboratory simulation. The laboratory simulation was designed to mimic the original market environment. Although there were systematic biases in predictions of true purchase behavior from the simulated data, the "compressed simulations" were reasonably valid in predicting market shares and promotion sensitivity for brands across consumers.

Consumer researchers have long been interested in studying choice behavior in simulated or labora-

tory environments. The laboratory environment offers both a high degree of control and the ability to collect a large amount of data quickly and inexpensively. With the growing popularity of personal computers offering "the potential for a more complete simulation of the shopping/purchasing experience" (Brucks 1988, p. 17), consumer researchers have increasingly turned to the computer-controlled laboratory environment (e.g., Brucks 1985; Burke and Srull 1988; Buyukkurk 1986; Huber, Holbrook, and Kahn 1986; Johnson et al. 1986; Painton and Gentry 1985).

Although most researchers use the computer laboratory environment to study process and choice behavior at one point in time, some researchers have recently been using the computer to study repeated choice behavior over a period of time. For example, Kahn and Louie (1990) and Kahn and Raju (1991) use a computer laboratory study to investigate the differential reactions of variety-seeking and brand-loyal subjects to price promotions. Krishna (1991) uses the computer laboratory to study dynamic promotion patterning, and Meyer and Assuncao (1990) investigate stockpiling over time in response to various price promotions. These

kinds of dynamic phenomena are key determinants of consumer purchase behavior of nondurable consumer packaged goods. Before the use of computer-simulated experiments, these dynamic processes were very difficult and expensive for researchers to monitor.

While the external validity of laboratory experiments (e.g., Gabor, Granger, and Sowter 1970; Green and Sri- nivasan 1978; Levin et al. 1983; Nevin 1974; Pessemier 1960; Sawyer, Worthing, and Sendak 1979) and of laboratory-generated market-share forecasts (e.g., Silk and Urban 1978; Urban and Katz 1983) has been examined, no one to our knowledge has investigated the external validity of measuring dynamic processes, or time compression, in computer-simulated market environments. In this article, we begin this process by comparing consumers' actual supermarket purchases over a seven-month period with their choice decisions collected in one sitting in a laboratory simulation. The simulation was designed to mirror the conditions of the supermarket environment during the seven-month period. In contrasting the simulation process with the actual purchase process, we compare static measures, such as market share, brand-set size, and number of goods purchased per shopping trip, as well as dynamic measures, such as switching and run lengths. We find that the simulation validates some measures well and others not as well. These results provide initial guidelines for the suitability of time-compressed environments for the study of dynamic behavior.

At the outset, we should state some caveats concern- ing the research design. Because of resource limitations, data were collected from a relatively small sample of 16 women, aged 18 to 65, who had used one or more of the test products during the last month. However, in the tradition of verbal protocol and decision-net re-

*Raymond R. Burke is associate professor of marketing at the Harvard Business School, Boston, Massachusetts 02163. Bari A. Harlam is assistant professor at the Graduate School of Business, Columbia University, New York, New York 10027. Barbara E. Kahn is the Stephen M. Peck Term Associate Professor of Marketing, and Leonard M. Lodish, the Samuel R. Harrell Professor of Marketing, at the Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104. We thank Paul Green and the three reviewers for their comments on earlier drafts of this article. We acknowledge the financial support of the Marketing Science Institute.

? 1992 by JOURNAL OF CONSUMER RESEARCH, Inc. e Vol. 19 * June 1992 All rights reserved. 0093-5301/93/1901-0007$2.00

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72 JOURNAL OF CONSUMER RESEARCH

search, we collected a large amount of information from each of these individuals. Specifically, we recorded par- ticipants' actual shopping behavior for a seven-month period and then administered a lengthy computerized questionnaire. Therefore, our sample sizes in terms of the number of purchases are actually quite large-up to 233 purchases for some product categories. We also monitored the supermarket environment for the seven- month period, tracking prices and promotional activity.

This approach is a first step in the process of examining how well dynamic choice behavior observed in the laboratory corresponds to real-world phenomena. Our findings will provide guidelines for subsequent validation studies with larger samples and help predict the generalizability of future simulation results in which the laboratory is used to test conditions outside the bounds of real-world market conditions.

In the remainder of this article, we begin by identi- fying differences between the actual and simulated environments that may affect the decision process. We then discuss our methodology for simulating purchase environments. Finally, we present the results of the study and discuss the implications.

SIMULATION FACTORS AND THE PURCHASE DECISION PROCESS

In this study, we define laboratory simulation as the attempt to translate the consumer's dynamic purchase processes in supermarket settings to a personal computer environment. In the computer simulation, the experimenter has control over all aspects of the market environment and, thus, has to make decisions about which aspects to present to the subject, which aspects to simplify, and which aspects to ignore. In this regard, computer simulations can be thought of as spanning a continuum, anchored on one end by very rudimentary simulations in which realism is sacrificed for simplicity (e.g., pen-and-pencil questionnaires) and on the other end by very sophisticated and realistic simulations (e.g., test markets).

When purchasing products, consumers typically progress through the stages of information search, alternative evaluation, choice, and postpurchase evaluation and learning (e.g., Bettman 1979; Engel, Black- well, and Miniard 1990; Howard 1989). In the next section, we discuss how consumer behavior in actual and simulated environments may differ at each stage in the purchase process and then predict how these effects might be influenced by the relative sophistication of the simulation.

Information-Search Stage For frequently purchased consumer packaged goods,

information search is usually very limited. Consumers have accumulated considerable brand and product knowledge. They therefore spend little time examining

alternative brands and attributes when making purchases (Howard 1989). In a field study of laundry de- tergent purchases, Hoyer (1984) observed that the me- dian purchase decision time was 8.5 seconds, which includes the time taken by customers to walk down the grocery aisle. Dickson and Sawyer (1990) found that, for coffee, toothpaste, margarine, and cold cereal, the mean category shopping time was less than 12 seconds, with about 42 percent of the shoppers spending five seconds or less.

In these mature product categories, consumers often base their decisions on brand recognition, brand loyalty, and/or "affect referral" (see Bettman 1979; Johnson and Russo 1984; Rossiter and Percy 1987; Wright 1975). Hoyer (1984) similarly argues that, for commonly purchased items, consumers use simple choice tactics, such as "buy what you bought last time" or "buy the cheapest brand." This is also consistent with Howard and Sheth's (1969) model, which suggests that, in the mature stage of the product life cycle, buying becomes routine or habitual.

The cues associated with these simplified decision rules, such as brand name and price, can be easily reproduced in computer simulations. We therefore would expect that, in product classes in which the purchase decision is either made in the supermarket on the basis of cues such as price or made in advance on the basis of brand name, consumer behavior in the simulated environment will accurately reflect behavior in a real grocery store. Of course, there are some categories for which this will not be the case. For example, with meat, deli, and produce products, consumers often squeeze, smell, and taste the goods before buying them. These cues cannot be easily reproduced in the laboratory. In other categories, such as vitamins or pain relievers, consumers might study detailed package information before making a purchase. This information would be harder to present in the computer simulation. We would therefore predict the following:

HI: Consumer choice behavior in the laboratory will more closely match actual shopping behavior in product categories in which purchase decisions are based on cues that can be easily reproduced in the simulation, such as price and brand name.

Not only are there significant differences between the simulated and actual environment, but there may also be considerable differences in the level of sophistication of laboratory simulations. We hypothesize that, when the simulation differs significantly from the supermarket setting in the manner in which specific information is presented to the consumer and when this specific information is critical in the subsequent decision process, differences between simulated behavior and actual purchase behavior become magnified. For example, when consumers are choosing among different sizes of a brand, physical attributes such as the size of the con-



DYNAMIC CONSUMER CHOICE 73

tainer and the weight of the package become salient. The ability of a laboratory simulation to forecast market share on the basis of size depends on the realism of the simulation and whether or not it imparts visual size information. Thus, we hypothesize the following:

H2: The correspondence between laboratory and actual market shares of goods by sizes (rather than by brands) will be higher in more realistic simulations, which impart visual size information, than it will be in rudimentary simulations.

Alternative-Evaluation Stage In the mature stage of the product life cycle, price

and in-store promotions often become separated from the brand image in the buyer's mind and exert an independent influence on choice (Howard 1989). This implies that, although consumers are not likely to change their attitude toward a brand, in-store promotions or pricing may nonetheless affect the evaluation of available alternatives at any particular point in time. For example, research suggests that consumers have an implicit reference price for a brand. When coupons or other price discounts are offered, consumers react pos- itively by purchasing the discounted brand because it is perceived to be a "good deal" (see Monroe 1973; Thaler 1985; Winer 1986).

The laboratory and the actual store environment may differ significantly in the manner in which price is presented to the consumer. Most of the previous work comparing laboratory environments with actual store environments has concerned the inclusion of price information. For example, Gabor et al. (1970) compared subjects' behavior in a simulated shopping experiment with actual behavior in the store. They found higher price sensitivity in the laboratory for some product classes and concluded that there was a greater emphasis on price in the simulation than had actually existed in the marketplace. Nevin (1974) notes that simulations generally assume that consumers have 100 percent awareness of all price offerings. Thus, if the actual in- store price awareness is anything less than 100 percent (which it invariably is), the laboratory estimates would tend to overstate the actual effects of price treatments. Thus, we hypothesize the following:

H3: Subjects will be more sensitive to price promotions (i.e., features') in the laboratory than to price promotions in the actual grocery store.

However, this sensitivity to price promotions in the laboratory should be lessened in simulations that more

realistically reflect the actual supermarket environment because factors other than price may become salient. Thus, we hypothesize the following:

H4: The correspondence between purchases made on promotion in the laboratory versus the supermarket will be higher in more realistic simulations than it will be in the rudimentary simulations.

Product Choice The laboratory and the actual supermarket also differ

at the choice stage of the decision process. In the simulation, the decision maker is an individual. In real-life situations, there may be other people involved in the decision at the point of purchase or prior to purchase. Out-of-stock conditions and seasonal or other extra- neous events will also vary between a simulation and the actual grocery store. In addition, time constraints differ between a simulation and actual shopping experiences.

In a simulation, it is difficult to impose a realistic budget constraint unless actual purchases are made by the subject using his or her own money. Any other im- position of a budget constraint in a simulation is some- what artificial. Without an explicit, realistic budget or space constraint (e.g., shopping cart, car, etc.), subjects in a simulated environment are likely to purchase a larger quantity of a brand than they might in the supermarket. Thus, we hypothesize the following:

H5: Without an explicit and realistic budget or space constraint in the laboratory, subjects will choose a larger quantity of product per choice occasion in the simulation than they would per shopping trip in a store.

Finally, potential demand effects in the laboratory may encourage respondents to alter their behavior in socially desirable ways when they are being observed. For example, diary panel companies typically wait some period of time before incorporating new panelists into the panel data because they believe that subjects tend to buy more premium brands and fewer "cheap" brands when they know someone is observing their behavior. Thus, we hypothesize the following:

H6: Subjects will be more likely to purchase na- tional brands, rather than the less expensive store brands, in the laboratory than in the actual supermarket.

Postpurchase Evaluation and Learning One of the major differences between actual and sim-

ulated dynamic choice processes is the time-compression factor. In the simulation, all purchase decisions are generally made over a very short time period, whereas in the actual shopping situation the decisions

'When we refer to price promotions or features in this study, we are referring to promoted prices that are advertised in the newspaper and that usually have a corresponding shelf marker at the point of purchase.




are made over a much longer period of time. In the time-compressed environment, previous decisions are likely to have a stronger impact on current decisions than they do in actual shopping trips because of increased salience and memory.

Because the products are not consumed between purchases in the simulation, there is no learning through consumption and no satiation. Further, in the simulation, it is less likely that the consumer will experience the desire for novelty or variety that is known to be a factor in actual purchasing (Howard 1989; Raju 1980).

All of the factors mentioned above imply that there will be less brand switching from one purchase occasion to the next in the laboratory than in the supermarket. Because there is no natural satiation possible in the laboratory, the total number of brands in the choice set over all purchase occasions should be smaller in the laboratory than in the supermarket. Thus, we hypothesize

H7: Subjects will switch less from one purchase occasion to the next in the laboratory simulation than in the actual purchase environment.

H8: Because there is no consumption of the product in the laboratory and thus no opportunity for physical satiation, the total number of brands in a subject's choice set over all purchase occasions will be smaller in the laboratory than in the supermarket.

As the above discussion indicates, we recognize that there are many differences between the simulation of dynamic choice behavior and the actual shopping experience. However, on the basis of past validation studies of laboratory research techniques for other kinds of choice behavior (e.g., Levin et al. 1983), we expect that the artificial environment will nonetheless offer impor- tant insights into actual dynamic purchase behavior.

EXPERIMENTAL METHODOLOGY

In setting up an experiment to compare computer- simulated market behavior with actual purchase behavior, many decisions about the design of the computer simulation have to be made. On the one hand, we wanted to capture as much of the complexity of the actual marketplace as possible to ensure external validity (Baldinger 1988). On the other hand, we wanted to see how much we could simplify the laboratory environment and still get reasonably valid predictions of behavior.

In our experiment we used two computer simulation techniques, a rudimentary laboratory and a realistic laboratory. In general, all of the decisions about the design of the rudimentary lab were made in keeping with a desire to find the easiest, simplest method of

operationalizing the task. In the realistic lab, all of the decisions were made to try to recreate accurately the in-store shopping experience.

Rudimentary Laboratory In the rudimentary laboratory, we designed a com-

puter procedure that could be easily replicated by other academic researchers with limited resources. In this procedure, we provided a verbal description of available brands. Every brand and every size of the brand were included and listed alphabetically. In the soft drink category, which was quite large, subjects were told to go through the list once and delete any items that they would never consider purchasing. These brands were then deleted from any subsequent listing of the category, making the number of brands more manageable.

Subjects were given the opportunity to request price information for any specific brand of interest. If a brand was featured in a newspaper ad that week, the brand name was listed in green, and if a store coupon was available, the brand name was listed in pink. The subject could specifically request additional information about the featured price or coupon discount of any brand, but this information was not automatically displayed. To simplify the presentation, all other brand attributes were unavailable. The subject could choose to purchase any number of units per choice occasion. The subjects were asked first to make all decisions for one product category and then to start on the next product category.

Realistic Laboratory At the other end of the spectrum, the realistic labo-

ratory was designed to mimic more closely the actual shopping experience. In this laboratory, the computer displayed photographic images of the fronts of brand packages, which were arranged on the shelf as they are in the grocery store. In addition, the prices of brands were listed below the brand packages on shelf cards. Unit pricing was not made available to simplify the visual presentation. Promotions were highlighted in the simulation by shelf markers (as they are in grocery stores). Thus, the computer screen looked like a pho- tograph of the brands exactly as they appeared in the grocery store.

Because each brand included in the simulation had to be purchased and photographed, a few brands in each product category were missing from the simulation list because they were stocked out at the time the brands were photographed. In the soft drink category, the number of brands was large enough to necessitate dis- playing three screens of brand images, rather than one, as was the case in the other categories. Subjects "walked" between the screens by using a trackball. In making their choices in the realistic simulation, subjects zoomed in on individual brands and incremented a




TABLE 1

DESCRIPTION OF PRODUCT CLASSES

Orange juice Paper Toilet towels Tuna tissue Fresh Frozen Soft drinks

Percentage of households buying 92.9 86.9 95.8 69 59 98 Purchase cycle (days) 44 51 35 45 54 23 Any trade deal (% of purchases made) 52 55 49 56 51 74

In-store display (% of purchases made) 27 28 23 5 12 51

Store coupon (% of purchases made) 4 4 4 8 2 4

Manufacturers' coupon (% of purchases made) 4 1 6 10 6 4

Feature (% of purchases made) 30 33 29 41 31 40

SOURCE.-The Marketing Fact Book, Information Resources, January-December 1989.

counter to indicate the quantity purchased. They could move freely between brands when making choices. The subjects went through all the product categories for a given week and then started on the next week.

Product Classes When selecting product classes for the validation

study, we wanted products that accounted for the largest volume in supermarkets. Therefore, we chose product classes that virtually all households buy and buy frequently (see Fader and Lodish 1990). We also wanted product classes that were promoted heavily in order to see how promotions might differentially affect behavior in the laboratory versus the grocery store. Products meeting these requirements tend to be (1) dry goods that are not perishable, are highly featured, and may use off-shelf display or (2) refrigerated and frozen goods that are highly featured but that cannot be easily displayed (Fader and Lodish 1990). Because products cannot be "experienced" in the simulation, we were most interested in looking at mature product classes with which consumers were already familiar. Finally, we wanted product classes in which the incidence of manufacturers' coupons was low because we could not record when manufacturers' coupons were available.

To span a broad spectrum and to reduce tedium for subjects who went through both simulations, we chose different product classes for the two simulations. For the rudimentary simulation, we used bathroom tissue and orange juice. For the realistic simulation, we se- lected paper towels and canned tuna. We did choose one common product category across both simulations-carbonated soft drinks-so that we could compare a rudimentary simulation with a sophisticated one in testing some of our hypotheses. We chose soft drinks as the common category because there is empirical evidence that this product category is one in which consumers make purchase decisions in the store on the basis of price and promotions (Consumer Reports, March

1988; Lal 1990), or in advance on the basis of brand name (Kerr 1990), and these cues are easily reproduced in the laboratory. In addition, earlier research (Nevin 1974) found stronger validity for laboratory results for the soft drink category as compared with the coffee category. Specific summary statistics about these categories are provided in Table 1.

Experimental Procedure

Initially, 18 suburban women who were 18-65 years old and who were the primary grocery shoppers for their families were recruited from a religious organization. They agreed to take part in a study on shopping. To qualify, these women had to shop primarily at one or both of two grocery stores in the local area that were equipped with UPC scanner equipment at the time of the study. These women saved their receipts from all grocery shopping trips over a seven-month period. The shopping receipts showed the date, time, brands, quantity, size, price, and store coupon usage for all purchases. These receipts were sent to the researchers each week in preaddressed, postage-paid envelopes. In exchange, a donation equaling 2 percent of the total amount spent was sent to the religious organization.

Weekly store information was also collected over the seven-month period. Specifically, local print advertising was monitored to record all store promotions and features. In addition, the stores provided weekly price lists for the five categories used in our study. We did not have data on stockouts, displays, or manufacturers' coupons.

Two months after the last receipt was collected, each subject was asked to participate in a computer questionnaire. At this juncture, we lost two subjects who could not attend the sessions run on the computer, leaving us with 16 subjects for the final analysis. Four- teen subjects participated in the rudimentary simulation, and 10 subjects participated in the realistic sim-




TABLE 2

DESCRIPTIVE STATISTICS OF SUBJECTS' ACTUAL PURCHASE PATTERNS

Paper Toilet Orange towels Tuna tissue juice Soft drinks

Number of items in product class 24 59 30 67 266 Number of brands in product class 12 28 14 17 50 Average number of purchases per person (total) 3.43 3.14 6.92 11.14 17.29 Average number of different items purchased

per person 2.86 2.14 3.25 6.62 7.54 Average number of different brands purchased

per person 2.71 1.71 2.33 3.36 5.54

ulation. Eight subjects participated in both simulations.2 In these eight cases, we alternated which simulation was run first. For each of the computer-simulated shopping trips, we re-created the conditions that actually occurred in the store during the weeks that the subjects made purchases in one of the categories of interest. Thus, each simulation was uniquely designed for each subject to match the supermarket conditions for the weeks she specifically shopped.

Within the simulation, subjects were asked to act as they would in a conventional grocery store and to choose the products they would buy when faced with the represented store conditions. Subjects were not asked to try and remember what they actually purchased and were not told that the environment was the same as it was when they sent in their receipts. Following the purchase decisions, the subjects were asked about usage in various product categories and about the computer experience. Both simulations were completed in one sitting; the rudimentary simulation took an average of 45 minutes to complete while the realistic simulation required an average of 30 minutes.

RESULTS Before testing the hypotheses, we report some general

descriptive statistics on consumers' actual purchase behavior in the test product categories. Table 2 shows that the soft drink category is different from the other categories in that more purchases were made per person and many more items were offered in the product class.

Table 3 provides various measures of how well the market shares recorded in the simulation matched the

actual shares recorded in the store. To get a benchmark of what a "best-case" correlation would be, we ran a bootstrap analysis. We randomly drew 10 samples of the data (with replacement) from the actual data base. The size of these samples was equal to the sample size of the actual data. Thus, we had random samples that were drawn from our known market-share population. We then calculated correlations between these boot- strapped samples and the actual data. These correlations represent an upper bound for the sample correlations because they only suffer from random sampling errors. Our samples in the laboratory suffer both from random sampling and because they may be drawn from a different distribution of market shares than the actual one. The range of Pearson correlations between the boot- strapped samples and the original data ranged from .95 to .99. By comparison, the range of Pearson correlations between the simulated data and the original data was .67-.96.

Another benchmark, the mean absolute difference in market-share points between predicted and actual shares, has ranged from an average difference of nine share points (Silk and Urban [1978] with ASSESSOR model) to a standard deviation of two share points (Ur- ban and Katz 1983) in past studies.

Finally, the third benchmark was borrowed from in- dustry. A market research analyst from a major consumer goods company said that his firm looked for at least an 80 percent index of coverage when assessing market-share predictions. In our experiment this means that the simulated market share should be within 80- 120 percent of the actual market share. While on the face of it, this measure of fit appears to be the most conservative, the low percentages of predictions within a plus-or-minus 20 percent coverage range are due, for the most part, to a large number of very small brands. When the market share of a brand is quite small (e.g., less than 3 percent), even very small absolute deviations can be large when measured in percentage terms. For the orange juice category in particular, this problem explains the rather poor performance of the simulation on the index of coverage measure.

2Because we had some subjects who participated in both laboratories, we very carefully tested for order effects. We found no significant differences in subjects' responses on any of our measures on the basis of which simulation they completed first. We also compared responses from subjects who did only one simulation with those who did two and again found no significant differences on the basis of this factor. Of course, finding no significant differences on these tests is not surprising, given the limited power of the statistical tests. However, we did not uncover any suspicious directional effects.




TABLE 3

STATISTICS FOR AGGREGATE MARKET-SHARE ANALYSIS

Realistic laboratory Rudimentary laboratory

Paper towels Tuna Soft drinks Toilet tissue Orange juice Soft drinks

Pearson correlation: Actual with laboratory shares .80 .67 .83 .75 .96 .96 p-Level .01 .05 .001 .001 .001 .001

Mean absolute difference in share points: Over all brands (SE) 6 (2) 9 (3) 2 (1) 4 (1) 2 (1) 1 (1) For brands with actual market share greater than

zero (SE) 8 (2) 12 (3) 3 (1) 6 (2) 3 (5) 1 (.2) Percentage of brands with simulated market

shares within five share points of actual (no. of brands) 60 (12) 50 (28) 83 (24) 79 (14) 89 (17) 100 (50)

Percentage of brands with simulated market share within 80 percent of actual (no. of brands) 40 (12) 44 (28) 42 (24) 43 (14) 29 (17) 66 (50)

Market share analyses: Total purchases madea (actual/simulation) 24/42 22/46 193/354 83/109 152/207 233/238

a1n subsequent statistical tests for market-share analyses, multiple purchases per person were assumed to be independent purchases.

Relevant sample sizes for aggregate market-share analyses are listed in Table 3. Sample sizes for individual analyses are listed in Table 4.

Information-Search Stage We hypothesized that, when simulations differed sig-

nificantly from the supermarket setting in the manner in which specific information relevant to the decision was presented, then differences between simulated and actual behavior would become magnified. Specifically, Hypothesis 1 predicts that the simulation should capture the choice behavior more accurately in product categories in which the purchase decision is made on easily reproduced cues such as price or brand name. As mentioned earlier, soft drinks are a category in which consumers tend either to make their decisions in advance on the basis of brand name or to buy whichever brand is on special. Using either of these decision rules, consumers are unlikely to read the labels or to pick up the product (Kerr 1990), two factors that are more difficult to simulate. Thus, to test this hypothesis we compared market shares for the simulated data and the actual purchase data in the soft drink category with the other categories.

As Table 3 shows, we find support for Hypothesis I in that the simulation does better in predicting actual market shares for the soft drink category than for the other categories across all five measures listed. In addition, Table 3 tells another story. One of the goals of this research is to determine the validity of computer simulations for predicting actual purchase behavior. While the results discussed above for the tests of the other hypotheses point out some of the differences between simulated behavior and actual behavior, Table

3 demonstrates how well the simulated environments can predict aggregate choice behavior across all product categories.

Hypothesis 2 predicts that the correspondence of market shares of goods by sizes would be closer in a more sophisticated, realistic laboratory than it would be in a rudimentary one.3 Comparing across the soft drink category, which is common to both laboratories, we find that the mean absolute deviation between size shares in the laboratory and in the supermarket for the rudimentary laboratory is .03 (SE = .02), and it is .01 (SE = .005) for the realistic laboratory. In support of the hypothesis, these differences are marginally significantly different (p < . 10).

Alternative-Evaluation Stage Hypothesis 3 asserts that subjects should be more

sensitive to promotions in the laboratory than in the supermarket. In Table 3,4 we compared the percentage of purchases made on feature in the laboratory with the percentage of those made in the store. We found different results for the two environments, thus also pro-

3Note that we cannot isolate which specific factors are causing the differences in results between the two laboratories, as there are many differences. However, each of the different characteristics between the two laboratories was chosen such that the realistic laboratory was more representative of purchase behavior and the rudimentary laboratory was easier for a researcher to operationalize.

4These and subsequent analyses are made across product classes. We tested for interactions between product classes and the type of environment (laboratory or store) and found no significant effects. We also checked to make sure that there was no directional effect that might indicate a potential interaction with a larger sample size. None was found. Thus it is appropriate to report the main effects of type of environment on our dependent variables.




TABLE 4

MEASURES OF PROMOTION SENSITIVITY FOR INDIVIDUAL-LEVEL ANALYSIS

Realistic laboratory Rudimentary laboratory

Actual Laboratory Actual Laboratory

Percentage of total purchases made on feature 26 31 15*** 31 ***

Percentage of trips in which a featured purchase was made 30 33 18** 30** Average quantity purchased:

Given a featured price .86** 2.06** .78* 1.41 *

Given no featured price 1.02** 1.63** 1.05* 1.31* Number of subjects in each laboratorya 10 10 14 14

aThese sample sizes are the same for all individual analyses. Averages across all purchases within a product class per person were used. A repeated-measure ANOVA was used to analyze the data because we had multiple entries for each subject (i.e., multiple product class averages).

*Difference between actual and laboratory is significant at the .05 level. **Difference between actual and laboratory is significant at the .01 level. ***Difference between actual and laboratory is significant at the .001 level.

viding support for Hypothesis 4, which stated that the correspondence between promotional purchases in the laboratory and in the supermarket would be stronger in the realistic simulation than in the rudimentary one. In the rudimentary laboratory, a significantly higher percentage of purchases were made on feature in the laboratory than were made in the actual store, as predicted. However, in the realistic laboratory, there was no significant difference between the simulated and actual environments. This difference is also evident when the percentage of purchases made on feature in the actual supermarket, as compared with the behavior in the simulation, is plotted for each subject in the two laboratory environments. As Figure 1 illustrates, the correspondence is closer in the realistic laboratory than in the rudimentary laboratory.

The significantly higher percentage of purchases made on feature in the rudimentary laboratory, as compared with the percentage of those made in the actual store, could be due to one of two (or both) reactions to promotions. We could observe a response in the laboratory because subjects were more likely to make a purchase given a featured price in the laboratory or because subjects were more likely to stockpile, that is, purchase larger quantities, given a featured price in the laboratory (see Table 4).

In testing this response, we again found different results in the two laboratory environments. In the rudimentary laboratory, we found that the percentage of trips on which a featured product was purchased relative to the total number of trips was significantly higher in the laboratory than it was in the supermarket (p - .01). In the realistic laboratory, there was no difference (p = .60). However, given that a product was purchased on feature, the quantity purchased in the laboratory was significantly higher for both the rudimentary and realistic laboratories (p < .03 and p < .01, respectively).

We also tested whether the increase in the number of goods purchased on feature in the laboratory compared with the store was greater than the increase in the

number of goods purchased off feature in the laboratory compared with the store. In both the rudimentary and the realistic laboratories, we found a marginally significant fan-shaped interaction (p < .10). In all cases, a larger quantity per shopping trip was made in the laboratory than in the store, but this increase was even greater when the product was featured than when it was not.

As discussed above, we found some general support for Hypothesis 4. To test Hypothesis 4 directly, we compared consumer behavior in the common category of soft drinks. In the rudimentary simulation, 46 percent of the choices were made on feature, compared with 28 percent in the actual store, which is a significant difference at the .05 level. However, in the realistic simulation, 48 percent of the choices in the laboratory were made on promotion, compared with 42 percent in the actual store, which is a nonsignificant difference (p > .40).

We also evaluated Hypothesis 4 by comparing the impact of features on the basis of multinomial logit (MNL) findings. We calibrated separate MNL models at the brand level in the soft drink product class for the actual supermarket purchase data and for the laboratory choice data. The independent variables for each of the models were brand-specific constants and a dummy variable that indicated whether the brand was featured.5 By comparing the feature dummy variable coefficients, we can examine differences in the impact of features between actual and simulated behavior across the laboratory environments.

Table 5 shows the feature variable coefficient estimates for each soft drink category MNL model. As ex-

5The dummy variable is set to one if any of the items within the brand are on feature during a particular purchase opportunity and zero otherwise. We have not included price in the MNL model because price and features are correlated, r = .82. The brand-specific constants ensure that the probabilities, when aggregated, will sum to the actual market share.




FIGURE 1

PERCENTAGE OF PURCHASES MADE ON FEATURE

Graphics Laboratory Text Laboratory

1.2 ~~~~~~~~~~~~~~~~~~1.2

0.8 -. 0.8

0 .6 i ....O... ... ........... .............. 0 .6

0.4 - ... 0.4 , ...

0.2 - ;

. . 0.2 . X . ..

0 2 4 6 8 10 12 0 2 4 6 8 10 12 14 16

Subject Number Subject Number

Actual I- Simulation - Actual -4I Simulation

pected, we find significant positive feature coefficients in all settings. In the rudimentary laboratory, the coefficient for the feature variable is significantly lower in the actual supermarket purchase-based model than it is in the laboratory-based model. However, in the realistic laboratory, there is no significant difference between the feature coefficients in the supermarket versus the laboratory.6

Product-Choice Stage Hypothesis 5 predicts that because there is no budget

or space constraint in the laboratory, subjects will choose larger quantities of goods than they would in the supermarket. As Table 6 shows, we find support for this hypothesis.

Hypothesis 6 states that, perhaps because of demand effects, subjects will be more likely to purchase store brands in the supermarket than in the laboratory. As Table 6 shows, we find mixed support. In the rudimentary laboratory, for soft drinks and for toilet tissue, subjects do buy a significantly larger share of store brands in the supermarket than in the laboratory. For orange juice there is no difference. In the realistic laboratory,

for paper towels and tuna, subjects buy a larger share of store brands in the laboratory than in the supermarket, contrary to the hypothesis, although the differences are not statistically significant.7 The disparity in these results may be a reflection of product class differences.

Postpurchase Evaluation and Learning Finally, Hypotheses 7 and 8 predict the effect of time

compression on the choice process. We would expect more switching behavior and larger choice sets in the actual grocery store than in the simulation. To test Hy- pothesis 7, we compared the average number of switches (normalized by dividing by the number of purchases the individual made in the product category minus one), the average run length, and the longest run length (normalized by dividing by the number of purchases). As Table 7 indicates, we find support for Hypothesis 7. We find a significantly lower percentage of switching, longer average runs, and the longest run's being a higher percentage of the total purchases in the simulated environment compared with the actual store environment. Thus, our results provide strong evidence that there is more switching in the actual store environment than

6We should point out that, because of the size and nonrandom aspect of the sample, the exact parameter estimates reported are not generalizable. However, the MNL models show convergent validity for our other measures and illustrate the potential for future model estimation of simulated data.

7Store brands of soft drinks were not used in the graphic simulation (because of the excessive number of brands in this category and the difficulty in visually recording all of them on the computer screen) so comparisons on store-brand market share cannot be made for this category.




TABLE 5

LOGIT COEFFICIENTS FOR MEASURING THE IMPACT OF PRICE FEATURES ON SOFT DRINK CHOICE

Actual Laboratory

Rudimentary laboratory: Logit coefficient 2.12* 2.51* t-Statistic 14.12 15.70

Realistic laboratory: Logit coefficient 2.54 2.36 t-Statistic 15.06 14.05

*Difference between actual and laboratory is significant at the .05 level.

there is in the laboratory. In addition to time compression, other differences between the laboratory and the store may be contributing to these results. For example, we did not have data on out-of-stock products, products on in-store displays, or manufacturers' coupons in the laboratory. These factors may increase brand switching in the actual grocery store.

To test Hypothesis 8, we compare the total number of brands in a subject's choice set (normalized by dividing by the number of purchases made) in the laboratory with the actual purchase behavior. As Table 7 shows, for both types of simulations, the brand-level choice set was larger in the actual shopping experience than it was in the laboratory. Thus, the subjects were sampling a larger number of brands in the supermarket than they were in the laboratory, supporting the notion that time compression reduces natural variety or novelty seeking.

CONCLUSIONS In this study, we identified various stages of the con-

sumer decision process and explored the ways in which simulated choice processes may differ from actual grocery shopping. We found systematic biases in predictions of true purchase behavior from the simulated process.

In the information-search stage of the decision process, consumers are collecting information relevant to their purchase. We hypothesized that simulations would do a better job forecasting brand market shares in product classes in which the information or cues used to make purchase decisions were accurately reproduced in the laboratory. In support of this prediction, we found stronger validity for the laboratory results in the soft drink category than for the other categories.

We also hypothesized that, when the simulation differs significantly from the supermarket setting in the manner in which specific information relevant to the decision is presented to the consumer, differences between simulated and actual purchase behavior become magnified. We tested this by comparing size-share predictions between the realistic and the rudimentary lab-

TABLE 6

MEASURES OF THE EFFECTS ON PRODUCT PURCHASE

Realistic Rudimentary laboratory laboratory


Average number of purchases per trip 1.37*** 2.68*** 1.34*** 1.69***

Market share of store brands by product class:

Toilet tissue .28*** .1 7*** Orange juice .19 .20 Soft drinks NA NA .09** .04** Paper towels .04 .10 Tuna .09 .22

"Difference between actual and laboratory is significant at the .01 level. *-Difference between actual and laboratory is significant at the .001 level.

oratories for a common product class. Although there were many differences between the two laboratories (making it difficult to determine precisely which factor was responsible for the effect), the realistic laboratory presented package-size information to the subjects in a manner more similar to the way it is presented in the supermarket. Supporting our hypothesis, we found that the realistic laboratory predicted market share based on size better than the rudimentary laboratory did.

In the alternative evaluation stage, price and in-store promotions can exert an independent influence on choice. We found that, in the rudimentary simulation, subjects were more sensitive to in-store price promotions in the laboratory than they were in the grocery store across a common product class. This increased sensitivity to promotions had two aspects. First, in the rudimentary laboratory, subjects were more likely to choose a featured item in the laboratory than they were in the supermarket. In addition, given the choice of a featured good, subjects in the rudimentary laboratory chose larger quantities in the laboratory than they did in the supermarket. In the realistic laboratory, some of this increased sensitivity was mitigated. The incidence of purchases made on feature was not significantly different between the realistic laboratory and the supermarket. However, given a purchase on feature, a larger quantity was again chosen in the realistic laboratory than was chosen in the supermarket.

The lack of budget or space constraints in both types of laboratory simulation seemed to cause subjects to choose larger quantities of goods in the laboratory than they did in the supermarket. In addition, perhaps because of demand effects, we found some cases in which subjects purchased fewer store brands in the laboratory than they did in the grocery store, although this was not a consistent result.

Finally, another difference between the actual and simulated dynamic processes was the time-compression




TABLE 7

MEASURES OF THE EFFECTS OF TIME COMPRESSION FOR INDIVIDUAL-LEVEL ANALYSIS

Realistic Rudimentary laboratory laboratory


Average switching rate .70** .40** .77*** .49*** Average run length 1.39*** 2.72*** 1.37** 3.43** Longest run per number

of purchases .44 .50 .31 .46** Brand-level set size per

number of purchases .57*** .30*** .40** .30**

"Difference between actual and laboratory is significant at the .01 level. ***Difference between actual and laboratory is significant at the .001 level.

factor. The time compression in both of the laboratories resulted in more routinized behavior than appeared in the actual marketplace. Across purchase occasions, subjects were likely to include fewer brands in their choice set and to switch between brands less frequently in the laboratory as compared with the actual grocery store. These results seem to point to more zero-order behavior in the supermarket as compared with the laboratory environment.

RESEARCH IMPLICATIONS

Our results hold some caveats for interpreting the results of time-compressed simulations. We found that, although laboratory simulations can capture general brand preferences (measured by brand shares at the individual level), there are systematic biases in the simulation when dynamic processes are monitored. When time is compressed, the influence of past purchases on current purchases is intensified. Thus, behavior is likely to be more zero order in the supermarket than it is in the laboratory. Consequently, care must be taken in generalizing results from laboratory studies in which long series of choices are made in a short period of time.

This does not mean that there is nothing to be learned from such laboratory studies. Like other researchers who have looked at the external validity of laboratory research (e.g., Levin et al. 1983; Sawyer et al. 1979) we believe that, although we cannot expect naturalistic studies to be duplicated exactly in the laboratory, relative comparisons within a laboratory context are generalizable. For example, even though the degree of variety-seeking and brand-loyal behavior or the reactions to promotion strategies found in the laboratory may differ significantly from the behavior observed in a supermarket, differences in relative behavior as a function of manipulated independent variables are still relevant.

In fact, as Sawyer et al. (1979) point out, sometimes the biases associated with laboratory experiments may provide a more conservative test than would field ex-

periments, perhaps suggesting greater confidence in decisions based on lab results. For example, in Meyer and Assuncao's (199 1) stockpiling research, subjects showed a biased tendency to underrespond to price promotions relative to a normative framework. Thus our findings, which show a tendency for respondents both to buy more and to respond to price promotions to a greater degree than they do in the real world, suggest that the underbuying bias reported by Meyer and Assuncao may be even more pronounced in natural environments.

In addition, our results suggest ways in which future simulations can be designed to increase the external validity of measures of dynamic processes. For example, it may be helpful in future studies of dynamic choice behavior to report elapsed time between shopping trips to the subjects. Perhaps by adding contextual information, such as the time of year and information about other members of the household, or by adding product- performance feedback, behavior in the laboratory may become more generalizable. It may also be valuable to introduce distractions between the simulated choice occasions to lessen the impact of past behavior on future behavior, reflecting more accurately real-world phenomena. Similarly, our results indicate that care should be taken in the manner in which promotions are presented to the subjects. There seems to be a tendency for price promotions to be too salient in the laboratory. The accurate depiction of price promotions in the realistic laboratory is a step in the right direction.

Finally, we hope our study can provide some practical guidelines for subsequent larger-scale validation studies and for helping researchers to make decisions about the advantages, disadvantages, and caveats in using a dynamic simulated computer environment to understand purchase processes. Within appropriate limits, these computer environments will allow us to study research issues that previously could not be explored.

[Received February 1991. Revised November 1991.]

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Documents

Comparing Dynamic Consumer Choice in Real and Computer-Simulated Environments