WHAT FEATURES DRIVE RATED BURGER CRAVEABILITY AT THE CONCEPT LEVEL?

WHAT FEATURES DRIVE RATED BURGER CRAVEABILITY AT THE CONCEPT LEVEL?

JACQUELINE BECKLEY and HOLLIS ASHMAN

Understanding and Insight Group Inc.

AND

ANDREA MAIER and HOWARD MOSKOWITZ'

Moskowitz Jacobs Znc. I025 Westchester Avenue

White Plains, New York 10604

Received for Publication February 19, 2003

ABSTRACT

This paper deals with the analysis of drivers for self-defined craveability assessed in an Internet-based, conjoint analysis task. The stimuli comprised 36 descriptions of restaurant hamburgers, including product features, benefits, restaurant names, and emotional reactions that might ensue after eating the hamburger. Elements were combined into concepts by experimental design, and the resulting concepts evaluated by 145 respondents, on the attribute of craveability. Models relating the presence/absence of concept elements to ratings revealed that statements about the hamburger itself were the most powerful, but that no single element was highest across all the respondents. Segmentation of the 145 individuals by the pattern of their individual utilities revealed four key segments. These are Elaborates who may be sensoly-oriented and respond strongly to product descriptions; Classics who like the notion of a grilled hamburger; Imaginers who respond to restaurant name and advertising copy; and Emotionals who respond to statements about food to descriptions how the eater feels after consumption.

INTRODUCTION

Food Craving and High Levels of Liking

Food craving is the popular term for a variety of different behaviors. Fundamentally, the notion of food craving is an uncontrollable desire for a

'Address all correspondence

Journal of Sensory Studies 19 (2004) 27-47. All Rights Reserved. OCopyright 2004 by Food & Nutrition Press, Inc.. Trumbull. Connecticut. 21

28 J. BECKLEY, H. ASHMAN, A . MAIER and H. MOSKOWITZ

product. Food cravings are not well understood scientifically, although there is substantial anecdotal evidence that some foods (e.g., chocolate) are highly craved (Hetherington 1996). There is a growing archival, science-based, refereed literature dealing with food cravings. Many deal with the standard products that engender cravings (Gibson and Desmond 1999; Hetherington and Macdiannid 1995; Macdiarmid and Hetherington 1995; Rozin ef al. 1991; Weingarten and Elston 1991). Furthermore, the popular literature is replete with references on food craving, as a visit to the web search engine, Google, quickly reveals. At the time of this writing (February, 2003) there are 140,000 web pages that deal in one way or another with food craving. Most of the popular press on cravings consists of news articles in the food or personals or health section (Waterhouse 1995), along with commercially oriented promotional material on products that are designed to curb food cravings.

Up to now, a great amount of work about craving has been done through relatively simplistic questionnaires, asking the respondents about what foods they crave, mood, etc. Much of this information is anecdotal. Indeed, in a great number of the web hits for the topic food craving one finds relatively few academic, rigidly controlled studies on the drivers of food craving; although recently several articles have appeared that deal with the issues (Hill and Heaton-Brown 1994; Macdiannid and Hetherington 1995; Michener and Rozin 1994; Pelchat 1997; Pelchat and Schafer 2000). Many of these articles deal with food craving in conjunction with body state issues. There is almost nothing that deals with the notion of craveability of a food, and the communication factors that may be associated with that craveability.

This paper will deal with craveability as a heightened desire for a food. At the anecdotal level, we see the term craving associated with a variety of foods. The emphasis is on the person who is doing the craving. It is a person that has the craving, and craving is never used without reference to the individual experiencing the craving. As noted above, much of this is association, and indeed much of the written material, appears in nonscientific media. There is some evidence from popular usage that food cravings are often associated with some fast food products, such as pizza and hamburger. To some extent, we can trace sales of these products to individuals who develop a craving, and rush out to the fast food restaurant to satisfy the craving. This craving is not the clinical type often considered in the literature, but rather it is the notion of craving as an intensified degree of liking. Issues of this sort have been dealt with by researchers with compendia of approaches appearing in edited volumes (Shepherd 1989).

There are two organizing principles behind the research reported here. The first is that subjects can rate the craveability of descriptions or product vignettes when directly instructed to do so. The second is that if these vignettes are systematically varied, and comprise both food and nonfood communications

WHAT FEATURES DRIVE RATED BURGER CRAVEABILITY 29

(e.g., emotional satisfaction or brand), then it becomes possible to identify the particular elements that act as drivers of the craveability rating.

The understanding of craveability has both scientific and business aspects. The specific features of a product that make it craveable constitutes an important topic for developers, if these features can be controlled. These physical features are the drivers of craveability. The proper knowledge of what features make an individual crave the food can thus lead to a better, more focused product development effort. In addition, however, the ability to link together product features and emotional benefits, as well as restaurant names, adds a new dimension to research. This dimension is less tangible, but for the lack of a better term might be called the more emotional aspects (Saguy and Moskowitz 2002).

Conjoint Analysis to Uncover Aspects of Food Craveability

One of the more productive research methods to identify the algebra of the customer’s mind is known as conjoint measurement (Green and Srinivasan 1990; Moskowitz 1994a). Conjoint measurement works by presenting combinations of statements to the customer, with the customer instructed to rate the combination on a simple scale (e.g., liking, fit to and end-use, etc.). Since the researcher knows what particular elements are present in a concept, and also knows the consumer rating, it is straightforward to relate the presence/absence of concept elements to the consumer rating. The result is a model or equation that shows the contribution of every element to the rating attribute (Box et al. 1978).

Conjoint measurement has been widely used by marketers in order to identify what factors of a product drive consumer interest (Wittink and Cattin 1989), and whether there exist different segments of the consumer world (Green and Krieger 1991). Topics as different as food irradiation on the one hand (Moskowitz et al. 2002), and snack foods on the other (Moskowitz 1994b), have lent themselves to investigation by this approach. It is important to emphasize that conjoint analysis is simply a research technique and an application of statistical analysis, which requires problem definition up front in order to be useful.

Conjoint measurement may provide new insight regarding drivers of craveability. The approach follows that used by a researcher in marketing. For the conjoint task craveability rather than liking or interest becomes the dependent variable. The outcome is a set of numbers showing how the different elements drive the craveability ratings.

Applying Conjoint Measurement Over the Internet

Conjoint measurement is powerful, but can be long and costly to imple- ment. The respondent has to be recruited to participate, come to a session,

30 J . BECKLEY, H. ASHMAN, A. MAIER and H. MOSKOWITZ

evaluate concepts, and then complete a questionnaire. The actual evaluation itself can last 30-45 min. When the conjoint analysis is done by telephone, with an interviewer reading the concepts, the interview can even be longer. Attempts are often made to mail out the conjoint analysis questionnaire to respondents, with the instructions to complete the questionnaire and mail back the ratings. The respondent is unaware of the underlying experimental design in the questionnaire, and simply responds to the concepts as a set of different stimuli.

When properly implemented, Internet-based conjoint measurement can provide the necessary data at a reasonable cost. The respondent must be hooked up to the Internet. If there are graphics, then ideally the hook up must be through a fast dialup modem (e.g., 28K or faster), or preferably through an even faster link (e.g., DSL or cable modem). For text based conjoint, the need for speed is not as great, although speed does make the task less onerous.

The validity of conjoint measurement over the Internet has been previously demonstrated, for a study run with respondents both at a prerecruit central location site, and at the Internet. The results were highly correlated, both at the level of the total panel, and at key subgroups. Furthermore, similar segments emerged from the data, suggesting that the same types of segments can be found in both research methods (Moskowitz ef d. 2000). The popularity of conjoint measurement on the Internet is growing, as can be attested to by the many research sites devoted to the approach (e.g., www.conjoint1online.com). Thus, for the purpose of studying consumer reactions, the researcher can accept Internet-based results as reflecting the types of results that would be found in central location interviews.

An additional way to ensure validity for Internet surveys uses passwords and cookies. The password ensures that only the respondents who are actually invited participate, especially if provision is made for the password to be embedded in the link. Furthennore, validity can be assured by placing a cookie in the respondent’s computer, so that the respondent cannot participate more than once. The cookie effectively shuts the respondent out of the study after the first participation.

Recently, the senior author implemented another method to ensure data quality (Moskowitz 2002). The method computes a measure of goodness-of-fit of the ratings to the concept elements. The goodness of fit statistic, the multiple RZ, can be computed on the ratings assigned by each respondent, since each respondent evaluates a complete set of concepts created by experimental design. Each individual participates in a complete study. By complete is meant that the specific combinations of concept elements rated by the individual are selected so that the researcher can compute a model or equation for that particular respondent relating the rating to the presencelabsence of the concept elements. The program returns with the multiple R2 value for that respondent. The higher the R2-square value for an individual model, the more that variations in the


ratings can be traced to variations in the concept element. That is, the more lawful the data is concluded to be. It does not matter what elements do well, or what do poorly. Nor does it matter how closely the individual agrees with the remaining population, nor even the range of ratings assigned. As long as there is a clearly lawful relation between the presencelabsence of concept elements and the ratings, one can conclude that the respondents ratings are valid. For this study, we selected an R2 value above 0.60 to represent a strong relation between the presence/absence of elements and ratings. This R2 value corresponds to stringent criterion of goodness-of-fit. In most studies, more than 80% of the respondents show this level of high R2, leading one to accept the results based upon this measure of consistency. One presumable reason for such a high proportion of consistent raters is that the disinterested respondents may drop out of the study before completing it, so their interviews are never counted in the total.

Response Heterogeneity, Subgroups in the Population, and Segmentation

People differ in what they like and dislike. This is easily seen at an everyday level by walking through a supermarket, to observe shelves loaded with different varieties of the same products, whether the varieties differ in terms of size of package, flavor, etc. These preference differences often transcend the more conventional ways that researchers divide respondents (e.g., by geo-demographics, or by self-defined product usage). One of the key features of conjoint measurement is its ability to reveal the nature of those preferences, often in cases where the respondent cannot articulate the preferences himself. This ability to identify the preferences can provide deeper insight into the drivers of craveability .

Recently, Moskowitz and his colleagues have suggested a deeper way to segment the consumers. This approach looks at the utility values from the conjoint measurement task. The consumers are divided by the patterns of the utility values. Originally developed for products (Moskowitz ez al. 1985), the method has been applied to concepts, and then to the results of conjoint measurement (Moskowitz 1996; Moskowitz and Rabino 1994). The method can be further simplified through the use of standard statistical packages, which divide consumers into segments according to some measure of the distance between these consumers. The distance measure for utilities can be defined as the value 1-R, where R = correlation between any two individuals on the utility values for the concept elements. Whichever segmentation method is used to divide respondents, the results come out as a division of respondents into like- minded groups, where individuals in the same segment show similar patterns of elements that they like, and elements that they dislike, respectively. This response-based approach generates differences among respondents that the more

32 J. BECKLEY, H. ASHMAN, A . MAIER and H. MOSKOWITZ

conventional segmentation methods miss. The more conventional methods first use larger scale questionnaires dealing with general topics, cluster respondents together into segments, and only later attempt to link segment differences to response differences (Mitchell 1983; Wells 1975).

Study Objectives

The objective of the study was to identify the impact on perceived craveability of different statements about fast food hamburgers. The statements comprised both product descriptions and emotional aspects of the eating situation. Through segmentation of individuals on the basis of their utility values, the study attempted to ascertain the existence and nature of segments of individuals with different points of view of what they considered to be drivers of craveability .

Method

Stimulus Elements. The concept elements were developed to span a wide range of different aspects of fast food hamburger - ranging from the nature of the product itself, to the end-benefit of eating the product, to brand name. The elements were created by looking at the different messages currently being targeted to consumers about hamburgers. These messages were taken from advertising on the Internet, in magazines, on television, and at the restaurant.

The 36 elements were placed into silos, comprising nine elements per silo. There were four silos, or basic groupings, as follows: Silo 1 = product features; Silo 2 = accompaniments and sensory features; Silo 3 = emotional benefits; Silo 4 = brands, promises, claims. The elements thus included a variety of product aspects in the concepts, not just product features. A single silo could contain two logically different categories or types of elements (e.g., brand names and benefits). Although a single silo could contain several categories, a single category never crossed silos. In this way one could be sure that a concept never comprised two elements from a single category, such as two brand names. The silo approach follows the design suggested by Beckley and Moskowitz (2002) for larger-scale studies that intermix emotional, product, brand, and other features of foods in a single conjoint analysis study.

The elements were combined into short, easy to understand concepts, comprising 2-4 elements. The particular strategy for assigning elements to concepts was done with the goal of allowing a statistical analysis of individual data by dummy variable regression analysis. The design strategy made each silo statistically independent of the other three. Consequently, every concept has four logical slots or positions that could be occupied, with one concept element per silo. However, some specific concepts were designated by the design to have either one or two null elements, or elements without any text. This strategy


eliminated the chance for multi-collinearity, and allowed for valid dummy variable regression at the individual respondent level. That utility value, the coefficient of the element from regression, is the contribution to craveability for the particular concept element. Finally, each respondent evaluated a unique set of 60 combinations, so the actual concepts differed from one respondent to another, even though the same 36 elements were tested. This strategy eliminates any possibility of a specific element combination biasing the results.

Respondents. The respondent comprised 145 colleagues, employees and friends. They were asked to participate in a short Internet-based survey, by e- mail invitation. The respondent specifications were obtained by a classification questionnaire attached to the conjoint measurement portion of the study. Table 1 summarizes the panel composition, and the additive constant for the models (discussed below).

TABLE 1. SPECIFICS OF THE RESPONDENT PANEL AND THE ADDITIVE CONSTANT FROM

MODELING USING DUMMY VARIABLE REGRESSION ANALYSIS

34 J. BECKLEY, H. ASHMAN, A. MAIER and H. MOSKOWITZ

The Internet-Based Interview. The interview comprised a set of screens. The first screen introduced the purpose of the study, including the nature of the scale. Screens 2-61 comprised the concepts themselves, according to the experimental design. The screens comprised a concept, and the rating question: How craveable do you think this hamburger is, based upon what you just read? The rating scale was anchored at both ends, with a scale value of 1 corresponding to not craveable at all to a scale value of 9 corresponding to extremely craveable. The strategy was designed to reduce the drop out from the Internet survey, a problem that continues to plague this type of research (MacElroy 2000).

The remaining screens comprised the classification questionnaire, and a thank-you page. A small rectangle at the top right of the screen showed the respondents the number of pages finished, and the number of pages remaining until the end of the interview. This information rectangle was updated with each new screen, and provided the respondent with a clear idea of how much longer they would need to participate.

Results

Creating Concept Models for Hamburger Craveability. Two models were created for each respondent, one dealing with magnitude of craveability, and one dealing with membership in the class of craveable.

Model I - Strength of Craveabiliry (Magnitude). For this model, the ratings were first recoded by multiplying by 11, in order to convert the 1-9 scale to a larger range, and make the coefficients easier to read. For each respondent the 36 independent variables (elements) were regressed against the recoded ratings by ordinary least squares in order to generate an additive model. This equation is expressed as follows:

craveability = k, + k,(Element 1) + k,(Element 2) ... k,,(Element 36)

For this first model the measure of the goodness-of-fit of the equation is the multiple R2. The R2 statistic varies between 0 (ratings are random), to 1 (ratings are completely predicted by the presence/absence of elements). The goodness-of- fit can be used as an index of consistency. This approach to consistency of ratings does not require that the respondent agree with any preconceived pattern of ratings. The approach simply requires that the ratings track known physical variables in a consistent way. Respondents with RZ values less than 0.6 were eliminated from the data set. Approximately 15% of the respondents had to be eliminated from the data due to low RZ values, leaving 145 respondents whose data is reported here. It is important to emphasize that the high consistency of


respondents cannot be attributed to chance and to memory, since the interview on the Internet went quickly, and each element appeared against many backgrounds. Arguably, the high R2values indicate validity or at a minimum they indicate consistency, at least in terms of what the respondent believed to be the features driving craveability.

Model 2 -Membership in a Class Through Recoding to a Binary Statistic. Another approach recodes the rating into a binary pair of numbers. A typical recoding would be 0 for ratings between 1 and 6, and 100 for ratings between 7 and 9, respectively. The binary numbers show whether the person feels the product described in the concept to be craveable or not craveable. Market researchers prefer to deal with incidence statistics, such as the proportion of individuals who feel that the concept denotes a food that is craveable. For aggregate data the coefficients show the proportion of individuals who feel that an element is craveable. In an alternative explanation, the coefficients show the additive conditional probability that the concept for the hamburger will be deemed craveable if the element is inserted into the concept. This paper uses results from Model 2 to determine the proportion of respondents who feel that an element is craveable. The two sets of coefficients, one from each model, correlate highly, so that decisions made using one set of coefficients will be materially the same as decisions made using the other set (Fig. 1). All analyses exception segmentation will be based on the results from Model 2.

I I /

-1 0 0 10 20 Coefflclent - Magnltude

FIG. 1 . SCATTER-GRAM OF 36 COEFFICIENTS FOR MAGNITUDE OF CRAVING (X AXIS) VERSUS PERCENT CRAVING (BINARY) (Y AXIS)

The same development and marketing decisions would be made by using either the actual ratings (Model 1). or the recoded ratings (Model 2).

36 J . BECKLEY, H . ASHMAN, A. MAIER and H. MOSKOWITZ

The Additive Constant - A Measure of Propensity to Call the Hamburger Craveable. The additive constant shows the conditional probability that a person will rate the hamburger concept as craveable (viz. 7-9) even if there are no concept elements. In the study each of the 60 concepts comprised at least two elements. Thus the additive constant is an estimated parameter, resulting from fitting the equation to the data. The additive constant provides a valuable index of the predisposition of the respondents to state that a concept is craveable. The additive constant will be high when the ratings for many of the concepts are high. The additive constant will be low when the ratings for many of the concepts are low. This is, of course, only half the story. The other half is the incremental (or decremental) impact of the elements.

The additive constant can be calculated for any subgroup by averaging together the additive constants of all individuals in that subgroup. This aggregation is possible because all models were created at the individual respondent level. As Table 1 shows, the constant is low (15), meaning that without any elements, only about 15% of the respondents would feel that fast food restaurant hamburgers are craveable. Subgroups differ from each other somewhat, but not dramatically. Males and females are similar, as are respondents who report different degrees of hunger. What may be interesting is that young respondents under the age of 21 feel that fast food hamburgers are craveable (constant of 42), as do individuals who eat in fast food restaurants several times per week, or more frequently (constant of 44). Finally, basic craveability for hamburger rises in the afternoon and late evening (see the time over 24 h).

Which Elements Drive Craveability - Total Panel. There is a clear hierarchy of elements that drive craveability. The most critical elements deal with the description of the hamburger and topping (e.g., Lofs of crispy bacon and cheese cover the grilled juicy hamburger on a lightly toasted bun) or show the pure simplicity of the product (e.g., Fresh grilled hamburger). Pure emotional aspects do not necessarily work for the total panel, but they may work for certain segments, as discussed below. Table 2 shows the impact scores for the total panel, for all 36 elements. The restaurants score at the bottom of the scale, suggesting that restaurant name itself does not generate a craving. It is the product description that does the work.

A sense of the relative importance of the elements can be obtained from norms for these types of conjoint studies. The norms, obtained by the senior author in over 1,000 studies conducted since 1992, suggest the following ranges:


Impact or utility score

a. > 15 Extremely strong performer, element substantially contributes b. >10 Very strong performer c. > 5 d. > 0 e. < 0 Negative, detracts from craveability

Significant performer (utilities > 6 tend to be significant by t tests) Not significant, modest impact, probably irrelevant

TABLE 2. IMPACT SCORES FOR INDIVIDUAL ELEMENTS, FOR THE TOTAL PANEL. EACH NUMBER SHOWS THE CONDITION PROBABILITY THAT THE CONCEPT FOR THE FAST

SCALE), IF THE ELEMENT IS PRESENT IN THE CONCEPT FOOD HAMBURGER WILL BE RATED CRAVEABLE (RATING 7-9 ON THE 9-POINT

38 J . BECKLEY, H. ASHMAN, A . MAIER and H. MOSKOWITZ

Subgroups and Individual Differences. One way to study individual differences breaks apart the total panel into key subgroups. A useful way to look at these differences uses the scatter-plot matrix (Systat 1997). The matrix plots every pair of subgroups. In addition, the scatter-plot provides a frequency plot of the different subgroups, in terms of the element impact values. That is, by looking at this frequency plot one quickly understands the nature of the distribution of impact values. Do they all cluster in the middle? Do they skew towards the low end, or the high end?

There are some key results worth mentioning briefly here:

Gender (Fig. 2A) - appears not to play a role in craving for fast food hamburgers. What males like, females like as well.

Males FIG. 2A. SCATTER-PLOT MATRIX FOR GENDER

Each point corresponds to one of the 36 elements.

Self-Rated Hunger (Fig. 2B) - as the degree of difference in self-rated hunger increases (from Hung0 = none, to Hung3 = Extreme), the dissimilarity of the patterns increases. That is, more intense hunger generates different patterns of impact values for the elements as compared to less intense hunger.

Hunger is interesting at a more micro-level for specific concept elements. With no hunger (Hunged), the elements show low utility values. The highest utility values are 9 for two product descriptions (Table 3). With higher levels of hunger, however, the utility values for the elements are also high. In all cases, however, the most powerful elements are product descriptions. Furthermore, there are different types of descriptions. Some of the descriptions deal with the product itself (e.g., grilled arum). Other descriptions deal with the accompaniments to the hamburger (e.g., crispy bacon, cheese). There is a sense of two different sets of elements achieving high impact - descriptions of the product itself, and descriptions of what comes with the product. Restaurant name,


emotional benefit, and the like do not play a key role for total panel, or for respondents divided by their different hunger states.

I Hung0 Hung1 Hung2 Hung3

FIG. 2B. SCATTER-PLOT FOR SELF-RATED HUNGER, FROM NONE (HungerO) TO HIGHEST (Hunger3)


Deeper Segmentation as Revealed by Clustering Utilities

The analysis of subgroups revealed different mind-sets in the population. There are more profound ways to divide consumers, with the property that individuals in the same segment share similar responses to the concept elements. This segmentation has been previously shown to be very powerful, and to transcend countries (Moskowitz 1996).

The approach divides consumers into clusters, with the property that individuals in the same clusters show similar patterns of utility scores, whereas individuals in different clusters showed different patterns of utility scores (Systat 1997). The measure of similarity of pattern is the Pearson correlation (R value), which varies from a high of + 1 .OO (perfect linear relation) to a middle level of 0 (no relation at all) to a low of -1 .OO (perfect inverse relation). The dissimilarity of patterns of impact values across the four segments appears clearly in the scatter-gram matrix (Fig. 3).


TABLE 3.

ELEMENTS. ALL ELEMENTS SCORING ABOVE 9 ARE SHOWN FOR EACH HUNGER LEVEL, ALONG WITH THEIR SCORES FOR THE OTHER HUNGER LEVELS

EFFECT OF SELF-REPORTED HUNGER LEVEL ON THE UTILITY OF CONCEPT

SegA Seg0 SegC SegD I I I I . 1

FIG. 3 . SCATTER-GRAM MATRIX FOR UTILITIES FROM THE FOUR CONCEPT-RESPONSE SEGMENTS



Although statistical procedures can divide the population of respondents according to different criteria, in the end the segmentation requires the insight of the researcher or marketer to determine whether the segments make sense. These data suggest at least four segments of more or less similar size, except for the fourth segment which is somewhat smaller (Table 4). The size of the segments in the general population cannot be determined with particular precision given the limited number of respondents (n = 145).

TABLE 4.

SEGMENTS EMERGING FROM THE 145 RESPONDENTS TOP-PERFORMING ELEMENTS FOR EACH OF FOUR CONCEPT-RESPONSE

The winning elements for these segments differ from segment to segment. All four segments respond strongly to food descriptions as drivers of craveability, but there are other messages that may attract the segments. Two of the

42 J. BECKLEY, H. ASHMAN, A. MAIER and H. MOSKOWITZ

four segments respond to aspects outside of food descriptions. Segment 4A responds to elaborate descriptions of the hamburger, with its accompaniments. Segment 4B responds to the word grilled. Segment 4C responds to the product, but also responds to the restaurant name. Segment 4D responds to emotional messages. The names assigned to the segments in Table 4 are simple convenient descriptors, based upon the winning elements, and are meant to be provisional.

It is generally instructive to identify, if possible, commonalities among the segments in term of external criteria, such as gender, age, and hunger level, etc. It would be nice, both from a scientific and from a business point of view, respectively, to discover rules governing membership in the segments. However, from these data there appears to be no simple rules determining segment membership. Table 5 shows that the segments are similar by gender composition. One possible difference comes from age. Older respondents tend to fall into the Elaborates and Classics segments. Younger respondents tend to fall into the Imagines and Emotionals segments. This finding warrants additional research, because it suggests that age may possibly co-vary with segment membership, with younger respondents attending to features other than the food itself.

TABLE 5 . PANEL COMPOSITION FOR THE FOUR SEGMENTS. EXCEF'TIONS TO THE

GENERAL PATTERN ARE PRESENTED IN BOLD

DISCUSSION AND CONCLUSIONS

Conjoint Measurement as a Research Method

One of the key benefits of conjoint measurement is its ability to understand the consumer's mind through experimental design and stimulus-response


analysis. The data in this study show that one can intermix different aspects of a communication about food, such as product features, emotions, and brands. Most panelists do not report problems rating a concept comprising these different types of messages. If panelists do have a problem with the conjoint analysis method, then typically this problem manifests itself in a report that the study is repetitive, and that the elements continue to reappear in a monotonous fashion.

A deeper issue is whether or not a consumer can validly report degree of craving? This is a more philosophical question. One answer is that the respondents appear to be consistent in their ratings, based upon the use of the multiple RZ statistic as a consistency measure. In turn, this consistency suggests a level of validity in the responses, simply because it is impossible for the typical respondent to give the appropriate answer to a concept whose compo- nents change from combination to combination. To the degree that the ratings are consistent (viz., the ratings can be related to the elements with a high degree of consistency), one can assume that the respondent answers honestly.

What is Special About the Winning Elements for the Total Panel?

The experimental design ensured that every one of the 36 hamburger elements appeared equally often. Yet only a few of the elements generated a utility value of 10 or higher, and a few more generated a utility value of 7 or higher. These elements tended to be descriptions of the product, or of a taste- oriented process (e.g., grilled). From this study and from others conducted similarly (Beckley and Moskowitz 2002), the winning elements that continue to emerge deal principally with the description of the food itself, rather than dealing with the other aspects surrounding the food (e.g., emotion). Emotion and brand may play a role, but at least in studies where concepts comprise food descriptions along with these other aspects, it is the food description that typically drives concept acceptance. This finding is worth noting because the segment data as discussed below show the existence of groups of individuals who do pay attention to brand and emotion, beyond just attending to the food.

Do Segments Really Exist?

Segmentation has been a mainstay of marketing for more than 30 years (Wells 1975). Segmentation has two sides, however. As presented here the segmentation analysis is primarily statistical, and only afterwards is there the attempt to assign a meaningful interpretation to the segments revealed through the formal statistical analysis. There remains the perennial possibility that the segments uncovered here may be statistical constructs without any particular meaning. Certainly most computer programs that provide k-means clustering can generate up to N-1 clusters, with N being the number of respondents in the


study. How then can the researcher feel confident that the segments thus uncovered have real meaning?

The results presented here provide one way to reassure that the segments have meaning. The segments were created by pure formal procedures based upon statistics, but without any interpretive judgment guiding that analysis. The four segments emerged by dividing the consumers into statistically homogeneous groups, based upon specific numerical criteria. The interpretation only began once the groups were established, and was based upon the common properties of those elements that achieved the highest utility values for a particular segment. The elements suggest themes, such as food descriptions for the Elaborates, and brand names for the Imaginers.

Future Directions and Research Opportunities

following three issues: Additional work in segmentation is needed to further understand the

(1) How many segments really best describe the data? Are four segments too

(2) Do the same segments emerge in different food and beverage categories? In studies with a large number of foods, Beckley and Moskowitz (2002) suggested that there are three general segments common to many foods (Elaborates, Classics, Lmaginers, respectively). It might be that these data confirm that general observation, with the Imaginers and Emotionals lumped together into one larger Imaginer segment.

(3) Can an assignment rule be cited in order to predict the likelihood of membership in a segment, using criteria other than response to the test concepts that were used to create the segments in the first place. Simple cross-tabulations of geo-demographics do not suffice to predict segment membership. Greene and Moskowitz (2000) were able to use information about respondents, purchasable from data companies, as inputs to a set of data-mining procedures (Rudd 2000). The data-mining procedures create an assignment rule to locate a respondent as a member of a specific concept response segment. That work was done for responses to jewelry concepts, and was subsequently validated in terms of increasing jewelry purchases by the use of the correct concepts in advertising flyers. Such data mining methods may prove valuable here as well.

IMXly?

Practical Application: Enhancing Product Development and Communication

A key aspect of conjoint analysis and segmentation is to aid the product developer and marketer. To the degree that the information improves product development, the research contributes to both basic science and business-oriented


R&D. To the degree that the information improves communication, the research contributes to basic science and business-oriented marketing. Neither business application detracts in any way from the scientific validity of the approach.

The key practical contribution is that segmentation allows the product developer to focus on the product features that drive acceptance. The segmentation allows the developer to identify four different groups of people, and then target the product development efforts towards those groups. For example, the product development initiative for the Elaborates might comprise these two features :

(1) Lots of crispy bacon and cheese cover the grilled juicy hamburger on a lightly toasted bun.

(2) Layers of burger, sauce, relishes, pickles, and lettuce on a moist sourdough sesame seed bun.

It is clear that the Elaborates want a fancy burger with many items on their product. In contrast, the Classics might want a very simple grilled burger. In that case the product development initiative for the Classics might comprise these two features:

(1) Fresh from the grill, made especially for you. (2) Fresh grilled hamburger.

The same type of analysis could also be done for communication, in order to identify what are the specific phrases to which the different segments respond.

Research Opportunities With the Concept-response Segments

If the segmentation is to prove useful from a scientific perspective, it should lead to new understanding of the consumer. Four questions that provide future research opportunities are: (1) Does the segmentation scheme apply to other foods, or only to hamburgers? (2) Do members of the same family fall into the same segment? (3) Does an individual show consistent segment membership across different

foods, or is segment membership for an individual food-specific? (4) What other behaviors of an individual, or what other physiological

characteristics co-vary with segment membership? The data suggest that degree of hunger may drive some response patterns to the concept elements. Is there a connection between hunger and segment membership, indicating that segment membership is labile, or is segment membership the key to response patterns, and hunger simply a secondary influencer?

46 J. BECKLEY, H. ASHMAN, A. MAIER and H. MOSKOWTZ

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WHAT FEATURES DRIVE RATED BURGER CRAVEABILITY AT THE CONCEPT LEVEL?

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