contrasting customer and operator concept and product requirements: the case of surimi

© Blackwell Science Ltd. 2002 Food Service Technology, 2, pp. 115–130 115

Contrasting customer and operator concept and productrequirements: the case of surimi

and the foodservice operator exhibit the same preferencepatterns. That is, do they like the same types of messagesabout products, and do they like the same types of prod-ucts? Foodservice operators, as surrogates for customersin the purchase of ingredients, may or may not share the preference patterns of their customers. That questioncan be answered by direct primary research.

Experimental design – identifying the stimulus-response connection

Traditional research subsumed under ‘voice of the con-sumer’ enables the foodservice professional to under-stand what consumers want. However, the voice of theconsumer has to be specified in terms of specifics, suchas communication and formulation. One could answerthe question by directly asking the respondents tochoose what they wish in terms of a set of communi-cations or a set of formulations. If the researcher islucky enough to include in the test set a winningconcept (for communication) or a winning product (forformulation) then the research will be successful. Theevaluation of alternative concepts and products willreveal these winners.

More often than not, however, the researcher doesnot really know what the consumer wants and executes

Peer review

Correspondence:Howard R. Moskowitz,Moskowitz Jacobs Inc.,White Plains, New York,USA. Tel. 914-421-7400,Fax: 914-428-8364. E-mail:[email protected]

Keywords:conjoint analysis,experimental design,consumer input

Abstract

This paper deals with the analysis of responses to experimentally designed surimi pro-totypes, and to concepts about ‘lobster’ surimi. Consumers and foodservice opera-tors evaluated systematically varied concepts and product prototypes varying on fourfactors. The results permit assessment of the differences in evaluative criteria betweenthe operators and the consumers. The two groups responded similarly to many of theconcept elements, but responded differently to elements dealing with emotion, ver-satility and price. The two groups were also similar in their evaluations of the 17 sys-tematically varied prototypes, but differed in the criteria that drove overall liking.Aroma, flavor, texture and acceptance were far more important as liking drivers for foodservice operators than they were for consumers. These results exemplifyapproaches that allow researchers and marketers to understand the different mind-sets of operators versus consumers.

Introduction

Increasing importance of consumer inputs

One of today’s biggest buzzwords is the so-called ‘voiceof the consumer’. A search of this phrase on a well-known Internet search engine, Google®, revealed 1130pages using this phrase in May 2002. This indicatesthat the consumer requirements for a foodserviceproduct must be taken into consideration if the productis to be successful. The foodservice operator mustunderstand what the consumer wants and provide it onthe menu for communication, and in the actual productfor satisfaction and subsequent repurchase.

Over the past 20 years foodservice research into con-sumer preferences has become the rule rather than theexception. Today, in the early 21st century, it is notunusual to see restaurant chains and their suppliersconduct consumer research to identify what the customer wants. It is, however, unusual to directlycompare customers and foodservice operators. Thesuppliers, who must satisfy both the purchasing constituency (foodservice operators) and the ultimatecustomer, usually do this type of research.

Given the emerging need for customer input, one ofthe key questions to ask is whether or not the customer

Howard R. Moskowitz* and Sebastiano Porretta†

*Moskowitz Jacobs Inc., White Plains, New York, USA; †Experimental Station for the Food Preservation Industry,Parma, Italy

the research in order to discover these preferred con-cepts and products. In such a situation, where knowl-edge is critical rather than simply the selection of awinner, the method of experimental design recom-mends itself. Experimental design comprises the systematic variation of the test stimulus (concept orproduct), the evaluation of the variations, and the subsequent discovery of patterns underlying winningversus losing stimuli. Through experimental design itbecomes quite easy in most cases to identify which particular parts of the test stimuli drive acceptance. The part of the stimuli consist of words in the case ofconcepts; ingredients in the case of products.

In an emerging category such as seafood substitutes(e.g. surimi) the lack of knowledge about consumerrequirements is even more pronounced (Sieffermanet al. 2002). Even in many well developed categoriesthe manufacturer often does not know what consumersreally want, or stated in a more politically correct way,how consumers will react to new products or emergingcategories. Therefore, a structure of research thatrapidly, quantitatively and clearly delineates the con-sumer preference patterns in an affordable way iswelcome. Experimental design for emerging categoriescan provide that structure of research.

Yet, there are caveats in researching new productareas, which apply here. In an emerging category onemust guard against invalidity of data. Invalidity cancome in many forms, but in the emerging category oneform is particularly pernicious. This is the false rejec-tion or false acceptance of a product or a concept basedupon incomplete information. Respondents in emerg-ing categories do not have the requisite experience witha product, and may respond incorrectly to conceptsbecause they really cannot imagine what the productwill be like. Similarly, if the product is new to the worldthen the respondent may reject it because the productdiffers from that to which they are accustomed. Yet,with a limited amount of exposure over a period ofmonths (far longer than the developmental researchperiod) the product may ultimately achieve acceptance.In spite of these caveats however, the business require-ments are such that they force developers and mar-keters to make decisions on the basis of limited, andperhaps unduly premature information that could bepartially wrong and perhaps somewhat misleading.Thus, the results found here must be considered in lightof the relative newness of surimi to the market.

Understanding the operator and customer’s minds byconjoint analysis

The stimulus-response approach has been used expen-sively by researchers in order to relate the communica-

116 Contrasting customer and operator requirements H. R. Moskowitz and S. Porretta

© Blackwell Science Ltd. 2002 Food Service Technology, 2, pp. 115–130

tions of a product or service to the acceptance or communication of feeling/tonality. One of the key meth-ods is known generically as conjoint analysis (Green & Srinivasan 1990). The approach works in a verystraightforward manner. The researcher creates a set ofmaterial known as concept elements, combines theseconcept elements into different combinations, and thentests the combinations with the target population. Eachconcept element appears in multiple combinations in amanner that ensures it to be statistically independentof every other element. That is, the elements appear asfree agents in the concepts. Statistical independenceensures that the data matrix can be analyzed by toolssuch as regression analysis, to build models relating thepresence/absence of the individual concept element tothe subjective rating (e.g. of interest). Through model-ing, the researcher can quantify the contribution ofeach of the concept elements.

Conjoint analysis has proved extraordinarily popu-lar in the world of primary research for a variety oftopics. The search for the term ‘conjoint analysis’ onGoogle® will come up with thousands of references to its use, varying dramatically, from theoretical toapplied, from product development to social policy,from academic studies to clearly commercial studies.The use of conjoint analysis has dramatically increasedover the past three decades (Cattin & Wittink 1982;Wittink & Cattin 1989). Recently, one of the authorshas discussed the use of conjoint analysis in early stagedevelopment of foodservice products and has presentedapproaches that make the method very easy to use(Moskowitz et al. 2002). Conjoint analysis is themethod of choice for this paper because of its power inrevealing the impact or utility of different messages forboth the foodservice operator and the customer. Theresults can be compared across the two groups forcommon elements in order to discover whether theoperators and customers share similar a mindset, andif they do not, then where do they differ from eachother, and by how much.

Concept evaluation by conjoint analysis has its prosand cons. The pros have already been presented above.The cons are also worth noting. They involve pri-marily additional effort, which are detailed below.

HomeworkConjoint measurement requires the collection of rawmaterials ahead of time. Conventional concept devel-opment creates the ‘best guess’ concepts in the sameway that conventional product development creates the‘best guess’ prototype. Conjoint measurement requireshomework. Homework is not pleasant, and is oftenshunned when the product developer or marketer needsa quick answer.

Longer fieldworkConventional concept testing evaluates one or a fewconcepts in an easy test situation. Conjoint meas-urement requires evaluation of many, systematicallyvaried combinations.

Statistical modelingConventional concept testing requires means and tests of difference. Conjoint measurement requiresregression analysis. The higher level analysis meansthat conjoint measurement is not instantaneously avail-able to all users, nor is it immediately and intuitivelyobvious.

The role of product evaluation

Like concept development, systematic product devel-opment can identify the product features and levels thatare optimal to the end user. In this case the researcheruses another version of experimental design that allowshim to create a set of equations. Each equation showshow a response attribute (e.g. liking, sensory attributelevel etc.) co-varies with the physical formulation.Often this equation is not linear; to accommodate the reality that as a formula or processing variableincreases, liking first increases, peaks, and then eitherflattens at an asymptote or drops down. Statistical textson experimentally design abound (Box et al. 1978;Khuri & Cornell 1987), and in the past two decadesthe use of experimentally designed products hasincreased. Current statistical packages often featureexperimentally designs (Systat 1997) that can immedi-ately be analyzed by means of the regression packages.

Experimental design of product formulations alsocomes with some cons. These are issues that must beconsidered when embarking on a research project. Theissues involve the choice of product variables and the effort involved in creating the prototypes, and aredetailed below.

HomeworkWhereas, conventional research simply requires stimulito test, experimentally designed products require thatthe researcher know what variables with which towork, and the appropriate levels to choose for the prototypes. This requirement to understand the pro-duct variables is by itself reasonable, but it may tax the product developer more accustomed to puttingtogether his ‘best shots’ for testing. Rather than relyingupon the artistry and experience of the developer,experimental design forces the developer to work in a systematic fashion. Sometimes this discipline is a psychological negative.

Developer timeAlthough scientists like to work with variables, theproduct developer has the business objective of comingup with an answer in a short time frame, with the leastexpenditure of effort and money. Experimental designrequires the creation of prototypes. That creation oftencosts a great deal of money, especially when plant runsare required. A lot of the time the business decisionbecomes the selection of the expedient in place of thecorrect.

ModelingResponse surface modeling requires statistical treat-ment of the data beyond the conventional tests of dif-ferences between two means, or the analysis of pairedpreference results. This higher order statistical modelmay intimidate.

Scope of the study

The original purpose of this study was to evaluate thepotential of a lobster surimi. Surimi is an artificialseafood product, typically formed to resemble crab.Lobster is very popular seafood, and the manufacturerhad identified seafood surimi as a possible new entrant.The objective was to quantify the attractiveness of alobster surimi concept and product, both to foodser-vice operators and to customers, respectively. The studyprovides an opportunity to understand the decision cri-teria of two important constituencies that would usethe product – consumers in their home and foodserviceoperators in restaurants. Thus, what began as a devel-opment project has been, in this paper, transformedinto a methodological investigation of responses to systematically varied products by consumers and professionals.

As there are two quite distinct phases of this study,concept and product, the paper will present each ofthese two phases separately. In that way the common-alties between the foodservice operator and the customer will emerge more clearly.

Method

Concepts

The test concepts were created in a systematic fashion,following the steps below.

Element creationThe concept elements were created by the manufacturerin concert with a marketing consultant. As the studywas geared towards both foodservice professionals andconsumers, two sets of concept elements were devel-

Contrasting customer and operator requirements H. R. Moskowitz and Porretta 117


oped. There were 64 elements for the consumer and 70elements for the foodservice professional. From this setof elements, 64 were identical. The elements were putinto categories. A category comprises a set of similartypes of elements (e.g. price). In several cases the textof the element was slightly changed in order to be rel-evant to the particular group. It will be those commonelements only that will be the focus of this paper. Therewere six other elements in the foodservice operator’sstudy that did not appear in the customer study. Theseadditional elements are not germane for the compara-tive analysis, and therefore not shown. The resultssection shows the list of elements

RestrictionsA set of restrictions was developed for the elements.One set of restrictions applied to consumer’s elements,one set to foodservice professional’s elements. Therestrictions involved pairs of elements that could notappear together.

Experimental DesignThe experimental design encompassing the concept ele-ments was created separately for each group. Theexperimental design created small, easy to read con-cepts, with the concept elements appearing indepen-dently from a statistical point of view as measured by the Pearson correlation between the elements. Aconcept could either have one element or no elementsfrom a category, but never more than one element. Thisbook keeping procedure ensured that a concept couldnever have elements from the same category that presented opposing messages. Furthermore, a conceptcould never have two elements that were restricted(from the set of restrictions).

Each group of respondents had a separate set of concepts created for it. Each concept comprised 3–6concept elements. Each element appeared 5 timesacross the full set of concepts. The concepts werecreated ahead of time, randomized, put into booklets,and then rated by the respondent on three specificattributes.

Products

Table 1 shows the experimental design. The design is afractional factorial, Box-Behnken design. The designallows the researcher to explore three levels of each offour variables. Ordinarily, four variables at three levelseach would require 27 prototypes for the full factorialdesign. The design is set up to require only 17, by theelimination of certain combinations. In practical terms,the effort to make the full set of 27 prototypes and the



cost to test all of them among operators and consumersdid not justify the full factorial set. However, R & Ddid recognize that it had to create sufficient and proper combinations to accommodate the possiblenon-linearity of acceptance with formula or processinglevels. This led to the specific design shown here.

The particular prototypes were created by the man-ufacturer, based upon knowledge of what could bevaried independently. For purposes of confidentialitythe levels are coded as 1, 2 and 3, respectively.Although the product developers knew what to vary,they did not know the particular combinations tocreate. These combinations were developed accordingto the experimental design.

Consumers and experts evaluated the different pro-totypes on a blind basis, with a concept introducing theidea. The set-up concept, however, presented at thestart of the product evaluation reiterated the fact thatthe products would be lobster surimi.

Study execution

The study comprised an extended, three-hour inter-view. Respondents, however, were recruited for fourhours, to allow for latecomers, field problems, etc.During this time the respondents evaluated 9 of 17 pro-totypes, rating the prototypes of surimi lobster on avariety of attributes. These attributes, presented inbooklets, will be discussed in the section on producttest concepts. Each respondent then rated 60 of the 108concepts on three attributes, to be discussed specificallyin the section on concepts.

Table 1 Experimental design for the prototypes

Surimi Slit ColorSample Level Flavoring Size Level

101 2 2 2 2102 2 2 3 3103 2 3 2 3104 2 3 3 2105 3 2 2 3106 3 2 3 2107 3 3 2 2108 3 3 3 3109 2 1 1 1110 3 1 1 1111 1 2 1 1112 1 3 1 1113 1 1 2 1114 1 1 3 1115 1 1 1 2116 1 1 1 3117 1 1 1 1

The respondents were pre-recruited to participate.Table 2 gives the specifics of the evaluation. The chore-ography of the study was run to maximize respondentparticipation, quality of data, and interest, followingmethods outlined by Moskowitz (1985).

Table 3 provides the panel composition. The foodservice operators were contacted from a list of customer companies currently doing business or havingpreviously done business with the company funding the study. Thus, all respondents in the foodservicegroup were familiar with the product, and recruited to participate as foodservice professionals, rather thanas consumers. All foodservice respondents were accep-tors of surimi on a personal basis as well, as ascertainedduring the telephone recruit interview.

During the actual interview (product evaluation,concept evaluation) the foodservice operators wereinstructed specifically to evaluate the stimuli from their

professional viewpoint, and not from their viewpointas consumers.

Product Results

There are two principal ways to analyze experimentaldata from these two groups of respondents – operatorsand consumers. These are:

Stimulus–Response: looking at the relation between thephysical variables and the subjective rating. This typeof analysis is typically done for product development,rather than to uncover relations between subgroups orrelations between attribute ratings.

Response–Response: looking at the relation betweentwo subjective attributes. This type of analysis com-pares the operators and the consumers, using the 17prototypes simply as vehicles by which to elicit reac-



Table 2 Specifics of the field procedure

PRE-RECRUITMENT & RE-SCREENING/SEATINGRespondents were pre-recruited via telephone to ensure qualification and attendance for a four-hour session. Consumers wererecruited based upon the requirement that they eat surimi either at home or in a restaurant. The word ‘surimi’ was defined as‘imitation seafood based upon fish, rather than seafood itself’. The foodservice professionals were recruited from a listprovided by the manufacturer.Upon arrival, respondents were re-screened to confirm qualification and seated randomly.

PRACTICE EXERCISERespondents completed a cracker practice exercise in order to become familiar with the types of questions, rating scale anduse of the computer cards.

CHECKING PROCEDUREAttending interviewers checked the rating cards for completion and respondents for comprehension after each and everyevaluation.

CONTROL SHEETSThe moderator explained the use of the control sheets to the respondents as a group. The control sheets listed the sequence ofproducts each respondent tested. The order of product trial was randomized for each respondent.

PRODUCT EVALUATIONThe moderator first presented a short description of lobster surimi, describing what it is and how it might be used in saladsand in hot dishes. The description was factual, couched in neutral terms, and only dealt with the product itself, and not withreasons that would be construed as ‘marketing-oriented’. The moderator took the respondents through the first productevaluation as a group. Each respondent received 3 flakes or chunks of lobster product on a plate.

REMAINING PRODUCT EVALUATIONSAfter the first product evaluation, respondents continued to evaluate the remaining products at their own pace. Allrespondents completed 9 product evaluations out of a possible 17.

BASE CONCEPT EVALUATIONThe moderator again read the same set-up description of lobster surimi. The moderator took the group through theirappropriate base concept. This concept specified the nature of the product

CONCEPT EVALUATIONThe Moderator explained the use of the concept control sheets and concept booklets. The moderator took the group throughthe first concept evaluation, reading all of the questions.Respondents evaluated 5 concept booklets out of 9 booklets. Each booklet contained 12 concepts.

CLASSIFICATIONRespondents completed the classification questionnaire to provide demographic and usage information

tions to stimuli. A key type of response–response analy-sis looks at the relation between a sensory attribute asthe independent variable and a liking or image attributeas the dependent variable.

This paper concentrates on the second set of analy-ses. Response–response analysis provides the researcherwith deeper insights into what features of a subjectivenature drive subjective responses. This will showwhether the mind of the operator is similar to or dif-ferent from the mind of the consumer. There are threeissues to be addressed, as described below:

Issue 1: On image and liking attributes, how closely do the operators and consumers agree?This first issue can be addressed by computing thePearson correlation coefficient between operators andconsumers, on an attribute by attribute basis, using the17 prototypes as cases. Table 4 reveals that there is arange of correlations. The highest correlation appearsfor the visual evaluation of the surimi products. Con-sumers and foodservice operators agree on what looksgood versus what does not. The correlations for the dif-ferent aspects of appearance range from 0.84 to 0.94.In contrast, for liking of flavor, aroma and texture thecorrelations are lower, and indeed there is virtually norelation between the texture liked by the foodserviceoperator and the texture liked by the consumer. Finally,



the two groups agree on similarity of appearance tolobster, but not on similarity to lobster flavor, or likethe lobster flavor.

Issue 2: What sensory attributes drive liking or image?A recurrent issue in product research are the ‘drivers ofliking’. This term refers to an understanding of howchanges in the liking rating covary with specific inde-pendent variables (Moskowitz 1981). These indepen-dent variables can either be formula/process variablesunder the researcher’s control or sensory attributesassigned by the respondents.

One can develop relations between sensory attributesas the independent variable and either image or likingattributes as the dependent variable. The relation musttake into account the fact that as a sensory attributeincreases liking first increases, peaks, and then dropsdown. This relation calls for a quadratic function. The relation may, in fact, be linear as it is for darknessof color versus liking of appearance. The relation isalmost identical for operators and consumers. Theindependent variable is the rating of darkness of colors,combining the highly correlated ratings of darknessassigned by operators and consumers. Figure 1(A)shows the relation between darkness and liking ofappearance. The relation is a fitted curve, with thepoints brought to the line. This type of analysis pro-

Table 3 Panel composition

Operator Consumer Operator Consumer

Gender EducationMale 18 35 Some High School 1 0Female 12 37 High School 5 9

Market Some College 11 31Chicago 11 24 College 9 21New Jersey 10 22 Graduate School 1 8Los Angeles 9 26 Other 0 3

Technical (e.g., nursing) 3 0

Age21-29 6 16 Foodservice occupation30-39 12 23 School, Hospital, College 940-49 6 19 Fast Food Or Family Style Restaurant 2150+ 6 14 Other

Marital status Foodservice management 13Single 11 23 Consumption of Imitation Lobster

(from any source, whether at homeor in a foodservice outlet)

Married 19 49 More than 2 times per week 2

Income (1,000’s) 1-2 times per week 210-19 0 9 4 times per month 620-39 10 16 1-3 times per month 4640-74 14 32 Once every 5-8 weeks 1675+ 6 15

vides a clear idea of the basic relation between two vari-ables, and the difference or similarity of the relation fordifferent groups.

Similar analyses can be run for any pair of ratingattributes. Figure 1(B) shows how the sweetness ofsurimi drives both liking of strength of flavor (top pairof curves) and perceived similarity to lobster (bottompair of curves). Operators and consumers show similarcurves for ratings of similarity to lobster flavor,meaning that they share the same concept of lobsterflavor (relative to sweetness). Operators, however, like stronger flavors, whereas consumers like weakerflavors. It is clear therefore that a respondent can dif-ferentiate between liking and image attributes. Other-wise, we would have seen similar curves for both‘similar to lobster flavor’, and ‘liking of strength offlavor’.

Finally, there are clear but not radical differencesbetween operators and consumers when it comes toliking of texture versus surimi springiness. Consumerslike more springy textures, as do operators. Operators,however, also accept very low springiness. They do notlike intermediate levels of springiness.

Issue 3: How does attribute liking drive overall liking?Another way to look at drivers of liking relates overallliking to attribute liking. The basic relation is an equa-tion of the form: Overall Liking = k0 + k1(AttributeLiking). This approach has been discussed previously(Moskowitz & Krieger 1995). It provides a metric ofrelative importance. If the coefficient k1 is high, thenthis means that small changes in attribute likingproduce large changes in overall liking. Conversely, ifthe coefficient k1 is low, then this means that smallchanges in attribute liking produce small changes inoverall liking. Table 4 shows the slopes, k1, for the

different evaluative attributes. The slopes are verysimilar for visual attributes, different for taste/flavorand aroma, and quite different for texture. Operatorsare far more driven by attribute liking of taste/flavorand texture than are consumers. That is, unit increasesin these attribute liking ratings covary with much largerchanges in the overall liking rating assigned by thefoodservice operator than the change assigned by theconsumer. This difference is worthy of additionalresearch, because it begins to reveal differences in judgment criteria.

Concept Modeling

The consumers rated the concepts on purchase intent,expected similarity to real lobster, and good taste. Purchase intent was rated on a five point purchase scale(from 5 = definitely would buy, to 1 = definitely wouldnot buy). The remaining two attributes were rated ona 0–100 scale, anchored at both ends to reduce ambi-guity. The foodservice operators rated the concept onpurchase intent (same five-point scale), and on per-ceived high quality and on versatility. These latter twoattributes were again rated on an anchored 0–100scale.

A separate model was created for each group ofrespondents, using the specific experimental designcreated for that group. The 64 common elements werethen extracted from the model for discussion here. It isimportant to note that the experimental design usesdummy variable regression analysis for the modeling.Therefore, the utility or impact variable of each elementis computed, and shows the effect on the dependentvariable of that element’s individual contribution. It isif this property of ‘absolute contribution to the depen-dent variable’ that enables the researcher to extract the



Table 4 Correlation between consumers and foodservice operators on evaluative and image characteristics, across the 17surimi prototypes, and relative importance of the attribute as a driver of overall liking as indexed by the slope k1 in the equa-tion: Overall Liking = k0 + k1(Attribute Liking)

Liking or image attribute Pearson Correlation Operator k1 Consumer k1

Like Outside Appearance 0.94 0.37 0.34Overall Appeal 0.93 0.34 0.38Like Inner Appearance 0.91 0.41 0.40Natural Appearance 0.86 0.41 0.37Similar To Lobster Appearance 0.86 0.40 0.41Like Appearance 0.84 0.39 0.38Overall Liking 0.63 NA NASimilar To Lobster Flavor 0.63 0.97 0.80Like Flavor Strength 0.59 0.93 1.02Like Aroma 0.46 0.83 0.75Like Lobster Flavor 0.21 0.98 0.60Like Texture -0.01 1.23 0.61



64 common elements from the consumer model, andfrom the operator model, respectively, and comparethem.

The dependent variables were the percent top twobox on the five point scale (definitely would buy; prob-ably would buy), and the ratings of the communicationvariables (similar to lobster, good taste for the con-sumers; high quality and versatility for the foodserviceoperators). It is important to note that consumers wererating the concepts on purchase intent for themselves,whereas operators were rating the concept on purchasefor their restaurant. The data for each concept wasaveraged across all the respondents who evaluated thatparticular concept, in order to provide both the per-centage statistic (purchase intent) or the average (com-munication attributes). The independent variables werethe presence/absence of the concept elements.

Each attribute generates its own regression model.The parameters of the regression models appear inTable 5. These include the R2 (percentage of responsevariability accounted for by the regression), and thestandard error of regression (the range of variation fora given estimate).

The following results emerge from the evaluation ofconcepts and the subsequent concept modeling bydummy variable regression.

Goodness of fitThe models show a good fit of the data for purchaseintent (% top two box), for estimates of similarity tolobster, and for high quality and versatility. These showR2 values between a high of 0.71 (viz., 71% of the vari-ability in the ratings accounted for by the elements),and a low of 0.63. These are very high significant(probability of chance <1%). The attribute of ‘expectedgood taste’, however, as rated by the consumer, showsa poorer fit of the regression model to the actual data(R2 = 0.55). This poorer fit suggests that the expecta-tion of ‘good taste’ may either not be easy to commu-nicate by a concept, or may differ across respondentsso much that there are different criteria, leading tonoisier results.

The additive constant differs quite dramatically forthe attribute of purchase interestThe additive constant for the consumers is 26 for theconcepts, meaning that the base level of interest is 26%top 2 box. This means that without any elements (ahypothetical situation), lobster surimi would be ratedas definitely/probably purchase by 26% of the con-sumers. In contrast, the additive constant for the food-service professional is 10, meaning only a very low10% of the foodservice professionals are interested inthe idea of lobster surimi, without any descriptions.

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the

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fo

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ars

for

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tinu

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sum

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tor

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op 2

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mila

r G

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or

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taur

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lth

& N

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19

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1 2

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0

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lik

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this

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tinu

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sum

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rvic

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pera

tor

% T

op 2

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mila

r G

ood

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2 B

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tilit

yB

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ase

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obst

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rcha

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or

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Self

Res

taur

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lity

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lity

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high

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ins

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The consumer is basically more interested, and the ele-ments play a role. The foodservice professional is basi-cally uninterested until the product is well specified bythe elements in the concept. At that point the elementsdo all the work for the foodservice professional. As thedata shows, many of the utilities are quite high for thefoodservice professional, even though the additive con-stant is low. This is an important finding, because itshows the different judgement criteria adopted by food-service operators vs. consumers.

Consumers find specific promises to be importantSome elements are very important for consumers(utility values > 10 for purchase intent). For consumers,the key communication elements are ‘The taste of reallobster without the calories, fat or cholesterol’ (utility= 19) and ‘all the taste, texture and flavor of ordinarylobster (utility = 15). These are strong promises.

Foodservice professionals react strongly to certainelementsOther elements are very important for foodservice, andindeed many of the elements score quite well amongthe foodservice professionals. The strongest performingelement deals with ‘waste’, or minimizing productioneffort: ‘No shells, no cleaning, no cooking, no waste’(utility = 29). Another element deals with taste promise,but in a more general sense than what consumers mightrespond to. This element is the following: 99% fat free,100% delicious (utility = 20). The promise is there, but the taste promise (100% delicious) is couched ingeneral language. A third element deals with use in aproduct: great for seafood chowder and soups (Utility= 18). Consumers are clearly sensitive to price, food-service professionals view very low prices as potentiallyless attractive products: Four of the elements dealt withdifferent levels of price. These price statements werealways seen separately (by design), and against differ-ent backgrounds. Nonetheless, when the ratings to the concepts are deconstructed into the components, itappears quite clear that the consumers track the price.As price increases the percent of top 2 box, purchaseintent decreases. Foodservice professionals show aslightly different pattern of reaction to price. Operatorsshow less interest in the very lowest priced surimi (thisproduct sells for $3.05 per pound), perhaps becausethat low price may implicitly, but not directly signallower quality. The sensitivity to price is a key issue formanufacturers of surimi. Thus, the results here need tobe confirmed, because if price signals quality to somedegree, then the manufacturer may be able to sellhigher quality surimi product to the operator. Thehigher quality product could cost slightly more, but theoperator may well be prepared to accept that added

cost. It is also important to qualify these results bynoting that the study did not deal necessarily with pro-fessionals involved in the actual purchase transaction(viz., the actual purchasing agent).

There is fair, albeit not perfect, agreement betweenthe consumers and the professionalsWe can see the agreement by inspecting the table to seewhere they disagree. There is no clear pattern underly-ing the agreements or disagreements. For instance, versatility is an important category for professionals,but not for consumers, and so the agreement is lower.Figure 2 shows a plot of the utilities. The letters in theplot are the first letters of the category.

Optimal concepts for the foodservice operator maydiffer from those for the consumerThe results here suggest that the two groups respondto different types of messages, in some but not in allcases. The optimal concepts are those that attain appealfor both groups, or if this is impossible, then conceptsthat are attractive to one group, and accepted, notrejected, by the other. These concepts can be created byusing the data to sort through different combinations,with different rules (e.g. maximize operator concepts,while maintaining consumer concepts). It may, in fact,not even be necessary to have the same concepts for thetwo groups, although from a marketing perspective itwould be good to maintain the same type of position-ing for both retail sales and foodservice.

Discussion

Value of working with operators as well as withconsumers, and with many rather with a few stimuli

A great deal of research in the product evaluation areadeals with consumers. Far less has been published onreactions of foodservice professionals. At some levelthis is due to the the types of studies that are run. Mostconventional studies deal with one or a few products,and look for consumer reactions. Rarely are the studiesdone with experimental design, rarely with product andconcept, and rarely in such a way that the researchercan probe deeply into the respondent’s mind. The lackof data comparing consumers and foodservice opera-tors is matched by the relative lack of data comparingconsumers and experts who have been trained insensory analysis (Moskowitz 1995).

The data presented here comprise an exception toconventional data. For one, the data are deeper andricher, because they deal with the reactions of the dif-ferent respondent groups to multiple products and con-cepts, not just to one. Therefore, it is possible to discern



different response patterns because the study comprisesseveral different stimuli, not just one. Second, thestimuli are systematically varied, allowing a variety ofdifferent types of concepts and a variety of sensoricallydifferent prototypes. Again, patterns can be deducedfar more easily than would be the typical case, whereinthe researcher investigates one product and/or oneconcept, or from time to time the fit between a productand a concept. That type of information is necessaryfor marketing decisions but does not, however, providethe researcher with insight into the mind of the respon-dent in the same way that a large study of many stimuliwould provide.

Drivers of liking for operators versus consumers

One of the goals of the research was to identify thedegree to which the operators and the consumersmatched in terms of product acceptance. These twopopulations agree in overall liking, and responses toappearance. What one group likes, the other grouplikes as well. They do not agree, however, in responsesto flavor and texture. This is important, because thelack of agreement may mean that foodservice operatorscould select products that consumers might not like, orlike less than could be the case. It is clear from Table 4that the differences are largest when it comes to an evaluation of texture, less so when it comes to an evaluation of appearance, or of overall liking.



Although there are differences between operators andconsumers, just knowing these differences and theirmagnitude may not suffice for the development of con-sumer science. An additional consideration is the natureof these differences at a more profound level. One ofthese additional considerations is the study of judge-ment criteria. What are the sensory attributes drivingoverall consumer acceptance versus overall foodserviceoperator acceptance. Table 4 and Fig. 1(A) show thatthese two groups of individuals are very similar in theirresponse to visual inputs. They differ somewhat withregard to responses to taste/flavor attributes, and howimportant the attribute of taste/flavor acceptance is tooverall liking (Table 4 and Fig. 1B). Consumers andfoodservice operators differ from each other most dra-matically with regard to texture (Table 4 and Fig. 1C).This type of analysis of liking drivers at a deeper levelbegins to uncover the nature of differences betweengroups. It does not yet tell us why operators have devel-oped different criteria from those used by consumers,but does show where the criteria differ.

Uncovering different mindsets using conjointmeasurement

Marketers to identify the appropriate features for aproduct, and the appropriate communications, respec-tively, have typically used conjoint measurement. In itstraditional implementation most researchers used con-joint measurement in order to identify the features ofthe product, and have left the communications portionof those features to another study, or quite often simplyto the judgement of marketing experts. That is, the traditional use of conjoint was either to develop theproduct itself, or to position the product in the con-sumer’s mind, but almost never in the same study. Inthis study both aspects, the rational and the emotional,were covered in the same conjoint study. The conceptelements comprise both statements about the productitself, and statements about why the consumer or the foodservice operator should buy the product. Theapproach of combining rational benefits with emo-tional benefits provides a very strong procedure forrapidly creating products because in a single study onecan determine both what the product should be andhow to communicate it.

As price can be addressed as part of the element setit is worthwhile looking at the results about price. Theconsumers are quite sensitive to price, whereas theoperators are less sensitive. As the price increased, consumer interest in the product dropped, whereas theoperator’s response to price was mixed. There was nolinear relation. This apparent lower sensitivity to pricedoes not necessarily mean that the price was less

-10 0 10 20 30Consumer Utility Values

-10

0

10

20

30

Op

erat

or

Uti

lity

Val

ue

s

H

P

Q

N

Q

P

C

PN

V

Q

C

E

E

C

M P

C B

I

U

N

E

H

M

U

H

I

HVI

NIC

E

N

C

U

VV

B

UI

U

U

M

EV

B

C

Q

M

E

B

H

C

U

N

ICEE

Figure 2 Scatterplot of utility values for consumers versusoperators. The 45-degree line shows the line of perfectagreement. The letters refer to the categories of the ele-ments. B = brand name; C = convenience; E = emotion; H =heritage; I = ingredient; M = price N = nutrition; P =package; Q = quality; U = usage; V = versatility.

irrelevant to the foodservice operator, but rather theoperator wasn’t particularly paying attention to thegradations of price alone. The decrease in utility for the very lowest price may have signaled low quality, atleast implicitly. (This low price did not signal lowquality explicitly, because the utility function forquality did not drop at the lowest price).

The stated prices generated utility values that wereeither 0 or positive, meaning that the price statementactually added to the interest in the product. Thismeans that the stated prices were below what therespondent expected to pay for the product. If theprices were around the consumer expectations orhigher, then we would see the utility values for theprices be 0 and negative, rather than 0 and positive.One hypothesis worth exploring is that consumers arealways price sensitive, no matter what the price levelmay be. Even if the price is less than they expect, theyare still sensitive to gradations. In contrast, foodserviceoperators may not be sensitive to price if the price is inthe right place, or less than they expect to pay. This dif-ference for ‘below expected or sale prices’ is worthexploring in far more detail. It may suggest substantivedifferences between business-to-consumer and busi-ness-to-business pricing.

From a practical point of view, it is important toknow what to stress to a potential buyer. If the indi-vidual responds to the rational aspects of a product,then it is important to stress those, whereas if an indi-vidual responds to the emotional aspects then thoseshould be stressed. This information has already beenpresented for regular advertising strategies (Golden &Johnson 1982). These data suggest that different typesof messages may be effective for the consumer vs. theoperator, opening up the issue of optimizing messagesfor different groups, based upon the utilities of the elements in the concept study.

It is clear from the concept research that the food-service operator works with a somewhat different setof criteria than does the consumer. Each group ofrespondents operates within its own framework. Con-sumers are interested in the product itself, and how itaffects them. Thus they are interested in high qualityand low prices. Operators, in contrast, take otherfactors into consideration as well, such as the ease withwhich the product is worked into recipes, the versatil-ity, and against all of that the potential cost (and thuspotential profit).

On the nature of business decisions grounded inknowledge

An over-arching theme of this paper is that one can discover the criteria for liking, and thus implicitly for

product quality, through systematic exploration ofproducts and concepts. This information provides thebusiness professional (operator, owner, manufacturer)with an idea of what is important in the product andin the communication. What is specifically important to professionals tells the manufacturer where to investand what to communicate. If price is critical, then thenatural business strategy is to maximize potential profitby cutting the price, cutting the product quality (tolower price), and emphasizing cost savings. If, in contrast, price is less critical, then the correspondingstrategy is to maximize profit by providing high qualityproducts, and communicating this quality by point-ing to specific sensory characteristics that reflect thequality. Whichever situation holds for a specificproduct and foodservice application, the knowledgeabout criteria for product quality can be used in orderto engineer a more ‘appropriate’ product for the food-service operator, and at the same time maintain per-ceived quality and value for the consumer. It becomesa matter of creating a product and communicationoption that fits the necessary criteria, and is accep-table to the two groups. The research provides the feedback, but it is the business consideration that mustprovide the necessary product and communicationdirection.

Note: All statistical calculations were done using Systat(1997).

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