The opposite pattern, no compensation , occurs when we put the nutritional label on the side of the package

Now these three previously compensating elements that had somehow ameliorated the health problem, no longer do so.

6. Putting the nutritional label on the side of the package drops interest and diminishes the effect of health mes-saging! Let ’ s look at columns D and E (health) and columns H and I (purchase intent) to see this opposite effect.

7. Location has two effects, therefore. When the label/

calorie information is presented in front of the

Chapter 16 Healthy Pasta: Nutritional Labeling and the Role of Messages 173

elements, ” at least in some situations. Before we jump into the approach, the reader can fi nd a worked example in the author ’ s book, Selling Blue Elephants:

How to make great products that people want before they even know they want them (Moskowitz and Gofman, 2007 ).

1. We begin by preparing the data for a specifi c type of analysis called “ scenario analysis, ” and then move on to make our discoveries.

2. Look at Figure 16.9 , which shows part of a very large Excel fi le comprising 8,208 rows, one for each package “ concept ” for each respondent. Recall that each of our 152 respondents evaluated 54 different combinations of concepts, which generates the 8,208 rows of data. Thus our Excel fi le comprised all the data that we will need for the analysis.

3. Each row shows the 24 elements, coded 1 (present) or 0 (absent), as well as four new, derived variables.

These four new variables are labeled with simple, easy - to - remember, and intuitively obvious names: stimulus has a nutritional label, A6, which is the nutrition label on the side, higher - calorie level).

4. We sort the entire data matrix by the newly created variable, ByA. The fi rst rows in the sort correspond to those where ByA is 0; the last set of rows corre-spond to those where ByA is 6, etc. Sorting is straightforward. The idea here is that there are seven such “ layers, ” each layer with its own specifi c nutri-tional label.

5. Now we are going to analyze the matrix. Instead of running one large regression relating all 24 elements (A1 … D6) versus the attribute rating (i.e., binary variable for healthfulness, 1 – 6 coded 0, 7 – 9 coded 100), we will run six separate regressions. Each regression analysis will correspond to one of the six layers (i.e., where the variable ByA takes on the value 1, 2, 3, 4, 5, and 6, respectively). We won ’ t analyze those test stimuli where the nutritional label is absent (i.e., where ByA = 0).

6. The dependent variable for the regression analysis is the binary variable for healthfulness (0 if the original rating for healthfulness was 1 – 6, 100 if the original rating for healthfulness was 7 – 9). The independent intent highly correlate with ratings of healthfulness. For

the two remaining groups who say they sometimes or never read labels, there is an increasing proportion that fall into the uncorrelated group, which treat healthfulness differently from purchase intent.

Finally, by looking at a fi ner - grained analysis of the data, we found that the nutritional labels “ set the stage ” for the performance of the health messages. The respon-dents recognize the increase of calories when it is put on the nutritional label and down - rate healthfulness and pur-chase intent. When the nutrition label is put in front of the package, respondents look for a health message that can compensate for the impact of higher calories. Three health elements do the work of compensation. The com-pensation dynamic does not occur when the nutritional labels and health messaging are placed on the side of the box, however.

Technical Appendix

Discovering Interactions among Pairs of Elements

In this appendix we present a straightforward way to fi nd interactions among pairs of stimulus elements, even if one doesn ’ t suspect that these interactions exist (see Gofman, 2006 ). Many researchers who work with exper-imentally designed stimuli have to limit themselves to main effects or the separate contributions of the different elements. There is a simple reason for the limits. Think about the number of possible interactions among two elements from different silos. In this study we have four silos (nutrition label, information splash, health message, and family message). Each of the silos, in turn, com-prises six elements. Therefore, for any particular pair of silos (i.e., nutrition label × health message), there are 36 different combinations (A1 × C1 … A6 × C6 = 36). In turn, with four silos, there are (4 × 3/2) or 6 pairs of silos.

With 36 pairs of elements, this means 216 possible com-binations, an altogether impossible task if we work in the conventional way, with a limited number of combina-tions tested by many people. Of course, if ahead of time we know the combinations of elements that we think will interact, then we can test that hypothesis by creating those specifi c combinations.

In this appendix we outline the approach, and then apply it to the role of nutrition label. We will fi nd these labels both to provide information and to act as guides or conductors, infl uencing the effect of other “ health

174 Part III Health and Hope

mate of the basic interest in the package, given only the label information.

9. The element values correspond to the utility values of the 18 elements. We only present the six utility values corresponding to the six elements of the nutri-tional message, where the “ action occurs. ”

10. The bottom line here is through the scenario analysis it becomes possible to see how one variable affects another. Scenario analysis looks at one element as a “ guide ” or “ director ” of other elements. By parti-tioning the set of elements in one silo into layers, and by creating the model “ layer - by - layer, ” one layer per element, it becomes possible to identify the interactive effects of two elements, the element defi ning the layer and the element whose impact or utility is being estimated.

References

Garretson , J.A. and Burton , S. ( 2000 ) “ Effects of nutrition facts panel values, nutrition claims, and health claims on consumer attitudes,

variables are the 18 elements in silos B, C, and D, respectively. The elements in silo A, nutritional label and its position, do not appear in the model. As just stated in Step 5, we run a separate equation for those test stimuli where ByA takes on the value 1 (i.e., the label has the regular number of calories, and appears in the front), then a separate equation for those test stimuli where ByA takes on the value 2, etc.

7. The outcome is a model for each layer. The model can be estimated for each attribute (healthfulness, purchase intent) by each option or level of the nutri-tional label (six in total). There are 12 such combina-tions. We see only 8 of the 12 in Figure 16.8 . 8. The additive constant is the conditional probability

of rating a specifi c pasta package as “ healthful ” (i.e., 7 – 9 on the 9 - point scale), without any elements. All we know is that the package corresponds to a spe-cifi c one of the six package alternatives, varying in calories and position of the nutritional label. Again, this additive constant corresponding to the specifi c package is an estimated parameter. It is a good

esti-UID Con A1

Figure 16.9 Example of the database in Excel. The fi rst column is the UID (i.e., the respondent unique identifi cation number). The Con is the concept number, which ranged from 1 to 54. The experimental design comprises 24 elements, four silos (A – D), each with six elements. At the right side are four new variables: ByA, ByB, ByC, and ByD.

Chapter 16 Healthy Pasta: Nutritional Labeling and the Role of Messages 175

Neuhouser , M.L. , Kristal , A.R. , and Patterson , R.E. ( 1999 ) Use of food nutrition labels is associated with lower fat intake . Journal of the American Dietetic Association 99 , 45 – 53 .

Nutritional Facts Panel . ( 2008 ) Guidance for Industry: A Food Labeling Guide . ( http://www.cfsan.fda.gov/label.html ).

Satia , J.A. , Galanko , J.A. , and Neuhouser , M.L. ( 2005 ) Food nutrition label use is associated with demographic, behavioural, and psycho-social factors and dietary intake among African Americans in North Carolina . Journal of the American Dietetic Association 105 , 392 – 402 .

U.S. Centers for Disease Control, Obesity Trends 2006, ( http://www.

cdc.gov/nccdphp/dnpa/obesity/trend/

perceptions of disease - related risks, and trust , ” Journal of Public Policy & Marketing 19 , 213 – 227 .

Gofman , A. ( 2006 ) Emergent scenarios, synergies, and suppressions uncovered within conjoint analysis . Journal of Sensory Studies 21 ( 4 ): 373 – 414 .

Goldberg , J.H. and Probart , C.K. ( 1999 ) Visual search of food nutrition labels . Human Factors 41 , 425 – 437 .

Jones , G. and Richardson , M. ( 2007 ) An objective examination of consumer perception of nutrition information based on healthiness rating and eye movement . Public Health Nutrition 10 , 238 – 244 . Moskowitz , H. and Gofman , A. ( 2007 ) Selling blue elephants: How to

make great products that people want before they even know they want them . Wharton School Publishing, Upper Saddle River , NJ .

Part IV