Soda Versus Cereal and SugarVersus Fat: Drivers of Healthful FoodIntake and the Impact of Diabetes
DiagnosisThis study examines how household members’ personal characteristics and key marketing factors affect thehealthfulness of food purchased for in-home consumption; it further considers how food intake changes followinga diagnosis of Type 2 diabetes in the household. Using a combination of grocery purchases over four years, surveydata about health status, and the nutrition content of 13 of the largest packaged food categories, this study showsthat households with higher education and nutrition interest consume fewer calories, sugar, and totalcarbohydrates, whereas those with higher self-control consume more, because they offset their lower intake of“unhealthy” categories (e.g., soft drinks) with higher intake of “healthy” categories (e.g., cereal). The consumptionof sugar and carbohydrates decreases significantly in response to a diabetes diagnosis, whereas the intake of fatand sodium increases. Education, nutrition interest, and self-control are not associated with healthier changes inresponse to a diagnosis, but younger and higher-income households, as well as those in which the diabetes patientis female, make healthier changes. These findings have notable implications for marketers, consumers, consumerresearchers, and public health professionals.
Keywords: health status, drivers of food choice, health halo bias, diabetes, self-control, household groceryshopping
Yu Ma is Assistant Professor, Department of Marketing, Business Eco-nomics, and Law, School of Business, University of Alberta (e-mail:[email protected]). Kusum L. Ailawadi is Charles Jordan 1911 TU’12Professor of Marketing, Tuck Business School, Dartmouth College (e-mail:kusum. [email protected]). Dhruv Grewal is Toyota Chair of Com-merce and Electronic Business and Professor of Marketing, Babson College(e-mail: [email protected]). The authors thank Information ResourcesInc. for providing the panel and survey data used in this research and theAlberta School of Retailing for a research grant awarded to the firstauthor. They also thank Ron Adner, Laurette Dube, Karen Gedenk, PeterGolder, John Hauser, Kevin Keller, Ellie Kyung, Ann McGill, Scott Neslin,Jason Riis, Scott Motyka, and seminar participants at the Tuck BusinessSchool at Dartmouth, University of North Carolina at Chapel Hill, Wash-ington University in St. Louis, HEC Paris, Maastricht University, Universityof Hamburg, Northeast Marketing Consortium, University of South Caro-lina, University of Salamanca, and 2012 Marketing Science Conferencefor their helpful comments. Leigh McAlister served as area editor for thisarticle.
Journal of MarketingVolume 77 (May 2013), 101 –120101
Consumers worldwide face serious health problems inthe form of increasing rates of obesity and incidenceof obesity-related diseases, such as Type 2 diabetes.
According to the Centers for Disease Control and Preven-tion (CDC), one-third of U.S. adults are obese (Flegal, Car-roll, and Ogden 2010), and one in ten people has been diag-nosed with diabetes, a rate predicted to increase to one inthree by 2050 (CDC 2010). Similar trends mark consumersin countries such as India and China as they adopt moreWesternized diets. The consequent burden is not only eco-
nomic, in the form of health care costs, but also social, inthe form of reduced quality of life. Public health and fooddecision-making researchers point to several reasons for theobesity epidemic. A widely noted factor is the easy avail-ability and heavy marketing of calorie-dense processedfoods and sugar-laden beverages. Increasingly, therefore,packaged food marketers are confronted with questionsabout the health implications of their products, demandingsome response.
Consumer researchers, public health professionals, andmarketers need a better understanding of the drivers of foodchoice and how to encourage consumers with health prob-lems to make necessary changes to their diets. Our researchaddresses these issues in the context of Type 2 diabetes byanswering three research questions. First, we consider howvarious factors identified in prior literature as barriers to orenablers of healthy choices affect households’ regular in-home food intake. Second, we study how households altertheir food intake in response to a household member’s diag-nosis of diabetes. Third, we determine whether the effectsof the different barriers and enablers change after such adiagnosis. We compile a unique data set to answer thesequestions, combining four years of monthly grocery pur-chases of a national panel of U.S. households together withsurvey data about the health status of household membersand information on the nutritional content of thousands offood items. We track purchases of and nutrient intake from13 of the largest packaged food categories among house-
holds with a diabetes diagnosis for several months beforeand after diagnosis and compare them with a matched con-trol group of households without diabetes. Next, for each ofour three research questions, we highlight how our workcontributes to the literature and the kinds of insights itdelivers.
RQ1: How do various consumer and marketing factors drivehealthy choices regarding food intake at home?
Public health, food decision making, and health-protectivebehavior research identifies multiple barriers to and enablersof good dietary choices. However, apart from some aggregatecomparisons of the prices and availability of healthy foodsin different demographic areas (French 2003; Horowitz etal. 2004; Jetter and Cassady 2006), most of this work isconducted in a lab setting manipulating single variables. Weaim to validate these findings with actual purchase behaviorin the marketplace, in which context consumers must man-age multiple goals, stimuli, and constraints.
We contribute to the literature by integrating the barriers/enablers that drive food choices into a model and quantify-ing their relative impact on the healthfulness of actual foodintake at home. We also assess how these factors affect thequantity versus quality of intake by examining not only thenumber of servings but also sugar, total carbohydrate, fat,sodium, and calorie intake from prominent food categoriesthat vary in perceived (and actual) healthfulness. We findthat households headed by highly educated people who areinterested in nutrition consume fewer unhealthy nutrients,though the impact of such consumer characteristics is muchless than that of price. In contrast, households whose headsexert self-control (e.g., not eating fast foods) actually takein more sugar, total carbohydrates, and calories. They offsettheir lower intake of unhealthy categories, such as carbon-ated soft drinks (CSDs), with higher intake of categoriesthat have a positive health perception, such as cereal.
RQ2: How does diabetes diagnosis change the healthfulness offood intake at home?
Public health research examines patients’ diet changesusing food-frequency questionnaires or short-term diaryrecall, often in small samples. Not only does recall tend tobe inaccurate, there is also evidence that desirable dietaryintake is overreported (Salvini et al. 1989) and overall foodconsumption is underreported, especially by obese patients(Salle, Ryan, and Ritz 2006). Furthermore, these studiesonly examine intake after diagnosis or the change frombefore to after diagnosis. Without accounting for trends andother market factors with a control group, they cannot iso-late the effect of diagnosis. These problems lead to conflict-ing findings across studies: they consistently report lowersugar intake among diabetics, but some studies find lowerfat intake (Niewind et al. 1990; Van de Laar et al. 2004),whereas others find higher levels (Shimakawa et al. 1993;Virtanen et al. 2000). Moreover, some find lower sodiumintake (Niewind et al. 1990), whereas others find the oppo-site (Munoz-Pareja et al. 2012; Vitolins et al. 2009).
We contribute to this literature by capturing the impactof diagnosis on the actual food intake of a large nationalsample in a way that is unobtrusive and relatively long-term
102 / Journal of Marketing, May 2013
and that combines the internal validity of a before-and-after-with-control group design with the external validity ofreal marketplace behavior. We also provide insight not justinto the average change in nutrient intake but also into howit varies with patient characteristics on the categories thechange is sourced from. For example, we find that house-holds reduce sugar intake but increase fat intake. Sugarreduction is sourced from beverages for which low-sugaralternatives are available but not from other hedonic cate-gories such as cookies and ice cream. The increase in fatcomes from categories such as processed meat and saltysnacks that are known to be unhealthy for diabetics but arelow in sugar. If the diabetes patient is female, householdstend to make healthier changes across a broader set of cate-gories than if the patient is male. Furthermore, if the patientis not taking prescription (Rx) medication for diabetes, thehousehold makes smaller changes across a wider set ofcategories, even though changes in total nutrient intakeremain similar to households in which the patient is Rxtreated.
RQ3: Do the effects of various barriers and enablers changeafter disease diagnosis?
Studies of the impact of consumer characteristics andmarketing factors on health behaviors and food choicestend to be in the context of risk prevention, and they focuson one-time choices instead of sustained behavioralchanges. Such findings may not apply to sustained diseasemanagement; for example, the extent to which consumersengage in automatic versus deliberative or implemental pro-cessing (Gollwitzer and Bayer 1999), are motivated to acton health-related knowledge (Moorman and Matulich1993), or deplete their self-control (Lord et al. 2010) likelychanges when a health threat has become a reality.
We contribute to this literature by comparing the impactof various barriers/enablers before and after diagnosis—thatis, when the consumer may be in risk prevention mode ver-sus in disease management mode. Only if a factor’s impactchanges after diagnosis can it help explain why somehouseholds manage the disease better than others. By usingseveral months of data before and after the diagnosis, wealso address sustained behavior changes (Rothman et al.2011). Our results show that people with high self-control orgreater education and nutrition interest do not respond anybetter to diagnosis than others; these characteristics matterless for disease management than for risk prevention. How-ever, households with higher incomes and younger patientsmake healthier changes, cutting more sugar and carbohy-drates. Those whose intake of sugar was higher to beginwith also make more substantial reductions. Family influ-ence matters as well: household size does not affect sugarreduction, which seems to be a salient goal of diabetics, butbigger families increase fat intake more and do not reducecarbohydrates as much as others.
In the next section, we detail our conceptual frameworkby identifying key factors that influence the healthfulness offood intake and developing hypotheses about their impact.We then describe our data and measures and present themethodology we use to examine our three research questions.After we present our empirical findings, we conclude with
some implications of these findings for marketers, consumerbehavior researchers, and public health professionals.
Conceptual Framework andHypotheses
The conceptual framework in Figure 1 guides our analysisof the research questions with four key elements. First, itcites the outcome of interest, namely, the healthfulness offood purchased for in-home intake. Second, it lists factorsthat are potential barriers to or enablers of healthy choices.Their impact on the healthfulness of food intake in normalcircumstances constitutes the focus of RQ1. Third, thisframework shows that a change in health status can moti-vate households to make changes in their intake (RQ2).Fourth, it highlights the possible moderation of the impactof barriers/enablers by diagnosis (RQ3) when consumersare in disease management mode. In addition to these mainelements, the framework shows that variation in intakeacross households and over time may be driven by factorssuch as household requirements, time effects, and regionaldifferences, which need to be controlled for. Finally, werecognize that diagnosis can change some consumer fac-tors, as depicted by the dotted arrow.Healthfulness of Food IntakeDietary guidelines for general health (U.S. Department ofAgriculture [USDA] 2010) recommend that U.S. con-sumers should (1) reduce their total calorie intake to ensuredesirable body weights; (2) reduce their fat (especially satu-rated and trans fats), sugar, sodium, and refined grainsintake; and (3) eat more fat-free or low-fat dairy, fresh veg-etables and fruit, whole grains, beans/legumes, and fish.
Drivers of Healthful Food Intake / 103
The dietary guidelines for managing Type 2 diabetes(American Diabetes Association 2008, 2012; Mayo Clinic2012) are similar, except with a stronger emphasis on con-trolling carbohydrate intake. Patients are advised to eatfiber-rich foods that are low in fat and calories and controlcarbohydrate intake to manage their blood glucose levelsand reduce weight. In addition, they are asked to reducesodium and fat intake because they are at higher risk forhypertension and heart disease. In terms of specific foods,they are advised to (1) reduce high-calorie snacks anddesserts; (2) avoid sugary drinks, including regular sodaand fruit drinks; (3) replace high-fat dairy with low-fat andfat-free dairy, processed grains with whole grains, andprocessed meats with lean meats or skinless poultry; and (4)eat fresh, nonstarchy vegetables and fruit, beans/legumes,and fish. In general, they need to replace processed foodwith fresh foods and limit portion sizes. As these guidelinesshow, the healthfulness of food intake refers to the con-sumption of calories and nutrients, and the emphasis is onlimiting calories and unhealthy nutrients. The only element(other than vitamins and minerals from fresh produce) thatconsumers are asked to increase is fiber. Accordingly, ourdiscussion and hypotheses focus on the reduction ofunhealthy intake.
Because dietary guidelines also cite the sources of calo-ries and nutrients, we move beyond total intake to considerthe food categories from which people get their calories.Intake from specific categories is also important to studybecause prior lab research has shown that consumers clas-sify food categories as unhealthy or healthy, and theirsearch, choices, and consumption are driven by whether acategory has a healthy perception. For example, Moormanet al. (2004) show that consumers who believe they are
Barriers/Enablers for Healthy Food IntakeHealth knowledgeSelf-controlAffordabilityHealth statusSocial influencePrior food habitsAvailability of healthy food
Disease DiagnosisGender of patientSeverity of disease
Control VariablesMarket and time effectsHousehold requirements... and so on
Healthfulness of Food IntakeTotalCalories, unhealthy and healthy nutrientintake
Sourced fromUnhealthy versus healthy food optionsLess versus more healthy options withinfood categories
FIGURE 1Conceptual Framework
RQ1
RQ2
RQ3
knowledgeable about nutrition are more selective of healthyversus unhealthy food categories than of less versus morehealthy options within a category. In addition, consumersunderestimate calories and overeat when they perceive thefood to be healthy (Chandon and Wansink 2007;Provencher, Polivy, and Herman 2009; Wansink and Chan-don 2006). According to Chernev and Gal (2010) andChernev (2011), consumers even lower their total calorieestimates when a healthy option is added to an unhealthymeal, especially if they are exerting dietary restraint. Distin-guishing intake from categories that are perceived as moreversus less healthy will enable us to determine whetherthese lab findings extend to food-for-home purchases in themarketplace, and they can provide insights into how adher-ence to dietary guidelines may be helped or hindered bycategory perceptions.Barriers to and Enablers of Healthy Food IntakeIf all foods were equal in appeal, availability, and afford-ability and if consumers were knowledgeable about theirhealthfulness, they would choose healthy foods. Instead,sweet and fatty foods taste better and are more difficult to
104 / Journal of Marketing, May 2013
resist; highly processed, energy-dense foods are cheaperand more widely available. Moreover, consumers lack bothsufficient knowledge to choose healthy foods and the abilityto accurately monitor how much they eat. We reviewed thenutrition and public health literature as well as the consumerbehavior literature on food decision making and health pro-tective behavior to identify the key factors that enablehealthy food choices or act as barriers. These factors arelisted in Figure 1, and we discuss each one in the followingsubsections. Table 1 summarizes our discussion by listingconceptual definitions and hypotheses for each factor.
Health knowledge. Health knowledge is the extent towhich consumers have enduring health-related cognitivestructures (Moorman and Matulich 1993). To make goodchoices, they need information about which foods arehealthy, as well as the cognitive ability and interest to usethat information (Bublitz, Peracchio, and Block 2010).Greater health knowledge should reduce unhealthy intake,especially in categories perceived as unhealthy (Moormanet al. 2004).
Self-control. Self-control is a person’s ability to overridenatural and automatic desires and behaviors and pursue
ConstructHypotheses for Effects on Unhealthy Intake
(Research Questions 1 and 3)Health KnowledgeEnduring health-related cognitive structures. Information about what is healthy and the cognitive ability and interest to utilize it.
RQ1: Health knowledge has a negative main effect on unhealthy intake.RQ3: The interaction of diagnosis with health knowledge has a negative
effect on unhealthy intake.
Self-ControlAbility to override natural and automatic desiresand behaviors and pursue long-term goals atthe expense of short-term temptations. Bothperceived self-control and actual behaviorreflecting self-control are relevant.
RQ1: Self-control has a negative main effect on unhealthy intake.RQ3: We cannot predict the direction of the interaction effect of diagnosis
and self-control.
AffordabilityExtent to which consumers can bear the finan-cial cost of engaging in healthy behaviors. Bothmarket cost and consumers’ ability to pay arerelevant.
RQ1: Cost of unhealthy foods has a negative main effect on unhealthyintake.
RQ3: We cannot predict the direction of the interaction effect of diagnosisand cost.
RQ1: Ability to pay has a positive main effect on unhealthy intake.RQ3: The interaction effect of diagnosis and ability to pay on unhealthy
intake is negative.Health StatusConsumers’ wellness of body and mind. Both perceived health and actual health are relevant.
RQ1: We cannot predict the main effect of health status on unhealthyintake.
RQ3: The interaction effect of diagnosis and health status on unhealthyintake is negative.
Social InfluenceInfluence of others in a person’s food choices.Presence and preferences of other family members are relevant for food-for-home choices.
RQ1: Influence from other family members has a positive main effect onunhealthy intake.
RQ3: We cannot predict the direction of the interaction effect of diagnosisand family influence.
Prior Food HabitsHabitual behavior that reflects food preferencesand can be automatic.
RQ1: Prior unhealthy food habits have a positive main effect on unhealthyintake.
RQ3: The interaction effect of diagnosis and prior unhealthy habits onunhealthy intake is negative.
Availability of Healthy FoodConsumers’ access to healthy food categoriesand healthy options within categories.
RQ1: Availability of healthy foods has a negative main effect on unhealthyintake.
RQ3: The interaction effect of diagnosis and healthy food availability onunhealthy intake is negative.
TABLE 1Factors Driving Healthfulness of Food Intake: Constructs and Hypotheses
long-term goals at the expense of short-term temptations(Bauer and Baumeister 2011, p. 65). Perceived self-controlhas a positive effect on intent to engage in risk preventionand healthy behaviors (Bandura 1977; Rogers 1983). How-ever, actual self-control is what directly drives healthychoices (Ajzen 2002). Self-control is required to avoidsweet, fatty, and salty foods (Chandon and Wansink 2011;Drewnowski 1995), whereas a positive health halo bias mayweaken the effect of self-control for categories that seemhealthy. We expect a negative effect of self-control onunhealthy intake, mainly in categories perceived asunhealthy.
Affordability. Defined as the extent to which consumerscan bear the financial cost of engaging in healthy behaviors,affordability reflects both consumers’ own financial abilityto pay and the cost in the market, both of which can barhealthy eating (Bihan et al. 2010; Horowitz et al. 2004; Jet-ter and Cassady 2006). People with more discretionaryincome are not constrained to buy less expensive, unhealthyfoods, but recent research has shown that such people justconsume more of everything—both good and bad (French,Wall, and Mitchell 2010). Therefore, we expect the cost ofunhealthy foods to have negative effects on their intake,whereas ability to pay should have a positive effect onunhealthy (and healthy) intake.
Health status. Health status refers to consumers’ well-ness of body and mind and reflects their physical and men-tal ability to engage in healthy behaviors (Moorman andMatulich 1993). It includes not just actual but also per-ceived health, both of which drive health-related behaviors(Martin 1997; Millunpalo et al. 1997). Health status may beassociated with better choices if consumers work to pre-serve this state; conversely, healthy consumers may becomecomplacent, whereas unhealthy consumers could feel pres-sure to improve their health. Therefore, we do not offer aprediction about the effect of health status.
Social influence. Social influence refers to the influenceof others in encouraging or interrupting healthy choices (fora review, see Bublitz, Peracchio, and Block 2010). It mayprompt overeating, due to the social facilitation of others, orrestrained eating, due to impression management (Herman,Roth, and Polivy 2003). Social influence can play a rolethrough family members at home and through those aroundwhom food is consumed in public. When a person eats athome with his or her family, the social facilitation effects arelikely to overwhelm impression management effects. Thepresence of other family members also increases total foodrequirements. Thus, we expect family influence to have apositive effect on unhealthy (as well as healthy) food intake.
Prior food habits. Household grocery purchase behav-iors tend to be automatic and habit driven (Ji and Wood2007; Khare and Inman 2006). We expect the effect of priorfood habits to be positive, because consumers purchasemore of what they are habituated to buying.
Availability of healthy food. Availability of healthy foodrefers to how accessible different foods are to consumers.Consumers in low-income and inner-city areas have limitedaccess to healthy foods (Horowitz et al. 2004; Jetter and
Drivers of Healthful Food Intake / 105
Cassady 2006), while easy availability of calorie-denseprocessed foods increases their consumption (Chandon andWansink 2011; Kessler 2009). If more healthy categoriesand healthy options within categories are available,unhealthy intake should decline.
Finally, food choice and consumption are influenced bysensory and other characteristics of the food itself, packagecharacteristics, and mood (see Bublitz, Peracchio, andBlock 2010; Chandon and Wansink 2011). Because ouranalysis is specific to each food category we study, we drawon category characteristics to explain any differences acrosscategories. In addition, because our focus is on total intakeover a sustained period of time, individual package charac-teristics and consumers’ situational frame of mind are notrelevant.Changes in Intake Due to Disease Diagnosis (RQ2)As we noted previously, the dietary guidelines for manag-ing Type 2 diabetes recommend control of sugar, total car-bohydrates, fat, and sodium. Thus, we expect a decrease inthe intake of these elements after a diagnosis, especially incategories perceived as unhealthy for diabetics. Sugardirectly increases blood glucose level; we predict the largestdecrease in sugar intake. Because diabetics control carbo-hydrate intake, but some low-carbohydrate foods such ascheese and processed meat are high in fat, we expect thesmallest reduction in fat intake. We also anticipate smallerreductions in the intake of hedonic foods, which are moredifficult to resist (Chandon and Wansink 2011) and may besought out to offset the emotional distress and mortalitysalience caused by a diabetes diagnosis (Ferraro, Shiv, andBettman 2005).
Gender of the diagnosed person may influenceresponse. Men exhibit more risky and fewer healthy behav-iors than women (Liang et al. 1999; Wardle et al. 2004) andgive lower priority to health when choosing foods (Fagerliand Wandel 1990). Men also rely on schema and heuristiccues, whereas women tend to scrutinize and processdetailed information (Meyers-Levy 1989; Meyers-Levy andSternthal 1991). Thus, we expect a greater reduction inunhealthy food intake across more categories when thehousehold member with diabetes is female.1
Finally, although diabetes is a serious and chronic dis-ease, variation exists in the severity of the symptoms andconsequences. According to protection motivation theory(Ajzen 1988; Rogers 1983), the severity of the negative out-comes increases motivation for behavior change. Therefore,we expect greater reductions in unhealthy intake when thedisease is more severe.Interaction of Diagnosis with Barriers/Enablers(RQ3)For any of the barriers/enablers in our framework to explainwhy some households make better changes than others,their effects must be moderated by diagnosis. But, whyshould their impact change in the postdiagnosis disease
1Because the gender and disease severity of the patient are onlyknown after diagnosis, main effects of these factors on food intakeare not applicable.
management mode? A serious disease such as diabetesshould motivate people to take better care of their health,and motivation promotes information processing, attitudinal,and behavioral change (MacInnis, Moorman, and Jaworski1991; Moorman and Matulich 1993). According to this logicof diagnosis = higher motivation, diagnosis should motivateconsumers to better use their health knowledge and healthstatus to reduce unhealthy intake. Indeed, Moorman andMatulich (1993) argue that such abilities drive healthybehaviors only when consumers are highly motivated.Thus, we expect a negative interaction of diagnosis withhealth knowledge and health status; that is, their negativeeffect on unhealthy intake should be stronger after diagno-sis. Similarly, when consumers are motivated, they shouldchoose more of the healthy foods available to them, so weexpect a negative interaction with healthy food availability.
However, self-control research has shown that successbolsters confidence and further action, whereas failure under-mines both (Rothman et al. 2011). Although self-control hasbeen shown to play an important role in risk prevention, the“failure” of having contracted the disease despite self-controlmay interrupt continued efforts. Managing multiple goalsrelated to diabetes (e.g., eat better, lose weight, exercise)also taxes people’s cognitive resources, and ego depletioninterrupts self-control (Bauer and Baumeister 2011;Bublitz, Peracchio, and Block 2010; Vohs and Heatherton2000). Given this tension between increased motivation andnegative feedback/ego depletion, the interaction of diagno-sis with self-control is an empirical question.
Diagnosis should also moderate the effect of affordabil-ity. On the cost side, realizing that they must improve theirdiet to manage the disease should make households lesslikely to buy unhealthy products simply because they arecheap or on promotion; however, new medical costs maymake them more price sensitive. Therefore, the interactionof a diabetes diagnosis with market costs cannot be pre-dicted. Because ability to pay helps households substitutehealthier (and more expensive) products for unhealthy ones,when they realize the importance of diet, we expect a nega-tive interaction of diagnosis with ability to pay.
Diagnosis can moderate the effect of social influence inmultiple ways. If family influence makes it more difficultfor the patient to change at-home eating, the interactionshould be positive. However, if family members also makehealthy changes, the interaction should be negative (greaterreduction in unhealthy intake). Thus, the direction of theinteraction is an empirical question. Finally, there is moreroom for improvement if unhealthy intake was habituallyhigh to begin with. Thus, we expect a negative interactionof diagnosis with prior food habits. Table 1 summarizes ourhypotheses for these interactions.
Data and MethodologyWe combine four sources of data to conduct our analysis.The first is a nationwide home-scan panel data set fromSymphonyIRI that tracks households’ weekly grocery pur-chases from January 2006 to December 2009. These pur-chases, which we aggregate to the monthly level, cover thecomplete set of IRI packaged goods categories and encom-
106 / Journal of Marketing, May 2013
pass not just grocery and drug stores but also mass storesand warehouse clubs. The second is a health survey admin-istered by SymphonyIRI in November of each year from2005 to 2008 that includes information on each householdmember’s health status and health-related perceptions andbehaviors. The third is a database of the nutrient content ofindividual items in the food categories we study. The fourthis a consumer survey we conducted to determine howhealthful each of the food categories is perceived to be.Selection of Food CategoriesOur analysis is based on 13 of the largest categories in theUnited States, accounting for more than 40% of total pack-aged food and beverage category sales as tracked by Sym-phonyIRI. Although fresh vegetables and fruit are recom-mended in all dietary guidelines, scanner data only trackpackaged foods with universal product codes. Therefore,we are unable to study intake of fresh foods.2 Becausepackaged foods are typically processed, and dietary guide-lines emphasize control of calories and unhealthy nutrientsfrom processed foods, we focus on intake of calories, sugar,total carbohydrates, fat, and sodium.3 Table 2 providesinformation on average nutrient content and calories perserving of the 13 categories. We obtained serving size fromthe eCFR (2011) database of reference amounts of foodscustomarily consumed per eating occasion. We do not usethe serving size on product labels because manufacturers canmanipulate them to make the nutrients per serving appearreasonable (Mohr, Lichtenstein, and Janiszewski 2012).
As the table shows, these categories vary significantlyin average nutrient content. They also differ in the standarddeviation of nutrient content. For example, CSDs, juice,and yogurt have high standard deviations of sugar contentin line with the availability of less versus more unhealthystockkeeping units (SKUs), whereas ice cream and cookieshave relatively low standard deviations, indicating the lackof healthier alternatives in these categories. Such variationhighlights the importance of examining not only the amountof a category but also the calorie and nutrient intake from it.It will also be helpful subsequently in understanding whythere is greater reduction of unhealthy intake after diagnosisin some categories than in others.
2However, we did examine unprepared frozen and canned veg-etables. Main effects of our model variables on number of servingswere largely as expected, but there was no significant change inintake due to diagnosis. Because they are a very small source ofsugar, fat, calories, and so on, we have not included them in ouranalysis. Among the 20 largest SymphonyIRI categories, wedeleted bread, because our nutrient database does not provide ade-quate coverage of that category, and beverages such as bottledwater, tea, coffee, and alcohol, because they are small sources ofthe nutrients we study.
3The guidelines recommend higher fiber intake, but we do notstudy it as a separate nutrient because the categories in our studydo not contain much fiber. The American Diabetes Associationstates that half the grams of fiber can be subtracted from totalgrams of carbohydrates if an item contains more than 5 grams offiber per serving. We performed our analysis with this modifiedmeasure of carbohydrate intake and found our results wereunchanged. We also performed our analysis with saturated fatinstead of total fat and found similar results.
Drivers of Healthful Food Intake / 107
TABLE 2
List of Product Categories and Nutrient Content
Perceived
Perceived
Total
Healthiness
Healthiness
Category
Sugar (g)
Carbohydrates (g)
Fat (g)
Sodium (mg)
Calories (kcal)
(General)
(Diabetes)
Cereal
10.8
(3.2)
28.7
(5.4)
2.9(2.3)
139
(88)
152.5(12.7)
H 4.0
(1.4)
UH3.1(1.7)
Cheese
.n.s.
.n.s.
7.7(1.7)
248(135)
99.6
(16.7)
H 4.4
(1.3)
H 4.0
(1.6)
Cookies
10.4
(1.5)
20.3
(1.4)
5.8(1.2)
91(41)
137.8(10.0)
UH1.9
(.9)
UH1.5
(.8)
Crackers
1.9
(1.7)
20.5
(2.4)
4.2(1.8)
226(101)
130.4
(11.9)
H 3.4
(1.3)
UH3.0(1.6)
CSDs
19.0(12.2)
19.4(12.3)
.n.s.
.n.s.
74.1
(46.9)
UH1.6
(.9)
UH1.5
(.9)
Frozen dinners
4.7
(2.2)
30.5(10.0)
9.7(4.5)
536(222)
256.3(83.0)
UH3.0(1.2)
UH2.7(1.5)
Ice cream
15.8
(2.6)
19.4
(2.8)
6.8(2.5)
56(25)
147.1(18.4)
UH2.3(1.2)
UH1.8(1.1)
Juices
26.4
(5.3)
28.4
(5.6)
.n.s.
42(101)
114.6(22.1)
H 4.6
(1.4)
UH3.0(2.0)
Milk
12.0
(.4)
12.6
(.4)
4.1(3.1)
157
(57)
122.2(24.5)
H 5.5
(1.3)
H 4.4
(1.6)
Processed meats
.n.s.
.n.s.
9.0(4.8)
529(223)
120.3(40.9)
UH2.9(1.4)
UH2.6(1.5)
Salty snacks
.n.s.
16.9
(4.9)
8.1(3.1)
245(145)
151.8(16.8)
UH2.0
( .9)
UH1.7
( .9)
Soup
3.6
(3.0)
20.5
(5.8)
5.6(3.1)
713(211)
157.3(40.6)
H 3.9
(1.4)
H 3.5
(1.7)
Yogurt
17.1
(4.5)
20.9
(4.8)
.9(.7)
91(29)
120.0(22.0)
H 5.5
(1.1)
H 4.7
(1.7)
UHPlaced in “unhealthy” group if mean perceived rating is not significantly greater than 3.
H Placed in “healthy” group if mean perceived rating is significantly greater than 3.
Notes: Calories and nutrients are average values per serving, with standard deviations in parentheses; perceived healthiness ratings are average values on a scale of 1(“very unhealthy”) to 7
(“very healthy”), with standard deviations in parentheses. n.s. = not a significant source of the nutrient (less than 1 gram
for sugar, and less than 1% of USD
A recommended daily allowance
for others).
To determine which categories have a less versus morepositive health perception in the minds of consumers, weconducted two surveys using an online panel. The first askeda sample of 190 U.S. adults to rate each of the categories inour study on how healthful they think it is in general. Thesecond asked a sample of 122 U.S. adults who either wereprediabetic or had Type 2 diabetes to rate each of the cate-gories on how healthful they think it is for diabetics. Fol-lowing Moorman and Slotegraaf (1999), we used a scalefrom 1 (“not at all healthy”) to 7 (“very healthy”). Table 2also includes average ratings from the consumer surveys.
We note that (1) there is reasonable but not perfect con-sistency between the objective nutrient content of the cate-gories and consumers’ perceptions of their healthfulnessand (b) there are a few differences in perceived healthful-ness of categories in general versus for diabetes patients. Inparticular, high-sugar and high-fat categories such as CSDs,ice cream, cookies, salty snacks, and frozen dinners are per-ceived as being unhealthy, with average ratings not greaterthan 3, both in general and for diabetes. Juice, cereal, andcrackers are not considered unhealthy in general, but theirperceived healthfulness is significantly lower for diabetes,which makes sense given their carbohydrate content. In ourempirical analysis, we distinguish more and less unhealthycategories, using the general healthfulness perceptions forexamining main effects of various barriers and enablers andthe diabetes-specific healthfulness perceptions for examin-ing response to diagnosis. In the remainder of this article,when we discuss “unhealthy” and “healthy” categories, it iswith reference to these consumer perceptions.Selection of SampleBeginning with the 24,390 households in our panel with atleast two years of survey and purchase data, we first createdtwo groups: those with diabetes in the household (5,980) andthose without (18,410). We drew our “diabetes households”from the former and our “control households” from the lat-ter. The health survey provides information on whether ahousehold member has diabetes at the time of the survey,that is, in November of a given year. Thus, if no memberreported having diabetes in November 2006 but one or morereported having the disease in November 2007, we knowthey were diagnosed sometime between November 2006 andNovember 2007. To ensure adequate pre- and postdiagnosisperiods, we include in our diabetes group households with adiagnosis between November 2006 and November 2007 orbetween November 2007 and November 2008. Because wedo not have the exact date of diagnosis and to make sure wehave clean pre- and postdiagnosis periods, we do not includein our analysis the year during which diagnosis occurred.Finally, to ensure that the diagnosed patient’s characteristicswould be represented in our model (which uses the averagevalues for the household heads), we only included thosehouseholds for which the patient is a household head. Thisprocedure provides a sample of 578 diabetes households,48.8% of which have a male diagnosed member.
We then used propensity scores (Rosenbaum and Rubin1983) to match the diabetes households to control house-holds. Matching each of the diabetes households to their
108 / Journal of Marketing, May 2013
two nearest neighbors using a minimum caliper criterion of1% without replacement provided a control group of 1,155households. The matching is done on average values of alldemographics and other characteristics for the householdheads. In addition, we select, to the extent possible, thesame pre- and postdiagnosis periods for a control householdas for the diabetes household to which it is matched. T-testsconfirm that there is no significant difference between thetwo groups on means of the matching variables. Details ofthe matching procedure are available upon request.MeasuresTable 3 summarizes our measures of the dependent variablesand the barriers/enablers in the conceptual framework ofFigure 1, along with representative studies from the litera-ture that use similar measures. A few points regarding ourmeasures deserve note. First, we measured the dependentvariables as monthly purchases of the household. Second,we averaged measures of all the consumer characteristicsfor the household head(s), who are defined as (up to two)persons responsible for most household decisions. All thepatients in our sample are household heads, so the patient’sown characteristics are always captured.4 Third, Figure 1allows for a potential impact of diagnosis on consumercharacteristics (e.g., health status, self-control), but thechanges in these characteristics in the data are too small tomodel. We use prediagnosis values to ensure that our resultsare not confounded by the changes, however small.
Fourth, all the categories are available throughout theperiod to all panelists in the stores where they shop, so wedid not include availability in our model. However, as dis-cussed previously, there is variation across categories in theavailability of less versus more healthy SKUs, and we usethis to explain our results across categories. Fifth, we mea-sure disease severity by whether the patient is being Rxtreated, because the likelihood of taking Rx drugs is higherif the disease is severe (63.8% are Rx treated in our sam-ple). Although Bolton and colleagues (Bolton, Cohen, andBloom 2006; Bolton et al. 2008) show that drugs mayboomerang, whereby those who use them reduce healthybehavior, we expect that the severity effect hypothesizedpreviously (i.e., greater reduction in unhealthy intake) ismore likely for a serious disease.
The hypothesized effects of the measures in Table 3 nat-urally follow those of the underlying constructs discussedpreviously and summarized in Table 1. However, a fewdeserve elaboration. First, nonprice promotions are relevantto affordability even though they do not reduce price,because consumers take promotions as a cue for a price cutand respond to them. Therefore, we expect their main effectto be positive. However, this response only occurs underautomatic and low-cognition decision making, which istypical of normal grocery shopping (Inman, McAlister, andHoyer 1990). Nonprice promotions become less effectivewhen consumers are more deliberative in their shopping, as
4We also repeated all our analyses using only the diagnosedmember’s characteristics and found no substantive difference inresults.
Drivers of Healthful Food Intake / 109
Effect on UnhealthyIntake
Construct MeasuresRepresentative References Main Interactiona
Healthfulnessof foodintake
Total IntakeHousehold’s total monthly intake of sugar, carbohy-drates, fat, sodium, and calories across the 13 categories = Number of servings purchased of an itemtimes the content per serving, summed across all products purchased in any of the 13 categories.
Category IntakeHousehold’s monthly intake of servings, sugar, carbo-hydrates, fat, sodium, and calories from each of the 13categories = Number of servings purchased of an itemtimes the content per serving, summed across all products purchased in a given category.
Niewind et al. 1990;Virtanen et al. 2000
____ ____
Healthknowledge
EducationHighest level of education (1 = “grade school or less,”and 8 = “postgraduate work”)
Nutrition Interest (Nut_int)a. I often read nutrition labels on food (1 = “disagree,”and 3 = “agree”)
b. How concerned are you about refined/processedfood? (1 = “not at all,” and 3 = “very”)
c. How concerned are you about trans fat in food? (1 =“not at all,” and 3 = “very”)
Moorman and Matulich 1993;Andrews, Netemeyer,and Burton 2009
–
–
–
–
Self-controlb Behavioral Control (Beh_ctrl)a. On a weekly basis how often do you exercise?b. On a weekly basis how often do you eat late at night?c. On a weekly basis how often do you eat at fast-foodrestaurants?
d. On a weekly basis how often do you take multivitamins?
(All on three point scales [1= “most days,” and 3 =“rarely/never”; a and d are reverse-coded)
Bagozzi, Moore, andLeone 2004;Baumeister et al.2006
– ?
Affordability IncomeHousehold income (1 = “$10K per year or less,” and 12 = “more than $100K”)
Net PriceWeighted average net price (after price promotions)per serving
Nonprice Promotion (Nonprice_promo)Weighted average percentage of SKUs on feature ordisplay without price cut
Bihan et al. 2010;Ailawadi and Neslin1998; Wansink 1996;Inman, McAlister, andHoyer 1990
+
–
+
–
?
–
Health status Perceived Health (Percv_hlth)a. My health is… (1 = “poor,” and 4 = “excellent”)b. I’m much healthier than most people my age (1 = “disagree,” and 3 = “agree”)
Age1= “18–29 years,” and 6 = “65 years and over”
Cole and Gaeth 1990;Millunpalo et al. 1997;Moorman and Matulich 1993
?
?
–
+
Socialinfluence
Family Size (Famsize)Number of members in household
+ ?
Prior foodhabits
Initial Intake (Init_intake)Household’s average monthly intake of category/nutrient in initialization period (first three months)
Ailawadi and Neslin1998
+ –
TABLE 3Measures of Constructs in Empirical Analysis
aInteraction with diagnosis.bGender of the household heads relates to self-control, but at least one of the household heads is female in more than 95% of our sample sothere is little variation on this variable.Notes: All consumer characteristics are average for the household heads.
might be expected after diabetes diagnosis. Therefore, weexpect a negative interaction of diagnosis with nonpricepromotions. Second, we expect a positive interaction ofdiagnosis with age in line with the arguments regardinghealth status in the previous section. (Older people arelower in health status, and we hypothesized the interactionwith health status to be negative.) In addition, the disease isclearly more aggressive if its onset is at a younger age andthe patient is likely to be exposed to its consequences over alonger period. Therefore, younger people should be moremotivated to reduce unhealthy intake after diagnosis.ModelWe estimated equations for total monthly intake of caloriesand the four nutrients across the 13 categories (five equa-tions) as well as monthly amount (number of servings),calorie, and nutrient intake for each individual category (sixequations per category). This enables us to examine howhouseholds change (1) their total intake of calories and nutri-ents from the largest food categories, (2) the amount of eachcategory, and (3) the extent to which they choose healthieroptions (e.g., lower sugar, lower fat) within the category. Thebasic model to quantify the difference-in-difference responseto diagnosis for each dependent variable is as follows:(1) Yhncm = 0 + 1Diabh + 2Posthm + 3 Diabh ¥ Posthm
+ ehncm,where Y is the dependent variable of interest (e.g., monthlygrams of sugar from a category) and the subscripts refer tohousehold h, variable (e.g., servings, calories, sugar) n,food category c, and month m; Diab is a dummy variablethat equals 1 for diabetes households; Post is a dummyvariable that equals 1 if the month is in the postdiagnosisperiod5; and 3 is the difference-in-difference response todiagnosis, that is, the amount by which the average pre- topostdiagnosis period change in Y differs for the diabetesversus control group.6
To examine whether response differs depending on thegender and treatment status of the diagnosed member, weincluded interactions of Female and NotRx (not treated byRx drugs) dummy variables with Diab ¥ Post. Furthermore,because we wanted to quantify the effects of the variablesin Table 3 on household intake and determine whether theseeffects changed after diagnosis, we included main effects ofthose variables and their interactions with Diab ¥ Post. Forcompleteness, we also included lower-order interactionswith Diab and Post. Finally, we included several controlvariables: market and month dummies, weighted averageprice and promotion of all the categories except the focalone to account for budget constraint effects, and averagemonthly expenditure on all IRI food categories in the ini-tialization period to account for unobserved differences in
110 / Journal of Marketing, May 2013
households’ total needs. This results in our final model (inwhich the variables are as defined in Table 3):(2) Yhncm = 0 + 1Posthm + 2Diabh + 3 Diabh ¥ Posthm
We mean-centered all continuous variables before esti-mation. We jointly estimated the five equations for totalintake of calories and nutrients using seemingly unrelatedregression (SUR). The largest variance inflation factor inany equation is 5.23, showing that multicollinearity is not aproblem. We estimated the category-level equations usingTobit because the categories are not purchased every monthby every household, leading to several observations withzero values of the dependent variables.7
ResultsIt is impractical to report estimates for all equations. There-fore, we organize our results according to the three researchquestions, drawing from the relevant model estimates asneeded.RQ1: Effects of Barriers/Enablers on UnhealthyIntake
Effects on total intake. Table 4 provides SUR estimatesof the main effects of our model variables on total intake ofcalories and the four nutrients. The underlying constructsthat these variables measure are also listed in the table.Given the presence of interactions, these main effects repre-sent the impact on prediagnosis period intake of the controlgroup. Adding in the interactions with Diab gives theimpact on prediagnosis period intake of the diabetes group.We did not hypothesize interactions with Diab, and most ofthese are indeed not significant. Even when they are signifi-cant, the sign and significance of the prediagnosis periodimpact is the same for the control and diabetes groups.Therefore, we discuss the main effects next.
The table shows that most of the effects are consistentwith our hypotheses. Education, nutrition interest, net price,family size, and initial intake all have the expected effectson intake of calories, sugar, carbohydrates, fat, and sodium.The effects of age and income are largely insignificant,
5There is a subscript h for the Post variable because the pre- andpostdiagnosis periods vary for different diabetes households andtherefore their matched control households.
6We tested for a difference in response during the first sixmonths of the postdiagnosis period versus subsequently. Becausethe difference was not significant in most cases, we report resultswith one postdiagnosis period effect.
7Using multivariate Tobit is econometrically intractable for sucha large number of equations. However, we estimated multivariatemodels across nutrients within a category as well as for a givennutrient across categories. Although the error correlations weresignificant, there were no substantive differences in the key modelcoefficients compared to the univariate Tobit results we report here.
reflecting the opposing mechanisms we discussed previ-ously. The effect of perceived health is largely negative,suggesting that those in good health are motivated to main-tain their status by reducing unhealthy intake. The only twocounterintuitive effects are those of behavioral control andnonprice promotion: the former are mostly positive, thoughwe expected the opposite, and the latter are mostly insig-nificant. However, the category level effects that we discussnext help explain these results.
To assess the relative importance of the differentvariables, we compute, for each nutrient, the change inintake for a one-standard-deviation increase in each variableand include it in parentheses in Table 4. The table showsthat initial intake has the largest effect, which is not surpris-ing because it captures heterogeneity in habits/preferences.This is followed by price and family size, both of whichhave substantial and approximately equal effects. The othervariables are substantially less important.
Effects on intake from unhealthy and healthy categories.Table 5 summarizes the main effects of the model variableson intake from unhealthy and healthy categories.8 We esti-
Drivers of Healthful Food Intake / 111
mated the Tobit models for each of the 13 categories, butwe used the meta-analytic procedure of adding Zs (Rosen-thal 1991, p. 93) to summarize the effect of each indepen-dent variable on each dependent variable in the two groups.The statistic shows whether the effect is statistically signifi-cant across categories in each group (e.g., Ailawadi et al.2010; Geyskens, Gielens, and Gijsbrecht 2010). Because itis only possible to change intake of a nutrient from cate-gories that contain it, we excluded categories with “n.s.” fora nutrient in Table 2.
Table 5 reveals some important insights. First, all thevariables in our model have the expected effects on calorieand nutrient intake from the unhealthy group. Education,nutrition interest, behavioral control, and net price havenegative effects, while nonprice promotion, family size, andinitial intake have positive effects. In addition, perceivedhealth has negative effects consistent with motivation topreserve good health. Second, we find the hypothesizedpositive effects of nonprice promotion in these category-level models despite an insignificant effect on total intake.This is because households do respond to such promotionsin individual categories, but budget constraints do not allowthem to respond simultaneously across several categories(as in the total intake model). The effects of cross-price andcross-promotion (weighted average in other categories),
8As noted in Table 3, categories whose average rating of generalhealthfulness is not significantly greater than 3 are placed in theunhealthy group. Our results are substantively unchanged if weuse a cutoff of 4.
TABLE 4Main Effects of Barriers/Enablers on Total Intake
(1,312.99) (2,260.38) (590.15) (12,028.25) (16,028.98)*p < .10.**p < .05.***p < .01.aFor example, read as follows: For a one unit (one standard deviation) increase in education, a household’s monthly intake of sugar across thecategories in our analysis decreases by 20.20 (25.91) grams.Notes: The effect on intake of a standard deviation change in the independent variables is reported in parentheses.
112 / Journal of Marketing, May 2013
TABLE 5
Main Effects of Barriers/Enablers on Intake from Unhealthy and Healthy Categories
Effect on
Effect on Sugar
Total Carbohydrate
Effect on Fat
Effect on Sodium
Effect on Calorie
Effect on Servings
Intake from
Intake from
Intake from
Intake from
Intake from
Intake from
UHH
UHH
UHH
UHH
UHH
UHH
Education
a–2.27**
1.40
–3.86***
1.38
–4.16***
–.29
–4.91***
–.73
–3.98***
.56
–2.97***
.14
Nutrition interest
–4.32***
1.25
–5.40***
1.78*
–5.42***
–1.03
–5.31***
1.05
–6.34***
1.21
–6.22***
.86
Behavior control
–2.16**
6.16***
–2.39**
5.81***
–2.87***
3.01***
–1.75*
4.52***
–3.05***
5.09***
–2.11**
5.39***
Income
–3.25***
.68
–1.78*
1.35
–1.43
1.69*
–1.61
1.85*
–2.22**
2.29**
–1.16
2.56**
Net price
–7.29***
–4.96***
–6.45***
–5.20***
–9.15***
–1.13
–15.45***
–8.94***
–10.69***
–5.80***
–8.52***
–5.27***
Nonprice prom
otion
12.71***
10.68***
13.60***
10.98***
11.92***
8.51***
38.99***
20.90***
23.47***
14.97***
12.99***
10.70***
Perceived health
–5.31***
.1–4.86***
1.2
–3.43***
1.77*
–4.60***
2.69***
–4.31***
2.12**
–5.74***
2.13**
Age
.14
2.97***
–1.41
2.26**
–1.03
–.57
.08
1.64
–1.18
1.65*
–.29
1.54
Family size
3.52***
9.97***
4.75***
9.65***
3.24***
9.68***
4.52***
11.43***
4.85***
10.62***
3.50***
10.62***
Initial intake
44.01***
76.84***
52.83***
79.80***
62.93***
74.22***
64.84***
78.45***
61.51***
87.87***
62.79***
85.90***
*p< .10.
**p< .05.
***p< .01.
a Read as follows: The meta-analytic Z for the main effect of education on su
gar intake from su
gar-containing food ca
tegories p
erceived as healthy is insignificant at +1.40. The meta-analytic Z for
the main effect of education on sugar intake from
sugar-containing food categories perceived as unhealthy is significantly negative at –2.27.
Notes: H denotes healthy categories, and UH denotes unhealthy categories. Num
bers are meta-analytic Z-statistics for the effect of each variable across the food categories in a given group.
which are not shown in the table but are control variables inthe category models, are negative, consistent with the exis-tence of a budget constraint.
Third, education and nutrition interest do not affectintake from foods in the healthy group. Thus, householdswith health knowledge pay attention to unhealthy versushealthy categories but not to less versus more healthyoptions in the healthy categories, consistent with Moormanet al. (2004), who document such patterns especially forsubjective knowledge. Fourth, high behavioral controlpeople take in more sugar, carbohydrates, and so on fromhealthy categories, which more than offsets lower intakefrom unhealthy ones. This is consistent with prior lab find-ings that health-conscious consumers may be most suscepti-ble to health halo biases. It also explains our previous find-ing of a positive effect of behavioral control on total intake.RQ2: Changes in Unhealthy Intake Due toDiagnosis
Change in total intake. Figure 2 summarizes thechanges in total intake of nutrients and calories across the13 categories made by diabetes households in response todiagnosis. It depicts with black bars the estimates of theDiab ¥ Post (or difference-in-difference) coefficient from
Drivers of Healthful Food Intake / 113
the SUR model of total intake. Because all the continuousvariables in the model are mean-centered, these estimatesrepresent the change in monthly intake by the average dia-betes household in which the diagnosed patient is a manwhose disease is being treated with Rx medication. Theblack bars show that, on average, such households reducesugar and total carbohydrate intake by 155.3 g and 103.7 g,respectively, but increase fat and sodium intake by 70.9grams and 2,041 mg, respectively, without making any sig-nificant change in calorie intake. It is worthwhile to com-pare this magnitude of the impact of disease diagnosis withthe impact of internal motivators such as nutrition interestand self-control on the one hand and marketing variablessuch as price on the other (Table 4). For sugar intake, forexample, the impact of net price is as strong as the impactof diagnosis, whereas the impact of internal motivators ismuch smaller.
We find no significant difference between Rx treatedand not Rx treated households with respect to change intotal intake. However, there is a difference for householdsin which the patient is female. The gray bars in the figuredepict the additional changes made by female patienthouseholds on top of those by male patient households andconfirm our hypothesis. Female patient households reducetotal carbohydrates and calories by 147.3 g and 1,086 kcalsmore, and they increase fat and sodium by 38.5 g and 2,232mg less than male patient households.
Change in intake from unhealthy and healthy cate-gories. To examine from which categories the total changesare sourced, we use the Diab ¥ Post response coefficients inthe category-level Tobit models. Because Tobit coefficientsdo not directly reflect the effect size, we compute the mag-nitude of response as follows for all statistically significantcoefficients. We set all continuous variables at their meansand dummy variables (Female and NotRx) at 0 and computethe expected value (Wooldridge 2002, chap. 16) of thedependent variable for four groups: pre- and postdiagnosisperiod for the diabetes and control groups. The magnitudeof response is the difference in difference (“post minus pre”for diabetes group minus “post minus pre” for controlgroup) of the expected value. Table 6 reports these magni-tudes. We also report the differential magnitudes for femaleand not Rx treated patient households.
We begin with average response from diabetes house-holds with a male Rx treated patient. They reduce sugar,and therefore carbohydrates, from CSDs and juice, both ofwhich are perceived as unhealthy for diabetes. Notably, thereduction comes not from consuming less but from choos-ing low-sugar alternatives within the categories. This is evi-dent in that there is no significant change in amount of juiceand an increase in amount of CSDs. Second, increased fatintake comes mainly from salty snacks and processedmeats. The corresponding increase in calories from thesecategories offsets the reduction from the two sugary bever-age categories. Essentially, these households focus on cut-ting sugar, and they do so where it is easiest—in categoriesin which low-sugar alternatives are available, not in cate-gories such as cookies and ice cream, whose combination offat and sugar is more difficult for the human palate to resist
FIGURE 2Average Changes in Intake Due to Diagnosis
50
0
–50
–100
–150
–200
Sugar/Carbohydrate (g)
500
0
–500
–1,000
–1,500
–2,000
Calories (kcal)
100
50
0
–50
–100
Fat (g)
3,000
2,000
1,000
0
–1,000
–2,000
–3,000
Sodium (mg)
Average changes for households with male patientAdditional changes for households with female patient
and which offer fewer low-sugar alternatives.9 The increasein salty snacks and processed meats, despite their unhealthyperception, may be because of their relatively glycemicload and consumption convenience. There is a smallincrease in intake from healthy categories such as yogurtand soup, but, for the most part, changes, both desirable(sugar and carbohydrate reduction) and undesirable (fat andsodium increase) are sourced from unhealthy categories.
The second column of Table 6, which shows the differ-ence in response when the diagnosed patient is a woman,reveals two major patterns, both of which are consistentwith our expectations. First, we observe changes across awider range of categories, and second, the changes arehealthier than for households with male patients. Thesehouseholds do not increase processed meats, and therefore
114 / Journal of Marketing, May 2013
fat and sodium intake, as much. In addition, they make sig-nificant reductions in intake of hedonic categories such asice cream and cookies and convenience categories such asfrozen dinners, thus reducing carbohydrates and caloriesmore. A seemingly counterintuitive result is the smallerreduction in sugar from CSDs compared with male patienthouseholds. This is probably because women consumemore diet drinks than men (Storey, Forshee, and Anderson2006), so there is less room to further reduce CSD sugarintake.
The third column of the table, which shows the differ-ence in response when the diagnosed patient is not Rxtreated, reveals a different, though not necessarily better orworse, pattern of changes. These households make smallerchanges in amounts of high-sugar and high-fat categoriessuch as CSDs, processed meats, and salty snacks, which isconsistent with our expectation of an overall weakerresponse. They also make small reductions in other cate-gories such as soup and milk. Overall, the changes aresmaller and spread over more categories.
9We do not observe a significant reduction in fat intake frommilk and yogurt, which offer low-fat options, because consumersare already primarily using the low-fat options. The share of full-fat milk in our data is only 20%.
Intake
Response in Households inWhich Patient Is Male and Rx
TreatedDifferential Response ifPatient Is Female
Differential Response if PatientIs Not Rx Treated
Sugara CSDs: –90.0 gJuice: –31.0 g
CSDs: +51.8 gIce cream: –13.9 g
HMilk: –22.6 g
Total carbohydrates CSDs: –93.0 gJuice: –31.4 gSalty snacks: +52.0 gHSoup: +5.8 g
Cookies: –26.3 gCrackers: +19.7 gCSDs: +54.8 gIce cream: –17.8 g
TABLE 6Change in Intake from Unhealthy and Healthy Categories Due to Diagnosis
HCategory is perceived as healthy for diabetes. All others are perceived as unhealthy.aFor example, read as follows: Households in which the patient is male and Rx treated reduce monthly sugar intake from CSDs by 90.0 g onaverage. The reduction is 51.8 g less (i.e., 90.0 – 51.8 = 38.2 g) if the patient is female, and the reduction is not statistically different from 90.0g if the patient is not Rx treated.Notes: Only categories with significant effects are included in the table.
RQ3: Moderation of the Effects of Barriers/Enablers by DiagnosisTable 7 provides estimates of the interaction between diag-nosis (Diab ¥ Post) and our model variables from the SURmodel of total intake. It shows that interactions with educa-tion, nutrition interest, price, and promotion are not signifi-cant, so these variables do not explain why some house-holds make better changes after diagnosis than others. Theeffects of behavioral control, age, income, family size, andinitial intake are significantly moderated by diagnosis, atleast for some nutrients. Because a similar pattern repeats inthe category level Tobit models, we do not report thoseresults. We simply highlight which categories the signifi-cant effects come from.
The interactions that are significant are largely consis-tent with our hypotheses. First, the effect of age becomesmore positive after diagnosis for sugar and carbohydrateintake: younger diabetes households cut sugar and carbohy-drates more than older ones. The category-level modelsshow that these effects come mainly from unhealthy cook-ies and ice cream but also from yogurt. However, the effectis not significant for fat and total calories, because youngerhouseholds offset the decrease in cookies and ice creamwith an increase in cheese, which is perceived as healthy.Second, the income interaction is negative for sugar, carbo-
Drivers of Healthful Food Intake / 115
hydrates, sodium, and calories: high income enables betterchanges after diagnosis. The ingredients are sourced from amix of categories such as frozen dinners, crackers, cheese,and milk.
Third, the effect of family size is positively moderatedby diagnosis for all except sugar. Change in sugar intakedue to diagnosis is not significantly different for small ver-sus large families, but the presence of other family mem-bers makes it more difficult to reduce intake of other nutri-ents. The category level results show that the effect comesmainly from ice cream, processed meat, and salty snacks.Patients appear to overcome family influence in cutting themost salient unhealthy nutrient, but they are susceptible tofamily influence for hedonic categories.
Fourth, the interaction effect of initial intake is negative,as we expected for sugar and carbohydrates. The category-level results show that this effect comes mainly from CSDs,cookies, and cereal. However, there are two categories,processed meat and crackers, in which the interaction isactually positive; that is, households whose intake washigher to begin with increase it even more after diagnosis.Finally, the interaction effect of behavioral control is posi-tive, though only marginally significant, for fat and sodium.Those with high behavioral control increase intake of thesenutrients more than others, and the increase is sourced fromprocessed meats.
TABLE 7Interaction Effects of Diagnosis and Barriers/Enablers on Total Intake
(–145.89) (–124.01) (13.68) (335.87) (–554.28)*p < .10.**p < .05.***p < .01.aFor example, read as follows: For a one unit (one standard deviation) increase in education, a household’s monthly intake of sugar across thecategories in our analysis decreases by 23.13 (29.66) grams more due to diagnosis.Notes: The effect on intake of a standard deviation change in the independent variables is reported in parentheses.
DiscussionThis study investigates how various factors, identified inprior lab research as barriers to or enablers of healthy foodchoices, affect the healthfulness of households’ normal gro-cery food purchases, how the healthfulness of food intakechanges after a diagnosis of Type 2 diabetes for a memberof the household, and whether the impact of the various fac-tors changes after the diagnosis. To accomplish theseresearch goals, we use a unique combination of households’grocery purchases over time, rich survey data on health sta-tus, and foods’ nutrition content. By identifying householdswith a new diagnosis and matching them to a control group,we conduct a before-and-after-with-control-group analysisof actual in-market response to diagnosis. The findings haveimportant implications for consumers and consumer behav-ior researchers, marketers, and public health professionals.Research ImplicationsOur integrated model confirms that consumer characteris-tics—education, nutrition interest, and self-control—andmarketing variables such as price and promotion affecthealthy food choices in the marketplace for 13 of the largestpackaged food categories. In addition, we assess the relativeimpact of these factors; habit is by far the most important,followed by price, whereas the impact of consumer charac-teristics tends to be substantially smaller. These resultsshow that the impact of phenomena such as nutrition label-ing or ego depletion should be established not just in the labor through surveys but in the marketplace, where other fac-tors have substantial effects.
Our finding regarding the main effects of self-control onfood choice corroborates the health halo bias: people withhigh self-control take in fewer calories and unhealthy nutri-ents from unhealthy categories such as CSDs and saltysnacks but offset this benefit with higher intake from cate-gories perceived to be healthy, such as cereal, milk, andyogurt. It is notable that the health halo bias is pervasiveand strong enough to affect total nutrient intake across awide range of categories consumed at home, and that it isexhibited with respect to self-control but not with respect toeducation or nutrition interest. It is also notable that typicalgrocery shopping is consistent with simple heuristics ofhealthy versus unhealthy categorization and more selectionacross the two types than within (Moorman et al. 2004), butdiagnosis makes people more deliberative and interruptssuch heuristics. Diabetes households make changes in bothhealthy and unhealthy categories, and they select healthieroptions within categories such as CSD.
The finding that households respond to a diabetes diag-nosis by cutting sugar but increasing fat suggests an avenuefor research on how consumers balance their food intake.Khare and Inman (2006, 2009) demonstrate that consumersbracket calories and individual nutrients across mealswithin a day and across days. Our results imply that suchbracketing may also occur across nutrients.
Our analysis of whether a diabetes diagnosis moderatesthe effects of these consumer and marketing variables has
116 / Journal of Marketing, May 2013
several implications for researchers. For the most part, theimpact of health knowledge and self-control does notchange after the diagnosis: those with high self-control orhealth knowledge do not respond better. Thus, previousfindings about the positive role of these variables in riskprevention do not extend to disease management. Thisunderscores the importance of studying response to actualhealth problems, not just risk perceptions, and of examiningsustained health-related behavior in the marketplace overtime, not just intentions and choices in the lab. We knowthat motivation increases healthy behaviors, but our find-ings suggest that the impact of intrinsic motivation differsfrom the motivation that results from a disease diagnosis.Perhaps disease diagnosis is an equalizer: even those withlow self-control and health knowledge realize they mustmake changes when they develop diabetes.
Our finding that diagnosis moderates the impact of fam-ily size on unhealthy intake underscores the challenge thatpatients face in not only controlling their own temptationsbut in having to do so in the face of family needs and pref-erences. Patients seem able to overcome family influencesin curtailing their sugar intake, which they likely view asmost important, but not in curtailing other types of intake.More research is needed to understand the influence of fam-ily and thus its management.
Finally, our finding that responses are neither better norworse among those who take Rx medication suggests theneed for more research. Consumers may lean on drugs as asubstitute for healthy behavior when they are dealing withproblems that are not immediately debilitating, such assmoking or being overweight, but when the health problemis a serious disease, they do change their behavior. Ourfindings show that patients who are not Rx treated makebroader, smaller changes, whereas those who take Rx makemore focused changes. Perhaps those whose disease is moresevere (and who therefore need Rx drugs) are more focusedon immediate and concrete goals (e.g., keep blood glucoselevels under control) and therefore make narrow changes,whereas those whose disease is less severe focus on longer-term, more abstract goals (e.g., eat healthy, lose weight) andmake broader changes (e.g., Liberman, Sagristano, andTrope 2002).Marketing ImplicationsOur research highlights the conundrum that packaged foodcompanies are facing: they must continue to increase theirsales and profits, but they also need to develop responsibleproducts and marketing strategies to respond to the growingobesity and public health crises. If marketers fail to offerhealthier food options that are priced competitively, theyare likely to suffer sales drops and come under increasedlegislative scrutiny, as evidenced by the alcohol andtobacco industries. The CSD industry already is in the spot-light (Bittman 2010). It is in the interests of stakeholders ofCoca-Cola and PepsiCo to encourage healthier productportfolios rather than resist this drive, as they are wont to do(Bauerlein 2011).
The finding that diabetes households make large switchesfrom high- to low-sugar CSDs and juice drinks reveals anopportunity for food companies to develop direct substitutesfor unhealthy foods without compromising taste and conve-nience. One reason we do not observe reduction in sugarand fat intake from products such as ice cream may bebecause these hedonic categories are more difficult to resist,but another is because these categories do not offer health-ier options (as the high mean and relatively low standarddeviation of sugar and fat content per serving indicate).
In addition, marketers should break from the currentnorm of pricing healthier alternatives at a premium. A com-parison of SKU prices in our data reveals that the leastunhealthy processed meat and cookies are priced at a pre-mium of as much as 50% compared with unhealthy options,presumably because marketers believe the health-consciousniche segment is less price sensitive. However, the impactof price on purchases is as great as that of disease diagnosis.If marketers make their healthier options more affordable,more consumers whose poor health is making them cutunhealthy intake can buy these options, instead of reducingtheir category consumption. Indeed, Wal-Mart (2011) hasrecently announced such an initiative. That this would workis evidenced by our finding that diabetes households make asubstantial switch from regular to low-sugar CSDs, a cate-gory in which there is no price premium for the latter. Thefinding that affordability, as reflected in income, becomesmore important after diagnosis, also underscores the needfor smart pricing.Policy ImplicationsGreater fat intake among households after a diabetes diag-nosis reveals the need for better communication, irrespec-tive of the reason for the increase. One solution is to includecounts of fat and calories, not just carbohydrates, in dia-betes meal plans. However, it is important to examine notonly the guidelines provided to patients but also how theyare processed. Goal pursuit research suggests that even ifinformation on the negative effect of fat is provided,patients may focus selectively on lowering sugar becausepursuing multiple goals is difficult and that goal is mademore salient by the need to regularly check blood glucoselevels (Cheema and Bagchi 2011). Our work shows thatconsumers recognize that processed meats and salty snacksare unhealthy in general, as well as for diabetics. However,they may feel licensed to indulge in salty snacks becausethey limit their intake of other unhealthy foods (Dhar andSimonson 1999), and they may see processed meats as thelesser of the evils in managing their disease. A solution is toclearly identify meal plans that are low in both sugar andfat, because making multiple goals seem easier to manage iseffective (Dalton and Spiller 2012).
It is also important to account for the finding that con-sumers, especially those with high self-control, overconsumesupposedly healthy foods. Nutrition programs for obesityand diabetes risk prevention should aim at reducing con-sumption of obviously unhealthy foods but also monitoring
Drivers of Healthful Food Intake / 117
intake of less unhealthy ones. Our finding that family influ-ences make it more difficult to reduce carbohydrate, fat, andsodium intakes highlights the need to educate not justpatients but also household decision makers. Family deci-sions often create conflict due to individual preferences andlimited resources, and changing diet requires the cooperationof family members. Therefore, communications, suggesteddiets, and interventions must be designed from the perspec-tive of the entire family. This is particularly important inhouseholds in which the patient is male, because our resultsshow that such households make less desirable changesthan female patient households.
Our finding that diagnosis moderates the effect of ageoffers both good and bad news. Younger people, who willbe exposed to the consequences of the disease for longer,are more careful about sugar intake. Yet they are not morecareful about fat and sodium intake, which increases theirrisk of hypertension and heart disease, problems to whichdiabetics are already susceptible. The costs, in both reducedquality of life and economic terms, are substantial.
Finally, our findings regarding the impact of price andincome reinforce a major challenge. Sensitivity to pricedoes not change after diagnosis, but it is the strongest factordriving regular food choices. In addition, income gainsimportance after diagnosis. High-income households makebetter changes after diagnosis, either because more healthyoptions are available in high-income markets or becausehigh-income households can afford to switch to healthier,fresh, more expensive foods. Financial considerations thusare key in normal shopping behavior and in facilitatingdesirable changes. They must be addressed by making lessexpensive healthy options available for economically disad-vantaged groups. Conversely, taxing vice products such astobacco has been effective in curtailing their intake (for asummary, see the International Agency for Research onCancer handbook). Some policy makers are consideringsimilar tactics to fight obesity, as evidenced by Denmark’sintroduction of a food fat tax (BBC 2011).
We conclude with some limitations of this work that wehope will spur additional field research on consumerresponse to major changes in health status. First, we cannotisolate the response of the patient from that of the house-hold in our data, but it is notable that we observe significantchanges even at the household level. Second, we investigatecategories that constitute a substantial portion of total foodpurchases, but we lacked data about the intake of freshfoods or food consumption away from home. Third, thechanges we document take place in the medium term, span-ning a period of several months before and after the diagno-sis. Further research might examine whether these changespersist over the long run. Fourth, we have not studied feed-back effects. Consumers’ ongoing effort is a function of thediscrepancy between their expected and achieved goals, soresearch should examine how the progress, or the lackthereof, toward blood glucose control and weight loss goalsaffects food purchase behaviors.
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