food marketing research articles

Measuring the Power of Food Marketing to Children: a Review of Recent Literature

• Diabetes and Obesity (CB Chan, Section Editor)
• Published: 20 November 2019
• Volume 8 , pages 323–332, ( 2019 )

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• Charlene Elliott 1 &
• Emily Truman 2  

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22 Citations

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Purpose of Review

This scoping review examines literature from the past 5 years (June 2014 to June 2019) across three databases (PubMed, MEDLINE, and Scopus) to detail how the persuasive power of child-targeted food marketing content is addressed and evaluated in current research, to document trends and gaps in research, and to identify opportunities for future focus.

Recent Findings

Eighty relevant studies were identified, with varied approaches related to examining food marketing techniques to children (i.e., experimental, survey, meta-analyses, mixed methods, content analyses, focus groups). Few studies specifically defined power, and studies differed in terms of techniques examined. Spokes-characters were the predominant marketing technique measured; television was the platform most analyzed; and dominant messages focused on health/nutrition, taste appeals, and appeals to fun/pleasure.

Mapping the current landscape when it comes to the power of food marketing to children reveals concrete details about particular platforms, methods, and strategies, as well as opportunities for future research—particularly with respect to definitions and techniques monitored, digital platforms, qualitative research, and tracking changes in targeted marketing techniques over time.

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Parents’ Beliefs About, and Attitudes Towards, Marketing to Children

When it comes to food marketing, a spokes-character typically refers to an animated object or character or an animate being used to promote a product. Examples include the Lucky the Leprechaun, Tony the Tiger, the Keebler Elves, Count Chocula, the Quaker Oats Quaker, and the Kool Aid Man. These are typically trademarked characters used consistently in association with a product over time. Spokes-characters, who have been specifically created to promote a product, are generally thought to be different from licensed characters (which are licensed from entertainment companies (e.g., movie or television characters, such as Disney Lion King or Marvel superheroes). However, as we note below, in the studies reviewed, the spokes-character captures a range of definitions, including licensed characters, media characters, and promotional characters.

Here we are referring specifically to the messaging used by food marketers to make claims about nutrition or health benefits of their products.

An advergame is an advertisement in the form of a game. Specifically, the product is integrated into elements of a videogame.

Note that several studies involve multiple platforms, such as Facebook and YouTube or Facebook and Instagram.

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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Acknowledgments

The authors would like to acknowledge the Helderleigh Foundation and the Canada Research Chairs program for support of this project.

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Canada Research Chair, Food Marketing, Policy and Children’s Health, Department of Communication, Media and Film, University of Calgary, Calgary, Alberta, T2N 1N4, Canada

Charlene Elliott

Department of Communication, Media and Film, University of Calgary, Calgary, Alberta, T2N 1N4, Canada

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Charlene Elliott holds a Canada Research Chair on Food Marketing, Policy and Children’s Health and has provided recommendations and advice to Health Canada on policy related to food marketing to children.

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Elliott, C., Truman, E. Measuring the Power of Food Marketing to Children: a Review of Recent Literature. Curr Nutr Rep 8 , 323–332 (2019). https://doi.org/10.1007/s13668-019-00292-2

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ISSN : 0309-0566

Article publication date: 20 January 2023

Issue publication date: 23 November 2023

Consumer food behavior has received considerable attention from marketers, researchers and regulators. With the rising obesity epidemic worldwide, the existing literature and previous reviews provide a limited understanding of consumers’ unhealthy food choices. To address this gap, this study aims to investigate consumer psychology for food choices in terms of mental processes and behavior.

Design/methodology/approach

This systematic literature review analyzed 84 research papers accessed from the Web of Science database and selected high-quality marketing journals. A detailed analysis identified themes arranged in an organizing framework. Gaps, limitations, convergence and ambivalent findings were noted to derive future research directions.

Major themes in the literature include food marketers’ actions (food stimuli and context), environmental influence (micro and macro) and consumer psychology and personal factors, leading to food choice related decisions. The antecedents and consequences of food choice healthiness are summarized. Several studies converged on the benefits of health motivations and goals, food literacy and customizing meals bottom-up on food choice healthiness.

Research limitations/implications

This review helps researchers gain state-of-the-art understanding on consumer psychology for food choices. It presents ambivalent and converging findings, gaps and limitations of extant research to inform researchers about issues that need to be addressed in the literature. This review presents future research questions to guide research on critical issues. This literature review contributes to marketing domain literature on consumer’s food well-being and overall well-being.

Practical implications

This review offers actionable insights for food marketers, policymakers and nongovernmental organizations to drive consumer demand for healthier foods, focusing on food labeling, food environment, message framing and raising consumer awareness.

Originality/value

This review offers current understanding of consumer psychology for food choices focusing on healthiness, an aspect lacking in previous literature reviews.

• Healthy eating
• Consumer psychology
• Food marketing
• Unhealthy food choices
• Eating behavior
• Food consumption

Khan, A.W. and Pandey, J. (2023), "Consumer psychology for food choices: a systematic review and research directions", European Journal of Marketing , Vol. 57 No. 9, pp. 2353-2381. https://doi.org/10.1108/EJM-07-2021-0566

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Food marketing influences children’s attitudes, preferences and consumption: a systematic critical review.

1. Introduction

2. materials and methods, 2.1. study selection, 2.2. data extraction, 3.1. marketing techniques evaluated, 3.2. description of studies, 3.3. coverage and impact of marketing techniques, 3.4. gaps in marketing techniques explored, 3.5. design and methodological gaps, 4. discussion, 4.1. strengths and weaknesses of selected studies, 4.2. priority areas identified for future research, 5. conclusions, supplementary materials, author contributions, acknowledgments, conflicts of interest.

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Smith, R.; Kelly, B.; Yeatman, H.; Boyland, E. Food Marketing Influences Children’s Attitudes, Preferences and Consumption: A Systematic Critical Review. Nutrients 2019 , 11 , 875. https://doi.org/10.3390/nu11040875

Smith R, Kelly B, Yeatman H, Boyland E. Food Marketing Influences Children’s Attitudes, Preferences and Consumption: A Systematic Critical Review. Nutrients . 2019; 11(4):875. https://doi.org/10.3390/nu11040875

Smith, Rachel, Bridget Kelly, Heather Yeatman, and Emma Boyland. 2019. "Food Marketing Influences Children’s Attitudes, Preferences and Consumption: A Systematic Critical Review" Nutrients 11, no. 4: 875. https://doi.org/10.3390/nu11040875

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• v.10(6); 2020 Dec

The healthy food marketing strategies study: design, baseline characteristics, and supermarket compliance

Karen glanz.

1 Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Annie Chung

Knashawn h morales, pui l kwong, douglas wiebe, donna paulhamus giordano.

2 Center for Research in Education and Social Policy, University of Delaware, Newark, DE, USA

Colleen M Brensinger

Allison karpyn.

Identifying effective strategies to promote healthy eating and reduce obesity is a priority in the USA, especially among low-income and minority groups, who often have less access to healthy food and higher rates of obesity. Efforts to improve food access have led to more supermarkets in low-income, ethnically diverse neighborhoods. However, this alone may not be enough to reduce food insecurity and improve residents’ diet quality and health. This paper summarizes the design, methods, baseline findings, and supermarket in-store marketing strategy compliance for a randomized trial of the impact of healthy food marketing on the purchase of healthier “target” food items. Thirty-three supermarkets in low-income, high-minority neighborhoods in the metropolitan Philadelphia area were matched on store size and percentage of sales from government food assistance programs and randomly assigned to the intervention or control group. Healthy marketing strategies, including increased availability of healthier “target” products, prime shelf-placement and call-out promotion signs, and reduced availability of regular “comparison” products, were implemented in 16 intervention stores for an 18 month period for over 100 individual food items. Six product categories were studied: bread, checkout cooler beverages, cheese, frozen dinners, milk, and salty snacks. The primary outcome measure was weekly sales per store in each product category for 1 year preintervention and 18 months during the intervention. Compliance with the marketing strategies was assessed twice per month for the first 6 months and once a month thereafter. Store and neighborhood characteristics were not significantly different between control and intervention stores. Intercept surveys with customers to assess shopping habits and grocery marketing environment assessments to examine the food promotion environment were completed in the same six food categories. In intercept surveys, 51.0% of shoppers self-identified as overweight and 60.6% wanted to change their weight. Shoppers who typically purchased one type of food over another commonly did so out of habit or because the item was on sale. Findings revealed that preintervention sales of healthier “target” or regular “comparison” items did not differ between intervention and control stores for 1 year prior to intervention implementation. Rates of compliance with the healthy marketing strategies were high, averaging 76.5% over the first 12 months in all 16 stores. If healthy in-store marketing interventions are effective in this scaled-up, longer-term study, they should be translated into wider use in community supermarkets.

Implications

Practice: This is a real-world pragmatic trial of a relatively low-cost, broadly feasible intervention strategy that—if effective—could be widely translated and implemented in large supermarkets.

Policy: Because bricks and mortar supermarkets remain the primary location where most Americans purchase food for themselves and their families, this study has broad potential for wide implementation and policy support if the results support the long-term effectiveness of placement and promotion strategies.

Research: Given the rapid deployment of healthy food access approaches across the USA without clear evidence on effectiveness, it is critical to conduct rigorous research using objective data to assess whether supermarkets in low-income neighborhoods can achieve positive health effects that may reduce health disparities in chronic diseases.

Introduction

As the prevalence of obesity has increased over the past two decades [ 1 ], public health experts have begun to focus on the environments that shape overeating, unhealthy food choices [ 2 ], and food insecurity. Food insecurity, defined as a lack of access to adequate or healthful food because of limited money or other resources, impacts 11.8% of American households [ 3 , 4 ]. Food-insecure households are at increased risk for a variety of poor health outcomes, including obesity and related comorbidities [ 5–7 ].

Retail grocery stores, the primary locations for food purchases, are pivotally positioned between consumers and the products they eat and are an opportune place to help influence food choices to favorably affect energy balance [ 8 ]. Efforts to increase access to supermarkets in disadvantaged urban and rural communities hold promise for promoting healthier diets [ 9 ]. But it is not clear what effect improved access to both healthy and unhealthy products will have on diet, food insecurity, or obesity. Several studies have found that simply having access to a neighborhood supermarket did not impact residents’ diet quality or obesity rates [ 10–13 ], while others found positive effects [ 14 ]. These data suggest the possibility that access, while necessary, may not be sufficient to drive healthier choices.

The development and/or reestablishment of supermarkets in low-income, ethnically diverse neighborhoods provides a unique opportunity to design and evaluate various strategies to promote the purchase of healthier products [ 8 , 15 ]. Increased availability of healthier foods, if translated to purchases that alter the products in people’s homes, has the potential to positively affect diet quality among those at greatest risk for obesity—low-income, ethnic minorities in food-insecure households. Yet, there is limited public health research examining how in-store marketing efforts affect the purchasing of healthier foods.

The largest number of community-based intervention studies in retail food stores has been in small stores (i.e., corner stores and bodegas)—and most have been pre–post studies and relied on manager and consumer self-reports of environmental and behavior change [ 16 ]. Previous supermarket interventions focused on healthier items have used point-of-purchase (POP) approaches (nutrition education posters, shelf-tags, and pop-out flyers) to increase awareness about the health attributes of targeted products [ 17 ]. POP interventions have had mixed results on the sale of healthier products; some studies report increased sales [ 18–22 ], while others found no change [ 18 , 23–30 ]. While price discounts (e.g., coupons) can be effective to increase sales [ 31–33 ], they require substantial ongoing investment. Overall, the existing literature on promoting healthier purchases in supermarkets have been conducted in middle-class areas, leveraged the health attributes of products, or used price discounts that are costly to sustain.

New research in low-income, high-minority neighborhoods that use representative data for shopper populations from store sales records, and supplements sales data with observations and surveys, can provide important new insights on how healthy food marketing strategies work. Such research can also shed light on how interventions in supermarkets affect diet quality in food-insecure groups. We previously conducted a large randomized pilot study in eight supermarkets, where we tested in-store marketing of healthier food options in five food and beverage categories, using simple placement and product availability strategies based on traditional marketing approaches. Those strategies significantly increased the sales of healthier, lower-calorie items in the milk and frozen dinner categories and of water in both the soda aisles and checkout beverage coolers, in intervention stores compared to control stores, during a 6 month intervention period [ 34 ]. The present study builds on the pilot study, with a larger sample and a longer-term intervention, thus investigating the scalability and sustainability of healthy in-store marketing strategies.

In this paper, we describe the design, methods, and baseline findings, as well as first year supermarket compliance with in-store marketing interventions for the scaled-up study. The primary aim of the study was to evaluate, in a randomized controlled trial design, the effects of in-store healthy food marketing strategies on sales of specific healthier items in six product categories (milk, frozen dinners, beverage checkout coolers, bread, salty snacks, and cheese). The main study hypothesis that sales of targeted healthier products would be significantly higher in the intervention stores after the intervention, and that the changes would be sustained for the duration of the intervention, drove the study design and methods described here. With respect to the baseline data reported in this paper, we hypothesized that there would be equivalence between intervention and control group stores with regard to store and shopper characteristics and that in-store marketing intervention compliance during the first 12 months would be as good or better than what was achieved in the pilot study.

Overall research strategy and conceptual framework

A randomized controlled trial design was used to test the impact of healthy food marketing strategies to promote the purchase of healthier “target” food items in six product categories: bread, checkout cooler beverages, cheese, frozen dinners, milk, and salty snacks. Thirty-three supermarkets in low-income, high-minority neighborhoods in the metropolitan Philadelphia area were randomly assigned to the intervention or control group. Healthy food marketing strategies, including prime shelf-placement and call-out promotion signs, increased “target” (healthier) product shelf space, and reduced regular “comparison” product shelf space, were implemented in 16 intervention stores for an 18 month period. The primary outcome measure was weekly sales per store in each product category for 1 year preintervention and 18 months during the intervention.

The foundational theoretical framework for this study was an ecological model of health behavior, which involves examining multiple levels of influences on food choice and its determinants [ 2 , 35 , 36 ]. Ecological models of health behavior emphasize the environmental and policy contexts of behavior while incorporating social and psychological influences. Ecological models lead to the explicit consideration of multiple levels of influence, thereby guiding the development of more comprehensive interventions [ 37 ]. These models focus on the relationships between people and their environments [ 2 , 35 , 36 ]. Key variables measured and analyzed in this study were at the organizational and individual levels. The study does not explicitly address the interpersonal and community levels, which are outside the scope of this study, and it is not a test of the full model. This study also draws on concepts from social marketing and consumer behavior, which blend psychology and economics to help understand behavior [ 38 , 39 ].

Recruitment and randomization of stores

Eligible stores were at least 35,000 square feet in size and located in urban or suburban communities with at least 50% of households below the state median income. Stores that received the pilot study intervention were excluded. In order for a store to participate, stores had to be willing to cooperate with the study randomization, assessment, and intervention procedures (if assigned to the intervention arm). Before randomization, stores were required to complete a corporate agreement to participate in the study and provide weekly sales data before and during the study period. Randomization was completed using matched pairs of stores to ensure a balance of relevant store-level factors (chain, region, and percentage of sales of Special Supplemental Nutrition Program for Women, Infants and Children (WIC) and Supplemental Nutrition Assistance Program (SNAP) food assistance programs). Randomization assignments were completed by the study biostatistician (K.H.M.) after enrollment of at least four stores. The target sample size of 32 stores was determined based on the minimum detectable effects (a small effect size of 1 total unit difference between groups) for three products included in the pilot study, assuming 80% power for testing the six product category outcomes between groups.

Interventions

Interventions in this study focused on two key strategic elements of the marketing mix: placement and promotion [ 38 ]. Strategies were initially identified through an integrative literature review of retail marketing strategies [ 40 ] and were refined in consultation with supermarket managers for the pilot study upon which this research builds [ 34 ]. Feasibility and sustainability in the retail setting were important considerations, thus pricing strategies, such as coupons and discounts, were not included. Products were selected as healthier items based on caloric content (i.e., lower-calorie products) in the milk, frozen dinners, beverage checkout coolers, salty snacks, and cheese sections of the stores and for whole-grain content in the bread section. As in the pilot study [ 34 ], product categories were selected for their high sales volume and potential for significant positive changes, as well as for price neutrality of the healthier target products in each category, that is, that the healthier product did not cost more than the comparison product ( Table 1 ). Prior to implementing the interventions, research staff assessed target and comparison product placement and availability on store shelves in each intervention and control store. They then created store-specific planograms for intervention stores to improve the shelf placement and availability of target items while reducing the availability of comparison items by approximately 30%. Store staff implemented and maintained the planogram changes with assistance from research staff. Placement interventions in the dairy section focused on promoting lower-calorie milk (skim, 1%, and 2%) while diminishing the presence of whole milk. The visual order of the milk displays was changed, and the number of facings, or the fronts of packages the consumer can see on the shelf, of whole milk were decreased by 30% while increasing the facings of the lower-calorie milk. In the other product categories (frozen dinners, beverage checkout coolers, bread, salty snacks, and cheese), target products were moved to eye level and the number of facings was increased. Promotion strategies included placing call-out signs produced by research staff with templates provided by each supermarket chain alongside the targeted products. The signs were on bright colored paper, laminated, with the product indicated in large text. No additional messages, health information, or claims were provided. Signage was rotated monthly (e.g., new colors/different target products) to increase the chances that customers would notice them [ 8 , 39 ]. Milk taste tests [ 41 ] were conducted at intervention stores each month. An example of the intervention in beverage coolers is shown in Fig. 1 .

Intervention products

Sample intervention: checkout cooler.

For primary outcome measures, stores provided weekly sales data for each of the selected products. The data included units sold of all selected products (e.g., higher-calorie versions and lower-calorie alternatives) in ounces or packages. All sizes of the selected products were included. These data were collected for 1 year preintervention and the 18 months during the intervention and were transmitted to the research team as Excel files. An indicator of healthy sales movement was created for each product category based on the proportion of sales for the healthier option compared to total sales in the product category, summarized as a percentage. This indicator could not be calculated for the targeted frozen dinners because there was no comparison or “less healthy” frozen meals matched to those products.

Secondary measures included compliance monitoring for the interventions and shopper intercept surveys . Compliance checks were conducted twice a month for 6 months and monthly thereafter (in intervention stores only). Compliance with the planned placement and promotion strategies was rated on a scale of 0%–100% at each compliance check using a protocol and formulas developed in the pilot study and with established interrater reliability. For example, if the planogram specified that a healthier target product was to be placed at eye level with 50% more facings than the regular (comparison) product, the compliance check examined how well the placement adhered to the plan. Intercept surveys, adapted from Davis et al. [ 42 ], were conducted with consumers who did most of their shopping at the participating store, at baseline and 12 and 18 months in all stores, with a convenience sample of 50 surveys per store per occasion. These brief 25-item surveys assessed customers’ background characteristics, shopping patterns, such as the number of shopping trips per week, and whether they used a shopping list and/or coupons. The surveys also assessed buying habits for each targeted product category, including the reasons for selecting a specific type or brand and whether they would try a different type or brand in the future. The 12 and 18 month intercept surveys assessed whether shoppers noticed the placement and promotion strategies for the healthier target products that were the focal points of the intervention in the stores.

Additional measures included store and neighborhood characteristics , including neighborhood race and income, proximity to public transportation and other food retail outlets, and indicators of crime that might affect shoppers’ experiences (robbery and assault). We also tracked the overall food promotion environment in the supermarkets, using the Grocery Marketing Environment Assessment tool, based on the Nutrition Environment Measures Survey in stores, a valid and reliable, widely used assessment of healthful food choices in retail settings [ 15 , 43 , 44 ] [data not reported here].

Statistical analysis

Statistical analyses reported here examine the equivalence of stores and shoppers in the outlets randomized to intervention and control arms of the study. We computed descriptive statistics on store and neighborhood characteristics, baseline data from intercept surveys, intervention compliance during the first year of the intervention, and weekly sales data for the selected products during the first year of the intervention.

We compared the mean sales (total movement and average weekly movement) during the preintervention period across intervention and control stores. In addition, we compared, within intervention group, the mean sales of target and comparison products. The distribution of each of the outcomes was right skewed, so a flexible family of models was chosen. Specifically, we applied marginal longitudinal models (or generalized estimating equations) with a gamma distribution and log link. Each model included sales data for all target and comparison products within a category with fixed effects for product type (e.g., whole wheat vs. white bread), intervention group (intervention vs. control), time in weeks, and the interaction between the three main effects. The interaction allowed us to test the difference between the sales for intervention stores versus control stores and the difference between the sales in target versus comparison products. In addition, a structured exchangeable covariance structure was included to account for the correlation of paired stores. Model estimate means and 95% confidence intervals were estimated from appropriate contrasts. For frozen dinners, separate models were fit for each type since not all stores carried all product types. In addition, a composite of only the products sold in all stores (turkey dinners and lasagna) was derived and analyzed. We used a p -value <.05 for assessing statistical significance. Models were fit using SAS version 9.4 (SAS Institute, Cary, NC).

Recruitment results

Recruitment was completed in two waves due to management changes in one previously committed supermarket chain, which led to their decision not to participate in the study. The second chain agreed that its stores would participate nearly 1 year after the first wave of recruitment began. Thirty-three supermarkets were randomized in the trial, one more than planned.

Store and neighborhood characteristics

Of the 33 supermarkets randomized in the trial, 16 were intervention stores and 17 were control stores. Stores were located in Southeastern Pennsylvania, Delaware, and southern New Jersey ( Fig. 2 ). The average store size was 60,501 square feet (range 36,000–87,000) and stores had been open for an average of 24 years. Sixteen percent of the stores’ sales were WIC/SNAP, with a range from 2.5% to 56.5%. Using the location of the stores based on their census tracts, we determined the store’s neighborhood characteristics ( Table 2 ). Across all stores, neighborhoods were 65.6% white, 24.1% black, and 5.8% Hispanic. Median household income averaged $52,325 per year and, on average, 11.7% of residents were below the poverty level. All but 3 of the 33 stores had access to a public transit stop within 0.25 miles of the store. There was an average of 15.2 supermarkets/small groceries/convenience stores within 2 km of a study store. In store neighborhoods, robbery averaged 271 per 100,000 population (above the national rate of 98) and aggravated assault averaged 351 per 100,000 (above the national rate of 249) [ 45 ]. Baseline percentage of healthy sales movement ranged from 15% to 60%. None of the store or neighborhood characteristics were significantly different between control and intervention stores.

SD standard deviation.

a Median household income for states in the study: PA $56,951; NJ: $76,475; DE: $63,036.

Enrolled locations ( N = 33).

Shopper intercept surveys

At baseline, 50 shopper intercept surveys were completed at each store for a total of 1,650 surveys. Shopper demographics and highlights of responses are summarized in Table 3 . Most shopper characteristics were similar between those at control and intervention stores. Respondents were an average (standard deviation) age of 55.2 (16.7) years with almost half (47%) of all surveyed shoppers between 50 and 69 years old. The majority of shoppers were female at 71.6%. Fifty-nine percent of shoppers identified themselves as non-Hispanic white, 31% non-Hispanic black, and 5% other or mixed race. Four percent identified as Hispanic ethnicity. There were more blacks and fewer whites in the Control stores ( p < .01). Thirty-five percent of shoppers surveyed had a high school education or less.

Baseline characteristics of shoppers from intercept interviews ( N = 1,650; some missing data)

With respect to perceived weight status, 51.1% considered themselves to be overweight and 60.6% wanted to change their weight, mainly to lose weight. On average, 46% of shoppers reported shopping at their grocery store more than once a week, a majority used a shopping list at least some of the time (71%), and 18% received SNAP benefits. Shoppers were asked to report whether or not they usually buy a particular brand or type of our six product categories, and the reasons why. The most important reasons for usually buying the same product type, across all product categories, were that it was a habit, that someone in the household likes it, or that it was on sale. Eighty-five percent of respondents answered yes for milk and 61% for bread. About 44% answered yes for salty snacks and cheese. The type and brand mattered the least for beverages and frozen dinners at 40% and 23%, respectively. As for placement and promotion of products, 61% reported that they notice the products located at endcaps but only 43% said that they notice special promotions and sales while shopping. About half of the shoppers noticed the drinks in the checkout coolers.

Average weekly sales in the 52 weeks preintervention

Table 4 shows the average weekly sales volume in intervention and control stores, for each of the target (healthier) products and comparison products (see Table 1 ), in the preintervention phase. Analyses revealed that, with the exception of some of the frozen dinners, there were no significant differences between intervention and control in baseline volume sales of the products included in the study. Statistical modeling for the main results will account for those few products where baseline differences need to be considered in main outcome analyses.

Average weekly sales 52 weeks preintervention

a Units of bread were in 16 or 20 ounce packages.

b Estimates of means and confidence intervals (CIs) for frozen dinners based on analyses stratified by product; all product types not at all stores.

c Sum of units sold for turkey and lasagna. Pasta, mac and cheese, and chicken nuggets were not carried in all stores.

Intervention compliance in the first 12 months

Overall compliance across product categories and stores averaged 76% (range 62%–88%). Scores varied over time between stores and product categories but did not decrease significantly through the year. Cheese and milk had the greatest mean compliance at 82%–85%. Bread had the lowest mean compliance at 65%. There were no individual stores that had continuously poor compliance.

Current efforts to address obesity and food insecurity recognize the complexity of these widespread problems, and the contributions of many influences on diet quality need further study [ 3 ]. Our study begins to bridge this gap by examining how effectively in-store interventions increase sales of more healthful foods. Key strengths of our study include the randomized design, incorporating marketing tactics alongside sustained changes in product positioning, encouraging changes within already high-selling product categories, interventions developed in conjunction with retailers serving residents in low- to moderate-income communities, and the use of objective sales data as the main outcome. An important challenge in the study, identified by responses to the intercept surveys, is that many shoppers habitually buy a particular type or brand of the target products in the study, especially for milk and bread. Findings also suggest that different product categories may have varying levels of product loyalty and price sensitivity and, therefore, be more or less influenced by certain in-store marketing strategies.

The present study is the only one of its size that examines objective sales data (an objective measure; not self-report) to test the impact of targeted in-store promotion of healthier alternative products, wherein product placement and promotion are implemented. Other studies are limited in their emphasis on nutrition education only, specific geographies or products, and small store formats [ 46–50 ]. A review of studies that sought to improve healthy food purchases in supermarkets or grocery stores found that few interventions utilized randomized designs (6 of 33 studies) and nearly all conducted interventions for less than 12 months [ 51 ], with the exception of three information-only interventions [ 52–54 ].

Prior research has also emphasized the critical nature of cooperative partnerships with retailers in order to implement and maintain in-store marketing interventions and has identified such participatory and translational research partnerships as critical to maximizing public health impact [ 55 , 56 ]. The present study approach and implementation findings illustrate successful partnerships in that compliance across categories and stores over a 12 month intervention period were high (62%−88%), averaged 76.5%, and did not decline over time. While there is no benchmark in the literature to define supermarket compliance in a study like ours, other studies [ 57 , 58 ] and reviews [ 55 ] have reported challenges to achieving health-promoting changes in store food environments, including product availability and placement. We used compliance-enhancing monitoring and feedback to achieve a rate that was four percentage points (or 5.4%) higher than in our pilot study [ 34 ]. Also, our collaborative approach to intervention development and maintenance maximizes the potential for wider-scale program replication.

In partnership with retailers, our team met regularly before the intervention began to codesign a feasible approach to product placement and shelf-tagging strategies using retailer templates whenever provided. Further, the team provided regular updates to the stores and chains on implementation, sales data collection in order to maintain and continue to build strong, effective relationships with supermarket management and staff. A primary goal of translational research in public health is to identify new approaches to support population health for which wide-scale adoption and implementation is possible [ 59 ]. In alignment with new thinking about best practices in research translation, the present study sought to optimize the potential for replicability in its approach to working with retailers.

The findings from the baseline data collected for this study provide a firm foundation for the ultimate analysis of the effectiveness of the healthy in-store food marketing strategies, given the comparability of stores and shoppers in intervention and control conditions. Data from the baseline intercept interviews suggest important issues to consider in analyzing and interpreting the main results of the trial. The compliance findings attest to the high level of implementation of the in-store marketing interventions, another key element for assuring internal validity of the trial findings.

Data on the relationship between dietary quality and food insecurity are mixed, and suggest that the relationship is complex and may vary by gender, age, and level of food insecurity [ 6 , 7 ] and are likely moderated in part by participation in programs like SNAP [ 60 ]. Households with very low food security shop more often in smaller stores [ 61 ] and low food security has also been found to be associated with high levels of consumption of high-fat dairy products, salty snack foods, and sugar-sweetened beverages [ 62 , 63 ].

The recruitment period for the study was longer than expected and occurred in waves rather than all at once. Ultimately, the study enrolled stores across two large retail chains; however, in the early phases of the study, the research team met with a range of store chain sizes and formats, including smaller and limited-assortment stores. Despite our openness to alternative store formats and chain sizes, our preliminary visits to those stores revealed that they did not carry the variety of products and potential brands planned for the intervention, and the store owners’ commitment to participate was insufficient to enroll and randomize those stores. Thus, while the enrollment period was more time-intensive then initially expected, the process likely supported a clear understanding of project goals and operational needs and created a partnership that contributed to sustained collaboration; no stores dropped out once the study began.

Translational Implications

The study described in this article provides an important, and the most robust to date , test of a key premise of an ongoing national policy of financing supermarkets in disadvantaged and minority communities, intended to increase food access [ 64 ] and facilitate healthier diets. Given the rapid deployment of these approaches across the USA without clear evidence on effectiveness, it is critical to conduct a rigorous study using objective data to assess whether supermarkets in low-income neighborhoods can achieve positive health effects that may reduce health disparities in chronic diseases.

The study design provides an important example of a real-world pragmatic trial of a relatively low-cost, broadly feasible intervention strategy that—if effective—could be widely translated and implemented in large supermarkets. In 2018, 49% of shoppers viewed the supermarket as their primary store, and supercenters, while still popular, have declined in overall use since 2010 as shoppers’ primary store for groceries (27% in 2010 vs. 24% in 2018) [ 65 ]. Even with the advent of new retail food sources, for example, the rise of dollar stores’ food sales, delivery, and online purchasing, the bricks and mortar supermarket remains the primary location where most Americans purchase food for themselves and their families. This study demonstrates the feasibility of implementing healthy food marketing strategies in supermarkets, a finding which, if associated with improvements in healthful food sales, will justify the widespread implementation of these low-cost approaches. Future research should examine the applicability of similar methods to food pantry settings [ 66 , 67 ] while recognizing that the layout of food pantries may be less standardized than that of large supermarkets.

Therefore, this study has broad potential for wider implementation if the results support the long-term effectiveness of these placement and promotion strategies for increasing sales of healthier products in low-income and food-insecure neighborhoods.

Acknowledgments:

The authors thank Erica Davis, Matt Phillips, Brean Flynn Witmer, Steve Menkes, Vicky Tam, Julia Orchinik, and Sarah Green and the supermarket managers and staff

This study was funded by grant number 1R01DK101629 from the National Institute of Diabetes, Digestive and Kidney Diseases of the National Institutes of Health.

Compliance with Ethical Standards

Conflicts of Interest: The authors declare that they have no conflicts of interest.

Authors’ Contributions: K.G. conceptualized and led the study and wrote the main draft of the paper. A.C. coordinated fieldwork and data collection. K.M. provided statistical expertise and conducted randomization of stores. P.K. conducted statistical analyses. D.W. led neighborhood characteristics analyses and assessments. D.P.G. managed the study operations. C.M.B. managed data cleaning and reduction for sales data. A.K. co-conceptualized the study and wrote significant sections of the paper. All authors reviewed, edited and approved the manuscript.

Ethical Approval: All procedures performed were in accordance with ethical standards. The study procedures were approved by the University of Pennsylvania IRB. This article does not contain any studies with animals.

Informed Consent: Informed consent was obtained from all individual participants and supermarkets that were included in the study.

Clinical Trials Registration: NCT02499211.

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Related Topics

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Professor of Health Promotion; Scientific Director of the Healthy Populations Institute, Dalhousie University

Senior Lecturer in Marketing, Retailing and Consumer Behaviour, University of Tasmania

Senior Nutrition Project Officer at Cancer Council NSW and PhD Student in Nutrition and Dietetics, University of Sydney

Director of Cancer Programs, Cancer Council NSW; PhD Candidate in Nutrition & Dietetics, University of Sydney

Professor of Marketing, University of Birmingham

Professor of Sociology and Associate Dean for Research & Graduate Affairs, College of Liberal Arts, Colorado State University

Professor of Dietetics, University of Sydney

Associate Professor, Canada Research Chair in Early Childhood: Diversity and Transitions, Mount Saint Vincent University

Senior Research Fellow, Alfred Deakin Institute, Deakin University

Senior Research Fellow, Deakin University

Professor of Law, Te Herenga Waka — Victoria University of Wellington

Professor of Commercial Law, Te Herenga Waka — Victoria University of Wellington

Professor, Antai College of Economics and Management, Shanghai Jiao Tong University

Assistant Professor, Nutrition and Dietetics, Memorial University of Newfoundland

Professor of Marketing and Consumer Behaviour, Queensland University of Technology

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HYPOTHESIS AND THEORY article

The food and beverage cues in digital marketing model: special considerations of social media, gaming, and livestreaming environments for food marketing and eating behavior research.

• 1 Department of Nutritional Sciences, The Pennsylvania State University, University Park, PA, United States
• 2 Department of Advertising and Public Relations, The Pennsylvania State University, University Park, PA, United States
• 3 Department of Agricultural Economics, Sociology and Education, The Pennsylvania State University, University Park, PA, United States
• 4 Department of Advertising, School of Communications, Brigham Young University, Provo, UT, United States
• 5 Department of Psychology, The University of Liverpool, Liverpool, United Kingdom

Digital marketing to children, teens, and adults contributes to substantial exposure to cues and persuasive messages that drive the overconsumption of energy dense foods and sugary beverages. Previous food marketing research has focused on traditional media, but less is known about how marketing techniques translate within digital platforms, such as social media, livestreaming, and gaming. Building upon previous theories and models, we propose a new model entitled food and beverage cues in digital marketing (FBCDM). The FBCDM model specifies key marking elements and marketing integration strategies that are common on digital platforms and are hypothesized to enhance the effects of advertising and incentive sensitization process. FBCDM also categorizes measurable outcomes into three domains that include brand, food, and social outcomes. Additionally, repeated marketing exposure and the resulting outcomes are hypothesized to have long term consequences related to consumer markets, consumption behavior, culture, and health. We include a discussion of what is currently known about digital marketing exposure within the outcome domains, and we highlight gaps in research including the long-term consequences of digital marketing exposure. The FBCDM model provides a conceptual framework to guide future research to examine the digital marketing of food and beverages to children and adolescents in order to inform government and industry policies that restrict the aggressive marketing of products associated with obesity and adverse diet related outcomes.

Introduction

Food and beverage marketing is a major contributor for establishing a preference and drive to eat energy-dense, nutrient-poor foods ( 1 – 3 ). Concern regarding exposure to food and beverage marketing and rising obesity rates has prompted policies to restrict advertising to children in some countries ( 4 ). Additionally, the World Health Organization has included the need to reduce exposure to this marketing as one of their specific aims to address the childhood obesity epidemic and encourages member states to use policies and legislation to achieve this goal ( 5 , 6 ). However, most policies attempting to restrict advertising to youth have primarily focused on television and do not adequately or directly address current technology and digital media ( 4 ). Popular digital platforms, such as Facebook, Instagram, Twitch, and Tik Tok, have monthly active users in the billions and have primarily adolescent and young adult audiences ( 7 – 10 ). Only recently has digital media been included in regulation discussions ( 11 , 12 ). Food and beverage marketing on digital media is especially concerning for children and adolescents given the broad range of digital exposures ( 11 , 13 ).

A wide variety of data driven marketing strategies on digital media platforms are being used by food and beverage brands to market directly to consumers in a manner that encourages consumption of energy drinks, sugary beverages, snack foods, and fast-food meals, which are linked with poor diet quality and adverse health outcomes when consumed in excess or in place of more nutrient dense foods ( 14 , 15 ). These strategies target children and adolescents through the use of emotional appeals, social influence, and interactive features. In a qualitative study, adolescents reported that food marketing contributed to their craving for and purchasing of specific brands, inducing emotional responses and prompting engagement with branded content or social media ( 16 ). However, digital platforms ( Table 1 ) evolve rapidly with advances in technology that can make understanding their impact on health difficult to track and regulate.

One emerging media format on digital platforms – livestreaming – provides a clear illustration for the complexity of digital media and the need for additional research on the effects of food marketing within these spaces. Livestreaming involves synchronous online broadcast by content creators, colloquially termed “streamers,” who have the ability to interact with their audience in real time. Streamers are similar to influencers on other social media platforms as they use their social capital to build financially lucrative partnerships with brands and promote those brands through their livestreams and on their social media pages. Influencer marketing across platforms is estimated to grow to a worth of $21 billion in 2023 with $5.20 return on investment for every dollar spent ( 17 ). Livestreaming originates from the online gaming community, but this medium has branched out rapidly into other content areas such as music, cooking, and beauty ( 18 ). The most popular video game livestreaming platform ( 19 , 20 ), Twitch, has 33.2 million users in the U.S alone, with billions of hours watched per year ( 21 ). In 2020, adolescents and young adults accounted for 37.8 and 40.6% of the total users, respectively ( 22 ). To reach this young audience, food brands are investing marketing efforts into not only the livestream platforms, but also with popular streamers and esports leagues that competitively play video games ( 19 , 23 ).

The foundational research of marketing techniques employed in traditional media (TV, movies, and print) offers a starting point at which to examine the possible implications of food marketing within digital platforms. Previous work on the effects of food marketing on eating behaviors has led to the development of a general explanatory model entitled the Reactivity to Embedded Food Cues in Advertising Model (REFCAM) ( 24 ). This model has been used previously to describe how food marketing is likely to influence and reinforce the consumption of advertised foods ( 24 ). In this model, food marketing is hypothesized to induce physiological and psychological processes that drive the viewer to seek out and consume the advertised foods, and this consumption in turn increases the individual’s susceptibility to future encounters with food marketing ( 24 ). Another model, the Hierarchy of Unhealthy Food Promotion Effects, provides greater description of the multitude of marketing effects ( 25 ). This model theorizes that direct effects on intake occur downstream of brand awareness, brand attitudes, and purchase intentions. The unique aspects of digital media platforms that influence marketing strategies and exposure are further described in the How Digital Marketers Target Youth framework. This framework presents the idea that digital marketing transcends platforms specific messaging, allows for dynamic engagement that includes user generated content, and leverages social influence to create particularly salient persuasive messaging ( 26 ). While these models provide broad utility to researchers, there is a need for a more specialized model to better account for the unique aspects of food and beverage presentation and medium integration that are now possible within digital media, and which set it apart from other media in terms of how it is experienced by young people.

Table 1 . Definition of key terms.

Accordingly, we propose to build upon and merge these models and their underlying theories with a revised model, entitled the food and beverage cues in digital marketing (FBCDM) model. This model highlights the various types of marketing elements (e.g., ads, endorsement) that can be present simultaneously and considers various levels of marketing integration (e.g., saturation, congruency, and social influence), that are possible on digital platforms (see Figure 1 ). Additionally, elements from the Hierarchy of Unhealthy Food Promotion Effects model are incorporated to acknowledge the multiple points of impact that food marketing may influence prior to food consumption ( 25 ). The effects of digital marketing are categorized into measurable outcomes that fall into three domains (brand, food, and social) and long-term impacts that span consumer behavior, consumption behavior, cultural norms, and health. This model also hypothesizes that individual susceptibility will alter how marketing is perceived and the resulting impacts of exposure. This conceptual model aims to provide a framework to inform future hypothesis testing that addresses the complexities and power of food marketing within digital multimedia. In the discussion that follows, we will review key components of the proposed FBCDM model with the aim of understanding their impact on consumer attitudes and behavior. To illustrate how these concepts are currently presenting within digital media, the livestreaming platform Twitch will serve as the baseline example, but we propose that these concepts are applicable to other digital outlets such as video and social media platforms.

Figure 1 . The food and beverage cues in digital marketing (FBCDM) model. (A) Marketing elements are defined here as the types of individual advertisements that are used within digital media. (B) The marketing elements are integrated into digital media in a variety of ways that may enhance the advertising effect process. These strategies include increased saturation, improved congruency, and powerful social influence. (C) Acute exposure outcomes can be categorized by brand specific, intake, and social based effects. (D) Repeated exposure and incentive sensitization process that reinforces the effects of marketing can lead to long term impacts on consumption behaviors, purchasing, and health.

Online marketing elements

Online platforms utilize a variety of marketing elements to convey their messages, including static advertising, video advertising, product placement, and endorsement ( Table 2 ). In this section, we describe each of these marketing elements highlighted in the FBCDM model, with a particular emphasis on what is known in relation to food marketing.

Table 2 . Description and examples of marketing elements.

Video commercials

Video commercials were the dominant marketing medium for food companies for many years, with many memorable marketing campaigns that have contributed substantially to brand development ( 27 ). Commercials use entertainment, emotional, and pop-cultural appeals to capture the attention of the viewer and establish brand identity and equity ( 28 , 29 ). The brand identity encompasses a set of characteristics or values of the brand that can resonate with a potential consumer or that can provide an aspirational ideal ( 30 ). Brand equity is financial benefit derived from consumers’ perception of the brand ( 31 ). These adverts have been integrated into many different digital platforms, including streaming services that take the place of television, often with the option given to the viewer to skip the commercial after 5 s.

Television commercials have been the primary exposure media in much food marketing research to date. Multiple systematic reviews have been conducted on the associations between television viewing and food intake and the acute effect of commercial exposure with eating behavior outcomes, such as food choice, preference, and snack intake ( 1 , 3 , 32 , 33 ). Consistently, exposure to food brand commercials leads to increased positive attitudes towards food brands and ultimately food choice ( 3 , 34 ). Particularly in adolescents exposure to commercial advertising has been found to be positively associated with social norms related to the consumption of energy dense foods and sugary beverages ( 34 ). The effect of commercials extends beyond the specific brand featured in the commercial and appear to impact intake of general product categories as well ( 35 ). The Quantity of food and beverage video commercials targeting children present on television has been associated with the amount of children’s reported intake of fast food and sugar sweetened drinks ( 36 ). Additionally, there is compelling evidence that experimental exposure to food commercials leads to an increase in quantity of food intake in children ( 33 , 37 ). Specifically, food commercials act as a powerful food cue that generally induces a desire to consume or seek out food ( 24 , 38 ).

Display advertising

Digital display advertising is most comparable to print advertising, in particular advertisements placed within magazines. In one study, exposure to snack food advertisements present in a children’s magazine were found to result in greater likelihood of choosing the advertised food for a snack ( 39 ). Children’s magazines have also been found to contain links to the food and beverage brands websites that encourage further engagement with the brand through giveaways and games ( 40 ). Another way that exposure to display advertising has been explored is through looking at outdoor built environments containing food brand billboards ( 41 , 42 ). The amount of outdoor food and beverage advertising has been associated with greater obesity risk ( 43 ).

Generally, the impact of display advertisements online is not as well studied in the food marketing literature. Often, display advertisements are taken out of the context in which they are normally viewed ( Figure 2 ). For example, isolated brand logos images have been used in fMRI research showing brain responses in reward regions. Brain response to food and beverage brand images differs among individuals of different weight status ( 44 ) and this brain response correlates with food intake when foods are presented in branded packaging ( 45 ). Similarly, display advertisements have been used to measure attentional bias to food cues through eye-tracking study designs. Greater attentional bias toward the advertisements was associated with greater snack food intake ( 46 ). Exposure to advertisements showing branded food items has been found to significantly increase brand attachment, which is an emotional connection to the brand that often relates to the identity of the consumer ( 47 , 48 ). Brand attachment is a strong predictor of purchasing behavior ( 49 ). The familiarity of the food products or brand logos and associations with palatability derived from exposure over time may be driving the effect on food choice and intake ( Table 2 ).

Figure 2 . Example of display advertising on Twitch livestream platform. Yellow squares indicate the location of food brands and products.

Product placement

Product placement is a form of advertising that is integrated directly into the media content with the aim to influence consumer attitudes and behavior toward the featured product ( Figure 3 ) ( 50 , 51 ). Product placement is an increasingly popular method of advertising currently being leveraged in television, movies, video games, and digital media ( 52 , 53 ). Brand logos and names can be included as text within digital platforms in the form of content titles and chat discussions. Integration of a product into the entertainment content may reduce consumers’ awareness of the brand placement or the persuasive intent ( 54 , 55 ). Brand placement in video games has been found to be relatively well received by users and it has been suggested that they may even enhance the realism of certain gaming experiences ( 56 ). Product placement influences consumer behavior through repeated exposure, unconscious messaging, and transfer of associations (such as positive emotions) from the entertainment content to the product or brand ( 57 , 58 ).

Figure 3 . Example of product placement on Twitch livestreaming platform and within a video game. (A) Product placement in the streamer’s environment. (B) Product placement within a popular video game. Yellow squares indicate location of food brand and product placement.

The impact of product placement on eating behavior is of considerable interest to researchers ( 59 – 61 ). Exposure to product placement both in television and movies has been shown to influence beverage and food choice in children towards the featured food, independent of brand recall ( 62 , 63 ). Product placement within digital media has been explored primarily in advergames, which use gamification of brands logos and characters ( 64 ). Both branded and unbranded food images have been found to influence food intake in children with a meta-analysis finding a small to moderate effect size ( 65 – 67 ).

Endorsement

Endorsement of a product by a TV or movie celebrity, musician, and athlete is a historically successful method of marketing used in traditional media, both in video commercials and static adverts ( 68 – 70 ). Food and beverage brands also frequently sponsor events that are linked with endorsements made by the participants of the event and impact children’s attitudes and perferences ( 70 , 71 ). The mechanism for effective endorsement is centered on meaning transfer and source attractiveness ( 72 , 73 ). Positive associations of the endorser are transferred to the brand, leading to an increase in brand equity ( 74 ). Moreover, the similarity, likability, and familiarity of the endorser to the viewer can enhance the effectiveness of the endorsement ( 75 ). In a reciprocal relationship, the traits of the endorser impact brand perceptions and the endorser becomes a part of the brand identity ( 76 ). In digital media, specifically social media, endorsement has evolved into influencer marketing, which can be described as a paid partnership between a brand and an influencer (i.e., any person who has a large following on social media) ( 77 ). Followers build connection and a sense of familiarity with the influencer that is in reality unknown to that person, defined as a parasocial relationship ( 78 ). Influencers use their social leverage and parasocial relationships with followers to increase awareness and positive attitudes toward the brands they promote. Moreover, viewers may regard the sponsorship of content creators as necessary to support content creation and believe that sponsorships are beneficial for both the influencer and the viewer ( 79 ).

Online endorsements by both celebrities and influencers are similarly influential on eating behavior ( 80 – 82 ). A systemic review found positive effects of endorsements on beliefs about foods, food choice, and intake in children younger than 12 years ( 83 ). Endorsement has been shown to not only increase food intake after exposure to the promotion, but also in subsequent exposure to the endorsing celebrity in a non-food related context ( 81 ). Although endorsements are sometimes used for healthier food products, such as the “Got Milk” campaigns, energy dense and nutrient poor foods comprise the majority of celebrity-based endorsements ( 84 , 85 ).

Integration of marketing elements in digital media

As our review of the marketing elements attests, prior research provides considerable evidence for the separate impact of video commercials, static adverts, product placement, and endorsement on eating behavior outcomes. However, digital media allows for enhanced integration opportunities using these marketing elements, which creates the potential for saturation, congruency, and social influence to emerge and alter the impact of the advertising effect process. The existing food marketing literature does not conceptualize marketing in the integrative sense and cannot fully account for multi-component strategies ( 86 ). However, the concepts of integrated marketing communications and omni-channel marketing, which relate to the coordinated dissemination of brand messaging across a variety of outlets to strategically increase reach and impact and providing an optimized customer experience across platforms, has been explored in the marketing literature ( 87 ). This model proposes that integrative effects warrant further explanation and exploration to increase understanding of the impact of digital food marketing on food behaviors. In the following sections, we discuss each aspect of integration in turn, using livestreaming to showcase integration into digital media.

The possibility of deploying multiple marketing elements in one setting provides an unprecedented way for advertisers to saturate digital media. Unlike traditional media, in digital media multiple marketing elements can be present on the screen simultaneously for extended periods of time, in addition to being embedded within the entertainment content itself. Consider, for example, the livestreaming platform Twitch: streamers appear on screen alongside product placements, with display advertisements layered over the stream and to the side of the broadcast; references to the brand appear in the stream title and in the profile information of the streamer; video commercials are periodically shown during the broadcast; and discussion of the advertised product can occur in the chat space. This is clearly witnessed in a Wendy’s sponsored Mario Cart livestream that created a Wendy’s restaurant Mario Cart course. Here the product and brand logo were heavily integrated into the media content, the video game setting, and the chat discussions (see Figure 4 ). The chat comments originate both from brand-controlled bots as well as organically from viewers.

Figure 4 . Saturation of marketing on Twitch livestreaming platform. Wendy’s logo and product embedded in Mario Cart livestream, with chat discussing Wendy’s. Yellow squares indicate the location of brand and product placement. Blue squares denote chat interaction related to the food brands.

The concept of saturation also extends out beyond the main platform of the marketing campaign. From the perspective of the marketers, this is known as integrated marketing communication and has long been used as a strategy for creating cohesive and synergistic messaging across multiple channels to maximize reach of a marketing campaign ( 88 , 89 ). For example, Figure 5 depicts layers of static advertising within a League of Legends Tournament with the KitKat logo being integrated into the game itself and into the broadcast content through an overlay advertisement. A simultaneous release of marketing materials was placed onto X, both on KitKat’s company page and the page of the event being broadcast (see Figures 5B , C ). In addition, a video commercial campaign was also featured across each of these platforms specifically targeting video gamers ( 90 ). Streamers often feature their social media handles during the livestream, which facilitates cross-platform saturation. Digital media presents complications to implementing integrated marketing communications as messaging is harder to control due to user-generated content and input of influencers in brand partnerships ( 88 ).

Figure 5 . Saturation of marketing across social media (Twitter) and livestreaming platform (Twitch). (A) Kit-Kat banner ad within a Twitch livestream and in the video game. (B) Kit-Kat twitter page mentioning livestreaming. (C) League of legends twitter featuring Kit-Kat. Yellow squares indicate location of embedded brands.

Saturation of marketing with repetitive exposure and multiple points of consumer contact increases the reach and power of the messaging. Prior research specific to multi-element food marketing and eating behavior outcomes is limited, as research has tended to focus on marketing elements in relative isolation. Some preliminary evidence suggests that combining marketing elements can enhance their impact. For example, combining video advertising with product placement in an advergame was shown to increase snack intake over video advertising alone ( 91 ). The effects of saturation within a single digital platform has not been explored in the food and beverage marketing literature.

More broadly, saturation may alter the impact of marketing exposure through the level of direct engagement with advertising and in turn the level of conscious processing ( 92 – 94 ). Low to moderate levels of saturation through a variety of marketing elements may lead to an accumulation of incidental contact over time that increases brand recall and nudges viewers to engage with featured brands ( 95 , 96 ). However, higher levels of saturation could decrease effectiveness through dampened engagement and perhaps an increased perception of intrusiveness ( 97 , 98 ).

Congruency refers to the degree of likeness or synergy between marketing messages or communications and the entertainment content and is thought to affect the degree of engagement with the marketing content ( 99 ). Congruency increases the relevance of the product to the viewer and reduces the perceived intrusiveness of the marketing elements. This is especially important for endorsement-based marketing, as a lack of congruency between the product and the endorser creates distrust ( 100 , 101 ). Although congruency reduces perceptions of intrusion, incongruent messages increase memory recall ( 102 ). The type of media content, viewership, and brands being marketed are important considerations for how congruency impacts attitudes and brand recall ( 102 ). Congruency has also been shown to play a role in food marketing, with one study demonstrating that it moderates the impact of advertising on purchasing intent ( 103 ).

Technology available within digital media allows for multiple strategies for creating congruency between media content and a brand. Lifestyle congruency, which is when the brand relates to shared characteristics of the viewership or those who engage with the specific media the brand is imbedded into, can be achieved with influencer type marketing practices ( 104 ). For example, energy drinks are heavily marketed on livestreaming platforms by popular streamers as an important component of their video game playing performance ( 23 ). These beverages are integrated into the group group identity of streamers and video game players. Influencers are able to relate a brand to their viewers by describing how they use a product and what they like about it. Livestream video game-based content often utilizes functional congruency, where the brand becomes a component of the game being played. This also creates a virtual direct experience with the product which is more impactful than viewing a branded message alone ( 105 , 106 ). Congruency may also be achieved by repeated placement of a brand within the entertainment content to enhance the relevance to the viewer. A clear example of this can be seen in the Twix “Pick your side” stream series: a video commercial for Twix plays before streaming content is viewed ( Figure 6A ), the Twix brand is integrated into a stream ( Figure 6B ) and featured on the Twitch home screen ( Figure 6C ). While the Twix candy bar does not directly relate to the video game play itself, elements are included in the overall streaming experience that enhance the congruency of the brand to the media content.

Figure 6 . Congruency of marketing and entertainment content on Twitch livestreaming platforms. (A) Video commercial before stream starts. (B) Brand integration in stream content. (C) Brand featured on homepage of Twitch’s website. Yellow squares indicate location of brand logos across the content.

Social influence

Perhaps one of the most unique and powerful aspects of digital media is the social interaction between content creators and viewers, as well as between other viewers that are perceived as peers. Social influence occurs both from branded content by “influencers” and by viewer generated content. This blurs the line between marketing and entertainment, which further obscures persuasive intent. To bring attention to when content is sponsored by brands, the US Federal Trade Commission as well as the United Kingdom implemented disclosure requirements (such as #ad and #sponsored). These disclosures increase awareness of advertising, but do not always reduce the effectiveness of the marketing messaging ( 107 ). The parasocial relationship between the viewer and content creator also likely plays a role in the effectiveness of advertising disclosure ( 108 , 109 ). Moreover, viewers may regard the sponsorship of content creators as necessary to support content creation and believe that sponsorships are beneficial for both the influencer and the viewer ( 79 ).

User-based content, both in reaction to branded content or independently, allows for the development of “brand communities” in which peers share their experiences and knowledge of the brand with other members ( 110 , 111 ). True to being a community, members develop norms, traditions, values, and specific language ( 110 , 111 ). Individuals align their behavior, including brand loyalty, to conform to these groups ( 112 ). Digital platforms enhance the ability of community members to communicate as well as share user created content, thus strengthening ties to the brand ( 113 , 114 ). Peers reinforce ideas and behaviors shared within these communities through “liking” and commenting on posts as well as sharing content ( 115 ).

Both branded and user-generated content can include modeling of eating behaviors. Behavioral modeling may contribute to a transfer of accepted eating behaviors and create new norms within an individual or community ( 116 , 117 ). The impact of behavioral modeling is likely to be exacerbated on digital media, whose audiences are largely adolescents and young adults and whose attitudes and food behaviors are more susceptible to social influence ( 79 , 118 , 119 ). It is well established that eating behavior can be altered by the influence of others within a given social setting, i.e., social facilitation ( 116 , 120 ). Social facilitation occurs between both familiar peers and unfamiliar peers ( 121 , 122 ) and seems likely to emerge on digital platforms by observing influencer behavior. In addition, social facilitation can emerge from perceptions of peer behavior or acceptance of a behavior ( 123 ). One example of behavioral modeling and peer reinforcement that occurred on a livestreaming platform involved a streamer partnership with Uber Eats and McDonalds. In addition to the streamer and audience working together to achieve a discount from McDonalds, viewers also observed the streamer ordering the food through Uber Eats, discussing his favorite foods on the menu, and subsequently consuming the food while watching another streamer. In essence, these actions model the behaviors that the food marketing companies would like to reinforce in their customers (see Figures 7A , B ).

Figure 7 . Eating behavioral modeling in Twitch livestreaming platforms. (A) Streamer eating McDonald’s with overlay ad and brand mentioned in stream title. (B) Uber eats Tweet about campaign. Red squares indicate the location of brands and products.

Acute exposure outcomes

The FBCDM model categorizes outcome measures to account for the variety of effects food marketing may exert on consumers. This aspect of the model incorporates concepts from the Hierarchy of Unhealthy Food Promotion Effects model ( 25 ). The following sections discuss existing digital food marketing research for each outcome type.

Brand outcomes

Marketing research measures outcomes in terms of brand reach, power, and equity. Important outcome measures that apply to food marketing research with a public health angle are brand awareness (i.e., recall, familiarity), brand attitudes, brand engagement, and purchasing behavior (both intent and actual purchase). The Hierarchy of Unhealthy Food Promotion Effects Model theorizes that these outcomes are important antecedents to consumption behaviors. These outcome alternatives provided valuable insight into the effects of digital marketing that are likely to impact eating behaviors as direct impacts on intake are difficult to measure. Experimental measurement of food intake directly due to digital marketing exposure is challenging as this media is difficult to recreate due to the use of influencers and targeted marketing that individualizes marketing exposures.

For example, recall of food marketing across digital platforms has been associated with self-reported purchase and intake behaviors ( 124 ). Greater recall of food marketing may be related to an individual’s attentional bias towards food or individual responsiveness to food cues in the digital environment. In a large sample of livestream users, higher food brand recall was associated with higher external food cue reactivity scores and reported craving of those products ( 125 ).

The context in which the food marketing is recalled may impact the attitudes toward the brand, which is important for establishing brand equity ( 126 ). Food attitudes have been found to moderate the relationship between brand recall and reported purchase and consumption of these marketed foods ( 124 ). Food marketing in digital media has been found to produce more favorable attitudes compared to traditional media ( 127 , 128 ). The use of digital media has also been associated with higher odds of engagement with multiple food brands, which includes following brands on social media, liking posts or brand pages, or sharing branded content ( 95 ). The lasting effect of a digital marketing campaign on positive brand engagement has been demonstrated by examining comments related to a fast-food brand on a popular livestreaming platform ( 129 ). Positive commentary increased during the campaign and remained higher than negative comments after the campaign ended. This is also supported by qualitative research in adolescents describing their own perceptions and experiences with food marketing across digital platforms ( 130 , 131 ). Adolescents in both studies mention the power of frequency, emotional appeal, and degree of relevance to them in shaping their attitudes toward the advertisement and the brand and likelihood of purchasing the products.

Food outcomes

In comparison to existing food marketing research using television as the exposure media, there are few studies within the digital media space that use measured food or beverage intake in response to acute exposure as the outcome. Most frequently studied are advergames with branded foods imbedded into the game ( 132 ). These studies have found a general effect of increased intake, increased intake of energy dense foods compared to lower energy dense foods, and a greater choice of the marketed product compared to an alternative food item ( 65 , 66 , 133 ). Additionally, two studies have examined the effect of social media influencers’ effect on snack intake ( 80 , 107 ). These studies found exposure to food marketing impacted both amount and type of food consumed. Similarly, a longitudinal study looking at children’s exposure to influencers on video web blogs (vlogs) found that frequency of watching the vlogs related to greater consumption of unhealthy beverages, but not snack food, 2 years later ( 134 ).

In place of direct intake measurement, there have been consistent findings using self-reported exposure to digital food marketing and food intake or preferences, specifically in adolescents. In a large sample of adolescents in Belgium, higher exposure to unhealthy food marketing across social media platforms was positively associated with greater intake and preference of those foods ( 135 ). Another study that used a narrow definition of digital marketing as receiving a text message or email from a food brand offering a price promotion or giveaway found that the frequency of these messages was associated with greater intake of fast food, sugary drinks, and salty snacks ( 136 ). This speaks directly to targeted marketing, which usually results from either location-based data on smart phones, use of food brand apps, or engagement with a brand within digital media. Higher frequency of self-reported engagement with food marketing on social media in the form of liking and sharing content also has been associated with increased intake of unhealthy food and beverages ( 137 ).

Social outcomes

Digital food marketing heavily utilizes social influence, and the effects of exposure may also contribute to perceptions of normative eating behavior and short-term trends. In addition to acute effects on intake, social influence also can contribute to the development of social norms that then contribute to general patterns of behavior ( 138 ). Social norm outcomes have primarily been explored using social media platforms Facebook, Instagram, YouTube, Tik Tok, and the adolescent population. Qualitative studies that interviewed adolescents about their perceptions of food marketing within digital media allude to the power of the normative messaging of marketing ( 130 ). Key themes included showing food consumed with friend groups who are having fun and who appear happy and depicting food consumed in large quantities. Additionally, a study examining self-reported exposure to food marketing on social media found that greater exposure was associated with higher descriptive norms, or the perceptions of their peer’s behavior, related to consumption of non-core foods (i.e., energy dense, nutrient poor) ( 135 ). Social norms related to consuming non-core foods also were found to mediate the relationship between exposure and intake. The effect of exposure to food marketing on social media on intake of non-core foods was in part due to perceptions that peers also consume these foods. In contrast, social norms were not found to mediate the relationship between brand recall of marketed foods within a livestream platform and self-reported intake and purchase behaviors ( 124 ).

Exposure to food marketing through the regular use of digital media may have a more general, rather than brand-specific, effect on eating behavior social norms. This may be in part due to the intersection between branded and user generated content that is often difficult to distinguish. The social endorsement of foods and eating behaviors, such as portion sizes, have been shown to influence perceived norms and engagement with marketing content ( 139 – 141 ). A study using Facebook found that adolescents had more positive response to unhealthy food marketing, were more likely to share or like an unhealthy food advertisement or peer post, and rated peers who had more posts of unhealthy than healthy food images more positively ( 140 ). Another social platform, Tik Tok, is a source of viral food trends, and adolescents report using this platform to engage with food based content to find new food items to try, watch food preparation techniques, and adopt diet trends ( 142 ). Using social based digital content to experimentally examine eating behavior outcomes has resulted in mixed outcomes, likely due to the challenge of recreating the saliency of real peers and influencers ( 141 , 143 ).

Long-term impacts

Digital media presents unique challenges for investigating the direct effects on both acute and long-term outcomes ( 144 ). The use of data to personalize food and beverage marketing, targeting of specific groups, lack of paid advertising disclosure, and user generated content complication the quantification of exposure and require invasive methods to track long term exposure ( 14 , 145 ). Emerging evidence shows a link between digital media use and brand awareness, food intake patterns, and social norms ( 80 , 118 , 124 , 135 ). However, clear direct effects of marketing exposure and eating behavior outcomes are more difficult to measure. There is a need for more comprehensive and contextual research to establish a clearer picture of the effects of food marketing within digital media to inform policy development. To date purposed bans on both online food and beverage advertising and restrictions on children’s digital data collection for the purpose of selling to markers have not been met with success due to political and industry pushback ( 146 , 147 ). The FBCDM may be used to develop targeted interventions or policy that balances restriction and impact. For example, the placement or frequency of marketing within a platform, depth of integration into entertainment content, or use of social appeals are potential targets.

Borrowing from the wealth of research on food marketing within traditional media and from what is known about marketing practices within digital media, hypotheses can be developed for potential long-term consequences. Repeated exposure to marketing, combined with positive experiences with the promoted product and brand, is hypothesized to establish brand affinity and loyalty. A loyal consumer base increases the market share for a brand, and this contributes to which products are readily available in consumer markets. Beyond specific brand outcomes, the general categories of food that are marketed are also hypothesized to contribute to overall dietary patterns.

Overwhelmingly high-energy-dense and nutrient-poor foods are marketed across digital platforms. Through the development of social norms around these foods and constant cues for their consumption, habitual intake occurs that may displace healthier food options. Ultimately, health outcomes are therefore hypothesized to be negatively impacted. Digital food marketing exposure may impact both diet quality and energy intake that relates to the risk of chronic disease, such as diabetes and cardiovascular disease, and obesity. Groups at higher risk for chronic disease and obesity due to health inequalities already are disproportionately targeted by food brands ( 95 ). Longitudinal data exploring long-term exposure is needed to better quantify the effects on health outcomes in both children and adults.

Digital media is complex, rapidly evolving, and used extensively for food and beverage marketing. It also affords marketers many new ways to create, combine, and disseminate increasingly persuasive advertising elements and strategies. For example, emerging augmented and virtual reality technology introduces additional immersion and integration of marketing opportunities for food and beverage brands ( 148 , 149 ). To address the challenges and opportunities presented by this evolving landscape, it is essential to gain an understanding of how food and beverage brands leverage technology within digital media and how viewers are engaging with marketing. The proposed FBCDM model presents a framework to direct research efforts on food and beverage marketing within digital media. The exact relationship and how different combinations of marketing elements and integration strategies contribute to exposure outcomes and long-term impacts warrants further exploration and consideration in future studies. There remains a need for empirical evidence testing of this model and application of the model to other forms of digital media as the primary example explored here was livestreaming. Merging marketing theory with existing food marketing literature can help inform study design to answer these questions more comprehensively. Future studies must carefully consider and account for the variety of manipulations that are possible on digital platforms and the context in which marketing is usually encountered. Another consideration that is important to address in future work would be the impact of individual differences and response to digital marketing as there may be both protective and susceptibility factors. Specifically, the disproportional exposure to unhealthy food and beverages on digital media platforms and parental influence are two areas that warrant further exploration ( 145 , 150 ). A more comprehensive understanding of digital food and beverage marketing is needed to inform policy that aims to reduce the negative consequences of exposure.

Data availability statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Author contributions

SM: Conceptualization, Writing – original draft, Writing – review & editing. KK: Writing – review & editing. FD: Writing – review & editing. MV: Writing – review & editing. JF: Writing – review & editing. RE: Writing – review & editing. EB: Writing – review & editing. TM: Conceptualization, Supervision, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: food marketing, digital media, eating behavior, social media, children and adolescent, health, policy

Citation: Maksi SJ, Keller KL, Dardis F, Vecchi M, Freeman J, Evans RK, Boyland E and Masterson TD (2024) The food and beverage cues in digital marketing model: special considerations of social media, gaming, and livestreaming environments for food marketing and eating behavior research. Front. Nutr . 10:1325265. doi: 10.3389/fnut.2023.1325265

Received: 20 October 2023; Accepted: 29 November 2023; Published: 06 February 2024.

Reviewed by:

Copyright © 2024 Maksi, Keller, Dardis, Vecchi, Freeman, Evans, Boyland and Masterson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sara J. Maksi, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Research article
• Open access
• Published: 10 April 2014

The effects of food advertising and cognitive load on food choices

• Frederick J Zimmerman 1 &
• Sandhya V Shimoga 1  

BMC Public Health volume  14 , Article number:  342 ( 2014 ) Cite this article

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Advertising has been implicated in the declining quality of the American diet, but much of the research has been conducted with children rather than adults. This study tested the effects of televised food advertising on adult food choice.

Participants (N = 351) were randomized into one of 4 experimental conditions: exposure to food advertising vs. exposure to non-food advertising, and within each of these groups, exposure to a task that was either cognitively demanding or not cognitively demanding. The number of unhealthy snacks chosen was subsequently measured, along with total calories of the snacks chosen.

Those exposed to food advertising chose 28% more unhealthy snacks than those exposed to non-food-advertising (95% CI: 7% - 53%), with a total caloric value that was 65 kcal higher (95% CI: 10-121). The effect of advertising was not significant among those assigned to the low-cognitive-load group, but was large and significant among those assigned to the high-cognitive-load group: 43% more unhealthy snacks (95% CI: 11% - 85%) and 94 more total calories (95% CI: 19-169).

Conclusions

Televised food advertising has strong effects on individual food choice, and these effects are magnified when individuals are cognitively occupied by other tasks.

Peer Review reports

The quality of the typical American diet has been eroding for decades, a development that some researchers have associated with the growth in food marketing [ 1 – 3 ]. Although each of the “4 P’s” of marketing—product [ 4 ], place [ 5 ], price [ 6 , 7 ], and promotion [ 8 ]—have contributed to the erosion of the American diet, that part of promotion that comprises television advertising has certainly played a significant role [ 9 – 14 ]. Even among adults, food advertising has strong effects [ 9 , 15 – 18 ].

Recent research in psychology and behavioral economics has shown that cognitive resources are inherently limited [ 19 , 20 ]. People are able to make attentive, rational-seeming decisions some of the time, but at other times decisions seem to be irrational, relying on such cognitive shortcuts as heuristics, social referencing, and habit [ 21 – 24 ].

In the particular area of behaviors around what and how much we eat, people seem to be so sensitive to such effects that eating itself has been described as an “automatic behavior” [ 25 ]. In several recent experiments, researchers have discovered that portion size, the behavior of nearby eaters, the accessibility of food, and even dubious health claims all affect the amount and types of food consumed [ 26 – 29 ].

A separate strand of research has shown that eating behaviors are sensitive to the depletion of cognitive resources at any given time (that is, to cognitive load). One study manipulated available cognitive resources by asking participants to memorize either a 2-digit or a 7-digit number, walk down a hallway to another room, and recall the number [ 30 ]. Along the way, participants were offered the choice of a chocolate cake or a fruit salad. Among those who had been given a 7-digit number 63% chose the chocolate cake, whereas among those remembering a 2-digit number only 41% chose the cake. Another study produced similar results among restrained eaters [ 31 ]. These results were accentuated when the cognitive load represented an ego threat to the participant [ 32 ].

There accordingly appears to be strong evidence that eating behaviors are highly sensitive to external cues (including advertising), and cognitive load tends to disinhibit eating. Putting these two strands of research together suggests that the effects of food advertising may be greater among those under a heavy cognitive load than among those whose cognitive resources are not so taxed.

Recent work has shown that foods of low nutritional quality are more heavily marketed in low-income and minority neighborhoods [ 33 – 37 ]. This finding, if replicated in other studies, may explain a part of the socioeconomic disparities in eating behaviors that have been observed. Yet it also raises a question: why might it be more attractive to advertise obesigenic foods in these vulnerable neighborhoods?

This conjecture may offer important insights into the causes that underlie socioeconomic disparities in dietary behaviors. If cognitive load potentiates the effects of obesigenic food advertising, then disparities in stress and cognitive load could translate into disparities in healthy eating behaviors.

This study tests whether food advertising has a significant effect on the types and quantity of food chosen in a free-choice environment, and explores how these effects of advertising differ when cognitive load is experimentally manipulated.

A secondary analysis presents these results stratified by the socioeconomic status of the participants.

This study used a 2x2 factorial design, with both advertising and cognitive load experimentally manipulated, to test the effects of food advertising on food choice overall and among subsets of participants who received either high-cognitive-load or low-cognitive-load tasks.

Participants were students at UCLA, recruited through posters, ads in the campus newsletter, and a campus-wide student participant pool maintained by the Anderson Behavioral Lab, a part of the UCLA Anderson School of Management. All willing students aged 18 or older and without any major self-reported health problems (such as asthma, diabetes, heart disease or depression) were eligible to participate. Participants were told that they would be participating in a study of “television viewing and short-term memory”. Those who completed the study were given a $10 gift certificate to on-campus stores and restaurants.

Participants who met the above inclusion criteria were randomly assigned to one of four groups:

High cognitive load + food advertising

Low cognitive load + food advertising

High cognitive load + non-food advertising

Low cognitive load + non-food advertising

Participants were invited in groups of 20 to view prerecorded movie segments interspersed with advertising. Each session included, in this order, a brief introduction to the study, 45 minutes of viewing, a brief break for snacks (including water and soda options), and the completion of a survey of demographic and other information. The entire session typically lasted just under an hour. Eligible enrollees were asked to enroll for a particular study session via an online scheduling system. The study slots were then randomly assigned to one of the four experimental arms, ensuring only that approximately equal number of sessions were conducted in morning, noon and afternoons.

The viewing consisted of a 3 blocks of content and each block included three 30-second commercials. Each block began with an introductory or transition screen displayed for 15 seconds, an introductory announcement such as that seen in movie theaters requesting that people silence their cell phones, and one, 30-second commercial. This introductory material— 45 seconds total—was followed by a 6-to-7-minute segment excerpted from a movie or television show, a second 30-second commercial break, a second movie or TV segment, and finally one more 30-second commercials. For those participants assigned to the food-advertising arm, the 2 of the 3 commercials were for an obesigenic food product—potato chips, chocolate candy and sugary soda. The order in which food commercial was introduced within each block varied. Each participant assigned to the food-advertising arm accordingly was exposed to 6, 30-second food commercials. In the intervention arm, 1 of the 3 commercials was for irrelevant products (such as cars, sneakers or cell phones). Those assigned to the control-advertising arm saw the same irrelevant commercials as in the food-advertising arm, and in addition saw additional non-food commercial in the place of food commercial in each block. Participants in both arms saw the same number of commercials and the same TV and movie programming. The movie and TV excerpts were chosen to be entertaining, but not highly stimulating, and to avoid mention of food, eating, or obesity-related topics. The same TV and movie excerpts were used in both arms. Additional file 1 reports the full schedule of viewing in both arms.

There were two parts to the cognitive task, a task involving remembering a number and a task involving tracking information on screen.

Immediately before the introductory screen of the third block, participants were shown a number for 7 seconds and asked to memorize the number. Participants were asked not to write the number down and were told that they would be asked to record the number on their final survey. Those assigned to the high-cognitive-load condition were asked to remember a 7-digit number. Those assigned to the low-cognitive-load condition were asked to remember a 2-digit number. These cognitive tasks were chosen because of their similarity to a previous experiment involving cognitive load and food choice [ 30 ]. The specific numbers are reported in Additional file 1 . The information task demanding high cognitive load required the participants to mentally keep track of the number of times a particular word was uttered in a movie segment. (For example, in the sector showing ‘Duck Dynasty’, the participants were asked to count the number of time the word duck is uttered by any of the actors.) At the end of that segment, they were required to write down the total count on the task answer sheet given to them.

In addition to memorizing a 2-digit number, the low cognitive load task was to answer a simple question per segment. (For example, in the ‘Duck Dynasty’ segment, the question asked about the show’s location, which was mentioned multiple times during the segment.)

At the beginning of the study, participants were informed of the study purpose and protocol and provided their verbal consent to participate. The study protocol was approved by the UCLA Institutional Review Board, approval #12-000323.

A variety of snack and drink items were made freely available to the participants during a break that took place after the viewing and before the survey. Participants were told that there were snacks on the table on one side of the room, and that they were invited to help themselves. These items included water, small bags of sliced apples, small bags of trail mix, granola bars, Coca Cola, small bags of M&M’s, Reese’s Peanut Butter Cups, Hershey’s Kisses, and Lay’s Potato Chips. Ads for Coca Cola, Hershey’s Kisses, M&M’s and Lay’s Potato Chips were included as part of the experimental manipulation in the food-advertising arm. Because no ads were presented for water, apples, trail mix or granola, these items were deemed healthy, with the candy, soda, and potato chips deemed unhealthy. These labels are intended as convenient descriptors only, as it is true that excessive consumption of, say, trail mix, would not be healthy.

Two main outcome variables were assessed: the number of snack items chosen and the total count of calories of food that was chosen. These outcomes were chosen to reflect each of the two distinct dimensions of food-related choices: the type of food chosen and the quantity chosen. Actual consumption of food was not a behavioral target of the experiment and was not observed in the study. Within each of these outcomes, the analysis separately tracks the number of calories from healthy and unhealthy items and the number of healthy and unhealthy items chosen.

The number and types of snack items (including drinks) were observed and recorded by one of the coauthors (SS). Discrete video recording of the snacks area permitted accurate assessment of the items taken by each study participant. Calorie counts were available for each of the healthy and unhealthy items.

The final questionnaire included questions on age, gender, year in school, major, exercise and sleeping habits, fast food consumption, soda consumption, and. television viewing habits. Following previous work on economic disparities in obesity, students were asked to provide the zip code of their parents’ address as a proxy for socioeconomic status [ 38 ]. Data from the 2011 American Community Survey, collected by the US Census, were used to determine the average income for each zip code. Participants were dichotomized into high vs low socioeconomic status according to whether the average income in their home zip code is above or below the within-sample median. Foreign students (N = 48) were dropped from these analyses.

Statistical analysis

The number of unhealthy snack items chosen is count data, with a Poisson distribution. A likelihood ratio test failed to reject the assumption of equidispersion (i.e., that the conditional mean and conditional variance of the outcome are equal; p-value = 0.38), suggesting that poisson is preferred to a negative-binomial regression. The Vuong test revealed no evidence of zero-inflation. Accordingly, the assumptions of Poisson regression could not be rejected and hence, it was the preferred model.

The Poisson regression was first conducted in the whole sample to test the main effects of advertising. To gain some purchase on the statistical meaning of the differences in the effects of advertising between high-cognitive-load and low-cognitive-load conditions two tests were conducted. First, an advertising-cognitive-load interaction term was added to the regressions and its significance was tested. If the coefficient on this term were significant, it would indicate that the analysis could reject the null hypothesis of no effect-modification of advertising by cognitive load.

Second, an equivalence test was conducted [ 39 ], using a two one-sided test (TOST) with a delta of 50 kcal for the total calories and 25% for the number of unhealthy snacks. The purpose of an equivalence test is to determine whether the observed point estimate, along with its entire confidence interval, is contained within a specified margin around some anchor, often either zero or some other known quantity. Unlike a statistical significance test, the purpose of an equivalence test is to test the magnitude of difference between an estimate and some other quantity. In this analysis, the question is whether the effects of advertising can be said to be of similar magnitude in a high-cognitive-load and a low-cognitive-load condition. Note that significant differences and equivalences are conceptually distinct: estimates in these two conditions could be statistically significantly different and yet equivalent; not statistically significantly different and yet not equivalent; or any other combination. The equivalence used a one-sided test of whether the interaction of cognitive load and advertising was associated with a change in either total calories or the number of unhealthy snacks of less than 50 calories or less than 25%, respectively.

The sample was then split into sub-samples of high-cognitive-load and low-cognitive-load, and the Poisson regression was conducted in each sample separately to test the effects of advertising under these distinct conditions.

Finally, as a secondary analysis, the samples were further stratified within cognitive-load arms by socioeconomic status, divided at the sample median (excluding the foreign-born participants). Again, the Poisson regression was conducted, this time in 4 distinct sub-samples of the data.

In each regression, the participant’s status in the food-advertising or non-food advertising arm is the only regressor.

The number of calories is a normally distributed variable, but truncated on the left at zero. With this distribution for the dependent variable, Tobit regression is indicated. As for the first outcome, the number of calories chosen was analyzed first in a Tobit regression of the whole sample, with tests for effect modification and equivalence (with a delta of 50 kcal), and then in a stratified regression by cognitive load and finally in a sub-analysis in which the sample was further stratified by socioeconomic status.

All analyses were carried out using Stata 10.1.

Sensitivity analyses

A small number (N = 3; <1%) of the participants were observed either to have written their number down when they were asked to remember it, or recalled a number that was substantially different than the number they had been given. The results reported here were analyzed without correcting for this protocol violation. However, an analysis in which these participants were dropped (not reported here) produced highly similar results.

Several additional analyses were conducted to test the robustness of the results to alternative specifications. These analyses included ordinary least squares regression instead of Poisson or Tobit, and analyses that were adjusted for the gender, parental SES, year in college, foreign citizenship and past food habits of the participant. All analyses produced results that were highly similar to those reported here.

Table  1 presents the descriptive statistics of the sample. Consistent with the randomization of the participants, there are few meaningful differences across the groups.

Figure  1 presents the unadjusted results graphically. The top panel reports results in terms of calories, and the bottom panel in terms of the number of snacks chosen. Results are broken down by individual food type, within the categories of healthy and unhealthy food. In the left pane is the simple comparison of food choices in the non-food-advertising arm and the food-advertising arm; in the right pane the effect modification by cognitive load is presented. In all comparisons, more food was taken in the food-advertising arm than in the non-food advertising arm. For calories, most of the increase was among the unhealthy foods, with the largest percentage increases for soda and chips. For number of items, there were large increases in the unhealthy foods, again with proportionately large increases for soda and chips. However, food advertising was also associated with an increase in the selection of apples, and with a decrease in selection of trailmix.

Calories and Number of snacks by experimental arm.

Table  2 presents a formal statistical analysis of these results. Three models are presented: the main effect of advertising, the effects of advertising controlling for the set of covariates included in Table  1 , and the effects of the advertising-cognitive-load interaction. Each model is executed for the total number of calories and the number of unhealthy snacks.

Those exposed to food advertising took a set of snacks with 65 more calories than those exposed to non-food advertising, and this difference is significant (p-value = 0.02; 95% CI: 10-121). Again, neither the effect modification by cognitive load nor the equivalence test achieved significance (p-values of 0.30 and 0.56, respectively).

Results of the Poisson estimation of the number of unhealthy snacks are reported with exponentiated coefficients, which can be interpreted as a percentage increase relative to the reference category. The exponentiated coefficient (rate ratio: RR) in the pooled regression is 1.28 (95% CI: 1.07 – 1.53). That is, those in the food-advertising group chose 28% more unhealthy snacks than those in the non-food advertising group. Neither the effect-modification of advertising by cognitive load nor the equivalence test was significant (p-values of 0.22 and 0.50, respectively). Low-income and foreign students chose more snacks and more total calories than non-foreign and high-income students. No other covariates were significant, and—as expected in a randomized experiment—the covariates collectively do not moderate the main effects.

The results of the Tobit regressions of number of calories are reported in Table  3 . In the low-cognitive-load group the effect was not significant for all calories, calories from healthy foods and calories from unhealthy foods. In the high-cognitive-load group the effect was significant for total calories and calories from unhealthy foods. Those in the food-advertising group chose a set of snacks with 94 more calories than the non-food advertising group (95% CI: 19-169); and their choice of unhealthy foods had 107 more calories than those of the non-food-advertising group (95% CI: 33-181). Accordingly, all of the additional calories associated with food advertising were from unhealthy foods.

The secondary stratified analyses using socioeconomic status revealed no statistically significant results, except that below-median-SES participants in the high-cognitive-load plus food-advertising arm chose snacks with 143 more calories than those in the high-cognitive-load plus non-food-advertising arm (95% CI: 37-249).

Stratified results of the Poisson regressions of the total number of snacks, unhealthy snacks, and healthy snacks are presented in Table  4 . In the low-cognitive-load group, the effect of food advertising is not significant for all snacks, healthy snacks and unhealthy snacks. In the high-cognitive-load group, those exposed to food advertising chose 28% more total snacks and 43% more unhealthy snacks (rate ratio 95% CIs: 1.07-1.54 and 1.11 – 1.85, respectively). The effect on healthy snacks was not significant.

The secondary analyses further stratifying these results by parent socioeconomic status revealed a significant effect among those in the high-cognitive-load group with below-sample-median income. In this group, the effect of food advertising was an 84% increase in the number of unhealthy snacks chosen (rate ratio 95% CI: 1.22 – 2.78), and this effect was significantly different than among the above-sample-median group. Those in the high-cognitive-load group with above-median SES had increases of 46% and 81%, respectively, in the number of snacks overall and the number of healthy snacks chosen (95% CIs” 1.10-1.95 and 1.20-2.71, respectively). The effect of food advertising was not significant in all other groups, and there were no other significant effect modifications by SES in any of the other regressions.

There is a clear qualitative difference between the high-cognitive-load group, for whom advertising has a large and statistically significant effect, and the low-cognitive-load group, for whom advertising has a smaller, and statistically insignificant effect. These differences appear to be magnified by the participant’s socioeconomic status, with low-SES individuals more susceptible to the effects of advertising than high-SES individuals.

These study results are similar to those found in Harris, Bargh, and Brownell (2009), which included 4 food advertisements (20 seconds each, as opposed to 3, 30-second advertisements here). Although the coding of the outcome in the two studies was too different to permit a direct comparison, the Harris et al. study found that those in the food-advertising group consumed 0.44 standard deviations more than in the control group, an effect of a broadly similar magnitude to that estimated here.

We are unaware of any study in the literature that examines whether the effect of advertising can be enhanced by cognitive load. Two studies have noted an interaction between restrained eating and either cognitive load [ 31 ] or advertising [ 9 ] on increased calorie choice in experimental settings. Another study found that emotional setbacks like a favorite sports team losing an important match can trigger overeating [ 40 ].

Our results suggest that the conjoint presence of both heavy cognitive load and food advertising might lead to significantly worse food choices. Other research has shown people often watch television while distracted in some way, for example by multi-tasking. To the extent that such multi-tasking induces cognitive load, the research here suggests that it may exacerbate the effects of advertising. In addition, evidence suggests that television viewing in childhood and adolescence has sustained effects into adulthood [ 13 , 41 ]. If low-SES children are more likely to be exposed to television advertising for obesigenic foods, the longevity of the effect may explain some of the results here. Participants’ prior exposure to food marketing was not assessed here, and this is a limitation of the present research.

Food advertising is much discussed in the public health literature, but most of the popular discussion around food advertising seems to focus on children [ 14 , 42 – 44 ], while scant attention is paid to adults. This study contributes to a very small but important body of literature that suggests that the effects of advertising are not limited to children.

The results of this study reinforce the research consensus that advertising is a potent force in food choice. Americans tend to resist calls for restrictions on marketing by invoking values around freedom. Yet it is worth closely examining the meaning of free choice [ 45 ]. In this experiment all participants were equally free to choose, and yet the study authors were able to manipulate this freedom, influencing choices through experimental conditions. In the world outside the lab, choices can also be manipulated [ 46 ]. Carefully studied experience from a ban on advertising to children in Québec shows that such a ban is effective in promoting healthier eating [ 47 ].

Previous research has found that those of low socioeconomic status may be especially likely to suffer from stress [ 48 – 50 ]. For example, one recent study in which race/ethnicity was strongly correlated with education and income, found that African-Americans had experienced an average of 1.92 stressors and American-born Latinos 1.90, against only 1.12 events for Whites [ 51 ]. It may be that the daily hassles and stressors experienced by minority and low-income communities operate in a similar way to the experimentally induced cognitive load described here. If so, that would suggest that people so exposed might be more than usually susceptible to the effects of food advertising.

Eating behavior is strongly influenced by cultural and environmental factors [ 52 ]. The results presented here raise the possibility that food marketing may be more potent in low-income neighborhoods than in high-income ones. Future research should attempt to replicate and extend this research to further examine patterns related to cognitive burden and socioeconomic factors.

Limitations

Participants in this study were all students at a top-ranked university, which may limit the external generalizability of the study. UCLA is one of the most ethnically and economically diverse universities in the country [ 53 ] and has the highest proportion of students receiving Pell Grants of any major university, an important indicator of economic diversity [ 54 ]. All the same, many of the results on food choice obtained to date have been conducted among college students, and research in the community would enhance confidence in the generalizability of the results.

Socioeconomic status in this study was measured by a proxy of parental zip code, which is clearly an imperfect measure. In the US, a zip code includes approximately 7,000-10,000 people. Because housing costs in the US tend to follow geographic patterns, zip codes tend to have some degree of economic homogeneity. Yet this homogeneity is not absolute, and there can be variations of income within zip code. In this data set the standard deviation of parental SES as measured by zip code proxy was 38% of the mean. This measure was used because the socioeconomic status of college students is hard to operationalize. An advantage of replicating these results in the community would be the ability to capture more reliable measures of socioeconomic status.

This research is motivated by the possibility that chronic cognitive load enhances the effect of chronic exposure to food advertising. Yet in the confines of this experiment neither chronic cognitive load nor chronic exposure to food advertising could be experimentally manipulated. It could be that the effects of chronic exposures are either greater or lesser than the very brief and relatively small doses manipulated in this experiment. Given how pervasive and profound both cognitive load and food advertising are in American society, other methods besides experimental manipulation will be necessary to tease out the causal roles and interactions of these two factors on eating behaviors.

“Marketing works”. These opening words of the Institute of Medicine’s report on food marketing to children [ 14 ] apply to adults as well as to children. These study results raise the possibility that food marketing may have disparate effects across different populations, disproportionately influencing the eating behaviors of some of the most vulnerable subgroups and potentially contributing to disparities in diet and in related health outcomes.

Abbreviations

Socio-economic status

Confidence interval.

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Home » Resources » Food marketing online » Food marketing in the digital age: A conceptual framework and agenda for research

Food marketing in the digital age: A conceptual framework and agenda for research

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The next few years will see a dramatic expansion of digital food and beverage marketing. The food industry is at the forefront of research and innovation in the interactive marketing arena, working with dozens of ad agencies, marketing firms, and high-tech specialists to design campaigns that take advantage of young people’s engagement with social networks, interactive games, mobile phones, online videos, and virtual worlds. 1 Major brands have significantly increased their spending for online display advertising, exhibiting double- and in some cases triple-digit growth. 2 For example, Coca-Cola’s spending was up 163 percent in 2009 from 2008; Dr. Pepper witnessed 427.9 percent growth; Kellogg’s was up 225.3 percent; PepsiCo 68.6 percent; Wendy’s 355.7 percent; General Mills 105.6 percent; and McDonald’s spent 47.4 percent more. 3

Food marketers pay particularly close attention to ethnic minorities. 4 As the fastest growing demographic sector, African-Americans and Hispanics are also important trendsetters who are influencing the consumption patterns, new media behaviors, and even many of the products of the broader youth market. 5 Today’s minorities are predicted to become the majority, comprising almost half of all American youth by 2050. 6

Six unique concepts define a framework for digital marketing

Intensive digital marketing campaigns for fast food, snacks, and sweetened beverages combine an integrated set of digital practices designed to engage children and youth continuously (see Figure 1). We have identified six defining concepts that constitute unique features of digital media and marketing.

Ubiquitous connectivity. Children and teens now move seamlessly, and often simultaneously, across a spectrum of platforms–from laptops to desktop computers to cell phones to televisions. 14 Marketers design their campaigns to take advantage of young people’s constant connectivity to technology, their geographic locations, and the “fluidity of their media experiences. The ubiquitous nature of new media makes it difficult for researchers to take into account the entirety of an individual’s interaction with marketing. Neither the “medium” nor the “message” can be easily identified or isolated. While it is still important to understand how youth respond to individual media platforms and marketing appeals, they cannot be examined in isolation. Rather, researchers will need to find ways of assessing synergies across platforms, as well as how these platforms and the marketing content within them reinforce each other and create multiplier effects.

Engagement. In contrast to the passive experience of watching television, the increasingly participatory environment of interactive media facilitates active engagement. 15 This is particularly the case for children and youth, whose enthusiastic involvement with social networks, blogs, text messaging, and online video makes them the most engaged demographic group. 16 Engagement is a fundamental goal of contemporary digital marketing. Rather than simply exposing consumers to a particular message, product, or service, engagement means creating an environment in which young people are actually interacting with the brand, befriending the product, and integrating it into their personal and social relationships. Engagement also refers to the emotional connection between consumers and brands, which can be measured through a variety of techniques, including neuromarketing, which uses the tools of neuroscience to test the impact of marketing on the brain. 17

User-generated content. Digital technologies make it possible for marketers to enlist youth in creating and distributing brand-related content, including advertisements, products, and packaging for their favorite products. In this way, youth are not passive viewers of commercial messages, but active practitioners in the marketing enterprise. User-generated-media campaigns employ a variety of techniques to encourage consumers to become involved in creating marketing messages. In most cases, companies create a template and provide incentives to foster participation. The practice turns the conventional model of advertising on its head, transforming children and teens from passive viewers of commercials into ad producers and distributors. This raises a number of key questions for researchers. For example, rather than focus on understanding the persuasive intent of an ad, scholars may want to explore other key issues such as how creating and promoting one’s favorite products may intersect with identity development, especially during the critical period of adolescence.

Social graph. Participatory Web 2.0 platforms–particularly social networking sites such as Facebook–are further enhancing marketers’ ability both to know the nature and extent of an individual’s social relationships, and use them for highly sophisticated social media marketing campaigns. Social networking platforms have added a feature to digital marketing that is distinct and important: the ability to tap into the social graph, which is the complex web of relationships among individuals facilitated and tracked online, enabling marketers to access and in”uence both individuals and their communities in ways that were never before possible. 22 Using a host of new techniques and measurement tools, social media marketers can know the breadth and depth of these online social relationships, as well as how they function, understanding who in”uences whom, and how the process of in”uence works. These social media campaigns target teens at a point in their lives when they are relying less on their parents and family and more on friends. There are many aspects of these new forms of digital marketing that need to be explored, including the role of peer in”uence in brand promotion, and the intersection of online social interactions with eating behaviors.

Immersive environments. State-of-the-art animation, high-definition video, and other multimedia applications are spawning a new generation of immersive environments, including interactive games and three-dimensional virtual worlds, which marketers are using for brand promotion. Immersive environments surround and engross a person with powerful, realistic images and sounds, creating an experience of being inside the action, a mental state that is frequently accompanied by “intense focus, loss of self, distorted time sense, effortless action.” 23 These environments often trigger a sense of “presence,” which is defined as “being there,” a subjective feeling as if one is actually physically in the virtual environment. 24 Immersive marketing techniques are designed to create particularly intense experiences, plunging users into the center of the action through the use of avatars or “first-person-shooter” devices that induce a strong sense of subjectivity, heighten emotional arousal, and trigger unconscious processes. The growing use of augmented reality, video, and 3-D virtual environments–already migrating to mobile platforms–means that all digital media experiences will be increasingly immersive in the not-too-distant future.

Scholars will need to take these six unique concepts into account when designing studies of how young people are responding to digital marketing, addressing, for example, the role that ” ow, presence, and subjective experience might play in making young people susceptible to food and beverage promotions. An important challenge for research on digital marketing campaigns will be understanding how the components interact in a unified framework with one another and with traditional media marketing.

Research evidence has already established that television commercials are an important contributing factor in youth consumption of unhealthful food. 27 The combination of strategies and techniques that snack food, soft drink, and fast food companies are using to reach and engage youth through digital marketing is potentially a great deal more powerful. Furthermore, while sugar, fats, and salt are particularly appealing to humans, these taste preferences may disproportionately affect African-Americans and Hispanics. 28 Food marketing encountered by African-American and Hispanic youth tends to promote less healthful foods, and is less likely to support positive nutrition. 29 Because digital media play such a powerful and influential role in the lives of these young people, digital food marketing targeted at ethnic minority groups may further amplify these effects. All children and adolescents are engaging with digital food marketing during critical periods of their development when they are being socialized into the larger culture, forging their own identities, and establishing the habits and behaviors that are likely to stay with them for the rest of their lives. Social norms, attitudes about food, and consumption patterns, may become routine, automatic, and in many ways, unconscious.

Rethinking the research paradigm

With the increasing concern about childhood and adolescent obesity, a few scholars have begun to turn their attention to food marketing in digital media. However, for the most part, these studies have been somewhat narrow in scope, focusing on those aspects of digital marketing (particularly food-company-sponsored advergames) that can be easily quantified and measured through content analysis and other traditional mass communication methods. 30 These studies have also relied primarily on the cognitive theoretical model that has dominated both research and public policy on children and advertising for the last several decades. 31 Drawing from Piaget’s theories of child development, three successive developmental stages have been identified during which children acquire increased abilities to understand advertisers’ intentions to persuade them. It is not until children reach the age of 7 or 8 that they have the cognitive ability to recognize the persuasive intent behind a television advertisement. 32 By age 12, children are able to articulate a more critical comprehension of advertising intent and become more skeptical. 33 According to this age-based, “cognitive defense” approach, regulatory safeguards are necessary only for the younger segment of the youth population. 34

In recent years, however, some scholars have begun to critique the cognitive model, suggesting other theoretical approaches for assessing the impact of contemporary marketing. 35 While no one theory can fully explain the complex ways in which contemporary food marketing influences the health behaviors of children and adolescents, we see several theoretical models and approaches that may be useful building blocks for helping scholars develop an understanding of how digital marketing works. Below, we briefly consider three: dual-process models of persuasion; models of affective response; and models of familiarity and social norms.

Models of familiarity and social norms. Given the ubiquity of digital media, exposure to marketing has become frequent and commonplace, engendering a level of familiarity that may go unnoticed yet result in significant marketing effects. According to the mere exposure effect model (also called the familiarity principle in social psychology), people exhibit a preference for things because they are familiar with them. 42 Young people are likely to develop positive associations with logos they encounter in various forms throughout their daily experiences. 43 If merely being exposed to a logo repeatedly and in different contexts can produce enhanced positive attitudes, what might be the impact of such practices as appropriating brand logos as part of one’s social network profile, or developing a video to demonstrate one’s loyalty to the brand and then distributing it among friends? Some evidence already points to such effects in a digital context. 44 This category of theories also highlights the importance of understanding norms related to digital marketing. Scholars have noted that unhealthful eating behaviors may emerge and flourish in environments where that behavior is viewed as normal and acceptable. 45 Repeated exposure to marketing stimuli–especially stimuli that are processed less consciously–may lead to perceived norms regarding specific foods and beverages. When the ubiquity of marketing brands and icons is combined with the various forms of engagement and integrated into social interactions, the impact of familiarity and social norms may be further intensified. It is critical for researchers to understand the effect of synergy across digital platforms, as well as digital synergy with other (traditional) marketing methods. Contextual factors must also be taken into account, especially when researching ethnic minority youth and other cultural subgroups. 46

Adolescent vulnerabilities. Adolescents are at serious risk for obesity, and the teen years are a critical developmental period, during which long-term consumer habits and eating behaviors are established and reinforced. 50 Yet, this age group has not received the same level of scholarly attention that has been focused on younger children, particularly with regard to food marketing. 51 Because of the emphasis on cognitive theory in much of the advertising effects research, scholars have viewed adolescents as more knowledgeable about marketer intentions, and thus better able to resist advertising and marketing influences. 52 However, recent research suggests that biological and psychosocial attributes of the adolescent experience may play an important role in how teens respond to marketing, making them more vulnerable than they were thought to have been in the past, especially when they are distracted, are in a state of high arousal, or are subjected to peer pressure. 53 These are exactly the conditions that digital marketing is often designed to induce. All of these issues need to be explored further if we are to understand fully how these processes work in digital food marketing. Some specific questions that should be addressed through empirical research are: What role does self-esteem play in contributing to a young person’s vulnerability to specific kinds of unhealthful foods, as well as specific forms of digital marketing promoting those foods? Do young people who are already overweight or obese have greater susceptibility to these forms of marketing? How do digital media increase arousal among teens? What types of media experiences (e.g., video games, online video, interactive television) are more likely to induce these states? What role does mood play in an adolescent’s vulnerability to digital marketing? How do digital media trigger mood variation?

Targeted digital marketing to ethnic minority groups. Driven by the sheer number and growth of minority youth, as well as by their favorable usage patterns and cultural trendsetting, digital marketers have made understanding and reaching minority youth a priority. Target marketing to African-American and Hispanic youth influences their consumption choices by affecting the awareness and availability of food-related information and options, and can contribute to perceived norms. 56 Further, research suggests that ethnic minority youth are more interested in, positive towards, and influenced by marketing than non-Hispanic whites. Moreover, minority youth are important cultural models who influence the behaviors of the larger youth population. 57 As a consequence, minority youth may be subject to multiple layers of vulnerability, given family circumstances, normative exposures to obesity, and the contexts in which they live. 58 Unfortunately, however, there is very limited research on ethnic minority youth and marketing, especially of direct relevance to digital. 59 In the face of such aggressive market research and digital promotion of unhealthful foods to African-American and Hispanic young people, research is urgently needed to address a number of key questions, including: What are the specific issues with regard to vulnerability and receptivity of digital marketing efforts among youth of color? When considering lower income youth of color, are there particular effects of digital marketing that depend on the settings and circumstances of the youths’ lives? Does geolocation targeting through mobile phones and other devices take particular advantage of those youth who are already more disadvantaged than others, such as those who live in unhealthy contexts?

Similarly, could cravings be triggered through mobile marketing by targeting young people with fast-food promotions and discount coupons when they are near a fast food restaurant? These types of campaigns could create powerful contexts in which resistance to marketing messages would be particularly challenging. Studies are needed to explore these and other related hypotheses.

Methods for studying digital media and marketing

Flexible and innovative approaches are needed to understand the complex ways that youth are interacting with this new commercial media culture. Much of this research will need to be collaborative and interdisciplinary, combining expertise from various fields to pose hypotheses that cut across disciplines and across levels of influence.

Research to inform policy

Studies that can apply what social scientists are learning to critical issues in the legal and policy arenas are also urgently needed. Though the government has limited ability to ban advertising and marketing content because of important constitutional protections for free speech, there are several areas where it can develop safeguards that may limit what marketers can do, especially in areas of particular sensitivity. 69 For example, practices that undermine rational decision-making in the marketplace and tap into the unconscious/subconscious processes may be inherently deceptive or unfair. 70 These may include augmented reality, immersive environments, and similar techniques. Other practices that warrant close attention are: integration of marketing and “content” to make the two indistinguishable; linking point of influence to point of purchase (for example, in mobile marketing campaigns); peer-to-peer strategies employed in the social graph, especially on social network platforms; prizes, contests, and other incentives designed to encourage participation in marketing strategies and to facilitate data collection; behavioral targeting, smart ads, and dynamic product placement; and the use of neuromarketing to develop implicit persuasion techniques aimed at underage youth. 71

Conclusion: Researchers must expand their investigations to accommodate the new framework for digital food marketing

Authors: Kathryn Montgomery, PhD, School of Communication, American University; Sonya Grier, PhD Kogod School of Business, American University; Jeff Chester, MSW Center for Digital Democracy; Lori Dorfman, DrPH Berkeley Media Studies Group, Public Health Institute.

This research was supported by a grant from the Robert Wood Johnson Foundation’s Healthy Eating Research program (grant #65063).

The authors thank the researchers whose work they studied and discussed over the course of the project, especially those who participated in two meetings we convened on digital marketing: one in April 2009 co-sponsored with the National Policy and Legal Analysis Network to Prevent Childhood Obesity (NPLAN), and the second co-sponsored with the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD).

Graphic design by Barbara Moulton.

1 . See, for example, Advertising Research Foundation, “NeuroStandards Collaboration,” http://www.hearf.org/assets/neurostandards-collaboration (viewed 26 Feb. 2011). Microsoft Advertising and Carat presented research involving Quick Service Restaurants (QSRs) and the influence of the Internet. “Digital is Core to the Journey: Mobile is Especially influential,” they explained. Microsoft Advertising and Carat, “The New Shopper: Today’s Purchase Path and the Media that Influences It,” n.d., http://advertising.microsoft.com/wwdocs/user/en-us/researchlibrary/researchreport/USOnline-Consumer-Retail-Research-Carat-Microsoft- Advertising.pdf (viewed 24 Feb. 2011). See also Jeff Chester and Kathryn Montgomery, “Interactive Food & Beverage Marketing: Targeting Children and Youth in the Digital Age,” May 2007, http://www.digitalads. org/documents/digiMarketingFull.pdf (viewed 25 Feb. 2011).

2 . While online advertising spending still constitutes a relatively small portion of overall U.S. advertising expenditures (approximately $27 billion for online in 2010 as compared to $173 billion overall), the numbers are growing steadily. Mike Sachoff, “Online Ad Spending To Outpace Print In 2010,” WebProNews, 8 Mar. 2011, http://www. webpronews.com/topnews/2010/03/08/onlinead- spending-to-outpace-print-in-2010 (viewed 26 Feb. 2011). Last year, according to comScore, “U.S. Internet users received a total of 4.9 trillion display ads,” an increase of more than 23 percent from the previous year “Total U.S. e-commerce spending reached $227.6 billion in 2010, up 9 percent versus the previous year.” comScore, “comScore Releases ‘The 2010 U.S. Digital Year in Review’: Report Highlights 2010 Digital Marketing Trends and Implications for 2011,” 8 Feb. 2011, http://www.comscore.com/Press_Events/ Press_Releases/2011/2/comScore_Releases_ The_2010_U.S._Digital_Year_in_Review (viewed 26 Feb. 2011).

3.  “Explore Ad Age’s Databases,” Advertising Age, http://adage.com/datacenter/ (registration required).

4.  For example, Mars’ M&M’s has used the Alcance Media Group advertising network, which “consists of hundreds of websites both U.S. and international that reach the Hispanic market.” Alcance Media Group, “Advertiser Solutions,” http://www. alcancemg.com/advertisers/solutions/advertisersolutions/ (viewed 20 Aug. 2010). Burger King franchises in key markets, similarly, used BK’s “Futbol Kingdom” campaign to drive “incredible results,” according to its Hispanic agency Macias Advertising, Facebook, http://www.facebook. com/group.php?gid=44583321445 (viewed 20 Aug. 2010). McDonald’s is placing an even greater emphasis on generating new revenues through targeting multicultural consumers. It is now one of the “ve top companies marketing to Hispanics, spending $100 million a year, according to Advertising Age . McDonald’s has made a major effort to become “the country’s leading partner with the Hispanic community,” including in areas related to “education, marketing, vendors/suppliers and employment.” The company claims to have “one of the highest percentages of Hispanics in top management positions at 12%,” and “Hispanic crews at many of the country’s 14,000 restaurants receive free English-language and other career development courses. The McDonald’s Hispanic Owner/ Operators Association (MHOA) is the country’s largest organization of Hispanic franchisees with 269 members who operate 900 restaurants in 35 states with revenues of almost $2 billion. In addition, McDonald’s utilizes more Hispanic suppliers than any other corporation.” Hispanic Public Relations Society of America, “Premio Awards: Corporation of the Year: McDonald’s Corporation,” 2009, http://www.hpra-usa.org/awards.html (viewed 20 Aug. 2010).

5.  Alan J. Bush, Rachel Smith, and Craig Martin, “The Influence of Consumer Socialization Variables on Attitude toward Advertising: A Comparison of African- Americans and Caucasians,” Journal of Advertising 28, n. 3 (1999): 13-24; Felipe Korzenny, Betty Ann Korzenny, Holly McGavock, and Maria Gracia Inglessis, “The Multicultural Marketing Equation: Media, Attitudes, Brands, and Spending,” Center for Hispanic Marketing Communication, Florida State University, 2006; George P. Moschis, Consumer Socialization: A Life-Cycle Perspective (Lexington, MA: Lexington Books, 1987); Nitish Singh, Ik-Whan Kwon, and Arun Pereira, “Cross-Cultural Consumer Socialization: An Exploratory Study of Socialization Influences across Three Ethnic Groups,” Psychology & Marketing 20, n. 10 (2003): 15; Carolyn A. Stroman, “Television’s Role in the Socialization of African American Children and Adolescents,” The Journal of Negro Education 60, n. 3 (1991): 314- 327; Gail Baker Woods, Advertising and Marketing to the New Majority (Belmont, CA: Wadsworth Publishing Company, 1995).

6.  U.S. Census, “An Older and More Diverse Nation by Midcentury,” 14 Aug. 2008, http://www.census.gov/ newsroom/releases/archives/population/cb08- 123.html (viewed 12 May 2009).

7.  J. M. McGinnis, J. A. Gootman, and V. I. Kraak, eds., Food Marketing to Children and Youth : Threat or Opportunity? (Washington, DC: Institute of Medicine, 2005).

8.  E. S. Moore and V. J. Rideout, “The Online Marketing of Food to Children: Is it Just Fun and Games?” Journal of Public Policy & Marketing 6, n. 2 (2007): 202-220; E. S. Moore, “It’s Child’s Play: Advergaming and the Online Marketing of Food to Children,” 2006, http://www.kff.org/entmedia/upload/7536. pdf (viewed 2 Oct. 2008); M. Story and S. French, “Food Advertising and Marketing Directed at Children and Adolescents in the U.S.,” International Journal of Behavioral Nutrition and Physical Activity 1 (2004): 1-3.; E. T. Quilliam, N. M. Rifon, M. Lee, H-J. Paek, and R. Cole, “Food Advergames Targeting Children: Prevalence, Effects, and Policy Implications,” paper presented to the conference, Consumer Culture and the Ethical Treatment of Children: Theory, Research & Fair Practice, East Lansing, 2009; L. M. Alvy and S. L. Calvert, “Food Marketing on Popular Children’s Web Sites: A Content Analysis,” Journal of the American Dietary Association 108, n. 4 (2008): 710-713; S. L. Calvert, A. B. Jordan, R. R. Cocking, eds., Children in the Digital Age: Influences of Electronic Media on Development (Westport, CT: Praeger, 2002): 57-70; S. L. Calvert, “Children as Consumers: Advertising and Marketing,” The Future of Children 18, n. 1 (2008): 205-234; D. Kunkel, B. L. Wilcox, J. Cantor, et al., “Report of the APA Task Force on Advertising and Children,” 20 Feb. 2004, http://www.apa.org/ releases/childrenads.pdf ; Kaiser Family Foundation, “The Role of Media in Childhood Obesity,” Feb. 2004, http://www.kff.org/entmedia/upload/The-Role- Of-Media-in-Childhood-Obesity.pdf (viewed 2 Oct. 2008); American Academy of Pediatrics Committee on Communications, “Policy Statement: Children, Adolescents, and Advertising,” Pediatrics 118, n. 6 (Dec. 2006): 2563-2569, http://pediatrics. aappublications.org/cgi/content/full/118/6/2563 (viewed 4 Oct. 2008); P. M. Valkenburg, “Media and Youth Consumerism,” Journal of Adolescent Health 27 (S 2000): 52-56; Jennifer L. Harris, Marlene B. Schwartz, Kelly D. Brownell, et al., “Cereal FACTS: Evaluating the Nutrition Quality and Marketing of Children’s Cereals,” Oct. 2009, http://www.cerealfacts.org/media/Cereal_FACTS_Report.pdf (viewed 10 Apr. 2010); E. O. Lingas, L. Dorfman, and E. Bukofzer, “Nutrition Content of Food and Beverage Products on Web Sites Popular with Children,” American Journal of Public Health 99 (2009):S587-S592; Jennifer L. Harris, Marlene B. Schwartz, Kelly D. Brownell, et al., “Fast Food FACTS: Evaluating Fast Food Nutrition and Marketing to Youth,” Yale Rudd Center for Food Policy and Obesity, Nov. 2010, http://www.fastfoodmarketing.org/ media/FastFoodFACTS_Report.pdf (viewed 21 Mar. 2001).

9.  For example, the six-volume series from the MacArthur Foundation initiative on Digital Media and Learning, published by MIT Press, has no titles dedicated to marketing, and the issue is covered in only a few of the research papers. The John D. and Catherine T. MacArthur Foundation Series on Digital Media and Learning, MIT Press, http://mitpress.mit.edu/catalog/browse/browse. asp?btype=6&serid=170 ; “Building the Field of Digital Media and Learning,” http://digitallearning.macfound.org/site/c.enJLKQNlFiG/b.2029199/ k.94AC/Latest_News.htm (both viewed 25 Mar 2009). Similarly, ethnographic scholars have been conducting research on how youth are using social networking platforms, but without consideration of marketing effects. See, for example, danah boyd and Nicole Ellison, “Social Network Sites: De”nition, History, and Scholarship,” Journal of Computer-Mediated Communication 13, n. 1 (Oct. 2007): 210-230; danah boyd, “Why Youth (Heart) Social Network Sites: The Role of Networked Publics in Teenage Social Life,” in David Buckingham, ed., Youth, Identity, and Digital Media , MacArthur Foundation Series on Digital Learning (Cambridge, MA: MIT Press, 2007): 119-142, http://www. mitpressjournals.org/doi/pdf/10.1162/ dmal.9780262524834.119 (viewed 9 Apr. 2010).

10.  Chester and Montgomery, “Interactive Food & Beverage Marketing: Targeting Children and Youth in the Digital Age”; S. A. Grier and S. K. Kumanyika, “The Context for Choice: Health Implications of Targeted Food and Beverage Marketing to African Americans,” American Journal of Public Health 98, n. 9 (2008): 1616-29. See also, for example, the research presentations given to the Advertising Research Foundation’s Youth Council, http://www. thearf.org/assets/youth-council?fbid=yqcPGDy_ HEW (viewed 10 Apr. 2010).

11.  Mira Lee, Yoonhyeung Choi, Elizabeth Quilliam, and Richard T. Cole, “Playing with Food: Content Analysis of Food Advergames,” The Journal of Consumer Affairs 43, n. 1 (2009): 129-154; Moore and Rideout, “The Online Marketing of Food to Children: Is it Just Fun and Games?”; Fareena Sultan, Andrew J. Rohm, and Tao (Tony) Gao, “Factor Influencing Consumer Acceptance of Mobile Marketing: A Two-Country Study of Youth Markets,” Journal of Interactive Marketing 23, n. 4 (2009): 308-320.

12 . McGinnis, Gootman & Kraak, eds., Food Marketing to Children and Youth: Threat or Opportunity? ; D. Kunkel, B. L. Wilcox, J. Cantor, et al., “Report of the APA Task Force on Advertising and Children,” 20 Feb. 2004, http://www.apa.org/releases/childrenads. pdf . (viewed 9 Apr. 2010).

13.  We have arrived at these concepts through a process of inductive analysis, drawing from several broad areas of research, including: 1) marketing and market research industry literature on new media, children, and youth; 2) academic studies–both quantitative and qualitative–on new media use by children and youth; and 3) analysis of the practices and techniques used by marketers to target youth, including, but not limited to, companies promoting food and beverage. The authors wish to thank the Robert Wood Johnson Foundation and the Healthy Eating Research Initiative for their support of this research. This paper is based on a longer report completed under the HER grant #65063.

14.  Victoria J. Rideout, Ulla G. Foehr, and Donald F. Roberts, “Generation M2: Media in the Lives of 8-18-Year-Olds,” Kaiser Family Foundation Study, Jan. 2010, http://www.kff.org/entmedia/upload/8010.pdf (viewed 14 Sept. 2010).

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19.  David Hallerman, “Behavioral Targeting: Marketing Trends,” eMarketer, 2008; I. Khan, B. Weishaar, L. Polinsky, et al., “Nothing but Net: 2008 Internet Investment Guide,” 2008, https://mm.jpmorgan. com/stp/t/c.do?i=2082C-248&u=a_p*d_170762. pdf*h_-3ohpnmv (viewed 23 Mar. 2009).

20.  Center for Digital Democracy and U.S. PIRG, “Complaint and Request for Inquiry and Injunctive Relief Concerning Unfair and Deceptive Online Marketing Practices. Federal Trade Commission Filing,” 1 Nov. 2006, http://www.democraticmedia. org/”les/pdf/FTCadprivacy.pdf ; Center for Digital Democracy and U.S. PIRG, “Supplemental Statement In Support of Complaint and Request for Inquiry and Injunctive Relief Concerning Unfair and Deceptive Online Marketing Practices,” Federal Trade Commission Filing, 1 Nov. 2007, http://www. democraticmedia.org/”les/FTCsupplemental_ statement1107.pdf ; Center for Digital Democracy and U.S. PIRG, “Complaint and Request for Inquiry and Injunctive Relief Concerning Unfair and Deceptive Mobile Marketing Practices”; EPIC, Center for Digital Democracy, and U.S. PIRG, “In the matter of Google, Inc. and DoubleClick, Inc., Complaint and Request for Injunction, Request for Investigation and for Other Relief, before the Federal Trade Commission,” 20 Apr. 2007, http://www.epic.org/ privacy/ftc/google/epic_complaint.pdf ; EPIC, Center for Digital Democracy, and U.S. PIRG, “In the matter of Google, Inc. and DoubleClick, Inc., Second Filing of Supplemental Materials in Support of Pending Complaint and Request for Injunction, Request for Investigation and for Other Relief,” 17 Sept. 2007, http://epic.org/privacy/ftc/google/supp2_091707. pdf (all viewed 12 Oct. 2009).

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54.  Lan#Nguyen#Chaplin and Deborah#Roedder John, “The Development of Self$Brand Connections in Children and Adolescents,” Journal of Consumer Research 32 (June 2005): 119-129.

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56.  Grier and Kumanyika, “The Context for Choice: Health Implications of Targeted Food and Beverage Marketing to African Americans”; S. A. Grier and J. Mensinger, et al., “Fast Food Marketing and Children’s Fast Food Consumption: Exploring Parental Influences in an Ethnically Diverse Sample,” Journal of Public Policy & Marketing 26, n. 2 (2007): 221-235.

57.  Alan J. Bush, Rachel Smith, and Craig Martin, “The Influence of Consumer Socialization Variables on Attitude toward Advertising: A Comparison of African- Americans and Caucasians,” Journal of Advertising 28, n. 3 (1999): 13-24; Felipe Korzenny, Betty Ann Korzenny, Holly McGavock, and Maria Gracia Inglessis, “The Multicultural Marketing Equation: Media, Attitudes, Brands, and Spending,” Center for Hispanic Marketing Communication, Florida State University, 2006; George P. Moschis, Consumer Socialization: A Life-Cycle Perspective (Lexington, MA: Lexington Books, 1987); Nitish Singh, Ik-Whan Kwon, and Arun Pereira, “Cross-Cultural Consumer Socialization: An Exploratory Study of Socialization Influences across Three Ethnic Groups,” Psychology & Marketing 20, n. 10 (2003): 15; Carolyn A. Stroman, “Television’s Role in the Socialization of African American Children and Adolescents,” The Journal of Negro Education 60, n. 3 (1991): 314- 327; Gail Baker Woods, Advertising and Marketing to the New Majority (Belmont, CA: Wadsworth Publishing Company, 1995).

58 . S. A. Grier and S. Kumanyika, “Targeted Marketing and Public Health,” Annual Review of Public Health 31(1): 349-369; Grier and Kumanyika, “The Context for Choice: Health Implications of Targeted Food and Beverage Marketing to African Americans”; Grier and Mensinger, et al., “Fast Food Marketing and Children’s Fast Food Consumption: Exploring Parental Influences in an Ethnically Diverse Sample”; S. Kumanyika and S. Grier, “Targeting Interventions for Ethnic Minority and Low-income Populations,” Future of Children 16, n. 1 (2006): 187-207.

59 . Grier, “African American & Hispanic Youth Vulnerability to Target Marketing: Implications for Understanding the Effects of Digital Marketing.”

60 . David A. Kessler, The End of Overeating: Taking Control of the Insatiable American Appetite (New York: Rodale, 2009): 34. See also Adam Drewnowski, “Energy Intake and Sensory Properties of Food,” American Journal of Clinical Nutrition 62, no. 5 Suppl. (1995): 1081S-1085S.

61 . Kessler, The End of Overeating: Taking Control of the Insatiable American Appetite , 34.

62 . Kessler, The End of Overeating: Taking Control of the Insatiable American Appetite , 61-64.

63 . Ashley N. Gearhardt, William R. Corbin, Kelly D. Brownell, “Preliminary Validation of the Yale Food Addiction Scale,” Appetite 52 (2009): 430-436, http://www.yaleruddcenter.org/resources/upload/ docs/what/addiction/FoodAddictionScaleArticle09. pdf (viewed 14 Sept. 2010).

64 . Chester and Montgomery, “Interactive Food & Beverage Marketing: Targeting Children and Youth in the Digital Age.”

65 . Terry T. Huang, Adam Drewnowski, Shiriki K. Kumanyika, and Thomas A. Glass, “A Systems- Oriented Multilevel Framework for Addressing Obesity in the 21st Century,” Preventing Chronic Disease 6, n. 3 (July 2009): 1-10.

66 . MacArthur Foundation, “Building the Field of Digital Media and Learning,” http://www.digitallearning. macfound.org/site/c.enJLKQNlFiG/b.2029199/k. BFC9/Home.htm (viewed 7 June 2009); S. Livingstone, “Do the Media Harm Children? Reflections on New Approaches to an Old Problem,” Journal of Children and Media 1, n. 1 (2007): 5-14.

67 . Robert V. Kozinets, Netnography: Doing Ethnographic Research Online (Newbury Park, CA: Sage Publications, 2009).

68 . These issues of new media research design are a central focus of the current Healthy Eating Research Initiative Round 5 grant, “De”ning Priorities and Optimal Research Designs for Studying the Impact of Digital Food Marketing on Adolescents” (Co-PIs Kathryn Montgomery and Sonya Grier).

69 . Angela Campbell, “Recent Federal Regulatory Developments Concerning Food and Beverage Marketing to Children and Adolescents,” Memo prepared for the Second NPLAN/BMSG Meeting on Digital Media and Marketing to Children for the NPLAN Marketing to Children Learning Community, Berkeley, CA, June 29-30, 2009, http://digitalads. org/documents/Campbell_NPLAN_BMSG_memo. pdf (viewed 26 Aug. 2010).

70 . Campbell, “Recent Federal Regulatory Developments Concerning Food and Beverage Marketing to Children and Adolescents.”

71 . These issues are developed in greater depth in a separate forthcoming report commissioned by the National Policy & Legal Analysis Network to Prevent Childhood Obesity (NPLAN): Kathryn Montgomery and Jeff Chester, “Digital Marketing to Children and Youth: Problematic Practices and Policy Interventions.”

72 . Alvy and Calvert, “Food Marketing on Popular Children’s Web Sites: A Content Analysis”; S. L. Calvert, A. B. Jordan, R. R. Cocking, eds., Children in the Digital Age: Infuences of Electronic Media on Development (Westport, CT: Praeger, 2002): 57-70; S. L. Calvert, “Children as Consumers: Advertising and Marketing,” The Future of Children 18, n. 1 (2008): 205-234; Kunkel, Wilcox, Cantor, et al., “Report of the APA Task Force on Advertising and Children”; Kaiser Family Foundation, “The Role of Media in Childhood Obesity,” Feb. 2004, http:// www.kff.org/entmedia/upload/The-Role-Of-Mediain-Childhood-Obesity.pdf (viewed 2 Oct. 2008); American Academy of Pediatrics Committee on Communications, “Policy Statement: Children, Adolescents, and Advertising,” Pediatrics 118, n. 6 (Dec. 2006): 2563-2569, http://pediatrics. aappublications.org/cgi/content/full/118/6/2563 (viewed 4 Oct. 2008); P. M. Valkenburg, “Media and Youth Consumerism,” Journal of Adolescent Health 27 (S 2000): 52-56.

73 . PepsiCo, “Frequently Asked Questions,” http:// pepsico10.com/2010/pepsico10-faq.htm ; Advertising Research Foundation, “ARF Announces Groundbreaking NeuroStandards Study,” 24 Sept 2010, http://www.thearf.org/assets/pr-2010-09- 24 (both viewed 20 Mar. 2011).

74 . Barbara Ortutay, “Investments Place Value of Facebook at $50 Billion,” MSNBC, 3 Jan. 2011, http://www.msnbc.msn.com/id/40885536/ns/ business-us_business/ (viewed 20 Mar. 2011).

75.  “Chocolate Charmer Campaign Media Mix,” n.d., http://www.scribd.com/doc/45468533/Cadbury-Campaign-Results-Dec-2010 ; Graham Charlton, “Q&A: David Buckingham on Nectar and Yahoo’s Ad Targeting Scheme,” Econsultancy, 13 Apr. 2010, http://econsultancy.com/us/blog/5740-q-a-davidbuckingham-on-nectar-and-yahoo-s-ad-targetingscheme (both viewed 20 Mar. 2011).

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Peer-reviewed

Research Article

What motivates consumers to buy organic foods? Results of an empirical study in the United States

Roles Conceptualization, Investigation, Project administration, Writing – original draft, Writing – review & editing

Affiliation Department of Business, University of Wisconsin-Parkside, Kenosha, Wisconsin, United States of America

Roles Conceptualization, Data curation, Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Faculty of Economic Sciences and Management, Nicolaus Copernicus University, Torun, Poland

• Raghava R. Gundala, 
• Anupam Singh

• Published: September 10, 2021
• https://doi.org/10.1371/journal.pone.0257288
• Peer Review
• Reader Comments

Consumers perceive organic foods as more nutritious, natural, and environmentally friendly than non-organic or conventional foods. Since organic foods developed, studies on consumer behavior and organic foods have contributed significantly to its development. The presesent study aims to identify the factors affecting consumer buying behaviour toward organic foods in the United States. Survey data are collected from 770 consumers in the Midwest, United States. ANOVA, multiple linear regression, factor analysis, independent t-tests, and hierarchical multiple regression analysis are used to analyze the collected primary data. This research confirms health consciousness, consumer knowledge, perceived or subjective norms, and perception of price influence consumers’ attitudes toward buying organic foods. Availability is another factor that affected the purchase intentions of consumers. Age, education, and income are demographic factors that also impact consumers’ buying behavior. The findings help marketers of organic foods design strategies to succeed in the US’s fast-growing organic foods market.

Citation: Gundala RR, Singh A (2021) What motivates consumers to buy organic foods? Results of an empirical study in the United States. PLoS ONE 16(9): e0257288. https://doi.org/10.1371/journal.pone.0257288

Editor: Ali B. Mahmoud, St John’s University, UNITED KINGDOM

Received: January 7, 2021; Accepted: August 30, 2021; Published: September 10, 2021

Copyright: © 2021 Gundala, Singh. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

What is organic food.

Foods that are cultivated without the application of chemical pesticides can be called organic foods [ 1 ]. The feed cannot include antibiotics or growth hormones for the food products labeled organic for foods derived from animals (e.g., eggs, meat, milk, and milk products) [ 2 ]. Organic foods are perceived as environmentally safe, as chemical pesticides and fertilizers are not used in their production. They also are not grown from genetically modified organisms. Furthermore, organic foods are not processed using irradiation, industrial solvents, or synthetic food additives [ 3 ]. Thus, these foods are considered environmentally safe, as they are produced using ecologically sound methods.

When the world’s population was low, almost all agriculture was primarily organic and near-natural. However, these traditional practices, passed from one generation to the next, did not produce enough food to meet the rapidly increasing global population’s demands. This led to the "green revolution," in which farmers used technological interventions to maximize outputs to meet the growing need for food for the increasing population [ 4 ]. Unfortunately, this increased food production also increased chemical pesticides and fertilizers, causing environmental and health issues.

Consumers worldwide are now more concerned with the environment [ 5 ]. They are sensitive to information about products, processing, and brands that might impact the environment [ 6 ]. Environmental issues are perceived as having a more direct impact on consumers’ well-being. Consumers who know environmental degradation activities are willing to buy organic foods [ 7 ].

Heightened awareness of the environment and the consumer’s desire to buy organic foods leads to increased corporate investment toward organic food production and marketing. They are thus initiating significant innovations in the organic food industry [ 8 ]. As a result, the organic food market is increasing [ 9 ]. In addition, effective campaigns create awareness about the environment. Because of these effective campaigns, consumers are now ready to spend more on green products [ 10 ].

Furthermore, people’s living standards have significantly improved in the past few decades. With these improvements, the demand for better lifestyles and food has also increased. The steady growth in purchases of organic foods is an emerging trend. Consumers want to learn what organic foods offer before purchasing decisions [ 11 ].

Global organic food market.

According to a recent report, the organic food market is expected to grow with a Compound Annual Growth Rate (CAGR) of 16% during 2015–2020. This growth might be due to consumers’ health concerns as they become aware of organic foods’ perceived health benefits. Further, rising income levels, changes in living standards, and government initiatives encourage the broader adoption of organic products [ 12 ].

Organic food market in the USA

In 2018, organic market sales were US$47.86 billion, and the market grew by 6.3% from 2017 to 2018 [ 13 ]. In 2017, the organic food market in the United States hit a record of US$45.2 billion in sales; this market consists of both the organic food market and the organic non-food market (see Fig 1 ). It is predicted that the organic food market will grow at a consistent pace as it matures. The demand for organic foods is flourishing as consumers seek nutritious and clean eating, which they perceive as suitable for their health and the environment.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Source: Statista.com .

https://doi.org/10.1371/journal.pone.0257288.g001

Understanding consumer buying behavior toward organic foods is essential to pursue better marketing and management of the market. This can help us learn about the consumer decision-making process on organic foods and understand how consumers’ attitudes and beliefs impact their consumption patterns. In addition, studying consumers’ willingness to pay a premium price and their response to organic food advertisements [ 14 ] is necessary for companies to succeed in this growing market.

This study focuses on exploring the factors influencing consumers’ buying behavior of organic foods. Although many factors can affect consumer buying behavior, we chose health consciousness, knowledge, subjective norms, price, and availability for this study based on Singh & Verma’s [ 1 ] study. Understanding these factors is vital for developing marketing strategies for successfully marketing of these products.

Theory and research hypotheses

Earlier research in the area of consumer buying behavior of organic foods discussed reasons why people buy. Even though there are some differences, the main reasons are product quality, concerns related to environmental degradation, and health-related issues [ 15 ]. Subsequent studies on consumer buying behavior of organic foods confirmed this [ 16 ]. Consumers tend to perceive organic foods as being healthier than conventional alternatives. This perception of organic foods is one of the most commonly cited reasons for purchasing them. In two studies [ 17 , 18 ], it became evident that consumers tend to have a positive attitude toward organic foods. However, they may not be purchasing organic foods due to environmental concerns. Instead, purchasing decisions are driven by the perceived health benefits the foods offer, the desire to fit in with a social group, try a new trend, or differentiate themselves from others [ 19 ].

Health consciousness (HEC)

Consumer attitudes are significantly influenced by their health consciousness [ 20 ]. Consumers mainly purchase organic foods due to health benefits [ 21 ]. Several studies show that health factors significantly influence consumers’ willingness to buy organic foods [ 22 – 26 ]. One of the significant reasons that influence consumers could be the deterioration of their health [ 22 ]; thus, consumers see consumers’ purchases as an investment for good health. Bourn and Prescott [ 27 ] found that organic foods have a competitive advantage over conventional foods due to organic foods’ nutritive attributes.

However, in a study conducted by Fotopoulos and Krystallis [ 28 ], taste is also another reason consumers buy organic foods. Even though many studies said that the perceived health benefits are the primary motivator, work by Tarkiainen and Sundqvist [ 29 ] and Michaelidou and Hassan [ 25 ] did not find it to be a compelling driver. In the earlier studies, the health benefit is the least significant influencer on organic foods. We examined our respondents’ thoughts on this topic with these different findings on the importance of health benefits. Based on the above, we formulated Hypothesis 1:

• H1: Health consciousness has a positive impact on buying behavior toward organic foods.

Consumer knowledge (CK)

The Theory of Reasoned Actions (TRA) supports our understanding of consumer behavior development by exploring the motivational influences on how consumers behave [ 30 ]. TRA offers a basis for predicting consumer attitudes and behavior [ 31 ]. Liu [ 20 ] further confirms that TRA is the best theory to predict consumer behavior about organic foods. Consumers want to be aware of what they are buying and satisfy their needs and wants. Therefore, knowledge is essential in impacting consumer behavior on foods.

Sapp [ 32 ] argued that knowledge involves a cognitive learning process. Consumer purchase intentions differ based on the consumers’ levels of expertise [ 33 ]. Consumers’ purchase of organic products cannot be separated from their knowledge and understanding of organic foods [ 34 , 35 ]. Recent research on consumer awareness and knowledge about organic foods found that consumer awareness worldwide is low relative to Europe’s awareness level. This elevated awareness about organic food is due to its market, which is well developed compared to the rest [ 3 , 36 – 39 ].

Studies also found that consumers’ knowledge about what is "organic" is inconsistent. For example, in one study, respondents assumed that organic foods are produced without pesticides, fertilizers, or growth regulators [ 40 ]. However, in a similar study done in the UK by Hutchings and Greenhalgh [ 41 ], respondents thought that "organic" farming is free from chemicals and is grown naturally. Further, respondents felt that organic foods are not intensively farmed.

In consumer purchase decisions of organic foods, awareness and knowledge about these products are essential. Smith and Paladino [ 42 ] conducted a study on factors affecting organic foods’ purchasing behavior. They found that learning about social and environmental issues will positively impact consumers’ purchase behavior. However, from the above, it is evident that consumers’ knowledge about organic foods is inconsistent. While they are likely to perceive that organic foods are pure, natural, and healthy, this perception might be based on their belief that organic foods are free from pesticides and chemical fertilizers. To evaluate the same, we proposed Hypothesis 2 as:

• H2: Consumer buying behavior is positively associated with consumer knowledge of organic foods.

Perceived or subjective norms (PSN)

Ajzen [ 43 ] defines perceived or subjective norms (PSN) as "a perceived social pressure to perform or not to perform a behavior." Finlay et al. [ 44 ] said subjective norms are individuals’ perceptions or opinions about what others believe the individual should do. Subjective norms had an impact on consumer purchase behavior in the research conducted by Shimp and Kavas [ 45 ], Sheppard et al. [ 46 ], and Bagozzi et al. [ 47 ]. Chang [ 48 ] tested the correlation between attitudes toward consumer behavior and subjective norms. This study also examined the link between norms and attitudes and found that subjective norms lead to behavior attitudes in a meaningful manner. From the above, we formulated Hypothesis 3 as:

• H3: Perceived or subjective norms will positively influence consumer buying of organic foods.

Perception of the price (PP)

Organic foods are priced higher than conventional foods. Aertsens et al. [ 49 ] and Hughner et al. [ 16 ] confirmed that price is a significant barrier to organic food choice. Padel and Midmore [ 50 ] and O’Doherty et al. [ 51 ] indicate that high prices are likely to impede future demand development; thus, price is crucial in organic food marketing. The research confirmed that consumers switch products due to high prices [ 52 ], and Gan et al. [ 53 ] found that higher costs hurt the chances of buying organic foods. However, Radman [ 54 ] concluded that some consumers have a positive attitude toward organic foods and are willing to pay a higher price. Meanwhile, Smith et al. [ 55 ] found that price does not significantly impact organic food purchases. Since there are contradictory findings on the relationship between price and organic foods, we decided to explore whether consumer perceptions of cost have any link to their buying behavior of organic foods, as stated in Hypothesis 4:

• H4: Perceived price of organic foods is negatively associated with consumer buying.

Availability of organic foods (AV)

Availability is one factor that encourages the purchase of organic foods [ 56 ]. Makatouni [ 24 ] reiterated that organic foods’ availability could be a barrier to consuming the same. In a study by Tarkiainen and Sundqvist [ 29 ], the authors showed that the easy availability of organic foods positively affected their purchase behavior. In a survey conducted by Young et al. [ 57 ], consumers prefer readily available products. Therefore, they do not want to spend time searching for organic products.

However, recently, retailers across the country have noticed the growing popularity of organic foods and have been adding organic foods to their shelves. Increased organic foods marketing by large retail outlets and specialty stores has made organic foods accessible to more consumers [ 58 ]. This discussion poses a question. Does availability have a positive impact on purchase behavior? We decided to test this using Hypothesis 5:

• H5: Availability of organic foods increases consumer buying behavior.

Purchase intention and actual buying behavior (PI and AB)

Planned behavior theory suggests that a reaction is a function of intentions and perceived behavioral control. Sheppard et al. [ 31 ] showed evidence that a relationship exists between choices and actions in different buying behavior types. Ajzen [ 43 ] stated that intentions or willingness could significantly predict actual buying behavior. Studies by Tanner and Kast [ 59 ] and Vermeir and Verbeke [ 60 ] found discrepancies between consumers who expressed favorable attitudes and actual purchase behavior. Hughner [ 16 ] found that, even though consumers have a positive attitude toward purchasing organic foods, very few people bought them. Based on the above, researchers believe that there is a relationship between attitudes and actions. This is in line with the study of Wheale and Hinton [ 61 ]. Attitudes toward organically grown food products might positively and significantly affect purchase behavior [ 62 ]. From this, it is assumed that the purchase of organic food results from an intent to purchase.

The attitude-behavior gap is a gap in consumers’ favorable attitude and actual purchase behavior of organic foods. This gap suggests that a positive attitude toward organic products might not always lead to a purchase. Many factors could influence this gap. Price, availability, and social influence, among many others, can create a discrepancy among consumer attitudes, purchase intentions, and actual buying behavior. We test the effects of influencing factors (HEC, CK, PSN, PP, AV) on purchase intent (PI) and actual buying behavior (AB).

• H6: Consumer attitudes toward organic foods mediate the association between influencing factors and purchase intention.
• H7: Consumer attitudes and purchase intentions mediate the association between influencing factors and actual buying behavior.

Sociodemographic factors

Behavior is not influenced by attitudes alone; many factors influence behavior. For example, Voon et al. [ 62 ] found that sociodemographic factors influence buying behavior. One significant factor is gender. For instance, Lockie et al. [ 63 ] confirm that women are more likely to have positive attitudes than men toward organic foods. Similarly, adolescent girls are more favorable than boys toward organic products [ 64 ].

Research has found that age also influences the purchase of organic foods. For example, Misra et al. [ 65 ] show that older individuals may be willing to buy organic foods due to health-related reasons. However, Cranfield and Magnusson [ 66 ] found that younger consumers are more likely to pay over 6% higher premiums to ensure that food products are pesticide-free. In addition, Rimal et al. [ 67 ] found that older individuals are less likely to buy organic foods than younger individuals. In contrast, younger people and women consider organic foods more essential and include them in their purchases [ 68 , 69 ].

In consumers’ demographic characteristics, income is another factor considered crucial for influencing the purchase of organic food. In two studies conducted by Govidnasamy and Italia [ 68 ] and Loureiro et al. [ 70 ], organic products are more frequently purchased by higher-income households. Likewise, Voon et al. ’s [ 62 ] research found that household income positively relates to organic food purchases. Further, women in the 30–45, with children and having a higher disposable income, include organic foods in their purchases [ 58 ].

Research by Cunningham [ 38 ] and O’Donovan and McCarthy [ 71 ] found a positive relationship between organic foods and education consumption. This is also true of Dettmann and Dimitri’s [ 58 ] work. According to their study, individuals with a higher education level are more likely to purchase organic foods than those with a lower education level. This was also discovered by Aryal et al. [ 72 ]. They showed that education is another factor that might influence the purchase of organic products.

Contrary to the above-referred research, some studies found a negative correlation [ 73 , 74 ]. These negative correlations are also confirmed by the analysis of Arbindra et al. [ 75 ]. They explain that organic food purchase patterns and levels of education are statistically significant.

Since there are different findings in the literature, we test the influence of demographic factors on buying, and the following hypotheses are formulated:

• H8a: The age of the consumer and buying behavior toward organic foods are significantly different.
• H8b: Gender and buying behavior toward organic foods are significantly different.
• H8c: Income and buying behavior toward organic foods are significantly different.
• H8d: Education and buying behavior toward organic foods are significantly different.

Research method

Primary data were collected using a questionnaire developed from prior studies [ 1 , 76 – 80 ]. The questionnaire has two sections. The first section contains questions about organic product purchase behavior, with responses measured on a 5-point Likert scale. The second section includes questions on respondents’ demographic information (see S1 Appendix ).

The questionnaire was pilot tested on 50 respondents to ensure question and response clarity. Changes were made where necessary based on the feedback of the pilot study. Convenience and snowball sampling methods were used. Online surveys were conducted by sending out the surveys to individuals known to both the researcher and the students taking a Market Research course during Spring 2019. These individuals were asked to pass on the survey to their friends and family members. The snowball sampling method was used to generate as many responses as possible during May-August 2019. Respondents were asked to participate in the study via email. The email sent to potential participants indicated that they voluntarily agreed to participate in the survey by clicking on the survey link. The email also mentioned that, at any time, they could stop participating by merely closing the browser, and their responses will not be saved. A total of 770 responses were received. After going through the questionnaires for completeness, a total of 502 surveys were used for further analysis. The study is approved by the Institutional Review Board of the University of Wisconsin-Stout as this involves a survey from the consumers based on their consents. Further, the data were analyzed anonymously.

Results and discussion

The respondents’ demographic profile is reported in Table 1 . The table indicates that 58% of the respondents are men, while the remaining 42% are women. The plurality (37%) of the respondents is 31–40 years old. Likewise, most (35%) are graduate students, followed by undergraduate students (28%) and postgraduates/Ph.D. (21%). The analysis also shows that respondents’ plurality has an annual income of over $100,000. The highest proportion of respondents (38%) has a family size of 1–2 members living in their households. This family size is closely followed by 3–4 people in the household (37%).

https://doi.org/10.1371/journal.pone.0257288.t001

Reasons for purchase of organic foods

Respondents were asked if they have ever bought organic food products, and 55.6% said yes. Then, these respondents were asked further questions about their purchases. When asked about the purchase frequency, 51.8% said they purchase organic food products weekly, 26% purchase at least once a month, and the remaining 21.6% purchase less frequently than once a month.

Respondents mentioned health consciousness as the primary reason for purchasing organic food. Further, non-use of pesticides, lower pesticide residues, environmentally friendly production, and perceived freshness are other reasons respondents choose to buy organic foods (see Fig 2 ). Health consciousness played an essential role in 48% of respondents, followed by pesticide-free (19%) and environmentally friendly (15%) considerations.

https://doi.org/10.1371/journal.pone.0257288.g002

To identify the factors influencing attitudes toward organic foods, Principal Components Analysis (PCA) using varimax rotation is conducted. Before applying the factor analysis, the Kaiser-Mayer-Olin (KMO) test and Bartlett’s test of sphericity are used to test data suitability. The result shows the KMO measure of sampling adequacy as 0.82. Thus, the value exceeds the cut-off value of 0.60. Bartlett’s test of sphericity (χ 2 = 2,082, df = 132, p < .001) is also significant. This indicates that the inter-item correlations are significant for PCA. KMO and Bartlett’s test results support the data [ 81 ]. The results are shown in Table 2 to ensure scale reliability. Each factor has a Cronbach’s alpha (α) value higher than the threshold value of 0.70 [ 82 ].

https://doi.org/10.1371/journal.pone.0257288.t002

Multiple linear regression analysis is performed to test hypotheses H1–H5. The analysis ascertains the impact of health consciousness, consumers’ knowledge, perceived or subjective norms, availability, and perception of the price on consumer attitude (AT). As shown in Table 3 , HEC, CK, PSN, PP, and AV account for 33% of the explained variances (F (5, 177) = 32.51, p < .001, R 2 = 0.33).

https://doi.org/10.1371/journal.pone.0257288.t003

According to the results, the H1(β = 0.37, p = .016); H2 (β = 0.47, p < .001); H3 (β = 0.34, p = .015); and H4 (β = 0.36, p = .001) are supported, as the β values are positive and significant. However, the values for H5 (β = 0.29, p = .117) are statistically non-significant. This shows that H5 is not supported. The findings confirmed that health consciousness, consumer knowledge, perceptive or subjective norm, and perception of the price affect respondents’ attitudes toward organic foods. However, it is also found that availability has no impact on consumers’ attitudes, at least in our sample.

The hierarchical regression method was applied to test the association between purchase intention and influencing factors (HEC, CK, PSN, PP, and AV) via the mediation of AT. The mediation was ascertained using Baron and Kenny’s [ 83 ] approach. Certain criteria must be met to declare the presence of mediation in the equation. The first necessary criterion is that the independent variable (IV) must affect the dependent variable (DV). The second criterion is that the IV must significantly influence the mediating variables. The third suggests the mediating variables must affect the DV. When all of the above conditions are met, a full mediation is confirmed if the IV no longer affects the DV after the mediator has been controlled for. Partial mediation occurs when the effect of the IV on the DV is reduced after the mediators are controlled for. The results indicate that all β values (for the effect on AT) are positive and significant: HEC (β = 0.17, p < .001), CK (β = 0.29, p < .030), PSN (β = 0.33, p < .020), PP (β = 0.39, p < .010), and AV (β = 0.24, p < .050; see Table 4 ). The presence of mediation is also confirmed, as Baron and Kenny’s criteria are met. Thus, H6, which predicts that the attitude mediates the relationship between the influencing factors and PI, is supported.

https://doi.org/10.1371/journal.pone.0257288.t004

According to the results reported in Table 5 , H7—which states that influencing factors have a positive effect on actual buying behavior via the mediating effect of attitude and purchase intention—is supported: AT (β = 0.24, p < .040) and PI (β = 0.26, p < .020). This confirms that AT and PI have a positive and significant effect on consumers’ actual buying behavior. Furthermore, AT and PI mediate the association between influencing factors and AB since the values of the corresponding regression coefficients of HEC, CK, PSN, PP, and AV are reduced when the effects of AT and PI are controlled for. These results support H7.

https://doi.org/10.1371/journal.pone.0257288.t005

Demographic differences in the actual buying behavior

An independent t-test is conducted to see if the actual purchase behavior changes are due to gender. Levene’s test ( Table 6 ) indicates that the p-value for gender is more significant than .05. The result confirms the homogeneous variance. Thus, the t-test is suitable for equal variance. Furthermore, the t-value of 0.08 (two-tailed) is higher than the significance level, suggesting a non-significant difference, implying that the mean values (-0.19 and -0.16) are not significant, supporting H8a.

https://doi.org/10.1371/journal.pone.0257288.t006

Table 7A below shows the results of the one-way ANOVA test. The findings suggest that respondents’ age (F = 7.01; p = .023) has a statistically significant effect on the purchase intention; thus, H8b is supported. However, further analysis of the respondents’ age groups is conducted using the least significant difference (LSD) test. The results of the LSD test, as depicted in Table 7B , indicate that the age group of 41–50 years has a statistically higher score than other age groups.

A. Age groups: ANOVA test. B. LSD test for respondent’s age groups.

https://doi.org/10.1371/journal.pone.0257288.t007

Hypothesis H8c is supported, as the ANONA test reveals that annual income (F = 8.22; p = .011) significantly affects purchase intention (see Table 8A ). Further, the LSD Test for income ( Table 8B ) implies that the income level of more than US$80,000 has a higher score on the actual purchase as compared to those with incomes lower than US$80,000.

A. Annual income: ANOVA test. B. Annual income: LSD test.

https://doi.org/10.1371/journal.pone.0257288.t008

According to Table 9A , the level of education (F = 7.05; p = .001) affects consumer purchase behavior toward organic foods. The LSD test ( Table 9B ) further clarifies that consumers hold postgraduate/Ph.D. Degrees have a higher score on the AB of organic food products than consumers with only a high school diploma or undergraduates. The test also shows that graduate degree-holders are more likely to purchase organic food than any other group.

A. Education levels: ANOVA Test. B. Education levels: LSD Test.

https://doi.org/10.1371/journal.pone.0257288.t009

Conclusions

This study tested Singh and Verma’s [ 1 ] model on US consumers. We initially investigated the factors influencing consumer attitudes. Then we studied how these influencing factors and attitudes together affect the actual buying behavior of consumers. There has always been a debate on consumers’ intention to purchase compared to their actual purchase. Evidence of previous studies suggests that actual purchase behavior is not always the consequence of intent to purchase. Consumers sometimes intend to buy but often fail to do so. Therefore, this study also looked at the impact of demographic variables (such as gender, income, education, and age) on the consumers’ actual buying. This study confirms that all five factors—namely, health consciousness, consumer knowledge, availability, perception of price, and subjective norms—influence consumer attitudes. In contrast, attitudes and purchases were found to have mediating roles between influencing factors and actual buying behavior toward organic foods.

Further, the t-tests and ANOVA test results explored a more in-depth understanding of the relationships between demographic factors and actual buying. LSD tests were conducted to understand which sub-group in a demographic variable is significantly different from its counterparts. The findings of this study suggest that gender does not affect the actual buying of organic foods. Meanwhile, income, age, and education do affect consumers’ actual purchases. Furthermore, the LSD test shows that 41–50 years of age, consumers are more likely to buy organic foods than those in other groups. Not surprisingly, income is found to be another critical determinant of actual buying decisions. This may indicate that income is directly proportional to organic food buying (i.e., the higher the income level, the more likely the consumer is to buy organic foods). The findings also indicate the same trend with education. Higher levels of education correspond to a higher likelihood of purchasing organic foods. This could be because education might increase the consumer’s knowledge, and informed consumers could be health-conscious and aware of organic foods’ benefits. Many studies have stated different reasons for buying organic foods in developed and developing countries. However, if we compare and contrast our research findings with recent work in developed countries, similar results have been obtained. Health consciousness, food safety, environmentally friendly procedures, consumer’s knowledge on organic foods, perceived or subjective norms, availability of organic foods, and demographic factors, like gender, education, and income are the most substantial reasons for buying organic food, irrespective of the country (developed or developing; [ 1 , 3 , 25 ].

Implications

The findings of this research may guide companies dealing with organic foods. The study suggests the companies can craft marketing strategies to increase consumers’ awareness of the benefits of organic food consumption. Providing additional information about the benefits of organic food products may help convince consumers to make the purchase. This study will be helpful to retailers to segment their consumers based on their demographics. The study will also help retailers understand the factors that are likely to influence consumers’ organic food purchases and design strategies to increase their sales. Since availability (access) is one factor in buying decisions, retailers should reach out to local shops/areas to enhance market coverage. As subjective norms are another significant factor, marketers should promote organic food consumption through family, celebrities, and society.

This study offers important implications but with some limitations. First, direct factors related to consumer purchase decisions were measured. The second limitation is the sampling. Since the data is collected using an online survey forwarded by students and researchers to others, it could constitute snowballing. Any data collected using snowballing should be cautiously used to generalize the outcomes. Further research in this area may consider advertisements, federal and state regulations, and consumption patterns of organic foods. Of course, in organic food consumption, more studies in different regions with a higher sample size would validate our findings.

Covid-19 pandemic crisis affecting all aspects of the population’s daily life, in particular, dietary habits [ 85 ]. However, Covid-19 perceptions on adopting healthy food habits are not investigated in the present study. Any further research in this area should consider post-pandemic behavior. Recent studies suggest that parental attitudes affects dietary habits [ 84 – 86 ]. Therefore, future research should also consider how parental attitudes influence the purchase of organic foods.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0257288.s001

S1 Appendix. Survey questionnaire.

https://doi.org/10.1371/journal.pone.0257288.s002

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