food marketing research articles

• Open access
• Published: 27 July 2022

A scoping review of outdoor food marketing: exposure, power and impacts on eating behaviour and health

• Amy Finlay 1 ,
• Eric Robinson 1 ,
• Andrew Jones 1 ,
• Michelle Maden 2 ,
• Caroline Cerny 1 , 3 ,
• Magdalena Muc 1 ,
• Rebecca Evans 1 ,
• Harriet Makin 1 &
• Emma Boyland 1  

BMC Public Health volume  22 , Article number:  1431 ( 2022 ) Cite this article

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There is convincing evidence that unhealthy food marketing is extensive on television and in digital media, uses powerful persuasive techniques, and impacts dietary choices and consumption, particularly in children. It is less clear whether this is also the case for outdoor food marketing. This review (i) identifies common criteria used to define outdoor food marketing, (ii) summarises research methodologies used, (iii) identifies available evidence on the exposure, power (i.e. persuasive creative strategies within marketing) and impact of outdoor food marketing on behaviour and health and (iv) identifies knowledge gaps and directions for future research.

A systematic search was conducted of Medline (Ovid), Scopus, Science Direct, Proquest, PsycINFO, CINAHL, PubMed, the Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials and a number of grey literature sources. Titles and abstracts were screened by one researcher. Relevant full texts were independently checked by two researchers against eligibility criteria.

Fifty-three studies were conducted across twenty-one countries. The majority of studies ( n  = 39) were conducted in high-income countries. All measured the extent of exposure to outdoor food marketing, twelve also assessed power and three measured impact on behavioural or health outcomes. Criteria used to define outdoor food marketing and methodologies adopted were highly variable across studies. Almost a quarter of advertisements across all studies were for food (mean of 22.1%) and the majority of advertised foods were unhealthy (mean of 63%). The evidence on differences in exposure by SES is heterogenous, which makes it difficult to draw conclusions, however the research suggests that ethnic minority groups have a higher likelihood of exposure to food marketing outdoors. The most frequent persuasive creative strategies were premium offers and use of characters. There was limited evidence on the relationship between exposure to outdoor food marketing and eating behaviour or health outcomes.

Conclusions

This review highlights the extent of unhealthy outdoor food marketing globally and the powerful methods used within this marketing. There is a need for consistency in defining and measuring outdoor food marketing to enable comparison across time and place. Future research should attempt to measure direct impacts on behaviour and health.

Peer Review reports

Advertising of foods and non-alcoholic beverages, (hereafter food advertising), particularly for items high in fat, salt and/or sugar (HFSS), has been identified as a factor contributing to obesity and associated non-communicable diseases globally [ 1 ]. People from more deprived backgrounds or ethnic minority groups are disproportionately targeted and exposed to greater food marketing across a range of platforms [ 2 ], and this may contribute to social gradients in obesity and associated health inequalities [ 3 ]. Marketing is defined by the American Marketing Association (AMA) as “the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large” [ 4 ], and advertising is a key aspect of marketing, which seeks to “inform and/or persuade members of a particular target market or audience regarding their products, services, organizations or ideas” [ 5 ]. The World Health Organization (WHO) assert that the impact that food marketing has on consumer behaviour is dependent on both ‘exposure’ and ‘power’ [ 6 ]. Exposure is the frequency and reach of the marketing messages and power is the creative content and strategies used, both of which determine the effectiveness of marketing [ 6 ]. Hierarchy of effects models of food marketing consider that the pathways for these effects are likely to be complex [ 7 ], with evidence demonstrating that food marketing impacts food purchasing [ 8 ], purchase requests [ 9 ], consumption [ 10 , 11 ] and obesity prevalence [ 12 ].

Evidence suggests that children are likely to be more vulnerable to marketing messages than adults [ 13 , 14 , 15 ]. Furthermore, it has been proposed that the scepticism towards advertising that is developed in adolescence does not equate to protection against its effects [ 16 ], leaving both young children and older adolescents vulnerable to the effects of food marketing [ 17 ]. For this reason, policies enacted generally aim to decrease the exposure or power of food marketing to children, and so this is where much of the research is focused. Despite this, it is apparent that adults are similarly affected by food marketing [ 18 ], and therefore also likely to benefit from restrictions [ 19 ].

In 2010, WHO called on countries to limit the marketing of unhealthy foods, specifically to children [ 6 ]. Various policies have since attempted to enforce restrictions on HFSS advertisements [ 20 ], however, restrictions outdoors remain scarce [ 21 ] and implementation and observation of such restrictions has been found to be inconsistent and problematic [ 22 ].

Previous reviews have collated the evidence on the exposure, power and impact of food advertising on television [ 23 , 24 , 25 ], advergames [ 26 , 27 ], sports sponsorship [ 28 , 29 ] and food packaging [ 30 , 31 ] and in some cases across a range of mediums [ 2 , 32 ]. An existing scoping review [ 33 ] documents the policies in place globally to target outdoor food marketing, and the facilitators and barriers involved in implementing these policies. The lack of effective policies for outdoor food marketing may reflect the comparatively little evidence or synthesis of evidence on outdoor marketing or its potential role in contributing to overweight and obesity, relative to that for other media. Additionally, there are challenges in measuring outdoor marketing exposure compared to television and online [ 34 ]. As countries such as the UK and Chile [ 35 ] move to strengthen restrictions on unhealthy food marketing via television, digital media and packaging, it is plausible that advertisements will be displaced to other media such as outdoor mediums so that brands can maintain or increase their exposure [ 36 , 37 ].

Despite being a longstanding and widely used format [ 38 ] there is no agreed definition for outdoor food marketing. This may have implications for the comparability of data across study designs, which has been reported as a limitation in previous reviews [ 11 , 39 ]. Identifying the common criteria used to define outdoor food marketing, alongside considering best practice methodologies for outdoor marketing monitoring and impact research, are important steps to support the generation of robust, comparable evidence to underpin public health policy development.

Given that 98% of people are exposed to outdoor marketing daily [ 40 ], it is an efficient form of marketing for brands [ 41 ], and is likely successful in influencing purchase decisions through targeting potential shoppers in places the brands are sold [ 42 ]. Food marketing through media such as television and advergames have been shown to impact eating and related behaviours such as purchasing [ 43 , 44 , 45 , 46 ], and the evidence on this marketing and body weight has satisfied the Bradford Hill Criteria [ 47 ], which is used to recognise a causal relationship between two variables. However, the impact that outdoor marketing has on eating related outcomes is less clear.

Therefore, this scoping review aims to (i) identify common criteria used to define outdoor food marketing, (ii) summarise research methodologies used, (iii) identify available evidence on the exposure, power (i.e. persuasive creative strategies within marketing) and impact of outdoor food marketing on behaviour and health with consideration of any observed differences by equity characteristics such as socioeconomic position and (iv) identify knowledge gaps and directions for future research.

Given the broad objectives, a scoping review [ 48 ] was conducted and reported in accordance with the Joanna Briggs Institute (JBI) methodology for scoping reviews [ 49 ] and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) [ 50 ]. The review was pre-registered on the Open Science Framework ( https://osf.io/wezug ).

Search strategy

A detailed search strategy was created by the research team (see supplementary material 1 ), which included an experienced information specialist (M.Ma), to capture both published and unpublished studies and grey literature. Search terms related to food, outdoor and marketing were developed based on titles and abstracts of key studies (identified from preliminary scoping searches) and index terms used to describe articles. For grey literature sources simple terms “outdoor food marketing” and “outdoor food advertising” were used. Searches were conducted between 21st January and 10th February 2021.

Databases searched for academic literature included Medline (Ovid), Scopus, Science Direct, Proquest, PsycINFO, CINAHL, the Cochrane Database of Systematic Reviews and the Cochrane Central Register of Controlled Trials. An additional supplementary PubMed search was conducted to ensure journals and manuscripts in PubMed Central and the NCBI bookshelf were captured. Grey literature searches were conducted of databases Open Access Theses and Dissertations, OpenGrey, UK Health Forum, WHO and Public Health England. Other searches for grey literature included government websites (GOV.uk), regulatory and industry body websites (World Advertising Research Centre Database, Advertising Standards Authority) and NGO sites (Obesity Health Alliance, Sustain).

Eligibility criteria

Primary quantitative studies assessing marketing of food and non-alcoholic beverage brands or products encountered outdoors in terms of exposure, power or impact were considered for inclusion. We defined both marketing and advertising as per the AMA definitions [ 4 , 5 ].

Examples of outdoor marketing included billboards, posters, street furniture and public transport. Exposure was defined as the volume of advertising identified, with consideration of the brands and products promoted. Power of outdoor marketing was defined as the strategies used to promote products (e.g. promotions, characters) [ 51 ]. Eligible behavioural impacts of outdoor marketing were food preference, choice, purchase, intended purchase, purchase requests and consumption. Health-related impacts were body weight and prevalence of obesity or non-communicable diseases. Non-behavioural outcomes were ineligible, e.g., brand recall, awareness, or attitudes.

Studies in which outdoor marketing could not be clearly isolated from other marketing forms [ 46 ], or food could not be isolated from other marketed products (e.g. alcohol and tobacco) were excluded. Studies of health promotion (e.g., public health campaigns) were ineligible. Qualitative studies and reviews were not eligible for inclusion; however, reference lists of relevant reviews were searched.

Selection of sources of evidence

The full screening process is shown as a PRISMA flow diagram (Fig.  1 ). Titles and abstracts were screened by one researcher (AF). Full text review was conducted independently by two researchers from a pool of four (AF, M. Mu, RE & HM). Disagreement was resolved by discussion and where necessary ( n  = 4 articles) a third reviewer (EB) was consulted. Covidence systematic review software was used to organise the screening of studies. Inter-rater reliability for the full-text screening was high, with estimated agreement of 95.7% and a Kappa score of 0.91.

PRISMA flow diagram

Data charting

The extraction template was developed and piloted prior to data extraction. For more detail on the information extracted from each article see supplementary material 2 . Discrepancies in extraction were resolved by discussion. As the aim was to characterise and map existing literature and not systematically review its quality, as is common in scoping reviews [ 52 ] quality assessment (e.g. risk of bias) was not undertaken.

Synthesis of results

Studies that defined outdoor food marketing were grouped to identify common criteria used in definitions. Methodologies used to measure exposure, power and impact are summarised. Studies were grouped into exposure, power and impact for synthesis, with relevant sub-categories to document findings related to equity characteristics. We deemed foods classed as “non-core”, “discretionary”, “unhealthy”, “less healthy”, “junk”, “HFSS”, “processed”, “ultra-processed”, “occasional”, “do not sell”, “poorest choice for health”, “less healthful”, “ineligible to be advertised”, and “not permitted” as unhealthy.

Study selection

After removal of duplicates from an initial 4177 records, 3093 records were screened. Ninety-eight articles were then full-text reviewed. Fifty-four studies were excluded here (supplementary material 3 ). After grey literature and citation searches, the final number of included studies was 53.

Characteristics of included studies

All studies ( n  = 53) measured exposure to outdoor food marketing, n  = 12 also measured power of outdoor food marketing, and n  = 3 measured impact. N  = 15 studies provided at least one criterion through which outdoor food marketing was defined, beyond stating the media explored.

Studies were conducted across twenty-one countries, the majority took place in the USA ( n  = 16) [ 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 ], and other high-income countries ( n  = 23) as categorised by the world bank [ 69 ] (Tables  1 , 2 and 3 ).

Of studies including participants ( n  = 7), three measured exposure of children between the ages of 10 and 14 [ 89 , 93 , 102 ], two surveyed caregivers of children aged 3–5 [ 70 ] (79.2% mothers) and 0–2 years [ 85 ] (100% mothers) and two studies collected data from adults in select census tracts [ 53 , 71 ].

What common criteria are used to define outdoor food marketing?

As shown in Tables 1 , 2 and 3 , the majority of studies ( n  = 33) encompassed a combination of outdoor media [ 53 , 54 , 55 , 58 , 60 , 61 , 62 , 65 , 66 , 67 , 68 , 72 , 73 , 75 , 76 , 78 , 79 , 80 , 80 , 81 , 82 , 83 , 84 , 87 , 88 , 89 , 91 , 92 , 94 , 97 , 98 , 102 , 103 , 104 ]. Many ( n  = 11) focused on advertising solely on public transport property [ 64 , 70 , 71 , 86 , 90 , 93 , 95 , 99 , 100 , 101 , 106 ], five studies focused exclusively on billboards [ 63 , 74 , 77 , 85 , 96 ] and four measured advertising outside stores or food outlets [ 56 , 57 , 59 , 105 ].

Outdoor food marketing was inconsistently defined across studies. All studies stated the media they were measuring and some defined marketing or advertising generally, but often not how it related to the outdoor environment. Studies that provided specific criteria for outdoor food marketing ( n  = 15) or an equivalent term (i.e. outdoor food advertising) beyond simply stating the media recorded are listed in Table  4 . Figure 2 represents the criteria referred to most frequently when defining outdoor food marketing.

Common criteria used to define outdoor food marketing

What methods are used to document outdoor food marketing exposure, power, and impact.

Most included studies ( n  = 49) were cross-sectional, although four were longitudinal [ 68 , 90 , 95 , 100 ]. The methods used to classify foods were inconsistent, for example, often local nutrient profiling models were used to classify advertised products as healthy or not healthy (e.g. [ 80 ]), however in some cases the number of advertisements for specific food groups were tallied (e.g. [ 56 ]). Forty-two studies assessed the frequency of food advertising through researcher visits to locations. In four [ 82 , 83 , 86 , 93 ] studies, researchers visited streets virtually, through Google Street view. Real, rather than potential exposure was measured in three studies [ 89 , 93 , 102 ]. In two cases, children wore cameras which documented advertisements encountered in their typical day [ 89 , 102 ], and in a final study, children wore a global positioning system (GPS) device so researchers could track when they encountered previously identified advertisements [ 93 ]. Self-reported retrospective exposure (frequency of encountering outdoor food advertising) was measured in three studies [ 70 , 71 , 85 ].

When measuring advertising around schools/places children gather, researchers typically created buffer zones, ranging from 100 m [ 81 ] to 2 km [ 105 ], with 500 m being the most frequent buffer size ( n  = 8) [ 77 , 78 , 86 , 87 , 88 , 95 , 97 , 104 ]. Four studies used multiple buffers [ 82 , 87 , 93 , 97 ], allowing for comparison between the area directly surrounding a school (e.g. < 250 m) with an area further away (e.g. 250-500 m) [ 87 ], one study compared advertising in Mass Transit Railway stations in school and non-school zones [ 90 ], and another used GPS point patterns to determine the extent of advertising around schools [ 92 ].

Content analysis was used to characterise the food types promoted and strategies used in the advertising. Two studies investigated price promotions [ 55 , 56 ], two identified promotional characters and premium offers [ 75 , 78 ], one specifically assessed child-directed marketing [ 57 ]. Others examined a mix of strategies including sports or health references, cultural relevance and emotional, value or taste appeals [ 54 , 73 , 74 , 76 , 79 ].

All three studies measuring the impact of outdoor food advertising used self-reported data. In a study conducted in Indonesia [ 70 ], caregivers reported the frequency of food advertising exposures in the past week, and their children’s frequency of intake of various confectionaries at home in the last week. In a study conducted in the US [ 53 ], individuals reported consumption of 12 oz. sodas in the last 24 hours, and odds of exposure was assessed by the extent of advertising in surrounding areas. In the UK study [ 71 ], participants reported exposure to HFSS advertising in the past week, and body mass index was calculated from participants’ reported height and weight.

What is known about exposure to outdoor food marketing?

Content of food marketing.

Fifty-three studies investigated outdoor marketing exposure (Tables 1 , 2 and 3 ), n  = 22 reported specifically on exposure around schools or places children gather, n  = 9 documented exposure on public transport and n  = 4 outside stores/establishments. The remaining n  = 21 measured exposure across multiple settings.

Food products were promoted in between 7.8% [ 64 ] and 57% [ 91 ] of advertisements, the mean across studies was 22.1%. Of food advertisements, a majority (~ 63%: range 39.3% [ 64 ] - 89.2% [ 89 ]) were categorised as unhealthy. Healthier foods were advertised far less, with studies generally reporting between 1.8% [ 97 ] and 18.8% [ 91 ] of food advertisements being for healthier products. Fast food ( n  = 17) and sugar-sweetened beverages (SSBs; n  = 22) were frequently n amed as some of the most advertised product types.

Coca Cola was frequently stated as the most prominent brand advertised [ 73 , 75 , 88 , 91 , 100 ]. Around 5% of all outdoor food advertisements in New Zealand [ 78 ] and Australia [ 100 ] were promoting a brand (rather than a specific product), however there were no brand only advertisements identified in a UK study [ 80 ].

Marketing to children

Over half of the studies included ( n  = 29) sought to examine children’s exposure to food advertising. One UK study [ 93 ] concluded that while it was unlikely that unhealthy products were advertised on bus shelters surrounding schools (100-800 m), children, particularly in urban areas, were likely to encounter advertising on their journeys to and from school. All other studies found food advertising to be prevalent around schools, often promoting unhealthy products, although in three studies, a minority of schools (20.4% [ 78 ], 15.4% [ 79 ], 33.3% [ 103 ]) did not have any food advertising nearby. Four studies found that there was more food advertising closer to schools or facilities used by children and adolescents, compared to areas further away from these facilities [ 87 , 97 ], specifically for unhealthy or processed foods [ 87 , 90 ] and snack foods [ 82 ], however one study found SSB advertisements increased as distance from schools increased [ 92 ].

Differences by socioeconomic position/ethnicity

Eight studies considered differences in exposure by ethnicity. Three of these found that ethnic minority groups were exposed to more food advertising [ 55 , 58 , 62 ], for example, schools in the US with a majority Hispanic population were found to have more total advertisements and establishment advertisements in surrounding areas [ 55 ], whilst in New York City, for every 10% increase in proportion of Black residents there was a 6% increase in food images and 18% increase in non-alcoholic beverage images [ 58 ]. In addition to this, associations were found between sugary drink advertisement density and Percentage of Asian or Pacific Islander residents and percentage of Black, non-Latino residents [ 62 ]. Two studies found that multicultural neighbourhoods had a higher proportion of food advertisements [ 94 ] and higher density of unhealthy beverage advertisements [ 61 ]. Unhealthy food [ 63 ] and beverage [ 61 ] advertising were found to be more prevalent in ethnic minority communities. Low-income communities with majority Black or Latino residents had greater odds of having any food advertising [ 53 ], generally more food and beverage advertising and greater unhealthy food space [ 61 ] compared to white counterparts. A US study [ 56 ] found that differences in exposure to food and beverage, and soda advertisements by ethnicity were no longer significant after controlling for household income.

Twenty-six studies considered differences in exposure by SES, five of these did not find a relationship [ 60 , 71 , 83 , 93 , 99 ]. Two studies showed that food and beverage advertisements were more prevalent in low SES communities [ 56 , 94 ]. Schools characterised by low SES had a higher proportion unhealthy food advertising nearby in two studies [ 78 , 104 ], although in one instance there was no significant difference in the number of unhealthy food advertisements [ 78 ]. One study conducted in Sweden [ 84 ] found no significant difference in the proportion of food advertisements by SES, however there was a significantly greater proportion of advertisements promoting ultra-processed foods in the more deprived region.

Foods more frequently advertised in low SES areas were: SSBs, hamburgers and kebabs, diet soft drinks, vegetable snacks, dairy with no added sugar [ 103 ], staple foods [ 91 ], flavoured milk and fruit juice [ 101 ]. Low income communities in the US had lower odds of fruit and vegetable advertisements at limited service stores [ 56 ] and a higher density of unhealthy beverages [ 61 , 62 ] compared with higher-income communities.

Two studies found no significant difference in the number of core advertisements by SES [ 98 , 100 ], however a study of outdoor food advertising in Uganda found that there were more healthy food advertisements in high income areas [ 75 ]. Advertisements for fast food, takeaways, hot beverages and soft drinks were found to be more frequent in high-income areas [ 91 , 96 , 101 ].

A study comparing four schools of varying deprivation found the school with lowest deprivation had no advertisements but there was no clear trend in extent of advertising by deprivation [ 105 ]. Two studies conducted in Mexico [ 81 ] and New Zealand [ 86 ] found outdoor food advertising to be more frequent around public schools than private schools, however a study in Uganda [ 75 ] found no significant difference in the number of core foods advertised around private and government funded schools, in all three studies private school was considered a proxy of high SES. In this New Zealand study, low decile areas had the greatest number of advertisements for non-core food, core food and non-core food and beverage, however when high decile schools were combined with areas around private schools, the greatest number of all food and beverage advertisements and non-core advertisements were found in high SES areas [ 86 ].

What is known about the power of outdoor food marketing?

Twelve studies documented the power of outdoor food marketing (Table  2 ). This was measured by quantifying the use of a range of persuasive creative strategies and child-directed marketing. The persuasive creative strategies observed across studies are shown in Fig.  3 .

Powerful creative strategies observed in studies

Observed power

There was evidence of variation in the use of persuasive creative strategies in outdoor advertising, with premium offers (e.g. buy one get one free [ 78 ]) utilised in between 7.84% [ 74 ] and 28.1% [ 78 ] of food advertisements, and the proportion of advertisements featuring a person or promotional character ranging from 2.8% [ 79 ] to 46.8% [ 73 ]. Other strategies frequently identified were appeals related to price [ 55 , 56 , 72 , 74 , 76 ], emotion [ 72 , 74 , 76 , 77 ] and taste [ 72 , 74 , 76 , 77 ].

The proportion of advertisements considered to be targeted just at children or young people ranged from less than 1% [ 58 ] to 10.4% [ 73 ]. Studies assessing appeals to children considered the use of cartoon characters, popular figures, child models or characters, colours or images, toys and the placement of the advertisement [ 58 , 64 , 73 , 83 ].

Often, the foods promoted using persuasive creative strategies were soft drinks [ 73 , 76 ], non-core foods [ 77 ] and fast foods [ 57 , 76 ], however one study [ 75 ] observed outdoor food advertising in Uganda and found that 15% of healthy food advertisements used promotional characters.

Differences by socioeconomic status/ethnicity

A US study [ 55 ] found that schools with a majority Hispanic population (vs. low Hispanic population) had significantly more advertisements featuring price promotions within half a mile of the school. Price promotions were also more frequent outside supermarkets in non-Hispanic Black communities in the US [ 56 ], although this was no longer significant after controlling for household income. Supermarkets in low-income communities were significantly more likely to have price promotions [ 56 ] and being located in middle-income (compared to high) and black communities was marginally associated with increased odds of child-directed marketing [ 57 ]. Sometimes, local culture was referenced in food advertising through persuasive creative strategies [ 54 , 76 , 77 ], for example a US study quantifying advertisements in a Chinese-American Neighbourhood [ 54 ] found food advertisements were frequently relevant to Chinese culture (58.9% of food and 59.04% of non-alcoholic beverage advertisements), often featuring Asian models.

What is known about the impact of outdoor food marketing?

Three studies (Table  1 ) [ 53 , 70 , 71 ] explored associations between exposure to outdoor food advertising and behavioural or health outcomes, two of these found a significant positive relationship. Lesser et al. (2013) [ 53 ] found that for every 10% increase in outdoor food advertisements present, residents consumed on average 6% more soda, and had 5% higher odds of living with obesity. In Indonesia [ 70 ] self-reported exposure to food advertising on public transport was associated with consumption of two specific HFSS products. No associations were found between exposure and consumption of the other eight products considered. A UK study [ 71 ] found no significant association between self-reported exposure to HFSS advertising across transport networks and weight status. No studies measured differences in impact in relation to equity characteristics.

Summary of main results

This review is the first to collate the criteria used to define outdoor food marketing, document the methods used to measure this form of marketing, and identify what is known about its exposure, power and impact.

Fifty-three studies were identified which met all eligibility criteria. In brief, of studies with a definition, the criteria referenced most were; on or outside stores/establishments; and stationary signs/objects. The methods used to research outdoor marketing include self-report data, virtual auditing, in-person auditing, and content analysis. There was little consistency in the approach used to classify foods as healthy or unhealthy, although nutrient profiling models were used in some studies.

Food accounted for an average of 22.1% of all advertisements, the majority of foods advertised were classed as unhealthy (63%). Ethnic minority groups were generally shown to have higher exposure to outdoor food advertising, but findings on differential exposure by SES were inconsistent.

Studies showed frequent use of premium offers, promotional characters, health claims, taste appeals and emotional appeals in outdoor food advertisements. There was limited evidence of relationships between exposure to food marketing and behavioural or health outcomes.

Eight out of fifteen studies (Fig.  2 ) stated that outdoor food marketing must be on or outside of stores or establishments, seven studies included stationary signs or objects in their definition and five studies stated that advertisements must be visible from the street or sidewalk. However, the defining criteria was inconsistent across the fifteen studies, and some of the most referenced criteria are problematic. Although stationary signage is an important aspect of outdoor marketing, this excludes forms of marketing on transport e.g. the exterior of buses. Equally, not all outdoor marketing may be “visible from the street or sidewalk”, this could exclude advertising on public transport property, i.e. station platforms. Additionally, the share of digital out-of-home advertising rose from 14% in 2011 to 59% in 2020 [ 107 ]. Three studies did aim to document digital advertising [ 60 , 63 , 90 ] through observing a digital board for a set amount of time. This medium is likely to become more prevalent over time globally, and there are challenges due to its changing and interactive nature [ 108 ]. The literature appears dominated by studies of advertising. This may reflect that most marketing encountered outdoors is advertising, conversely, it may be that the literature is yet to consider some newer forms of marketing, such as increased digital platforms. It will be important for future research to consider the evolving nature of outdoor marketing and how this should be measured.

Only fifteen studies defined outdoor food marketing as a term. This has likely been a factor influencing the heterogeneity observed across studies (e.g. differences in scope), as inconsistencies in defining a factor can negatively impact the development of an evidence base [ 109 ]. Researchers should endeavour to work towards an agreed definition, perhaps through use of the Delphi method of consensus development [ 110 ], in order to improve consistency in the resulting research. However, this method can be open to bias if the researchers are of the same background as the experts involved [ 109 ] therefore it is important that any definition developed aligns with criteria used by industry to reduce likelihood of bias.

Methods used in outdoor food marketing research

Outdoor food marketing exposure and impact were measured using self-reported data, which may lack validity, as advertising can influence brand attitudes whether consciously or unconsciously processed [ 111 ]. While it can be useful to know the extent that individuals process advertising, this may not be a true representation of exposure. Equally, participants may alter their response to appear socially desirable which has previously resulted in misreporting of height and weight data [ 112 ].

Using Google Street View as an auditing tool is beneficial in saving time and resources whilst gathering large samples [ 113 ], however almost one third of advertisements in one study were unable to be identified [ 83 ], therefore systematically searching the streets in sample areas, and taking photographs for later reference is a more reliable method. Buffer areas are a useful tool for measuring advertising, particularly around specific sites such as schools, although stating advertising was present “around schools” has different meanings when comparing 100 m to 500 m, or to 2 km. GPS and wearable camera technology can identify how individuals encounter food marketing in the routes they use to travel through their environment. These methods should be replicated globally as a more objective measure of individual exposure to outdoor food marketing, although care must be taken in regard to privacy and ethical considerations.

There was little consistency in the methods used to identify persuasive creative strategies, which is typical in the field of food marketing [ 23 ]. The heterogeneity observed could be reduced through adherence to protocols for the monitoring of food marketing such as those developed by WHO [ 51 ] and INFORMAS [ 114 ]. This would improve comparability of future outdoor food marketing data across countries and time points which would better support policy action in this area. Nutrient profiling models are a useful tool for food categorisation, as opposed to grouping foods as “everyday” and “discretionary” or “core” and “non-core”, however, profiling models differ due to cultural differences in diet [ 106 , 115 ]. There is a need to balance the data required for country-level policy relevance with international comparability. Watson et al. (2021) [ 106 ] propose an amalgamation of the WHO EURO NPM and WHO Western Pacific models.

Exposure to outdoor food marketing

Marketing platforms outdoors remain accessible for the food industry and are relatively unrestricted. This is reflected by the extent of advertised food products (22.1%) and the proportion of those that were unhealthy (63%), which is problematic as discrepancies between the food types frequently promoted and dietary recommendations have been linked to changes in dietary norms and food preferences [ 111 ]. Whilst fruits and vegetables should make up 40% of daily intake [ 116 ], these products were rarely promoted. These findings are comparable to global data of other marketing formats, for example, a benchmarking study found that on average, 23% of advertisements on TV were for foods or beverages, and other studies have found 60–70% of food advertisements to be unhealthy across social media [ 117 , 118 , 119 ] and in print [ 120 ].

This knowledge adds to the existing evidence reporting the extent of children’s exposure through multiple forms of marketing [ 2 , 13 , 121 ]. Whilst efforts are being made to restrict their advertising exposure through other sources such as TV, for consistency, more must be done to protect children in the outdoor environment.

There is no consensus on clear trends in exposure by SES. In part, contradictory findings within this review, such as targeting of wealthier consumers, may reflect the occurrence of a nutrition transition occurring in low income countries, characterised by increased reliance on processed foods [ 122 ] which are more available to those with more disposable income. Further research should attempt to develop clear consensus on the differential exposure to outdoor food marketing by SES in both high- and lower-income countries.

Power of outdoor food marketing

The lack of research into the power of outdoor food marketing is most likely a result of the lack of established definitions and classifications for the powerful characteristics of marketing and in particular, child appeal of marketing [ 123 ]. The most frequent persuasive creative strategies identified across the twelve studies documenting power were premium offers, promotional characters, health claims, taste appeals and emotional appeals, similar to those identified in television food marketing [ 23 ]. These strategies are particularly salient to children: spokes-characters can be effective in influencing children’s food choice, preference, awareness and attention [ 124 ], whilst premium offers (e.g. collectible toys) can influence children’s likeability and anticipated taste of the promoted food [ 125 ] and can prompt choice of healthier meals [ 125 , 126 ]. One study found that children were more likely to choose unhealthy food products if they featured nutrient content claims such as “reduced fat, source of calcium” [ 127 ]. In this study participants were exposed to unknown brands, it is anticipated that larger responses would be present in brands recognised by participants. Future research should attempt to determine the success of different strategies in influencing behaviour, particularly as the rise in digital media used outdoors may increase the potential for power through increasing the variation and sophistication of outdoor marketing techniques. Policy in this field is largely focused on advertising directed at children, although it is important for research and policy to reflect that due to persuasive creative strategies used, advertising not wholly directed at children can still appeal to them [ 83 ].

Impact of outdoor food marketing

There is evidence that outdoor advertising exposure is related to consumption of SSBs and odds of obesity, Previous reviews on the impact of food marketing on television and digital media have found compelling evidence of a relationship between exposure and food intake [ 44 ], attitudes and preferences [ 24 ]. However, the small number of studies measuring impacts of outdoor food marketing in this review were correlational and therefore cannot demonstrate causality. This lack of evidence is likely preventing policy progress in this area. It is likely that the lack of studies measuring impact of outdoor food marketing is due to the difficulty in controlling for confounding variables in external settings [ 128 ] or replicating this form of marketing in a lab compared to other formats such as television. It is clear that unhealthy food marketing is prevalent outdoors, but our understanding of the resultant impacts is underdeveloped and must be further examined through experimental research.

Experimental research will enable clearer understanding as to whether outdoor food marketing influences behaviour as television and digital marketing do [ 10 , 24 , 129 ]. Understanding the impact of outdoor food marketing on body weight would require longitudinal research, although it is difficult to separate the impact of marketing from secular trends. Additionally, there is increasing recognition that attributing a behavioural outcome to a single marketing communication can be problematic and does not appropriately reflect the cumulative effects of multiple, repeated exposures [ 7 ]. Purchase data in response to marketing campaigns could be a useful indicator of marketing impact [ 130 ], however gathering sales data from industry is problematic. This could be made possible through changes such as those proposed by the UK national food strategy, and supported by the NGO sector [ 131 ], calling for mandatory annual reporting of product sales for large food companies [ 132 ]. Although this is only proposed in the UK, if the strategy is successful in encouraging companies to make changes to formulations or the proportion of healthy products available, this strategy may be adopted elsewhere.

The review was pre-registered, allowing for transparency in approach and reporting of results and the methodology and reporting of the review were robust and consistent with guidelines from both the PRISMA extension for scoping reviews and the JBI methodology. The systematic search strategy ensured a wide range of databases were searched and the identification of a large number of potentially relevant studies. The use of multiple independent reviewers in the full-text screening and data extraction ensured all relevant data was captured accurately.

Limitations

As this is a scoping review, non-peer reviewed sources such as letters to editors [ 96 ], conference abstracts [ 99 ] and grey literature [ 104 ] were included if they met inclusion criteria. Government websites beyond the UK were not included, which is a limitation of our searches, however multiple grey literature sources that were not UK based would have captured relevant international materials. The majority of studies included are focused on advertising and while some marketing aspects are considered, this review does not encompass all marketing communications. However, our searches were designed with thesaurus terms to capture words related to marketing that might not have been realised from a public health perspective. Therefore, it is likely that the relevant literature from marketing disciplines was identified, and is just limited.

As quality assessment was not deemed appropriate, there is potential for error and bias within the included studies, similarly, inaccuracies may arise from the self-reported data used in four studies. Although there were no limitations by language, no translation was required and all eligible studies were published in English. Further, discrepancies in the conduct and reporting of studies make it difficult to collate data and draw firm conclusions.

This review has documented the research on outdoor food marketing exposure, power, and impact. There is substantial heterogeneity in the criteria used to define and methods used to measure outdoor food marketing. Future research will benefit from using a consistent definition and measurement tools to allow for improved comparability between studies. Whilst all the studies documented exposure, few recorded the powerful strategies used in outdoor food marketing and it is still largely unknown how this marketing influences behaviour and ultimately health. In order to inform policy, further research will benefit from examining the causal processes through which outdoor marketing may influence behaviour and health outcomes.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the OSF repository, https://osf.io/b65jy/ .

Abbreviations

American Marketing Association

Global Positioning System

High in fats, salt and sugar

International Network for Food and Obesity/Non-communicable Diseases Research, Monitoring and Action Support.

Nutrient Profiling Model

Place, race/ethnicity/culture/language, occupation, gender/sex, religion, education, socioeconomic status, social capital

Socioeconomic status

Sugar-sweetened beverages

World Health Organization

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EB and ER were involved in the conception of the study. The review protocol, design and extraction instrument were developed by AF, EB, ER, AJ and M.Ma. The search strategy was developed by AF and M.Ma. Searches, title and abstract screening and full text screening were completed by AF. A second independent full text screening of articles was completed by RE, HM and M.Mu. Data extraction was completed by AF, RE, HM and M.Mu. The manuscript was written by AF and EB. All authors read and approved the final manuscript.

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Finlay, A., Robinson, E., Jones, A. et al. A scoping review of outdoor food marketing: exposure, power and impacts on eating behaviour and health. BMC Public Health 22 , 1431 (2022). https://doi.org/10.1186/s12889-022-13784-8

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Rebalancing the marketing of healthier versus less healthy food products

* E-mail: [email protected]

Affiliation MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom

Published: March 24, 2022

• https://doi.org/10.1371/journal.pmed.1003956
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Citation: Adams J (2022) Rebalancing the marketing of healthier versus less healthy food products. PLoS Med 19(3): e1003956. https://doi.org/10.1371/journal.pmed.1003956

Copyright: © 2022 Jean Adams. 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.

Funding: JA is supported by the MRC Epidemiology Unit, University of Cambridge [Medical Research Council grant number MC/UU/00006/7] mrc.ukri.org . The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: JA reports being a member of the PLOS Medicine editorial board.

Provenance: Commissioned; not externally peer reviewed

We live in a world increasingly saturated with marketing for less healthy foods [ 1 ]. One study found that children in New Zealand see an average of 27 instances of marketing for less healthy foods and only 12 for healthier foods, each day [ 2 ]. Food marketing involves activities across the 4 Ps of the marketing mix: product, place, price, and promotion. We are encouraged to buy less healthy food products through their placement in prominent store locations such as checkouts, end of aisles, and store entrances; price discounts; and promotions including advertising, cartoon tie-ins, and celebrity endorsements.

Systematic reviews have confirmed the effectiveness of these marketing techniques to influence purchasing and consumption of less healthy foods [ 3 – 5 ]. Indeed, the documented power of food marketing has led the World Health Organisation to recommended limiting exposure as an overarching and enabling “best buy” to improve diets [ 6 ].

Supermarkets remain the location of about 70% of food spend in the United Kingdom [ 7 ]. The concentration of food marketing in grocery stores can feel particularly overwhelming with parents describing the “temptation” as “like a trip to the zoo every week” for their children [ 8 ]. As such, supermarkets may be particularly important venues for addressing food marketing.

In 2 accompanying Research Articles in PLOS Medicine , Piernas and colleagues used nonrandomised approaches to study the impacts on sales of a range of strategies to rebalance the marketing of healthier versus less healthy products in 3 large UK supermarket chains [ 9 , 10 ]. Across the 2 papers, 7 different interventions were implemented that changed the relative availability of healthier versus less healthy products (2 interventions), removed less healthy products from prominent positions, placed healthier products at eye level, offered price discounts on healthier products, increased signage on healthier products, and applied a range of entertainment tie-in promotions on healthier products (one intervention each). These variously had the intention to encourage substitution of less healthy products with healthier alternatives or to reduce purchasing of less healthy foods without substitution.

Increasing the relative availability of healthier products, removing less healthy products from prominent positions and price promotions on healthier products were all associated with changes in unit sales in the expected direction, although associations with changes in nutrients purchased were sometimes more modest. In contrast, moving healthier products to eye level and increasing signage were not associated with changes in sales. These findings are particularly timely in England where a range of measures to reduce exposure to marketing of less healthy foods in retail environments are due to be implemented from October 2022 [ 11 ].

Piernas and colleagues worked in collaboration with large UK supermarket chains. That the chains were prepared to innovate to support public health indicates that rebalancing marketing towards healthier products may not be as burdensome to the sector as it has sometimes claimed [ 12 ]. It also strengthens the external validity of these studies giving an indication of how customers react in real-world environments.

However, that the supermarket chains decided what the interventions should be also imposes limitations on wider interpretation of the findings. Each of the 7 different interventions applied to different categories of foods without any rationale made explicit to the research team—for example, chocolate confectionary was removed from prominent positions, higher fibre breakfast cereals were placed at eye level, and price discounts were applied to fruit and vegetables. This makes it hard to determine whether observed impacts were unique to specific combinations of intervention and food category. Indeed, rather than particular marketing interventions being more effective than others across the board, it is possible that complex interplays between food category, marketing intervention, and other contextual aspects (such as shop and customer characteristics) interact to produce changes in sales.

The “squeezed balloon effect” proposes that restrictions on specific aspects of marketing may lead to compensatory increases in others [ 13 ]. For example, restricting television advertising of less healthy foods during and around children’s programmes in the UK was associated with increased exposure of adults to these adverts [ 14 ]. Wider compensation between, as well as within, media (for example, TV restrictions leading to more online marketing) may also be expected. It is possible that supermarkets willing to engage in university-assessed marketing changes may have self-policed any simultaneous compensatory activities, and, anyway, these would not necessarily have been identified in the studies by Piernas and colleagues. Any real-life compensation as the whole grocery sector adapts to government-imposed marketing restrictions may be difficult to predict. This reinforces the need for postimplementation evaluation.

The squeezed balloon effect means that the most effective marketing restrictions may be those that target marketing of the same products through multiple simultaneous interventions. In Chile, near-simultaneous implementation of front-of-pack warning labels, advertising restrictions, and a prohibition of sales in schools of products high in calories, sodium, sugar, or saturated fat were associated with substantial declines in purchases of targeted foods and nutrients [ 15 ]. This approach is also the underlying strategy in England where near-simultaneous restrictions on TV and online advertising of less healthy foods are planned for the whole of the UK alongside the England-specific bans on location and price-based promotions [ 16 ].

Despite the innovative approach in England, neither the regulations on TV and online advertising of less healthy foods nor on price and location-based promotions of these foods have cleared the parliamentary process. The UK government recently accepted an amendment to the TV and online advertising restrictions to give the Secretary of State for Health and Social Care power to delay implementation [ 17 ]. The restrictions on price and location-based promotions may be under threat of being dropped altogether [ 18 ].

Piernas and colleagues’ studies add to the accumulating evidence that restricting marketing on less healthy foods and encouraging marketing on healthier foods may be an effective way to support public health. Theory and a range of evidence suggest that simultaneous restrictions on a variety of different types of less healthy food marketing are likely to be the most effective ways of reducing exposure to this marketing. The UK government has proposed this approach in England on a number of occasions. That implementation continues to hang in the balance is a sad indictment of our collective inability to create a world that supports everyone to eat in the way they want to, rather than the way the marketers want for us.

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• 6. World Health Organization. ‘Best buys’ and other recommended interventions for the prevention and control of noncommunicable diseases. Geneva: WHO; 2017.
• 7. Department for Environment Food & Rural Affairs. Family Food 2018/19. London: National Statistics; 2020.
• 13. World Health Organization Regional Office for Europe. Evaluating implementation of the WHO set of recommendations on the marketing of foods and non-alcoholic beverages to children: Progress, challenges and guidance for next steps in the WHO European Region. Copenhagen, Denmark: The World Health Organization Regional Office for Europe; 2018.

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• Published: 08 October 2017

Children’s everyday exposure to food marketing: an objective analysis using wearable cameras

• L. N. Signal 1 ,
• J. Stanley 1 ,
• M. Smith 1 ,
• M. B. Barr 1 ,
• T. J. Chambers 1 ,
• J. Zhou 2 ,
• A. Duane 2 ,
• C. Gurrin 2 ,
• A. F. Smeaton 2 ,
• C. McKerchar 1 ,
• A. L. Pearson 3 ,
• J. Hoek 4 ,
• G. L. S. Jenkin 1 &
• C. Ni Mhurchu 5  

International Journal of Behavioral Nutrition and Physical Activity volume  14 , Article number:  137 ( 2017 ) Cite this article

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Over the past three decades the global prevalence of childhood overweight and obesity has increased by 47%. Marketing of energy-dense nutrient-poor foods and beverages contributes to this worldwide increase. Previous research on food marketing to children largely uses self-report, reporting by parents, or third-party observation of children’s environments, with the focus mostly on single settings and/or media. This paper reports on innovative research, Kids’Cam, in which children wore cameras to examine the frequency and nature of everyday exposure to food marketing across multiple media and settings.

Kids’Cam was a cross-sectional study of 168 children (mean age 12.6 years, SD = 0.5) in Wellington, New Zealand. Each child wore a wearable camera on four consecutive days, capturing images automatically every seven seconds. Images were manually coded as either recommended (core) or not recommended (non-core) to be marketed to children by setting, marketing medium, and product category. Images in convenience stores and supermarkets were excluded as marketing examples were considered too numerous to count.

On average, children were exposed to non-core food marketing 27.3 times a day (95% CI 24.8, 30.1) across all settings. This was more than twice their average exposure to core food marketing (12.3 per day, 95% CI 8.7, 17.4). Most non-core exposures occurred at home (33%), in public spaces (30%) and at school (19%). Food packaging was the predominant marketing medium (74% and 64% for core and non-core foods) followed by signs (21% and 28% for core and non-core). Sugary drinks, fast food, confectionary and snack foods were the most commonly encountered non-core foods marketed. Rates were calculated using Poisson regression.

Conclusions

Children in this study were frequently exposed, across multiple settings, to marketing of non-core foods not recommended to be marketed to children. The study provides further evidence of the need for urgent action to reduce children’s exposure to marketing of unhealthy foods, and suggests the settings and media in which to act. Such action is necessary if the Commission on Ending Childhood Obesity’s vision is to be achieved.

Over the past three decades the global prevalence of childhood overweight and obesity has increased by 47% [ 1 ]. Excess adiposity during childhood and adolescence is associated with an increased risk of many serious health conditions and has lifetime consequences for children’s health, well-being, and productivity [ 2 , 3 , 4 ].

Marketing of energy-dense nutrient-poor (EDNP) foods and beverages contributes to the worldwide increase in childhood obesity [ 5 ] by encouraging the repeat purchase and consumption of foods that do not meet nutritional guidelines [ 6 , 7 , 8 ]. Internationally, it is estimated that 60% to 90% of food marketing to children is for pre-sugared breakfast cereals, soft drinks, savoury snacks, confectionery and fast foods [ 8 ]. The World Health Organization (WHO) Commission on Ending Childhood Obesity (ECHO) recommends reducing children’s exposure to, and the power of, marketing of unhealthy foods [ 5 ]. ECHO states that “settings where children and adolescents gather (such as schools and sports facilities or events) and the screen-based offerings they watch or participate in, should be free of marketing of unhealthy food and sugar-sweetened beverages” [5, p.18]. According to the WHO Regional Office for Europe Nutrient Profiling Model [ 9 ], foods not recommended to be marketed to children include confectionery, sweet snack food, ice-cream, iced confectionery and sugar-sweetened and artificially-sweetened beverages. In New Zealand, the industry self-regulating Children’s Code for Advertising Food states that “food advertisements should not undermine the food and nutrition policies of Government, the Ministry of Health Food and Nutrition Guidelines nor the health and well-being of children” ([ 10 ], p.21.)

Previous studies quantifying children’s exposure to food and beverage marketing have concluded that promotions encouraging the consumption of EDNP products are ubiquitous in children’s environments [ 6 , 11 , 12 , 13 , 14 , 15 ]. Yet, despite this important work, little is known about children’s actual daily exposure to food marketing. This knowledge gap exists because previous research has largely used self-report, reporting by parents, or third-party observation of children’s environments. Further, it often focuses on single settings [ 16 , 17 , 18 ] (outdoors) and/or media (television) [ 11 , 14 , 19 , 20 ]. This paper reports on innovative research, Kids’Cam, which used wearable cameras to examine the frequency and nature of New Zealand (NZ) children’s everyday exposure to food and non-alcoholic beverage marketing (hereafter food marketing) across multiple media and settings [ 21 ]. Marketing exposure was examined by socioeconomic status and ethnicity (including whether the magnitude of any ethnic differences varied with socioeconomic status), as childhood obesity is strongly patterned by these factors [ 21 ].

Study design

Kids’Cam was a cross-sectional study of 168 Year 8 children (typical age range 11 to 13 years) in the Wellington region of NZ. Children were asked to wear a camera around their neck from when they got up in the morning until going to bed for four consecutive days (Thursday to Sunday, to capture both weekday and weekend exposures). They were advised to remove the camera in situations where privacy could be expected (e.g. toilet or shower facilities), if they felt uncomfortable, when swimming or playing vigorous sport, or if requested. The camera automatically captured a 136° image of the front-facing scene approximately every seven seconds. Data were collected over a 12-month period from July 2014 to June 2015 to allow for seasonal variations. Full details of the study methods (including sample size calculations) are published elsewhere [ 22 ]. The study protocol is available at https://diet.auckland.ac.nz/content/kidscam

Sampling and recruitment

Sampling and recruitment were conducted in two stages, first at school level and then child level. The number of Year 8 children enrolled across all schools in the Wellington region was collated using aggregate school enrolment data from the Ministry of Education, and schools were sampled with probability-proportional-to-size stratified random sampling by school decile Footnote 1 (low decile = 1–3, medium decile = 4–7, high decile = 8–10) and student ethnicity Māori (indigenous population), Pacific (mostly second generation migrants from Pacific Islands), and NZ European (NZE). This sampling strategy facilitated comparisons of marketing exposure by socioeconomic status and ethnicity, and gave a total of nine sampling strata. Randomly selected schools were invited to participate.

In consenting schools, a maximum of 20 Year 8 children were randomly selected from the class list, stratified by ethnicity, using R 3.2.4 (R Institute, Vienna). The school principal or lead teacher reviewed the list of students to identify children who did not meet the study criteria ( n  = 5 over the study period). The first 15 eligible children were invited to participate, and the first six children on the list who returned signed consent forms (including parental consent) were selected to participate. The number of children invited exceeded the number of participants required in order to achieve recruitment of four to six children per school (as per the sampling strategy), and reduce the burden on the schools from multiple rounds of invitation.

Data collection and management

Written consent was gained from children and their parents and basic demographic data were collected via parental questionnaire. A briefing session was held with participating children the day before the cameras were first worn to explain the project. Following data collection, cameras were collected and images downloaded, with children given the opportunity to review and delete any photos before the researchers viewed them. At this review, height and weight were measured to determine age- and gender-specific BMI, using the extended international body mass index cut-offs [ 22 ]. Approved images were downloaded to a password-protected server, saved in secure cloud storage, and backed up to a password-protected external hard drive. Approximately 1.3 million images were recorded that could be coded for the presence of food marketing.

Coding of image data

Image coding was performed using a coding protocol to guide content analysis [ 23 ]. Customised software enabled manual coding of each image. Marketing was defined as “any form of commercial communication or message that is designed to, or has the effect of, increasing recognition, appeal and/or consumption of particular products and services” ([ 24 ], p.9). A three-tiered framework was used to code each relevant image for setting, marketing medium and food product category, based on the WHO food marketing framework [ 9 ]. Key settings codes were home, school, food venues, recreation venues and other public spaces. Key marketing media codes were product packaging, signs, in-store marketing, print media, screen and merchandise.

MB, TC and four other health science students undertook the coding. A half day training workshop was held with all coders and coders were then given access to the dataset for a number of days to become familiar with it. Once coders felt comfortable, reliability testing was conducted, with each coder achieving 90% concurrence with model answers on a test dataset of 115 images before coding commenced. Coders were supervised by MS, MB and TC to ensure consistency. Uncertain codes were noted as such and checked by MB or TC.

All foods were classified as either recommended (core) or not recommended (non-core) to be marketed to children based on the WHO Regional Office for Europe Nutrient Profiling Model [ 9 ], with some modifications (e.g. a ‘fast food’ category was added which included all commercially prepared food products sold at quick service restaurants). All fast food was classified as not recommended to be marketed to children as it is typically high in saturated fat and sodium and low in fiber [ 25 ]. Marketing in convenience stores and supermarkets was too extensive to code individually and was therefore excluded from this analysis. Codes were only assigned to an image where 50% or more of a brand name or logo could be clearly seen by the coder. Individual images could be coded for multiple marketing media and product categories.

Further processing of the coded data included determining the number of marketing exposures for each unique exposure code (defined as the combination of setting, medium and product type for that code). A marketing exposure was defined as starting on the first instance of an image with a particular setting/medium/product code; subsequent images were counted as part of the same exposure. An exposure was considered to have ended when 30 s had elapsed since the last recorded code of that setting/medium/product code (defined using the image timestamps). Any subsequent code for that same combination after this 30 s limit was counted as the start of a new exposure sequence.

The number of exposures was summed for each unique exposure code by child; aggregate counts were determined for each child to estimate total exposures to core and non-core foods, and exposure by setting, medium, and product type. Cleaning and aggregation of coded data was completed in R version 3.2.3 (R Institute, Vienna).

Data analysis

All statistical analysis was conducted in Stata 12 (StataCorp, College Station, TX, USA). Data analysis for study outcomes accounted for the complex sampling by using inverse sampling weights to account for over- and under-sampling of groups by ethnicity and school decile relative to their share of the Year 8 population in the Wellington region, and inferential statistics incorporated elements to handle sample stratification and clustering of children within schools (95% confidence intervals, p -values) [ 26 ] using Stata’s svy prefix commands and associated weighting options.

Descriptive analysis of the overall cohort was undertaken to describe children by ethnicity, school decile group, age, gender, individual deprivation (NZiDep) [ 27 ] and BMI status. Schools participating in the study were described by sub-region within the greater Wellington area and school decile group.

Descriptive analysis of rates of core and non-core food marketing exposures for each child was undertaken by taking the total number of exposures (by core and non-core foods) and dividing by the total number of photos for that child, with this number subsequently re-scaled as an exposure rate for a ten hour day. These were summarised within the major sampling groups (ethnicity and school decile stratum) as median and interquartile ranges of the daily rates, weighted for the sampling design.

Subsequent analysis of rates of marketing exposures used Poisson regression methods, as appropriate for count-based numerator data, analysed separately for core and non-core food marketing exposures. Rates and rate ratios were presented with 95% confidence intervals (95% CI). Results were reported as rates per day of photos (i.e. per 10 h of photographs). Each photo was specified as contributing seven seconds of exposure time (seven seconds being the median interval between images) for the Poisson regression.

Rates of core and non-core exposures per day were analysed using Poisson regression models. Separate models were constructed for core and non-core food exposures. For each, an initial model looked at differences by ethnicity, adjusted for child gender and age (treated as a linear covariate); a second model added school decile group (area level socioeconomic position) to this first model. A third model examined our primary research question of whether ethnic group differences in overall rates of marketing exposures differed across school decile group, by including interaction terms between these two variables. P -values are reported for hypothesis tests of these interaction terms and fully stratified results are presented when these hypothesis tests were significant. These results are presented in the additional files as rates within each ethnicity/school decile stratum, and as rate ratios comparing exposure rates between ethnic groups, as calculated separately within each school decile stratum.

Participating schools and children

Sampling and recruitment of schools and children are summarised in Fig. 1 . All 93 schools with Year 8 students in the Wellington region were eligible to be sampled. Twenty-eight schools were approached across the nine sampling strata and 16 consented to participate (57%). Of the 443 children invited to participate, 192 gave consent (43%) and 168 participated (38%). Sociodemographic information for participating children is presented in Table 1 . Most children were 12 years old (75%: mean = 12.6 years, SD = 0.5) with approximately equal numbers of girls and boys (52.7% female). Just over half the children were of normal weight or underweight (57.5%); with the remainder overweight or obese (42.5%). The lower part of Table 1 shows location and school decile for participating schools. The number of children in each sampling stratum is reported in Additional file 1 , along with a summary of the number of photos available for analysis within each stratum.

Sampling and recruitment flow diagrams for schools and children, by ethnicity and school decile stratum

Rates of marketing exposures

Rates of marketing exposures per day for core and non-core foods are presented in Table 2 . The mean rate for core food was 12.3 marketing exposures per day; for non-core foods, the mean rate was 27.3 marketing exposures per day, more than twice that for core foods. Additional file 2 reports the median and interquartile range of daily exposure to core and non-core food marketing: the interquartile range spread from 15 to 34 non-core exposures per day.

Most core food marketing exposures occurred at home or school (5.5 and 5.3 exposures per day, making up 45% and 43% of all core exposures respectively) (see Table 2 and Fig. 2 , top panel); for non-core food marketing exposures, the majority happened either at home (33% of all non-core exposures) or in public spaces other than food or recreation venues (30% of all non-core exposures). One-fifth of non-core food marketing exposures occurred at school (19%). Additional file 3 gives further detail regarding the settings in which marketing exposures occurred: for example, most exposures in other public spaces were on the street or on shop fronts.

Mean rate (and 95% CI) of core and non-core food marketing exposures per day (10 h of photographs) by setting (top panel) and medium (bottom panel)

The majority of marketing exposures were in the form of food packaging (see Table 2 and Fig. 2 , bottom panel), at a mean rate of 9.1 exposures per day for core foods (74% of core exposures) and 17.4 exposures for non-core foods (64% of non-core exposures). The remaining marketing exposures were mostly signs (21% and 28% of core and non-core food marketing exposures, respectively) (see Fig. 3 for images of marketing).

Sign for sugary drink in public space, sign for sugary drink in public place, product packaging for snack food at school, product packaging for sugary drink at home

Types of non-core food product marketing exposures

Marketing exposure rates for specific non-core food product categories are presented in Table 2 . The largest share was for sugary drinks (mean rate of 9.1 exposures per day, 33% of non-core exposures) followed by fast food (22% of non-core exposures), confectionery (11% of non-core exposures) and snack foods (10% of non-core exposures). Foods making up the remainder of non-core marketing exposures (24% of exposures) are listed in Table 2 .

Rates of marketing exposures by child ethnicity and school decile stratum

The mean exposure rates for core and non-core foods are presented in Additional file 4 , stratified by ethnicity and school decile stratum. The rate of exposure for non-core foods was higher than for core foods in all strata.

Initial analysis for core foods compared exposure by ethnicity, adjusted for gender and age (Table 3 , model 1). Māori children had non-significantly higher rates of exposure compared to NZE (RR = 1.55, 95% CI 0.68, 3.56); and Pacific children had similar rates of exposure to NZE (RR = 0.98, 95% CI 0.59, 1.61). Adding school decile group into the model did not appreciably change ethnic differences (Table 3 , model 2). Compared to middle-decile children, children at higher decile schools had higher exposure to core foods (RR = 1.60, 95% CI 1.03, 2.48); while children at lower decile schools had non-significantly higher rates of such exposure (RR = 1.18; 95% CI 0.80, 1.73; reference is middle decile group). The third model incorporated a formal interaction test between ethnicity and school decile group, which was non-significant, suggesting that ethnic patterns were similar across school decile groups (F 4, 15 = 1.99; p  = 0.1481).

Analysis of ethnic differences in non-core exposures (adjusted for child gender and age; Table 3 , right hand column, model 1) showed non-significantly higher rates of exposure to non-core foods for Māori children relative to NZE (RR = 1.18, 95% CI 0.90, 1.55) but not for Pacific children (RR = 0.99, 95% CI 0.84, 1.16). Differences in exposure by school decile group appeared minimal (Table 3 ) and adjustment of ethnic differences for school decile group did not appreciably change estimates from those in the initial model. A third model, incorporating interaction terms, suggested that ethnic differences in non-core exposures differed across the three school decile groups (F 4, 15 = 4.58, p  = 0.013). These results are presented in Additional files 4 and 5 . In brief, there was reasonably strong evidence for ethnic differences in the lowest school decile group (Māori RR = 1.20, 95% CI 0.97, 1.47; Pacific RR = 1.50, 95% CI 1.19, 1.89; both relative to NZE).

Children in this study were exposed to non-core food marketing, food not recommended to be marketed to children, 27.3 times a day on average across all settings, excluding convenience stores and supermarkets. Exposure to non-core food marketing was more than twice that of exposure to core food marketing (12.3 times a day). Most non-core exposures occurred at home, in public spaces and at school. Food packaging was the predominant marketing medium, followed by signs. Product packaging is commonly used to attract attention, provide information about product attributes and encourage purchase at point-of-sale [ 16 ]. Product packaging is particularly salient as children are the population group most vulnerable to such food marketing [ 20 ].

Children were most exposed to non-core marketing for sugary drinks, fast food, confectionary and snack foods, a finding consistent with previous research [ 11 , 14 , 28 , 29 , 30 , 31 ]. A notable exception is exposure to marketing for high-sugar, low-fibre breakfast cereals which comprised only 2.5% of all non-core marketing. Research in the UK and Australia found high rates of such marketing on television [ 11 , 30 ].

Although televisions, smart phones, tablets and computers often appeared in the images, screen-based marketing is likely under-reported in the current study as content on screens was often not clear enough to meet coding criteria in the images. Research across 11 countries in 2010 reported five food advertisements per hour of television. A 2014 national survey of NZ children aged 6–14 found 88% watch television each day, 44% of whom watch more than an hour a day [ 32 ] thus potentially seeing five food advertisements daily on television alone, considerably more than the 0.2 exposures per day identified across all screen types in the current study. Food marketing on new media is also of concern (e.g. websites, social media and apps) and may have even greater impact than traditional media e.g. television [ 33 ]. NZ children engage with the internet frequently, with 66% accessing it daily [ 32 ].

Exposure to non-core food marketing was higher than for core foods in all school decile strata. Core exposures were more common in the high school decile groups; while for non-core exposures, there were no significant differences in exposure by these school decile groups. Similarly, while Māori children had higher exposure to both core and non-core marketing than NZE children, these results were not statistically significant in the adjusted models. The more complex model incorporating interaction terms suggested that ethnic group differences were somewhat varied across school decile group, with stronger evidence in the lowest school decile group for Pacific and Māori children.

To our knowledge, this is the first study to objectively measure children’s exposure to food marketing in their everyday environments across multiple settings and in multiple media. The use of automated wearable cameras enabled unprecedented access to children’s worlds, recording their exposures with food marketing as they occurred. This methodology overcomes many of the limitations inherent in using self-report or proxy report data [ 34 ]. Further, it comprehensively documented children’s actual exposure to marketing, with the important exceptions of marketing on screens, and in convenience stores and supermarkets. This is a major advantage of the Kids’Cam methodology: documenting actual exposure is challenging in third-party environmental observation studies, particularly in private contexts such as the home.

While this research provides some of the most robust data yet analysed on children’s exposure to food marketing, it does have limitations. First, the images do not determine if a child actually sees the marketing in the image. For example, the child could be looking away, although given the extent of food marketing in children’s environments they may still see marketing. Secondly, the decision to only code an image if 50% or more of a brand name or logo could be clearly seen is likely to underestimate the exposure to marketing, as does the exclusion of marketing in convenience stores and supermarkets, where marketing is likely to be extensive [ 35 ]. Further, the use of still photography may have missed some exposures. However, excluding screens, convenience stores and supermarkets, the ratio of more than two non-core food marketing exposures for every one core exposure is likely to be consistent, despite these limitations. The participation rate ( n  = 192 or 43% of invited children consenting to participate; with space for 168 participants, or 38% of the full invitation list participating) was reasonable for a study that required ongoing engagement by the children over several days. It remains possible that those children and families consenting to participate were systematically different from children who did not participate. Finally, while the sample size was determined prior to the study commencing, the number of participants was limited by the study budget and timeframe. This meant that some analyses (e.g. comparisons of exposure rates by ethnic group) might have had sub-optimal power to detect differences between groups, which is reflected in the relatively wide confidence intervals for these estimates. These specific estimates should be interpreted with caution.

Further real time research is needed on children’s exposure to marketing in convenience stores and supermarkets and on screens to complement this research. Further exploration of potential ethnic differences appears warranted, but will require a substantially larger sample size to improve statistical precision and power. Use of photo elicitation [ 36 ] with children who wore cameras would likely elicit valuable data on the meaning of food marketing and enable exploration of effective means for intervention from children’s perspective. Manual data coding was resource intensive, taking a total of 1440 person-hours. While this was an extensive undertaking, the richness of the resulting dataset made it worthwhile: the children collected 2553 h of image data from their perspective, giving insight into settings that would have been difficult to study as a participant observer. Ancillary studies also benefitted from this initial coding, as settings and other image characteristics were already available to researchers, which reduces processing times in these subsequent studies [ 37 , 38 ]. Further, automated image recognition has the potential to aid analysis and reduce manual coding time requirements [ 39 , 40 ]. The Kids’Cam method has the potential to validate other methods, e.g. surveys of school food policies, with in-depth analysis of the actual food environment [ 41 ]. Comparative research of children’s exposure to food marketing in other jurisdictions would further strengthen the global body of evidence.

This research suggests that children live in an obesogenic food marketing environment that promotes obesity as a normal response to their everyday environment [ 42 ]. Children are more than twice as likely to be exposed to non-core food marketing, not recommended to be marketed to children [ 9 ], than core food marketing, and to be exposed multiple times a day across various settings and via multiple media. All children, regardless of socio-economic position, were exposed to more non-core than core food marketing, and there appears to be some ethnic patterning.

Particularly concerning is the amount of exposure in school, an environment where children’s health is required to be protected under NZ law [ 43 ], and which the ECHO Commission states should be free of such marketing [ 5 ]. Exposure in public places is an arena for central and local governments globally. Given that over two-thirds of marketing is in the form of food packaging, consideration should be given to plain packaging in some specific cases (e.g. sugar sweetened beverages) as a highly effective intervention in this arena [ 44 ].

The ECHO Commission is right to call for the reduction of children’s exposure to marketing of unhealthy foods [ 5 ]. This research provides further evidence of the need for action and suggests both settings and media in which to act. Urgent action is required if the vision of the Commission on Ending Childhood Obesity is to be achieved.

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Acknowledgements

We gratefully thank the children, parents, caregivers and schools who let us into their lives. We also thank Ryan Gage and the fourth year medical students who assisted with the coding, especially Saskia Campbell, Ryan Cullen and Richard Kennedy.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

This research was funded by a Health Research Council of New Zealand Programme Grant (13/724), and supported by Science Foundation Ireland (grant 12/RC/2289), a European Commission FP7 International Research Staff Exchange Scheme (IRSES) funding award (2011-IRSES-295157-PANAMA) and a University of Otago Wellington Equipment Grant.

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L. N. Signal, J. Stanley, M. Smith, M. B. Barr, T. J. Chambers, C. McKerchar & G. L. S. Jenkin

Insight Centre for Data Analytics, Dublin City University, Belfield, Dublin, Ireland

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Department of Geography, Environment and Spatial Sciences, Michigan State University, 673 Auditorium Rd, East Lansing, MI, 48825, USA

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LS, MS, MB, JS, GJ, ALP, JH, CG, AFS, and CNM conceived the idea and developed the study design. JZ, AD, CG, and AS developed the coding software. LS, MS, MB, TC, and GJ collected the data. JS led the data management and analysis. LS, MS, MB, TC, CM, JH and CNM participated in the data analysis. LS provided overall leadership of the research. All authors contributed to this manuscript and approved the final version.

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Ethical approval was obtained from the University of Otago Human Ethics Committee (Health) (13/220) to study any aspect of the world children live in and their interaction with it; as such, children were blinded to the primary food marketing focus of the study. All participating children, parents and schools signed written consent to participate in the study.

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Additional files

Additional file 1:.

Number of children and photos by ethnicity/school decile sampling stratum, and mean number of photos per child in each stratum. (DOCX 15 kb)

Additional file 2:

Median and interquartile range of per-child rates of exposure per day to core and non-core items, by school. (DOCX 15 kb)

Additional file 3:

Mean rate of core and non-core food marketing exposures (per day, with 95% CI, from Poisson regression) by setting with aggregated and detailed setting information (with percentage share of all exposures by setting). (DOCX 16 kb)

Additional file 4:

Mean rate (and 95% CI) of core and non-core marketing exposures per day (10 h of photographs), by school decile stratum and ethnicity of child. (DOCX 98 kb)

Additional file 5:

Rate ratios for differences in non-core food marketing exposures (from Poisson regression, with 95% CI) by interaction school decile group and ethnicity, adjusted for gender and age. (DOCX 15 kb)

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Signal, L.N., Stanley, J., Smith, M. et al. Children’s everyday exposure to food marketing: an objective analysis using wearable cameras. Int J Behav Nutr Phys Act 14 , 137 (2017). https://doi.org/10.1186/s12966-017-0570-3

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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).

15 . Yochai Benkler, The Wealth of Networks: How Social Production Transforms Markets and Freedom (New Haven, Connecticut: Yale University Press, 2006): 272.

16. . Yahoo! and OMD, “Truly, Madly, Deeply Engaged: Global Youth, Media and Technology,” 2005, http:// www.iabaustralia.com.au/Truly_Madly_Final_ booklet.pdf (viewed 27 Mar. 2007).

17.  See A.K. Pradeep, The Buying Brain: Secrets for Selling to the Subconscious Mind (New York: Wiley, 2010).

18 . Ward Hanson, Principles of Internet Marketing (Cincinnati, OH: South Western College Publishing, 1999).

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).

21 . Hallerman, “Behavioral Targeting: Marketing Trends”; Khan, Weishaar, Polinsky, et al., “Nothing but Net: 2008 Internet Investment Guide.”

22.  A. Iskold, “Social Graph: Concepts and Issues,” ReadWriteWeb, 12 Sept. 2007, http://www. readwriteweb.com/archives/social_graph_concepts_and_issues.php (viewed 2 Oct. 2008).

23 . Allen Vamey, “Immersion Unexplained,” The Escapist, 8 Aug. 2006, http://www. escapistmagazine.com/articles/view/issues/ issue_57/341-Immersion-Unexplained (viewed 26 Aug. 2010).

24.  Hairong Li, Terry Daugherty, and Frank Biocca, “Impact of 3-D Advertising on Product Knowledge, Brand Attitude, and Purchase Intention: The Mediating Role of Presence,” Journal of Advertising 31, n. 3 (Fall 2002): 43.

25.  Wendy L. Johnson-Askew, et al, “Decision Making in Eating Behavior: State of the Science and Recommendations for Future Research,” Annals of Behavioral Medicine 38, Suppl. (2009).

26.  Mary Story, et al., “Creating Healthy Food and Eating Environments: Policy and Environmental Approaches,” Annual Review of Public Health 29 (2008): 253-272.

27 . The National Academies, “Food Marketing Aimed at Kid Influences Poor Nutritional Choices, IOM Study Finds; Broad Effort Needed to Promote Healthier Products and Diets,” press release, 6 Dec. 2005, http://www8.nationalacademies.org/onpinews/ newsitem.aspx?RecordID=11514 (viewed 26 Mar. 2007); McGinnis, et al., eds., Food Marketing to Children and Youth: Threat or Opportunity?

28.  Harris, Brownell, and Bargh, “The Food Marketing Defense Model: Integrating Psychological Research to Protect Youth and Inform Public Policy.”

29 . Hae-Kyong Bang and Bonnie B. Reece, “Minorities in Children’s Television Commercials: New, Improved, and Stereotyped,” Journal of Consumer Affairs 37, n. 1 (2003): 42-66; Grier and Kumanyika, “The Context for Choice: Health Implications of Targeted Food and Beverage Marketing to African Americans”; Kristen Harrison, “Fast and Sweet: Nutritional Attributes of Television Food Advertisements with and without Black Characters,” Howard Journal of Communications 17, n. 4 (2006): 16; Corliss Wilson Outley and Abdissa Taddese, “A Content Analysis of Health and Physical Activity Messages Marketed to African American Children During After-School Television Programming,” Archives of Pediatrics & Adolescent Medicine 160, n. 4 (2006): 4; Lisa M. Powell, Glen Szczypka, and Frank J. Chaloupka, “Adolescent Exposure to Food Advertising on Television,” American Journal of Preventive Medicine 33, n. 4 (2007): S251-S256.

30 . Moore and Rideout, “The Online Marketing of Food to Children: Is it Just Fun and Games?”; Moore, “It’s Child’s Play: Advergaming and the Online Marketing of Food to Children”; Story and French, “Food Advertising and Marketing Directed at Children and Adolescents in the U.S.”; Lee, Choi, Quilliam, and Cole, “Playing with Food: Content Analysis of Food Advergames.”

31.  Alvy and Calvert, “Food Marketing on Popular Children’s Web Sites: A Content Analysis”; Calvert, Jordan, and Cocking, eds., Children in the Digital Age: Inlfuences of Electronic Media on Development ; Calvert, “Children as Consumers: Advertising and Marketing”; 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”; American Academy of Pediatrics Committee on Communications, “Policy Statement: Children, Adolescents, and Advertising”; Valkenburg, “Media and Youth Consumerism.”

32.  Kunkel, Wilcox, Cantor, et al., “Report of the APA Task Force on Advertising and Children”; D. Roedder- John, “Consumer Socialization of Children: A Retrospective Look at Twenty-“ve Years of Research,” Journal of Consumer Research 26, n. 3 (1999): 183-213.

33 . S. Livingstone and E. J. Helsper, “Does Advertising Literacy Mediate the Effects of Advertising on Children? A Critical Examination of Two Linked Research Literatures in Relation to Obesity and Food Choice,” Journal of Communication 56, n. 3 (2006): 560-584.

34 . Dale Kunkel, “Children and Television Advertising,” in D. G. Singer and J. L. Singer, eds., Handbook of Children and the Media (Thousand Oaks, CA: Sage Publications., 2001): 375-393; Dale Kunkel, “Kids Media Policy Goes Digital: Current Developments in Children’s Television Regulation,” in J. A. Bryant & J. Bryant, eds., The Children’s Television Community: Institutional, Critical, Social Systems, and Network Analyses (Mahwah, NJ: Lawrence Erlbaum Associates, 2006): 203-228; Montgomery, Generation Digital: Politics, Commerce, and Childhood in the Age of the Internet ; A. Nairn and C. Fine, “Who’s Messing with My Mind? The Implications of Dual-process Models for the Ethics of Advertising to Children,” International Journal of Advertising 27, n. 3 (2008): 447-470; Agnes Nairn, “Changing the Rules of the Game: Implicit Persuasion and Interactive Children’s Marketing,” 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:// www.digitalads.org/documents/Nairn_NPLAN_ BMSG_memo.pdf (viewed 26 Aug. 2010).

35 . C. Pechmann, L. Levine, & S. Loughlin, et al., “Impulsive and Self-conscious: Adolescents’ Vulnerability to Advertising and Promotion,” Journal of Public Policy & Marketing 24, n. 2 (2005): 202-221; Nairn and Fine, “Who’s Messing with My Mind? The Implications of Dual-process Models for the Ethics of Advertising to Children”; Peter Wright, Marian Friestad, and David M. Boush, “The Development of Marketplace Persuasion Knowledge in Children, Adolescents, and Young Adults,” Journal of Public Policy & Marketing 24, n. 2 (2005): 222-233; Harris, Brownell, and Bargh, “The Food Marketing Defense Model: Integrating Psychological Research to Protect Youth and Inform Public Policy.”

36 . Nairn and Fine, “Who’s Messing with My Mind? The Implications of Dual-process Models for the Ethics of Advertising to Children.”

37 . Harris, Brownell, and Bargh, “The Food Marketing Defense Model: Integrating Psychological Research to Protect Youth and Inform Public Policy”; Sharmistha Law and Kathryn A. Braun, “I’ll Have What She’s Having: Gauging the Impact of Product Placements on Viewers,” Psychology and Marketing 17, n. 12 (Dec. 2000): 1059-1075; S. Auty and C. Lewis “The ‘Delicious Paradox’: Preconscious Processing of Product Placements by Children,” in L. J. Shrum, ed., The Psychology of Entertainment Media: Blurring the Lines Between Entertainment and Persuasion (London: Psychology Press, 2003): 117-33; Nairn, “Changing the Rules of the Game: Implicit Persuasion and Interactive Children’s Marketing.”

38.  A. E. Eagly and S. Chaiken, “Process Theories of Attitude Formation and Change: The Elaboration Likelihood Model and Heuristic Systematic Models,” in A. E. Eagly & S. Chaiken, eds., The Psychology of Attitudes (Ft. Worth, TX: Harcourt Brace Jovanovich, 1993): 305-325; Harris, Brownell, and Bargh, “The Food Marketing Defense Model: Integrating Psychological Research to Protect Youth and Inform Public Policy”; Livingstone & Helsper, “Does Advertising Literacy Mediate the Effects of Advertising on Children?”

39.  Nairn, “Changing the Rules of the Game: Implicit Persuasion and Interactive Children’s Marketing.” Louis J. Moses, “Research on Child Development: Implications for How Children Understand and Cope with Digital Media,” 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://www.digitalads.org/documents/Moses_NPLAN_BMSG_memo.pdf (viewed 26 Aug. 2010).

40.  J. A. Bargh, “Losing Consciousness: Automatic Influences on Consumer Judgment, Behavior and Motivation,” Journal of Consumer Research 29, n. 2 (2002): 280-286; G. J. Fitzsimons, J. W. Hutchinson, P. Williams, et al., “Non-conscious Influences on Consumer Choice,” Marketing Letters 13, n. 3 (2002): 269-279.

41.  Harris, Brownell, and Bargh, “The Food Marketing Defense Model: Integrating Psychological Research to Protect Youth and Inform Public Policy.”

42 . R. B. Zajonc, “Attitudinal Effects of Mere Exposure,” Journal of Personality and Social Psychology 9 (1968): 1-27.

43 . Harris, Brownell, and Bargh, “The Food Marketing Defense Model: Integrating Psychological Research to Protect Youth and Inform Public Policy,” 223.

44 . C. Y. Yoo, “Unconscious Processing of Web Advertising: Effects on Implicit Memory, Attitude Tsoward the Brand, and Consideration Set, Journal of Interactive Marketing 22, n. 2 (2008): 2-16.

45 . Janet Hoek and Phillip Gendall, “Advertising and Obesity: A Behavioral Perspective” Journal of Health Communication 11, n. 4 (June 2006): 409-423.

46.  Grier and Kumanyika, “The Context for Choice: Health Implications of Targeted Food and Beverage Marketing to African Americans.”

47 . S. K. Kumanyika, M. C. Whitt-Glover, T. L. Gary, et al., “Expanding the Obesity Research Paradigm to Reach African American Communities,” Preventing Chronic Disease 4, n. 4 (2007): 1-22.

48 . J. M. McPhersony, L. Smith-Lovin, and J. M. Cook, “Birds of a Feather: Homophily in Social Networks,” Annual Review of Sociology 27 (2001): 415-444.

49 . David Honig and Lewis Steckler, “Who Do you Know?” Media6degrees, n.d., http:// media6degrees.com/wp-content/themes/md6/ documents/who-do-you-know.pdf (viewed 12 Sept. 2010); McPhersony, Smith-Lovin, and Cook, “Birds of a Feather: Homophily in Social Networks.”

50 . Kelly D. Brownell, Marlene B. Schwartz, Rebecca M. Puhl, Kathryn E. Henderson, and Jennifer L. Harris, “The Need for Bold Action to Prevent Adolescent Obesity,” Journal of Adolescent Health 45, n. 3, Suppl. (Sept. 2009): S8-S17. M. Story, J. Sallis, and T. Orleans, “Adolescent Obesity: Towards Evidence- Based Policy and Environmental Solutions,” Journal of Adolescent Health 45, n. 3, Suppl. (Sept. 2009): S1-S5.

51 . McGinnis, et al., eds., Food Marketing to Children and Youth: Threat or Opportunity? Story and French, “Food Advertising and Marketing Directed at Children and Adolescents in the U.S.” Livingstone & Helsper, “Does Advertising Literacy Mediate the Effects of Advertising on Children?”

52 . M. Goldberg, K. Niedermeier, L. Bechtel, and G. Gorn, “Heightening Adolescent Vigilance toward Alcohol Advertising to Forestall Alcohol Use,” Journal of Public Policy and Marketing 25, n. 2 (2006): 147-159.

53.  Pechmann, Levine, & Loughlin, et al., “Impulsive and Self-conscious: Adolescents’ Vulnerability to Advertising and Promotion”; Frances M. Leslie, Linda J. Levine, Sandra E. Loughlin, & Cornelia Pechmann, “Adolescents’ Psychological & Neurobiological Development: Implications for Digital Marketing,” 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/Leslie_ et_al_NPLAN_BMSG_memo.pdf (viewed 26 Aug. 2010); J. N. Giedd, “The Teen Brain: Insights from Neuroimaging,” Journal of Adolescent Health 42, n. 4 (2008): 335-343; E. R. McAnarney, “Adolescent Brain Development: Forging New Links?” Journal of Adolescent Health 42, n. 4 (2008): 321-323; L. Steinberg, “Risk Taking in Adolescence: New Perspectives from Brain and Behavioral Science,” Current Directions in Psychological Science 16, n. 2 (2007): 55-59; L. Steinberg, “A Social Neuroscience Perspective on Adolescent Risktaking,” Developmental Review 28, n. 1 (2008): 78-106; T. McCreanor, H. M.Barnes, & M. Gregory, et al., “Consuming Identities: Alcohol Marketing and the Commodi”cation of Youth Experience,” Addiction Research & Theory 13, n. 6 (2005): 579-590; R. L. Collins, P. L. Ellickson, & D. McCaffrey, et al., “Early Adolescent Exposure to Alcohol Advertising and its Relationship to Underage Drinking,” Journal of Adolescent Health 40, n. 6 (2007): 527-534.

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.

55 . F. G. Castro, “Physiological, Psychological, Social, and Cultural Influences on the Use of Menthol Cigarettes among Blacks and Hispanics,” Nicotine & Tobacco Research 6, Suppl. 1 (2004): S29- 41; Grier, “African American & Hispanic Youth Vulnerability to Target Marketing: Implications for Understanding the Effects of Digital Marketing”; S. McDermott, & B. Greenberg, “Parents, Peers and Television as Determinants of Black Children’s Esteem,” in R. Bostrom, ed., Communication Yearbook (Beverly Hills, CA: Sage, 1984): 164-177.

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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How marketing classes can rescue ‘ugly produce’ from becoming food waste

PhD Candidate, Marketing, Carleton University

Associate Professor of Marketing, Carleton University

Associate Professor, Marketing, Carleton University

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At a time of rising food costs and growing food insecurity , a large percentage of food grown for consumption never reaches our tables.

Indeed, some estimates suggest that approximately 40 per cent of fruits and vegetables never even leave farms . Much of it gets rejected by wholesalers and retailers based on irregularities in weight, size or shape.

This desire for cosmetically appealing food also extends to consumers, as we often prefer picture-perfect produce . Unsurprisingly, this wanton waste takes a significant environmental toll, with an estimated eight to 10 per cent of global greenhouse gas emissions tied to unconsumed food .

Showing ugly produce some love

Some companies have taken strides to counter food waste. A prominent example in the United States is Misfits Market, which launched in 2018.

By buying misshapen and ugly produce and reselling it at discount prices in subscription boxes, Misfits Market has grown into a billion-dollar business .

Closer to home, Loblaw Companies’ “naturally imperfect” line offers visually unappealing produce at lower prices, while newcomers such as Montréal-based Food Hero are developing apps to reduce a different but persistent form of waste by helping customers find deals on food approaching its best-by date.

Despite such encouraging efforts, there’s still a lot of work to do on changing attitudes and behaviours to alleviate waste. This has become an important academic issue, and is increasingly being tackled by those of us in marketing, a field that has perpetuated this cycle of waste .

In a recent study, we introduced our RESCUER framework designed to expose students to food waste and to generate behavioural changes. We developed it over three years through research assignments undertaken by students in our classes at Carleton University. We used 90 reflective essay assignments alongside 63 sets of surveys (administered pre- and post-assignment) to develop the framework.

Steps towards change

RESCUER stands for the steps in the process of learning, action and change undertaken by students, and combines passive and active modes of learning .

We first engaged students with resources — “passive” forms of learning through lectures and curated readings on food waste , irregularly shaped produce and sustainable practices .

Next, students engaged in an experiential learning exercise that had them actively planning, shopping for and preparing a salad with food waste issues in mind, before writing reflective journals about their experiences. Journaling allows students to articulate their feelings, thoughts and values, leads them to examine and challenge pre-conceived assumptions, practices and policies, and encourages them to be more alert when shopping for and preparing food.

We next accounted for the social influences of family, friends and peers on sustainability-minded behaviours.

Throughout the process, students developed a greater cognizance of food waste, and these issues became more readily and consistently resonant when shopping. The process also resulted in underlying problem-salience — the spontaneous evocation of the food waste problem in consumers’ minds as soon as they need to buy or prepare food.

Finally, we identified factors that expedite learning and adoption processes, such as the availability of recycling and composting facilities at home and access to retailers that support sustainable practices and provide price discounts.

Student comments

The results? Well, students emerged with a much deeper understanding of food waste and an increase in responsible attitudes and behaviours. This increase in responsibility is evident in the comments from students about RESCUER, including:

“I am cognizant of the negative effects that food abnormalities have on the environment due to food waste issues. On that account, I will surely change some of my habits to match my perceived identity. Seeing myself as, and wanting to be more of, a pro-environmental person, I want my actions regarding food waste to match this desired self-identity.”

The students’ newfound awareness also translated into more responsible consumption behaviours. They started choosing imperfect produce, as one student reported:

“I bought abnormal carrots and green onions and even considered some oddly shaped bell peppers in my purchase decisions.”

They also became less picky about expiration dates, according to another student who was conscious of preventing waste:

“Completing this assignment has increased my awareness to ensure to take the foods on the shelves that are approaching their best-before date as opposed to selecting the freshest option each time.”

Another responsible action is in how students spread what they have learned, as one noted:

“I am certainly going to share what I have learned from the readings with friends and family.”

These qualitative findings are further validated by our survey results. A comparative analysis was conducted before and after the framework’s implementation. It revealed that students’ awareness, understanding and actions related to sustainability were all improved after having completed the exercise.

Educators can change attitudes

Overall, we’ve seen our RESCUER framework cultivate a shift towards responsible consumption, and it also situates marketing education within a sustainability narrative.

Ours is an example of how educators can play a crucial role in changing attitudes and actions, and in equipping future professionals with tools to tackle the challenges of sustainability .

Conversations about what sustainability entails , how it can be encouraged and its integration into education is more relevant than ever as we strive for ways to work towards a more sustainable future.

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About 1 in 10 restaurants in the U.S. serve Mexican food

Mexican culture is widely established in America’s restaurants. Some 11% of restaurants in the United States serve Mexican food, according to a Pew Research Center analysis of data from SafeGraph , which curates information about millions of places of interest around the globe, and the user review site Yelp.

Although especially common in California and Texas, Mexican restaurants are found in a large majority of counties in the U.S. Some 37.2 million people in the U.S. trace their ancestry to Mexico , making Mexican Americans by far the largest Hispanic origin group in the nation.

Pew Research Center conducted this analysis to examine the geographic distribution and characteristics of Mexican restaurants in the United States, including Puerto Rico. We focused on Mexican restaurants because Mexican Americans are the nation’s largest Hispanic origin group – and because Mexican food is so widespread in the U.S. Throughout this analysis, we use phrases such as “Mexican restaurants” and “restaurants that serve Mexican food” interchangeably.

To conduct this analysis, we purchased restaurant data from  SafeGraph , which curates information about millions of places of interest around the globe. “Restaurants” are places defined by the North American Industry Classification System (NAICS) as “Restaurants and Other Eating Places” (NAICS code 7225). When we collected this data on March 23, 2023, SafeGraph had records for 788,018 operational restaurants nationwide.

We used SafeGraph’s  category tags to build an initial list of Hispanic or Latino restaurants in America. This list includes restaurants tagged with the following categories: Argentine, Brazilian, Cuban, Mexican, Peruvian, Portuguese and Spanish, along with catchall categories for “Caribbean food” and “Latin American food.” 

As part of this analysis, we also matched the restaurants in the SafeGraph data with data from the review site Yelp, using the official Yelp API . We matched the Yelp restaurant identifiers (restaurant name and address) to the corresponding restaurants in the SafeGraph dataset using Python’s FastLink package, an implementation of the Fellegi-Sunter probabilistic record linkage model. This matching supplemented the SafeGraph data with more detailed food origin and dish categories, as well as restaurant details such as average price range.

Of the 101,009 restaurants with some sort of Hispanic or Latino food category tag in the SafeGraph data, we were able to find matching entries for 92,718 restaurants (92%) on Yelp. After examining a selection of unmatched restaurants, we found two main reasons why. In some cases, the business did not have any reviews on Yelp (the Yelp API does not return information for businesses with no user-contributed enhancements ). In other cases, these restaurants had closed between the time we purchased the SafeGraph data in March 2023 and when we conducted the Yelp matching in September 2023.

After matching the original SafeGraph records with the Yelp data, the final combined dataset included restaurants serving the following types of Hispanic or Latino food: Argentine, Brazilian, Colombian, Cuban, Dominican, Haitian, Honduran, Mexican, Nicaraguan, Peruvian, Portuguese, Puerto Rican, Salvadoran, Spanish, Trinidadian and Venezuelan, as well as categories for “Caribbean food” and “Latin American food.” Our analysis of restaurants other than Mexican is limited due to the small number tagged this way.

The data also included tags for specific types of food such as “Tex-Mex,” “tacos” and “empanadas.” Restaurants with “Tex-Mex” and “tacos” tags are included in the “Mexican food” category, while those tagged with “empanadas” are included in the “Latin American food” category, unless the restaurant is already tagged with a more specific category.

Individual restaurants can be tagged with multiple categories. For instance, a restaurant may include tags for “Mexican food” and “Salvadoran food.” These restaurants are counted under all categories listed in the dataset.

County-level population estimates for the U.S. come from table B01003 of the American Community Survey’s 5-year 2019 estimates, which include counties and county equivalents (such as Fairbanks North Star Borough, Alaska).

Which states and counties have the most Mexican restaurants?

This analysis finds that 85% of U.S. counties have at least one Mexican restaurant. In turn, the counties that don’t have Mexican restaurants tend to have small populations. The 15% of counties without any Mexican restaurants have about 4 million people living in them. That is just 1% of the total U.S. population.

Related: 71% of Asian restaurants in the U.S. serve Chinese, Japanese or Thai food

Mexican restaurants are most common in California and Texas. These two states, which are home to a majority of the Mexican American population , have around 40% of all Mexican restaurants in the country: 22% are in California, while 17% are in Texas.

In California, Los Angeles County alone is home to 30% of the state’s Mexican restaurants. In Texas, 17% of the state’s Mexican restaurants are in Harris County, which includes Houston; 9% each are located in Bexar County, which includes San Antonio, and in Dallas County.

Florida, New York and Illinois also contain large numbers of Mexican restaurants. Each state has 4% of the nationwide total of these restaurants. All told, 51% of all Mexican restaurants in the U.S. are in California, Texas, Florida, New York or Illinois.

Where do Mexican restaurants make up the largest share of eateries?

In addition to examining which parts of the country have the most Mexican restaurants, we also looked at where they make up the largest share of restaurants.

By this metric, Mexican restaurants make up an especially large share of all restaurants in Southwestern states that border Mexico. They account for 22% of all restaurants in New Mexico, 20% in Texas, 18% in Arizona and 17% in California.

At the county level, there are 10 where Mexican restaurants account for more than 33% of all restaurants. Eight of these 10 counties are in Texas, and most are along the U.S.-Mexico border. (This analysis excludes counties that have fewer than 15 restaurants of any type.)

What are some common features of Mexican restaurants?

This analysis finds that 22% of Mexican restaurants nationwide are “fast food” restaurants, 12% specialize in serving tacos, 8% are classified as food trucks or carts, and 6% offer “Tex-Mex” food.

Mexican restaurants also tend to be modestly priced. Among restaurants with pricing data, 61% of Mexican restaurants are rated as one “dollar sign” on Yelp’s four-point pricing scale. Less than 1% of all Mexican restaurants nationwide – just 251 in total – have a rating of three or four dollar signs on the Yelp scale. Around a quarter of these more expensive Mexican restaurants are in Los Angeles County; Cook County, where Chicago is located; and New York County, home of Manhattan.

How common are other types of Latino or Hispanic restaurants in the U.S.?

Mexican Americans are the largest Hispanic group in the U.S., but 40% of the nation’s Latinos claim another Hispanic origin . Yet our analysis finds that only 2% of U.S. restaurants serve Hispanic or Latino cuisine other than Mexican.

The most common types of non-Mexican Hispanic restaurants include Caribbean, Cuban, “Latin American,” Peruvian, Salvadoran and Spanish restaurants. But none makes up more than 1% of restaurants nationwide. (There are other types of Hispanic restaurants in addition to these, but they each make up 0.1% or less of restaurants nationwide and are not included in this analysis.)

Put differently, Mexican restaurants account for the vast majority of Hispanic or Latino restaurants of any kind. And although many non-Mexican restaurants also offer Mexican food, the reverse is less often true. For example, 38% of Salvadoran and 25% of Honduran restaurants in the U.S. also serve Mexican food. But just 3% of Mexican restaurants also serve other kinds of Hispanic or Latino food.

Hispanic or Latino restaurants that are not Mexican are also much less geographically widespread than Mexican restaurants. Fully 85% of U.S. counties have at least one Mexican restaurant, but 29% have some type of Latino or Hispanic restaurant that is not primarily Mexican.

These Latino or Hispanic restaurants make up a relatively large share of restaurants in places like Florida – especially in and around Miami-Dade County – or in New York and New Jersey near New York City. But even in these areas, Mexican restaurants make up for a comparable – and sometimes larger – share of all restaurants than those serving other Hispanic or Latino food.

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Regina Widjaya is a computational social scientist focusing on data science at Pew Research Center

Sono Shah is an associate director focusing on data science at Pew Research Center

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Original research article, the artificial sweetener neotame negatively regulates the intestinal epithelium directly through t1r3-signaling and indirectly through pathogenic changes to model gut bacteria.

• 1 Department of Botany, Jahangirnagar University, Dhaka, Bangladesh
• 2 Biomedical Research Group, School of Life Science, Anglia Ruskin University, Cambridge, United Kingdom

Introduction: Recent studies have indicated considerable health risks associated with the consumption of artificial sweeteners. Neotame is a relatively new sweetener in the global market however there is still limited data on the impact of neotame on the intestinal epithelium or the commensal microbiota.

Methods: In the present study, we use a model of the intestinal epithelium (Caco-2) and microbiota ( Escherichia coli and Enterococcus faecalis ) to investigate how physiologically-relevant exposure of neotame impacts intestinal epithelial cell function, gut bacterial metabolism and pathogenicity, and gut epithelium-microbiota interactions.

Results: Our findings show that neotame causes intestinal epithelial cell apoptosis and death with siRNA knockdown of T1R3 expression significantly attenuating the neotame-induced loss to cell viability. Similarly, neotame exposure results in barrier disruption with enhanced monolayer leak and reduced claudin-3 cell surface expression through a T1R3-dependent pathway. Using the gut bacteria models, E. coli and E. faecalis , neotame significantly increased biofilm formation and metabolites of E. coli , but not E. faecalis , reduced Caco-2 cell viability. In co-culture studies, neotame exposure increased adhesion capacity of E. coli and E. faecalis onto Caco-2 cells and invasion capacity of E. coli . Neotame-induced biofilm formation, E.coli -specific Caco-2 cell death, adhesion and invasion was identified to be meditated through a taste-dependent pathway.

Discussion: Our study identifies novel pathogenic effects of neotame on the intestinal epithelium or bacteria alone, and in co-cultures to mimic the gut microbiome. These findings demonstrate the need to better understand food additives common in the global market and the molecular mechanisms underlying potential negative health impacts.

Introduction

Artificial sweeteners have emerged as an essential dietary additive, serving as substitutes for sugar in low-calorie foods and beverages, as well as pharmaceuticals and cosmetics ( 1 , 2 ). According to global market reports on artificial sweeteners, saccharin, sucralose, aspartame, neotame, acesulfame potassium, and cyclamate are widely accepted artificial sweeteners that have been approved as safe by the US Food and Drug Administration ( 3 ). Due to their widespread use, artificial sweeteners have a predicted global market value of USD 3 billion by the end of 2025 ( 4 ). Whilst the traditional sweeteners, acesulfame potassium, sucralose, saccharin and aspartame, have been consumed by the public for many years there are more recently developed artificial sweeteners which herald the next generation of sweet additives ( 5 ). Neotame is one such new sweetener which was developed in the 1990′s with the commercial benefits of greater sweetness potential and improved stability compared to existing sweeteners ( 6 , 7 ). Whilst neotame is a non-nutritive additive, it is rapidly metabolized and eliminated with no apparent physiological accumulation in the body ( 8 ). Feeding studies with neotame in mice and other test animals did not show adverse physical symptoms, water consumption, or clinical pathology evaluations, therefore the sweetener is considered safe for consumption and was approved by FDA in 2002 and EFSA in 2010( 8 ) The acceptable daily intake (ADI) of neotame is 2 mg/kg body weight per day which is no more than 10 mM per day in an individual with average weight ( 8 ) Given the different available forms of neotame, such as agglomerated, encapsulated, co-crystallized with sugar and cyclodextrin complexes, the sweetener is easy and cost-effective to use for food manufacturing and, as such, is found in a range of drinks, sauces, savory and sweet foods, and chewing gums ( 7 , 9 ). Despite widespread global use of neotame, there are surprisingly few research studies on the biological and physiological effects of the sweetener. Given our emerging knowledge of the health impacts of other artificial sweeteners ( 10 ), there is a need to focus studies on the impact of neotame on human health.

Numerous epidemiological studies have highlighted the potential benefits of artificial sweeteners in promoting weight loss and aiding individuals with glucose intolerance and type 2 diabetes mellitus ( 2 ), however other research has demonstrated negative health outcomes associated with artificial sweetener consumption ( 11 – 13 ). Of particular relevance in recent studies is the impact of artificial sweeteners on dysbiosis of the gut microbiota. The gut microbiota can play a crucial role in regulation of a range of metabolic, neurological, and immune-related conditions and the link between diet and microbiota is apparent ( 14 – 16 ). It is now well-understood that the artificial sweeteners acesulfame potassium, aspartame, sucralose and saccharin have a significant impact on the presence of certain taxa in the microbiota with increased Actinobacteria, Bacteroides, Parabacteroides, Staphylococcus and Providencia phylum noted following exposure to sweeteners ( 1 , 17 – 19 ). Worryingly, studies also demonstrate the ability of these sweeteners to cause stress-induced conjugative transfer of antibiotic resistance genes ( 20 , 21 ). We have previously demonstrated that exposure to acesulfame potassium, aspartame, sucralose and saccharin significantly enhances the pathogenic characteristics of model gut bacteria with a focus on biofilm formation ( 22 ). Interestingly, there were differences observed between different sweeteners and bacteria, for example saccharin, sucralose and aspartame induced biofilm formation in E. coli whereas in E. faecalis , aspartame exposure increased biofilm formation and saccharin and sucralose had no effect ( 22 ). As well as the disruptive effects of artificial sweeteners directly on the gut microbiota, our previous studies also showed that model gut bacteria exposed to sucralose and aspartame displayed significant adhesion to and invasion of mammalian gut epithelial cells. This pathogenic profile was accompanied by increased epithelial cell death. Worryingly, our previous studies also noted sweetener-induced breakdown of the intestinal epithelium, in the absence of microbiota, associated with oxidative stress, increased permeability and dysregulated claudin expression at the epithelial cell junctions, specifically reduced claudin-3 levels ( 23 ). We further identified the role of the sweet taste receptor, a G-protein coupled receptor called T1R3, in mediating the negative effect of sweeteners on the intestinal epithelium. Indeed, studies showed that inhibition of the sweet taste sensing, either through siRNA knockdown of T1R3 or through exposure to the pan-taste inhibitor, zinc sulfate, significantly attenuated any negative effects of traditional artificial sweeteners on both bacteria and intestinal epithelial cells ( 22 – 24 ). Whilst our studies and others demonstrate the potential negative impact of artificial sweeteners on the gut epithelium and microbiota, these focus on traditional artificial sweeteners such as saccharin, sucralose, aspartame and acesulfame potassium. Given the relatively recent development of neotame, there are limited such studies performed on this sweetener. In vitro studies using a bioluminescent bacterial panel indicate some toxicity of neotame which is strain-dependent ( 25 ). In vivo studies provide more detail and demonstrate that long-term neotame feeding in mice, over 4 weeks, reduces and alters α-diversity and β-diversity respectively in the microbiome. Interestingly, this study showed that neotame-induced metabolic changes in the microbiota with elevated levels of fatty acids and cholesterol and decreased levels of metabolites, such as malic acid, mannose-6-phosphate and glyceric acid, in fecal samples ( 26 ). This is further indicative of a changing microbiome profile following neotame exposure and a subsequent negative effect on host ability to absorb fatty acids and lipids. These studies with neotame indicate that the newer synthetic sweetener has potential negative effects on the microbiota but provide limited information on the changes at a bacteria-specific level. Therefore, research is needed to better understand how neotame impacts gut bacteria and how they interact with the intestinal epithelium. In the present study, we utilize two model gut bacteria which are predominantly identified in the microbiota and a human cell model of the intestinal epithelium to investigate this area of research.

Materials and methods

Enterococcus faecalis ( E. faecalis , 19433™) and Escherichia coli (E. coli , 10418) were purchased from ATCC (Middlesex, UK) and NCTC (Salisbury, UK), respectively. Field isolates of Shigella spp., Escherichia coli ESBL producer, Enterococcus faecalis and Enterococcus faecium were collected, as previously described ( 27 ), from bird feces in the Cambridge area. Bacterial media and blood agar plates was purchased from Oxoid (ThermoFisher, Hampshire, UK). Silencing RNA (siRNA) for T1R3 and a DharmaFECT™ reagent were obtained from Dharmacon (Cambridge, UK). Antibodies directed against claudin 3 were purchased from Abcam (Cambridge, UK), while T1R3 and actin antibodies were obtained from Santa Cruz Biotechnology (Santa Cruz, CA). An annexin V kit was purchased from BD Pharmingen (Wokingham, UK). For bacterial growth curve experiment and biofilm assay, sterile, flat-bottom, non-treated polystyrene 96-well plates were purchased from CytoOne (StarLabs, Milton Keynes, UK). Phosphate Buffered Saline (PBS) was obtained from Gibco (ThermoFisher, Hampshire, UK). All other reagents, including human colon carcinoma cell line, Caco-2, and neotame were purchased from Sigma-Aldrich (Dorset, UK).

Bacterial and mammalian cell culture

Bacterial cells were grown aseptically at 37°C on solid media for single colonies, or in liquid media with shaking (150 rpm) for growth measurements. E. faecalis and E. coli , were propagated using brain heart infusion (BHI) agar/broth and nutrient agar/broth respectively. Human colon carcinoma cells (Caco-2) were cultured in Eagle's Minimum Essential Media containing 10% fetal bovine serum and 1% penicillin/streptomycin (1 U/mL penicillin, 1 μg/mL streptomycin), and used between passages 35 and 50.

Mammalian cell viability and apoptosis measurement

Differentiated Caco-2 cells were grown to 60% confluence in T-25 flasks prior to exposure with neotame (0.01 μM to 10 mM) for 24 h. Integrity of the Caco-2 cell monolayer was validated on Transwell inserts using transepithelial electrical resistance (TER) (EVOM 2 ; World Precision Instruments, Herts, UK). Resistance higher than or equal to 800 Ω.cm 2 was considered appropriate for experiments ( 28 ). Neotame was dissolved in the vehicle control (H 2 O) and sterile filtered to prepare a working stock solution. For cell viability assays, neotame-treated cells were incubated with MTT reagent (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) for 2 h at 37°C. Absorbance was then assessed at 450 nm using a microplate reader (Tecan Sunrise). For apoptosis and cell death assays, adherent and floating cells were collected and incubated with a binding buffer, annexin V, and propidium iodide for 15 min in the dark. Cells were then analyzed with an Accuri C6 Flow cytometer (BD Biosciences), and the percentage of positive cells for annexin V and propidium iodide was calculated with FlowJo (V10.2, Oregon, USA).

siRNA transfections in mammalian cells

Caco-2 cells were transiently transfected with siRNA specific to T1R3 or non-specific control siRNA using a DharmaFect™ 2 reagent, as per manufacturer's guidelines. Cells were transfected at a seeding density of 0.5 × 10 4 cells per well of a 96-well plate, 2.5 × 10 4 cells per well of a Transwell insert, or 1.5 × 10 5 cells per well of a 6-well plate. Transfected cells were plated onto Transwell inserts or 96-well plates for an analysis of permeability and whole-cell ELISA, respectively. At 24 h post-transfection, cells were exposed to neotame (0.01 μM to 10 mM) or vehicle control (H 2 O) for a further 24 h. Experiments were then performed as outlined in ‘ Whole cell ELISA and epithelial monolayer permeability in mammalian cells .' To confirm knockdown of T1R3 at 48 h post-transfection, cells were lysed with a radioimmunoprecipitation assay buffer, resuspended in a Laemmli buffer, and subjected to immunoblot analysis. Immunoblot analyses were performed on 10% SDS-PAGE using a primary antibody specific to T1R3 (ab150525) and β-actin (AM4302) at a dilution of 1:1,000 and secondary antibody dilutions of 1:5000. Image J software (version 1.52d) was used to quantify Western blots and T1R3 expression was normalized to actin expression.

Whole cell ELISA and epithelial monolayer permeability in mammalian cells

Caco-2 cells (1 × 10 4 cells per well) were plated on black-walled 96-well plate for 24 h, followed by exposure to neotame (0.01 μM to 10 mM), or the vehicle control (H 2 O) for a further 24 h. Where stated, cells were first transfected with siRNA for 24 h, treated with neotame, and then rinsed once with Dulbecco's phosphate-buffered saline (DPBS) and fixed using 1% paraformaldehyde at room temperature for 10 min. Whole cell ELISA was performed as previously described ( 29 ) in non-permeabilized Caco-2 cells using antibodies specific to claudin 3 or T1R3 or IgG control. Fluorescent-conjugated secondary antibodies were measured at a 1 s exposure time using a florescent plate reader (Victor, Perkin Elmer), and measurements from blank wells (no primary antibody) were subtracted to provide the presented data. To confirm changes in claudin 3, cells were lysed with a radioimmunoprecipitation assay buffer, resuspended in a Laemmli buffer, and subjected to immunoblot analysis. Immunoblot analyses were performed on 10% SDS-PAGE using a primary antibody specific to claudin 3 (ab214487) and β-actin (AM4302) at a dilution of 1:1,000 and secondary antibody dilutions of 1:5,000. Image J software (version 1.52d) was used to quantify Western blots and claudin 3 expression was normalized to actin expression.

Epithelial monolayer permeability was assessed using the fluorescein isothiocyanate (FITC)-dextran permeability assay. Caco-2 cells were plated onto Transwell filters for 24 h, followed by exposure to neotame (0.01 μM to 10 mM), or the vehicle control (H 2 O) for a further 24 h. Where stated, cells were first transfected with siRNA. Permeability was measured by adding FITC-conjugated to 20 kDa dextran (FD20) to media in the upper chamber of the Transwell filter to a concentration of 5 μg/μl. FD20 was allowed to equilibrate for 180 s at 37°C, and a sample (100 μl) of media from the lower chamber was collected and analyzed at 488 nm using a Victor TM X3 multiplate reader (Perkin Elmer). Permeability (%) was calculated by fluorescence accumulated in the lower chamber divided by fluorescence in the upper chamber, which was then multiplied by 100.

Bacterial growth curve determination

A single bacterial colony of E. coli or E. faecalis was inoculated aseptically into nutrient broth or BHI broth, respectively, supplemented with neotame at a range of concentrations from 0.1 to 1,000 μM, or vehicle [double-distilled water (ddH 2 O)] and allowed to grow for up to 4 days. Growth was recorded as absorbance at 600 nm (A600) using Victor TM X3 multiplate reader (Perkin Elmer).and values were normalized to 0 μM at 0 h (as 1). In addition, E. coli (MG1655), Shigella spp., E. coli ESBL producer, E. faecium were used in the investigation. Bacteria were grown for 24 h, at 37°C in Luria-Bertani (LB) broth supplemented with neotame at a range of concentrations from 2 mM to 2 μM, or vehicle (ddH 2 O). Following incubation, the absorbance was read at 620 nm using a spectrophotometer (Tecan'Sunrise).

Biofilm formation assay

Biofilm formation of E. coli and E. faecalis was measured after exposure to neotame (100 μM) using the indirect crystal violet biofilm formation assay as described previously ( 22 ) with some modifications. In addition, E. coli MG1655 and field isolates of Shigella spp. and Enterococcus faecium were assessed. Bacterial cultures were propagated in LB broth with neotame (1mM) and also in LB broth with sterilized ddH 2 O (vehicle). A single bacterial colony was inoculated into 10 ml of the corresponding liquid media supplemented with sweetener or vehicle (H 2 O) in presence or absence of zinc sulfate. Absorbance at 600 nm was measured on Victor TM X3 fluorescent plate reader (Perkin Elmer) to ensure equal bacterial cell numbers, and the overnight culture was transferred into liquid media (1:200) supplemented with artificial sweeteners. After vortexing, 200 μL was transferred into sterile 96-well plasticware plates and grown aerobically for 48 h at 37°C. The supernatant was removed, and wells were washed twice with ddH2O to remove loosely associated bacteria. Each well was stained with 150 μL 0.1% Gram crystal violet for 20 min at room temperature. After staining, wells were washed with ddH 2 O three times. The retained crystal violet was solubilised by adding 200 μL 30% acetic acid and incubating at 37°C for 5 min. The quantitative analysis of biofilm formation was performed by measuring absorbance at 600 nm using Victor TM X3 fluorescent plate reader (Perkin Elmer). The biofilm forming units were calculated by dividing the absorbance of crystal violet retained with the absorbance of the total bacterial growth and was normalized to the control (as 1).

Bacterial adhesion assay

Adhesion of the model gut bacteria to Caco-2 cells following artificial sweetener exposure was measured as previously described ( 22 ) with some modifications. Caco-2 cells were seeded on 24-well tissue culture plates (7.5 × 10 4 cells/well) and incubated in humidified condition (90%) at 37°C and 5% CO 2 for 48 h, following exposure to artificial sweeteners for 24 h. Meanwhile, a single colony of E. coli and E. faecalis was inoculated into respective media supplemented with neotame (100 μM) in the presence or absence of zinc sulfate (100 μM), or vehicle (ddH 2 O) and incubated overnight at 37°C with shaking at 150 rpm. Bacteria were then washed twice with 500 μL serum and antibiotic-free EMEM media by centrifuging at 4,000 rpm (2683 × g ) for 10 min at 37°C (accuSpinTM 1R, Fisher Scientific, Thermo Electron Corporation LED GmbH, Osterode, Germany) and re-suspended in EMEM without antibiotics. Caco-2 cell monolayers were washed twice with 500 μl PBS, and then EMEM (490 μL; without antibiotics) was added to each well. The total number of adherent Caco-2 cells was measured by performing a cell count. Bacterial suspension (10 μL) was added on the Caco-2 cells at a multiplicity of infection (MOI) 1:300 for an infection incubation time of 1 h. After the infection period, the cells were washed twice with 500 μL of sterile PBS and the Caco-2 cells were lysed with 500 μL of 0.5% Triton X-100. The number of viable bacteria was determined by spread-plating serial dilutions of the cell suspension on respective solid media, followed by overnight incubation at 37°C and then counting colony forming units. Bacterial adhesion was expressed as ratio of total bacteria attached per viable Caco-2 cells (normalized to 100). Each assay was performed in triplicate with the successive passage of Caco-2 cells.

Bacterial invasion assay

The ability of bacterial to invade Caco-2 cells was measured as previously described ( 22 ). Briefly, Caco-2 cells were seeded on 24-well tissue culture plates for 36 h followed by exposure to neotame for a further 24 h. The cell monolayer was rinsed with sterile PBS and antibiotic-free EMEM media was added for the bacterial invasion assay. In parallel, bacteria were exposed to neotame and prepared for infection. The number of adhered Caco-2 cells that were subjected to bacterial infection was determined by performing a cell count. Caco-2 cell monolayer was infected with bacteria at MOI 1:300 for 1 h at 37°C. The monolayer was washed once with 500 μL PBS and fresh cell culture medium (500 μL) was added containing 100 μg/mL gentamicin for E. coli and 100 μg/mL gentamicin along with 50 μg/mL ampicillin for E. faecalis and incubated at 37°C for 30 min to kill the external-adhered bacteria. The cell monolayer was washed twice with PBS and then lysed with 0.5% Triton X-100 in PBS.

The number of viable colony-forming units were determined by diluting and plating the samples onto solid media and incubating overnight at 37°C. The results were expressed as the ratio of intracellular bacteria compared with the control (normalized to 100). Each assay was performed in triplicate with the successive passage of Caco-2 cells.

Cytotoxicity assay

The cytotoxic effect of neotame-mediated bacterial metabolites on intestinal epithelial cells was performed following the protocol previously described ( 22 ), and cell viability was measured by using the Cell Counting Kit-8 (CCK-8), as per manufacturer's guidelines. Caco-2 cells were grown on 96-well plates (1 × 10 4 cells/well) and incubated for 48 h at 37°C in humidified condition with 5% CO 2 . Simultaneously, E. coli or E. faecalis was grown in 10 ml of respective liquid media supplemented with 100 μM of neotame with or without 100 μM zinc sulfate or vehicle for 24 h. The cultures were centrifuged at 4,000 rpm (2,683 × g) for 15 min at 4°C and supernatant was collected, filter-sterilized (0.22 μM membranes; Millipore, USA). 50 μl of the soluble bacterial factors (supernatant) and 50 μl antibiotic-free EMEM was added to the Caco-2 cell monolayer. Cells were incubated for 24 h followed by measurement of cell viability using CCK-8 reagent assessed as absorbance at 450 nm using a microplate reader (Tecan Sunrise TM , Switzerland).

Statistical analysis

All data sets were statistically analyzed using GraphPad Prism (version 7.05). Analysis was performed using either a one-way or two-way ANOVA with Tukey Multiple comparisons post-hoc test where relevant. Statistical significance is considered where p < 0.05. Data is presented as mean ± standard error mean (S.E.M.) unless otherwise stated and sample size (n number) is included in the figure legend for each study.

Neotame causes epithelial cell damage and disruption of the intestinal epithelial monolayer

Our previous studies have demonstrated that artificial sweeteners, sucralose, saccharin and aspartame, significantly reduce viability of intestinal epithelial cells ( 23 ) therefore our first experiments in this study assessed the impact of neotame on the intestinal epithelium in vitro at a range of physiological concentrations. Given that the ADI for neotame is approximately equivalent to 10 mM, we used up to this concentration for initial studies ( 8 ). There was a significant increase in Caco-2 cell viability at 1,000 μM neotame concentration exposure with higher concentrations showing very little cell viability (0.025 ± 0.005 a.u. for 10 mM as compared to 0.993 ± 0.042 a.u for 0 mM control) ( Figure 1A ). These findings were mirrored by cell death studies which noted a significant increase in cell death from 100 μM and higher ( Figure 1B ) and significant apoptosis of Caco-2 cells from 10 μM neotame exposure and higher ( Figure 1C ). Given the excessive cell death noted at 10 mM neotame concentration, further studies were performed up to 1,000 μM only. Permeability of the epithelial cell monolayer showed a significant increase at 1 μM neotame and higher ( Figure 1D ). Whilst increased monolayer permeability at 100 μM and higher could be reasonably expected since Caco-2 cell death would result in leak across the monolayer, findings at 1 μM and 10 μM neotame suggest increased leak due to paracellular junction breakdown. Indeed, whole cell and cell surface expression of the tight junction molecule, Claudin 3, was significantly decreased at 10 μM and higher in Caco-2 cells ( Figures 1E , F ). Taken together, these data demonstrate the neotame causes intestinal epithelial cell death at high concentrations (100 μM and higher) and leak across the epithelial monolayer at lower concentrations (1–100 μM).

Figure 1 . Neotame causes epithelial cell death and disruption of the intestinal epithelial monolayer. Caco-2 cells were exposed to neotame at a range of concentrations for 6 h (B, C) and 24 h (A, D, E, F) . Cell viability (A) was assessed using MTT assay and cell death and apoptosis was assessed using flow cytometry (B, C) . Epithelial monolayer permeability was determined using FITC-dextran Transwell assay (D) and claudin 3 expression at the Caco-2 cell surface was assessed using whole cell ELISA (E) and Western blotting with Caco-2 cell lysates (F) . Data are presented as mean ± S.E.M, n = 6-8. * p < 0.05 vs. vehicle for neotame (0 μM).

Neotame regulates Caco-2 cell viability and intestinal epithelial barrier function through T1R3-dependent signaling

We next sought to establish whether this is a direct effect of neotame on sweet taste receptors in intestinal epithelial cells, rather than an indirect chemical effect of neotame. As we have previously demonstrated the presence of the sweet taste receptor T1R3, but not T1R2, in intestinal epithelial cells ( 23 ), we investigated Caco-2 cell viability, apoptosis and leak in cells transiently transfected with siRNA specific to the human sweet taste receptor, T1R3. siRNA knockdown of T1R3 expression was confirmed using Western blot [ Figures 2A (i), (ii)] and whole cell ELISA ( Figure 2B ) with both techniques showing a significant decrease in T1R3 expression. Knockdown of T1R3 attenuated the cytotoxic ( Figure 2C ) and pro-apoptotic ( Figure 2D ) effects of neotame, as well as the increased monolayer permeability ( Figure 2E ) and reduced Claudin 3 expression observed at Caco-2 cell surface ( Figure 2F ). These data demonstrate that neotame-induced damage to the intestinal epithelium in vitro , both barrier disruption and cell death via apoptosis, is mediated by the sweet taste receptor, T1R3.

Figure 2 . Neotame regulates Caco-2 cell viability and intestinal epithelial barrier function through T1R3-dependent signaling. T1R3 expression was silenced in Caco-2 cells using T1R3-specific siRNA and compared to non-specific (ns) siRNA. Knockdown of protein levels was confirmed using Western blotting, representative blot [ (A) i] and quantification [ (A) ii] shown, and whole cell ELISA (B) . Following siRNA transfection, Caco-2 cells were exposed to neotame at a range of concentrations for 6 h (D) and 24 h (C, E < F) . Cell viability (C) was assessed using MTT assay and cell apoptosis (D) was assessed using flow cytometry. Epithelial monolayer permeability (E) was determined using FITC-dextran Transwell assay and claudin 3 expression (F) at the Caco-2 cell surface was assessed using whole Cell ELISA. Data are presented as mean ± S.E.M, n = 6–8. * p < 0.05 vs. non-specific siRNA, vehicle for neotame (0 μM).

Exposure to neotame significantly increases biofilm formation by E. coli and E. faecalis , and cytotoxicity by E. coli only, in a zinc-dependent manner

In physiological settings, the intestinal epithelium is in close association with the gut microbiota and therefore any dietary substances which impact the microbiota will also impact the epithelial barrier. Of note, biofilm formation of gut bacteria significantly disrupts the integrity of the intestinal epithelial monolayer through mechanical force exertion from the biofilm as well as the release of bacterial factors when in a biofilm ( 30 ). We have previously demonstrated that the artificial sweeteners, saccharin, sucralose and aspartame, significantly increase biofilm formation in model gut microbiota bacteria, E. coli NCTC and E. faecalis ( 22 ). Therefore, our next studies sought to understand the effect of neotame on these model bacteria. We first investigated whether neotame had an impact on planktonic bacterial growth of E. coli NCT and E. faecalis and noted no significant change at a range of concentrations, 0.1–1,000 μM, and timepoints up to 96 h ( Figures 3A , D ). These studies were confirmed in other model gut bacteria ( Table 1 ) demonstrating a robust absence of sweetener-induced effect on bacteria growth across different species. We next sought to investigate the impact of neotame on biofilm formation of model gut bacteria, E. coli and E. faecalis . Neotame exposure, at 100 μM, significantly increased biofilm formation in both bacteria ( Figures 3B , E ). The pan sweet taste inhibitor, zinc sulfate, was used to investigate the role of sweet taste sensing in regulating this pathogenic effect ( 22 , 24 ). Interestingly, zinc sulfate exposure with neotame significantly blocked the increase in biofilm formation observed in both bacteria ( Figures 3B , E ).

Figure 3 . Exposure to neotame significantly increases biofilm formation by E. coli and E. faecalis , and cytotoxic effect of soluble bacteria factors released from E. coli , in a zinc-dependent manner. Bacterial growth of E. coli (A) and E. faecalis (D) was measured over 96 h following exposure to neotame at a range of concentrations. Absorbance was measured at 600 nm and normalized to vehicle at 0 h. Biofilm formation of E. coli (B) and E. faecalis (E) was measured, using crystal violet assay, following exposure to neotame (100 μM) in the presence and absence of zinc sulfate (100 μM) for 24 h. Cytotoxicity in Caco-2 cells was measured following 24 h exposure to bacterial supernatant, where E. coli (C) and E. faecalis (F) were incubated with neotame (100 μM) in the presence and absence of zinc sulfate (100 μM) for 24 h. Data was normalized to vehicle for neotame and presented as mean ± S.E.M, n = 6–8. * p < 0.05 vs. vehicle for neotame and zinc sulfate (0 μM).

Table 1 . Neotame does not impact planktonic growth of different model gut microbiota bacterial species.

The release of soluble factors from bacteria in a biofilm is associated with pathogenic effects ( 30 ). Therefore, we next studied whether neotame induces a change in released bacterial factors which can affect Caco-2 cells. Following 24 h exposure with neotame at 100 μM, solubilised releasate from E. coli and E. faecalis , called E. coli -neotame or -vehicle and E. faecalis -neotame or -vehicle, was collected and Caco-2 cells were exposed to each for 24 h. We observed a significant decrease in Caco-2 cell viability following exposure to E. coli -neotame compared to E. coli -vehicle ( Figure 3C ). In contract, E. faecalis -neotame had no impact on Caco-2 cell viability ( Figure 3F ). Interestingly, the cytotoxic effect of releasate from E. coli exposed to 100 μM neotame on Caco-2 cells ( Figure 3C ) was significantly higher than the effect of 100 μM neotame alone on Caco-2 cell viability ( Figure 1A ) (% change for neotame only: 1.41 ± 2.13 vs. % change for E. coli-neotame releasate: 44 ± 4.40, p < 0.05). Furthermore, incubation of E. coli with neotame and zinc sulfate blocked the cytotoxic effect of releasate from the bacteria ( Figure 3C ). Taken together, these data demonstrate the neotame exposure has a significant effect on biofilm formation of E. coli and E. faecalis through a taste-dependent pathway. Furthermore, neotame also causes E. coli to produce soluble factors which result in mammalian cell toxicity through a taste-dependent pathway.

Neotame significantly disrupts the Caco-2 cell—Bacteria interaction in a zinc-dependent and -independent manner

Bacterial adhesion to and invasion of intestinal epithelial cells represent the initial phases of pathogenic characteristics in many disorders. Therefore, we next investigated the effect of neotame exposure on the adhesive and invasive capability of model gut bacteria with Caco-2 cells. Both E. coli and E. faecalis treated with 100 μM neotame displayed significantly higher adhesion to Caco-2 cells ( Figure 4A ). In the presence of the pan sweet taste inhibitor, zinc sulfate, neotame-induced adhesion of E. coli and E. faecalis to Caco-2 cells was attenuated ( Figures 4B , C ). Likewise, exposure to neotame significantly increased E. coli invasion but had no effect on the invasive capacity of E. faecalis ( Figure 4D ). Whilst zinc sulfate treatment significantly reduced neotame-induced invasion of E. coli into Caco-2 cells, it did not completely abrogate invasion caused by the sweetener ( Figure 4E ). Unsurprisingly, zinc sulfate had no impact on the invasive capacity of E. faecalis ( Figure 4F ). Taken together, these data demonstrate that neotame significantly increases the pathogenic effect of two model gut bacteria on human intestinal epithelial cells, to different degrees, through a taste-dependent mechanism.

Figure 4 . Neotame significant disrupts the Caco-2 cell—bacteria interaction in a zinc-dependent and -independent manner. Bacteria cell adhesion to (A–C) and invasion of (D–F) Caco-2 cells was measured following E. coli (A, B, D, E) and E. faecalis (A, C, D, F) exposure to neotame (100 μM) in the presence and absence of zinc sulfate (100 μM). Bacteria adhesion index is expressed as ratio of total bacteria attached per viable Caco-2 cells (normalized to 100) and bacteria invasion index is expressed as the ratio of intracellular bacteria compared with the control (normalized to 100). Data is presented as mean ± S.E.M, n = 5–6. * p < 0.05 vs. vehicle for neotame and zinc sulfate (0 μM), # p < 0.05 vs. vehicle for zinc sulfate only.

Artificial sweeteners have historically been regarded as safe additives to enhance the sweet taste profile of a wide range of commercial products however, recent research suggests that certain sweeteners may disrupt the gut microbiota, and thus have a negative effect on host health. In the present study, we investigate the effect of the relatively new synthetic sweetener, neotame, on models of gut bacteria and the human intestinal epithelium. Our findings are the first to demonstrate that neotame can damage the intestinal epithelium directly, through the sweet taste receptor, T1R3, and indirectly, through stimulating pathogenic changes in model gut bacteria which are closely associated with the epithelium ( Figure 5 ). The negative effect of neotame on the epithelium-microbiota relationship in the gut has the potential to influence a range of gut functions resulting in poor gut health which impacts a range of conditions including metabolic and inflammatory diseases, neuropathic pain, and neurological conditions ( 31 – 34 ).

Figure 5 . Schematic to show the direct (T1R3-dependent) and indirect (bacteria-dependent) impact of the artificial sweetener neotame on the intestinal epithelial cell.

The health impacts of artificial sweeteners have been an area of recent interest with the World Health Organization publishing a 2023 report outlining that these non-nutritive additives should not be used as a weight-control aid ( 10 ). This is following a slew of studies which demonstrate the effect of sweeteners on the gut microbiota to influence gastric hormones and glucose intolerance ( 1 , 11 ). Although the effects of traditional sweeteners on the gut microbiota are well understood, newer sweeteners on the market, such as neotame, have not yet been fully investigated. At nearly double the sweet taste perception of sucralose, neotame is an intensely sweet additive which provides no source of energy and is rapidly metabolized and eliminated ( 35 ). As such, it is increasingly used as an artificial sweetener in food production and therefore widely consumed in the diet. In the present study, we investigated the biological effects of neotame on the human intestinal epithelial cell line, Caco-2, and noted cell death, mediated by apoptosis. At concentrations higher than 100 μM we see a switch from pro-apoptotic cells to dead cells suggestive of a toxic effect of neotame. This is similar to previous findings with the artificial sweeteners saccharin and aspartame found to increase cell death in a variety of different cell types including cancer, neuroprogenitor and pancreatic islet cells ( 23 , 36 – 38 ). Caco-2 cells are a well-established model of the intestinal epithelium with differentiation resulting in a well-organized brush border and a range of molecular transporters and enzymes expressed to mimic the intestinal epithelium in vivo ( 39 ). However, these are colon carcinoma cells cultured to mimic the gut milieu, that is, without the humoral, neurological, muscular or immunological elements associated with the gut lumen environment ( 40 ). There is thus a need for the use of gut organoid models or in vivo feeding studies to investigate the negative impact of neotame on intestinal epithelial cell function, however our studies provide a good indication that this sweetener would significantly disrupt the epithelium in either of these physiological models. In contrast to the human cells, the different model gut bacteria studied in the present work, E. faecalis, Shigella, E. faecium , and a range of E. coli , pathogenic and non-pathogenic did not show any changes in growth curve in response to neotame exposure at concentrations between 0 and 2 mM. Whilst some studies demonstrate similar outcomes with different bacteria exposed to a range of artificial sweeteners, such as saccharin, aspartame and sucralose ( 23 , 41 ), studies on multi-drug resistant bacteria such as Acinetobacter baumannii and Pseudomonas aeruginosa in the presence of sweeteners such as acesulfame potassium, sucralose and saccharin show significant bactericidal effects ( 42 ). These differences in the literature may be due to the differences in concentration of sweetener studied. For example, de Rios et al studied sweeteners up to 440 μM ( 42 ) and Wang et al investigated concentrations in the 30–80 mM range, which are significantly higher than the concentrations described to be physiological ( 43 ) whereas our studies focused on sweeteners at 100 μM. Hence, the contradictory results from recent research could be explained due to differences in the concentration of the sweeteners used. Another key difference is the use of neotame in the present study, as opposed to previous studies on more traditional artificial sweeteners such as sucralose, saccharin and acesulfame potassium. This highlights the need for further studies on the toxic effects of more recently-developed artificial sweeteners using a range of mammalian and bacterial cell models to map potential health impacts of these additives.

In mammalian cells, the G-protein coupled receptors T1R2 and T1R3 have been established to be sweet taste receptors which responds to sugars and artificial sweeteners in a range of oral and extra-oral locations ( 44 ). We have previously identified T1R3 only in the intestinal epithelium ( 23 ) and, as such, investigated whether neotame-induced cell death and barrier disruption was mediated through a direct effect on the sweet taste receptor or through a non-specific indirect chemical effect on the intestinal epithelial cells. Following molecular inhibition of T1R3, cell death and apoptosis following exposure with neotame was completely abolished. Likewise, neotame-induced epithelial barrier permeability and claudin 3 internalization was abolished in T1R3-siRNA cells. We have previously identified the pivotal role of T1R3 in mediating epithelial cell damage induced by artificial sweeteners saccharin and aspartame ( 23 ) and, whilst not unexpected, there are studies where sweeteners impact cell function independently of the sweet taste receptor ( 45 ). Interestingly, bacteria have not been identified to have a homologous sweet taste receptor but our findings here demonstrate the ability of E. coli and E. faecalis to respond to neotame. Zinc sulfate is a potent but crude inhibitor of sweet taste sensing mediated by T1R3 ( 24 ) which we demonstrate to block neotame-induced pathogenic effects in both bacteria. Whilst this supports the notion that there is a type of zinc-sensitive sweet taste sensor in E. coli and E. faecalis , further studies are needed to identify the specific mechanism through which bacteria can respond to artificial sweeteners. It is possible that sweeteners may induce an oxidative stress response in bacteria, as demonstrated by Yu et al with elevated superoxide production in fecal bacteria following exposure to high concentrations of saccharin, sucralose, aspartame or acesulfame potassium ( 21 ). Indeed, both ROS- and SOS-related genes are upregulated following exposure to the sweeteners suggesting there may be multiple bacterial sweet taste sensors which can respond to sweetener stimulus ( 20 , 21 , 46 ). In the present study, we identify a range of pathogenic responses elicited by exposure of E. coli and E. faecalis to neotame, including biofilm formation and increased adhesion to and invasion of mammalian cells. Our studies used laboratory strains of each bacteria, grown individually and in aerobic conditions, as opposed to the gut microbiota setting where over 100 trillion bacteria co-exist in an anaerobic microenvironment ( 47 ). Whilst this poses a potential limitation to the studies performed, our research clearly demonstrates that neotame causes pathogenic changes to model bacteria which are associated with a significant risk to human health. For example, the National Institute of Health have linked 60–80% of all microbial infections with biofilm formation ( 48 ) and entero-adherent and entero-invasive E. coli have been closely aligned to a range of gastrointestinal disorders including diarrhea, intestinal inflammation, and subsequent syndromes ( 49 ). Therefore, understanding the impact of neotame on the pathogenic changes occurring in the gut microbiota, and the underlying mechanisms which cause these changes, is vital to understanding how sweeteners impact human health.

Artificial sweeteners are consumed in a range of different food and drink products across the population and therefore it is challenging to assess what are the physiological concentrations of neotame which the intestinal epithelium and microbiota would be exposed to in a standard diet. Previous in vivo studies used a range of concentrations of neotame from 0.75 mg/kg body weight in mice to a range of 10–500 mg/kg body weight in pigs ( 26 , 50 ). The acceptable daily intake in humans is up to 2 mg/kg body weight which, considering the average adult weight and gastric fluid volume is equivalent to 40 mg/L ( 8 , 51 , 52 ). There is little evidence around the accumulation concentration of artificial sweeteners in the intestine however, given known concentrations of sweeteners in commercial products, it is possible that following consumption of a diet soft drink, for example, the intestine could be exposed to up to 2 mM sweetener ( 2 ). In the present study, we investigated the effect of neotame at concentrations ranging from 0.1 to 50 mM but noted intestinal epithelial cell death at 0.1 mM and intestinal barrier disruption at 1 μM. Furthermore, co-culture studies with E. coli or E. faecalis demonstrated pathogenic effects at 100 μM, which is lower than the expected concentration in many food and drink, and the acceptable daily intake ( 2 , 8 ). It is worth noting, however, that studies were performed following 24 h exposure to neotame whereas transit time in the intestine is 5 h therefore it is possible that the epithelium and gut microbiota would not be exposed to sustained sweetener for as long as was studied ( 51 ). Further studies on a range of shorter time points of neotame exposure would therefore provide a more physiological review of the impact of the sweetener on the intestine.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

Ethical approval was not required for the studies on humans in accordance with the local legislation and institutional requirements because only commercially available established cell lines were used.

Author contributions

AS: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. LL: Writing – review & editing, Methodology, Investigation, Formal analysis. CW: Supervision, Project administration, Writing – review & editing, Methodology. HC: Writing – original draft, Visualization, Investigation, Formal analysis, Data curation, Conceptualization, Writing – review & editing, Supervision, Project administration, Methodology.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Studies were funded by internal sources, no external funding.

Acknowledgments

We would also like to thank Dr. Plamen Iliev for his technical support in the tissue culture laboratories.

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: artificial sweeteners, neotame, intestinal epithelium, gut, microbiota, nutrition

Citation: Shil A, Ladeira Faria LM, Walker CA and Chichger H (2024) The artificial sweetener neotame negatively regulates the intestinal epithelium directly through T1R3-signaling and indirectly through pathogenic changes to model gut bacteria. Front. Nutr. 11:1366409. doi: 10.3389/fnut.2024.1366409

Received: 06 January 2024; Accepted: 02 April 2024; Published: 24 April 2024.

Reviewed by:

Copyright © 2024 Shil, Ladeira Faria, Walker and Chichger. 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: Havovi Chichger, havovi.chichger@aru.ac.uk

This article is part of the Research Topic

Noncaloric Artificial Sweeteners and their Impact On Human Health

News Release -- Public Market Food Truck Rodeos 2024 Season Kicks Off Wednesday with Unstuck

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The Food Truck Rodeos draw tens of thousands of hungry attendees and dozens of food trucks selling a bounty of creative dishes, drinks, and desserts bursting with Rochester flavor. This event series, co-hosted by Rohrbach Brewing Company, will take place on the last Wednesday of the month, April through September.

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After Wednesday’s kick-off with Unstruck, the remining music lineup will be:

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Food and social media: a research stream analysis

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• Published: 18 February 2023

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• Ruth Areli García-León   ORCID: orcid.org/0000-0002-8984-2348 1 &
• Thorsten Teichert   ORCID: orcid.org/0000-0002-2044-742X 1  

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Interest in food and online communication is growing fast among marketing and business scholars. Nevertheless, this interest has been not exclusive to these areas. Researchers from different disciplines have focused their research on different concepts, target populations, approaches, methodologies, and theoretical backgrounds, making this growing body of knowledge richer, but at the same time difficult to analyze. In order to have a broader overview of this topic, this study analyzes the existent literature regarding food and social media in social sciences in order to identify the main research streams and themes explored. With this purpose, the present paper uses bibliometric methods to analyze 1356 journal articles by means of factor and social network analysis. The study contributes by revealing 4 clusters containing 11 dominant research streams within the social sciences, determining the linkages among the main research discourses, and recommending new future topics of research.

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1 Introduction

Food and social media is highly a controversial topic. While some studies point out that the use of social media can be associated with an increase of unhealthy food intake and Body Mass Index (BMI) (Coates et al. 2019a ; Khajeheian et al. 2018 ), other studies, as well as the OECD and the American Heart Association suggest that the use of social media could be used to sensitize the population regarding obesity and to promote public health regarding food (Chau et al. 2018 ; Li et al. 2013 ; OECD 2017 ).

People use the World Wide Web and social media to seek and share information, for social interaction, and to be part of a social network (Kavanaugh et al. 2005 ; Whiting and Williams 2013 ). Billions of opinions are shared on social networks every day (Mostafa 2019 ), breaking barriers across geographical distance and bringing people closer (Rimjhim et al. 2020 ). Social networks and online communities facilitate consumer-to-consumer communication (Sloan et al. 2015 ), and influence consumers’ opinions, attitudes, consumption experiences, brand perceptions, purchasing decisions, as well as post-purchase communication and evaluation, among others (Jansen et al. 2009 ; Mangold and Faulds 2009 ; Teichert et al. 2020 ).

The rapid growth of online communication among consumers has increased academic interest in electronic word of mouth (e-WOM). Zinko et al. ( 2021 ) define e-WOM as the “web-mediated exchange of information which occurs when one person tells another about their experience with a service or product” (p. 526). E-WOM includes blogs, online reviews, ratings, messages posted on online groups, and social media posts (Hennig-Thurau and Walsh 2003 ). Either as a topic of consumer health, sustainability, or as an opportunity for management development, studies regarding food and social media are gaining importance. Scholars from different disciplines have used different approaches, methodologies, theoretical backgrounds, and populations targets to address this topic. Additionally, due to the novelty of some internet-based communication tools, and the rapid emergence of additional ones, new concepts, definitions, and approaches are emerging too, making this growing body of knowledge difficult to explore.

Although the scope of food and social media research has partly been disclosed in literature reviews, these focus on a particular sub-segment of food consumption, a specific target population, area of research, research method, or a specific new technology or social media. For example, Chau et al. ( 2018 ) centered their research on the role of social media in nutrition interventions for adolescents and young adults. Rounsefell et al. ( 2020 ) explored the impact of social media exposure to image-content on body image and food choices in young adults. Chapman et al. ( 2014 ) analyzed literature regarding the use of social media for public health communication in order to explore the potential of social media as a tool to combat foodborne illness. De Veirman et al. ( 2019 ) studied the persuasive power of social media influencers over young children. Dute et al. ( 2016 ) examined literature regarding the promotion of physical activity, healthy nutrition, and overweight prevention among adolescents and students, through mobile apps. Allman-Farinelli and Gemming ( 2017 ) explored the state of the art in dietary assessment, using smartphone and digital technology regarding technology mediated interventions for dietary change. Tao et al. ( 2020 ) studied the use of text mining as a big data analysis tool for food science and nutrition. And Ventura et al. ( 2021 ) analyzed the topic of food in social media from a consumer-oriented point of view. However, there are no studies offering a general overview of a broad sample of articles within the social sciences regarding food and the use of social media.

Given this, the aim of this paper is to provide a broad bibliometric review for marketing and business scholars, companies, and organizations on past and current research regarding food and social media within the social sciences, in order to reveal the main addressed topics, as well as for suggesting future topics of research in this field of knowledge. To achieve the results, this research uses the co-word analysis of Keywords. Co-word analysis (Callon et al. 1983 ) is a type of bibliometric method which seeks to find connections among concepts that co-occurs in document abstracts, titles, or keywords as assessed by the authors (Zupic and Čater 2015 ). By conducting a co-word analysis of keywords, the present study aims to reveal the main research streams regarding food and social media studied in the social sciences. First, statistical analyses are applied to identify research streams as well as their interconnections in an objective manner. Single research streams are then analyzed in detail by a manual inspection of their key publications. Focal issues of past and current research are highlighted and opportunities for future research are identified.

2 Methodology

2.1 co-word analysis.

One of the most used bibliometric methods is co-citation analysis. Nevertheless, while co-citation analysis connects documents, authors, or journals in order to find the intellectual structure, the knowledge base, or influences on a research field (Small 1977 ; Zupic and Čater 2015 ) the co-word analysis uses the actual words contained in documents to determine relationships among concepts that represent a conceptual space of a field (Zupic and Čater 2015 ). In co-citation analysis, it is assumed that the more two items are cited together, the more likely is that their content is related, and since it takes time to accumulate citations, the analysis reflects the state of the field in the past and not how it could look now or tomorrow (Zupic and Čater 2015 ). In this regard, the co-word analysis offers a more actual state of the field since authors choose the words, concepts, titles, and keywords that best represent their studies. In their articles, authors construct different realities linking scientific and technical concepts that are shared by a specific research community (Callon et al. 1983 ). Therefore, the co-word analysis is more content-driven than the co-citation analysis.

The main target of this analysis is the keywords contained in the articles since keywords are chosen by the authors because they represent in a few words, the main content of the study. Web of Science database (WoS) is frequently used for bibliometric studies in management and organization, and it contains different valuable bibliographical data for indexed documents that include title, article type, authors, keywords, keywords plus, abstract and subject categories or areas, among others (Zupic and Čater 2015 ). Besides the Author Keywords, WoS provides Keywords Plus. They are index terms automatically generated from the titles of cited articles in an article that augment traditional keyword retrieval (Clarivate 2020 ). Therefore, this research analyzes the Author Keywords and the Keywords Plus provided by WoS.

2.2 Identification of literature

The search of documents was made on WoS by using a Keywords string containing the main concepts related to the objective of the research (see Fig.  1 for the overall design, search string, and interim steps taken). Although most of the well-known social media such as Youtube or Twitter appeared in the 2000s, some authors consider that the development of social media started during the 80 s with the introduction of USENET, a type of internet discussion system, real-time online chat services such as Compu Serve’s CB Simulator (1980), the Internet Relay Chat (IRC) (1988), or AOL’s chat rooms (1989) (Edosomwan et al. 2011 ; Lake 2009 ; Sajithra and Patil 2013 ). Others establish this development in the 90 s when the World Wide Web became public and web blogs, list-servers, and e-mail services allowed users to form online communities exploding networked communication (Simonova et al. 2021 ; van Dijck 2013 ). Therefore, in order to have a broader number of articles and consequently a broader scope regarding food and social media research in Social Sciences, the timespan 1990 to 2021 and the citation indexes Social Sciences Citation Index (SSCI) and Emerging Sources Citation Index (ESCI) were used as limiters. The ESCI extends the scope of publications of WoS by including around 3,000 peer-reviewed publications that although they are not yet recognized internationally, meet the WoS high-quality criteria (Francis 2021 ). Besides, Articles, Reviews, or Early Access articles were included in order to capture the most recent published works. Early Access articles in WoS Core Collection are fully indexed articles that the publisher makes available online in a nearly final state (e.g. Articles in Press, Published Ahead of Print, Online First, etc.), they lack publication date, volume, issue, and page number (Clarivate 2021 ).

Sample generation process by steps

With this information, an initial database of 1400 records was created on July, 20 of 2021. Nevertheless, only articles containing Author Keywords and/or Keywords Plus were included; therefore, 29 articles without author Keywords and Keywords Plus were removed. In the end, just 1371 were included in the next analysis.

A first analysis of Keywords contained in the 1371 articles was made by using the KHCoder, a text-mining and text-analysis application ( https://khcoder.net/en/ ). To avoid the analysis of joined words separately, a total of 31 words strings, also called Force Pick Up Words, were chosen to extract different words as one concept (e.g. qualitative_research, corporate_social_responsibility) (see Table S1 in Supplementary material). The word frequency list revealed a total of 3,716 keywords and a total of 21,027 mentions. In order to include just the most representative concepts in the analysis, just concepts mentioned more than 5 times were included. Hence, just 655 Keywords representing 75.81% of all mentions were included in the second analysis.

The second step was an analysis of concepts, conducted by both researchers, in order to find similarities among words due to meaning, writing differences, use of abbreviations, or use of signs to unite words.

After this analysis, a list of 413 Keywords or “code words” containing the initial 655 Keywords was generated (the complete list of words and code words (*) could be seen in Table S2 in Supplementary material). This list of code words was introduced to KHCoder in order to generate a crosstab containing the concepts included in every article. As a result, 15 articles containing none of the 413 Keywords were discarded for further analysis.

2.3 Data analysis

The data were analyzed by using the package UCINET 6 (Borgatti et al. 2002 ), one of the most used software for network visualization (Zupic and Čater 2015 ), in order to generate an overall concept co-occurrence matrix. By executing a core-periphery analysis the core keywords contained in the food and social media literature were separated from the periphery keywords. The stable solution was found in 50 iterations (fitness = 0.609).

Then, a factor analysis was conducted using SPSS in order to group keywords based on their co-occurrences. Factor analysis can determine which indicators, in this case, keywords, may be grouped together. Factor analysis is known as a data reduction technique (Sallis et al. 2021 ). In order to identify groups of bibliometric data, researchers have used different statistical techniques such as factor analysis, cluster analysis, multidimensional scaling, or multivariate analysis (Chen et al. 2016 ; Leydesdorff and Welbers 2011 ; Ravikumar et al. 2015 ; Wang et al. 2012 ; Yang et al. 2012 ), although, for practical use, some authors have not found a difference between cluster analysis and factor analysis (Lee and Jeong 2008 ).

The use of factor analysis has a long tradition in co-word analysis. Considered a quantitative form of content analysis, it can substitute commonly practiced techniques for content analysis, providing precision and validity in the resulting categories while investing less time and resources (Leydesdorff and Welbers 2011 ; Simon and Xenos 2004 ). Many studies have used factor analysis in co-word analysis as a reliable method to discover linkages among scientific documents. For example, by using the words contained in the titles and abstracts of research articles, Leydesdroff ( 1989 ) used factor analysis and cluster analysis to find linkages among biochemistry documents. Leydesdorff and Hellsten ( 2005 ) studied words related to stem-cell by using factor analysis. Leydesdorff and Zhou ( 2008 ) used factor analysis to analyze words of journal titles using Chinese characters. Wang et al. ( 2014 ) analyzed keywords from core journals in the field of domestic knowledge discovery by using factor and cluster analysis. Yan et al. ( 2015 ) analyzed the intellectual structure of the field of the Internet of Things by means of factor and cluster analysis of keywords. Gan and Wang ( 2015 ) used factor analysis to map the intellectual structure of social media research in china by using keywords, and Sun and Teichert ( 2022 ) used factor analysis to study the research landscape of ‘scarcity’ by using author keywords.

In the specific application field of bibliometrics, the method identifies different research streams (Kuntner and Teichert 2016 ). By reducing the number of variables in a dataset, the factor analysis finds patterns and therefore, the underlying structure of the data (Wendler and Gröttrup 2016 ). There are different methods to extract factors. This study applied a principal component analysis (PCA) with an orthogonal factor rotation Varimax with Kaiser Normalization of 15 iterations. Varimax is a very popular rotation method in which each factor represents a small number of variables and each variable tends to be associated with one or a small number of factors (Abdi 2003 ). It enhances clarity, interpretability, and efficiency when distinguishing among the extracted factors (Simon and Xenos 2004 ). PCA finds the linear combination between indicators that extract the most variance in the data and uses both common and specific variance to extract a solution (Sallis et al. 2021 ). Therefore, in order to find the main research streams regarding food and social media, the number of variables (i.e. Keywords) was reduced to identify the underlying structure based on the overall variance. By performing factor analysis, determined keywords are assigned to determined factors based on their factor loadings. Factor loads (FL) inform about the representativeness of a determined keyword for a determined factor, and the usage of a keyword in a research stream (Kuntner and Teichert 2016 ; Sun and Teichert 2022 ). That means that the keywords assigned to one factor are more likely to co-occur than the keywords of other factors. Therefore, by using this method, factors were interpreted as single research streams.

As a result of the analysis, 12 factors emerged, which explain 51.175% of the total variance (see Table S3 in Supplementary material for the complete concepts per factor). Factor 11 was found to address issues related to the pharmaceutical industry and the Food and Drug Administration of United States (FDA) guidance documents. This factor was omitted in the further analysis, as it primarily addresses the pharmaceutical industry does not have a direct relationship with food and social media.

In order to further identify group similarities across research streams, a cluster analysis in SPSS was conducted. Cluster analysis finds natural groups present in the data, but hidden, by identifying important and defining properties (Sallis et al. 2021 ). This analysis revealed four main research clusters that the researchers named: Psychological Research Realm, Action-Oriented Research, Broader Communication Issues, and Service Industry Discourse (see Table 12 for a summary of research clusters and their characteristics).

3 Results and discussion

In the following, the four different clusters of research are explained in detail considering the most representative publications of every factor or research stream.

3.1 Psychological research realm

The Psychological Research Realm contains four research streams; therefore, it is the biggest of the four clusters. These research clusters address mainly, the impact of social media use on consumers. It includes the streams “online tools for healthy diet intervention programs,” “food and use of apps,” “online food advertising exposure,” and “social media and mental disorders.”

3.1.1 Research stream on “online tools for healthy diet intervention programs” (Factor 1)

The first research stream explains 18.94% of the variance of keyword relationships, indicating a research stream of first-highest distinction. While obesity and diet were the most often listed keywords (130 and 123 mentions), the research stream was best represented (in terms of factor loadings) by the keywords diet (FL = 0.922) , followed by intervention. Program, related to (physical) activity, nutrition, prevention, adult, overweight, and association constitute the remainders of the top ten keywords. An inspection of the remaining 103 keywords confirms this focus on application-oriented topics from the perspective of healthy diet interventions. Thus, this research stream clearly addresses the topic “use of online tools for healthy diet intervention programs.”

Representative publications of this research stream (see Table 1 ) reference each more than 14 keywords of factor 1. Regarding theories and conceptualizations, most of the articles refer to healthy diets and the use of online tools. Thus, an inclusive and shared research discourse can be diagnosed.

A closer look at these articles (selected by maximum number of referenced keywords) provides insights about the methods used, the online tools evaluated, as well as the types of insights gained from this research discourse (Table 1 right columns). These articles address the use of online tools for healthy diet intervention programs by using randomized and controlled trial groups, among others. The studies analyze the development of novel online tools as well as the efficacy of other healthy diet intervention tools.

The consumption of junk foods, fast foods, sugar-sweetened beverages, and carbonated drinks and beverages is associated with higher body mass index in children and adolescents due to their high content of free sugar and energy (Gupta et al. 2019 ). In order to promote public health sensitizing the population regarding obesity, the use of social media and new technologies has been recommended by the OECD and the American Heart Association (Li et al. 2013 ; OECD 2017 ).

In this regard, this research stream contains protocols of novel internet-based intervention tools to promote healthy diets (Helle et al. 2017 ; Røed et al. 2019 ), as well evaluations about the effectivity of online tools for intervention programs, and for the delivery of healthy eating information and recipes, among others. Ahmad et al. ( 2020 ) evaluated the effect of the family-based intervention program (REDUCE) on children’s eating behaviors and dietary intake via face-to-face and social media by using Facebook and a WhatsApp group to deliver information about the intervention and as platforms of interaction and problem solving. The authors found small changes in consumption of unhealthy snacks, as well as fruits and vegetables, without clinical impact. Dumas et al. ( 2020 ) explored the effects of an evidence-informed healthy eating blog written by a registered dietitian, finding no effects on dietary intakes, food-related behaviors, and body weight.

While these former studies did not reveal a strong positive impact, there are other studies showing positive results. For example, with the aim of evaluating the value of social media for delivering healthy diet interventions, Chau et al. ( 2018 ) found that the majority of the studies associated with this topic, from 2006 to 2016, showed positive outcomes regarding the use of only basic social media features. Tobey et al. ( 2019 ) evaluated the success of the Food Hero marketing campaign and suggest that in order to disseminate recipes to low-income audiences through social marketing campaigns, is recommended to understand the target audience, to add healthy/customizable recipes to family “go-to” recipe rotations considering the generational influences on family meals, and to create websites that meet the target audience criteria (e.g. simple and visually interesting).

By delivering healthy diet interventions through social media or online tools, studies in this research stream targeted mainly parents. Future research might evaluate the efficacy of social media or novel online tools by targeting parents and children separately, and by delivering strategies designed for each group.

3.1.2 Research stream on “online food advertising exposure” (Factor 5)

Explaining 2.78% of the variance of keyword relationships, the fifth research stream indicates a research stream of fifth-highest distinction. Here, the most often mentioned keywords were marketing and advertising (82 and 63 mentions). However, in terms of factor loadings, the research stream was best represented by the keywords advertising (FL = 0.915) , followed by marketing. Exposure related to (unhealthy) food, television, advergame, beverage, celebrity, youtube, and endorsement constitute the remainders of the top ten keywords. The inspection of the remaining 14 keywords confirms the online advertising exposure approach. Thus, this research stream clearly addresses the topic “online food advertising exposure.”

Representative publications (see Table 2 ), selected by the highest number of reference keywords, reference each more than 6 keywords of factor 5, and address the concept of influencer marketing , and among other social media, they analyze mainly YouTube videos, sharing an inclusive research discourse.

A closer look at these articles reveals that four of six articles of this research stream were led by the same author. In general, the articles of this research stream address the exposure to food advertising online by means of content analysis, questionnaires, and multivariate analysis, among others.

Regarding food and beverage marketing content on social media, Kent et al. ( 2019 ) found that although children and adolescents are exposed to unhealthy food and beverage marketing on social media, adolescents were more highly exposed to food marketing than children through user‐generated, celebrity‐generated content, and other entertainment content. Regarding food and beverage products featured on YouTube videos of influencers who are popular with children, it was found that less healthy products were the most frequently featured, branded, presented in the context of eating out, described positively, not consumed, and featured as part of an explicit marketing campaign, than healthy products (Coates et al. 2019b ).

Studies in this research stream have proved the persuasive power of social media influencer promotion of food, and their impact on children’s food intake, even when including a protective disclosure, due to their credibility and familiarity with children. Some authors situate social media influencers as a new type of advertising source that combines the merits of e-WOM and celebrity endorsement (De Veirman et al. 2019 ). YouTubers featuring videos of food and beverages high in fat, sugar, and/or salt (HFSS) are valued highly by children because they are viewed to fulfill their needs. Children develop sympathetic attitudes towards YouTubers because they are not strangers to them (Coates et al. 2020 ). Children look up to popular influencers who have certain celebrity status and are willing to identify with them while taking on their lifestyles, attitudes, and beliefs. Therefore, (marketing) messages spread by them are perceived as highly credible WOM, rather than as advertising, due to their perceived authenticity (i.e., they have no commercial interests) (De Veirman et al. 2019 ).

It has been discovered that children exposed to influencer marketing in a YouTube video of a branded unhealthy snack (with and without an advertising disclosure) consumed more of the marketed snack and significantly increased intake of unhealthy snacks specifically whereas the equivalent marketing of healthy foods had no effect. Therefore, it has been concluded that influencer marketing increases children's immediate intake of the promoted snack, even when including a “protective” advertising disclosure, which does not reduce the effect of influencer marketing (Coates et al. 2019a , 2019c ). Results reveal that increasing the promotion of healthy foods on social media could not be an effective strategy to encourage healthy dietary behaviors in children (Coates et al. 2019c ).

In sum, most of the articles in this research stream address children and adolescents’ exposure to unhealthy food influencer marketing contained in YouTube videos. Further research could evaluate the use of influencer marketing on children for healthy food intake, not just in YouTube, but also in other video content social media like TikTok, or Instagram. Other studies could compare different target groups (e.g. adults, adolescents, and children) in different countries.

3.1.3 Research stream on “social media and mental disorders” (Factor 8)

The eights research stream explains 1.93% of the variance of keyword relationships, indicating a research stream of eight-highest distinction. The research stream was best represented (in terms of factor loadings) by the keywords depression (FL = 0.793) , followed by anxiety. The same words were, as well the most listed keywords (18 and 17 mentions) . Addiction, disorder, symptom, distress, psychological, stress, well-being, and personality constitute the remainders of the top ten keywords. An inspection of the remaining 6 keywords confirms this focus on application-oriented topics from the perspective of mental disorders. Thus, this research stream clearly addresses the topic “social media and mental disorders.”

Representative publications of this research stream (see Table 3 ) reference each more than 4 keywords of factor 8. Regarding theories and conceptualizations, although this research stream has not a leading theory, they analyze different mental disorders and their relationship with social media. Thus, an inclusive and shared research discourse can be diagnosed.

A closer look at these articles (selected by maximum number referenced keywords) provides insights about the methods applied and types of insights gained from this research discourse (Table 3 right columns). These articles address social media use and mental disorders by using questionnaires, addiction scales, and personality inventories, among others. Hence, antecedents and consequences of social media use and mental disorders are analyzed.

Regarding the antecedents of addictive behaviors, it was found that personality traits and gender, as well as certain mental disorders, are associated with different behavioral addictions. For example, the profiles “elevated levels of gaming and pornography addictions” as well as “highest levels of all addictions” are predominantly male, while the profile “elevated levels of study, Facebook, shopping, and food addictions” are almost exclusively female (Charzynska et al. 2021 ). Besides, it was concluded that “individuals higher in anxiety sensitivity/hopelessness used food or alcohol to cope which, in turn, significantly predicted unhealthy snacking, and hazardous drinking, respectively” (Reaves et al. 2019 , p. 921).

Regarding the use of social media and its impact on mental disorders, Kicali et al. ( 2021 ) found that although food addiction is associated with some personality traits, personal habits, and psychiatric symptoms, more than five hours a day of social media consumption hat a direct relationship with internet and eating addiction. Kircaburun et al. ( 2021 ) found that a Problematic YouTube Use (PYU), which refers to different activities like watching specific YouTube channels or viewing online video games, is associated with loneliness and depression. Other works in this research stream explored images shared on social media and their relationship with mental disorders. E.g., Bogolyubova et al. ( 2018 ) concluded that while in Russian language people shared more images of food with hashtags for stress, images of alcohol were associated with stress hashtags, and hashtags for fear were related to the “scary” in popular culture and not to psychological distress.

Other works in this research stream addressed the impact of the COVID-19 Pandemic on mental health. Bountress et al. ( 2021 ) determined that instead of a single overarching COVID-19 impact, there are discrete impacts of various COVID-related factors. Therefore, they suggest a five-factor COVID model (i.e. exposure, worry, housing/food instability, social media, substance use) which is able to predict the risk of mental health symptomology, as well as other adverse sequelae of the COVID-19 pandemic at large. On the other hand, Panno et al. ( 2020 ) confirmed that COVID-19 related distress is associated with alcohol problems, social media, and food addiction symptoms. Following this line of research, future research might explore further the use of social media for mental health.

3.1.4 Research stream on “food and the use of apps” (Factor 12)

The twelfth research stream explains 1.32% of the variance of keyword relationships, and is the research stream of twelfth-highest distinction. Mobile and adoption, were the most often listed keywords (24 mentions each). Nevertheless, the research stream was best represented (in terms of factor loadings) by the keywords application (FL = 0.621) , followed by mobile. The remainders of the top five words were (Smart)phone and app. A closer look at the main keywords confirms its orientation to application-oriented topics from the perspective of the use of apps, focusing clearly on the topic “food and the use of apps.”

Representative publications reference each more than 2 keywords of factor 12 (see Table 4 ). Although this research stream has not a leading theory, most of the articles investigate the topic of food and the use of apps, sharing an inclusive research discourse. The representative publications chosen by the highest number of referenced keywords (Table 4 right columns), address the use of apps in relation to food by means of literature review, questionnaires, and interviews, mainly. Among others, social media content, as well as antecedents, and contingencies regarding food tourism are analyzed.

Information Communication Technology (ICT) (e.g. internet; mobile technology; and social media platforms among others) influence the daily living activities of persons, specifically Instrumental Activities of Daily Living (IADL) (e.g. activities requiring complex problem solving, cognitive function, coordination, and scheduling) (Quamar et al. 2020 ). In this regard, children interact with and consume visual advertising when visiting sites or applications related to online gaming (23%), food and distribution (18%), entertainment (8%) and fashion (8%), and when using smartphones with Internet access, Chilean children receive 14 min per hour of use of visual advertising more than from other media, such as television (Feijoo-Fernandez et al. 2020 ).

Regarding the antecedents of the use of mobile phones and apps for service purposes, it was found that the adoption of services and apps is driven by individual’s mobile phone technology maturity and business development (Paas et al. 2021 ). An analysis of user’s feedback on Twitter of four prominent food delivery apps and app store reviews of these apps revealed that the main concerns of users are related to issues regarding customer service, orders, food, delivery, time, app, money, drivers, and restaurants (Williams et al. 2020 ). Regarding mobile dining (e.g. use smartphone apps, to find restaurants, to read food menus, to select food, and to order it) it was found that consumers’ purchase intention is shaped by perceived values (i.e. navigation system, review valence, credibility, as well as service, and food quality) (Shah et al. 2020 ).

Other studies explored the use of smartphone apps for healthy lifestyles and dietary change. While Allman-Farinelli and Gemming ( 2017 ) concluded that apps have proven to be effective for glycemic control but not yet regarding weight loss and food intake, other studies found that monitoring apps enable users to set targets and monitor themselves. Besides, it is possible to acquire tailored feedback, and subsequently to raise awareness and increase motivation regarding dietary intake and physical activity. Moreover, apps with incorporated social features, characterized as social media, facilitate social interaction and support, can provide social comparison and social support (Dute et al. 2016 ). Concerning the development of smartphone apps to reduce sugar-sweetened beverage consumption among disadvantaged young adults in nonurban settings or indigenous communities, Tonkin et al. ( 2017 ) identified the importance of design to facilitate comprehension, and that in order to increase satisfaction the use of social features such as audio, leader boards, games, and team challenges could be helpful.

Studies in this research stream explored the use of specific apps for service purposes or dietary change, in just one region or sample. Further research could conduct comparative studies among apps, with different target groups in different geographical areas or regions.

3.2 Action-oriented research

This research cluster analyzes the content of social media and its impact on consumers' food risk information seeking and perception, behavioral intention and buying of green products online, as well as food tourism for destination image and its promotion. It includes the research streams “online food risk communication,” “behavioral intention and buying online,” and “social media and food tourism.”

3.2.1 Research stream on “online food risk communication” (Factor 3)

This research stream of third-highest distinction explains 3.79% of the variance of keyword relationships. Communication and risk were the most often listed keywords accounting 151 and 102 mentions respectively. However, in terms of factor loading, it was best represented by the keywords ( food) safety (FL = 0.827) , followed by ( risk) communication. The remainders of the top ten keywords were the keywords public and (risk) perception related to safety, (food) risk, crisis, and amplification . The remaining 35 keywords indicate its focus on themes from the perspective of online communication, addressing clearly the topic “online food risk communication.”

Table 5 displays the representative publications of this research stream, which reference each more than 8 keywords of factor 3. Most of them address the risk communication concept, sharing therefore an inclusive research discourse. These articles address the topics of online media consumption and food risk by means of surveys and quantitative content analysis, among others. They focus mainly on the coverage of topics related to health risk, consumers´ food risk information seeking, and consumers´ risk perception.

Some studies in this research stream explore how online information sources cover different healthy risk themes. For example, during the 2008 Irish dioxin contamination of food, Shan et al. ( 2014 ) found that social media responded faster than traditional media, using offline and online media news messages as primary sources, in reporting limited topics. Related to the coverage of biological, chemical, nutritional food risks, and related safety issues, Tiozzo et al. ( 2020 ) discovered that the most widely covered topics were nutritional risks and news about outbreaks, controls, and alerts. Moreover, national sources covered food risks, especially during food emergencies whereas thematic sources devoted major attention to nutritional topics.

In regard to the antecedents of consumers’ online information seeking behavior, concerning food safety issues, Wu ( 2015 ) concluded that Facebook use intention is determined by risk perception, emotion, social trust, and support. Regarding Genetic Modification (GM) issues, (Hanssen et al. 2018 ) discovered that the frequency with which people seek information is low, and it is driven by a positive attitude toward science and technology, trust in organizations, negative trust in regulations, as well as by gender and educational level. As a tool for food safety risk, specifically, to combat foodborne illness, Chapman et al. ( 2014 ) identified that the use of social media could be helpful for public health and food safety risk, since social media provide access to real people´s discussions and feedback, allow communicators to reach people where they are, create communities, and can be used to build credibility by providing decision-making evidence.

Regarding risk perception, some studies in this research stream found that risk perception depends on the topics and the online source used by consumers. For example, mixed media have a stronger positive relationship regarding public risk perception (PRP), than traditional media or internet social media (Niu et al. 2022 ). And, in the case of bovine spongiform encephalopathy (BSE), individuals exposed to more internet news had higher risk perceptions in terms of how BSE could affect themselves, while respondents exposed to social networking sites were concerned about how the disease could affect others (Moon and Shim 2019 ).

With most of the articles of this research stream addressing risk perception, or consumers’ food risk information seeking, further research could explore how social media could be used effectively for public health and food safety risk by using quantitative and qualitative methods of research.

3.2.2 Research stream on “behavioral intention and buying online” (Factor 4)

The fourth research stream explains 3.02% of the variance of keyword relationships, indicating a research stream of fourth-highest distinction. The research stream was best represented (in terms of factor loadings) by the keywords organic (FL = 0.765) , followed by purchase, although attitude and intention were the most often listed keywords (79 and 66 mentions) . Theory and (planed) behavior related to buying, food-intake , belief, and acceptance, were the remainders of the top ten keywords. As it can be confirmed by analyzing the remaining 20 keywords, the focus of this research stream relies on the perspective of behavioral intention, addressing thus the topic of “behavioral intention and buying online.”

Representative publications of this research stream (see Table 6 ), selected by the highest number of referenced keywords, contain each more than 7 keywords of factor 4. Addressing the Theory of Planned Behavior (TPB) and/or the Theory of Reasoned Action (TRA) (Ajzen and Fishbein 1980 ), most of the articles address the concept of “behavioral intention” regarding green, or organic products, showing an inclusive and shared research discourse.

With six of seven articles using TPB or TRA, this research stream addresses the topic of behavioral intention regarding green products by means of structural equation modeling.

The TPB is an improved version or extension of the Theory of Reasoned Action (TRA) (Ajzen 1991 ; Hofmeister-Tóth et al. 2011 ). The TPB differs from the TRA, “in that it takes into account perceived as well as actual control over the behavior under consideration” (Ajzen 1985 , p. 12). Ajzen ( 1985 ) explains that actions are controlled by intentions. Therefore, the TPB is a model that predicts behavior based on the intention to perform the behavior and the perceived behavioral control where the attitude towards the behavior , the subjective norm, and the perceived behavioral control influence intention (Aertsens et al. 2009 ).

Studies of this research stream concluded that the information contained in social media tools can influence the intention to perform a behavior regarding green or organic products. Considering green cosmetics purchase intentions, Pop et al. ( 2020 ) point out that social media can increase consumers’ environmental concerns, consumers’ attitudes, subjective norms, altruistic and egoistic motivations, and therefore consumers’ green cosmetics purchase intentions. By using the value-belief-norm theory and the elaboration likelihood model, Jaini et al. ( 2019 ) discovered that e-WOM communications influences consumers’ green cosmetics purchase decisions, with personal norm affecting this choice, especially when they are actively involved in obtaining positive feedback via e-WOM communication. In addition, pro-environmental beliefs, which eventually affect consumers’ personal norms, are affected positively by hedonic, and altruistic value.

Regarding organic food, it was confirmed that consumers’ attitudes towards organic food can be shaped by social media forums and informative webpages featuring product quality and certification. They have a great moderating effect on purchase ratings and reviews that positively influence consumers’ online impulse buying behavior (Tariq et al. 2019 ). Background factors like information (i.e., social media information and labeling), individual (i.e., health consciousness and purchase attitude), and social (i.e., self-perceived vegetarian and environmentalism), impact consumers’ intention of purchasing organic food (Li and Jaharuddin 2021 ). Lim and Lee-Won ( 2017 ) discovered that dialogic retweets (i.e. retweeting user mentions addressed to an organization), are more persuasive than monologic tweets because dialogic retweets lead to a higher level of subjective norms, more favorable attitudes toward behavior, and greater intention to adopt the behavior advocated by an organic food organization in the messages. On the other hand, a lifestyle of health and sustainability influences the attitude of customers toward sustainable consumption and therefore, consumers’ sustainable consumption behavior (Matharu et al. 2021 ). Furthermore, regarding western imported food products in a Muslim country, Bukhari et al. ( 2020 ) found that product attributes, price, self-concept, brand trust, personality, and religiosity are positively correlated with consumers’ purchase intention in Pakistan.

This research stream concluded that the information contained in social media can influence the intention to consume green or organic products. Nevertheless, it is known that there is an intention-behavior gap, identified between positive attitudes toward organic products and actual purchase behavior (Padel and Foster 2005 ; Pearson et al. 2011 ). Thus, further research could explore, by means of mixed methods, how social media could reduce the intention-behavior gap.

3.2.3 Research stream on “social media and food tourism” (Factor 10)

The tenth research stream explains 1.54% of the variance of keyword relationships, indicating a research stream of tenth-highest distinction. While image (58 mentions) and destination, (content) analysis and instagram (30 mentions each) were the most often listed keywords, the research stream was best represented (in terms of factor loadings) by the keywords destination (FL = 0.645) , followed by authenticity. Place, related to travel, culinary, image, wine, and gastronomy constitute the remainders of the top ten keywords. These 10 keywords in this research stream confirm the application-oriented topics from the perspective of food tourism. Therefore, this research stream clearly addresses the topic “social media and food tourism.”

Representative publications of this research stream (see Table 7 ) reference each more than 2 keywords of factor 10. Regarding theories and conceptualizations, although this research stream has not a leading theory, they analyze food tourism and its relationship with social media. Thus, an inclusive and shared research discourse can be determined.

A closer look at these articles (selected by maximum number of referenced keywords) provides insights about the methods applied and types of insights gained from this research discourse (Table 7 right columns). These articles address food tourism related to social media use by means of content analysis, semi-structured interviews, and literature review, among others. The articles analyzed social media content, as well as antecedents and contingencies regarding social media and food tourism.

The use of social media to increase destination image or to promote a food destination is the main focus of this research stream. Over the past two decades, the key themes regarding food tourism were authenticity through food experiences, the offer of unique food experiences, food tourism and sustainability, as well as the use of food destination in marketing; nevertheless, Okumus ( 2021 ) suggests that future studies should focus on the role of social media in promoting food tourism experiences, among others. In this regard, Filieri et al. ( 2021 ) found that on Instagram, users communicate their destination brand love through photographs of some destination attributes (e.g. people, food, weather, etc.) accompanied by specific positive emotions (e.g. attractiveness, pleasure, amazement, etc.) or providing emotional support during a destination crisis. Besides, Ramirez-Gutierrez et al. ( 2021 ) concluded that in TripAdvisor, tourists’ communications of gastronomic experiences contain both aesthetic and personal values.

Other studies in this research stream reveal social media strategies and how specific online tools can help to promote food destinations. While memories influence positively the loyalty for a food destination (Bachman et al. 2021 ), the description of food on TikTok brings an effect of intention to travel and to obtain information, impacting the affective image of a destination and increasing potential tourists’ attention (Li et al. 2020 ). As a tool to advertise food-based cities, Yu and Sun ( 2019 ) recommend the use of Instagram to attract the attention of consumers including hashtags to reach more users and to generate interactivity. Moreover, the endorsement of celebrity chefs on social media can help to promote cities as culinary destinations by giving provocativeness (i.e. attractiveness and customer engagement), credibility (i.e. trustworthiness, leading, and reliability), and supportiveness (i.e. localism and match-up) (Demirkol and Cifci 2020 ). Besides, Vrontis et al. ( 2021 ) suggest that the support interactions between destination managers and stakeholders by using online technology; can be transformed into a word-of-mouth source that could affect perceptions and sustainable development of the territory producing the place brand.

Finally, by conducting a content analysis of 600 Instagram images containing the hashtag #fitspiration, Tiggemann and Zaccardo ( 2018 ) found that most images of women contained objectifying elements, and only one body type: thin and toned. Authors point out that although ‘fitspiration’ images may be inspirational for viewers, they contain elements that could affect negatively the viewer’s body image.

This research stream analyzed the role of social media in food tourism on Instagram, TikTok, and Tripadvisor. Further research might explore the use of further social media tools in order to enrich this research stream with comparisons among tools and countries.

3.3 Broader communication issues

This research cluster analyses online communications regarding Alternative Food Networks (AFN), online communication, and eating disorders, as well as the analysis of online food related data by means of novel tools. This cluster includes the research streams “sustainable food communication online,” “analysis of online food related data,” and “online communication and eating disorders.”

3.3.1 Research stream on “sustainable food communication online” (Factor 6)

Explaining the 2.66% of the variance of keyword relationships, this research stream of sixth-highest distinction was best represented (in terms of factor loadings) by the keyword sustainability (FL = 0.727) , followed by agriculture, although network and sustainability were the most often listed keywords (68 and 60 mentions) . The remainders of the top ten keywords, were the words innovation , system, economy, chain, alternative, supply, and farmer . The remaining 24 keywords confirm the focus on sustainable food communication. Thus, this research stream clearly addresses the topic “sustainable food communication online.”

The most representative articles of this research stream (see Table 8 ) were selected by the highest number of keywords referenced, in this case, each more than 6 keywords of factor 5. Without a leading theory, most of the articles rely on the concept of AFN, and local food networks or systems. They address the topic of sustainable food and online communication, linked both by means of content analysis, data mining, semi-structured interviews, surveys, and participant observation, among others. Media content is investigated, as well as antecedents and contingencies regarding sustainable food communication online.

Regarding the antecedents of the use of internet communications, in this research stream, it was found that initiators and participants of AFN are individual shoppers and nascent activists that organize strategies, build networks, and use internet communications to extend their reach, and expand linkages to emancipatory spaces of global and social justice movements (Schumilas and Scott 2016 ). Online spaces (e.g. websites and social media platforms) supplement the socio-material connections in AFNs’ offline spaces providing a ‘virtual reconnection’ or an additional real for reconnection (Bos and Owen 2016 ). By using social media, participants in citizen-drive initiatives (e.g. for waste-prevention) create collaborative local networks to develop green/sustainable consumption practices (Campos and Zapata 2017 ). Exploring communications with the hashtag #sustainability on Twitter, Pilar et al. ( 2019 ) discovered six communities (i.e. Environmental Sustainability, Sustainability Awareness, Renewable Energy and Climate Change, Innovative Technology, Green Architecture, and Food Sustainability), and 6 hashtags related to sustainability (i.e. innovation, environment, climate change, corporate social responsibility, technology, and energy).

Regarding the use of online communications by producers and intermediaries, it was found that producers establish consumers’ trust by satisfying the consumer´s desire for safe food, and that they use social media to construct food materiality and the perception of this materiality in order to fit the consumer´s ideal of freshness (Martindale 2021 ). Besides, Kummer and Milestad ( 2020 ) discovered that social media is used as an advertising tool in the growing practice of box schemes (i.e. a type of locally oriented distribution system used by community supported agriculture (CSA) farms or enterprises) in Europe. Other works in this research stream studied the motivations for buying sustainable agricultural products (e.g. Ashtab and Campbell 2021 ).

Further research could explore not just the use of social media for communication, but also how these communications influence behavior-change and sustainable food consumption among their participants.

3.3.2 Research stream on “analysis of online food related data” (Factor 7)

The seventh-highest distinction research stream explains 2.14% of the variance of keyword relationships. In terms of factor loadings, the keywords (sentiment) analysis (FL = 0.74) , and tweet are the main keyword representing this research stream . The top ten keywords were led by twitter with 102 mentions, followed by (sentiment) analysis and datum with 35 mentions each. Halal, detection, topic , mining, classification, and sentiment are the remainders of the top ten keywords. Analyzing all keywords, it can be confirmed the use of words related to methods for the analysis of online data. Therefore, this research stream addresses the topic of “analysis of online food related data.”

Although the representative publications (see Table 9 ), with more than 5 keywords of factor 7, do not share a leading theory, they share a research discourse by analyzing Twitter communications. With three articles led by the same author, articles in this research stream address the analysis of online data related to food by means of social network analysis, data mining, and sentiment analysis. Media content, antecedents, and contingencies regarding the analysis of online food related data are analyzed.

Many studies in this research stream emphasize the use of different methods and tools to analyze online communication data. By using opinion mining techniques, Mostafa ( 2019 ) analyzed food sentiments regarding halal food expressed on Twitter detecting a generally positive sentiment toward halal food, as well as a heterogeneous group of halal food consumers divisible by concern for food authenticity, self-identity, animal welfare attitudes, and level of religiosity. By using social network analysis Mostafa ( 2021 ) examined the structure, dynamics, and influencers in halal food networks, founding that few social mediators or “influencers” control the diffusion of information through a small world preferential attachment network that links digital halal food consumers. The same author analyzed Wikipedia’s clickstream data in order to study users’ halal food navigation strategies on Wikipedia servers discovering that only a few articles or “influencers” within close-knot communities control the flow of halal food information (Mostafa 2022 ).

As well the use of geocoding has an important place in this research stream. By using geocoding, Rimjhim et al. ( 2020 ) analyzed data from Twitter and Wikipedia, to know how the conversational discourse on online social networks vary semantically and geographically over time finding that although there is a significant homogenization in online discussion topics, despite geographical distance, it is not similar across all topics of discussion and location. Zhang et al. ( 2020 ) explored individuals’ emotions and cognition of cultural food differences among people from South and North China by using the machine learning method of natural language processing (NLP) by posting on the Zhihu Q&A platform the question “What are the differences between South and North China that you ever know?” They found that food culture is the most popular difference among people from North and South China and that individuals tend to have a negative attitude toward food cultures that differ from their own. Analyzing geo-located and reciprocal user mention and reply tweets over the course of the 2016 primary and presidential elections in the United States, Koylu ( 2019 ) found that the discourse was divided between election-related discussions of the political campaigns and candidates, and civil rights, being the last the more dominant. Ullah et al. ( 2021 ) propose an architecture to store data to accelerate the development process of the machine learning classifiers using rule-based and logistic regression.

The contribution of this research stream to the social sciences lies, without doubt, in the novel approaches to analyzing online data. Further research could extend the use of these tools in their research or propose new ones. And, since most studies analyze text, it is recommended the development of tools to analyze images.

3.3.3 Research stream on “online communication and eating disorders” (Factor 9)

The ninth research stream explains 1.76% of the variance of keyword relationships. Blog and site were the most often listed keywords (62 and 38 mentions), but in terms of factor loadings, the stream was best represented by the keywords discourse (FL = 0.557) , followed by blog. An inspection of the remaining seventeen keywords, confirms the eating disorders approach. Hence, this research stream studies the topic of “online communication and eating disorders.”

Without a leading theory, representative publications of this research stream (see Table 10 ) analyze online communication related to eating disorders, sharing the same discourse. Articles address online communication related to eating disorders by means of virtual ethnography, netnography, and interpretative phenomenological analysis, among others. They analyze web and social media content as well as antecedents and contingencies regarding online communication and eating disorders.

Some studies in this research stream explore online narratives, experiences, and discussions regarding eating disorders (ED) online. By using content analysis of ‘food porn’ websites and blogs, as well as participant observation and interviews regarding ‘pro-anorexia’ websites, Lavis ( 2017 ) found that participants “eat” in, and through cyberspace, beyond and among bodies. Cinquegrani and Brown ( 2018 ) explored narratives of experiences and conceptualizations through online social media forums regarding the eating disorder Orthorexia Nervosa (ON), a fixation on eating proper food accompanied by excessive exercise. The authors found three main narratives: pursuit (i.e. the individuals are on a quest to ‘better’ themselves), resistance to the illness narrative, and the recovery (i.e. after accepting the ‘illness narrative’). The authors suggest considering ON a lifestyle syndrome embodied in social and cultural processes. By analyzing TikTok posts containing the hashtag (#) EDrecovery, Herrick et al. ( 2021 ) concluded that creators share their personal experiences with recovery by using popular (or viral) video formats, succinct storytelling, and the production of educational content.

Other studies explored online conversations in order to understand how individuals confer value and meaning to ‘healthy’ eating behaviors. Consumers are active co-producers of value and meaning regarding the impact of green products on their health and the environment, and their understanding of health and sustainability is affected by cultural meanings and pleasure, which lead them to attribute additional unsubstantiated traits to certain products ascribed as virtuous (Yeo 2014 ). Examining the visual and textual framings of ‘superfoods’ on social media, it was found that superfoods are a marker of idealized identity mobilized by using postfeminist, neoliberal, and food justice discourses (Sikka 2019 ), the healing potential of veganism is derived from a passionate investment of the self that redefines young women’s ways of being in the world (Costa et al. 2019 ).

In sum, this research contributes to the understanding of the complexity of eating disorders by uncovering the processes and meanings of eating disorders and how they are portraited online. Some studies in this research stream also discloses how individuals confer meaning to healthy eating behaviors and how an idealized identity ascribes virtuous attributes to some foods. Further research could explore if this initially idealized identity of healthy foods leads to future eating disorders.

3.4 Service industry discourse on “food online reviews in the service industry” (Factor 2)

One research stream was found in this cluster, which possesses an integrative discourse: “food online reviews in the service industry.” This research stream explains 9.87% of the variance of keyword relationships, indicating a research stream of second-highest distinction. While word-of-mouth and satisfaction were the most often listed keywords (77 and 60 mentions), the research stream was best represented (in terms of factor loadings) by the keywords hotel (FL = 0.868) , followed by ( online) reviews. Performance and (consumer) satisfaction related to restaurant, service, hospitality constitute the remainders of the top ten keywords. An inspection of the remaining 49 keywords confirms this focus on application-oriented topics from the perspective of the service industry. Thus, this research stream addresses the topic “food online reviews in the service industry.”

Representative publications of this research stream (see Table 11 ) reference each more than 10 keywords of factor 2. Regarding theories and conceptualizations, most of the articles refer to electronic word of mouth (e-WOM) and online review. Thus, an inclusive and shared research discourse can be diagnosed.

A closer look at these articles (selected by maximum number of referenced keywords) provides insights about the methods applied and types of insights gained from this research discourse (Table 11 right columns). These articles address online food reviews as an indicator of service quality, linking both by means of regression analysis or structural equation modeling. Antecedents, consequences as well as contingencies of online food reviews are analyzed.

In a narrow effects perspective, Kim et al. ( 2016 ) found that the number of online reviews correlates with restaurant performance. By analyzing online customer comments on Yelp.com, Bilgihan et al. ( 2018 ) found that a focus on selected menu offerings, food, ambiance, and service can create buzz in social media. Addressing the broader scope of tourism industry, Abrudan et al. ( 2020 ) studied customer review scores on booking.com to analyze the impact of different hotel facilities on customers’ overall ratings, confirming the special relevance of food service for hotel ratings. Another analysis of online reviews from 68 online platforms however did not confirm such a special relevance of food services, with hotel attributes, including quality of rooms, Internet provision, and building to impact hotel performance most (Phillips et al. 2017 ). Altogether, these works highlight the importance of food reviews as drivers of positive consumer feedback primarily in the restaurant industry but less so in the broader hospitality industry.

Other works critically reflect on the antecedents of consumers’ online food reviews. Investigating consumers´ personal drivers to write food reviews, Liu et al. ( 2020 ) found that personal motivation, and especially altruism, influences the posting of negative consumer online reviews. Cambra-Fierro et al. ( 2020 ) discovered that a company’s corporate social responsibility can steer consumers to identify and link themselves to brands generating buy-back and recommendation behaviors. These works thus reveal behavioral drivers on the creation of food reviews both at the consumer and company level. Finally, several works investigate contingencies regarding the effects of food reviews: Zinko et al. ( 2021 ) found that reviewer-submitted (food) images influence consumers’ attitudes only when they are consistent with the review text. This contingency perspective on the effects of food reviews in social media seems the more needed given that previous research, as outlined above, came to divergent conclusions about the impact of online food reviews on consumers’ service ratings.

With most articles in this research stream addressing written food reviews online on different social media, further research might analyze not just the use of written messages, but as well the use of images in online reviews.

3.5 Patterns of the overall research system

The previous analyses were restricted to the level of single research streams. To complement this perspective, the relationship between research streams is analyzed by means of a network analysis. Hereto, a multidimensional scaling of the linkages of the top-ten keywords per factor is calculated and visualized in Fig.  2 . While the size of nodes displays the relative mentioning frequency of each keyword, their positioning within the figure informs about their overall centrality and connectedness. Although the largest nodes or most often mentioned keywords are communication, diet, risk, and obesity , this chart indicates a clear focality on the keyword communication .

Network Visualization of Factors´ Top-10-Keywords Relations

The closeness of single keywords indicates their relationship with each other, and with other research streams. To ease interpretation, each factors’ keywords are marked in different colors. Thus, the distance between keywords stemming from different research streams reveals not only their closeness but as well interconnections between their respective research streams. For example, obesity and diet are closely linked to advertising . This implies close connections between the discourses on “Online Tools for Healthy Diet Intervention Programs” (factor 1, marked in red) and “Online Food Advertising Exposure” (factor 5, marked in dark green). While these two discourses assume a different actor perspective, zooming into consumers’ or marketers’ interest, they nonetheless discuss related topics from a complementary perspective.

In contrast, a large distance among words or factors shows a weak relationship or missing links between research streams; for example, a large distance can be observed among keywords related to “Sustainable Food Communication Online” (factor 6) and to “Social Media and Food Tourism” (factor 10). This shows that these two research streams are not yet strongly related. Future research might contribute by linking those different perspectives together.

Furthermore, the location of keywords related to “Social Media and Mental Disorders” (factor 8) at the outer skirt of the figure reveals that this research stream is a truly peripheral discourse. Finally, the method-driven discourse on “Food Online Reviews in the Service Industry” (factor 2) is clearly more related to the core discourse, to twitter and the different methods of analysis.

4 Conclusions and implications

This study presents a bibliometric analysis of the research conducted regarding food and social media within the social sciences. By using co-word analysis, this study evaluated 413 main Keywords contained in 1356 articles by means of factor and social network analysis. The study shows that the number of studies conducted on this topic has increased rapidly, indicating a growing interest in food and social media. Besides, the results reveal four main research clusters (i.e. Psychological Research Realm, Action-Oriented Research, Broader Communication Issues, and Service Industry Discourse) containing the main topics of research.

The Psychological Research Cluster analyzes online tools for healthy diet intervention programs, the use of apps for service purposes or dietary change, the exposure of children and adolescents to influencer marketing in YouTube videos, as well as the antecedents and consequences of social media use and mental disorders. The Action-Oriented Research cluster analyzes online food risk communication, behavioral intention and buying online, as well as the use of social media for food tourism. The Broader Communication Issues cluster studies sustainable food communication online, online food related data, and the relationship between online communication and eating disorders. Finally, the Service Industry Discourse cluster explores online reviews in the service industry.

Future research could transfer topics in order to have a broad scope of research. For example, the insights gained on the discourse “food and the use of apps” (factor 12), could be transferred to studies regarding “online food risk communication” (factor 3). A further alternative is to transfer the potential of the sophisticated text-mining as method of analysis used in the discourse “analysis of online food related data” (factor 7) enriched by picture mining, in order to address research questions related to how food is perceived and marketed (e.g. factor 6). Another possibility is to intersect, for example, the topic of factor 1, which addresses more positive psychological constructs in detail, and factor 8, which addresses topics more related to clinical psychology. Further integration of theoretical models stemming from psychology (e.g. factor 1 and factor 2) into the practically oriented joint discourse on service industry setting (Factor 2). More theoretical foundations might help to generate broader insights. Other studies could compare target groups (e.g. comparing adults, adolescents, and children), in different countries, regarding the same topics (e.g. fast-food intake while consuming social media). Additionally, the analysis of texts or reviews could be enriched through the analysis images, or by developing tools to analyze images. Other ideas are summarized in Table 12 , and elaborated in the discussion of the single research streams above.

By suggesting future research directions, this study help scholars to find relevant future topics of research in this area of study. The findings presented in this study can be beneficial for marketing and business scholars, as well as companies, and organizations interested in understanding the relationships between food and social media.

Data availability

On request.

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Does food marketing need to make us fat? A review and solutions

Pierre chandon.

INSEAD, Fontainebleau, and a member of ICAN, Paris, France

Brian Wansink

Charles H. Dyson School of Applied Economics and Management at Cornell University, Ithaca, NY, USA

Food marketing is often singled out as the leading cause of the obesity epidemic. The present review examines current food marketing practices to determine how exactly they may be influencing food intake, and how food marketers could meet their business objectives while helping people eat healthier. Particular attention is paid to the insights provided by recently published studies in the areas of marketing and consumer research, and those insights are integrated with findings from studies in nutrition and related disciplines. The review begins with an examination of the multiple ways in which 1) food pricing strategies and 2) marketing communication (including branding and food claims) bias food consumption. It then describes the effects of newer and less conspicuous marketing actions, focusing on 3) packaging (including the effects of package design and package-based claims) and 4) the eating environment (including the availability, salience, and convenience of food). Throughout, this review underscores the promising opportunities that food manufacturers and retailers have to make profitable “win-win” adjustments to help consumers eat better.

INTRODUCTION

Biology and natural selection have created strong food preferences. Individuals around the world want easy access to a variety of tasty, convenient, inexpensive, and safe foods that can be eaten in large quantities. By catering to, and stimulating, these biological interests, food marketers have been accused of contributing to the growing problem of global obesity. 1–5 After all, the food industry (which includes food and beverage producers, as well as retailers, restaurants, and food services companies) employs savvy and creative marketers who have pioneered many of the tools of modern marketing. 6,7 At the same time, it is important to understand that the marketers and the executives who guide them are torn between satisfying the desires of various consumers, the demands of their shareholders, and the concerns of public health organizations, which largely perceive the food industry as the new tobacco industry (because both industries have used similar tactics, such as emphasizing personal responsibility, massive lobbying, pre-emptive self-regulation, etc.). 8,9 For these reasons, it is useful to review and integrate much of the overlooked evidence on how food marketing influences food intake and to examine how food marketers could continue to grow their profits without growing their customer's body mass index (BMI).

This review article examines and integrates the literature from marketing, consumer research, and related social science disciplines, which is not in the commonly referenced databases for health and medicine, such as PubMed, and is therefore often unknown to nutrition researchers. By incorporating this information, this review updates the existing reviews in the field, 10,11 which are rapidly becoming outdated given the breadth of more current research. For the purpose of this review, marketing is defined in accordance with the definition of the American Marketing Association as “the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large.” This article focuses on the direct effects of marketing activity under the direct control of food marketers, often referred to as the 4 Ps of “product,”“price,”“promotion,” and “place.” Specific focus is placed on the factors that influence how much consumers eat, and in particular, whether they overeat (which is defined as eating more than one realizes). Yet, it is important to remember that food/energy intake is not synonymous with weight gain, let alone obesity. 12 Because of this review's focus on marketing and food intake, many influencers of food intake that are not under the direct control of food marketers are excluded (e.g., physical activity, pro-social marketing, personal, cultural, and social norms about food, eating, dieting, incidental emotions, etc.).

Food marketers influence the volume of food consumption through four basic mechanisms that vary in their conspicuousness. 1) The short- and long-term price of food, as well as the type of pricing (e.g., a straight price cut or quantity discount), can influence how much people purchase and eventually consume. Pricing efforts are generally conspicuous and lead to deliberate decisions. 2) Marketing communications, including advertising, promotion, branding, nutrition, and health claims, can influence a consumer's expectations of the sensory and non-sensory benefits of the food. Marketing communications comprise the most recognized form of influence and the one most closely scrutinized by marketing and non-marketing researchers. The influence of marketing communication can sometimes be as conspicuous as price changes, but consumers are not always aware of some of the newest forms of marketing communication (e.g., “advergaming,” package design, or social media activities) and, even when they are aware of the persuasive intent behind these tools, they may not realize that their consumption decisions are being influenced. 3) The product itself, including its quality (composition, sensory properties, calorie density, and variety) and quantity (packaging and serving sizes) also influence in a variety of ways how much of the product consumers eat. This area has been frequently researched as marketing communication. 4) The eating environment, including the availability, salience, and convenience of food, can be altered by marketers. Compared to the breadth of the domain, this is the least frequently studied area, yet it is the one most likely to be driven by automatic, visceral effects outside the awareness and volitional control of consumers.

PRICING: HOW LONG- AND SHORT-TERM PRICE REDUCTIONS STIMULATE INTAKE

Some food products like milk, meats, fruits, and vegetables are often sold as commodities. With commodities, short-term prices are determined by supply and demand on world markets and long-term price changes are determined by efficiency gains in the production, transformation, and distribution of food rather than by marketers. The most notable change in this respect is the relative steep decline in the price of food over the last 50 years, particularly for branded, processed foods that are high in sugar and fat, and for ready-to-eat foods, which are prepared away from home. 13–17

Yet most food products are not commodities; instead, they are branded products that are differentiated in the eyes of consumers thanks to the ways in which they are advertised, formulated, packaged, distributed, and so on. With these branded products, marketers can establish their own price depending on which consumer segment they wish to target. Advances in marketing segmentation have enabled companies to direct price cuts to only the most susceptible consumer segments, which increases their efficiency. Table 1 summarizes key findings about the effects of price on overeating, innovative solutions tested by marketers to mitigate its effects, and suggestions for using price to improve food consumption decisions.

Pricing and consumer welfare.

Effects of long-term price changes

Econometric studies suggest that lower food prices have led to increased energy intake. 13–17 Even though the average price elasticity of food consumption is low (−0.78), it can be quite high in some categories (e.g., −1.15 for soft drinks) and for food prepared away from home. For example, one econometric study 18 using data from the 1984–1999 national Behavioral Risk Factor Surveillance System found that a 10% increase in prices at fast-food and full-service restaurants was associated with a 0.7% decrease in the obesity rate.

These conclusions are reinforced by the results of randomized controlled trials which demonstrate the causal effects of price changes. Longitudinal field experiments in cafeterias 19–21 have found that price changes above 25% significantly influence consumption of beverages or snacks, but also of fruit and vegetables, and they have stronger effects than nutrition labeling, which sometimes backfire because of negative taste inferences. One of the most thorough studies 22 also varied food budgets over time and found strong and comparable same-price elasticity in two studies for healthy (−1 and −1.7, respectively) and unhealthy (−0.9 and −2.1, respectively) foods. In contrast, the cross-price elasticities were four times smaller and only occurred when children had a very low budget, showing that children do not consider healthy foods to be a substitute for unhealthier ones.

The only exception to the rule that higher prices reduce consumption comes from a study showing that higher prices at an all-you-can-eat pizza restaurant led to higher consumption of pizza, probably because of the psychology of “sunk costs,” which leads people to try to eat “their money's worth.” 23 Interestingly, monetary (and normative) rewards do not seem to have any adverse effects on children's intrinsic motivation for the food. 24 In general, consumers appear to have learned that lower-priced foods are as hedonically satisfying as higher-priced foods, with the exception of a few categories, such as wine, for which determining good taste is ambiguous. 25 For example, in a recent study, Austrian consumers thought that price was unrelated to the quality of foods, which is not surprising given that the correlation between price and quality in that country was estimated by experts at only 0.07. 26

Effects of temporary price promotions and quantity discounts

Until recently, it was believed that price promotions simply shifted sales across brands or across time. However, it has now become clear that temporary sales promotions can lead to a significant increase in consumption. 27,28 Probably the best evidence of this comes from a randomized controlled field experiment involving 1,104 shoppers. 29 This study found that a 12.5% temporary price discount on healthier foods increased the purchase volume of these foods by 11% among the low-income consumers who received the coupons. The effect persisted even 6 months after the promotion had been stopped. In comparison, nutrition education and suggestions for substituting healthier food for less healthy food had no effect, whether alone or combined with the price discounts. However, the discounts on healthy food did not reduce purchases of unhealthy food.

Price deals can influence the speed of consumption even when the food has already been purchased (such as by another family member) and is, therefore, an irreversible sunk cost; this should not, in theory, influence consumption because the cost cannot be recovered, no matter when, or how quickly, the food is consumed. Nevertheless, studies have found that people accelerate the consumption of products perceived to have been purchased at a lower price. 30 This happens because a reduced past price is seen as an indication that the product will be discounted again in the future 31 or simply because the reduced sunk cost means that consumers feel they do not have to wait for a special occasion to consume the product perceived to be cheaper. 32

Marketers also reduce the relative price of food by offering quantity discounts with larger package sizes or multi-unit packs, which is a powerful driver of supersizing. 33 Although there are exceptions, most studies found that quantity discounts generally lead to stockpiling and increased consumption, especially for overweight consumers. 27,34 One study found that during weeks in which multi-unit packages were purchased, consumption of orange juice increased by 100% and cookies by 92%, but there was no change in consumption of non-edible products. 32 The authors replicated this effect in a field experiment in which the quantity of food was randomly manipulated while keeping its price constant; they found that large purchase quantities influenced consumption by making the food salient in the pantry or fridge, and not just by reducing its price.

Beyond the degree of the incentive, the form of the promotion and the payment mechanism can also influence energy intake. One study suggests that consumers prefer price discounts to bonus packs for guilt-inducing “vice” foods, but preferred bonus packs to price discounts for “virtue” foods because it is easy to justify buying them in larger quantity. 35 By definition, “vices” are foods that are preferred when considering only the immediate consequences of consumption and holding delayed consequences fixed, whereas the opposite is true for “virtues.” 36 The greater difficulty of justifying purchases of unhealthy foods also explains why they are more likely to be purchased when people pay for their grocery purchases via credit card than when they pay cash – a more painful form of payment which elicits a higher need for justification. 37 On the other hand, people are more likely to purchase and consume indulgent high-calorie ice-creams when paying cash than when paying with a credit card, 38 possibly because in this case they have the opposite goal of rewarding themselves.

Overall, all the studies reviewed here clearly show that pricing is one of the strongest – if not the strongest – marketing factors predicting increased energy intake and obesity, and this is why lower-income consumers are predominantly affected by these conditions. Conversely, the power of pricing means that it holds the key to many of the “win-win” solutions detailed in Table 1 . However, price is not the only determinant of food choices and it cannot alone explain rising obesity rates. 18 Unlike price, which arguably influences consumption through deliberate processes that people are aware of, food communication influences food perceptions and preferences often beyond volitional control and sometimes outside conscious awareness.

PROMOTION: HOW MARKETING COMMUNICATION STIMULATES INTAKE

Advertising and promotions are one of the most visible and studied actions of food marketers. They include advertising, both on traditional media channels and on non-traditional non-media channels, such as online, in-store, in movies, television programs or games, sponsorship or organization of events, in the street, and so on. Food marketers also communicate in more indirect ways by branding the entire product category (e.g., the “Got Milk?” campaign), the ingredients (e.g., acai), and by making nutrition or health claims in their advertising or on their packages. These claims are distinct from the mandatory nutrition information about calories, nutrient levels, and serving sizes, whose effects are reviewed elsewhere. 39–44 Table 2 summarizes the effects of marketing communication and shows how they can also improve food choices.

Marketing communications (promotion) and consumer welfare.

Marketing communication informs people about product attributes, like the price or where it can be purchased. Marketing communication also increases awareness of the brand and food, which leads consumers, particularly children, to try fewer foods and to only search for brands they already know rather than the brand that would have the highest nutritional and hedonic qualities. 45–47 Moving beyond awareness, communication enhances a consumer's expectations of the sensory and non-sensory benefits (such as the social and symbolic value) associated with the purchase and consumption of a particular food. Even if it fails at changing the expected benefits of consumption, marketing communication can influence the importance of these benefits, for example, by making taste a more important goal than health. This may explain why nutrition ranks last in surveys of the drivers of food choices, after taste, cost, and convenience. 48,49

Advertising and promotion effects

The food industry is among the top advertisers in the US media market. Children and adolescents are exposed to increasing levels of television advertising, mostly for nutritionally poor snacks, cereals, candies, and other food with a high fat, sodium, or added sugar content. 50–52 As with all consumer goods marketers, food marketers are diverting budgets from television, print, radio, or outdoor advertising to more recent forms of communication on new media (including web sites, all types of video games, social networks, product placement, point-of-purchase advertising, etc.) and through packaging, direct marketing, public relations, and event sponsorship. 53 The message communicated in these ads is that eating these foods is normal, fun, and socially rewarding.

Given how much food marketers spend on communication, and particularly television advertising, it is surprising that a link between television advertising and energy intake is still perceived to be controversial by some. Some researchers contend that television advertising only affects brand preferences and not overall energy intake, while others demand an extremely high bar before any conclusion can be drawn. 54–56 Part of the explanation for the duration of the controversy is that, unlike other factors, such as price or portion size changes, advertising is a complex multi-dimensional intervention. Two campaigns can vary in their reach, frequency, scheduling, targeting, message strategy, and execution. In combination, this makes it difficult to conclusively estimate reliable effects using non-experimental real-world data.

Television viewing or television advertising? The correlation between television viewing and obesity is well established. Television viewing is associated with unhealthy snacking. Eating in front of the television also distracts, and therefore slows awareness of satiety. 57–59 Although television viewing also reduces calorie expenditures directly (by displacing physical activity) or indirectly (by advertising cars, games, and indoor toys that promote a sedentary lifestyle), studies suggest that the effects of television viewing on calorie expenditure are too weak to materially impact obesity. 57,60,61 Still, these studies cannot disentangle the effects of television viewing from the effects of television advertising .

One of the reasons it is difficult to estimate how television advertising influences energy intake is because there is very little natural variation in real-world exposure to television advertising for food, requiring one to make many statistical assumptions. In this context, probably the most convincing studies use real-world data from Québec's ban on television advertising aimed at children in French-speaking television networks. A first study 62 showed that the ban reduced the quantity of children's cereals in the homes of French-speaking children in Québec, but not for English-speaking children who continued to be exposed to the same amount of food advertising through US television stations. Another study 63 concluded that the Québec ban also significantly reduced fast-food consumption because French-speaking families in Québec with children eat less often in fast-food restaurants than English-speaking families with children, but no such difference are found between families without children or between French- and English-speaking families living in Ontario. These results are corroborated by other experimental studies in schools and summer camps, which showed that exposure to television advertising for unhealthy foods increased the likelihood that these foods would be chosen on a single consumption occasion as well as for longer time periods, and that the largest effects occurred among obese children. 64,65

In summary, reviews of this literature suggest that food advertising moderately influences the diet of children (though not of teens). There is not, however, enough evidence to rule out alternative explanations regarding its effects on obesity itself. 57,66–68 It is also suggested that food advertising interacts with other marketing factors, such as price promotions, and with factors not directly under the control of marketers, such as social norms, to influence obesity to a degree which would be very hard to establish precisely.

Branding and labeling effects

Food and ingredient branding. Branding is the creation of names, symbols, characters, and slogans that help identify a product and create unique positive associations which differentiate it from the competition and create additional value in the consumer's mind. 69 The name of the food (brand name or generic category name) has a strong influence on how consumers' expectations of how tasty, filling, or fattening the food will be, which are often uncorrelated with reality. 70,71 Well-known brands, but also simple descriptions like “succulent,” can influence taste expectations, consumption experience, and retrospective evaluations of the taste, and then lead to increased sales, especially for non-experts. 72–74 For example, a recent study 75 showed that branding the same food as a “salad special” versus “pasta special,” or as “fruit chews” versus “candy chews” increased dieters' perceptions of the healthfulness or tastiness of the food as well as its actual consumption. Interestingly, name changes had no impact on non-dieters and disappeared when dieters were asked to consider the actual ingredients (versus the name), and when looking only at dieters with a high need for cognition. Consumers also form expectations about the product from any attribute associated with the product, from the presence of licensed or brand-owned character, 53 to the firmness of its container. 76

Beyond the name of the food, communication about the nutrient composition and the presence (and number) of specific macro nutrients or ingredients (especially fat content, but also energy density, fiber, sugar content, unfamiliar long-worded ingredients, and so on) can strongly impact food expectations. 77–79 As with any communication, the framing of the information matters also for nutrition information. Food is perceived to be leaner and higher quality when labeled “75% fat-free” than “25% fat.” 36,80 For example, vinegar improves the taste of beer, but only when it is described as a “special ingredient,” not when it is described as vinegar, and only when the description is provided prior to the consumption. 81 This suggests that branding influences the interpretation of the sensory experience and does not just modify the retrospective interpretation of the experience. In fact, marketing descriptions of a milkshake as “indulgent” or “sensible” influences physiological satiation, as measured by gut peptide ghrelin. 82 Neuroimaging studies confirm that these marketing actions influence not just self-reported liking, but also its neural representations, suggesting that these effects are not merely influenced by social cues and that marketing actions modify how much people actually enjoy consuming the food. 25

Health and nutrition claims. Although nutrition and health claims are regulated, the decision of whether or not to use them rests with the food marketers. In past years, marketers have become increasingly likely to make heavy use of nutrition claims (including “low fat” or “rich in omega 3”), “structure-function” claims (“proteins are essential for growth”), health claims (“supports immunity”), vague unregulated claims or health sales (including “smart choice” or “good for you”), or the use of third-party ratings or endorsements (including “Kosher,”“Halal,”“organic,” or the heart check mark of the American Heart Association). Some of these claims can improve brand evaluation and sales, although these effects are not universal and are influenced by comparisons with other foods in the same category and by how they influence taste expectations. 43,83

Studies have shown that simpler, more prescriptive health claims, such as color-coded traffic lights, have stronger effects. 84,85 A field experiment found that simple color coding of cafeteria foods with a green, yellow, or red label (for “healthy,”“less healthy,” and “unhealthy” foods) improved sales of healthy items and reduced sales of unhealthy items. 86 Providing category benchmarks for each ingredient and nutrient (average or range) helps consumers process nutrition information, while summarizing information in a graphic format is particularly helpful for illiterate consumers. 87,88 Food marketers could also choose to provide information about recommended serving sizes (which is only mandatory in the United States). One study found that, although adding serving size information reduced granola intake for both overweight and normal-weight consumers, it had no impact if the granola was labeled as “low fat.” 89 The same authors found that promoting smaller serving sizes did not influence intake or satiety ratings, especially among overweight people. This could be because most consumers think that the entire content of the package is the appropriate serving size and perceive USDA serving sizes as an arbitrary unit designed to allow a comparison of nutrition facts across products, rather than as a general guide to how much people should consume. 90,91

Beyond evaluating whether health claims are scientifically true, an important question to examine is how they are understood by consumers. Recent reviews have identified many sources of confusion. 92–94 First, although the relationship between any nutrient and health is almost always curvilinear, consumers expect it to be monotonic (“more is better”). Second, consumers may not realize that they are already taking too much of a particular nutrient (e.g., protein intake in Western countries). Third, wording can be misleading; such as when “provides energy” is understood as “energizing.” Finally, some claims are based on flimsy science, or they overstate research findings. For these reasons, health claims are likely to become even more regulated, and to be only allowed for general products as opposed to specific brands, for example.

Health halos. The branding and labeling of food often operate by relying on people's natural tendency to categorize food as intrinsically good or bad, healthy or unhealthy, regardless of how much is eaten. 95 When branding and labeling efforts emphasize one aspect of the food as healthy, it can lead to a “health halo,” whereby people generalize that the food scores highly on all nutrition aspects, including weight gain. 96–98 In one study, 89 we found lower calorie estimations for granola than for M&Ms, a product with the same calorie density but considered less healthy than granola. The same study also found that labeling both products as “low fat” reduced calorie estimation and increased the amount that people served themselves or consumed, especially for people with a high body mass index. In another study, 96 we found evidence for health halos created by the name of a restaurant or the food available on a restaurant menu. For example, meals from the sandwich chain SUBWAY® were perceived to contain 21% fewer calories than same-calorie meals from McDonald's. These results were replicated with other foods and restaurant brands. 99

Related studies showed that adding a healthy food to an unhealthy food could lead to calorie estimations that were lower than for the unhealthy food alone. For example, one study found that a hamburger alone was perceived to have 761 calories but the same hamburger and a salad was thought to have only 583 calories. 100 This “negative calorie” illusion created by adding a healthy food to an unhealthy food is particularly strong among people who are on a diet. 101 Different biases, or contrast effects, occur when people estimate calories sequentially instead of simultaneously. 102

Overall, the finding that people expect that they can eat more, and do, when marketing actions lead the food to be categorized as healthy is robust and is replicated independently of people's BMI, gender, or restrained eating. 103,104 This boomerang effect seems to occur because people feel that they can eat more of the healthy food, or can eat more unhealthy, but tasty, food after choosing healthy food without guilt and without gaining weight. 96,105,106 In fact, simply considering the healthier option without actually consuming it, or forced choice of healthy food can be enough to allow some consumers to vicariously fulfill their nutrition goals, which makes them hungrier and entices them to choose the most indulgent food available. 107,108

To fully understand the effects of health claims, however, we must look at their impact on choice and purchase and not just on consumption volume when they are freely provided. When examining purchases, the results are mixed. First, studies have shown that people generally expect food presented as “unhealthy” to taste better, and that these effects persist even after actual intake, 109 although another study found this only among people who are not on a diet. 75 These results, coupled with the earlier findings that taste expectations are the strongest driver of food choices, imply that positioning food as healthy may not necessarily increase total energy consumption if the higher intake per consumption occasion is compensated by fewer consumption occasions (or fewer consumers).

The net effect of health claims probably depends on brand and individual characteristics, and is stronger for some claims than others. For example, differences in taste expectations about food, specifically when described as “low fat,” as opposed to branded as “healthy” in general, have been found between men and women, 110 and mostly influence unfamiliar brands. It is also unlikely to influence foods strongly categorized as healthy or unhealthy. This could explain the null effect of some of the studies and some of the earlier opposite findings. 111,112 The negative association between health and taste is less pronounced in Europe, where people tend to associate “healthy” with freshness and higher quality, and thus sometimes healthier can be tastier. 113,114

PRODUCT: HOW MARKETING STIMULATES INTAKE BY CHANGING THE FOOD ITSELF

Although marketing is most readily associated with communication and pricing, marketers are also closely involved with product development decisions. This includes making decisions about the “quality” of the food and also its “quantity.” The effects of changes in the product on overeating are summarized in Table 3 . This table also shows how some food marketers have found ways to mitigate these changes and provide avenues for further win-win strategies.

Product and consumer welfare.

Product quality: effects of the composition, sensory, and nutritional properties of the food

In addition to being a source of nourishment, food is a source of hedonic pleasure and stimulation. Hence, it is not surprising that one of the primary goals of food marketing is to improve the palatability of the food. At a basic level, palatability generally increases energy intake because people in rich countries can choose to eat only what they like. 115 Although improving palatability and the sensory and nutritional properties of food are largely driven by advances in food science, marketing plays an important role because it helps incorporate the expressed and latent desires of consumers and, above all, the role of perception. For example, advances in market research can correct for the fact that some people may not like a given amount of sweetness simply because they are not as sensitive to it as much as others or because they have a different interpretation of a scale label such as “extremely sweet.” 116,117 This is particularly important because taste perception and preferences are not the same for people with a high and low BMI. 118

Food composition. Flavor is a seamless combination of taste and predominately smell, but it is also enhanced by adding different layers of flavors; combining different forms (solid or liquid), textures, colors, or temperatures also influences flavor perceptions due to multisensory taste integration as well as consumers' expectations. 119–121 These factors can directly impact energy intake independent of their impact on flavor. People tend to consume more calories from liquid than from comparable solid foods of the same energy density because the lower bite effort and shorter sensory exposure postpone satiation. 122

Because people associate certain colors with certain foods and flavors, food marketers have long used colors to improve taste expectations. For example, some colors, especially those with strong flavor expectations, can influence the perceived sweetness of food and play a very important role in helping consumers discriminate between different foods, sometimes bigger than the role played by taste or brand information. 72,123 Even advertisements that evoke multiple sensory experiences can enhance taste perceptions. 124

Up to a certain level, adding sugar, fat, and salt, especially in combination, improves palatability, but does not increase the satiating power of the food in the same proportion. 125,126 Accordingly, food marketers have expanded the supply of food rich in fat or added sugar, such as sweetened beverages, which have accounted for a large proportion of the added supply of calories in recent decades. 127,128 Even though it is true that the percentage of calories consumed from fat has declined in the United States, this percentage decrease is the result of an increase in total energy intake; fat consumption itself has not decreased. 129 Interestingly, adding ingredients reduces the perception that the food is natural, which is an important criteria for food choices, whereas subtracting ingredients (e.g., skim milk) does not. 130

Food marketers have changed the composition of foods not just to increase palatability but also to respond to public concerns about a particular ingredient or to regulatory changes. Surprisingly perhaps, responses to mandatory nutrition labeling have been mixed. One study suggested that the Nutrition Labeling and Education Act of 1990 led food marketers to improve the level of taste-neutral positive nutrients, such as vitamins, in their core brands (especially those with a weak nutritional profile) and to introduce healthier brand extensions with similar levels of positive nutrients but with lower levels of negative nutrients, especially in junk food categories. 131,132 However, despite these advances, the average nutritional quality of food products sold in grocery stores had actually worsened compared to pre-NLEA levels and compared to similar food products unregulated by the NLEA. 132 This is largely driven by established brands, which account for a large portion of people's diet (e.g., dinner food) and whose nutritional quality has slightly deteriorated. This may be because companies are afraid of reducing levels of negative nutrients (e.g., fat or sodium) in their flagship brands for fear that it may decrease flavor expectations and because companies prefer to compete on taste rather than on nutrition, which can now be more easily compared.

Calorie density and sensory variety. The biggest share of marketing budgets, and most new product introductions, tend to be for calorie-dense foods with a variety of flavors. 2 Unfortunately from a public health perspective, it is well established that calorie density – the number of calories per unit of food – increases energy intake over the short term, such as during an afternoon snack. This happens because people prefer calorie-dense food and tend to eat the same volume of food regardless of its calorie density. 133–135 One of the explanations for this finding is that, instead of paying attention to internal signals of satiation, they focus on external signals, which are often biased. 136 In one study, unsuspecting diners were served tomato soup in bowls that were refilled from tubing that ran under the table and up into the bottom of the bowls. People with varying BMI levels eating soup from these “bottomless” bowls ate 73% more soup than those eating from normal bowls, but these diners estimated that they ate only 4.8 calories more. 137

It is well known that food variety, both within and across meals, increases consumption volume because it reduces sensory-specific satiety within a meal and it reduces monotony across meals. 138–140 The variety effect is independent of macronutrient content and energy density; it is also independent of individual characteristics such as gender, weight, and dietary restraints, and is only somewhat reduced with age. Research in marketing has focused on perceived (versus true) variety. It has shown that increasing the number of colors and the organization, duplication, and symmetry of an assortment can influence perceived variety, which then influences the perceived quantity of food and, ultimately, how much food is chosen. 141–144 Food marketers have explored many ways to increase perceived variety, including distraction, varying condiments, or giving people illusory choice over what they eat. 138

Wanting versus liking. Despite the links between sensory stimulation, palatability, and consumption, the availability of tasty, highly palatable foods is neither a necessary nor a sufficient cause of over-consumption. 145,146 While a highly satisfying meal can lead one person to not want to eat dessert, it can trigger the desire in another person. In fact, highly palatable food samples actually enhance subsequent consumption of similar foods and may prompt people to seek any other type of rewarding food. 147 Even then, people eat beyond the level at which their appetite is satisfied, which is why people eat and drink less when asked to focus on taste satisfaction. 148 Conversely, mental stimulation can create habituation. Simply imagining eating 30 pieces of cheese reduces consumption, increases satiation for the imagined food, and reduces subsequent wanting for the food, but not its hedonic liking. 149

More generally, there is converging evidence that food decisions are influenced by motivational “wanting”– the salience or reinforcement value of eating – and not just by hedonic “liking”– the pleasure derived from sensory stimulation. 150,151 So although there is no doubt that marketing has played a role in developing more complex, palatable, and rewarding foods which people cannot easily resist or stop eating, 2 the hedonic effects of sensory properties are again just one of many drivers of energy intake.

Product quantity: altering package and serving sizes

Trends in serving and package sizes. With the exception of some specific foods that must be sold in standardized sizes (e.g., wine and liquor), most food and beverage manufacturers are free to choose the size and description (e.g., “medium” or “value” size) of the packages and servings that they sell. Product package and serving sizes have grown rapidly over the past decades and are now almost invariably larger than the USDA recommended serving sizes. 152–154 While this is a trend in much of the developed world, such “supersizing” is particularly common in the United States and has been identified as one reason why obesity has increased faster in the United States than in other developed countries. 155–157

Larger package sizes almost always have lower unit prices (by volume or weight), except in the rare instances when there is more competition on the smaller sizes or when smaller sizes are used as loss leaders by retail stores. 158 Marketers can reduce the unit price of larger products and hence increase consumer value because of their lower packaging costs. More importantly, larger servings and packages provide greater absolute margins because the marginal cost of the extra food is often minimal compared to its perceived value for the consumer. For food retailers and restaurants with high fixed costs (such as high real estate, labor, or marketing costs), reducing serving sizes, and hence average consumer expenditure, would require a huge increase in traffic to break even – which is why the few restaurant chains that have tried this tactic have mostly stopped promoting these items or stopped offering them altogether. In fact, it can even be optimal for food marketers to price the incremental quantity below its marginal cost if their products are bought by two distinct consumer segments: one willing to pay more for smaller portion sizes that help them control their intake, and the other unconcerned about overeating and willing to buy larger quantities to obtain the lower unit price. 36,159 As a result, larger package sizes are typically more profitable for food marketers, and they benefit from a higher perceived economic and environmental value, a win-win in all aspects but convenience and consumption control.

Supersizing effects. There is considerable evidence that, with the exception of children under 3 years of age who still self-regulate naturally, larger package and serving sizes significantly increase consumption. 30,32,91,160–163 These studies have shown that the increased energy intake due to supersizing (as well as the decrease in energy intake due to downsizing) often reach a 30% change in calorie intake and are not followed by caloric compensation for up to 10 days. 164–166 Supersized servings can even increase the consumption of bad-tasting foods, such as stale 5- and even 14-day-old popcorn. 167,168

Even “virtual” serving sizes can influence consumption. Simply adding unobtrusive partitions (e.g., colored papers in between the cookies inside the package or a red Pringle chip between every seven yellow ones in a tube) can reduce intake. 169,170 However, partitioning may only work when people pay attention to the partition. One study 171 found that 93% of the purchasers of a king-size pack containing two single-serving candy bars intended to consume both within one day, often because they had not noticed that smaller sizes of candy bars were available for purchase. This is consistent with earlier results indicating that people take package size as a cue for appropriate serving size. 90,91

The effects of package size on consumption are strongly influenced by the range of the other sizes available and by the serving size chosen by other consumers. One study 172 found that people in hypothetical choice scenarios avoided the largest or smallest drink sizes. Such aversion to extremes causes consumers to choose larger size drinks when the smallest drink size is dropped or when a larger drink size is added to a set. Social modeling studies have shown that larger package and serving sizes can also have an indirect, passive, impact on energy intake, since people tend to imitate how much other people choose, particularly if the person that they have observed is not obese. 173–175

There are important exceptions to this rule, however. Small units of products such as 100-calorie packs may increase consumption volume on one consumption occasion more than regular-size packs for hedonic products and when people's self-regulatory concerns have been activated, or for restrained eaters. 176,177 These studies show that, unlike larger package sizes, small units “fly under the radar” and encourage lapses in self-control because the consumption of these small packages fails to activate healthy eating goals. However, these effects do not seem to hold for long periods, whereupon small sizes do lead to reduced calorie intake. 164,178

One of the explanations for why large packages and servings increase consumption is the social norm that people should clean their plate. 153,179 However, this norm cannot explain why large packages also increase the pouring of inedible products such as shampoo, cooking oil, detergent, dog food, and plant food. Nor does it explain why large packages of M&Ms, chips, and spaghetti increase consumption in studies where even the smaller servings were too large to eat in one sitting. 30,163,180 Another explanation is that larger serving sizes are used as an indication of the “normal” or “appropriate” amount to consume. Even if people do not clean their plate or finish the package, the large size presented to them gives them the liberty to consume past the point where they might otherwise stop with a smaller but still unconstrained supply. 91 This explanation is consistent with the finding that supersized servings increase energy intake even when people eat in the dark. 181 Other studies have shown that people associate larger servings with higher status and that people are therefore more likely to supersize when they want to signal status, for example, when they are made to feel powerless. 182

A final, and important, reason is that people are simply unaware of how large the supersized servings and packages are. 183,184 Information about food size, volume, or calorie content is not always easily available (such as in restaurants or at home once the food is no longer in its original packaging). Even in retail settings, where size information is available (on the front of the packages or on the shelf tags), few people read it, preferring to rely on visual estimations of the package's weight or volume to infer the amount of product that it contains. 185,186 Many studies have shown that people's perception of serving sizes is inelastic (it changes more slowly than it should). 187–191 On average, a 100% increase in serving size only looks like a 50–70% increase. As a result, whereas small servings tend to be accurately estimated, large servings are greatly underestimated. 188 These perceptual biases are very robust and even trained dieticians exhibit a strong diminishing sensitivity as the size of the meal increases. They are independent of the individual's BMI or interest in nutrition, and they have been replicated by other researchers across a variety of food categories. 99 Stated simply, meal size, not body size, explains serving size errors. People with a high BMI tend to underestimate their calorie intake more than people with a low BMI 192 because they tend to select larger meals, not because they are intrinsically worse (or biased) size estimators. 189

Size labeling. The size labels used for food and beverages (such as “short” or “large” and also “biggie” or “petite”) have acquired meanings among consumers, who are generally able to rank order them accurately. 193 In reality however, these labels mask huge discrepancies because a small size from one restaurant or brand can be larger than a medium size from another. 194 For example, McDonald's abandoned its supersize 42-oz beverages and 200-g fries, while other fast-food chains retained the serving size but simply renamed the “king” a “large.” 51,195 These labels are important because they influence size perceptions, preferences, and actual consumption. One study 196 found that “labeling down” (labeling a large serving “medium”) had a stronger impact on size perception than “labeling up” (labeling a small serving “large”). In addition, these authors found that smaller labels made people eat more but think that they eat less.

A few studies have shown that marketers can influence impressions of size by changing the visual representations on the package itself. Containers that attract more attention are perceived to contain more product. 197 Two recent studies 198,199 showed that people expected packages with pictures of the product on the bottom or on the right of the package to be heavier. Finally, simply showing more products on the packaging has been shown to increase size perception and consumption, especially when consumers are paying attention. 200 It is important to note that most of these studies were conducted in lab settings or in homes and not in in-store environments. Still, the key conclusion is that the quantity of food, and not just its quality, can have large effects on short-term intake and that consumers are largely unaware of these effects.

PLACE: HOW MARKETING CHANGES TO THE EATING ENVIRONMENT STIMULATE INTAKE

In the same way that food is more than nourishment, eating is more than food intake. It is a social activity, a cultural act, and a form of entertainment. Paradoxically, eating is also mostly a mindless habitual behavior that is strongly influenced by the environment, often without volitional input. 201,202 In this context, the most subtle and perhaps the most effective way marketing influences consumption is by altering the eating environment and making food accessible, salient, and convenient to consume. As for the other ways food marketing can influence overeating, Table 4 summarizes the key findings as well as existing and new solutions to reverse the effects of marketing changes to the eating environment.

Eating environment (place) and consumer welfare.

Access, salience, and convenience

Access. One of the biggest goals of food marketers is to facilitate access to food by making food easier to purchase, prepare, and consume. Obviously, food availability is a key factor since food that is not available cannot be consumed. 203 In addition, the sheer availability of a variety of palatable foods can derail the homeostatic system designed to regulate food intake. 2 For example, one study found that overweight men on a 3,000 calorie diet did not stick to their diet and consumed an average of 4,500 calories when given access to two free vending machines. 204 This pattern also holds for healthy foods. 205

On a more general level, convenient, ready-to-eat food is now available in many developed countries almost anytime, anywhere. One can buy food not only in restaurants, grocery stores, and coffee bars, but also in gas stations, pharmacies, kiosks, places of work, schools, and in the hospital. We can also have food delivered almost immediately at home or elsewhere. Food which used to be bought in small family-owned stores is now bought in small or large outlets belonging to multi-national corporations with strong marketing skills and vast resources. Improvements in the marketing and distribution of food, as well as food policies such as subsidies of calorie-dense sugar and starch, explain why the total supply of calories has increased tremendously since the 1970s, reaching 3,900 kcal per person and per day in the United States and between 3,400 and 3,600 kcal in other wealthy countries; the exception to this pattern is Japan, where food supply is only 2,700 kcal and where, not coincidentally, obesity is almost nonexistent. 4

It is true that the metabolism of obese people requires a higher calorie intake and hence that the increased supply of food is a consequence, and not just a cause, of rising obesity rates. 206 In addition, an increased part of the larger food supply is lost to waste and spoilage, although the estimates of how much is wasted vary between 25% and 40% of the food supply. 207,208 Still, the increased calorie supply cannot be attributed entirely to increasing food waste or to the higher energy requirements of heavier bodies. In fact, many prominent obesity researchers argue 4 that the rise in food energy supply is more than sufficient to explain the rise in obesity in the United States from the 1970s.

Access to food is greatly facilitated by the increased availability of ready-to-eat food prepared away from home, particularly in quick-service restaurants. Whereas spending on at-home food remained stable between 1982 and 2007, expenditure on away-from-home food in the United States increased by 16%, and now represents 49% of all food expenditures. 209 Econometric studies have suggested that the increased availability of fast food (but not full-service restaurants) is a strong predictor of local obesity trends. 18,210,211 Other studies show that proximity to grocery stores (but not to convenience stores) was associated with a lower BMI, possibly because grocery stores offer more healthful foods. 212 However, these findings were mitigated by a recent study 213 which showed that only the proximity to fast-food restaurant significantly influences BMI (particularly for women), whereas proximity to grocery stores or other restaurants does not seem to matter.

Salience. In today's cluttered stores and pantries, marketers know that availability, awareness, and even preferences are not sufficient to generate sales; food visibility must be maximized at the point of purchase and at the point of consumption. For example, eye-tracking studies 47,48 showed that simply increasing the number of facings on a supermarket shelf or placing familiar foods on top of the shelf (versus the bottom) increased the chances that these brands would be noticed, considered, and chosen. One study 214 found that making healthy foods easier to order at a fast-food restaurant by displaying them conspicuously on the menu led to a significant increase in sales. Displaying healthier food more conspicuously in cafeterias of school lunchrooms (by placing them on eye-level shelves and conveniently at various points in the cafeteria line) also increases their consumption. 86 Finally, another study conducted at a fast-food restaurant found that a stronger manipulation of salience, asking consumers whether they would like to downsize their side dishes, was accepted by one-third of consumers and was significantly more effective than calorie labeling. 215 Importantly, the smaller side dishes were not compensated by larger entrees.

The salience (or visibility) of food at home also increases energy intake. When jars of 30 chocolate candies were placed on the desks of secretaries, those in clear jars were consumed 46% more quickly than those in opaque jars. 216 Another study 32 showed that simply placing a food magnet on the refrigerator reminding people of food that they had bought in large quantities was enough to trigger consumption of ready-to-eat food. Spreading products in the pantry (versus stacking them) can increase people's awareness that the product is available and increase the likelihood of consumption. 187 The increased intake of visible foods occurs because their salience serves as a continuously tempting consumption reminder. While part of this may be cognitively based, part is also motivational. Simply seeing or smelling a food can increase reported hunger, devalue other goals, and stimulate salivation and consumption, even when sated. 147,217–219 Salience can also be generated by asking people to write a detailed description of the last time they ate soup or by asking them when they intend to eat. 220–222

Convenience. One of the strongest trends in food marketing is the focus on improving the convenience of food preparation and consumption. For most people, with the exception of specific festive occasions, food preparation is a cost of inconvenience that consumers are increasingly less willing to pay. 223 Food marketers have responded to the preference for improved convenience by reducing preparation time and increasing the share of ready-to-eat food. Supporting the role of convenience, studies have shown that increased consumption is largely driven by increased consumption frequency rather than by increased consumption quantity per meal. 223 The same study showed that between 1978 and 1996 energy intake increased more for snacks (+101%) than for breakfast (+16%), lunch (+21%), and dinner (−37%). The gains were highest among married women who now spend less time preparing food at home. This may also explain why maternal employment is associated with childhood obesity. 224 Convenience also explains the success of “combo” meals at fast-food restaurants, which combine a sandwich, a side, and a beverage. In fact, one study 225 showed that consumers place a higher value on a “bundled” combo meal, even after controlling for the effect of price discounts, because they reduce transaction costs and increase the saliency of the “featured” items on the menu board.

Convenience also interacts with other factors such as serving size and salience. In one study, 32 we stockpiled people's pantries with either large or moderate quantities of eight different foods. We found that stockpiling increased consumption frequency but only for ready-to-eat products, and that this effect leveled off after the eighth day, even though plenty of food remained in stock. Interestingly, we found that stockpiling increased the quantity consumed per consumption occasion of both ready-to-eat and non-ready-to-eat foods throughout the entire two-week period. With ready-to-eat foods, this was due to the higher visibility because of stockpiling.

Shape and size of serving containers

About 70% of a person's caloric intake is consumed using serving aids such as bowls, plates, glasses, or utensils. 226 The size of bowls and plates obviously influences energy intake for the 54% of Americans who say that they “clean their plates” no matter how much food they find there. 227 This can influence energy intake simply because people (and not just those who clean their plates) rely on visual cues to terminate consumption. If a person decides to eat half a bowl of cereal, the size of the bowl will act as a perceptual cue that may influence how much is served and subsequently consumed. Unfortunately, many of these cues are misleading. A number of studies have shown that people in Western societies overestimate the height of a cylindrical object (such as a drinking glass) compared to its width. 228–230 For example, one of these studies found that the elongation caused people to unknowingly pour and drink 88% more juice or soft drink into a short, wide glass than into a tall, narrow one of the same volume. 229

Another visual bias, the size-contrast or Delboeuf illusion, suggests that a given amount of product looks smaller on a larger plate than on a smaller plate. 231–233 A study showed that people who were given 24 oz. bowls of ice cream served and consumed about 20% more ice cream than those given 16 oz. bowls. 234 Larger serving containers increase consumption even when a constant amount of food is served on the bowl (versus people serving themselves). 30,163 On the other hand, other studies 235,236 found that using a smaller plate did not reduce energy intake in lab studies in which subjects were repeatedly eating the identical food in isolation.

Recent studies have started to link these results with work in psychophysics and to look at the interaction effects of size and shape on size perceptions and preferences. 237,238 An important finding has been that the lack of sensitivity to increasing sizes is even stronger when packages and servings increase in all three dimensions (height, width, and length) compared to when they only increase in one dimension. 191 This could explain why the effect is stronger for cups, glasses, and bowls (3D objects) than for plates (essentially 2D). The same authors have shown that because people underestimate volume changes that occur in three dimensions, they pour more beverage into conical containers (e.g., cocktail glasses where volume changes in three dimensions) than into cylindrical containers (where volume changes in one dimension). In addition, people's preference for supersizing is higher when products grow in one dimension. Although some studies have shown that part of these effects is mediated by attention, 180,197 other studies 190,239 suggest that they are mostly caused by people failing to compound the changes of multiple dimensions.

Atmospherics of the purchase and consumption environments

Retailers, restaurants, and food service companies can influence the ambient characteristics of the point of purchase and of the point of consumption (e.g., its temperature, lighting, odor, noise, and so on). Some atmospheric dimensions, such as temperature, have direct physiological effects. Studies have shown that people consume more energy when the ambient temperature is outside the thermo neutral zone, the range in which energy expenditure is not required for homeothermy. 240 For this reason, it has been argued that obesity could be linked to the reduction in the variability in ambient temperature brought about by air conditioning. 241 For example, consumption increases more during prolonged cold temperatures than in hot temperatures because of the body's need to regulate its core temperature. 242

Dimmed or soft lighting appears to influence consumption by lengthening eating duration and by increasing comfort and disinhibition. Harsh lighting makes people eat faster and reduces the time they stay in a restaurant, whereas soft or warm lighting (including candlelight) generally causes people to linger and likely enjoy an unplanned dessert or an extra drink. 243,244 Ambient odors can influence food consumption through taste enhancement or through suppression. 123,245 For example, one study 147 found that exposure to an appetizing odor increased soft drink consumption during movie-watching and that exposure to an offensive odor decreased consumption without people being aware of these effects.

The presence of background music is associated with higher food intake 246 and it is even linked with choice in supermarkets. In the context of restaurants, soft music generally encourages a slower rate of eating, longer meal duration, and higher consumption of both food and drinks. 247 When appealing music is played, individuals dine longer, feel more comfortable and disinhibited, and are more likely to order a dessert or another drink. 248 This is because when it improves affective responses (environmental affect, mood or arousal), background music reduces perception of time duration. 249 In contrast, when music or ambient noise is loud, fast, or discomforting, people tend to spend less time in a restaurant. 250 A recent meta-analysis found that music also influences shopping in a large range of retail contexts, that slower tempo, lower volume, and familiar music increase shopping duration, whereas loud, fast, disliked music increases perceived time duration. 251

All of these findings highlight the role of distraction in influencing consumption or intake volume. 58 For example, one study found that eating while watching TV or eating with friends (but not with strangers) impaired the ability to self-monitor, decreased the attention given to the food itself, and led to higher energy intake. 59 Other studies found that eating while distracted reduced satiation and impaired memory of past consumption, which reduced the time until the next eating episode. 252 Indeed, amnesiac patients have been found to eat the same meal multiple times in a row if they are told that it is dinner time. 253,254 Distraction influences taste perception (e.g., reduces sensory-specific satiety) and increases subsequent consumption volume by emphasizing the affective (versus cognitive) drivers of taste. One study 255 found that distraction while sampling food increased enjoyment as well as the subsequent choice of the relative vice (chocolate cake) versus the relative virtue (fruit salad).

Although one of the least studied ways marketers can influence consumption, the impact of the eating environment is powerful and multifaceted – and often overlooked by consumers. 201,256 Overall, these studies show that consumption volume is influenced by the eating environment, by facilitating access to the food, increasing its salience and the convenience of its preparation, but also by modifying the shape and size of serving containers as well as temperature, brightness, ambient odors, and music.

The food manufacturing and retailing industries have evolved tremendously and now include numerous innovative and fast-growing organization that are either nonprofit or with strong concerns for public health and the environment. 257 However, the majority of the food eaten in developed countries is still manufactured and distributed by traditional for-profit, and often publicly listed, companies. 258 For-profit food marketers are not focused on making people fat but on making money. In a free market, for-profit food companies that are less profitable than their competitors are likely to end up being acquired by their rivals or to go bankrupt. In this context, the mission assigned to most food marketers is to understand what different consumer segments desire and to profitably offer it to them. In general, what many people want in the short term is tasty, inexpensive, varied, convenient, and healthy foods – roughly in that order of benefit importance. The marketer's mandate is to help identify and create foods that deliver these benefits better; to communicate these benefits; to profitably package, price, and distribute these foods; and to protect these innovations by branding the food so that it acquires unique and positive associations in the mind of consumers. In this respect, food marketers have been very successful and have pioneered many marketing innovations now used in other industries.

Yet, as this review has shown, the vast ingenuity and resources of food marketers have created a myriad of ways in which food marketing influences consumption volume and, hence, may promote obesity. Although television advertising has attracted the bulk of the attention of researchers, it is merely the tip of the iceberg. It is neither the most innovative nor the most powerful way food marketing works, and its importance is declining.

To summarize how food marketing has made us fat, it is most likely through increased access to continuously cheaper, bigger, and tastier calorie-dense food. Two contentions are also offered here: 1) Researchers have overestimated the impact that deliberate decision-making has on food intake. For this reason, the effects of nutrition information, health claims, and informational advertising, have had a smaller impact than is believed. However, this probably does not apply to price and access to food, which are two important influencers of food intake that mostly operate through deliberate decision-making. 2) Researchers have underestimated the impact that peripheral factors and mindless habitual behavior have on food intake. For this reason, the effects of brand associations; calorie density and sensory complexity of food; the size and shape of portions, packages, and serving containers; and the convenience and salience of food stimuli in the eating environment. That is, the effects of the product and the place (the eating environment) have had a greater impact than believed.

Future research opportunities

Despite decades of work, what we presently know about how food marketing influences consumption is still dwarfed by what we do not know, creating many opportunities for impactful research and ensuring that no review will ever be complete and final. Yet, we should have realistic expectations regarding what research can do. This review shows that food marketing can influence consumption in many inter-related ways and that food consumption is governed by a complex set of dynamic interactions. In this context it is unlikely that any amount of research will be able to “prove” general statements such as “front-of-package health claims improve consumption decisions” because the magnitude and direction of the effects will depend on the implementation and will vary dynamically across consumer segments, consumption occasions, and the type of food studied.

One of the most important areas for future research, therefore, is to examine how the short-term effects reviewed here, which are often investigated only in single-consumption occasions in a lab, also hold when examined across time. Longer time horizons are particularly important because habituation and compensation can offset short-term effects. Ideally, these new studies would combine the best aspects of studies from 1) consumer research (including rich psychological insights and multi-method testing), 2) nutrition (including longitudinal designs, representative participants, biomarkers of calorie intake, and expenditures), and 3) health economics (including population-level interventions and analyses, and policy implications). As such, they would provide the necessary link between specific marketing actions, individual short-term food choices, and long-term population weight gain.

As shown in the tables, the factors leading people to eat more can also lead them to eat less, to promote consumption of healthier food, and more generally increase the importance people attach to health over taste, price, and convenience when making food decisions. For example, we have reviewed studies showing that consumption of healthy and unhealthy food responds similarly to price reductions, 22 that it is possible to incentivize children to prefer healthier food, 24 and that smart downsizing can lead people to prefer smaller servings. 191 In general, there is a wide range of profitable changes that businesses could make to help consumers eat better and eat less. What is important to understand is that these solutions need to fit both supply and demand in the food marketing value chain. In this respect, Tables 1 – 4 show that much of the leading thinking in this area of win-win approaches has been in food retailing, such as with supermarkets, cafeterias, and restaurants. Thanks to the longer time that consumers spend with food retailers, changes to their marketing have the highest potential to impact consumption.

Finally, it will be important to examine the interplay of marketing factors and cultural, social, and individual characteristics. Although obesity is a global problem, most of the studies reviewed here were conducted among North American consumers and often among undergraduate students. Yet, we know that culture, age, income, education, and a host of other socioeconomic factors influence food decisions. For example, there are important differences between how Americans, Europeans, and Asians approach food and eating. Beliefs that are taken for granted in a US context, for example, that unhealthy food is tastier or that external cues influence satiation, may not apply elsewhere. 113,114,136,259

Policy implications

After reviewing the studies outlined here, one may question the effectiveness of the policy changes being suggested by regulators. It is beyond the scope of this paper to examine all the policy interventions designed to fight obesity, and we need to be mindful of the many factors mentioned in the introduction that influence food decisions that are not under the control of food marketers. What this review underscores is that many such changes will come with either modest results or unanticipated results due to how consumers and companies respond. Consider mandatory nutrition information. As a rule, mandatory information disclosure has the intended effect when there is a consensus among consumers about the valence of the information. This occurs when an attribute (like trans-fats, or fibers) is universally seen as negative or positive. However, mandatory disclosure may backfire if the information is about attributes that are not uniformly valued – like calories, salt, fat, or sugar content – which are seen by some as a signal of rich taste. In this case, companies may actually choose to compete on less transparent attributes like taste and to target taste-conscious consumers. 132

By highlighting the effects of unobtrusive environmental factors on energy intake, the findings in this review support the current “small steps” approach to obesity prevention. 260 This approach recognizes that obesity is not a moral weakness but a normal response to the changing environment. As such, it stands in contrast with traditional public health efforts that have focused on providing science-based nutrition information and have exhorted people through didactic and sometimes moralizing appeals to change their dietary habits. A small steps approach focuses on adopting smaller, more sustainable goals. It recognizes that self-control is a limited and often absent resource and focuses less on persuasion and more on benevolent interventions that “nudge” consumers into making slightly better but repeated food choices without thinking about it. 261 This is done mostly by altering the eating environment, for example, by substituting calorie-dense drinks, like soft drinks, with water or diet soft drink in cafeterias, surreptitiously improving food composition, indirectly promoting smaller packages on menus (by eliminating quantity discounts and adding an extra small size to the range), storing tempting food out of reach and healthier alternatives within reach, using smaller cups and bowls, and pre-plating food instead of using family-style service. The small steps approach is not designed to achieve major weight loss among the obese but to prevent obesity for the 90% of the population that is gradually becoming fat by eating 60–100 calories too many per day. 262,263 It should be paired with smarter public education campaigns to rebrand health by associating it with stronger identity-based appeals, such as sustainability, animal welfare, or even national security. 264

Acknowledgments

The authors thank the editor and the reviewers for their help in the review process, as well as France Bellisle, Sybille Kranz, Jason Riis, Jennifer Harris, Margaret Sullivan, Erin Sharp, participants in the collective expertise on food behaviors organized by INRA (P. Etiévant, F. Bellisle, J. Dallongeville, F. Etilé, E. Guichard, M. Padilla, and M. Romon-Rousseaux), and participants in the Society for Nutrition Education and Behavior 2011 Preconference for their feedback. A less comprehensive review targeted to marketing scholars is available in Foundations and Trends in Marketing : Vol. 5: no. 3, 2010, pp. 113–196.

Declaration of interest.