Mobile app-etite: Consumer attitudes towards and use of mobile technology in the context of eating behaviour Journal of Direct, Data and Digital Marketing Practice December 2015, Volume 17, Issue 2, pp 114–129 | Cite as Allison E Doub (1) Email author ([email protected]
) Aron Levin (1) Charles Edward Heath (1) Kristie LeVangie (1) 1. Department of Human Development and Family Studies, Pennsylvania State University, United States Papers First Online: 16 November 2015 Received: 01 July 2015 Revised: 01 July 2015 5 Shares 1.9k Downloads 1 Citations
Abstract Understanding how consumers use mobile devices and mobile applications to support their eating behaviour, such as creating grocery lists, gathering meal ideas and ordering food from restaurants, is important for business, marketing and health professionals who seek to reach consumers through mobile technology. This study identified segments of users and non-users of food-related technology and described differences in their demographic characteristics, food-related app use and interest in food-related app functionality. Results revealed that 22 per cent of participants were highly engaged with technology and food, while just 12 per cent were disengaged from both. The remaining two-thirds of participants were evenly split between those who were engaged with technology and food generally but were ambivalent about food-related apps, and those who were utilitarian in their approach to food and disengaged from food-related apps. There were segment differences based on age such that younger adults (ages 18–34) were more likely to be engaged with technology and food than older adults (ages 55+). Technology and food-engaged segments reported the highest levels of use of select food-related apps, but even well-known apps were not highly used, indicating room for market expansion. Findings are discussed in the context of app development, digital advertising and nutritional health interventions.
Keywords mobile marketing eating behaviour mobile devices 2PhD is a Professor of Marketing and Director of the Marketing Research Partnership Program at Northern Kentucky University. His research focuses
on the impact of marketing strategies such as sports sponsorship and celebrity endorsements on consumers’ attitudes and behaviours. 3MBA is an Instructor of Marketing at Northern Kentucky University. His research explores beauty and aesthetics in the context of consumer
behaviour, e-commerce and social media marketing. Download article PDF
Introduction Internet-connected mobile devices (eg smartphones, tablet computers, smart watches) and mobile applications (‘apps’) are becoming ubiquitous parts of modern life, yet little is known about how consumers use mobile technology to support their eating behaviour, such as grocery shopping, meal planning, cooking and eating at restaurants. As of January 2014, nearly 60 per cent of adults in the United States owned a smartphone and 42 per cent owned a tablet computer. 1 At least half of adult cellphone users have downloaded apps onto their mobile device(s). 2 Apps are software programs that serve numerous functions, including communication, productivity, entertainment, shopping, social networking and tracking health behaviours. 3 Influencing food purchasing Previous research has described consumers’ general app use3, 4, 5 and their use of apps for health tracking 6 and weight loss.7, 8, 9 Fewer studies have examined app use in the context of everyday choices about purchasing and consuming food. In 2014, US consumers spent $765.1 bn at food-at-home retailers (eg grocery stores) and $624.8 bn on food-away-from-home retailers (eg restaurants). 10 Business and health professionals who seek to influence consumers’ food purchasing decisions through digital marketing would benefit from additional insight about the characteristics of users and non-users of food-related mobile applications. The current study addressed this gap in the literature by conducting an online survey with a large, diverse sample of adult mobile device owners.
Consumer engagement with mobile devices and apps Growing consumer spend on apps There is a substantial and growing market for mobile apps. 11 Most commercially available mobile devices come pre-installed with several apps (eg internet browsing, calendar, weather) and users can download additional apps from distributors such as Google Play, Apple’s iOS App Store and the Amazon AppStore. At the end of 2014, Google Play offered ~1.43 million apps for consumers to download, Apple’s iOS App store offered ~1.21 million and the Amazon Appstore offered ~293,000. 12 One study recently found that mobile device users downloaded an average of nine apps per month. 13 Apps may generate revenue through pay-per-download, in-app purchases and/or in-app advertising models. 11 During the first week of January 2015 alone, consumers worldwide spent nearly $500 m in Apple’s App store. 14 Furthermore, overall revenues from mobile content, including mobile applications, has been estimated to surpass $65 bn by 2016. 15 Daily demands of feeding As apps become increasingly diverse, understanding consumer attitudes towards and use of specific categories of apps, including food-related apps, becomes increasingly important to business, marketing and health professionals trying to engage new and existing consumers.16, 17 There is scant empirical evidence on how the internet and mobile technology support consumers in meeting the daily demands of feeding themselves and their families, such as using digital displays to facilitate recipe preparation 18 or ordering at restaurants.19, 20 Research is needed that describes consumers’ attitudes towards and use of food-related mobile technology and apps, such as apps that support users in planning, purchasing and socially sharing meals and snacks. Food and drinks are popular topics on social media and are the top content shared on Pinterest,21, 22, 23 which can be accessed via mobile applications. In 2012, 60 per cent of adults reported tracking their weight, diet or exercise routines, but less than 10 per cent of adults reported using their mobile device to do so, 6 suggesting that this category of app has market expansion potential. Decision to download and use apps Previous research suggests that demographic characteristics of consumers are associated with the likelihood of downloading apps. Younger, more educated, wealthier and non-rural-dwelling adults are more likely to download apps, as are Black, non-Hispanic adults. 2 In 2013, 77 per cent of 18– 29 year olds in the United States downloaded apps, compared to 59 per cent of 30–49 year olds, 33 per cent of 50–64 year olds and just 14 per cent of adults over the age of 65. 2 Less is known about the mechanisms that underlie consumers’ decisions about whether to download and consistently use food-related apps. Predictors of intention to use Theoretical frameworks offer more mechanistic insight into why some, but not all, consumers adopt new technologies such as purchasing mobile devices or downloading apps. The Technology Acceptance Model 24, 25 (TAM) and its extensions, unified theory of acceptance and use of technology (UTAUT 26 and UTAUT2), 27 suggest that the following factors predict consumer intention to engage with new technologies: value, ease of use, social norms and pressures, resources available to the individual, hedonic motivation, perceived price value, previous experience, and habit. These factors predicted behavioural intention to use mobile internet technology in a sample of 1,512 adults in Hong Kong, 27 for example. Curiosity and desire for knowledge Similar to the TAM and its extensions,24, 25, 26, 27 consumer values theory 28 proposes that purchase intent is predicted by perceived utility (ie practical value), social influence and hedonic motivation. Consumer values theory goes on to suggest that purchasing behaviour is influenced by epistemic values, meaning that consumers are likely to purchase products that stimulate curiosity and/or help them acquire knowledge. 28 Consumer values theory also asserts that values are context-specific (eg a restaurant finder app may be more useful in a new city than it is in a familiar one). 28 Wang et al. 29 found support for the consumer values framework such that conditional value predicted behavioural intention to use mobile applications as mediated by functional, social, emotional and epistemic values in a sample of 282 primarily young adults in Taiwan. Together, these theories suggest that information about consumers’ attitudes (ie perceived values) and behaviours (eg previous experiences with apps, habits) may be more useful than demographic factors alone in explaining why some but not all consumers engage with food-related mobile technology and applications.
Study aims Segments and profiles of users The first goal of the current study is to use a segmentation analysis approach 30 to identify discrete segments of mobile device users based on their self-reported attitudes and behaviours related to technology in general, food and nutrition topics, and internet, mobile device and app use in food contexts. Our second goal is to describe the segment profiles based on their demographic characteristics, use of well-known food-related apps, and attitudes towards food-related app functionality and mobile digital marketing. Potential applications of this study include identifying potential customers who could be targeted through marketing efforts for new and existing apps; informing the development and redesign of food-related apps; generating novel ideas for in-app advertising; and identifying intervention opportunities to improve nutritional health.
Methodology Participants An external sampling company, Survey Sampling International LLC, recruited 615 participants between 29 December 2013 and 2 January 2014. Eligible participants were 18 years of age or older, used a mobile device, such as a smartphone or tablet computer, and had at least one app installed on their primary mobile device. Recruitment priority was given to individuals who were at least 75 per cent responsible for the grocery shopping and meal planning for their household. Participants were recruited to reflect approximately the 2014 United States Census Bureau statistics on gender, age, race/ethnicity and geographic region. 31 Participants also reported their marital status, the number of children in the household, education and household income. Descriptive statistics are reported in Table 1. Table 1 Participant demographic characteristics Demographic attribute:
N=615 n (per cent)
18–24 years of age
25–34 years of age
35–44 years of age
45–54 years of age
55–64 years of age
65+ years of age
Children<18 years in household Yes
High school diploma or less
Some graduate work or post-grad degree 117 (29)
Less than $35,000
Prefer not to answer
Mobile device and application use Measures Participants reported what type of mobile device(s) they owned, the number of mobile applications they had on their primary mobile device, and whether they had a favourite food-related app.
Segmentation variables A series of questions assessed participants’ current attitudes and behaviours related to food, technology and the use of mobile applications in food contexts (eg preparing meals and snacks, grocery shopping, dining out). All items were rated on a five-point Likert scale ranging from ‘Strongly agree’ (1) to ‘Strongly disagree’ (5) or ‘Always’ (1) to ‘Never’ (5). Consistent with the previously described theoretical models24, 25, 26, 27, 28 and supporting empirical research,27, 29 this study segmented participants based on their responses to these questions. Pearson correlations and factor analyses were run separately for attitudinal and behavioural items to identify cohesive groups of items that measured the same constructs and to eliminate items that performed poorly. Attitudinal and behavioural factors The correlation and factor analysis results supported two attitudinal factors: Attitudes towards technology (eight items; Cronbach’s =0.87; eg ‘Technology helps make my life more organized’.). Attitudes towards food and nutrition (six items; Cronbach’s =0.82; eg ‘I enjoy seeking out new recipes’.). And three behavioural factors: Digital food exploration (eight items; Cronbach’s =0.92; eg ‘I post pictures of dishes that I cook myself on my social networking profile’.). Digital food-related information seeking (five items; Cronbach’s =0.83; eg ‘I look up reviews of restaurants before deciding whether or not to try them’.). Food-related mobile device and app use (16 items; Cronbach’s =0.96; eg ‘I use more than one app while grocery shopping’. See Appendix .) Subscale mean scores were calculated for each participant. Segmentation profile variables Participants reported their demographic characteristics and their awareness and use of five popular food-related mobile applications: Pinterest, Instagram, allrecipes, OpenTable and Yelp. Reponses were categorical and included ‘Currently use regularly’, ‘Have on my device, but rarely use’, ‘Have heard of, but have not used’ and ‘Have never heard of/Not familiar’. These five apps were selected because they represent a range of possible food-related behaviours: exploring and saving food and drink-related web content (Pinterest); socially sharing ones’ own and browsing others’ personal food and drink photos (Instagram); searching for recipes (allrecipes); exploring and placing reservations at restaurants (OpenTable); and discovering new food and drink locations through geo-location and reading reviews (Yelp). Interest in food-related apps Participants responded to five hypothetical scenarios relevant to food-related mobile applications and marketing. Questions probed participants’ attitudes towards: Mobile ordering: ‘Imagine that you are standing in line at your favourite fast food restaurant. How interested would you be in the ability to order items from your mobile device?’ Mobile payment: ‘How interested would you be in the ability to pay your restaurant or grocery bill using ONLY your mobile device?’ Product source: ‘Imagine you are in the produce aisle looking for some fresh fruit to make your favourite dessert. How interested would you be in the ability to scan a quick response (QR) or bar code and learn about the origin/source of the product you are considering?’ Product-specific recipe ideas: ‘How interested would you be in the ability to scan a QR or bar code and pull up a new recipe using the ingredient you were scanning?’ and Nutrition-based product recommendations: ‘Imagine you are in the snack aisle looking for something to purchase. How interested would you be in an app that recommends healthier options based on the item you are thinking about purchasing?’ Responses were rated on a five-point scale, ranging from ‘Very interested’ (1) to ‘Not interested at all’ (5).
Analyses Descriptive statistics Calculating clusters and profiles Frequencies, means and standard deviations were calculated for the demographic, mobile device use and app use variables.
Segmentation analysis A two-stage cluster analysis procedure was used. 32 First, a hierarchical cluster analysis (Ward’s method) was performed to approximate the number of clusters. 33 Second, two possible cluster solutions were explored using k-means clustering. 34 The final number of clusters was decided based on conceptual clarity of the cluster centres.
Segmentation profiles Chi-square tests of independence examined segment differences based on demographic characteristics and food-related app use. To explore whether segments differed in their interest in food-related mobile application functionality or mobile marketing, a one-way ANOVA with post-hoc Bonferroni contrasts analyses was performed. Analyses were conducted using IBM SPSS version 22.
Results Mobile device and application use Smartphones were the most common mobile device, used by 91 per cent of participants (n=559), with 48 per cent (n=294) owning Android phones, 35 per cent (n=215) owning iPhones and less than 10 per cent owning Blackberry, Windows or other brands of smartphones. Sixty-five per cent of all participants (n=401) owned a tablet and 38 per cent (n=233) owned an eReader. Participants reported an average of 25.59 apps on their primary mobile device (SD=26.80; Range=0–200). Seventy-three per cent of participants (n=449) reported having a food-related app on their mobile device.
Segmentation analysis A four-cluster solution The results of the two-step clustering procedure supported a four-cluster solution. The k-means cluster centres for the attitudinal and behavioural constructs are shown in Table 2. Table 2 K-means cluster centres by segment on attitudes and behaviours involving technology and food Segment name
Cluster center (M) values Attitudes towards Attitudes towards food technology and nutrition
Digital food exploration
Digital food-related information seeking
Food-related mobile device and app use
App-disengaged Food Utilitarians (n=205)
Food-focused App Experimenters (n=203)
App-engaged Food Lovers 1.41 (n=132)
App- and Food-disengaged 3.56 (n=75)
Note: Responses were rated on a five-point scale, ranging from ‘Strongly agree’ (1) to ‘Strongly disagree’ (5) or ‘Always’ (1) to ‘Never’ (5). Thus, lower scores represent higher levels of endorsement; (N=615). App-disengaged Food Utilitarians Segment 1 contained 33 per cent of participants (n=205). This segment was characterized by slightly favourable attitudes towards technology and food and nutrition topics, and sometimes-to-often using technology to seek out information about food. However, they rarely used digital technology to explore or socially share food, and rarely used food-related apps. Due to participants’ seemingly utilitarian views towards the intersection of food and mobile technology, this segment was labelled ‘App-disengaged Food Utilitarians’. Food-focused App Experimenters Segment 2 contained 33 per cent of participants (n=203). These participants reported highly favourable attitudes towards technology and food and nutrition topics (eg enjoy experimenting with new foods), and reported often using digital technology to seek information about food (eg read restaurant reviews). However, they only sometimes used mobile devices and apps to explore and socially share food, and only sometimes using apps to support day-to-day eating behaviour. For these reasons, this segment was labelled ‘Food-focused App Experimenters’. App-engaged Food Lovers Segment 3 contained 22 per cent of participants (n=132). These participants reported highly favourable attitudes towards technology in general (eg enjoy the convenience of online ordering) and food and nutrition topics (eg believe that food preparation is an expression of art). They also reported often-to-always using digital technology to explore and share food ideas (eg share food photos on social media) and to seek information about food (eg read product reviews). They often used apps in the context of everyday eating behaviour (eg use apps to create a grocery list, plan weekly menus, check prices in store, share product reviews). Thus, they were labelled ‘App-engaged Food Lovers’. App- and Food-disengaged Segment 4 was the smallest segment and contained 12 per cent of participants (n=75). These participants reported ambivalent-to-negative attitudes towards technology and food and nutrition topics (eg disagree that meal time is a social event), and reported sometimes-to-rarely using technology to gather information about food. They also reported rarely-to-never using mobile technology to explore or share food, and rarely-to-never used foodrelated apps. As such, they were labelled ‘App- and Food-disengaged’.
Segmentation profiles Demographics Segments were not significantly different based on gender, race/ethnicity, household income or marital status (data not shown); however, there were significant differences in age (see Table 3). More 18–34 year olds (‘Millenials’) were classified as ‘Food-focused App Experimenters’ and ‘Appengaged Food Lovers’, whereas more older adults ages 55–64 and 65+ were classified as ‘App- and Food-disengaged’ or ‘App-disengaged Food Utilitarians’ (p<0.001). Table 3 Results of chi-square test and descriptive statistics for segment by age Segment name
Age n (per cent)
18–34 35–44 45–54 55–64
App-disengaged Food Utilitarians (n=205) 48 (23) 40 (20) 35 (17) 53 (26) 29 (14)
Food-focused App Experimenters (n=203) 90 (44) 47 (23) 30 (15) 32 (16) 4(2.0)
App-engaged Food Lovers (n=132)
53 (40) 36 (27) 29 (22) 11 (8) 3 (2.3)
App- and Food-disengaged (n=75)
10 (13) 17 (23) 26 (35) 16 (21)
Notes: X2 (12, n=615)=100.07, p<0.001, Cramer’s V=0.23; (N=615). Presence of children There were also significant segment differences based on whether children below 18 years of age were present in the household (p<0.001). This relationship maintained significance even when only participants between 18 and 44 years of age were included, suggesting that having children in the household did not simply reflect age differences. More participants 18–44 years of age who reported having children in the household were ‘Appengaged Food Lovers’ and fewer were ‘App-disengaged Food Utilitarians’ compared to participants 18–44 years of age who did not have children in the household (see Table 4). Table 4 Results of chi-square tests and descriptive statistics by segment based on children in the household Segment name
Children<18 years of age in the household n (per cent)
App-disengaged Food Utilitarians (n=88) 36 (20)
Food-focused App Experimenters (n=137) 66 (38)
App-engaged Food Lovers (n=89)
App- and Food-disengaged (n=16)
Notes: X2 (12, n=330)=20.86 p<0.001, Cramer’s V=0.25); Limited to participants 18–44 years of age (n=330). Food-related app use There were significant segment differences in their awareness and use of Pinterest, Instagram, allrecipes, OpenTable and Yelp (see Table 5). Consistent with the attitudinal and behavioural responses that classified them by segment, ‘App-engaged Food Lovers’ reported the highest levels of current use across all apps examined, followed by ‘Food-focused App Experimenters’. ‘App-disengaged Food Utilitarians’ and ‘App- and Food-disengaged’ participants rarely-to-never used these apps. Even among the most engaged segment, ‘App-engaged Food Lovers’, current use of these food-related apps peaked at only 55 per cent. OpenTable appeared to have particularly low current use, and was endorsed by just 1–26 per cent of each segment. Table 5 Results of chi-square test and descriptive statistics by segment based on current use of selected food-related mobile applications n (per cent)
Segment name Currently use Pinteresta
Currently use Instagramb
Currently use allrecipesc
Currently use OpenTabled
Currently use Yelpe
App-disengaged Food Utilitarians (n=205)
Food-focused App Experimenters (n=203)
App-engaged Food Lovers (n=132) 61 (46)
App- and Food-disengaged (n=75)
aX2 (12, n=615)=148.62, p<0.001, Cramer’s V=0.28). bX2 (12, n=615)=196.16, p<0.001, Cramer’s V=0.33). cX2 (12, n=615)=201.84, p<0.001, Cramer’s V=0.33). dX2 (12, n=615)=130.41, p<0.001, Cramer’s V=0.27). eX2 (12, n=615)=190.88, p<0.001, Cramer’s V=0.32).
Notes: Numbers in parentheses indicate column percentages; (N=615). Interest in food-related apps and mobile marketing There were significant segment differences in their interest in food-related app functionality and mobile digital marketing (p<0.001). ‘App-engaged Food Lovers’ reported the highest level of interest across all categories — they were somewhat-to-very interested in using mobile technology and/or apps to facilitate ordering food, paying for food, learning the source of ingredients within products, obtaining product specific recipe ideas and getting nutrition-based product recommendations. ‘Food-focused App Experimenters’ were somewhat interested in these features, while the ‘Appdisengaged Food Utilitarians’ and ‘App- and Food-disengaged’ segments were neither interested nor disinterested, or somewhat disinterested. Across all segments, participants reported the most interest in apps that would facilitate ordering food.
Discussion and future directions Segments and mobile behaviour The goal of this study was to identify and describe segments of consumers who were using or not using mobile technology and applications in the context of typical eating behaviour (eg during dinner preparation, while grocery shopping, to gather recipe ideas, to share food photos on social media). We identified four unique consumer segments based on their attitudes towards technology; attitudes towards food and nutrition; use of the internet and mobile devices to explore and socially share food; use of the internet and mobile devices to seek information about food/restaurants; and use of mobile devices and apps to support everyday food-related tasks. Engaged with technology and food Twenty-two per cent of participants were characterized as having highly-favourable attitudes and behavioural engagement with technology and food — they enjoyed activities such as sharing photos of their meals online with their social networks, browsing the web for recipes, using apps while grocery shopping and ordering food from their mobile devices. This segment may perceive food-related apps as being easy to use, practical, enjoyable and social. These individuals may be among the first to target for interventions that aim to improve nutritional health through apps and mobile websites, for example by making healthy food options among the easiest to order, promoting social sharing of nutritious meal selections, and allowing customers to learn more about the ingredients and nutrition information of their meal choices. Promoting healthy options Thirty-three per cent of participants were classified as being interested in food (eg reading restaurant reviews, socially sharing food photos, recipe browsing), but were only occasionally using apps to support these food-related interests. This segment may respond to mobile digital marketing or nutritional health interventions that are situated within the contexts that they are already going to, for example, including branded food products within recipes that are shared on familiar recipe websites (eg allrecipes) or partnering with social media influences (eg highly followed Instagram or Pinterest users) to promote healthy foods and evidence-based nutrition information. Practical value of apps Another 33 per cent of participants were not highly interested in exploring food online or using food-related apps, yet reported somewhat favourable attitudes towards technology generally and used online sources to find out more information about food (eg read product and restaurant reviews). This segment may be most interested in food-related apps that are perceived to have practical or price value, such as saving time or money. These individuals may also be motivated to adopt mobile applications that help them easily meet health-related goals, such as tracking their weight, counting calories, or time-efficient, healthful meal planning. Pinterest and Instagram Recent data from the Pew Research Center suggests that Pinterest and Instagram are increasingly popular social media platforms. 35 Our results suggest that approximately one in two ‘Food-engaged App lovers’ used the mobile apps for Pinterest and Instagram, while approximately one in three ‘Foodengaged App Experimenters’ used these apps. Pinterest and Instagram, which have functional, social and hedonic value, may be platforms that business, marketing and health professionals could target first to reach consumers with food-related marketing. Parents more engaged than non-parents Consistent with previous research on mobile application use 2 and social media engagement 35 in the United States, younger participants were more engaged with technology and apps in the context of food. However, our findings also suggested that among younger adults, parents (ie individuals living with children below 18 years of age) had more favourable attitudes towards technology and food compared to non-parents. Feeding children is an important and potentially time-consuming parenting task with important implications for children’s health and weight status, particularly during infancy and early childhood.36, 37 Parents may be more engaged with technology and food compared to non-parents because they have the added responsibility of feeding their children as well as themselves. Future studies should continue to explore this relationship, as parents have expressed interest in mobile devices as a way to learn more about feeding their children, which may be an opportunity to intervene for childhood obesity prevention. 38 In-store technology In a 2015 report, the National Restaurant Association identified in-store tablet computers and mobile applications as technology trends for consumers, restaurateurs and chefs. 39 Our findings suggest that consumers may be most interested in apps that facilitate food ordering. 40 This has important implications for health policies related to menu labelling requirements regarding nutrition information. Posting information about the caloric, fat and sugar content of meals within the check-out or final ordering screen with opportunities to swap items for healthier choices may be one opportunity to improve nutritional health. Another mobile ordering feature could show consumers how their order compares to federal nutritional guidelines to encourage customers to select healthier meals. Future studies should identify consumer segments that are currently using or interested in using apps that target nutrition, as well as what app functionalities predict sustained healthy eating behaviour. 41 Influencing health choices Only 12 per cent of participants were characterized as disengaged from both technology and food, which suggests that developing and marketing mobile application functionality or mobile digital marketing campaigns that appeal to users’ interest in either domain is likely to have broad appeal. 11 That said, professionals aiming to improve health might need to assess individuals’ interest in mobile technology prior to recommending apps for nutritional health. Future research should explore whether there are specific mobile application functions that would make consumers who are currently ambivalent about apps intend to use them, or whether other factors such as perceived ease of use or price values, which were not fully explored in this study, are more important. Finally, additional research should investigate how a combination of mobile touch points (eg a coupon for a free item, mobile ordering and mobile payment) influences food purchasing decisions. 42
Strengths and limitations Highly food-involved consumers This study is among the first in the academic literature to explore the intersection of mobile technology and typical eating behaviour. This theoryand data-driven research provides critical foundational knowledge of consumers’ attitudes towards and use of mobile devices and apps in food-related contexts upon which future studies may build. 43 There are a few limitations of this study that should be considered when interpreting the results. Participants were recruited based on their responsibility for meal planning and grocery shopping in their household; thus, these results may only be generalized to individuals who are highly involved with household food purchasing decisions. This study relied on self-report data for mobile application use, rather than data collected from mobile devices, which would be a more precise method of measurement. Given that this is a novel area of research, the questionnaire items were developed specifically for this project and would benefit from further validation, even though Cronbach’s met and exceeded acceptable levels (>0.70). Measuring technology use and eating Additionally, attitudinal and behavioural constructs were moderately-to-highly correlated (Pearson’s r ranging from 0.58 to 0.87, p<0.001), suggesting that we segmented participants based on closely related constructs. Future studies should develop measures of consumers’ use of mobile technology specifically to support eating behaviour based upon the constructs included in the TAM,24, 25 its extensions26, 27 and consumer values theory,28, 29 such as practical value, price value, perceived ease of use, social norms and pressures and previous experience.
Conclusions Mobile marketing opportunities Mobile devices and apps are increasingly part of daily living, including contexts in which consumers make decisions about what, where and how much to eat or feed their families. As consumers adopt apps to support them in purchasing, preparing and consuming food, it is important that business, marketing and health professionals understand how apps impact consumer choices. Our findings suggest that consumers who are highly engaged with mobile technology and food are most likely to be young adults and parents. Across all consumers, mobile ordering was an appealing functionality. The results from this study may be applied to strategic digital marketing campaigns and the development of new mobile applications within the business and health promotion sectors.
Notes Acknowledgements The authors thank the research participants and all members of the Curiosity InsightStream research team who contributed to the collection of these data. This study was reviewed and approved as exempt by the Institutional Review Board at Northern Kentucky University and the Institutional Review Board at Pennsylvania State University. Source of support: Support for manuscript preparation was provided to AED by Agriculture and Food Research Initiative Grant no. 2011-67001-30117 from the USDA National Institute of Food and Agriculture, Childhood Obesity Prevention Challenge Area — A2121. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE1255832. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the USDA or the NSF. Contributions. Allison E. Doub was primarily responsible for the research questions, data analyses, interpretation, and drafting and revising of the manuscript. Aron Levin shared responsibility for the development of the research questions and contributed to the interpretation of the analyses and drafting and revising of the manuscript. C. Edward Heath contributed to the interpretation of the analyses and drafting and revising of the manuscript. Kristie LeVangie was responsible for the conception and design of the larger research study from which the data for the current study were drawn, including participant recruitment, measure development and data provision to Allison E. Doub and Aron Levin. All authors read and approved the final manuscript.
Appendix Table A1
• I am willing to take surveys on my mobile device.
• I am interested in watching videos on my mobile device.
• I like to keep my personal pages updated with information about my life.
• I wish there was one app for all my needs. Attitudes towards technology a • Technology helps make my life more organized.
• My mobile device is an extension of my personality.
• I like the convenience of being able to make reservations from my mobile device.
• I like the convenience of being able to place an online order using my mobile device.
• I enjoy seeking out new recipes.
• I like to experiment with new foods.
• Mealtime is a social event. Attitudes toward food and nutrition a • Food preparation is an expression of art.
• It’s important for me to be able to look up the nutritional content of restaurant or grocery items.
• The source where my food is coming from is important to me.
• I post pictures of dishes I have in restaurants on my social networking profile(s).
• I post pictures of dishes that I cook myself on my social networking profile(s).
• I contribute to a food blog.
• I look for inspiration on Pinterest and other social media sites. Digital food exploration a • I track my food intake on my mobile device (ie keep a food diary).
• I use my mobile device to locate local farmers’ markets.
• I use my mobile device to locate healthier meal options.
• I look for apps that help me manage my weight.
• When I find a food item I like, I typically recommend it to people I know.
• I look up reviews of restaurants before deciding whether or not to try them. Digital food-related information seeking a
• I look up recommendations of dishes within restaurants I visit before deciding whether or not to try them.
• I look up menus using my mobile device for restaurants I am considering or planning to visit.
• Before purchasing a product online, I typically read online reviews submitted by others.
• I use more than one app when grocery shopping.
• I use more than one app for my dining out experience.
• I look actively for the next great food app.
• I scan QR codes on products or advertisements that interest me.
• I share my opinions about products and services by posting reviews and ratings online.
• I plan a weekly menu on my mobile device before heading to the grocery store.
• I create a shopping list on my mobile device before heading to the grocery store.
• I monitor my food’s expiration dates using my mobile device. Food-related mobile device and app • I use my mobile device to locate food items on the shelves within my preferred store. useb • While in the store, I check item availability and pricing at other locations.
• While in the store, I research information about products I am thinking of purchasing.
• While in the store, I call or text a family member or friend about a product I am thinking of purchasing.
• I look up reviews of products I am thinking about purchasing while I am in the store.
• While in the store, I purchase items on my mobile device if I find them at a better price somewhere else.
• I use my mobile device to look up measurement conversions while cooking (ie # of teaspoons in a tablespoon).
• I use an app that allows me to store all of my shopper card information in my mobile device for easy access when shopping. aPrompt: ‘How much do you agree or disagree with the following statements?’ Rating scale: 1=‘Strongly agree’; 2=‘Somewhat agree’; 3=‘Neither
agree nor disagree’; 4=‘Somewhat disagree’; 5= ‘Strongly disagree’. bPrompt: ‘Thinking about the following statements, please indicate how frequently or infrequently you do each action’. Rating scale: 1=‘Always’;
2=‘Often’; 3=‘Sometimes’; 4=‘Rarely’; 5=‘Never’.
References Pew Research Center. (2014) Device ownership over time. Pew Research Center’s Internet and American Life Project. http://www.pewinternet.org/data-trend/mobile/device-ownership/, accessed 9 January 2015. Google Scholar (http://scholar.google.com/scholar? q=Pew%20Research%20Center.%20%282014%29%20Device%20ownership%20over%20time.%20Pew%20Research%20Center%E2%80%99s%20 Internet%20and%20American%20Life%20Project.%20http%3A%2F%2Fwww.pewinternet.org%2Fdata-trend%2Fmobile%2Fdeviceownership%2F%2C%20accessed%209%20January%202015.) Duggan, M. (2013) ‘Cell phone activities’. Pew Research Center’s Internet and American Life Project. http://www.pewinternet.org/files/oldmedia/Files/Reports/2013/PIP_Cellpercent20Phonepercent20Activitiespercent20Maypercent202013.pdf (http://www.pewinternet.org/files/oldmedia/Files/Reports/2013/PIP_Cellpercent20Phonepercent20Activitiespercent20Maypercent202013.pdf), accessed 9 January 2015. Verkasalo, H., López-Nicolás, C., Molina-Castillo, F. J. and Bouwman, H. (2010) ‘Analysis of users and non-users of smartphone applications’, Telematics and Informatics, Vol. 27, No. 3, pp. 242–55. CrossRef (http://doi.org/10.1016/j.tele.2009.11.001) Google Scholar (http://scholar.google.com/scholar_lookup?title=Analysis%20of%20users%20and%20nonusers%20of%20smartphone%20applications&author=H.%20Verkasalo&author=C.%20L%C3%B3pez-Nicol%C3%A1s&author=FJ.%20MolinaCastillo&author=H.%20Bouwman&journal=Telematics%20and%20Informatics&volume=27&issue=3&pages=242-55&publication_year=2010) Böhmer, M., Hecht, B., Schöning, J., Krüger, A. and Bauer, G. (2011) Falling asleep with Angry Birds, Facebook and Kindle: A large scale study on mobile application usage. in Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, 30 August – 2 September 2011, New York, NY. Association for Computing Machinery (ACM). Google Scholar (http://scholar.google.com/scholar? q=B%C3%B6hmer%2C%20M.%2C%20Hecht%2C%20B.%2C%20Sch%C3%B6ning%2C%20J.%2C%20Kr%C3%BCger%2C%20A.%20and%20 Bauer%2C%20G.%20%282011%29%20Falling%20asleep%20with%20Angry%20Birds%2C%20Facebook%20and%20Kindle%3A%20A%20large %20scale%20study%20on%20mobile%20application%20usage.%20in%20Proceedings%20of%20the%2013th%20International%20Conference%2 0on%20Human%20Computer%20Interaction%20with%20Mobile%20Devices%20and%20Services%2C%2030%20August%20%E2%80%93%202 %20September%202011%2C%20New%20York%2C%20NY.%20Association%20for%20Computing%20Machinery%20%28ACM%29.) Falaki, H., Mahajan, R., Kandula, S., Lymberopoulos, D., Govindan, R. and Estrin, D. (2010) Diversity in smartphone usage. in Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, 15–18 June 2010, New York, NY. Association for Computing Machinery (ACM). Google Scholar (http://scholar.google.com/scholar? q=Falaki%2C%20H.%2C%20Mahajan%2C%20R.%2C%20Kandula%2C%20S.%2C%20Lymberopoulos%2C%20D.%2C%20Govindan%2C%20R. %20and%20Estrin%2C%20D.%20%282010%29%20Diversity%20in%20smartphone%20usage.%20in%20Proceedings%20of%20the%208th%20I nternational%20Conference%20on%20Mobile%20Systems%2C%20Applications%2C%20and%20Services%2C%2015%E2%80%9318%20June% 202010%2C%20New%20York%2C%20NY.%20Association%20for%20Computing%20Machinery%20%28ACM%29.) Fox, S. and Duggan, M. (2013) ‘Tracking for health’. Pew Research Center’s Internet and American Life Project, http://pewinternet.org/Reports/2013/Tracking-for-Health.aspx (http://pewinternet.org/Reports/2013/Tracking-for-Health.aspx), accessed 9 January 2015. Azar, K. M. J. et al. (2013) ‘Mobile applications for weight management: Theory-based content analysis’, American Journal of Preventive Medicine, Vol. 45, No. 5, pp. 583–589. CrossRef (http://doi.org/10.1016/j.amepre.2013.07.005) Google Scholar (http://scholar.google.com/scholar_lookup?title=Mobile%20applications%20for%20weight%20management%3A%20Theorybased%20content%20analysis&author=KMJ.%20Azar&journal=American%20Journal%20of%20Preventive%20Medicine&volume=45&issue=5&p ages=583-589&publication_year=2013) Wharton, C. M., Johnston, C. S., Cunningham, B. K. and Sterner, D. (2014) ‘Dietary self-monitoring, but not dietary quality, improves with use of smartphone app technology in an 8-week weight loss trial’, Journal of Nutrition Education and Behaviour, Vol. 46, No. 5, pp. 440–444. CrossRef (http://doi.org/10.1016/j.jneb.2014.04.291) Google Scholar (http://scholar.google.com/scholar_lookup?title=Dietary%20selfmonitoring%2C%20but%20not%20dietary%20quality%2C%20improves%20with%20use%20of%20smartphone%20app%20technology%20in%2 0an%208week%20weight%20loss%20trial&author=CM.%20Wharton&author=CS.%20Johnston&author=BK.%20Cunningham&author=D.%20Sterner&jour nal=Journal%20of%20Nutrition%20Education%20and%20Behaviour&volume=46&issue=5&pages=440-444&publication_year=2014) Pagoto, S. et al. (2014) ‘Tweeting it off: Characteristics of adults who tweet about a weight loss attempt’, Journal of the American Medical Informatics Association, Vol. 21, No. 6, pp. 1032–1037. CrossRef (http://doi.org/10.1136/amiajnl-2014-002652) Google Scholar (http://scholar.google.com/scholar_lookup? title=Tweeting%20it%20off%3A%20Characteristics%20of%20adults%20who%20tweet%20about%20a%20weight%20loss%20attempt&author=S. %20Pagoto&journal=Journal%20of%20the%20American%20Medical%20Informatics%20Association&volume=21&issue=6&pages=10321037&publication_year=2014) United States Department of Agriculture Economic Research Service. (2015) ‘Monthly retail sales for food at home and food away from home’, http://www.ers.usda.gov/data-products/food-expenditures.aspx (http://www.ers.usda.gov/data-products/food-expenditures.aspx), 3 March 2015. Ghose, A. and Han, S. P. (2014) ‘Estimating demand for mobile applications in the new economy’, Management Science, Vol. 60, No. 6, pp. 1470– 1488. CrossRef (http://doi.org/10.1287/mnsc.2014.1945) Google Scholar (http://scholar.google.com/scholar_lookup? title=Estimating%20demand%20for%20mobile%20applications%20in%20the%20new%20economy&author=A.%20Ghose&author=SP.%20Han&j ournal=Management%20Science&volume=60&issue=6&pages=1470-1488&publication_year=2014) appfigures. (2014) App stores growth accelerates in 2014. http://blog.appfigures.com/app-stores-growth-accelerates-in-2014/ (http://blog.appfigures.com/app-stores-growth-accelerates-in-2014/), accessed 3 March 2015. Khalaf, S. (2014) ‘App install addiction shows no signs of stopping’. Flurry analytics, http://www.flurry.com/blog/flurry-insights/app-installaddiction-shows-no-signs-stopping (http://www.flurry.com/blog/flurry-insights/app-install-addiction-shows-no-signs-stopping), accessed 11 January 2015. Kosner, A. W. (2015) ‘Apple app store revenue surge and the rise of freemium app pricing’. Forbes, http://www.forbes.com/sites/anthonykosner/2015/01/11/apple-app-store-revenue-surge-and-the-rise-of-the-freemium/ (http://www.forbes.com/sites/anthonykosner/2015/01/11/apple-app-store-revenue-surge-and-the-rise-of-the-freemium/), accessed 3 March 2015. Johnson, L. (2013) ‘Mobile content revenue to hit $65bn by 2016: Study, Mobile Marketer’, http://www.mobilemarketer.com/cms/news/research/15313.html (http://www.mobilemarketer.com/cms/news/research/15313.html), accessed 3 March 2015. Lin, Y.-H., Fang, C.-H. and Hsu, C.-L. (2014) ‘Determining uses and gratifications for mobile phone apps’, Future Information Technology: Lecture Notes from Electrical Engineering, Vol. 309, pp. 661–668. CrossRef (http://doi.org/10.1007/978-3-642-55038-6_103) Google Scholar (http://scholar.google.com/scholar_lookup? title=Determining%20uses%20and%20gratifications%20for%20mobile%20phone%20apps&author=Y-H.%20Lin&author=CH.%20Fang&author=CL.%20Hsu&journal=Future%20Information%20Technology%3A%20Lecture%20Notes%20from%20Electrical%20Engineering&volume=309&pag es=661-668&publication_year=2014) Ruggiero, T. E. (2000) ‘Uses and gratifications theory in the 21st century’, Mass Communication and Society, Vol. 3, No. 1, pp. 3–37. CrossRef (http://doi.org/10.1207/S15327825MCS0301_02) Google Scholar (http://scholar.google.com/scholar_lookup? title=Uses%20and%20gratifications%20theory%20in%20the%2021st%20century&author=TE.%20Ruggiero&journal=Mass%20Communication%2 0and%20Society&volume=3&issue=1&pages=3-37&publication_year=2000) Buykx, L. and Petrie, H. (2011) What cooks need from multimedia and textually enhanced recipes. Proceedings of the 2011 IEEE International Symposium on Multimedia (ISM). 5–7 December 2011, Dana Point, CA. Google Scholar (http://scholar.google.com/scholar? q=Buykx%2C%20L.%20and%20Petrie%2C%20H.%20%282011%29%20What%20cooks%20need%20from%20multimedia%20and%20textually %20enhanced%20recipes.%20Proceedings%20of%20the%202011%20IEEE%20International%20Symposium%20on%20Multimedia%20%28ISM %29.%205%E2%80%937%20December%202011%2C%20Dana%20Point%2C%20CA.) Okumus, B. and Bilgihan, A. (2014) ‘Proposing a model to test smartphone users’ intention to use smart applications when ordering food in restaurants’, Journal of Hospitality and Tourism Technology, Vol. 5, No. 1, pp. 31–49. CrossRef (http://doi.org/10.1108/JHTT-01-2013-0003) Google Scholar (http://scholar.google.com/scholar_lookup? title=Proposing%20a%20model%20to%20test%20smartphone%20users%E2%80%99%20intention%20to%20use%20smart%20applications%20w hen%20ordering%20food%20in%20restaurants&author=B.%20Okumus&author=A.%20Bilgihan&journal=Journal%20of%20Hospitality%20and% 20Tourism%20Technology&volume=5&issue=1&pages=31-49&publication_year=2014) Spence, C. and Piqueras-Fiszman, B. (2013) ‘Technology at the dining table’, Flavour, Vol. 2, No. 1, pp. 16. CrossRef (http://doi.org/10.1186/2044-7248-2-16) Google Scholar (http://scholar.google.com/scholar_lookup? title=Technology%20at%20the%20dining%20table&author=C.%20Spence&author=B.%20PiquerasFiszman&journal=Flavour&volume=2&issue=1&pages=16&publication_year=2013) Chang, S., Kumar, V., Gilbert, E. and Terveen, L. (2014) Specialization, homophily, and gender in a social curation site: Findings from Pinterest. in Proceedings of the 17th ACM conference on Computer Supported Cooperative Work and Social Computing. 16–19 February 2014, Baltimore, MD. Association for Computing Machinery (ACM). Google Scholar (http://scholar.google.com/scholar? q=Chang%2C%20S.%2C%20Kumar%2C%20V.%2C%20Gilbert%2C%20E.%20and%20Terveen%2C%20L.%20%282014%29%20Specialization %2C%20homophily%2C%20and%20gender%20in%20a%20social%20curation%20site%3A%20Findings%20from%20Pinterest.%20in%20Procee dings%20of%20the%2017th%20ACM%20conference%20on%20Computer%20Supported%20Cooperative%20Work%20and%20Social%20Comp uting.%2016%E2%80%9319%20February%202014%2C%20Baltimore%2C%20MD.%20Association%20for%20Computing%20Machinery%20% 28ACM%29.) Gilbert, E., Bakhshi, S., Chang, S. and Terveen, L. (2013) ‘I Need to Try This?’: A statistical overview of Pinterest. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 27 April – 2 May 2013, Paris, France. Google Scholar (http://scholar.google.com/scholar? q=Gilbert%2C%20E.%2C%20Bakhshi%2C%20S.%2C%20Chang%2C%20S.%20and%20Terveen%2C%20L.%20%282013%29%20%E2%80%98I %20Need%20to%20Try%20This%3F%E2%80%99%3A%20A%20statistical%20overview%20of%20Pinterest.%20in%20Proceedings%20of%20th e%20SIGCHI%20Conference%20on%20Human%20Factors%20in%20Computing%20Systems%2C%2027%20April%20%E2%80%93%202%20M ay%202013%2C%20Paris%2C%20France.) Hall, C. and Zarro, M. (2013) ‘Social curation on the website Pinterest.com’, Proceedings of the American Society for Information Science and Technology, Vol. 49, No. 1, pp. 1–9. Google Scholar (http://scholar.google.com/scholar_lookup? title=Social%20curation%20on%20the%20website%20Pinterest.com&author=C.%20Hall&author=M.%20Zarro&journal=Proceedings%20of%20th e%20American%20Society%20for%20Information%20Science%20and%20Technology&volume=49&issue=1&pages=19&publication_year=2013) Davis, F. D. (1989) ‘Perceived usefulness, perceived ease of use, and user acceptance of information technology’, Management Information Systems Quarterly, Vol. 13, No. 3, pp. 319–40. CrossRef (http://doi.org/10.2307/249008) Google Scholar (http://scholar.google.com/scholar_lookup? title=Perceived%20usefulness%2C%20perceived%20ease%20of%20use%2C%20and%20user%20acceptance%20of%20information%20technolog y&author=FD.%20Davis&journal=Management%20Information%20Systems%20Quarterly&volume=13&issue=3&pages=31940&publication_year=1989) Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989) ‘User acceptance of computer technology: A comparison of two theoretical models’, Management Science, Vol. 35, No. 8, pp. 982–1003. CrossRef (http://doi.org/10.1287/mnsc.35.8.982) Google Scholar (http://scholar.google.com/scholar_lookup? title=User%20acceptance%20of%20computer%20technology%3A%20A%20comparison%20of%20two%20theoretical%20models&author=FD.%20 Davis&author=RP.%20Bagozzi&author=PR.%20Warshaw&journal=Management%20Science&volume=35&issue=8&pages=9821003&publication_year=1989) Venkatesh, V., Morris, M. G., Davis, G. and Davis, F. D. (2003) ‘User acceptance of information technology: Toward a unified view’, Management Information Systems Quarterly, Vol. 27 No. 3, pp. 425–78. Google Scholar (http://scholar.google.com/scholar_lookup? title=User%20acceptance%20of%20information%20technology%3A%20Toward%20a%20unified%20view&author=V.%20Venkatesh&author=MG .%20Morris&author=G.%20Davis&author=FD.%20Davis&journal=Management%20Information%20Systems%20Quarterly&volume=27&issue=3& pages=425-78&publication_year=2003) Venkatesh, V., Thong, J. Y. L. and Xu, X. (2012) ‘Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology’, Management Information Systems Quarterly, Vol. 36, No. 1, pp. 157–78. Google Scholar (http://scholar.google.com/scholar_lookup? title=Consumer%20acceptance%20and%20use%20of%20information%20technology%3A%20Extending%20the%20unified%20theory%20of%20 acceptance%20and%20use%20of%20technology&author=V.%20Venkatesh&author=JYL.%20Thong&author=X.%20Xu&journal=Management%2 0Information%20Systems%20Quarterly&volume=36&issue=1&pages=157-78&publication_year=2012) Sheth, J. N., Newman, B. I. and Gross, B. L. (1991) ‘Why we buy what we buy: A theory of consumption values’, Journal of Business Research, Vol. 22, No. 2, pp. 159–70. CrossRef (http://doi.org/10.1016/0148-2963(91)90050-8) Google Scholar (http://scholar.google.com/scholar_lookup? title=Why%20we%20buy%20what%20we%20buy%3A%20A%20theory%20of%20consumption%20values&author=JN.%20Sheth&author=BI.%20 Newman&author=BL.%20Gross&journal=Journal%20of%20Business%20Research&volume=22&issue=2&pages=159-70&publication_year=1991) Wang, H.-Y., Liao, C. and Yang, L.-H. (2013) ‘What affects mobile application use? The roles of consumption values’, International Journal of Marketing Studies, Vol. 5, No. 2, pp. 11–22. CrossRef (http://doi.org/10.5539/ijms.v5n2p11) Google Scholar (http://scholar.google.com/scholar_lookup? title=What%20affects%20mobile%20application%20use%3F%20The%20roles%20of%20consumption%20values&author=HY.%20Wang&author=C.%20Liao&author=LH.%20Yang&journal=International%20Journal%20of%20Marketing%20Studies&volume=5&issue=2&pages=11-22&publication_year=2013) Assael, H. and Roscoe, M. (1976) ‘Approaches to market segmentation analysis’, Journal of Marketing, Vol. 40, No. 4, pp. 67–76. CrossRef (http://doi.org/10.2307/1251070) Google Scholar (http://scholar.google.com/scholar_lookup? title=Approaches%20to%20market%20segmentation%20analysis&author=H.%20Assael&author=M.%20Roscoe&journal=Journal%20of%20Market ing&volume=40&issue=4&pages=67-76&publication_year=1976) US Census Bureau. (2014) ‘Population estimates’, http://www.census.gov/popest/data/index.html (http://www.census.gov/popest/data/index.html), accessed 10 February 2015. Punj, G. and Stewart, D. W. (1983) ‘Cluster analysis in marketing research: Review and suggestions for application’, Journal of Marketing Research, Vol. 20, No. 2, pp. 134–148. CrossRef (http://doi.org/10.2307/3151680) Google Scholar (http://scholar.google.com/scholar_lookup? title=Cluster%20analysis%20in%20marketing%20research%3A%20Review%20and%20suggestions%20for%20application&author=G.%20Punj&a uthor=DW.%20Stewart&journal=Journal%20of%20Marketing%20Research&volume=20&issue=2&pages=134-148&publication_year=1983) Johnson, S. C. (1967) ‘Hierarchical clustering schemes’, Psychometrika, Vol. 32, No. 3, pp. 241–54. CrossRef (http://doi.org/10.1007/BF02289588) Google Scholar (http://scholar.google.com/scholar_lookup? title=Hierarchical%20clustering%20schemes&author=SC.%20Johnson&journal=Psychometrika&volume=32&issue=3&pages=24154&publication_year=1967) Hartigan, J. A. and Wong, M. A. (1979) ‘Algorithm AS 136: A K-means clustering algorithm’, Applied Statistics, Vol. 28, No. 1, pp. 100–108. CrossRef (http://doi.org/10.2307/2346830) Google Scholar (http://scholar.google.com/scholar_lookup?title=Algorithm%20AS%20136%3A%20A%20Kmeans%20clustering%20algorithm&author=JA.%20Hartigan&author=MA.%20Wong&journal=Applied%20Statistics&volume=28&issue=1&pages =100-108&publication_year=1979) Duggan, M., Ellison, N. B., Lampe, C., Lenhart, A. and Madden, M. (2015) ‘Social media update 2014, Pew Research Center’s Internet and American Life Project’, http://www.pewinternet.org/2015/01/09/social-media-update-2014/ (http://www.pewinternet.org/2015/01/09/social-media-update2014/), accessed 12 January 2015. Faith, M. S., Scanlon, K. S., Birch, L. L., Francis, L. A. and Sherry, B. (2004) ‘Parent-child feeding strategies and their relationships to child eating and weight status’, Obesity Research, Vol. 12, No. 11, pp. 1711–1722. CrossRef (http://doi.org/10.1038/oby.2004.212) Google Scholar (http://scholar.google.com/scholar_lookup?title=Parentchild%20feeding%20strategies%20and%20their%20relationships%20to%20child%20eating%20and%20weight%20status&author=MS.%20Faith& author=KS.%20Scanlon&author=LL.%20Birch&author=LA.%20Francis&author=B.%20Sherry&journal=Obesity%20Research&volume=12&issue= 11&pages=1711-1722&publication_year=2004) Savage, J. S., Fisher, J. O. and Birch, L. L. (2007) ‘Parental influence on eating behaviour: Conception to adolescence’, Journal of Law, Medicine & Ethics, Vol. 35, No. 1, pp. 22–34. CrossRef (http://doi.org/10.1111/j.1748-720X.2007.00111.x) Google Scholar (http://scholar.google.com/scholar_lookup? title=Parental%20influence%20on%20eating%20behaviour%3A%20Conception%20to%20adolescence&author=JS.%20Savage&author=JO.%20Fi sher&author=LL.%20Birch&journal=Journal%20of%20Law%2C%20Medicine%20%26%20Ethics&volume=35&issue=1&pages=2234&publication_year=2007) Sharifi, M. et al. (2013) ‘Leveraging text messaging and mobile technology to support pediatric obesity-related behaviour change: A qualitative study using parent focus groups and interviews’, Journal of Medical Internet Research, Vol. 15, No. 12, pp. e272. CrossRef (http://doi.org/10.2196/jmir.2780) Google Scholar (http://scholar.google.com/scholar_lookup? title=Leveraging%20text%20messaging%20and%20mobile%20technology%20to%20support%20pediatric%20obesityrelated%20behaviour%20change%3A%20A%20qualitative%20study%20using%20parent%20focus%20groups%20and%20interviews&author=M. %20Sharifi&journal=Journal%20of%20Medical%20Internet%20Research&volume=15&issue=12&pages=e272&publication_year=2013) National Restaurant Association. (2014) What’s hot 2015 culinary forecast, http://www.restaurant.org/News-Research/Research/What-s-Hot (http://www.restaurant.org/News-Research/Research/What-s-Hot), accessed 12 January 2015. Nasridinov, A., Yoon, S. and Park, Y.-H. (2014) ‘A database design for pre-ordering system based on prediction of customer arrival time’, Frontier and Innovation in Future Computing and Communication, Lecture Notes in Electrical Engineering, Vol. 301, pp. 653–659. CrossRef (http://doi.org/10.1007/978-94-017-8798-7_75) Google Scholar (http://scholar.google.com/scholar_lookup?title=A%20database%20design%20for%20preordering%20system%20based%20on%20prediction%20of%20customer%20arrival%20time&author=A.%20Nasridinov&author=S.%20Yoon&autho r=YH.%20Park&journal=Frontier%20and%20Innovation%20in%20Future%20Computing%20and%20Communication%2C%20Lecture%20Notes%20i n%20Electrical%20Engineering&volume=301&pages=653-659&publication_year=2014) Lieffers, J. R. L., Vance, V. A. and Hanning, R. M. (2014) ‘Use of mobile device applications in Canadian dietetic practice’, Canadian Journal of Dietetic Practice and Research, Vol. 75, No. 1, pp. 41–47. CrossRef (http://doi.org/10.3148/75.1.2014.41) Google Scholar (http://scholar.google.com/scholar_lookup? title=Use%20of%20mobile%20device%20applications%20in%20Canadian%20dietetic%20practice&author=JRL.%20Lieffers&author=VA.%20Va nce&author=RM.%20Hanning&journal=Canadian%20Journal%20of%20Dietetic%20Practice%20and%20Research&volume=75&issue=1&pages=4 1-47&publication_year=2014) Persaud, A. and Azhar, I. (2012) ‘Innovative mobile marketing via smartphones’, Marketing Intelligence and Plan, Vol. 30, No. 4, pp. 418–43. CrossRef (http://doi.org/10.1108/02634501211231883) Google Scholar (http://scholar.google.com/scholar_lookup? title=Innovative%20mobile%20marketing%20via%20smartphones&author=A.%20Persaud&author=I.%20Azhar&journal=Marketing%20Intelligenc e%20and%20Plan&volume=30&issue=4&pages=418-43&publication_year=2012) Bentley, M. E. et al. (2014) ‘Formative research methods for designing culturally appropriate, integrated child nutrition and development interventions: An overview’, Annals of the New York Academy of Sciences, Vol. 1308, No. 1, pp. 54–67. CrossRef (http://doi.org/10.1111/nyas.12290) Google Scholar (http://scholar.google.com/scholar_lookup? title=Formative%20research%20methods%20for%20designing%20culturally%20appropriate%2C%20integrated%20child%20nutrition%20and%2 0development%20interventions%3A%20An%20overview&author=ME.%20Bentley&journal=Annals%20of%20the%20New%20York%20Academy %20of%20Sciences&volume=1308&issue=1&pages=54-67&publication_year=2014)
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About this article Cite this article as: Doub, A., Levin, A., Heath, C. et al. J Direct Data Digit Mark Pract (2015) 17: 114. http://doi.org/10.1057/dddmp.2015.44 DOI (Digital Object Identifier) http://doi.org/10.1057/dddmp.2015.44 Publisher Name Palgrave Macmillan UK Print ISSN 1746-0166 Online ISSN 1746-0174 About this journal Reprints and Permissions
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