Your Fries are Less Fattening than Mine: How Food Sharing Biases Fattening Judgments Without Biasing Caloric Estimates

Abstract

Food sharing has become quite popular over the last decade, with companies offering food options specifically designed to be shared. As the popularity has grown, so too has concerns over the potential negative impact on consumer health. Despite companies' explicit claims to the contrary, critics maintain that food sharing may be encouraging excessive caloric intake. The current article provides the first systematic exploration of why this may be happening. Three main and two supplementary studies suggest that food sharing reduces perceived ownership, which, in turn, leads people to mentally decouple calories from their consequence. Thus, sharing can reduce the perceived fattening potential of a consumption episode without biasing caloric estimates. This phenomenon persists even when explicit caloric information is provided, and it applies to both healthy and unhealthy foods. Importantly, we establish a relevant downstream consequence by illustrating that people tend to subsequently select calorie-dense foods after underestimating the fattening potential of a shared consumption episode. A roadmap for future research and practical implications are discussed.

Content Hungry: How the Nutrition of Food Media Influences Social Media Engagement

“…Not only does food promotion influence what people eat (Spence et al., 2016; Taylor, Noseworthy, & Pancer, 2019), but it may also shape their social dynamics in terms of what they share with others, ultimately influencing and normalizing what others eat. This is important given that obesity results from overnutrition (Livingston & Zylke, 2012) and people tend to underestimate the negative consequences of caloric intake when they engage in shared consumption (Taylor & Noseworthy, 2021). Future research could explore whether this also occurs in a virtual setting where content is shared.…”

Boosting engagement with healthy food on social media

“…Food consumption research has emphasized that what people eat is influenced by their social environment. For example, seeing others eat unhealthy food (McFerran et al , 2010) and sharing food (Taylor and Noseworthy, 2021) can inadvertently increase people’s caloric intake, eating more than they would otherwise. Thus, given recent research demonstrating that people engage more with unhealthy food media content (Pancer et al , 2022) and that social media algorithms promote content that receives more engagement (Gillespie, 2016; Hogan, 2015; Zulli, 2018), people are likely exposed to more unhealthy food content online.…”

“…This concern is amplified based on prior work on the normalization of food consumption, where social influence can skew actual eating habits (McFerran et al, 2010;Taylor and Noseworthy, 2021). Given that 70% of people in the USA use social media (Pew Research Center, 2021), that food-related content pages are some of the most popular on EJM 56,11 social media (Tubular, 2021) and that food choices are influenced by social cues (McFerran et al, 2010;Taylor et al, 2019;Taylor and Noseworthy, 2021), helping boost healthconscious content can ensure that people are exposed to healthier food alternatives online. In an effort to respond to these potential challenges, the current work investigates whether shifting the viewer's mindset prior to exposure could boost engagement with healthy foods.…”

“…And third, with more exposure to unhealthy foods, consumer perceptions of what is considered normal eating habits may skew to be unhealthier. This concern is amplified based on prior work on the normalization of food consumption, where social influence can skew actual eating habits (McFerran et al, 2010;Taylor and Noseworthy, 2021). Given that 70% of people in the USA use social media (Pew Research Center, 2021), that food-related content pages are some of the most popular on EJM 56,11 social media (Tubular, 2021) and that food choices are influenced by social cues (McFerran et al, 2010;Taylor et al, 2019;Taylor and Noseworthy, 2021), helping boost healthconscious content can ensure that people are exposed to healthier food alternatives online.…”

How to calculate, use, and report variance explained effect size indices and not die trying

“…Behavioral scholars are trained and comfortable with significance tests, but are often less familiar with effect sizes, despite their essential role in research. For instance, we reviewed recent articles published in the Journal of Consumer Psychology (2021, volume 31, issue 4) and the Journal of Consumer Research (2021, volume 48, issue 3) and found that in some cases effect size indices were not reported (Catlin et al, 2021; Davidson & Theriault, 2021; Hovy et al, 2021; Rathee, 2021), other papers were qualitative (Bajde & Rojas‐Gaviria, 2021; Kozinets et al, 2021), another (Janiszewski & van Osselaer, 2021) did not report data but endorsed reporting effect sizes, and Bayes indices were reported in still another (Taylor & Noseworthy, 2021), although regression coefficients and means are likely to be reported (Davidson & Theriault, 2021). In other articles, different effect sizes were reported, sometimes eta‐squared (Biswas et al, 2021; Donnelly et al, 2021; Gupta & Hagtvedt, 2021; van der Lans et al, 2021), or partial eta‐squared (Florack et al, 2021; Han & Broniarczyk, 2021; Lei & Zhang, 2021; Steffel & Williams, 2021), sometimes Cohen's d (Donnelly et al, 2021; Lei & Zhang, 2021; Rocklage et al, 2021; Steffel & Williams, 2021), or other indices (such as for categorical data, Cheng et al, 2021; Kim & Yoon, 2021).…”