“Browsing to a healthy diet”
Examining the relationship between Instagram engagement and the intention to
eat healthy as an extension of the Theory of Planned Behavior
Yasmin Jabbarová Student ID number: 11939133
Master’s Thesis
Graduate School of Communication Master’s programme Communication Science
Under the supervision of: mw. dr. S.E. Susanne Baumgartner Date of completion: 26. 6. 2020
Abstract
The present thesis investigates the relationship between different types of Instagram engagement with healthy food posts and the intention to follow a healthy diet among
emerging adults. The Theory of Planned Behavior was used as the theoretical framework with active, interactive and passive engagement with healthy food posts on Instagram acting as an extension of the model. Further, this study aimed to find out if the relationship between Instagram engagement and the intention to eat healthy is moderated by social comparison. The quantitative research was employed with a single time point cross-sectional survey design with 188 respondents. The results suggested that Instagram engagement with healthy food posts did not predict healthy eating intentions above the predictive strength of the Theory of Planned Behavior predictors, namely attitude, subjective norms and perceived behavioral control. However, without the Theory of Planned Behavior variables present in the model, passive engagement with healthy food posts predicted the intention to eat healthy. Social comparison ended up moderating only the relationship between active Instagram engagement with healthy food posts and the intention to follow a healthy diet. This study showed that to a certain extent Instagram engagement is a significant component of eating intention,
contributing to the body of research focusing on social eating norms. The implications of the results and recommendations for future research are discussed in the end.
Keywords
Theory of Planned Behavior, social eating norms, Instagram engagement, emerging adults, social comparison
Introduction
A nutritious and a balanced diet is a key part of a healthy lifestyle, and following a healthy diet decreases the risk of an early death (Sotos-Prieto et al., 2017). A healthy diet can be defined as one being high in fruits, vegetables, legumes, nuts and whole grains and being low in sweetened beverages, sodium and red and processed meat (Sotos-Prieto et al., 2015). According to Birch (1999) the initial food preferences which later influence the food choices start during childhood. The diet of children and subsequently adolescents is generally
controlled or partially influenced by their parents. In contrast, young adults enter the period of their lives in which they start to make their own food consumption decisions (Deshpande, Basil & Basil, 2009). For this reason, it is important to examine which factors influence young adults’ food choices.
One of the frequently used models to predict the intention to follow a healthy diet is the Theory of Planned Behavior (TPB) developed by Ajzen (1991). The influencing factors in TPB are the attitude towards the behavior, subjective norms, which are the opinions of an individual’s social circle, and perceived behavioral control (PBC), which refers to how easy or difficult the individual perceives taking part in the chosen behavior (Ajzen, 1991). Although there is not an extensive amount of studies focusing on employing TPB on food choices of young adults (Åstrøm & Rise, 2001), Conner and Norman (2002) have shown that TPB successfully predicts the intention to follow a healthy diet.
Next to the predictors mentioned in TPB, another potential factor influencing food choices could be social media (Zilberman & Kaplan, 2014) since they allow users to share, discuss and find food-related inspiration together. Research has shown (Vaterlaus, Patten, Roche & Young, 2014) that the food choices of young adults are indeed influenced by social media. The most popular social media platform among young people is currently Instagram
(Huang & Su, 2018) which is quite popular for sharing and looking up food related posts (Chung et al., 2017).
However, previous studies on the effects of social media on healthy food intake did not distinguish between the effects of different types of Instagram engagement. This might be important because previous research has distinguished between three types of Instagram engagement, namely active, interactive and passive engagement and found different consequences on psychological well-being for each type of Instagram use (Yang, 2016), therefore there might be different outcomes in eating behavior based on Instagram engagement. Additionally, the studies focusing on the influence of social media on the intention to eat healthy thus far have not included TPB in researching intentions to follow a healthy diet (Vaterlaus et al., 2014; Chung et al., 2017). Therefore, in order to provide a full picture, this study will examine how different types of Instagram engagement predict the intention to follow a healthy diet alongside the TPB predictors.
Moreover, it has been shown that the implications of social media use are different for various users. In fact, Instagram users with high social comparison tendencies seem to
experience Instagram differently and have different psychological outcomes compared to Instagram users with low social comparison tendencies (Yang, 2016). A majority of studies researching the outcomes of Instagram use related to social comparison have focused on the negative implications of Instagram use. However, Meier and Schäfer (2018) focused on possible positive outcomes of social comparison on Instagram and found a link between inspiration based on social comparison on Instagram and positive affect, thus achieving an increase in well-being. The current study will investigate how the relationship between Instagram engagement and the intention to eat healthy differs for individuals with different levels of social comparison. To conclude, the research question is:
“To what extent does active, interactive and passive engagement with healthy food content on Instagram predict the intention to eat healthy among emerging adults above the effects of attitude, subjective norms and perceived behavioral control, and is this relationship moderated by social comparison?”
Theoretical framework
Instagram engagement and emerging adults
In order to better understand different potential influences on food choices, it is essential to first understand who makes these choices. The current study focuses on emerging adults. As Arnett (2014) describes, young adulthood is a complicated life period which starts at age 18 and usually comes to an end around 29 years old. However, the beginning and the end of emerging adulthood depends on specific features of each individual. Emerging adults often experience high amounts of uncertainty and anxiety but also a lot of freedom of choice. According to Arnett (2014), young adults are still in a life phase of identity exploration and seeking new possibilities in life. Emerging adults have the option to essentially transform themselves and become a different person from their adolescent self. Some of these
opportunities come when leaving the family house and making independent decisions. Since young adulthood is a time of uncertainty, emerging adults often look for sources to guide them through their discovered freedom (Arnett, 2014). One of the potential sources could be media. Emerging adults are avid media users. According to Coyne, Padilla-Walker and Howard (2013) young adults spend more time on using media than on any other activity. Furthermore, Padilla-Walker, Nelson, Carroll and Jensen (2010) reported that emerging adults spend around three and a half hours per day on the Internet, with social networking sites (SNS) being one of the frequently mentioned activities. Consistently, the Pew Research Center (2018) showed that 88% of 18 to 29-year-olds use SNS. Moreover, according to the
Pew Research Center (2018) the most popular SNS among emerging adults is Instagram with 71% of respondents between the ages of 18 and 24 years old indicating they own an
Instagram profile. Instagram is a popular mobile application designed primarily for sharing pictures and photos while often being used to share and look up food related posts (Chung et al., 2017). Additionally, 81% of young adults use Instagram on a daily basis with 55% reporting using the platform several times a day (Pew Research Center, 2018).
Correspondingly, Huang and Su (2018) confirm that Instagram is currently the most popular platform among young people.
According to Yang (2016) Instagram use can be reflected through three distinct engagement categories: passive use, interactive use and active use. Instagram browsing, is defined as passively consuming Instagram content without contributing to it by either posting or by commenting on the posts of others. Interactive Instagram use refers to activities such as liking and commenting on others’ posts, therefore interacting and socializing with other Instagram users. Lastly, active Instagram use represents creating and posting Instagram content without an interactive element such as tagging other Instagram users in the posts. For that reason it can also be called “broadcasting”.
As of now, there is a lack of literature focusing on these different types of Instagram engagement and their influence on emerging adults. Yang (2016) focused on the three types of Instagram engagement but in relation to loneliness among young adults. The study (Yang, 2016) showed that interactive Instagram use was linked to lower levels of loneliness and greater psychological well-being. Similarly, passive use was also associated with lower levels of loneliness. Contrastingly, broadcasting Instagram use was shown to be linked to higher levels of loneliness (Yang, 2016). In terms of eating behavior, Chung et al. (2017)
interviewed women about their food tracking behavior on Instagram. Publishing posts about one’s eating habits technically corresponds with active Instagram use according to Yang
(2016). The study by Chung et al. (2017) focused mostly on motivations behind food tracking on Instagram but found various outcomes of active Instagram use regarding eating. The results were rather mixed, with some participants reporting positive outcomes such as achieving their health goals but some saying they experienced an increase in negative behaviors. The results indicate that different types of Instagram use, such as posting about food, could be linked to different outcomes in well-being, in that people can experience positive or negative outcomes by engaging with food related content on Instagram.
Given the above, it is important to understand the different effects of different type of Instagram engagement on eating behavior, of emerging adults. In order to explore this association, it is essential to understand which other factors influence food choices.
Theory of Planned Behavior
A popular model to use in health behavior research is TPB, which is an extended model of the Theory of Reasoned Action (Ajzen & Fishbein, 1980). Ajzen (1991) explains that as the intention to perform a behavior rises, the more likely will an individual take part in the
behavior. The central variables in the model are attitude, subjective norms and PBC which all predict the intention to engage in a specific behavior.
Attitude depicts the degree to which an individual perceives a behavior as favorable or unfavorable (Ajzen, 1991). Along with values, knowledge and beliefs an attitude consists of a conviction of whether taking part in a behavior results in positive or negative outcomes (Hackman & Knowlden, 2014).
The next predictor in the TPB is subjective norms. Ajzen (1991) describes subjective norms as a social factor which relates to the perceived social pressure of an individual to either partake or not partake in a behavior. Subjective norms consist of “injunctive normative beliefs” which is one’s assumption on whether the people who are important to them would
approve or disapprove of the behavior and “descriptive normative beliefs” based on the observed behavior of other people (Ajzen, 2015).
The final predictor of intention is the perceived behavioral control (PBC) which reflects the perceived ease or difficulty an individual feels while performing a behavior, therefore the amount of control one feels over their own actions. It is expected that PBC refers not only to previous experiences but to anticipated difficulties of the behavior as well (Ajzen, 1991). So for example, if one anticipates difficulties in maintaining a healthy diet, it leads to a reduced intention to follow a healthy diet (Ajzen, 2015).
A meta-analysis by Hackman and Knowlden (2014) shows that TPB has been used numerous times to predict aspects of healthy eating such as fruits and vegetables intake, or breakfast consumption among young adults. Additionally, it has been shown that TPB successfully predicts the intention to follow a healthy diet (Conner & Norman, 2002).
However, out of all the predictors included in TPB, subjective norms have been found to be the weakest and at times even insignificant predictor of intention (Payne, Jones & Harris, 2004). Armitage and Conner (2001) point to one of the possible explanations being the subjective norms within the TPB framework fail to reach the important aspects of social influence. Studies analyzing TPB in terms of healthy eating came to the same conclusion, that subjective norms have the weakest role of predicting the intention to eat healthy (Chan, Prendergast & Ng, 2016). Consistently, Åstrøm and Rise (2001) reported a weak predictive effect of subjective norms on the intention to follow a healthy diet among emerging adults.
Although subjective norms in TPB seem to be a rather weak predictor, other studies have shown that social influence strongly influences food choices. For example, when exposing participants to a message portraying a social healthy food norm they were more likely to make healthy food choices compared to a group which was exposed to a social unhealthy food message (Mollen, Rimal, Ruiter, & Kok, 2013). Additionally, Higgs (2014) showed that
people have a tendency to adjust the amount and type of food they consume according to the members of their social group such as family members or friends. According to Higgs (2014) social influence is the primary predictor of eating behavior. The possible mechanisms of social eating norms could include the need to fit in or the desire to behave, therefore, to eat correctly. Furthermore, experimental research has shown that when presented with a “healthy” and an “unhealthy” option, participants chose the healthy food option if they thought a previous participant has also chosen the healthy food item (Prinsen, Ridder & Vet, 2013).
It might be argued that the effects of subjective norms in TPB are lower because they miss crucial aspects of social influence that are important for today’s young people. As an
example, SNS could be a source of social influence, specifically Instagram since it is a social media platform where one can follow their peers or aspirational strangers in order to observe their behavior, therefore representing the type of social influence TPB might be lacking (Jackson & Luchner, 2018). Notably, the effect of following social norms in eating behavior seems to be stronger when people are strangers compared to when they know each other (Higgs, 2014). This might be due to the fact that individuals could idealize a stranger and identify them with an identity or social status that they strive to achieve, therefore modelling the behavior for social approval reasons (Higgs, 2014). Considering a lot of people follow strangers on their social media accounts it might explain why there may be even stronger effects in terms of eating behavior influenced by SNS use than from direct peers (Jackson & Luchner, 2018).
Additionally, people tend to comply to social eating norms even when no one is present but they are given indications about someone else’s past eating behavior. This indicates that besides social approval, people observe other people’s eating habits to establish a point of reference for appropriate eating behavior (Cruwys et al., 2014). Relatedly, SNS, especially
Instagram, provide an easy access to eating behavior of other people. Specifically Instagram contains a considerable amount of food related content with a lot of people posting about their eating habits and food preferences (Chung et al., 2017). Consequently, people striving to follow a healthy diet could either get unconsciously influenced in their food choices by strangers or they could be using Instagram profiles of others as a point of reference for a healthy eating behavior. It is therefore not surprising that the eating behavior of adolescents gets influenced by peers through social media in the same way as it gets during real life interactions (Bevelander, Anschütz, Creemers, Kleinjan & Engels, 2013). The effects should be similar for emerging adults. In fact, Vaterlaus et al. (2014) reported that young adults credit social media with expanding their food choices by giving them access to meal ideas and recipes that their peers are cooking as well as giving them a platform to show their own cooking skills. Consistently, Petit, Cheok and Oullier (2016) reported healthy food posts on social media could encourage people to eat healthy. Emerging adults could be using
Instagram to find nutritional inspiration or peer interaction about subjects of interest such as healthy eating. As Chan et al. (2016) note as well, it is important to investigate social media behaviors, in addition to the traditional TPB factors.
Given the above, the present study extends existing research of TPB by adding Instagram engagement as a predictor of eating behavior among emerging adults with the following hypotheses:
H1: Attitude towards healthy eating, subjective norms, and perceived behavioral control predict the intention to eat healthy.
H2: Instagram engagement with healthy food content on Instagram will predict the intention to follow a healthy diet above the predictive effects of attitudes, subjective norms, and perceived behavioral control.
Considering that all three types of Instagram engagement were shown to have different consequences on emerging adults and the lack of current literature to form a hypothesis on, the following sub-research question will be investigated:
Sub-RQ: “Which of the types of Instagram engagements (active, interactive and passive) with healthy food content on Instagram is the strongest predictor of the intention to follow a healthy diet among emerging adults?”
Social comparison
The concept of social comparison originates from a paper “A Theory of Social Comparison Processes” by Festinger (1954) according to which individuals like to evaluate themselves according to an objective measure such as opinions and abilities of others.
However, if there is no objective measure available, the evaluation is based upon comparison with other people; preferably the kind to which the individual perceives as the most similar to themselves (Festinger, 1954). There are three types of possible comparisons; downward comparison, horizontal comparison and upwards comparison. Downward comparison occurs when one compares oneself to someone perceived as inferior, therefore the result is often a boost of self-esteem. Horizontal comparison refers to comparing oneself to someone
perceived as equal. Finally, upward comparison occurs when individuals compare themselves to someone they perceive as superior in some way. Often this type of comparison leads to negative psychological outcomes such as a drop in self-esteem (Meier & Schäfer, 2018).
SNS like Instagram provide an essential base for social comparison among emerging adults. Users have an opportunity to create their profiles which they can later compare to
profiles of other users and evaluate themselves (Lee, 2014). Appel, Gerlach and Crusius (2016) additionally point to the fact that social media makes information such as number of likes, comments or friends of other people more accessible than it is in real life which can also affect aspects of social comparison. These elements could be amplified with Instagram use as the platform is photocentric with its posts often altered to seem more appealing than in real life (Vannucci, Ohannessian, & Gagnon, 2019).
According to the Differential Susceptibility to Media Effects Model (Valkenburg, Peter & Walther, 2016) people can be more or less susceptible to media influences, therefore changing the potential strength of an effect. The effects can differ based on either
developmental, dispositional or social susceptibility factors. The dispositional susceptibility refers to the extent to which media influences differ based on factors such as gender or specific personality traits (Valkenburg et al., 2016). One of the possible personality traits that could affect the relationship between Instagram engagement and the intention to eat healthy is social comparison since the implications of SNS use differ for each user. Indeed, Instagram users with high social comparison tendencies experience different outcomes compared to users with low social comparison tendencies. Yang (2016) reported participants using Instagram interactively and who scored low on social comparison showed lower levels of loneliness compared to participants who had higher social comparison tendencies. Previous studies mostly focused on the negative influences of SNS on its users in terms of social comparison, such as damaging self-esteem and increase in depressive symptoms (Meier & Schäfer, 2018). However, Meier and Schäfer (2018) found a link between inspiration on Instagram and social comparison. The findings show that individuals who perceive Instagram as a source of motivation and inspiration achieve an increase in positive well-being through social comparison (Meier & Schäfer, 2018). Finally, evidence shows that individuals get
influenced in their eating behavior by other people who they identify as inspirational (Higgs, 2014).
Based on these findings the relationship between Instagram engagement and the intention to follow a healthy diet could differ for Instagram users who have a tendency for social comparison, therefore the final hypothesis is:
H3: The relationship between Instagram engagement and the intention to follow a healthy diet is moderated by social comparison, in that the effect is stronger for those having a higher tendency for social comparison.
Methods
Research design and sample
The research was a cross-sectional survey design with the data collected through an online questionnaire. The cross-sectional design was chosen for its advantages such as time saving, easy accessibility and low cost. The questionnaire was designed as an individual self-report. Respondents accessed the survey through an online link which began with an
introductory text informing the participants about the purpose of the research, followed by information about data protection and privacy concerns. After reading all the provided
information, the participants could either sign the consent for participating in the study or exit the survey. The questionnaire was distributed through the author’s social media accounts such as Facebook and Instagram. Considering this thesis mainly focuses on Instagram users, collecting data through SNS was deemed suitable for the purpose of this research.
Additionally, data was collected through survey exchange websites such as SurveySwap and SurveyCircle with an aim to collect a more international and representative sample compared to only using the friends of the author. Thus, the sampling method was a non-probability convenience sampling and the data collection lasted one month.
The final sample consisted of 188 respondents after excluding four people from the analysis due to their age being either below or above the age criteria of emerging adults, which was 18 to 30 years old. More than half of the participants identified as female (68.1%) with males representing 30.9% of the sample. Additionally, 2 respondents (1.1%) preferred not the disclose their gender. The age of participants ranged from 19 to 30 years old with the average age being 24 years old (M = 24.22, SD = 2.24). In regards to nationality, most respondents ended up being from the Netherlands (19.1%) with a close second being Czech Republic (17.6%) and third Germany (9%). Further, the majority of respondents finished their Bachelor’s degree (60.6%) or Master’s degree (22.3%) with a smaller portion finishing only high school (14.4%), having a professional degree (2.1%) or a doctorate degree (0.5%).
Measures
Independent variables
Attitude. The scale measuring attitude developed by Åstrøm and Rise (2007) initially consisted of three items such as “To eat healthy foods regularly in the future is reasonable” and “To eat healthy foods regularly in the future is useful” with a 7-point Likert answer scale ranging from 1 = “strongly disagree” to 7 = “strongly agree”. Factor analysis with the
principal axis factoring method (Direct Oblimin rotation) was conducted in order to test the scale. The factor analysis showed one factor with an Eigenvalue over 1 explaining 62.65% of the variance with a satisfactory sampling adequacy (KMO = .55) and a successful Bartlett’s test of sphericity (χ2 = 152.72, p < .001). However, the scale showed a low internal
consistency with a Cronbach’s alpha of .60. Removing the item “To eat healthy foods regularly in the future is boring” led to a substantial improvement of the scale’s reliability (α = .84, M = 6.23, SD = 0.84). High scores indicated a favorable attitude towards healthy eating.
Subjective norms. The subjective norms scale developed by Brouwer and Mosack (2015) included four items with a 7-point Likert answer scale ranging from 1 = “strongly disagree” to 7 = “strongly agree”. The items consisted of statements such as “People who are important to me think I should eat a healthy diet” and “People who are important to me would approve of my eating a healthy diet”. An exploratory factor analysis with the principal axis factoring method (Direct Oblimin rotation) showed an adequate sample size (KMO = .64) and successful Bartlett’s test of sphericity (χ2 = 110.73, p < .001). However, the factor analysis also revealed two factors with an Eigenvalue over 1 with the first factor explaining 48.20% of the variance and the second 25.27% of the variance. Consistent with Brouwer and Mosack’s (2015) report, the initial Cronbach’s was quite low (α = .60) but after deleting the item which loaded on the second factor “I feel under social pressure to eat healthy” the scale reliability improved (α = .69, M = 5.24, SD = 1.10). Thus, only the items which loaded on the first factor were used to construct the scale. High scores suggested perceiving one’s own social circle as supportive of following a healthy diet.
Perceived behavioral control. The scale measuring PBC was developed by Povey, Conner, Sparks, James and Shepherd (2000) and consisted of items such as “How confident are you that you could eat a healthy diet if you wanted to?” and “To what extent is it up to you whether you eat a healthy diet?” with a 7-point Likert answer scale ranging from 1 = “not at all confident” or 1 = “no extent at all” to 7 = “extremely confident” or 7 = “a great extent” based on the wording of the question. A factor analysis with a principal axis factoring (Direct Oblimin rotation) showed one factor with an Eigenvalue over 1 explaining 59.37% of the variance with a satisfactory sampling adequacy (KMO = .71) and successful Bartlett’s test of sphericity (χ2 = 219.51, p < .001). The scale was also deemed reliable (α = .75, M = 5.34, SD = 0.97). High scores implied high perception of own’s PBC towards healthy eating.
Instagram engagement. The scale developed by Yang (2016) measuring three different types of Instagram use was used to measure Instagram engagement. For the purpose of this study, the scale was altered to measure types of Instagram engagement with healthy food posts. The scale used in the questionnaire consisted of five items with two items measuring interactive Instagram use, one item measuring active Instagram use and two items measuring passive Instagram use. However, a factor analysis with a principal axis factoring method (Direct Oblimin rotation) revealed only two factors with an Eigenvalue over 1 which
combined explained 79.3% of the variance with a successful sampling adequacy (KMO = .70) and Bartlett’s test of sphericity (χ2 = 408.43, p < .001). Thus, the individual items from the scale were kept but instead they were assigned to one of the two newly created categories: “active Instagram engagement” or “passive Instagram engagement”. Active engagement consisted of two items “How often do you post/upload posts about healthy food content on your Instagram profile without tagging anyone?” and “How often do you comment on or reply to posts about healthy food on Instagram?” with a 5-point Likert answer scale ranging from 1 = “never” to 5 = “always” with a reliable scale (α = .76, M = 1.57, SD = 0.84). The second dimension, passive Instagram engagement, consisted of three items ““How often do you “like” posts about healthy food on Instagram?”, “How often do you browse your Instagram home page/newsfeed to view/read posts about healthy food without leaving comments?” and “How often do you check out profiles on Instagram featuring content about healthy food without leaving a like or comments?” with a 5-point Likert answer scale ranging from 1 = “never” to 5 = “always” with a scale which was also deemed reliable (α = .84, M = 2.67, SD = 1.16).
Dependent variable
Intention. The scale for the dependent variable was developed by Povey et al. (2000) and consisted of two items “I want to eat a healthy diet in the future” and “I intend to eat a healthy diet in the future” with a 7-point Likert scale ranging from 1 = “strongly disagree” to 7 = “strongly agree”. The two items correlated highly (r = .80, p < .001) and were thus averaged into a mean index (M = 5.80, SD = 1.04). High scores showed a strong intention to follow a healthy diet.
Moderator variable
Social comparison. Social comparison was measured by 11 items taken from the Iowa-Netherlands Comparison Orientation Measure (Gibbons & Buunk, 1999). Participants indicated their answers on a 7-point Likert scale ranging from 1 = “strongly disagree” to 7 = “strongly agree” on items such as “I often compare myself with others with respect to what I have accomplished in life” or “I am not the type of person who compares often with others” with the second item having to be reverse coded. A factor analysis with a principal axis factoring method (Direct Oblimin rotation) was conducted. The test of sampling adequacy was successful (KMO = .82) as well as the Bartlett’s test of sphericity (χ2 = 551.68, p < .001). However, the factor analysis revealed three factors with an Eigenvalue over 1. Seven out of the 11 items loaded on one factor explaining 35.11% of the variance. In the end, these 7 items were chosen since they seemed to represent the concept of social comparison well and they created a scale which was deemed reliable (α = .80, M = 6.43, SD = 0.98). Higher scores pointed to a higher tendency for social comparison.
Control variables
In order to achieve results that are as valid as possible, Instagram use frequency, gender and age were included as control variables.
Instagram use frequency. The scale was first developed by Ross et al. (2009) for Facebook use and later adapted by Lup, Trub, and Rosenthal (2015) to fit Instagram use. One item measured the frequency of Instagram use by asking participants to report how many minutes they spend using Instagram daily, with 6 choices including “10 minutes or less”, “11– 30 minutes”, “31–60 minutes”, “1–2 hours” and “2–3 hours”. Additionally, the option “more than 3 hours” was included in the questionnaire by the author in order to achieve the most precise measurements about frequency of Instagram use. On average participants reported spending between 31-60 minutes a day using Instagram (M = 3.04, SD = 1.42).
Gender. Gender was measured by a multiple choice question with options “male” (n = 58), “female” (n = 128), “other” and “do not want to disclose” (n = 2) (M = 1.71, SD = 0.52).
Age. Participants reported their age in years using an open-ended question in the questionnaire. As mentioned in the previous section, four cases were excluded for exceeding the age limit which resulted in 188 cases ranging from 19 years old to 30 years old (M = 24.22, SD = 2.24).
All the items chosen for the scale construction are included in Appendix A in order to increase the replicability of this study.
Results
In order to explore any initial hints of relationships between the analyzed concepts, we conducted a correlational analysis between all the measured variables. The results can be found in Table 1. It is important to note that even though the table shows that passive Instagram engagement and the intention to eat healthy are not significantly correlated, the analysis showed that they were almost significantly correlated (p = .053).
Additionally, none of the control variables significantly predicted the intention to eat healthy. A multiple regression was conducted in order to explore the predictive effects on the control variables on the intention to eat healthy. Age had a very weak and non-statistically significant predictive effect on the intention to follow a healthy diet (b = .03, t = 0.21, p = .836, 95% CI [-0.05, 0.06], b* = .01). Gender, therefore in terms of the regression analysis being male, also did not significantly predict the intention to eat healthy (b = -.16, t = 1.21, p = .229, 95% CI [-0.43, 0.10]) with a considerably weak predictive power, b* = .07. Finally, Instagram use did not show any predictive effects on the intention to eat healthy either with a very weak predictive effect (b = .02, t = 0.37, p = .714, 95% CI [-0.07, 0.11], b* = .02).
Table 1: Correlations among all the independent, dependent and control variables (N = 188).
Note: * p <.05. ** p <.01. *** p <.001.
Hypothesis testing
To test the first two hypotheses (H1 and H2) a hierarchal multiple regression was conducted. The first model included the TPB variables (attitude, subjective norms and PBC)
and the control variables (age, gender, and frequency of Instagram use) as predictors and the intention to follow a healthy diet as the dependent variable. The model was statistically significant, F (6, 180) = 20.42, p < .001 and predictive of about 41% of the variance in the intention to eat healthy (R2 = .41). As for the individual predictors, attitude towards healthy eating positively predicted the intention to follow a healthy diet, b = .49. A one-unit increase in attitude predicted an increase in the intention to eat healthy with 0.49 points. This effect was statistically significant, t = 6.17, p < .001, 95% CI [0.33, 0.64] and moderate in size, b* = .39. Further, subjective norms towards healthy eating also positively predicted the intention to follow a healthy diet, b = .21. All else equal, a one-unit increase in subjective norms predicted a rise in the intention to eat healthy of 0.21 points. The effect was statistically significant, t = 3.69, p < .001, 95% CI [0.10, 0.32], but moderate in size, b* = .22. Lastly, the results showed that PBC towards healthy eating had a positive predictive effect on the intention to follow a healthy diet, b = .27. All else equal, scoring an additional one-unit in PBC predicted an increase in the intention to eat healthy of 0.27 points. The effect was statistically significant, t = 4.00, p < .001, 95% CI [0.14, 0.41]. The effect was moderate in size, b* = .25.
Consequently, H1 was accepted.
The second regression analysis tested H2, therefore the assumption that Instagram engagement with healthy food posts predicted the intention to follow a healthy diet above the predictive effects of the TPB variables. The regression model included the active and passive Instagram engagement variables, the control variables as well as TPB variables as predictors and the intention to follow a healthy diet as the dependent variable. The model was
statistically significant, F (8, 178) = 15.76, p < .001 and predicted about 42% of the variance in the intention to eat healthy (R2 = .42). Active Instagram engagement with healthy food posts did not predict the intention to follow a healthy diet, b = .10, t = 1.18, p = .240, 95% CI [-0.07, 0.26] , b* = .08. Likewise, the results for passive Instagram engagement with healthy
food posts did not show any predictive effects on the intention to eat healthy, b = .04, t = 0.64, p = .526, 95% CI [-0.09, 0.17] , b* = .05. Based on these results, the H2 was rejected.
Another multiple regression model was constructed in order to analyze the sub-RQ, therefore to see which type of Instagram engagement with healthy food posts was the
strongest predictor of the intention to follow a healthy diet. The control variables (age, gender and the frequency of Instagram use) were included besides the active and passive Instagram engagement variables as predictors. The intention to eat healthy was the outcome variable. However, the regression model was not statistically significant, F (5, 181) = 1.75, p = .125 and predicted only about 5% of the variance in the intention to follow a healthy diet (R2 = .05). Likewise, controlling for age, gender and the frequency of Instagram use, the results did not show a significant predictive effect of active Instagram engagement with healthy food posts on the intention to follow a healthy diet, b = -.12, t = -1.16, p = .249, 95% CI [-0.31, 0.08], b* = .09. Nevertheless, although the model as a whole was not significant, the results showed that passive Instagram engagement with healthy food posts significantly predicted the intention to eat healthy, b = .16. All else equal, an additional one-unit increase in passive Instagram engagement with healthy food posts predicted an increase in the intention to follow a healthy diet of 0.16 points, t = 2.03, p = .044, 95% CI [0.01, 0.31]. However, the predictive effect was weak in size, b* = .18. To answer the RQ, it can be concluded that passive
engagement with healthy food posts was a stronger predictor of the intention to eat healthy than an active engagement with healthy food posts.
Lastly, a multiple regression moderation analysis via PROCESS (Model 1) in SPSS was conducted in order to establish if the relationship between Instagram engagement and the intention to eat healthy was moderated by social comparison. However, the multiple
regression model was not statistically significant for neither the active Instagram engagement, F (6, 180) = 2.08, p = .057, nor the passive Instagram engagement with healthy food posts, F
(6, 180) = 1.45, p = .196. The model with active Instagram engagement as a predictor
explained about 4% of the variance in the intention to follow a healthy diet (R2 = .04) and the model with passive Instagram engagement with healthy food posts explained only about 2% of the variance in the intention to eat healthy (R2 = .02). Further, the relationship between passive Instagram engagement with healthy food posts and the intention to follow a healthy diet was not significantly moderated by social comparison (p = .860). However, although the model for active Instagram engagement was not significant, the results showed that the relationship between active Instagram engagement with healthy food posts and the intention to eat healthy was moderated by social comparison. This effect was negative, therefore, active engagement with healthy food posts predicted lower intention to eat healthy but only for the participants who scored low on social comparison, b = -.38, t = -2.34, p = .020, 95% CI [-0.70, -0.06]. Although the results showed a moderation, we expected those high in social comparison to be more affected, and this was not supported by the data, therefore H3 was rejected.
Discussion
The present study aimed at analyzing to what extent does active, interactive and passive engagement with healthy food posts on Instagram predict the intention to eat healthy among emerging adults, and if this relationship differs for individuals with social comparison tendencies. Additionally, the goal was to find out if the potential predictive effect of
Instagram engagement surpasses the predictive strength of attitude, subjective norms and perceived behavior control. The analysis revealed that Instagram engagement with healthy food posts did not predict the intention to eat healthy above the three predictors from the Theory of Planned Behavior. However, without the presence of attitude, subjective norms and PBC, passive Instagram engagement with healthy food posts had a predictive effect on the
intention to eat healthy, revealing it to be a stronger predictor than active Instagram
engagement with healthy food posts. Finally, the relationship between Instagram engagement with healthy food posts and the intention to eat healthy did not differ for participants with high tendencies for social comparison. However, for participants with low social comparison tendencies active Instagram engagement with healthy food posts predicted a lower intention to eat healthy. Implications and further explanations will be discussed in the following paragraphs.
In line with previous research, attitude, subjective norms, and PBC towards healthy eating predicted the intention to eat healthy (Hackman & Knowlden, 2014). Consistent with findings from Hackman and Knowlden (2014), attitude turned out to be the strongest
predictor of intention and PBC was a moderate predictor of the intention. Similarly, subjective norms were shown to be the weakest predictor. The finding is consistent with previous research (Åstrøm & Rise, 2001; Chan, Prendergast & Ng, 2016; Payne, Jones & Harris, 2004). Armitage and Conner (2001) explained that subjective norms within the TPB framework might not reach the important aspects of social influence, therefore having a weak predictive effect on the intentional outcomes. This reasoning may also apply to Instagram engagement not correlating with subjective norms, in that the measurement of subjective norms within the TPB fails to capture the essence of social influence that might relate to Instagram engagement. Ultimately, the results establishing the predictive effects of attitude, subjective norms and, PBC on the intention to eat healthy were expected and confirmed the existing research surrounding TPB.
Although based on previous studies it was expected that there are three types of Instagram engagement with healthy food posts (passive, active, interactive), the data revealed that there were only active versus passive types of engagement. Yang (2016) points out that Instagram browsing for young adults could be not as passive as it seems and recommends
future adjustments to labelling. It seems that the types of Instagram engagement might differ based on the researched topic. Eating intentions might relate to different types of Instagram engagement compared to other aspects of well-being. For example, our findings showed that “liking” corresponded with passive engagement, therefore contrasting the findings of Yang (2016) about depressive symptoms. Future studies should further research relevant
adjustments to types of Instagram engagement based on the topic of analyses.
Nevertheless, in the current study, the predictive effects of Instagram engagement did not have an effect above the TPB factors. However, without the presence of the TPB
predictors in the model, passive Instagram engagement with healthy food posts predicted the intention to eat healthy among emerging adults. From a statistical perspective, the predictive effects of the TPB variables might be so strong that they suppress the predictive power of passive Instagram engagement with healthy food posts.
Surprisingly, opposed to active Instagram engagement, passive Instagram engagement predicted the intention to eat healthy. Previous research has yet to analyze eating behavior as an outcome in relations to social media engagement, however, most of the findings have pointed to passive SNS use as leading to negative outcomes in well-being such as an increase in depressive symptoms and loneliness (Escobar-Viera et al., 2018; Verduyn et al., 2015). It might be that in the case of the intention to follow a certain dietary behavior, browsing through food content on Instagram gives people inspiration to eat healthy, therefore resulting in an improvement in well-being. In fact, Vaterlaus et al. (2014) previously reported that emerging adults acknowledged social media for increasing the food variety and motivation for positive health behavior. Furthermore, Cruwys et al. (2014) reported that people complied to social eating norms even if there was nobody else present in the room with them. This
supports the idea that observing eating behavior of other people serves as a point of reference for appropriate food consumption. Yang (2016) points out that replacing the label “passive
Instagram use” with “content consumption” might be more accurate. Our findings indicate that consuming a certain amount of healthy food content could lead to learning and adopting healthy eating habits, therefore acquiring a point of reference for eating intentions.
Nonetheless, active Instagram engagement did not predict the intention to eat healthy. According to Sheldon & Bryant (2015) the main motivation behind active Instagram
engagement in general is the need for social interaction. However, healthy food posts on Instagram are often generated by health influencers (Nathalia, Kansius, Felicia & Kalpikasari, 2016), therefore commenting on these types of posts is not likely to result in any meaningful social interaction which might discourage users from actively engaging. Further, our findings showed that attitude towards healthy eating and active Instagram engagement with healthy food posts were negatively correlated. It might be that individuals who already had a positive attitude towards healthy eating did not have the need to post about it or interact with healthy foods posts on Instagram. Additionally, the analysis revealed that the means of active and passive Instagram engagement with healthy food posts were rather low in the sample, meaning only a few of the participants partake in the behavior which might explain the results. Considering as of now, there is a lack of literature focusing on this phenomenon, future research should further investigate the nuances of passive and active Instagram engagement with eating intentions.
The most unexpected result was the finding that for people with low tendencies of social comparison, actively engaging with healthy food posts on Instagram predicted lower intention to eat healthy. Although it might seem surprising at first, a study by Chung et al. (2017) focusing on motivations behind healthy food tracking on Instagram might offer an explanation. Active Instagram engagement with healthy food posts included broadcasting, therefore posting pictures of healthy meals on one’s Instagram profile. According to Chung et al. (2017), among other reasons, individuals use Instagram to track their eating behavior in
order to inform, motivate and emotionally support others. Instagram users who do not have tendencies to compare themselves to others might be using the platform for these reasons. Chung et al. (2017) argue that for some of the participants the food tracking led to negative consequences such as being less likely to engage in healthy behavior as the participants reported feeling under social pressure to serve as a role model. To conclude, individuals who do not tend to compare themselves to others might be actively using Instagram for food-related reasons in order to motivate others. The responsibility of serving as a role model for their followers could result in negative consequences such as losing the motivation to eat healthy.
The present study does not come without limitations. Firstly, the used sampling method was not of a random probability nature which might have resulted in a not enough
representative sample. Furthermore, the data was collected on a self-report basis which always poses the danger of resulting in non-reliable results, especially when the intention to eat healthy is studied. People can have non-accurate perception of their dietary habits or they can try answering in socially desirable ways (Bryman, 2016). Additionally, the means of passive and active Instagram engagement with healthy food posts showed that the participants in our sample did not tend to take part in the behavior, which likely influenced the results. Finally, considering that the data for the present thesis was collected at a single time point through a cross-sectional research design, it is not possible to conclude a causational effect of Instagram engagement with healthy food posts on the intention to eat healthy. Future research should try to establish a causal direction through an experimental or longitudinal research design. Furthermore, the present study could be extended by analyzing the relationship between different types of Instagram engagement with healthy food posts and the actual behavior of eating healthy.
Conclusion
The present thesis adds to the existing body of research focusing on TPB and social eating norms. Furthermore, it lays a foundation for upcoming studies to analyze different types of social media use and their outcomes on various aspects of well-being. The present study indicated that to a certain extent passive Instagram engagement with healthy food posts predicts the intention to eat healthy. Moreover, the relationship between active Instagram engagement and the intentional outcomes can differ for each user based on individual differences such as social comparison tendencies. As Higgs and Thomas (2016) point out, social influences on eating intentions and behavior embody a thriving research area which is only meant to grow. In light of taking a personalized approach to researching different audiences, it is important to keep in mind that emerging adults experience a specific life period during which social context and influences might differ compared to other age groups. Considering that social media, especially Instagram, are gaining popularity it is important to keep researching their impact on different aspects of well-being (Lup, Trub, & Rosenthal, 2015). Researching and understanding how people make their food choices could lead to an improvement in bringing proper dietary changes and therefore decreasing the risk of obesity among the population.
References
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Ajzen, I. (2015). Consumer attitudes and behavior: the theory of planned behavior applied to food consumption decisions. Italian Review of Agricultural Economics, 70(2), 121-138.
Appel, H., Gerlach, A. L., & Crusius, J. (2016). The interplay between Facebook use, social comparison, envy, and depression. Current Opinion in Psychology, 9, 44-49. doi: 10.1016/j.copsyc.2015.10.006
Åstrøm, A. N., & Rise, J. (2001). Young adults' intention to eat healthy food: Extending the theory of planned behaviour. Psychology and Health, 16(2), 223-237.
Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta‐ analytic review. British journal of social psychology, 40(4), 471-499.
Arnett, J. J. (2014). Emerging adulthood: The winding road from the late teens through the twenties. Oxford University Press.
Bevelander, K. E., Anschütz, D. J., Creemers, D. H., Kleinjan, M., & Engels, R. C. (2013). The role of explicit and implicit self-esteem in peer modeling of palatable food intake: A study on social media interaction among youngsters. PloS one, 8(8).
Birch, L. L. (1999). Development of food preferences. Annual review of nutrition, 19(1), 41-62.
Brouwer, A. M., & Mosack, K. E. (2015). Expanding the theory of planned behavior to predict healthy eating behaviors. Nutrition & Food Science, 45(1), 39–53.
Chan, K., Prendergast, G., & Ng, Y. L. (2016). Using an expanded Theory of Planned Behavior to predict adolescents' intention to engage in healthy eating. Journal of international consumer marketing, 28(1), 16-27.
Chung, C. F., Agapie, E., Schroeder, J., Mishra, S., Fogarty, J., & Munson, S. A. (2017). When personal tracking becomes social: Examining the use of Instagram for healthy eating. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 1674-1687).
Clark, J. L., Algoe, S. B., & Green, M. C. (2018). Social network sites and well-being: the role of social connection. Current Directions in Psychological Science, 27(1), 32-37. Conner, M., Norman, P., & Bell, R. (2002). The theory of planned behavior and healthy
eating. Health psychology, 21(2), 194.
Coyne, S. M., Padilla-Walker, L. M., & Howard, E. (2013). Emerging in a digital world: A decade review of media use, effects, and gratifications in emerging
adulthood. Emerging Adulthood, 1(2), 125-137.
Cruwys, T., Bevelander, K. E., & Hermans, R. C. (2014). Social modeling of eating: A review of when and why social influence affects food intake and choice. Appetite, 86, 3-18. Deshpande, S., Basil, M. D., & Basil, D. Z. (2009). Factors influencing healthy eating habits
among college students: An application of the health belief model. Health marketing quarterly, 26(2), 145-164.
Escobar-Viera, C. G., Shensa, A., Bowman, N. D., Sidani, J. E., Knight, J., James, A. E., & Primack, B. A. (2018). Passive and active social media use and depressive symptoms among United States adults. Cyberpsychology, Behavior, and Social Networking, 21(7), 437-443.
Festinger, L. (1954). A theory of social comparison processes. Human relations, 7(2), 117-140.
Fishbein, M., & Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Gibbons, F. X., & Buunk, B. P. (1999). Individual differences in social comparison:
development of a scale of social comparison orientation. Journal of personality and
social psychology, 76(1), 129.
Greenwood, S., Perrin, A., & Duggan, M. (2016). Social media update 2016. Pew Research Center, 11(2).
Hackman, C. L., & Knowlden, A. P. (2014). Theory of reasoned action and theory of planned behavior-based dietary interventions in adolescents and young adults: a systematic review. Adolescent health, medicine and therapeutics, 5, 101.
Higgs, S. (2014). Social norms and their influence on eating behaviours. Appetite, 86, 38-44. Higgs, S., & Thomas, J. (2016). Social influences on eating. Current Opinion in Behavioral
Sciences, 9, 1-6.
Huang, Y. T., & Su, S. F. (2018). Motives for Instagram use and topics of interest among young adults. Future Internet, 10(8), 77.
Jackson, C. A., & Luchner, A. F. (2018). Self-presentation mediates the relationship between self-criticism and emotional response to Instagram feedback. Personality and
Individual Differences, 133, 1-6.
Lee, S. Y. (2014). How do people compare themselves with others on social network sites?: The case of Facebook. Computers in Human Behavior, 32, 253-260.
Lee, E., Lee, J. A., Moon, J. H., & Sung, Y. (2015). Pictures speak louder than words: Motivations for using Instagram. Cyberpsychology, behavior, and social networking, 18(9), 552-556.
Lup, K., Trub, L., & Rosenthal, L. (2015). Instagram# instasad?: exploring associations among instagram use, depressive symptoms, negative social comparison, and strangers followed. Cyberpsychology, Behavior, and Social Networking, 18(5), 247-252.
Meier, A., & Schäfer, S. (2018). The positive side of social comparison on social network sites: How envy can drive inspiration on Instagram. Cyberpsychology, Behavior, and Social Networking, 21(7), 411-417.
Mollen, S., Rimal, R. N., Ruiter, R. A., & Kok, G. (2013). Healthy and unhealthy social norms and food selection. Findings from a field-experiment. Appetite, 65, 83-89. Nathalia, T. C., Kansius, C., Felicia, E., & Kalpikasari, I. A. A. (2016, November). The
Influence of food blogger to the intention of consuming healthy food. In International
Conference on Tourism, Gastronomy, and Tourist Destination (ICTGTD 2016).
Atlantis Press.
Padilla-Walker, L. M., Nelson, L. J., Carroll, J. S., & Jensen, A. C. (2010). More than a just a game: video game and internet use during emerging adulthood. Journal of youth and adolescence, 39(2), 103-113.
Payne, N., Jones, F., & Harris, P. R. (2004). The role of perceived need within the theory of planned behaviour: A comparison of exercise and healthy eating. British Journal of Health Psychology, 9(4), 489-504.
Petit, O., Cheok, A. D., & Oullier, O. (2016). Can food porn make us slim? How brains of consumers react to food in digital environments. Integrative, Food, Nutrition and Metabolism, 3, 251-255.
Povey, R., Conner, M., Sparks, P., James, R., & Shepherd, R. (2000). The theory of planned behaviour and healthy eating: Examining additive and moderating effects of social influence variables. Psychology & Health, 14(6), 991-1006.
Prinsen, S., de Ridder, D. T., & de Vet, E. (2013). Eating by example. Effects of environmental cues on dietary decisions. Appetite, 70, 1-5.
Rivis, A., & Sheeran, P. (2003). Descriptive norms as an additional predictor in the theory of planned behaviour: A meta-analysis. Current Psychology, 22(3), 218-233.
Ross, C., Orr, E. S., Sisic, M., Arseneault, J. M., Simmering, M. G., & Orr, R. R. (2009). Personality and motivations associated with Facebook use. Computers in Human Behavior, 25(2), 578-586.
Ruggiero, T. E. (2000). Uses and gratifications theory in the 21st century. Mass communication & society, 3(1), 3-37.
Sheldon, P., & Bryant, K. (2015). Instagram: Motives for its use and relationship to narcissism and contextual age. Computers in human Behavior, 58, 89-97. Smith, A., & Anderson, M. (2018). Social Media Use 2018. Pew Research Center
Smock, A. D., Ellison, N. B., Lampe, C., & Wohn, D. Y. (2011). Facebook as a toolkit: A uses and gratification approach to unbundling feature use. Computers in Human Behavior, 27(6), 2322-2329.
Sotos-Prieto, M., Bhupathiraju, S. N., Mattei, J., Fung, T. T., Li, Y., Pan, A., Willett, WC., Rimm, EB. & & Hu, F. B. (2015). Changes in diet quality scores and risk of
cardiovascular disease among US men and women. Circulation, 132(23), 2212-2219. Sotos-Prieto, M., Bhupathiraju, S. N., Mattei, J., Fung, T. T., Li, Y., Pan, A., Willett, WC.,
Rimm, EB. & Hu, F. B. (2017). Association of changes in diet quality with total and cause-specific mortality. New England Journal of Medicine, 377(2), 143-153. Valkenburg, P. M., Peter, J., & Walther, J. B. (2016). Media effects: Theory and Research.
Annual Review of Psychology, 67, 315-338.
Vannucci, A., Ohannessian, C. M., & Gagnon, S. (2019). Use of multiple social media platforms in relation to psychological functioning in emerging adults. Emerging Adulthood, 7(6), 501-506.
Vaterlaus, J. M., Patten, E. V., Roche, C., & Young, J. A. (2014). # Gettinghealthy: The perceived influence of social media on young adult health behaviors. Computers in Human Behavior, 45, 151-157.
Verduyn, P., Lee, D. S., Park, J., Shablack, H., Orvell, A., Bayer, J., Ybarra, O., Jonides, J., & Kross, E. (2015). Passive Facebook usage undermines affective well-being:
Experimental and longitudinal evidence. Journal of Experimental Psychology:
General, 144(2), 480.
Yang, C. C. (2016). Instagram use, loneliness, and social comparison orientation: Interact and browse on social media, but don't compare. Cyberpsychology, Behavior, and Social Networking, 19(12), 703-708.
Zilberman, D., & Kaplan, S. (2014). What the Adoption Literature can teach us about Social Media and Network Effects on Food Choices. What the Adoption Literature Can Teach Us about Social Media and Network Effects on Food Choices. doi:
Appendix A: Items used for the scale constructions
Attitude (Åstrøm & Rise, 2007)
1. To eat healthy foods regularly in the future is reasonable. 2. To eat healthy foods regularly in the future is useful.
Subjective norms (Brouwer & Mosack, 2015)
1. People who are important to me think I should eat a healthy diet.
2. People who are important to me would approve of my eating a healthy diet. 3. People who are important to me want me to eat healthy.
PBC (Povey et al., 2000)
1. How confident are you that you could eat a healthy diet if you wanted to? 2. How much control do you feel you would have over eating a healthy diet? 3. How easy or difficult do you think it would be for you to eat a healthy diet?
Active Instagram engagement (Yang, 2016)
1. How often do you post/upload posts about healthy food content on your Instagram profile without tagging anyone?
2. How often do you comment on or reply to posts about healthy food on Instagram?
Passive Instagram engagement (Yang, 2016)
1. How often do you browse your Instagram home page/newsfeed to view/read posts about healthy food without leaving comments?
2. How often do you check out profiles on Instagram featuring content about healthy food without leaving a like or comments?
3. How often do you “like” posts about healthy food on Instagram?
Social comparison (Gibbons & Buunk, 1999)
1. I often compare myself with others with respect to what I have accomplished in life. 2. If I want to learn more about something, I try to find out what others think about it. 3. I always pay a lot of attention to how I do things compared with how others do things. 4. I often compare how my loved ones (boy or girlfriend, family members, etc.) are doing with how others are doing.
5. I am not the type of person who compares often with others. (reverse coded)
6. If I want to find out how well I have done something, I compare what I have done with how others have done.
7. I often compare how I am doing socially (e.g., social skills, popularity) with other people.
Intention (Povey et al., 2000)
1. I want to eat a healthy diet in the future. 2. I intend to eat a healthy diet in the future.
Instagram use (Lup et al., 2015)
How much time do you spend on Instagram daily? (You can see this information by going to your Instagram profile, clicking the three horizontal lines in the upper right corner and then clicking on “Your activity”.
1. 10 minutes or less 2. 11–30 minutes
3. 31–60 minutes 4. 1–2 hours 5. 2–3 hours
