Making healthy behaviour trending
The influence of trending minority norms on food
choice and the role of need for uniqueness
Celine van Weerdenburg June 2019
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Making healthy behaviour trending
The influence of trending minority norms on food choice and the role of
need for uniqueness.
Master thesis in the context of the master’s degree in Communication Science at the University of Amsterdam
Celine van Weerdenburg
Student number: 10980806 Supervisor: dr. S. Mollen
Word count: 9.236
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Abstract
Food delivery services have made high-calorie food easily accessible. To reverse the obesity epidemic, healthy eating interventions should be developed. The goal of this study was to extend prior research that has looked at trending minority norms in the context of sustainable behaviour by exploring the effectiveness of trending norms in the domain of health
behaviour. The study contributes to social norm research through experimentally testing whether trending norms could be used to encourage health behaviour which is performed by a minority. More specifically, how people’s food choice is influenced by a trending minority norm message. Trending minority norms highlight the fact that a certain minority behaviour is increasing in popularity. Participants (n = 268) in an online experiment were exposed to one of four messages: a trending minority norm, a majority norm, a minority norm or control message. The main analysis was conducted among participants (n = 101) who correctly reported being exposed to either a descriptive norm or control message. Findings showed that the trending minority norm message did not result in more healthy food choices,
compared to a minority norm, majority norm or the control condition. Need for uniqueness did not influence the relationship between exposure to a social norm message and food choice. Taken together, these results suggest that trending minority norms as well as majority norms might not be suitable in a situation where most people have the intention to eat unhealthily. Implications of the findings are discussed.
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Table of content
Introduction 5
Theoretical framework 8
Social eating norms 8
Majority vs. minority norms 9
Trending minority norms 10
Need for uniqueness 13
Method 15
Participants and design 15
Procedure and materials 16
Measures 18
Results 20
Randomisation check 20
Norm manipulation 21
Perceived healthiness food choice 23
Intention to threat 23 Main analysis 24 Health intention 26 Discussion 27 Limitations 30 Conclusion 32 References 32 Appendix I Questionnaire 37
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Introduction
“Never doubt that a small group of thoughtful, committed people can change the world. Indeed, it is the only thing that ever has” – Margaret Mead (Lutkehaus, 2008).
Many countries are looking for solutions how to reverse the rise in obesity (Swinburn et al., 2011). Over the past 50 years obesity prevalence has increased exponentially (Blüher, 2019). In 2017, more than half of the Dutch citizens aged 18 and above were overweight and 14% obese (CBS, 2018). Where smoking used to be the number one lifestyle related risk factor for our health, obesity is now the biggest health concern of our society (Osborn & Morley, 2016).
The increased availability and consumption of energy-dense food has contributed to the obesity epidemic (Swinburn et al., 2011). Innovative distribution systems – like food delivery services – have made high-calorie food easily accessible and convenient (Swinburn et al., 2011). To reverse the obesity epidemic, energy dense junk food should be replaced by the consumption of food low in energy-density, such as fruit and vegetables (Robinson, Fleming & Higgs, 2014). However, in an environment where energy-dense food is highly visible it is difficult to make a healthy food choice (Swinburn et al., 2011).
Most food choices are made when individuals are under conditions of low self-control which makes it hard to resist tasty but unhealthy food (Salmon, Fennis, de Ridder, Adriaanse & de Vet, 2014). Therefore, scientific attention has shifted from educating people about nutritional values, towards developing healthier eating interventions that alter aspects of the environment (Higgs, Liu, Collins & Thomas, 2019). Salmon et al. (2014) suggest that under conditions of low self-control, people tend to rely on simple information cues, like social proof (Salmon et al., 2014). One strategy which can be used as social proof is a descriptive norm message (Cialdini et al., 2006). Descriptive norm messages inform individuals about the behaviour of other people. People use this information to base their decision on: if many others are doing it, it must be correct (Mollen, Rimal, Ruiter & Kok, 2013). Several studies
6 have shown that descriptive norm messages which inform people about eating habits of others, could encourage healthy food choices (Mollen et al., 2013; Prinsen, de Ridder & de Vet, 2013; Salmon et al., 2014).
However, when most people do not choose a healthy option, descriptive norms might be harmful. For example, most people do not choose a healthy meal while ordering food online. If people see the message that only a few other customers have ordered a low-calorie option, they are more likely to order a high-calorie meal because – according to the norm – most others do (Mortensen et al., 2019; Stok, de Ridder, de Vet & de Wit, 2012). More research is therefore needed on how to use social norm interventions when promoting a health behaviour, which is not the norm behaviour (Higgs & Thomas, 2016).
Recently, the use of trending minority norms has gained scientific attention. Two studies have found that trends might be effective when the communication of descriptive norms would be detrimental (Mortensen et al., 2019; Sparkman & Walton, 2017). Thus, in cases when a minority performs the desired behaviour. A trending minority norm highlights that a behaviour performed by a small group of people is increasing in popularity (e.g. ‘48% of University students engage in one or more water conservation behaviours, this has increased from 37% in 2 years previous.’; Mortensen et al., 2019). Mortensen et al. (2019) and Sparkman and Walton (2017) looked at trending norms in sustainable and prosocial behaviours. Both studies concerned relatively easy-to-implement or onetime behaviours (e.g. donating to an environmental cause). More research is needed to understand the
effectiveness of trending minority norms across different behaviours. Therefore, the aim of this study is to explore the effectiveness of trending norms in the domain of health behaviour, more specifically how people’s food choice is influenced by trending minority norms. Food choices are often driven by habits, which makes the behaviour more difficult to change (Walker, Gregory & Verplanken, 2015). It is therefore of interest to examine the external validity of the findings of Mortensen et al. (2019) and Sparkman and Walton (2017), considering behaviour which is more difficult to affect. During the last decade, more
7 (Bigliardi & Galati, 2013). If this trend, that more and more people are choosing healthy foods, is visible to other consumers, this might encourage them to choose a healthy option as well. Investigating whether trending norms could influence people’s food choice can provide a key to reverse the obesity epidemic. This study thereby contributes to social norm research through experimentally testing whether trending norms could be used to encourage health behaviour which is performed by a minority.
Whether people conform to behaviour performed by an increasing number of people might depend on individual characteristics.For some people, conforming to the behaviour of a minority might be frightening because of the risk of disapproval, while others enjoy being outside the norm (Imhoff & Erb, 2009). People who experience a high need for uniqueness do not find it gratifying to think or do the same as other people. They tend to actively avoid conforming to a majority (Imhoff & Erb, 2009). In addition, they consume products that are unpopular because this might indicate uniqueness (Zaggl, Hagenmaier & Raasch, 2018). This might also apply when people with a high need for uniqueness experience that they are one of the few who engage in a trend, making them innovators or leaders (Ruvio, 2008).
Imhoff and Erb (2009) state that when the need for uniqueness is high, people may agree more with a minority to increase the feeling of being different from others. Ruvio (2008) also found that individuals seek uniqueness, but within certain boundaries. The desire for social approval tends to still dominate the need for uniqueness. Therefore, it could be argued that a trending minority norm gives the consumer some social approval while still
representing a diverging choice.
Prior research showed that trending norms could influence behaviour (Mortensen et al., 2019; Sparkman & Walton, 2017), however it remains unknown for whom a trending minority will (not) influence their decision. To deepen our understanding of trending norms, more research is needed to uncover conditions under which trending norms most strongly influence behaviour. In addition to investigating the effects of trending minority norms on health behaviour performed by a minority, it is therefore of interest to examine if need for
8 uniqueness might explain for whom a trending minority norm message will influence their decision. Therefore, the research question that will be addresses in this study follows:
What is the effect of a trending minority norm message compared to a minority norm, a majority norm or a control message on people’s food choice in an online food ordering environment, and does this depend on need for uniqueness?
Theoretical framework
Social eating norms
Eating is often a social occasion and the people around us have a large impact on the food decisions we make. Our dining partners influence what we choose to eat and how much we consume (Higgs, 2015; Cruwys, Bevelander & Hermans, 2015). To illustrate, we tend to eat more when those around us consume lots of food, and less when others eat less (Cruwys et
al., 2015).
People do not necessarily have to be in each other’s presence to be influenced by each other’s eating decisions. Mere information about other people’s eating behaviour can also affect food choices (Higgs, 2015). However, there are some conditions that must be met before information about other people’s behaviour can influence one’s own behaviour. This can be explained by the focus theory of normative conduct which has two main premises (Cialdini, Reno & Kallgren, 1990). First, normative information can only influence behaviour when people pay attention to it. Paying attention to (information about) what other people eat, is a prerequisite for influence on, for example, one’s own food choices. Second, the theory distinguishes between two types of social information, both with a unique effect on behaviour when paid attention to (Jacobsen, Mortensen & Cialdini, 2011).
The first type of social information is referred to as descriptive social norms (Cialdini et al., 1990). Descriptive norms are the perceived frequency or commonness of a behaviour and relate to what the majority does. These norms highlight how other people in a social group behave (e.g. ‘Most people eat healthily’; Higgs et al., 2019). The second type of social information is the injunctive norm, which refers to the perceived amount of social approval for
9 a specific behaviour (e.g. ‘Most people favour healthy eating’; Higgs et al., 2019; Cialdini et al., 1990).
It is necessary to distinguish between the two types of norm information because they both relate to a different goal (Cialdini et al., 1990). Whereas injunctive norms are related to the goal of maintaining social approval, descriptive norms are related to the goal of making correct decisions (Cialdini et al., 1990). Descriptive norms can be used as social proof and function as a guide when making decisions (Cialdini et al., 2006). If the majority chooses an unhealthy option, other people will probably choose this option as well by using a rule of thumb: if a lot of people are doing it, this must be the right way to behave.
Most research about the influence of social norm messages on eating behaviours show that injunctive norms are not as effective as descriptive norms (Mollen et al., 2013; Stok, de Ridder, de Vet & de Wit, 2014; Lally, Bartle & Wardle, 2011). This could be explained by the fact that most people agree that healthy eating is appropriate behaviour (Higgs et al., 2019). A small set of studies has shown that descriptive norm effects occur when choosing between healthy and unhealthy food (e.g. Mollen et al., 2013; Prinsen et al., 2013; Salmon et al., 2014). For example, Mollen et al. (2013) found in a field experiment that a healthy descriptive norm message (e.g. ‘Every day more than 150 students have a tossed salad for lunch here.’) resulted in more healthy choices compared to a no norm control condition. Salmon et al. (2014) showed that when a healthy option is associated with social norm information (e.g. a pie chart which indicated that most previous participants had chosen the healthy food), participants choose the healthy option more often than the unhealthy option. This suggests that text-based descriptive norm messages could be effective in influencing people’s food choice (Robinson et al., 2014). Consequently, it could be argued that social norms can be used to persuade people to make a healthier food choice (Higgs & Thomas, 2016).
10 Majority norms refer to what most people do, thus stating the typical behaviour in a certain situation (e.g. ‘73% of Dutch university students eat sufficient fruit’; Stok et al., 2012). Majority descriptive norms can have a positive effect on healthy food choices (Mollen et al., 2013; Prinsen et al., 2013; Salmon et al., 2014), unless most people have chosen the unhealthy option (e.g. Burger et al., 2010; Prinsen et al., 2013). For example, when empty chocolate wrappers indicated that previous participants in an experiment had chosen the unhealthy snack, participants were more likely to choose the same unhealthy snack (Prinsen et al., 2013). Burger et al. (2010) found the same results in a similar kind of study, where empty snack wrappers indicated which choice previous participants had made. A descriptive majority norm message is thus problematic if the unhealthy norm is well known.
Most research about descriptive norms and healthy eating has focused on majority norms (Stok et al., 2012). In contrast, minority norms describe a rare behaviour which is only performed by a minority (e.g. ‘30% of the people eat the recommended amount of vegetables per day’; Stok et al., 2012). Stok et al. (2012) explored the influence of providing minority norm information versus majority norm information regarding other people’s fruit
consumption on participants’ fruit intake. Their results show that participants who saw the minority norm message and identified with the referent group in the message, decreased their fruit intake significantly compared to participants who saw the majority norm message and identified with the referent group in the message. Highlighting that a minority in your social environment performs a recommended behaviour may thus negatively influence the likelihood that others engage in the desirable behaviour (Stok et al., 2012).
It seems challenging for those who wish to use norms to stimulate behaviours that most people are not currently performing (Mortensen et al., 2019). This does not mean that public health campaigns should communicate false information about the norm, but rather that the message should be framed in a different way (Stok et al., 2012).
11 Mortensen et al. (2019) argue that trends could be used when descriptive norms should be avoided. Trending minority norms highlight the fact that a certain minority behaviour is increasing in popularity (e.g., ‘Some people are changing and now eat less meat’; Sparkman & Walton, 2017). Mollen et al. (2013) already mentioned in their research that it might be beneficial to develop messages that highlight the “special” nature of the minority that performs a healthy behaviour. They point to the diffusion of innovations theory (Rogers, 1962). This theory states that innovators – who are frequently in a minority position – are often influential in spreading something new in society. If this increase in behaviour is salient, it could stimulate other people to conform to this norm even though the behaviour is
performed by a minority.
Trending minority norms might explain how a small group can cause change in behaviour (Sparkman & Walton, 2017). People have a sensitivity to information about change in collective behaviour (Sparkman & Walton, 2017). Maglio and Polman (2016) showed that an increase in probability feels like something is about to happen and this changes people’s behaviour regarding those trends. Sparkman and Walton (2017) predicted that when people expect this behaviour to become the future norm they will adapt their own behaviour to fit this norm. They call this preconformity. Sparkman and Walton (2017) based their ideas on the attribution theory of Kelley (1967): seeing change in other people’s behaviour might lead to an increased perceived importance that others place on that behaviour.
Recently, it has been empirically tested that trending minority norms are effective in encouraging behaviour that is different from an existing norm (Mortensen et al., 2019; Sparkman & Walton, 2017). Mortensen et al. (2019) tested the effect of trending minority messages compared to a minority norm only in two different prosocial domains: water conservation and donating to environmental causes. The trending minority message differed from the minority message by adding a sentence which highlighted that the number of people performing the behaviour has increased in the last years (e.g., ‘Research from 2018 has found that 48% of University students engage in one or more water conservation behaviours,
12 this has increased from 37% in 2 years previous.’; Mortensen et al., 2019). Their results show that trending minority messages were more effective in affecting prosocial behaviour than minority messages which did not include a trend or a control message (informational message without a norm). This suggests that when minority norms communicate that a growing number of people performs a certain behaviour, it could have a positive effect on prompting health behaviours which are not prevalent. Trending norms thus seem to avoid the negative effects of minority norms that describe that only some people perform the desired behaviour (Mortensen et al., 2019) and could be used when persuading people into making a healthier choice. Sparkman and Walton (2017) also found that exposure to trending minority norms can affect intentions and prosocial behaviour (i.e., meat consumption and water conservation). In three online and two field experiments they showed that trending minority norms increased interest in reducing meat consumption, doubled the percentage of
participants who ordered a meatless lunch at a café and reduced laundry loads and water use (Sparkman & Walton, 2017).
Both studies show that trends are an effective strategy to use normative information, when behaviour is not yet performed by a majority, compared to a minority norm or control message. Within sustainable behaviours, it has been found that trend information could reverse the detrimental effects of a minority norm. However, Mortensen et al. (2019) and Sparkman and Walton (2017) both tested onetime behaviour (donating to an environmental cause) or behaviour which is easy to implement (water conservation). It remains unknown if trending minority norms could also affect behaviours which are more difficult to change. For example, food choices are often driven by habits which makes it more difficult to influence behaviour (Walker et al., 2015). More research is needed to test the external validity of trending minority norms across different health behaviours (Mortensen et al., 2019). From an applied perspective, it is crucial to understand if trending minority norms could be used in social norm interventions when promoting a health behaviour, which is not the norm behaviour.
13 food choice, compared to a minority norm only or a control message. Mollen et al. (2019) and Sparkman and Walton (2017) did not compare the effects between a trending minority norm and majority norm message. Previous research has shown that majority norms have a positive effect on healthy food choices (Mollen et al., 2013; Prinsen et al, 2013; Salmon et al., 2014). Therefore, it is hypothesized that a trending minority norm would not lead to more healthy food choices compared to a majority norm.
H1a: A trending minority norm message will lead to more healthy food choices
compared to both the control message and minority norm message, but not a majority norm message.
In addition, based on the findings of Sparkman and Walton (2017) it is predicted that exposure to trending minority norm messages can affect intentions. Although not the main focus of this study, it is hypothesized that a trending minority norm would increase intention to order a healthy meal in the future, compared to both the control message and minority norm message, but not a majority norm message.
H1b: A trending minority norm message will lead to a higher intention to order a healthy meal in the future compared to both the control message and minority norm message, but not a majority norm message
Need for Uniqueness
A trending minority norm might be an appropriate message frame when promoting healthy food choices that are not yet normative. However, for some people it might feel
uncomfortable to do something different than most people (Imhoff & Erb, 2009). Whether people diverge from the majority depends – among other things - on one’s need for uniqueness (NfU) (Imhoff & Erb, 2009). To some extent, we all strive for a certain level of uniqueness. Yet, some people do not like the feeling of being too similar to others (Imhoff & Erb, 2009). People who have a high need for uniqueness (NfU) actively avoid conforming to a majority (Imhoff & Erb, 2009). Individuals with a high NfU tend to make choices which are
14 unusual or unpopular because “being outside the norm” can give them the feeling of
uniqueness (Imhoff & Erb, 2009). In contrast, people with a low need for uniqueness most often follow the behaviour of others and conform to social norms (Simonson & Nowlis, 2000).
Duval (1976) demonstrated that participants with a high NfU conformed less easily to the majority than participants with a low score on NfU. Imhoff and Erb (2009) also tested whether people with a high need for uniqueness resist majority influence. Their results show that while participants with a low NfU conformed to majority influence, participants with a high NfU score did not. This supports the idea that people with a high need for uniqueness may resist social influence of the majority: when striving to be unique, conforming to the majority seems to be a less attractive option.
The results of Imhoff and Erb (2009) also hint towards a minority influence (although non-significant) in case of high level of need for uniqueness. This means that people with a high level of NfU choose a behaviour which is performed by a small number of people rather than conforming to a majority. Imhoff and Erb (2009) argue that people might choose the minority option because (and not despite that) there are only “few who do it”. At the same time, conforming to a minority includes risking disapproval. Snyder and Fromkin (1980) state that striving for uniqueness seems restricted by the desire for social approval and conformity. People pursue a state of uniqueness, however this is counterbalanced with an individual’s desire for social approval (Ruvio, 2008). People with a high NfU might also be sensitive to the influence of a trending minority. A trending minority norm could give people with higher levels of need for uniqueness some social approval, while still representing a diverging choice.
Although empirical evidence about the relationship between social norms and need for uniqueness is scars, there is some evidence that people with a high need for uniqueness actively avoid conforming to a majority (Imhoff & Erb, 2009). Need for uniqueness might therefore be a key variable when the question is raised of who will engage in minority
behaviour (Schumpe & Erb, 2015). The results of Imhoff and Erb (2009) showed that people with a high NfU might be influenced by a minority norm. This research extends these
15 preliminary findings by experimentally testing whether need for uniqueness might explain for whom a trending norm will (not) influence their food choice. Based on the research of Imhoff and Erb (2009) it is expected that people with a high level of need for uniqueness will resist majority influence while confronted with norm information about other people’s food choices. When people have the desire for social approval, but at the same time have a high need for uniqueness, people might be influenced by a trending minority norm. In this study it is therefore proposed that a trending minority norm will lead to an increase in healthy food choices, and this will especially count for people with a high need for uniqueness.
H2: A trending minority norm message will lead to more healthy food choices
compared to both the minority norm message and control message, but not a majority norm message. This effect will be more pronounced if the participant has a high level of need for uniqueness.
The concepts in this research are shown in the model in Figure 1.
Figure 1. Conceptual Model
Method
Participants and design
In this study, the effects of descriptive norm messages (i.e. minority norm, trending minority norm and majority norm) on food were tested in an online experiment. To answer the research question, a single factor between-subjects experiment has been conducted with
Need for uniqueness Message type
[1] Minority norm
[2] Trending minority norm [3] Majority norm [4] Control message Food choice [1] Unhealthy [2] Healthy H2 H1a Health intention H1b
16 four conditions (message type: minority norm vs. trending minority norm vs. majority norm vs. control). The continuous moderator need for uniqueness was measured. The dependent variables in this research were the imaginary food choice participants made (healthy or unhealthy) and the intention to order a healthy meal in the future. Although it was not the main focus of this study, intention effects are reported.
Participants were recruited through the university’s participant pool and the researcher’s own network. In total, 334 people agreed to participate in the study of whom 292 fully completed the experiment (87.43%). Nine participants were excluded from the study because they were underaged. Participants were asked to describe their diet by indicating whether they eat meat, are flexitarian, vegetarian or vegan. They were also asked to report any food allergies. Being vegan or having food allergies might hinder participants in making an imaginary food choice on Thuisbezorgd because they would not be able to eat the meal in real life. Therefore, people who reported having a milk allergy, are lactose intolerant (n = 10) or follow a vegan diet (n = 5) were excluded. The sample therefore consisted of 268
participants (68 men, 200 women). The age of the participants ranged from 18 and 80 years old (M = 33.46, SD = 14.53). Around 71.3% of the participants indicated to follow or to have completed a higher professional (Hbo) or university education. Most participants indicated to have used a meal delivery service before (84.33%). However, most of them do not use it frequently: forty-seven percent (n = 126) uses it less than monthly and around twenty-percent uses it once a month (n = 53). The procedure and materials were approved by the
university’s ethical committee.
Procedure and materials
People were invited to participate in the online survey that was accessible via a link. Participants that were recruited through the university’s research website participated in exchange for course credit. Three gift cards were raffled among serious participants in the study (either from the university’s research website or the online link). The participants were randomly assigned to one of the four conditions.
17 Participants were recruited to take part in a study in which they were asked to ‘review an online food delivery application’. The cover story presented the study as research about a new version of the Thuisbezorgd smartphone app. Thuisbezorgd is the biggest home food delivery service in the Netherlands. The company operates as an intermediary between the consumer and the restaurant.
Participants were first informed about the protection of their privacy and anonymity in the study and that during the study, participants were able to stop at any time. Participants were also informed that within seven days after participation they could withdraw their
consent to the use of their data. If participants agreed with the informed consent, they started with the experiment. First some demographic (i.e., age, gender and education level) and other general questions (i.e., food allergies and experience with food delivery services) were asked. Participants who reported to be younger than 18 years old were immediately
excluded from the experiment. Subsequently participants were asked to answer some statements about themselves as an online consumer. These statements included filler items about attitudes towards online shopping and items which measured their need for
uniqueness. The filler statements about online shopping were included to fit with the coverstory.
Subsequently, participants were exposed to a screenshot of the Thuisbezorgd app, which was adapted to reflect the different experimental conditions and the control condition. They were instructed to imagine themselves wanting to order a meal for dinner that evening. They could take as much time as they wanted to inspect the six different options that were shown, of which three were healthy (salads) and three unhealthy (burger, hotdog and fries). On average, participants looked nearly forty seconds at the screenshot (M = 39.10, SD = 32.80). No prices were displayed with the meals to prevent this from influencing participants food choice. See Appendix II for the manipulations. The order in which the healthy and unhealthy meals were shown was counterbalanced to make sure that participants food choice did not depend on the order in which they saw the (un)healthy meals.
18 random assignment. In each condition, the norm message was placed between the first two dishes to make it look like an advertisement. In the minority norm condition participants were exposed to the following message: “Did you know that 32% of the people in your area order a healthy meal?”. In the trending minority condition this text continued with “This has
increased from 22% in the last two years”. People in the majority norm condition were
exposed to the statement: “Did you know that 72% of the people in your area order a healthy meal?”. Participants in the control condition were exposed to a statement about the option to order lunch at Thuisbezorgd: “Did you know you could order lunch at Thuisbezorgd?”. Earlier in the experiment participants had reported in which province they live. This question was used to create a referent group with whom a participant could perceive a shared identity in the norm messages (Higgs, 2015). The referent group is referred to in the descriptive norm message as “the people in your area”.
After viewing the screenshot of the app, participants were first asked to report their desired food choice for that evening. Participants chose one out of six options that were accompanied with a picture of the meal and proceeded with the questionnaire. Participant’s intention to order a healthy meal the next time they would use a meal delivery service was measured. To fit with the cover story, three items measured attitudes towards the
Thuisbezorgd app. Participants were then asked to indicate how healthy they thought their chosen meal was and the percentage of people who they thought order healthy meals via the Thuisbezorgd app or website. To check whether participants had seen the descriptive norm message, they were asked to indicate whether they saw a message and if so, which
message they saw. To conclude, participants described what they thought the purpose of the study was and where debriefed on the actual purpose of the study.
Measures
Demographic variables, such as gender, age and education level were measured. Diet
habits were assessed by asking participants whether they eat meat or are either flexitarian, vegetarian or vegan. Food allergies were measured, answered with yes or no. People with a
19 food allergy indicated which type (open-ended). Experience with delivery services was
measured by asking participants whether they had used a meal delivery service before and how often (1 = less than monthly – 4 = once a week). If they had experience with a meal delivery service, they also indicated their most frequently used website. Experience with delivery services was recoded into ‘no experience’ (= ‘0’) and ‘experienced’ (= ‘1’).
Food choice (multiple choice) was recoded into two groups. One that reflected healthy choices (salads, coded as ‘2’) and another that reflected unhealthy choices (burger, fries or hotdog, coded as ‘1’). Most participants chose an unhealthy meal (65.7%, n = 176) compared to a healthy meal (34.3%, n = 92).
Need for Uniqueness was assessed using seven items based on the scale developed by Tian, Bearden and Hunter (2001), each answered on a 7-point Likert-scale ranging from ‘do not agree at all’ (1) to ‘fully agree’ (7). An example item is: “I hate products or brands that are bought by everyone”. The full list of items can be found in Appendix I. The scale for Need for Uniqueness was unidimensional (EV = 3.90, R2 = 55.76) and reliable (α = 0.85, M = 3.59,
SD = 1.22). One item (i.e. “Ik koop zelden producten omdat andere mensen vinden dat je dit
moet kopen”) loaded below 0.45. However, this item was included in the scale since deleting the item would not increase reliability.
Health intention. Participants intention to order a healthy meal in the future was measured with four items based on Stok et al. (2012), each answered on a 7-point Likert-scale ranging from ‘do not agree at all’ (1) to ‘fully agree’ (7). An example item is: “The next time I will use a meal delivery service I expect to order a healthy meal”. The full list of items can be found in Appendix I. The scale for health intention was unidimensional (EV = 3.49, R2 = 87.34) and reliable (α = 0.95, M = 3.90, SD = 1.55).
Attention check was used to indicate whether participants read the questionnaire
carefully. Using a control question, participants were instructed to select ‘Else’ as their answer. Out of 268 participants, 36.9% (n = 99) read the question correctly and selected ‘Else’ as their answer. No participants were excluded from the analysis based on the
20 attention check. Instead, those who did not recognize the norm message correctly were excluded from the main analysis (see results section).
Plan of analysis
First, a randomization check is conducted. Second, to check whether the norm manipulations were successful an independent t-test and ANOVA are conducted. Then, an intention to treat analysis is conducted on the full sample as described above. A hierarchal logistic regression analysis was performed on a subsample of people who correctly identified the descriptive norm message they had seen to test the main hypotheses. Finally, although not the focus of this study, an ANOVA is conducted to test whether exposure to one of the norm conditions influenced participants intention to order a healthy meal the next time they use a food delivery service.
Results
Randomisation check
To control for possible confounding variables, several randomization checks were performed. First, two ANOVA’s were conducted to check if participants’ education level and age was comparable across the different conditions. The ANOVA had message type (majority, minority, trending minority, control) as independent variable, and either education level or age as dependent variable. The first ANOVA showed that participants’ education level, F(3, 264) = 0.27, p = .847, was not significantly different across conditions. Participants age also did not significantly differ across conditions, F(3, 264) = 0.25, p = .862. Second, to check the distribution between conditions for gender, a Chi-square test was conducted. There were no significant differences between conditions on gender,
2 (3, N = 268) = 1.42, p = .701. Lastly,to check if participants experience with meal delivery services was comparable across conditions, a Chi-square test was conducted. The Chi-square test showed that experience with delivery services did not differ significantly across conditions,
2 (3, N = 268) = 1.12, p =21 .797. This means that the randomization was likely successful, and the groups are
comparable on the main demographic characteristics. Therefore, these variables will not be controlled for in the analysis. See Table 1 for an overview of the demographic characteristics of participants per condition on the full sample.
Table 1Demographic characteristics of participants per condition, full sample
Norm manipulation
Among the 268 participants in the four conditions, 101 (37,7%) correctly recalled seeing (in the experimental conditions) or not seeing (in the control condition) a specific (norm)
message (see Table 2). Those who did not recognize the message they were exposed to in the delivery app correctly were excluded from the main analysis. This resulted in a final sample of 101 participants, resulting in n = 22, n = 32, n = 21, and n = 26, in the control condition, minority norm condition, trending minority condition and majority norm condition, respectively (see Table 3). The main analysis was conducted with the final sample of 101 participants. The intention to threat analysis was conducted with the full sample of 268 participants. Control condition Minority norm condition Trending minority norm condition Majority norm condition Total Gender Male 14 19 19 16 68 Female 53 48 48 51 200 Age Mean 33.45 32.21 33.96 34.21 33.46 Standard deviation 13.93 15.11 14.74 14.55 14.53 Education level Lbo, vmbo . 2 . 1 3 Mavo 3 3 3 3 12 Mbo 8 7 8 14 37 Havo, vwo 8 6 8 3 25 Hbo 15 15 20 15 65 Wo 33 34 28 31 126 Total 67 67 67 67 268
22 Table 2. Overview of the number of correct and incorrect answers on message recognition per condition
Correct Incorrect Total
Minority norm 32 (47.8%) 35 (52.2%) 67
Trending minority norm 21 (31.3%) 46 (68.7%) 67
Majority norm 26 (38.8%) 41 (61.2%) 67
Control message 22 (32.8%) 45 (67.2%) 67
Total 101 (37.7%) 170 (62.3%) 268 (100%)
Table 3. Demographic characteristics of participants per condition in the subsample
To test whether descriptive norm manipulations were successful, a norm perception question was asked to measure the difference in perceptions of the descriptive norm. Therefore, participants estimated the percentage of people who ordered a healthy meal on Thuisbezorgd that day on a slider scale (ranging from 1 to 100%). An ANOVA was
conducted with message type (minority only, majority only, trending minority and control) as independent variable and perceived percentage of healthy orders as dependent variable. Against predictions, the F-test showed no significant differences between the conditions, F (3,97) = 1.06, p = .371. The perception of the norm was approximately the same for people in the control condition (M = 32.36, SD = 15.86, n = 22), minority only condition (M = 28.25, SD = 11.46, n = 32), trending minority condition (M = 27.24, SD = 12.73, n = 21) and majority
Control condition Minority norm condition Trending minority norm condition Majority norm condition Total Gender Male 6 8 6 5 25 Female 16 24 15 21 76 Age Mean 32.77 29.78 32.14 29.15 30.76 Standard deviation 14.56 13.01 15.80 12.29 13.67 Education level Lbo, vmbo . 2 . . 2 Mavo 1 2 . 1 4 Mbo 2 3 1 2 8 Havo, vwo 3 2 3 . 8 Hbo 5 4 7 7 23 Wo 11 19 10 16 56 Total 22 32 21 26 101
23 condition (M = 34.58, SD = 24.30, n = 26). Bonferroni post-hoc comparisons did not reveal any significant differences between groups (ps > .837). This indicates that the manipulation of the norm perception was not successful.
Perceived healthiness food choice
To check whether people indeed perceived the hamburger, hotdog and fries to be unhealthy and the salads to be healthy an independent samples t-test was conducted with food choice (healthy versus unhealthy) as independent variable and ratings of healthiness of food choice (1 = very unhealthy – 7 very healthy) as dependent variable. As expected, participants who chose fries, a burger or a hotdog for dinner rated their food choice as less healthy (M = 2.93, SD = 1.13, n = 176) than those who chose a salad (M = 5.08, SD = 0.97, n = 92), t(266) = -15.52, p < .001.
Intention to treat analyses
An intention to treat analysis was performed for food choice (healthy or unhealthy) on the full sample of 268 participants. A hierarchical logistic regression analysis was run to determine the effects of the descriptive norm messages on the likelihood to choose a healthy meal. In the first step, dummy variables for message type in which all norm messages and the control message are compared to the trending minority norm were entered in the model, as well as the main effect of need for uniqueness.
In contrast to the hypothesis, the participants exposed to a trending minority norm did not show a higher likelihood to choose a healthy meal than those exposed to a control message, B = -0.01, SE = 0.36, p = .986, odds ratio = 0.99, [CI = 0.49-2.02]. In addition, the participants exposed to a trending minority norm did not show a higher likelihood to choose a healthy meal than those exposed to a minority norm message, B = -0.16, SE = 0.37, p = .655, odds ratio = 0.85, [CI = 0.41-1.74], nor to the majority norm message, B = -0.25, SE = 0.66, p = .698, odds ratio = 0.78, [CI = 0.11-1.60].
24 uniqueness and message type was added to the model. No significant interaction effects were found. Taken into account the participants’ level of need for uniqueness, those who were exposed to a minority message, B = -0.23, SE = 0.31, p = .473, odds ratio = 0.80, [CI = 0.43-1.48], or control message, B = -0.42, SE = 0.29, p = .151, odds ratio = 0.66, [CI = 0.37-1.17], did not show a higher likelihood to order a healthy meal compared to the trending norm condition. As predicted, no interaction effects were found of need for uniqueness and the majority norm condition compared to the trending norm condition, B = -0.29, SE = 0.31, p = .355, odds ratio = 0.75, [CI = 0.41-1.38]. See Table 4 for comparisons between conditions.
Table 4 Intention to treat analyses. Odds ratios of healthy food choice
Main analyses
The main analyses were performed on the 101 participants who correctly identified the norm or control message they had been exposed to. The same hierarchical logistic regression analysis was conducted to determine whether a trending minority norm message resulted in more healthy food choices than a minority norm message only or no norm. In the first step of the regression the main effects of message type and need for uniqueness were entered. Against predictions, the difference between the trending minority norm and minority norm condition or control condition did not reach a level of significance. Those exposed to a trending minority message did not show a higher likelihood to choose a healthy meal than those exposed to a minority message B = -0.31, SE = 0.62, p = .616, odds ratio = 0.73, [CI =
B (SE) OR [CI]
Hierarchical regression model Step 1
Control vs. trending minority -0.01 (0.36) 0.99 [0.49-2.02] Minority vs. trending minority -0.16 (0.37) 0.85 [0.41-1.74] Majority vs. trending minority -0.15 (0.37) 0.86 [0.42-1.77]
Step 2
Control vs. trending*NfU -0.42 (0.29) 0.66 [0.37-1.17] Minority vs. trending*NfU -0.23 (0.31) 0.80 [0.43-1.48] Majority vs. trending*NfU -0.29 (0.31) 0.75 [0.41-1.38]
25 0.22-2.47] or control message B = -0.17, SE = 0.67, p = .794, odds ratio = 0.84, [CI = 0.23-3.11]. Therefore, the hypothesis that a trending minority norm would result in more healthy food choices compared with the control condition and minority condition was not confirmed. As expected, the odds to make a healthy food choice in the trending minority condition did not differ significantly from the odds to choose a healthy meal in the majority condition, B = -0.25, SE = 0.66, p = .698, odds ratio = 0.78, [CI = 0.22-2.80].
In the second step of the regression, the interaction term between need for
uniqueness and message type was added to the model. It was hypothesized that the effect of a trending minority norm message would be more pronounced if the participants had a high level of need for uniqueness, compared to a minority norm only or a control message, but not a majority norm message. In contrast to the hypothesis, no significant interaction effect was found. Taken into account the participants’ level of need for uniqueness, those who were exposed to a minority message, B = -0.89, SE = 0.69, p = .198, odds ratio = 0.41, [CI = 0.11-1.60], or control message, B = -0.69, SE = 0.68, p = .309, odd ratio = 0,41, [CI = 0.13-1.90], did not show a higher likelihood to order a healthy meal compared to those exposed to a trending norm message. As predicted, no interaction effect was found between participants’ need for uniqueness level and those exposed to a majority message, B = -0.24, SE = 0.56, p = .666, odd ratio = 0,78, [CI = 0.26-2.37], compared to those exposed to a trending minority message. The second hypothesis was therefore not confirmed. See Table 5 for comparisons between conditions.
26 Table 5 Main analyses. Odds ratios of healthy food choices
Health intention
To test whether the social norm condition influenced participants’ intention to order a healthy meal in the future, an ANOVA was conducted. Message type (minority, majority, trending vs control) was used as an independent variable and the scale for health intention as dependent variable. First, the ANOVA was conducted on the full sample of 268 participants. The F-test showed no significant effect of message type on participant’s health intention, F(3, 264) = 0.45, p = .720. Bonferroni post-hoc comparisons did not reveal any significant differences between groups (ps = 1.000). A second analysis with the subsample of 101 participants who correctly identified the norm or control message they had been exposed to also showed an insignificant effect, F(3, 97) = 0.42, p = .736. There were no significant differences in the participants’ intention to make a healthy food choice the next time they would use an online delivery service between the control condition (M = 4.01, SD = 1.46), minority condition (M = 3.70, SD = 1.36), trending condition (M = 3.56, SD = 1.45) or majority condition (M = 3.76, SD = 1.17). Bonferroni post-hoc comparisons did not reveal any significant differences between groups (ps = 1.000). Therefore, the hypothesis that a trending minority norm message will increase people’s intention to make a healthy food choice compared to a minority norm message or control message, but not a majority norm, was not confirmed.
B (SE) OR [CI]
Hierarchical regression model Step 1
Control vs. trending minority -0.17 (0.67) 0.84 [0.23-3.11] Minority vs. trending minority -0.31 (0.62) 0.73 [0.22-2.47] Majority vs. trending minority -0.25 (0.66) 0.78 [0.22-2.80]
Step 2
Control vs. trending*NfU -0.69 (0.68) 0.41 [0.13-1.90] Minority vs. trending*NfU -0.89 (0.69) 0.41 [0.11-1.60] Majority vs. trending*NfU -0.24 (1.53) 0.78 [0.26-2.37]
27
Discussion
The goal of this study was to test the effects of trending minority norm messages on people’s food choices and the role of need for uniqueness. Therefore, an online experiment was conducted in which trending minority, minority and majority norms were communicated, as well as a control message. Based on prior research by Mortensen et al. (2019) and
Sparkman and Walton (2017) it was predicted that a trending minority norm message would result in more healthy food choices, compared to both a control message as well as a
minority norm message, but not a majority norm message. In contrast to the prediction, there were no differences between different norm messages, nor the control message on food choice. Those exposed to a trending minority norm message did not make more healthy food choices than those in the control, minority or majority condition. In addition, there were no differences between different norm messages, or the control message on participants’ intention to make a healthy food choice the next time they would use an online delivery service.
The current study extends prior research that has looked at trending minority norms in the context of sustainable behaviours by testing the effectiveness in health behaviours. More specifically, how people’s food choice is influenced by trending minority norms. Trending minority norm messages have been shown to positively influence behaviours which are not yet performed by a majority, compared to a minority norm or control message (Sparkman & Walton, 2017; Mortensen et al., 2019). In the current study, however, highlighting a trending norm did not result in healthier choices when people are asked to imagine themselves ordering food online. A reason for this may be that taste overrules the influence of social norms (Higgs, 2015). Pliner and Mann (2004) found that social norms did not influence participants to make a healthy snack choice when the food was perceived as less tasty. Raghunathan, Naylor and Hoyer (2006) argue that people consume energy-dense food not despite its unhealthiness, but rather because of it. They showed in their study that people intuitively believe that the less healthy the food, the tastier it would be. This unhealthy = tasty
28 intuition could have affected participants food choices. To counter the unhealthy = tasty bias, future research could make the healthy meals more appealing. Instead of using salads as the healthy option, plant-based hamburgers could be used, but without any energy-dense
toppings like cheese. Current findings contrast those of previous research in which social norms have been found to influence people’s food choice (e.g. Mollen et al., 2013; Prinsen et al., 2013; Salmon et al., 2014). Although it has been demonstrated that mere information about other people’s eating behaviour can affect food choices (Higgs, 2015), there might be some boundaries to this influence. People have pre-existing preferences when it comes to eating and sometimes even have clear routines, for example with breakfast (Cruwys et al., 2015).
In the current study most participants indicated to have used meal delivery services before, however, most of them did not use it frequently. A possible explanation for the
nonsignificant results could be that most people see these online meal delivery services as a treat and do not look at health aspects while ordering food. In the current study, two thirds of the participants chose an unhealthy meal. Previous research has looked at the influence of social norms on food choices, among other things, in a laboratory setting (Salmon et al., 2014). This is a situation in which food choices are made in the moment, while it is likely that people decide to order something unhealthy in advance while using a food delivery service. As an illustration, some participants in the current study indicated in the comment section that they cook healthily at home and when they use a delivery service they intentionally order something unhealthy (e.g.: “I cook healthily myself, Thuisbezorgd is a way to sin for me”). This is in line with the previously mentioned unhealthy = tasty inference. If people have a hedonic goal, they are more likely to select an unhealthy meal, presumably because they think that it would taste better (Raghunathan et al., 2006). Future research could use different food options, which both fulfil hedonic preferences. For example, a pizza as
unhealthy option, which is high in calories and saturated fat. The healthier option could be a pizza with a cauliflower crust, which contains fewer calories.
29 feel more certain about what they like or dislike about food, then people do about the
appropriate amount to eat in certain situations (Cruwys et al., 2015). In other words, social norms might have less influence on food choices than the amount of food consumed. As Cialdini et al. (2006) state, descriptive norms function as a guide when making decisions and are related to the goal of making correct decisions. If people already know what they like and dislike, they do not have to look at others while choosing for themselves. Previous research has demonstrated that our dining partners influence the amount of food we consume (Higgs, 2015; Cruwys et al., 2015). Future research could investigate whether trending norms are successful in situations where the appropriate amount of consumption is not yet known – for example with portion size - or when it is not known how tasty specific meals are.
In addition, the current study explored the effects of trending norms on relatively difficult to change behaviour. Previous studies that did find significant results have focused on easy-to-implement or onetime behaviours (e.g. donating to an environmental cause and water conservation; Mortensen et al., 2019, Sparkman & Walton, 2017). Repeatedly
selecting a behaviour in a certain context can lead the behaviour to become automatic (Walker et al., 2015). In the context of food delivery services this would mean that choosing an unhealthy meal each time that you use a meal delivery service becomes a standard for ordering food online. If this happens, the behaviour is automatically triggered by
environmental cues and information can no longer easily have an influence (Walker et al., 2015).
Contrary to expectations, need for uniqueness did not influence the relationship between exposure to a social norm message and food choice. People with a high need for uniqueness like to use products before others do to distinguish themselves, and it was predicted that this would also apply for adopting to other behavioural trends. Contrary to previous research, no significant interaction results were found between message type and need for uniqueness. Considered the participants’ level of need for uniqueness, those who were exposed to a minority message or control message did not show a higher likelihood to order a healthy meal compared to those exposed to a trending norm message. Ruvio (2008)
30 showed that people express uniqueness through consumption but this is constrained by one’s own desire for social approval. It could be that people do not perceive these
boundaries while ordering food online. People with a high level of need for uniqueness make choices which are not common to differentiate themselves from others (Imhoff & Erb, 2009). However, people’s food choice through a meal delivery service cannot be seen by others and therefore people might focus only on their own (hedonic) preferences. Current research on need for uniqueness has focused on products or attitudes that could be seen by other people (e.g. Imhoff & Erb; Zaggl et al., 2018). However, when people’s food choice is not likely to be noticed by others, it could be that people with a high need for uniqueness do not feel the need to differentiate themselves by choosing contrary to the majority. Zaggl et al. (2018) propose a product-specific need for uniqueness, instead of a general desire for uniqueness. In a situation where other people do see what kind of food you order (e.g. a canteen or restaurant), need for uniqueness might indeed be a behavioural motivator. Highlighting minority food trends in a canteen or (food) festival might trigger one’s need for uniqueness and thereby can help to distinguish oneself. Need for uniqueness might therefore be more appropriate as a moderator in another context.
Limitations
The primary limitation of the current study is that norm perceptions were not affected by the norm messages participants had seen. To test whether norm perceptions were affected by the descriptive norm message, participants estimated the percentage of people who ordered a healthy meal on Thuisbezorgd. Against predictions, each condition estimated the
percentage of people roughly the same. This might indicate that the descriptive norm messages were not convincingly enough. Mortensen et al. (2019) found effects of the trending norm when using minorities which almost became a majority. However, the percentages used in this study were not as close to becoming a majority. Regarding the majority norm message, it might be that the descriptive norms were not in line with the actual perceptions of the participants. Future research should pre-test the effectiveness of the norm
31 manipulations on the perceived norm to make sure the descriptive norm message is
convincing and in line with perceptions of the participants.
Only thirty-seven percent of the participants in the study reported correctly which social norm message they had seen, and this resulted in the exclusion of more than half of the participants. Therefore, it is important to manipulate norm perceptions in a different way so that participants notice the norm message and as a result the conditions clearly differ in perceived norm. To make the descriptive norm more salient a pop-up message could be used.
A final consideration is that participants in the current study were instructed to make an imaginary food choice. To make the experience of ordering food online more realistic, future research could make use of an actual app where participants can hover, scroll and click on items to make their decision.
Conclusion
Innovative food distribution systems – like food delivery services – have made high-calorie food easily accessible and convenient. This study tested whether trending minority norm messages could influence people to make a healthier food choice. Stimulating behaviours that most people are not currently performing can be challenging. Previous research has shown that trending norms provide a potential solution within prosocial behaviour. However, in the field of health behaviours findings did not support the expectations. The results suggest that trending minority norms as well as majority norms might not be suitable in a situation where most people have the intention to eat unhealthily. More research, therefore, is needed to uncover when trending minority norms are appropriate to use when the target health behaviour is not the prevalent one.
32
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37
Appendix I: Questionnaire
Factsheet
Beste deelnemer,
Welkom bij dit onderzoek. Graag wil ik je vragen om de onderstaande tekst goed door te lezen. Dit onderzoek wordt uitgevoerd onder verantwoordelijkheid van Amsterdam School of Communication (ASCoR), onderdeel van de Universiteit van Amsterdam.
Het onderzoek waarvoor ik je medewerking wil vragen is getiteld “Het beoordelen van een maaltijdbezorging app”. In dit onderzoek ga je de app van een maaltijd bezorgservice bekijken, waarna je wordt gevraagd hierover enkele vragen te beantwoorden. Het onderzoek duurt 5 tot 10 minuten. Onder de deelnemers die de vragenlijst serieus hebben ingevuld worden drie bol.com bonnen ter waarde van €15,- verloot.
Omdat dit onderzoek wordt uitgevoerd onder de verantwoordelijkheid van ASCoR, Universiteit van Amsterdam, heb je de garantie dat:
1. Je anonimiteit is gewaarborgd en dat je antwoorden of gegevens onder geen enkele voorwaarde aan derden worden verstrekt, tenzij je hiervoor van tevoren uitdrukkelijke toestemming hebt verleend. 2. Je zonder opgaaf van redenen kunt weigeren mee te doen aan het onderzoek of je deelname voortijdig kunt afbreken. Ook kun je achteraf (tot 7 dagen na deelname) je toestemming intrekken voor het gebruik van je antwoorden of gegevens voor het onderzoek.
3. Deelname aan het onderzoek geen noemenswaardige risico’s of ongemakken met zich meebrengt en je niet met expliciet aanstootgevend materiaal zult worden geconfronteerd.
Voor meer informatie over dit onderzoek en de uitnodiging tot deelname kun je te allen tijde contact opnemen met Celine van Weerdenburg (celine.vanweerdenburg@student.uva.nl) of haar
scriptiebegeleider Saar Mollen (s.mollen@uva.nl; 020-5253680), Universiteit van Amsterdam, Postbus 15793, 1001 NG Amsterdam. Mochten er naar aanleiding van je deelname aan dit onderzoek klachten of opmerkingen bij je zijn, dan kun je contact opnemen met het lid van de Commissie Ethiek van de afdeling Communicatiewetenschap, per adres: ASCoR secretariaat, Commissie Ethiek, Universiteit van Amsterdam, Postbus 15793, 1001 NG Amsterdam: 020-5253680, ascor-secr-fmg@uva.nl. Een vertrouwelijke behandeling van je klacht of opmerking is daarbij gewaarborgd.
Ik hoop je hiermee voldoende te hebben geïnformeerd. Hartelijk dank voor je deelname aan dit onderzoek.
Met vriendelijke groet, Celine van Weerdenburg
38 Informed consent
Ik verklaar hierbij op voor mij duidelijke wijze te zijn ingelicht over de aard en methode van het onderzoek, zoals uiteengezet in de uitnodigingsmail voor dit onderzoek.
Ik stem geheel vrijwillig in met deelname aan dit onderzoek. Ik behoud daarbij het recht deze instemming weer in te trekken zonder dat ik daarvoor een reden hoef op te geven. Ik besef dat ik op elk moment mag stoppen met het onderzoek.
Als mijn onderzoeksresultaten worden gebruikt in wetenschappelijke publicaties, of op een andere manier openbaar worden gemaakt, dan zal dit volledig geanonimiseerd gebeuren. Mijn
persoonsgegevens worden niet door derden ingezien zonder mijn uitdrukkelijke toestemming. Als ik meer informatie wil, nu of in de toekomst, dan kan ik me wenden tot Celine van Weerdenburg (celine.vanweerdenburg@student.uva.nl).
Voor eventuele klachten over dit onderzoek kan ik me wenden tot het lid van de Commissie Ethiek namens ASCoR, per adres: ASCoR secretariaat, Commissie Ethiek, Universiteit van Amsterdam, Postbus 15793, Amsterdam; 020-5252689, ascor-secr-fmg@uva.nl.
39 Demographic data
Allereerst willen we je enkele algemene vragen stellen. 1. Wat is je leeftijd? ___ 2. Wat is je geslacht? Man Vrouw Anders
3. Wat is de hoogste opleiding die je op het moment volgt of hebt afgerond? Basisonderwijs Lbo, vmbo Mavo Mbo Havo, vwo Hbo Wo
4. In welke provincie woon je? Noord-Holland Zuid-Holland Zeeland Groningen Friesland Drenthe Overijssel Gelderland Flevoland Noord-Brabant Limburg Utrecht
5. Hoe omschrijf je jouw huidige eetpatroon? Vleeseter
Flexitariër Vegetariër Veganist
