TRAFFIC LIGHT LABELS, EGO DEPLETION AND HEALTH
CONSCIOUSNESS: THE EFFECTS ON FOOD CHOICES MADE IN
RESTAURANTS
Master Thesis
MSc Marketing Management Faculty of Economics and Business
17 June 2019
CHARLOTTE VICTORIA ANNA SARS Student number: S3482413 Diephuisstraat 45 9714 GV Groningen tel: 0620995944 E-Mail: c.v.a.sars@student.rug.nl First supervisor: Martine van der Heide
PREFACE
You are about to read my master thesis about the effects of traffic light labels, resource depletion and health consciousness on the food choices made in restaurants. This thesis was written as a final product to complete the master Marketing Management at the University of Groningen. First of all, I would like to acknowledge that I could not have written this thesis without my supervisor Martine van der Heide. Without her continuous enthusiasm for this field of research, and her ongoing help where necessary, it would have been a completely different process. Furthermore, I would like to thank my family and best friend for their continuous support along the way. I hope that you will enjoy reading my thesis and that it leaves you with new insights.
TRAFFIC LIGHT LABELS, EGO DEPLETION AND HEALTH CONSCIOUSNESS: THE EFFECTS ON FOOD CHOICES MADE IN RESTAURANTS
ABSTRACT
This research aimed to find out whether the addition of traffic light labels (TLL) on restaurant menus could help counteract the rising obesity epidemic. Additionally, being in a state of ego depletion and being a highly health conscious individual was expected to moderate this relationship. An experimental between-subjects design was used to research the described relationships, in which healthiness of a chosen three-course restaurant menu was measured in terms of total calories ordered. State of ego depletion was manipulated, and participants were randomly allocated to either a control menu condition containing only calorie information, or to a traffic light label menu condition in which TLL were added. The results of this study do not only indicate that the amount of calories ordered was not significantly lower in the TLL condition than in the control condition (t(138) = 0.732, p = 0.465), there was also no significant difference in the calories ordered for resource depleted people and non-resource depleted people (t(188) = -0.996, p = 0.321). Finally, the expected three-way interaction between TLL, state of ego depletion, and health consciousness was also found to be non-significant (p = 0.8521). Follow-up analysis, however, revealed an interaction effect between age and TLL (p = 0.0180), where younger people are more positively affected by TLL than older people.
Keywords: traffic light labels; food labeling; nutrition; public health; ego depletion; health
consciousness; restaurant
TABLE OF CONTENTS
INTRODUCTION 1 LITERATURE REVIEW 4 METHODS 11 RESULTS 17 DISCUSSION 28 CONCLUSIONS 31 REFERENCES 33APPENDIX 1: HEALTH CONSCIOUSNESS SCALE 40
APPENDIX 2: SPSS OUTPUT 41
2.1 DESCRIPTIVES 41
2.2 TRAFFIC LIGHT CONDITION OUTPUT 42
2.3 RESOURCE DEPLETION MANIPULATION 48
2.4 HEALTH CONSCIOUSNESS MEASUREMENT 53
2.5 FOLLOW-UP ANALYSIS 62
LIST OF TABLES AND FIGURES
FIGURE 1: CONCEPTUAL MODEL
FIGURE 2: MENU CONTROL CONDITION FIGURE 3: MENU TLL CONDITION FIGURE 4: FREQUENCY OF DINING OUT
FIGURE 5: CALORIES ORDERED PER COURSE, TLL CONDITION VS. CONTROL CONDITION
FIGURE 6: INTERACTION TLL CONDITION AND RESOURCE DEPLETION ON TOTAL CALORIES ORDERED
FIGURE 7: MEAN HEALTH CONSCIOUSNESS SCORES OVER 9 ITEMS
FIGURE 8: GRAPHICAL VIEW OF THREE-WAY INTERACTIONS TLL, RESOURCE DEPLETION AND HEALTH CONSCIOUSNESS
FIGURE 9: GRAPHICAL VIEW OF AGE & TLL ON CALORIES ORDERED TABLE 1: CHARACTERISTICS PER CONDITION
TABLE 2: MODEL FITTING INFORMATION ORDINAL LOGISTIC REGRESSION TABLE 3: REGRESSION RESULTS THREE-WAY INTERACTION TLL, RESOURCE
DEPLETION AND HC
TABLE 4: REGRESSION MODEL WITH CONTROL VARIABLES
TABLE 5: REGRESSION MODEL TLL, AGE AND RESOURCE DEPLETION
LIST OF ABBREVIATIONS
TLL: Traffic light labels
TL: Traffic Light
HC: Health Consciousness
NHANES: National Health and Nutrition Survey
INTRODUCTION
It is generally known that obesity is becoming an enormous health issue all over the world. The National Health and Nutrition Survey (NHANES) collects data since the 1970’s and this data reveals a consistent increase of overweight and obesity among adults and young people, not depending on the race, ethnicity, gender or age in the USA (Kelly, Barlow, Rao, Inge, Hayman, Steinberger & Daniels, 2013). When zooming into the Netherlands specifically, statistics show that a large increase in the number of obese people in the Dutch population has taken place. During the year 2017, nearly 50 percent of all people aged over twenty years old were overweight, compared to only 33 percent in the 1980’s (CBS, 2018). A long time ago, obesity was a rarity, now, it is an epidemic which major health organizations are currently considering to be a disease.
Although food consumption mainly happens at home, people do tend to go out for dinner more often. During the year 2017, revenues for the Out-of-Home Market increased by 4.1% compared to the year before. Per Dutch resident this comes down to an increase of €50 for out-of-home consumption (FoodService Instituut, 2018). Interestingly, research has shown that food consumed in restaurants has a large effect on caloric intake, which can lead to weight gain. A study by Todd, Mancino & Lin (2010) showed that every extra meal or snack consumed away from home can add on average 134 calories for that day. These extra amounts of calories consumed in restaurants is therefore another factor leading to an increase of obesity rates. To counteract this epidemic, several nutrition label initiatives have been initiated.
Nutrition labelling is an example of an intervention aimed at helping to make the food selection environment more favorable for healthy choices by providing consumers with information about the nutrient content of options (Cowburn & Stockley, 2005). However, when looking at people’s understanding of nutritional information, consumers mostly report finding nutrition labelling confusing, especially when technical and numerical terms are being used (Cowburn & Stockley, 2005). This implies a need for investigating other forms of nutrition labels which might be easier to understand.
rating systems constructed to outline the key nutritional characteristics of food products (Nathan, Yaktine, Lichtenstein & Wartella (Eds.), 2012). Different forms of FOP labels exist, from industry initiated forms in the USA, to Traffic Light (TL) systems and others (Center for Science in the Public Interest, 2010). FOP labels are becoming highly important, as is shown by an FDA survey, which found that 67% of their respondents indicate to often consult FOP labels before making a purchase (Choinière & Lando, 2010). However, the fact that there is no standardization in FOP labels makes it difficult for consumers to compare different foods based on nutrition information. Besides that, it is known that people in general often use heuristics to make decisions, and therefore, when under time pressure, consumers are more sensitive to food manufacturers highlighting healthy facets of a product that is overall actually unhealthy (Schofield & Mullainathan, 2008).
There is one specific type of nutritional labeling that has proven to be effective due to its simplicity: traffic light labeling (TLL). This specific type of nutrition label is the subject of this research. According to Sonnenberg, Gelsomin, Levy, Riis, Barraclough & Thorndike (2013) traffic light food labels do not only cause people to consider their health at the point-of-purchase, but they also increase the probability that consumers will make healthier choices. Basically what is meant by traffic light labeling is that colors are placed next to each nutrient of a product with colors similar to traffic lights. Green indicates a healthy choice based on nutrients, orange indicates medium, and red indicates an unhealthy choice (Drichoutis, Nayga, Lazaridis, 2011). This colored indication of nutritional information simplifies the understanding for consumers, and thereby intends to stimulate healthy choices. Combining calorie information with traffic light labels results in reduced calories ordered, and therefore seems to be an effective tool. Testing the effects of these traffic light labels in a restaurant setting is the focus of this research.
It is expected that the effects of TLL in restaurants is moderated by the state of ego depletion one is in. Studies have found that resisting one desire lessens the ability to resist the next, which is called depletion (Baumeister, Bratslavsky, Muraven & Tice, 1998; Schmeichel, 2007). Knowing that asserting self-control is especially difficult when being hungry (Schmeichel, 2007; Wang, Novemsky, Dhar & Baumeister, 2010), makes it likely that this is especially difficult in a restaurant setting. Thus, when people are in a state of ego depletion, they experience more difficulties in resisting temptations.
lifestyle (Gould, 1990; Kaskutas & Greenfield, 1997). Research by Gould (1990) also found that health consciousness is positively related to avoiding calorie dense foods. Thus, it is expected that the more health conscious someone is in itself, the more one will be able to still make healthy choices when in an ego depleted state, by using TLL labels.
More specifically, this research aims to answer the following questions: (1) What is the effect of Traffic Light Labels (vs. calorie information only) on the healthiness of a three course restaurant meal choice? (2) Is this effect moderated by a person’s health consciousness and state of ego depletion?
This study builds on existing literature on the topic of traffic light labels by focusing on the Dutch restaurant market. Although previous studies provided evidence of the effects of TLL (e.g. Sacks, Veerman, Moodie, Swinburn, 2011), this effect was not yet elaborately tested in relation to state of ego depletion and health consciousness. Moreover, much of previous lines of research focused on the effects of adding calorie information and/or TLL in fast-food or chain restaurant settings in the United States (e.g. Downs, Wisdom, Wansink, & Loewenstein, 2013; Elbel, Gyamfi, & Kersh, 2011). This study adds to existing literature streams by specifically focusing on a sit-down restaurant setting, and testing whether being in a state of ego depletion and/or being a highly health conscious individual enhances the effects of TLL on the healthiness of menu courses ordered.
LITERATURE REVIEW
Restaurant meal choices
Eating out of home is associated with higher energy intake, less dietary quality, and a heightened chance of weight gain and obesity (Lachat, Nago, Verstraeten, Roberfroid, Van Camp, & Kolsteren, 2011). Food choices made whilst eating out are generally more energy dense, which contributes to excessive energy intake, and therefore frequently eating outside of home leads to weight gain (Bezerra, Curioni, & Sichieri, 2012). These findings show that it is especially important for consumers to make well-thought choices in an out-of-home setting. However, it is known that consumers make food choices without taking into account their healthiness (Chance, Gorlin & Dhar, 2014). This can be explained by several factors. The benefits of these unhealthy options are often certain and immediate, whereas possible costs are uncertain and seem to be far in the future. The opposite is true for healthy food options. For these, benefits are uncertain and seem to be far in the future although costs are certain and immediate (Milkman, Rogers & Bazerman, 2008; Wertenbroch, 1998).
Another issue that makes choosing healthy options in a restaurant setting difficult for people, is the fact that people tend to treat each consumption occasion as separate and negligible (Kirby & Guastello, 2001) and by doing so, people tend to tell themselves that they will make healthier food choices in the future (Khan & Dhar, 2007), which allows them to make an indulgent choice now during their dining experience (Fishbach & Dhar, 2005; Khan & Dhar, 2006). This research aims to find ways to stimulate people to make healthier choices on the spot rather than in the future.
Food labeling
Food labeling can be used as a strategy to increase awareness of healthy food choices among consumers. Different types of food labeling try to change people’s behavior, using the so-called “nudging techniques”. According to Thaler and Sunstein (2009, p.6) nudging can be defined as: “..., any aspect of the choice architecture that alters people’s behavior in a predictable way without forbidding any options or significantly changing their economic incentives. To count as a mere nudge, the intervention must be easy and cheap to avoid.” These nudges can motivate people to consider their health at the point-of-purchase, and also increase the probability that eventually these consumers will make healthier choices (Sonnenberg et al., 2013). An example of a nutrition intervention which uses the nudging technique is front-of-package labels. Well-known FOP label strategies are reference intake labels, nutritional information panels, “Nutrition Keys” labeling system and traffic light labels (Hawley, Roberto, Bragg, Liu, Schwartz, & Brownell, 2012).
Although these nudging food labels are already commonly used on supermarket foods, they are only lately slowly being introduced in the restaurant setting. However, restaurant nutrition information is not always easy for people to use when ordering, since many establishments only provide this information online. In order to make nutrition information more accessible for customers, menu labeling regulations have been adopted in the US, and now require chain restaurants to provide calorie information on their menu boards (Food and Drug Administration, 2014). Research on the effects of these types of interventions is mixed and limited to the United States. Some research indicates that consumers may not even want to see nutrition information on a restaurant menu, or may overestimate their use of nutrition labels (Grunert & Wills, 2007). Furthermore, many controlled studies found a non-significant reduction in calories, after stating nutrition information in a restaurant setting (Long, Tobias, Cradock, Batchelder & Gortmaker, 2015).
Traffic light labeling
Traffic light labels can serve as a more simplified nutrition intervention. Seeing that nutrient information containing technical terms is too difficult for most consumers to understand, providing only calorie information might not be sufficient to counteract the obesity epidemic. Traffic light labels can be an answer to this problem, since this system uses a simplified color scheme to identify the healthiness of food. This traffic light labeling system has demonstrated to be effective in promoting healthier choices (Sonnenberg et al, 2013).
A modeling study in Australia actually found that traffic light labeling can serve as a cost effective method for preventing obesity (Sacks, Veerman, Moodie, Swinburn, 2011). Other proof comes from research in a large hospital cafeteria by Thorndike, Sonnenberg, Riis, Barraclough & Levy (2012). These researchers found that a traffic light color scheme led to an increase of sales of healthy items (marked as green) and a reduce of sales of unhealthy items (marked as red). This is in line with what Gorton, Ni Mhurchu, Chen, & Dixon (2009) found with their research on traffic light labels in New Zealand. These researchers found that traffic light labels were best understood and most frequently preferred among New Zealand study participants.
An explanation of why this traffic light label system might be so effective is that using these colors helps to attract consumer attention (Jones and Richardson, 2007), and people have learned about the meaning of these colors throughout their lives. The color red is generally associated with danger, prohibition and need for vigilance (Elliot, Maier, Binser, Friedman & Pekrun, 2009). The same applies to traffic contexts, the color red indicates a need to stop (Bargh, 1992). The color green, on the other hand, is less provoking than red (Wilson, 1966) and is often the opposite color of red (Fehrman & Fehrman, 2004). The color green is overall associated with safety (Caivano, 1998) and is considered by consumers as quieting and agreeable. It is also known that when a color is associated with a specific meaning, it basically functions as a subtle prime (Elliot et al, 2009).
compared to a control condition (no information) in a full-service restaurant. The results showed that only presenting numeric calorie information does not have a significant impact on calorie intake. However, the addition of traffic lights to numeric labels did reduce the total amount of calories consumed. Based on these research findings, the following hypothesis applies:
H1: Adding a traffic light label system has a positive effect on the healthiness of a three-course
restaurant meal choice.
State of ego depletion
It is often suggested that people engage in more unhealthy food choices when they are in a state of low self-control. Therefore, it is assumed that in order to resist these temptations and to act in line with long term health goals, people need a sufficient level of self-control (Hofmann, Friese, & Wiers, 2008; Schwarzer, 2008). Many interventions in healthy eating behavior are based on the assumption that people have sufficient levels of self-control at the point they make a food choice (Herman & Polivy, 2011). However, this is rarely true. Most food choices are made mindlessly, when people are not able or willing to exert self-control (Bargh, 2002; Wansink & Sobal, 2007). This research aims to research the effects of the low self-control conditions under which most food choices are made.
The phenomenon of ego depletion is based on the fact that after an initial act of exerting self-control (e.g. such as making a couple of choices or inhibiting impulses), people are less able and less willing to exert self-control on a subsequent task (Baumeister, Bratslavsky, Muraven, & Tice, 1998; Inzlicht & Schmeichel, 2012; Muraven & Baumeister, 2000; Vohs, Baumeister, Schmeichel, Twenge, Nelson, & Tice, 2008). Under these ego depletion conditions, people do not have the resources available or lack motivation to exert self-control over their behavior and decisions. This makes it difficult for people to weigh the pros and cons of several options and to make a deliberate decision. Rather, decision making becomes more automatic and impulsive under these conditions (Fennis, Janssen, & Vohs, 2009; Hofmann, Friese, & Strack, 2009; Janssen, Fennis, Pruyn, & Vohs, 2008). During this state of low self-control, people often prefer tempting but unhealthy food options as these are often more appealing in the short term (Bruyneel, Dewitte, Vohs, & Warlop, 2006; Vohs & Heatherton, 2000; Wang, Novemsky, Dhar, & Baumeister, 2010).
Ridder, Adriaanse & de Vet, 2014). Heuristics can be defined as simple decision rules that simplify the decision making process (Gigerenzer & Gaissmaier, 2011). Using these heuristics is especially effective in influencing behavior under conditions of low self-control, when people do not have the capacity or motivation to make a well-thought off decision (Fennis et al., 2009; Salmon et al., 2014). Heuristic-based choices are made more often under cognitive loads that temporarily occupy limited cognitive resources. Moreover, self-control can also be depleted by the very act of making a decision, which leaves cognitive resources unavailable for making subsequent thoughtful choices (Vohs et al., 2008). Although heuristics are known to be a useful strategy for the promotion of unhealthy foods, they seem to be seldom associated with healthy food products. Thus the focus of this research will be to test whether TLL can serve as a heuristic in a state of ego depletion.
A study by Koenigstorfer, Groeppel-Klein, & Kamm (2014) found that traffic light colors on nutrition labels in an in-store decision making context helped consumers in a state of low self-control to make healthier food choices. The use of traffic light labels succeeded in diverting attention away from unhealthy foods, that has previously been observed in consumers who try to restrict themselves (Fishbach, Friedman, and Kruglanski 2003; Vohs and Heatherton, 2000). Based on the results of this research, one would expect this mechanism to also work in a restaurant decision setting. Therefore, it is expected that a state of ego depletion moderates the effect of traffic light labels on the healthiness of three-course restaurant menu choices. Thus, people whose resources are depleted and therefore are in a state of ego depletion, will choose a three-course menu in a more automatic manner. Traffic light labels are known to simplify the overview of healthy options and therefore it is expected that in an automatic mode traffic light labels will aid decision making. I therefore propose the following hypothesis:
H2: The effect of TLL on the healthiness of a three-course restaurant menu choice is more
pronounced for people in a state of ego depletion.
Health consciousness
bad health, by partaking in healthy behaviors and by being aware of their health (Gould, 1988; Plank and Gould, 1990; Kraft and Goodell, 1993; Newsom, McFarland, Kaplan, Huguet & Zani, 2005).
Gould (1990) also revealed that health consciousness is positively correlated with taking vitamins and avoiding high-caloric foods. The same study found that people high in health consciousness talk and read more about health and also study ingredient labels more thoroughly. Foods with health and nutritional benefits are important to health-conscious consumers (Glanz, Basil, Maibach, Goldberg, and Snyder, 1998). Several studies have identified reasons for the increase in health consciousness among restaurant visitors including disease prevention, weight control and personal appearance (Fitzgerald, Heary, Kelly, Nixon, and Shevlin, 2013; Kang, Jun, and Arendt, 2015). However, the most important returning reason is enhanced personal health and well-being.
Howlett, Burton, Bates & Huggins (2009) claim that it is reasonable to expect that the effect of nutrition labels on consumer’s choices depends on the degree to which objective information counters prior expectations and that this is especially true for more motivated and health-conscious people. Thus, one might have certain expectations of the amount of calories in a certain dish presented on the restaurant menu, but the calorie nutrition label might actually show them a different amount which creates an expectancy-disconfirmation. Moreover, health conscious consumers usually have a tendency to avoid unhealthy food and have a desire to perceive calorie amounts as lower and they could therefore evaluate indulgent treats differently (Choi, Li, & Samper, 2019). In contrast, consumers with low health motivation are less inclined to avoid indulgent foods and therefore need a little help from nutrition labels. Thus, one would expect that the more health conscious an individual is, the more this person is motivated to carefully think about his/her’s restaurant menu choices.
ego depletion, and people are less able to think about their choices, traffic light labels can only help people make healthier choices when these individuals are conscious of their health. Concluding, a three-way interaction is expected between TLL, state of ego depletion and health consciousness, resulting in the following hypothesis:
H3: The moderating impact of ego depletion on the effect of TLL will in turn be moderated by
health consciousness; the impact of TLL’s will be more pronounced for individuals who are depleted, and this impact will be more pronounced for highly health conscious individuals.
Conceptual Model
FIGURE 1
CONCEPTUAL MODEL
The hypotheses mentioned earlier led to the conceptual model proposed above. This model displays the following effects: a direct effect of traffic light labels on the healthiness of a three course restaurant meal, a moderating effect of state of ego depletion on the direct effect mentioned before, and finally a three-way interaction between consumers’ health consciousness, state of ego depletion and traffic light labeling.
METHODS
Participants and design
The data was collected by means of an online survey which was conducted between the end of April and the beginning of May. Participants were selected by means of a convenience sample. An online survey was developed and distributed among the author’s personal network and social media channels. The target group aimed at was all people aged over 16 years old.
This research followed an experimental design with two experimental factors. Specifically, this research equaled a full-factorial 2 (TLL vs. no TLL) x 2 (ego depletion vs. non-ego depletion) x health consciousness between-subjects design. In this design people were randomly assigned to one of the TLL conditions and to one of the depletion conditions. State of ego depletion was manipulated by creating an ego depletion condition and a control condition.
Procedure
Participants participated in an online survey. After receiving information about the study and giving consent to participation, participants were introduced to the manipulation task.
Participants were randomly allocated to either the depletion condition or the control condition. A self-control manipulation task was adapted from previous research (Schmeichel & Vohs, 2009). In this manipulation task, participants were randomly assigned to write a story in one or two ways. In the free-writing condition participants were asked to write a story about a trip they had recently taken. Participants in the restricted condition received an additional instruction: these participants were not allowed to use the letters a and n anywhere in their story. Thus, this group was required to exercise self-control by prohibiting the use of particular letters during the writing task, whereas the other group had no restrictions and thus had little need to exercise self-control while writing. Both groups were told to write at least five sentences. Right after the resource depletion manipulation task, participants responded to the manipulation checks, “How hard was it to complete the essay?” and “How much effort do you feel you put into completing the essay?” on a 7-point scale. There was a strong, positive correlation between these two items, which was statistically significant (r = .590, n = 182, p = 0.000).
enjoying each other’s company whilst some jazz music was playing in the background, when the waiter hands them the menu. Subsequently, participants were asked to study the menu as they would do during a “normal” restaurant visit. Here, participants were randomly assigned to either a condition showing a menu containing calorie indications only, or to a condition showing a menu containing calorie indications as well as traffic light labels indicating the relative healthiness of the dishes. Green dishes contained the least amount of calories, whereas red contained the most. Orange dishes contained a moderate amount of calories. The exact classification is explained later.
After participants clicked the “next” button, they were asked to place their order with the waiter. They were asked to order one starter, one main course and one dessert. Later, the participant’s health consciousness was assessed by means of the validated health consciousness scale by Gould (1988). Finally, the survey ended with demographic questions as well as control variable checks.
Measurement
The dependent variable in this research was “ healthiness of a three course meal choice” and is measured in terms of total amount of calories ordered. In this research, the assumption was made that high amounts of calories are associated with more “unhealthy choices”. It is commonly known that “body weight remains stable as long as the number of calories consumed equals the number expended through physical activities and metabolic processes. When energy intake increases above expenditure, the excess is used to build new tissue, and weight gain results” (Katan & Ludwig, 2010, p.65). Therefore, assuming that one does not significantly increase his/her’s exercise pattern when going out for dinner, one would expect that consuming more caloric meal choices leads to negative weight increases. Thus, in this research, the dependent variable was measured in terms of calorie levels.
FIGURE 2
FIGURE 3
Furthermore, state of ego depletion was manipulated and participants were randomly allocated to either an ego depletion group or a control group. The manipulation check afterwards intended to test whether the ego depletion condition actually depleted a person’s resources.
Additionally, health consciousness was measured on a validated scale by Gould (1988). This scale consists of nine items which are measured on a 5-point Likert scale. A “1” indicates that the statement does not at all describe the participant, and a “5” indicates that the statement describes the participant very well. This Health Consciousness Scale was found to be reliable with a Cronbach’s Alpha of 0.92 (M = 3.64, SD = 0.772).
RESULTS
A total of 202 respondents participated in the online survey, of which 20 responses in total were eventually deleted from the dataset due to incompleteness. Of the remaining 182 responses, 28.9% was male and 66.8% female (table 1). The respondents mean age was 32.8 years old. All data analyses were conducted by means of SPSS software.
Conditions
TABLE 1
CHARACTERISTICS PER CONDITION
TLL Menu + Resource Depletion TLL Menu + free writing Control Menu + Resource Depletion Control Menu + free writing Total Participants, n (%) 35 (19.2) 49 (26.9) 44 (24.2) 54 (29.7) 182 (100)
Age in years, mean (SD) 34.3 (13.2) 33.9 (13.4) 30.9 (14.3) 32.6 (14.5) 32.8 (13.8) Female, n (%) 20 (57.1) 32 (65.3) 31 (70.5) 44 (81.5) 127 (69.8) Male, n (%) 15 (42.9) 17 (34.7) 13 (29.5) 10 (18.5) 55 (30.2) Currently on a diet, n (%) 3 (8.6) 4 (8.2) 8 (18.2) 8 (14.8) 23 (12.6) Colorblind, n (%) 0 (0) 2 (4.1) 2 (4.5) 0 (0) 4 (2.2) Dietary restrictions, n (%) 8 (22.9) 7 (14.3) 11 (25) 11 (20.4) 37 (20.3) Notice calorie information, n (%) 34 (97.1) 48 (98) 42 (97.7) 49 (90.7) 176 (95.6) Used calorie information, n (%) 9 (25.7) 16 (32.7) 13 (30.2) 17 (31.5) 55 (30.2) Noticed TLL, n (%) 34 (97.1) 45 (91.8) n.a. n.a. 80 (94.1) Used TLL, n (%) 5 (14.3) 12 (24.5) n.a. n.a. 17 (20) Average HC Score, n (SD) 3.5 (0.59) 3.6 (0.78) 3.6 (0.98) 3.8 (0.67) 3.6 (0.77)
As it was believed that frequency of dining out could influence the choices made in the restaurant setting, this had to be checked as well. The results revealed that on average most people go out for dinner once a month (30.2%), followed by twice a month (22.5%) and every couple of months (21.4%). Thus, in general, it is common for the respondents to go out for dinner frequently. Figure 4 illustrates this below.
FIGURE 4
FREQUENCY OF DINING OUT
FIGURE 5
CALORIES ORDERED PER COURSE TLL CONDITION VS. CONTROL CONDITION
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
Truffled egg & asparagus (371 kcal) Creamy Burrata (488 kcal) Salmon Bruschetta (730 kcal)
Starter choices
TL Control 0% 5% 10% 15% 20% 25% 30% 35% 40% 45%Swordfish alla siciliana (290 kcal)
Calabrian Risotto (660 kcal)
Spring tagliatelle carbonara (858 kcal)
Main choices
TL Control 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%First of all, the first figure reveals that the Creamy Burrata (488 calories) was the most popular choice among the TL condition participants, whereas the Truffled egg & asparagus (371 calories) was the most popular choice among the control condition participants. It also becomes clear that compared to the control condition, less people in the TL condition chose for the most unhealthy option, the Salmon Bruschetta (730 calories).
When looking at the main course choices, differences are still relatively small between the two groups. More people chose the Swordfish (290 calories) in the TL condition compared to the control condition, whereas more people chose the unhealthiest option, the Spring Tagliatelle (858 calories) in the control condition compared to the TL condition.
Finally, when looking at the dessert choices made, it becomes clear that more people in the TL condition chose the healthiest option, the Vanilla Ice Cream (142 calories) and less the unhealthiest option, the Chocolate Fondant (678 calories) compared to the control condition. However, in order to understand whether these differences are also statistically significant, an independent samples t-test was executed.
Before the independent samples t-test was executed to test for any differences in the group means, a test for normality was done. Although the Shapiro-Wilk test for normality was significant (Control condition p = 0.007, TL Condition p = 0.016), an inspection of the Q-Q Plots revealed that total calories ordered were normally distributed for both conditions. Moreover, whether there was homogeneity of variance was assessed by Levene’s Test for Equality of Variances (p = 0.138). Therefore, an independent t-test was run on the data with a 95% confidence interval (CI) for the mean difference. It was found that the amount of calories ordered was not significantly lower in the TL label condition than in the control condition (t(138) = 0.732, p = 0.465).
After further inspection of the data, it was found that 20.3% of the respondents indicated to have dietary restrictions. However, even after excluding these respondents from analysis, no significant difference in calories ordered were found for people in the TL label condition compared to the control condition (t(143) = 1.516, p = 0.132). The same applies for excluding people who were on a diet at the time of filling in the survey (t(157) = 1.342, p = 0.181).
A simple linear regression was used to predict amount of calories ordered from TL labels. This variable insignificantly predicted calories ordered, F(1, 188) = 0,120, p = 0.730,
R² = 0.001.
the model fit for starter, main and dessert choices, the non-significant chi-square statistics (table 2) indicate that all models do not give a significant improvement over the baseline intercept-only model. This means that the models do not give better predictions than if someone just guessed based on the marginal probabilities for the outcome categories.
TABLE 2
MODEL FITTING INFORMATION ORDINAL LOGISTIC REGRESSION
Dependent variable Model -2 Log Likelihood 2 df p
Starter choice Intercept only Final
20,557
20,140 0.416 1 0.519
Main choice Intercept only Final
19,144
18,747 0.397 1 0.529
Dessert choice Intercept only Final
20,191
19,142 1.049 1 0.306
Link function: Logit
Based on the findings of the linear regression analysis and the ordinal regression analysis, hypothesis 1 was rejected, meaning that adding a traffic light label on a menu does not have a positive effect on the healthiness of a three-course restaurant meal choice.
State of ego depletion as moderator
First, it was important to test whether the resource depletion manipulation task had been effective. The effect of the manipulation was checked by means of asking the respondent how much effort they felt they put into writing the essay. This was tested on a 7 point Likert scale with 1 indicating “a great deal” and 7 “none at all”. An independent samples t-test was conducted to compare effort scores for people in the resource depletion condition and people in the control condition. There was a significant difference in the effort scores for resource depleted people (M = 2.98, SD = 1.39) and non-resource depleted people (M = 4.36, SD = 1.38), where 2.98 is a higher score than 4.36 on the scale of 1-7 as mentioned above; t(172) = 6.808,
p = 0.000. These results suggest that the resource depletion condition was actually more
difficult and therefore, the manipulation can be considered to have been successful.
Next, an independent samples t-test was also conducted to compare the amount of calories ordered for people in the resource depletion condition and people in the control condition. There was no significant difference in the calories ordered for resource depleted people (M = 1342,3, SD = 460.14) and non-resource depleted people (M = 1410.2, SD = 471);
t(188) = -0.996, p = 0.321. These results suggest that the resource depletion condition had no
As proposed in hypothesis 2, the moderating effect of being in a state of ego depletion had to be assessed. Figure 6 below illustrates this interaction. Although it can be seen that participants in the condition of resource depletion and traffic light labels seem to order relatively less calories in total, compared to the control conditions, according to the follow-up regression analysis this effect does not seem to be statistically significant.
The Process Macro from Hayes (2012) was used to perform a regression analysis to test for this possible moderating effect. After recoding the variables TL condition and Resource depletion condition into effects and mean centering these, this analysis revealed an overall non-significant effect for the model (R² = 0.0215, F(3, 178) = 1.3042, p = 0.2746). Moreover, the main effect of resource depletion was not found to be statistically significant either (p = 0.5860). Additionally, the interaction term was also non-significant (p = 0.1415), indicating that resource depletion does not seem to moderate the effect between TL labels and healthiness of three course restaurant meal. This can also be seen from the visualization below.
FIGURE 6
INTERACTION TLL CONDITION AND RESOURCE DEPLETION ON TOTAL CALORIES ORDERED
Health Consciousness
As mentioned before, health consciousness was measured in this research using the scale developed by Gould (1988). This scale is based on nine items and was found to be reliable for testing health consciousness with a Cronbach’s Alpha of 0.919.
To further investigate the number of constructs and structure of this measure, an exploratory factor analysis was conducted. The Kaiser-Meyer-Olkin measure of sampling adequacy was .919, above the commonly recommended value of .6, and Bartlett’s test of sphericity was significant (χ2 (36) = 1027.918, p < .05). Finally, the communalities were all above .3 (see appendix 2.4), further confirming that each item shared some common variance with other items. Given these overall indicators, factor analysis was deemed to be suitable with all 9 items. Inspection of the scree plot (appendix 2.4) revealed a clear loading on one factor.
A mean health consciousness score was generated for all participants over these 9 items and this revealed an average health consciousness score of 3.64 over all 182 participants. Figure 7 indicates the variation in health consciousness scores in the sample.
FIGURE 7
The three-way interaction as proposed in hypothesis 3 was measured using Model 3 of the PROCESS Macro by Hayes (2012). After mean-centering the variables and running the regression analysis, the results revealed a non-significant overall effect for the model (R² = 0.0402, F(7, 174) = 1.0398, p = 0.4051). The table below (Table 3) shows all the results from the regression analysis.
TABLE 3
REGRESSION RESULTS THREE-WAY INTERACTION TLL, RESOURCE DEPLETION AND HC
B t p Constant 1430,3688 48.8396 0.0000 TL Effect -39,9333 -1.3635 0.1745 Resource Depletion Effect 11,4871 0.3922 0.6954 Intercept 1 -36,0888 -1.2322 0.2195 HC Effect -16,5345 -0.3998 0.6898 Intercept 2 60,4313 1.4612 0.1458 Intercept 3 43,7740 1.0584 0.2913 Intercept 4 -7,7240 -0.1868 0.8521
When looking at the main effect for health consciousness on calories ordered, we find another non-significant effect (p = 0.6898). Furthermore, the expected three-way interaction was found to be non-significant as well (p = 0.8521), indicating that hypothesis 3 had to be rejected. This implies that the moderating impact of ego depletion on the effect of TLL is not in turn moderated by health consciousness. The graph (figure 8) below visualizes this non-significant three-way interaction.
FIGURE 8
Follow-up analyses
Although all three hypotheses were rejected, it was still interesting to execute a follow-up analysis. Another regression analysis was run using Hayes (2012) PROCESS Macro. In this regression, the independent variable TLL and the moderating variable Resource Depletion were tested on the DV total calories ordered whilst controlling for age, diet restrictions, color-blindness, dieting, frequency of dining out and gender. The results of this regression can be seen in table 4 below. The model with resource depletion as moderator was found to be significant (R² = 0.1272, F(9, 172) = 2.7858, p = 0.0045) after including the following covariates: age, diet restrictions, color-blindness, dieting, frequency of dining out and gender.
TABLE 4
REGRESSION MODEL WITH CONTROL VARIABLES
B t p Constant 1391,157 2.8044 0.0056 TL Effect -26,7160 -0.9441 0.3465 Resource Depletion Effect 12,8792 0.4602 0.6460 Intercept 1 -39,1131 -1.3881 0.1669 Age -8,7334 -4.1856 0.0000 Diet_restriction 91,8774 1.2677 0.2066 Colorblind -46,8048 -0.2461 0.8059 Diet Frequency_dining_out Gender 23,8478 11,0894 -10,9665 0.2663 1.0421 -0.1796 0.7903 0.2988 0.8577
Age was the only variable in this model to be found to significantly predict the healthiness of a three course restaurant meal (b = -8.7334, t(172) = -4.1856, p = 0.000). After running Model 3 of the PROCESS Macro regression again using age as a second moderator (Z), a significant interaction effect was found between age and TLL (b = 5.0302, t(174) = 2.5013, p = 0.0133). However, there was no evidence of a possible three-way interaction between TLL, age, and resource depletion (b = 1.8943, t(174) = 0.9420, p = 0.3475).
TABLE 5
REGRESSION MODEL TLL, AGE AND RESOURCE DEPLETION
B t p
Constant 1420,6932 51.8632 0.000
TL Effect -25,5402 -0.9407 0.3481
Age Effect -7,7529 -3.9093 0.0001
Intercept 1 -4,7356 2.3868 0.0180
Resource Depletion Effect 10,7722 0.3954 0.6930
The regression analysis revealed that people aged one standard deviation (SD = 13.85) below the mean age of 32.84, statistically significantly ordered less calories (p = 0.0206) than people around the mean age (p = 0.3477), and people aged one standard deviation above the mean age (p = 0.2970).
When looking at the Johnson-Neyman regions, it becomes clear that approximately until the age of 25, people statistically significantly ordered less calories. Roughly from the age of 25 on, TLL had no effect on the calories ordered, until the age of 70, where TLL started to have an effect on calories ordered again. Nevertheless, in the latter case, the effect is reversed, as people aged above 70 seem to start ordering more calories when they see TLL on a menu. This effect is also visualized in the graph below (figure 9). Here, young people represent people aged one standard deviation below the mean (M = 32.84, SD = 13.85), and older people represent people aged one standard deviation above the mean (M = 32.84, SD = 13.85). The graph illustrates how older people tend to order less calories than younger people. Additionally, the graph visualizes that the TLL menu was more effective for younger people, as it led to less calories ordered for this group, whereas it led to slightly more calories ordered by older people, compared to the control menu.
FIGURE 9
GRAPHICAL VIEW OF EFFECT OF AGE & TLL ON CALORIES ORDERED
1200 1250 1300 1350 1400 1450 1500 1550 1600 1650
Younger people Older people
C
alorie
s orde
re
d
Effect of age & TLL on calories ordered
DISCUSSION
Traffic light labels
Although calorie information and traffic light labels on supermarket products seem to be the rule nowadays, providing this information on restaurant menus remains an exception. This study aimed to find out how effective the addition of TLL on a restaurant menu is on stimulating customers to make healthier choices in a restaurant setting. The results revealed that adding a TLL system on a restaurant menu, does not necessarily lead to healthier choices. This is in line with findings from a study by Ellison et al. (2013). These researches conducted a randomized-controlled experiment in a full-service restaurant on a university campus. In this study they randomly assigned customers to either a menu without energy content labels, one with numeric calorie labels or one with numeric calorie labels plus traffic light labels. Their results revealed no differences in options chosen.
Interestingly, in the present study, a high level of respondents (94%) indicated to have seen the TL labels, but only 20% indicated to have used these labels consciously in their decision making. This is congruent with the results of a study by Sacks, Rayner & Swinburn (2009), who researched the effects of traffic light labels on the purchases of ready meals and sandwiches. The findings of this study indicate that the introduction of a system of traffic light labels had no apparent effect on the relative healthiness of consumer purchases. A study by Dumanovsky, Huang, Nonas, Matte, Basselt & Silver (2011) gathered surveys from fast food restaurants in New York City before nutrition labeling, and participants after labeling across fast food chains at different locations, and did not detect a significant difference in mean energy content of foods purchased either. Thus, it appears that people are less easily influenced than was expected.
calories. Still, participants might have assumed that all dishes on the menu were relatively healthy, which might have led them to choose without thinking through the consequences.
State of ego depletion
Another finding of this study, contrasting the hypothesis, is that being in a state of ego depletion does not moderate the effect of traffic light labels on the healthiness of dishes chosen in a restaurant. This finding contrasts previous work by de Ridder, Adriaanse & de Vet (2014) who found that people make fewer healthy food choices under low self-control conditions. They also found that this effect is reversed when the healthy option is associated with the social proof heuristic. This might be an explanation for what happened in this research as well. Perhaps respondents noticed the calorie information and suspected that they were expected to use this information in their decision making. Another reason why ego depletion was in this case not an effective moderator, could be that this experiment took place online, and although people were asked to order food, they knew that they would not actually receive this food. This might have let them to make less realistic orders. Furthermore, although the results of the manipulation check revealed that people in the resource depletion manipulation were actually depleted, it is not possible to rule out that people in the free-writing condition were not depleted at the moment they started taking the survey. This might have minimized the differences between the groups.
The follow-up analysis of this study also revealed that younger people seem to be more influenced by traffic light labels than older people. However, due to the small sample size used in this study, this finding should be further investigated in future studies in order to make more relevant assumptions.
Health Consciousness
healthy foods motivates customers to seek out nutritional information and to make healthier choices. Likewise, Ellison, Lusk & Davis (2013) found that numeric calorie labels are most likely to influence less health conscious consumers, and symbolic calorie labels reduce caloric intake among all levels of health consciousness. Again, the results of this research contrasts these findings, in that no difference was found in calories ordered for people low on health consciousness or high on health conscious, not depending on the TLL condition or the control condition. An explanation for this might be that the respondents of this study were relatively homogeneous, and therefore so were their health consciousness scores. Thus, the differences between the groups might have been too small to find any difference in outcome. Furthermore, respondents’ answers might have been influenced by the fact that it was an online experiment.
Limitations
There are some limitations to this study. First of all, the sample turned out to be smaller than aimed for at the beginning. The aim was to have a minimum of fifty respondents per condition. However, due to incompleteness, twenty responses had to be deleted from the dataset during analysis, leaving the study with only 182 respondents. This relatively small sample may have influenced the results, seeing that it only represents a very small part of the overall population. Furthermore, although age was found to be a possible moderator, it is difficult to fully judge on this finding due to the small sample size. Only seven people were aged over 60 years old, and therefore not much can be said about the choices made by seniors.
A further limitation is the fact that an online experiment was used for this study. The online environment made the restaurant setting less realistic, as respondents were not actually going to consume the dishes they ordered and this may have influenced the choices made by them. Moreover, due to this online experiment, respondents could answer the survey anytime they liked. Consequently, respondents did not necessarily participate during dinner time or when they were hungry. This may have influenced their menu choices too.
Another limitation is that, although this research controlled for dietary restrictions, one cannot control taste. Therefore, people might have chosen dishes based on liking or not liking. Nevertheless, that is something that could happen in a real life scenario as well.
the choice was made to focus solely on calories to determine healthiness, since it is commonly known that higher amounts of calories do contribute to an increase in weight.
Suggestions for further research
Seeing that there is still much confusion as to whether the addition of traffic light labels can act as a mechanism to reduce caloric intake in restaurants, future research could benefit from researching this issue using a larger study sample. As of now, no recommendation can be made on whether or not to introduce a TLL menu system in the Netherlands. More specifically, it might be interesting to research whether the traffic light label system in restaurants could work in the Netherlands specifically, or whether this is country dependent, by comparing multiple countries in future research.
Additionally, although the results of this study do not indicate an effect for traffic light labels in a restaurant setting, using a larger sample, the effects might be different. Moreover, contrasting to this research, it would be wise to vary the types of traffic light labeling in order to find out which type works best. This research used a colored round shape in front of the dish to indicate the relative healthiness of the dish, but perhaps other types could be more effective. Perhaps a TLL system, as is used with FOP labels on supermarket products, indicating different nutrient values with colors, could be effective in a restaurant setting as well. Seeing that this works in the supermarket setting, it would be wise to test this in the restaurant setting more extensively too. In any case it might be interesting to vary the types of labeling, calorie labels only vs. adding TLL vs. adding other nutrient values (e.g. fat, sugars etc.) on restaurant menus to find out which is most effective.
Finally, seeing that the findings of this study indicate that there might be different traffic light label effects depending on age, future research should investigate this effect further.
CONCLUSIONS
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APPENDIX 1: HEALTH CONSCIOUSNESS SCALE
TABLE 1
