What makes people leave LESS food? Testing effects of smaller portions and information in a behavioral model

Appetite 139 (2019) 127–144

Contents lists available at ScienceDirect

Appetite journal homepage: www.elsevier.com/locate/appet

What makes people leave LESS food? Testing effects of smaller portions and information in a behavioral model

T

Bettina A. Lorenz-Walthera, Nina Langena,∗, Christine Göbelb, Tobias Engelmannc, Katrin Bienged, Melanie Speckd, Petra Teitscheidb a

Institute of Vocational Education and Work Studies, Technische Universität Berlin, Marchstraße 23, 10587, Berlin, Germany FH Münster-University of Applied Sciences, iSuN – Institut für Nachhaltige Ernährung, Corrensstraße 25, 48149, Münster, Germany c Faktor 10 – Institut für nachhaltiges Wirtschaften gemeinnützige GmbH, Alte Bahnhofstraße 13, 61169, Friedberg, Germany d Wuppertal Institute for Climate, Environment and Energy, Research Group Sustainable Production and Consumption, Doeppersberg 19, 42103 Wuppertal, Germany b

A R T I C LE I N FO

A B S T R A C T

Keywords: Food waste Out-of-home consumption Consumer behavior Structural equation model Leftovers

To contribute to a better understanding of consumer food leftovers and to facilitate their reduction in out-ofhome settings, our study analyzes the effects of two common intervention strategies for reducing leftovers in a holistic behavioral model. Based on a quasi-experimental baseline-intervention design, we analyzed how the display of information posters and the reduction of portion sizes take an effect on personal, social and environmental determinants in a structural equation model. Applying data from online surveys and observations among 880 guests (503 baseline, 377 intervention) during two weeks in a university canteen, the suggested model allows to assign effects from the two interventions on plate leftovers to specific changes in behavioral determinants. Portion size reductions for target dishes are found to relate to lower levels of plate waste based on conscious perception, represented in smaller portion size ratings. Effects from seeing information posters are found to base on changed personal attitudes, subjective norms and perceived behavioral control. However, depending on how an individual reacts to the information (by only making an effort to finish all food or by making an effort and additionally choosing a different dish in the canteen) there are opposite effects on these determinants and consequently also on plate leftovers. Overall, the differentiated results on intervention effects strongly support the benefits of more holistic and in-depth analyses of interventions to reduce plate leftovers and therefore to contribute to more sustainable food consumption in out-of-home settings.

1. Introduction Although absolute estimates about global food waste face high levels of uncertainty, statements that one third of all food produced is wasted (Gustavsson, Cederberg, Sonesson, van Otterdijk, & Meybeck, 2011) highlight the potential and relevance for its reduction. Since eating food away-from-home becomes more and more common (European Commission, 2011), settings such as school or work canteens have to be considered to increase sustainable food consumption by reducing food waste (Reisch, Eberle, & Lorek, 2013). Specifically plate waste constitutes a relevant share of avoidable waste in service settings (Betz, Buchli, Göbel, & Müller, 2015; Engström & Carlsson-Kanyama, 2004; Heikkilä, Reinikainen, Katajajuuri, Silvennoinen, & Hartikainen, 2016; Lorenz, Hartmann, Hirsch, Kanz, & Langen, 2017; Silvennoinen, Heikkilä, Katajajuuri, & Reinikainen, 2015). To positively change food consumption behavior away-from home towards more sustainable

∗

behavior, a number of studies suggests the use of information basedinstruments such as signs, labels and posters (Kallbekken & Sælen, 2013; Lassen et al., 2014; Manomaivibool, Chart-asa, & Unroj, 2016; Martins, Rodrigues, Cunha, & Rocha, 2015; Morley et al., 2013). Similarly, choice architectural changes such as the arrangement of food offers or different portion sizes are supported (Dayan, 2011; Freedman, Bartoli, & Wagle, 2012; Lahne & Zellner, 2015; Wansink & van Ittersum, 2013). However, most of these intervention-type studies measure only descriptive changes in behavior and do not consider the underlying behavioral processes. Regarding plate leftovers, only two studies have applied a broader approach to analyze leftover behavior from a statusquo perspective. For a fixed setting, they show that the occurrence of leftovers in canteens is linked to a variety of personal, social and environmental factors which can jointly be integrated into a general model (Lorenz, Hartmann, Hirsch, et al., 2017; Lorenz, Hartmann, & Langen, 2017).

Corresponding author. E-mail address: [email protected] (N. Langen).

https://doi.org/10.1016/j.appet.2019.03.026 Received 23 March 2018; Received in revised form 26 September 2018; Accepted 23 March 2019 Available online 06 April 2019 0195-6663/ © 2019 Elsevier Ltd. All rights reserved.

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

times. That's better than taking a lot once.” (Kallbekken & Sælen, 2013) as part of a more behavioral economics oriented intervention was found to change behavior towards lower levels of plate waste. Accordingly, we decided to design and test the effects of an information poster which highlights the issue of food waste and addresses procedural knowledge about how guests can avoid plate leftovers. Related to the former, it was posted that the kitchen team at the university canteen aims to avoid food waste and asks for help as well as that the canteen may sometimes not provide a full range of dishes in order to avoid unnecessary waste. Related to the latter, two strategies to avoid receiving excess amounts of food at the canteen were posted. First, a reminder to only take so much food that one is able to finish it with a focus on the possibility to choose as many side dishes as one wishes. Second, an invitation to ask for a smaller portion of a dish at the food service counter. The poster was displayed well-visibly in the canteen. Since the poster originally was presented in German, Fig. 1 shows the original German poster together with an English translation.

The aim of this study is to apply this general model for an analysis of behavior change by assessing the effects from two common1 interventions to reduce leftovers in a university canteen. First, decreasing portion sizes and second, providing information on the issue of food waste and on how guests can avoid plate leftovers. Thereby, our study provides a more holistic understanding on how different interventions influence individuals’ behavior and considers direct as well as indirect intervention effects. 2. Study design 2.1. Empirical setting Based on a project cooperation, data was collected in a university canteen in Germany, serving approximately 3000 meals for lunch every day. Generally, the menu offered in the canteen each day composes of four regular main dishes (two main dishes with meat and one vegan dish, see Appendix 1) which can be complemented with servings of side dishes (served separately in small bowls). Every day, at least two types of starchy side dishes and two types of vegetable side dishes (one type of salad and one type of cooked vegetables) as well as an extended variety of desserts (fruit salad, curd desserts etc.) are available. In addition to those regular menu items, the canteen provides two special dishes (one with meat, one vegetarian) every day, representing a fixed composition of main and side dishes as well as a salad- and pasta-bar. Meal prices for students are subsidized and amount to EUR 2.30 to EUR 5.50 for a hot main dish including side dishes. An overview of the full meal plan offered during the weeks of data collection for this study is presented in Appendix 1.

2.2.2. Second intervention: reduce portion size Regarding the second type of intervention, different studies show that the perception and impact of portion sizes on food consumption volumes goes beyond conscious evaluation. A well-known example for such effects are studies that show how larger or smaller portion sizes of food are not necessarily related to larger or smaller portion size ratings of guests in out-of-home settings but nevertheless change consumption volumes and food leftovers (Diliberti et al., 2004; Freedman & Brochado, 2010; Wansink & van Ittersum, 2013). A potential explanation for these effects is that portion sizes perform as heuristic or visual cue for food consumption and leftovers (Scheibehenne, Todd, & Wansink, 2010a; Wansink, Painter, & North, 2005). Besides, portion sizes from a social perspective may be interpreted as consumption norm that states an adequate or socially acceptable amount of food intake (Wansink & van Ittersum, 2013). Accordingly, we decreased the portion size of specific target dishes in the canteen. In cooperation with the canteen management and under consideration of general recommendations on more sustainable diets in industrialized countries by decreased meat consumption (Notarnicola, Tassielli, Renzulli, Castellani, & Serenella, 2016), three meaty main dishes (one dish on Monday, Tuesday and Wednesday) out of the regular canteen menu were identified as target intervention dishes (see Appendix 1). A reduction in portion sizes was realized by either serving smaller pieces of meat (initial portions of 140 g were reduced to 120 g) or using smaller scoops for serving sauces that contain meat (resulting in a decrease from 100 g of sauce per portion to 83 g).

2.2. Design of two interventions to decrease plate leftovers When reviewing existing research that has considered plate leftovers mainly from the perspective of evaluating food intake, two types of interventions are particularly prominent in changing food-related behavior. First the provision of information in the form of labels or posters (Kallbekken & Sælen, 2013; Lassen et al., 2014) and second the manipulation of portion sizes (Diliberti, Bordi, Conklin, Roe, & Rolls, 2004; Freedman & Brochado, 2010; Geier, Rozin, & Doros, 2006; Lorenz & Langen, 2018; Vermeer, Steenhuis, & Poelman, 2014). 2.2.1. First intervention: provide information Regarding the first type of intervention, it is stated that targeted information on the one hand provide knowledge to individuals which then relates to potential changes in food-related attitudes (Barr, 2007; Gawronski & Bodenhausen, 2006; Tarkiainen & Sundqvist, 2005) and in perceived behavioral control over specific aspects of food choice and consumption (i.e. healthy behavior) (Conner, Norman, & Bell, 2002; Kim, Ham, Yang, & Choi, 2013; Vyth et al., 2011). On the other hand, it is stated that information may rather activate the use of existing knowledge, attitudes, social norms or intentions instead of adding to it (Thomas, Puig Ribera, Senye-Mir, & Eves, 2016). A useful distinction with regards to nutritional knowledge and its effect on behavior describes declarative and procedural knowledge (Worsley, 2002). Whereas declarative knowledge refers to cognitive or more explicit knowledge about facts, procedural knowledge refers to more implicit knowledge about how to do something (Worsley, 2002). Especially the provision and addressing of procedural knowledge is relevant for changing food-related behavior i.e. increasing the consumption of fruit and vegetables at a worksite canteen (Lassen, Thorsen, Trolle, Elsig, & Ovesen, 2004) or decreasing food waste in private households (Stancu, Haugaard, & Lähteenmäki, 2016). At a descriptive level, the display of posters stating that guests in a hotel should “Visit our buffet many 1

3. Behavioral model for the Analysis of Plate Leftovers To analyze behavioral changes in response to interventions from a holistic perspective, our analyses base on a baseline-intervention comparison for an extended set of behavioral factors and their interaction in determining individuals’ plate leftovers in a structural equation model (SEM, see Fig. 2). The constructs considered are closely related to the model by Lorenz, Hartmann, Hirsch, et al. (2017). Our model jointly addresses three dimensions of behavioral determinants, namely personal, social and environmental factors. A detailed overview of all hypotheses relating to structural model relationships is available from Appendix 2. Linked to personal factors, seven hypotheses (P1 –P7, see Fig. 2) relate to the classical Theory of Planned Behavior (TPB) framework with behavioral intention (H-P1), attitudes (H-P4), subjective norms (H-P3) and perceived behavioral control (PBC, H-P2) (Ajzen, 1991). Besides the successful integration of classical TPB determinants for plate leftovers in canteens (Lorenz, Hartmann, Hirsch, et al., 2017; Lorenz, Hartmann, & Langen, 2017) the TPB framework is also supported in modelling other sustainability related food behaviors such as the purchase of organic (Arvola et al., 2008), regional (Lorenz,

As shown in the review by Lorenz and Langen (2018). 128

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

Fig. 1. Poster with information to decrease plate leftovers in a canteen. On the left: English translation of the German poster (on the right) in original design. E4ii Palatability

E4i

P7ii Affec ve A tude

Envrionment A tude

E2

P4ii P7i

E6

Time Pressure (1=yes)

S1

Presence of others (males)

P5 Cogni ve A tude

P6ii

E5ii

P4i

Inten on

P1

Plate Le overs S2

P6i P3

E1

P2

Subjec ve Norm PBC

Por on Size E3

Presence of others (females)

E5iii

E5i

Type of Food Choice

Fig. 2. Structural model combining personal (P), Social (S) and Environmental (E) Determinants for the analysis of plate leftovers in a canteen.

while men tend to increase their food intake (Bell & Pliner, 2003; Cavazza, Guidetti, & Butera, 2015, 2017), we differentiate the effect of social presence based on gender (H-S1, H-S2, see Fig. 2 and Appendix 2). Finally, originating from thoughts of behavioral economics, an increasing number of studies supports that food-related behavior is not only determined by personal and social factors but as well by situational determinants and aspects of the physical environment (Just & Wansink, 2009; Just, Wansink, Mancino, & Guthrie, 2008; Story, Kaphingst, Robinson-O’brien, & Glanz, 2008). Two situational aspects of food that are relatively straightforward in their direct effect on plate leftovers are ratings of portion sizes (H-E1) and ratings of palatability (H-E2) (Betz et al., 2015; Göbel et al., 2014). Moreover, indirect impacts from those ratings via personal factors (PBC, H-E3 and attitudes, H-E4i, H-E4ii) are included based on findings by Lorenz, Hartmann, Hirsch, et al. (2017). Naturally, different dishes served in a canteen may obtain different average portion size (H-E5i) and taste (H-E5ii) ratings, especially when these dishes refer to different levels of flexibility in composition, i.e. a buffet dish compared to a dish with pre-portioned components (Guthrie & Buzby, 2001). Specific types of food such as vegetables and starchy side dishes moreover were found to refer to higher shares of plate leftovers than meat (Betz et al., 2015; Ferreira, Martins, & Rocha, 2013) which is accounted for by a direct effect from food choices on leftovers (H-E5iii). A final environmental factor that is considered in our model is time pressure (H-E6), relating to findings that time pressure during lunch significantly changes the consumption rates of lunch for children at school (Cohen et al., 2016; Price & Just, 2014).

Hartmann, & Simons, 2015) or healthy foods (Jun & Arendt, 2016) as well as waste related behaviors (Sirieix, Lála, & Kocmanová, 2017; Young, Russell, Robinson, & Barkemeyer, 2016). In line with some of these studies, two modifications are included as additional personal factors in the model: - a differentiation between cognitive and affective attitudes (H-P4i, HP4ii) which is found to better represent food-related attitudes (Arvola et al., 2008; Kang, Jun, & Arendt, 2015; Tarkiainen & Sundqvist, 2005) - a construct of personal or moral norm which determines attitudes (H-P7i, H-P7ii) and is interlinked with subjective norms is stated to improve the modelling of sustainability-related food behaviors (Arvola et al., 2008; Bamberg & Möser, 2007; Lorenz et al., 2015); since classical behavior specific measures of personal norms were found to face differentiation issues from behavioral intention for the specific behavior of plate leftovers (Lorenz, Hartmann, & Langen, 2017), we decided to apply a general environmental attitude by Haws et al., (2014) as an alternative measure. Interrelated with personal factors, social factors are identified as a relevant determinant of food-related behavior by a large body of research (Brindal, Wilson, Mohr, & Wittert, 2015; Furst, Connors, Bisgoni, Sobal, & Falk, 1996; Higgs, 2015; Lusk & Briggeman, 2009; Sirieix et al., 2017). Besides the consideration of social determinants in the TPB (P4 and P5i), more direct effects from the presence of other people are stated to influence food choices and consumption (Cruwys, Bevelander, & Hermans, 2015). In line with specific findings that women tend to decrease their food intake in the presence of others

129

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

component, respondents were asked whether they had finished this type of food or not. Second, plate leftovers were observed by videotaping in high-resolution returned trays in the main scullery (where trays from two out of three return points for trays are cleared) of the university canteen. Leftovers were visually estimated from those videos. Since the visual estimation in this form relates to unpackaged leftovers served on separate standardized plates, sufficient reliability of this estimation method was assumed with reference to past research (Hanks, Wansink, & Just, 2014). An overview of all measured indicators and constructs for the baseline and intervention data collection may be obtained from Table 1. Regarding the majority of indicators presented in Table 1, which relate to stated measures, a smartphone-optimized online survey was programed for data collection. It also included questions on socio-demographics, respondents’ familiarity with the university canteen and a short block of complementary questions on personal nutrition and sustainable consumption. In the intervention data collection, it moreover was asked whether respondents had already participated in the baseline survey. Overall, answering the online survey in a pre-test with 10 respondents proved to be sufficiently easy and took between 20 and 30 min.

3.1. Additional hypotheses on intervention effects To evaluate the effects of an exposition to information and to decreased portion sizes within the presented model, three additional hypotheses are formulated in reference to the suggested behavior change from similar interventions described in section 2.2. Regarding effects of the designed poster, it is assumed that information on the one hand changes personal attitudes towards plate leftovers. On the other hand, the practical suggestion to ask for smaller portions and to be careful about not taking too much food is predicted to increase individuals’ personal control over leftovers. Finally, the notion that “the kitchen team makes a great effort to avoid food waste” is assumed to increase perceptions of social norms that food leftovers should be avoided. Hypothesis I1. Being exposed to an information poster on how to avoid food leftovers in the canteen … i) … increases positive cognitive attitude towards finishing all food. ii) … increases positive affective attitude towards finishing all food. Hypothesis I2. Being exposed to an information poster on how to avoid food leftovers in the canteen increases the perceived behavioral control over having plate leftovers.

4.2. Collection of data and final sample

Hypothesis I3. Being exposed to an information poster on how to avoid food waste increases perceived subjective norms in favor of finishing all food. In contrast to an information effect based on conscious processing, the effect of decreased portion sizes is presumed to influence leftovers without noticing. Accordingly, three hypotheses are formulated:

To analyze behavior changes by two interventions, a collection of data for the defined indicators was carried out at two points in time. First, a baseline data collection took place during one week (MondayFriday) in November 2016. Second, six weeks later in December 2016, after a full meal cycle in the canteen was finished and the same dishes were offered again, the intervention of reduced portion sizes was carried out. Finally, after three full meal cycles in the university canteen in April 2017, the information posters were prominently displayed in the canteen (while portion sizes of target dishes remained constantly at the reduced level) and an additional data collection (again during one week, Monday-Friday) took place. Fig. 3 provides a schematic overview about the procedure of data collections and interventions. During the baseline and intervention collection, guests of the university canteen were approached by two research assistants at two (out of three) central points of tray return between 11:30 a.m. and 2:30 p.m. (corresponding to the opening hours between 11:15 a.m. and 2:15 p.m.) and asked to participate in an online survey about “eating lunch at the university canteen”. If they were interested, a link to a smartphoneoptimized online survey with an individual participation code was handed to them. After this, the same participation code was written on the tray of the guest with a non-permanent marker by the research assistants. Thereby, trays of guests who were interested in participating in the survey could be identified from the observational videotape in the main scullery and could be matched with his or her answers. Among a total of 18,944 videotaped trays (8749 in baseline and 10,195 in intervention) on nearly 26 h of video material, 1630 trays during baseline and 1672 trays during intervention data collection were identified as coded by the research assistants. Survey participation reached N = 508 respondents during baseline and N = 413 during intervention. No respondent in the intervention indicated to have participated in the baseline survey as well. Hence, it can be assumed that there is no bias from repeated participation in the intervention data collection. After clearing data (based on a minimum survey process time of 8 min compared to a mean process time of 24 min and on a maximum share of 5% missing values) 880 respondents (503 baseline and 377 intervention) were considered as valid sample. However, due to repeated technical problems during the videotaping process, leading to insufficient contrast and blurred images, only 556 visual estimations of plate leftovers (293 baseline and 263 intervention) could unequivocally be assigned to participation codes in the online survey. We base subsequent analyses on the total valid sample (stated leftover behavior) and on additional exclusive analyses considering stated as well as

Hypothesis I4. Dishes with decreased portion sizes directly decrease plate leftovers from those dishes. Hypothesis I5. The portion size ratings for dishes with decreased portion sizes do not differ from the ratings for dishes with regular portion sizes. Hypothesis I6. The taste ratings for dishes with decreased portion sizes do not differ from the ratings for dishes with regular portion sizes. 4. Methodology 4.1. Measurement of model components To measure the relevant factors leading to plate leftovers in the university canteen, measurement systems in the form of reflective indicators2 were set up for seven of the determinants in the general model (see Fig. 1), namely behavioral intention, cognitive and affective attitudes, subjective norms, PBC, environmental attitude and the palatability of food. Based on the definitions by Lorenz, Hartmann, Hirsch, et al. (2017) and with reference to recommendations on the reflective measurement of constructs in structural equation modelling, at least three indicators were defined for each latent variable in the model (Ding, Velicer, & Harlow, 1995). The remaining environmental or situational factors as well as the exposure to the two interventions were measured directly, either in continuous form (portion sizes perception, time pressure, potential reactions to information exposure) or by dummy variables (presence of others for male and female respondents, type of food choices, exposure to interventions either by choosing a target dish or by stating to have seen or reacted to the information poster). The measurement of the central outcome of the model, plate leftovers, was conducted in two ways. First, stated plate leftovers were measured by a set of binary responses. For each chosen food 2 For background information on the setup of measurement systems in structural equation modelling, the authors would like to recommend Coltman, Devinney, Midgley, and Venaik (2008).

130

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

Table 1 Indicators and constructs measured at baseline and final data collection. Construct

Items

Reference

Cognitive Attitude

Having leftovers is beneficial/harmful** Having leftovers is valuable/valueless** Having leftovers is ideal/unacceptable** Having leftovers makes me feel happy/unhappy** Having leftovers makes me feel relaxed/annoyed** Having leftovers makes me feel satisfied/unsatisfied** Others think people should finish all food* x Their opinion is important to me* Others finish all food on their plate* x I try to do the same* Others may criticize me if I don't finish all food* x Their critics make me feel uncomfortable* Finishing all food on my plate is usually easy to me* I could always finish all food on my plate if I wanted to* Predicting the right amount of food at food choice is easy* I will do my best to empty my plate* I generally try not to leave food* I somewhat expect to have leftovers* [reversed measurement] It is relevant for me that the products that I use do not harm the environment* I generally consider the environmental impacts of my activities* My consumption behavior is influenced by considerations about environmental impacts* I am concerned about the waste of resources* I am willing to accept inconveniences for being more environmental friendly* I would consider myself to be conscious about the environment* Visual appearance of food today (1–5 stars) Taste of food today (1–5 stars) Smell of food today (1–5 stars) The portion size of my food today was too small/too large (7-point bipolar) My lunchtime today was unusually short/unusually long (7-point bipolar scale) Index for visually estimated leftovers from videos based on chosen servings of food component: 0 = no leftover, 1 = leftover equivalent to less than 0.5 servings of one food component, 2 = leftover equivalent to about 0.5 servings of one food component; 3 = leftover equivalent to more than 0.5 servings of one food component; 4 = leftover of a full serving of one food component Index for stated leftovers based on the question whether any of the food components chosen was not finished during lunch. 0 = no leftover, 1 = one component with leftovers, 2 = 2 components with leftovers, 3 = 3 or more components with leftovers Stated food choice from a list of all available food offers at the day of survey participation, creation of three dummy variables: vegetarian vs. meat, salad bar vs. service line, special dish with fixed components and special name against regular dishes with variable component-based selection Stated presence of other persons during lunch: creation of four dummy variables based on three groups for male and female participants: eating alone, eating with one other person, eating with two or more other persons Dummy variable for answering “yes”: Do you recognize the following sign/poster from your visit to the university canteen? Two dummy variables for answering “yes”: Have you reacted to the poster in any of the following ways? i) I have chosen a different food/smaller portion sizes; ii) I tried to finish the food that I chose Creation of a dummy variable if one of the target dishes was chosen at baseline or intervention

Crites, Fabrigar, & Petty, (1994)

Affective Attitude

Subjective Normb

Perceived Behavioral Controla

Intentiona

Environ-mental Norm

Palatabilitya

Portion Sizea Time Pressurea Observed Plate Leftovers

Stated Plate Leftovers

Food Choicea

Presence of Othersa

Viewing of Information Reaction to Information

Exposure to Portion Size Change

Crites, Fabrigar, & Petty, (1994)

Cialdini, Kallgren, and Reno (1991), Cruwys et al. (2015) Armitage and Conner (2001)

Ajzen (2006)

Haws, Winterich, and Naylor (2014)

Finkbeiner (2013), Hermans, Larsen, Peter Herman, and Engels (2012)

Connors and Rozell (2004)

Hierarchy adapted from Pulos and Leng (2010)

*measurement on five-point Likert Scale, **measurement on 7-point bipolar scale. a Measured identically by Lorenz, Hartmann, Hirsch, et al., 2017. b Although the items are equivalent to measures by Lorenz, Hartmann, Hirsch, et al., 2017, an alternative calculation was applied, leading to strictly positive subjective norms. Meal cycles (every 6 weeks)

4.3. Validation of measurement systems for latent variables 1

Means to reduce le overs Data Collec ons

2

3

4

5

6

Prior to the full model estimation, a confirmatory factor analysis (CFA) was carried out to ensure a reliable and valid representation of latent variables by the defined measurement systems. Thereby, it was considered that the validation of measurement needs to hold for both, the baseline as well as the intervention sample. Moreover, the distribution of empirically measured indicators provided for a violation of the assumption of multivariate normal distribution (see Appendix 3). Hence, a grouped CFA was conducted under application of a maximum likelihood estimator with robust standard errors (for an extensive discussion on the use of maximum likelihood estimation in CFA and structural equation modelling, see Yuan and Bentler (2000); Enders (2001)). Generally, the results (see Appendix 4) of this analysis support the suggested measurement systems with sufficient reliability (> 0.6) for the latent variables and sufficient individual loadings (> 0.7) as well as

PorƟon size reducƟons (unconsciously nudging) InformaƟon Baseline

Interven on

Fig. 3. Sequence of data collections and interventions.

observed leftovers for the reduced sample. We assume that due to the technical nature of observation issues, observational data is missing at random. All subsequent analyses were conducted using Mplus Software (Version 7.3).

131

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

homogeneous age and living situation. On average, the sample in the intervention collection with an average age of 24.3 years is slightly younger than in the baseline collection with 24.7 years. This tendency also is represented in slightly more undergraduate students, a slightly higher share of people living alone and finally a higher share of participants with a monthly net income below EUR 900 (see Table 2).

average variance extracted (AVE, > 0.5) for most of the assigned indicators (for reference values see Bagozzi and Yi (1988)). Specific shortcomings regarding single criteria are observable for subjective norms, PBC and behavioral intention in the baseline sample. However, these local shortcomings were accepted with regards to the remaining criteria (i.e. low indicator loadings for one item do not lead to a general low reliability of the latent variable). Finally, discriminant validity or a clear differentiation between measurement systems for different latent variables was checked by applying the Fornell-Larcker-Criterion (Fornell & Larcker, 1981). All measurement systems except for cognitive and affective attitudes in the baseline sample were adequately differentiated. Concerning the theory-implied close association between cognitive and affective attitudes this was not considered a structural issue in the model. Hence, with the aim to represent different aspects of attitudes (see section 3.1), both constructs were included in subsequent analyses.

5.2. Descriptive differences between baseline and intervention Starting with a comparison of the measured indicators, representing individual perceptions, there are only small differences in distribution and means between the baseline and intervention group. This also is represented in non-significant differences based on standard T-tests (see Appendix 3). Similarly, the social context for meals in the university canteen with regards to the number of people with whom respondents went for lunch is comparable between both weeks. Both, male as well as female respondents dominantly went for lunch in a group with at least two more other people. Only a low share of men and an even lower share of women had lunch on their own (see Table 3). Buffet, special (fixed components) and extra (burger, sausage, fries) dishes were more popular during the baseline than during the intervention. Conversely, regular component based vegetarian dishes gained in popularity during the second data collection. Notably, the share of regular component based dishes that contain meat and also of the three intervention dishes remained constant between baseline and intervention (see Table 3). In comparison to total sales of the university canteen during the collection of data, the food choices of our sample may be considered representative. Stated plate leftovers are nearly equally distributed between the baseline and intervention group (see Table 3) and relatively uncommon with more than 85% stating that they did not have any leftovers at the day of survey participation. In contrast to the invariance of stated leftovers there are significant differences (p < 0.05) between observed leftovers during baseline and intervention with a significantly lower share of plates with leftovers during intervention (see Table 3). Moreover, - under consideration of randomly missing observations - observed leftovers suggest a higher share of people that have leftovers compared to their stated measures. Additional support for this suggestion may be drawn from a supplementary visual analysis of trays that could not be unequivocally assigned to a specific respondent. For the remaining 1339 trays during baseline and 1409 trays during intervention, a share of 37% and 25% respectively had at least some plate leftovers. When comparing finally stated and observed plate leftovers descriptively for the intervention dishes, a tendency of decreasing leftovers from reduced portion sizes occurs (see Table 4). Although people who chose intervention dishes at both points in time had more often plate leftovers (stated was well as observed measures) than people who chose other dishes, the share of people without plate leftovers increased more for them from baseline to intervention (from 63% to 78% for observed leftovers) than for the average of other dishes (from 74% to 80% for observed leftovers see Table 4). Due to the high difference in sample size and the low number of people who generally chose the intervention dishes, these tendencies however are not manifested as significant differences. Moreover, it cannot be determined whether differences exclusively relate to the changes in portion sizes or to other determinants (i.e. the provision of information). In contrast to the observable effect from reduced portion sizes, a comparison for the exposition to information provides less clear results (see Table 5). For observed leftovers, people who recall to have seen the information poster appear to have lower leftovers than people who cannot recall having seen the poster. This interrelation does not hold for stated leftovers. The two types of stated reactions to seeing the poster (choosing a different dish; making an effort to finish all food) have opposite effects on plate leftovers (see Table 5). Whereas reacting only by making an effort to finish all food appears to relate to less leftovers, reacting by choosing a different dish appears to relate to more leftovers.

4.4. Analysis of intervention effects in the structural equation model To finally apply the suggested SEM for the analysis of intervention effects, two different estimations with robust maximum likelihood estimators were conducted. First, a grouped model estimation for the baseline and intervention sample was conducted to evaluate the effect from portion sizes changes. To do this, the defined paths representing effects for the choice of an intervention dish were compared between the baseline (regular portion sizes) and intervention sample (reduced portion sizes). Moreover, the grouped model was applied to determine whether the display of information has a general effect on the constructs covered in our model. Second, an estimation only for the intervention sample was conducted to evaluate the effects of being consciously exposed to information in contrast to not being consciously exposed (not having recognized). To meaningfully compare results from the estimates of a SEM for the baseline and intervention sample and hence to interpret the effects of the two interventions, it was necessary to first assure a similar overall model fit and measurement invariance for all latent constructs across the two samples. Regarding the former, basic goodness-of-fit criteria were applied: a preliminary SEM estimation under the exclusion of dummy variables yielded that the suggested model structure provides good model fit to both samples (baseline/intervention: CMin/df = 1.22/1.21, CFI = 0,976/0,971, TLI = 0,973/ 0,967, RMSEA = 0.026/0.029; for reference values see Byrne (2012)). Regarding the latter, Chi-Square difference tests were applied to see whether a nested model with forced invariance of factor loadings and factor intercepts between baseline and intervention significantly decreased the overall model fit (see Appendix 5). Results of these tests imply that measurement invariance can be assumed for the baseline and intervention sample based on non-significant difference statistics. In other words, there are no structural differences in indicators and their assignment to latent variables between the baseline and intervention group. 5. Results The subsequent section first provides an overview of the sample in the baseline and intervention data collection. Second, descriptive differences in measured variables between baseline and intervention sample are analyzed and finally, structural findings on the general model and the impact of the two interventions within the model are presented. Thereby, also a differentiation between stated and observed leftover behavior as dependent variable is considered. 5.1. Sample properties For both, baseline and intervention, there are similar sample properties (see Table 2). Naturally, the sample in a university canteen mainly composes of students and hence of a population with 132

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

Table 2 Sociodemographic characteristics for 808 guests at a university canteen. Sample Distribution (valid %, Nbase = 503, Ninterv = 377) Gender Age Visitor Status Education Household size Household composition Relationship status Monthly net income

Baseline Intervention Baseline Intervention Baseline Intervention Baseline Intervention Baseline Intervention Baseline Intervention Baseline Intervention Baseline Intervention

50% 51% 50% 54% 89% 87% 56% 62% 32% 31% 91% 90% 58% 60% 51% 59%

male 19–23 years student under-graduate 2 persons no children single below EUR 900

50% 49% 27% 24% 10% 12% 34% 33% 27% 33% 5% 6% 34% 34% 18% 13%

female 24–28 years university employee post-graduate 1 person 1 child permanent relationship EUR 1300- EUR 2600

18% 13% 1% 1% 6% 3% 17% 16% 4% 4% 8% 5% 14% 14%

> 28 years

5% 9%

< 19 years

3% 1% 24% 20%

other

external guest postdoc 3 persons

4 or more persons

2 or more children married EUR 900 - EUR 1300

17% 14%

> EUR 2600

Table 3 Descriptive comparison of leftovers and environmental determinants. Baseline Food Choices (Nbase = 503/Ninterv = 377)

Social Context of Females (Nbase = 249/Ninterv = 178)

Social Context of Males (Nbase = 252/Ninterv = 186)

Stated Leftovers (Nbase = 503/Ninterv = 377)

Observed Leftovers (Nbase = 293/Ninterv = 263)

Buffet dish Special dish Extra dish Regular dish, vegetarian Regular dish, meat No intervention dish Intervention dish Eat alone Eat with one other person Eat with other persons Eat alone Eat with one other person Eat with other persons No leftovers 1 component 2 components 3 or more components No leftovers < 0.5 servings 0.5 servings > 0.5 servings

105 106 38 141 64 49 19 50 180 29 53 170 429 35 31 8 214 57 17 5

Intervention 21% 21% 8% 28% 13% 10% 8% 20% 72% 12% 21% 67% 85% 7% 6% 2% 73% 19% 6% 2%

61 47 33 146 54 36 13 39 126 30 33 123 326 32 13 4 210 28 16 9

Difference 16% 12% 9% 39% 14% 10% 7% 22% 71% 16% 18% 66% 86% 8% 3% 1% 80% 11% 6% 3%

n.s. ** n.s. ** n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s. n.s.

*

Significance based on a Pearson Chi-Square statistics. “n.s.” = non-significant (significance level p* < 0.05, ** < 0.01).

controlling for potential other differences and changes.

It should be noted that all people who stated to have reacted by choosing a different dish (N = 18/12) have as well stated to have reacted by making an effort to finish all food. Again, one cannot surely assign the observed differences to the provision of information without

Table 4 Descriptive differences in leftovers from reduced portion sizes. Baseline

Stated leftovers

Observed leftovers

Observed leftovers (no) linked questionnaire

No leftovers 1 component 2 components 3 or more components No leftovers < 0.5 servings 0.5 servings > 0.5 servings No leftovers At least some leftovers

Intervention

Intervention dish, N = 49/30

Other dishes, N = 454/263

Intervention dish, N = 36/27

Other dish, N = 341/236

80% 14% 2% 4% 63% 23% 7% 7% Baseline, N = 1339 63% 37%

86% 6% 7% 1% 74% 19% 6% 1%

83% 8% 3% 6% 78% 19% 4% 0% Intervention, N = 1409 75% 25%

87% 9% 4% 1% 80% 10% 6% 4%

Different sample sizes relate to the difference in the valid sample for stated/observed leftovers. 133

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

Table 5 Descriptive differences in leftovers from exposition to information. Intervention

Stated leftovers

Observed leftovers

No leftovers 1 component 2 components 3 or more components No leftovers < 0.5 servings 0.5 servings > 0.5 servings

Baseline N = 503/293

Reacting by choice, N = 18/12

Reacting by eating, N = 59/40

recall of poster, N = 122/86

no recall of poster, N = 255/177

67% 28% 6% 0%

85% 14% 2% 0%

84% 11% 3% 2%

88% 7% 4% 1%

85% 7% 6% 2%

75% 17% 8% 0%

85% 8% 5% 3%

86% 7% 3% 3%

77% 12% 7% 3%

73% 19% 6% 2%

Different sample sizes relate to the difference in the valid sample for stated/observed leftovers.

or plate leftovers in the baseline sample (when portion sizes are regular). However, in the intervention sample choosing one of these dishes has a highly significant negative impact on portion size perceptions while effects on palatability as well as on plate leftovers remain nonsignificant (see Table 6). Overall, the effect on portion size perceptions results in a small but significant indirect effect on plate leftovers (−0.045, p < 0.01) from choosing reduced portion size dishes during the intervention. However, neither the direct effect of choosing an intervention dish on portion size perceptions nor the total effect of choosing an intervention dish on leftovers is significantly different between the baseline and intervention data collection based on a Wald Test.

5.3. Estimation results for the structural equation model Based on the preliminary model, applied for tests of measurement invariance, dummy variables for different types of food choices, social presence and time pressure were tested separately as well as interactively for their behavioral contribution in the SEM. Since time pressure in general (referring to hypothesis E6) and specific types of food choices (special dishes compared to all other dishes, vegetarian food choices compared to choices containing meat) did not yield any significant effect in the baseline or intervention group, those variables were excluded from further analyses and will not be presented. 5.3.1. General results Overall, the grouped model with N = 880 (baseline = 503 and intervention = 377) which was applied for an analysis of portion size effects yields a very good overall fit with a robustness corrected CMin/ df = 1.25, a CFI = 0.959, a TLI = 0.955 and a RMSEA = 0.027. The model supports most of the suggested causal relationships and reaches sufficient R2 values for behavioral intention (0.383 in baseline and 0.328 in intervention) and for stated leftover behavior (0.202 in baseline and 0.180 in intervention). An overview of the estimation results including the reference to the hypotheses tested is provided in Table 6. Focusing briefly on the general theoretically defined causal relationships of the model, most of the seven hypotheses related to personal factors are supported by significant structural model estimates. Exceptions are in the baseline group a general non-significant impact from cognitive attitudes on intention (H-P4i) and a non-significant effect from subjective norms on intention (H-P3). Considering the effects of social presence, their inclusion appears relevant based on significant direct effects on leftovers. However, the results are inconsistent between baseline and intervention and cannot support hypothesis H-S1 and H-S2. Finally, reviewing results based on the hypotheses for environmental determinants, the central constructs of portion size and palatability are supported in their direct effect on leftovers (H-E1 and H-E2). Portion size perceptions moreover significantly determine PBC (H-E3). In contrast, no significant effect of palatability ratings on attitudes is found (H-E4i and H-E4ii). Regarding the effect of specific types of food choices on palatability, portion size perceptions and plate leftovers (H-E5i, ii, iii), buffet dishes compared to regular componentbased dishes at both points in time receive larger portion size ratings but also directly relate to lower plate leftovers.

5.3.3. Results on the provision of information The general presence of information in the intervention data collection is not manifested in changed averages of indicators for attitudes (H-I1), subjective norms (H-I2) or PBC (H-I3) (see Appendix 3). Additionally, it was tested whether fixing the structural model relationships of those constructs between baseline and intervention provided a significant change in the model fit (see Appendix 5). The insignificant results of this test show that the general presence of information in the intervention sample is not manifested in any structural model changes for the effects of cognitive and affective attitudes, subjective norms and PBC. Therefore, the display of information did not yield any theoryrelated changes between the baseline and intervention sample in general. To include reactions to the display of information in more detail, an intervention-only model with a set of dummy variables was computed. First, a dummy variable when study participants recalled they had seen the poster during their canteen visit and second, a more differentiated set of three dummy variables, referring to (a) people who only recall seeing information but do not state any reactions to them, (b) people who recall information and state to have made additional effort to finish all food and (c) people who recall information and state to have chosen different or smaller dishes. In line with the condition that the overall model needs to sufficiently fit in both of the samples the estimation for the intervention sample only (N = 377) yielded comparable results with a good overall model fit (robustness corrected CMin/df = 1.25, a CFI = 0.959, a TLI = 0.955 and a RMSEA = 0.027) and similar structural relationships. Since the inclusion of a general dummy variable for recognizing the information poster did not yield any significant estimation results, only the results for the differentiated reaction dummy variables are presented in Table 7. In line with the hypotheses on a positive effect of information on cognitive and affective attitudes, reporting a reaction (making an effort to finish all food (N = 59)) to the information poster is related to a significant increase in both types of attitudes (H-I1i, ii) as well as in subjective norms (H-I3) that favor the avoidance of leftovers (see Table 7). However, choosing different dishes in reaction to the information poster (N = 18) yields effects opposite to the assumption of

5.3.2. Results on the reduction of portion sizes Regarding the presumed effects of reduced portion sizes, a comparison of the defined relationships of the choice for one of the three intervention dishes in the baseline and intervention sample was conducted. The choice of an intervention dish (compared to the baseline category of choosing other regular component-based dishes) does not have any significant effects on palatability ratings, portion size ratings 134

135 0.266** [H-P7i] (0.608**) [H-P5] (0.319**) [H-P6ii] n.s. [H-E4ii] -

-

-

-

-

0.201** [H-P7ii] (0.608**) [H-P5] (0.289**) [H-P6i] n.s. [H-E4i] -

-

-

Affective Attitude

0.438** [H-P7ii] (0.624**) [H-P5] (0.299**) [H-P6ii] n.s. [H-E4ii] -

0.295** [H-P7i] (0.624**) [H-P5] (0.243**) [H-P6i] n.s. [H-E4i] -

Cognitive Attitude

Affective Attitude

Cognitive Attitude

-

-

(n.s.) (0.289**) [H-P6i] (0.319**) [H-P6ii] -

Subjective Norm

-

-

(n.s.) (0.243**) [H-P6i] (0.299**) [H-P6ii] -

Subjective Norm

Results in parentheses = covariance, (R) = reference category for dummy variables. For each hypothesized relationship, the referring hypothesis is indicated in [ ]. “n.s.” = non-significant (significance level p* < 0.05, ** < 0.01), “-“ = no direct effect defined.

Environment Attitude Cognitive Attitude Affective Attitude Subjective Norm PBC Intention Portion Size Palatability Women alone couple group Men alone couple group (R) Food Buffet Choice Extra Regular (R) Intervention R2

Intervention, N = 377

Environment Attitude Cognitive Attitude Affective Attitude Subjective Norm PBC Intention Portion Size Palatability Women alone couple group Men alone couple group (R) Food Buffet Choice Extra Regular (R) Intervention R2

Baseline, N = 503

Table 6 Grouped estimation results (unstandardized) for baseline and intervention.

-

-

−0.239** [H-E3] -

PBC

-

-

−0.196** [H-E3] -

PBC

0.328

-

n.s. [H-P4i] 0.261* [H-P4ii] 0.284** [H-P3] 0.343** [H-P2] -

Intention

0.383

-

n.s. [H-P4i] 0.222* [H-P4ii] n.s. [H-P3] 0.494** [H-P2] -

Intention

n.s. [H-I6]

n.s. [H-I5]

n.s. [H-E5ii] n.s. [H-E5ii] n.s. [H-I6]

0.214* [H-E5i] n.s. [H-E5i] −0.348* [H-I5]

Palatability

n.s. [H-E5ii] n.s. [H-E5ii]

0.417** [H-E5i] n.s. [H-E5i]

Portion Size

-

Palatability

-

Portion Size

n.s. [H-I4] 0.180

−0.179** [H-E5iii] n.s. [H-E5iii]

−0.077** [H-P1] 0.125** [H-E1] −0.071* [H-E2] n.s. [H-S1] n.s. [H-S1] 0.145** [H-S1] n.s. [H-S2] n.s. [H-S2]

Stated Leftovers

n.s. [H-I4] 0.202

−0.091* [H-E5iii] n.s. [H-E5iii]

−0.114** [H-P1] 0.130*** [H-E1] −0.107** [H-E2] 0.313* [H-S1] 0.159* [H-S1] n.s. [H-S1] n.s. [S2] n.s. [S2]

Stated Leftovers

B.A. Lorenz-Walther, et al.

Appetite 139 (2019) 127–144

136

Affective Attitude 0.206** [H-P7ii] (0.563**) [H-P5] (0.331**) [H-P6ii] n.s. [H-E4ii] n.s. [H-I1ii] 0.781** [H-I1ii] n.s. [H-I1ii] -

Cognitive Attitude

0.151* [H-P7i] (0.563**) [H-P5] (0.216**) [H-P6i] n.s. [H-E4i] n.s. [H-I1i] 0.516** [H-I1i] −0.255* [H-I1i]

-

-

-

-

-

-

(n.s.) (0.216**) [H-P6i] (0.331**) [H-P6ii] −0.531* [H-I3] 0.693** [H-I3] n.s. [H-I3]

Subjective Norm

Results in parentheses = covariance, (R) = reference category for dummy variables. For each hypothesized relationship, the referring hypothesis is indicated in [ ]. “n.s.” = non-significant (significance level p* < 0.05, ** < 0.01), “-“ = no direct effect defined.

React by choosing React by eating Only see poster

Indirect Intervention Effects

Environment Attitude Cognitive Attitude Affective Attitude Subjective Norm PBC Intention Portion Size Palatability Info Dummies react: choice react: eat only see not seen (R) Women alone couple group Men alone couple group (R) Food Buffet Choice Extra Regular (R) Intervention R2

Intervention, N = 377

Table 7 Estimation results (unstandardized) for the intervention sample with information effects.

-

-

-

−0.225** [H-E3] −0.612* [H-I2] n.s. [H-I2] n.s. [H-I2]

PBC

−0.463** 0.383** n.s.

0.323

-

-

n.s. [H-P4i] 0.273** [H-P4ii] 0.275** [H-P3] 0.345** [H-P2] -

Intention

n.s. [H-E5ii] n.s. [H-E5ii] n.s. [H-I6]

0.214* [H-E5i] n.s. [H-E5i] −0.348* [H-I5]

-

Palatability

-

-

Portion Size

0.035* −0.029* n.s.

n.s. [H-I4] 0.178

−0.179** [H-E5iii] n.s. [H-E5iii]

n.s. [H-S1] n.s. [H-S1] 0.145** [H-S1] n.s. [H-S2] n.s. [H-S2]

−0.076** [H-P1] 0.125** [H-E1] −0.071* [H-E2] -

Stated Leftovers

B.A. Lorenz-Walther, et al.

Appetite 139 (2019) 127–144

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

attitudes as well as a more general environmental attitude (in line with the suggested inclusion of personal norms). In contrast to studies noting the separate relevance of cognitive and affective attitudes to determine organic (Aertsens, Verbeke, Mondelaers, & van Huylenbroeck, 2009) and regional food consumption (Lorenz et al., 2015) as well as recycling behavior (Tonglet, Phillips, & Read, 2004), we find that affective attitudes alone determine the intention for plate leftovers. A potential explanation for this finding is the high correlation between cognitive and affective attitudes in our model, indicating that cognitive and affective attitudes towards food leftovers only rarely differ and hence that the differentiation does not provide additional insights for the determination of leftover behavior. Regarding the inclusion of a general environmental measure of attitude in form of the green scale by Haws et al. (2014), our findings imply a dependency of leftover-related attitudes on global sustainability-related attitudes. Thereby, we extend findings on the relevance of green values or attitudes for sustainable product choices (Haws et al., 2014; Maniatis, 2016) to sustainable foodrelated decisions. This also supports the use of general environmental communication campaigns to enhance sustainable food consumption in out-of-home settings specifically for reducing food leftovers. Besides subjective norms, our results also support the relevance of social presence. Although we cannot fully replicate past findings stating that women tend to decrease food intake in relation to (especially male) meal companions while men tend to increase their food intake (Bell & Pliner, 2003; Clendenen, Herman, & Polivy, 1994; Young, Mizzau, Mai, Sirisegaram, & Wilson, 2009), a differentiated perspective on the gender of individuals and the number of people they are eating with appears relevant with regards to leftovers in our model. Considering finally the effects from situational factors, portion size and palatability ratings are relevant for food leftovers. In line with findings by Lorenz, Hartmann, Hirsch, et al. (2017), portion size perceptions are a significant determinant of PBC, supporting the assumption of interrelations between personal and situational factors that lead to food leftovers. In relation to this, different types of food choices are found to on the one hand directly take an influence on food leftovers. On the other hand, different food choices also relate to difference portion size and palatability ratings and thereby indirectly contribute to different levels of plate leftovers. Exemplarily, choosing food from a buffet is directly related to lower levels of plate leftovers (Pirani & Arafat, 2015). However, it is as well related to larger portion size ratings. To our knowledge, no study has considered differences in plate leftovers between buffets and regular à la carte dishes under consideration of portion-size perceptions. Potential explanations for the counter-intuitive effects may relate to a higher personal responsibility over portion sizes in a buffet situation which presumably could either be represented in more awareness about the portion size of food and/or in lower leftovers due to confirmation bias. For the three intervention dishes in this field experiment for which portion sizes were reduced, our findings support the broad body of research suggesting that decreased portion sizes in out-of-home settings can contribute to decreased food consumption and waste (Berkowitz, Marquart, Mykerezi, Degeneffe, & Reicks, 2016; Freedman & Brochado, 2010; Wansink & van Ittersum, 2013). However, the overall effects of this intervention are – though significant – only small in size. In contrast to assumptions of subconscious effects from portion size manipulations (Wansink et al., 2005; Wansink & van Ittersum, 2013), we find that reduced food leftovers from reduced portion sizes relate to significantly smaller perceived portion sizes and hence to consciously processed determination. An explanation for this may be provided by notions that portion size perceptions strongly relate to visual cues (DiSantis et al., 2013; Scheibehenne, Todd, & Wansink, 2010b; Wansink & van Ittersum, 2013). Since reduced portion sizes in the chosen setting had to be offered on the regular canteen dishware and were accompanied by side dishes of constant portion sizes, guests probably could easily determine smaller portion sizes. From a practical perspective, our results support the usefulness of reduced portion sizes as a means to

increased PBC (H-I2) and opposite to the presumed positive impact on subjective norms (H-I3). When people state to react to information by choosing different dishes as an addition to making an effort to finish all food, this together relates to a lower intention to finish all food as well as to higher levels of stated leftovers. When people in contrast only are reacting by making an effort to finish all food, this relates to higher levels of intention to finish all food and thereby to significantly lower stated plate leftovers. 5.4. Comparison of results for stated and observed leftovers Based on the estimation results for the full valid sample of 880 participants with stated leftovers as dependent variable, supplementary estimations were conducted for the reduced sample of 556 respondents for whom it was possible to match observations of leftovers with stated responses in the questionnaire with observed leftovers as alternative dependent variable. Thereby, the SEM was specified identically to the previous analyses except for a replacement of stated leftovers with observed leftovers as the central dependent variable. Regarding overall model fit for the grouped model a slightly worse but still acceptable overall model fit is reached (robustness corrected CMin/df = 1.28, a CFI = 0.936, a TLI = 0.931 and a RMSEA = 0.033). Besides, the structural relationships in the model are very similar to the full sample estimates (see Appendix 6-7 for comparison with Tables 6 and 7). However, a relevant difference to the results on stated leftovers are nonsignificant effects from intention and palatability on observed leftovers in the baseline group. This also relates to a substantially lower R2 for observed leftovers of 0.112 in the baseline group while it remains comparably high at 0.165 in the intervention group. Regarding the effect of the portion size intervention, there is no direct significant effect of the choice of an intervention dish on observed leftovers (H-I4) and on palatability ratings (H-I6) in either the baseline or intervention sample. However, there is a highly significant effect from choosing an intervention dish on portion size perception (H-I5) in the intervention sample. Compared to the findings on stated leftovers, the effect increases in magnitude from −0.348 (stated leftovers) to −0.409 (observed leftovers, see Appendix 6). Consequently, for the model on observed leftovers, there is a significant difference between baseline and intervention not only for the choice effect of an intervention dish on portion size perception (Wald Test Chi-Square = 5.73, p = 0.017) but also for the indirect effect of choosing an intervention dish on observed leftovers (Wald Test Chi-Square = 3.829, p = 0.050). Regarding the model fit for the intervention-only model, again a good overall model fit and similar structural relationships to the model for stated leftovers are reached in the reduced sample with observed leftovers (robustness corrected CMin/df = 1.18, a CFI = 0.952, a TLI = 0.947 and a RMSEA = 0.029, see Appendix 6). Besides, no differences from the findings on stated leftovers are observable for intervention effects from information apart from a non-significant effect from stating to have chosen different dishes in reaction to the info poster on PBC (H-I2, see Appendix 7). 6. Discussion Our results support the useful application of a holistic model to determine plate leftovers in a university canteen and to analyze effects of different interventions to decrease leftovers. In line with past analyses on leftovers in canteens (Lorenz, Hartmann, Hirsch, et al., 2017; Lorenz, Hartmann, & Langen, 2017) as well as with studies on other food- and sustainability-related consumer behavior (Cook, Kerr, & Moore, 2002; Mahon, Cowan, & McCarthy, 2006; Tarkiainen & Sundqvist, 2005), the central elements of the TPB (intention, attitudes, subjective norms and PBC) are supported as relevant personal determinants of leftovers. Moreover, two extensions that have been suggested for modelling food- and sustainability related consumer behavior are supported: a differentiation between affective and cognitive 137

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

general ineffectiveness of information but to differentiated effects on individuals. Based on the differentiation, it may be possible to improve the provided information (i.e. by more focusing on individuals' attempts to finishing all food than on changing food choices) or to improve the context leading to negative effects of choice-reactions to information (i.e. by making it easier to choose different food options or decreased portion sizes). Regarding the former, it also should be considered that simply finishing all food on one's plate without altering food choices could lead to increased calorie intake (Robinson & Hardman, 2015) and potentially adverse health effects. Hence, future research should explore to what extent individuals' net food intake changes from increasing effort to finish all food (potentially also under consideration of spill-over effects to meals outside the canteen setting) and should elaborate on creating environments where food choices as a reaction to information about waste can easily be adapted. A final point of this section relates to differences between the estimates for stated and observed leftovers. The presented structural model provides good overall fit in relation to stated leftovers as well as observed leftovers and also the presented intervention effects hold for both measures as dependent variable. However, observed leftovers in the baseline sample are determined by intention only at a non-significant level and R2 values for observed leftovers are consistently lower in the baseline and intervention sample than the R2 for stated leftovers. Especially participants in the baseline sample had lower stated leftovers than observed leftovers (85% of the baseline sample stated to have finished all food but only 73% of observed trays had no leftovers compared to the intervention sample with 86% stating to have no leftovers against 80% observed trays without leftovers). One reason for this gap in the baseline group and the resulting non-significant relationship between intention and observed behavior may be social desirability bias in stated measures (Zander & Hamm, 2010) which also has been detected in relation to stated food waste measures for private households (Langen, Göbel, & Waskow, 2015). Another reason may be an unawareness of personal food leftovers, i.e. if small leftovers of a food component are not considered as waste and hence are not represented in stated leftovers. If individuals are not aware of exerting a specific behavior, they are unable to transfer behavioral intentions into action (Carrington, Neville, & Whitwell, 2014). In line with Hinton et al. (2013) we strongly support the complementary use of stated and observed measures of behavior when conducting research on food-related behaviors in out-of-home settings.

reduce leftovers. However, for the setting of a university canteen, our findings question research which defines the effect of reduced portion sizes as a nudge working fully subconsciously. In contrast, we find that individuals recognize smaller portion sizes when being presented on regular plates. Accordingly, we suggest that openly offering different sizes of dishes may provide a better and more consumer oriented way of decreasing leftovers in this type of setting. The offering of smaller portion sizes as addition to regular offers has been found to decrease calorie intake in canteens as well as fast food restaurants (Schwartz, Riis, Elbel, & Ariely, 2012; Vermeer, Steenhuis, Leeuwis, Heymans, & Seidell, 2011). An evaluation of the effect of information posters that were presumed to influence attitudes, subjective norms and PBC in favor of decreased leftovers provides highly interesting mixed findings. In a grouped model, no significant differences in mean values for any of the indicators that theoretically may change from an information intervention are observable between and intervention. In line with this nonsignificant overall effect of information display, findings in the intervention-only model imply no significant differences between guests who stated to have seen the information but have not reacted to them and guests who did not recall the information, apart from a more negative cognitive attitude towards finishing all food. This is in line with findings that the display of nutrition information does not change food choices when individuals show low involvement (Hamdan, Story, French, Fulkerson, & Nelson, 2005) or when information stand in conflict with individual motives and attitudes (Hoefkens, Pieniak, Van Camp, & Verbeke, 2012). In contrast, guests who stated to have reacted to the information by making additional effort to finish all food also stated more positive cognitive and affective attitudes towards finishing all food as well as more positive subjective norms in favor of avoiding leftovers. Conversely, individuals who additionally stated to have chosen different or fewer dishes in reaction to the information did not show more favorable attitudes towards avoiding plate leftovers and had significantly lower subjective norms and PBC. Therefore, people who stated to react to information only by trying to finish all food had significantly lower plate leftovers than people who had not recognized the information. People who stated to react to information by trying to finish all food and by choosing a different dish in the canteen had significantly higher plate leftovers. In line with our findings on the portion size intervention, the changes in leftovers by reactions to information were – though significant – very small in size. Apparently, individuals who stated to have altered their food choice perceived to have less personal influence over finishing all food and to face meal companions who were less supportive of finishing all food. A potential explanation may be drawn from the basic notation that PBC “(…) is assumed to reflect past experience as well as anticipated impediments and obstacles.” (Ajzen, 1991, p. 188). Accordingly, it may be possible that individuals were facing difficulties in their attempt to choose smaller portions or alternative dishes at the day of their visit in the canteen which lead to decreased PBC. The same mechanism may as well hold for the negative relationship of an attempted different food choice to subjective norms. With regards to research stating that food choices in canteens are embedded into social context and may also provide a means to impression management (Cruwys et al., 2015; Vartanian, 2015), changed food choices from individuals as a reaction to the information on food leftovers may provide social tension not necessarily based on the topic of food leftovers but on the topic of changes in the meal choice. Overall, our results support the broad number of studies and initiatives that apply information to change food-waste-related behaviors for the setting of a university canteen. However, they also show that specific aspects of and reactions to information can have diverse and potentially context-driven effects on behavior. Typical analyses of information effects which only focus on aggregate behavior changes therefore may miss relevant details and give away potential to sustainably change behavior. For our study, no aggregate effect of provided information was observable. However, this did not relate to a

6.1. Limitations When discussing the results of our study, certain limitations regarding the applied study design and analyses should be taken into account. A set of general limitations of the applied model have been addressed by Lorenz, Hartmann, Hirsch, et al. (2017). A relevant point is the reverse order of measurement for behavioral intention and behavior which was accepted since an advance measurement of behavioral intention may have considerably biased the behavior at a specific meal. Besides, situational factors cannot be measured before a meal takes place. Therefore, a pre-measure of intention would have required a repeated collection of data from individuals with probably a high share of incomplete data. Moreover, there are different constructs which have been stated to provide relevant extensions to the applied model, i.e. an inclusion of determinants for specific types of food choices or a more in-depth consideration of social determinants. Since the focus of this study was to see whether specific interventions can be analyzed in their effects on individuals in a more holistic way than only observing descriptive behavioral changes, we decided to focus on the most relevant determinants of behavior in relation to those interventions rather than extending the initial model. One needs to consider that this study bases on a sample of guests in a university canteen that mainly are students. Although this limits the application of results to a general population, this was accepted due to the high requirements for 138

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

findings and provide more general recommendations on how to effectively reduce leftovers in a canteen setting.

a suitable setting of conducting data collections and interventions. The design of a baseline and intervention comparison was well applicable for this analysis of intervention effects and yielded significant results on how they change individuals’ behavior. In comparison to descriptive analyses of leftovers and interventions that are in place simultaneously, our analyses allow us to separate their effects based on theoretically defined relationships in the applied model. Nevertheless, this approach only provides for aggregate comparisons and relies on stated exposure to interventions (i.e. the recognition of information posters). In line with past research, we assumed that certain changes in personal determinants for leftovers, i.e. a more positive attitude towards finishing food, are caused by receiving and processing information about the issue of food waste. An alternative interpretation of a positive association between the two constructs may be that people with a more positive attitude towards finishing all food are more attentive to information on the topic of food waste and more likely to comply with this information. In order to address this question, individual pre-post comparisons for interventions in a realistic canteen setting may provide additional insights. Thereby, a more reliable method of observing leftovers should be applied since the missing data on observed leftovers puts limitations to our comparison between stated and observed measures of leftovers. Although it can be assumed that data of observations was missing at random, the technical shortcomings lead to a low sample size (N = 293/263) and therefore potentially to less accurate model estimates. Finally, it should be considered that – as participation in our online survey was voluntary – our results may suffer from participation bias. When considering the results on observed plate leftovers from our sample to a general coding of all videotaped trays during the baseline and intervention collection, participants in our survey tended to have more plate leftovers than the average guest in the university canteen. Whereas in our sample 73% of participants during baseline and 80% of respondents during intervention did not have any observed plate leftovers, 83% of all videotaped trays in the university canteen during baseline (7256 of 8749 trays) and during intervention (8436 of 10,195 trays) were generally coded not to have any plate leftovers on them. This on the one hand may indicate that participation bias did not lead to a participation of guests with lower leftovers. On the other hand, it may indicate that participation bias may relate to an overstated intervention effect in our results. This again strongly supports our suggestion for a consideration of intra-personal intervention effects to validate our

7. Conclusion To contribute to a better understanding of how interventions can change individuals' plate leftovers in out-of-home settings, this study applies a holistic behavioral model in a baseline-intervention design. Our results for a sample of 880 guests at a university canteen (503 baseline, 377 intervention) provide insights on how two common interventions in form of a reduction of portion sizes and the provision of information reduce leftovers. Thereby, intervention effects are assigned to specific changes in personal, social and environmental determinants of stated as well as observed plate leftovers in two structural equation models (one grouped model and one intervention-only model). Two major implications from the conducted analyses are first that smaller portion sizes for three meaty target dishes significantly reduce observed plate leftovers from those dishes. However, in contrast to past research stating that reduced portion sizes work subconsciously, the intervention effect in a setting with standardized dishware and regular guests relates to smaller portion size perceptions of guests. Second, a provision of information can reduce stated as well as observed plate leftovers when guests react to this information by increased effort to finish their chosen food. This relates to increased positive attitudes towards finishing all food and more favorable subjective norms. Contrary, information can also increase leftovers when guests react to this information by choosing other dishes than they initially intend to choose. This relates to potential obstacles to deviate from initial food choices represented in lower levels of PBC and less favorable subjective norms. Overall, the results and implications from this research strongly support the benefits of more holistic and in-depth analyses of interventions to reduce plate leftovers and thereby change individuals’ food-related behaviors towards more sustainable consumption patterns in out-of-home settings. Acknowledgements This research was carried out within the project NAHGAST funded by the German Federal Ministry of Education and Research (Grant Number 01UT1409B) and was supported by a PhD scholarship of the Deutsche Bundesstiftung Umwelt.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.appet.2019.03.026. Appendix Appendix 1. Menu Plan during Collection of Data

Monday

Menu 1 Menu 2

Menu 3

Menu 4 (vegan)

Tuesday

Hash brown with broccoli *Pasta "Carbonara" and nuts (veg) *Chicken escalope with Hash browns with curry-fruit sauce cream cheese filling (veg) Beef roulade with bacon, Mediterranean onions and pickles chicken escalope with onion and vegetables Mashed potatoes with Asian vegetable stew vegetables (vegan) with mini spring rolls (vegan)

Wednesday

Thursday

*Fresh sausage with onion sauce Vegetarian Bolognese (veg)

Friday

Student Price Menu price (incl. three side dishes): EUR2.30-EUR 3.30 single prices EUR 1.55-EUR 2.55

Grated vegetable schnitzel "Italia" with tomatoes and mozzarella (veg) Salmon lasagna

Goulash with onion and pepper

Pasta bake with herbal sauce (veg) Schnitzel with mushroom cream sauce

Roasted turkey leg with red wine sauce

Sautéed Pollack with mustard sauce

Sweet rice with coconut milk and strawberries (vegan)

Spicy stew with potatoes and rocket (vegan)

Vegetable stew with bulgur and curry sauce (vegan)

139

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al. Special 1

Schnitzel with pepper sauce and tomato rice

Special 2

Chicken fricassee with asparagus and champignons, served with rice -

Stew Side Dishes

Vegetable stew with Beef roulade with cream sauce beef and pineapple, and mashed potatoes served with rice Schnitzel with pepper Roasted gnocchi with beef stew sauce and french fries

Homemade lentil stew with sausage rice (org), potatoes, green potato wedges, salad, carrots bulgur, green salad, mixed vegetables

Homemade stew with beans and smoked pork mashed potatoes, tomato rice, green salad, mixed vegetables, Mediterranean vegetables

Chicken stew with pasta

EUR 2.30-EUR 5.50

Gyro with tzatziki, salad and french fries

Roasted goose leg with red cabbage and potato dumplings Goulash with cream sauce and rice

Homemade vegetable stew (veg) potato dumplings, pasta (org), french fries, green salad, red cabbage, apple sauce

Homemade vegetable Stew (veg) potatoes, rice, potato dumplings, endive salad, red cabbage, peas

EUR 1.65

Desserts

Fruit salad, rice pudding, sweet quark, yogurt, bakeries and two daily changing offers (i.e. chocolate pudding)

Extra Offers

Burger, vegetarian burger, curry sausage, french fries salad bar, pasta bar

Menu price EUR 0.25 Single price EUR 0.65 Menu price EUR 0.25 Single price EUR 0.95 -

veg = vegetarian dish, org = organic, * = intervention dish for reducing portion sizes.

Appendix 2. Hypotheses Defining General Structural Relationships in the Behavioral Model

Hypothesis

Presumed Causal Relationship

Reference

H-P1 H-P2

Plate leftovers in a canteen are negatively influenced by the behavioral intention to finish all food on one's plate. The behavioral intention to finish all food on one's plate in a canteen is positively determined by high perceived behavioral control over finishing all food on one's plate. The behavioral intention to finish all food on one's plate in a canteen is positively determined by subjective norms that are in favor of finishing all food. The behavioral intention to finish all food on one's plate in a canteen is positively determined by a positive cognitive attitude. The behavioral intention to finish all food on one's plate in a canteen is positively determined by a positive affective attitude. Positive cognitive attitude towards finishing all food are positively correlated with positive affective attitude towards finishing all food. Subjective norms that are in favor of finishing all food are positively correlated with positive cognitive attitude towards finishing all food. Subjective norms that are in favor of finishing all food are positively correlated with positive affective attitude towards finishing all food. A general positive environmental attitude positively determines positive cognitive attitudes towards finishing all food. A general positive environmental attitude positively determines positive cognitive attitudes towards finishing all food. Women who are eating in company have more leftovers than women who are eating alone. Men who are eating in company have less leftovers than men who are eating alone. Plate leftovers are positively related to larger perceived portion sizes of the chosen meal components. Plate leftovers are negative related to higher palatability ratings of the chosen meal components. Larger portion sizes of meal components negatively determine perceived behavioral control over finishing all food on one's plate. Higher palatability ratings of meal components positively determine positive cognitive attitudes towards finishing all food. Higher palatability ratings of meal components positively determine positive affective attitudes towards finishing all food. Different types of food choices with respect to their content of meat and their flexibility in portion size create higher ratings of palatability. Different types of food choices with respect to their content of meat and their flexibility in portion size create larger portion size ratings. Different types of food choices with respect to their content of meat and their flexibility in portion size create independent from their effect on palatability and portion size ratings different levels of plate leftovers. Time pressure during lunch increases plate leftovers.

Ajzen (2001); Lorenz, Hartmann, Hirsch, et al. (2017); Lorenz et al. (2017b)

H-P3 H-P4i H-P4ii H-P5 H-P6i H-P6ii H-P7i H-P7ii H-S1 H-S2 H-E1 H-E2 H-E3 H-E4i H-E4ii H-E5i H-E5ii H-E5iii H-E6

Ajzen (2001); Arvola et al. (2008); Tarkiainen and Sundqvist (2005)

Arvola et al. (2008); Bamberg and Möser (2007); Lorenz et al. (2015)

Bell and Pliner (2003); Cavazza et al. (2015,2017) Lorenz, Hartmann, Hirsch, et al. (2017); Lorenz et al. (2017b)

Cohen et al. (2016), Price and Just (2014)

Appendix 3. Descriptive Properties of Measured Indicators

Construct

Palatability

Portion Size Affective Attitude

Cognitive Attitude

Indicator

appearance taste smell happy/unhappy relaxed/annoyed satisfied/unsatisfied beneficial/harmful valuable/valueless ideal/unacceptable

Sample

Mean

Variance

Skewness

Kurtosis

T-Statistic*

base

iv

base

iv

base

iv

base

iv

base

iv

503 503 503 503 503 503 503 503 503 503

366 366 366 377 377 377 377 377 377 377

3.47 3.66 3.56 3.73 4.75 4.42 4.96 4.75 4.71 4.80

3.39 3.60 3.45 3.73 4.79 4.42 4.93 4.66 4.58 4.76

0.83 0.74 0.71 0.89 1.32 1.92 1.62 1.10 1.13 0.89

0.91 0.75 0.70 0.94 1.20 1.83 1.40 1.07 1.04 0.82

−0.27 −0.41 −0.38 −0.37 −0.04 −0.39 −0.42 0.22 0.46 0.30

−0.30 −0.52 −0.12 −0.23 0.28 −0.36 −0.30 0.15 0.41 0.18

−0.24 −0.08 0.34 1.73 0.74 0.27 0.32 0.36 0.21 0.68

−0.24 0.23 0.07 1.78 0.30 0.47 0.67 0.96 1.09 1.71

140

1.158 0.977 1.855 −0.015 −0.521 −0.054 0.428 1.268 1.827 0.491

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al. Environ-mental Norm

PBC

Subjective Norm

Intention

products don't harm the environment environmental impacts (activities) environmental impacts (consumption) concerned about the waste of resources accept inconveniences for environment consider myself to be conscious finishing is easy I could always finish predicting the right amount others think others finish others criticize will do my best generally try expect leftovers

481 479 471 477 475 486 494 488 488 433 455 463 498 501 492

342 336 337 345 340 345 364 358 362 303 334 342 373 374 370

3.50 3.33 3.37 3.69 3.54 3.41 4.14 3.87 3.76 2.48 2.12 1.51 4.36 4.34 2.75

3.50 3.33 3.39 3.75 3.50 3.40 4.06 3.87 3.74 2.51 2.11 1.47 4.31 4.33 2.89

1.01 1.13 1.10 1.16 1.05 1.02 0.91 1.44 1.03 1.08 1.04 0.70 0.83 0.91 2.20

1.02 1.20 1.09 1.07 1.06 1.13 0.99 1.45 1.13 1.22 1.07 0.52 0.85 0.86 2.38

−0.59 −0.33 −0.34 −0.67 −0.56 −0.34 −1.20 −0.84 −0.78 0.58 1.05 2.04 −1.50 −1.61 0.34

−0.44 −0.27 −0.38 −0.75 −0.46 −0.31 −1.05 −0.86 −0.68 0.55 1.09 1.81 −1.41 −1.46 0.17

−0.06 −0.50 −0.48 −0.20 −0.22 −0.40 1.13 −0.40 −0.07 −023 0.90 4.39 1.75 2.15 −1.36

−0.35 −0.72 −0.48 0.09 −0.44 −0.55 0.59 −0.32 −0.36 −0.38 0.81 3.66 1.59 1.67 −1.53

−0.014 0.026 −0.228 −0.869 0.494 0.124 1.252 0.012 0.376 −0.376 0.135 0.710 0.796 0.124 −1.290

*Test-Statistic based on Student T-Test with a critical value (p < 0.05) of ± 1.96.

Appendix 4. Overview of CFA Results

Standardized Factor Loading

Affective Attitude

Cognitive Attitude

Environmental Norm

PBC

Subjective Norms

Intention

Palatabilty

AA_1 AA_2 AA_3 CA_1 CA_2 CA_3 PN_1 PN_2 PN_3 PN_4 PN_5 PN_6 PC_1 PC_2 PB_3 SN_1 SN_2 SN_3 BI_1 BI_2 BI_3 PA_1 PA_2 PA_3

Factor Reliability

AVE

Maximum Squared Correlation

With Latent Construct

base

iv

base

iv

base

iv

base

iv

0.866 0.796 0.864 0.817 0.531 0.781 0.810 0.871 0.852 0.768 0.707 0.814 0.854 0.660 0.433 0.719 0.631 0.631 0.222 0.719 0.631 0.625 0.708 0.921

0.740 0.795 0.846 0.859 0.745 0.841 0.816 0.848 0.867 0.725 0.754 0.795 0.934 0.674 0.401 0.787 0.625 0.478 0.301 0.876 0.853 0.632 0.765 0.841

0.88

0.84

0.71

0.63

0.59

0.46

Cognitive Attitude

0.76

0.86

0.52

0.67

0.59

0.46

Affective Attitude

0.92

0.92

0.65

0.64

0.14

0.09

Affective Attitude

0.70

0.73

0.45

0.50

0.24

0.12

Intention

0.70

0.71

0.44

0.46

0.18

0.23

Affective Attitude

0.67

0.59

0.45

0.53

0.24

0.12

PBC

0.80

0.79

0.58

0.56

0.06

0.01

PBC/ SN

Appendix 5. Model Results for Difference Testing

Specifications for Grouped Models (baseline and intervention)

Free Parameters CMin df Scaling Correction Factor (MLR estimator) CMin Baseline CMin Intervention Robustness corrected Cmin difference* compared to Model 1 [df] Robustness corrected Cmin difference* compared to Model 1i [df]

Model 1

Model 2

Model 3

measurement invariance

variance in indicator loadings

variance in indicator loadings and measurement invariance, fixed structural paths intercepts related to information

154 886.460 652 1.09 457.699 428.761 -

172 863.794 634 1.089 447.692 416.102 22.757 [18]

190 852.881 616 1.091 442.769 410.113 33.398 [36]

141 897.773 665 1.092 462.161 435.612 -

11.313 [13]

33.979 [31]

44.892 [49]

-

* referring to Satorra and Bentler (2010). df = degress of freedom.

141

Model 1i

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

Appendix 6. Grouped Estimation Results (unstandardized) for Observed Leftovers

Baseline, N = 293

Env. Attitude Cognitive Attitude Affective Attitude Subjective Norm PBC Intention Portion Size Palatability Female alone couple group Male alone couple group (R) Food Buffet Choice Extra Regular (R) Intervention R2

Cognitive Attitude

Affective Attitude

Subjective Norm

PBC

Intention

Portion Size

Palatability

Observed Leftovers

0.329** (0.667**) (0.174*) n.s. -

0.484** (0.667**) (0.256**) n.s. -

(n.s.) (0.174*) (0.256**) -

−0.200** -

n.s. 0.371** n.s. 0.410** -

-

-

n.s. 0.166** n.s. 0.649* 0.302* n.s. n.s. 0.240*

-

-

-

-

-

0.523** n.s.

0.221* n.s.

−0.174* n.s.

-

-

-

-

0.386

n.s.

n.s.

n.s. 0.112

Cognitive Attitude

Affective Attitude

Subjective Norm

PBC

Intention

Portion Size

Palatability

Observed Leftovers

0.217** (0.608**) (0.289**) n.s. -

0.262** (0.608**) (0.319**) n.s. -

(n.s.) (0.289**) (0.319**) -

−0.278** -

n.s. n.s. 0.325* 0.356** -

-

-

−0.236** 0.158** −0.154* n.s. n.s. 0.518* n.s. n.s.

-

-

-

-

-

n.s. n.s.

−0.246* n.s.

n.s. n.s.

-

-

-

-

0.343

−0.409*

n.s.

n.s. 0.165

Intervention, N = 263

Env. Attitude Cognitive Attitude Affective Attitude Subjective Norm PBC Intention Portion Size Palatability Female alone couple group Male alone couple group (R) Food Buffet Choice Extra Regular (R) Intervention R2

Results in parentheses = covariance, (R) = reference category for dummy variables. “n.s.” = non-significant (significance level p* < 0.05, ** < 0.01), “-“ = no direct effect defined.

Appendix 7. Estimation Results (unstandardized) for Information and Observed Leftovers

Intervention, N = 263

Environment Attitude Cognitive Attitude Affective Attitude Subjective Norm PBC Intention Portion Size Palatability Information react: choice react: eat only see not seen (R) Female alone couple group Male alone couple group (R) Buffet

Cognitive Attitude

Affective Attitude

Subjective Norm

PBC

Intention

Portion Size

Palatability

Observed Leftovers

0.185* (0.568**) (0.240**) n.s. n.s. 0.475* −0.301*

0.231* (0.568**) (0.265**) n.s. n.s. 0.761** n.s.

(n.s.) (0.240**) (0.265**) −0.650* 0.716** −0.316*

−0.262** n.s. n.s. n.s.

n.s. n.s. 0.367** 0.304** -

-

-

−0.234** 0.158** −0.153* -

-

-

-

-

-

-

-

n.s. n.s. n.s. n.s. n.s.

-

-

-

-

-

n.s.

n.s.

n.s.

142

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al. Food Choice

Extra Regular (R) Intervention

-

-

-

-

-

n.s.

n.s.

n.s.

-

-

-

-

0.327

−0.409*

n.s.

n.s. 0.165

R2

Indirect Intervention Effects −0.422** 0.392** n.s.

React by choosing React by eating Only see poster

n.s. n.s. n.s.

Results in parentheses = covariance, (R) = reference category for dummy variables. “n.s.” = non-significant (significance level p* < 0.05, ** < 0.01), “-“ = no direct effect defined.

org/10.1016/j.jada.2003.10.012. Cook, A. J., Kerr, G. N., & Moore, K. (2002). Attitudes and intentions towards purchasing GM food. Journal of Economic Psychology, 23(5), 557–572. https://doi.org/10.1016/ S0167-4870(02)00117-4. Crites, S. L., Fabrigar, L. R., & Petty, R. E. (1994). Measuring the affective and cognitive properties of attitudes: Conceptual and methodological issues. Personality and Social Psychology Bulletin, 20(6), 619–634. https://doi.org/10.1177/0146167294206001. Cruwys, T., Bevelander, K. E., & Hermans, R. C. J. (2015). Social modeling of eating: A review of when and why social influence affects food intake and choice. Appetite, 86, 3–18. https://doi.org/10.1016/j.appet.2014.08.035. Dayan, E. (2011). Nudge to obesity II : Menu positions influence food orders. Judgement and Decision Making, 6(4), 333–342. Diliberti, N., Bordi, P. L., Conklin, M. T., Roe, L. S., & Rolls, B. J. (2004). Increased portion size leads to increased energy intake in a restaurant meal. Obesity Research, 12(3), 562–568. https://doi.org/10.1038/oby.2004.64. Ding, L., Velicer, W. F., & Harlow, L. L. (1995). Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices. Structural Equation Modeling: A Multidisciplinary Journal, 2(2), 119–143. https://doi.org/10.1080/10705519509540000. DiSantis, K. I., Birch, L. L., Davey, a., Serrano, E. L., Zhang, J., Bruton, Y., et al. (2013). Plate size and children's appetite: Effects of larger dishware on self-served portions and intake. Pediatrics, 131(5), e1451–e1458. https://doi.org/10.1542/peds.20122330. Enders, C. K. (2001). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6(4), 352–370. https://doi.org/10.1037//I082-989X.6.4.352. Engström, R., & Carlsson-Kanyama, A. (2004). Food losses in food service institutions Examples from Sweden. Food Policy, 29(3), 203–213. https://doi.org/10.1016/j. foodpol.2004.03.004. European Commission (2011). Analysis associatied with the roadmap to a resource efficient europe Part II. Commission staff working paper. SEC. 2011)). Retrieved from http:// www.ncbi.nlm.nih.gov/pubmed/19097363. Ferreira, M., Martins, M. L., & Rocha, A. (2013). Food waste as an index of foodservice quality. British Food Journal, 115(11), 1628–1637. https://doi.org/10.1108/BFJ-032012-0051. Finkbeiner, O. (2013). Lebensmittelabfälle in der Betriebsverpflegung - erfassung von Mengen und Ursachen. München: AVM. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Freedman, M. R., Bartoli, C., & Wagle, A. (2012). Food intake patterns and plate waste among community meal center guests in san Jose, CA. Journal of the Academy of Nutrition and Dietetics, 112(9), A89. https://doi.org/10.1016/j.jand.2012.06.322. Freedman, M. R., & Brochado, C. (2010). Reducing portion size reduces food intake and plate waste. Obesity, (18), 1864–1866. Furst, T., Connors, M., Bisgoni, C. A., Sobal, J., & Falk, l. W. (1996). Food choice: A conceptual model of the process. Appetite, 26(3), 247–266. https://doi.org/10.1006/ appe.1996.0019. Gawronski, B., & Bodenhausen, G. (2006). Associative and propositional processes in evaluation: An integrative review of implicit and explicit attitude change. Psychological Bulletin, 132(5), 692–731. Geier, A. B., Rozin, P., & Doros, G. (2006). Unit bias - a new heuristic that helps explain the effect of portion size on food intake. Psychological Science, 17(6), 521–525. Göbel, C., Blumenthal, A., Niepagenkemper, L., Baumkoetter, D., Teitscheid, P., & Wetter, C. (2014). Bericht zum Forschungs- und Entwicklungsprojekt „ Reduktion von Warenverlusten und Warenvernichtung in der AHV - ein Beitrag zur Steigerung der Ressourceneffizienz “ November 2014. Muenster: Institut für Nachhaltige Ernährung und Ernährungswirtschaft Fachhochschule Muenster (iSuN). Gustavsson, J., Cederberg, C., Sonesson, U., van Otterdijk, R., & Meybeck, A. (2011). Global food losses and food waste. Rome. Guthrie, J. F., & Buzby, J. C. (2001). Several strategies may lower plate waste in school feeding programs. FoodReview, 25(2), 36–42. Hamdan, S., Story, M., French, S. A., Fulkerson, J. A., & Nelson, H. (2005). Perceptions of adolescents involved in promoting lower-fat foods in schools: Associations with level of involvement. Journal of the American Dietetic Association, 105, 247–251. https:// doi.org/10.1016/j.jada.2004.11.030. Hanks, A. S., Wansink, B., & Just, D. R. (2014). Reliability and accuracy of real-time visualization techniques for measuring school cafeteria tray waste: Validating the quarter-waste method. Journal of the Academy of Nutrition and Dietetics, 114(3), 470–474. https://doi.org/10.1016/j.jand.2013.08.013. Haws, K. L., Winterich, K. P., & Naylor, R. W. (2014). Seeing the world through GREENtinted glasses: Green consumption values and responses to environmentally friendly

References Aertsens, J., Verbeke, W., Mondelaers, K., & van Huylenbroeck, G. (2009). Personal determinants of organic food consumption: A review. British Food Journal, 111(10), 1140–1167. https://doi.org/10.1108/00070700910992961. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T. Ajzen, I. (2001). The nature and operation of attitudes. Annual Review of Psychology, 52(1), 27–58. Ajzen, I. (2006). Constructing a TPB Questionnaire : Conceptual and methodological considerations. Time 2002, 1–13. June 5 https://doi.org/10.1002/hep.22759. Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned Behaviour : A meta-analytic review. British Journal of Social Psychology, 40, 471–499. Arvola, A., Vassallo, M., Dean, M., Lampila, P., Saba, A., Lähteenmäki, L., et al. (2008). Predicting intentions to purchase organic food: The role of affective and moral attitudes in the Theory of Planned Behaviour. Appetite, 50(2–3), 443–454. https://doi. org/10.1016/j.appet.2007.09.010. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1177/ 009207038801600107. Bamberg, S., & Möser, G. (2007). Twenty years after hines, hungerford, and tomera: A new meta-analysis of psycho-social determinants of pro-environmental behaviour. Journal of Environmental Psychology, 27(1), 14–25. https://doi.org/10.1016/j.jenvp. 2006.12.002. Barr, S. (2007). Factors influencing environmental attitudes and behaviors: A U.K. Case study of household waste management. Environment and Behavior, 39https://doi.org/ 10.1177/0013916505283421. Bell, R., & Pliner, P. L. (2003). Time to eat: The relationship between the number of people eating and meal duration in three lunch settings. Appetite, 41(41), 215–218. https://doi.org/10.1016/S0195-6663(03)00109-0. Berkowitz, S., Marquart, L., Mykerezi, E., Degeneffe, D., & Reicks, M. (2016). Reducedportion entrées in a worksite and restaurant setting: Impact on food consumption and waste. Public Health Nutrition, 19(16), 1–7. https://doi.org/10.1017/ S1368980016001348. Betz, A., Buchli, J., Göbel, C., & Müller, C. (2015). Food waste in the Swiss food service industry – magnitude and potential for reduction. Waste Management, 35, 218–236. https://doi.org/10.1016/j.wasman.2014.09.015. Brindal, E., Wilson, C., Mohr, P., & Wittert, G. (2015). Eating in groups: Do multiple social influences affect intake in a fast-food restaurant? Journal of Health Psychology, 20(5, SI), 483–489. https://doi.org/10.1177/1359105315576607. Byrne, B. M. (2012). Structural equation modeling with Mplus. New York: Routledge. Carrington, M. J., Neville, B. A., & Whitwell, G. J. (2014). Lost in translation: Exploring the ethical consumer intention-behavior gap. Journal of Business Research, 67(1), 2759–2767. https://doi.org/10.1016/j.jbusres.2012.09.022. Cavazza, N., Guidetti, M., & Butera, F. (2015). Ingredients of gender-based stereotypes about food. Indirect influence of food type, portion size and presentation on gendered intentions to eat. Appetite, 91(April), 266–272. https://doi.org/10.1016/j.appet. 2015.04.068. Cavazza, N., Guidetti, M., & Butera, F. (2017). Portion size tells who I am, food type tells who you are: Specific functions of amount and type of food in same- and opposite-sex dyadic eating contexts. Appetite, 112, 96–101. https://doi.org/10.1016/j.appet.2017. 01.019. Cialdini, R. B., Kallgren, C. A., & Reno, R. R. (1991). A focus theory of normative conduct: A theoretical refinement and reevaluation of the role of norms in human behavior. Advances in Experimental Social Psychology, 24(20), 201–234. Clendenen, V. I., Herman, C. P., & Polivy, J. (1994). Social facilitation of eating among friends and strangers. Appetite. https://doi.org/10.1006/appe.1994.1030. Cohen, J. F. W., Jahn, J. L., Richardson, S., Cluggish, S. A., Parker, E., & Rimm, E. B. (2016). Amount of time to eat lunch is associated with children's selection and consumption of school meal entrée, fruits, vegetables, and milk. Journal of the Academy of Nutrition and Dietetics, 1, 123–128. https://doi.org/10.1016/j.jand.2015. 07.019. Coltman, T., Devinney, T. M., Midgley, D. F., & Venaik, S. (2008). Formative versus reflective measurement models: Two applications of erroneous measurement. Journal of Business Research, 61(12), 1250–1262. https://doi.org/10.1016/j.jbusres.2008.01. 013. Conner, M., Norman, P., & Bell, R. (2002). The theory of planned behavior and healthy eating. Health Psychology, 21(2), 194–201. https://doi.org/10.1037//0278-6133.21. 2.194. Connors, P. L., & Rozell, S. B. (2004). Using a visual plate waste study to monitor menu performance. Journal of the American Dietetic Association, 104(1), 94–96. https://doi.

143

Appetite 139 (2019) 127–144

B.A. Lorenz-Walther, et al.

industry. Journal of Cleaner Production, 132, 129–145. https://doi.org/10.1016/j. jclepro.2015.07.146. Price, J., & Just, D. R. (2014). Lunch, recess and nutrition: Responding to time incentives in the cafeteria. Preventive Medicine, 71, 27–30. https://doi.org/10.1016/j.ypmed. 2014.11.016. Pulos, E., & Leng, K. (2010). Evaluation of a voluntary menu-labeling program in fullservice restaurants. American Journal of Public Health, 100(6), 1035–1039. https:// doi.org/10.2105/AJPH.2009.174839. Reisch, L., Eberle, U., & Lorek, S. (2013). Sustainable food consumption: An overview of contemporary issues and policies. Sustainability: Science, Practice and Policy, 9(2), 7–25. https://doi.org/10.1016/j.foodpol.2017.01.007. Robinson, E., & Hardman, C. A. (2015). Empty plates and larger waists: A cross-sectional study of factors associated with plate clearing habits and body weight. European Journal of Clinical Nutrition, 1–3. October https://doi.org/10.1038/ejcn.2015.218. Satorra, A., & Bentler, P. M. (2010). Ensuring positiveness of the scaled difference chisquare test statistic. Psychometrika, 75(2), 243–248. https://doi.org/https://doi.org/ 10.1007/s11336-009-9135-y. Scheibehenne, B., Todd, P. M., & Wansink, B. (2010a). Dining in the dark : The importance of visual cues for food consumption and satiety. Appetite, 55(3), 710–713. https://doi.org/10.1016/j.appet.2010.08.002. Scheibehenne, B., Todd, P. M., & Wansink, B. (2010b). Dining in the dark. The importance of visual cues for food consumption and satiety. Appetite, 55(3), 710–713. https://doi. org/10.1016/j.appet.2010.08.002. Schwartz, J., Riis, J., Elbel, B., & Ariely, D. (2012). Inviting consumers to downsize fastfood portions significantly reduces calorie consumption. Health Affairs, 31(2), 399–407. https://doi.org/10.1377/hlthaff.2011.0224. Silvennoinen, K., Heikkilä, L., Katajajuuri, J.-M., & Reinikainen, A. (2015). Food waste volume and origin: Case studies in the Finnish food service sector. Waste Managementhttps://doi.org/10.1016/j.wasman.2015.09.010. Sirieix, L., Lála, J., & Kocmanová, K. (2017). Understanding the antecedents of consumers' attitudes towards doggy bags in restaurants: Concern about food waste, culture, norms and emotions. Journal of Retailing and Consumer Services, 34(August 2016), 153–158. https://doi.org/10.1016/j.jretconser.2016.10.004. Stancu, V., Haugaard, P., & Lähteenmäki, L. (2016). Determinants of consumer food waste behaviour: Two routes to food waste. Appetite, 96, 7–17. https://doi.org/10.1016/j. appet.2015.08.025. Story, M., Kaphingst, K. M., Robinson-O’brien, R., & Glanz, K. (2008). Creating healthy food and eating environments: Policy and environmental approaches. Annual Review of Public Health, 29, 253–272. https://doi.org/10.1146/annurev.publhealth.29. 020907.090926. Tarkiainen, A., & Sundqvist, S. (2005). Subjective norms, attitudes and intentions of Finnish consumers in buying organic food. British Food Journal, 107(11), 808–822. https://doi.org/10.1108/00070700510629760. Thomas, E. L., Puig Ribera, A., Senye-Mir, A., & Eves, F. F. (2016). Promoting healthy choices in workplace cafeterias: A qualitative study. Journal of Nutrition Education and Behavior, 48(2), 138–145. e1 https://doi.org/10.1016/j.jneb.2015.11.001. Tonglet, M., Phillips, P. S., & Read, A. D. (2004). Using the theory of planned behaviour to investigate the determinants of recycling behaviour: A case study from Brixworth, UK. Resources, Conservation and Recyling, (41), 191–214. Vartanian, L. R. (2015). Impression management and food intake. Current directions in research. Appetite, 86, 74–80. https://doi.org/10.1016/j.appet.2014.08.021. Vermeer, W. M., Steenhuis, I. H. M., Leeuwis, F. H., Heymans, M. W., & Seidell, J. C. (2011). Small portion sizes in worksite cafeterias: Do they help consumers to reduce their food intake? International Journal of Obesity, 35(9), 1200–1207. 2005 https:// doi.org/10.1038/ijo.2010.271. Vermeer, W. M., Steenhuis, I. H. M., & Poelman, M. P. (2014). Small, medium, large or supersize? The development and evaluation of interventions targeted at portion size. 2005 International Journal of Obesity, 38(Suppl 1), 13–18. SUPPL. 1 https://doi.org/ 10.1038/ijo.2014.84. Vyth, E. L., Steenhuis, I. H. M., Heymans, M. W., Roodenburg, A. J. C., Brug, J., & Seidell, J. C. (2011). Influence of placement of a nutrition logo on cafeteria menu items on lunchtime food choices at Dutch work sites. Journal of the American Dietetic Association, 111(1), 131–136. https://doi.org/10.1016/j.jada.2010.10.003. Wansink, B., Painter, J. E., & North, J. (2005). Bottomless bowls: Why visual cues of portion size may influence intake. Obesity Research, 13(1), 93–100. https://doi.org/ 10.1038/oby.2005.12. Wansink, B., & van Ittersum, K. (2013). Portion size me: Plate-size induced consumption norms and win-win solutions for reducing food intake and waste. Journal of Experimental Psychology: Applied, 19(4), 320–332. https://doi.org/10.1037/ a0035053. Worsley, A. (2002). Nutrition knowledge and food consumption-can nutrition knowledge change behaviour? Asia Pacific Journal of Clinical Nutrition, 11, 579–585. https://doi. org/10.1046/j.1440-6047.11.supp3.7.x. Young, M. E., Mizzau, M., Mai, N. T., Sirisegaram, A., & Wilson, M. (2009). Food for thought. What you eat depends on your sex and eating companions. Appetite, 53(2), 268–271. https://doi.org/10.1016/j.appet.2009.07.021. Young, W., Russell, S. V., Robinson, C. A., & Barkemeyer, R. (2016). Can social media be a tool for reducing consumers' food waste? A behaviour change experiment by a UK retailer. Resources, Conservation and Recycling, 117, 9. https://doi.org/10.1016/j. resconrec.2016.10.016. Yuan, K.-H., & Bentler, P. M. (2000). Three likelihood-based methods for mean and covariance structure analysis with nonnormal missing data. Sociological Methodology, 30, 165–200. https://doi.org/10.1111/0081-1750.00078. Zander, K., & Hamm, U. (2010). Consumer preferences for additional ethical attributes of organic food. Food Quality and Preference, 21(5), 495–503. https://doi.org/10.1016/j. foodqual.2010.01.006.

products. Journal of Consumer Psychology, 24(3), 336–354. https://doi.org/10.1016/j. jcps.2013.11.002. Heikkilä, L., Reinikainen, A., Katajajuuri, J., Silvennoinen, K., & Hartikainen, H. (2016). Elements affecting food waste in the food service sector. Waste Management, 56, 446–453. https://doi.org/10.1016/j.wasman.2016.06.019. Hermans, R. C. J., Larsen, J. K., Peter Herman, C., & Engels, R. C. M. E. (2012). How much should I eat? Situational norms affect young women's food intake during meal time. British Journal of Nutrition, 107, 588–594. https://doi.org/10.1017/ S0007114511003278. Higgs, S. (2015). Social norms and their influence on eating behaviors. Appetite, 86(1), 38–44. https://doi.org/https://doi.org/10.1016/j.appet.2014.10.021. Hinton, E. C., Brunstrom, J. M., Fay, S. H., Wilkinson, L. L., Ferriday, D., Rogers, P. J., et al. (2013). Using photography in “The Restaurant of the Future”. A useful way to assess portion selection and plate cleaning? Appetite, 63, 31–35. https://doi.org/10. 1016/j.appet.2012.12.008. Hoefkens, C., Pieniak, Z., Van Camp, J., & Verbeke, W. (2012). Explaining the effects of a point-of-purchase nutrition-information intervention in university canteens: A structural equation modelling analysis. International Journal of Behavioral Nutrition and Physical Activity, 9(1), 111. https://doi.org/10.1186/1479-5868-9-111. Jun, J., & Arendt, S. (2016). Understanding healthy eating behaviors at casual dining restaurants using the extended theory of planned behavior. International Journal of Hospitality Management, 53, 106–115. Just, D. R., & Wansink, B. (2009). Smarter lunchrooms: Using behavioral economics to improve meal selection. Choice, 24(3) (nn-nn). Just, D. R., Wansink, B., Mancino, L., & Guthrie, J. (2008). Behavioral economic concepts to encourage healthy eating in school cafeterias. Economic research report, Vol. 68. Kallbekken, S., & Sælen, H. (2013). “Nudging” hotel guests to reduce food waste as a winwin environmental measure. Economics Letters, 119(3), 325–327. https://doi.org/10. 1016/j.econlet.2013.03.019. Kang, J., Jun, J., & Arendt, S. W. (2015). Understanding customers' healthy food choices at casual dining restaurants: Using the Value-Attitude-Behavior model. International Journal of Hospitality Management, 48, 12–21. https://doi.org/10.1016/j.ijhm.2015. 04.005. Kim, E., Ham, S., Yang, I. S., & Choi, J. G. (2013). The roles of attitude, subjective norm, and perceived behavioral control in the formation of consumers' behavioral intentions to read menu labels in the restaurant industry. International Journal of Hospitality Management, 35, 203–213. https://doi.org/10.1016/j.ijhm.2013.06.008. Lahne, J., & Zellner, D. A. (2015). The great is the enemy of the good: Hedonic contrast in a coursed meal. Food Quality and Preference, 45, 70–74. https://doi.org/10.1016/j. foodqual.2015.05.009. Langen, N., Göbel, C., & Waskow, F. (2015). The effectiveness of advice and actions in reducing food waste. Waste and Resource Management, 168(2), 72–86. Lassen, A. D., Beck, A., Leedo, E., Andersen, E. W., Christensen, T., Mejborn, H., ... Tetens, I. (2014). Effectiveness of offering healthy labelled meals in improving the nutritional quality of lunch meals eaten in a worksite canteen. Appetite, (75), 128–134. https:// doi.org/10.1016/j.appet.2013.12.005. Lassen, A. D., Thorsen, A. V., Trolle, E., Elsig, M., & Ovesen, L. (2004). Successful strategies to increase the consumption of fruits and vegetables: Results from the Danish “6 a day” work-site canteen model study. Public Health Nutrition, 7(2), 263–270. https://doi.org/10.1079/PHN2003532. Lorenz, B. A.-S., Hartmann, M., Hirsch, S., Kanz, O., & Langen, N. (2017). Determinants of plate leftovers in one German catering company. Sustainability, 9, 807. https://doi. org/10.3390/su9050807. Lorenz, B. A.-S., Hartmann, M., & Langen, N. (2017). What makes people leave their food? The interaction of personal and situational factors leading to plate leftovers in canteens. Appetite, 116, 45–56. https://doi.org/10.1016/j.appet.2017.04.014. Lorenz, B. A.-S., Hartmann, M., & Simons, J. (2015). Impacts from region-of-origin labeling on consumer product perception and purchasing intention - causal relationships in a TPB based model. Food Quality and Preference, 45, 149–157. https://doi. org/10.1016/j.foodqual.2015.06.002. Lorenz, B. A.-S., & Langen, N. (2018). Determinants of how individuals choose, eat and waste: Providing common ground to enhance sustainable food consumption out-ofhome. International Journal of Consumer Studies, 42(1), 35–75. https://doi.org/10. 1111/ijcs.12392. Lusk, J. L., & Briggeman, B. C. (2009). Food values. American Journal of Agricultural Economics, 91(1), 184–196. https://doi.org/10.1111/j.1467-8276.2008.01175.x. Mahon, D., Cowan, C., & McCarthy, M. (2006). The role of attitudes, subjective norm, perceived control and habit in the consumption of ready meals and takeaways in Great Britain. Food Quality and Preference, 17(6), 474–481. https://doi.org/10.1016/ j.foodqual.2005.06.001. Maniatis, P. (2016). Investigating factors influencing consumer decision-making while choosing green products. Journal of Cleaner Production, 132, 215–228. https://doi. org/10.1016/j.jclepro.2015.02.067. Manomaivibool, P., Chart-asa, C., & Unroj, P. (2016). Measuring the impacts of a save food campaign to reduce. Food Waste on Campus in Thailand, 38(2), 13–22. Martins, M. L., Rodrigues, S. S. P., Cunha, L. M., & Rocha, A. (2015). Strategies to reduce plate waste in primary schools – experimental evaluation. Public Health Nutrition, 19(8), 1517–1525. https://doi.org/10.1017/S1368980015002797. Morley, B., Scully, M., Martin, J., Niven, P., Dixon, H., & Wakefield, M. (2013). What types of nutrition menu labelling lead consumers to select less energy-dense fast food? An experimental study. Appetite, 67, 8–15. https://doi.org/10.1016/j.appet. 2013.03.003. Notarnicola, B., Tassielli, G., Renzulli, P. A., Castellani, V., & Serenella, S. (2016). Environmental impacts of food consumption in Europe. Journal of Cleaner Production, 140, 753–765. https://doi.org/10.1016/j.jclepro.2016.06.080. Pirani, S. I., & Arafat, H. A. (2015). Reduction of food waste generation in the hospitality

144