What makes people leave their food? The interaction of personal and situational factors leading to plate leftovers in canteens

Appetite 116 (2017) 45e56

Contents lists available at ScienceDirect

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

What makes people leave their food? The interaction of personal and situational factors leading to plate leftovers in canteens Bettina Anne-Sophie Lorenz a, b, *, Monika Hartmann a, Nina Langen b a b

Institute for Food and Resource Economics, University of Bonn, Germany €t Berlin, Germany Institute of Vocational Education and Work Studies, Technische Universita

a r t i c l e i n f o

a b s t r a c t

Article history: Received 5 September 2016 Received in revised form 22 March 2017 Accepted 15 April 2017 Available online 20 April 2017

In order to provide a basis for the reduction of food losses, our study analyzes individual food choice, eating and leftover behavior in a university canteen by consideration of personal, social and environmental determinants. Based on an extended literature review, a structural equation model is derived and empirically tested for a sample of 343 students. The empirical estimates support the derived model with a good overall model fit and sufficient R2 values for dependent variables. Hence, our results provide evidence for a general significant impact of behavioral intention and related personal and social determinants as well as for the relevance of environmental/situational determinants such as portion sizes and palatability of food for plate leftovers. Moreover, we find that environmental and personal determinants are interrelated and that the impact of different determinants is relative to perceived time constraints during a visit of the university canteen. Accordingly, we conclude that simple measures to decrease avoidable food waste may take effects via complex and interrelated behavioral structures and that future research should focus on these effects to understand and change food leftover behavior. © 2017 Elsevier Ltd. All rights reserved.

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

1. Thematic background Sustainability in food production and consumption has gained substantial attention in international politics and research (see e.g. the UN sustainable development goals set up in September 2015, (United Nations, 2016)). Within this context, the reduction of food losses along the food supply chain is declared a specific target (FAO, 2013). Even though data on food waste in the European member states comes still with a high level of uncertainty, the Fusions Project estimated an annual food waste for the year 2012 of 88 million tons in EU-28 member states of which about 53% occur at household level and 12% in the food service sector (Stenmarck, Jensen, Quested, & Moates, 2016). Apart from research on private households (Evans, 2011; Graham-Rowe, Jessop, & Sparks, 2014; €, & Reinikainen, Katajajuuri, Silvennoinen, Hartikainen, Heikkila 2014; Langen, Goebel, & Waskow, 2015) the relevance of consumers for food waste has been addressed in the food service sector €m by estimates in Finland (Katajajuuri et al., 2014), Sweden (Engstro & Carlsson-Kanyama, 2004) and Germany (Kranert et al., 2012),

* Corresponding author. Institute for Food and Resource Economics, University of Bonn, Germany. E-mail address: [email protected] (B.A.-S. Lorenz). http://dx.doi.org/10.1016/j.appet.2017.04.014 0195-6663/© 2017 Elsevier Ltd. All rights reserved.

where plate leftovers at the guest level have been estimated to contribute between 17% (Katajajuuri et al., 2014) and 55% € m & Carlsson-Kanyama, 2004) to all food waste. Moreover, (Engstro they have been identified as important sources of avoidable food waste since relevant shares of leftovers are uneaten vegetables and €m & Carlsson-Kanyama, 2004; Silvenstarchy side dishes (Engstro €, Katajajuuri, & Reinikainen, 2015). Relating to the noinen, Heikkila trend of increased out-of-home consumption of food, the European Commission named “standardised portion sizes in restaurants and canteens” as one of the factors that contribute to avoidable food waste (European Union, 2016). Considering research on foodrelated behavior in general, the emphasis on portion sizes as single factor for food leftovers in out-of-home catering appears too simplistic. Various studies indicate that food choice and eating behavior in out-of-home consumption settings relate to a wide set of personal, social and situational factors such as food-related values (Lusk & Briggeman, 2009), attitudes (Mahon, Cowan, & McCarthy, 2006; Sparks, Hedderley, & Shepherd, 1992), social norms (Cruwys, Bevelander, & Hermans, 2015), personal comfort (Byker, Farris, Marcenelle, Davis, & Serrano, 2014) and choice design (Hanks, Just, Smith, & Wansink, 2012). Moreover, studies that have specifically considered plate leftovers, mainly to determine consumption of specific food types (e.g. fruit and vegetables),

46

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

support an extended behavioral framework in order to understand what makes people leave their food on the plate (Lowe et al., 2010; WRAP, 2011). There are only few studies that have applied a comprehensive behavioral model for the analysis of food leftovers (Finkbeiner, 2013; Mahon et al., 2006). One limitation of those studies is that leftovers were either measured in an aggregate form or by individuals' self-reported behavior. Accordingly, the aim of the paper is to test an extended behavioral framework concerning the relevance of determinants related to food choice and eating on plate leftovers in the out-of-home setting of canteens, with plate leftovers being measured by researchers’ visual estimation. Based on an extended literature review, a structural model is derived considering three dimensions of influencing factors: (physical) environment, social contexts and personal behavioral determinants. The model is empirically tested for a dataset of observational and survey data, collected among 343 students in a university canteen. Finally, we relate our results to past studies in the research area and draw conclusions on the interaction of environmental, social and personal determinants for the presence of food leftovers in catering. 2. Derivation of research hypotheses In order to provide a theoretical basis on the analysis of individuals’ food leftover behavior, we consider studies on foodrelated behaviors in out-of-home settings, which mostly focus on specific determinants for food-related behavior. By linking findings for personal, social and environmental factors, research hypotheses are derived and translated into a structural model. 2.1. Personal determinants of food leftovers A prominent research area with respect to consumer behavior in catering facilities are the personal determinants of food choice and consumption from a health perspective (Graham-Rowe, Jessop, & € m & Thunstro € m, 2015; Scholderer, Kügeler, Sparks, 2015; Nordstro Olsen, & Verbeke, 2013; Schulte-Mecklenbeck, Sohn, de Bellis, Martin, & Hertwig, 2013). Generally, the Theory of Planned Behavior (TPB) is an extensively applied model for individual health- and food related behaviors (Aertsens, Verbeke, Mondelaers, & van Huylenbroeck, 2009; Tonglet, Phillips, & Read, 2004). It states that behavior can be determined by measuring behavioral intention (Ajzen, 1991). Behavioral intention itself is influenced by three constructs: first by attitudes towards the behavior; second by perceived social norms (subjective norms) or the evaluation of relevant others with respect to the behavior; and third by perceived behavioral control (PBC) over the behavior (Ajzen & Fishbein, 2005; Ajzen, 1991). With respect to the application of the TPB for sustainable consumer behaviors, such as regional and organic food purchasing (Arvola et al., 2008; Lorenz, Hartmann, & Simons, 2015; Tarkiainen & Sundqvist, 2005), healthy food consumption habits (Cook, Kerr, & Moore, 2002; Mahon et al., 2006; Sparks et al., 1992), as well as waste recycling behavior (Barr, 2007; Graham-Rowe et al., 2014), it has partially been suggested to add personal norms as a fourth construct to determine behavioral intention. Independent of studies applying the TPB, research supports the relevance of personal norms or of constructs closely related to personal norms (i.e. self-identity, personal value systems) for sustainability related food consumption practices such as purchasing “suboptimal” (visually imperfect) food products in supermarkets (de Hooge, Oostindjer, Aschemann-Witzel, Normann, Loose, & Almli, 2017) or the purchase of sustainable dairy products (Vermeir & Verbeke, 2006). Personal norms measure a normative evaluation of the behavior based on personal value systems. They have been

found to have a direct influence on behavioral intention as well as an indirect effect from their influence on attitudes (Arvola et al., 2008; Harland, Staats, & Wilke, 1999). Moreover, personal norms have been shown to be interrelated with subjective norms (Aertsens et al., 2009; Arvola et al., 2008; Vermeir & Verbeke, 2008). Accordingly, three hypotheses are formulated for the behavioral determinants of plate leftovers in a university canteen. H1. Food leftovers at lunch are negatively influenced by the behavioral intention to finish all food on one's plate in the university canteen. H2. The behavioral intention to finish all food at lunch is positively determined by… (a) …a positive attitude towards finishing all food at lunch in the university canteen. (b) …high perceived personal control over finishing all food at lunch in the university canteen. (c) …subjective norms which support finishing all food at lunch in the university canteen. H3. Personal norms in favor of finishing all food at lunch in the university canteen… (a) …are positively correlated with subjective norms that support finishing all food at lunch in the university canteen. (b) …strengthens consumers' positive attitude towards finishing all food at lunch in the university canteen.

2.2. Social influences on food leftovers Interrelated with personal determinants of food leftovers, foodrelated behavior in general has been stated to be influenced by social context (Cruwys et al., 2015; King, Weber, Meiselman, & Lv, 2004; Story, Kaphingst, Robinson-O'brien, & Glanz, 2008). One aspect of social context is already considered in the TPB (see section 2.1, hypotheses H2(c) and H3(a)). Based on an extended review on food-related behaviors, Cruwys et al. (2015) conclude that social influences go beyond this dimension and also include seeking affiliation with others. Moreover, besides having an indirect influence on behavior via behavioral intention, social context and specifically the presence and behavior of other persons has been found to influence food choice and eating behavior directly and partly unconsciously (Cruwys et al., 2015; Mollen, Rimal, Ruiter, & Kok, 2013). A comparably large body of research specifically addresses influences of social presence on food consumption in relation to various mediating factors such as extended meal duration (Bell & Pliner, 2003), or different social relationships between eating companions (Brindal, Wilson, Mohr, & Wittert, 2015; Salvy, Jarrin, Paluch, Irfan, & Pliner, 2007). Specifically for a university canteen setting, Young, Mizzau, Mai, Sirisegaram, and Wilson (2009) show that the presence of others tends to decrease food intake of females. Hence, we assume: H4. Plate leftovers at lunch in the university canteen are positively influenced by the presence of other persons during lunch.

2.3. Environmental determinants of food leftovers Besides personal and social determinants of food-related behavior, a growing literature body deals with the impact of environmental or situational factors on food choice and eating in catering settings and the respective impacts on nutritional meal

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

47

quality and food leftovers (Hanks et al., 2012; Thiagarajah & Getty, 2013; Wansink, 2004). Within these studies, the impact of the palatability of food and of food portion sizes on food consumption are prominent areas of research. Related to palatability, various studies state that the palatability of food (visual appearance, smell and taste of food) is one of the greatest drivers of food-related behaviors (Eertmans, 2006; Pliner & Mann, 2004; Tuomisto, Tuomisto, Hetherington, & Lappalainen, 1998). Accordingly, low ratings of palatability have been found to be a major stated reason €bel, & Müller, 2015; Go €bel et al., for food leftovers (Betz, Buchli, Go 2014). Different studies aiming to increase fruit and vegetable consumption (and to decrease plate waste of fruit and vegetables) in out-of-home settings have focused on palatability as measure of successful behavioral change (Brug, Tak, te Velde, Bere, & de Bourdeaudhuij, 2008; Cooke et al., 2011). Besides aspects of taste, differences in portion sizes have as well been found to determine food consumption and food leftovers in various settings (Dinis, Martins, & Rocha, 2013; Freedman & Brochado, 2010; Hermans, Larsen, Peter Herman, & Engels, 2012). Thereby, bigger portion sizes have been related to unconsciously increased food consumption but as well to increased plate leftovers which were found being nearly proportional to the initial amount of food on a plate (Wansink & van Ittersum, 2013). It may be assumed that depending on whether food is served as a fixed portion or in a buffet form (i.e. for salad) - portions sizes have higher or lower impact on perceived control to finish all food and on leftovers (Berkowitz, Marquart, Mykerezi, Degeneffe, & Reicks, 2016). Nevertheless, also self-served portion sizes may e due to insufficient abilities to estimate serving sizes and personal appetite e relate to inadequate amounts of food on a plate (Schwartz & ByrdBredbenner, 2006; Wansink & van Ittersum, 2013). Accordingly, the situationally perceived portions size and the perceived palatability of food at a specific day are hypothesized to take a direct influence on plate leftovers.

food valuation and plate leftovers (e.g. Lusk & Briggeman, 2009). In this respect, the type of food chosen is likely of relevance for con€, sumers’ evaluation of palatability (Tuorila, Palmujoki, Kyto €rnwall, & Vehkalahti, 2016). Besides, previous studies in caterTo ing settings have found that food leftovers are not equally distributed over all food components. Generally, vegetables and starchy side dishes are left over in greater quantities than meat (Betz et al., 2015) and self-served or more flexible menus create less leftovers than standardized portion sizes (Guthrie & Buzby, 2001). Both aspects potentially make the choice of food also a direct factor of food leftovers (Betz et al., 2015; Ferreira, Martins, & Rocha, 2013). This leads to the following hypotheses:

H5. Food leftovers at lunch in the university canteen are positively related to perceived larger portion sizes of food.

H10. Time pressure during lunch…

H6. Food leftovers at lunch in the university canteen are negatively related to a higher perceived palatability of food. Additionally to the direct impacts of situational portion size and palatability on plate leftovers, research implies that the formation of behavioral determinants generally is based on experiences that may be related to environmental variables (Ajzen, 2001). Therefore, two additional links and indirect influences of palatability and portion size via behavioral variables will be considered in our model. In line with studies finding that people cognitively justify plate leftovers regularly with large portion sizes or unfavorable taste in catering (Betz et al., 2015), we consider two relationships in our model. First, we assume that individual attitudes towards leftovers are determined by the situationally perceived palatability of food based on notions that palatability is one dimension of food related attitudes and accordingly of the eating and leftover behavior (Roininen, 2001). Second, we regard portion sizes as a situational reason for food leftovers and thus consider the perception of large portion sizes as a specific determinant of perceived behavioral control (Ajzen, 1991). H7. Higher perceived palatability of food in the university canteen creates more positive attitudes towards finishing all food. H8. Larger perceived portion sizes in the university canteen create lower ratings of perceived behavioral control over finishing all food at lunch in the university canteen. Additional aspects have been identified to influence consumers'

H9. Specific types of food choice (vegetarian vs. meat and fixed component vs. flexible menu) at lunch in the university canteen… (a) …create higher ratings of palatability than other choice types. (b) …create higher amounts of plate waste at lunch in the university canteen, independently from their effect on palatability. Finally, time pressure may, independently from other determinants, provide a causal reason for plate leftovers as past research has shown that an extended time for a meal is related to e et al., 2016; higher food intake and lower plate leftovers (Entre Price & Just, 2014; Wansink, 2004). Moreover, Scheibehenne, Miesler, and Todd (2007) have assumed that time pressure may not only be a direct determinant for food leftovers. Instead, it may shift the relevance of determinants for behavior from more elaborated personal to more automatic environmental cues. Hence, following this argument we considered the impact of time pressure first as direct influence on plate leftovers (H10a) and second as contextual determinant thus, investigating its relative impact on environmental versus personal behavioral determinants (H10b).

(a) …increases plate leftovers. (b) …increases the relevance of environmental variables and decreases the relevance of behavioral determinants on plate leftovers. Overall, the derived research hypotheses lead to a structural framework consisting of 11 variables if Hypothesis 10a (see Fig. 1) or of 10 variables in case Hypothesis 10b is considered. 3. Methodology Based on the theoretical structural model, the subsequent section displays the variables and indicators relevant for the empirical analysis conducted. Moreover, the collection of data and analyses prior to the final estimation of structural relationships are presented. 3.1. Definition of measurement systems To test the theoretical model derived in section 2, six of the structural variables (Fig. 1) were set up in the form of reflective multidimensional latent constructs (personal norm, attitude, subjective norm, perceived behavioral control, intention and palatability). With reference to past consumer studies and methodological considerations of model convergence and improper solutions (i.e. linear dependencies between indicators), those constructs were defined by measurement systems of three variables each (see Table 1); a detailed discussion on adequate

48

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

H9

+

Aƫtude

+

H7

+

H3

Palatability

H2

Personal Norm

H6 H3

+

SubjecƟve Norm

+

+ PBC

IntenƟon

H2

-

-

H8

-

+

H4

Plate LeŌovers

H1

+

H2

Presence of Others

H5

PorƟon Size

H9

Food Choice

+

H10

Time Pressure

Source: own illustration Fig. 1. Structural model for food leftover behavior.

Table 1 Measurement of behavioral and environmental variables. Construct

Items

Reference b

Having leftovers is bad/good [reversed measurement] Having leftovers makes me feel satisfied/unsatisfiedb Having leftovers makes me feel happy/unhappyb Subjective Norm Others finish all food on their platea x I try to do the samea Others think people should finish all fooda x Their opinion is important to mea Others may criticize me if I don't finish all fooda x Their critics make me feel uncomfortablea Predicting food amount at food choice is easya Perceived Behavioral Finishing all food on my plate is usually easy to mea Control I could always finish all food on my plate if I wanted toa Intention I will do my best to empty my platea I somewhat expect to have leftoversa [reversed measurement] I generally try not to leave fooda Personal Norms I am not the type of person to leave food on my platea Leaving food on your plate is wronga I don't think that it makes any difference whether I finish all food on my plate or nota [reversed measurement] Palatability Visual appearance of food today (1e5 stars) Taste of food today (1e5 stars) Smell of food today (1e5 stars) Portion Size The portion size of my food today was too small/too large (7-point bipolar) Time Pressure My lunchtime today was unusually short/unusually long (7-point bipolar scale) Plate Leftovers Index for visually estimated leftovers based on chosen servings of food component: 0 ¼ no leftover, 1 ¼ leftover equivalent to less than 0.5 servings of one food component, 2 ¼ leftover equivalent to about 0.5 servings of one food component; 3 ¼ leftover equivalent to more than 0.5servings of one food component Food Choice 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 Presence of Others Stated presence of other persons during lunch: creation of two dummy variables based on groups: eating alone, eating with one other person, eating with two or more other persons

Attitude

a b

Crites, Fabrigar, and Petty (1994)

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

Ajzen (2006)

Cook et al. (2002)

Finkbeiner (2013), Hermans et al. (2012)

Connors and Rozell (2004)

measurement on five-point Likert Scale. measurement on seven-point bipolar scale.

ratios between measured indicators and latent constructs may be obtained from Ding, Velicer, and Harlow (1995). The remaining environmental variables were directly measured by sets of dummy variables (types of food choice and presence of others) or in continuous form (portions size and time pressure) as displayed in Table 1. The latter also holds for the dependent variable (plate leftovers) which were measured based on visual estimation of digital photographs. This measurement approach has been stated to provide reliable approximations of weighted food leftovers when food is not packaged and when different meal components are served on separate plates which both applied to our setting (Hanks,

Wansink, & Just, 2014; Hinton et al., 2013). 3.2. Collection and clearing of data Data collection was carried out during three days (Tuesday to Thursday) within one week in December 2015 (during the regular semester) at a university canteen in Germany. The canteen had two service lines, one for hot dishes which were (expect for one daily special meal) served based on components (main dishes, soup, starchy and vegetable side dishes and desert) and one for salad in a buffet form. A full plan of food served during data collection is

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

displayed in Appendix 1. The canteen's kitchen opening hours were from 11:00am until 2:00pm. During this time, approximately 1500 meals were served per day. Meal prices for students generally were very low due to food subsidies. During data collection, the prices were standardized at EUR 1.65 for main dishes, EUR 1.00 for soup and EUR 0.60 for side dishes. Prices for the special dish, which already included side dishes, varied between EUR 3.40 and EUR 4.20. The collection of data combined visual measurement of individual plate leftovers via photographs together with an online (smartphone optimized) questionnaire. The questionnaire was available in English as well as in German and had been pretested by native speakers of the respective languages as well as by bilingual university staff in order to ensure the comparability of questions. It composed of queries related to the indicators displayed in Table 1, personal determinants (i.e. general beliefs) of food choice and eating, specific perceptions of food related to the day of participation and sociodemographic characteristics. Questions were ordered according to their specificity and their potential to bias subsequent questions (Krosnick & Presser, 2009). Accordingly, questions regarding behavioral intention, attitudes and personal norms were asked prior to questions on subjective norms and perceived behavioral control. Moreover, the order of items for each question was randomized to keep biases to a minimum. Guests of the university canteen were informed by two posters that there was “a general survey on having food at the university canteen” and that they could participate at the central point of tray return. Besides, guests were approached by three research assistants when returning their trays. Guests who were interested in participating in the survey received an individualized card with an online link and QR code transferring them to the survey. Moreover, a picture of each participant's tray with the individualized card was taken before the guests returned their tray to the canteen staff (see Fig. 2). In order to estimate plate leftovers of each food component, avoidable leftovers were rated by two independent coders according to the four-point-scale of Connors and Rozell (2004) (see Table 1). Overall, 825 pictures of 825 different individual trays were taken during three days, of which 383 successfully could be matched to a completed online survey questionnaire. The valid sample consisted of 343 participants who met two criteria: (1) they were all guests with student status (compared to university employee or guest status), serving as a proxy for a group of sociodemographic characteristics and comparable lifestyles and (2) had no missing answers in the directly measured indicators. While the former criteria led to an exclusion of 35, the latter implied an exclusion of five initial participants. 3.3. Exploratory and confirmatory factor analysis Following standard procedures of structural equation model analyses with empirical data (i.e. Brown, 2006; Byrne, 2012) exploratory and confirmatory factor analyses (EFA/CFA) were conducted prior to the general model estimation to test for the validity

49

of the defined measurement systems. The applied software was SPSS Statistics and Mplus software. As these analyses revealed that indicators of personal norms were highly correlated with indicators from attitude, subjective norms, perceived behavioral control and intention, the construct personal norms had to be excluded from the subsequent analysis. For the remaining five latent constructs, results of the confirmatory factor analysis revealed sufficient factor loadings (standardized loading between 0.5 and 0.95) and sufficient composite reliability (above 0.6) for all constructs besides intention (for reference values see Bagozzi & Yi, 1988). In order to overcome the latter shortcoming, a two-indicator measurement was defined for intention, excluding the indicator “I somewhat expect to have leftovers”. Results for the adjusted CFA are displayed in Appendix 2. Regarding discriminant validity, a comparison of correlation between latent constructs and their average variance extracted (AVE) revealed sufficient differentiation between the constructs according to Fornell and Larcker (1981) (see Appendix 2). 3.4. Model estimation A standard estimation method for CFA and corresponding structural equation models is covariance based Maximum Likelihood estimation of model parameters so that the observed variance-covariance matrix of measured indicators matches the theoretically structured model best (Brown, 2006). However, our data violated the condition of normal distribution for measured variables with moderate skewness (<2) and kurtosis (<7) (see Appendix 2). An alternative model estimation via distribution-free Generalized Least Squares under mean-imputation for missing values indicated comparable estimation results to the Maximum Likelihood estimates regarding overall model fit, factor loadings and regression weights. Accordingly, it was decided to apply Full Information Maximum Likelihood due to the usage of a higher share of data provided by the dataset. 4. Results 4.1. Sample properties Since the final sample of 343 participants only composed of student guests in a university canteen, it provides a relatively homogeneous group with respect to sociodemographic characteristics (see Table 2). The majority of our sample composed of younger students who did not hold a university degree yet. Accordingly, their average age was 22.8 years with 80% of the sample in the age range between 18 and 27. As expected, the income situation was relatively homogeneous with 73% stating a monthly net income below EUR 1300. Compared to general statistics (*national averages) on students in Germany, this sample may be regarded representative concerning average age (*24 years), gender (*48% female), and monthly net income (*864 EUR). Participants of this study were more often single than the German average of students (*43%) and had more

Fig. 2. Exemplary photographs of observed food leftovers (original photographs in color and high resolution).

50

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

Table 2 Sample Properties for 343 students. Characteristic

Sample Distribution (N ¼ 343)

Gender Age

52% 55%

male 19-23 years

48% 21%

female 24-28 years

Faculty of enrollment

56%

Mathematics and Natural Sciences

29%

Agriculture

University degree Household size Household composition Relationship status Monthly net income

63% 35% 90% 61% 60%

no degree yet one person no children single below EUR 900

33% 28% 7% 34% 14%

Bachelor, Diploma or Master two persons one child in permanent relationship EUR 1300 to EUR 2600

often children (*5%) (Middendorff, Apolinarski, Poskwsky, Kandulla, & Netz, 2013). Moreover, the sample is biased towards students in subjects from faculties stated in Table 2 (*mathematic and natural sciences, including agricultural sciences 20%; medical sciences 6%). 4.2. Descriptive findings Over the three days of our study, 11% of meals were salads from the salad bars and 36% of chosen main components were vegetarian (see Appendix 1). In this respect food choices of participants were similar to general estimates of the university canteen as on average, 15% of meals are salads from the buffet and about 30% of chosen main components (exclusive stew and soup) are vegetarian. 258 participants (75%) of our sample hardly had any plate leftovers against 57 (17%) participants with moderate (equivalent to maximum 0.5 servings of one meal component) and 28 participants (8%) with considerable plate leftovers (equivalent to more than 0.5 servings of one meal component). This stands in line with findings that 78% of young adults in a UK study stated to “always clear their plate” (Robinson & Hardman, 2015). Most of our participants went to lunch with more than one other person (68%) or with one other person (24%) against 8% having lunch on their own. Considering finally the rating of portion sizes, about half of our sample (53%) rated their portion size as adequate (central answer on the bipolar scale from 1 ¼ too small to 7 ¼ too big), while 32% of participants rated the portion sizes as rather too small and 15% as rather too big. Regarding the distribution of indicators for the latent variables, descriptive statistics are provided in Appendix 3. 4.3. Structural model for food leftovers Based on the refined measurement systems from the CFA (section 3.3), a structural model was estimated. Dummy variables for specific types of food choices (see Table 1) and for the presence of others during lunch (see Table 1) were tested separately as well as interactively in their impact on leftover behavior. Moreover, dummy variables were included for sociodemographic features (gender, monthly net income and age by split half method), however, since only gender had a significant impact on any other variable in our model, it was the single sociodemographic characteristic considered in the subsequent analyses. Overall, the final structural model (8 independent variables, 5 dependent variables) reached a good fit to the empirical data with a minimum Chi-Square discrepancy CMin ¼ 300.05 under 192 degrees of freedom (CMin/df ¼ 1.56), a Comparative Fit Index (CFI) ¼ 0.923, a Tucker Lewis Index (TLI) ¼ 0.909 and a Root Mean Square Error of Approximation (RMSEA ¼ 0.041) (for reference values see Bagozzi & Yi, 1988; Brown, 2006). Estimation results for the regression weights and correlations on the structural level are

14% 10% 10% 1% 2% 14% 3% 4% 13% 13%

>28 years <¼18 years Medicine Philosophy Doctoral degree three persons two or more children married EUR 900 to EUR 1300 above EUR 2600

displayed in Fig. 3. With respect to the theoretically derived determinants, the estimated model supports most assumed structural relationships and some dummy-variable impacts at significance levels of a < 0.05. The hypotheses related to personal norms (H3a and H3b) could not be tested as this latent construct had to be excluded from the structural model (as discussed above). Regarding the hypotheses linked to the behavioral constructs, the general impact of behavioral intention on leftover behavior (H1) as well as the impact of attitudes (H2a), subjective norms (H2b) and perceived behavioral control (H2c) on behavioral intention are supported by the estimates (see Fig. 3). Overall, the model reaches a sufficient R2 of 0.438 for intention and an R2 of 0.199 for the plate leftover behavior. Generally, and independently from the other determinants in the structural model, male participants had lower leftovers than female participants. Regarding the hypotheses on environmental variables, the assumed relationships between the presence of others and food leftovers (H4) cannot be supported since both dummy variables contribute insignificant estimation results. In contrast to this, the assumed relationships between perceptions of food in form of portions size (H5) and palatability (H6) and food leftovers are substantiated by our empirical analysis. The same applies to the impacts from these two environmental variables on specific behavioral constructs: palatability of food positively influences stated attitudes towards finishing all food (H8) and larger perception of portion sizes decrease the perception of personal control over leftovers (H7). Regarding the dummy variables on different types of food choice (H9), ‘choosing a special dish with fixed components versus a regular component-based choice’ and ‘choosing food from the salad bar versus food from the regular service line’ both have a significant positive impact on taste (see Fig. 3). Whereas the indirect influence on leftovers of choosing from the salad bar is supported by an additional direct negative impact on leftovers, a twofold perspective is observed in the model for choosing a special dish: in contrast to the negative indirect impact from improved taste, it also has a direct positive impact on the amount of food leftovers (see Fig. 3). Finally, time pressure as direct environmental determinant of leftovers (H10a) is not supported by the model estimates. To additionally test hypothesis 10b, the model estimation was repeated for a grouped model with one group consisting of those participants who rated their lunchtime with 1e3 on a seven-point bipolar scale from 1 ¼ unusually short to 7 ¼ unusually long (N ¼ 105) and one model for the remaining participants (N ¼ 238). Situational variables that had no significant impact in the general model (meat versus vegetarian food choice, presence of others) were excluded from this analysis. Compared to the general model, the grouped model (Fig. 4) reveals comparable goodness of overall model fit (CMin/df of 406.10/297 ¼ 1.37, CFI ¼ 0.922, TLI ¼ 0.913,

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

0.123*

Aƫtude

Palatability

51

Special Dish (1=yes)

0.156*** 0.176***

0.393***

Salad Bar (1=yes)

0.285*** -0.170*** SubjecƟve Norm

0.164***

0.252*** -0.179***

IntenƟon

Plate LeŌovers R2=0.199

R2=0.438

0. 406***

-0.112**

0.240*** -0.136**

PBC

PorƟon Size

-0.359***

Presence of one (1=yes)

Meat Dish (1=yes)

Presence of more (1=yes)

Time Pressure (1=yes)

Gender (1=male) Fig. 3. Standardized estimation results for the structural model (N ¼ 343); ***p < 0.01; **p < 0.05; *p < 0.1.

0.045 (0.123*)

Aƫtude

0.115 (0.346***) SubjecƟve Norm

Palatability

0.431*** (0.378***)

IntenƟon

PBC

-0.280*** (-0.397***)

Plate LeŌovers

0.205** (0.261***)

0. 473*** (0.377***)

0.219*** (0.182***)

-0.054 (-0.102)

-0.120 (-0.182***)

R2=0.514 (R²=0.416)

Special Dish (1=yes) Salad Bar (1=yes)

-0.108 (-0.040)

-0.289*** (-0.143**)

0.277*** (0.256***)

0.184*** (0.103)

PorƟon Size

R2=0.238 (R²=0.187) -0.221** (-0.077)

Gender (1=male) Fig. 4. Standardized estimation results for group with short lunchtime (N ¼ 105, results bold and underlined) and with regular/extended lunch time (N ¼ 238, results in parentheses); ***p < 0.01; **p < 0.05; *p < 0.1.

RMSEA ¼ 0.046). Both groups contribute to a similar extent to the CMin value of the model with 188.48 (time pressure) and 217.62 (no time pressure) respectively. Estimates for participants without time pressure (Fig. 4, results in parentheses) remain comparable to the results for the general model with significant impacts of behavioral determinants and environmental variables as well as significant interactions between both. Though gender and most of the food choice type effects are insignificant in the model, the share of explained variances in intention and plate leftovers remains similar to the general model. The results, however, change when people under time pressure (N ¼ 105) are considered (Fig. 4, underlined results in bold font). Whereas structural relationships within the behavioral part of the model (attitudes, subjective norms, perceived behavioral control

and intention) and within most of the environmental part of the model (palatability, portion size, type of food choice ¼ special dish with the exception of type of food choice ¼ salad bar) remain significant, this does not hold for the interaction between both parts of the model. In fact, we observe that the link between palatability and attitude, and even more important between behavioral intention and leftover behavior becomes insignificant and also the impact from perceived portion size on perceived behavioral control decreases in magnitude compared to the general model. Finally, for participants under time pressure, gender becomes a significant determinant for leftover behavior (see Fig. 4). Remarkably the explained variance for intention and behavior increase compared to the general model. Overall, the grouped model supports the hypothesis that in the

52

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

case of time pressure, leftover behavior becomes dominated by environmental factors and is no longer significantly influenced by behavioral determinants (H10b). 5. Discussion At a general level, our model supports the relevance of behavioral, social and environmental determinants in explaining plate leftovers in a university canteen setting. Reviewing the behavioral determinants in the model, the classical TPB components of behavioral intention are supported in our analysis, as is the impact of intention on observed food leftover behavior. This expands findings on the successful application of this theory for the consumption of takeaway foods (Mahon et al., 2006), as well as the purchase of organic (Tarkiainen & Sundqvist, 2005) and genetically modified food (Cook et al., 2002). An extension of the TPB by personal norms as suggested by Aertsens et al. (2009) and by Arvola et al. (2008), however, could not be substantiated by our results. In contrast to these two studies on organic foods, it was not possible to distinguish personal norms from other behavioral constructs with our dataset. Thereby our study indicates the close interrelation between personal norms on the one hand and attitude, subjective norms, perceived behavioral control and intention on the other hand. Thus, one might conclude that the dimension of personal norms is with respect to plate leftover behavior implicitly considered by those other behavioral constructs (Ajzen & Fishbein, 2005; Arvola et al., 2008). From a practical perspective, our results support efforts to decrease food leftovers in out of home settings by providing information and recommendations to individuals that target personal attitudes and provide advice to increase personal ability to take control over the own behavior. Examples of such interventions may be “mindful eating” campaigns (Stites et al., 2015; Timmerman & Earvolino-Ramirez, 2010) or practically oriented sustainability education campaigns (Redman & Redman, 2014). Regarding the relevance of social influences on food-related behavior, our model confirms the significant impact of subjective norms on students' intention to prevent plate leftovers, although it is notable that the relative impact of perceived behavioral control and attitudes on intention is greater than the impact of subjective norms. Thereby, the relevance of descriptive and injunctive social norms as highlighted in the past for fruit and vegetable consumption (Thompson, Bachman, Baranowski, & Cullen, 2007), for the choice of healthy versus unhealthy food in university canteens (Mollen et al., 2013), as well as for the amount of food that female students consume at a meal (Hermans et al., 2012) is supported. On the other hand, the presence of others as situational social influence which has been suggested to determine the amount of food chosen and consumed in comparable settings (Young et al., 2009) did not have any significant impact in our model. An explanation for this non-significance may be that most of our participants did eat with more than one other person and that the university canteen in general is very crowded during lunchtime. This changes the meaning of the question ‘whether a person had lunch on his/ her own’ towards the question ‘whether a person had lunch sitting next to people he/she is familiar with or not’. Although Clendenen, Herman, & Polivy state that “the relationship of dining companions is an important factor contributing to social facilitation” (1994, p. 1), they also conclude that this relationship only moderates a general impact on food consumption by the presence of other people. In contrast to this, other studies found relevant changes in the influence of the presence of others on food consumption based on gender (Brindal et al., 2015), the type of relationship between people (Salvy et al., 2007), as well as the situation-specific perception of others i.e. based on body type (McFerran, Dahl,

Fitzsimons, & Morales, 2010). Generally, the application of existing social studies to our approach may be limited since those studies focused on social determinants without including personal and environmental determinants. Most environmental determinants of the derived theoretical framework have been supported empirically. Moreover, our empirical models confirm the interplay of personal and environmental factors that influence university students' food leftover behavior. First, our findings support the relevance of perceived portion size and taste for plate leftovers as found in other studies (Betz et al., 2015). Freedman, Bartoli, and Wagle (2012) show that (objectively) smaller portion sizes in a university canteen lead to lower levels of plate waste and different studies published by Wansink and colleagues support this relationship for restaurant settings (Wansink & Payne, 2008; Wansink & van Ittersum, 2013). Our results for portion size are in line with these findings assuming a correlation between larger portion sizes and plate leftovers. However, since we only consider the individually perceived portion size, our results may be biased compared to objective portion size measurements. Past research indicates that the perception of portion sizes does not necessarily align to objective portion size measurement (Diliberti, Bordi, Conklin, Roe, & Rolls, 2004; Wansink & van Ittersum, 2013; Wansink, Painter, & North, 2005). Regarding the link between the different types of food choice (e.g. salad bar vs. regular service line) on the one hand and palatability as well as food leftovers on the other hand two differentiations e special dishes versus regular component-based dishes and food from the salad bar versus food from the regular service line e proved to be significant. Notably, we found that special dishes and salad bar dishes positively influence the palatability rating thereby indirectly reducing plate leftover. However, whereas salad bar dishes additionally also directly decrease plate leftovers, special dishes directly increase plate leftovers. Since, there was no significant impact of both types of dishes on portion size perceptions, we can rule out this factor as explanation for the direct impacts on leftovers. A related reason may be the composition of meals which are e compared to the regular component-based meals of different main courses and side dishes e more variable for salad from the buffet but more fixed for the special dishes with a predefined set of components. It remains however unclear why such a relation would not be reflected in ratings of taste or portion size. Potentially, the choice of a special dish may be related to its irregular presence in the lunch menu of the canteen thereby positively influencing consumers’ perceptions. However, no study could be found that has analyzed effects of this kind yet. Comparisons may be drawn from studies indicating that descriptive names of lunch options positively influence the perception of regular and new food options (Morizet, Depezay, Combris, Picard, & Giboreau, 2012; Wansink, van Ittersum, & Painter, 2005). Applying our findings on portion sizes, palatability and types of meal choices to efforts for decreasing leftovers, we may support many of the empirical studies that have shown the effectiveness of environmental interventions in changing food choices and consumption. Specifically, the provision of optional smaller portion sizes (Berkowitz et al., 2016; Vermeer, Steenhuis, & Poelman, 2014; Vermeer, Steenhuis, Leeuwis, Heymans, & Seidell, 2011) may be beneficial since it combines the potential benefits of portion sizes with an increase in flexibility of meal composition for the individual. Finally, the impact of time as environmental factor was analyzed (a) as direct determinant of leftovers and (b) as conditional factor for relationships between other structural constructs. Regarding the former we could not find a link between time pressure and food leftovers in our aggregate model. In this respect our model seems to contradict findings for company canteens by Finkbeiner (2013) and e et al., 2016; Price & for school canteens by various authors (Entre

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

Just, 2014; WRAP, 2011). One reason for this insignificant result may be the measurement of time pressure based on only one item. However, our estimation results of the grouped model support notions that human behavior under time pressure becomes more dominated by environmental factors and related automatic cues instead of behavioral rational considerations such as behavioral intention (Evans, 2008). Our estimates moreover show that females tend to have more plate leftovers than males and that e based on automatic and environmental influences under time pressure e this tendency becomes stronger. This is in line with general tendencies of female students to apply habitual plate clearing less frequently than male students (Robinson & Hardman, 2015). Overall, the grouped estimation also reveals that under time pressure, leftover behavior is dominated by environmental factors and independent from stated behavioral intention, leading also to almost separate model of behavioral determinants on the one hand and environmental determinants on the other. Accordingly, it may be suggested to consider time as a relevant mediator for the effectiveness of interventions targeting personal attitudes and control beliefs which may decrease with time pressure as well as for interventions targeting environmental cues which conversely may increase with time pressure. 5.1. Limitations and implications for future research In interpreting the results of this study, a number of limitations need consideration. First, there were different restrictions with respect to the collection of data. On the one hand, the order of the data acquisition is not in line with the TPB framework. As described in section 3.2, individual plate leftovers were first observed and documented and subsequently students took part in the online survey. This was not only a more convenient order of data collection but also considered reasonable as a reversed order and thus a questionnaire on food leftovers directly before measuring food leftovers would likely have biased participants’ behavior. Nevertheless, this implies that behavioral intention is measured after the actual behavior. A tripartite data collection with a survey on intentional constructs, an observation of behavior regarding food leftovers well after the first survey and a second survey regarding situational factors at the day of observation would be optimal but hardly realizable. Given that behavioral constructs are considered as relative stable over time the chosen design seems justified. On the other hand, our data collection especially with respect to social and environmental determinants only covers a limited number of factors. Exemplarily, it may have been beneficial to collect additional data on the situational-social determinants during lunch, such as the type of relationships to present others as well as their behavior with respect to leftovers and to investigate the personal perception of time pressure in more detail. Besides, it would have been interesting to include situational factors which take an impact on food choices and their perception with respect to portion size and taste in our survey in order to provide more specific recommendations on the reduction of leftovers. Second, the sample is a convenience sample comprised exclusively of students. Due to the high homogeneity in our sample, one should be tentative to apply our findings to other social groups or settings, since it can be expected that the level and determinants of consumers’ leftover behavior differ according to sociodemographics such as age, income and education. Third, personal norms could not be distinguished from other behavioral determinants, indicating that personal norms may be incorporated in other components of our model. Nevertheless, future research may investigate whether a different measurement system of personal norms or alternative related constructs such as self-image are of relevance for consumers’ food leftover behavior.

53

Finally, we would like to address this latter aspect as one additional limitation. The share of explained variance in behavioral intention is 45% and in leftover behavior is 20%. Although those values, according to a meta-analytic review by Armitage and Conner (2001, pp. 471e499), are in line with other TPB applications, they indicate that a relevant proportion of behavior cannot be captured by the considered behavioral and situational model components. One explanation might be that conflicting intentions and attitudes are not considered in the estimated models. Individuals may e.g. have a positive intention towards finishing the plate, but at the same time this intention could be modified by an intention to lose weight. Other factors ignored in the estimated models are costs of the meal (persons on a tight budget may feel more obliged to finish the plate e though this factor can be assumed to be of more relevance in other out-of-home consumption settings where prices are not subsidized as they are in a university canteen) or the feeling of being full. In order to overcome the named limitations, we suggest to conduct data collections under consideration of additional variables and samples with more diverse sociodemographic background which may e from a methodological perspective e also require larger sample sizes compared to our study. Besides, effort should be made to analyze behavior over longer time periods and potentially also repeatedly for participating individuals. This may provide better insights in how environmental and situational determinants interact in different potential combinations. Presumably, these efforts may also improve the performance of our model in explaining variation of leftover behaviors and hence the potential to understand and improve the effects of interventions to reduce leftovers. 6. Conclusion We derived and tested a model which represents an extension of the TPB and jointly considers environmental, social and behavioral factors as determinants of individual food leftover behavior in a catering setting. Our results for a sample of 343 students at a university canteen provide support for the relevance of personal and environmental factors for the presence of food leftovers and hence also for initiatives which aim to decrease leftovers either by changing personal determinants (i.e. by providing information and teaching competences) or by taking influence via environmental changes (i.e. changing portion sizes). Importantly, we moreover find that environmental/situational and behavioral/personal determinants are interrelated and that the relevance of determinants is relative to time constraints. Besides, our analyses yield a comparably low though significant impact of social factors (subjective norms) on food leftovers. In line with the general aim of providing insights to decrease avoidable food waste in form of plate leftovers by our research, our study shows that food leftovers are determined by a complex set of interrelated factors. Hence, we may assume that simple measures to decrease avoidable food waste may take effects via complex behavioral structures. In order to understand the underlying processes, we suggest future research to analyze the separate and joint effects of prominent interventions such as adapted portion sizes or information campaigns within comparable behavioral models. Ultimately, this should facilitate the development of more integrated approaches on changing food waste behaviors. Acknowledgements This research was funded in form of a doctoral scholarship by the Deutsche Bundesstiftung Umwelt.

54

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

Appendix 1. Menu and meal choices for the days of data collection.

Tuesday Main component, vegetarian Main component, meat Soup/stew Special dish Salad bar “Other” dishes Number of observed dishes Side dishes Every day

Choices Wednesday [%]

Choices Thursday [%]

Choices [%]

vegetarian spring roll with 12 peanut sauce (V)

spaghetti with vegetable bolognese (V)

38

tofucurry “Sumatra” (V)

13

beef goulash with pepper 41

Krakauer sausage with mustard sauce

34

curry with turkey and mango-carrot sauce

23

green pea stew (V) baked beans with sausage and potatoes salad from the buffet (V) falafel (V), only side dishes (V) 146*

tomato soup (V) catfish “Tandoori” with grilled vegetables and tagliatelle salad from the buffet (V) falafel (V), vegetarian spring roll (V) 130*

4 4

13 8//19

12 8

vegan lentil stew (V) goose with potato dumplings and red cabbage//potato dumplings with vegetables, pesto and salad (V) salad from the buffet (V) only side dishes (V), steak (pork)

10 13

100

77*

100

22 4 9 12 100

rice, potato dumplings, boiled mashed potatoes, parsley potatoes, mixed curry-rice, pasta, cauliflower potatoes, red cabbage vegetables one dessert (pudding or quark with different flavors), side salad (pre-portioned), large salad from the buffet (self-served)

V ¼ vegetarian dish; *including choices of two main components.

Appendix 2. Results of the confirmatory factor analysis.

Standardized Factor Loadings

Having leftovers is bad/good Having leftovers makes me feel satisfied/ unsatisfied Having leftovers makes me feel happy/ unhappy Others finish all food on their plate x I try to do the same Others think people should finish all food x their opinion is important to me Others may criticize me if I don't finish all food x this makes me feel uncomfortable I will do my best to empty my plate I generally try not to leave food Predicting food amount at food choice is easy Finishing is usually easy to me I could always finish all food on my plate if I wanted to Visual appearance Taste Smell

Sum of Squared Loadings Attitudes

0.765 1.617 0.646

Square Maximum Root AVE correlation in model

With construct

Squared Sum Sum of of Loadings Residual Variances

Composite Reliability

AVE

4.818

0.805

0.857

0.654 0.808

0.522

Intention

3.775

1.057

0.781

0.623 0.790

0.420

Intention

2.161

1.530

0.585

0.651 0.807

0.522

Attitudes

4.145

0.964

0.811

0.647 0.804

0.491

Intention

4.800

0.809

0.856

0.655 0.809

0.258

Intention

0.784 Subjective 0.573 1.294 Norms 0.569 0.801 Intention PBC

Taste

0.653 1.094 0.817 0.495 1.487 0.936 0.605 0.604 1.624 0.783 0.804

Appendix 3. Descriptive properties of indicators

Variable

Mean

Standard Deviation

Minimum

Maximum

Skewness

Kurtosis

I will do my best to empty my plate I generally try not to leave food Having leftovers is good/bad Having leftovers makes me feel satisfied/unsatisfied Having leftovers makes me feel happy/unhappy Others finish all food on their plate x I try to do the same Others think people should finish all food x their opinion is important to me Others may criticize me if I don't finish all food x this makes me feel uncomfortable Predicting food amount at food choice is easy

4.18 4.29 5.21 5.21 4.94 10.84 14.33 6.06 4.21

1.04 1.00 1.15 1.37 1.12 5.95 5.24 4.40 1.01

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

5.00 5.00 7.00 7.00 7.00 25.00 25.00 25.00 5.00

1.22 1.58 0.61 0.57 0.22 0.46 0.19 1.49 1.38

0.70 2.07 1.14 0.41 0.63 0.57 0.12 2.84 1.23

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

55

(continued ) Variable

Mean

Standard Deviation

Minimum

Maximum

Skewness

Kurtosis

Finishing is usually easy to me I could always finish all food on my plate if I wanted to Visual appearance Taste Smell

3.79 3.94 3.36 3.59 3.52

1.32 1.04 0.94 0.91 0.84

1.00 1.00 1.00 1.00 1.00

5.00 5.00 5.00 5.00 5.00

0.78 0.90 0.23 0.54 0.27

0.74 0.03 0.38 0.19 0.02

References Aertsens, J., Verbeke, W., Mondelaers, K., & van Huylenbroeck, G. (2009). Personal determinants of organic food consumption: A review. British Food Journal, 111(10), 1140e1167. http://doi.org/10.1108/00070700910992961. Ajzen, I. (1991). The theory of planned behavior. Orgnizational Behavior and Human Decision Processes, 50, 179e211. http://dx.doi.org/10.1016/0749-5978(91)90020T. Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52(1), 27e58. http://doi.org/10.1146/annurev.psych.52.1.27. Ajzen, I. (2006). Constructing a TPB Questionnaire: Conceptual and methodological considerations. Time, 2002(June 5), 1e13. http://doi.org/10.1002/hep.22759. Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracin, B. T. Johnson, & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173e221). New York: Psychology Press. http://doi.org/10.1080/00224545.1956. 9921907. Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned Behaviour: A meta-analytic review. €hteenm€ Arvola, A., Vassallo, M., Dean, M., Lampila, P., Saba, A., La aki, 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(2e3), 443e454. http://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), 74e94. http://doi.org/10. 1177/009207038801600107. Barr, S. (2007). Factors influencing environmental attitudes and behaviors: A U.K. Case study of household waste management. Environment and Behavior, 39. http://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, 215e218. http://doi.org/10.1016/S0195-6663(03)00109-0. Berkowitz, S., Marquart, L., Mykerezi, E., Degeneffe, D., & Reicks, M. (2016). es in a worksite and restaurant setting: Impact on food Reduced-portion entre consumption and waste. Public Health Nutrition, 19(16), 1e7. http://doi.org/10. 1017/S1368980016001348. €bel, C., & Müller, C. (2015). Food waste in the Swiss food service Betz, A., Buchli, J., Go industry e magnitude and potential for reduction. Waste Management, 35, 218e226. http://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), 483e489. http://doi.org/10.1177/1359105315576607. Brown, T. A. (2006). In D. A. Kenny (Ed.), Confirmatory factor analysis for applied research. New York: The Guilford Press. Brug, J., Tak, N. I., te Velde, S. J., Bere, E., & de Bourdeaudhuij, I. (2008). Taste preferences, liking and other factors related to fruit and vegetable intakes among schoolchildren: Results from observational studies. British Journal of Nutrition, 99(S1), 7e14. http://doi.org/10.1017/S0007114508892458. Byker, C. J., Farris, A. R., Marcenelle, M., Davis, G. C., & Serrano, E. L. (2014). Food waste in a school nutrition program after implementation of new lunch program guidelines. Journal of Nutrition Education and Behavior, 46(5), 406e411. http://doi.org/10.1016/j.jneb.2014.03.009. Byrne, B. M. (2012). Structural equation modeling with Mplus. New York: Routledge. 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), 201e234. Clendenen, V. I., Herman, C. P., & Polivy, J. (1994). Social facilitation of eating among friends and strangers. Appetite. http://doi.org/10.1006/appe.1994.1030. 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), 94e96. http://doi.org/10.1016/j.jada.2003.10.012. Cooke, L. J., Chambers, L. C., Anez, E. V., Croker, H. A., Boniface, D., Yeomans, M. R., et al. (2011). Eating for pleasure or profit: The effect of incentives on children's enjoyment of vegetables. Psychological Science, 22(2), 190e196. http://doi.org/ 10.1177/0956797610394662. Cook, A. J., Kerr, G. N., & Moore, K. (2002). Attitudes and intentions towards purchasing GM food. Journal of Economic Psychology, 23(5), 557e572. http:// dx.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), 619e634. http://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, 3e18. http://doi.org/10.1016/j.appet.2014.08.035. 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), 562e568. http://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), 119e143. http://doi.org/10.1080/10705519509540000. Dinis, D., Martins, M. L., & Rocha, A. (2013). Plate waste as an indicator of portions inadequacy at school lunch. International Journal of Biological, Biomolecular, Agricultural, Food and Biotechnological Engineering, 7(7), 187e190. Eertmans, A. (2006). Sensory-affective and other determinants of food choice. Katholieke Universiteit Leuven. € m, R., & Carlsson-Kanyama, A. (2004). Food losses in food service inEngstro stitutions examples from Sweden. Food Policy, 29(3), 203e213. http://doi.org/ 10.1016/j.foodpol.2004.03.004. e, M., Cohen, J. F. W., Jahn, J. L., Richardson, S., Cluggish, S. A., Parker, E., et al. Entre (2016). Amount of time to eat lunch is associated with Children's selection and e, fruits, vegetables, and milk. Journal of the consumption of school meal entre Academy of Nutrition and Dietetics, 1, 123e128. http://doi.org/10.1016/j.jand. 2015.07.019. European Union. (2016). Stop food waste - european commission. Retrieved July 1, 2016, from http://ec.europa.eu/food/safety/food_waste/stop/index_en.htm. Evans, J. S. B. T. (2008). Dual-processing accounts of reasoning, judgment and social cognition. Annual Review of Psychology, 59, 255e278. Evans, D. (2011). Beyond the throwaway Society: Ordinary domestic practice and a sociological approach to household food waste. Sociology, 46(1), 41e56. http:// doi.org/10.1177/0038038511416150. FAO. (2013). Food wastage footprint - impacts o natural resources. Rome. Retrieved from www.fao.org/docrep/018/i3347e/i3347e.pdf. Ferreira, M., Martins, M. L., & Rocha, A. (2013). Food waste as an index of food service quality. British Food Journal, 115(11), 1628e1637. http://doi.org/10.1108/ BFJ-03-2012-0051. €lle in der Betriebsverpflegung - erfassung von Finkbeiner, O. (2013). Lebensmittelabfa 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), 39e50. 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. http://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 (Silver Spring, Md.), 18(9), 1864e1866. http://doi.org/10. 1038/oby.2009.480. €bel, C., Blumenthal, A., Niepagenkemper, L., Baumkoetter, D., Teitscheid, P., & Go 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. Graham-Rowe, E., Jessop, D. C., & Sparks, P. (2014). Identifying motivations and barriers to minimising household food waste. Resources, Conservation and Recycling, 84, 15e23. http://doi.org/10.1016/j.resconrec.2013.12.005. Graham-Rowe, E., Jessop, D. C., & Sparks, P. (2015). Predicting household food waste reduction using an extended theory of planned behavior. Conservation and Recycling, 101, 194e202. Guthrie, J. F., & Buzby, J. C. (2001). Several strategies may lower plate waste in school feeding programs. FoodReview, 25(2), 36e42. Hanks, A. S., Just, D. R., Smith, L. E., & Wansink, B. (2012). Healthy convenience: Nudging students toward healthier choices in the lunchroom. Journal of Public Health (United Kingdom), 34(3), 370e376. http://doi.org/10.1093/pubmed/ fds003. 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), 470e474. http://doi.org/10.1016/j.jand.2013.08.013. Harland, P., Staats, H., & Wilke, H. A. M. (1999). Explaining proenvironmental intention and behavior by personal norms and the theory of planned behavior’. 1Journal of Applied Social Psychology, 29(12), 2505e2528. 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, 588e594. http://doi.org/10.1017/ S0007114511003278. Hinton, E. C., Brunstrom, J. M., Fay, S. H., Wilkinson, L. L., Ferriday, D., Rogers, P. J.,

56

B.A.-S. Lorenz et al. / Appetite 116 (2017) 45e56

et al. (2013). Using photography in “the restaurant of the future”: A useful way to assess portion selection and plate cleaning? Appetite, 63, 31e35. http://doi. org/10.1016/j.appet.2012.12.008. de Hooge, I. E., Oostindjer, M., Aschemann-Witzel, J., Normann, A., Loose, S. M., et al. (2017). This apple is too ugly for me! Food Quality and Preference, 56, 80e92. http://doi.org/10.1016/j.foodqual.2016.09.012. €, L., & Reinikainen, A. Katajajuuri, J. M., Silvennoinen, K., Hartikainen, H., Heikkila (2014). Food waste in the Finnish food chain. Journal of Cleaner Production, 73, 322e329. http://doi.org/10.1016/j.jclepro.2013.12.057. King, S. C., Weber, A. J., Meiselman, H. L., & Lv, N. (2004). The effect of meal situation, social interaction, physical environment and choice on food acceptability. Food Quality and Preference, 15, 645e653. http://doi.org/10.1016/j.foodqual.2004.04. 010. €lbig, A., … Scherhaufer, S. Kranert, M., Hafner, G., Barabosz, J. H. S., Leverenz, D., Ko (2012). Ermittlung der weggeworfenen Lebensmittelmengen und Vorschl€ age zur Verminderung der Wegwerfrate bei Lebensmitteln in Deutschland. Stuttgart. Retrieved from http://www.bmelv.de/SharedDocs/Downloads/ Ernaehrung/WvL/Studie_Lebensmittelabfaelle_Langfassung.pdf? __blob¼publicationFile. Krosnick, J. A., & Presser, S. (2009). Question and questionnaire design. In J. D. Wright, & P. V. Marsden (Eds.), Handbook of survey research (2nd ed.). San Diego: Elsevier http://doi.org/10.1111/j.1432-1033.1976.tb10115.x. €bel, C., & Waskow, F. (2015). The effectiveness of advice and actions in Langen, N., Go reducing food waste. Waste and Resource Management, 168(2), 72e86. Lorenz, B. A., 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, 149e157. http://doi.org/10.1016/j.foodqual.2015.06.002. Lowe, M. R., Tappe, K. A., Butryn, M. L., Annunziato, R. A., Coletta, M. C., Ochner, C. N., et al. (2010). An intervention study targeting energy and nutrient intake in worksite cafeterias. Eating Behaviors, 11(3), 144e151. http://doi.org/10.1016/j. eatbeh.2010.01.002. Lusk, J. L., & Briggeman, B. C. (2009). Food values. American Journal of Agricultural Economics, 91(1), 184e196. http://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), 474e481. http://doi.org/10. 1016/j.foodqual.2005.06.001. McFerran, B., Dahl, D. W., Fitzsimons, G. J., & Morales, A. C. (2010). Might an overweight waitress make you eat more? How the body type of others is sufficient to alter our food consumption. Journal of Consumer Psychology, 20(2), 146e151. http://doi.org/10.1016/j.jcps.2010.03.006. Middendorff, E., Apolinarski, B., Poskwsky, J., Kandulla, M., & Netz, N. (2013). Die wirtschaftliche und soziale Lage der Studienden in Deutschland 2012. Hannover. Retrieved from www.sozialerhebung.de. Mollen, S., Rimal, R. N., Ruiter, R. A. C., & Kok, G. (2013). Healthy and unhealthy social norms and food selection. Findings from a field-experiment. Appetite, 65, 83e89. http://doi.org/10.1016/j.appet.2013.01.020. Morizet, D., Depezay, L., Combris, P., Picard, D., & Giboreau, A. (2012). Effect of labeling on new vegetable dish acceptance in preadolescent children. Appetite, 59(2), 399e402. http://doi.org/10.1016/j.appet.2012.05.030. € m, J., & Thunstro € m, L. (2015). The impact of price reductions on individuals' Nordstro choice of healthy meals away from home. Appetite, 89, 103e111. http://doi.org/ 10.1016/j.appet.2015.01.023. Pliner, P., & Mann, N. (2004). Influence of social norms and palatability on amount consumed and food choice. Appetite, 42(2), 227e237. http://doi.org/10.1016/j. appet.2003.12.001. Price, J., & Just, D. R. (2014). Lunch, recess and nutrition: Responding to time incentives in the cafeteria. Preventive Medicine, 71, 27e30. http://doi.org/10.1016/j. ypmed.2014.11.016. Redman, E., & Redman, A. (2014). Transforming sustainable food and waste behaviors by realigning domains of knowledge in our education system. Journal of Cleaner Production, 64, 147e157. http://doi.org/10.1016/j.jclepro.2013.09.016. Robinson, E., & Hardman, C. A. (2015). Empty plates and larger waists: A crosssectional study of factors associated with plate clearing habits and body weight. European Journal of Clinical Nutrition, (October), 1e3. http://doi.org/10. 1038/ejcn.2015.218. Roininen, K. (2001). Evaluation of food choice Behavior: Development and validation of health and taste attitude scales. University of Helsinky. Retrieved from http:// ethesis.helsinki.fi/julkaisut/maa/elint/vk/roininen/evaluati.pdf. Salvy, S. J., Jarrin, D., Paluch, R., Irfan, N., & Pliner, P. (2007). Effects of social influence on eating in couples, friends and strangers. Appetite, 49(1), 92e99. http://doi. org/10.1016/j.appet.2006.12.004. Scheibehenne, B., Miesler, L., & Todd, P. M. (2007). Fast and frugal food choices: Uncovering individual decision heuristics. Appetite, 49(3), 578e589. http://doi. org/10.1016/j.appet.2007.03.224. Scholderer, J., Kügeler, J. O., Olsen, N. V., & Verbeke, W. (2013). Meal mapping. Food Quality and Preference, 30, 47e55. Schulte-Mecklenbeck, M., Sohn, M., de Bellis, E., Martin, N., & Hertwig, R. (2013). A lack of appetite for information and computation. Simple heuristics in food choice. Appetite, 71, 242e251. Schwartz, J., & Byrd-Bredbenner, C. (2006). Portion Distortion: Typical portion sizes selected by young adults. Journal of the American Dietetic Association, 106(9),

1412e1418. http://doi.org/10.1016/j.jada.2006.06.006. Silvennoinen, K., Heikkil€ a, L., Katajajuuri, J.-M., & Reinikainen, A. (2015). Food waste volume and origin: Case studies in the Finnish food service sector. Waste Management. http://doi.org/10.1016/j.wasman.2015.09.010. Sparks, P., Hedderley, D., & Shepherd, R. (1992). An investigation into the relationship between perceived control, attitude variability and the consumption of two common foods. European Journal of Social Psychology, 22, 55e71. Stenmarck, A., Jensen, C., Quested, T., & Moates, G. (2016). Estimates of European food waste levels (Stockholm). Stites, S. D., Singletary, S. B., Menasha, A., Cooblall, C., Hantula, D., Axelrod, S., … Phipps, E. J. (2015). Pre-ordering lunch at work. Results of the what to eat for lunch study. Appetite, 84, 88e97. http://doi.org/10.1016/j.appet. 2014.10.005. 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, 253e272. http://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), 808e822. http://doi.org/10.1108/00070700510629760. Thiagarajah, K., & Getty, V. M. (2013). Impact on plate waste of switching from a tray to a trayless delivery system in a university dining hall and employee response to the switch. Journal of the Academy of Nutrition and Dietetics, 113(1), 141e145. http://doi.org/10.1016/j.jand.2012.07.004. Thompson, V. J., Bachman, C. M., Baranowski, T., & Cullen, K. W. (2007). Self-efficacy and norm measures for lunch fruit and vegetable consumption are reliable and valid among fifth grade students. Journal of Nutrition Education and Behavior, 39(1), 2e7. http://doi.org/10.1016/j.jneb.2006.06.006. Timmerman, G. M., & Earvolino-Ramirez, M. (2010). Strategies for and barriers to managing weight when eating at restaurants. Preventing Chronic Disease, 7(3), A60. 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, 191e214. Tuomisto, T., Tuomisto, M. T., Hetherington, M., & Lappalainen, R. (1998). Reasons for initiation and cessation of eating in obese men and women and the affective consequences of eating in everyday situations. Appetite, 30(2), 211e222. http:// doi.org/S0195666397901422 ([pii]). € , E., To €rnwall, O., & Vehkalahti, K. (2016). School meal Tuorila, H., Palmujoki, I., Kyto acceptance depends on the dish, student, and context. Food Quality and Preference, 46, 126e136. http://doi.org/10.1016/j.foodqual.2015.07.013. United Nations. (2016). United Nations sustainable development agenda - goal 12: Ensure sustainable consumption and production patterns. Retrieved December 9, 2016, from http://www.un.org/sustainabledevelopment/sustainableconsumption-production/. 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 (2005), 35(9), 1200e1207. http://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. International Journal of Obesity (2005), 38(Suppl 1), 13e18. http:// doi.org/10.1038/ijo.2014.84. Vermeir, I., & Verbeke, W. (2006). Sustainable food Consumption: Exploring the consumer “attitude e behavioral intention” gap. Journal of Agricultural and Environmental Ethics, 19(2), 169e194. http://doi.org/10.1007/s10806-005-54853. Vermeir, I., & Verbeke, W. (2008). Sustainable food consumption among young adults in Belgium: Theory of planned behaviour and the role of confidence and values. Ecological Economics, 64(3), 542e553. http://doi.org/10.1016/j.ecolecon. 2007.03.007. Wansink, B. (2004). Environmental factors that increase the food intake and consumption volume of unknowing consumers. Annual Review of Nutrition, 24(217), 455e479. http://doi.org/10.1146/annurev.nutr.24.012003.132140. Wansink, B., Painter, J. E., & North, J. (2005). Bottomless bowls: Why visual cues of portion size may influence intake. Obesity Research, 13(1), 93e100. http://doi. org/10.1038/oby.2005.12. Wansink, B., & Payne, C. R. (2008). Eating behavior and obesity at Chinese buffets. Obesity (Silver Spring, Md.), 16(8), 1957e1960. http://doi.org/10.1038/oby.2008. 286. 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), 320e332. http://doi.org/10. 1037/a0035053. Wansink, B., van Ittersum, K., & Painter, J. E. (2005). How descriptive food names bias sensory perceptions in restaurants. Food Quality and Preference, 16(5), 393e400. http://doi.org/10.1016/j.foodqual.2004.06.005. WRAP. (2011). Food waste in schools. Banbury. Retrieved from http://www.wrap. org.uk/downloads/Report_into_the_Nature_and_Scale_of_Waste_produced_ by_schools_in_England.2f3077e3.5723.pdf. 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), 268e271. http://doi.org/10.1016/j.appet.2009.07.021.