Repeat Purchasing Behavior of Visitors of a Company Lunch Restaurant

1 1 ABSTRACT 23 Objective: To characterize lunch compositions and lunch habits of regular registered visitors of a4 lunch restaurant. 5 Design: Lunch selections (n=1541) of registered regular visitors of a company lunch restaurant6 were monitored over a period of 40 consecutive weekdays.7 Setting: An instrumented company lunch restaurant.8 Subjects: Regular registered visitors of the company lunch restaurant.9 Results: The lunch selections were analysed with respect to lunch composition and repeated10 selections of the same visitors (lunch habits). 11 Lunch composition. Lunch energy averaged 46646 kCals (SD 2541) and was positively correlated12 with price, weight, number of items, and energy from fat and negatively correlated with energy13 from fibres, proteins and carbohydrates (including sugars) (p<0.01). Inclusion of drinks in lunches14 added to weight, variety, sugar content and energy (in the case of non-dairy drinks). In contrast,15 soup also added to weight but was associated with reduced lunch energy, even though energy from16 fat increased. but added little to variety, reduced energy from sugars, and was associated with17 reduced lunch energy content. 18 Lunch habits: Lunch selections were relatively stable with regard to energy from fat and19 carbohydrates, somewhat less stable with regard to energy from proteins and fibers and relatively20 unstable with regard to energy from sugars. The stability for the nutrient non-specific parameters21 fell between the least and most stable nutrient parameters. 22 Conclusions: The results suggest that healthier dietary lunch patterns can be promoted by reducing23 the variety of lunch items and their fat content. Inclusion of non-dairy beverages or soups may also24 contribute to a healthier pattern because they add lunch weight but not lunch energy. 251 26 2 INTRODUCTION 2728 Dutch people, like most people in other parts of the Western world, eat too much and too29 unhealthy1. More specifically, the consumption of certain nutrients and foods is too high (e.g.,30 saturated fat, added sugar and salt) whereas the consumption of others (e.g., fruits, fish and31 vegetables) is too low2. Current international guidelines3 for total daily intake recommend 250032 kCals for males and 2000 kCals for females with 20-35 per cent of the energy coming from fat, 40-33 65% from carbohydrates (of which no more than 25% from added sugars), 10-35% from proteins34 (or 0.8 grams of proteins per kilogram body weight) and 25-38 grams of fibres. Overconsumption is35 typically associated with excessive consumption of carbohydrates and fats, whereas consumption of36 proteins may actually reduce intake due to its satiating or filling properties4, 5. Overconsumption is37 also associated with meal variation; people tend to consume more from a varied meal than from a38 monotonous meal. This phenomenon is typically explained by sensory specific satiety or the39 declining satisfaction generated by the consumption of a certain type of food, and the consequent40 renewal in appetite resulting from the exposure to a new flavor or food6. The form in which41 nutrients are consumed may be important as well. Beverages and other calorie-containing liquid42 foods are associated with low satiation and relatively high energy intake7 with the possible43 exception of soups which seem to reduce energy intake8. 44 Given these facts it is not surprising that the focus of guidelines towards improved diets has started45 to shift from individual nutrients to combinations of foods or dietary patterns9 which requires46 knowledge on what the individual patterns are; i.e., the combinations of foods typically consumed47 during breakfast, lunch, and dinner and any other meal moments. Dietary patterns should ideally be48 collected at the level of individual consumers and over multiple meals to assess the stability of49 individual meal patterns. Insight in intra-and inter individual variation over meals provides50 information on determining factors behind repeat meal selections. For example, if repeated food51 choice is primarily driven by meal energy, then successful intervention strategies may focus on2 52 dietary modifications that leave the meal energy unaltered. If possible, the assessment of dietary53 patterns should be based on objective measurements rather than self-report, which typically leads to54 underreporting10. Finally, dietary patterns are probably assessed best in situations where the meals55 are typically consumed and where people show their natural food-related behaviours, e.g., in one’s56 home or lunch restaurant11. 57 The present study objectively characterizes lunch selections of individual regular visitors of a58 company lunch restaurant in the Netherlands, a representative of the out-of-home eating category59 that increasingly contributes to unhealthy diets12. The study focusses on the nutrient and energy60 composition of individual lunches and of repeated lunch selections of individual consumers to61 provide input for dietary interventions. Associations are explored between lunch energy, specific62 nutrients, and variables such as price, weight and number of lunch items. Specifically, hypothesis63 are tested regarding positive associations between lunch energy and lunch variation or beverages,64 and negative associations between lunch energy and protein content or soup. 65 The study also verifies the stability of lunch selections over time made by individual consumers.66 Actual repeated dietary lunch patterns are characterized for individual consumers in terms of67 nutritional and energy variables as well as variables such as weight and number of lunch items, and68 price. Previous studies have demonstrated effects on food choice and food perception of non-69 nutritional variables such as the number and colour of the food items of which a meal is composed,70 the weight and size of the containers in which the food is presented, branding, packaging and71 price13, 14. We hypothesize an inverse relationship between the importance of a variable for repeated72 lunch choice and its variability over repeat lunches. For example, if lunch selections are primarily73 driven by economical rather than health motifs we expect that price remains relatively constant over74 repeated lunch selections while health-related variables, such as energy, are more variable. 3 75 3 Methods 7677 3.1 Test location 78 79 Repeat lunch selection behavior was measured in an instrumented company lunch restaurant named80 the “Restaurant of the Future” (RoF) located in Wageningen, The Netherlands. The RoF is set-up as81 a regular company buffet restaurant in which employees of Wageningen University & Research82 Centre can select and consume their lunch. The restaurant is open during 48 weeks per year on83 weekdays between noon and 1.30 p.m. Lunch prices are comparable to those in similar other lunch84 restaurants. The lay-out of the buffets in the Restaurant follows a free-flowing principle, i, e. the85 buffets are distributed evenly across the buffet area assuring customers access from all directions to86 each buffet. Each buffet contains a specific group of products and is labeled accordingly. During87 their first visit visitors fill in an informed consent, and a questionnaire soliciting demographic88 information as well as information regarding food habits and preferences. This procedure was89 developed in collaboration with the Medical Ethical Committee of Wageningen University. Next,90 they receive an ID card that allows them to use the cash registers, which are used to record91 personalized sales. Visitors are not informed about individual studies and whether or not their sales92 data are used. At the time of the study approximately 550 people had registered as RoF visitors, of93 which approximately 300 visited the RoF on a frequent basis. 94 3.2 Nutritional Information 9596 During the test period, approximately 330 foods and beverages were offered to the visitors.97 Nutritional information per serving was compiled from the online NEVO food composition98 database15 and consumption intake information was converted from foods to nutrients using VBS99 food conversion software16 and the Portion size table17. The recipes were coded according to100 standardized guidelines that are used by nutritionists of the Division of Human Nutrition of4 101 Wageningen University; correspond in general to the EUROFIR guidelines18, 19. Nutritional102 information was complemented with the lunch item’s weight and price.103 3.3 Study Set Up. 104 105 Lunch selections were recorded per individual registered consumer over a period of 8 weeks or 40106 working days. Visitors were not aware of their participation in this particular study. Visitors were107 included in this study when they 1) had registered, and 2) visited the restaurant at least twice during108 the test period. Lunches were included that consisted of at least two items and were not109 complemented by items brought in from outside the restaurant.110 3.4 Statistical Analysis.111 3.4.1 Lunch composition.112 Correlational analysis was carried out to verify associations between lunch parameters. Lunches113 were categorized whether or not they included soups, dairy drinks and non-dairy drinks. Mixed114 models ANOVAs were used to test systematic differences between various types of lunches while115 compensating for differences in visiting frequencies between visitors. Visitors IDs were entered as116 random factors and lunch type as fixed factor.117 3.4.2 Stability of lunch selections of individual consumers (lunch habits).118 The ratio of the within and between subject variance was used to quantify the stability of repeated119 lunch selections of individual visitors for each lunch parameter. For example, a relatively low ratio120 for lunch energy indicates that repeated lunch selections of individual visitors in terms of lunch121 energy are relatively stable whereas the variations between visitors are relatively large. Variance122 components were estimated per lunch parameter with visitor ID as random factor. All analyses were123 carried out with IBM SPSS Statistics v.19.5 124 4 RESULTS 125126 4.1 General. 127128 During the test period, 168 visitors, 48% females and 52% males, met all inclusion criteria. One129 hundred thirty-four of these visitors were native speakers; the others were English-speaking. The130 age of the average visitors was 39.4 yrs. (SD 10.4) with a BMI of 23.5 kg/m 2 (SD 3.0) Over the 40-131 day test period, the visitors visited the Restaurant on average 9.3 times (SD 7.6) and selected 1541132 lunches at an average cost of 4.21 euro (SD 1.4), consisting of 3.6 items (SD 1.1) with a combined133 weight of 528 grams (SD 157). 134 4.2 Lunch composition. 135136 The average lunch contained 4646 kCals of energy (SD 2541). Thirty four per cent (SD 14.4) of the137 lunch energy was derived from fat, 47% (SD 14.4) from carbohydrates (of which 17 % (SD 15.4)138 from sugars), 18% (SD 7.1) from proteins and 1.1 % (SD 1.0) from dietary fibres.139 Correlation coefficients between consumer and lunch variables are given in Table 1 and show140 significant positive correlations between lunch energy and price, weight, lunch variation (number of141 lunch items), and energy from fat and significant negative correlations with energy from proteins,142 carbohydrates (including energy from sugars) and fibres. Age and BMI of the visitors did not143 correlate with lunch energy.144 TABLE 1 ABOUT HERE145 The effects of inclusion of soup and beverages on total lunch energy and lunch composition are146 summarized in Table 2 together with information regarding the visitors who selected these lunches.147 Inclusion of a beverage (either dairy or non-dairy) add variety (i.e. add items) and weight to the148 lunch. Inclusion of a dairy beverage is associated with higher lunch energy whereas inclusion of149 other beverages has no effect on lunch energy, and inclusion of soup is associated with somewhat6 150 lower lunch energy. Not surprisingly, the nutrient balance of the lunch is affected by the selected151 beverage. Lunches with milk are relatively high in (energy from) proteins and low in fibres and152 carbohydrates whereas lunches with other beverages are relatively low in proteins and fat and high153 in carbohydrates (and especially sugars). The nutrient balance of lunches with soup is relatively154 similar to that of lunches without soup. Visitors who select milk for their lunches are typically male,155 somewhat older, and somewhat heavier. Soups are also selected by somewhat older visitors,156 irrespective of gender. 157 TABLE 2 ABOUT HERE158159 4.3 Stability of lunch selections of individual consumers.160 Stabilities of lunch selections are shown in Table 3 for selected lunch parameters that are either161 nutrient specific or non-specific. Lunch selections are relatively stable with regard to energy from162 fat and carbohydrates, somewhat less stable with regard to energy from proteins and fibers and163 relatively unstable with regard to energy from sugars. The stability for the nutrient non-specific164 parameters falls between the least and most stable nutrient parameters with somewhat better165 stability for weight and energy than for price and number of items. 166 TABLE 3 ABOUT HERE1671681697 170 5 DISCUSSION 171172 5.1 Lunch composition 173 174 The lunch that is typically selected in the test restaurant consists of 3 or 4 items with a combined175 weight of about 530 grams, a cost of about 4.20 euro, and a total energy of about 440 kCals. Lunch176 energy increases with price, weight, number of lunch items, and relative energy from fat and177 decreases with relative energy from proteins, carbohydrates (including sugars) and fibres. The178 relation between energy, price and weight is to be expected: more money buys more food with more179 energy. The relation between energy and number of items is less obvious and may be caused by180 sensory specific satiety. Sensory specific satiety is typically used to explain the phenomenon that181 more food is consumed when a larger variety of food is offered. The present results suggest that182 sensory specific satiety is also involved when a larger variety of foods is selected. Visitors may183 already anticipate reduced satiation and compensate by selecting more food. 184 Previous studies suggest that proteins are more filling – or satiating- than carbohydrates or fat. This185 study supports that notion because low energy lunches are characterized by relatively high protein186 content. These lunches are also characterized by high fibre content. Fibres contain relatively little187 energy but add much volume to the food resulting in low energy-dense foods. These foods are188 typically more satiating than high energy-dense foods20, 21. Lower energy lunches also contain189 relatively much sugar which is unexpected because sugars are generally seen of one of the190 contributors to overconsumption, especially if consumed in the form of carbonated beverages. In191 our study, energy from sugar was also associated with consumption of dairy and non-dairy192 beverages (table 2). In addition to sugar, inclusion of beverages in lunches also add to the lunch193 weight and the variety (i.e. the lunch contains more items). Unexpectedly, the increase in sugar,194 weight and variety is associated with higher energy only when the beverage is a dairy one. In other195 cases, lunches with non-dairy beverages are not associated with more energy. It has to be noted8 196 however, that the non-dairy beverages selected in the lunch restaurant were typically non-197 carbonated juices, whereas overconsumption is typically associated with carbonated beverages. 198 Our results show striking differences between lunches with beverages, either dairy or non-dairy,199 and lunches with soup. Whereas beverages add to weight, variety, sugar content, and energy (in the200 case of dairy beverages), soup also adds to weight but adds little to variety. In contrast to beverages,201 soup reduces sugar content, and is associated with reduced lunch energy content. These results202 correspond well to results from other studies that demonstrate the satiating properties of soups203 compared to other liquids8. Obviously, comparisons of lunches with and without beverages have to204 be regarded with caution because they may be consumed by consumer segments that differ with205 regard to gender, age and BMI and probably many other variables that were not included in this206 study (table 2).207 The average lunch contained approximately 440 kCals of energy which is relatively low compared208 to averages reported by others. However, these estimations are based on the dietary patterns of other209 countries which may differ from the Dutch patterns. In the present study, 34 per cent of the lunch210 energy came from fat and 47% from carbohydrates which is in line with the national guidelines for211 daily intake. Seventeen per cent of the lunch energy came from proteins, which equates to212 approximately 0.27 grams of protein per kg of bodyweight, which is in line with the recommended213 daily consumption.214 Lunch selections were relatively stable with regard to energy from fat and carbohydrates, somewhat215 less stable with regard to energy from proteins and fibers and relatively unstable with regard to216 energy from sugars. The stability for the nutrient non-specific parameters fall between the least and217 most stable nutrient parameters with somewhat better stability for weight and energy than for price218 and number of items. (Table 3). The stability of repeated lunch selections in terms of energy and219 nutritional protein, fat and carbohydrate composition is probably not based on information provided220 by the restaurant because this information is either absent, as in the case of freshly prepared items,9 221 or typically ignored by visitors as in the case of packaged items. Hence, this stability probably222 reflects preferences of visitors for specific lunch categories with specific energies and composition,223 e.g., visitors who routinely selects snacks or salads. Even though different snacks or salads may be224 selected and/or offered during repeat visits, variation in energies and composition between items225 from the same category is probably typically smaller than variation between categories resulting in226 greater stability. The relative low stability of lunch selections in terms of energy from sugar227 suggests that intake of sugar is more occasionally than habitually. High-sugar desserts may be228 selected on “special” occasions (e.g., “it is Friday”) but ignored during the rest of the time, possibly229 for health reasons. 230 In contrast to energy and nutritional composition, lunch weight can actually be monitored by the231 visitor via the weight of the tray. Visitors may anticipate that a certain weight of food selected from232 specific lunch categories is typically satiating enough to get them through the remainder of their233 working day without feeling hungry. Anticipated satiety was demonstrated by others to be a major234 determinant of portion size22. 235 The fact that visitors seem to base their lunch selections again and again on specific criteria236 suggests that these selection criteria satisfy their needs although there is always the possibility that237 visitors compensate during breakfast, dinner, and/or snacking for the nutritional variations during238 lunch. 239 Obviously, extrapolation of the current results to healthy dietary patterns in general has to be240 regarded with caution given the fact that the current study relates to only one meal moment for a241 relatively homogeneous group of consumers and a restricted lunch assortment. Nevertheless, the242 results offer some insights in the way dietary patterns may be changed into healthier ones. The243 results suggest that healthier dietary lunch patterns with less energy can be promoted by reducing244 the variety of lunch items and their fat content. Inclusion of non-dairy beverages or soups may also245 contribute to a healthier pattern because they add lunch weight but not lunch energy.24610 247 6 REFERENCES 248249 1. Kreijl CF van, Knaap A GAC, van Raaij JMA (eds.) (2006). Our Food, Our Health: Healthy diet250 and safe food in the Netherlands. Bilthoven, the Netherlands : RIVM, Report 270555009.251 2. Hulshof KFAM, van Rossum C TM, Buurma-Rethans EJM et al (2003). Resultaten van de252 voedselconsumptiepeiling, in TNO report nr. V6000, TNO, RIVM: Zeist, Utrecht 2003.253 3. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein,254 and Amino Acids (Macronutrients) (2005). Food and Nutrition Board (FNB). The national255 academies press. Washington D.C.256 4. Westerterp-Plantenga MS, Rolland V, Wilson SAJ, Westerterp KR (1999). Satiety related to 24 h257 diet-induced thermogenesis during high protein/carbohydrate vs. high fat diets measured in a258 respiration chamber. Eur J Clin Nutr; 53, 495–502.259 5. Eisenstein J, Roberts SB, Dallal G, Saltzman E. (2002). High-protein weight-loss diets: are they260 safe and do they work? A review of experimental and epidemiologic data. Nutr Rev; 60, 189–200.261 6. Rolls BJ, Rolls ET, Rowe EA, Sweeney K (1981). Sensory specific satiety in man. Physiol262 Behav.;27(1):137-42263 7. Mattes R (2006). Fluid calories and energy balance: The good, the bad, and the uncertain 2006264 Physiol Behav; 89, 66-70.265 8. Mattes R. Soup and satiety (2005). Physiol Behav; 83, 739-747266 9. Editorial Dietary Patterns and Dietary Makeovers. Public Health Nutr 2004; 7, 473–474.267 10.Briefel RR, Sempos CT, McDowell MA et al (1997). Dietary methods research in the Third268 National Health and Nutrition Examination Survey: underreporting of energy intake. Am J Clin269 Nutr; 65, 1203–1209.270 11.De Castro, JM (2000).Eating behaviour: lessons from the real world of humans. Nutrition; 16, 800-271 813.11 272 12.O’Dwyer NA, McCarthy SN, Burke SJ et al. (2005).The temporal pattern of the contribution of fat273 to energy and of food groups to fat at various eating locations: implications for developing food-274 based dietary guidelines. Public Health Nutr; 8, 249–257.275 13.Zampollo F, Wansink B, Kniffin KM et al. (2012). Looks Good Enough to Eat. How Food Plating276 Preferences Differ Across Cultures and Continents. Cross-Cultural Res; 46, 31-49. 277 14.Piqueras-Fiszman B, Harrar V, Alcaide J et al. (2011). Does the weight of the dish influence our278 perception of food? Food Qual Pref; 22, 753-756. 279 15.NEVO: Dutch food composition database. Netherlands Nutrition Centre. 2001, The Hague, the280 Netherlands: Netherlands Nutrition Centre.281 16.NEVO, VBS Komeet, in hv07. 2008: Arnhem, The Netherlands. VBS Food calculation system.282 17.Donders-Engelen M, van der Heijden L, Hulshof KFAM (2003). Maten, gewichten en283 codenummers. Wageningen University, department of Human Nutrition.284 18.Reinivuo H, Bell S, Ovaskainen ML (2009). Harmonization of recipe calculation procedures in285 European food composition databases, J Food Comp Anal 22: 410–413. 286 19.Bell S, Vasquez-Caicedo AN, Hartman BM et al (2006) Report on Nutrient Losses and Gains287 Factors used in European Food composition Databases.288 20.Rolls BJ, Bell EA, Waugh BA (2000). Increasing the volume of food by incorporating air affects289 satiety in men. Am J Clin Nutr 72: 361-368. 290 21.Rolls BJ, Bell EA (1999). Intake of fat and carbohydrate: role of energy density. Eur J Clin Nutr291 53, 166-173. 292 22.Brunstrom JM, Rogers PJ (2009). How many calories are on our plate? Expected fullness, not293 liking, determines meal-size selection. Obesity 17: 1884–1890.29429512 296 Table 1: correlation coefficients between lunch parameters related to the visitors (BMI, age), lunch297 energy (total energy and energy from nutrients or % Ener), and weight, price and number of lunch298 items. (n=1541)299 n i r s e r a b t e r g t o b a r u a i P F S F C ) l a m m m m m s c o o o o o e r r r r r l K ( F F F F F c i . . . . . t d t y r r r r r j r i h g e e e e e t r A f g I s n n n n n i . j e e i e E E E E E M o r n e L B N P W E % % % % % Leeftijd 1 .307** .071** -.077** .106** -.042 .062* -.039 .013 -.073** .119** BMI .307** 1 .045 -.005 -.007 -.003 .046 .016 -.032 -.096** .053* No. Articles .071** .045 1 .374** .300** .466** -.124** .128** -.058* -.134** -.180** Prijs -.077** -.005 .374** 1 .698** .636** -.039 .154** -.133** -.025 -.165** Weight .106** -.007 .300** .698** 1 .556** .042 .057* -.068** -.140** .024 Energy (Kcal) -.042 -.003 .466** .636** .556** 1 -.161** .324** -.220** -.345** -.352** %Ener. From Protein .062* .046 -.124** -.039 .042 -.161** 1 -.185** -.309** .072** -.078** %Ener. From Fat -.039 .016 .128** .154** .057* .324** -.185** 1 -.875** -.436** -.462** %Ener. From Carbs .013 -.032 -.058* -.133** -.068** -.220** -.309** -.875** 1 .331** .489** %Ener. From Fiber -.073** -.096** -.134** -.025 -.140** -.345** .072** -.436** .331** 1 .128** %Ener. From Sugar .119** .053* -.180** -.165** .024 -.352** -.078** -.462** .489** .128** 1 **. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed). 300 301 m m m m m s o o o o o e r r r r r l F F F F F c i . . . . . t d t y n r r r r r j i r ) i r h s g l e e e e e t e r r a A f g b a t I s n n n n n i e . j r e g e t i c o e E E E E E b M o a r n r u e a i K L B N P W E ( % P % F % C % S % F Leeftijd 1 ,308** ,073** -,082** ,102** -.050 .008 -.023 .024 ,136** -,087** BMI ,308** 1 .046 -.003 -.004 -.005 .004 .031 -.027 ,053* -,101** No. Articles ,073** .046 1 ,370** ,297** ,467** -,170** ,149** -.047 -,201** -,064* Prijs -,082** -.003 ,370** 1 ,700** ,679** ,091** ,138** -,186** -,196** -,054* Weight ,102** -.004 ,297** ,700** 1 ,586** ,115** .042 -,096** .028 -,194** Energy (Kcal) -.050 -.005 ,467** ,679** ,586** 1 -,075** ,322** -,257** -,378** -,324** %Ener. From Protein .008 .004 -,170** ,091** ,115** -,075** 1 -,239** -,337** -,080** -.006 %Ener. From Fat -.023 .031 ,149** ,138** .042 ,322** -,239** 1 -,831** -,441** -,434** %Ener. From Carbs .024 -.027 -.047 -,186** -,096** -,257** -,337** -,831** 1 ,482** ,371** %Ener. From Sugar ,136** ,053* -,201** -,196** .028 -,378** -,080** -,441** ,482** 1 ,077** %Ener. From Fiber -,087** -,101** -,064* -,054* -,194** -,324** -.006 -,434** ,371** ,077** 1 **. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed). 30213 303 Table 2: Parameters of lunches that contain dairy (milk) or non-dairy beverages or soup with 304 respect to visitors (gender, BMI, age), lunch energy (total energy and energy from nutrients or % 305 Ener), and lunch weight and price. Visitors values are actual means, lunch values are estimated 306 means for the mixed model ANOVAs. Estimated values in bold and italic differ significantly from 307 the control in the row below (p<0.01) except for those indicated with an * (p<0.05). 308 Actual means Estimated means from mixed model analysis used for paper s ) e n s r s i r l s b e ) a i a t b o g r t F ) C o r u r a a k ) g y u s ( ( S s P F C s s g E r t e y y ( r r m r r r l y h a e g e e e e e ( a e r t r g I n n n n n I i T c e . e M i e E E E E E o M g r o n N % B A P N W E % % % % % Lunch with dairy beverage 464 64 24.2 44.4 4.2 3.9 620.0 456* 22.1 32.2 44.9 21.2 0.9 Lunch without dairy beverage 1077 55 23.0 38.1 4.2 3.4 476.0 417.0 16.1 33.7 48.9 17.3 1.2Lunch with non-dairy beverage 329 54 23.8 39.1 4.6 3.9 570.0 433.0 15.3 27.3 56.2 29.0 1.3 Lunch without non-dairy beverage 1212 59 23.3 40.2 4.1 3.5 514.0 430.0 19.1 34.9 45.0 15.5 1.1Lunch with soup 848 58 23.3 41.9 4.2 3.7 587.0 417* 17.7 33.4 47.9 18.1 1.0 Lunch without soup 693 58 23.5 37.6 4.3 3.4 453.0 448.0 18.9 32.8 47.1 19.3 1.2 309 Numbers in bold and italic: p<0.01, except * (p<0.05) 310 Actual means Estimated means from mixed model analysis used for paper s ) e n s r s i r l s b e ) a a i t b o g r t F o ) C r r a u a k ) g u y s ( ( P C s F S s s g E t r e y y ( r r r m r r l y h a e e e e g e e ( a r e t r g I n n n n n I i T c e . e M i e E E E E E o r M g o n N % B A P N W E % % % % % Lunch with milk 461 64 24.2 44.4 4.2 3.9 620.0 451.9 22.5 31.1 45.6 21.0 0.8 Lunch without milk 1075 55 23.0 38.1 4.2 3.4 476.0 471.7 17.3 33.7 47.7 15.2 1.3Lunch with drink (no milk) 325 54 23.8 39.1 4.6 3.9 570.0 504.5 15.9 28.4 54.5 27.1 1.2 Lunch without drink (no milk) 1211 59 23.3 40.2 4.1 3.5 514.0 455.4 19.6 34.1 45.1 14.2 1.1Lunch with soup 846 58 23.3 41.9 4.2 3.7 587.0 452.3 18.0 34.1 46.9 16.8 1.0 Lunch without soup 690 58 23.5 37.6 4.3 3.4 453.0 482.2 19.9 31.5 47.4 17.0 1.2 311 Numbers in bold and italic: p<0.01, except * (p<0.05) 312 31314 314 Table 3: Ratios between estimated within and between visitor variances for nutrient specific and315 unspecific lunch parameters.Nutrient non-specific Nutrient specific s e t a r d s y n h s i s r o e e a b t r r g s t o b a r u a i t y m P F F C S h g e e r g t n n n n n i I c e i E E E E E e o n r p w N E % % % % % Within participant 1.530 19047.000 .920 25056.000 18.490 52.350 50.820 127.400 .320 Between participant 1.750 27552.000 1.180 37968.000 35.310 155.980 159.530 129.370 .650 316 Ratio 0.87 0.69 0.78 0.66 0.52 0.34 0.32 0.98 0.49317Nutrient non-specific Nutrient specific s e t a r d s y n h s i s r o e e a b t r r g s t o b a r u a i t y m P F F C S h g e e r g t n n n n n i I c e i E E E E E e o n r p w N E % % % % % Within participant 1.7 19907.0 0.9 28781.9 20.8 56.7 53.8 130.7 0.3 Between participant 1.8 27410.6 1.2 38519.3 52.4 153.5 158.8 122.6 0.6 318 Ratio 0.94 0.73 0.78 0.75 0.40 0.37 0.34 1.07 0.51319320 32132232315

Repeat Purchasing Behavior of Visitors of a Company Lunch Restaurant

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