What’s for lunch? Eliciting preferences for food on university campus: discrete choice experiment protocol

What’s for lunch? Eliciting preferences for food on university campus: discrete choice experiment protocol | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Study protocol What’s for lunch? Eliciting preferences for food on university campus: discrete choice experiment protocol Irina Pokhilenko, Nafsika Afentou, Lin Fu, Mickael Hiligsmann, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4436883/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. Food choices are influenced by habits, experiences, as well as various socioeconomic factors. Understanding these drivers can mitigate negative effects of poor nutrition and yield societal benefits. Preference elicitation methods like discrete choice experiments help understand people’s food preferences revealing factors influencing choices the most, such as nutritional content or cost of a meal. This information can be helpful in developing tailored meal-based interventions and informing food policies. Universities, as anchor institutions, are increasingly concerned with health, wellbeing, and sustainability of their students and staff. Yet, there is limited evidence on food preferences in university settings. This paper outlines a discrete choice experiment protocol to compare lunch preferences among university staff and students across six European countries, aiming to inform campus food policies. Methods. Attributes and levels were derived from a systematic literature review of preference-based studies focused on the drivers of meal choices and validated in the focus group with students and staff from participating universities. The attributes in the discrete choice experiment include nutritional content, price, time to access a meal, sensory properties of a meal, naturalness of the ingredients, and meal size. The survey was piloted in think-aloud interviews with students and staff in participating universities. We will collect preference data, along with data on participants’ sociodemographic characteristics, food-related behaviour, opinions about food, experience of food insecurity, physical activity, and body composition, using an online survey. Preference data will be analysed using random parameter logit and latent class models. Discussion. This study will be the first to investigate lunch preferences of university students and staff across six European countries, informing campus food policies. While campus food systems may not always align with students’ and staff preferences, incorporating them into policy-making can enhance satisfaction and well-being. Strengths include an international focus, inclusion of complementary variables, and involvement of potential respondents in all phases of developing this research. Acknowledging limitations, such as varying lunch habits, the study aims to provide valuable insights for improving university food policies and overall community well-being. discrete choice experiment food preferences university students workplace Figures Figure 1 Figure 2 Introduction Every day, people make decisions about purchasing and consuming food. These decisions are guided by habits, past experiences, as well as a wide range of socioeconomic and cultural factors [ 1 ]. Food consumption has a direct impact on people’s health. Poor nutrition is associated with negative health (e.g. obesity, hypertension, cancer) (Schlesinger et al, 2019; Schwingshackl et al, 2017; Johnson et al, 2013), and[ 2 – 4 ], economic and environmental consequences (e.g. lower educational attainment, reduced workplace productivity, and greenhouse gas emissions) [ 5 – 7 ]. Having a better understanding of the drivers of food consumption choices can help mitigate some of these negative consequences and lead to health and economic benefits for individuals and society. Preference elicitation methods are designed to help understand core preferences. There are several approaches to eliciting preferences, including discrete choice experiments (DCEs), that are gaining popularity in food research [ 8 ]. A DCE is a preference elicitation method that identifies the product attributes that influence people’s choices. For example, Livingstone and colleagues (2021) conducted a DCE to understand the relative importance of factors influencing young adults’ food choices [ 9 ]. They found that nutrition content and cost of a meal were the two most important influences of food choices, while preparation time was the least important. Preferences also varied by demographic and health characteristics. The authors concluded that a DCE is a suitable method for understanding the complexity of food choice behaviours and can generate useful evidence to support the design of tailored meal-based interventions. While the majority of DCEs in food research have targeted the general population, several studies have been conducted to elicit people’s preferences for various attributes of meals in specific contexts such as schools [ 10 , 11 ] and prisons [ 12 ]. Such evidence can be helpful in providing more nuanced understanding of food choice drivers in specific settings to inform local or organizational policies. For example, Rusmevichientong et al. (2021) used a DCE to quantify the relative importance of snack attributes among middle-school children suggesting that price and whole grains were the most important attributes for the choice of snack among the respondents [ 10 ]. Universities represent a unique setting as they include diverse populations in terms of age, ethnicity, and cultural backgrounds. With being both a place of education and a workplace, universities act as ‘anchor institutions’ within a food system with responsibility for providing a healthy environment for their staff and students. Universities are becoming increasingly interested in the health and wellbeing of their students and staff as well as in becoming more environmentally sustainable. This is evidenced by, for example, The UK Healthy Universities Network aiming to support universities with developing and implementing ‘whole university’ approaches to health, wellbeing, and sustainability [ 13 ], and the work of The Association for the Advancement of Sustainability in Higher Education (AASHE) with empowering higher education staff and students to drive sustainability innovation [ 14 ], among other initiatives. However, there is a paucity of evidence on preferences for food provision in university settings, and understanding the drivers of students’ and staff preferences for food offered on campus will provide valuable evidence for informing university food policy. This paper presents the protocol for conducting a DCE to elicit and compare preferences of university staff and students for lunch, as lunch is the most commonly bought meal on campus by both students and staff, across different country settings. The study will be conducted within six university campuses located within six European countries (France, Hungary, Italy, Spain, Sweden, and the United Kingdom). Methods Context This study will be conducted in universities participating in the EUniWell alliance [ 15 ], including University of Birmingham (The United Kingdom), University of Florence (Italy), Linnaeus University (Sweden), Nantes University (France), Semmelweis University (Hungary), and University of Murcia (Spain). Characteristics of each university are presented below (Table 1 ). Table 1 University characteristics University of Birmingham* University of Florence Linnaeus University Nantes university Semmelweis University University of Murcia Number of students 36,933 53,612 33,000 43,000 14,024 31,015 Proportion of international students 31% 8% 2% 12% 35% 3% Number of staff 9,053 4,399 2,100 4,500 2,095 3,839 Proportion of international staff 24% Unknown Unknown Unknown Unknown Unknown Location Urban Urban and surroundings Urban and surroundings Urban and surroundings Urban Urban and surroundings Number of campuses 3 (Edgbaston, Selly Oak, and the Dubai campus overseas) 7 2 (Växjö and Kalmar) 7 (Nantes, Carquefou and 2 surrrounding cities, Saint-Nazaire, Roche/Yon) The university does not have a campus. Instead, its faculties, departments, hospitals, clinics, libraries, sport and accommodation facilities are scattered throughout the capital city of Budapest 5 Type of campus (city or campus-based) Campus-based Both city and campus-based Campus-based City and campus-based City-based Campus-based *all estimates exclude the students and the staff at the Dubai campus of the University of Birmingham Study design: Discrete choice experiment (DCE) A DCE is a method for eliciting preferences for the attributes of a product or service. Using survey methodology, DCEs assess preferences by asking individuals to make choices or trade-offs between hypothetical options that differ according to their attributes. This method is based on the principle that people derive utility, i.e., wellbeing, for a product/service from its attributes, and that the choices revealed through a DCE enable inferences on the relative contribution of each attribute and level to the overall utility of a product or a service. DCEs are based on strong theoretical underpinning (random utility theory) and are currently seen as the gold standard for evaluating preferences [ 16 ]. The development of a DCE involves a series of steps including the identification of attributes and levels, experimental and instrument design, data collection, and statistical analysis [ 17 ]. The overview of the study steps to develop the DCE is presented in Fig. 2 below, including a systematic literature review to identify the initial list of attributes and levels, focus group discussion using the nominal group technique approach to validate the list of attributes and levels, and think-aloud interviews to pilot test the survey. Figure 1. Steps to develop the discrete choice experiment survey Identification of attributes and levels To identify potentially relevant attributes and attribute levels, we conducted a systematic literature review of preference-based studies focused on the drivers of meal choices [ 18 ]. The objectives of the review were to summarise the evidence generated from DCEs and other preference-based methods to understand meal-choice; and to identify a list of attributes for the development of a DCE to investigate demand for lunch on campus. We constructed a comprehensive search strategy and searched Web of Science, Scopus, Medline, Embase, PsychINFO, EconLit, and CINAHL to identify eligible studies. After title/abstract and full-text screening, 33 studies were included in the review. The important constructs, in terms of attributes and their levels, were extracted from the identified studies. They were clustered into groups corresponding to similar themes (e.g. taste, price), and ranked in terms of frequency of being included in the identified DCEs as well as their relevance to the project setting. The clarity and mutual exclusivity of the attributes on the list were further discussed by the project team. This resulted in the initial list of nine potentially relevant attributes accompanied by descriptions and corresponding levels (Table 2 ). Table 2 List of attributes, descriptions and levels generated from the literature Attribute Description Levels 1 Environmental impact Negative impact of food production on the environment (e.g. CO2 emissions, water usage, animal welfare) Low; moderate; high 2 Food origin Origin of the ingredients Locally sourced; imported; unknown 3 Healthiness Meal that is good for your health Healthy; neutral; unhealthy 4 Price Price paid for food Cheap; average; expensive 5 Time How fast the food is to access (including walking to the food outlet and waiting for the meal to be prepared) Fast; moderate; slow 6 Sensory properties of a meal Appearance, smell, taste, texture, and colour of the meal OK; good; very good 7 Familiarity Familiarity with the food options available Not very familiar; somewhat familiar; very familiar 8 Naturalness The extent of processing during the food production process Minimally processed; processed; ultra-processed 9 Serving size Size of the portion Small; average; big Validation of the list of attributes and levels From this initial list, the final list of attributes accompanied by descriptions and levels were determined using the nominal group method in a focus group. This technique has frequently been used to select attributes for DCEs [ 19 ]. The original aim was to recruit one member of staff and one student from each participating university, however due to difficulties with recruitment and delays with obtaining ethics approvals, the final sample for the nominal group included eight participants from five out of the six participating universities. The participants were presented the initial list displayed in Table 2 and asked to: Discuss the clarity and mutual exclusivity of the attributes. Discuss the clarity of the descriptions. Prioritise the attributes in terms of their importance when selecting a meal. Discuss the appropriateness of the levels. The session was conducted online and led by a moderator (IP). During the session, the participants were asked to think about choosing what to eat for lunch on a typical day on campus, when they did not bring their own lunch. During the session, participants discussed the initial list of attributes presented in Table 2 ; as a result of this discussion some attributes were combined (e.g. healthiness and naturalness) and new attributes were introduced (e.g. variety of meal options available and environment in which a meal is consumed). Participants were asked to provide written consent prior to participation and received shopping vouchers (value of £25/€30) In accordance with good practices, we aimed to include a maximum of 6 attributes in the final list to avoid overburdening the DCE respondents [ 20 ]. Therefore, the focus group participants were asked to select the top-6 attributes they found most important when choosing lunch on campus. The results of the voting are presented in Table 3 below. Table 3 Results of the attribute prioritization Order of importance based on individual voting Attribute 1st Price of a meal 2nd Nutritional content of a meal 3rd Time it takes to walk to the outlet and wait for a meal to be served 4th Sensory properties of a meal 5th Variety of meal options available 6th Naturalness of the ingredients used to prepare a meal 7th Serving size of a meal 8th Environment in which a meal is consumed 9th Familiarity with a meal 10th Environmental impact of a meal Based on the focus group discussion and voting, and subsequent discussion amongst the project team, the final list of attributes, descriptions, and levels were developed (Table 4 ). It is important to note that even though the focus group participants placed variety of meal options in the top-6, it was excluded from the final list of attributes, because variety was an attribute describing a collection of meal offers, rather than the attributes of a single meal. Table 4 Final list of attributes, descriptions and levels Factor Description Level Nutritional content How well a meal is able to meet your nutritional and dietary needs Insufficient; neutral; sufficient Price Price paid for a meal 20% below average; average; 20% above average Time How fast the food is to access (including walking to the food outlet and waiting for the meal to be prepared) Fast; moderate; slow Sensory properties of a meal Appearance, smell, taste, texture, and colour of a meal Poor; OK; very good Naturalness The extent of processing of the ingredients during the food production process Minimally processed; processed; ultra-processed Meal size Amount of food you receive in a meal Small; medium; large Design of choice tasks The DCE choice tasks were designed based on the final list of attributes and corresponding levels displayed in Table 3 using dcreate package in Stata version 17.0 (StataCorp LLC, College Station, TX). This package allows for the development of an efficient design that maximizes the precision of estimates by using a-priori information on the levels for each attribute that were generated from the nominal group discussion. In line with good practice, we generated 3 blocks of 8 choice tasks. Furthermore, we included a test-retest validity question, a question that repeats twice in each choice, to test the consistency of respondents’ preferences. The test-retest validity questions are there as a validation check as if any respondent ‘fails’, they are excluded from further analysis. Within the DCE, each respondent will be randomly allocated to one of the three blocks of choice tasks. Choice scenarios will be presented using visual aids to ease comprehension. Figure 2 below shows an example choice task. Figure 2. Example of a choice set in the discrete choice experiment Survey design The DCE will be incorporated within a larger survey that will also include questions about participants’ sociodemographic characteristics, food-related behaviour (e.g. typical source of lunch, usual diet, food allergies), opinions about food, experience of food insecurity, physical activity, and body composition. We will also assess the level of burden of survey completion. The original survey will be developed in the English language and translated into other languages (French, Swedish, Italian, Hungarian, and Spanish) by an external translation agency. Translations will be checked for correctness by the researchers (native speakers) in each participating university. Pilot test of the DCE survey Earlier versions of the survey were pilot tested with seven staff members and six students from the participating universities using a think-aloud interview approach. During these interviews we assessed the comprehensibility of the survey and the difficulty of completing it. Overall, participants found the survey understandable albeit rather lengthy and suggested some clarifications, for example, adding definitions to the listed diet types and eating patterns. It took them on average 15–20 minutes to complete. Based on the feedback from the pilot, we added clarifications and reduced the number of choice tasks from 12 to 8. All pilot participants signed consent forms and received a shopping voucher (value of £25/€30). The final version of the DCE survey is included in Supplementary File 1. Data collection and sampling To conduct the DCE survey, we will use Qualtrics XM (Qualtrics, Provo, UT) for the respondents from all participating universities except for the Linnaeus University, for which the survey will be developed using Survey&Report (Artisan). The invitation to participate will be distributed to students and staff from the six participating universities using various media (e.g. university newsletters, flyers, directed emails, social media promotion, etc). In our study, we will aim to recruit at least 100 respondents from each category, i.e. 100 staff members and 100 students, from each participating university. This was sufficient according to the common rule-of-thumb estimation for DCEs [ 21 ]. Statistical analysis The participant characteristics will be described using Stata software, version 17.0 [ 22 ]. Data on preferences will be analysed using Nlogit 6 [ 23 ]. Since all attribute levels are categorical, they will be coded using the effects-coding approach. For this analysis, one level of each attribute is omitted and non-omitted variables are assigned a value of 1 when they are present, and 0 when another non-omitted variable is present. In effects coding, non-omitted variables are assigned the value of -1, when an omitted variable is present. Effects coding yields a unique coefficient for each attribute level included in the study. We will first analyse the choice data using a random parameter logit model that will capture preference heterogeneity. We will also estimate subgroup random parameter logit models to assess if the preferences varied as a function of participant characteristics (sociodemographic characteristics and health-related behaviours). Finally, we will estimate a latent class model to identify preference classes based on participants’ preferences. All participants who completed the DCE-part of the survey and passed the test-retest validity check will be included in the main analysis. Preference data from all participants regardless of the test-retest validity check will be analysed separately in a secondary analysis. Ethics Prior to their participation in the survey, all participants will be provided with clear information on the study aims and objectives and asked to provide consent. Participants will have the option to drop out of the survey at any point without providing a reason or facing consequences. Ethical approval for this study was sought from the university ethics committee in each participating university. This study was approved by the ethics committees of the University of Birmingham (ERN_1270-Jun2023), University of Murcia (M10/2023/046), Nantes University (n°031020230), University of Florence (n. 304 granted on 21/02/2024), and Swedish Ethical Review Authority (Dnr 2023-07604-01). Discussion To the best of our knowledge, this will be the first study to elicit preferences of university students and staff for lunch on campus in six universities in six European countries contributing evidence to inform university food system policies. Food systems, including those on university campuses, are influenced by a multitude of factors including business considerations and statutory regulations and may not always be in line with what the customers, i.e. students and staff members, prefer, or offer food choices that maximise health and environmental outcomes. Incorporating their preferences in how food policies are developed can offer a new perspective to decision-makers and help enhance the satisfaction and well-being of university students and staff. Studies adopting a DCE design require a well-defined research question and scope for specifying the context for making choices [ 17 ]. Therefore, our DCE will focus on a lunch-time meal bought on campus. Our findings will likely to be less applicable to other eating occasions, such as breakfast or snack, but given that the majority of students and staff spend time on campus during day time, we can reasonably assume that the largest proportion of food consumption on campus occurs at lunch. Attributes pertaining to the overall experience of out-of-home meal consumption, such as food offer (e.g. variety of meal options), environment in which meals are consumed (e.g. whether food is served by waiters or self-serviced), or the social element of eating were not included in our DCE, even though such attributes have been found to be important drivers of meal choice in previous preference-based studies [ 24 , 25 ]. Specific focus on meal attributes will enable us to examine and compare preferences for those attributes that are universally applicable to meals regardless of the country and university context. At the same time, the relative importance of the included attributes will offer valuable insights that will inform university food policy. A particular strength of this study will be its international focus given that the proposed survey will be simultaneously conducted in six universities across six European countries. This research will help to fill the gap in the literature on the determinants of demand for food on campus and potential differences between various countries. We followed good practice guidelines for development of DCEs in the healthcare setting [ 17 ], including conducting a comprehensive literature review to identify the initial list of attributes and levels [ 18 ], a focus group discussion, and pilot think-aloud interviews with university students and staff from the participating universities. This will ensure the DCE is applicable to the different country settings. This is an important strength of this study given the underreporting of this step in previous DCE research [ 8 ]. The results will be reported through various means including academic conferences and a peer-reviewed publication to communicate the findings to the academic community, and through reports and policy briefs to reach university policymakers. Another strength of the study is through the deliberate inclusion of complementary variables aimed at improving our understanding of preference formation. First, the identification of relationships between these preferences and specific behaviours, such as the source of lunch, dietary habits, and food allergies and intolerances, is in line with the principles of behavioural economics [ 26 ]. Through the incorporation of these variables, we will provide a more nuanced understanding of the determinants of food choices among university staff and students. Moreover, because we consider food involvement, i.e., the importance of food for the subject, we will add a layer of depth to the analysis, capturing a motivational dimension that explains the extent to which subjects engage in information processing during food decision-making processes. It is important to acknowledge that a proportion of university students and staff typically bring their own lunch to campus, and this proportion may vary across country settings. However, we assume that the majority of respondents would still buy lunch on campus from time to time. While we will not be able to explore this in-depth, in our survey, we will account for the average number of days spent on campus by the participants and their typical source of lunch. The heterogeneity of food systems across the different campuses, as well as any underlying cultural differences, might present challenges to the conduct of the DCE. We plan to mitigate this by incorporating the local context knowledge by working closely with research partners from each university, and with involving potential respondents in the design and pilot stages, developing translated versions of the survey, and accounting for heterogeneity in the analysis through the inclusion of a wide range of explanatory co-variates. In summary, by taking into consideration the interrelationships of food preferences with related behaviours and food involvement, our study will provide valuable insights for practical applications within the university setting, with the ultimate goal of contributing to food policy that will lead to a positive impact on the university community well-being. Declarations Ethics approval and consent to participate This study was approved by the ethics committees of the University of Birmingham (ERN_1270-Jun2023), University of Murcia (M10/2023/046), Nantes University (n°031020230), University of Florence (n. 304 granted on 21/02/2024), and Swedish Ethical Review Authority (Dnr 2023-07604-01). Focus group and pilot interview participants signed informed consent forms prior to participation. Consent for publication This manuscript does not contain any personal data. Availability of data and materials Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. Competing interests The authors declare that they have no competing interests Funding This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101035821. Authors' contributions All authors (IP, NA, LF, MH, CW, MH, LDN, FR, AJE, KB, ÉCC, KO, PP, SRDM, EFB, and EF) made substantial contributions to securing funding for this work, to the conception and design of the work. IP, NA, CW, MH, LDN, FR, AJE, KB, ÉCC, KO, PP, SRDM, EFB, and EF contributed to the acquisition, analysis, and interpretation of the focus group and pilot interview data. MH provided technical expertise on the analysis of the discrete choice experiment data. IP drafted the manuscript. All authors contributed to revising the manuscript. All authors approved the submitted version of the manuscript and agreed both to be personally accountable for the author's own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4436883","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Study protocol","associatedPublications":[],"authors":[{"id":308943108,"identity":"ba51c50e-e3db-4848-b905-12783e4f6ae7","order_by":0,"name":"Irina 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University","correspondingAuthor":false,"prefix":"","firstName":"Cornelia","middleName":"","lastName":"Witthoft","suffix":""},{"id":308943113,"identity":"c4db3118-83d1-4e44-9ffb-3f6141ec9233","order_by":5,"name":"Mohammed Hefni","email":"","orcid":"","institution":"Linnaeus University","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Hefni","suffix":""},{"id":308943114,"identity":"69882d22-b469-4a20-8ceb-bc9f150302b8","order_by":6,"name":"Leonie Dapi Nzefa","email":"","orcid":"","institution":"Linnaeus University","correspondingAuthor":false,"prefix":"","firstName":"Leonie","middleName":"Dapi","lastName":"Nzefa","suffix":""},{"id":308943115,"identity":"6689e415-8ebe-4798-8cec-7ee968eaa338","order_by":7,"name":"Filippo Randelli","email":"","orcid":"","institution":"University of Florence","correspondingAuthor":false,"prefix":"","firstName":"Filippo","middleName":"","lastName":"Randelli","suffix":""},{"id":308943116,"identity":"6c4d8718-87da-42ef-b52d-c3f6cc4b08c8","order_by":8,"name":"Anna Julia Elias","email":"","orcid":"","institution":"Semmelweis University","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"Julia","lastName":"Elias","suffix":""},{"id":308943117,"identity":"3c96d137-1cbd-4edc-bbf8-0fc463d0e507","order_by":9,"name":"Krisztina Bartos","email":"","orcid":"","institution":"Semmelweis University","correspondingAuthor":false,"prefix":"","firstName":"Krisztina","middleName":"","lastName":"Bartos","suffix":""},{"id":308943118,"identity":"d55bb8b7-f0fd-42f0-a814-21f817e66bfa","order_by":10,"name":"Éva Csajbókné Csobod","email":"","orcid":"","institution":"Semmelweis University","correspondingAuthor":false,"prefix":"","firstName":"Éva","middleName":"Csajbókné","lastName":"Csobod","suffix":""},{"id":308943119,"identity":"ffe3cff0-8d50-431a-9181-a0daa0ef841a","order_by":11,"name":"Khadija Ouguerram","email":"","orcid":"","institution":"Nantes Université, INRAE, UMR 1280","correspondingAuthor":false,"prefix":"","firstName":"Khadija","middleName":"","lastName":"Ouguerram","suffix":""},{"id":308943120,"identity":"0ec725c9-d803-4a3e-a7c5-d023d14b3c90","order_by":12,"name":"Patricia Parnet","email":"","orcid":"","institution":"Nantes Université, INRAE, UMR 1280","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Parnet","suffix":""},{"id":308943121,"identity":"13d9174f-2382-4527-b87b-2fcb4d399a23","order_by":13,"name":"Salvador Ruiz-de-Maya","email":"","orcid":"","institution":"University of Murcia","correspondingAuthor":false,"prefix":"","firstName":"Salvador","middleName":"","lastName":"Ruiz-de-Maya","suffix":""},{"id":308943122,"identity":"d82d2b61-c1a2-45b6-b849-552360f81375","order_by":14,"name":"Elvira Ferrer-Bernal","email":"","orcid":"","institution":"University of Murcia","correspondingAuthor":false,"prefix":"","firstName":"Elvira","middleName":"","lastName":"Ferrer-Bernal","suffix":""},{"id":308943123,"identity":"4031266e-875e-4e49-9317-9baafce43b3b","order_by":15,"name":"Emma Frew","email":"","orcid":"","institution":"University of Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Frew","suffix":""}],"badges":[],"createdAt":"2024-05-17 13:00:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4436883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4436883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57941467,"identity":"f1c61ce3-8579-4307-a0bb-c946f20510cd","added_by":"auto","created_at":"2024-06-07 18:57:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24994,"visible":true,"origin":"","legend":"\u003cp\u003eSteps to develop the discrete choice experiment survey\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4436883/v1/7fc652b6af7232c1daa4f780.png"},{"id":57941469,"identity":"67d90a7c-07ae-4fac-9c61-3f45d7884903","added_by":"auto","created_at":"2024-06-07 18:57:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108833,"visible":true,"origin":"","legend":"\u003cp\u003eExample of a choice set in the discrete choice experiment\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4436883/v1/df87eed0c1ecdf0d4b9a04cf.png"},{"id":71653228,"identity":"fb0e2e58-4245-4d6e-ba3c-4d621790184d","added_by":"auto","created_at":"2024-12-17 12:38:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":628197,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4436883/v1/ac91efa6-4c21-4816-8c5b-0f540786d3a2.pdf"},{"id":57941468,"identity":"213ae04b-76c6-42df-8d60-fcdf764d1456","added_by":"auto","created_at":"2024-06-07 18:57:19","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":872863,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4436883/v1/3b30e1147f75a74cd1cb89dd.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"What’s for lunch? Eliciting preferences for food on university campus: discrete choice experiment protocol","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEvery day, people make decisions about purchasing and consuming food. These decisions are guided by habits, past experiences, as well as a wide range of socioeconomic and cultural factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Food consumption has a direct impact on people\u0026rsquo;s health. Poor nutrition is associated with negative health (e.g. obesity, hypertension, cancer) (Schlesinger et al, 2019; Schwingshackl et al, 2017; Johnson et al, 2013), and[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], economic and environmental consequences (e.g. lower educational attainment, reduced workplace productivity, and greenhouse gas emissions) [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Having a better understanding of the drivers of food consumption choices can help mitigate some of these negative consequences and lead to health and economic benefits for individuals and society.\u003c/p\u003e \u003cp\u003ePreference elicitation methods are designed to help understand core preferences. There are several approaches to eliciting preferences, including discrete choice experiments (DCEs), that are gaining popularity in food research [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A DCE is a preference elicitation method that identifies the product attributes that influence people\u0026rsquo;s choices. For example, Livingstone and colleagues (2021) conducted a DCE to understand the relative importance of factors influencing young adults\u0026rsquo; food choices [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. They found that nutrition content and cost of a meal were the two most important influences of food choices, while preparation time was the least important. Preferences also varied by demographic and health characteristics. The authors concluded that a DCE is a suitable method for understanding the complexity of food choice behaviours and can generate useful evidence to support the design of tailored meal-based interventions.\u003c/p\u003e \u003cp\u003eWhile the majority of DCEs in food research have targeted the general population, several studies have been conducted to elicit people\u0026rsquo;s preferences for various attributes of meals in specific contexts such as schools [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and prisons [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Such evidence can be helpful in providing more nuanced understanding of food choice drivers in specific settings to inform local or organizational policies. For example, Rusmevichientong et al. (2021) used a DCE to quantify the relative importance of snack attributes among middle-school children suggesting that price and whole grains were the most important attributes for the choice of snack among the respondents [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUniversities represent a unique setting as they include diverse populations in terms of age, ethnicity, and cultural backgrounds. With being both a place of education and a workplace, universities act as \u0026lsquo;anchor institutions\u0026rsquo; within a food system with responsibility for providing a healthy environment for their staff and students. Universities are becoming increasingly interested in the health and wellbeing of their students and staff as well as in becoming more environmentally sustainable. This is evidenced by, for example, The UK Healthy Universities Network aiming to support universities with developing and implementing \u0026lsquo;whole university\u0026rsquo; approaches to health, wellbeing, and sustainability [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and the work of The Association for the Advancement of Sustainability in Higher Education (AASHE) with empowering higher education staff and students to drive sustainability innovation [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], among other initiatives. However, there is a paucity of evidence on preferences for food provision in university settings, and understanding the drivers of students\u0026rsquo; and staff preferences for food offered on campus will provide valuable evidence for informing university food policy.\u003c/p\u003e \u003cp\u003eThis paper presents the protocol for conducting a DCE to elicit and compare preferences of university staff and students for lunch, as lunch is the most commonly bought meal on campus by both students and staff, across different country settings. The study will be conducted within six university campuses located within six European countries (France, Hungary, Italy, Spain, Sweden, and the United Kingdom).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eContext\u003c/h2\u003e \u003cp\u003eThis study will be conducted in universities participating in the EUniWell alliance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], including University of Birmingham (The United Kingdom), University of Florence (Italy), Linnaeus University (Sweden), Nantes University (France), Semmelweis University (Hungary), and University of Murcia (Spain). Characteristics of each university are presented below (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUniversity characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity of Birmingham*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniversity of Florence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLinnaeus University\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNantes university\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSemmelweis University\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUniversity of Murcia\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36,933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53,612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14,024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31,015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of international students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9,053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of international staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban and surroundings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban and surroundings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUrban and surroundings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUrban and surroundings\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of campuses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (Edgbaston, Selly Oak, and the Dubai campus overseas)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (V\u0026auml;xj\u0026ouml; and Kalmar)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (Nantes, Carquefou and 2 surrrounding cities, Saint-Nazaire, Roche/Yon)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe university does not have a campus. Instead, its faculties, departments, hospitals, clinics, libraries, sport and accommodation facilities are scattered throughout the capital city of Budapest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of campus (city or campus-based)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCampus-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoth city and campus-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCampus-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCity and campus-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCity-based\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCampus-based\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*all estimates exclude the students and the staff at the Dubai campus of the University of Birmingham\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design: Discrete choice experiment (DCE)\u003c/h2\u003e \u003cp\u003eA DCE is a method for eliciting preferences for the attributes of a product or service. Using survey methodology, DCEs assess preferences by asking individuals to make choices or trade-offs between hypothetical options that differ according to their attributes. This method is based on the principle that people derive utility, i.e., wellbeing, for a product/service from its attributes, and that the choices revealed through a DCE enable inferences on the relative contribution of each attribute and level to the overall utility of a product or a service. DCEs are based on strong theoretical underpinning (random utility theory) and are currently seen as the gold standard for evaluating preferences [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The development of a DCE involves a series of steps including the identification of attributes and levels, experimental and instrument design, data collection, and statistical analysis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe overview of the study steps to develop the DCE is presented in Fig.\u0026nbsp;2 below, including a systematic literature review to identify the initial list of attributes and levels, focus group discussion using the nominal group technique approach to validate the list of attributes and levels, and think-aloud interviews to pilot test the survey.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 1. Steps to develop the discrete choice experiment survey\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of attributes and levels\u003c/h2\u003e \u003cp\u003eTo identify potentially relevant attributes and attribute levels, we conducted a systematic literature review of preference-based studies focused on the drivers of meal choices [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The objectives of the review were to summarise the evidence generated from DCEs and other preference-based methods to understand meal-choice; and to identify a list of attributes for the development of a DCE to investigate demand for lunch on campus. We constructed a comprehensive search strategy and searched Web of Science, Scopus, Medline, Embase, PsychINFO, EconLit, and CINAHL to identify eligible studies. After title/abstract and full-text screening, 33 studies were included in the review. The important constructs, in terms of attributes and their levels, were extracted from the identified studies. They were clustered into groups corresponding to similar themes (e.g. taste, price), and ranked in terms of frequency of being included in the identified DCEs as well as their relevance to the project setting. The clarity and mutual exclusivity of the attributes on the list were further discussed by the project team. This resulted in the initial list of nine potentially relevant attributes accompanied by descriptions and corresponding levels (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of attributes, descriptions and levels generated from the literature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttribute\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative impact of food production on the environment (e.g. CO2 emissions, water usage, animal welfare)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow; moderate; high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrigin of the ingredients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocally sourced; imported; unknown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealthiness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeal that is good for your health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHealthy; neutral; unhealthy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrice paid for food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCheap; average; expensive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow fast the food is to access (including walking to the food outlet and waiting for the meal to be prepared)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFast; moderate; slow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensory properties of a meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAppearance, smell, taste, texture, and colour of the meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOK; good; very good\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamiliarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamiliarity with the food options available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot very familiar; somewhat familiar; very familiar\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaturalness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe extent of processing during the food production process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimally processed; processed; ultra-processed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eServing size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSize of the portion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSmall; average; big\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the list of attributes and levels\u003c/h2\u003e \u003cp\u003eFrom this initial list, the final list of attributes accompanied by descriptions and levels were determined using the nominal group method in a focus group. This technique has frequently been used to select attributes for DCEs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The original aim was to recruit one member of staff and one student from each participating university, however due to difficulties with recruitment and delays with obtaining ethics approvals, the final sample for the nominal group included eight participants from five out of the six participating universities.\u003c/p\u003e \u003cp\u003eThe participants were presented the initial list displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and asked to:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDiscuss the clarity and mutual exclusivity of the attributes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDiscuss the clarity of the descriptions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePrioritise the attributes in terms of their importance when selecting a meal.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDiscuss the appropriateness of the levels.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe session was conducted online and led by a moderator (IP). During the session, the participants were asked to think about choosing what to eat for lunch on a typical day on campus, when they did not bring their own lunch. During the session, participants discussed the initial list of attributes presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; as a result of this discussion some attributes were combined (e.g. healthiness and naturalness) and new attributes were introduced (e.g. variety of meal options available and environment in which a meal is consumed). Participants were asked to provide written consent prior to participation and received shopping vouchers (value of \u0026pound;25/\u0026euro;30)\u003c/p\u003e \u003cp\u003eIn accordance with good practices, we aimed to include a maximum of 6 attributes in the final list to avoid overburdening the DCE respondents [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, the focus group participants were asked to select the top-6 attributes they found most important when choosing lunch on campus. The results of the voting are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the attribute prioritization\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrder of importance based on individual voting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttribute\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrice of a meal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNutritional content of a meal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime it takes to walk to the outlet and wait for a meal to be served\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensory properties of a meal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariety of meal options available\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNaturalness of the ingredients used to prepare a meal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eServing size of a meal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironment in which a meal is consumed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamiliarity with a meal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental impact of a meal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the focus group discussion and voting, and subsequent discussion amongst the project team, the final list of attributes, descriptions, and levels were developed (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It is important to note that even though the focus group participants placed variety of meal options in the top-6, it was excluded from the final list of attributes, because variety was an attribute describing a collection of meal offers, rather than the attributes of a single meal.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFinal list of attributes, descriptions and levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutritional content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow well a meal is able to meet your nutritional and dietary needs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInsufficient; neutral; sufficient\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrice paid for a meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20% below average; average; 20% above average\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow fast the food is to access (including walking to the food outlet and waiting for the meal to be prepared)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFast; moderate; slow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensory properties of a meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAppearance, smell, taste, texture, and colour of a meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor; OK; very good\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNaturalness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe extent of processing of the ingredients during the food production process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimally processed; processed; ultra-processed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeal size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmount of food you receive in a meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall; medium; large\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDesign of choice tasks\u003c/h2\u003e \u003cp\u003eThe DCE choice tasks were designed based on the final list of attributes and corresponding levels displayed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e using dcreate package in Stata version 17.0 (StataCorp LLC, College Station, TX). This package allows for the development of an efficient design that maximizes the precision of estimates by using a-priori information on the levels for each attribute that were generated from the nominal group discussion. In line with good practice, we generated 3 blocks of 8 choice tasks. Furthermore, we included a test-retest validity question, a question that repeats twice in each choice, to test the consistency of respondents\u0026rsquo; preferences. The test-retest validity questions are there as a validation check as if any respondent \u0026lsquo;fails\u0026rsquo;, they are excluded from further analysis. Within the DCE, each respondent will be randomly allocated to one of the three blocks of choice tasks. Choice scenarios will be presented using visual aids to ease comprehension. Figure\u0026nbsp;2 below shows an example choice task.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFigure 2. Example of a choice set in the discrete choice experiment\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSurvey design\u003c/h2\u003e \u003cp\u003eThe DCE will be incorporated within a larger survey that will also include questions about participants\u0026rsquo; sociodemographic characteristics, food-related behaviour (e.g. typical source of lunch, usual diet, food allergies), opinions about food, experience of food insecurity, physical activity, and body composition. We will also assess the level of burden of survey completion. The original survey will be developed in the English language and translated into other languages (French, Swedish, Italian, Hungarian, and Spanish) by an external translation agency. Translations will be checked for correctness by the researchers (native speakers) in each participating university.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePilot test of the DCE survey\u003c/h2\u003e \u003cp\u003eEarlier versions of the survey were pilot tested with seven staff members and six students from the participating universities using a think-aloud interview approach. During these interviews we assessed the comprehensibility of the survey and the difficulty of completing it. Overall, participants found the survey understandable albeit rather lengthy and suggested some clarifications, for example, adding definitions to the listed diet types and eating patterns. It took them on average 15\u0026ndash;20 minutes to complete. Based on the feedback from the pilot, we added clarifications and reduced the number of choice tasks from 12 to 8. All pilot participants signed consent forms and received a shopping voucher (value of \u0026pound;25/\u0026euro;30). The final version of the DCE survey is included in Supplementary File 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData collection and sampling\u003c/h2\u003e \u003cp\u003eTo conduct the DCE survey, we will use Qualtrics\u003csup\u003eXM\u003c/sup\u003e (Qualtrics, Provo, UT) for the respondents from all participating universities except for the Linnaeus University, for which the survey will be developed using Survey\u0026amp;Report (Artisan). The invitation to participate will be distributed to students and staff from the six participating universities using various media (e.g. university newsletters, flyers, directed emails, social media promotion, etc). In our study, we will aim to recruit at least 100 respondents from each category, i.e. 100 staff members and 100 students, from each participating university. This was sufficient according to the common rule-of-thumb estimation for DCEs [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe participant characteristics will be described using Stata software, version 17.0 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Data on preferences will be analysed using Nlogit 6 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Since all attribute levels are categorical, they will be coded using the effects-coding approach. For this analysis, one level of each attribute is omitted and non-omitted variables are assigned a value of 1 when they are present, and 0 when another non-omitted variable is present. In effects coding, non-omitted variables are assigned the value of -1, when an omitted variable is present. Effects coding yields a unique coefficient for each attribute level included in the study.\u003c/p\u003e \u003cp\u003eWe will first analyse the choice data using a random parameter logit model that will capture preference heterogeneity. We will also estimate subgroup random parameter logit models to assess if the preferences varied as a function of participant characteristics (sociodemographic characteristics and health-related behaviours). Finally, we will estimate a latent class model to identify preference classes based on participants\u0026rsquo; preferences. All participants who completed the DCE-part of the survey and passed the test-retest validity check will be included in the main analysis. Preference data from all participants regardless of the test-retest validity check will be analysed separately in a secondary analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003ePrior to their participation in the survey, all participants will be provided with clear information on the study aims and objectives and asked to provide consent. Participants will have the option to drop out of the survey at any point without providing a reason or facing consequences. Ethical approval for this study was sought from the university ethics committee in each participating university. This study was approved by the ethics committees of the University of Birmingham (ERN_1270-Jun2023), University of Murcia (M10/2023/046), Nantes University (n\u0026deg;031020230), University of Florence (n. 304 granted on 21/02/2024), and Swedish Ethical Review Authority (Dnr 2023-07604-01).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this will be the first study to elicit preferences of university students and staff for lunch on campus in six universities in six European countries contributing evidence to inform university food system policies. Food systems, including those on university campuses, are influenced by a multitude of factors including business considerations and statutory regulations and may not always be in line with what the customers, i.e. students and staff members, prefer, or offer food choices that maximise health and environmental outcomes. Incorporating their preferences in how food policies are developed can offer a new perspective to decision-makers and help enhance the satisfaction and well-being of university students and staff.\u003c/p\u003e \u003cp\u003eStudies adopting a DCE design require a well-defined research question and scope for specifying the context for making choices [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, our DCE will focus on a lunch-time meal bought on campus. Our findings will likely to be less applicable to other eating occasions, such as breakfast or snack, but given that the majority of students and staff spend time on campus during day time, we can reasonably assume that the largest proportion of food consumption on campus occurs at lunch.\u003c/p\u003e \u003cp\u003eAttributes pertaining to the overall experience of out-of-home meal consumption, such as food offer (e.g. variety of meal options), environment in which meals are consumed (e.g. whether food is served by waiters or self-serviced), or the social element of eating were not included in our DCE, even though such attributes have been found to be important drivers of meal choice in previous preference-based studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Specific focus on meal attributes will enable us to examine and compare preferences for those attributes that are universally applicable to meals regardless of the country and university context. At the same time, the relative importance of the included attributes will offer valuable insights that will inform university food policy.\u003c/p\u003e \u003cp\u003eA particular strength of this study will be its international focus given that the proposed survey will be simultaneously conducted in six universities across six European countries. This research will help to fill the gap in the literature on the determinants of demand for food on campus and potential differences between various countries. We followed good practice guidelines for development of DCEs in the healthcare setting [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], including conducting a comprehensive literature review to identify the initial list of attributes and levels [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], a focus group discussion, and pilot think-aloud interviews with university students and staff from the participating universities. This will ensure the DCE is applicable to the different country settings. This is an important strength of this study given the underreporting of this step in previous DCE research [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The results will be reported through various means including academic conferences and a peer-reviewed publication to communicate the findings to the academic community, and through reports and policy briefs to reach university policymakers.\u003c/p\u003e \u003cp\u003eAnother strength of the study is through the deliberate inclusion of complementary variables aimed at improving our understanding of preference formation. First, the identification of relationships between these preferences and specific behaviours, such as the source of lunch, dietary habits, and food allergies and intolerances, is in line with the principles of behavioural economics [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Through the incorporation of these variables, we will provide a more nuanced understanding of the determinants of food choices among university staff and students. Moreover, because we consider food involvement, i.e., the importance of food for the subject, we will add a layer of depth to the analysis, capturing a motivational dimension that explains the extent to which subjects engage in information processing during food decision-making processes.\u003c/p\u003e \u003cp\u003eIt is important to acknowledge that a proportion of university students and staff typically bring their own lunch to campus, and this proportion may vary across country settings. However, we assume that the majority of respondents would still buy lunch on campus from time to time. While we will not be able to explore this in-depth, in our survey, we will account for the average number of days spent on campus by the participants and their typical source of lunch.\u003c/p\u003e \u003cp\u003eThe heterogeneity of food systems across the different campuses, as well as any underlying cultural differences, might present challenges to the conduct of the DCE. We plan to mitigate this by incorporating the local context knowledge by working closely with research partners from each university, and with involving potential respondents in the design and pilot stages, developing translated versions of the survey, and accounting for heterogeneity in the analysis through the inclusion of a wide range of explanatory co-variates.\u003c/p\u003e \u003cp\u003eIn summary, by taking into consideration the interrelationships of food preferences with related behaviours and food involvement, our study will provide valuable insights for practical applications within the university setting, with the ultimate goal of contributing to food policy that will lead to a positive impact on the university community well-being.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics committees of the University of Birmingham (ERN_1270-Jun2023), University of Murcia (M10/2023/046), Nantes University (n\u0026deg;031020230), University of Florence (n. 304 granted on 21/02/2024), and Swedish Ethical Review Authority (Dnr 2023-07604-01). Focus group and pilot interview participants signed informed consent forms prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript does not contain any personal data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData sharing is not applicable to this article as no datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis project has received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation programme under grant agreement No 101035821.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors (IP, NA, LF, MH, CW, MH, LDN, FR, AJE, KB,\u0026nbsp;\u0026Eacute;CC, KO, PP, SRDM, EFB, and EF) made substantial contributions to securing funding for this work,\u0026nbsp;to the conception and design of the work. IP, NA, CW, MH, LDN, FR, AJE, KB,\u0026nbsp;\u0026Eacute;CC, KO, PP, SRDM, EFB, and EF contributed to the\u0026nbsp;acquisition, analysis, and interpretation of the focus group and pilot interview data. MH provided technical expertise on the analysis of the discrete choice experiment data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIP drafted the manuscript. All authors contributed to revising the manuscript. All authors\u0026nbsp;approved the submitted version of the manuscript and agreed both to be personally accountable for the author\u0026apos;s own contributions and to ensure that questions related to the accuracy or integrity of any part of the work, even ones in which the author was not personally involved, are appropriately investigated, resolved, and the resolution documented in the literature.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank focus group and pilot interview participants for their time and help in developing the survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNeumark-Sztainer D, Story M, Perry C, Casey MA. Factors influencing food choices of adolescents: findings from focus-group discussions with adolescents. 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Patient preferences in the medical product lifecycle. Patient-Patient-Centered Outcomes Res. 2020;13(1):7\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBridges JF, Hauber AB, Marshall D, Lloyd A, Prosser LA, Regier DA, et al. Conjoint analysis applications in health\u0026mdash;a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value health. 2011;14(4):403\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfentou NFL, Frew E, Pokhilenko I. Systematic literature review of discrete choice experiments of meal preferences. [Article under review.]. In press. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHiligsmann M, van Durme C, Geusens P, Dellaert BG, Dirksen CD, van der Weijden T et al. Nominal group technique to select attributes for discrete choice experiments: an example for drug treatment choice in osteoporosis. Patient Prefer Adherence. 2013:133\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21(2):145\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Bekker-Grob EW, Donkers B, Jonker MF, Stolk EA. Sample size requirements for discrete-choice experiments in healthcare: a practical guide. Patient-Patient-Centered Outcomes Res. 2015;8:373\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStataCorp. Stata Statistical Software: Release 17. College Station. TX: StataCorp LLC; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNlogit. Superior Statistical Analysis Software 2024 [ \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.limdep.com/products/nlogit/\u003c/span\u003e\u003cspan address=\"https://www.limdep.com/products/nlogit/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErnawati H, Suwandojo DPEH. Consumer preferences for indonesian food. J Indonesian Econ Business: JIEB. 2019;34(3):280\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandwehr SC, Hartmann M. Is it all due to peers? The influence of peers on children's snack purchase decisions. Appetite. 2024;192:107111.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user\u0026rsquo;s guide. PharmacoEconomics. 2008;26:661\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"discrete choice experiment, food preferences, university, students, workplace","lastPublishedDoi":"10.21203/rs.3.rs-4436883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4436883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground. \u003c/strong\u003eFood choices are influenced by habits, experiences, as well as various socioeconomic factors. Understanding these drivers can mitigate negative effects of poor nutrition and yield societal benefits. Preference elicitation methods like discrete choice experiments help understand people’s food preferences revealing factors influencing choices the most, such as nutritional content or cost of a meal. This information can be helpful in developing tailored meal-based interventions and informing food policies. Universities, as anchor institutions, are increasingly concerned with health, wellbeing, and sustainability of their students and staff. Yet, there is limited evidence on food preferences in university settings. This paper outlines a discrete choice experiment protocol to compare lunch preferences among university staff and students across six European countries, aiming to inform campus food policies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods. \u003c/strong\u003eAttributes and levels were derived from a systematic literature review of preference-based studies focused on the drivers of meal choices and validated in the focus group with students and staff from participating universities. The attributes in the discrete choice experiment include nutritional content, price, time to access a meal, sensory properties of a meal, naturalness of the ingredients, and meal size. The survey was piloted in think-aloud interviews with students and staff in participating universities. We will collect preference data, along with data on participants’ sociodemographic characteristics, food-related behaviour, opinions about food, experience of food insecurity, physical activity, and body composition, using an online survey. Preference data will be analysed using random parameter logit and latent class models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion. \u003c/strong\u003eThis study will be the first to investigate lunch preferences of university students and staff across six European countries, informing campus food policies. While campus food systems may not always align with students’ and staff preferences, incorporating them into policy-making can enhance satisfaction and well-being. Strengths include an international focus, inclusion of complementary variables, and involvement of potential respondents in all phases of developing this research. Acknowledging limitations, such as varying lunch habits, the study aims to provide valuable insights for improving university food policies and overall community well-being.\u003c/p\u003e","manuscriptTitle":"What’s for lunch? Eliciting preferences for food on university campus: discrete choice experiment protocol","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 18:57:11","doi":"10.21203/rs.3.rs-4436883/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4631dd2-fd55-4552-aa10-600ab2adce49","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-17T12:38:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 18:57:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4436883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4436883","identity":"rs-4436883","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}