Introducing the Front-Of-Pack Acceptance Model: the role of usefulness and ease of use in European consumers’ acceptance of Front-Of-Pack Labels

References

• Andreeva VA, Egnell M, Handjieva-Darlenska T, Talati Z, Touvier M, Galan P, Hercberg S, Pettigrew S, Julia C. 2020. Bulgarian consumers' objective understanding of front-of-package nutrition labels: a comparative, randomized study . Arch Public Health. 78(1):35–39.
   PubMed Web of Science ®Google Scholar
• Ares G, Aschemann-Witzel J, Curutchet MR, Antúnez L, Machín L, Vidal L, Giménez A. 2018. Product reformulation in the context of nutritional warning labels: exploration of consumer preferences towards food concepts in three food categories. Food Res Int. 107:669–674.
   PubMed Web of Science ®Google Scholar
• Arrúa A, Curutchet MR, Rey N, Barreto P, Golovchenko N, Sellanes A, Velazco G, Winokur M, Giménez A, Ares G. 2017. Impact of front-of-pack nutrition information and label design on children's choice of two snack foods: Comparison of warnings and the traffic-light system. Appetite. 116:139–146. doi:https://doi.org/10.1016/j.appet.2017.04.012.28428151
   PubMed Web of Science ®Google Scholar
• Bagozzi RP. 1980. The nature and causes of self-esteem, performance, and satisfaction in the sales force: a structural equation approach. J Bus. 53(3):315.
   Web of Science ®Google Scholar
• Bastian M, Heymann S, Jacomy M. 2009. Gephi: an open source software for exploring and manipulating networks. BT – International AAAI Conference on Weblogs and Social. International AAAI Conference on Weblogs and Social Media. 3(1):361–362. www.aaai.org.
   Google Scholar
• Bhatti S, Srivastava R. 2003. Participation of working women in decision-making process as consumer. Int J Cons Stud. 27(3):228–228.
   Google Scholar
• Bro R, Smilde AK. 2014. Principal component analysis. Anal Methods . 6(9):2812–2831.
   Web of Science ®Google Scholar
• Byrne BM. 2013. Structural equation modeling with Mplus: basic concepts, applications, and programming. London: Routledge.
   Google Scholar
• Carmines EG, Zeller RA. 1979. Reliability and validity assessment. New York, NY: Sage Publications.
   Google Scholar
• Caswell JA, Padberg DI. 1992. Toward a more comprehensive theory of food labels. Am J Agric Econ. 74(2):460–468.
   Web of Science ®Google Scholar
• Chang CC, Yan CF, Tseng JS. 2012. Perceived convenience in an extended technology acceptance model: mobile technology and English learning for college students. AJET. 28(5):809–826.
   Web of Science ®Google Scholar
• Cheung GW, Rensvold RB. 2002. Evaluating goodness-of-fit indexes for testing measurement invariance. Struct Equ Modeling. 9(2):233–255.
   Web of Science ®Google Scholar
• Compeau DR, Higgins CA. 1995b. Computer self-efficacy: development of a measure and initial test. MIS Quarterly. 19(2):189–211.
   Web of Science ®Google Scholar
• Crockett RA, King SE, Marteau TM, Prevost AT, Bignardi G, Roberts NW, Stubbs B, Hollands GJ, Jebb SA. 2018. Nutritional labelling for healthier food or non-alcoholic drink purchasing and consumption. Cochrane Database Syst Rev. 2:CD009315.
   PubMedGoogle Scholar
• Croker H, Packer J, Russell SJ, Stansfield C, Viner RM. 2020. Front of pack nutritional labelling schemes: a systematic review and meta-analysis of recent evidence relating to objectively measured consumption and purchasing. J Hum Nutr Diet. 33(4):518–537.
   PubMed Web of Science ®Google Scholar
• Cronbach LJ. 1951. Coefficient alpha and the internal structure of tests. Psychometrika. 16(3):297–334.
   Web of Science ®Google Scholar
• Davis FD. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Manag Inf Syst. 13(3):319–339.
   Web of Science ®Google Scholar
• Davis FD. 1993. User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. Int J Man-Mach Stud. 38(3):475–487.
   Google Scholar
• Davis FD, Venkatesh V. 2004. Toward preprototype user acceptance testing of new information systems: implications for software project management. IEEE Trans Eng Manage. 51(1):31–46.
   Web of Science ®Google Scholar
• De Temmerman J, Heeremans E, Slabbinck H, Vermeir I. 2021. The impact of the Nutri-Score nutrition label on perceived healthiness and purchase intentions. Appetite. 157:104995.
   PubMed Web of Science ®Google Scholar
• Dietvorst BJ, Bartels DM. 2020. Consumers object to algorithms making morally relevant decisions because of algorithms’ consequentialist decision strategies (December 22, 2020). Available at SSRN: https://ssrn.com/abstract=3753670 or https://doi.org/http://dx.doi.org/10.2139/ssrn.3753670.
   Google Scholar
• Dishaw MT, Strong DM. 1999. Extending the technology acceptance model with task–technology fit constructs. Inf Manage. 36(1):9–21.
   Web of Science ®Google Scholar
• Dréano-Trécant L, Egnell M, Hercberg S, Galan P, Soudon J, Fialon M, Touvier M, Kesse-Guyot E, Julia C. 2020. Performance of the front-of-pack nutrition label Nutri-Score to discriminate the nutritional quality of foods products: a comparative study across 8 European Countries. Nutrients. 12(5):1303.
   Web of Science ®Google Scholar
• Dubois P, Albuquerque P, Allais O, Bonnet C, Bertail P, Combris P, Lahlou S, Rigal N, Ruffieux B, Chandon P. 2021. Effects of front-of-pack labels on the nutritional quality of supermarket food purchases: evidence from a large-scale randomized controlled trial. J Acad Mark Sci. 49(1):119–138.
   Web of Science ®Google Scholar
• Ducrot P, Méjean C, Julia C, Kesse-Guyot E, Touvier M, Fezeu L, Hercberg S, Péneau S. 2015a. Effectiveness of front-of-pack nutrition labels in French adults: results from the NutriNet-Sante cohort study. PLoS One. 10(10):e0140898.
   PubMed Web of Science ®Google Scholar
• Ducrot P, Méjean C, Julia C, Kesse-Guyot E, Touvier M, Fezeu LK, Hercberg S, Péneau S. 2015b. Objective under- standing of front-of-package nutrition labels among nutritionally at-risk individuals. Nutrients. 7(8):7106–7125.
   PubMed Web of Science ®Google Scholar
• Ducrot P, Julia C, Méjean C, Kesse-Guyot E, Touvier M, Fezeu LK, Hercberg S, Péneau S. 2016. Impact of different front-of-pack nutrition labels on consumer purchasing intentions: a randomized controlled trial. Am J Prev Med. 50(5):627–636.
   PubMed Web of Science ®Google Scholar
• Dulcic Z, Pavlic D, Silic I. 2012. Evaluating the intended use of Decision Support System (DSS) by applying Technology Acceptance Model (TAM) in business organizations in Croatia. Procedia – Soc Behav Sci. 58:1565–1575.
   Google Scholar
• Egnell M, Ducrot P, Touvier M, Allés B, Hercberg S, Kesse- Guyot E, Julia C. 2018a. Objective understanding of Nutri-Score Front-Of-Package nutrition label according to individual characteristics of subjects: comparisons with other format labels. PLoS One. 13(8):e0202095.
   PubMed Web of Science ®Google Scholar
• Egnell M, Talati Z, Hercberg S, Pettigrew S, Julia C. 2018b. Objective understanding of front-of-package nutrition labels: an international comparative experimental study across 12 countries. Nutrients. 10:1542.
   Web of Science ®Google Scholar
• Egnell M, Talati Z, Galan P, Andreeva VA, Vandevijvere S, Gombaud M, Dréano-Trécant L, Hercberg S, Pettigrew S, Julia C. 2020. Objective understanding of the Nutri-score front-of-pack label by European consumers and its effect on food choices: an online experimental study. Int J Behav Nutr Phys Act. 17(1):146–113.
   PubMed Web of Science ®Google Scholar
• Egnell M, Talati Z, Pettigrew S, Galan P, Hercberg S, Julia C. 2019. Vergleich von Front-of-Pack-Kennzeichnungen zur Aufklärung deutscher VerbraucherInnen über den Nährwert von Lebensmitteln: Farbkodierte Kennzeichnungen übertreffen alle anderen Systeme. Ernahrungs Umschau. 66(5):76–84. https://doi.org/https://doi.org/10.4455/eu.2019.020.
   Google Scholar
• European Commission. 2020. Inception Impact Assessment: Proposal for a revision of Regulation (EU) No 1169/2011 on the provision of food information to consumers. Retrieved at: https://www.essna.com/wp-content/uploads/2021/01/Inception-Impact-Assessment-FIC-F2F.pdf.
   Google Scholar
• European Parliaments and the Council. 2020. Farm to Fork Strategy | Food Safety. https://ec.europa.eu/food/farm2fork_en.
   Google Scholar
• Featherman MS, Pavlou PA. 2003. Predicting e-services adoption: a perceived risk facets perspective. Int J Hum Comput Stud. 59(4):451–474.
   Web of Science ®Google Scholar
• Fialon M, Egnell M, Talati Z, Galan P, Dréano-Trécant L, Touvier M, Pettigrew S, Hercberg S, Julia C. 2020. Effectiveness of Different Front-of-Pack Nutrition Labels among Italian Consumers: Results from an Online Randomized Controlled Trial. Nutrients. 12(8):2307 doi:https://doi.org/10.3390/nu12082307.
   PubMed Web of Science ®Google Scholar
• Finkelstein EA, Li W, Melo G, Strombotne K, Zhen C. 2018. Identifying the effect of shelf nutrition labels on consumer purchases: Results of a natural experiment and consumer survey. Am J Clin Nutr. 107(4):647–651.
   PubMed Web of Science ®Google Scholar
• Fishbein M, Ajzen I. 1975. Beliefs, attitude, intention, and behavior. Reading (MA): Addison-Wesley.
   Google Scholar
• Forina M, Armanino C, Raggio V. 2002. Clustering with dendrograms on interpretation variables. Anal Chim Acta. 454(1):13–19.
   Web of Science ®Google Scholar
• Fornell C, Larcker DF. 1981. Structural equation models with unobservable variables and measurement error: algebra and statistics. J Mark Res. 18(3):382–388.
   Web of Science ®Google Scholar
• French Health Ministry. 2021. Nutri-Score. retrieved at: https://www.santepubliquefrance.fr/determinants-de-sante/nutrition-et-activite-physique/articles/nutri-score.
   Google Scholar
• Galan P, Egnell M, Salas-Salvadó J, Babio N, Pettigrew S, Hercberg S, Julia C. 2020. Understanding of different front-of-package labels by the Spanish population: results of a comparative study. Endocrinol Diabetes Nutr (Engl Ed). 67(2):122–129.
   PubMed Web of Science ®Google Scholar
• Garaus M, Wolfsteiner E, Wagner U. 2016. Shoppers’ acceptance and perceptions of electronic shelf labels. J Bus Res. 69(9):3687–3692.
   Web of Science ®Google Scholar
• Gefen D. 2004. What makes an ERP implementation relationship worthwhile: linking trust mechanisms and ERP usefulness. J Manage Inf Syst. 21(1):263–288.
   Web of Science ®Google Scholar
• Gefen D, Straub DW. 2004. Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services. Omega. 32(6):407–424.
   Web of Science ®Google Scholar
• Goodhue DL. 1995. Understanding user evaluations of information systems. Manage Sci . 41(12):1827–1818.
   Web of Science ®Google Scholar
• Goodhue DL, Thompson RL. 1999. Task-technology fit and individual performance Study. MIS Quarterly. 23(2):145–236. MIS Quarterly. 1995;19(2). 213.
   Web of Science ®Google Scholar
• Grunert KG, Wills JM. 2007. A review of European research on consumer response to nutrition information on food labels. J Public Health. 15(5):385–399.
   Google Scholar
• Han J, Moon H, Oh Y, Chang JY, Ham S. 2020. Impacts of menu information quality and nutrition information quality on technology acceptance characteristics and behaviors toward fast food restaurants' kiosk. Nutr Res Pract. 14(2):167–174.
   PubMed Web of Science ®Google Scholar
• Hardgrave BC, Johnson RA. 2003. Toward an information systems development acceptance model: the case of object-oriented systems development. IEEE Trans Eng Manage. 50(3):322–336.
   Web of Science ®Google Scholar
• Horton RP, Buck T, Waterson PE, Clegg CW. 2001. Explaining intranet use with the technology acceptance model. J Inf Technol. 16(4):237–249.
   Web of Science ®Google Scholar
• Hu LT, Bentler PM. 1999. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 6(1):1–55.
   Web of Science ®Google Scholar
• Huang L, Lu MT, Wong BK. 2003. The impact of power distance on email acceptance: evidence from the PRC. Comput Inf. 44(1):93–101.
   Google Scholar
• Igbaria M, Schiffman SJ, Wieckowski TJ. 1994. The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology. Behav Inf Technol. 13(6):349–361.
   Web of Science ®Google Scholar
• Igbaria M, Tan M. 1997. The consequences of information technology acceptance on subsequent individual performance. Inform Manage. 32(3):113–121.
   Google Scholar
• Italian Economic Development Ministry. 2021. NutrInform Battery. Retrieved at: https://www.nutrinformbattery.it.
   Google Scholar
• Jackson CM, Chow S, Leitch RA. 1997. Toward an understanding of the behavioral intention to use an information system. Decision Sci. 28(2):357–389.
   Web of Science ®Google Scholar
• Jacquier C, Bonthoux F, Baciu M, Ruffieux B. 2012. Improving the effectiveness of nutritional information policies: assessment of unconscious pleasure mechanisms involved in food-choice decisions. Nutr Rev. 70(2):118–131.
   PubMed Web of Science ®Google Scholar
• Johnston R. 2014. Confirmatory Factor Analysis (CFA). In: Encyclopedia of quality of life and well-being research. The Netherlands: Springer; p. 1194–1195. https://doi.org/https://doi.org/10.1007/978-94-007-0753-5_524
   Google Scholar
• Julia C, Ducrot P, Péneau S, Deschamps V, Méjean C, Fézeu L, Touvier M, HErcberg S, Kesse-Guyot E. 2015a. Discriminating nutritional quality of foods using the 5-color nutrition label in the French food market: consistency with nutritional recommendations. Nutr J. 14:100.
   PubMed Web of Science ®Google Scholar
• Julia C, Hercberg S. 2017. Nutri-Score: evidence of the effectiveness of the French front-of-pack nutrition label. Ernahrungs Umschau. 64:181–187.
   Web of Science ®Google Scholar
• Julia C, Kesse-Guyot E, Ducrot P, Péneau S, Touvier M, Méjean C, Hercberg S. 2015b. Performance of a five category front-of-pack labelling system – the 5-colour nutrition label – to differentiate nutritional quality of breakfast cereals in France . BMC Public Health. 15(1):179–179.
   PubMedGoogle Scholar
• Kim YG, Woo E. 2016. Consumer acceptance of a quick response (QR) code for the food traceability system: application of an extended technology acceptance model (TAM). Food Res Int. 85:266–272.
   PubMed Web of Science ®Google Scholar
• King WR, He J. 2006. A meta-analysis of the technology acceptance model. Inf Manage. 43(6):740–755.
   Web of Science ®Google Scholar
• Kline RB. 2015. Principles and practice of structural equation modeling. London: Guilford Publications.
   Google Scholar
• Langfelder P, Horvath S. 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 9(1):1–13.
   PubMedGoogle Scholar
• Leonard-Barton D, Deschamps I. 1988. Managerial influence in the implementation of new technology. Manage Sci. 34(10):1252–1265.
   Web of Science ®Google Scholar
• Ma YJ, Gam HJ, Banning J. 2017. Perceived ease of use and usefulness of sustainability labels on apparel products: application of the technology acceptance model. Fash Text. 4(1):1–20.
   Google Scholar
• Marangunić N, Granić A. 2015. Technology acceptance model: a literature review from 1986 to 2013. Univ Access Inf Soc. 14(1):81–95.
   Web of Science ®Google Scholar
• Mazzù MF, Romani S, Baccelloni A, Simonetti E. 2021b. Search of superiority: exploring the effectiveness gap of front-of-pack nutritional labels. An assessment of consumer’s decision-making process toward healthier food choices. J Food Sci Nutr. 7:103.
   Google Scholar
• Mazzù MF, Romani S, Baccelloni A, Gambicorti A. 2021a. A cross-country experimental study on consumers' subjective understanding and liking on front-of-pack nutrition labels . Int J Food Sci Nutr. 72(6):833–815.
   PubMed Web of Science ®Google Scholar
• Mazzù MF, Romani S, Gambicorti A. 2020. Effects on consumers' subjective understanding of a new front-of-pack nutritional label: a study on Italian consumers. Int J Food Sci Nutr. 72(3):357–366.
   PubMed Web of Science ®Google Scholar
• Moon JW, Kim YG. 2001. Extending the TAM for a World-Wide-Web context. Inf Manage. 38(4):217–230.
   Web of Science ®Google Scholar
• Mun YY, Jackson JD, Park JS, Probst JC. 2006. Understanding information technology acceptance by individual professionals: toward an integrative view. Inf Manage. 43(3):350–363.
   Web of Science ®Google Scholar
• Nguyen TTH, Nguyen N, Nguyen TBL, Phan TTH, Bui LP, Moon HC. 2019. Investigating consumer attitude and intention towards online food purchasing in an emerging economy: an extended TAM approach. Foods. 8(11):576.
   PubMed Web of Science ®Google Scholar
• Oh S, Ahn J, Kim B. 2003. Adoption of broadband Internet in Korea: the role of experience in building attitudes. J Inf Technol. 18(4):267–280.
   Web of Science ®Google Scholar
• Otnes C, McGrath MA. 2001. Perceptions and realities of male shopping behavior. J Retailing. 77(1):111–137.
   Web of Science ®Google Scholar
• Palan S, Schitter C. 2018. Prolific.ac—a subject pool for online experiments. J Behav Exp Finance. 17:22–27.
   Web of Science ®Google Scholar
• Pavlou PA, Gefen D. 2004. Building effective online marketplaces with institution-based trust. Inf Syst Res. 15(1):37–59.
   Web of Science ®Google Scholar
• Peters-Texeira A, Badrie N. 2005. Consumers’ perception of food packaging in Trinidad, West Indies and its related impact on food choices. Int J Cons Stud. 29(6):508–514.
   Google Scholar
• Pierce TP, Willy C, Roncace R, Bischoff J. 2014. Extending the technology acceptance model: Policy Acceptance Model (PAM). AJHS. 5(2):129–144.
   Google Scholar
• Plouffe CR, Vandenbosch M, Hulland J. 2001. Intermediating technologies and multi‐group adoption: a comparison of consumer and merchant adoption intentions toward a new electronic payment system. J Prod Innov Manag: Int Publ Prod Dev Manag Assoc. 18(2):65–81.
   Web of Science ®Google Scholar
• R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
   Google Scholar
• Richbell S, Kite V. 2007. Night shoppers in the “open 24 hours” supermarket: a profile. Intl J Retail Distrib Mgt. 35(1):54–68.
   Google Scholar
• Schepers J, Wetzels M. 2007. A meta-analysis of the technology acceptance model: investigating subjective norm and moderation effects. Inf Manage. 44(1):90–103.
   Web of Science ®Google Scholar
• Sherif CW, Sherif M. 1967. Attitude, ego-involvement, and change. New York: Wiley.
   Google Scholar
• Shim S, Gehrt KC, Holikova S. 1999. Shopping orientation-based segmentation of U.S. grocery shoppers. J Food Prod Mark. 5(2):1–19.
   Google Scholar
• Slatten LAD. 2012. Something old and something new: using the technology acceptance model to evaluate nonprofit certification. Int J Org Theory Behav. 15(3):423–449.
   Google Scholar
• Sobal J, Bisogni CA. 2009. Constructing food choice decisions. Ann Behav Med. 38(S1): S37–S46.
   PubMedGoogle Scholar
• Sprague RH Jr. 1980. A framework for the development of decision support systems. MIS Quarterly. 4(4):1–26.
   Web of Science ®Google Scholar
• Szajna B. 1996. Empirical evaluation of the revised technology acceptance model. Manage Sci. 42(1):85–92.
   Web of Science ®Google Scholar
• Talati Z, Egnell M, Hercberg S, Julia C, Pettigrew S. 2019a. Consumers’ perceptions of five front-of-package nutrition labels: an experimental study across 12 countries. Nutrients. 11(8):1934.
   Web of Science ®Google Scholar
• Talati Z, Egnell M, Hercberg S, Julia C, Pettigrew S. 2019b. Food choice under five front-of-package nutrition label conditions: an experimental study across 12 countries. Am J Public Health. 109(12):1770–1775.
   PubMed Web of Science ®Google Scholar
• Talati Z, Pettigrew S, Ball K, Hughes C, Kelly B, Neal B, Dixon H. 2017. The relative ability of different front-of-pack labels to assist consumers discriminate between healthy, moderately healthy, and unhealthy foods. Food Qual Prefer. 59:109–113.
   Web of Science ®Google Scholar
• UK Health Minister. 2019. Guide to creating a front of pack (FoP) nutrition label for pre-packed products sold through retail outlets (2019). Retrieved at: https://www.food.gov.uk/sites/default/files/media/document/fop-guidance_0.pdf.
   Google Scholar
• Venkatesh V, Thong JYL, Xu X. 2012. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly. 36(1):157 doi:https://doi.org/10.2307/41410412.
   Web of Science ®Google Scholar
• Venkatesh V. 2000. Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf Syst Res. 11(4):342–365.
   Web of Science ®Google Scholar
• Venkatesh V, Bala H. 2008. Technology acceptance model 3 and a research agenda on interventions. Decis Sci. 39(2):273–315.
   Web of Science ®Google Scholar
• Venkatesh V, Davis FD. 1996. A model of the antecedents of perceived ease of use: development and test. Decis Sci. 27(3):451–481.
   Web of Science ®Google Scholar
• Venkatesh V, Davis FD. 2000. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci. 46(2):186–204.
   Web of Science ®Google Scholar
• Venkatesh V, Morris MG. 2000. Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly: Manage Inf Syst. 24(1):115–136.
   Web of Science ®Google Scholar
• Venkatesh V, Morris MG, Davis GB, Davis FD. 2003. User acceptance of information technology: toward a unified view. MIS Quarterly: Manage Inf Syst. 27(3):425–478.
   Web of Science ®Google Scholar
• World Health Organization. 2000. Obesity. https://www.who.int/health-topics/obesity#tab=tab_1.
   Google Scholar
• Wu L, Chen JL. 2005. An extension of trust and TAM model with TPB in the initial adoption of on-line tax: an empirical study. Int J Hum Comput Stud. 62(6):784–808.
   Web of Science ®Google Scholar