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PERCEPÇÃO DE VALOR EM DISPOSITIVOS TECNOLÓGICOS GERADORES DE INFORMAÇÕES ALIMENTARES DE PRODUTOS ALIMENTÍCIOS CUSTOMIZADOS

Calegari, Luiz Philipi; Fettermann, Diego Castro; Zandonai, Giuliano;

Artigo Completo:

Em produtos alimentícios, a disponibilidade de informações sobre o produto (rótulos por exemplo), influencia perante a percepção de valor. Mas, à medida que o produto alimentício é customizado, a personalização dessas informações torna-se necessária, fato este que pode criar dificuldades na adoção de Customização em Massa (CM). A utilização de dispositivos que usufruem de tecnologia inteligente (smart technology) é um meio capaz de auxiliar os consumidores no momento da escolha dos produtos alimentícios, e possibilitam a customização de informações. A partir de uma pesquisa de mercado sobre dispositivos que geram informações sobre produtos alimentícios, foram identificados 5 atributos considerados na composição desses dispositivos: (i) portabilidade, (ii) precisão, (iii) personalização de dieta, (iv) análise de qualidade do produto alimentício e (v) preço. Este estudo possui como objetivo, identificar quais desses atributos direcionam para uma maior agregação de valor ao cliente. Realizou-se então um projeto experimental desenvolvido por análise conjunta baseada em escolha, com planejamento fatorial fracionado 25-1. Para a coleta de dados foi realizada a abordagem metodológica survey, com número de 130 respondentes. Como contribuições, este estudo traz resultados para o direcionamento da pesquisa para composição de um dispositivo com finalidade de fornecer informações sobre o produto alimentício customizado em massa.

Artigo Completo:

Palavras-chave: customização em massa, restrições alimentares, personalização, informações alimentares, dispositivos tecnológicos, percepção de valor,

Palavras-chave:

DOI: 10.5151/cbgdp2017-103

Referências bibliográficas
  • [1] ARENS-VOLLAND, A. G.; SPASSOVA, L.; BOHN, T. Promising approaches of computer-supported dietary assessment and management—Current research status and available applications. International Journal of Medical Informatics, v. 84, n. 12, p. 997-1008, 2015.
  • [2] ASCHEMANN-WITZEL, J., GRUNERT, K. G., VAN TRIJP, H. C., BIALKOVA, S., RAATS, M. M., HODKGINS, C., ... & KOENIGSTORFER, J. Effects of nutrition label format and product assortment on the healthfulness of food choice. Appetite, v. 71, p. 63-74, 2013.
  • [3] ASIOLI, D., NæS, T., ØVRUM, A., & ALMLI, V. L.. Comparison of rating-based and choice-based conjoint analysis models. A case study based on preferences for iced coffee in Norway. Food Quality and Preference, v. 48, p. 174-184, 2016.
  • [4] BALCOMBE, K., FRASER, I., LOWE, B., & MONTEIRO, D. S. l. Information customization and food choice. American Journal of Agricultural Economics, v. 98, n. 1, p. 54-73, 2016.
  • [5] BALTAS, G. Nutrition labelling: issues and policies. European Journal of Marketing, v. 35, n. 5/6, p. 708-721, 2001.
  • [6] BOLAND, M. Innovation in the food industry: Personalised nutrition and mass customisation. Innovation, v. 10, n. 1, p. 53-60, 2008.
  • [7] BRUZZONE, F., VIDAL, L., ANTÚNEZ, L., GIMÉNEZ, A., DELIZA, R., & ARES, G.. Comparison of intensity scales and CATA questions in new product development: Sensory characterisation and directions for product reformulation of milk desserts. Food Quality and Preference, v. 44, p. 183-193, 2015.
  • [8] BURTON, S., HOWLETT, E.; TANGARI, A. H.. Food for thought: How will the nutrition labeling of quick service restaurant menu items influence consumers’ product evaluations, purchase intentions, and choices?. Journal of Retailing, v. 85, n. 3, p. 258-273, 2009.
  • [9] CAMPBELL, J.; PORTER, J. Dietary mobile apps and their effect on nutritional indicators in chronic renal disease: A systematic review. Nephrology, v. 20, n. 10, p. 744-751, 2015.
  • [10] CHEN, P. H.; LIANG, Y. H.; LIN, T. C. Implementing a cooking and dietary management system using RFID technology. Mathematical Problems in Engineering, v. 2014, 2014.
  • [11] CICIA, G.; DEL GIUDICE, T.; SCARPA, R. Consumers’ perception of quality in organic food: a random utility model under preference heterogeneity and choice correlation from rank-orderings. British Food Journal, v. 104, n. 3/4/5, p. 200-213, 2002.
  • [12] CLARET, A., GUERRERO, L., AGUIRRE, E., RINCÓN, L., HERNANDÉZ, M. D., MARTINEZ, I., ... & RODRIGUEZ-RODRIGUEZ, C. Consumer preferences for sea fish using conjoint analysis: Exploratory study of the importance of country of origin, obtaining method, storage conditions and purchasing price. Food Quality and Preference, v. 26, n. 2, p. 259-266, 20
  • [13] COSKUN, A. F., WONG, J., KHODADADI, D., NAGI, R., TEY, A., & OZCAN, A. A personalized food allergen testing platform on a cellphone. Lab on a Chip, v. 13, n. 4, p. 636-640, 20
  • [14] DA SILVEIRA, G.; BORENSTEIN, D.; FOGLIATTO, F. S. Mass customization: Literature review and research directions. International Journal of Production Economics, v. 72, n. 1, p. 1-13, 2001.
  • [15] DARBY, A., STRUM, M. W., HOLMES, E., & GATWOOD, J.. A Review of Nutritional Tracking Mobile Applications for Diabetes Patient Use. Diabetes Technology & Therapeutics, v. 18, n. 3, p. 200-212, 2016.
  • [16] DE BRUIN, J., SCHUH, C., SEELING, W., LUGER, E., GALL, M., HUTTERER, E., ... & SCHINDLER, K.. Assessing the feasibility of a mobile health-supported clinical decision support system for nutritional triage in oncology outpatients using Arden Syntax. Artificial Intelligence in Medicine, 2015.
  • [17] DE PELSMAEKER, S.; DEWETTINCK, K.; GELLYNCK, X. The possibility of using tasting as a presentation method for sensory stimuli in conjoint analysis. Trends in Food Science & Technology, v. 29, n. 2, p. 108-115, 2013.
  • [18] DECLOEDT, A. I.; VAN LANDSCHOOT, A.; VANHAECKE, L Fractional factorial design-based optimisation and application of an extraction and UPLC-MS/MS detection method for the quantification of phytosterols in food, feed and beverages low in phytosterols. Analytical and Bioanalytical Chemistry, v. 408, n. 27, p. 7731-7744, 2016.
  • [19] DIMARA, E.; SKURAS, D. Consumer demand for informative labeling of quality food and drink products: a European Union case study. Journal of Consumer Marketing, v. 22, n. 2, p. 90-100, 2005.
  • [20] DURAY, R, WARD, P. T., MILIGAN, G. W., & BERRY, W. L. Approaches to mass customization: configurations and empirical validation. Journal of Operations Management, v. 18, n. 6, p. 605-625, 2000.
  • [21] FETTERMANN, D. C.; ECHEVESTE, M. E. S.; MARTINS, V. L. M.. Configurador de produto para a customização em massa na indústria automobilística. Produto & Produção, v. 13, n. 1, 2012.
  • [22] FRANCO, R. Z., FALLAIZE, R., LOVEGROVE, J. A., & HWANG, F. Popular Nutrition-Related Mobile Apps: A Feature Assessment. JMIR mHealth and uHealth, v. 4, n. 3, 2016.
  • [23] FRIEDMAN, J., HASTIE, T., & TIBSHIRANI, R. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, v. 28, n. 2, p. 337-407, 2000.
  • [24] HAIR, Joseph F., ANDERSON, R. E., BABIN, B. J., & BLACK, W. C. Multivariate Data Analysis: A global perspective. Upper Saddle River, NJ: Pearson, 2010.
  • [25] HEBDEN, L.; CHEY, T.; ALLMAN‐FARINELLI, M. Lifestyle intervention for preventing weight gain in young adults: a systematic review and meta‐analysis of RCTs. Obesity Reviews, v. 13, n. 8, p. 692-710, 2012.
  • [26] HEIDE, M.; OLSEN, S. O. Influence of packaging attributes on consumer evaluation of fresh cod. Food Quality and Preference, v. 60, p. 9-18, 2017.
  • [27] KRIFLIK, L. S.; YEATMAN, Heather. Food scares and sustainability: a consumer perspective. Health, Risk & Society, v. 7, n. 1, p. 11-24, 2005.
  • [28] LINDLEY, D. Imperfect information for consumers. Consumer Policy Review, v. 17, n. 3, p. 74, 2007.
  • [29] LOUVIERE, J. J.; HENSHER, D. A.; SWAIT, J. D. Stated choice methods: analysis and applications. Cambridge University Press, 2000.
  • [30] MAKARONA, E., PETROU, P., KAKABAKOS, S., MISIAKOS, K., & RAPTIS, I.. Point-of-Need bioanalytics based on planar optical interferometry. Biotechnology Advances, v. 34, n. 3, p. 209-233, 2016.
  • [31] MATTILA, E., KORHONEN, I., SALMINEN, J. H., AHTINEN, A., KOSKINEN, E., SÃRELÃ, A., ... & LAPPALAINEN, R. Empowering citizens for well-being and chronic disease management with wellness diary. IEEE Transactions on Information Technology in Biomedicine, v. 14, n. 2, p. 456-463, 2010.
  • [32] MILLER, J. R. Attribute blocks: Visualizing multiple continuously defined attributes. IEEE Computer Graphics and Applications, v. 27, n. 3, p. 57-69, 2007.
  • [33] NEETHIRAJAN, S.; JAYAS, D. S. Nanotechnology for the food and bioprocessing industries. Food and Bioprocess Technology, v. 4, n. 1, p. 39-47, 2011.
  • [34] NELSON, M. E., LAYNE, J. E., BERNSTEIN, M. J., NUERNBERGER, A., CASTANEDA, C., KALITON, D., ... & SINGH, M. A. F. The effects of multidimensional home-based exercise on functional performance in elderly people. The Journals of Gerontology: Series A, v. 59, n. 2, p. M154-M160, 2004.
  • [35] OGAWA, S.; PILLER, F. T. Reducing the risks of new product development. MIT Sloan Management Review, v. 47, n. 2, p. 65, 2006.
  • [36] OKUMUS, B.; BILGIHAN, A. Proposing a model to test smartphone users' intention to use smart applications when ordering food in restaurants. Journal of Hospitality and Tourism Technology, v. 5, n. 1, p. 31-49, 2014.
  • [37] REITBERGER, W.; SPREICER, W.; FITZPATRICK, G. Situated and mobile displays for reflection on shopping and nutritional choices. Personal and Ubiquitous Computing, v. 18, n. 7, p. 1721-1735, 2014.
  • [38] SCHERER, C.; EMBERGER-KLEIN, A.; MENRAD, K. Biogenic product alternatives for children: Consumer preferences for a set of sand toys made of bio-based plastic. Sustainable Production and Consumption, v. 10, p. 1-14, 2017.
  • [39] SCHULDT, J. P. Does green mean healthy? Nutrition label color affects perceptions of healthfulness. Health Communication, v. 28, n. 8, p. 814-821, 2013.
  • [40] TAO, H., BRENCKLE, M. A., YANG, M., ZHANG, J., LIU, M., SIEBERT, S. M., ... & KAPLAN, D. L. Silk‐Based Conformal, Adhesive, Edible Food Sensors. Advanced Materials, v. 24, n. 8, p. 1067-1072, 2012.
  • [41] VAN DER MERWE, D., KEMPEN, E. L., BREEDT, S., & DE BEER, H. Food choice: student consumers' decision‐making process regarding food products with limited label information. International Journal of Consumer Studies, v. 34, n. 1, p. 11-18, 2010.
  • [42] VANDERROOST, M., RAGAERT, P., VERWAEREN, J., DE MEULANAER, B., DE BAETS, B., & DEVLIEGHERE, F. The digitization of a food package’s life cycle: Existing and emerging computer systems in the logistics and post-logistics phase. Computers in Industry, v. 87, p. 15-30, 2017.
  • [43] VASILJEVIC, M.; PECHEY, R.; MARTEAU, T. M. Making food labels social: The impact of colour of nutritional labels and injunctive norms on perceptions and choice of snack foods. Appetite, v. 91, p. 56-63, 2015.
  • [44] VOLKOVA, E.; MHURCHU, C. N. The influence of nutrition labeling and point-of-purchase information on food behaviours. Current Obesity Reports, v. 4, n. 1, p. 19-29, 2015.
  • [45] WANG, F.; GU, H.; SWAGER, T. M. Carbon nanotube/polythiophene chemiresistive sensors for chemical warfare agents. Journal of the American Chemical Society, v. 130, n. 16, p. 5392-5393, 2008.
  • [46] WEARING, J., NOLLEN, N., BEFORT, C., DAVIS, A. M., AGEMY, C. K. iPhone app adherence to expert-recommended guidelines for pediatric obesity prevention. Childhood Obesity, v. 10, n. 2, p. 132-144, 2014.
Como citar:

Calegari, Luiz Philipi; Fettermann, Diego Castro; Zandonai, Giuliano; "PERCEPÇÃO DE VALOR EM DISPOSITIVOS TECNOLÓGICOS GERADORES DE INFORMAÇÕES ALIMENTARES DE PRODUTOS ALIMENTÍCIOS CUSTOMIZADOS", p. 985-993 . In: . São Paulo: Blucher, 2017.
ISSN 2318-6968, DOI 10.5151/cbgdp2017-103

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