Sugary and Carbonated Beverage Consumption in an Example of Generation Z - Small Sample: Health Risks for Young People


International Research Journal of Economics and Management Studies
© 2026 by IRJEMS
Volume 5  Issue 5
Year of Publication : 2026
Authors : Nadiya Dubrovina, Veronika Grimberger, Vira Dubrovina, Maike Scheipers, 5Jana Peliova
irjems doi : 10.56472/25835238/IRJEMS-V5I5P118

Citation:

Nadiya Dubrovina, Veronika Grimberger, Vira Dubrovina, Maike Scheipers, 5Jana Peliova. "Sugary and Carbonated Beverage Consumption in an Example of Generation Z - Small Sample: Health Risks for Young People" International Research Journal of Economics and Management Studies, Vol. 5, No. 5, pp. 151-165, 2026. Crossref. http://doi.org/10.56472/25835238/IRJEMS-V5I5P118

Abstract:

This article aims to study consumer choices for sweet and carbonated beverages among Generation Z consumers and, using quantitative methods, identify key types of consumer behavior, establish similarities or differences that indicate specific preferences for consuming sweet or carbonated beverages, and formulate recommendations for preventing excess weight and obesity based on consumer type. The article examines the problems of analyzing consumer choice of sweet and carbonated drinks using the method of analytic hierarchy of T. Saaty and multidimensional scaling methods. The following popular beverages were selected as a set of sweet and carbonated drinks: Cola, Pepsi, Fanta, packaged in plastic or glass bottles; sweet Ice Tea varieties prepared from green and black tea with various flavor additives and packaged in plastic bottles; sweet drinks based on black coffee, packaged in closed plastic cups; apple juice and orange juice with added sugar or sweeteners, packaged in plastic or glass bottles; carbonated mineral water, packaged in plastic or glass bottles. Four groups were identified, characterizing the consumer preferences for sweet and carbonated beverages among Generation Z. The first and fourth groups were characterized by lower levels of consumption of beverages such as Cola, Pepsi, and Fanta, and a high preference for carbonated mineral water. This means that this group exhibits lower health risks associated with consuming sugary and carbonated beverages. The first group comprised 20% of observations, while the fourth group comprised 10%. The second group, the largest (60% of observations), exhibited the highest level of health risk due to their high consumption of Cola, Pepsi, and Fanta. In the third group (10% of observations), the health risk from consuming sugary and carbonated beverages was moderate. Thus, various taste and behavioral preferences in the choice of sweet and carbonated drinks were identified, which are of sufficient importance for determining the target group and developing programs to promote healthy eating, prevent obesity and the development of diabetes mellitus associated with metabolic disorders and endocrine system functioning.

References:

[1] Abbasalizad Farhangi, M., Mohammadi Tofigh, A., Jahangiri, L., Nikniaz, Z., & Nikniaz, L. (2022). Sugar-sweetened beverages intake and the risk of obesity in children: An updated systematic review and dose-response meta-analysis. Pediatric obesity, 17(8), e12914. https://doi.org/10.1111/ijpo.12914
[2] Artime, E., Spaepen, E., Zimner-Rapuch, S., Lampropoulou, A., Adam, A., Lin, X., Shang, M., Seager, S., Le Roux, C. W., & Dicker, D. (2025). Epidemiology Landscape and Impact of Overweight and Obesity in Adults: Multi-country Results from the IMPACT O Study. Advances in Therapy, 42(10), 5148–5163. https://doi.org/10.1007/s12325-025-03333-1
[3] 3.Beal, T., Le, T. D., Trinh, H. T., Burra, D. D., Béné, C., Huynh, T. T., Jones, A. D. (2020). Child overweight or obesity is associated with modifiable and geographic factors in Vietnam: Implications for program design and targeting. Nutrients, 12(5), 1286. https://doi.org/10.3390/nu12051286
[4] Berghöfer, A., Pischon, T., Reinhold, T. et al. (2008). Obesity prevalence from a European perspective: a systematic review. BMC Public Health 8, 200. https://doi.org/10.1186/1471-2458-8-200
[5] Blundell, J. E., Baker, J. L., Boyland, E., Blaak, E., Charzewska, J., de Henauw, S., … & (additional authors). (2017). Variations in the Prevalence of Obesity Among European Countries, and a Consideration of Possible Causes. Obesity Facts, 10(1), 25–37. https://doi.org/10.1159/000455952
[6] Bodzsar, E. B., & Zsakai, A. (2014). Recent trends in childhood obesity and overweight in the transition countries of Eastern and Central Europe. Annals of Human Biology, 41(3), 263–270. https://doi.org/10.3109/03014460.2013.856473
[7] Carrillo Larco, R. M., Guzman Vilca, W. C., Tarazona Meza, C., Xu, X., & Bernabe Ortiz, A. (2025). Prevalence of preclinical and clinical obesity in adults: Pooled analysis of 56 population based national health surveys. PLOS Global Public Health, 5(7), e0004838. https://doi.org/10.1371/journal.pgph.0004838
[8] Centers for Disease Control and Prevention. (2025, December 3). Adult obesity prevalence maps. U.S. Department of Health and Human Services. https://www.cdc.gov/obesity/data-and-statistics/adult-obesity-prevalence-maps.html
[9] Deierlein, A. L., Raynor, H. A., Andres, A., Byrd Bredbenner, C., Fisher, J. O., Fung, T., Palacios, C., Tobias, D. K., Hoelscher, D. M., Anderson, C. A. M., Booth, S. L., Gardner, C. D., Giovannucci, E., Stanford, F. C., Talegawkar, S. A., Taylor, C. A., Webster, A., Kingshipp, B. J., Nevins, J., Cole, N. C., Bahnfleth, C. L., Becker, B. J., Higgins, M., Scinto Madonich, S. R., Ming, J., Butera, G., Terry, N., & Obbagy, J. (2024). Sugar Sweetened Beverages and Growth, Body Composition, and Risk of Obesity: A Systematic Review with Meta Analysis. USDA Nutrition Evidence Systematic Review. https://doi.org/10.52570/NESR.DGAC2025.SR23
[10] Hout MC, Papesh MH, Goldinger SD. (2012). Multidimensional scaling. Wiley Interdiscip Rev Cogn Sci. 2013 Jan;4(1):93-103. doi: 10.1002/wcs.1203. Epub 2012 Oct 8. PMID: 23359318; PMCID: PMC3555222.
[11] Jakobsen, D. D., Brader, L., & Bruun, J. M. (2023). Association between Food, Beverages and Overweight/Obesity in Children and Adolescents—A Systematic Review and Meta-Analysis of Observational Studies. Nutrients, 15(3), 764. https://doi.org/10.3390/nu15030764
[12] Knai, C., Suhrcke, M., & Lobstein, T. (2007). Obesity in Eastern Europe: An overview of its health and economic implications. Economics & Human Biology, 5(3), 392–408. https://doi.org/10.1016/j.ehb.2007.08.002
[13] Koh, K., Grady, S. C., Darden, J. T., & Vojnovic, I. (2018). Adult obesity prevalence at the county level in the United States, 2000-2010: Downscaling public health survey data using a spatial microsimulation approach. Spatial and spatio-temporal epidemiology, 26, 153–164. https://doi.org/10.1016/j.sste.2017.10.001
[14] Li, H., Xiang, X., Yi, Y., Yan, B., Yi, L., Ding, N., Yang, J., Gu, Z., Luo, Q., Huang, Y., Fan, L., & Xiang, W. (2024). Epidemiology of obesity and influential factors in China: a multicenter cross-sectional study of children and adolescents. BMC Pediatrics, 24, 498. https://doi.org/10.1186/s12887-024-04970-1
[15] Matrosova, E., Tikhomirova, A., Matrosov, N., Dmitriy, K. (2021). Visualization of T. Saati Hierarchy Analysis Method. In: Samsonovich, A.V., Gudwin, R.R., Simões, A.d.S. (eds) Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020. BICA 2020. Advances in Intelligent Systems and Computing, vol. 1310. Springer, Cham. https://doi.org/10.1007/978-3-030-65596-9_32
[16] Myers, C. A., Slack, T., Martin, C. K., Broyles, S. T., & Heymsfield, S. B. (2015). Regional disparities in obesity prevalence in the United States: A spatial regime analysis. Obesity (Silver Spring, Md.), 23(2), 481–487. https://doi.org/10.1002/oby.20963
[17] Nguyen, M., Jarvis, S. E., Tinajero, M. G., Yu, J., Chiavaroli, L., Mejia, S. B., Khan, T. A., Tobias, D. K., Willett, W. C., Hu, F. B., Hanley, A. J., Birken, C. S., Sievenpiper, J. L., & Malik, V. S. (2023). Sugar-sweetened beverage consumption and weight gain in children and adults: a systematic review and meta-analysis of prospective cohort studies and randomized controlled trials. The American journal of clinical nutrition, 117(1), 160–174. https://doi.org/10.1016/j.ajcnut.2022.11.008
[18] Olaya, B., Moneta, M. V., Domènech-Abella, J., & et al. (2015). Country-level and individual correlates of overweight and obesity among primary school children: A cross-sectional study in seven European countries. BMC Public Health, 15, 1053. https://doi.org/10.1186/s12889-015-1809-z
[19] Orsini, F., D’Ambrosio, F., Scardigno, A., Ricciardi, R., & Calabrò, G. E. (2023). Epidemiological Impact of Metabolic Syndrome in Overweight and Obese European Children and Adolescents: A Systematic Literature Review. Nutrients, 15(18), 3895. https://doi.org/10.3390/nu15183895
[20] Saaty Thomas L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology Volume 15, Issue 3, June 1977, Pages 234-281. https://doi.org/10.1016/0022-2496(77)90033-5
[21] Saha, J., Chouhan, P., Ahmed, F., Ghosh, T., Mondal, S., Shahid, M., Fatima, S., & Tang, K. (2022). Overweight/Obesity Prevalence among Under-Five Children and Risk Factors in India: A Cross-Sectional Study Using the National Family Health Survey (2015–2016). Nutrients, 14(17), 3621. https://doi.org/10.3390/nu14173621
[22] Webber, L., Sacks, G., Besson, H., et al. (2012). Modelling obesity trends and related diseases in Eastern Europe. Public Health, 126(11), 1007–1015. https://doi.org/10.1016/j.puhe.2012.08.005
[23] Wong, M. C. S., Huang, J., Wang, J., Chan, P. S. F., Lok, V., Chen, X., Leung, C., Wang, H. H. X., Lao, X. Q., & Zheng, Z.-J. (2020). Global, regional and time-trend prevalence of central obesity: a systematic review and meta-analysis of 13.2 million subjects. European Journal of Epidemiology, 35, 673–683. https://doi.org/10.1007/s10654-020-00650-3
[24] Zhang, L., Chen, J., Zhang, J., et al. (2021). Regional disparities in obesity among a heterogeneous population of Chinese children and adolescents. JAMA Network Open, 4(10), e2131040. https://doi.org/10.1001/jamanetworkopen.2021.31040
[25] http://cda.psych.uiuc.edu/mds_509_2013/readings/systat_scaling_manual.pdf

Keywords:

Sugary and Carbonate Beverages, Consumer Choice, Analytical Hierarchy Process, Multidimensional Scaling, Overweight and Obesity, Health Risk.