Global consumers before and during the COVID-19 pandemic: What aspects characterize digital consumer behavior?

Autores

DOI:

https://doi.org/10.5585/remark.v22i4.23468

Palavras-chave:

Market situation information, E-commerce, Pandemics

Resumo

Purpose: Our work assessed patterns of intra and inter-regional e-commerce behavior ex-ante and during the COVID-19 pandemic. 

Design/methodology/approach: The research was conducted under a quantitative approach, using a non-experimental longitudinal design focusing on the evolution of groups. Initially, relevant variables were selected from the Passport Euromonitor International Lifestyle survey database for the 2019–2021 period, in a sample of forty countries, for which a cluster analysis and the subsequent parametric and non-parametric tests of comparison between groups were performed, considering socioeconomic, demographic, cultural and e-commerce related variables.

Findings: The patterns of digital consumer behavior in the countries under analysis showed changes during the pandemic, moving from characteristics of greater heterogeneity before COVID-19 to a more homogeneous scenario among consumers in different countries.

Theoretical and methodological implications: This work delve into digital consumer patterns during the COVID-19 pandemic. Additionally, the methodological contribution of the research highlights the use of data clustering techniques for behavioural segmentation, being a replicable example for other researchers.

Originality/value: There is a new term proposed to specific characteristics of the e-commerce consumer where socioeconomic, demographic, and cultural variables are added as a complement to the characterization of the Level of Sophistication by the Digital Consumer.

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Biografia do Autor

Luz Elena Barrantes-Aguilar, Universidade da Costa Rica, Escola de Economia Agrícola e Agronegócios – UCR

Mestrado

Luis Ricardo Solís-Rivera, Universidade da Costa Rica, Escola de Economia Agrícola e Agronegócios – UCR

Mestrado

Alexis Villalobos, Universidad de Costa Rica

Estación Experimental Agropecuaria Fabio Baudrit Moreno, Universidad de Costa RicaUCR

Referências

Blazquez-Resino, J. J., Gutierrez-Broncano, S., & Golab-Andrzejak, E. (2021). Neuroeconomy and neuromarketing: The study of the consumer behaviour in the COVID-19 context. Frontiers in Psychology, 13(7), 1–2. https://doi.org/10.3390/su13073791

Bodea, T., & Ferguson, M. (2014). Segmentation, revenue management, and pricing analytics. Routledge.

Carlis, J., & Bruso, K. (2012). RSQRT: An heuristic for estimating the number of clusters to report. Electronic Commerce Research and Applications, 11(2), 152–158. https://doi.org/10.1016/j.elerap.2011.12.006

Chan, T. K. H., Cheung, C. M. K., & Lee, Z. W. Y. (2017). The state of online impulse-buying research: A literature analysis. Information & Management, 54(2), 204–217. https://doi.org/10.1016/j.im.2016.06.001

Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014). NbClust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61, 1–36. https://doi.org/10.18637/jss.v061.i06

Di Crosta, A., Ceccato, I., Marchetti, D., La Malva, P., Maiella, R., Cannito, L., Cipi, M., Mammarella, N., Palumbo, R., Verrocchio, M. C., Palumbo, R., & Di Domenico, A. (2021). Psychological factors and consumer behavior during the COVID-19 pandemic. PLOS ONE, 16(8), e0256095. https://doi.org/10.1371/journal.pone.0256095

Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of Business Research, 117(June), 284–289. https://doi.org/10.1016/j.jbusres.2020.06.008

Duarte, M., Moro, S., & Ferreira da Silva, C. (2022). Does cultural background influence the dissemination and severity of the COVID-19 pandemic? Heliyon, 8(2). Scopus. https://doi.org/10.1016/j.heliyon.2022.e08907

E-commerce Europe. (2021). Impact of the coronavirus on e-commerce: Survey results report (January; pp. 1–12). https://ecommerce-europe.eu/wp-content/uploads/2021/01/Coronavirus-Survey-Report-January-2021.pdf

Economic Commission for Latin America and the Caribbean (ECLAC). (2020). Report on the economic impact of coronavirus disease (COVID-19) on Latin America and the Caribbean. United Nations. https://doi.org/10.18356/497b1332-en

Eger, L., Komárková, L., Egerová, D., & Mičík, M. (2021). The effect of COVID-19 on consumer shopping behaviour: Generational cohort perspective. Journal of Retailing and Consumer Services, 61, 102542. https://doi.org/10.1016/j.jretconser.2021.102542

Fairlie, R., & Fossen, F. M. (2022). The early impacts of the COVID-19 pandemic on business sales. Small Business Economics, 58(4), 1853–1864. https://doi.org/10.1007/s11187-021-00479-4

Faqih, K. M. S. (2022). Internet shopping in the Covid-19 era: Investigating the role of perceived risk, anxiety, gender, culture, and trust in the consumers’ purchasing behavior from a developing country context. Technology in Society, 101992. https://doi.org/10.1016/j.techsoc.2022.101992

Fisher, R. A. (1935). The design of experiments. Oliver & Boyd.

Fordellone, M., & Vichi, M. (2020). Finding groups in structural equation modeling through the partial least squares algorithm. Computational Statistics & Data Analysis, 147, 106957. https://doi.org/10.1016/j.csda.2020.106957

Frasquet, M., Mollá, A., & Ruiz, E. (2015). Identifying patterns in channel usage across the search, purchase and post-sales stages of shopping. Electronic Commerce Research and Applications, 14(6), 654–665. https://doi.org/10.1016/j.elerap.2015.10.002

French, J. (2016). The importance of segmentation in social marketing strategy. En Segmentation in Social Marketing: Process, Methods and Application (pp. 25–40). Springer Science+Business Media Singapore. https://doi.org/10.1007/978-981-10-1835-0_3

Garcia, P., Jacquinot, P., Lenarčič, Č., Lozej, M., & Mavromatis, K. (2021). Global models for a global pandemic: The impact of COVID-19 on small euro area economies (SSRN Scholarly Paper Núm. 3940805). https://doi.org/10.2139/ssrn.3940805

García Pérez de Lema, D., Calvo Flores, A., Hansen, P., Leiva Bonilla, J. C., & Somohano Rodríguez, F. M. (2021). Impacto económico de la crisis COVID-19 sobre la mipyme en Iberoamérica (pp. 1–68). http://faedpyme.upct.es/sites/default/files/article/157/informecompleto.pdf

Garín-Muñoz, T., López, R., Pérez-Amaral, T., Herguera, I., & Valarezo, A. (2019). Models for individual adoption of eCommerce, eBanking and eGovernment in Spain. Telecommunications Policy, 43(1), 100–111. https://doi.org/10.1016/j.telpol.2018.01.002

Gefen, D. (2000). E-commerce: The role of familiarity and trust. Omega, 28(6), 725–737. https://doi.org/10.1016/S0305-0483(00)00021-9

Guthrie, C., Fosso-Wamba, S., & Arnaud, J. B. (2021). Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. Journal of Retailing and Consumer Services, 61, 102570. https://doi.org/10.1016/j.jretconser.2021.102570

He, Z., Jiang, Y., Chakraborti, R., & Berry, T. D. (2022). The impact of national culture on COVID-19 pandemic outcomes. International Journal of Social Economics, 49(3), 313–335. Scopus. https://doi.org/10.1108/IJSE-07-2021-0424

Hofstede, G. (2011). Dimensionalizing Cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1). https://doi.org/10.9707/2307-0919.1014

Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193–218. https://doi.org/10.1007/BF01908075

Jílková, P., & Králová, P. (2021). Digital consumer behaviour and eCommerce trends during the COVID-19 crisis. International Advances in Economic Research, 27(1), 83–85. https://doi.org/10.1007/s11294-021-09817-4

John Hopkins University. (2022). Coronavirus Resource Center. En Coronavirus COVID-19 Global Cases by the Center for System Science and Engineering (CSSE). https://coronavirus.jhu.edu/map.html

Kaminskiy, K. (2014). Internationalisation of E-commerce: Peculiarities of the Russian market. Comparative study of online shopping preferences among Russian , Turkish and Korean populations . (pp. 1–64). https://www.researchgate.net/publication/269167275_Internationalisation_of_E-commerce_Peculiarities_of_the_Russian_market_Comparative_study_of_online_shopping_preferences_among_Russian_Turkish_and_Korean_populations

Kao, K. C., Rao Hill, S., & Troshani, I. (2021). A cross-country comparison of online deal popularity effect. Journal of Retailing and Consumer Services, 60, 102402. https://doi.org/10.1016/j.jretconser.2020.102402

Kidane, T. T., & Sharma, R. R. K. (2016). Influence of culture on E-commerce and vice versa. 8-10 March 2016, 87–94. Scopus.

Kirk, C. P., & Rifkin, L. S. (2020). I’ll trade you diamonds for toilet paper: Consumer reacting, coping and adapting behaviors in the COVID-19 pandemic. Journal of Business Research, 117, 124–131. https://doi.org/10.1016/j.jbusres.2020.05.028

Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583–621. https://doi.org/10.2307/2280779

Kumar, V., Alshazly, H., Idris, S. A., & Bourouis, S. (2021). Evaluating the impact of COVID-19 on society, environment, economy, and education. Sustainability, 13(24), 13642. https://doi.org/10.3390/su132413642

Le, M. T. H. (2021). Examining factors that boost intention and loyalty to use Fintech post-COVID-19 lockdown as a new normal behavior. Heliyon, 7(8), e07821. https://doi.org/10.1016/j.heliyon.2021.e07821

Lebart, L., Piron, M., & Morineau, A. (2000). Statistique exploratoire multidimensionnelle.

Lee, N. R. (2016). How and why segmentation improves ROI. En T. Dietrich, S. Rundle-Thiele, & K. Kubacki (Eds.), Segmentation in Social Marketing: Process, Methods and Application (pp. 61–74). Springer Science+Business Media Singapore. https://doi.org/10.1007/978-981-10-1835-0_5

Levene, H. (1960). Robust tests for equality of variances. En I. Olkin, Contributions to probability and statistics: Essays in honor of Harold Hotelling (pp. 278–292). Stanford University Press.

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Undefined. https://www.semanticscholar.org/paper/Some-methods-for-classification-and-analysis-of-MacQueen/ac8ab51a86f1a9ae74dd0e4576d1a019f5e654ed

Magidson, J., & Vermunt, J. K. (2002). Latent class models for clustering: A comparison with K-means. 20, 9.

Mahase, E. (2020). China coronavirus: WHO declares international emergency as death toll exceeds 200. BMJ, 368, m408. https://doi.org/10.1136/bmj.m408

Malhotra, N. K., Ulgado, F. M., Agarwal, J., Shainesh, G., & Wu, L. (2005). Dimensions of service quality in developed and developing economies: Multi‐country cross‐cultural comparisons. International Marketing Review, 22(3), 256–278. https://doi.org/10.1108/02651330510602204

Mehta, S., Saxena, T., & Purohit, N. (2020). The new consumer behaviour paradigm amid COVID-19: Permanent or transient? Journal of Health Management, 22(2), 291–301. https://doi.org/10.1177/0972063420940834

Merhi, M. (2021). Multi-country analysis of e-commerce adoption: The impact of national culture and economic development. Pacific Asia Journal of the Association for Information Systems, 13(3), 86–108. https://doi.org/10.17705/1pais.13304

Mohammed, Z. A., & Tejay, G. P. (2017). Examining privacy concerns and ecommerce adoption in developing countries: The impact of culture in shaping individuals’ perceptions toward technology. Computers and Security, 67, 254–265. Scopus. https://doi.org/10.1016/j.cose.2017.03.001

Morisada, M., Miwa, Y., & Dahana, W. D. (2019). Identifying valuable customer segments in online fashion markets: An implication for customer tier programs. Electronic Commerce Research and Applications, 33, 100822. https://doi.org/10.1016/j.elerap.2018.100822

Organisation for Economic Co-operation and Development (OECD). (2020). E-commerce in the times of COVID-19. Unpacking E-commerce, October, 1–10.

Park, J., Han, H., & Park, J. (2013). Psychological antecedents and risk on attitudes toward e-customization. Journal of Business Research, 66(12), 2552–2559. https://doi.org/10.1016/j.jbusres.2013.05.048

Peng, Q., Meng, W., He, H., & Yu, C. (2004). WISE-Cluster: Clustering e-commerce search engines automatically. Proceedings of the Interntational Workshop on Web Information and Data Management, 111. https://doi.org/10.1145/1031453.1031473

R Core Team. (2019). R: A language and environment for statistical computing (3.6.1). R Foundation for Statistical Computing. https://www.R-project.org/

Ramadan, Z., & Nsouli, N. Z. (2022). Luxury fashion start-up brands’ digital strategies with female Gen Y in the Middle East. Journal of Fashion Marketing and Management, 26(2), 247–265. https://doi.org/10.1108/JFMM-10-2020-0222

Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7

Sari, J. N., Nugroho, L. E., Ferdiana, R., & Santosa, P. I. (2016, octubre). Review on customer segmentation technique on ecommerce [Text]. American Scientific Publishers. https://doi.org/10.1166/asl.2016.7985

Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591–611. https://doi.org/10.2307/2333709

Sharma, A., Fadahunsi, A., Abbas, H., & Pathak, V. K. (2022). A multi-analytic approach to predict social media marketing influence on consumer purchase intention. Journal of Indian Business Research, 14(2), 125–149. https://doi.org/10.1108/JIBR-08-2021-0313

Sheth, J. (2020). Impact of Covid-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117, 280–283. https://doi.org/10.1016/j.jbusres.2020.05.059

Shi, C., Wei, B., Wei, S., Wang, W., Liu, H., & Liu, J. (2021). A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. EURASIP Journal on Wireless Communications and Networking, 2021(1), 31. https://doi.org/10.1186/s13638-021-01910-w

Starczewski, A., & Krzyżak, A. (2015). Performance evaluation of the silhouette index. En L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, & J. M. Zurada (Eds.), Artificial Intelligence and Soft Computing (pp. 49–58). Springer International Publishing. https://doi.org/10.1007/978-3-319-19369-4_5

Sturgeon, T., Van Biesebroeck, J., & Gereffi, G. (2008). Value chains, networks and clusters: Reframing the global automotive industry. Journal of Economic Geography, 8(3), 297–321. https://doi.org/10.1093/jeg/lbn007

Sugar, C. A., & James, G. M. (2003). Finding the number of clusters in a dataset. Journal of the American Statistical Association, 98(463), 750–763. https://doi.org/10.1198/016214503000000666

Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., & Satoto, B. D. (2018). Integration K-Means clustering method and elbow method for identification of the best customer profile cluster. IOP Conference Series: Materials Science and Engineering, 336, 012017. https://doi.org/10.1088/1757-899X/336/1/012017

Thorndike, R. L. (1953). Who belongs in the family? Psychometrika, 18(4), 267–276. https://doi.org/10.1007/BF02289263

Tolman, E. C. (1932). Purposive behavior in animals and men (pp. xiv, 463). Century/Random House UK.

Verschuur, J., Koks, E. E., & Hall, J. W. (2021). Global economic impacts of COVID-19 lockdown measures stand out in high-frequency shipping data. PLOS ONE, 16(4), e0248818. https://doi.org/10.1371/journal.pone.0248818

Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845

Weber, E. U., & Hsee, C. (1998). Cross-cultural differences in risk perception, but cross-cultural similarities in attitudes towards perceived risk. Management Science, 44(9), 1205–1217. https://doi.org/10.1287/mnsc.44.9.1205

World Health Organization (WHO). (2020). Health inequity and the effects of COVID 19: Assessing, responding to and mitigating the socioeconomic impact on health to buid a better future. https://apps.who.int/iris/bitstream/handle/10665/338199/WHO-EURO-2020-1744-41495-56594-eng.pdf?sequence=1&isAllowed=y

Wu, R.-S., & Chou, P.-H. (2011). Customer segmentation of multiple category data in e-commerce using a soft-clustering approach. Electronic Commerce Research and Applications, 10(3), 331–341. https://doi.org/10.1016/j.elerap.2010.11.002

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Publicado

18.12.2023

Como Citar

Barrantes-Aguilar, L. E., Solís-Rivera, L. R., & Villalobos, A. (2023). Global consumers before and during the COVID-19 pandemic: What aspects characterize digital consumer behavior? . ReMark - Revista Brasileira De Marketing, 22(4), 1614–1644. https://doi.org/10.5585/remark.v22i4.23468

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Special Issue: Applications of neurosciences to the marketing field