Traditional Research versus Advanced Algorithms: “Is Survey in the Last Days?



Main Article Content

Marcelo Luiz Dias da Silva Gabriel
https://orcid.org/0000-0001-8861-0783 orcid
Jose Afonso Mazzon
https://orcid.org/0000-0003-1556-520X orcid
Giuliana Isabella
https://orcid.org/0000-0002-4502-4327 orcid
Ricardo Limongi França Coelho
https://orcid.org/0000-0003-3231-7515 orcid
Evandro Luiz Lopes
https://orcid.org/0000-0002-2780-4215 orcid
Vinicius Andrade Brei
https://orcid.org/0000-0002-0502-4533 orcid

Abstract

Objective: to examine the contemporary challenges faced by the survey method in the administration field, particularly in marketing, due to the emergence of new technologies and changes in respondent behavior. Provocations: with the rise of artificial intelligence, the traditional survey method is increasingly being questioned. Issues such as response validity, respondent fatigue, and proliferation of behavioral data obtained through automated means cast doubt on the survey’s effectiveness in capturing actual consumer behavior. Additionally, new legislation may introduce restrictions that could impact data collection via surveys. Conclusions: although not obsolete, the survey method must reinvent itself to remain relevant. Integrating new technologies, such as artificial intelligence, and combining them with qualitative methods are suggested paths to improve research effectiveness in an environment heavily influenced by technological advancements. The future of the survey depends on its ability to adapt and complement other emerging approaches.



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How to Cite
Gabriel, M. L. D. da S., Mazzon, J. A., Isabella, G., Coelho, R. L. F., Lopes, E. L., & Brei, V. A. (2024). Traditional Research versus Advanced Algorithms: “Is Survey in the Last Days?. Journal of Contemporary Administration, 28(4), e240246. https://doi.org/10.1590/1982-7849rac2024240246.en
Section
Provocations

References

Evans, J. R., & Mathur, A. (2018). The value of online surveys: A look back and a look ahead. Internet Research, 28(5), 854- 887. https://doi.org/10.1108/IntR-03-2018-0089
Faria, F. P. (2024, janeiro 9). O aumento da taxa de não resposta: As pesquisas diante de seu maior desafio? – Parte I. Blog do IBRE. https://blogdoibre.fgv.br/posts/o-aumento-da-taxade-nao-resposta-pesquisas-diante-de-seu-maior-desafioparte-i
Franzke, A., Bechmann, A., Zimmer, M., & Ess, C. M. (2020). Internet Research: Ethical Guidelines 3.0. The Association of Internet Researchers. https://aoir.org/reports/ethics3.pdf
Fricker, R. D., & Schonlau, M. (2002). Advantages and disadvantages of internet research surveys: Evidence from the literature. Field Methods, 14(4), 347-367. https://doi.org/10.1177/152582202237725
Goffin, K., Lemke, F., & Koners, U. (2010). Surveys and interviews. In K. Goffin, F. Lemke, & U. Koners (Eds.), Identifying hidden needs (pp. 27-51). Palgrave Macmillan. https://doi.org/10.1057/9780230294486_2
Groves, R. M., & Harris-Kojetin, B. A. (2017). Using multiple data sources and state-of-the-art estimation methods in federal statistics: Frameworks, methods, and assessment. The National Academies Press. https://www.nationalacademies.org/our-work/using-multiple-data-sources-and-state-of-theart-estimation-methods-in-federal-statistics-frameworksmethods-and-assessment
Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., & Suman, R. (2022). Artificial intelligence (AI) applications for marketing: A literature-based study. International Journal of Intelligent Networks, 3(1), 119-132. https://doi.org/10.1016/j.ijin.2022.08.005
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50. https://doi.org/10.1007/s11747-020-00749-9
Hulland, J., Baumgartner, H., & Smith, K. M. (2018). Marketing survey research best practices: Evidence and recommendations from a review of JAMS articles. Journal of the Academy of Marketing Science, 46(1), 92-108. https://doi.org/10.1007/s11747-017-0532-y
Jenkins, K. (2023). Synthetic data and public policy: Supporting realworld policymakers with algorithmically generated data. Policy Quarterly, 19(2). https://doi.org/10.26686/pq.v19i2.8234
Ikegwu, A., Nweke, H. F., Anikwe, C. V., Alo, U. R., & Okonkwo, O. R. (2022). Big data analytics for data-driven industry: A review of data sources, tools, challenges, solutions, and research directions. Cluster Computing, 25(2), 3343-3387. https://doi.org/10.1007/s10586-022-03568-5
Kamakura, W. A., & Wedel, M. (1996). Statistical data-fusion for cross-tabulation. Working Paper. University of Groningen.
Kordzadeh, N., & Ghasemaghaei, M. (2021). Algorithmic bias: Review, synthesis, and future research directions. European Journal of Information Systems, 31(3), 388-409, https://doi.org/10.1080/0960085X.2021.1927212
Lei nº 14.874 (2024). Lei nº 14.874, de 28 de maio de 2024, que dispõe sobre a pesquisa com seres humanos e institui o Sistema Nacional de Ética em Pesquisa com Seres Humanos. Diário Oficial da União. https://www.planalto.gov.br/ccivil_03/_ato2023-2026/2024/lei/l14874.htm
Li, P., Castelo, N., Katona, Z., & Sarvary, M. (2024). Determining the validity of large language models for automated perceptual analysis. Marketing Science, 43(2), 239-468. https://doi.org/10.1287/mksc.2023.0454
Little, R. J. A. (1993). Statistical analysis of masked data. Journal of Official Statistics, 9, 407-426.
Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135-155. https://doi.org/10.1007/s11747-016-0495-4
Mazzon, J. A., & Hernandez, J. M. D. C. (2013). Brazilian scientific production in marketing in the period 2000- 2009. Revista de Administração de Empresas, 53(1), 67-80. https://doi.org/10.1590/S0034-75902013000100007
Meyer, B., Mok, W., & Sullivan, J. (2015). Household surveys in crisis. Journal of Economic Perspectives, 29(4), 199-226. https://doi.org/10.1257/jep.29.4.199
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixedmethod approach. Journal of Business Research, 98, 261-276. https://doi.org/10.1016/j.jbusres.2019.01.044
Molnar, C. (2018). Interpretable machine learning (2nd ed.). Leanpub. https://leanpub.next/interpretable-machine-learning
Nardi, P. M. (2018). Doing survey research: A guide to quantitative methods. Routledge. Rubin, D. B. (1993). Discussion: Statistical disclosure limitation. Journal of Official Statistics, 9, 462-468.
Ruiz-Real, J. L., Uribe-Toril, J., Torres, J. A., & De Pablo, J. (2021). Artificial intelligence in business and economics research: trends and future. Journal of Business Economics and Management, 22(1), 98-117. https://doi.org/10.3846/jbem.2020.13641
Sampaio, C. H., Perin, M. G., Luce, F. B., Santos, M. J. D., Santini, F. D. O., Oliveira, M. O. R. D., & Lenz, G. D. S. (2012). Pesquisa científica da área de marketing no Brasil: Uma revisão da primeira década do século 21. Revista de Administração Contemporânea, 16, 459-478. https://doi.org/10.1590/S1415-65552012000300008
Savage, N. (2023, April 23). Synthetic data could be better than real data. Nature. https://doi.org/10.1038/d41586-023-01445-8
Sturgis, P., & Luff, R. (2020). The demise of the survey? A research note on trends in the use of survey data in the social sciences, 1939 to 2015. International Journal of Social Research Methodology, 24, 691- 696. https://doi.org/10.1080/13645579.2020.1844896
Sudbury-Riley, L., & Kohlbacher, F. (2016). Ethically minded consumer behavior: Scale review, development, and validation. Journal of Business Research, 69(8), 2697-2710. https://doi.org/10.1016/j.jbusres.2015.11.005
Tourangeau, R., & Yan, T. (2007). Sensitive questions in surveys. Psychological Bulletin, 133(5), 859. http://dx.doi.org/10.1037/0033-2909.133.5.859
Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitativequantitative divide: Guidelines for conducting mixed methods research in information systems. MIS Quarterly, 37(1), 21-54. https://doi.org/10.25300/MISQ/2013/3