Palavras-chave
Ciclo de inteligência
Governança da inteligência
Capacidades Dinâmicas
Inteligência de decisão
Decisão de adoção de tecnologia
PLS-SEM
Inteligência artificial no setor público
Como Citar
Resumo
Objetivo: Este estudo operacionaliza a inteligência competitiva (IC) como uma capacidade organizacional mensurável para decisões estratégicas de adoção de tecnologia. Utiliza o ciclo de inteligência para estruturar como uma organização do setor público coleta, analisa, governa e age sobre sinais relativos a uma nova tecnologia, neste caso a inteligência artificial (IA), e testa empiricamente esse modelo de capacidade.
Metodologia/Abordagem: Um modelo de equações estruturais por mínimos quadrados parciais (PLS-SEM) reflexivo é estimado a partir de uma pesquisa primária aberta com 203 funcionários públicos de nível médio e sênior nos seis países do Conselho de Cooperação do Golfo (CCG). A análise reporta análise fatorial exploratória, testes KMO e de Bartlett, confiabilidade e validade convergente, validade discriminante pelos critérios de Fornell-Larcker e HTMT com inferência por bootstrap, diagnósticos de colinearidade, estimação de caminhos por bootstrap de 5.000 reamostragens, mediação, tamanhos de efeito e relevância preditiva validada por validação cruzada.
Originalidade/Relevância: O estudo desloca a IC de uma lente interpretativa para um modelo de capacidade validado empiricamente em um contexto organizacional real e não educacional, conectando o ciclo de inteligência, a governança da inteligência, as capacidades dinâmicas e a inteligência de decisão a um resultado estratégico de adoção de tecnologia.
Principais resultados: As dimensões da capacidade explicam 70 por cento da variância nos resultados estratégicos de IA, com forte relevância preditiva (Q-quadrado = 0,68). A infraestrutura técnica, a base de coleta, é o principal determinante (beta = 0,66) e a governança contribui de forma significativa (beta = 0,21), enquanto a prontidão organizacional é uma condição construída pela infraestrutura e pela governança, e não um determinante independente. Todos os construtos apresentam alta confiabilidade (alfa de 0,92 a 0,96) e validade convergente (AVE de 0,87 a 0,94), e a validade discriminante é sustentada pelo critério de inferência HTMT.
Contribuições teóricas/metodológicas: O artigo contribui com uma operacionalização transferível do ciclo de inteligência como um modelo estrutural validado, uma leitura centrada na governança das decisões de adoção de tecnologia e um modelo de mensuração de IC reprodutível aplicável a diferentes contextos organizacionais.
Referências
Ackermann, A., Henke, J., Chevalère, S., et al. (2025). The effects of adaptive learning technologies on student performance. npj Science of Learning, 10, 12. https://doi.org/10.1038/s41539-024-00293-z
Ali, M. R., Fatemi, S. H., Boskabadi, A., Nikfar, M., Ugwuoke, J., & Ali, H. (2024). Generative AI in engineering and STEM education: A review of opportunities and challenges. Education Sciences, 14(6), 643. https://doi.org/10.3390/educsci14060643
AlShloul, S., Mazhar, T., Abbas, Q., et al. (2024). Artificial intelligence-enabled adaptive learning systems: A review. Computers and Education: Artificial Intelligence, 7, 100219. https://doi.org/10.1016/j.caeai.2024.100219
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
Biloš, S., & Budimir, B. (2024). Understanding generative AI adoption among students: An extended technology acceptance model. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 863-880. https://doi.org/10.3390/jtaer19020045
Calof, J. L., & Wright, S. (2008). Competitive intelligence: A practitioner, academic and inter-disciplinary perspective. European Journal of Marketing, 42(7/8), 717-730. https://doi.org/10.1108/03090560810877114
Celik, I. (2023). Exploring the determinants of artificial intelligence (AI) literacy: Digital divide, computational thinking, cognitive absorption. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228-239. https://doi.org/10.1080/14703297.2023.2190148
Dake, D. K., Bada, G. K., & Dadzie, A. E. (2023). Internet of Things (IoT) applications in education: Benefits and implementation challenges in tertiary institutions. Journal of Information Technology Education: Research, 22, 311-338. https://doi.org/10.28945/5183
Dervenis, K., Kyriatzis, P., Stoufis, S., & Fitsilis, P. (2022). Assessing the readiness of educational institutions for AI adoption. In Proceedings of the 6th International Conference on Algorithms, Computing and Artificial Intelligence (pp. 1-6). https://doi.org/10.1145/3564982.3564990
Dishman, P. L., & Calof, J. L. (2008). Competitive intelligence: A multiphasic precedent to marketing strategy. European Journal of Marketing, 42(7/8), 766-785. https://doi.org/10.1108/03090560810877141
Duong, C. T. (2024). Factors influencing the continuance intention to use ChatGPT among university students. Journal of Research in Innovative Teaching & Learning. https://doi.org/10.1108/JRIT-01-2024-0006
Gandolfi, E. (2025). Generative AI as an adaptive tutoring layer in intelligent learning environments. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00403-3
Gao, M., Cheah, J.-H., Lim, X.-J., & Luo, X. (2024). Determinants of generative AI adoption and continued use among university students. International Journal of Management Education, 22(3), 100958. https://doi.org/10.1016/j.ijme.2024.100958
Glickman, M., & Sharot, T. (2025). How human-AI feedback loops alter human perceptual and decision-making behavior. Nature Human Behaviour, 9, 1-12. https://doi.org/10.1038/s41562-024-02077-2
Hair, J. F., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Kassab, M., DeFranco, J., & Laplante, P. (2020). A systematic literature review on Internet of Things in education: Benefits and challenges. Journal of Computer Assisted Learning, 36(2), 115-127. https://doi.org/10.1111/jcal.12383
Kavitha, M., & Joshith, V. P. (2025). Generative AI adoption in higher education: An expectation-confirmation perspective. Journal of Educational Technology Systems. https://doi.org/10.1177/00472395251380294
Kayalı, E., Yavuz, M., Balat, Ş., & Çalışan, M. (2023). Investigating students' use of ChatGPT through the technology acceptance model. Australasian Journal of Educational Technology, 39(5), 1-18. https://doi.org/10.14742/ajet.8915
Koch, K., Carolus, A., Wienrich, C., & Latoschik, M. E. (2024). The influence of AI literacy on perceived usefulness and trust in generative AI. Heliyon, 10(20), e39686. https://doi.org/10.1016/j.heliyon.2024.e39686
Liengaard, B. D., et al. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in PLS-SEM. Decision Sciences, 52(2), 362-392. https://doi.org/10.1111/deci.12445
Lintner, M. (2024). A systematic review of AI literacy scales. npj Science of Learning, 9, 50. https://doi.org/10.1038/s41539-024-00264-4
Ma, X., & Chen, Y. (2024). Exploring the relationship between generative AI use and student engagement using structural equation modeling. IEEE Access, 12, 1-14. https://doi.org/10.1109/ACCESS.2024.3468378
Mansoor, A., et al. (2024). Students' perceptions and adoption of generative AI tools in higher education. Frontiers in Communication, 9, 1478476. https://doi.org/10.3389/fcomm.2024.1478476
Mohiuddin, G., et al. (2023). Edge-enabled mobile learning for the Internet of Things in smart education. SAGE Open, 13(3). https://doi.org/10.1177/21582440231190337
Naqvi, R. A., Ganjoo, R., Rowe, N., Pashine, S., & Mishra, S. (2025). Generative AI in education: Adoption, perceptions, and outcomes. Frontiers in Artificial Intelligence, 8, 1571527. https://doi.org/10.3389/frai.2025.1571527
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Ng, D. T. K., et al. (2024). Reviewing the impact of generative AI on student learning outcomes in higher education. British Journal of Educational Technology, 55(5), 1-22. https://doi.org/10.1111/bjet.13411
Pellissier, R., & Nenzhelele, T. E. (2013). Towards a universal competitive intelligence process model. South African Journal of Information Management, 15(2), a567. https://doi.org/10.4102/sajim.v15i2.567
Rohrbeck, R., & Kum, M. E. (2018). Corporate foresight and its impact on firm performance: A longitudinal analysis. Technological Forecasting and Social Change, 129, 105-116. https://doi.org/10.1016/j.techfore.2017.12.013
Sandu, C., Gide, A., & Elkhodr, M. (2024). The role of the Internet of Things and artificial intelligence in education: Opportunities and challenges. Discover Education, 3, 126. https://doi.org/10.1007/s44217-024-00126-6
Shahzad, M. F., Xu, S., An, X., & Asif, M. (2025). Effects of ChatGPT usage on students' academic performance and continuance: A structural model. SAGE Open, 15(1). https://doi.org/10.1177/21582440251368594
Sharma, P. N., Liengaard, B. D., Hair, J. F., Sarstedt, M., & Ringle, C. M. (2022). Predictive model assessment and selection in composite-based modeling using PLS-SEM. European Journal of Marketing, 56(6), 1662-1677. https://doi.org/10.1108/EJM-08-2020-0636
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Wang, X., & Yu, Z. (2025). A meta-analysis of the effects of ChatGPT on students' learning. PLOS ONE, 20(2), e0299018. https://doi.org/10.1371/journal.pone.0299018
Wang, Y., Li, T., Cheung, A. C. K., & Wong, G. K. W. (2025). The effects of generative AI on academic writing and learning autonomy. International Journal of Educational Technology in Higher Education, 22, 9. https://doi.org/10.1186/s41239-025-00547-9
Xu, W., & Ouyang, F. (2022). The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal of STEM Education, 9(1), 59. https://doi.org/10.1186/s40594-022-00377-5
Yin, M., Goh, E., & Hu, X. (2024). Learning analytics for self-regulated learning in online environments. International Journal of Educational Technology in Higher Education, 21, 60. https://doi.org/10.1186/s41239-024-00480-3
Zapata-Rivera, J., Torre, I., Lee, J., Sarasa-Cabezuelo, A., Ghergulescu, I., & Libbrecht, P. (2024). Adaptive learning analytics and intelligent feedback systems. Frontiers in Artificial Intelligence, 7, 1532896. https://doi.org/10.3389/frai.2024.1532896
Zhang, L., et al. (2024). Integrating sensors and artificial intelligence in smart classrooms: A review of engagement-aware learning environments. Sensors, 24(17), 5487. https://doi.org/10.3390/s24175487