Palavras-chave
Efetividade da inteligência competitiva
Efetividade da tomada de decisão
Prontidão tecnológica
PLS-SEM
Visão Baseada em Recursos
Teoria das Capacidades Dinâmicas
Como Citar
Resumo
Objetivo: Este estudo examina o efeito das capacidades de análise linguística na efetividade da inteligência competitiva, avaliando também o papel mediador da efetividade da tomada de decisão e o papel moderador da prontidão tecnológica.
Metodologia/abordagem: O estudo utilizou um desenho de pesquisa explicativo de corte transversal, baseado em respostas de 312 profissionais que atuam em inteligência competitiva, analytics, ciência de dados e funções relacionadas à estratégia. As hipóteses foram testadas por meio de modelagem de equações estruturais com mínimos quadrados parciais (PLS-SEM), utilizando bootstrapping.
Originalidade/Relevância: O estudo conceitualiza a análise linguística como uma capacidade organizacional, e não apenas como uma ferramenta técnica. Ao integrar a Visão Baseada em Recursos (Resource-Based View) e a Teoria das Capacidades Dinâmicas (Dynamic Capability Theory), explica como análises orientadas a texto podem gerar valor estratégico nos processos de inteligência competitiva.
Principais resultados: Os resultados mostram que as capacidades de análise linguística afetam positivamente a efetividade da inteligência competitiva (β = 0.413, p < 0.001) e a efetividade da tomada de decisão (β = 0.524, p < 0.001). A efetividade da tomada de decisão também afeta positivamente a efetividade da inteligência competitiva (β = 0.318, p < 0.001) e medeia parcialmente a relação principal (β indireto = 0.167; VAF = 28,8%). Além disso, a prontidão tecnológica fortalece a relação entre capacidades de análise linguística e efetividade da inteligência competitiva (β = 0.219, p < 0.001). O modelo estrutural explicou 48,7% da variância na efetividade da inteligência competitiva.
Contribuições teóricas/metodológicas: Este estudo contribui para a literatura de inteligência competitiva ao conectar as capacidades de análise linguística com a efetividade da inteligência por meio de caminhos diretos e indiretos. Também identifica a prontidão tecnológica como uma importante condição de contorno, ampliando as explicações baseadas em capacidades sobre como as organizações criam valor a partir de análises orientadas a texto em ambientes de inteligência e suporte à decisão.
Referências
Alafi, K. K., Ismaeel, B., Almarshad, M. N., Al-Habash, M. A., & Al-Aqrabawi, R. (2024). Analysis of competitive intelligence in retail management in the Jordanian market from the consumer’s perspective. Journal of Intelligence Studies in Business, 13(3), 24–38.
Alzghoul, A., Khaddam, A. A., Abousweilem, F., Irtaimeh, H. J., & Alshaar, Q. (2024). How business intelligence capability impacts decision-making speed, comprehensiveness, and firm performance. Information Development, 40(2), 220–233. https://doi.org/10.1177/02666669221108438
Arslan, M., Riaz, Z., & Cruz, C. (2023). Revolutionizing management information systems with natural language processing for digital transformation. Procedia Computer Science, 225, 2835–2844. https://doi.org/10.1016/j.procs.2023.10.276
Chatterjee, S., Chaudhuri, R., & Mikalef, P. (2022). Examining the dimensions of adopting natural language processing and big data analytics applications in firms. IEEE Transactions on Engineering Management, 71, 3001–3015. https://doi.org/10.1109/TEM.2022.3202871
Chen, D., Esperança, J. P., & Wang, S. (2022). The impact of artificial intelligence on firm performance: An application of the resource-based view to e-commerce firms. Frontiers in Psychology, 13, Article 884830. https://doi.org/10.3389/fpsyg.2022.884830
Elkaabi, K., Mamouny, A., & Elmaallam, M. (2025). The impact of artificial intelligence tools on the competitive intelligence process: A systematic literature review. In Proceedings of the 2025 International Conference on Circuit, Systems and Communication (ICCSC) (pp. 1–7). IEEE. https://doi.org/10.1109/ICCSC66714.2025.11135245
Elrehail, H., Aljahmani, R., Taamneh, A. M., Alsaad, A. K., Al-Okaily, M., & Emeagwali, O. L. (2024). The role of employees’ cognitive capabilities, knowledge creation and decision-making style in predicting firm performance. EuroMed Journal of Business, 19(4), 943–972.
Gao, J., Ren, L., Yang, Y., Zhang, D., & Li, L. (2022). The impact of artificial intelligence technology stimuli on smart customer experience and the moderating effect of technology readiness. International Journal of Emerging Markets, 17(4), 1123–1142. https://doi.org/10.1108/IJOEM-06-2021-0975
Helfat, C. E., Kaul, A., Ketchen, D. J., Jr., Barney, J. B., Chatain, O., & Singh, H. (2023). Renewing the resource-based view: New contexts, new concepts, and new methods. Strategic Management Journal, 44(6), 1357–1390. https://doi.org/10.1002/smj.3500
Hossain, M. R., Mahabub, S., Al Masum, A., & Jahan, I. (2024). Natural language processing in analyzing electronic health records for better decision making. Journal of Computer Science and Technology Studies, 6(5), 216–228. https://doi.org/10.32996/jcsts.2024.6.5.18
Huy, P. Q., & Phuc, V. K. (2025). Unveiling how business process management capabilities foster dynamic decision-making for sustainable digital transformation. Business Process Management Journal, 31(8), 67–103. https://doi.org/10.1108/BPMJ-06-2024-0467
Ibrahim, A. A., Ahmad, S. Z., & Abu Bakar, A. R. (2025). Impact of competitive intelligence on firm sustainable competitiveness and performance: The mediating role of strategic design collaboration. Management Research Review, 48(2), 231–257. https://doi.org/10.1108/MRR-04-2024-0280
Jamil, K., Zhang, W., Anwar, A., & Mustafa, S. (2025). Exploring the influence of AI adoption and technological readiness on sustainable performance in Pakistani SMEs. Sustainability, 17(8), Article 3599. https://doi.org/10.3390/su17083599
Kalyampudi, P. L. (2025). Natural language processing for business intelligence and market analysis. In Artificial intelligence and machine learning in management science: Emerging research and applications (p. 97).
Katebi, P., Ehdaie, S., & Jalilian, H. (2022). Analysis of the effect of competitive intelligence on strategic decision making in SMEs. International Journal of Management, Accounting & Economics, 9(6). https://doi.org/10.5281/zenodo.6977619
Khaddam, A. A., Alzghoul, A., Abusweilem, M. A., & Abousweilem, F. (2023). Business intelligence and firm performance: A moderated-mediated model. The Service Industries Journal, 43(13–14), 923–939. https://doi.org/10.1080/02642069.2021.1969367
Leoni, L., Ardolino, M., El Baz, J., Gueli, G., & Bacchetti, A. (2022). The mediating role of knowledge management processes in AI use in manufacturing firms. International Journal of Operations & Production Management, 42(13), 411–437. https://doi.org/10.1108/IJOPM-05-2022-0282
Madureira, L., Popovič, A., & Castelli, M. (2023). Competitive intelligence empirical validation and application. Journal of Information Science, 51(1), 164–183. https://doi.org/10.1177/01655515231191221
Malcalm, E., Asiedu, E., Majeed, M., & Sakara, A. (2025). Natural language processing on firm performance: The role of employee performance. In International Conference on Computational Complexity and Intelligent Algorithms (pp. 117–134). Springer.
Neiroukh, S., Emeagwali, O. L., & Aljuhmani, H. Y. (2025). AI capability and organizational performance: Mediating role of decision-making. Management Decision, 63(10), 3501–3532. https://doi.org/10.1108/MD-10-2023-1946
Olujimi, P. A., & Ade-Ibijola, A. (2023). NLP techniques for automating responses to customer queries. Discover Artificial Intelligence, 3(1), Article 20. https://doi.org/10.1007/s44163-023-00065-5
Punukollu, M. (2023). NLP applications in pharmaceutical text mining for competitive intelligence. Essex Journal of AI Ethics and Responsible Innovation, 3, 594–635.
Sawant, P., & Sonawane, K. (2024). NLP-based smart decision making for business and academics. Natural Language Processing Journal, 8, Article 100090. https://doi.org/10.1016/j.nlp.2024.100090
Shukla, S., Singh, J., Nassa, V. K., Saba, M., Bhatia, J., & Elangovan, M. (2024). AI-driven deep learning for competitive intelligence. In 2024 Asian Conference on Innovation in Technology (ASIANCON) (pp. 1–5). IEEE.
Silva, D., & Bacao, F. (2022). Enhancing competitive intelligence acquisition through embeddings and visual analytics. In EPIA Conference on Artificial Intelligence (pp. 599–610). Springer.
Somaya, J., Hasna, E. E., Abdelwahed, H. E., Laure, P., & Mariem, M. (2024). Automating information extraction using NLP. In Proceedings of the 4th International Conference on Advances in Communication Technology and Computer Engineering (ICACTCE’24) (pp. 507–518). Springer. https://doi.org/10.1007/978-3-031-94623-3_44
Taherdoost, H., & Madanchian, M. (2023). Artificial intelligence and sentiment analysis: A review. Computers, 12(2), Article 37. https://doi.org/10.3390/computers12020037
Tan, C. N. L., Liu, D., Dou, J., & Chung, H. F. (2025). AI capabilities and sustainable competitiveness in logistics. Journal of Enterprise Information Management. Advance online publication. https://doi.org/10.1108/JEIM-06-2025-0494
Tsiu, S. V., Ngobeni, M., Mathabela, L., & Thango, B. (2025). Data mining and BI in SMEs performance. Businesses, 5(2), Article 22. https://doi.org/10.3390/businesses5020022
Tyagi, N., & Bhushan, B. (2023). NLP in smart city applications. Wireless Personal Communications, 130(2), 857–908. https://doi.org/10.1007/s11277-023-10312-8
Vysotska, V. (2024). Modern state and prospects of information technologies development for natural language content processing. COLINS (2), 198–234.
Wang, B., Ma, K., Wu, L., Qiu, Q., Xie, Z., & Tao, L. (2022). Visual analytics for geological content extraction. Ore Geology Reviews, 144, Article 104818. https://doi.org/10.1016/j.oregeorev.2022.104818
Wang, W., & Liu, C. (2023). Dynamic capability theory and intelligent manufacturing performance. Electronics, 12(6), Article 1374. https://doi.org/10.3390/electronics12061374
Yu, Q., Xing, C., He, Y., Ahn, S., & Na, H. J. (2026). Hybrid NLP and deep learning for performance prediction. Electronics, 15(2), Article 443. https://doi.org/10.3390/electronics15020443
Zhang, H., Zhang, C., & Wang, Y. (2024). Technology development of NLP. Information Processing & Management, 61(1), Article 103574. https://doi.org/10.1016/j.ipm.2023.103574
Zhang, Z., Qin, W., & Xu, H. (2024). Webpage information extraction for competitive intelligence. Scalable Computing: Practice and Experience, 25(5), 4138–4152. https://doi.org/10.12694/scpe.v25i5.3078
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