Competitive Intelligence Capability for Technology-Adoption Decisions: An Intelligence-Cycle Framework for Public-Sector AI Adoption

Yajie Xing; Saltanat Biibosunova; Fang Liu; Xiaohang Zheng

doi:10.37497/eagleSustainable.v16i.491

Autores

Yajie Xing Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan https://orcid.org/0009-0003-1402-6033
Saltanat Biibosunova Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan https://orcid.org/0000-0003-3258-8972
Fang Liu Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan https://orcid.org/0009-0002-3591-6257
Xiaohang Zheng Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan https://orcid.org/0009-0003-5198-4409

DOI:

https://doi.org/10.37497/eagleSustainable.v16i.491

Palavras-chave:

Inteligência competitiva, Ciclo de inteligência, Governança da inteligência, Capacidades Dinâmicas, Inteligência de decisão, Decisão de adoção de tecnologia, PLS-SEM, Inteligência artificial no setor público

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.

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Biografia do Autor

Yajie Xing, Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan

Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan.

Saltanat Biibosunova, Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan

Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan.

Fang Liu, Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan

Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan.

Xiaohang Zheng, Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan

Kyrgyz State University named after I. Arabaev, Bishkek, Kyrgyzstan.

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