Leveraging Artificial Intelligence (AI) in Competitive Intelligence (CI) Research

Joseph F. Hair; Misty  Sabol

doi:10.24883/eagleSustainable.v15i.469

Autores

Joseph F. Hair University of South Alabama, Alabama https://orcid.org/0000-0002-9226-9693
Misty Sabol Mitchell College of Business, United States https://orcid.org/0000-0002-0259-918X

DOI:

https://doi.org/10.24883/eagleSustainable.v15i.469

Palavras-chave:

Inteligência Artificial (IA), Modelos de Linguagem de Grande Escala (LLMs), Pesquisa Acadêmica, Inteligência Competitiva (IC), Modelos GPT

Resumo

Objetivo: O rápido avanço da inteligência artificial (IA) tem influenciado significativamente a pesquisa e as práticas acadêmicas, levando universidades a elaborarem diretrizes para o uso de modelos de linguagem de grande escala (LLMs) por estudantes. No entanto, ainda há um debate em andamento entre periódicos e conferências acadêmicas sobre a necessidade de declarar o uso da IA na elaboração de manuscritos. Este artigo tem como objetivo explorar diferentes perspectivas sobre o uso de LLMs na pesquisa científica, especialmente no contexto da inteligência competitiva (IC), e oferecer diretrizes para pesquisadores da área sobre como utilizar eficazmente ferramentas de IA, como os modelos GPT.

Método: O estudo realiza uma revisão abrangente da literatura existente sobre a integração da IA na pesquisa acadêmica, com foco específico nas capacidades de modelos generativos, como o ChatGPT-4, o Scholar GPT e o Consensus GPT. Esses modelos, desenvolvidos pela OpenAI, são avaliados quanto à sua utilidade em diversas etapas do processo de pesquisa, incluindo a revisão de literatura, a análise qualitativa e a análise de dados. A análise destaca que a qualidade dos resultados gerados por IA depende diretamente da especificidade das instruções fornecidas pelo usuário.

Resultados: Embora os LLMs tenham demonstrado grande potencial para aprimorar revisões de literatura, pesquisas qualitativas e análises de dados, o estudo identifica que suas capacidades completas na pesquisa acadêmica ainda são pouco exploradas. A pesquisa evidencia tanto as preocupações com uma possível “contaminação” dos trabalhos científicos pelo uso da IA, quanto os benefícios que esses modelos oferecem, especialmente quando utilizados de forma estratégica.

Conclusões: O artigo apresenta um guia estruturado para pesquisadores da área de negócios, com ênfase especial em profissionais que atuam com inteligência competitiva, sobre como integrar eficazmente modelos de linguagem baseados em IA ao longo do processo de pesquisa. Os achados ressaltam a importância da especificidade dos comandos e oferecem recomendações práticas para o uso estratégico dos LLMs, visando aumentar a eficiência da pesquisa e a qualidade dos resultados.

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Biografia do Autor

Joseph F. Hair, University of South Alabama, Alabama

Dr. Joseph F. Hair is an American author, consultant, and professor. He currently serves as a Distinguished Professor of Marketing, holds the Cleverdon Chair of Business, and is the Director of the PhD program at the Mitchell College of Business, University of South Alabama. Previously, he held the positions of Senior Scholar for the DBA program at the Michael J. Coles College of Business at Kennesaw State University, and the Copeland Endowed Chair of Entrepreneurship at the Ourso College of Business Administration, Louisiana State University.

Dr. Hair has authored over 100 editions of his books, including Multivariate Data Analysis (8th edition, 2019), which has been cited over 201,000 times, Essentials of Business Research Methods (5th edition, 2023), A Primer on Partial Least Squares Structural Equation Modeling – PLS (3rd edition, 2022), Essentials of Marketing Research (6th edition, 2024), and MKTG (14th edition, 2024). He is renowned for his contributions to marketing research and multivariate data analysis.

From 2018 to 2024, Clarivate Analytics recognized Dr. Hair as part of the top 1% of all Business and Economics professors worldwide for his impactful research and contributions to the field.

Misty Sabol, Mitchell College of Business, United States

Dr. Misty Sabol is an experienced instructor specializing in Marketing, Supply Chain Management, and Analytics. Her research focuses on diverse areas such as statistics and methodologies, innovation, creativity, and ecosystems. Dr. Sabol holds a Doctor of Business Administration from the University of Dallas, a Master’s in Management from the University of Alabama, and a Bachelor’s in Business Administration from the University of New Orleans.

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