Leveraging Artificial Intelligence (AI) in Competitive Intelligence (CI) Research - Hair, Joseph F.; Sabol, Misty (Journal of Sustainable Competitive Intelligence, 2024)
Objective: The rapid advancement of artificial intelligence (AI) has significantly influenced research and academic practices, prompting universities to create guidelines for student use of large language models (LLMs). However, there is ongoing debate among academic journals and conferences regarding the necessity of reporting AI assistance in manuscript development. This paper aims to explore diverse perspectives on the use of LLMs in scholarly research, particularly within the context of competitive intelligence (CI), and to offer guidelines for CI researchers on how to effectively leverage AI tools like GPT models. Method: The study conducts a comprehensive review of existing literature on the integration of AI in academic research, focusing specifically on the capabilities of generative AI models such as ChatGPT-4, Scholar GPT, and Consensus GPT. These models, developed by OpenAI, are evaluated for their utility in various stages of the research process, including literature review, qualitative analysis, and data analysis. The analysis emphasizes how the quality of AI-generated outputs depends on the specificity of the user's input. Results: While LLMs have demonstrated significant potential in enhancing literature reviews, qualitative research, and data analysis, the study finds that their full capabilities in academic research remain underexplored. The research highlights both the concerns about potential "contamination" of scholarly work through AI use and the benefits these models offer, especially when used strategically. Conclusions: The article presents a structured guide for business researchers, with particular emphasis on those engaged in competitive intelligence, to integrate AI language models effectively throughout the research process. The findings underline the importance of input specificity and provide practical recommendations for leveraging LLMs to enhance research efficiency and output quality.