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Introduction
Further study is needed before we can fully understand how AI can be used as a research tool, despite its growing popularity in the academic world, while there are currently significant gaps of how AI can be used most effectively in the context of theory building.
Goals and Methods
The overarching goal of this study is to analyze how AI can aid in the development of theory and theoretical frameworks. This is why a conceptual methodological strategy coupled with a critical stance has been employed. An in-depth understanding and critical examination of the phenomenon being studied (i.e., AI and nexus with theoretical advancement) may lead to theoretical links, conceptualization, and specific directions for the qualitative research community.
Results
As a result of the foregoing, researchers need acknowledge and address some drawbacks of AI, such as trustworthiness issues. Some dynamics of AI emerge and these are connected to AI theory development: "inspiration," "technical and practical support," "advancement of a researcher's comprehension," and "providing insights." Despite this, the study highlights several prerequisites and key considerations that may help researchers employ AI in theory development. This includes amongst others establishing the researcher's cognitive and evaluative skills that must not be overlooked when utilizing AI systems. Despite the emphasis on the unusual and convoluted nature of the theory-building process, the importance of both AI tools and the researcher's own cognitive input is stressed.
Conclusions
No theory or even a sound theoretical foundation for a concept/process can be derived from the "synthesis" of AI-generated output. Despite the potential benefits of using AI in analysis, the "automatic" generation of some clusters may not always provide a thorough explanation of a phenomenon or a theory. Researchers' cognitive input and evaluation skills are still valued, even though AI can assist the process of theory advancement.
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