Research With ChatGPT - How to use #ChatGPT to Write a Research Introduction?

ChatGPT can speed up the hardest parts of a research introduction—turning a topic into structured arguments, drafting gap statements, and suggesting theory and contribution formats—but it only works if the researcher already understands how academic papers are built and is willing to verify everything against Google Scholar, Web of Science, and target journals.

The core workflow starts with the introduction’s purpose: establish why the topic matters, connect the focal construct to related variables, identify what remains untested, and then justify the study’s theoretical and practical contribution. Using an example topic—entrepreneurial leadership, knowledge management, and project success—the process begins by asking ChatGPT for arguments about the importance of entrepreneurial leadership for modern, project-based organizations. The output is treated as raw material: it can provide a more organized paragraph structure and candidate lines of reasoning, but it often needs reshaping so it fits the paper’s narrative and the journal’s expectations. A key caution is that ChatGPT-generated text may include incomplete or incorrect references, so the researcher must cross-check claims and citations in real databases.

Next comes linkage: the introduction needs a clear bridge between entrepreneurial leadership and knowledge management, and then toward project success. ChatGPT can generate a draft argument explaining how entrepreneurial leadership might influence knowledge management processes in project-based settings, again with the expectation that the researcher reads, edits, and validates the logic using existing literature.

The “gaps and limitations” section is handled differently because it depends on what has or hasn’t been studied. Since ChatGPT’s knowledge may be outdated (the transcript notes uncertainty around free versions and coverage up to roughly 2020–2021), gap identification should rely on direct searches in Google Scholar and Web of Science. The researcher checks whether studies already examine relationships among the variables; if not, the introduction can credibly claim novelty. ChatGPT can still help by offering a structure for presenting gaps—especially when no direct studies exist—but the underlying evidence must come from verified sources.

Finally, theory selection and contributions require the same verification discipline. ChatGPT can suggest candidate frameworks (the transcript mentions knowledge-based view as a preferred option over resource-based view for linking knowledge management concepts, though the researcher may disagree and must confirm via literature). It can also provide templates for writing study contributions—such as entrepreneurial leadership as the independent variable, knowledge management as a mediator, and project success as the dependent variable—but those templates should be compared against how papers in the intended journals actually phrase contributions.

Overall, ChatGPT is positioned as an assistant for drafting and structuring—particularly useful for non-native English speakers—while the credibility of the introduction still depends on reading research papers, correcting references, and grounding every claim in database-verified scholarship.