Research With ChatGPT - Using ChatGPT to Search a Theory

Choosing a theory is the difference between describing a relationship and explaining it—especially when using ChatGPT to justify how servant leadership connects to outcomes like environmental behavior or identity empowerment. The core message is that theory must do the “why”: it specifies how concepts relate and why they relate that way, turning empirical findings into knowledge contributions that reviewers expect to see supported by theoretical concepts, propositions, and hypotheses.

The transcript frames theory as essential in business and management research because it moves work beyond surveys that merely report what exists. A strong theoretical foundation can both predict and explain why some organizations perform better than others. For any model linking two variables—such as servant leadership improving environmental behavior—researchers need a theoretical framework that clarifies the mechanism behind the link. That “why element” becomes the theoretical contribution: it’s not enough to show correlation; the research must explain the logic connecting leadership behaviors to employee or organizational outcomes.

From there, the practical workflow uses ChatGPT as an idea generator, then relies on Google Scholar to validate whether the suggested theory has actually been used in relevant contexts. Because ChatGPT can produce incorrect references, the process emphasizes checking supporting literature rather than trusting citations blindly. The method begins by prompting ChatGPT with the research focus—e.g., “servant leadership and environmental behavior”—and asking what theory could explain the relationship. ChatGPT may suggest candidates such as social learning theory or theory of planned behavior.

But the transcript stresses that fit depends on the level of analysis. Social learning theory is described as learning from leaders’ behaviors, making it more applicable when environmental behavior is measured at the individual level. If environmental behavior is measured at the organizational level instead, the researcher should test whether theory of planned behavior (or another theory) has been applied to organizational environmental behavior. The validation step uses Google Scholar search strings combining the constructs (e.g., servant leadership, environmental behavior) with “theory,” then opening candidate papers to confirm whether the theory is used as intended and whether it appears in an environmental and organizational context.

The same logic applies to identity-related questions. When asked for a theory connecting servant leadership to self-identity, ChatGPT may propose social identity theory or self-categorization theory. The transcript notes that leadership-to-identity relationships are often explained with social identity theory; self-categorization theory may be less common. If existing studies have not used self-categorization theory for that specific relationship, it could represent a meaningful theoretical contribution—provided the researcher verifies the gap by reading the literature.

Overall, the transcript presents a repeatable approach: use ChatGPT to generate theory candidates, use Google Scholar to verify real usage and context fit, read the papers to understand the theory’s meaning, and only then decide whether the theory can credibly explain the “why” behind the leadership–outcome relationship.