ChatGPT for Research: ChatGPT to Adapt a Questionnaire

TL;DR

Copy the original validated scale items before attempting any adaptation to a new construct.

Briefing Cornell Notes

Briefing

Adapting an existing questionnaire for a new construct—like turning “strategic intent” items into “strategic sustainability intent”—can be done quickly with ChatGPT, but only after the researcher locks down key definitions. The core workflow is to copy the original scale items, add the new context (sustainability), and then iterate until the statements clearly reflect the intended concept.

In the first example, the researcher starts with a strategic intent scale and wants to measure strategic sustainability intent instead. The initial attempt is straightforward: reuse the original items and insert sustainability language. The resulting statements, however, come out vague and not fully aligned with how sustainability is being conceptualized. To fix that, the researcher asks ChatGPT for a definition of sustainability, then uses that definition as explicit context inside the prompt. With that added framing, the revised items become more specific—for instance, shifting from generic sustainability wording to statements that incorporate future generations and sustainable competitive advantage. The end result is a set of questionnaire items that better match the study’s sustainability construct rather than just adding a label.

A second example applies the same approach to a different construct: entrepreneurial leadership. The researcher again copies existing items and prompts ChatGPT to transform them into “green entrepreneurial leadership” indicators. The revised statements replace the original focus on production/services with environmentally oriented language, such as generating radical improvement ideas for environmentally friendly aspects of products and services.

Across both cases, the practical takeaway is that ChatGPT can transform scale items efficiently, but the quality depends on conceptual clarity. Before prompting, the researcher needs to understand what “sustainability” (or “green”), what “strategic intent,” and what “entrepreneurial leadership” mean in the specific study context. Without those definitions, the model may produce items that are only loosely connected to the intended construct. With clear definitions and iterative refinement, existing validated scales can be adapted to new scenarios—changing the context while preserving the underlying measurement structure—so the questionnaire better supports valid data collection and analysis.

Cornell Notes

ChatGPT can adapt existing questionnaire items to measure a new construct, such as converting “strategic intent” into “strategic sustainability intent.” The process starts by copying the original scale items, then prompting for modifications that embed the new context. When the first draft items feel unclear, adding an explicit definition of sustainability to the prompt improves alignment—leading to more detailed statements that reflect the study’s conceptualization (including future generations and sustainable competitive advantage). The same method works for other constructs, like transforming entrepreneurial leadership items into “green entrepreneurial leadership” indicators by swapping in environmentally focused language. The key requirement is conceptual clarity: define the constructs first, then iterate on the wording.

How does adding a sustainability definition change the adapted questionnaire items?

When the researcher first inserts sustainability language into strategic intent items, the statements are not clear enough. After asking ChatGPT for a definition of sustainability and including that definition in the prompt, the revised items become more detailed and conceptually aligned. For example, a statement shifts from seeking sustainable competitive advantage in general terms to seeking sustainable competitive advantage while considering the needs of future generations—making the instrument reflect the study’s sustainability conceptualization.

What is the basic step-by-step workflow for transforming an existing scale with ChatGPT?

First, copy the original questionnaire items from the strategic intent scale. Second, prompt ChatGPT to change the items to measure the new construct (strategic sustainability intent). Third, review the wording for clarity and fit. If the items feel vague, add explicit conceptual context—such as a sustainability definition—to the prompt and regenerate. Finally, compare and paste the revised items into the questionnaire for use in the study.

Why might the first round of adapted items be insufficient?

The initial modifications may only add the new label (“sustainability”) without capturing how sustainability is defined in the study. That can leave statements unclear or too general. The transcript emphasizes that without understanding and defining key concepts (sustainability/green, strategic intent, and the leadership construct), the model’s output may not fully match the intended measurement.

How does the “green entrepreneurial leadership” example demonstrate the same adaptation principle?

The researcher copies items intended to measure entrepreneurial leadership and prompts ChatGPT to convert them into indicators for “green entrepreneurial leadership.” The revised items replace the original focus on radical improvements in production/services with radical improvements in environmentally friendly aspects of products and services. This shows how the model can preserve the scale’s structure while changing the context to match the new construct.

What does the transcript suggest is necessary before using ChatGPT for scale adaptation?

It stresses that the researcher should first understand what “green” or “sustainability” means, what “entrepreneurial leadership” means, and what “strategic intent” means in their specific study. That conceptual grounding is what enables better prompts and higher-quality questionnaire items, rather than relying on generic sustainability wording.

Review Questions

What prompt addition helped improve clarity in the strategic sustainability intent items, and what did it change in the wording?
Describe how the entrepreneurial leadership items were transformed into green entrepreneurial leadership indicators.
Why is conceptual definition (e.g., sustainability/green) treated as a prerequisite for effective scale adaptation?

Key Points

1
Copy the original validated scale items before attempting any adaptation to a new construct.
2
Prompt ChatGPT to modify items by embedding the new context (e.g., sustainability) rather than only swapping keywords.
3
If the revised items feel unclear, iterate by adding explicit conceptual definitions (such as a sustainability definition) into the prompt.
4
Use the adapted wording to ensure it reflects the study’s conceptualization, not just the topic label.
5
Apply the same method across constructs by changing the context (e.g., entrepreneurial leadership to green entrepreneurial leadership).
6
Conceptual clarity about each construct (strategic intent, sustainability/green, leadership) is necessary to get questionnaire items that fit the intended measurement.

Highlights

Including a sustainability definition inside the prompt turned vague sustainability wording into items that explicitly reference future generations and sustainable competitive advantage.

The adaptation method preserves the original scale’s measurement structure while changing the scenario—strategic intent becomes strategic sustainability intent, and entrepreneurial leadership becomes green entrepreneurial leadership.

First-draft outputs may be insufficient when the new construct isn’t clearly defined; prompt iteration fixes alignment.

Transformations can be done by replacing the focus of improvement ideas—from production/services to environmentally friendly product and service aspects.

Topics

Questionnaire Adaptation
Strategic Sustainability Intent
Green Entrepreneurial Leadership
Scale Transformation
Prompt Iteration