Designing an Interview Guide for Grounded Theory Research: The Case of AI-Driven Transformation in High-Reliability Organizations for Aviation

This chapter presents a systematic approach to designing a semi-structured interview guide, which serves as the primary qualitative instrument in a theory-generating mixed-methods study. While existing methodological frameworks provide only broad guidance for interview design, this study fills a critical research gap by applying these principles directly to the underexplored context of Artificial Intelligence (AI) transformation in High-Reliability Organizations (HROs) within aviation. The overarching research seeks to develop a substantive Grounded Theory that explains how HROs, particularly Air Navigation Service Providers (ANSPs), evolve into Intelligent HROs through the adoption of AI. Guided by Grounded Theory principles, the interview guide is intentionally inductive, crafted to elicit rich, narrative data that enable the emergence of theory rather than the validation of pre-existing constructs. This chapter contributes to both methodology and practice by providing a transparent, replicable model specifically tailored for investigating AI adoption in ANSPs. The model addresses the unique challenges of safety–critical environments, where conventional change management often doesn’t work. The design process follows the practical framework of Joungtrakul et al. (2023), beginning with the translation of the central research question into a broad, open-ended “grand tour” prompt. This is complemented by a flexible probing toolkit addressing conditions, strategies, socio-technical dynamics, and consequences of transformation. The chapter underscores the importance of constructing a rigorous yet adaptive qualitative instrument, one that evolves alongside theoretical sampling and analysis, thereby ensuring the resulting theory is authentically grounded in the lived experiences of practitioners operating in high-stakes environments.