Unlocking New Perspectives: AI-Assisted Reanalysis of Entrepreneurial Leaps in IT Firms

Abstract

Firm growth has been a critical subject in entrepreneurship literature for decades (Penrose, 1959), typically seen as sequential stages. Levie and Lichtenstein (2010) identified over a hundred stage models proposed between 1962 and 2006, which have faced criticism for being empirically untenable (Brown et al., 2017) (Phelps et al., 2007). Our perspective views firm growth as a non-linear, multidimensional phenomenon that cannot be reduced to predefined steps or purely quantitative measures. This paper presents a methodological extension of a prior qualitative research project on "entrepreneurial leaps" (Sternad & Mödritscher, 2022) as transitional growth phases between dynamic states within firms (Levie & Lichtenstein, 2010). The original study employed a multiple-case design and involved in-depth interviews with leaders from 24 companies across three industries in Austria, Germany, and Italy. 
In the current paper, we focus exclusively on a subset of that data: the interviews conducted with IT firms. These cases are now re-analysed using artificial intelligence tools, allowing us to compare AI-driven interpretations with the original human-coded analysis. By narrowing the scope to IT firms, we aim to ensure thematic consistency and explore whether AI can detect alternative patterns, highlight overlooked connections, or offer new theoretical insights within a well-defined industry context.
This comparison aims to assess the methodological implications of using AI for qualitative analysis and examine potential differences in interpretation, structure, and emphasis between human and machine-driven research. This study contributes to the emerging discourse on hybrid research methodologies in entrepreneurship studies and offers reflections on the strengths and limitations of AI in analysing complex, narrative-based data.