Narrating Authority Through Storytelling in Entrepreneurial Podcast Interviews

Abstract

How do entrepreneurs claim the authority to lead an enterprise that was never theirs alone? Although the discursive construction of leadership has attracted growing interest, most existing work is qualitative and case-limited, and leadership authority has rarely been examined as a discursive phenomenon at scale. Drawing on discursive, relational, and narrative leadership theory, this study analyzes 59 entrepreneurial podcast interviews from the Innovation Fuel series, recorded between 2020 and 2026, combining lexicon-based authority detection, sentence-embedding, topic modeling, and clustering. Under the stated lexical rules, soft classifications account for 89.8% of transcripts, and individually voiced and collectively voiced authority language routinely co-occur within the same narratives. Borrowed authority is used as a provisional interpretive label for legitimacy claimed individually yet drawn from teams and networks; it is offered as a testable interpretation rather than a separately validated construct. Recorded guest gender is neither associated with the episode-level collective ratio (p = .205) nor recoverable from the analyzed discourse out-of-sample (cross-validated AUC of about 0.59), and gender labels are interspersed in the embedding space. Because complete transcripts include host speech, these findings cannot be attributed to guest speech alone. Crucially, each apparent result is submitted to a matched null test: the soft-authority prevalence and the gender null survive, whereas apparent cluster and topic authority bands are shown to be aggregation artifacts over poorly separated groups. Distinguishing patterns that survive such tests from those that do not, the study models a transparent, self-auditing approach to computational discourse analysis and contributes a reproducible workflow for leadership communication research on large corpora of naturally occurring talk.