Operational, marketing and psychological dimensions of food truck entrepreneurship

Purpose This paper explores the operational and psychological dimensions of food truck entrepreneurship and introduces an experimental approach that combines qualitative interviews with AI-assisted analysis. We examine how owners address pricing, promotion, regulation and adaptability and how entrepreneurial traits and adaptive strategies shape practices in this competitive context. Design/methodology/approach The study adopts a two-part qualitative design. Study 1 involved interviews with nine food truck owners in two US cities, conducted until data saturation, to identify operational challenges and strategies. Study 2 reanalyzed transcripts from the most popular trucks (n = 3) using ChatGPT-4 as a heuristic coding assistant. AI outputs identified linguistic cues linked to Big Five traits and adaptive practices, which were triangulated with transcripts and validated through researcher consensus. Findings Study 1 highlights challenges in pricing, regulation, and scalability and emphasizes the centrality of technology such as social media and digital payments. Study 2 shows how traits including openness, conscientiousness, agreeableness and extraversion inform adaptive strategies such as menu experimentation, route flexibility, event-based shifts and customer engagement. AI offered efficiency and linguistic sensitivity but remained exploratory, prompt-sensitive and bias-prone, requiring human oversight. Research limitations/implications As a qualitative two-city study, with Study 2 limited to a subset of owners, generalizability is constrained. ChatGPT-4 is not a psychometric instrument; outputs are model- and prompt-dependent and should be treated as hypothesis-generating. We outline a transparent protocol (AI-assisted segmentation, human coding and consensus validation) and call for multi-site replication and validated psychometric scales in future work. Originality/value This paper offers a rare owner-centric view linking personality traits to adaptive strategies in food truck entrepreneurship. Methodologically, it demonstrates how AI can complement qualitative research as a co-analyst, accelerating pattern discovery while underscoring the need for bias awareness, replication safeguards and rigorous human interpretation.