Identification of financing and sponsorship models for a legume-specific market information dashboard through LLM-supported analyses of qualitative expert interviews

Abstract

Abstract In niche markets such as the European legume market, transparency is crucial to promote competitive conditions and enable informed decisions. The LeguDash dashboard aims to increase the transparency of the legume market through a publicly accessible and interactive information platform thereby facilitating legume cultivation and increasing sustainability in cropping systems. Guideline-based qualitative interviews with selected experts were conducted to discuss financing and sponsorship models. The interviews were subjected to qualitative content analysis, both manually and using a Large Language Model (LLM)-based methodology. ChatGPT was used for the LLM-supported analysis. As a result of the interviews, various financing and sponsorship models were proposed, including public funding, subscription-based paid content with a paywall and a model where companies provide data in exchange for free access. Flexible, customizable solutions that can be integrated into existing systems are required. Methodologically, the LLM-supported content analysis of the qualitative data material provided valuable, partly inconsistent and partly corrective, insights. These insights can be used to fine-tune manual analysis characterized by specialist context knowledge beyond the immediate interview content. LLM-enriched manual analysis can potentially enable a deeper and more contextually appropriate analysis of the qualitative data. Therefore, LLM-assisted analyses of qualitative interviews seem promising to complement manual qualitative research for agribusinesses.

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