Automated identification of business models

Abstract

As business data grows in volume and complexity, there is an increasing demand for efficient, accurate, and scalable methods to analyse and classify business models. This study introduces and validates a novel approach for the automated identification of business models through content analysis of company reports. Our method builds on the semantic operationalisation of the business model that establishes a detailed structure of business model elements along with the dictionary of associated keywords. Through several refinement steps, we calibrate theory-derived keywords and obtain a final dictionary that totals 318 single words and collocations. We then run dictionary-based content analysis on a dataset of 363 annual reports from young public companies. The results are presented via a web-based software prototype, available online, that enables researchers and practitioners to visualise the structure and magnitude of business model elements based on the annual reports. Furthermore, we conduct a cluster analysis of the obtained data and combine the results with the extant theory to derive 5 categories of business models in young companies.