dc.contributor.author | Milei, Pavel | |
dc.contributor.author | Votintseva, Nadezhda | |
dc.contributor.author | Barajas Alonso, Ángel Antonio | |
dc.date.accessioned | 2024-10-02T14:28:13Z | |
dc.date.available | 2024-10-02T14:28:13Z | |
dc.date.issued | 2025-01 | |
dc.identifier.citation | Information Processing & Management, 62(1): 103893 (2025) | spa |
dc.identifier.issn | 03064573 | |
dc.identifier.uri | http://hdl.handle.net/11093/7547 | |
dc.description.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. | en |
dc.description.sponsorship | Universidade de Vigo/CISUG | |
dc.language.iso | eng | spa |
dc.publisher | Information Processing & Management | spa |
dc.rights | Attribution 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.title | Automated identification of business models | en |
dc.type | article | spa |
dc.rights.accessRights | openAccess | spa |
dc.identifier.doi | 10.1016/j.ipm.2024.103893 | |
dc.identifier.editor | https://linkinghub.elsevier.com/retrieve/pii/S0306457324002528 | spa |
dc.publisher.departamento | Economía financeira e contabilidade | spa |
dc.publisher.grupoinvestigacion | Empresa internacional e capital intelectual | spa |
dc.subject.unesco | 5311 Organización y Dirección de Empresas | spa |
dc.date.updated | 2024-10-02T10:39:39Z | |
dc.computerCitation | pub_title=Information Processing & Management|volume=62|journal_number=1|start_pag=103893|end_pag= | spa |