{"title":"Automated Identification of Business Models","authors":"Pavel Milei , Nadezhda Votintseva , Angel Barajas","doi":"10.1016/j.ipm.2024.103893","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 1","pages":"Article 103893"},"PeriodicalIF":7.4000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457324002528","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing.
We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.