Automated Identification of Business Models

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Information Processing & Management Pub Date : 2024-09-26 DOI:10.1016/j.ipm.2024.103893
Pavel Milei , Nadezhda Votintseva , Angel Barajas
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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.
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自动识别商业模式
随着商业数据的数量和复杂性不断增加,对高效、准确、可扩展的商业模式分析和分类方法的需求也与日俱增。本研究介绍并验证了一种通过公司报告内容分析自动识别商业模式的新方法。我们的方法以商业模式的语义操作化为基础,建立了商业模式元素的详细结构以及相关关键词字典。通过几个改进步骤,我们校准了理论派生的关键词,并获得了最终词典,其中包含 318 个单词和搭配词。然后,我们对一个包含 363 份年轻上市公司年报的数据集进行了基于词典的内容分析。分析结果通过一个基于网络的软件原型(可在线获取)进行展示,使研究人员和从业人员能够根据年报直观地了解商业模式要素的结构和规模。此外,我们还对获得的数据进行了聚类分析,并将结果与现有理论相结合,得出了年轻公司商业模式的 5 个类别。
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来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
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.
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