{"title":"Exploring the enablers of data-driven business models: A mixed-methods approach","authors":"Reza Dabestani , Sam Solaimani , Gazar Ajroemjan , Kitty Koelemeijer","doi":"10.1016/j.techfore.2025.124036","DOIUrl":null,"url":null,"abstract":"<div><div>One of the critical objectives underlying the digital transformation initiatives of numerous enterprises is the introduction of novel data-driven business models (DDBMs) aimed at facilitating the creation, delivery, and capture of value. While DDBMs has gained immense traction among scholars and practitioners, the implementation and scaling leave much to be desired. One widely argued reason is our poor understanding of the factors that enable DDBM's effective implementation. Using a mixed-methods approach, this study identifies a comprehensive set of enablers, explores the enablers' interdependencies, and discusses how the empirical findings are of value in DDBMs' implementation from theoretical and practical viewpoints.</div></div>","PeriodicalId":48454,"journal":{"name":"Technological Forecasting and Social Change","volume":"213 ","pages":"Article 124036"},"PeriodicalIF":13.3000,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technological Forecasting and Social Change","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0040162525000678","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
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Abstract
One of the critical objectives underlying the digital transformation initiatives of numerous enterprises is the introduction of novel data-driven business models (DDBMs) aimed at facilitating the creation, delivery, and capture of value. While DDBMs has gained immense traction among scholars and practitioners, the implementation and scaling leave much to be desired. One widely argued reason is our poor understanding of the factors that enable DDBM's effective implementation. Using a mixed-methods approach, this study identifies a comprehensive set of enablers, explores the enablers' interdependencies, and discusses how the empirical findings are of value in DDBMs' implementation from theoretical and practical viewpoints.
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