探索数据驱动业务模型的支持因素：混合方法方法

IF 13.3 1区管理学 Q1 BUSINESS

Technological Forecasting and Social Change Pub Date : 2025-02-08 DOI:10.1016/j.techfore.2025.124036

Reza Dabestani , Sam Solaimani , Gazar Ajroemjan , Kitty Koelemeijer

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引用次数: 0

摘要

众多企业数字化转型计划的关键目标之一是引入新的数据驱动业务模型（dbms），旨在促进价值的创建、交付和获取。虽然dbms在学者和实践者中获得了巨大的吸引力，但其实现和可扩展性仍有很多需要改进的地方。一个被广泛争论的原因是我们对使DDBM有效实现的因素理解不足。使用混合方法，本研究确定了一组全面的促成因素，探索了促成因素之间的相互依赖关系，并从理论和实践的角度讨论了经验发现如何在数据库管理系统的实现中具有价值。

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Exploring the enablers of data-driven business models: A mixed-methods approach

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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来源期刊

Technological Forecasting and Social Change Multiple-

CiteScore

21.30

自引率

10.80%

发文量

813

期刊介绍： Technological Forecasting and Social Change is a prominent platform for individuals engaged in the methodology and application of technological forecasting and future studies as planning tools, exploring the interconnectedness of social, environmental, and technological factors. In addition to serving as a key forum for these discussions, we offer numerous benefits for authors, including complimentary PDFs, a generous copyright policy, exclusive discounts on Elsevier publications, and more.