Supporting Data-Driven Business Model Innovations

Purpose: This paper synthesizes existing research on tools and methods that support data-driven business model innovation, and maps out relevant directions for future research. Design/methodology/approach: We have carried out a structured literature review and collected and analysed a respectable but not excessively large number of 33 publications, due to the comparatively emergent nature of the field. Findings: Current literature on supporting data-driven business model innovation differs in the types of contribution (taxonomies, patterns, visual tools, methods, IT tool and processes), the types of thinking supported (divergent and convergent) and the elements of the business models that are addressed by the research (value creation, value capturing and value proposition). Research limitations/implications: Our review highlights the following as relevant directions for future research. Firstly, most research focusses on supporting divergent thinking, i.e. ideation. However, convergent thinking, i.e. evaluating, prioritizing, and deciding, is also necessary. Secondly, the complete procedure of developing data-driven business models and also the development on chains of tools related to this have been under-investigated. Thirdly, scarcely any IT tools specifically support the development of data-driven business models. These avenues also highlight the necessity to integrate between research on specifics of data in business model innovation, on innovation management, information systems and business analytics. Originality/value: This paper is the first to synthesize the literature on how to identify and develop data-driven business models, and to map out (interdisciplinary) research directions for the community. Keywords: Business model innovation, data-driven business models, research agenda. Article classification: Literature review

研究目的：本文整合现有关于支撑数据驱动商业模式创新（data-driven business model innovation）的工具与方法的相关研究，并为未来研究勾勒出相关方向。
设计/研究方法/路径：鉴于该领域尚处于相对新兴的发展阶段，我们开展了结构化文献综述，共收集并分析了33篇文献，数量适中且未过于庞杂。
研究发现：当前关于支撑数据驱动商业模式创新的相关文献，在贡献类型（分类法、模式、可视化工具、方法、信息技术工具与流程）、所支持的思维类型（发散思维与聚合思维）以及研究所涉及的商业模式要素（价值创造、价值获取与价值主张）等方面存在差异。
研究局限与启示：本综述指出了以下值得关注的未来研究方向：其一，绝大多数研究聚焦于支撑发散思维（即创意构思阶段），但聚合思维，即评估、优先级排序与决策制定，同样不可或缺；其二，数据驱动型商业模式的完整开发流程，以及与之相关的工具链开发，尚未得到充分研究；其三，几乎没有专门针对数据驱动商业模式开发的信息技术工具。这些研究路径同时凸显了将商业模式创新中的数据特性研究、创新管理、信息系统与商业分析等领域进行跨域整合的必要性。
研究创新性与价值：本文首次整合了关于如何识别与开发数据驱动商业模式的相关文献，并为学界绘制了跨学科研究方向图谱。
关键词：商业模式创新（Business Model Innovation）、数据驱动商业模式（data-driven business models）、研究议程（research agenda）
文章分类：文献综述（Literature Review）