The Data-Driven Business Value Matrix - A Classification Scheme for Data-Driven Business Models

Breitfuss, Gert; Fruhwirth, Michael; Pammer-Schindler, Viktoria; Stern, Hermann; Dennerlein, Sebastian Maximilian

doi:10.18690/978-961-286-280-0.42

Cited by 10 publications

(10 citation statements)

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“…Since a DDBM's value architecture and purpose, as well as the form of its value-oriented data processing, influence perspectives regarding value realization from data-and since this investigation particularly aims to explore the differences and similarities between these perspectives-the twelve DDBMs of the QM execution phase were clustered in advance into four QM execution clusters (A-D). To form these clusters, the twelve DDBMs were scrutinized according to the following aspects of value realization from data: (a) the DDBMs' main design, purpose, and their forms of value architecture and data processing (Exner et al, 2017;Hartmann et al, 2016) as well as degree of business renewal (Breitfuß et al, 2019;Schüritz & Satzger, 2016); (b) the nature of their data usage to realize value (Becker, 2016;Wamba et al, 2015;Woerner & Wixom, 2015); and (c) the dynamic market and business implications of value realization from data (El Sawy & Pereira, 2013;Förster et al, 2019;Zhang et al, 2015). Grounding on these aspects, the twelve DDBMs of the QM execution phase were clustered as follows:…”

Section: Data Selectionmentioning

confidence: 99%

Employee perspectives on value realization from data within data-driven business models

2022

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Firms are innovating data-driven business models (DDBMs) to realize value from data. Yet, making DDBMs work is challenging, and DDBMs often fall short of expected value realization. One reason for this shortfall is that firms do not know how employees, who decisively influence a DDBM’s value realization, view this complex and multi-facetted topic. We think it is necessary to understand the employees’ perspectives, the dimensions that build these perspectives and the characteristics employees are particularly interested in regarding value realization from data. We address this research gap by applying the Q-methodology to examine the perspectives among 70 employees across twelve DDBMs at a German automotive manufacturer. This yields eight perspectives, e.g., data advocacy, data caution or data practical. By exploring these perspectives, we provide a first groundwork on how employees view and appraise value realization from data which adds to the strive for mastering value realization from data within DDBMs.

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Section: Data Selectionmentioning

confidence: 99%

Employee perspectives on value realization from data within data-driven business models

2022

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“…We excluded papers that did not fit this study's scope: Purely conceptual papers (see Lambert (2006a), Lambert (2006b), Lambert (2015), Lambert and Davidson (2013), Groth and Nielsen (2015), or Kamprath and Halecker (2012)); papers with a different focus, such as taxonomies for business model development tools (see Szopinski et al (2017) or Szopinski et al (2019a)), classifications of literature (see Burkhart et al (2011)), taxonomies with other analytical objects (see Berger et al (2018) or Hanelt et al (2015)) or other classification types (see Endres et al (2019), Breitfuss et al (2019), or Abdollahi and Leimstoll (2011)).…”

Section: Phase 1: Structured Literature Reviewmentioning

confidence: 99%

Designing business model taxonomies – synthesis and guidance from information systems research

et al. 2021

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Classification is an essential approach in business model research. Empirical classifications, termed taxonomies, are widespread in and beyond Information Systems (IS) and enjoy high popularity as both stand-alone artifacts and the foundation for further application. In this article, we focus on the study of empirical business model taxonomies for two reasons. Firstly, as these taxonomies serve as a tool to store empirical data about business models, we investigate their coverage of different industries and technologies. Secondly, as they are emerging artifacts in IS research, we aim to strengthen rigor in their design by illustrating essential design dimensions and characteristics. In doing this, we contribute to research and practice by synthesizing the diffusion of business model taxonomies that helps to draw on the available body of empirical knowledge and providing artifact-specific guidance for building taxonomies in the context of business models.

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“…The tool aims to facilitate idea generation and service design process by providing exemplary components of data-driven services. We evaluated the initial DSCs version in a first design cycle (Breitfuss et al, 2020). Evaluation results of test workshops showed that revising the DSCs in terms of usefulness and usability would improve the tool.…”

Section: Introductionmentioning

confidence: 99%

Gamification and Employee Well-being: A Systematic Literature Review

Lehtoranta,

Xi,

Hamari

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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Using data can significantly improve service design and development. However, for businesses, developing data-driven services can be challenging. To address this, we have developed the Data Service Cards (DSCs), a card-based tool to inspire the design of data-driven services. This paper presents two cycles of a design science research (DSR) project, focusing on the second cycle of redesign and evaluation of the DSCs. We conducted a two-step evaluation, including surveys and external expert ratings of data-driven service ideas. Survey results indicate that the DSCs are a valuable tool for developing data-driven services and external experts consider services designed using DSCs to be of higher quality. With the DSCs, we provide practitioners with a tool that facilitates and improves service design and supports digital transformation. Further, we contribute to DSR literature with a rigorous experimental procedure and to service innovation by supporting the early stages of data-driven service innovation.

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The Data-Driven Business Value Matrix - A Classification Scheme for Data-Driven Business Models

Cited by 10 publications

References 0 publications

Employee perspectives on value realization from data within data-driven business models

Employee perspectives on value realization from data within data-driven business models

Designing business model taxonomies – synthesis and guidance from information systems research

Gamification and Employee Well-being: A Systematic Literature Review

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