A Song of Data and Sustainability: A Framework of Sustainable and Data-driven Business Models

At present, society needs sustainable actions of the economy to reduce climate change and decrease social injustice. Simultaneously, data is becoming the most accessible and valuable source of the current decade. Business Models are emerging that make use of this data to foster sustainable development. To support practitioners and researchers in designing and investigating sustainable data-driven business models (SDDBMS), we examined existing SDDBMs. Through our analysis, we detected 92 specific characteristics of SDDBMs, which we categorized within 12 dimensions and four meta-dimensions into a taxonomy. Thus, we created a framework for the existing SDDBMs landscape. By conducting a distribution analysis, we were able to determine the current limitations of this landscape and the commonly chosen characteristics.

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Publication Details

Journal
ScholarSpace (University of Hawaii at Manoa)
Published
2025-12-23
Primary Topic
Big Data and Business Intelligence
Type
article
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article

A Song of Data and Sustainability: A Framework of Sustainable and Data-driven Business Models

Frederik Möller, Johanna Paeplow
ScholarSpace (University of Hawaii at Manoa)
Big Data and Business Intelligence
article

A Song of Data and Sustainability: A Framework of Sustainable and Data-driven Business Models

Frederik Möller, Johanna Paeplow
article en

Abstract

At present, society needs sustainable actions of the economy to reduce climate change and decrease social injustice. Simultaneously, data is becoming the most accessible and valuable source of the current decade. Business Models are emerging that make use of this data to foster sustainable development. To support practitioners and researchers in designing and investigating sustainable data-driven business models (SDDBMS), we examined existing SDDBMs. Through our analysis, we detected 92 specific characteristics of SDDBMs, which we categorized within 12 dimensions and four meta-dimensions into a taxonomy. Thus, we created a framework for the existing SDDBMs landscape. By conducting a distribution analysis, we were able to determine the current limitations of this landscape and the commonly chosen characteristics.

ScholarSpace (University of Hawaii at Manoa)
Openalex Percentile: Top 25%
Big Data and Business Intelligence
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