
Creating, sharing, and using data are expected to lead to the development of new innovative business models. This study investigates the interplay between business model change, ethical data practices, and participation in data ecosystems in fostering data-driven innovation. Using survey data from 1200 European companies, analyzed through partial least squares structural equation modeling (PLS-SEM), the findings reveal that while firms recognize the potential benefits of data, business model change alone is insufficient to drive innovation. Instead, active engagement in data ecosystems and adherence to ethical data practices together have a significant positive impact on data-driven innovation. This research contributes to the business model innovation literature by highlighting the role of ecosystems and ethical governance in shaping sustainable data-driven innovations. This study also provides practical insights for firms seeking to transition toward more collaborative and ethically grounded data-driven business models.
TA168, ethical data practices, data ecosystems, T1-995, business model innovation, data-driven innovation, Technology (General), Systems engineering
TA168, ethical data practices, data ecosystems, T1-995, business model innovation, data-driven innovation, Technology (General), Systems engineering
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
