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Dynamic Capabilities for the Twin Transformation Climb: A Capability Maturity Model.

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Title: Dynamic Capabilities for the Twin Transformation Climb: A Capability Maturity Model.
Authors: Breiter, Katharina1,2,3 (AUTHOR), Crome, Carlotta1,2,4 (AUTHOR), Oberländer, Anna Maria1,2,5 (AUTHOR), Schnaak, Feline1,2 (AUTHOR) feline.schnaak@fit.fraunhofer.de
Source: Information Systems Frontiers. Dec2024, Vol. 26 Issue 6, p2205-2226. 22p.
Subjects: Capability maturity model, Digital transformation, Dynamic capabilities, Design science, Dynamic models
Abstract: Digital transformation and sustainability transformation are at the top of organizations' agendas to remain competitive. While guidance on both transformations exists separately, even more research on integrating digital and sustainability transformation, namely twin transformation, is required. Specifically, deeper knowledge about relevant twin transformation capabilities and progress is needed for effective implementation. To enhance the understanding and provide corresponding guidance, we developed a twin transformation capability maturity model focusing on dynamic capabilities required to realize twin transformation based on a structured literature review and interviews with 13 experts. Further, we demonstrated its use with a technology service provider. Our contribution is twofold: First, accounting for organizations' twin transformation starting points in terms of their digitalization and sustainability experience and expertise, we reveal three pathways to becoming a twin transformer. Second, our work provides an overview of 45 relevant twin transformation capabilities structured along six capability dimensions and four maturity stages. Our work also provides relevant practical implications supporting organizations in assessing their twin transformation maturity building the foundation for targeted capability development. [ABSTRACT FROM AUTHOR]
Copyright of Information Systems Frontiers is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Digital transformation and sustainability transformation are at the top of organizations' agendas to remain competitive. While guidance on both transformations exists separately, even more research on integrating digital and sustainability transformation, namely twin transformation, is required. Specifically, deeper knowledge about relevant twin transformation capabilities and progress is needed for effective implementation. To enhance the understanding and provide corresponding guidance, we developed a twin transformation capability maturity model focusing on dynamic capabilities required to realize twin transformation based on a structured literature review and interviews with 13 experts. Further, we demonstrated its use with a technology service provider. Our contribution is twofold: First, accounting for organizations' twin transformation starting points in terms of their digitalization and sustainability experience and expertise, we reveal three pathways to becoming a twin transformer. Second, our work provides an overview of 45 relevant twin transformation capabilities structured along six capability dimensions and four maturity stages. Our work also provides relevant practical implications supporting organizations in assessing their twin transformation maturity building the foundation for targeted capability development. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Information Systems Frontiers is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10796-024-10520-y
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        Text: English
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      – SubjectFull: Dynamic capabilities
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              M: 12
              Text: Dec2024
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