ESG-Driven Innovation

A research project on innovation, strategy, and business model transformation in the ESG era.

Team ESG-Driven Innovation

Image: Privat

ESG-Driven Innovation

The research project “ESG-Driven Innovation” explores how companies adapt their innovation strategies and business models in response to growing environmental and societal demands. It builds on the so-called ESG criteria — Environmental, Social, and Governance — which encompass ecological responsibility, social standards, and principles of good corporate governance. Over the past years, these criteria have evolved into a central framework guiding companies, investors, policymakers, and society at large, increasingly shaping the dynamics of innovation.

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Q3 2026 (July – September) coming soon
Q2 2026 (April – June) The second quarter marked the project's transition into full empirical work. A central methodological question crystallised: how reliably do established, classification-code-based systems identify green patents? Preliminary work suggested these conventional approaches can both overcount environmental innovation and overlook it, missing genuinely green technologies that fall outside predefined code categories. The team put the text-based approach into practice on patent data, developing a pipeline to detect environmentally relevant patents directly from patent text and to quantify where text-based and code-based detection diverge. This work matured into the project's first research paper, "Overcounted and Overlooked: Text-Based Evidence on Noise and Coverage Gaps in Green Patent Classification," which was presented at the R&D Management Conference in Manchester, UK on 23 June 2026, where it received the Best Track Paper Award.
Q1 2026 (January – March) The quarter focused on advancing both the conceptual and technical foundations of the project. The greenness indicator framework was strengthened theoretically, with refined criteria for distinguishing environmentally relevant technologies beyond a binary green/non-green classification. Initial methodological work explored how established patent classifications can be combined with information from patent texts for a more nuanced identification of environmental innovations. In February, the team moved from concept to implementation, applying text analysis methods to patent data in practice. March was spent consolidating these efforts — streamlining and robustifying the analytical pipeline to ensure it could reliably support the empirical work planned for the months ahead.
Q4 2025 (October – December) The project launched its initial phase, with early analyses indicating that ESG-oriented strategies have a significant impact on firms' innovation processes and business model decisions. Preliminary findings suggest that companies integrating ESG considerations into their innovation strategies tend to develop more sustainable products and adapt their value creation logics accordingly. Building on these insights, the team developed initial ideas for a conceptual framework to differentiate patents according to their contribution to environmentally oriented innovation. In December, a productive workshop with our industry partner provided a first opportunity to assess this framework, align methodological perspectives, and clarify data requirements — resulting in agreed next steps for refining the indicator framework and advancing the project's empirical research.

NEWS

Q3 2026 (July – September)

coming soon

Q2 2026 (April – June)

The second quarter marked the project's transition into full empirical work. A central methodological question crystallised: how reliably do established, classification-code-based systems identify green patents? Preliminary work suggested these conventional approaches can both overcount environmental innovation and overlook it, missing genuinely green technologies that fall outside predefined code categories.

The team put the text-based approach into practice on patent data, developing a pipeline to detect environmentally relevant patents directly from patent text and to quantify where text-based and code-based detection diverge. This work matured into the project's first research paper, "Overcounted and Overlooked: Text-Based Evidence on Noise and Coverage Gaps in Green Patent Classification," which was presented at the R&D Management Conference in Manchester, UK on 23 June 2026, where it received the Best Track Paper Award.

Q1 2026 (January – March)

The quarter focused on advancing both the conceptual and technical foundations of the project. The greenness indicator framework was strengthened theoretically, with refined criteria for distinguishing environmentally relevant technologies beyond a binary green/non-green classification. Initial methodological work explored how established patent classifications can be combined with information from patent texts for a more nuanced identification of environmental innovations.

In February, the team moved from concept to implementation, applying text analysis methods to patent data in practice. March was spent consolidating these efforts — streamlining and robustifying the analytical pipeline to ensure it could reliably support the empirical work planned for the months ahead.

Q4 2025 (October – December)

The project launched its initial phase, with early analyses indicating that ESG-oriented strategies have a significant impact on firms' innovation processes and business model decisions. Preliminary findings suggest that companies integrating ESG considerations into their innovation strategies tend to develop more sustainable products and adapt their value creation logics accordingly.

Building on these insights, the team developed initial ideas for a conceptual framework to differentiate patents according to their contribution to environmentally oriented innovation. In December, a productive workshop with our industry partner provided a first opportunity to assess this framework, align methodological perspectives, and clarify data requirements — resulting in agreed next steps for refining the indicator framework and advancing the project's empirical research.