Browsing by Subject "ESG"

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    Do ESG Strategies Drive Green Innovation in Emerging Economies?
    (Wiley, 2025-10-28) Fülöp, Melinda Timea; Cifuentes Faura, Javier; Ciencia Política, Antropología Social y Hacienda Pública; Facultad de Economía y Empresa
    This study examines how environmental, social, and governance (ESG) strategies influence green innovation in small and medium-sized enterprises (SMEs) in emerging economies. Grounded in the resource-based view and stakeholder theory, we argue that ESG practices enhance green innovation and that this relationship is moderated by a firm's innovation orientation. Using data from 317 Romanian SMEs analyzed using partial least-squares structural equation modeling (PLS-SEM), we find that all three ESG dimensions positively affect green innovation, with environmental practices being the most influential. Innovation orientation significantly strengthens these effects, particularly in the case of environmental strategies. These findings contribute to ESG and innovation literature by providing practical evidence from a transitional economy and highlighting the complementary role of ESG and innovation orientation. The study suggests that Romanian SMEs-and, by extension, those in similar emerging markets-can boost green innovation by adopting integrated ESG strategies supported by an innovation-driven culture. Policymakers are encouraged to complement ESG reporting mandates with initiatives that develop SMEs' innovation capabilities and create a more sustainable and competitive business landscape.
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    Predicting ESG controversies in banks using machine learning techniques
    (Wiley, 2025-02-04) Dipierro, Anna Rita; Jiménez Barrionuevo, Fernando; Toma, Pierluigi; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Mistreating environmental, social, and governance (ESG) concerns has serious drawbacks in organizations of any type, and even more in banks. Deeply revolutionized in its taxonomy of risks, banking sector is herein evaluated in its integration of ESG parameters that, when lacking, leads to ESG-related controversies (ESGC). Thereby, this research approaches the almost uncharted territory of ESGC in banks, by means of machine learning. Aiming at selecting the set of features that are relevant in ESGC prediction, techniques belonging to feature selection are used over a real panel dataset of 140 banks evaluated for a wide set of features over 2011–2020 time-span. We find the power that governance-employees dynamics detains in making out-of-sample predictions and forecasting of ESGC banks' risk. Finally, we provide implications for researchers, practitioners and regulators, further confirming the need for the rapid inroads that machine learning tools are actually making in the banking toolkit and in the regulatory technology.