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Beyond digital transformation: a multi-mixed methods study on big data analytics capabilities and innovation in enhancing organizational performance

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Beyond digital transformation: a multi-mixed methods study on big data analytics capabilities and innovation in enhancing organizational performance

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dc.contributor.author Orero-Blat, Maria es_ES
dc.contributor.author Palacios Marqués, Daniel es_ES
dc.contributor.author Leal-Rodriguez, Antonio Luis es_ES
dc.contributor.author Ferraris, Alberto es_ES
dc.date.accessioned 2024-09-06T18:15:49Z
dc.date.available 2024-09-06T18:15:49Z
dc.date.issued 2024-06 es_ES
dc.identifier.issn 1863-6683 es_ES
dc.identifier.uri http://hdl.handle.net/10251/207585
dc.description.abstract [EN] Digital transformation (DT) and Big Data Analytics Capabilities (BDAC) enable SMEs to adapt to rapidly changing markets, innovate, and maintain relevance in the digital age. This research explores the impact of DT on SME performance through the lens of BDAC and innovation, from a multi-methods approach and applying the dynamic capabilities view. It asserts that simply investing in DT doesn't ensure enhanced performance. Analyzing 183 Spanish SMEs from various sectors, the study highlights the need for creating specific conditions that enable DT to positively impact performance. The integration of PLS-SEM and fsQCA methodologies provides a comprehensive analysis of BDAC as pivotal in optimizing SME performance through DT, emphasizing the necessity of strategic alignment with innovation. This nuanced approach, combining the predictive power of PLS-SEM and the configurational insights of fsQCA, demonstrates that investment in DT alone is insufficient without fostering conditions conducive to innovation. Our empirical insights offer actionable guidance for managers utilizing BDA or contemplating technological investments to elevate firm performance which go in the direction of increasing their innovation capabilities. Additionally, these findings equip policymakers with a nuanced understanding, enabling the design of tailored measures promoting DT in SMEs anchored in the nuances of BDAC and innovation capabilities. es_ES
dc.description.sponsorship Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The data collection of this study was funded by Cátedra de Empresa y Humanismo of University of Valencia, and the authors want to acknowledge Prof. Tomás Gonzalez Cruz for his help and support along the development of this research. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Review of Managerial Science es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Digital transformation es_ES
dc.subject Big data analytics capabilities es_ES
dc.subject Innovation es_ES
dc.subject Performance es_ES
dc.subject PLS-SEM es_ES
dc.subject FsQCA es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.title Beyond digital transformation: a multi-mixed methods study on big data analytics capabilities and innovation in enhancing organizational performance es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11846-024-00768-8 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Organización de Empresas - Departament d'Organització d'Empreses es_ES
dc.description.bibliographicCitation Orero-Blat, M.; Palacios Marqués, D.; Leal-Rodriguez, AL.; Ferraris, A. (2024). Beyond digital transformation: a multi-mixed methods study on big data analytics capabilities and innovation in enhancing organizational performance. Review of Managerial Science. https://doi.org/10.1007/s11846-024-00768-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11846-024-00768-8 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.pasarela S\523651 es_ES
dc.contributor.funder Universitat de València es_ES


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