Conceptual model for establishing the relationship between digital transformation and organizational performance in electrical power companies

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Abstract

The purpose of this study is to establish a conceptual model of structural equations that examines the relationship between digital transformation and organizational performance in the electric power sector, using the theories of Dynamic Capabilities (DC), Resources-Based View (RBV), and Knowledge-Based Vision (KBV). The study proposes a conceptual model of 27 relationships (hypotheses) that incorporates the theories of DC, RBV, and KBV, which are used to evaluate the relationship between digital transformations and organizational performance in the electric power sector. We conducted surveys with industry professionals to validate the model. Through the analysis of the survey data, the study establishes the relationship between digital transformations and organizational performance, highlighting the crucial role of knowledge in generating value for organizations. The study findings showed that the proposed model, which incorporates the theories of DC, RBV, and KBV, effectively models and measures organizational processes in the electric power sector. Our results show that digital transformation, particularly in Big Data, has a significant positive impact on organizational performance. Furthermore, the study highlights the important role of knowledge creation and management in driving organizational value in the context of digital transformation. Specifically, the study allows us to conclude that we can express organizational processes in causal relationships and measure them through data, with the interaction of these elements producing knowledge as a key element in generating value in organizations.

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Rosero-Garcia, J., & Montano-Salamanca, W. (2024). Conceptual model for establishing the relationship between digital transformation and organizational performance in electrical power companies. International Journal of Management and Sustainability, 13(2), 423–447. https://doi.org/10.18488/11.v13i2.3767

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