A Corpus-based empirical study on energy enterprises digital transformation

Abstract

[Objective/Significance]This study aims to explore the role and mechanism of dynamic KM for the digital transformation of energy enterprises. This paper conducts an empirical study on mechanism of energy enterprises’ digital transformation from the perspective of KM, which is likely to contribute to relevant academic literatures. [Method/Process]On the basis of the KM theory, the SECIE Model was constructed, together with a self-built Corpus-based empirical model; Corpus’ AHP-DEMATEL evaluation system applies to verify both reliability and consistency of the model by Concordance and Matrix. [Results/Conclusions]The research results show that KM during the energy firm digitalization is evidently a dynamic process, rather than static, in which there are 5 key elements(with corpus word hits), namely, learning (942 hits), embed (771 hits), internalize (685 hits), adapt (614 hits), integration (315 hits), that are identified and worth paying attention to. Other than that , those five factors are essential for influencing the digital transformation of enterprises; empirical study confirms that they all play a positive and significant role. Knowledge integration possesses the greatest weight(0.28), followed by internalization(0.19), learning(0.18) and adaptation(0.18), with embedding(0.16) being much lower. Based on the results, this paper draws a conclusion: the mechanism of KM for promoting digital transformation of energy enterprises lies in the integration and optimization of digital resources. “Self-transcendence knowledge” is very pivotal in the role of knowledge management in promoting energy firms to swiftly respond to future digital opportunities.