Indonesia State Electricity Company (PLN) has transformed its business process into a more modern practice with the intention of boosting efficiency and enhancing service quality. In the digital economy space, customer expectation is more complex, influenced by the network effect and public opinion. Facing these facts, it is imperative for PLN to be able to listen to its customers' voices using publicly available social media channels. Understanding and acting upon large-scale opinions and conversations is a nontrivial task. To filter large-scale data, we employ Natural Language Processing (NLP) methodology called Latent Dirichlet Allocation (LDA) to find the dominant topics in public discourse. It is followed by UTAUT2 model implementation to further clarify the most critical issues found. Finally, this study modified the e-ServQual and e-RecServQual methodologies to propose the best and most personalized solution for each issue. By using the proposed approach, PLN could accelerate the implementation of data-driven decision-making and induce sustainability.
Keywords: Customer voice; Public discourse; Topic modeling; UTAUT2; e-RecServQual; e-ServQual.
© 2023 The Authors. Published by Elsevier Ltd.