Research on improving the competitiveness of live music venues based on hybrid probabilistic information and cumulative prospect theory

Abstract

Enhancing the competitiveness of live music venues (LMVs) carries significant implications for the sustainable development of the music industry. This study proposes a quantitative evaluation method that integrates mixed probabilistic information with cumulative prospect theory (CPT), aiming to accurately assess the competitiveness of live music venues. Online customer reviews (OCR) were adopted, core requirement attributes were identified using the Latent Dirichlet Allocation (LDA) model, and sentiment scores were derived through employing the Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory (BERT-BiLSTM) model. Subsequently, a satisfaction model based on the Probabilistic Linguistic Term Set (PLTS) framework was constructed to quantitatively assess customer satisfaction levels. Building on this, the competitiveness index(CI) of LMVs was determined using a hybrid probabilistic information approach. CPT was then applied to evaluate the competitiveness rankings of different types of live music venues. Finally, the sensitivity and comparative analyses confirm the high accuracy, robustness, and generalizability of the proposed methodology. The results of this study deepen enterprises’ understanding of customer consumption behavior patterns and clarify the core indicators influencing competitiveness. This provides valuable insights for strategic adjustments aimed at enhancing competitiveness and offers a solid foundation for the sustainable development of the live music industry.