Business model innovation strategies of enterprises in the tourism economy based on Bayesian networks and deep learning models under the influence of organisational inertia

Abstract

This study integrates Bayesian network (BN) and deep learning (DL) models to create a framework for analysing the impact of organisational inertia on business model innovation strategies in the tourism economy. It also employs artificial intelligence (AI) technology to optimise innovation decisions. By comparing models such as graph neural network (GNN), transformer, and reinforcement learning (RL), the study demonstrates the multi-dimensional performance advantages of the optimised BN model. The experiment focuses on core indicators, including innovation investment ratio, market response speed, innovation success rate, revenue growth rate, customer satisfaction, and competitiveness index in the tourism economy. These results suggest that the optimised model is highly adaptable and effective in addressing organisational inertia and enhancing innovation strategies for enterprises operating in the tourism economy.