Article: Business model innovation strategies of enterprises in the tourism economy based on Bayesian networks and deep learning models under the influence of organisational inertia Journal: International Journal of Dynamical Systems and Differential Equations (IJDSDE) 2025 Vol.14 No.6 pp.522 - 543 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Huiting Wang; Jiayi Yu; Tiantian Qu; Jiaqian Liu

Addresses: School of Automotive Business, Hubei University of Automotive Technology, Shiyan, Hubei, 442002, China ' College of Industrial Management, Gyeongsang National University, Jinju, 52828, South Korea ' School of Automotive Business, Hubei University of Automotive Technology, Shiyan, Hubei, 442002, China ' School of Automotive Business, Hubei University of Automotive Technology, Shiyan, Hubei, 442002, China

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.

Keywords: organisational inertia; Bayesian network; business model; economic creative strategy; deep learning; artificial intelligence; tourism economy.

DOI: 10.1504/IJDSDE.2025.151441

International Journal of Dynamical Systems and Differential Equations, 2025 Vol.14 No.6, pp.522 - 543

Received: 02 Aug 2025
Accepted: 09 Oct 2025
Published online: 30 Jan 2026 *

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

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