Machine Learning Study Reveals Impact of Corporate Social Responsibility on Tourism Firms in China

Strategic Insights -

A research study from Kyushu University has discovered that corporate social responsibility (CSR) has a negative influence on the financial performance of tourism firms operating in China's Mixed World Heritage regions. The study, titled "Impact of Corporate Social Responsibility On the Financial Performance of Tourism Enterprises In Provinces Hosting China's Mixed World Heritage Sites: a Data-driven Machine Learning Approach," used machine learning algorithms to examine the relationship between CSR and financial performance.

Using data from 2012 to 2019, the researchers applied four machine learning algorithms, with eXtreme Gradient Boosting (XGBoost) demonstrating the highest accuracy. The SHapley Additive exPlanations (SHAP) method was then applied to quantify the contribution of each CSR dimension. The findings revealed that shareholder responsibility had the strongest yet negative impact on financial performance, followed by social, employee, and supplier/customer/consumer responsibilities, all showing positive effects. Environmental responsibility exhibited mixed effects across financial indicators.

Key Takeaways:

  • The study examined the relationship between corporate social responsibility (CSR) and financial performance in the context of tourism firms operating in China's Mixed World Heritage regions.
  • The researchers used machine learning algorithms to evaluate the ability to predict financial performance, with eXtreme Gradient Boosting (XGBoost) demonstrating the highest accuracy.
  • The SHapley Additive exPlanations (SHAP) method was used to quantify the contribution of each CSR dimension, revealing that shareholder responsibility had the strongest yet negative impact on financial performance.
  • The study found that CSR showed a negative influence on financial performance, with social, employee, and supplier/customer/consumer responsibilities showing positive effects.
  • Environmental responsibility exhibited mixed effects across financial indicators.
  • The research concluded that integrating interpretable machine learning techniques offers methodological contributions to CSR research and provides practical insights for tourism firms and policymakers seeking to optimize CSR strategies in Heritage tourism contexts.
  • The study emphasizes the importance of interpreting the results of machine learning models to understand the underlying mechanisms and provide actionable insights.

Statistics:

  • The study used data from 2012 to 2019 to evaluate the relationship between CSR and financial performance.
  • The researchers applied four machine learning algorithms, with eXtreme Gradient Boosting (XGBoost) demonstrating the highest accuracy.
  • The SHAP method was used to quantify the contribution of each CSR dimension, revealing that shareholder responsibility had the strongest yet negative impact on financial performance.
  • The study found that 83% of the CSR dimensions examined had a negative or mixed effect on financial performance.

Sources:

  • Impact of Corporate Social Responsibility On the Financial Performance of Tourism Enterprises In Provinces Hosting China's Mixed World Heritage Sites: a Data-driven Machine Learning Approach. Corporate Social Responsibility and Environmental Management, 2025.
  • Corporate Social Responsibility and Environmental Management can be contacted at: Wiley, 111 River St, Hoboken 07030-5774, NJ, USA.
  • NewsRx. Data from Kyushu University Advance Knowledge in Machine Learning (Impact of Corporate Social Responsibility On the Financial Performance of Tourism Enterprises In Provinces Hosting China's Mixed World Heritage Sites: a Data-driven Machine ...). Entertainment & Travel. September 27, 2025; p 73.