Exploring Hajj pilgrim satisfaction with hospitality services through expectation-confirmation theory and deep...

Area of Science:

• Hospitality Management
• Artificial Intelligence in Tourism
• Psychological Theories in Service Evaluation

Background:

• The Hajj pilgrimage attracts millions of Muslims globally, making pilgrim satisfaction with hospitality services critical.
• Recent years saw reduced Hajj participation due to the COVID-19 pandemic, highlighting the need to understand pilgrim experiences.
• Psychological theories and advanced analytics are increasingly used to evaluate service quality in the hospitality sector.

Purpose of the Study:

• To develop and validate a novel deep learning model for assessing Hajj pilgrim satisfaction with hospitality services.
• To apply the expectation-confirmation theory within a machine learning framework to analyze pilgrim feedback.
• To provide actionable insights for improving hospitality services during the Hajj.

Main Methods:

• Development of a novel deep learning model integrating the expectation-confirmation theory.
• Training and testing the model using hotel review data specifically from the Hajj context.
• Utilizing big data analytics and artificial intelligence for psychological theory evaluation.

Main Results:

• The proposed deep learning model demonstrated high predictive accuracy, achieving 97% for Hajj pilgrim satisfaction.
• The model effectively leveraged the expectation-confirmation theory to analyze sentiment and predict satisfaction levels.
• The findings offer a data-driven approach to understanding pilgrim experiences.

Conclusions:

• The deep learning model provides an accurate method for evaluating Hajj pilgrim satisfaction with hospitality.
• Insights gained can guide service improvements to enhance the overall Hajj pilgrim experience.
• This research demonstrates the potential of AI in applying psychological theories to the hospitality industry.