Fulfilling Customer Contentment: The Impact of Passenger’s Preferences and Characteristics on the Use of Chatbots for Booking and Inquiries

Abstract

This study examines factors influencing passenger satisfaction with AI chatbots for airline bookings and inquiries at Ninoy Aquino International Airport (NAIA). It focuses on passenger preferences, chatbot adaptability, data privacy, and the effectiveness of AI chatbots in building trust and satisfaction. The research also explores strategies to enhance chatbot performance while addressing concerns about privacy and emotional intelligence. Data were gathered from 53 passengers at NAIA and four informants: an airline pilot, a cybersecurity expert, and two IT specialists. Using a mixed-method approach, the study combined quantitative techniques such as surveys and descriptive analysis with qualitative methods like thematic analysis of interviews. Key themes included ease of use, privacy concerns, and customer support. Results showed that while AI chatbots improved convenience and efficiency, passengers raised concerns about data privacy and the lack of emotional intelligence, which affected trust and satisfaction. Despite these issues, passengers preferred user-friendly, adaptable, and personalized chatbot features. Demographic factors showed no significant influence on preferences, adaptability, privacy concerns, or chatbot effectiveness. The study concludes that addressing privacy concerns with strong legal frameworks, adding multilingual options, and making chatbots more human-like are essential to improving passenger satisfaction. Recommendations include enhancing chatbot personalization, strengthening data security, and maintaining human oversight to ensure a balanced and effective customer service system.

Introduction

Artificial Intelligence (AI) chatbots have become increasingly important in the aviation sector, especially for handling customer service tasks such as flight bookings and inquiries. This technology helps reduce human intervention, minimize operational costs, and improve service efficiency by offering 24/7 availability and real-time responses. In the Philippines, major airlines operating in busy airports are using AI chatbots to streamline customer service, addressing the rising demand for faster and more accurate assistance.

The study explores key factors affecting passenger satisfaction with airline AI chatbots, focusing on:

• Passenger preferences over human agents

• Adaptability of chatbots through machine learning and error correction

• Data privacy concerns

• Effectiveness in building trust and loyalty

Theoretical and Conceptual Framework:

The study is grounded in the SERVQUAL model, which emphasizes five service quality dimensions: reliability, tangibility, responsiveness, assurance, and empathy. These are reflected in chatbot design through features like real-time communication, reliability, personalized service, and data protection.

The conceptual framework outlines four main components of chatbot quality affecting passenger satisfaction:

1. Preference – Ease of use and convenience over human agents

2. Learning adaptability – Ability to improve over time via AI training

3. Data privacy – Secure handling of personal data

4. Effectiveness – Accurate, clear, and trustworthy responses

Key Insights from Literature:

• AI chatbots enhance customer loyalty and satisfaction (Chen, 2023; Yoo, 2022)

• Emotional intelligence, error handling, and natural language understanding are critical (Bilquise, 2022; Izadi, 2024)

• Ethical concerns and trust play a major role in adoption (Afroogh et al., 2022; Naga, 2024)

• Personalized, human-like interaction increases perceived enjoyment and engagement (de Sá Siqueira, 2023; Toader, 2020)

• Surveys show that frequent flyers appreciate chatbot efficiency and accessibility (Garcia et al., 2024)

Methodology:

The study used a quantitative descriptive design supported by qualitative interviews, surveying 53 airline passengers across NAIA Terminals 1–4. Respondents varied in age, airline type (legacy vs. low-cost), and flight frequency. The data collection tools included questionnaires and interviews, and thematic analysis was applied to interpret qualitative feedback.

Findings (Respondent Profiles):

• Majority were Gen Z (62.3%)

• Most flew with legacy carriers (73.6%)

• Terminal 3 had the highest passenger responses (41.5%)

• Frequent flyers made up 62.3% of the sample

Expert Interviews:

Insights from a pilot, cybersecurity personnel, and two IT specialists emphasized the importance of:

• Security and data protection

• Human oversight and ethical boundaries

• Emotional understanding and user-centered design

Research Questions & Hypothesis:

The study investigates the relationship between passenger characteristics (age, flight frequency, airline type, terminal) and satisfaction with AI chatbot use. It hypothesizes that no significant relationship exists between these demographics and satisfaction metrics (preference, adaptability, privacy, effectiveness).

Significance of the Study:

• Passengers: Helps understand how chatbots meet their needs

• IT Developers: Informs chatbot design for better user experience

• Airline Companies: Offers strategies to enhance customer service

• Academics: Provides a foundation for future AI-related research

Conclusion

AI chatbots are revolutionizing the aviation industry by offering scalable, efficient, and accessible customer service solutions. While their use increases efficiency and convenience, their success hinges on continuous improvement in emotional intelligence, data protection, and personalized engagement. The study aims to help airlines and developers refine chatbot systems to meet evolving passenger expectations and build stronger customer relationships.

References

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Copyright

Copyright © 2025 Isidro Karyl E. Namoro, Nathan Zinan P. Catindig, Julia Cassandra F. Dela Cruz, Julius Romeo Naga, Jean Matthew S. Puntero, Ezekiel F. Victor, Marianne Shalimar G. Del Rosario. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.