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This study aims to redefine approaches to metrics in service marketing by examining the utility of partial least squares – structural equation modeling (PLS-SEM) and eXplainable Artificial Intelligence (XAI) for assessing service quality, with a focus on the airline industry.

Using the Airline Passenger Satisfaction data set from Kaggle platform, this study applies PLS-SEM, facilitated by ADANCO software and XAI techniques, specifically using the SHapley Additive exPlanations TreeExplainer model. This study tests several hypotheses to validate the effectiveness of these methodological tools in identifying key determinants of service quality.

PLS-SEM analysis categorizes key variables into Delay, Airport Service and In-flight Service, whereas XAI techniques rank these variables based on their impact on service quality. This dual-framework provides businesses a detailed analytical approach customized to specific research needs.

This study is constrained by the use of a single data set focused on the airline industry, which may limit generalizability. Future research should apply these methodologies across various sectors to enhance a broader applicability.

The analytical framework offered here equips businesses with the robust tools for a more rigorous and nuanced evaluation of service quality metrics, supporting informed strategic decision-making.

By applying advanced analytics to refine service metrics, businesses can better meet and exceed customer expectations, ultimately elevating the societal standard of service delivery.

This study contributes to the ongoing discourse on artificial intelligence interpretability in business analytics, presenting an innovative methodological guide for applying PLS-SEM and/or XAI in service marketing research. This approach delivers actionable insights, not only in the airline sector but also across diverse business domains seeking to optimize service quality.