Exploring personality moderation effects on satisfaction and loyalty in the airline industry

Leon, Steven; Leon, Abigail

doi:10.3926/jairm.357

Cited by 2 publications

(1 citation statement)

References 61 publications

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“…In Figure 8, non-loyal customers have nearly three times the weight of loyal customers, and unlike loyal customers whose weight is concentrated on positive impacts, non-loyal customers have almost 1.5 times the weight of negative impact while having similar positive impact weight as loyal customers. Non-loyal customers are more concerned about the negative aspects of each service, so airlines can improve the loyalty program service level or lower the loyalty program price while improving service level to some extent, which will benefit the satisfaction of non-loyal customers and may also convert them into loyal customers [9,10]. In Figure 9, individual travelers have nearly four times more weight than business travelers and are mainly concentrated in negative weight impacts.…”

Section: Variable Comparisonmentioning

confidence: 99%

Neural Network in Aircraft Customer Satisfaction Prediction

2023

AEMPS

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The aviation service market has become increasingly competitive in recent years, and airlines are constantly seeking new ways to attract more customers and improve their market share. One effective method of assessing customer satisfaction is through evaluating the quality of airline services. In this paper, the target is to explore the influence of various airline services to customer satisfaction. The study identified four key service types that have a significant impact on customer satisfaction. These include Wi-Fi service, online boarding pass printing service, business class customer service, and personal travel customer service. Improving the quality of these services or lowering their price may result in higher satisfaction evaluations, thus attracting more customers and increasing market share. The study found that improving the quality of services for business class customers can have a significant impact on satisfaction levels. For personal travel customers, offering specific promotions or service upgrades can lead to higher satisfaction evaluations. Additionally, for all types of customers, offering high-quality economy class services can significantly reduce negative evaluations and increase the likelihood of repeat business. Overall, the findings suggest that airlines should focus on improving the quality of these key service types to attract more customers and improve their market share. By offering high-quality services at a reasonable price, airlines can enhance their reputation and build a loyal customer base, leading to long-term success in the competitive aviation industry.

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Section: Variable Comparisonmentioning

confidence: 99%

Neural Network in Aircraft Customer Satisfaction Prediction

2023

AEMPS

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Using PLS-SEM and XAI for causal-predictive services marketing research

Goktas,

Dirsehan

2024

JSM

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Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Social implications By applying advanced analytics to refine service metrics, businesses can better meet and exceed customer expectations, ultimately elevating the societal standard of service delivery. Originality/value 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.

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Exploring personality moderation effects on satisfaction and loyalty in the airline industry

Cited by 2 publications

References 61 publications

Neural Network in Aircraft Customer Satisfaction Prediction

Neural Network in Aircraft Customer Satisfaction Prediction

Using PLS-SEM and XAI for causal-predictive services marketing research

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