Airline Passenger Satisfaction Research Articles

A Comprehensive Analysis of Airline Passenger's Satisfaction through Classification Model

Passengers' satisfaction with airlines is one of the important determinants of competitive advantage for airlines with globalization and customer-oriented aviation industries of today. Customer satisfaction directly impacts brand loyalty, customer retention, and profitability, becoming an important key performance indicator for airline operators. The research examines a dataset of over 120,000 passengers, evaluating various aspects of their flying experience such as inflight services, comfort, punctuality, and overall satisfaction. An in-depth analysis of the key factors for customer satisfaction is done based on several data analyses and using different machine learning techniques like Logistic Regression, K-Nearest Neighbors, Decision Tree, and Random Forest. Through these techniques, our results show that, among others, service quality, punctuality, and comfort are the essential requirements for customer satisfaction. Conclusively, this study offers actionable recommendations for airlines that are vital in improving passenger satisfaction, which is an important factor in developing effective customer loyalty in the highly competitive airline business. The K-Nearest Neighbors (KNN) model performed best with an F1 score of 0.93, excelling in balancing precision and recall. Other models like Decision Tree and Random Forest were also used, with Random Forest providing robustness due to its ability to handle large datasets without overfitting, while Logistic Regression gave interpretable results but had the lowest F1 score at 0.89.

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

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.

Drivers and outcomes of airline passenger satisfaction: A Meta-analysis

In the competitive environment of air transport management, airline passenger satisfaction is critical to customer loyalty, operational feedback, financial performance, and regulatory compliance and rankings. Research on airline passenger satisfaction has evolved and matured, shaped by industry dynamics and passenger expectations. However, only some studies have systematically assessed the key drivers and outcomes of airline passenger satisfaction. Consequently, this research provides a meta-analysis of the relationships between the critical drivers of passenger satisfaction and attitudinal and behavioural outcomes within the airline industry. A meta-analysis of 95 empirical studies revealed that perceived service quality and perceived value positively impact passenger satisfaction, with varying effect sizes. Passenger satisfaction had a significant direct and indirect effect on various behavioural intentions and on the willingness to recommend an airline, with gender moderating the relationship between perceived value and satisfaction. Among the perceived service quality dimensions, in-flight service had the greatest effect and was critical to passenger satisfaction. A conceptual model containing mechanisms to achieve affirmative behavioural responses through passenger satisfaction is provided for airline practitioners to formulate long-term success.

How Has COVID-19 Affected Airline Passenger Satisfaction? Evaluating The Passenger Satisfaction of European Short-Haul Low-Cost Carriers Pre- and Post-COVID-19

This paper investigates whether there has been a change in passenger satisfaction drivers for the three largest short-haul low-cost carriers in Europe before and after COVID-19. User-generated content on the Skytrax platform was used as the data source for passenger satisfaction, and these secondary data were scraped using the Web Scraper tool. Binary logistic regression was used for the classification model related to passenger satisfaction, and ROC analysis was used to evaluate the classification performance of the model. The findings suggested that the service attributes of seat comfort, cabin staff services, and ground services are significant predictors of value for money, and the value for money is a significant determinant of overall satisfaction in both periods. Additionally, it was revealed that ground service is the most important determinant of the value for money perception. The results also indicate that in the post-COVID-19 period, the predictive power of seat comfort has decreased while the predictive power of ground services has increased.

Examining the Connection between Service Failure and Recovery with Airline Passenger Satisfaction with Air Asia

Examining the Connection between Service Failure and Recovery with Airline Passenger Satisfaction with Air Asia

Federated Learning-based neural networks for airline passenger satisfaction prediction

Preserving user data privacy is paramount for airlines. However, achieving highly accurate predictions of passenger satisfaction while safeguarding the privacy of individual companys data remains a considerable challenge. To achieve this objective, we utilized a privacy-preserving machine learning technique known as Federated Learning (FL). FL allows model training on decentralized devices while ensuring data security and privacy. The FL process comprises client training processes, communication rounds, and weight aggregation. We simulate FL principles using multiple processes to enable distributed learning. Clients preprocess data and train local models, ensuring data privacy. Communication rounds involve clients downloading the global model, local training, and transmitting updates to a central server. Weight aggregation methods like Federated Averaging merge these updates, preserving data privacy. Additionally, we leverage Artificial Neural Networks (ANNs) as foundational techniques. ANNs consist of input, hidden, and output layers, with weight adjustments based on real value differences to achieve accuracy. Our approach combines FL with ANNs to demonstrate FLs potential in privacy-preserving predictive analytics. We use the Airline Passenger Satisfaction dataset for modeling and evaluate the impact of neural network depth and submodel quantity on prediction accuracy. Our experimental results reveal that neither the depth of neural networks nor the number of submodels significantly affects prediction accuracy. FL emerges as a promising approach to balance data privacy and prediction accuracy effectively.

Pengaruh Kualitas Pelayanan Petugas Check-In Counter terhadap Kepuasan Penumpang Maskapai Citilink di Bandar Udara Internasional I Gusti Ngurah Rai Bali

This research aims to determine the influence of service quality on passenger satisfaction on Citilink airline passenger satisfaction at I Gusti Ngurah Rai Bali. This research uses quantitative methods. The data collection technique is by distributing statements (questionnaires) to a sample of 109 respondents. The sampling technique uses the Purposive Sampling technique, namely a sampling technique with certain decisions. Hypothesis testing uses multiple regression with t test and coefficient of determination. The results of the analysis show that service quality can be seen to have a significant positive impact on passenger satisfaction on Citilink airlines amounting to 14,884 on customer satisfaction. Influence of service quality variables. The research results show that 67.1% of Citilink airline customer satisfaction is influenced by service quality which consists of five dimensions, tangible, reliability, responsiveness, assurance and empathy. The remaining 32.9% is influenced by other variables not studied such as brand image, ticket prices and discounts.

Analysis of Airline Passenger Satisfaction Using Decision Tree and Naïve Bayes Algorithms

In the dynamic landscape of airline services, comprehending the intricacies that mold customer satisfaction is paramount to elevating overall service quality. This study aspires to dissect these pivotal elements, contributing nuanced insights that can propel the enhancement of customer satisfaction within the industry. A multifaceted investigation encompasses analyzing demographic data, exploring underlying factors significantly shaping passenger satisfaction, and identifying the most adept model for forecasting imminent passenger satisfaction outcomes. A model was meticulously crafted by leveraging a decision tree algorithm to discern the substantial variables influencing passenger satisfaction. Simultaneously, the Naïve Bayes algorithm was harnessed to prognosticate forthcoming passenger satisfaction. The findings underscore the diverse facets of the flying experience impacting satisfaction, with both ctree and rpart decision tree algorithms spotlighting critical factors, such as online boarding, inflight entertainment, WiFi service, class, and travel type. The Naïve Bayes algorithm demonstrates around 87% accuracy in predicting passenger satisfaction, underscoring its efficacy in discerning patterns within this complex realm.

Pengaruh Kualitas Pelayanan Dan Harga Tiket Terhadap Kepuasan Penumpang Maskapai Batik Air Di Bandar Udara Abdulrachman Saleh Malang

Passenger satisfaction is a very important thing for an airline to be one of the benchmarks to see how far the company's success is in carrying out its vision and mission. The purpose of this study was to determine partially significant Customer Service Quality towards Batik Air Airline Passenger Satisfaction at Abdulrachman Saleh Airport Malang. To find out ticket prices partially significant to Batik Air Airline Passenger Satisfaction at Abdulrachman Saleh Airport Malang. To determine the effect of Customer Service Quality and Ticket Prices simultaneously on Batik Air Airline Passenger Satisfaction at Abdulrachman Saleh Airport Malang. This research uses descriptive quantitative methods. And multiple linear analysis technique. The number of samples used is 100 respondents. Data collection techniques in this study using a questionnaire. Data processing in this study uses the SPSS software program. The conclusion of this study can be seen that the Quality of Customer Service has a positive and significant influence on Passenger Satisfaction, Ticket Prices have a positive and significant influence on Passenger Satisfaction. Simultaneously it is known that the Quality of Customer Service and Ticket Prices have a positive and significant influence on Batik Air Airline Passenger Satisfaction at Abdulrachman Saleh Airport Malang.

Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis

Air transportation is vital due to technological advancements and globalization. It is affordable and accessible worldwide, providing efficient services to reach destinations globally. This discussion focuses on full-service airlines that offer online-based services. Previous research indicates that available facilities and services influence passenger satisfaction. Previous research on customer satisfaction showed a correlation between satisfaction and services without accurate figures. In the present study, the customer satisfaction figure is measured using the Naive Bayes and K-Nearest Neighbour (K-NN) algorithm to obtain a tested level of accuracy. In this analysis, we will compare the effectiveness of Naive Bayes and K-NN algorithms in classifying airline passenger satisfaction. The results show that the accuracy of the Naive Bayes method of the two algorithms is higher than the K-NN method. The accuracy value of the Naive Bayes method is 84.48%, while the accuracy value of the K-NN method is 65.38%. From the test results, the precision value for Naive Bayes is 82.25%, and K-NN is 67.35%. Furthermore, the recall value for Naive Bayes is 82.43%, and K-NN is 74.33%.

Prediction of US airline passenger satisfaction using machine learning algorithms

Due to the COVID-19 pandemic, the U.S. financial system and economy have also been severely affected. The U.S. airline industry has been hit particularly hard by the COVID-19 pandemic. Additionally, the aviation industry is also full of competition. One of the ways to attract customers and compete with other airline companies is by improving their service quality. Therefore, this study aims to predict the satisfaction of airlines based on the machine learning model and discover which features are more correlated with the target variable. In this study, the dataset consists of 129,880 observations and 1 target, 22 features or attributes (not including the identification). In this study, the result showed that the features that slightly correlate more with customer satisfaction are 'Online boarding', 'Inflight entertainment', 'Seat comfort', 'On-board service', 'Leg room service', 'Cleanliness', 'Flight Distance' and 'Inflight wifi service'. Then, K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC), Logistic Regression (LR), Random Forest (RF), Naïve Bayes (NB) and AdaBoost were used to build the classification models. Data cleaning, exploratory data analysis, feature selection and One Hot Encoding were also performed before building the models. Finally, the models were evaluated based on their accuracy, precision, recall and F1-score. The results suggest that the champion model for this study is Random Forest, which achieved 89.20% accuracy, 93.04% precision and 88.80% F1-score. The results of this study can be used as a guide in applying machine learning to predict the satisfaction of airline passengers. This can also contribute to attracting passengers by improving the airline service quality.

Pengaruh Service Performance dan Fasilitas Baggage Conveyor Belt terhadap Kepuasan Penumpang Maskapai Lion Air Dalam Pengambilan Bagasi di Bandar Udara Mopah Merauke


 Mopah Airport Merauke is an airport located in the city of Merauke, South Papua, Indonesia. The purpose of this study was to determine whether the effect of service performance and baggage conveyor belt facilities on Lion Air airline passenger satisfaction in baggage claim at Mopah Airport Merauke. This research uses quantitative research methods. This research was conducted at Mopah Airport Merauke where the objects of this research were passengers or the people in the city of Merauke. The source of the data used in this study was obtained from distributing questionnaires to respondents of Lion Air airline passengers at Mopah Airport Merauke. The number of samples in this study is 100 respondents determined by the slovin formula. The results of this study were concluded to answer the formulation of the problem, that: 1. The Influence Variable of Service Performance (X1) has an effect on the Passenger Satisfaction Variable (Y) as evidenced by the calculated T value on the variable effect of service performance (X1) which is greater than T table, namely 8,636 > 1,660 with a Sig value. smaller than the probability value of 0.000 <0.05; 2. The Baggage Conveyor Belt Facility Variable (X2) has an effect on the Passenger Satisfaction Variable (Y) as evidenced by the calculated T value being greater than T table, namely 11,118 > 1,660 with a Sig. smaller than the probability value of 0.000 <0.05; 3. The effect of service performance (X1), the baggage conveyor belt facility (X2) has an effect on the passenger satisfaction variable (Y) of 0.604, it is known from the R Square value, which means that the variables X1, X2 affect the Y variable by 60.4%.
 Keywords: Effect of Service Performance, BCB Facilities, Mopah Airport Merauke

An Optimized Deep Learning Approach for Improving Airline Services

The aviation industry is one of the most competitive markets. The most common approach for airline service providers is to improve passenger satisfaction. Passenger satisfaction in the aviation industry occurs when passengers’ expectations are met during flights. Airline service quality is critical in attracting new passengers and retaining existing ones. It is crucial to identify passengers’ pain points and enhance their satisfaction with the services offered. The airlines used a variety of techniques to improve service quality. They used data analysis approaches to analyze the passenger point data. These solutions have focused simply on surveys; consequently, deep-learning approaches have received insufficient attention. In this study, deep neural networks with the adaptive moment estimation Adam optimization algorithm were applied to enhance classification performance. In previous studies, the quality of the dataset has been ignored. The proposed approach was applied to the airline passenger satisfaction dataset from the Kaggle repository. It was validated by applying artificial neural networks (ANNs), random forests, and support vector machine techniques to the same dataset. It was compared with other research papers that used the same dataset and had a similar problem. The experimental results showed that the proposed approach outperformed previous studies. It has achieved an accuracy of 99.3%.

Performance Comparison of K-Means and DBSCAN Methods for Airline Customer Segmentation

Organizations are now fully embracing ideas such as customer success, customer loyalty, customer experience management and customer satisfaction. The application of these concepts must be based on three pillars of technology, process and people, to ensure that the organization ultimately has satisfied, loyal and successful customers. In today's competitive environment, as in all sectors, gaining great services in the aviation industry can provide a competitive advantage. With this study, it is aimed to help aviation companies to know how their services should meet the needs of customers and to obtain passenger satisfaction. Customer segmentation is widely used, which groups objects according to the similarity difference on each object and provides a high level of homogeneity in the same cluster or a high level of heterogeneity between each group. The aim of this study is to examine airline passenger satisfaction by using data mining methods including K-Means and Density-based spatial clustering of applications with noise (DBSCAN) clustering algorithms to reveal the service quality importance for customer satisfaction. K-Means algorithm achieved slightly better results than DBSCAN algorithm with a Silhouette value of 0.1450671.

An analysis on airline passenger satisfaction through services provided by international airline

An analysis on airline passenger satisfaction through services provided by international airline

An analysis on airline passenger satisfaction through services provided by international airline

International airlines have been seeing a consistent change in menu based on their client's interest. This created an impact on the design of an advanced menu that is compiled for a complete diet to provide full nourishment based on airline. Food and beverage service quality has become a feature for international airline companies in contending with one another and in keeping their esteem and noticeable quality in the choice of their specific airline travellers, as the extending wants of aircraft passengers lead to a healthy eating regimen with a customised menu in regard to flight catering. International airlines of various destinations has expanded the variety of food and refreshment with quality according to passengers, further in customising the diet with their desires, and through quality of food and beverages, in order to retain their passengers especially to get them back later on airline. The objective of this study is to identify the quality of food and beverage provided to international airline travellers. Secondly, on knowing the various expectations of passengers like onboard facilities, by upgrading the menu as per passenger's expectation and by the nature of airline administrations, in order to satisfy them with the quality of service.

Genetic Algorithm Optimization on Nave Bayes for Airline Customer Satisfaction Classification

Airline companies need to provide satisfactory service quality so that people do not switch to using other airlines. The way that can be used to determine customer satisfaction is to use data mining techniques. Currently, the website www.kaggle.com has provided Airline Passenger Satisfaction data consisting of 22 attributes, 1 label and 25976 instances which are included in the supervised learning data category. Based on several previous studies, the Naïve Bayes algorithm can provide better classification performance than other classification algorithms. Several studies also state that the use of Naive Bayes can be optimized using Genetic Algorithm (GA) to obtain better performance. The use of Genetic Algorithm for Nave Bayes optimization in classifying Airline Passenger Satisfaction data requires further research to ensure the performance of the given classification. This study aims to compare the use of the Naive Bayes algorithm for the classification of Airline Passenger Satisfaction with and without GA optimization. The data validation process used in this study is to use split validation to divide the dataset into 95% training data and 5% testing data. The test results show that the use of GA on Naive Bayes can improve the classification performance of Airline Passenger Satisfaction data in terms of accuracy and recall with an accuracy value of 85.99% and a recall of 87.91%.

Investigating airline passenger satisfaction: Data mining method

In the current competitive environment, winning excellent services in the aviation industry can gain competitive advantages. Aviation companies should understand how their services satisfy customers' needs and want to achieve passenger satisfaction. This study examines airline passenger satisfaction using a data mining method. As for service attributes, we conclude that: (1) online/mobile boarding, (2) inflight wi-fi service, (3) baggage handling, and (4) inflight entertainment is the top 4 crucial service to be improved by the airline to gain passenger satisfaction.

Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

Business Intelligence in Airline Passenger Satisfaction Study—A Fuzzy-Genetic Approach with Optimized Interpretability-Accuracy Trade-Off

The main objective and contribution of this paper is the application of our knowledge-discovery business-intelligence technique (fuzzy rule-based classification systems) characterized by genetically optimized interpretability-accuracy trade-off (using multi-objective evolutionary optimization algorithms) to decision support related to airline passenger satisfaction problems. Recently published and accessible at Kaggle’s repository airline passengers satisfaction data set containing 259,760 records is used in our experiments. A comparison of our approach with an alternative method (using SAS-system’s accuracy-oriented prediction tools to determine the attribute importance hierarchy) is also performed showing the advantages of our method in terms of: (i) discovering the actual hierarchy of attribute significance for passenger satisfaction and (ii) knowledge-discovery system’s interpretability-accuracy trade-off optimization. The main results and findings of our work include: (i) an introduction of the modern fuzzy-genetic business-intelligence solution characterized both by high interpretability and high accuracy to the airline passenger satisfaction decision support, (ii) an analysis of the effect of possible "overlapping" of some input attributes over the other ones in order to discover the real hierarchy of influence of particular input attributes upon the airline passengers satisfaction, and (iii) an extended cross-validation experiment confirming high effectiveness of our approach for different learning-test splits of the data set considered.