Airline Passengers Research Articles

Challenges and coping strategies in airline service under crosscultural context

This study explores the challenges faced by airline cabin service from the perspectives of globalization and intercultural communication, as well as potential strategies to address these issues. The paper first reviews and analyzes the intercultural interaction problems that have emerged in airline cabin service, including language barriers, cultural clashes, and the misunderstandings and conflicts they bring. Subsequently, through field research and in-depth interviews, this article proposes and demonstrates a series of strategies to address these issues, including providing cultural adaptability training, enhancing cabin crew's language proficiency and intercultural communication skills, and improving the quality of service. Through the study and analysis of these strategies, this paper concludes that intercultural sensitivity and adaptability are crucial for improving the quality of airline cabin service and passenger satisfaction, providing theoretical basis and practical reference for airlines.

A privacy-preserving federated learning approach for airline upgrade optimization

PurposeA key issue of making upgrade decisions is to match the most relevant upgrade offers to the right customers at the right time. To optimize upgrade strategies and profitability, companies seek to break “data silos” between themselves and other business partners for a more holistic view of customers' consumption experiences. However, multi-source data fusion may lead to potential privacy leakage. To overcome these two challenges in data silos and privacy protection, this study introduced a privacy-preserving federated learning (FL) approach and explained the process of using FL in modeling airline passengers’ willingness to pay for upgrade offers. Design/methodology/approachFederated learning is a new confidential computing technique that allows companies to train a model cooperatively by exchanging model parameters instead of the actual raw data, which might include customers' privacy sensitive information. Using a case study of a Chinese airline company, this study demonstrated how FL-based upgrade models using multi-source data can be developed to improve the accuracy of predicting customers' willingness to pay for upgrades while preserving customers’ personal data privacy. FindingsComparing with traditional unilateral model using single-source data, the federated logistic regression and SecureBoost models demonstrate better model performance. This indicates that the proposed FL approach can enhance the accuracy of modeling airline passengers' willingness to pay for upgrade offers while preserving passengers’ data privacy. The findings also show that the FL-based models generally took longer running time than the traditional unilateral model due to the design of FL approach in ensuring data privacy. OriginalityThis study contributes to the literature of upgrade optimization by introducing the new FL approach for developing machining learning models to predict customers’ reaction to upgrade offers. Although we focus on the airline industry in our case study, the proposed FL approach can be applied to other industries with a similar issue of upgrade optimization such as hotels or cruise lines, and car rental.

Tradeoffs between privacy and screening efficiency for air passengers – Implications for tourism supply chain management

ABSTRACT This study employs the stated preference method to explore airline passengers’ choices during security screening and examines their trade-offs between privacy and efficiency. We compare two Asian economic hub cities (i.e. Hong Kong and Shanghai), with 500 business and leisure travellers sampled from each city. A generalised mixed multinomial logit model is estimated. The results suggest that travellers display varied levels of privacy concerns, including biometrics, biographical data, identifiers, and behavioural information. The participants from Shanghai expressed a willingness to go through fingerprint scanning and to provide facial images. The travellers in Hong Kong displayed notable reluctance to receive iris scans and disclose political and religious affiliations, and they were particularly hesitant about sharing medication records. Despite some differences between the two markets, both groups strongly opposed smartphone tracking, including access to search history and GPS location. In general, business (vs. leisure) travellers, men (vs. women), individuals with higher education levels, and those with higher personal monthly incomes strongly preferred fast-track schemes that utilised personal data. Our findings highlight the possibility of making more effective transitions to high-tech screening methods that meet the changing expectations of travellers if total screening time can be significantly decreased.

A novel approach to measure perceived customer centricity in the passenger airline industry: Index development and validation

This study addresses a significant gap in current research by developing and validating a comprehensive index for measuring perceived customer centricity in the passenger airline industry. The index was derived using a rigorous scale development methodology based on three surveys with a total of 963 participants. The process involved construct definition, item generation, and item validation through exploratory and confirmatory factor analyses. The final measurement index comprises 19 items across four dimensions (Customer Care & Experience, Communication & Product, Sustainability, and Value) and demonstrates strong discriminant and convergent validity, providing a reliable tool for assessing customer perceptions. Therefore, this research introduces the first validated measure of perceived customer centricity specifically designed for the airline industry and offers practical insights for airline managers, enabling them to identify specific areas where customer perceptions fall short and to make targeted improvements. Additionally, the index serves as an alternative to the industry-wide adopted Net Promoter Score (NPS). It extends current literature by providing an industry-specific scale and offers a practical instrument for enhancing customer-centric strategies in real-world applications, thereby fostering long-term, mutually beneficial relationships. Additionally, it provides empirical evidence of the multidimensional nature of customer centricity and offers a validated tool that can be applied in future research to explore customer perceptions and their impact on airline performance, thus satisfying both practical and academic needs.

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.

Pengaruh Service Quality terhadap Repurchase Intention melalui Customer Satisfaction: Perspektif Penumpang Lion Air

The development of air transportation in Indonesia is increasingly rapid, and low-cost airlines (low-cost carriers) dominate the market share of domestic airline passengers in Indonesia. Lion Air is one of the leading low-cost airlines (LCC), which has a high number of passengers. This research aims to test the influence of service quality on customer satisfaction, service quality on repurchase intention, and the relationship between service quality on repurchase intention which is mediated by customer satisfaction. The research methodology used is descriptive-quantitative causality. The data analysis technique uses structural equation modeling with partial least squares (SEM-PLS) with SMARTPLS 3 software. The results of hypothesis testing show that service quality (PV, RD, SI) has a statistically significant influence on customer satisfaction and also state that service quality (EC, SA) does not have a significant influence statistically on customer satisfaction. Service quality has a direct impact on the likelihood of customers making repeat purchases, just as customer satisfaction also influences the likelihood of repeat purchases. Customer satisfaction is influenced by service quality, which in turn influences repurchase intentions.

Airline performance measurement using bias-corrected network data envelopment analysis estimates: Evidence from a turbulent era

This study's objective is to estimate airline performance and identify the most resilient business model during periods affected by global crises, including financial crises, natural disasters, pandemics, terrorist attacks, and post-crisis periods. A balanced panel of 80 airlines, classified into full-service carriers (FSC), low-cost carriers (LCC), high perceived quality, and low perceived quality, during 2008–2021 is used in this work. The data is sourced from the ICAO Data+ and Skytrax databases. Following the recent literature on airline efficiency, we consider airlines' internal structure, which unfolds into three stages: (a) operations, (b) services, and (c) sales. Also, we employ a novel three-stage multi-parametric bias correction (MPBC) slacks-based measure (SBM) network data envelopment analysis (NDEA) approach to estimate system- and stage-specific efficiencies. It has been proven that the MPBC estimates are consistent, while the MPBC-SBM-NDEA approach addresses the limitations of conventional NDEA and SBM-NDEA models. This work highlights that airline passengers have become more price-sensitive, selecting value-for-money airline products and services after the Global Financial Crisis. Given this trend and the remainder of the study's findings, LCCs offering value-for-money products and services and operating smaller-scale networks with smaller plane capacities are less vulnerable to external shocks and are easier to rebound during post-crisis periods.

Feelings and Physics: Emotional, Psychological, and Other Soft Human Requirements, by Model‐Based Systems Engineering

ABSTRACTTraditionally, engineering encourages requirements statements that are objective, testable, quantitative, atomic descriptions of system technical behavior. But what about “soft” requirements? When products deliver psychologically or emotionally based human experiences, subjective descriptions may frustrate engineers. This challenge is important for products appealing to senses of style, enjoyment, fulfillment, stimulation, power, safety, awareness, comfort, or similar emotional or psychological factors. Automobiles, buildings, consumer products, packaging, graphic user interfaces, airline passenger compartments and flight decks, and hospital equipment provide typical examples. This paper shows how model‐based systems engineering helps solve three related problems: (1) integrating models of “soft” human experience with hard technical product requirements, (2) describing how to score traditional “hard” technology products in terms of “fuzzier” business and competitive marketplace issues, and (3) coordinating marketing communication and promotion with the design process. The resulting framework integrates the diverse perspectives of engineers, stylists, industrial designers, human factors experts, and marketing professionals.

Influence of Delegating Leadership on Employee Performance at the International Airports in Kenya

The type of stewardship engaged by the senior management of a firm has a direct bearing on the employee performance. There is no ideal style of leadership that fits all organizations and hence the top echelon has to select the most appropriate bearing that will drive the firm in the desired direction and bear the sought after outcomes. Several airports in Africa today continue to experience low employee performance as a result of the type of leadership selected by the top management and hence the need for diligence when picking the type of stewardship to be deployed in African Airports. This paper sought to establish the influence of delegation on employee performance at four international airports in Kenya, namely: Jomo Kenyatta International Airport, Nairobi; Moi International Airport, Mombasa; Kisumu International Airport and Eldoret International Airport. Positivism philosophy was preferred in this study because it advocates for the use of accurate and verifiable evidence that is free from assumptions and opinions of the researcher. A survey research was used to conduct the study. The respondents were first line; middle level and senior level managers of the five departments involved in the processing of air passengers within the four international airports in Kenya. These were; airport security employees, port health and quarantine services employees, passenger airlines operating within the four international airports in Kenya employees, immigration and border control employees and finally the customs services employees. Data was gathered from primary sources using self-administered structured questionnaires. The data analysis was done using descriptive and inferential statistics. From the Linear regression, the value of p was established as p<.05 (p value of 0.00 and ꞵ=0.474) and therefore the null hypothesis was rejected indicating that delegation has a significant impact on employee performance.

The adoption of self-service check-in kiosks among commercial airline passengers

This study examines passenger adoption of self-service check-in kiosks based on the technology acceptance model (TAM) and its extension. This study investigated the relationships between three independent variables (technology self-efficacy, need for human interaction, and perceived enjoyment) and two TAM's cognitive variables (perceived usefulness and ease of use). The analysis of these relationships is novel in aviation self-service technology research. Structural equation modeling (SEM) was employed to analyze 346 valid responses. The analysis reveals that enjoyment enhances the perception of self-service check-in kiosks as useful and easy to use, resulting in greater intention to use them. Moreover, passengers with higher confidence in their technological self-efficacy are more likely to perceive check-in kiosks as helpful. However, passengers may perceive kiosks as less useful when they require more human interaction. The results of this study contribute to the current knowledge on aviation self-service adoption. Additionally, managerial implications are offered to improve the future of airlines' passenger services.

Navigating loyalty and trust in the skies: The mediating role of customer satisfaction and image for sustainable airlines

This study aims to pinpoint the key factors influencing customer loyalty and trust within the airline industry. The study investigates the impact of service quality (assessed through the SERVQUAL model), pricing fairness, and perceived value on passengers' loyalty and trust in airlines. Additionally, the research delves into the mediating influences of passenger satisfaction and airline image in this domain. The data has been collected from the passengers who have lately used air transport to travel. The respondents included 400 passengers and were targeted using a purposive sampling technique. The findings reveal a significant positive correlation between service quality, price fairness, and perceived value, as well as passengers' loyalty and trust in airlines. Furthermore, we discovered that customer satisfaction and the airline's image played crucial roles as mediators, enhancing our understanding of the core mechanisms driving loyalty and trust formation among passengers.

Evaluating Domestic Passenger Satisfaction with In-Flight Catering Services

In-flight catering services are an essential part of the passengers' experience during travel. However, existing literature reveals that in-flight catering services have changed from time to time due to the increasing number of options, menu dishes, and new preparation methods and materials. Therefore, the main objective of this study is to examine the factors that affect passengers' satisfaction with in-flight catering services. Hence, the data were collected through an online survey among airline passengers using a snowball sampling method. The snowball sampling method was chosen for its ability to reach a diverse range of participants. The quantitative data were analyzed using SPSS version 22 software. Descriptive analyses and correlation analyses were used for the empirical study. Results revealed that environmentally friendly food packaging has the highest mean among the three variables: packaging, menu choices, and food quality. This proves that environmentally friendly food packaging is the most prevalent factor in passengers’ satisfaction with in-flight catering services. The result showed that three factors, packaging, menu choices, and food quality, made a unique and statistically significant contribution to the passengers' satisfaction. From these three factors, we would like to identify which factor most influences passengers' satisfaction with in-flight catering services.

Forecasting airline passengers’ satisfaction based on sentiments and ratings: An application of VADER and machine learning techniques

To the best of the authors' knowledge, research predicting airline passengers' satisfaction based on their sentiments and ratings is seldom sighted. Additionally, the literature reveals that most studies have primarily concentrated on specific airlines or routes, neglecting to conduct a comparative analysis of satisfaction levels across numerous airlines and routes. Hence, this research aims to predict passengers' satisfaction by combining the sentiment of their reviews and ratings on various parameters like food, entertainment, seat comfort, ground service, and value for money. Using the "Skytrax Airline Reviews" dataset, which contains data about 81 airlines and 64440 reviews, our research analyzes and predicts airline passengers' satisfaction based on sentiments and ratings using nine popular machine learning techniques. The study found that the LightGBM obtains an accuracy of 97 percent in predicting customer satisfaction. The results further reveal that 'value for money' and 'ground service' are crucial factors in determining the passengers' satisfaction, whereas 'entertainment' had no significant impact. Our study thus provides a valuable tool for predicting airline industry customer satisfaction and gives insight into the factors contributing to passenger satisfaction. These findings can further help airlines better understand their passengers' needs and improve their services accordingly.

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.

Strategic Customer Segmentation: Harnessing Machine Learning For Retaining Satisfied Customers

This research paper explores the burgeoning field of machine learning and its application in strategic customer segmentation within the aviation industry. Leveraging the Airline Passenger dataset, this study assesses the potential of various machine learning classifiers to enhance customer retention by effectively segmenting satisfied customers. Our methodology involves a comparative analysis of five machine learning classifiers: Random Forest, K-Nearest Neighbors (KNN), Decision Tree, Naive Bayes, and Artificial Neural Network (ANN). Each classifier is rigorously tested and evaluated based on key performance metrics including accuracy, precision, recall, and F1-score. The results indicate a diverse range of classifier effectiveness. Notably, the Random Forest classifier outperforms others with outstanding metrics: accuracy, precision, recall, and F1-scoreof 0.96. Decision Tree follows closely, also achieving high performance with a score of 0.95 across all metrics. Naive Bayes and ANN demonstrate respectable performance, with accuracyscores of 0.86 and 0.90 respectively. In contrast, KNN presents lower but consistent performance, with all metrics at 0.75. These quantitative findings highlight the nuanced performancedifferences among classifiers, emphasizing the critical role of algorithm selection in achieving precise customer segmentation. This study provides significant insights into the application of machine learning for strategic customer retention in the aviation sector, presenting practical implications for airlines aiming to optimize their segmentation strategies and retain satisfied customers. By showcasing the varying performances of different classifiers, this research contributes to the broader discourse on integrating machine learning into customer-centric strategies, ultimately aiding airlines to engage and retain their customer base more effectively.

Key characteristics of perceived customer centricity in the passenger airline industry: A systematic literature review

The airline industry is operating in a highly competitive and saturated market and is considered to be one of the most dynamic service industries in the world. Maintaining a high level of passenger satisfaction and incorporating customer centricity into an airline's strategy can be the key competitive advantage. Despite the growing interest of scholars and managers in customer centricity, research on this topic in the airline industry has just started. However, it is key to understand how a firm's intentions to become customer-centric finally translate into customer perceptions. For this purpose, the authors developed a perceived customer centricity framework which is tailored to the environment of the passenger airline industry. A systematic literature review was conducted to review and analyze key characteristics and measures of perceived customer centricity in the airline industry. The authors identified 15 distinct key characteristics (measures) in literature and conclude that airlines need to focus on various aspects to truly be customer-centric. Investing in sustainable practices, employee training, modern in-flight experiences, competitive pricing, technological infrastructure, and positive brand associations all contribute to customer centricity. Understanding how these elements are interconnected allows airlines to customize their efforts, meeting current and future passenger needs, and ultimately creating a standout customer-centric culture in the competitive airline industry. By formulating concrete measures this paper establishes a foundation for future perceived customer centricity research, such as surveys, and presents alternative measures to the widely adopted net promoter score (NPS).

Pengaruh Kualitas Pelayanan Petugas Check-In Counter KNP Kokapura terhadap Kepuasan Penumpang Maskapai Wings Air di Bandar Udara Internasional Jendral Ahmad Yani Semarang

General Ahmad Yani Semarang International Airport (SRG) is an airport located in the city of Semarang. KPN Kokapura Ahmad Yani must pay attention to services and facilities in the Check-In Counter area because they can affect passenger satisfaction. The main objectives of this research are as follows: 1) to find out whether there is an influence on the service quality of KPN Kokapura Check-In Counter officers on passenger satisfaction with Wings Air at Jendral Ahmad Yani International Airport Semarang and 2) to find out how much influence the Service Quality of Check-In Officers has. KPN Kokapura Counter on Wings Air Passenger Satisfaction at Jendral Ahmad Yani International Airport, Semarang. This research method uses quantitative methods. The number of samples in this research was 100 respondents. The respondents in this research were Wings Air passengers at Ahmad Yani International Airport, Semarang. The sampling technique in this research uses a Non-Probability Sampling technique with a Purposive Sampling approach, namely a sampling technique based on certain criteria that will be used as samples. Data analysis techniques use data management and drawing conclusions. The research results show that the significant value is 0.000<0.05 and the calculated t value is 3.604>t table value 1.660, which means that there is a positive influence on the quality of the KPN Kokapura Check-In Counter service on the satisfaction of Wings Air airline passengers at Jendral Ahmad Yani International Airport, Semarang. Based on the R square (R2) value of 0.117. shows that the Check-In Counter officer's service quality variable has an effect of 11.7% on passenger satisfaction. Meanwhile, 88.7% is explained by variables other than the independent variables in the research.

Pengaplikasian Data Analisis pada Data Penundaan Penerbangan dan Kinerja Maskapai di Suatu Bandara

The aviation industry is growing very significantly due to increased mobility, economic expansion and current technological advances. However, as mobility increases and the number of airline passengers increases, airlines experience problems in overcoming flight schedule delays or in other words delays. Data analysis plays an important role in processing large amounts of data to find hidden insights in data. The aim of writing this service journal is to identify days with the highest average minutes of delay, months that experience the highest average delays, and at what times flight delays often occur. . This service object uses secondary data obtained from the Kaggle site. It is hoped that this journal can be used as material for consideration in decision making and improving the operational performance of each airline involved.

Crossing the Line at Altitude: Passenger Sexual Misconduct in the Skies, 1999-2021

ABSTRACT Over the past decade, a range of news outlets and social media platforms has increasingly featured reports of airline passenger misconduct, particularly those involving verbal and physical assault. Less attention has been paid to passenger sexual misconduct. To improve our understanding of the nature and types of air passenger sexual misconduct cases, we draw on data from 77 incidents reported from 1999 to 2021 to develop a multi-classification scheme of misconduct and examine the precipitating factors as well as related circumstances involved. Our findings demonstrate passenger sexual misconduct takes three forms – sexual assault, sexual deviance, and sexual harassment – and highlight the complexity of circumstances, factors, and types of victims (i.e. passengers, flight attendants, and crew) involved in these reports.

Examining cross-cultural patterns in customer citizenship behavior: A cross-cultural exploration between Turkish and British airline passengers

Customer citizenship behavior (CCB) refers to actions that provide value to companies, such as offering feedback, being patient with disruptions, and assisting other customers. This is especially important in the airline industry, where services are provided collectively in a shared environment. Recognizing that the factors influencing CCB might differ based on national culture is crucial, particularly for the international operations of the airline industry. However, the impact of national culture on shaping CCB in this industry remains unclear. Using three theoretical lenses, namely expectation confirmation theory (ECT), social exchange theory (SET), and cultural dimensions theory (CDT), this study aims to investigate how national culture affects the formation of CCB through the proposed conceptual model. To achieve this, the study gathered responses from 743 passengers—both Turkish (n = 323) and British (n = 420)—using a structured web-based questionnaire. The analysis employed partial least squares structural equation modeling (PLS-SEM) and multigroup analysis (MGA). The results indicated that factors like service quality and value for money have a positive influence on customer satisfaction. Customer satisfaction has a positive impact on behaviors such as giving feedback, advocating for the company, assisting others, and being patient with disruptions. The proposed relationships are also mediated by value for money and customer satisfaction. This study stands as the first effort to investigate how national culture moderates the formation of CCB in the context of the airline industry.