Airline Passengers Research Articles

Liability of Airlines That Unilaterally Cancel Flights in Indonesia

Background, Aviation is one of the significant forms of business deregulation in Indonesia. The existence of aircraft makes it easy for passengers to move between regions quickly and efficiently for various purposes. The commercial air transportation sector is growing rapidly, as seen by the increasing number of airlines providing flight services and the rising public interest in using these services. However, in reality, many consumer rights have not been fully fulfilled by airlines, especially in the case of flight delays. For this reason, strict legal regulations are needed to regulate airlines' responsibilities to protect consumer rights. In its implementation, it turns out that passengers submit many complaints to the airline due to various problems. Aim, Clear and firm legal regulations are needed to regulate airlines' responsibilities to protect passengers' rights. This research focuses on two main issues, namely how the airline's liability to passengers is related to flight schedule delays and how to claim compensation for airline passengers related to flight schedule delays. Methods, This study uses a normative juridical approach by analysing laws and regulations related to legal protection for consumers. The data obtained was analysed qualitatively to understand the issue more deeply. Results, The study results show that the Regulation of the Minister of Transportation Number 89 of 2015 expressly regulates the rights of consumers affected by flight delays and stipulates the obligations of airlines to aggrieved passengers. Conclusions, The legislation was made to ensure that passengers get safety and security in running flights, as well as passengers, are more independent and know the importance of the issuance of the law. Implication, Furthermore, the regulation can be used as a foothold for passengers' legal protection against problems.

Optimal baggage allowance design for passenger airlines with pooling effect for baggage demand

Optimal baggage allowance design for passenger airlines with pooling effect for baggage demand

Comparative analysis between Garuda Indonesia and Lion Air: A study of Indonesia’s leading airlines

<p>This paper compares Garuda Indonesia and Lion Air, two leading Indonesian airlines, focusing on their operational, financial, and strategic differences. Garuda, a state-owned full-service airline, targets business and international travelers with a focus on premium services and safety, while Lion Air, a low-cost carrier (LCC), caters to budget-conscious passengers through affordability and extensive domestic coverage. The study highlights Indonesia’s reliance on air travel due to its archipelagic geography and explores how these airlines shape market competition and passenger experiences. Employing qualitative and quantitative analysis, the paper uses industry reports, financial data, and customer reviews to examine their operational efficiency and market strategies. It finds that while Lion Air’s low-cost model is cost-effective, its safety record impacts customer trust. Garuda Indonesia, on the other hand, struggles with financial sustainability. The study suggests that a balanced regulatory approach could enhance national connectivity and economic growth by supporting both service models.</p>

Attitudes of airline passengers under uncertainty - A case study of the COVID-19 pandemic in Poland

Attitudes of airline passengers under uncertainty - A case study of the COVID-19 pandemic in Poland

A comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfaction

A comparative study of statistical and intelligent classification models for predicting airlines passenger management satisfaction

Passenger Traffic Trends and Barriers: Post-Implementation of the Regional Connectivity Scheme (RCS) – UDAN

The Regional Connectivity Scheme (RCS) – UDAN (Ude Desh Ka Aam Nagrik) was launched in 2016 as a strategic initiative by Ministry of Civil Aviation (MOCA), Government of India to enhance regional air connectivity and make air travel affordable for the masses in semi-urban and rural areas. While the scheme has supported the expansion of air services to underserved and unserved airports, its long-term sustainability and effectiveness remain subjects of analysis. This study examines passenger traffic trends and barriers post-implementation of UDAN, identifying factors affecting its success and challenges that hinder its continued growth and sustainability. The research aims to analyse passenger growth patterns, assess the operational and economic barriers faced by the operators, evaluate the effectiveness of Government incentives through Viability Gap Funding (VGF), and compare UDAN with global regional air connectivity models such as the U.S. Essential Air Service (EAS) and Europe’s Public Service Obligation (PSO) routes. A mixed-method research approach is adopted to incorporate the quantitative analysis of DGCA and AAI passenger traffic data (2017–2023) and qualitative insights from the surveys and interviews with passengers, airline operators, and policymakers. Findings to reveal the initial passenger trends and growth on UDAN routes (2017–2020), followed by stagnation and a 39.39% decline in 2022–23 due to operational inefficiencies, low demand, and airline sustainability challenges. Infrastructure constraints, airline profitability issues, passenger awareness, and policy execution gaps emerges as critical barriers. The study concludes that while UDAN has successfully expanded regional connectivity, but continued policy refinements, financial sustainability strategies, and multimodal integration are essential for its long-term success. Recommendations include extended financial support, infrastructure upgrades, and improved airline participation incentives to ensure the financial and operational viability of regional air connectivity. Keywords: Regional Connectivity, Passenger Traffic Trends, Viability Gap Funding, Airline Sustainability, Infrastructure Barriers

Foreseeing the future of robotics in service sector: unveiling factors influence individual behavior to adopt services robots

Purpose The artificial intelligence-enabled service robot has the potential to serve in consumer market. However, there is limited knowledge of how users across the globe respond to this innovation. Therefore, the purpose of this study is to develop an integrated research model led by three known theories, namely, diffusion of innovation, theory of planned behavior and DeLone and McLean model, and investigates consumer intention to adopt robot-enabled services. Design/methodology/approach The quantitative research design is selected in this research. Data were collected through structured questionnaires. Overall, 221 airline passengers participated in the service robot survey. These responses were further analyzed with a structural equation modeling approach. Findings Statistical findings have revealed that jointly perceived behavioral control, compatibility, innovativeness, information quality, system quality and service quality explained R2 50.3% variance in user attitude to adopt service robots. In addition to that robot appearance and user attitude have explained a large variance R2 48.9 % in user intention to adopt service robots. Nevertheless, results have revealed an insignificant moderating impact of robot appearance on user attitude and intention to adopt service robots. Practical implications Practically, this research has suggested that information quality, adequate system quality and service quality boost user confidence and encourage users to adopt services led by robots. Similarly, this research has suggested that compatibility brings ease to the use of robot technology and innovativeness attracts technology users. Therefore, policymakers should focus on compatible and innovative characteristics of robot services. Moreover factors such as subjective norms and perceived behavioral control have shown a positive impact in measuring user intention to adopt service robots and hence need managerial attention. Originality/value The current research is original, as it gives deep insight into understanding user behavior toward the adoption of service robots. Moreover, the integration of three known theories, namely, DeLone and McLean model, the diffusion of innovation model and the theory of planned behavior in service robot setting has made current research more unique. Similarly, this study is consistent with United Nations sustainable development goals and ignites SDG progress by promoting artificial-driven robotic technology in the services sector.

Comparison of Supervised Learning Algorithms for Predicting Airline Passenger Satisfaction

Service quality and airline passenger satisfaction are the main factors in business success in the modern aviation industry. This research compares the performance of supervised learning algorithms, namely K-Nearest Neighbor (K-NN), Naïve Bayes, Decision Tree, Random Forest, and Support Vector Machine (SVM), to predict passenger satisfaction. The k-fold cross-validation method with k=20 was applied to ensure comprehensive model evaluation by dividing the data proportionally. Using a high value of ???? was chosen to optimize the stability of the model estimates, reduce the risk of overfitting, and produce more accurate evaluation metrics. The research results show that the Random Forest algorithm provides the highest accuracy of 95.78%, followed by Decision Tree (93.82%) and K-NN (91.85%). These results indicate that the Random Forest algorithm better classifies passenger satisfaction than other algorithms. This research confirms the potential of machine learning algorithms as a practical solution in data analysis to support strategic decision-making, especially for airlines that want to improve customer experience.

Minimizing passenger disruption and airline costs in hurricane recovery: A multimodal rescheduling approach

Minimizing passenger disruption and airline costs in hurricane recovery: A multimodal rescheduling approach

Decoding Airline Passenger Sentiment Dynamics Using Transformer‐Based Models and Time‐Series Clustering

ABSTRACTThis study proposes a novel framework, namely the Sentiment Trend Analysis on Reviews (STAR). The STAR framework incorporates time‐series clustering into sentiment detection to discover distinct airline passenger sentiment patterns, followed by transformer‐based analysis to identify customer concerns from review text that drives negative sentiments. By applying the STAR framework to U.S. airlines, we identified notable differences in sentiment trends between low‐cost carriers (LCCs) and full‐service carriers (FSCs). Passengers are consistently dissatisfied with LCCs' baggage handling procedures and fees throughout the year and are more concerned about bookings and refunds during the Christmas holidays. For FSC passengers, flight cancellations and delays rank among their top concerns even during periods without major holidays. This study provides guidance for researchers and airlines to respond to the dynamics of customers' expectations and design recovery actions.

Body Scanners at Airports: The Dilemma between Aviation Security and Passenger Privacy Rights

The aviation industry has tightened its security measures due to the increasing threat of attacks at airports. The use of body scanners can be considered an important step in enhancing flight security by detecting prohibited items carried by passengers. However, this can raise privacy concerns, as it may reveal sensitive information about the passengers' bodies. Although security checks at airports are often seen as an extension of aviation security, the implementation and practices of these checks must adhere to human rights and individual privacy. Therefore, the Chicago Convention of 1944 safeguards the safety of international civil aviation and protects the rights of passengers. Although the use of body scanners can be seen as a security measure, it must be carefully considered to balance the need for safety with the protection of individual privacy. This article aims to explain the implications of body scanners on the privacy rights of airline passengers. This research employs normative legal research, and the research method applied in this study is a literature review utilizing books, scientific journals, and other sources that regulate body scanners and passenger privacy rights at airports.

How do differences in airline passengers’ satisfaction with connectivity modes affect last-mile travel choices? A SALC modeling based on RRM

How do differences in airline passengers’ satisfaction with connectivity modes affect last-mile travel choices? A SALC modeling based on RRM

Dimensioning Space-Air-Ground Integrated Networks for In-Flight 6G Slice Orchestration

Dimensioning Space-Air-Ground Integrated Networks for In-Flight 6G Slice Orchestration

Airline revenue management, distribution and passengers: market trends in a technology driven triangle

PurposeThe aim of this paper is a first attempt to map and describe airline information systems (IS) market, in the context of the latest trends affecting air services distribution, revenue management (RM) and passengers in the airline industry.Design/methodology/approachRM evolution has greatly affected airline revenues and load factors in a global environment characterized by fierce competition. The distribution of air services has shifted gradually from a model dependent on travel agents and other intermediaries toward a new trend of do-it-yourself (DIY) bookings through the Internet. The resulting new and significantly more complex environment mandates the connection between revenue management systems (RMS) and reservation systems (RS) through third parties (aggregators) that have emerged and are ready to perform this task. In this context, a market review has been carried out, following the approach of a structured literature review to illustrate this ecosystem.FindingsData collected from the vendors depict the market size and capabilities, highlighting that the market for IS in the airline industry is based on a relational triangle of dependence and competition that includes RS, RMS and aggregators.Originality/valueMapping both technological and functional backgrounds and the operation of current distribution systems and their linking with RMS and methods is deemed a necessity for identifying and describing the mechanisms that are defining the B2B relationship and the subsequent need to improve the ways current distribution schemes combined with RM applications are addressing changing market needs, reflecting the new behavioral models of the customers.

The Forecasting for Number of Airplane Passengers at International Airport Soekarno Hatta, Jakarta Using Some Time Series Models

Indonesia is a vast country comprising five major islands that serve as the economic centers. Consequently, transportation methods, such as airplanes, are essential for facilitating economic transactions between these islands. The number of airline passengers is a crucial component in fostering the growth of the air transportation industry, which significantly influences the economy between islands. Therefore, it is essential to conduct periodic analyses of passenger numbers. A forecast analysis that predicts the number of airline passengers for the upcoming period will offer valuable insights, enabling the air transportation sector to continue its successful development. This research focuses on forecasting the monthly number of airplane passengers for the upcoming year at Soekarno-Hatta Airport, utilizing passenger data from January 2015 to August 2024. Four distinct time series models will be employed in this analysis: additive decomposition, multiplicative decomposition, Holt-Winters additive, and Holt-Winters multiplicative. The most critical aspect of evaluating the best model involves analyzing the smallest error results from each model, utilizing the Mean Absolute Error (MAE) testing tool for this purpose. The Holt-Winters additive model emerges as the superior choice, while both the decompose additive and decompose multiplicative models were unable to generate data with sufficiently narrow gaps, particularly during the significant decline in passenger numbers caused by the COVID-19 pandemic.

Exploring Aviation Accidents and Related Costs: A Critical Literature Review across Global and Greek Skies

Safety has a direct impact on the economic policy of an organization, especially when it concerns critical sectors such as military, commercial, and general aviation. It is apparent that such organizations prioritize aviation safety with great attention since even a single accident can cost not only human lives but also the financial survival of the respective company (Chance & Ferris, 1987). According to data from the National Transportation Safety Board (NTSB), the rate of accidents per 100,000 flight hours for U.S. air carriers has significantly declined over time, dropping from 1.77 in 1965 to 0.20 in 1990, and further decreasing to 0.13 in 2020 (NTSB, 2021). Also, the Massachusetts Institute of Technology (MIT) revealed a significant decline in airline passenger fatalities by considering the total number of passengers boardings. Specifically, this rate stood at one fatality for every 7.9 million passenger boardings in 2020, which is a notable improvement from one fatality per 2.7 million boardings between 1998 and 2007. Looking further back in history, the risk of fatality for commercial airline passengers was one death per 750,000 boardings during the period of 1978-1987 and one death per 350,000 boardings between 1968 and 1977 (Barnett, 2020). Nevertheless, despite aviation is currently safer than ever, accidents still occur globally. In this context, and by drawing on the literature, a critical review is conducted so as to analyze the financial consequences of aviation accidents and explore accident rates globally. Also, a special focus on Greece is attempted to facilitate comparison between worldwide and national accident rates. The aforementioned goals were pursued by discriminating between military, commercial and general aviation in order to pinpoint differences between these sectors. It is envisaged that through this process, additional knowledge will be built regarding the accident cost in aviation. By acknowledging that diagnosis is the initial response to overcoming challenges, it is envisioned that analyzing the current accident rates and costs will aid in the development of more advanced mitigation strategies aimed at safety optimization. Keywords: Transportation Safety, Aviation Industry, Cost Analysis, Regulation.

Revisiting Passengers’ Perceptions of Airline Service Quality: A Theory-Driven Machine Learning Approach Using Big Data

ABSTRACT Understanding the discrepancy between passenger expectations and airline offerings is critical for improving service quality and customer satisfaction. This study expands the current knowledge regarding airline service quality dimensions by investigating actual passenger experiences. Analyzing 1,389,689 online reviews from TripAdvisor.com, structural topic modeling identified 29 airline service quality factors. Drawing upon AIRQUAL and SERVQUAL as theoretical frameworks, five unique dimensions emerged: flight attendance service, flight schedule, in-flight entertainment, in-flight meal, and baggage. Moreover, regression analysis highlights the significance of flight attendant services and hidden critical factors, such as attention to child passengers and special requests, in enhancing customer satisfaction. This study provides theoretical implications by uncovering airline service quality factors recognized by passengers and suggesting five potential service quality dimensions suitable for the airline context. Practically, the current study guides airline managers in understanding a passenger viewpoint and avoiding the service gap by combining machine learning, econometric approaches, and existing theories.

The Impact of the Characteristics of Self-Service Technologies on Customer Experience Quality: Insights for Airline Companies

Research on self-service technologies (SSTs) has not been fully developed, and it is still open to debate with many aspects concerning its effect on customer experience and potential outcomes empirically, especially in the airline industry. Studies regarding these technologies and their potential impact are needed in the airline industry as they represent an integral part of the tourism industry. Previous studies on the airline industry have merely focused on SSTs and their impact on customer adoption, tendency to use, and satisfaction. The SSTs have the capacity to influence how customers perceive their experience in the overall process of getting a service. Thus, customer experience quality (CXQ) is influenced by the perceived characteristics of SSTs. The literature on the impact of SSTs on CXQ is considerably limited in general, particularly in the airline industry. More research on this issue is needed, especially following the outbreak of COVID-19; thus, this study aims to investigate a model that integrates the impact of the perceived characteristics of SSTs as antecedents and outcomes of CXQ. The research design of this study is based on a mixed-method approach: a preliminary study consisting of two qualitative investigations and a main study through face-to-face questionnaires with airline passengers. Structural equation modeling (SEM) is applied to the data collected from passengers traveling with the airline company in Turkey (N=501) through questionnaires applied as mall intercepts. The results of this study include extending the CXQ dimensions to add consistency and institutionalism, hence contributing to the service and tourism literature. Furthermore, this research provides actionable insights for managers in airline businesses to invest more in SSTs to improve their CXQ, customer satisfaction, and positive word-of-mouth communication (WOM).

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.

Environmental certification and the financial performance of passenger airlines. The mediating effect of image perception, asset management and employee behaviour

Environmental certification and the financial performance of passenger airlines. The mediating effect of image perception, asset management and employee behaviour