Search Results (45)

Flight delays represent a significant challenge for airlines, airports, and passengers, impacting operational costs and customer satisfaction. Traditional prediction methods often rely on complex statistical analysis and mathematical models that may not be easily implementable. This study proposes scorecards as an innovative and simplified approach to forecast flight delays. Historical flight data from the United States were used, incorporating variables such as departure and arrival times, flight routes, aircraft types, and other factors related to delay. Exploratory data analysis identified key variables influencing delays, and scorecards were constructed by assigning weights, normalizing, and scaling variables to improve interpretability. The model was validated using test datasets, and predictive performance was evaluated by comparing forecast delays with actual results. The results indicate that scorecards provide accurate and interpretable predictions of flight delays. This method facilitates the identification of critical factors that contribute to delays and allows for an estimation of their likelihood and duration. Scorecards offer a practical tool for airlines and airport operators, potentially enhancing decision-making processes, reducing delay-related costs, and improving service quality. Future research should explore the integration of scorecards into operational systems and the inclusion of additional variables to increase model robustness and generalizability. Full article

This research aims to identify patterns and root causes of aircraft downtimes by comparing various forecasting models used in the aviation industry to prevent AOG events effectively. At its heart, this study explores innovative forecasting models using time series analysis, time series modeling and binary classification to predict spare part usage, reduce downtime, and tackle the complexities of managing inventory for diverse aircraft fleets. By analyzing both data and insights shared by aviation industry experts, the research offers a practical roadmap for enhancing supply chain efficiency and reducing Mean Time Between Failures (MTBF). The thesis emphasizes how real-time data integration and hybrid forecasting approaches can transform operations, helping airlines keep spare parts available when and where they are needed most. It also shows how precise forecasting is not just about saving costs, it is about boosting customer satisfaction and staying competitive in an ever-demanding industry. In addition to data-driven insights, this research provides actionable recommendations, such as embracing predictive maintenance strategies and streamlining logistics. These steps aim to ensure smoother operations, fewer disruptions, and more reliable service for passengers and operators alike. Full article

Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports. Full article

This research introduces a novel framework that allows the comparative evaluation of airlines based on passengers’ flight experiences. The proposed framework combines a typical and a simulation-based extension of the AHP in a group decision-making environment to elicit rankings of various airlines. The first option (T-AHP) generates rankings by combining individual passengers’ preferences using the geometric mean synthesis rule. The second option (S-AHP) simulates the stochastic characteristics of the responses, aiming to handle the inherent uncertainty and the variety of preferences obtained by the customers. The rankings are derived by mapping the decision space according to the evaluation criteria implemented and passengers’ preference dimensions. The proposed options are illustrated through a case study where four airlines are evaluated using 51 satisfaction dimensions (sub-criteria). Although the derived results indicate similar rankings, those obtained by the S-AHP option are more stable and robust, with greater discriminatory capacity compared to those of its typical counterpart (T-AHP). Full article

This research tackles an essential gap in understanding how passengers prefer to interact with artificial intelligence (AI) or human agents in airline customer service contexts. Using a mixed-methods approach that combines statistical analysis with fuzzy set theory, we examine these preferences across a range of service scenarios. With data from 163 participants’ Likert scale responses, our qualitative analysis via fuzzy set methods complements the quantitative results from regression analyses, highlighting a preference model contingent on context: passengers prefer AI for straightforward, routine transactions but lean towards human agents for nuanced, emotionally complex issues. Our regression findings indicate that perceived benefits and simplicity of tasks significantly boost satisfaction and trust in AI services. Through fuzzy set analysis, we uncover a gradient of preference rather than a stark dichotomy between AI and human interaction. This insight enables airlines to strategically implement AI for handling routine tasks while employing human agents for more complex interactions, potentially improving passenger retention and service cost-efficiency. This research not only enriches the theoretical discourse on human–computer interaction in service delivery but also guides practical implementation with implications for AI-driven services across industries focused on customer experience. Full article

This study investigates the integration of customization and personalization approaches in aviation maintenance through Aviation Technical Support as a Service (ATSaaS) platform. Through a comprehensive survey of 86 small and medium-sized airlines, combined with mathematical modeling of fault detection systems, the study develops and validates a hybrid framework that integrates traditional maintenance approaches with AI-driven solutions. The comparative analysis demonstrates that the hybrid model significantly outperforms both pure customization and pure personalization approaches, achieving a 95% fault detection rate compared to 75% for customization-only and 88% for personalization-only models. The hybrid approach also showed superior performance in predictive maintenance effectiveness (96%), operational downtime reduction (92%), and cost optimization (90%). The research presents three architectural frameworks for ATSaaS implementation—customization-based, personalization-based, and hybrid—providing a structured approach for different airline categories. Large airlines, with their extensive technical expertise and complex operational requirements, benefit from enhancing their customized maintenance programs with personalization tools, improving overall maintenance efficiency by 23%. Simultaneously, smaller operators, often constrained by limited resources, can use ATSaaS platforms to access sophisticated maintenance capabilities without extensive in-house expertise, reducing operational costs by 35% compared to traditional approaches. The study concludes that the successful integration of customization and personalization through ATSaaS platforms represents a promising direction for optimizing aviation maintenance operations, supporting the industry’s movement toward data-driven, adaptive maintenance solutions. Full article

The purpose of this study is to identify airline asymmetric attributes that affect customer satisfaction based on the three-factor theory and to build an Asymmetric Impact-Sentiment Analysis (AISA) for resource allocation prioritization. We identified food and beverage service as a basic factor; inflight service as a one-dimensional factor; and seat comfort, ground service, and airline seat class as attractive factors. AISA analysis results showed that food and beverage services should be prevented from falling into customer dissatisfaction through Urgent Action (cell II), and in-flight services should be improved continuously to increase satisfaction (cell IV). Low-priority improvement is required for seat comfort, which is an attractive factor (cell VI), and strong maintenance is required for ground service and airline seat class as strength factors (cell V). The first contribution, the asymmetric relationship between customer opinions for service attributes and satisfaction, was verified. Second, this paper extended the IPA to the text mining-based asymmetric AISA. Full article

Based on the projected growth in passenger air traffic and the need for better utilization of existing capacities, the level of service (LOS) concept in the design and planning of airport terminal facilities is crucial. By monitoring and quickly responding to expected changes in passengers’ and airlines’ needs, better utilization of airport terminal facilities in the passenger terminal can be achieved. The factors that influence the level of service (LOS) from the passenger perspective were evaluated in order to improve the user experience. Definitions of the level of service, key indicators of customer satisfaction, and a decision-making process using the analytical hierarchy process (AHP) method are described. A survey questionnaire was developed, passengers’ preferences were collected, and an analysis of the results was conducted. A hierarchical AHP decision-making model with associated criteria and sub-criteria was developed to determine the optimal level of service for low-cost carriers. Finally, by using the AHP model, new spatial–temporal parameters for the optimal level of service (LOS) for low-cost carriers (LCCs) are proposed, developed, and presented. The main objective is to adjust the existing LOS concept considering the business characteristics of low-cost carriers, in order to improve the efficiency of airport terminal facilities. Full article

This study examines the impact of Customer-based Brand Equity (CBBE) on passengers’ Willingness to Pay Premium (WPP) for airline tickets, comparing low-cost and flag airlines. The research is prompted by the competitive nature of the industry and the need to comprehend passenger preferences, focusing on brand image, brand awareness, and service attributes as key variables influencing CBBE. The survey data collected from 489 recent travelers were analyzed through Partial Least Squares Structural Equation Modelling (PLS-SEM) and Multigroup Analysis (MGA), generating two quantitative analyses: first, the model was analyzed for airlines in general, and second, a multi-group analysis was performed to understand how the model behaves through price tiers. The findings indicate the significant influence of the chosen variables on both CBBE and WPP. A distinguishing factor lies in the differentiation between low-cost and flag airlines, revealing differing impacts on CBBE and WPP. This research contributes to the branding literature by expanding CBBE’s application to services, especially in the airline sector. It also builds on existing knowledge of WPP’s importance in service industries. Segmenting airline price tiers offers actionable insights for management strategies. In conclusion, this study augments the knowledge of CBBE, providing valuable managerial implications, guiding brand-tailored strategies to increase passengers’ willingness to pay premium. Full article

This paper presents an advanced method for forecasting flight fares that combines aspect-based sentiment analysis (ABSA) with deep learning techniques, particularly the gated recurrent unit (GRU) model. This approach leverages historical airline ticket transaction data and customer reviews to better understand airline fare dynamics and the impact of customer sentiments on pricing. The aspect analysis extracts key service aspects from customer feedback and provides insightful correlations with airfare. These were further categorized into nine groups for sensitivity analysis, which offered a deeper understanding of how each group influences customers’ attitudes. This ABSA-driven forecasting method marks a departure from traditional models by utilizing sentiment data alongside airline transaction data to improve the predictive accuracy. Its effectiveness is demonstrated through metrics including a root mean square error (RMSE) of 0.0071, a mean absolute error (MAE) of 0.0137, and a coefficient of determination (R²) of 0.9899. Additionally, this model shows strong prediction performance in both short- and long-term fare predictions. It not only advances airfare forecasting methods but provides valuable insights for decision makers of airline industry to refine the pricing strategies or make improvements when it is indicated some services require further attention. Full article

The applications of artificial intelligence (AI) and natural language processing (NLP) have significantly empowered the safety and operational efficiency within the aviation sector for safer and more efficient operations. Airlines derive informed decisions to enhance operational efficiency and strategic planning through extensive contextual analysis of customer reviews and feedback from social media, such as Twitter and Facebook. However, this form of analytical endeavor is labor-intensive and time-consuming. Extensive studies have investigated NLP algorithms for sentiment analysis based on textual customer feedback, thereby underscoring the necessity for an in-depth investigation of transformer architecture-based NLP models. In this study, we conducted an exploration of the large language model BERT and three of its derivatives using an airline sentiment tweet dataset for downstream tasks. We further honed this fine-tuning by adjusting the hyperparameters, thus improving the model’s consistency and precision of outcomes. With RoBERTa distinctly emerging as the most precise and overall effective model in both the binary (96.97%) and tri-class (86.89%) sentiment classification tasks and persisting in outperforming others in the balanced dataset for tri-class sentiment classification, our results validate the BERT models’ application in analyzing airline industry customer sentiment. In addition, this study identifies the scope for improvement in future studies, such as investigating more systematic and balanced datasets, applying other large language models, and using novel fine-tuning approaches. Our study serves as a pivotal benchmark for future exploration in customer sentiment analysis, with implications that extend from the airline industry to broader transportation sectors, where customer feedback plays a crucial role. Full article

Despite an increased emphasis on improvement in airline service quality concerning consumer behavior, such as passenger repurchasing as a result of their behavioral intention over the last several decades, there is still much less concern with the nature of airline service quality than should exist in the so-called “logistics service quality” and less concern with examining the specific behavioral intention preceding repurchasing behavior together with the theory of planned behavior. As such, this study aims to explore these issues, along with the psychological factors of the theory of planned behavior, that can lead to repurchasing behavior via word-of-mouth intention (WOMI). With an online survey of 383 respondents experienced with flying, the results reveal that the logistics service quality and each determinant in the theory positively influence a passenger’s repurchasing behavior through WOMI. Accordingly, service marketers can implement service design and apply integrated marketing communication by learning from repurchasing behavior that was formed by the given factors to retain their existing customers. Moreover, this study is the first to empirically and explicitly validate dimensions of airline services through the lens of logistics that are deemed fit with the nature of the airlines. It advances the understanding of theory approaching and connects what has hampered its advancement in a body of knowledge, simultaneously in a context of airline context where it should not be relegated to transportation and consumer and service orientation. Full article

Monitoring customer satisfaction in the airline service industry is critical for improving service quality and meeting consumer expectations. Modern and comprehensive quality of service measurement tools offer firms critical information about how consumers perceive quality and their service quality expectations. It is vital to assess service quality in airline transportation, which is becoming more popular in comparison to other modes of transportation, resulting in increased competition. Businesses should know their clients well and make adjustments by properly analyzing their expectations if they want to compete in the market and enhance the quality of their services. As a consequence of this, we decided to utilize a model called the analytical hierarchy process (AHP) in order to determine how passengers in Saudi Arabia evaluate the level of service that is offered by airlines. Using the analytic hierarchy process (AHP) approach to model the five SERVQUAL dimensions and 22 sub-criteria, the purpose of the study effort was to determine the criteria for improving airline services. For the purpose of the study, the service from three different airlines was chosen and assessed based on their overall quality performance. Systematically, the AHP-based approach is presented for rating the airlines according to the Saudi aviation services. According to the observations, airlines should focus more on reliability, assurance, responsiveness, and empathy and less on tangibles. The sub-criteria also state that the airlines’ top aim should be to deliver accurate services on the first try. According to the AHP analysis, Saudi Airlines ranked first, followed by flynas and then flydeal. The findings of this study have consequences for decisions about the effective monitoring of the total airline system in order to enhance the delivery of high-quality services that would increase customers’ pleasure, which is the aim of airline services. Full article

This study uses the Kano model and importance–satisfaction analysis (ISA) to assess airline service quality by identifying the prioritised service quality attributes (SQA) for business travellers. The study aims to produce suggestions for airline executives on how to allocate resources in the most effective way to enhance the quality of service and increase customer satisfaction. A conceptual framework divides business travellers into four Clusters based on the behavioural variables of flight length and cabin class. For each Cluster, business traveller expectations for fourteen SQAs were assessed through using the Kano model while integrating the ISA. The empirical phase employs a 38-item questionnaire that was shared on various frequent flyer and business travel forums. Additionally, this study utilises an adapted qualitative questionnaire where four airline managers expressed their perceptions on how they think business travellers perceive the fourteen SQAs. The analysis reveals four categories, namely ‘concentrate here’, ‘keep up the good work’, ‘low priority’, and ‘possible overkill’, exhibiting the importance and satisfaction of the fourteen SQAs. Findings show that resource allocation was adequate on only five attributes out of fourteen. The analysis of the airline manager responses shows differences in their assessment when compared to business travellers for two tangible attributes. Full article

Background: The purpose of this study is to analyse the role of the main 50 US cargo airports and 25 air cargo airlines in the logistics of e-commerce companies from 2000 to 2020, to highlight the importance of airports in the logistics and e-commerce industries. Methods: A review of the relevant literature on airports, air cargo carriers, logistics, and e-commerce sectors was undertaken to understand the link between them. The data were collected using four criteria: airport category, airport location, top 25 air cargo carriers, and other relevant data from the Federal Aviation Administration, International Air Transport Association, Organization for Economic Co-operation and Development, US Department of Transportation, amongst many others. Results: The findings reveal that there is a consolidated relationship between airports, air cargo airlines, and e-commerce, which has been especially evident during the COVID-19 pandemic. Airports and air cargo carriers are identified as the most relevant partners in the e-commerce industry. This is because of the e-commerce sector and its users’ demand for speed and reliability in the interaction between the demand for and supply of products and services. Conclusions: The pandemic has changed the way in which organizations operate and is likely to create new demand from companies and users in the aviation and e-commerce industries. E-commerce companies are highly dependent on the quality and efficiency of air cargo airlines and airports because they need to provide a good shipping service for their products to customers. Full article