This study aims to understand airline passengers' satisfaction trends by analyzing the most influential factors on satisfaction before and during the COVID-19 pandemic. The sample consists of a dataset with 9745 passenger reviews published on airlinequality.com. The reviews were analyzed with a sentiment analysis tool calibrated for the aviation industry for accuracy. Machine learning algorithms were then implemented to predict review sentiment based on airline company, travelers' type and class, and country of origin. Findings show passengers were unhappy before the pandemic, aggravated after the COVID-19 outbreak. The staff's behavior is the main factor influencing passengers' satisfaction. Predictive modeling showed that it is possible to predict negative review sentiments with satisfactory performance rather than positive reviews. The main takeaway is that passengers, after the pandemic, are most worried about refunds and aircraft cabin cleanliness. From a managerial standpoint, airline companies can benefit from the created knowledge to adjust their strategies in agreement and meet their customers' expectations.

In Summer 2022, after a lean COVID-19 spell of almost three years, many airlines reported profits and some airlines even outperformed their pre-pandemic records. In context of the perceived recovery, it is interesting to understand how different markets have gone through the pandemic challenges. In this study, we perform a spatial and temporal dissection of the recovery process the global aviation system went through since May 2020. At the heart of this study, we investigate the patterns underlying market entry decisions during the recovery phase. We identify a rather heterogeneous type of recovery as well as its underlying drivers. We believe that our work is a timely contribution to the research on COVID-19 and aviation, complementary to the existing studies in the literature.

This research investigates the number of on-time flights (OTFs) at European airports and how this number is influenced by an airport's flight connectivity. We conduct a spatial statistical analysis of the spatial context relationship using econometric models, and the interaction between the number of airport's on-time flights (OTFs) and flight connectivity. Using 2017 and 2018 data, we characterize the relationship between a European airport's air connectivity index (ACI) and the number of flights that depart or arrive at a gate within 15 min of schedule (OTFs). We also analyze the relationship between OTFs at a given airport and those of neighboring airports. As the distances between airports increase, autocorrelation shifts from a positive to a negative sign meaning that at greater distances, airports' on-time performance is less dissimilar. We find that before the pandemic and the ensuing global travel shutdown, a spatially lagged term of ACI improves the model's ability to account for variations in OTFs across airports. Flight delay propagation in the air transport system caused delays to occur due to the shared resources underlying an initially delayed flight and subsequent flights. This analysis offers a rational for increasing airport connectivity as a way of improving the share of on-time flights of European airports.

The aviation industry is one of the sectors that has been heavily impacted by the pandemic. While the major body of literature has focused on passenger experience and behaviour, this study focuses on airport employees instead-their experiences, perceptions, and preferences following the emergence of COVID-19. More than 1000 participants from 4 major airports-Amsterdam Airport Schiphol, Singapore Changi Airport, Taipei Taoyuan Airport, and Zurich Airport-representing over 10 different occupations, have provided a variety of sentiments about the airport as an employment ecosystem in the wake of COVID-19. Quantitatively and qualitatively surveying four different airports enabled a cross-border analysis of the results to identify interesting geographic contrasts, as well as global themes, among the responses. Regional differences regarding, the feeling of preparedness, confidence in measures, and optimism are presented. A significant difference in confidence in non-pharmaceutical measures between employees from Asian and European airports is shown. Wants and needs such as better physical/IT workplace infrastructure and more flexibility regarding job scope and hours are pointed out. The results of this research provide insights for future airport employee experience research by outlining areas to study in greater detail. Furthermore, practical implications for airport stakeholders and companies arising from the challenges experienced by the workforce are laid out to provide guidance to prepare for similar circumstances in the future and navigate the aftermath of and recovery from the pandemic.

The changes that have come about at airports in recent decades in the areas of security, deregulation, and technological advances have affected both airport management and the passenger experience at airport facilities. In addition, all around the world, the airport sector has been struck by the COVID-19 pandemic during 2020 and 2021. Using a broad sample of data taken from Airport Service Quality (ASQ) surveys and robust econometric methodology, specifically, an Ordered Logit model with Principal Component Analysis, this paper seeks to cover the gap in the academic literature regarding the effect of a worldwide pandemic on passenger satisfaction at airports during the 2015-2021 period, while taking into consideration the passenger profile and journey and airport attributes. It takes as its reference a Spanish regional airport, which had been experiencing a strong expansion process prior to the pandemic. With respect to the variables linked to the passenger profile, a differential behavior is observed in satisfaction depending on nationality, motive for travel, and destination. In addition, the four facility- and airport process-related dimensions are significant, with cleanliness and comfort standing out above all others. These are even more important in a health emergency scenario such as is currently being experienced. Lastly, 2021 is shown to cause a downturn in the positive passenger satisfaction with the airport that had been observed during the first year of the pandemic. Therefore, more long-term management is required alongside the initial rapid and efficient action taken by airports, with up-to-date information for passengers to internalize the inconveniences associated with this long-drawn-out period of uncertainty.

For nearly three years with the COVID-19 pandemic, China has implemented a set of strict policies to control the flux of potential virus carriers in cross-border flights: The so-called Circuit Breaker mechanism. In this study, we review the evolution of this mechanism - a rather unique experiment in the global aviation system - from a data-driven perspective. Specifically, we perform an investigation on the extent of violations and their potential drivers. In total, 183 events are analyzed covering the period from epidemic outbreak in early 2020 to December 2021. In addition to describing the spatial extent and temporal evolution, we develop a regression model which helps us to better understand the universal patterns. By dissecting an under-investigated phenomenon, we believe that our study contributes to the rich literature on aviation and COVID-19, not only in the specific context of China, but also by assessing some of the challenges and potential of containing a global health threat using strict aviation policies.

The COVID-19 pandemic has raised air transport stakeholders' concerns about the state of the market, the potential timing of recovery, and recouping long-haul traffic. Passengers' travel confidence must be restored, and air travel safety awareness raised. This paper estimates the immediate and long-term effects of COVID-19 on air transport markets and forecasts timescales for recovery of the markets for domestic and international flights in nine African countries. Intervention analysis and SARIMAX are employed for the analysis, using monthly time-series data from August 2003 to December 2021. The empirical results show that air transport is significantly elastic to the pandemic. It is forecast that air transport recovery may take around 28 months for domestic flights and 34 months for international flights, starting from 2020. The simulation analysis suggests that passenger flights may rebound to pre-crisis levels between 2022 and 2023. In general, the pandemic-induced fluctuations in the aviation market and the nature of the rebound may be considered to be part of a cyclical process rather than a structural change.

The COVID-19 pandemic had been a major crisis for the air transport industry due to its global reach, duration, and continuing uncertainty. Demand for air travel fell globally by around 90% in the period immediately following the introduction of lockdown restrictions which induced significant revenue loss for the industry and led to widespread bankruptcies and job losses. Within this extremely challenging business environment, commercially operated airports have struggled. This paper investigates how airport management has been impacted by this sudden and prolonged fall in the demand for air travel. Specifically, the UK case was studied through the Business Model Canvas, with documentary evidence supplemented with 31 in-depth interviews from the Government, airports, airlines, and other aviation organisations and from a variety of stakeholder roles within airports across the country. Interviewees were asked about how airport business models responded to COVID-19 and how they were likely to change in the future as a consequence. The findings suggest that COVID-19 encouraged airports to restructure key components in their business models. Fundamentally, airports have significant fixed costs, and it has been especially challenging to run terminals and operations with little or no revenue from conventional channels. The study finds airports were introducing more flexibility into their cost base while diversifying their revenue streams into areas such as developing business parks and enhancing retail portfolios. This is leading to a restructuring of airport business models to improve resilience to future systemic shocks. Overall, 4 future airport business drivers and approaches have emerged: 1) Cost-effectiveness and minimisation, 2) Diversification of revenue streams and intensified commercial activities, 3) Enhanced digitalisation and operational efficiency, and 4) Sustainability focused approach.

COVID-19 has had a major negative impact on the travel industry, especially on the aviation sector. Along with travel restrictions to contain the spread of the virus, a drastic drop in demand-mainly caused by the decrease in the willingness to travel-has also been registered. This study explores the impact of COVID-19 on airline pricing curves, in terms of the price level, price dispersion, and the extent to which intertemporal price discrimination is applied. By analyzing all major European flights departing from and arriving in Italy, the results reveal a 31% overall decrease in airline price per kilometer. Additionally, price dispersion dropped, and price discrimination intensity was found to have decreased as a result of COVID-19. These outcomes can be explained in light of two major impacts of the pandemic on air travel demand, namely the variation in the passenger mix and travelers' higher price sensitivity. Further analyses indicate that-along with other market and flight characteristics-market concentration, introduced interventions to prevent and control COVID-19, and airline- and destination-types play an important role in determining prices, price dispersion, and the price discrimination intensity.

Covid-19 pandemic affected aviation severely, resulting in unprecedented reduction of air traffic. While aviation is slowly re-gaining traffic volumes, we use the opportunity to study the arrival performance in the Terminal Maneuvering Area (TMA) in non-congested scenarios. Applying flight efficiency and environmental performance indicators (PIs) to the historical data of arrivals to Stockholm Arlanda and Gothenburg Landvetter airports, we discover noticeable inefficiencies, despite significant reduction of traffic intensity. We analyze the impact of such factors as weather and traffic intensity on arrival efficiency in isolated scenarios when only one factor dominates: isolated scenario with low traffic and isolated scenario with good weather conditions. Our analysis uncovers that weather has a stronger influence than traffic intensity on the vertical efficiency, while traffic intensity has stronger effect on the lateral efficiency. Impact of traffic intensity on the lateral efficiency might be explained by frequent hold-on patterns and flight trajectory extensions due to vectoring in high traffic conditions. Further investigation is needed to explain weather and vertical/lateral efficiency correlations, the conclusions might be country-specific.

The timely handling of passengers is critical to efficient airport and airline operations. The pandemic requirements mandate adapted process designs and handling procedures to maintain and improve operational performance. Passenger activities in the confined aircraft cabin must be evaluated for potential virus transmission, and boarding procedures should be designed to minimize the negative impact on passengers and operations. In our approach, we generate an optimized seat allocation that considers passengers' physical activities when they store their hand luggage items in the overhead compartment. We proposed a mixed-integer programming formulation including the concept of shedding rates to determine and minimize the risk of virus transmission by solving the NP-hard seat assignment problem. We are improving the already efficient outside-in boarding, where passengers in the window seat board first and passengers in the aisle seat board last, taking into account COVID-19 regulations and the limited capacity of overhead compartments. To demonstrate and evaluate the improvements achieved in aircraft boarding, a stochastic agent-based model is used in which three operational scenarios with seat occupancy of 50%, 66%, and 80% are implemented. With our optimization approach, the average boarding time and the transmission risk are significantly reduced already for the general case, i.e., when no specific boarding order is specified (random boarding). If the already efficient outside-in boarding is used as a reference, the boarding time can be reduced by more than 30% by applying our approach, while keeping the transmission risk at the lowest level.

This paper develops three scenarios for the aviation industry's recovery from COVID-19 until 2030 by utilizing the scenario methodology. Besides the short- and mid-term pandemic development, the study takes into account the industry's adaptation to changes in the market environment, e.g., toward sustainability and hygiene requirements. The resulting scenarios include the expected point in time of full air traffic recovery to pre-crisis levels. Subsequent implications suggest that most COVID-19-related hygiene measures along the travel chain disappear after the pandemic is contained. Some measures might serve as a differentiator between airline business models, while others are expected to become a new standard. Implications for environmental awareness and resulting operational and technical measures include changes in society's attitude toward traveling post-pandemic, especially in light of varying levels of environmental awareness. The presented scenarios help to identify the range of plausible development paths, thus building the basis for future model-based research.

This study conducts a detailed analysis of the response of China's low-cost carriers (LCCs) to the threats posed by the pandemic from a route network perspective, aiming to explore the resilience of LCCs and Chinese airlines. Using geographic visualization and network analysis, we evaluate and compare the network connectivity of each Chinese LCC to see the change patterns, then elaborate on the network connection of Spring Airlines. The major results are: the LCC sector has not recovered, but some of them exceed the pre-pandemic levels in a less deregulated environment; different LCCs show different recovery patterns; Spring Airlines outperforms the other four LCCs in terms of network connectivity. The recovery process is supported by various external factors, such as the reduction of new confirmed COVID-19 local cases and international flights, the re-open of inter-provincial tour groups and tourism demand, the nationwide rebound activities promoted by the central government, and the supporting policies, especially new slot allocation processes issued by CAAC. The case study further indicates the effects of high-speed rail (HSR) and regional subsidy measures on the tactical actions of Springs in route planning. This paper serves as a referential case for the LCCs worldwide and has good application for the recovery of other LCCs in other countries. Moreover, the study conducted in this time window offers a chance to assess the development of Chinese airlines in a not fully deregulated aviation environment. It contributes to the debate on the theory of air network resilience.

This paper examines the short-term market reaction of the airline industry to the declaration of COVID-19 as a global pandemic and to the announcements of the effectiveness of COVID-19 vaccines in the US. Using an event study, we observe a negative and statistically significant stock price reaction to the announcement of COVID-19 as a global pandemic. In contrast, we find a positive impact on the stock market due to the announcements of the effectiveness of COVID-19 vaccines in the US. These results are consistent with the investor sentiment hypothesis and the asset-pricing perspective. The empirical results also show a higher stock market reaction to the announcement of the effectiveness of the Pfizer-BioNTech COVID-19 vaccine in the US compared to the announcements of the effectiveness of subsequent vaccines. This result is explained by the innovation race competition effect and the greater reduction in investor uncertainty levels. These reactions were reinforced or mitigated by firm-specific characteristics such as liquidity, size, leverage, ownership concentration, state control and business model (i.e., low-cost full-service).

Airport service quality (ASQ) is a competitive advantage for airport management in today's airport market. Since the COVID-19 health crisis has unprecedentedly influenced airport regulations and operations, effective measurement of ASQ has become crucial for airport administrations. Surveying travelers' attitudes is useful for ASQ assessment but collecting responses could be time-consuming and costly. Therefore, this paper adopts a data-driven crowdsourcing approach to study ASQ during the COVID-19 pandemic by investigating Google Maps reviews from the 98 busiest U.S. airports. To do so, this study develops a topical ontology of keywords regarding ASQ attributes and uses a sentiment tool to derive passengers' attitudes. Through sentiment analysis, Google Maps reviews show more positive sentiment toward and but remain constant about during COVID-19. The lexical salience-valence analysis (LSVA) is then applied to explain such changes by tracking the sentiment of frequent words in reviews. Through correlation and regression analysis, this study demonstrates that is significantly related to , and in pre-and post-COVID periods. Additionally, the effect of , , , and on significantly differs between the two periods. The findings illustrate the effectiveness of leveraging online reviews and offer practical implications for what matters to air travelers, especially in the COVID-19 context.

The COVID-19 pandemic has created unexpected demand for air cargo in terms of rapid mobility of critical basic needs. Air cargo carriers aim to maximize their profits by taking advantage of the current demand and using their limited capacity in the right place. At this point, some of the qualifications of the airports in the places where demand plays a crucial role in this decision of the carriers. Thus, evaluating the factors considered in the airport selection for air cargo carriers during the COVID-19 period is curious. This study proposes a triangular fuzzy Dombi-Bonferroni best-worst method (BWM) framework with vast flexibility to establish the priority preferences of factors considered in selecting airports. The fuzzy BWM model becomes a superior decision support system by combining the Bonferroni mean operator's ability to consider interrelationships between attributes and the flexibility of the Dombi operator. In this sense, we highlight eighteen criteria based on five airport aspects: location, physical features, performance, costs, and reputation. Findings reveal that the foremost aspects are location and costs, whereas the most crucial factors are airport charges and handling charges. The study suggests that airports should follow a low-price policy for airport-related charges without compromising their sustainability to have a share of the increasing number of air cargo flights, especially during the COVID-19 period, when airline passenger flights are decreased. The study is crucial in deciding the strategy and policy of air cargo carriers and airports during the pandemic period.

Travel restrictions as well as travellers' increased risk perceptions have changed travel patterns around the globe during the COVID-19 pandemic. As such, the aviation industry has been particularly affected by the changing environment. Several airlines have reacted to travellers' rising concerns about becoming infected with the COVID-19 disease by introducing safety measures to guarantee a safe journey. Although research has noted the relevance of good communication during crises, the impact of communicating safety measures by rational (safety) advertising appeals on air travel intention has not been explored thus far. The current study investigates consumers' reactions to two different types of advertising appeals during the COVID-19 pandemic and their effect on air travel intentions and airline recommendation intentions. An online experiment reveals that travellers react more positively to safety as compared to emotional advertising appeals during the COVID-19 pandemic. Furthermore, the results confirm the hypothesized mediating effect of perceived health risk and trust in the airline on the impact of safety appeals in terms of air travel intention and airline recommendation. The results of this study uncover the underlying mechanisms that have driven consumers' air travel intentions during the COVID-19 pandemic and offer various theoretical and managerial implications.

This comparative study analyses the effectiveness of the communications delivered via Facebook by two Spanish airlines, Iberia and Air Europa. Using various indicators, the publications posted by the two Spanish airline companies (Iberia and AirEuropa) on this social media site during the COVID-19 pandemic are examined using two time-frames, namely the "strict lockdown" (between March 14th and June 21st, 2020) and the "new normal" (between June 22nd and September 30th). In this study we examined a total of 39 Iberia's and 49 AirEuropa's posts for the former period and 49 Iberia's and 89 AirEuropa's posts for the latter one. By analysing the followers' reactions to these posts, the work seeks to identify the relationship between use of different contents and three variables: brand popularity, customer brand engagement, and virality. Two time periods are selected, one spanning national lockdown as a result of the state of emergency declared in Spain and the other relating to the subsequent 'new-normal' as emergency restrictions began to be lifted. The results show that the Facebook posts created by Iberia that included informative messages, references to COVID-19, with hashtags, and allusions to corporate social responsibility-were more popular than Air Europa posts. Iberia posts generated greater customer brand engagement and virality, and received more positive reactions in terms of and

One of the purposes of Artificial Intelligence tools is to ease the analysis of large amounts of data. In order to support the strategic decision-making process of the airlines, this paper proposes a Data Mining approach (focused on visualization) with the objective of extracting market knowledge from any database of industry players or competitors. The method combines two clustering techniques (Self-Organizing Maps, SOMs, and K-means) via unsupervised learning with promising dynamic applications in different sectors. As a case study, 30-year data from 18 diverse US passenger airlines is used to showcase the capabilities of this tool including the identification and assessment of market trends, M&A events or the COVID-19 consequences.

This paper investigates the impacts of COVID-19 on the implementation of Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). By using the Automatic Dependent Surveillance-Broadcast (ADS-B) aviation data, the forecast methods of Gompertz and Logistic curves and four COVID-19 scenarios, we find the following results. First, the international aviation activities of developing countries are on the track of rapid growth, while the trends of developed countries are relatively slow or even close to saturation. Second, our results provide retrospective support for the decision of the ICAO Council to revise the implementation baseline of CORSIA. The adjustment of the baseline has saved countries considerable purchasing offsetting costs, especially for China, the United States, the United Arab Emirates, and the United Kingdom. Third, although the adjustment of the baseline can lower the economic pressure of the global aviation industry, CORSIA will still bring considerable financial burden to international aviation enterprises.

The economic downturn and the air travel crisis triggered by the recent coronavirus pandemic pose a substantial threat to the new consumer class of many emerging economies. In Brazil, considerable improvements in social inclusion have fostered the emergence of hundreds of thousands of first-time fliers over the past decades. We apply a two-step regression methodology in which the first step consists of identifying air transport markets characterized by greater social inclusion, using indicators of the local economies' income distribution, credit availability, and access to the Internet. In the second step, we inspect the drivers of the plunge in air travel demand since the pandemic began, differentiating markets by their predicted social inclusion intensity. After controlling for potential endogeneity stemming from the spread of COVID-19 through air travel, our results suggest that short and low-density routes are among the most impacted airline markets and that business-oriented routes are more impacted than leisure ones. Finally, we estimate that a market with 1% higher social inclusion is associated with a 0.153%-0.166% more pronounced decline in demand during the pandemic. Therefore, markets that have benefited from greater social inclusion in the country may be the most vulnerable to the current crisis.