Yield Management—A Sustainable Tool for Airline E-Commerce: Dynamic Comparative Analysis of E-Ticket Prices for Romanian Full-Service Airline vs. Low-Cost Carriers

Abstract

For many years, the air travel market has been one of the most regulated sectors in economy. The airline industry was fast-growing until the COVID pandemic. However, it is still a main business sector considered by economists and policymakers highly competitive. Air transport has specific features contributing to the final price of a travel ticket and differentiating airline companies. The aim of this paper is to analyze if the concept of yield management is applied by the Romanian national full-service airline TAROM and to highlight which criteria could be used for evaluating airline companies, the differences between a full-service carrier (FSC) and a low-cost carrier (LCC) model, and the types of pricing techniques used by airline companies, of which consumers could take advantage. The methodology is based on the dynamic pricing analysis by a continuous online simulation method for the Romanian full-service carrier and two low-cost carriers. The research results confirm that for the investigated companies, yield management involves strategic control of available resources meaning to sell the product to the right customer at the right time for the right price and thus fill in a gap in international research. Being extremely competitive and customer-oriented, airline companies serve as an example for other businesses in which yield management can be applied.

1. Introduction

For many years, the air travel market has been one of the most regulated sector of the economy [1] until the present worldwide situation of pandemic time. The airline industry was for long a fast-growing industry and still is a main business sector for all European countries [2], being considered by economists and policy-makers highly competitive [3] and a key area for reform [4] in the tourism field and other connected sectors. Today, tourism owes a lot to the modernization and diversification of the roads and means of transport. In fact, the link between tourism and transport is asymmetrical. Economic development worldwide is getting a significant boost from air transport [5]. Transport is the intermediate and the main element without which tourism could not exist, while the opposite cannot be true [6]. Air transport has increased accessibility and broadened the horizon of possibilities by initiating and expanding the touristic phenomenon, respectively the emergence of new types in tourism. An important group of travelers is represented by young people and for this category, transport price is very important considering their willingness to pay as consumers [3,7,8,9]. The price of air transport for users continues to fall, after adjusting for inflation [5].

Air transport has a lot of specific features [10]: a higher price of the transport service; comfort; a sophisticated infrastructure; accessibility; security; safety; fastness; regularity; long and medium routes; all convertibility contributing to the final price of a travel ticket and, moreover, differentiating airline companies. Regarding price policy, specialists consider that [11] a regular flight is efficient for an occupancy rate of at least 30%, while a charter flight is efficient for at least 80% occupancy rate.

Thus, while transport studies mainly focus on its usefulness, such as travel to workplace, school, or other places of daily activities, tourism and leisure travel are an integrated part of a widespread transport system and it is almost impossible to carry them out in the absence of a way to travel [12]. In these circumstances, the transport analysis is based on the fundamental concept of demand in relation to the journey made for any purpose [6].

According to a World Tourism Organization (WTO) study, air transport has gained important shares in recent years compared to other types of transport in terms of demand, e.g., for 2018, there was a demand of 57% for air transport, 37% for road transport, 4% for naval transport and 2% for rail transport [13]. The highest figure of 57% for air transport demonstrates its importance and its high degree of use and trust by the population. According to IATA, the International Air Transport Association [5], the airlines transported over 4.378 billion passengers in 2018 with a 6.9% increase compared to 2017 and a RPK (Revenue Passenger Km) of 8330 billion USD, with a 7.4% increase compared to the previous year. The distribution on continents is as follows: Asia-Pacific 37%, Europe 27%, North America 23%, Middle East 6%, South and Latin America 5%, and Africa 2% [14]. For Europe, the increase of arrivals is exponential: 176 million in 1999, 251 million in 2003, and 713 million in 2018. The Eurocontrol statistics mention that the traffic rate in Europe is 2.6%, reaching 11.2 million flights in 2019. However, these figures do not take into consideration the present pandemic situation. The numbers are divided between full-service carriers (FSCs) and low-cost carriers (LCCs). LCCs have emerged as important players in many service industries, the most predominant being called low-cost or discount airlines [15]. IATA [5] forecasts that consumers benefit from lower travel costs, more routes, and would spend 1% of the world GDP on air transport in 2020 [5].

The aim of this research is to compare the model of a FSC with LCCs from perspective of the yield management strategy for e-tickets. The specific research objectives are:

• to identify specific models for FSCs versus low-cost airlines by applying statistical analysis and models by using financial indicators;

• to analyze by a continuous simulation method e-ticket prices for national FSC (TAROM) and LCCs (Ryanair and Wizz air) during two months in high season on the most frequent flight routes.

In terms of economic characteristics, air transport is a well-developed industry meaning that it offers reliable, safe, and relatively cheap services. Air transport networks are an integrated part of any modern society. Just-in-time production is seen as an important key to effective management [16]. The air transport industry can be considered the center of globalization for other industries, due to its role in supporting world trade, international investment, and tourism activities.

The implementation of a successful business model which best meets customer needs became crucial for airlines [3]. Liberalization allows airlines to optimize their networks within and across continental markets [17], the rapid growth of LCCs leading to increased competition and stimulating traffic calling for the removal of restrictions on capacity, frequency, pricing, and entry [17].

This research is the first one taking into consideration the Romanian FSC TAROM compared with two LCCs: Ryanair, the main low-cost carrier [18] in Europe [19], and Wizz Air, filling in a gap in international research. The principal arguments for comparing the Romanian FSC TAROM with LCCs are: (1) the lack of research for Romanian companies and (2) the financial indicators of the Romanian airline are somewhat contradictory: even if the company registered in the last years (2014–2021) only losses and the average of booked passenger load factors are under 70% for the same period, the total revenue per booked passenger are close to double that of LCCs.

2. Literature Review

Ageron and Zembri [19] mention a famous quote from Sir Freddie Laker: the 20th century belonged largely to full-service, high-cost airlines; the 21st century will be reserved to low-cost airlines. The FSC sector is characterized by the existence of national companies, also called traditional or network-making exchanges with other countries [3,20]

The basic model of FSCs is based on the hypothesis that consumers have a high level of utility and consequently they are not price sensitive, but sensitive to a high quality and diversity of services; i.e., two different vertical quality products [21] (economy versus business, refundable tickets versus non-refundable tickets, VIP lounge access versus no VIP lounge access), Internet access, flexibility, maximum comfort, the main airports, loyalty compensations, exchangeable tickets option, etc. [22].

The emergence of LCCs is a major consequence of establishing The European Community Airspace, as European aviation has been regulated by highly restrictive bilateral air service agreements among concerned countries [23].

The basic model of LCCs is the remodeling of the standard production chain and a concentration of all expenses, which highlighted the original character and diversity of this movement, which is sociological, economic, and strategic. The basic strategy is emphasized by Ryanair Company, the most cost-effective LCC in Europe: the minimum service is equal to the minimum price [24]. The low-cost company’s strategy radically changed the structure and business model of FSCs, which showed new approaches regarding operations, infrastructure use, distribution, and passenger services [25].

Thus, the LCC model is based on a series of distinctive elements allowing to obtain a competitive advantage [26,27]: the use of secondary airports, online reservations, point-to-point network, a single class of services, use of facilities for a fee, no-frills, high labor productivity, common type of fleet, and intensive use of aircraft. Most LCCs do not segment the market based on willingness to pay for air ticket with different conditions and restrictions [9,21] but for saving costs [3] and braking capacity between consumers with different willingness to pay [28]

Different from FSCs, LCCs provide only basic functions, while other transportation products are offered separately or are not offered at all (i.e., additional charges for luggage, food and beverage, hot seats) [29].

The fare categories applicable to scheduled airlines operated by private airlines are so diverse that passengers may have paid between 8 and 15 different price levels for the same flight, with differences between 5% and 100%. In the attempt to expand and strengthen their market position, FSCs use techniques associated with pricing and distribution strategy.

There is a vast literature regarding FSC and LCC pricing strategies, for example Slovakia for Kosice airport [30] including Ryanair and Wizz Air, a state of the art in terms of pricing policy for FSCs versus LCCs [3] and for Spain [31]. For Romania, the only research was carried out in 2011 on a sampling of 766 respondents [32] using the Price-Sensitive Meter method. Moreover, Escobari, Rupp, and Meskey [33] identified a higher price during office hours (when business travelers are likely to buy, especially from FSCs) and lower prices in the evening (when leisure travelers are more likely to purchase) and Otero, Escallon, Lopez et al. [34] proposed a stochastic dynamic model for the optimal timing of promotions for Latin America. FSCs are facing a trade-off between growth and operating profits, while LCCs manage to simultaneously pursue growth-oriented strategies and improved profitability [35]. Overall, empirical evidence attests that, on average, LCCs’ prices are lower than those of FSCs [36].

An example of a detailed study is shown in Figure 1 regarding the average price of low-cost companies made by Deutsches Zentrum fuer Luft-und Raumfahrt (DLR) [37].

We can structure the characteristics of FSCs and LCCs as follows in Table 1 [3]:

Currently, there are also other innovative strategies used by FSCs. For example, the Chinese aviation authority asked the five airlines offering service between Shanghai and Beijing to form an express shuttle alliance [38]. Moreover, Merkert and Webber [39] draw the attention to the importance of seasonality in the seat factor more than the average airfare. Lardeux, Sabatier, Delahaye et al. [40] investigated the yield optimization for airlines from ticket resell by simulating buy-back campaigns for four flights. The differences in prices and qualities also appear to affect market structure despite flag carriers’ incumbency advantages [31]. Although airline companies differ in their cost structure, quality of services, and set of supplied products [23], their main goal is to increase revenue and maximize profit. One of the main objectives valid for both FSCs and LCCs is to sell the right inventory unit to the right type of customer at the right time and for the right price [21], which is called dynamic pricing or price optimization. Individual application of dynamic pricing in airline industry is also referred to as price discrimination, revenue management, and yield management [41].

The subject of yield management in air transportation has been largely approached in different studies, for example McAfee and Te Velde [40], Gaggero and Piga [42], and Malighetti et al. [18]. Vinod [43] approaches the evolution of yield management in the airline industry in a comprehensive and accessible way, while Williams [44] estimates a model to evaluate the welfare effects of dynamic airline pricing.

Airline companies use different kinds of pricing strategies to determine the optimal ticket prices: long-term pricing policies, yield pricing, or dynamic pricing, associated with dynamic adjustment of ticket prices in response to various influencing factors [45,46,47]. Airlines need to evolve their revenue management by complementing flight pricing with dynamic bundling, ancillary pricing and assortment [48].

3. Short Presentation of the Investigated Companies—TAROM and Low-Cost Companies

The most important KPIs of the three airline companies (the Romanian national airline company TAROM for FSCs, Ryanair and Wizz Air for LCCs) used for the research objectives are presented comparatively in Table 2.

Regarding the services offered by TAROM company, they are: two classes (Economy and Business), rent a car, accommodation, cargo, travel insurances, menus on board, etc.

In Romania, the Ryanair company places third, after Wizz Air and Blue Air and from the marketing point of view, the company promotes low level of prices, destination diversity, and excellent services. The basic price policy for Ryanair is load factor active—yield passive. Generally, Ryanair reduces four types of costs: aircraft equipment and financing costs, employee costs, the cost of client service, and airport costs.

The Wizz Air model is based on no printed tickets, use of secondary airports, and catering services at request and surcharge. The company is the leader in Romanian air transport: 42.3% market share, while Ryanair owns 29.8% market share and Easy Jet 6.3%. As seen in Table 2, both investigated LCCs have a passenger load factor above the international average of 81.9% for 2018 according to IATA [5].

4. Materials and Methods

Following Deutsches Zentrum fuer Luft-und Raumfahrt (DLR) [37] analysis, our aim was to highlight different pricing techniques adopted by airlines, investigating how each company has changed the price of a standard e-ticket over time. The study consists in the successive simulation [57] of a plane ticket purchase for a determined flight during two months in the high season [53]. Neither the flight nor the passenger profile during the study was changed, only the booking dates. Thus, the pricing techniques of different airlines are highlighted and then compared.

To make the dynamic pricing analysis as realistic as possible, a series of parameters were used in the simulation process. Initially, two flights were chosen, round-trip: one oriented to southwest: Bucharest–Madrid (from Friday to Sunday) and another to northeast: Bucharest–London (from Saturday to Monday) for morning departures and late arrivals. The simulation was carried out for two months, from Friday (12 March 2021) to Saturday (8 May 2021). The weekly examined data covered the flights from 7–9 May 2021 and 8–10 May 2021. In international literature, there is another result linked to this destination [58] analyzing which low-cost airlines operating from a secondary airport competes with full-service airlines serving a main airport in a multiple airport region.

Regarding consumer profile for simulation, a young person (18–25 years old) was chosen based on the well-known fact that clients of low-cost companies are young people sensitive to price [59] and internet users for reservations [1,60].

For the leading company TAROM, the simulation was carried out for the economy class.

For this research and accordingly with the research objectives, the quantitative analysis was conducted by using:

• Comparative analysis of the investigated companies air transport models for two important elements, network used and costing, and linked to the costs, the pricing policies [29]. These results are presented in Section 5.1. Conventionally, comparative analysis based on the statistical methods are used for the explanation of statistically significant differences and/or the explanation of similarities. The Student t test was applied to test if there are statistically significant differences between means of variables from Table 2 for FSC TAROM and LCCs. The linear regressions with Enter method was used to identified specific models for FSC TAROM and LCCs. For the regression models, the total operating revenue was established as dependent variable. The independent variables for models were booked passenger load factor (%), number of passengers booked (million), and the number of aircrafts. Comparative analysis is conducted mainly to explain and gain a better understanding of the causal processes involved in the creation of an event, feature, or relationship usually by bringing together variations in the explanatory variable or variables [61]. The results are presented in Section 5.1.

• Dynamic pricing analysis [45,60] for the two types of the investigated airline companies, taking into consideration an important factor of the pricing strategies for airlines—the reservation moment [2,7]—because the reservation moment or the time of purchase substantially influences the prices for the same flight [33,62]. Dynamic pricing uses data to understand and act upon any number of changing market conditions, maximizing the opportunity for revenue management and implicitly for yield management strategy. To emphasize the differences between e-tickets prices during the two months, for graphical representations, the water fall graphics was chosen. The results are presented in Section 5.2.

For a better understanding of the research framework we detailed, step by step, in Figure 2, the data, methods, and a short explanation for the chosen methods according to the aim and objectives of the research, separately for two main objectives of the research.

Data regarding the Romanian FSC TAROM and the two LCCs which are more active in Romania-Ryanair and Wizz Air companies were applied using all methods mentioned above. The analysis was performed for a period of two months [63] by using the continuous simulation method [57,64,65,66] and e-ticket prices were collected from the companies’ websites [21].

5. Results

5.1. Comparative Analysis of Air Transport Models: FSCs versus LCCs

This section presents the results of the comparative analysis by applying the statistical methods for the financial indicators from Table 2, comparatively for national FSC TAROM and LCCs. The results for descriptive statistics are presented in Table 3.

To test if there are statistically significant differences between mean values for variables, the Student t test was applied, the results being presented in Table 4.

According to the results from Table 4, apart from the booked passenger load factor, for the rest of the variables, the Student t test indicates statistically significant differences between the FSC TAROM and the LCCs.

The results of multilinear regression analysis indicate a good value for R² both for national FSC and LCCs (Table 5) and a statistically significant model (Table 6) according to ANOVA results.

In Table 7 are presented the results for standardized and unstandardized coefficients for regressions models together with collinearity statistics. It can be observed that all the VIF values from collinearity diagnosis are between 1 and 10, so there are no collinearity effects between the independent variables from the models.

Equation (1) for Model 1—the national FSC TAROM—is:

Total operating revenues = 200.372 + 115.227 number of passengers booked − 5.287 booked passenger load factor + 6.093 number of aircrafts(1)

Therefore, we can conclude that for FSC TAROM, the national leading company is increasing with 1 unit of number of passengers booked, the total operating revenue increase with 115.227 units. At increasing with 1 percent of booked passenger load factor, the total operating revenue decrease with 5.287 units, and at increasing with 1 unit of number of aircrafts, the total operating revenue increase with 6.093 units.

Equation (2) for Model 2—the LCCS—is:

Total operating revenues = −2197.711 + 52.002 Number of passengers booked + 28.816 Booked passenger load factor + 0.636 number of aircrafts(2)

Therefore, we can conclude that for the LCCS at increasing with 1 unit of number of passengers booked, the total operating revenue increase with 52.002 units. At increasing with 1 percent of booked passenger load factor, the total operating revenue increase with 28.816 units. For LCCs, the number of aircrafts is not statistically significant.

For both types of analysis, the main idea is based on the differences between LCCs and FSCs regarding the cost elements and, of course, the yield management concept (Table 3) resulting from IATA data. The incurred costs are positioned at the base of each company model. Several studies and data from IATA show that the cost structure of LCCs is more than 50% lower than the cost structure of a traditional airline (Table 8). The comparative analysis is based on the simulation using both data from IATA and features of the low-cost air transport model of Vasigh, Fleming, and Tacker [26]. This result shows how the LCCs arrive at this price calculation and how many benefits it can get in detail per available seat kilometer (ASK) or offered seat kilometer (SKO) cost.

Data from Table 8 present the cost advantages of a LCC such as Ryanair or Wizz Air for example. Not all LCCS can obtain these cost advantages, but the difference reaches a value of 40–60% [3], which explains the low prices offered by LCCs. The results from Section 5.2 will support this calculation. Another distinctive and differentiating element is the network used, which represents the set of lines and modes of communication, channels that serve the same geographical unit and depend on the same company [67]: the hub and spoke network or the point-to-point network.

The hub and spoke network is composed of large airports, the hub represents the correspondence center, the central airport, where the passenger traffic is intense; it contains on average 3 daily connections to at least 40 destinations. The hub allows companies to focus their activities on a main airport, where they can reduce their overall cost and benefit from economies of scale, satisfying the consumer, thanks to the consolidated infrastructure and staff. It is also cost-effective for offering various passenger services, as well as providing traffic to peripheral areas. Thus, according to (1) each passenger wishing to travel from point A to B or from point A to C, A to D must cross through the hub-H. Therefore, the sum of passengers flying from A to H is equal to the sum of the demand on lines AB, AC, and AD, which explains the presence of 2–3 regular flights per day.

For point-to-point networks: the European low-cost network is a network that is characterized by maximum density due to the large number of LCCs and their expansion, but the territory served by them is not uniform and, from the criteria The aim of profitability was to open a new network, which does not aim to cover the global territory of Europe, but to focus on major tourist routes [60].

Today, airline companies use the yield management method or real-time pricing, often known as a synonym for revenue management [3,7]. The development and use of yield management models have resulted in companies offering a variety of different types of fares for the tickets on the same route [7]. According to Gőnenç and Nicoletti [68,69] synchronization and increased flight frequency fully compensate for this loss of time and well-being for low-cost clients.

In Figure 3, the distribution of airlines companies according to the total operating revenue and number of passengers booked is presented, and in Figure 4, the distribution of airlines companies according to booked passengers load factor.

5.2. Dynamic Pricing Analysis of FSC TAROM versus LCCs

The simulation does not offer general results regarding the price difference between different Mediterranean and Nordic flights. Many other factors must be specified for a relevant analysis: money exchange differences, purchasing power of national population, vacation periods in each country, regulatory issues, distances, flight duration, etc. Generally, Bucharest–Madrid e-tickets are more expensive than Bucharest–London e-tickets for all investigated companies, mainly due to flight time, which for the Bucharest–London route is on average 3 h and a half, while for Bucharest–Madrid is 4 h. The initial objective of the simulation was not to determine which company is cheaper as their products are absolutely different; however, the data obtained allow some further comments.

Starting from the e-ticket prices obtained by simulation during 2 months before the departure date for flight routes Bucharest–Madrid and Bucharest–London for TAROM and the LCCs (Ryanair and Wizz Air), we calculated:

• the absolute modifications with fixed base for 2 months moment;

• the relative modifications with fixed base for 1 day moment.

We mention that the established moments for simulations, for all flight routes, were: 2 months, 7 weeks, 6 weeks, 5 weeks, 1 month, 3 weeks, 2 weeks, 1 week, 3 days, 2 days, and 1 day before the departure date.

Figure 5a,b and Figure 6a,b present the absolute and relative modifications with fixed base of e-ticket prices for the Bucharest–Madrid flight for Ryanair and TAROM. The simulation shows that the low prices of the LCC Ryanair are not always as low as they claim. The last-minute price of the Ryanair ticket is at least equal to the price of a TAROM ticket bought in advance. Another thing which could be concluded is that a passenger of a FSC such as TAROM is also interested in buying long in advance. TAROM company’s fares on the Bucharest–Madrid flight increased almost constantly throughout the process. The closer the flight date, the more expensive the ticket. The price difference for the same ticket is 200 Euros: a round trip on 7–9 May costs 250 Euros if purchased 7 weeks before the flight, compared to 450 Euros if booked the day before flight, May 6. This significant difference demonstrates the use of the yield management technique and price differentiation by traditional companies. Prices are not correlated with the company’s long-term costs, they are the result of calculations for short-term profit maximization.

Ryanair has as significantly different pricing strategy. In its case, for Bucharest–Madrid route price, variations depending on the purchase date are very low. This poor dispersion of tariffs seems impressive for a LCC and comes against many stated preconceptions, but it is not completely incomprehensible. Ryanair calculates long-term marginal costs of production which are relatively low given the drastic reduction of operating costs. Theoretically, it is neither necessary nor desirable to change this price too much over time as it already allows profit to be maximized continuously, involving more long-term constraints and being less dependent on short-term variables such as the date of ticket booking.

Figure 7a,b and Figure 8a,b present the absolute and relative modifications with fixed base of e-tickets prices for a Bucharest–London flight for Ryanair and TAROM.

The significant price variations of Ryanair indicate that it applied price differentiation, maximizing short-term revenues. Thus, some hypotheses can be formulated to explain these results. The curves are much more irregular compared to the previous case, which could be explained by sudden peaks in demand, forcing the low-cost company to integrate short-term factors in the price calculation, not being interested in increasing the passenger load factor of the aircraft. Another phenomenon frequently used in the case of LCCs that could also explain these price variations is the initiation of promotional campaigns on similar or complementary routes.

For a broader analysis and to highlight the different pricing techniques, a comparative analysis was also conducted for the two LCCs, Wizz Air and Ryanair. The pricing techniques are different not only for company type, FSC or LCC, but also depending on the company itself, including on direct competitors, and we are referring to those operating the same Bucharest–London flight segment. Figure 7a, Figure 8a and Figure 9a,b present the absolute and relative modifications with fixed base of e-tickets prices for a Bucharest–London flight for Ryanair and Wizz Air. As seen in Figure 7a and Figure 8a, although Ryanair has significant price variations on this route as a result of its campaigns previously analyzed, the same cannot be concluded for Wizz Air, which is rather known for greater price stability, as its figures are lower than the competing company’s.

An explanation for Wizz Air’s lower prices is the opening of its London base in 2018 as it mostly operates in central and eastern Europe, while its pricing policy aims to gain market share and increase number of customers, by also consolidating its image, notoriety, market domination, and the increase of the confidence at the level of this sales segment, which currently for Wizz Air is a beginner one, not centralized as in the case of its competitor.

Figure 10 presents the price differences within the national FSC and LCCs comparing the Friday–Sunday flight (7–9 May 2021) to the Saturday–Monday flight (8–10 May 2021). The incurred costs are considered to be the same for any day, therefore the noticeable price difference could only be explained by the aim of customer segmentation. The differences between the low-cost companies and the leading company’s pricing systems are evident. The fares on both return flights for Ryanair are almost identical throughout the simulation; the company does not differentiate the price according to the flight day. In contrast, for TAROM, the price difference is often important and varies over time. One month before the flight, the cost for a TAROM ticket is the same for both considered flights, but for the day before the flight, the price difference is 100 Euros, a 350 Euro price for the 8–10 May flight and a 450 Euro price for the 7–9 May flight.

The prices of leading companies tend to integrate more short-term factors than LCCs which highlights the tariff fluctuation. Generally, there were higher prices for May 7–9 flights than for May 8–10 flights, since the first segment includes a full weekend where the flow of passengers is more important and consequently the fares are increased, while the second includes a weekday—Monday, allowing the reduced prices.

6. Discussion and Conclusions

The study on the variation of prices depending on the date of e-ticket purchase offers the possibility of validating the hypotheses regarding the pricing strategies of different airlines. If LCCs such as Ryanair and Wizz Air tend to charge on long-term and maximize their profit more than the revenue, less influence on fares is determined by the purchase date.

The results of the comparative analysis of the air transport models based on the data from Table 2 by applying complementary the statistical methods between national Romanian airline and LCCs show that there are so many other factors influencing e-ticket prices: type of market, competition, seasonality [21], fuel costs, income and market structure, demand and operational factors [34], or the non-price factors, e.g., convenience of schedules, frequent flyer programs, quality on in-flight service, etc. [70]. Moreover, based on the results of Student t test our research identified that only for variable booked passenger load factor (%), there are no statistically significant differences between FSC and LCCs for the analyzed period. There are differences between FSC and LCCs regarding two important KPIs for airline companies: the average income/passenger and number of passengers booked (million persons).

The best predictors for total operating revenues (mil. Euro) are somewhat different: (1) for TAROM the best predictors are given by the number of passengers booked, booked passenger load factor, and the number of aircrafts with p-value < 0.05; (2) for low-cost companies, only the number of passengers booked are significant for p-value < 0.05 together with booked passenger load factor but for a p-value = 0.071.

Our comparative analysis emphasizes that the fares with the highest values are recorded one day before the flight, while the lowest are offered two months before the flight date, a fact specified in the study conducted by DLR [2019] [37], where the advance booking period is three months. Passengers consider it logical that airfares are cheaper when bought a long time in advance and very expensive when purchased shortly before departure [1,3,33,71] As seen in our simulation, the variation of flight ticket price confirms the results of the DLR study, i.e., between 100 Euro and 200 Euro for tickets purchased one day in advance and between 21 Euro and 56 Euro for tickets purchased two months in advance. These data are valid not only in the case of LCCs, as demonstrated in the analysis conducted by DLR, but are also representative for leading companies, generally in more proportions according to the calculated values.

Because LCCs do not implement flight alliance strategies, compared to traditional ones, which have code sharing contracts, these results lead to the conclusion that LCCs have two elements of differentiation comparing with FSCs:

• offering only direct point-to-point flights without the possibility of correspondence, still having major airports, for example Dublin for Ryanair;

• implantation on secondary airports, often regional airports, not frequented by FSCs, away from large urban centers; a traveler who wants to cross from point A to C is obliged to buy two independent tickets).

Our results are in line with the research results of Kelemen, Pilat, Mako et al. [28] showing that Ryanair had set up an absolute price competition for Wizz Air (the study case is for April 2019) and its only goal in that period was to get the most possible market share.

Our research results highlight that in the case of air transport, two practical phenomena are represented by:

• a discrepancy between the multiple prices for the same fare category and the same traveled distance;

• a continuous decrease of the average income per passenger compared to the regular fare, due to the multiplication of reduced fares determined by competition, the desire to maintain a competitive advantage by costs, and the use of promotional fares to attract new categories of passengers.

This research emphasizes many statistically significant differences resulting from the fact that these types are characterized by very different fundamental business models and correspondingly also target groups and confirm the opinion of Wehner, J. M. Lopez-Bonilla, L. M. Lopez-Bonilla et al. [3].

The differences between a FSC and a LCC model are more and more perceptible, both in terms of pricing strategies as shown by the present simulation results and in terms of distribution policy or by their definition of products.

The present research reinforces that the competitive strategies used by LCCs are based on price and targeting small and medium class customers, while network/leading airlines rely more on the quality of services and tend to conquer middle- and upper-class customer segments.

The case study allows the formulation of some conclusions, namely:

• the use of yield management techniques and price differentiation by airlines; its main instruments: ticket class, service restriction, and reservation moment—allowing airlines to divide consumers into groups according to their elasticity [7].

• the fare offer is the result of competition, the tendency to cover costs and maximize profit, considering the filling quota of the aircraft and the total generated revenue.

• the existence of a multitude of tariffs on different days of the week for the same flight, even for the same services, within the same company; thus, our research results confirm the results of Mantin and Koo [72] regarding the strong weekend effects for airfare dispersion, but not for price level.

• low prices of LCCs are not always as low as they claim, and the last-minute price of the LCCs is at least equal to the price of the traditional company bought in advance;

• low-cost companies tend to charge on long-term and expect a lower variation of the approached tariffs, respectively the reverse in the case of FSCs;

• companies carry out promotional campaigns on similar or complementary routes, where tariffs for the central segments are adapted to regulate traffic for the entire network.

The limits of the research are linked to not considering the pandemic period (2020–2021) as a particular situation and period for airline e-tickets buying behavior worldwide and particularly for the Romanian consumer. Even the continuous simulation of e-tickets was conducted in the time with COVID-19 pandemic restrictions for 2021. For future research, the authors intend to reconsider the continuous simulation process during the summertime for the most used travel routes for Romanian consumers.

The research results point out that price differentials also appear to have some relevant effect on market share and may facilitate entry and penetration by new competitors where they benefit from lower unit costs [34] such as the two investigated LCCs, Ryanair and Wizz Air. Our results reveal that for both FSCs and LCCs, the price generally increases as departure is approaching [21,63]. The consumers are aware of companies’ policies, but companies discriminate consumers by the moment of purchase [7].

Being extremely competitive and customer oriented, airlines serve as an example for many other businesses [2], the yield management being applied in many other activities, i.e., hotels [7].

The conclusion emphasized by our research is that for the Romanian FSC compared with the two LCCs, yield management involves strategic control of available resources, meaning to sell the product to the right customer at the right time for the right price [2,73].

Author Contributions

Conceptualization, F.D.O., M.K. and M.R.G.; methodology, F.D.O. and M.R.G.; validation, M.K., M.R.G. and F.D.O.; formal analysis, M.R.G. and F.D.O.; investigation, F.D.O., M.K. and M.R.G.; data curation, F.D.O., M.K. and M.R.G.; writing—original draft preparation, F.D.O., M.K. and M.R.G.; writing—review and editing, M.K., F.D.O. and M.R.G.; supervision, M.R.G. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Comparative average prices of low-cost airlines with advanced booking. (Source: made by the authors according to DLR data).

Figure 2. The research framework. (Source: made by the authors).

Figure 3. Distribution of airlines companies according to total operating revenue and number of passengers booked.

Figure 4. Distribution of airlines companies according to total operating revenue and booked passenger load factor.

Figure 5. Absolute modifications of the prices for flight Bucharest–Madrid for Ryanair and TAROM. (Source: authors’ contribution according to the data published on [38,39,41]).

Figure 6. Relative modifications of the prices for flight Bucharest–Madrid for Ryanair and TAROM. (Source: authors’ contribution according to the data published on [38,39,41]).

Figure 7. Absolute modifications of the prices for flight Bucharest–London for Ryanair and TAROM. (Source: authors’ contribution according to the data published on [38,39,41]).

Figure 8. Relative modifications of the prices for flight Bucharest–London for Ryanair and TAROM. (Source: authors’ contribution according to the data published on [38,39,41]).

Figure 9. Absolute and relative modifications of the prices for flight Bucharest–London for Wizz Air (Source: authors’ contribution according to the data published on [52,55]).

Figure 10. The price difference for Bucharest–London flight by days of the week. (Source: authors’ contribution according to the data published on [41,45]).

Table 1

Characteristics of network/leading carriers and low-cost airlines.

	FSCs	LCCs
Generic strategy	differentiation	cost leadership
Flight plan	many connecting flights	only direct flights
Collaboration	yes	no
Airports served	big hubs, medium-sized	primarily small
Geographical coverage	worldwide	continental
Fleet	heterogeneous	homogeneous

Source: [3].

Table 2

Financial indicators for FSCs and LCCs for 2014–2021.

Indicator	2014	2015	2016	2017	2018	2019	2020	2021
TAROM
Total revenue per booked passenger (mil. euro)	258	256	239	221	303	315	130.5	177.3
Net income (mil. euro)	−26	−6.3	−10.5	−37.7	−34.2	−33.9	−87.9	−65.5
Passenger booked (mil. people)	2.33	2.39	2.33	2.34	2.88	3.16	0.84	1.5
Booked passenger load factor (%)	66.0	70.0	68.1	71.6	73.17	74.90	64.31	71.30
Number of aircrafts	23	23	21	23	25	25	29	29
Number of employees	1969	1880	1841	1776	1794	1790	1532	1305
Total revenue per booked passenger (euro)	110.7	107.1	102.6	94.1	105.21	99.68	155.35	118.20
Ryanair
Total revenue per booked passenger (mil. euro)	5036	5654	6535	6647	7151	7697	8494	1635.8
Net income (mil. euro)	522	866	1559	1315	1450	885	648.7	1015.1
Passenger booked (mil. people)	81.7	90.6	106.4	120	130.3	142	149	28
Booked passenger load factor (%)	83	88	93	94	95	96	95	71
Number of aircrafts	297	308	341	383	431	-	-	471
Number of employees	8992	9394	11,458	13,026	14,583	16,840	17,268	15,016
Number of destinations	186	189	200	207	216	219	242	225
Total revenue per booked passenger (euro)	61.6	62.4	61.42	55.40	54.88	54.17	57.17	59.5
Wizz Air
Total revenue per booked passenger (mil. euro)	1011.8	1227.3	1429.1	1571.2	1948	2319.1	2761.3	0.739
Net income (mil. euro)	87.7	183.2	192.9	225.3	275.1	291.6	281.1	576
Passenger booked (mil. people)	13.9	16.5	20.0	23.8	29.6	34.6	40.02	10.18
Booked passenger load factor (%)	85.7	86.7	88.2	90.1	91.3	92.8	93.6	64.0
Number of aircrafts	46	55	67	79	93	112	121	137
Number of employees	1650	2040	2396	3033	3686	4550	4400	3960
Total revenue per booked passenger (euro)	65.1	64.7	62.6	65.73	65.43	58.9	54.6	44.1

Source: authors’ contribution using data from airline companies’ Internet pages and reports [49,50,51,52,53,54,55,56].

Table 3

Descriptive statistics for research variables.

National/Low-Cost Airline Company			Total Operating Revenues (mil. euro)	Net Income (mil. euro)	Number of Employee (Number)	Number of Passengers Booked (Million Persons)	Booked Passenger Load Factor (%)	Number of Aircraft	Average_Income_Per Passenger
national	N	Valid	8	8	8	8	8	8	8
		Missing	0	0	0	0	0	0	0
	Mean		237.475000	−37.7500	1735.88	2.2213	69.9225	24.75	111.6628
	Median		247.500000	−34.0500	1792.00	2.3350	70.6500	24.00	106.1565
	Std. Deviation		61.4195583	27.20882	214.245	0.73771	3.59060	2.915	19.04193
	Minimum		130.5000	−87.90	1305	0.84	64.31	21	94.44
	Maximum		315.0000	−6.30	1969	3.16	74.90	29	155.36
low-cost	N	Valid	16	16	16	16	16	14	16
		Missing	0	0	0	0	0	2	0
	Mean		3819.896188	648.3563	8268.25	64.7875	87.9625	210.07	59.2313
	Median		2540.200000	549.0000	6771.00	37.3100	90.7000	129.00	60.4600
	Std. Deviation		2826.0140520	482.63066	5699.770	50.84816	8.90579	153.497	5.72049
	Minimum		0.7390	87.70	1650	10.18	64.00	46	44.10
	Maximum		8494.0000	1559.00	17268	149.00	96.00	471	65.73

Table 4

The Student t test results.

		Levene’s Test for Equality of Variances		t-Test for Equality of Means
		F	Sig.	t	df	Sig. (2-Tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
									Lower	Upper
Total operating revenues (mil. euro)	EVA	49.412	0.000	−3.545	22	0.002	−3582.4211	1010.5483	−5678.1702	−1486.6721
	EVNA			−5.068	15.028	0.000	−3582.4211	706.8371	−5088.7616	−2076.0807
Net income (mil. euro)	EVA	18.162	0.000	−3.973	22	0.001	−686.1062	172.69178	−1044.2470	−327.9654
	EVNA			−5.668	15.190	0.000	−686.1062	121.04054	−943.8172	−428.3952
Number of employee (number)	EVA	35.697	0.000	−3.204	22	0.004	−6532.375	2038.616	−10760.205	−2304.545
	EVNA			−4.578	15.085	0.000	−6532.375	1426.954	−9572.371	−3492.379
Number of passenger booked (millions persons)	EVA	46.217	0.000	−3.441	22	0.002	−62.5662	18.1815	−100.2725	−24.8599
	EVNA			−4.921	15.013	0.000	−62.5662	12.7147	−89.6650	−35.4674
Booked passenger load factor (%)	EVA	2.160	0.156	−5.462	22	0.000	−18.0400	3.3028	−24.8896	−11.1903
	EVNA			−7.039	21.475	0.000	−18.0400	2.5629	−23.3627	−12.7172
Number of aircraft	EVA	50.963	0.000	−3.378	20	0.003	−185.321	54.853	−299.743	−70.900
	EVNA			−4.516	13.016	0.001	−185.321	41.037	−273.965	−96.678
average_income_passenger	EVA	5.339	0.031	10.319	22	0.000	52.4315	5.0809	41.8943	62.9686
	EVNA			7.618	7.639	0.000	52.4315	6.8825	36.4287	68.4343

Source: authors’ calculations. Note: EVA = Equal variances assumed; EVNA = Equal variances not assumed.

Table 5

Model summary.

National/Low-Cost Airline Company	Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
national	1	0.994 a	0.988	0.979	8.9065289
low-cost	2	0.995 a	0.989	0.986	289.6254814

^a Predictors: (Constant), number of aircraft, booked passenger load factor (%), number of passengers booked (millions persons).

Table 6

ANOVA results.

National/Low-Cost Airline Company	Model		Sum of Squares	df	Mean Square	F	Sig.
national a	1	Regression	26,089.230	3	8696.410	109.628	0.000 b
		Residual	317.305	4	79.326
		Total	26,406.535	7
low-cost a	2	Regression	7,6854,239.442	3	25,618,079.814	305.403	0.000 b
		Residual	838,829.194	10	83,882.919
		Total	77,693,068.636	13

^a Dependent variable: Total operating revenues (mil. euro). ^b Predictors: (Constant), number of aircraft, booked passenger load factor (%), number of passengers booked (millions persons).

Table 7

The regression coefficients for regression models.

National/Low-Cost Airline Company	Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity Statistics
			B	Std. Error	Beta			Tolerance	VIF
national a	1	(Constant)	200.372	85.436		2.345	0.079
		Number of passengers booked (million persons)	115.227	11.860	1.384	9.715	0.001	0.148	6.755
		Booked passenger load factor (%)	−5.287	1.877	−0.309	−2.817	0.048	0.249	4.008
		Number of aircraft	6.093	2.120	0.289	2.874	0.045	0.297	3.371
low-cost a	2	(Constant)	−2197.711	1222.691		−1.797	0.102
		Number of passengers booked (million persons)	52.002	4.588	0.911	11.334	0.000	0.167	5.990
		Booked passenger load factor (%)	28.816	14.240	0.106	2.024	0.071	0.390	2.561
		Number of aircraft	0.636	1.133	0.040	0.561	0.587	0.213	4.690

^a Dependent variable: Total operating revenues (mil. euro).

Table 8

Comparative analysis of costing elements: the differences between LCCs and FSCs.

	Cost Reduction	Weight Share
The cost supported by the FSCs		100%
Factors that reduce cost
Higher seat density	−15%	85%
Increasing aircraft productivity	−5%	80%
Low crew costs	−3%	77%
Fewer ground teams	−4%	73%
Low airport and landing costs	−6%	67%
Unique type of aircraft fleet	−2%	65%
Reduced station/outsourced handling costs	−10%	55%
No free board service	−6%	49%
Lack of commission	−3%	46%
Low sales and booking costs	−3%	43%
Reduced administrative expenses	−2%	41%
Higher seat density	−15%	85%
The cost supported by the LCCS		41%

Source: authors’ contribution based on data from IATA.

References

1. Szopinski, T.S.; Nowacki, R. The Influence of Purchase Date and Flight Duration over the Dispersion of Airline Ticket Prices. Contemp. Econ.; 2015; 9, pp. 353-366. [DOI: https://dx.doi.org/10.5709/ce.1897-9254.174]

2. Lohmeier, V.; Hess, S. Airline Pricing Strategies. 2009; Available online: https://www.diva-portal.org/smash/get/diva2:158427/FULLTEXT01.pdf (accessed on 5 May 2020).

3. Wehner, C.; Lopez- Bonilla, J.M.; Santos, J.A. State of the art of pricing policy in air transport &network carries vs. Low-cost airlines. Tour. Manag. Stud.; 2018; 14, pp. 32-40.

4. Forsyth, P. The gains from the liberalisation of air transport—A review of reform. J. Transp. Econ. Policy; 1998; 32, pp. 73-92.

5. IATA. Economic Performance of the Airline Industry—2019 End-Year Report. 2019; Available online: www.iata.org/economics (accessed on 5 May 2020).

6. Goeldner, C.R.; Ritchie, J.R.B. Tourism, Principles, Practices, Philosophies; 11th ed. John Wiley & Sons Inc.: Hoboken, NJ, USA, 2009.

7. Belova, A. Price Strategy of the International Airline Market. Working Paper. 2015; Available online: http://paneldataconference2015.ceu.hu/Program/Alexandra-Belova.pdf (accessed on 5 May 2020).

8. Kraemer, A.; Friesen, M.; Shelton, T. Are airline passengers ready for personalized dynamic pricing? A study of German consumers. J. Rev. Pricing Manag.; 2018; 17, pp. 115-120. [DOI: https://dx.doi.org/10.1057/s41272-017-0122-0]

9. Sonnenschein, J.; Smedby, N. Designing air ticket taxes for climate change mitigation: Insights from a Swedish valuation study. Clim. Policy; 2019; 19, pp. 651-663. [DOI: https://dx.doi.org/10.1080/14693062.2018.1547678]

10. Gheorghilaș, A. Geografia Turismului. Metode de Analiză în Turism (Tourism Geography. Analisys Methods for Tourism); 3rd ed. Editura Universitară: Bucharest, Romania, 2014; 188.

11. Snak, O.; Baron, P.; Neacșu, N. Economia Turismului (Tourism Economy); Editura Expert: Bucharest, Romania, 2003; 309.

12. Gheorghe, C. Transporturi Turistice Aeriene și Ticketing (Air Transport and Ticheting); Editura Pro Universitară: Bucharest, Romania, 2012; 12.

13. UNWTO. Tourism Highlights; 2018; Available online: https://www.slovenia.info/uploads/dokumenti/unwto_tourism_highlights_2018.pdf (accessed on 5 May 2020).

14. IATA. Available online: https://t2t.ro/statistici-iata-2017-record-de-peste-4-1-miliarde-de-pasageri-pe-zboruri/ (accessed on 14 May 2019).

15. Marcus, B.; Anderson, C.K. Revenue management for low-cost providers. Eur. J. Oper. Res.; 2008; 188, pp. 258-272. [DOI: https://dx.doi.org/10.1016/j.ejor.2007.04.010]

16. Button, K. Wings across Europe. Towards an Efficient European Air Transport System; Routledge: Hampshire, UK, 2017.

17. Fu, X.; Oum, T.H.; Zhang, A. Air Transport Liberalization and Its Impacts on Airline Competition and Air Passenger Traffic. Transp. J.; 2010; 49, pp. 24-41. [DOI: https://dx.doi.org/10.2307/40904912]

18. Malighetti, P.; Paleari, S.; Redondi, R. Pricing strategies of low-cost airlines: The Ryanair case study. J. Air Transp. Manag.; 2009; 15, pp. 195-203. [DOI: https://dx.doi.org/10.1016/j.jairtraman.2008.09.017]

19. Ageron, P.; Zembri, P. Le Transport Aérien:Une Mondialisation Réussie; La Documentation Française: Paris, France, 2016.

20. Tinard, Y. Le Tourisme, Economie et Management; Ediscience International: Paris, France, 1994.

21. Fedorco, L.; Hospodka, J. Airline Pricing Strategies in European Airline Market. 2013; Available online: http://pernerscontacts.upce.cz/30_2013/FeMArco.pdf (accessed on 5 May 2020).

22. Liao, C.-N.; Chen, Y.-J. Role of exchangeable tickets in the optimal menu design for airline tickets. OMEGA Int. J. Manag. Sci.; 2019; 89, pp. 151-163. [DOI: https://dx.doi.org/10.1016/j.omega.2018.10.004]

23. Kontsas, S.; Mylonakis, J. Pricing competition policy in the European airlines industry: A firm behavior model proposal. Innov. Mark.; 2008; 4, pp. 23-27.

24. RYANAIR. Available online: https://investor.ryanair.com/wp-content/uploads/2018/07/Ryanair-FY-2018-20F.pdf (accessed on 10 April 2019).

25. Smyth, M.; Pearce, B. Airline Cost Performance. An Analysis of the Cost Base of Leading Network Airlines Versus No-Frills, Low-Cost Airlines (LCCs); IATA Economics Briefing IATA: Montreal, QC, Canada, 2018.

26. Vasigh, B.; Fleming, K.; Tacker, T. Introduction to Air Transport Economics: From Theory to Applications; 2nd ed. Ashgate Publishing Limited: Hampshire, UK, 2008.

27. Law, C.C.H. The Impact of Long Haul, Low Cost Flights: Linking Thailand to Japan And South Korea. Tour Hosp. Manag. Croat.; 2022; 28, pp. 211-231. [DOI: https://dx.doi.org/10.20867/thm.28.1.11]

28. Gallego, G.; Lee, H. Callable products with dependent demand. Nav. Res. Logist.; 2020; 67, pp. 185-200. [DOI: https://dx.doi.org/10.1002/nav.21899]

29. Schlumberger, C.E.; Weisskopf, N. Ready for Takeoff? The Potential for Low-Cost Carriers in Developing Countries; The World Bank: Washington, DC, USA, 2014.

30. Kelemen, M.; Pilát, M.; Makó, S.; Rozenberg, R.; Tobisová, A. Pricing policy aspects in competitive fight between low-cost airlines on Kosice airport. J. KONBiN; 2019; 49, pp. 331-341. [DOI: https://dx.doi.org/10.2478/jok-2019-0017]

31. Fageda, X.; Luis Jimenez, J.; Perdiguero, J. Price rivalry in airline markets: A study of a successful strategy of a network carrier against a low-cost carrier. J. Transp. Geogr.; 2011; 19, pp. 658-669. [DOI: https://dx.doi.org/10.1016/j.jtrangeo.2010.07.006]

32. Georgescu, B.; Nistoreanu, P.; Anghel, L.D.; Negoi, R.; Moise, D. Identifying the optimum price—strategy of airlines companies for customers attraction and loyalty. Proceedings of the 7th International Conference on Management of Technological Changes; Alexandroupoli, Greece, 1–3 September 2011; Book 1 pp. 141-144.

33. Escobari, D.; Rupp, N.G.; Meskey, J. An analysis of dynamic price discrimination in airlines. South. Econ. J.; 2019; 85, pp. 639-662. [DOI: https://dx.doi.org/10.1002/soej.12309]

34. Otero, D.F.; Escallon, M.; Lopez, C.; Akhavan-Tabatabaei, R. Optimal timing of airline promotions under dilution. Eur. J. Oper. Res.; 2019; 277, pp. 981-995. [DOI: https://dx.doi.org/10.1016/j.ejor.2019.03.011]

35. Maung, Y.S.Y.; Douglas, I.; Tan, D. Identifying the drivers of profitable airline growth. Transp. Policy; 2022; 115, pp. 275-285. [DOI: https://dx.doi.org/10.1016/j.tranpol.2021.11.007]

36. Avogadro, N.; Malighetti, P.; Redondi, R.; Salanti, A. A tale of airline competition: When full-service carriers undercut low-cost carriers fares. J. Air Transp. Manag.; 2021; 92, 102027. [DOI: https://dx.doi.org/10.1016/j.jairtraman.2021.102027]

37. Available online: https://www.dlr.de/dlr/desktopdefault.aspx/tabid-10002/ (accessed on 12 June 2019).

38. Zhu, C.; Hu, J.Q. Revenue management games with government mandate. OMEGA Int. J. Manag. Sci.; 2020; 93, 102103. [DOI: https://dx.doi.org/10.1016/j.omega.2019.08.010]

39. Merkert, R.; Webber, T. How to manage seasonality in service industries—The case of price and seat factor management in airlines. J. Air Transp. Manag.; 2018; 72, pp. 39-46. [DOI: https://dx.doi.org/10.1016/j.jairtraman.2018.07.005]

40. Lardeux, B.; Sabatier, G.; Delahaye, T.; Boudia, M.; Tonnet, O.; Mathieu, P. Yield optimizations for airlines from ticket resell. J. Rev. Pricing Manag.; 2019; 18, pp. 213-227. [DOI: https://dx.doi.org/10.1057/s41272-018-00167-1]

41. McAfee, R.P.; Te Velde, V. Dynamic pricing in the airline industry forthcoming. Handbook on Economics and Information Systems; Hendershott, T.J. Elsevier: Amsterdam, The Netherlands, 2006.

42. Gaggero, A.A.; Piga, C.A. Airline market power and intertemporal price dispersion. J. Ind. Econ.; 2011; 59, pp. 552-577. [DOI: https://dx.doi.org/10.1111/j.1467-6451.2011.00467.x]

43. Vinod, B. The Evolution of Yield Management in the Airline Industry; Springer International Publishing: Berlin/Heidelberg, Germany, 2021; [DOI: https://dx.doi.org/10.1007/978-3-030-70424-7]

44. Williams, K. The welfare effects of dynamic pricing: Evidence from airline markets. Econometrica; 2022; 90, pp. 831-858. [DOI: https://dx.doi.org/10.3982/ECTA16180]

45. Narangajavana, Y.; Garrigos-Simon, F.J.; García, J.S.; Forgas-Coll, S. Prices, prices and prices: A study in the airline sector. Tour. Manag.; 2014; 41, pp. 28-42. [DOI: https://dx.doi.org/10.1016/j.tourman.2013.08.008]

46. Wang, Y. Dynamic pricing considering strategic customers. Proceedings of the 2016 International Conference on Logistics; Kraków, Poland, 1–3 June 2016; Informatics and Service Sciences (LISS): Sydney, NSW, Australia, 2016; pp. 1-5.

47. Abdella, J.A.; Zaki, N.M.; Shuaib, K.; Khan, F. Airline ticket price and demand prediction: A survey. J. King Saud. Univ. Comput. Inf. Sci.; 2021; 33, pp. 375-391. [DOI: https://dx.doi.org/10.1016/j.jksuci.2019.02.001]

48. Schubert, D.; Sys, C.; Macario, R. Customized airline offer management: A conceptual architecture. J. Revenue Pricing Manag.; 2022; 21, pp. 553-563. [DOI: https://dx.doi.org/10.1057/s41272-021-00358-3]

49. TAROM. Available online: https://www.tarom.ro/sites/default/files/sf_31.12.2017.pdf (accessed on 8 April 2019).

50. TAROM. Available online: https://www.tarom.ro/&files/situatiile-financiare-tarom-2015_1.pdf (accessed on 8 April 2019).

51. RYANAIR. Available online: https://corporate.ryanair.com/about-us/history-of-ryanair/ (accessed on 8 April 2019).

52. RYANAIR. Available online: https://www.ryanair.com/ro (accessed on 10 April 2019).

53. RYANAIR. Available online: https://investor.ryanair.com/wp-content/uploads/2022/07/Ryanair-2022-Annual-Report.pdf (accessed on 20 August 2022).

54. WIZZ AIR. Available online: https://corporate.WizzAir.com/en-GB/investor_relations/governance (accessed on 8 April 2019).

55. WIZZ AIR. Available online: https://WizzAir.com/ro-ro/informatii-si-servicii/despre-noi/noutati/#/ (accessed on 8 April 2019).

56. WIZZ AIR. Available online: Wizz_Air_Holdings_Plc_Annual_report_and_accounts_2018.pdf (accessed on 8 April 2019).

57. Deschinkel, K.; Farges, J.L.; Delahaye, D. Optimizing and assigning price levels for air traffic management. Transp. Res. Part E-Logist. Transp. Rev.; 2002; 38, pp. 221-237. [DOI: https://dx.doi.org/10.1016/S1366-5545(02)00007-8]

58. Pels, E.; Njegovan, N.; Behrens, C. Low-cost airlines and airport competition. Transp Res. Part E-Logist. Transp. Rev.; 2009; 45, pp. 335-344. [DOI: https://dx.doi.org/10.1016/j.tre.2008.09.005]

59. Koenigsberg, O.; Muller, E.; Vilcassim, N.J. EasyJet (R) pricing strategy: Should low-fare airlines offer last-minute deals?. QME Quant. Mark. Econ.; 2008; 6, pp. 279-297. [DOI: https://dx.doi.org/10.1007/s11129-007-9036-2]

60. Skare, V.; Gospic, D. Dynamic pricing and customers’ perceptions of price fairness in the airline industry. Tourism; 2015; 63, pp. 515-528.

61. Pickvance, C. The four varieties of comparative analysis: The case of environmental regulation. J. Ho. Built. Envirn.; 2005; 16, pp. 7-28. [DOI: https://dx.doi.org/10.1023/A:1011533211521]

62. Groves, W.; Gini, M. On Optimizing Airline Ticket Purchase Timing. ACM Transac. Intell. Sys. Tech.; 2015; 7, pp. 1-28. [DOI: https://dx.doi.org/10.1145/2733384]

63. Bilotkach, V.; Gorodnichenko, Y.; Talavera, O.; Zubenko, I. Are airlines’ price-strategies different?. J. Air Transp. Manag.; 2006; 16, pp. 1-6. [DOI: https://dx.doi.org/10.1016/j.jairtraman.2009.04.004]

64. Chiu, C.C.; Tsao, C. The optimal airline overbooking strategy under uncertainties. Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2004; pp. 937-945.

65. Straka, M.; Hurna, S.; Bozogan, M.; Spirkova, D. Using continuous simulation for identifying bottlenecks in specific operation. Int. J. Sim. Model.; 2019; 18, pp. 408-419. [DOI: https://dx.doi.org/10.2507/IJSIMM18(3)477]

66. Kunnumkal, S.; Topalogu, H. Simulation-based methods for booking control in network revenue management. Proceedings of the 2010 Winter Simulation Conference; Baltimore, MD, USA, 5–8 December 2010; pp. 1890-1897.

67. Decker, M. Structures et stratégies des compagnies aériennes à bas coùts. Le Tourbulences du « Low-Cost » dans le Ciel Européen; Chaos International: Strasbourg, France, 2009.

68. Available online: http://www.senat.fr/rap/r15-180/r15-180_mono.html (accessed on 13 April 2019).

69. Gőnenç, R.; Nicoletti, G. Le transport aérien de passagers: Réglementation, structure du marché et performance. Rev. Économique De L’ocde; 2001; 32, pp. 203-254. [DOI: https://dx.doi.org/10.1787/eco_studies-v2001-art6-fr]

70. Gayle, P.G. Does Price Matter? Price and Non-Price Competition in the Airline Industry. 2004; Available online: http://fmwww.bc.edu/repec/esNASM04/up.6683.1074879536.pdf (accessed on 28 April 2019).

71. Mumbower, S.; Garrow, L.A. Using Online Data to Explore Competitive Airline Pricing Policies Case Study Approach. Transp. Res. Rec.; 2010; 2184, pp. 1-12. [DOI: https://dx.doi.org/10.3141/2184-01]

72. Mantin, B.; Koo, B. Weekend effect in airfare pricing. J. Air Transp. Manag.; 2010; 16, pp. 48-50. [DOI: https://dx.doi.org/10.1016/j.jairtraman.2009.07.002]

73. Available online: https://centreforaviation.com/analysis/reports/european-aviation-wizz-air—Ryanair-exceed-pre-crisis-fleets-593558 (accessed on 10 September 2022).