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The purpose of this research is to investigate the efficiency of the Airline-within-Airline business model (a form of coopetition) in the rapidly growing airline industry and compare it to other business models. The core question addressed in this study is how coopetition impacts organizational performance and whether it brings measurable economic benefits. Data Envelopment Analysis (DEA) is the primary methodology to assess the outcomes of strategic alliances in the airline industry. A dataset comprising records for 52 Airlines structured into 25 decision-making units (DMUs) is utilized for this analysis. Our findings suggest that coopetition brings measurable economic benefits in terms of organizational performance. The Airline-within-Airline business model, in particular, is examined, and its efficiency is compared to other models. As a result, all airlines were divided into three groups by their efficiency, which allowed the formation of a pull of possible partners for further strategic partnerships. In conclusion, this research highlights the potential economic advantages of coopetition within the airline industry, shedding light on the drivers of coopetitive interactions. It underscores the importance of considering coopetition as a viable framework for enhancing organizational performance and market competitiveness. Only a few studies were accomplished measuring the efficiency of business models with a focus on strategic alliances, network cooperation, and coopetition. This research enables the selection of strategic partners and the final choice of the partner for possible coopetitive partnerships based on data-driven assessment.
School of Management, American University Kyiv (AUK), Ukraine
Sumy State University, Ukraine
Iryna Heiets
School of Engineering, RMIT University, Australia
*Address all correspondence to: shvindina.hannah@gmail.com
1. Introduction
The research is aimed to investigate the efficiency of the Airline-within-Arline business model (as a form of coopetition) comparatively to other business models to evaluate the strategic alliances’ outcomes. To accomplish this goal in the research, Data Envelopment Analysis (DEA) is performed to explain the choice toward coopetition or strategic alliances. In the chapter, it will be demonstrated that coopetition brings economic benefits that can be measurable in relation to organizational performance and can be operable as a framework for new partner choice and creates a new perspective for choosing the partners for the next alliance.
Coopetition as a strategic management theory can be referred to as the seminal work of Brandenburger and Nalebuff [1]. Since then, many strategists and scholars have developed and enriched research in coopetition. There are several academic streams that have become traditional and are newly formed in a sphere of coopetition research. The typology of the coopetition interactions was investigated deeply in terms of types of situations and the number of actors [2, 3] to reveal the antecedents of the phenomenon. There is still a big discussion about coopetition that is spontaneous rather than a teleological process, and scholars define the coopetition as a process, or the series of consistent actions of competitors on rules establishment to compete and cooperate in order to achieve current agreements [2, 4]. Another significant stream is a perception of coopetition as a phenomenon performed comprehensively in the systematic review [5]. The third stream assumes that coopetition is a behavior formed in response to global hypercompetition [6], later formulated as a reflection of behavioral approaches to strategy [7]. The conceptualization of coopetition still develops; a particular group of researchers considers coopetition as a paradox or a set of interrelations that are in logical contradictions [8, 9, 10]. The complexity of coopetition and tension between competition and cooperation logic [11] necessitates managing the coopetitive tension [12, 13]. This revolutionary mindset became a core of business modeling [14, 15] and an inspiration for the studies on ecosystems as a construct for strategy [16]. Yet, there are only a few papers evaluating the performance of coopetition. Nevertheless, coopetition was claimed to be a strategy that leads to superior performance [1]. Thus, coopetition proved to be helpful in shrinking product life cycles, risk sharing, and increasing market power, as it is shown in the research of Gnyawali and Park [17], the possible synergy in joint R&D was investigated by Osarenkhoe [18]. Coopetition was proved to be related to increasing the capacity to innovate [14] and improving market performance [19]. At the same time, a company that chooses a coopetition strategy does not definitely win; the strategy can also lead to losses. So, there should be an operable toolbox that helps to evaluate the coopetition strategy’s success. Data Envelopment Analysis (DEA) performed well for the potential partner assessment for the strategic alliance according to the previous findings [20]. This method became a core element of the current research for two reasons: (1) it allows evaluating multi-criterion systems and providing the targets for the further strategic planning; (2) this technique allows to obtain the performance evaluations using disparate data and comparing different market operators; (3) this approach may be implemented for the partner selection among the most attractive in terms of effectiveness.
This research investigation focused on 52 global airline companies using public financial reports (annual reports). The companies were classified into three groups: low-cost airline companies, full-service airline companies, and strategic alliances groups. We examine the perceived outputs of the companies addressing the following research questions:
Which business model is more effective regarding technical efficiency – low-cost or full-service airline companies?
Are the strategic alliances (Airline-within-Airline companies) that formed as a combination of full-service and low-cost service airlines more effective?
This chapter contributes to the strategic management literature by introducing a comparatively new decision-making approach, namely Data Envelopment Analysis (DEA), that makes airline companies feasible candidates for alliance strategies.
The remainder of this chapter is organized as follows: it discusses relevant literature, outlines the methodology, examines the findings, and the discussion of the results. The final section concludes with limitations and directions for future research.
The coopetition as a multifaced phenomenon, a process, a mindset, and an approach to create new superior competitive advantages, has been investigated since 1990. We should note several remarkable overviews that were done by scholars from different research teams. In their work, Czachon and Mucha-Kuś [21] performed a historical overview of coopetition term appearance and dissemination of the coopetition theory during the period from 1997 to 2010. Notably, according to the authors, alliances literature formed more than 83% of all studies on coopetition [21]. Another overview of coopetition studies development during more than two decades revealed specifics of the interaction process and outcomes (or motives) of the coopetition [22]. The systematic review of the papers in the period 1996–2013 [23] was performed for selected 82 papers and allowed to distinguish several dimensions of the coopetition research (roles, content, process, levels, and theoretical perspectives). A systematic review of 142 papers on coopetition [5] highlighted the main topics and domains in the coopetition research, such as the extensive discussion about the nature of the drivers (internal, relation-specific, and external drivers of coopetition), nature of the coopetition process, emphasized a tension as a central part of that discussion, as well as outcomes of coopetition (classified into four categories: innovation, knowledge related, firm performance, and relational outcomes). Dorn and her colleagues [24], on the other hand, performed a systematic review of 169 contributions (papers and book chapters) that resulted in a matrix form where the axes were formed as phases of coopetition (antecedents, initiation phase, managing and shaping phase, and evaluation phase) and levels of interactions (inter-firm level, intra-firm level, and network level). This framework enabled to synthesize all the selected studies’ topics and identified the directions for further research. Later, the bibliometric analysis via specific software (BibExcel) was performed to reveal new challenges in coopetition studies, and it was done based on more than 400 papers [25]. The researcher used Clarivate Analytics Web of Science to reveal the most contributing authors in the field and their main contributions to understanding coopetition. The partnerships and alliances proved to be the core element in the debates about coopetition-based innovation and the problem of creating and capturing value. A year later, the research on coopetition based on Scopus Databased was published [26], where besides the TOP contributing authors and publishing trends, the cluster analysis was performed for 664 papers on coopetition. The investigation revealed the link between 75 leading terms, where strategic alliances were identified as closely related to studies on business models, ecosystems, open systems, and competitive advantages.
The literature provides several explanations for the motives of the strategic alliances and, therefore, outcomes of the coopetition. For instance, a positive relationship between coopetitive strategy and financial performance was found in the case of SMEs [27] and for strategic alliances with competitors [11]. Another benefit is innovativeness [17, 28], which occurs in the form of the shrinking of the life cycle of the new production and the acceleration of the knowledge transfer. Even though coopetition can be a positive, neutral, or negative-sum game, the combination of the partner’s characteristics and environmental factors will determine the outcome [29]. However, coopetition is claimed to foster innovations, reduce costs through risk and cost-sharing, improve competitiveness [15], and in other words, generate higher results. Empiric research [30], based on the exploration of the data from 1991 to 2005 on firms’ performance, confirmed that cooperation with competitors leads to better performance. The researchers argue that through co-procurement, co-marketing, co-distribution, chain-store co-management, and integrated information systems, value creation takes place at a higher level that results in higher quality of the product and service, wider choice for the customer, and lower production costs. The key determinant of the success of coopetition is a pooling of the expertise and resources that creates additional opportunities to increase productivity, enlarge the economies of scale, and improve the experience curve [30].
The attention to coopetition issues and strategic alliances still grows, as it can be demonstrated through Google Trends Tool (Please see Figure 1).
Figure 1.
Google search trends for the keywords of the research: (a) coopetition (b) strategic Alliance and (c) airline Alliance (generated by Google trends tool for the data period 2004–2020).
Though the interest among the public and scholars in strategic alliances is not as high as in the 2000s, airline alliances are still the focus of discussion. There are few papers that unite the abovementioned topics, and one of the most cited works is [31], according to data gained through Harzing’s Publish or Perish Tool. In their research, Amankwah-Amoah and Debrah [31] classified motives for airline alliances into two categories: (1) functional efficiency improvement (ticketing, scheduling, sales, and so on), that leads to increasing capacity in reaching the market or competitiveness of the operator; (2) improvement of partners’ utilization of their size and structure to achieve collective gains. Czakon and Dana [32] revealed four phases in airline global industry development and antecedents of dyadic and network coopetition in the industry and suggested that coopetition is formed as an imitation of the industrial game to avoid external pressure and, moreover to create bigger value. Continuing the discussion about the airline alliance outcomes, Law and Breznik [33] defined additional benefits, such as access to global coverage routes, flight operations co-management, joint advertising, and equipment sharing. In another research, the multilateral alliance positional cycle was offered [34] to explain the motives and failures of the multilateral airline alliances. Researchers argue that the merger logic is not associated with superior economic rent but with the resource race for new combinations of resources, opportunities, and abilities to achieve superior rents.
2.2 Understanding strategic alliances business models
In the current research, the dynamic of three groups is investigated: low-cost airline companies, full-service airline companies, and strategic alliance groups (Airline-within-Airline). Several studies have been done to identify the low-cost carrier (LCC) business model; for instance, Gillen and Lall [35] defined and explained the original concept of LCC, which operates the network with short-haul point-to-point services at selected secondary airports. A specialist by the name of John G. Wensveen has mentioned in his paper that the airline industry has gotten used to the word “Low-Cost Carrier” [36]. But unfortunately, the public has various opinions on what low-cost carrier exactly means. There were multiple definitions, and a few of them include cheap carriers, low-cost and high-value airlines, and new-generation carriers [36]. The development of the low-cost carrier is related to three factors [37]: (1) air transport and the economic cycle are always a pair to start with; (2) air transportation is one of the limiting factors that contribute to the world society and populations; (3) liberalizations in the air transport sector encourages the start of airline businesses and services to the populations. Furthermore, the competition in the global aviation market has become strong in the last decade, with Ryanair, EasyJet, Southwest, JetBlue, WestJet, and Wiss Air offering record-low fares across European and USA destinations.
According to Hunter [38], Full-Service Network Carriers (FSNCs) are a type of airline business that uses a differentiation strategy in providing flights to big hub airports. The FSNC is normally large in scale and uses the hub and spoke model to connect flights from major cities by linking with feeder routes. The FSNCs use various aircraft types for different flight operations and sectors ranging from a mixture of short-haul, medium-haul, and long-haul routes. As for the FSNCs market, it is typically highly competitive with other FSNCs, such as the quality of service and high service image which they provide in terms of frequent scheduling of flights and flexibility they offer to passengers. In addition, FSNCs also provide passengers with comprehensive inflight services, a broad range of ground services, and the usage of principal airports [38]. Gillen [35] mentioned that the full-service model is based on predictions regarding product services and geographical aspects of providing passengers with a wide range of destinations for them to choose from.
The FSNCs and LCCs are widely discussed in the literature, as well as the strategy and competition between legacy carriers and budget airlines. On the other hand, few studies have addressed the issue of Airline-within-Airline (AwA) as a response to low-cost initiatives. According to Graf [39], Airline-within-Airline are two incompatibilities in simultaneously operating different business models within one grouping. The FSNC, offering premium products, decided to adopt a low-cost airline that operates in the opposite manner. Cost leadership is the critical strategy, LCCs follow in contrast to FSNCs. The airlines also have a high level of autonomy in one group. Graham and Vowles [40] mentioned that LCC is a part of the Airline-within-Airline business model to address leisure and tourist routes to protect the business class.
One of the first successful AwA cases is the Qantas Group, which includes Qantas Airways (International and Domestic) and low-cost carrier Jetstar. The Vueling is a Spanish low-cost airline based in Barcelona, which is now the largest airline in Spain. In 2011, the Singapore Airlines Group acquired a majority stake in Scoot for low-cost operations. The following year, Scoot was established as a part of this group. Eurowings operates all Lufthansa short-haul flights. In this in-depth case study, the authors selected the main 12 groups of airlines that present the Airline-within-Airline business model and include low-cost airlines in their management structure (see Figures 2 and 3 and Table 2 of Appendix A).
Figure 2.
The main airlines selected for research classified into three groups, depending on the airline business models.
Figure 3.
Business-models airlines within airlines (FSNCs are highlighted in blue color and LCCs are highlighted in yellow color).
2.3 Data envelopment analysis
Data Envelopment Analysis (DEA) is chosen to prove the hypothesis of the efficiency of AwA business models comparatively to LCCs and FSNCs Business Models, or on the contrary, to find the failure of the AwA business models for several reasons: (a) it allows evaluating multi-criterion systems and providing the targets for the further strategic planning; (b) this technique allows to obtain the performance evaluation using disparate data and to compare different market operators; (c) this approach may be implemented for the partner selection among the most attractive in terms of effectiveness. Moreover, this method is relatively easy to use via software (DEAP program).
DEA was first developed by Charners et al. [41] and later performed and explained by several scholars, for instance, Boussofiane et al. [42]. Tim Coelli developed and presented software that makes DEA operable and manageable for researchers [43]. After that, the prime for DEA began, as it enables measuring the relative efficiency of the decision-making unit (DMU) compared to other DMUs, using different performance indicators in one framework. DEA is applicable to various decision analysis problems, such as constructing performance indices to evaluate the productivity of the airports [44] or to choose the supplier [45].
This chapter offers to apply DEA in evaluating the overall performance of different business models with the orientation to evaluate the performance of the coopetition model.
The problem of evaluating strategic decisions and business models is a necessity to construct the integral index and applicable benchmarking system using comparable parameters [46]. DEA, as a nonparametric statistic method, not only allows the combining of different key aspects of the organizational functioning, including endogenous (i.e., costs) and exogenous (revenue or market share, for instance) into strategic analysis, and therefore identifying the leaders of the strategic game.
The scope of the study was restricted to passenger airlines based on open information. The research data were collected from annual reports; these reports indicate the true costs of the airlines. More specifically, the quantitative study was designed to test the operational efficiency depending on different types of airline business models for the major 52 airlines operating in the global world. Data sources were extracted from the public files for five full service network carriers, nine low-cost airlines, and twelve major group airlines and accumulated in a spreadsheet. A larger sample allowed for comparison analysis, a method permitting the examiner to assess the differences between the dependent variables. Data analysis and results reflect the data collected for the eligible airlines. The internet-based strategy was effective and produced results during the data collection process.
The focus was on collecting data related to the extent to which operational efficiency was determined by the passenger load factor and total revenue of the companies. We should add that the load factor is the dimensionless ratio of revenue passenger-miles/revenue passenger-kilometers (RPM/RPK) and available seat miles/available seat kilometers (ASM/ASK), which are acknowledged as one the main indicators of the attractiveness of the airlines. The escalating aircraft maintenance and repair expenses (or MRO), labor costs, the rising costs of fuel, total assets, and net debt were selected as input elements. Accessing unbiased data was critical. These measures provided visibility of the true time statistics regarding the airlines’ operational efficiency.
A quantitative research methodology was used in the study because the data collected were numerical data. This research was based on secondary data sources. There is a debate among academicians and practitioners of strategic management as to the extent that the process should be more quantitative as opposed to more qualitative since quantitative analysis is better for planning the airline cooperative strategy.
3.2 Data processing and data limitations
Any research has limitations according to its nature and capacities. There were some limitations in this quantitative method that could not be easily accomplished through the given resources available. At the same time, the authors did not have any authorization to research the confidential information of airlines. For instance, the authors could not collect data from Qatar Airlines, Etihad Airways, or Air Astana. Furthermore, airline groups collect financial and operating data without reflecting them for each individual airline.
Therefore, all the information used to investigate and analyze airline efficiency was gathered by the researchers individually for every airline (DMU) from airline websites, annual reports, and other reliable websites. All data were presented in different currencies depending on official reports of the airlines. The data extracted from the reports were not presented in a unified way despite most of the operators declaring International Financial Reporting Standards as the common rule. For instance, THAI uses cabin factor (%) as the numeric ratio of revenue passenger-miles/revenue passenger-kilometers (RPM/RPK) and available seat miles/available seat kilometers (ASM/ASK), and load factor by this company refers to the ratio of revenue ton-kilometers (RTK) to available ton-kilometers (ATK). Other companies use the load factor traditionally, as we mentioned above. These differences in presenting information about finance and business processes were diminished by data processing, such as the unification of the data presentation by conversion to the same measures and indices, standardization of the data, and conversion of the financial data to US$ as a current currency. However, the differences in the financial reports of Hanjin Kal Group were so significant that it led to the decision to exclude them from the sample.
An additional limitation was that reports had several dates of the reporting traditionally for different parts of the world. Nevertheless, data were reconciled to a similar time period (depending on the last financial report, from 30 to 09-18 to 30–06-19, all the reports were for the 2018 year). The authors put all the financial data in US$ using the official currency exchange rate on the date of each report to make data comparable. After extracting data, the unification of the indices, and currency conversion, the sample was transformed into 25 DMUs, as shown in Table 3 of Appendix A.
The Data Envelopment Analysis (DEA) was used to construct performance indices. The set contains data for 25 DMUs; the data were processed via DEAP software, resulting in CRS efficiency, VRS efficiency, and their combination for each performance indicator.
The first stage of the data analysis after the data extraction, standardization, and conversion into comparable type (by using the currency exchange rate on the date of the reports) is ranking of the airlines by the set of performance indicators (or efficiency measures). The revenue leaders are Lufthansa Group, IAG, Emirates Group, and Southwest Airlines. The TOP-10 Revenue Group is presented mainly by Airline-within-Airline companies, except Southwest Airlines. Only Thai Group and Copa Holding do not demonstrate high (comparatively) performance in terms of revenue (Please see Figure 4).
Figure 4.
Fragments of ranking among the airlines (revenue and Total assets), where blue color means airlines-within-airlines business models, LCCs are highlighted in green color, and FSNCs are highlighted in orange color (constructed by authors using the initial data set).
The comparison of the performance indices allows us to suggest the revenue-related decisions in forming the alliances. At the same time, the cost indicators may be more informative in terms of the operative performance of the alliances (please see the Costs Rankings in Appendix B, Figure 5).
Figure 5.
Rankings among the airlines (constructed on initial data set).
DEA results are presented in Table 1 below. These results were generated by using DEAP software, the principles and specifics of which were explicitly presented in the relevant guide [43]. In our case, we have a 7-dimensions model (two inputs and five outputs) that enables us to construct the rankings in terms of the operative efficiency of the companies. Examining the DEA results reveals some interesting conclusions for the different airlines. If we roughly divide the sample into three groups (leaders, followers, and challengers) by combining CRS and VRS results, we may present the following groups:
Group “Leaders” include three Airline-within-Airline Groups (Lufthansa Group, Aeroflot Group, and Emirates Group), three low-cost carriers (Ryan Air, Wizz Air, and Air Asia), and three FSNCs (TAP Air Portugal, Croatia Airlines, and Turkish Airlines).
Groupe “Followers” include four Airline-within-Airline Groups (ANA Group, Air Canada Group, Copa Holding, and IAG), four low-cost carriers (Azul Brazilian Airlines, EasyJet, WestJet Southwest Airlines), and one full-service network carrier (Finnair).
Groupe “Challengers” consists of three Airline-within-Airlines Groups (Qantas Group, Thai Group, and Air France-KLM Group), two LCCs (Norwegian Air and JetBlue Airways).
There is one more company, Air New Zealand, that gains the lowest indices in terms of technical efficiency, and in that case, we assume this company is an “outsider.”
Efficiency measures from DEA calculations for 7-dimensions model.
CRS means constant returns to scale (refers to the fact that output will change by the same proportion as inputs are changed)
VRS means variable returns to scale (production/service technology may impact increasing, constant and decreasing returns to scale)
However, there are some interesting findings. Regarding technical efficiency, LLCs are more effective than FSNCs, but FSNCs are more effective than Airline-within-Airline Groups. The point is that the Emirates Group does not choose a cost-effective strategy; the Group’s target is a market expansion which is evident from the revenue and total assets analysis. The technical efficiency analysis answers the questions about the quality of decisions that transform inputs into outputs, or the quality of management in general. Still, DEA does not answer the questions, for instance, whether the companies are effective in achieving their strategic objectives, which may differ.
The choice of DEA as the main tool for quantitative analysis was made for many reasons mentioned above in the current research. One of the advantages of DEA is that it offers an adequate measurement of the productivity of decision-making units and allows evaluation of the quality of management. As it was previously argued [44], DEA is not a good beacon for cost-efficiency analysis but rather for technical efficiency, which resonates with our research aim. In other words, in the current research, the relative efficiency achieved by decision-making units was evaluated to demonstrate the difference in the technical efficiency between airlines and, therefore, the difference in the quality of decisions and management performance. The next step of the research might be the reverse transformation of the management performance results in understanding the most effective management strategies.
The results indirectly confirm the argument of Amankwah-Amoah and Debrah [31] for the functional efficiency improvement that can be the motive for strategic alliances forming. Yet, size is a crucial parameter for management performance. Croatia Airlines, which has the smallest fleet (12 units), demonstrated one of the highest technical efficiencies. It means the revenue-related decisions on forming an alliance may lead to the loss of technical efficiency due to poor restructuring.
If we agree that the logic for coopetition is not rent-seeking behavior but a search for the best combination of resources, knowledge, and practices, then the pool of the most attractive partners is the “Leaders” Group. For instance, the potential partners for Airline-within-Airline groups are the most efficient low-cost carriers (Ryan Air, Wizz Air, and Air Asia) and full-service network carriers (TAP Air Portugal, Croatia Airlines, and Turkish Airlines). Strategic Alliances efficiency may be improved by expanding the number of the coopetitive partners and managing them based on the best practices. On the other hand, if the leading companies (i.e., Ryan Air) do not intend to lose their technical efficiency due to unavoidable restructuring, they may continue to lead the market using individual strategies.
The aviation industry is considered one of the major economic drivers of the world as well as a sign of regional development. In 2019 alone, there were 4.54 billion air travel passengers all around the globe, and 90% of all cross-continent e-commerce was done by air. The aviation industry accounted for 35% of world trade. Legacy carriers with full services have been a successful business model until the low-cost carrier revolution, which has completely changed the industry dynamics and forced FSNC’s to rethink their strategy. The LCCs are the most viable strategy now, with various airlines reporting more profits and passenger numbers than their counterparts while growing at a faster pace and stronger market share. The biggest FSNCs established LCCs as the subsidiary airlines in order to increase market extension and competitive advantage.
The findings of this study have several implications for both academic research and practitioners in the aviation industry. This research contributes to the existing academic literature by focusing on the effectiveness of airline business models, specifically the Airline-within-Airline (AwA) model as a form of coopetition. It expands the knowledge base in the field of aviation management and strategy. It might be further discussed, whether it is pure coopetition or not, while the results proved undoubtfully the excellence of functional efficiency for this type of business models. There are only a few studies that used Data Envelopment Analysis for justification of strategic decisions, and in this study, the use of Data Envelopment Analysis (DEA) for evaluating the efficiency of different airline business models, including strategic alliances, provides a quantitative framework for assessing their performance. This methodology can be a valuable tool for future research in various industries. At the same time, by examining the quality of decisions (technical efficiency) for each decision-making unit, this research identifies best practices in partner selection for coopetitive partnerships. Such insights can inform future research on coopetition strategies across industries. The approach of using data from annual reports of the airlines for exploratory research can serve as a model for researchers in aviation management and related fields.
The research offers practical guidance for airlines and aviation companies in selecting strategic partners for coopetitive alliances. By using data-driven assessment and DEA, practitioners can make informed decisions about potential partners, thereby enhancing the effectiveness of their coopetition strategies. The study’s focus on a diverse set of international airlines, including Qantas, Emirates, Lufthansa, and others, offers insights applicable across the global aviation industry. Practitioners can draw lessons from these cases to improve their own strategic decision-making. Overall, the novelty of this research lies in its quantitative evaluation of the efficiency of different airline business models, its application of DEA to partner selection, and its focus on the coopetitive dynamics in the aviation industry. These findings can enrich the knowledge of academic scholars and decision-makers in the industry in the complex world of airline management and strategy. Further research may combine quantitative and qualitative data on the best management practices and different strategies performance based on primary data for the Group of Leaders.
Initial data of the sample (indicators extracted from annual reports and open data sources).
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Written By
Hanna Shvindina and Iryna Heiets
Submitted: 14 June 2023Reviewed: 02 October 2023Published: 19 November 2023
Open access peer-reviewed chapter - ONLINE FIRST
