The air transportation industry is a vital service sector that supports other significant economic sectors such as tourism, manufacturing, international commerce, and business, as well as contributing to the national GDP [1]. Furthermore [2] portrayed it as essential to the US economy, with the government bailing it out in times of crisis and taxing it when required. Over the last few decades, the goal of long-term profitability and competitive advantages has influenced many company strategies [3]. The operating settings in which airlines operate are far from uniform. The diversity of policies, geographies, and economies throughout the world necessitates the development of a set of tailored strategies, which can be reflected in broad templates or business models built to adapt to specific needs. The purpose of this study is to examine the most sustainable of such business models in the Nigerian context, by first identifying the current business models pursued by airlines in the country, followed by a study of their sustainability from two key perspectives: market presence and product and organizational architecture. Although there is a wealth of literature on airline business models and their evolution in shifting global contexts, there has been little investigation into their relevance in the Nigerian context. The Nigerian environment is diverse enough to necessitate its own set of tactics, some of which may be distinct enough to be part of a new strategy template or business model.

In the last few decades, the search for sustainable-term profitability and competitive advantages has influenced many company strategies. The operational settings in which airlines find themselves are far from uniform. The diversity of regulations, geographies, and economies throughout the world necessitate the development of a set of tailored strategies, which can be represented in broad templates or business models geared to address the problems posed by unique operating environments. This study investigated the most sustainable business model in the Nigerian context. The main objective of this paper is to determine the main factors affecting market share of airline at the specific airport.

The word “sustainability” originally came from the Latin word called “sustinere” this means “to hold” [4]. There is a wealth of literature on airline sustainability modelling that uses a range of methodologies. Country economic indicators have been regarded as critical indicators for predicting performance in numerous airline sustainability modelling studies since the early 2000s. Previous research has confirmed that Gross Domestic Product [5], Human Development Index [6] [7], and Foreign Direct Investment [8] are the primary country economic key indicators that influence airline performance. As a result, the combination of those indicators was identified as the economic performance latent variable in this study. In this study, we define financial performance as a collection of well-known financial metrics. The most widely used performance metrics in airline sustainability modelling include total assets [9] [10], operational profit [7], and total revenue [11]. The combination of total assets, operational profit, and total revenue is defined as the financial performance hidden variable in this study. [6] [12] conducted studies that validated the influence of nation economic variables on airline financial performance.

Since the early 1990s, operational performance metrics have been widely used by a large number of organizations to measure present performance, identify requirements for improving performance, and enable the attainment of far-fetched strategic goals [13]. Recently, operational performance measures have gained global traction as a growing number of organizations and businesses around the world have shifted their focus and reliance away from the traditional method based primarily on financial performance measures and toward a variety of non-traditional value indices [13]. The primary operational performance measures in the airline business are revenue passenger per kilometre, revenue ton-kilometres [14] [15], and the number of departures [9]. Previous research has shown that operational performance has an influence on financial performance, both logically and practically, and that the link between economic performance and operational performance is validated [16].

Many researchers have been drawn to Classical-SEM in recent years as a frequently used technique for tasks such as data analysis in airline disciplines such as sustainability [17], low cost [18], job satisfaction [19], and service quality [20]. This application gives an improved form of linear regression with the main purpose of investigating the hypothesis that observes a covariance matrix for a collection of measured indicators equal to the covariance matrix provided by the hypothesized model. The normal distribution of residuals is a critical assumption in linear analysis. Otherwise, a typical technique can be used to compute the sample covariance matrix. Bayesian-SEM allows researchers to use previous information to update current information for the parameters of interest. To gather current information, the Gibbs sampler [21] is used. The Gibbs sampler [21] is used to collect samples of variable size to summarize the posterior distribution for defining the parameters of interest. Point estimates, standard deviations, and interval estimates may be generated using these samples to conclude. The Bayesian technique is appealing because it allows previous knowledge to be used to update current information about the parameters of interest.

The company’s business plan explained how it produces money and competes in its sector [22]. The profit margin of a firm is affected by the business model it chooses [23]. In the airline sector, there are two primary business models: network (full-service) and low-cost (discount) airlines. The network carrier model implements a diversification strategy by increasing domestic destinations, covering international flights, offering varied seating arrangements (business, economy, and first-class), maintaining a complex structure of hubs, and delivering high-quality service. Low-cost (discount) airlines, on the other hand, focus on reduced airfares. To save operational expenses, bargain airlines offer shorter routes, and point-to-point destinations rather than through complex hubs, and flights are mostly domestic. Discount airlines use a similar model aircraft fleet, a minimal infrastructure, a single seating layout, and fewer amenities and flight services than network airlines [24].

[25] investigated the relationship between profitability and airline business models as proposed by [24] and discovered that network airlines had a higher long-term consistent profit margin than their low-cost counterparts. Potter also noted that, although retaining high operational expenses, the old network (full-service) business model provides adequate service differentiation, resulting in larger profit margins in the long term. As a competitive advantage, [26] proposed that airlines include corporate culture into their business models. Given the airline industry’s severe rivalry, the choice of a business model remains important to the airline’s profit margin [23].

The type of data used in this study is a time series. As a result, the researcher employs time series econometrics on the variables to investigate the impact of sales growth, liquidity, leverage, asset tangibility, leasing cost, and fuel cost on the dependent variable profit. Furthermore, regression analysis is a mathematical way of accomplishing this. Multiple Regression Analysis refers to a regression analysis that contains more than two explanatory variables [27]. Finally, multiple regression models may handle a large number of associated explanatory variables. Naturally, when we add additional elements to our model for explaining dependent variable variation, we can explain more of the variation in dependent variable variation. As a result, multiple regression analysis may be utilized to develop more accurate models for predicting the dependent variable.

Another sort of airline sustainability analysis is the cost function. In this sort of study, researchers look at two categories of cost indicators. The first sort of cost indicator is determined by operational performance:

Cost indicator = f ( operational indicators )

Based on the aforementioned function, [28] created their models. The second sort of indicator is a financial indicator, which is a function of cost and operational indicators;

Financial indicator = f ( cost indicators , operational indicators )

Based on the aforementioned function, [29] [30] created their models. However, combining two forms of modelling, particularly with the leveraging of a country’s economic performance, is uncommon. Therefore, this study considers cost performance as the fourth latent variable with a combination of operating cost, labour cost, and fuel cost indicators based on the [28] study.

Figure 1 depicts the predicted study model with latent variables, and their indicators demonstrate the influence of economic performance on cost performance and operational performance on financial performance. The diagram shows how the first three constructions are linked. As a result, the current study model incorporates four constructs.

Market share modelling was employed as a sustainability metric for the airline

business in this study. Under the research modelling approach described above, regression analysis was done to identify the best set of explanatory variables to include in the model. Given the objective of this model (estimating market share for the leading airline at a certain airport) and the goal of constructing a robust model, having too many variables might be difficult to interpret, evaluate, and maintain. Another issue with a high number of variables is the presence of multicollinearity, which can reduce model accuracy. Panel data is used in the regression analysis given in this article. The market is the dependent variable at the airport. It is measured as a percentage of the number of passengers carried by airlines at the selected airport, annually.

The explanatory variables used in the multiple linear regression models were:

Fare = the average ticket price on the route calculated as flag airline annual revenue divided by the total number of passengers in that year.

Freq = the frequency share of the airline at that airport measures the number of flights offered by an airline at the particular airport with the total number of flights offered by all airlines operating at that airport.

Comp = the number of competitors per destination at that airport measures the level of competition at the airport and is calculated as the total number of airlines operating at the airport.

PAX = the number of passengers carried by airlines measured by the total number of passengers lifted by the flag airline.

The factors considered to describe the value of a market share may be loosely divided into two groups. The first group comprises variables that characterize some airline features, whereas the second set includes variables that account for market factors linked to rivals. The regression analysis is performed aggregately of all the airlines, and the estimate equation is provided as a linear function by:

LnMS = C + β 1 LnFare + β 2 LnComp + β 3 LnFreq + β 4 LnPax + e

Table 1 shows the summary findings of descriptive statistics. R and R² variables

have an excellent match, according to statistics. The R² values indicate that a high part of the airline market share might be explained by a few variables. In the regressions, the F statistic indicates that the variables are significant. This finding should be understood more broadly in the sense that the number of observations is sufficient to demonstrate any link between the specified explanatory factors and the dependent variable.

The Nigeria Civil Aviation Authority’s yearly bulletin and other pertinent sources provided the majority of the data for this study. The data collection spans the years 2015 to 2019, with over 663 data points collected along all routes in the domestic network. Market level factors, which are used to explain an airline’s overall market share, are the same for all similar city-pairs in a market. As a result, the statistics for these variables are reported in terms of routes.

The Nigerian airline industry has been so unstable over the years. The airline industry contributes to the reduction of unemployment and increases growth and development. The value of the market share of a specific airline, although a rough parameter, could be very valuable information for the airline’s top management who, at every moment, has to be aware of the airline’s position against its competitors in certain segments of the market or even in the overall market. By gaining higher portions of markets, airlines have a chance to maximize their revenue. Market share modelling was used in the process of selecting the most suitable set of explanatory variables to be included in the model. Having in mind the purpose of this model (market share estimation for the dominant airline at the particular airport) and the aim of developing the robust model, too many variables could be very hard to understand and analyze and very expensive to maintain. The values of R² suggest that the large proportion of flag airline market share could be explained by selected variables. The F statistic in all regressions shows that the variables are significant. In the aggregate market share modelling, fare coefficients from model estimations are large (−2.177) and in line with signs in the literature. The number of competitors (−1.384) exhibited the correct signs since competition affects market share adversely. This was expected because this parameter measures the level of competition which normally has a negative influence on the market share of the airline especially if that airline previously operated in the regulated market. The estimated frequency coefficient (0.557) indicates that potential travellers prefer routes with high flight frequency, and the marginal effects of different airline market are different. Although coefficients of passengers indicate that travellers prefer routes with higher travel demand, only the model estimates suggest significantly different marginal effects for the different numbers of airline passengers.

We will also like to acknowledge our colleagues in the aviation industry; Kingsley Chima, Ayodele Omueti, Percival Uwaechue, Engr. Maxwell Brown and the Nigeria Airways (WT) and Virgin Nigeria (VK) unite family. To Engr. Francis Igwe and Franka Akpasubi of NCAA, we deeply appreciate your support.

The authors declare no conflicts of interest regarding the publication of this paper.

Amaechi, C.O., Ibe, C.C., Ejem, E.A. and Okeudo, G.N. (2022) Empirical Study of Parameters of Airlines Sustainability in Nigerian Domestic Network. Journal of Transportation Technologies, 12, 357-368. https://doi.org/10.4236/jtts.2022.123022