Financial Performance and Sustainable Corporate Reputation: Empirical Evidence from the Airline Business

Abstract

A good corporate reputation is essential, and it is becoming increasingly relevant for both companies and stakeholders. In this context, Fortune magazine publishes an annual ranking of corporate reputation, therefore listing the most admired companies in the world. Since corporate reputation is considered an intangible asset, it is difficult for competing firms to create similar products or services in the long run. Numerous researchers have concluded that financial performance is strongly connected to corporate reputation. The purpose of this study was to elicit the importance of financial performance in determining an adequate sustainable level of corporate reputation. Using financial indicators and the VIKOR method for a sample of airline companies, we compared the rankings generated by the VIKOR method to the ones published by Fortune. Empirical results showed that when introducing additional financial indicators in the VIKOR method, the VIKOR ranking tends to become similar to the Fortune one. When we introduced 10 financial indicators in the VIKOR method, we obtained similar rankings in 8 of the 10 years of analysis. Our study addresses stakeholders who follow corporate reputation details and may assist them in formulating investment decisions by relying on financial results.

1. Introduction

Reputational risk is becoming increasingly important for companies, especially when considering the upsurge in the use of social media and the internet [1], where bad news spreads very quickly [2]. Corporate reputation raises interest among stakeholders because it is important for company value, especially in the long run. Since reputation is one of the intangible elements that is extremely difficult to imitate, it turns into a valuable source of competitive advantage [3]. Investors, managers, recruiters and other stakeholders have begun relying on company reputation when making decisions [4]. Hammond and Slocum [5] argued that corporate reputation often captures stakeholders’ perception of the company quality. In this context, important stakeholder decisions are based on corporate reputation, and, at the same time, reputation emerges as a result of stakeholders’ perceptions. Therefore, stakeholders (as recipients of information embodied in corporate reputation) collectively influence the financial performance of companies in the long run through their decisions to provide or withhold resources [6].

Financial performance assesses the overall condition of a company over a period of time, and it encompasses financial information measured by several indicators such as profitability, liquidity, cost-effectiveness, etc. [7]. Maqbool et al. [8] argued that financial performance has a major influence and plays a determining role in selecting investment paths. Financial information is employed by two categories of users, namely external and internal. External users employ financial performance data in order to analyze potential investment solutions. Internal users examine the information to ensure a profitable, healthy and continuously growing business, in other words, a sustainable business.

Recent research has focused on defining the relationship between corporate reputation and financial performance by answering two important questions: Assuming there is a positive or negative relationship between the two concepts, does corporate reputation have an influence on financial performance? Or does financial performance affect reputation? [9]. In this regard, researchers have reported mixed results. According to Sabate and Puente [9], this inconsistency of results stems from the use of multi-sector samples and the diversity of methodologies for constructing and determining corporate reputation. The inconsistency in defining corporate reputation makes it difficult to be quantified accurately. It also limits researchers’ ability to test theories regarding the determination and use of reputation [10].

The most widely used database on company reputation is the annual survey conducted by Fortune magazine, which appears under the headline of “The Most Admired Companies”. Fortune publishes this annual ranking of 50 companies with the best reputations across all industries. Over the years, some researchers have argued that Fortune’s ranking is heavily influenced by the past financial performance of companies. Using data from these rankings, Brown and Perry [11] concluded that 55% of corporate reputation could be explained by companies’ reported financial performance. Thus, we deem that financial performance is a major indicator in the methodology of determining corporate reputation. Given the amount of financial information that influences reputation, we can infer that a potential investor must not follow both corporate reputation and financial performance indicators. Instead, the investor can make investment decisions based on financial results achieved by the company [12].

Our study advances the idea of aggregating certain financial indicators to establish a hierarchy of the selected airline companies in the sample. We employed a wide variety of indicators related to profitability, financial leverage, growth, liquidity and business operations. By means of the VIKOR method, we assigned a rank to each company in the sample and compiled an overall ranking, which showed the importance of performance indicators for determining corporate reputation.

Overall, the study examined the degree to which corporate reputation is influenced by the financial performance of each airline. The VIKOR method is often used in the field of finance in the process of choosing the best option from several alternatives. In our study, the ranking produced by VIKOR was compared to the ranking produced by Fortune in order to identify potential similarities.

As stated by Sabate and Puente [9], results obtained from a wide range of samples may entail specific differences from the way reputation and financial performance are determined across industries. Stakeholders vary because of internal or external factors within a particular industry (e.g., legislative framework, environmental obligations, safety requirements). Thus, the impact of such factors tends to be homogeneous for one industry; nevertheless, when we talk about several industries, the level of impact varies. We focused on the airline industry in order to avoid certain discrepancies in the analysis and to have a sensible comparison of results.

Over the last 3 years, the airline industry has faced huge challenges, being one of the first industries affected by COVID-19 [13], with flight disruptions that led the industry to a temporary collapse [14,15]. Moreover, the military conflict in Ukraine has affected the industry because of flight restrictions and increasing inflation [16,17]. Nevertheless, airline companies have started to recover and resume their activities in order to reach a level similar to the pre-pandemic one. Job creation, the transportation of goods and people, expenses that companies register in a country and tourism are just a few elements that measure the economic impact generated by the airline industry. According to IATA [18], the air transportation industry, including airlines and supply chains, is estimated to generate USD 1.6 billion of the Romanian GDP. Globally, the revenue from the airline industry is expected to reach USD 782 billion in 2022 (a 54.5% increase as compared to 2021). The main industry revenues include passenger revenues and freight revenues, while the largest cost element is fuel, which accounts for 24% of total industry costs [19].

2. Literature Review

The extant literature provides a wealth of information that focuses on the relationship between corporate reputation and financial performance. Corporate reputations have been linked to above-average profit, employees’ loyalty toward a company and social responsibility activities performed by companies [5]. Hence, many studies have linked the two concepts based on their mutual influence. Eberl and Schwaiger [3] argued that the overall assessment of corporate reputation was explained by past financial performance, an idea also suggested by Roberts and Dowling [20]. Similarly, Sabate and Puente [9] stated that past financial performance had a strong effect on subsequent reputation, while reputation seemed less related to a measure of subsequent performance [10]. Some researchers have argued that Fortune rankings are strongly influenced by prior financial performance. For example, using the 1991 Fortune data, Brown and Perry [11] concluded that more than half of the variation in overall rantings could be explained by financial measures.

Stakeholders are often mentioned while trying to explain the relationship between corporate reputation and financial performance. When a company has a good overall reputation, stakeholders are satisfied with that company’s performance. Every company has a wide range of stakeholders. Cornell and Shapiro [21] divided stakeholders into two categories: those who have explicit contracts (shareholders per se) and those who have implicit contracts with the company (e.g., customers, employees). Hammond and Slocum [5] argued that corporate reputation was often related to stakeholders’ perception.

Neville et al. [6] stated that stakeholders were recipients of the information embedded in corporate reputation who collectively influenced company financial performance by deciding to withhold or provide resources. In this context, a good reputation provides several benefits, such as maintaining current customers and developing the portfolio of potential customers, which can lead to a higher profitability [3]. In a literature review of past and present definitions, Gotsi and Wilson [22] viewed corporate reputation as “a stakeholder’s overall assessment of a company over time”. This assessment can also be expressed through the stakeholder’s direct experiences with the company [3]. It is important to note that the concept of reputation may differ according to the diversity of stakeholders. For instance, a company may have a good reputation among investors and a poor reputation among customers. Ali et al. [23] reported that stakeholders significantly moderate the effect of reputation on financial performance. According to Gatzert [2], if the level of corporate reputation mitigates after certain financial challenges of the company, then it means that reputation is directly linked to financial performance.

Certain researchers perceive social responsibility as a bridge between company reputation and financial performance. In order to invest in social responsibility, a company needs profit in the first place. Other researchers believe that if social responsibility activities fulfil stakeholders’ expectations better, then such activities will probably lead to an improvement in corporate reputation [5]. In this sense, Hammond and Slocum [5] found that stakeholders determine company reputation based on its social responsibility actions, which are indicative of a good management [5]. Weak social responsibility activities can decrease a company’s ability to raise investment capital by transforming the company into a riskier investment. Neville et al. [6] considered that reputation was a vehicle in determining the relationship between corporate social responsibility and business performance. Wang and Berens [24] argued that corporate social responsibility actions were effective means of establishing an overall good reputation that could lead to financial benefits for the company [24]. It has been also suggested that corporate social responsibility goes against the maximization of company value because it diverts valuable resources away from the company’s core business activities [25].

The literature offers different interpretations regarding the relationship between corporate reputation and financial performance, with some authors believing that certain aspects of reputation still require further research, such as the definition of the term, the manner of measuring reputation, etc. [26,27,28]. Numerous researchers and managers believe that a favorable reputation is the most valuable intangible resource of an organization because it reduces stakeholders’ uncertainty about its future performance, develops public trust and strengthens competitive advantage [27,29,30,31]. According to Ito et al. [32], corporate reputation viewed as an intangible asset influences corporate value. Moreover, an increasing competition in today’s globalized economy incentivizes the identification of factors that lead to sustainable competitive advantages [33,34].

Fryxel and Wang [35] stated that the Fortune database provides an accurate measure of financial performance. Sabate and Puente [9] noticed that the literature has reported several proposals for determining corporate reputation and, although there have been high levels of criticisms regarding the significant financial bias of the Fortune survey, this methodology is still the most widely used in empirical research. For that matter, all reputation metrics have supporters and challengers [22]. Numerous methods for determining reputation have appeared in the literature (e.g., AMAC GMAC index, Global Most Admired Companies-Fortune, Reputation Quotient) [3], and all have been challenged.

Overall, as Flanagan et al. [10] reported, the annual Fortune magazine survey “The Most Admired Companies” is a widely available yet controversial measure of corporate reputation ranking results. It is published every year in February, and it is eagerly awaited by the business community. So far, no satisfactory method of determining corporate reputation has been identified. According to Pires and Trez [27], the relationship between the Fortune ratings and company financial performance (initially identified by Brown and Perry [11]) exists, albeit in a weakened form.

3. Methodology and Variables of Interest

Empirical results determined by assessing a company’s financial performance provide a basis for comparing the company both with itself across different periods and with other players in the same industry [36].

The main objective of the study was to determine key factors that influence financial performance in order to eliminate variables with a negative impact and to emphasize factors that have a positive impact on the business for a sustainable development on the economic market [37].

In this study, we used 10 representative indicators to capture company financial performance, i.e., ROA (Return on assets), EPS (Earnings per share), TDTC (Total Debt to Total Capital), CR (Current ratio), QR (Quick ratio), EBITDA margin, Net margin, Gross margin and ART (Accounts receivable turnover). Nine of these variables are financial indicators and one is a non-financial variable specific to the airline industry (Load factor or LF). The indicators are presented in Table 1.

According to Riley et al. [38], Behn and Riley [39] and Liedtka [40], when using financial statements, details regarding non-financial performance are also taken into account. Combined with financial information, non-financial details assess much more accurately future financial performance.

In the following we present details on the variables of interest:

• Earnings per share (EPS) is an indicator capturing return on capital growth. It is determined as the ratio of annual company net result to the number of shares. The higher a company’s ratio, the more profitable the company is.

• Total Debt to Total Capital (TDTC) is a measurement of a company’s financial leverage. It is calculated by dividing the company’s total debt by its total capital, which comprises total debt and shareholders’ equity.

• Load factor (LF) is an indicator that measures the percentage of available seating capacity that is filled with passengers [41].

• Current ratio (CR) is a liquidity indicator that measures a company’s ability to pay its short-term obligations. This indicator is determined by dividing current assets to current liabilities.

• Quick ratio (QR) measures the ability of the company to use its highly liquid assets in order to respond immediately to short-term financial debts. The indicator is often called the “acid test” and is computed by dividing current assets after subtracting inventory to current liabilities.

• Return on assets (ROA) measures the performance of company assets. In our case, ROA is determined as the ratio of net income to total assets.

• EBITDA margin is a performance metric that indicates a company’s profitability from operating activities. EBITDA represents earnings before interest, taxes, depreciation and amortization.

• Net margin (NM) measures how much net income was generated as a percentage of revenue. It is computed by dividing net income to total revenue.

• Gross margin (GM) is the amount of money a company retains after incurring direct costs associated with producing goods and/or services.

• Accounts receivable turnover (ART) captures the state of company health regarding its success in collecting receivables from clients. The indicator is determined as the ratio of net credit sales to average accounts receivable.

Our study aimed to build rankings based on company financial performance over a 10-year timespan from 2012–2021. The sample (Appendix A) included airline companies listed as having the most valuable and strongest brands in the world according to the website brandirectory.com [42]. It produces an annual ranking by calculating the brand value via the “royalty relief” methodology, which establishes the amount a company would be willing to pay to license its brand as if it owned it. The ranking is determined each year, and depending on the availability of public information regarding each company, the sample varies between 20 and 24 companies per year.

The basis for calculating and determining rankings is represented by the 10 performance indicators from Table 1. Our sample also includes three companies with the best reputations in the airline industry according to the Fortune ranking.

As we can see in Table 2, only three companies were included in the Fortune top 10 ranking with best reputations in the airline industry. Thus, Southwest Airlines has recorded the best results by ranking 7th in 3 years (2013, 2015, 2016). Delta Air Line entered this ranking only in 2014 and ranked 48th. Singapore Airlines did not reach the Fortune ranking in 2016, and its best results were in 2014 and 2019 (18th rank).

The purpose of this study was to observe whether, based on a ranking that depends on 10 performance indicators of airline companies, the three companies listed by Fortune would establish a new ranking. We therefore introduced a new approach in order to observe more accurately rank changes by: (1) determining rankings with seven performance indicators, and (2) introducing one indicator at a time until we reached 10 indicators in total.

This paper aims to verify two research hypotheses:

Hypothesis 1 (H1).

The number of financial indicators determines the level of corporate reputation.

Hypothesis 2 (H2).

The recorded financial performance has a major impact on corporate reputation.

Data collection was carried out using the Thomson Reuters database [44], which provided all financial and non-financial data for each airline company.

The multi-criteria decision-making method (MCDM) is a widely applied tool in determining the best solution among several alternatives with multiple criteria or attributes [45]. An MCDM problem can be expressed using a decision matrix as follows:

(1)$D=\begin{array}{c}{A}_1\\ A_2\\ \begin{array}{c}⋮\\ \begin{array}{c}{A}_i\\ \begin{array}{c}⋮\\ A_m\end{array}\end{array}\end{array}\end{array}\stackrel{\begin{array}{cccc}{x}_1 & x_2 & x_3 & x_4 \end{array}}{\left[\begin{array}{c}{x}_{11} x_{12}\cdots x_{1j}\cdots x_{1n}\\ \begin{array}{c}{x}_{21} x_{22}\cdots x_{2j}\cdots x_{2n}\\ \begin{array}{ccc}⋮ & ⋮& \begin{array}{cc}⋮ & ⋮\end{array}\end{array}\\ \begin{array}{c}{x}_{i1} x_{i2}\cdots x_{ij}\cdots x_{in}\\ \begin{array}{ccc}⋮& ⋮ & \begin{array}{cc}⋮& ⋮\end{array}\end{array}\end{array}\end{array}\\ x_{m1} x_{m2}\cdots x_{mj}\cdots x_{mn}\end{array}\right]}$

where $A_i$ represents the ith alternative, $i$ = 1, 2, …, $m$; $x_j$ represents the jth criterion, $j$ = 1, 2, …. $n;$ and $x_{ij}$ is the performance of alternative $A_i$ in relation to the jth criterion. The procedures for determining the best solution to an MCDM problem include calculating the utilities of alternatives and ranking these utilities. The alternative solution with the highest utility is considered to be the optimal solution [45]. For details on MCDM methods, see Zeleny [46].

VIKOR is a method for optimizing multiple criteria in a complex system that focuses on ranking and selecting from a set of alternatives among conflicting criteria [47,48]. Its role is to determine a multi-criteria ranking index based on particular measure of closeness to the ideal solution [49]. The VIKOR method has been applied by some researchers in the case of MCDM.

Steps 1 and 2 determine utility measure and regret measure for alternatives regarding each criterion.

• Step 1.. Calculate $x_i^{\ast }$ and $x_i^{-}$.

(2) $x_i^{\ast }=max\left[\left(x_{ij}\right)|j=1, 2, \dots , m\right]$

(3) $x_i^{-}=min\left[\left(x_{ij}\right)|j=1, 2, \dots ,m\right]$

• Step 2.. Compute the values of $S_j$ and $R_j$.

(4)$S_j={\displaystyle \sum }_{i=1}^n{w}_i\frac{x_i^{\ast }-x_{ij}}{x_i^{\ast }-x_i^{-}}$

(5)$R_j=max\left[w_j\left(\frac{x_i^{\ast }-x_{ij}}{x_i^{-}-x_{ij}}\right)\right]$

where $i=1,2,\dots ,n$, $S_j$ and $R_j$ represent the utility measure and the regret measure for alternative $x_j$, respectively, and $w_j$ is the weight of the ith criterion.

• Step 3.. Compute the minimum and maximum amounts of the results in Step 2.

Compute the values $S^{\ast }, R^{\ast }$.

(6)$S^{\ast }=min\left(S_j\right), S^{-}=max\left(S_j\right), j=1, 2, \dots , m$

(7)$R^{\ast }=min\left(R_j\right), R^{-}=max\left(R_j\right), j=1, 2, \dots , m$

• Step 4.. Calculate the VIKOR index. Determine the value of $Q_j$ for $j=1, 2, \dots , m$ and rank the alternatives by values of $Q_j$. The VIKOR index can be expressed as follows:

(8)$Q_j=\vartheta \left[\frac{S_j-S^{\ast }}{S^{-}-S^{\ast }}\right]+\left(1-\vartheta \right)\left[\frac{R_j-R^{\ast }}{R^{-}-R^{\ast }}\right]$

where $Q_j$ represents the jth alternative of the VIKOR value, $j=1, \dots ,m$; $S^{\ast }=min(S_j); S^{-}=\max \left(S_j\right); R^{\ast }=\min \left(R_j\right); R^{-}=\max \left(R_j\right)$; and $\vartheta$ is the weight for the strategy of maximum group utility (usually set to 0.5). Moreover, $1-\vartheta$ is the weight of the individual regret. The index of $Q_j$ tends to majority agreement clearly when $\vartheta <0.5$, and the index of $Q_j$ indicates an overall negative attitude.

• Step 5.. Draft the company ranking determined in an ascending order of the final score.

4. Data Analysis

Our study analyzes financial and non-financial data from a number of airline companies, which are listed as the most valuable brands in the world. The company sample varies across years due to the volume of financial information that is not publicly available (see Table 3).

Depending on the year of reference, two or three companies in the sample were listed in the Fortune rankings (i.e., Southwest Airlines, Delta Air Lines, Singapore Airlines) as entities with the best corporate reputations in the airline industry. The financial rates included in the analysis were determined based on the balance sheet, income statement, cash flow statement and various company reports retrieved from the Thomson Reuters database.

We obtained matrices consisting of company performance indicators from each of the 10 years (2012–2021). Using the VIKOR method, we were able to aggregate company results and generate rankings. We assigned equal weights to each criterion, as we considered financial indicators to have equal importance for the purpose of this study. Thus, for each of the 10 financial indicators, we obtained a weight of 10%.

In the following, we exemplify the VIKOR method for the year 2021. The financial results of the airline companies are presented in Table 4.

The first step of the method was to separate financial performance indicators into two categories: maximum or minimum indicators. We determined the best and worst values for each criterion. $x_i^{\ast }$ represents the best value for each criterion, while $x_i^{-}$ represents the worst results for each criterion. The results are presented in Table 5.

The second step was determined using the third and fourth mathematical formulas presented above. The results are shown in Table 6. The value of S was obtained by adding up the results from multiplying the weights of the criteria by the data from each company. The importance level was constant for each criterion (10%). The value of R was represented by the highest value obtained from multiplying criteria by the normalized data from each company.

In Table 7, we included $S^{\ast },$ $R^{\ast }$, $S^{-}$ and $R^{-}$, where $S^{\ast }$ means the lowest value of $S_j$ (the best value), $R^{\ast }$ means the lowest value of $R_j$, $S^{-}$ means the highest value of $S_j$ and $R^{-}$ means the highest value of $R_j$.

After completing all steps and determining $Q_j$ for each year, the VIKOR method was fully applied. The company ranking was compiled in ascending order. The results are shown in Table 8.

Following the aggregation of performance indicators using the VIKOR method, the ranks occupied by each company are shown in Table 8. Therefore, ranks were set for each year in the analyzed timeframe. At a glance, it can be seen that Ryanair recorded the best positions: It ranked first eight times; in 2014, it was second; and in 2021, it was third. The companies Southwest Airlines and easyJet also recorded very good positions in the ranking. At the bottom of the overall rankings is Korean Air, which registered the worst performance in the sample, ranking last several times.

Table 9 reports the final results of the study. The table lists the airline companies Southwest Airline, Delta Air Lines and Singapore Airlines with the best corporate reputations according to Fortune rankings. We also displayed the rankings determined using the VIKOR method. In most years, companies maintained the same order in both rankings. Thus, we conclude that performance indicators are extremely important when measuring corporate reputation.

Before arriving at the final result, we simulated several ranking variants. Hence, we noticed that as we increased the number of performance indicators, the rankings generated by the VIKOR method were more similar to the Fortune ones.

We obtained the first ranking for the same companies. In this case, the VIKOR method included only 7 performance indicators. The results are shown in Table 10. We noted that, in 5 years, the VIKOR ranking did not follow the Fortune ranking.

Continuing the simulation, we introduced 8 performance indicators. As one can see, during the 10 years of analysis, the Fortune ranking and the VIKOR ranking followed the same order in half of the cases. The results are shown in Table 11.

We also continued the simulation and introduced 9 performance indicators. In this case, one can identify an improvement since the same order of companies was reported by both rankings. Hence, the same rankings were obtained in 7 out of the 10 years. The results are shown in Table 12.

Following the introduction of the 10 performance indicators, we noticed an improvement as compared to the 9-indicator ranking and a major improvement as compared to the 7-indicator ranking. Thus, the more financial performance indicators we introduced as a basis for calculation, the closer the results were to the Fortune rankings. The final results are shown in Table 9 and Table 13.

Indeed, in the 10 years of analysis, 8 years registered similar results in both rankings. In 2015 and 2019, the order of the rankings differed. Potential explanations could be the following: (1) The date of the reporting period for Delta and Southwest ends on 31 of December, while for Singapore it ends on 31 March. (2) Differences in measuring reputation, depending on financial performance indicators, a different importance (higher or lower) given to each indicator or different expectations of stakeholders (e.g., for investors, profitability indicators have a higher importance). In our study, we allocated equal importance weights to each indicator. (3) According to Roberts and Dowling [20], there are two types of reputation (financial and residual), hence it is likely that in these 2 years, the residual reputation was superior to the financial one.

5. Conclusions and Discussion

The literature accounts for a fairly strong link between financial performance and corporate reputation. Most studies have focused on how financial performance influences corporate reputation, while others have focused on stakeholders’ reactions, the concept of corporate social responsibility (CSR), the intangibility of corporate reputation and ways to determine it. Many researchers believe that corporate reputation is built on the past financial performance of companies. Stakeholders are often brought into the discussion as it is argued that corporate reputation often embodies their perceptions.

Other researchers have tackled the concept of corporate social responsibility since it has a positive influence on corporate reputation due to certain company activities. It has been often argued that corporate reputation is a valuable intangible asset that provides a competitive advantage in the long run, it is difficult to replicate and it ensures a sustainable development of the business. Moreover, studies have focused on accurately measuring corporate reputation and testing extant methodologies, some of which are quite controversial.

This study has certain limitations. First, it comprised a 10-year period of analysis from 2012 to 2021. Future research might expand the timeframe by considering another decade. Second, the company sample varied across years. Third, our calculus was grounded on 10 performance indicators, namely nine financial performance indicators and one industry-specific performance indicator. Other research might expand the set of financial indicators and investigate different aspects from running a business.

We employed the VIKOR multi-criteria decision-making method (often used in the literature) in order to determine rankings and assign places to each company. Empirical results showed that the VIKOR annual ranking of the three airline companies resembled Fortune ranking in 8 out of the 10 years analyzed. Thus, we concluded that financial performance is highly important for the manner in which corporate reputation is built.

The results validated our research hypotheses and provided additional evidence on the link between the two concepts. The novelty of our study stems from the manner of linking the two concepts via the VIKOR method while focusing on the airline industry.

We showed that by introducing more financial performance indicators into the VIKOR model, the two rankings (Fortune and VIKOR) generated similar rankings for most cases. When we introduced seven random indicators into the VIKOR algorithm, rankings were similar only for 4 years. After using 10 indicators, similar hierarchies resulted in 8 out of the 10 years. Hence, we can infer that the more financial performance indicators are introduced into the VIKOR model, the more similar the two hierarchies become. Moreover, our research can assist stakeholders’ decisions of investing in a company since both methods yield similar results.

After analyzing data and comparing results, we concluded that a large number of financial indicators determined the level of corporate reputation. Given the fact that in the majority of years, company rankings were identical, our hypothesis that financial performance largely influences corporate reputation was supported. Therefore, the sustainable development of business activities can be achieved in the airline industry.

We believe that a company’s good reputation is largely driven by good financial performance results. In this context, our study addresses potential investors who closely follow the corporate reputation ranking provided by Fortune and company financial performance in order to make informed business decisions.

Author Contributions

Conceptualization, L.M.B. and A.-D.C.; Formal analysis, L.M.B., A.N. and A.-D.C.; Funding acquisition, A.N.; Investigation, L.M.B., A.N. and A.-D.C.; Methodology, L.M.B., A.N. and A.-D.C.; Project administration, L.M.B.; Resources, L.M.B., A.N. and A.-D.C.; Supervision, L.M.B.; Writing—original draft, L.M.B., A.N. and A.-D.C.; Writing—review and editing, L.M.B., A.N. and A.-D.C. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviation are used in this manuscript:

EPS	Earnings per share
TDTC	Total debt percentage of total capital
LF	Load factor
CR	Current ratio
ROA	Return on assets
ART	Accounts receivable turnover
QR	Quick ratio
CSR	Corporate social responsibility
NM	Net margin
GM	Gross margin

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

Tables

Table 1

Variables of interest.

Category	Indicator	Method of Computation
Growth	Earnings per share (EPS)	Net annual income/Number of shares
Leverage	Total Debt to Total Capital (TDTC)	Debt/(Debt + Shareholders Equity)
Operating	Load factor (LF)	Average Load/Peak Load
Liquidity	Current ratio (CR)	Current assets/Current liabilities
	Quick ratio (QR)	(Current assets–Inventory)/Current liabilities
Profitability	Return on assets (ROA)	Net income/Total assets
	EBITDA margin (EBITDA)	(EBITDA/Total sales) × 100
	Net margin (NM)	(Net income/Net sales) × 100
	Gross margin (GM)	(Gross profit/Revenue) × 100
Turnover	Accounts receivable turnover (ART)	Net credit sales/Average accounts receivable

Table 2

Companies with best corporate reputations.

	Southwest Airlines	Delta Air Lines	Singapore Airlines
2021	14	23	28
2020	11	19	28
2019	11	28	18
2018	8	31	32
2017	8	31	33
2016	7	30	-
2015	7	39	19
2014	9	48	18
2013	7	-	31
2012	10	-	23

Source: www.fourtune.com (accessed on 15 May 2022) [43].

Table 3

Number of companies included in the sample.

Year	No. of Companies
2012	20
2013	24
2014	24
2015	23
2016	24
2017	24
2018	24
2019	24
2020	24
2021	23

Table 4

Original data matrix.

	2021	EPS	TDTC	LF	CR	ROA	EBITDA	NM	ART	QR	GM
1	Delta Air Lines	0.37	87.4%	75.30%	0.76	0.39%	2.20%	0.9%	15.74	0.71	31.9%
2	American Airlines	−2.62	123.9%	75.30%	0.91	−3.10%	−10.26%	−6.7%	20.99	0.82	49.3%
3	United Airlines	−5.16	87.0%	72.20%	1.19	−3.08%	−7.73%	−8.0%	16.66	1.14	41.8%
4	Southwest Airlines	1.36	50.7%	78.50%	1.97	2.76%	0.28%	6.2%	12.70	1.91	61.8%
5	Air China	−0.15	73.4%	68.63%	0.33	−6.47%	5.81%	−25.3%	6.86	0.31	−15.2%
6	China Southern	−0.10	70.2%	71.25%	0.34	−3.39%	15.90%	−10.8%	8.99	0.33	−2.6%
7	China Eastern	−0.10	77.3%	67.71%	0.32	−4.63%	12.39%	−19.8%	5.44	0.30	−20.1%
8	Air Canada	−6.93	100.0%	63.00%	1.45	−12.10%	−22.87%	−56.3%	9.59	1.42	19.9%
9	Lufthansa	−2.97	78.8%	61.60%	0.92	−5.35%	1.72%	−13.1%	4.56	0.87	34.0%
10	ANA	−2.34	68.5%	34.83%	1.88	−4.43%	−1.32%	−13.9%	8.08	1.82	−2.9%
11	Ryanair	−0.91	53.9%	71.00%	0.98	−7.50%	−21.54%	0.0%	24.62	0.98	−10.1%
12	Japan Airlines	−3.11	52.4%	36.60%	1.19	−8.10%	−11.29%	−26.6%	6.93	1.53	100.0%
13	Turkish Airlines	0.59	68.6%	67.90%	0.73	3.68%	28.85%	9.0%	5.77	0.69	22.1%
14	Qantas	−0.57	94.9%	68.00%	0.45	−9.13%	−8.74%	−31.3%	8.90	0.41	19.4%
15	Singapore Airlines	−0.21	40.8%	30.10%	2.25	−2.20%	17.16%	−12.5%	5.47	2.22	19.7%
16	Air France	−3.00	131.0%	59.20%	0.91	−10.82%	5.00%	−23.0%	6.94	0.87	28.4%
17	easyJet	−1.82	63.1%	72.50%	1.56	−9.40%	−30.66%	−58.9%	9.32	1.51	3.7%
18	Airasia	−0.16	164.5%	74.00%	0.27	−18.65%	−57.29%	−202.6%	1.55	0.25	−88.2%
19	Alaska Airlines	3.10	40.1%	73.60%	0.98	3.31%	2.49%	7.5%	12.04	0.97	45.8%
20	Jetblue Airways	−0.48	51.0%	76.00%	0.95	−1.35%	−6.18%	−3.0%	39.59	0.93	55.2%
21	Aeroflot	−0.16	122.5%	80.30%	0.70	−3.24%	23.47%	−7.0%	9.07	0.63	43.4%
22	Hainan Airlines	0.03	90.7%	74.69%	1.30	2.63%	4.06%	11.9%	1.05	1.23	−25.8%
23	Cathay Pacific	−0.10	55.4%	31.10%	0.67	−2.75%	21.28%	−12.1%	7.06	0.64	47.2%

Table 5

Computation of ${\mathrm{x}}_{\mathrm{i}}^{\ast }$ and ${\mathrm{x}}_{\mathrm{i}}^{-}$.

Best $x_i^{\ast }$	3.10	40.1%	80.30%	2.25	3.68%	28.85%	11.9%	39.59	2.22	100.0%
Worst $x_i^{-}$	−6.93	164.5%	30.10%	0.27	−18.65%	−57.29%	−202.6%	1.05	0.25	−88.2%

Table 6

Computation of $S_j$ and $R_j$.

	EPS	TDTC	LF	CR	ROA	EBITDA	NM	ART	QR	GM
Weigh	10%	10%	10%	10%	10%	10%	10%	10%	10%	10%
Delta Air Lines	0.027	0.038	0.010	0.075	0.015	0.031	0.005	0.062	0.077	0.036
American Air	0.057	0.067	0.010	0.068	0.030	0.045	0.009	0.048	0.071	0.027
United Airlines	0.082	0.038	0.016	0.054	0.030	0.042	0.009	0.059	0.055	0.031
Southwest Air	0.017	0.009	0.004	0.014	0.004	0.033	0.003	0.070	0.016	0.020
Air China	0.032	0.027	0.023	0.097	0.045	0.027	0.017	0.085	0.097	0.061
China Southern	0.032	0.024	0.018	0.096	0.032	0.015	0.011	0.079	0.096	0.054
China Eastern	0.032	0.030	0.025	0.097	0.037	0.019	0.015	0.089	0.097	0.064
Air Canada	0.100	0.048	0.034	0.040	0.071	0.060	0.032	0.078	0.041	0.043
Lufthansa	0.061	0.031	0.037	0.067	0.040	0.031	0.012	0.091	0.069	0.035
ANA	0.054	0.023	0.091	0.019	0.036	0.035	0.012	0.082	0.020	0.055
Ryanair	0.040	0.011	0.019	0.064	0.050	0.058	0.006	0.039	0.063	0.059
Japan Airlines	0.062	0.010	0.087	0.054	0.053	0.047	0.018	0.085	0.035	0.000
Turkish Airlines	0.025	0.023	0.025	0.077	0.000	0.000	0.001	0.088	0.078	0.041
Qantas	0.037	0.044	0.025	0.091	0.057	0.044	0.020	0.080	0.092	0.043
Singapore Air	0.033	0.001	0.100	0.000	0.026	0.014	0.011	0.089	0.000	0.043
Air France	0.061	0.073	0.042	0.068	0.065	0.028	0.016	0.085	0.069	0.038
easyJet	0.049	0.018	0.016	0.035	0.059	0.069	0.033	0.079	0.036	0.051
Airasia	0.033	0.100	0.013	0.100	0.100	0.100	0.100	0.099	0.100	0.100
Alaska Airlines	0.000	0.000	0.013	0.064	0.002	0.031	0.002	0.071	0.063	0.029
Jetblue Airways	0.036	0.009	0.009	0.066	0.023	0.041	0.007	0.000	0.065	0.024
Aeroflot	0.033	0.066	0.000	0.078	0.031	0.006	0.009	0.079	0.081	0.030
Hainan Airlines	0.031	0.041	0.011	0.048	0.005	0.029	0.000	0.100	0.050	0.067
Cathay Pacific	0.032	0.012	0.098	0.080	0.029	0.009	0.011	0.084	0.080	0.028

Table 7

Values for $S_j$, $R_j$ and $Q_j$ (2021).

Company	${\mathit{S}}_{\mathit{j}}$	${\mathit{R}}_{\mathit{j}}$	${\mathit{Q}}_{\mathit{j}}$
Delta Air Lines	0.3759	0.0767	0.3169
American Airlines	0.4328	0.0711	0.2825
United Airlines	0.4170	0.0824	0.4278
Southwest Airlines	0.1894	0.0698	0.0785
Air China	0.5121	0.0969	0.7043
China Southern	0.4578	0.0965	0.6557
China Eastern	0.5053	0.0975	0.7061
Air Canada	0.5465	0.1	0.7729
Lufthansa	0.4741	0.0909	0.5906
ANA	0.4265	0.0906	0.5498
Ryanair	0.4082	0.0641	0.1671
Japan Airlines	0.4495	0.0871	0.5182
Turkish Airlines	0.3576	0.0878	0.4577
Qantas	0.5315	0.0919	0.6482
Singapore Airlines	0.3160	0.1	0.5967
Air France	0.5438	0.0847	0.5577
easyJet	0.4443	0.0785	0.3956
Airasia	0.8438	0.1	1
Alaska Airlines	0.2755	0.0715	0.1682
Jetblue Airways	0.2781	0.0657	0.0889
Aeroflot	0.4131	0.0807	0.4019
Hainan Airlines	0.3811	0.1	0.6464
Cathay Pacific	0.4635	0.0980	0.6817
${\mathit{S}}^{\mathit{\ast }}$; ${\mathit{R}}^{\mathit{\ast }}$	0.1894	0.0641
${\mathit{S}}^{-};$ ${\mathit{R}}^{-}$	0.8438	0.1

Table 8

Score and rank of each company determined by VIKOR.

	Rank(2021)	Rank(2020)	Rank(2019)	Rank(2018)	Rank(2017)	Rank(2016)	Rank(2015)	Rank(2014)	Rank(2013)	Rank(2012)
Delta Air Lines	6	15	9	10	7	7	8	17	4	13
American Air	5	14	20	11	16	14	4	5	21	20
United Airlines	9	21	5	4	5	5	5	8	12	14
Southwest Air	1	5	3	2	2	2	7	4	5	6
Air China	20	17	12	17	20	18	16	16	15	-
China Southern	18	7	22	20	23	22	-	-	-	15
China Eastern	21	19	18	21	24	21	20	20	22	-
Air Canada	22	13	7	8	6	13	9	12	18	14
Lufthansa	14	20	21	14	13	17	17	15	16	-
ANA	12	6	13	19	15	19	19	18	20	-
Ryanair	3	1	1	1	1	1	1	2	1	1
Japan Airlines	11	8	8	7	9	8	10	7	7	2
Turkish Airlines	10	12	11	15	19	23	18	13	17	12
Qantas	17	4	10	9	12	11	13	24	14	13
Singapore Air	15	18	16	13	11	15	14	11	10	9
Air France	13	22	15	18	17	20	22	22	23	-
easyJet	7	2	4	3	8	6	2	1	3	5
Airasia	23	23	17	12	10	9	15	14	13	3
Alaska Airlines	4	9	6	6	4	4	3	3	2	4
Korean Air	-	10	24	23	14	24	23	23	24	16
Jetblue Air	2	3	2	5	3	3	6	6	6	7
Aeroflot	8	16	19	22	21	12	21	21	11	11
LATAM Airlines	-	-	-	-	-	-	-	19	19	17
Hainan Airlines	16	24	23	24	18	10	11	10	8	10
Cathay Pacific	19	11	14	16	22	16	12	9	9	8

Table 9

Final results.

10 Indicators	2012		2013		2014		2015		2016
	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR
Southwest Airlines	10	6	7	5	9	4	7	7	7	2
Delta Air Lines	-	13	-	4	48	17	39	8	30	7
Singapore Airlines	23	9	31	10	18	11	19	14	-	15
	2017		2018		2019		2020		2021
	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR
Southwest Airlines	8	2	8	2	11	3	11	5	14	1
Delta Air Lines	31	7	31	10	28	9	19	15	23	6
Singapore Airlines	33	11	32	13	18	16	28	18	28	15

Table 10

Final results of the ranking with 7 performance indicators in the VIKOR method.

7 Indicators	2012		2013		2014		2015		2016
	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR
Southwest Airlines	10	11	7	9	9	9	7	8	7	4
Delta Air Lines	-	10	-	3	48	17	39	10	30	8
Singapore Airlines	23	8	31	7	18	7	19	12	-	11
	2017		2018		2019		2020		2021
	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR
Southwest Airlines	8	3	8	2	11	1	11	2	14	1
Delta Air Lines	31	9	31	13	28	10	19	16	23	7
Singapore Airlines	33	13	32	11	18	15	28	19	28	14

Table 11

Final results of the ranking with 8 performance indicators in the VIKOR method.

8 Indicators	2012		2013		2014		2015		2016
	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR
Southwest Airlines	10	10	7	9	9	9	7	12	7	5
Delta Air Lines	-	11	-	3	48	18	39	14	30	10
Singapore Airlines	23	8	31	7	18	6	19	7	-	11
	2017		2018		2019		2020		2021
	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR
Southwest Airlines	8	5	8	3	11	4	11	2	14	1
Delta	31	9	31	14	28	10	19	16	23	8
Singapore Airlines	33	13	32	11	18	15	28	17	28	13

Table 12

Final results of the ranking with 9 performance indicators in the VIKOR method.

9 Indicators	2012		2013		2014		2015		2016
	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR
Southwest Airlines	10	8	7	5	9	1	7	7	7	2
Delta Air Lines	-	11	-	4	48	17	39	9	30	8
Singapore Airlines	23	6	31	9	18	10	19	12	-	14
	2017		2018		2019		2020		2021
	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR
Southwest Airlines	8	2	8	2	11	3	11	5	14	1
Delta Air Lines	31	8	31	10	28	8	19	15	23	6
Singapore Airlines	33	11	32	13	18	15	28	18	28	14

Table 13

Final results of the ranking with 10 performance indicators in the VIKOR method.

10 Indicators	2012		2013		2014		2015		2016
	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR
Southwest Airlines	10	6	7	5	9	4	7	7	7	2
Delta Air Lines	-	13	-	4	48	17	39	8	30	7
Singapore Airlines	23	9	31	10	18	11	19	14	-	15
	2017		2018		2019		2020		2021
	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR	Rank Fortune	Rank VIKOR
Southwest Airlines	8	2	8	2	11	3	11	5	14	1
Delta Air Lines	31	7	31	10	28	9	19	15	23	6
Singapore Airlines	33	11	32	13	18	16	28	18	28	15

Appendix A

The sample includes the following companies: Delta Air Lines, American Airlines, United Airlines, Southwest Airlines, Air China, China Southern, China Eastern, Air Canada, Lufthansa, ANA, Ryanair, Japan Airlines, Turkish Airlines, Qantas, Singapore Airlines, Air France, easyJet, Airasia, Alaska Airlines, Korean Air, JetBlue Airways, Aeroflot, Hainan Airlines and Cathay Pacific.

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