The quality effect on the profitability of US airline companies

Abstract

This study was conducted to determine if quality had any effects on US airline economic performance. Therefore, four service quality indexes used in the US air transportation industry were included together with two economic performance indicators, passenger revenues and return on investment (ROI). Longitudinal data from the US airline companies from 2006 to 2015 were used to determine if airline activity or profitability increased when service quality improved. The results of this research demonstrated and confirmed the positive and significant influence of service quality on the ROI of US airline companies. Meanwhile, a non-significant effect was found for quality on airline passenger revenues. As none of the previous research studies have considered the four quality indexes altogether, the findings of this work could encourage airline companies to invest in quality, since this policy can have a positive consequence for their profitability.

Keywords

airlines air transportation economic performance key performance indicators quality

Introduction

Why do airlines focus on the quality they offer to the passengers (PAXs)? Is quality an influencing factor that ensures the profitability and a higher performance of airline companies? Which are the variables that play an important role when analysing this correlation? These questions, in various combinations, have led to carry out the present study.

Several empirical studies have been conducted during the last few years to explore the potential relationship between quality and profitability in the airline industry (Caruana, 2002; Mellat-Parast et al., 2015; Steven et al., 2012).

It is in the airlines’ best interest to offer a high standard of service quality, first, to reach a high level of customer satisfaction; but airlines are focused likewise on improving the quality provided to PAXs (and perceived by them) to differentiate themselves from competitors; not only by pricing (Gursoy et al., 2005). Achieving a high passenger satisfaction is one of the greatest assets (Akamavi et al., 2015; Archana and Subha, 2012). A passenger who is not satisfied with the services offered by the airline will most likely become less reliable to continue its relationship with that airline, as far as the airline industry is a global business operating in an open and liberal market.

These arguments justify the objective of this article, which is to determine and analyse the effect that service quality exerts on airline economic performance. To do so, we consider four service quality indexes applied in the US airline industry for determining their influence on passenger revenues and return on investment (ROI) of a sample of US airline companies. According to International Air Transport Association (2016), North American Airlines are leading the performance of this industry, and since 2013, they have been generating more than the half of the total profits of the global airline industry, being in 2016, 19.2 billion dollars over the global number of 36.3 billion dollars. Even though, it is essential to mention, that precisely in relation to the quality of service offered to the customers, the US airlines were underperforming through several years (Yu, 2007). This statement can be confirmed, as the US consumer satisfaction was ranked below the tax authority Internal Revenue Service (IRS), for instance, throughout several years. It is since 2012 that the US airline companies started slowly to rank ahead the IRS in customer satisfaction-related terms (Jones, 2014).

The structure of the article is organized as follows. It first offers a conceptual approach on how quality and performance are measured in the airline business. Thereafter, it describes the academic literature on what has been said about quality and the potential relationship between quality and profitability; besides it offers the hypotheses that have been built for this study. Subsequently, ‘Methodology’ section describes the methodology applied, how data has been collected and variables measured to test the established hypotheses. ‘Results’ section outlines the results that have been obtained from this research. The final section presents the conclusions that can be drawn from this study with a discussion on key findings, as well as limitations of the current study and proposed further research.

Based on the findings of this study and considering that the airline business affects several sectors in the economy related to, among others, transportation, infrastructure and tourism, this article can be useful for managers, shareholders, stakeholders, as well as for researchers in this field of interest.

Conceptual approach

How to measure quality in the airline industry?

From the airline business point of view, it is fundamental to provide a high level of service quality to PAXs to retain customer patronage, market share and profitability (Doppelt and Nadeau, 2013; Hussain et al., 2015; Morash and Ozment, 1994; Park et al., 2005). In the transportation field, the importance of aspects as passenger satisfaction and the measurement of service quality are increasingly recognized (Shen et al., 2016). A relevant method for analysing airline service quality in the airline industry is the use of publicly available, secondary data and indexes. Even if the purpose of this article is not to detail an exhaustive list of available sources, the most relevant indexes in the United States, and used in this study, are shown in Table 1 with the aim of giving an overview about the most important quality-related indexes applied in the US airline industry.

Table 1.

Summary of quality-related indexes applied in the US airline industry.

Index name	Approach	Measurement
ACSI (2016)	With its origin in 1994, the index measures customer satisfaction across the US economy offers information of benchmarks of a multitude of industries. In the case of the airline industry, ACSI analyses passengers’ satisfaction of US Airlines’ customers. It includes nine major airlines and several small carriers; data are updated once a year.	Summarizes opinions of customers about elements are as follows: – Flight schedule options – Check-in – Reservations – On-time arrival – Baggage handling – Loyalty programs – Flight crew courtesy and helpfulness – In-flight services – Seat comfort – Call centre – Website
AQR (2016)	Developed in 1991, the AQR index provides a summary of quality ratings taken month by month, reflecting US airlines that show at least 1% of domestic passenger volume. This rating system allows comparing the performance between the different domestic airlines.	Based on consumer surveys and subjective opinions, the index takes into account a weighted average of several elements and is calculated as follows: $AQR= \frac{(+ 8.36 \times OT) + (- 8.03 \times DB) + (- 7.92 \times MB) + (- 7.17 \times CC)}{(8.63 + 8.03 + 7.92 + 7.17)}$
JD Power Airline Satisfaction Index	The index measures the satisfaction of airline passengers, considering LCCs and network carriers. Besides, it compares airline performance and provides benchmark information of those two market segments.	Summarizes the satisfaction of customers of their air travel experiences, considering – Cost and fees – In-flight service – Boarding/deplaning/baggage – Flight crew – Aircraft – Check-in – Reservation
NPS (2016)	Developed in 2003, the NPS measures the tendency or possibility of a customer to confirm and recommend a good service to a close person.	Calculated as the average according to the answers of customers, they have to give with a score between 0 and 10 (being 10 most likely) on the question: ‘How likely are you recommend (this company), to your friends, family or business associates?’

Source: Created by the authors based on the webpage information of each index.

Note: ACSI: American Customer Satisfaction Index; AQR: Airline Quality Rating; NPS: Net Promoter Score; LCC: low cost carriers.

How to measure performance in the airline industry?

Beside the common indicators that can be used in all industries (financial, non-financial, etc.), the airline business, as every business does, has its own specific key performance indicators (KPIs) that should be calculated and understood in order to analyse the performance of airline companies (Amat et al., 2011; Belobaba et al., 2009; Katz and Garrow, 2014; Massachusetts Institute of Technology (MIT), 2015–2016). The most important KPIs of this business, and used for this article, are as follows:

– Load factor: The load factor is one of the most useful KPIs that was analysed in several studies to identify its impact on the average operating costs per flight (Bilotkach et al., 2014; Zuidberg, 2014). According to the MIT (2015–2016), the load factor refers to ‘the number of revenue passenger miles (RPMs)¹ expressed as a percentage of available seat miles (ASMs)’². It was widely accepted by the airline industry as the principal measure of commercial performance (Brenner, 1982), as it shows the maximization of a route due to economies of traffic density (Bilotkach et al., 2014).

– In line with Zuidberg (2014), airlines can reduce their operating unit costs per passenger by maximizing their load factor, as this does not lead to relevant higher operation costs per aircraft movement. Thus, airlines are able to increment their profit if they do so with their load factor, without any need to raise significantly operating costs. However, there are cost increases in fuel, catering, handling, among others, that could represent an increase per seat, approximately a 10–15% of the marginal revenue derived from this new occupied seat (Doganis, 2009). Nevertheless, due to the enormous changes in the pricing policy over the last two decades, the KPI revenue per ASM replaced latterly in some way this KPI.

– Company size refers to the company size of the airline measured through the total number of employees, as flight personnel (i.e. technical flight crew) and ground personnel (handling, maintenance, etc.). Thus, this KPI is used as a size variable of the airline companies, measured as full-time equivalent employees by year.

– PAX refers to the total number of PAXs that fly and, that are generating, in general terms (except free tickets), passenger revenues. This KPI is also necessary for calculating other operating metrics as, for instance, the RPM, that measures the average of each flown mile paid by each passenger, as well as the revenue per passenger, that stands for the generated average income per passenger.

Although the variables load factor and PAX are related, it is very important to point out that those two variables do not need to have necessarily the same tendency. Companies with wide body fleet can reflect an increase in the number of PAXs carried, but being the load factor lower than for instance for a company using narrow fleet, with less PAXs on board than the wide body company.

In addition to these specific KPIs of the airline business, we will use for the purpose of measuring profitability, the ROI that is applied as the most common ratio by different financial analysts to ascertain the best investment plans (Bhunia et al., 2011), as far as higher levels of assets imply higher capital needs and financial costs (debt plus equity) to be balanced (González et al., 2000). In this study, we will not make use of financial profitability, as we are focused on performance that is not influenced by the financial structure; and return on equity considers how to finance firm’s assets, depending on the capital structure that affects the ratio. For instance, a positive financial leverage would imply that the financial profitability would be higher than the economic profitability (Yoon and Jang, 2005).

Literature review on quality and performance in the airline industry and research hypotheses

Literature review

What has been said about quality in the airline industry?

To find a response to this question, a literature review has been carried out using primarily the database (Web of Science, 2016) that permits us to find out and obtain academic journals and papers related to the topic. The period considered for this literature review started on 1993, approximately when the open skies policy came into force, and finished on September 2016. The keywords used for carrying out the literature review have been as follows:

– quality, service quality, air transportation and airlines.

These keywords have been introduced in the database Web of Science with the following combinations through the topic search:

quality or service quality and

air transportation or airlines.

Based on a previous literature review carried out by Kalemba and Campa-Planas (2017), 16 concepts related to quality have been used to classify the findings of the literature reviewed (see Table 2).

Table 2.

Literature review on quality of parameters used.

		Total	Sandada and Matibiri (2016)	Delbari et al. (2016)	Merkert and Assaf (2015)	Chow (2014)	Choi et al. (2015)	Steven et al. (2012)	Mellat-Parast and Fini (2010)	Rhoades and Waguespack (2008)	Association of European Airlines (2008)	Park (2007)	Liou and Tzeng (2007)	Chen and Chang (2005)	Gilbert and Wong (2003)	Chang and Yeh (2002)	Bowen and Headley (2000)	Truitt and Haynes (1994)	Elliot and Roach (1993)
Attributes used for measuring service quality
1	Preflight service	5								X		X			X			X	X
2	In-flight service	6								X		X	X	X				X	X
3	Postflight service	7				X				X	X		X				X	X	X
4	Courtesy and image of the company	5								X		X	X		X	X
5	Handling passenger	6										X	X	X		X	X	X
6	Features aircraft	5								X			X			X		X	X
7	Flight schedule	5								X	X	X	X				X
8	Punctuality	8				X			X	X	X		X				X	X	X
9	Safety	3											X		X		X
10	Pricing	1										X
11	Service quality	3			X		X					X
12	Total quality management	1							X
13	Customer satisfaction	5	X		X		X	X				X
14	Customer perception and expectation	2	X		X
15	Airline alliances	1		X
16	Others	3	X							X			X
	Total parameters used by each author	66	3	1	3	2	2	1	2	8	3	8	9	2	3	3	5	6	5

Source: Created by the authors from the literature reviewed and mentioned.

This literature review related to the quality context in the airline business takes into consideration several authors’ definitions of parameters corresponding to service quality and shows that the most important concepts have been punctuality and postflight service. Meanwhile, the punctuality concept considers on-time performance, on-time arrival, travel time, turnaround time and delay; the postflight service concept includes aspects as needs of PAXs after the flight, such as baggage handling or mishandled baggage (Kalemba and Campa-Planas, 2017). Like Table 2 in structure, Table 3 has been developed to show a summary of the attributes used for measuring quality through the four selected quality indexes, which were explained previously.

Table 3.

Parameters mentioned by the literature and used by the four selected quality indexes.

		Total number of literature review	Total number of quality indexes	ACSI	Wichita	JD Power Airline Satisfaction Index	NPS
Attributes used for measuring service quality
1	Preflight service	5	2	X		X
2	In-flight service	6	2	X		X
3	Postflight service	7	3	X	X	X
4	Courtesy and image of the company	5	2	X		X
5	Handling passenger	6	1		X
6	Features aircraft	5	1	X
7	Flight schedule	5	2	X	X
8	Punctuality	8	2	X	X
9	Safety	3	-
10	Pricing	1	-
11	Service quality	3	-
12	Total quality management	1	-
13	Customer satisfaction	5	4	X	X	X	X
14	Customer perception and expectation	2	-
15	Airline alliances	1	-
16	Others	3	1	X
	Total parameters used by each index		20	9	5	5	1

Source: Created by the authors from the literature reviewed and mentioned and information coming from airlines quality indexes.

Note: ACSI: American Customer Satisfaction Index; NPS: Net Promoter Score.

While Table 2 shows that the most important parameters in previous researches have been punctuality and postflight service, Table 3 shows that the most important parameters the indexes are based on are customer satisfaction just as postflight service. Therefore, there are no significant differences between the parameters that the quality indexes consider and the academic literature that has been reviewed.

The quality–profitability link in the airline business

The relationship between quality and performance of a company has been identified and extensively discussed in the academic literature. Several studies based on management examined the link between both concepts, being either positive (Clemes et al., 2011; Nicolau and Sellers, 2010; Sun and Kim, 2013) or negative (Bounds et al., 1994; Easton, 1993; Reger et al., 1994). However, the focus of this study laid primarily on the quality–profitability link in the airline industry, where the results have been shown in Figure 1.

Figure 1.

Concept map literature review. Source: Created by the authors based on the literature reviewed.

Figure 1 shows, therefore, that quality does not have a direct influence on profitability, as there are several factors in-between that play an important role and are significant for the relationship; therefore, our findings of Figure 1, based on the literature reviewed, confirm the theory of the service profit chain that outlines the importance of people – both employees and customers – for establishing a relationship between quality and profitability through components as satisfaction and loyalty, among others.

Thus, based on the literature reviewed, the starting point of the chain is the customer service that according to Merkert and Assaf’s (2015) leads, in case of being positive, to an increased level of perceived service quality and helps afterwards to improve the relationship between the airline company and the customers. This, in turn, creates customer satisfaction and increments the willingness to pay for the service. Others emphasize a direct influence of customer service (Steven et al., 2012) and perceived quality (Anderson and Mittal, 2000; Caruana, 2002; Kotler and Keller, 2012; Mosahab et al., 2010) on the level of customer satisfaction.

Hence, when perceptions are higher than expectations, the customer will be satisfied and that, consequently, results in customer loyalty and/or retention (Anderson and Mittal, 2000; Caruana, 2002; Mosahab et al., 2010; Steven et al., 2012). In consequence, a high degree of customer loyalty drives to an increase of the profitability of the airline companies. That means, the more loyal customers an airline company has, the easier it will outperform competitors. This high level of performance and profitability, in turn, leads to a higher competitiveness of the airlines (Delbari et al., 2016). It is also important to mention the feedback of the chain that shows that customer service is the consequence of the highly competitive pressure existing in the airline industry (Pakdil and Aydin, 2007).

It is true that airlines that can understand PAXs’ needs, wishes and behaviours have a better position to aspire an improvement of their performance that maximizes sales and drives profitability through positive passenger experiences and loyalty (Power, 2016).

Research hypotheses

Once considered the literature reviewed and the objectives of this research, our conceptual model, illustrated in Figure 2, shows a relationship model between quality, defined through the four quality indexes, and financial indicators, as well as between airline KPIs and the financial ratios.

Figure 2.

Conceptual model and hypotheses. Source: Created by the authors.

Based on the model in Figure 2, our hypotheses development is as follows.

The relationship between quality and the firm’s performance and especially profitability has received considerable interest among authors (Caruana, 2002; Mellat-Parast, 2015; Steven et al., 2012). However, most of the empirical works considered only one quality index, and none of the studies quality has been considered as the average of a mix of four quality indexes. We therefore propose our first two hypotheses as follows:

Hypothesis 1: The better the service quality offered by the airlines, the higher their activity measured through passenger revenues.

Hypothesis 2: The better the service quality offered by the airlines, the higher their profitability measured through ROI.

Furthermore, there are additional variables that may affect the airlines’ performance and profitability. One of those variables that should be considered and that are essential in the airline business is size variables (Steven et al., 2012). In this study, we make use of the number of PAXs and the company size, measured through the number of employees. This relationship has not been studied in any previous study. Thus, our third hypotheses (3a and 3b) are as follows:

Hypothesis 3a: The higher the value of size variables (company size, PAX), the higher the profitability of the airlines measured through ROI.

Hypothesis 3b: The higher the value of size variables (company size, PAX), the higher the activity of the airlines measured through passenger revenues.

Methodology

Sources of information

Two different typologies of information sources have been used. Firstly, for the financial ratios and airline KPIs, it has been used the Airline Data Project (ADP) built by the MIT in the context of their Global Airline Industry Program. The ADP is developed based on reliable information provided by the US Department of Transportation giving the users an overview about data on the aircraft and employee productivity (MIT, 2015–2016). Secondly, for the quality indexes, we have used their respective scoring gained by the airlines that are included in the ADP. Data were taken from the index own public websites.

Sample collection

The period considered for both sources was longitudinal time-series data from 2006 to 2015. The airline companies and years considered that form the sample included in this study are as follows:

American Airlines;

Continental, until 2011; from 2012, merged with United Airlines;

Delta Air Lines;

Northwest Airlines, until 2009; from 2010, merged with Delta Air;

Lines;

United Airlines;

US Airways;

America West Airlines, until 2007, merged with US Airways;

Southwest Airlines;

JetBlue Airways;

AirTran Airways, until 2013; from 2014 ,merged with Southwest;

Airlines;

Frontier Airlines;

Virgin America, operating since 2007;

Alaska Airlines;

Hawaiian Airlines; and

Allegiant Air.

In relation to quality, the indexes considered during the period 2006–2015 for this study are as follows:

American Customer Satisfaction Index (ACSI);

Wichita’s Airline Quality Rating;

JD Power’s Airline Satisfaction Index; and

Net Promoter Score (NPS).

Measurement of variables

The established statistical model consists of three variables: dependent, explanatory and control variables (see also Figure 2).

Dependent variables

This study includes two dependent variables: passenger revenues to measure the airlines’ activity and ROI to measure the firm’s profitability. Passenger revenues show the total coming from airline operations (scheduled and non-scheduled flights), including PAXs, excess baggage and other transport-related items. Therefore, this variable shows the income related to the number of PAXs that have flown in a certain period. The second variable, ROI, measures the return on an airline’s investment in relation to the investment’s costs. This KPI is very useful to benchmark an airline’s profitability, as it measures the relationship between earnings before interest and taxes and total assets.

Explanatory variables

In our models, we have included only one explanatory variable, namely, the quality index of the airlines. This variable has been measured through the average of the standardized quality indicators: ACSI, Wichita AQR, JD Power Airline Satisfaction Index and NPS.

Control variables

We have used three control variables that allow us considering the effects of other variables not related to quality that can also affect an airlines’ performance

– Firstly, the load factor that measures the occupancy of the plane, as the ratio of RPM between ASM. As already explained previously (Belobaba et al., 2009; MIT, 2015–2016), higher occupancy should assume higher profitability and activity, as this business has a high component of fixed costs and a low component of variable costs. Therefore, an additional passenger generates, in most of the cases, a marginal profit, as far as the marginal revenue is higher than the variable cost.

– Secondly, the variable SIZE that has been considered as company size, being the annual total number of employees (FTS). A higher number of employees should imply a higher number of flights. Until these flights are sold at the right prices, a higher number of employees should imply likewise higher revenue (MIT, 2015–2016).

– And, finally, the number of PAXs for carrying out the robustness checks, reflecting the total number of people moved. Before the open skies policies, an increase of PAXs implied a proportional increase of revenues, as far as fixed pricing policies were established. Since the implementation of RM policies, new pricing policies have been applied (Butler and Keller, 1999; Eldad, 2005), and a higher number of PAXs should imply higher profitability too, when a good relationship between occupancy and price is achieved. This relationship is measured through the ASM ratio.

Both variables, the number of PAXs and company size, have been used as a logarithmic transformation of the original number.

Results

The descriptive statistics and the correlation matrix, respectively, of the dependent, explanatory and control variables included in the estimation models are shown in Tables 4 and 5, based on the statistical analyses carried out using the program R, version 3.3.2 (R Development Core Team, 2016).

Table 4.

Descriptive statistics.

Variables	Minimum	Maximum	Mean	SD
R	0.020	41.08	10.939	11.305
ROI	−0.275	0.245	0.050	0.080
Q	−3.337	1.671	−0.098	0.898
lf	0.621	0.909	0.823	0.040
lPAX	5.950	11.897	10.216	1.164
lSIZE	6.735	11.502	9.572	1.318

Note: SD: standard deviation; R: revenues; ROI: return on investment; Q: quality; lf: load factor; lPAX: log PAX; PAX: passenger lSIZE: log composition size.

Table 5.

Correlation matrix.

Variables	R	ROI	Q	lf	lPAX	lSIZE
R	1
ROI	−0.096	1
Q	−0.380	0.440	1
lf	0.228	0.130	0.046	1
lPAX	0.819	−0.162	−0.244	−0.075	1
lSIZE	0.893	−0.227	−0.422	0.027	0.956	1

Note: R: revenues; ROI: return on investment; Q: quality; lf: load factor; lPAX: log PAX; PAX: passenger; lSIZE: log composition size.

Table 4 reports that the correlation between both company size variables, namely PAX and SIZE is very high, suggesting therefore the suitability of including only one company size variable in the statistical model to avoid problems of multicollinearity between explanatory variables (Kalemba et al., 2016).

As mentioned before, the logarithmic transformation of the control variable SIZE, measured through the number of employees, has been considered as a general measure of the company size (Le et al., 2006; Wu, 2008).

Panel data methodology has been applied during the estimation process, due to the unobservable firm-specific effects that could affect the dependent variables. For the response to our proposed hypotheses, we determined to run two regressions for REVENUES and ROI, respectively. The specification models were as follows:

{REVENUES}_{i t} = β 11 {Quality}_{i t - 1} + β 12 {Load factor}_{i t} + β 13 {Size}_{i t} + ε_{i t}

{ROI}_{i t} = β 21 {Quality}_{i t - 1} + β 22 {Load factor}_{i t} + β 23 {Size}_{i t} + ε_{i t}

As seen in both models, we decided to consider the effect of Quality _it ₋₁ as the quality of 1 year does not have such a fast effect on the profitability of the airlines of the same year. Results for both specification models are reported in Table 6. F tests and Hausman-type tests have been conducted to determine the choice between pooled OLS, fixed or random effects models. The most appropriate estimation method for the models depends on the properties of both the individual as well as the idiosyncratic errors (Croissant and Millo, 2008).

Table 6.

Estimation models.

	Model 1 (fixed effects)		Model 2 (pooling)
	Dependent variable: R		Dependent variable: ROI
Variables	β	t	β	t
Intercept	–	–	−0.218	−0.899
Q _it _-1	−1.101	−1.197	0.045***	4.278
lf _it	61.773***	4.139	0.357	1.297
lSIZE _it	23.602***	13.104	−0.003	−0.345
Hausman test (fixed vs. random)	55.356***		7.793⁺
Adjusted R ²	0.605		0.237
F	70.176***		8.739***

Note: R: revenues; ROI: return on investment; Q: quality; lf: load factor; lSIZE: log composition size. All coefficients are standardized β weights and t-values are also given.

***p < 0.001; **p < 0.01; *p < 0.05; ⁺ p < 0.1.

In this case, the most appropriate model for model 1 was the fixed effects model, which means that the individual error is correlated with the regressors. On the contrary, pooled OLS was proven to be the most efficient estimator for β because the individual component of error was missing altogether.

The results of the fixed effects estimation of model 1 are provided in column 1 of Table 6. As the table shows, a non-significant effect was found for quality on airlines’ activity measured through passenger revenues (β11 = −1.101), reason why hypothesis 1 is not supported. Therefore, the findings of this study do not confirm the positive influence of service quality on airlines’ revenues. On the contrary, the effects of the control variables load factor and size show a positive sign of the coefficient, that is, β12 is positive (β12 = 61.773; p < 0.001) and β13 is also positive (β13 = 23.602; p < 0.001), which demonstrates the positive influence of the load factor and company size on the airlines’ revenues (hypothesis 3b supported). In conclusion, the incomes that airlines receive from their normal business activities are higher in big companies with a higher number of employees and also when their production is higher in comparison to their capacity. On the contrary, better service quality does not seem to directly affect airlines’ incomes.

Table 6 also reports a pooled OLS estimation of model 2. Column 2 shows that airline’s profitability is positively influenced by service quality (β21 = 0.045; p < 0.001), as it was hypothesized (hypothesis 2 is supported), confirming that a better service quality may not affect airlines’ revenues but, at the end, it improves the economic profitability of the airline companies. However, despite the confirmation of this result, the limited value of the adjusted R² (0.237) suggests that there are some other relevant variables that are not included in our model and that are affecting the airlines’ ROI. The variable SIZE and the load factor do not have a significant effect on the airlines’ economic profitability (β22 = 0.357; β23 = −0.003), therefore hypothesis 3a is not supported. Additionally, some robustness checks have been carried out to confirm whether our findings regarding the influence of quality on economic performance are robust to alternative specifications of the models. Results are reported in Table 7, which takes into consideration the logarithmic transformation of the number of PAX as the company size variable, substituting the logarithmic transformation of the company size (variable SIZE).

Table 7.

Estimation models: robustness checks.

	Model 1 (fixed effects)		Model 2 (pooling)
	Dependent variable: R		Dependent variable: ROI
Variables	β	t	β	t
Intercept	–	–	−0.195	−0.757
Q _it ₋₁	−1.065	−1.1765	0.046***	4.651
lf _it	6.732	0.453	0.344	1.245
lPAX _it	26.436***	13.386	−0.004	−0.430
Hausman test (fixed vs. random)	53.341***		8.708*
Adjusted R ²	0.612		0.238
F	73.056***		8.768***

Note: R: revenues; ROI: return on investment; Q: quality; lf: load factor; lPAX: log PAX; PAX: passenger. All coefficients are standardized β weights and t-values are also given.

***p < 0.001; **p < 0.01; *p < 0.05; ⁺ p < 0.1.

Table 7 indicates, contrary to our established hypothesis 1, a non-significant effect for quality on airlines’ revenues (β11 = −1.065). At the same time, a positive and significant influence was proven for company size, measured as the logarithmic transformation of the number of PAXs (β13 = 26.436; p < 0.001) on revenues, supporting therefore our hypothesis 3b. Moreover, the results confirm the positive and significant effect of service quality on airlines profitability, measured through the ROI of the companies’ sample of this study (β21 = 0.046; p < 0.001), supporting our hypothesis 2.

Conclusions

Discussion and contribution

The purpose of this article is to provide a contribution explaining the quality–profitability link in the airline business.

Airline companies have been modifying their business model in the last decades in order to adapt to the new situation of competition, legal regulations, tourism changes, world globalization, among other facts. Due to these changes, the airlines have been focused on different new strategies and one of them has been the quality provided as a way to differentiate from their competitors (Chen and Hu, 2013; Gursoy et al., 2005), having a specific customer-oriented attitude. It is fundamental to determine the quality concept in this business, due to the variety of needs of PAXs. There are some who could appreciate the service provided on the ground or in-flight, while other PAXs could prefer a lower fare or a better flight connection, for instance, over any other consideration. In addition, finance plays a crucial role in any business. Therefore, we have considered the airline’s success (Butler and Keller, 1999), addressed in this study through the analysis of the effects of quality on economic performance.

The main objective of this research is to focus on testing the outcomes of service quality on US airlines’ performance. For the analysis, four service quality indexes that are well-known and recognized in the US airline business and two economic performance indicators have been considered. That implied to figure out, firstly, two main research approaches, considering, on one hand, the effects of service quality on economic profitability and, on the other hand, the quality effect on airline passenger revenues. And, secondly, a third research approach, analysing the effect of size on airlines’ profitability. Consistent with our hypothesis, and considering the effects on airlines’ profitability, the results confirm the positive and significant influence of service quality on the ROI of the analysed US airlines. However, contrary to our assumption, there is a non-significant effect of quality on airline passenger revenues. At the same, this does not mean that airlines with a higher activity loose therefore quality and profits.

Additionally, a positive and significant influence was proven for company size on airlines’ revenues for both estimation models, the general and the robustness check, measured as the company size and number of PAXs, respectively.

Even though this results in relation to business profitability are consistent and similar to previous studies related to hospitality and tourism management (i.e. Sun and Kim, 2013), this research has been developed exclusively for airline companies, with several appropriate quality indexes, following a different research method. A positive relationship between quality and firms’ performance has been confirmed in research studies related to different businesses (i.e. hospitality business), but not in case of the airline industry, where no exclusive study for this industry has been previously developed. Furthermore, none of the studies included four quality indexes, fact that reaffirms the importance and the effect that quality has on the profitability the airline companies.

Therefore, we consider that the statistical outcome, the positive relationship between service quality and profitability, can encourage the airline management to continue improving service quality in their companies; this would be a core competitive advantage for the development of any airline company. Airline managers should enforce this opportunity to contribute to the airline company’s benefits and, therefore, respond to the political and economic transformations of recent years. This research also provides an important contribution to the academic community, especially for researchers in the airline sector.

Limitations and further research

However, this study is limited due to the restriction to a certain number of US airline companies where data have been available. Despite the explored results and the evidence for the positive relationship between quality and ROI in the US airline industry, there are several issues that still require a more detailed examination and analysis. In particular, in a further research, quality and economic factors of non-US companies have yet to be analysed, as this could enrich the given results of this study. By the same token, research could be extended to other rating systems such as Skytrax (2016) and Airline Ratings (2016) that consider different sources to build their quality-related indexes.

Furthermore, differentiating the relationship between the quality concept and profitability by passenger segments could strengthen our research results. That is to say, separating business travellers from leisure travellers and seeing their contribution and effects of quality for each segment on airline’s profits. Moreover, an analysis taking into account the contrast between low cost carriers and full cost carriers could be realized as both consider different determinants of quality.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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