Building Brand, Building Value: The Impact of Customer-Based Brand Equity on Airline Ticket Premium Pricing

Abstract

This study examines the impact of Customer-based Brand Equity (CBBE) on passengers’ Willingness to Pay Premium (WPP) for airline tickets, comparing low-cost and flag airlines. The research is prompted by the competitive nature of the industry and the need to comprehend passenger preferences, focusing on brand image, brand awareness, and service attributes as key variables influencing CBBE. The survey data collected from 489 recent travelers were analyzed through Partial Least Squares Structural Equation Modelling (PLS-SEM) and Multigroup Analysis (MGA), generating two quantitative analyses: first, the model was analyzed for airlines in general, and second, a multi-group analysis was performed to understand how the model behaves through price tiers. The findings indicate the significant influence of the chosen variables on both CBBE and WPP. A distinguishing factor lies in the differentiation between low-cost and flag airlines, revealing differing impacts on CBBE and WPP. This research contributes to the branding literature by expanding CBBE’s application to services, especially in the airline sector. It also builds on existing knowledge of WPP’s importance in service industries. Segmenting airline price tiers offers actionable insights for management strategies. In conclusion, this study augments the knowledge of CBBE, providing valuable managerial implications, guiding brand-tailored strategies to increase passengers’ willingness to pay premium.

1. Introduction

In 2019, almost 6 million flights departed from Europe [1], and there were almost 39 million flights worldwide [2]. The desire to see the world which had been closed off by the pandemic made people excited to get back to travelling, helping the industry to get back on its feet after serious losses. Now that flights are being re-established after the COVID-19 pandemic, these numbers are increasing, making the worldwide airline industry grow again. This industry, as we all know, is very competitive. With so many flight routes to choose from, so many schedules and different flight conditions, it becomes vital to understand what passengers value, what makes them choose their flights, and what makes them willing to pay more for the service. To understand this, we first need to focus on how to measure the value customers attribute to a brand/service, which brings us to customer-based brand equity.

Customer-based brand equity (CBBE) reflects the added value of a brand’s name compared to an identical competitor’s offer [3]. A brand is said to have positive CBBE when customers react more positively to its marketing stimuli than to their competitors’ [4]. We can find different approaches to CBBE in the literature. However, for this research, we choose to test the impact of brand image [5], brand awareness [4] and service attributes [5] on CBBE due to their perceived importance in the airline industry. One of the goals for management when investing in performance increases for these variables is to see an impact on willingness to pay a premium price, an impact which has also been studied by authors such as Malarvizhi et al. [6] and Chatzipanagiotou et al. [7].

Although there is some research on CBBE, its application in service areas is still scarce and open to exploration. For service industries, there is usually more risk adjacent to the purchase [8,9] due to the lack of physical product and its heterogeneity, making the customers more inclined to choose based on the value they attribute to the brand. This makes CBBE an important topic in service industries, and the airline industry is no exception. In the literature, there is a lack of consensus on what variables most influence CBBE, allowing for some space to test new ideas. In addition, despite all the research on the influence of CBBE on purchase intention, the relationship still needs to be explored regarding willingness to pay premium (WPP). This opens the path to new research connecting CBBE and WPP. Finally, considering the aviation industry, there are two separate groups: low-cost airlines and flag airlines. Due to their differences in service level, and the scarce literature on the different impacts of CBBE for different price tier services, this is also a topic to explore and where we can arrive at some interesting findings about this difference, such as the higher impact of constructs for one of the price tiers, and vice versa. The intangible nature of services, especially in the airline industry, increases perceived risk for consumers due to the lack of a physical product and inherent service variability. This reliance on brand value for risk mitigation renders CBBE important in service industries. Additionally, the impact of CBBE on WPP remains underexplored, despite extensive research on CBBE’s influence on purchase intention. This research addresses these gaps by examining the relationship between CBBE and WPP in the airline industry, considering factors such as brand awareness, service attributes, and brand image, particularly within different price tiers (low-cost vs. flag airlines).

With these research gaps in mind, the objectives of this paper are the following: (i) to investigate the relationship between brand awareness, service attributes, brand image, CBBE, and their impact on WPP within the airline industry; (ii) to examine the differences in the model’s behavior between low-cost and flag airlines in order to understand the implications for management strategies within each price tier.

To do so, we conducted a quantitative PLS-SEM analysis and a quantitative MGA through the same questionnaire, where 489 respondents who travelled recently were incentivized to answer about an airline company of their choice. Our findings suggest that all chosen variables influence CBBE and WPP, apart from brand awareness, whose impact on WPP was not proven significant. Through IPMA, it was possible to see which variables have more impact on WPP and which variables perform better in the model; the results led us to conclude that CBBE has high performance, making it an important variable for management to invest in, since it will bring the highest impact. Even though all variables are important, their impact is, as predicted, different between the two price tiers. Through MGA, the model was proven to perform significantly differently between the two price tiers. Thus, this study contributes to marketing research with a validated model of CBBE for the service industry, specifically the airline sector, and as a study of the impact of the CBBE model on WPP for different price tiers.

2. Literature Review

2.1. Key Concepts

Customer-based brand equity stems from brand equity, which is a measure of a brand’s value for both consumers and companies. Brand equity from the firm’s perspective relates to the organization’s financial worth, impacting risks, cash flows, entry barriers, and costs. In this study, we focus on consumer perceptions of brand equity, known as customer-based brand equity. This concept reflects the added value a brand offers to customers through intangible assets tied to the brand, creating emotional connections and influencing consumer responses to marketing efforts. Customer-based brand equity is established when consumers have strong brand associations and familiarity with the brand.

The definition of customer-based brand equity varies among researchers. Keller [4] focuses on knowledge structures shaping brand image and awareness. Chatzipanagiotou et al. [7] present a process-oriented approach to CBBE, highlighting three blocks: brand building, understanding, and relationship. These blocks encompass elements like brand essence, customer understanding, and relationship facilitation.

This research proposal aims to explore brand image, awareness, and service attributes as factors influencing customer-based brand equity. Service attributes will be studied independently due to their significant impact in service industries. The study will also investigate outcomes linked to CBBE such as brand preference, word of mouth, purchase intention, loyalty, and willingness to pay a premium for airline services.

2.2. Hypotheses Development and Conceptual Model

According to Keller [4], brand image can be described as the consumer’s perceptions about a specific brand, created by the brand associations the consumer holds in memory. Thus, brand associations are responsible for a customer’s image of a brand, and their strength, uniqueness and favorability drive a positive brand image, also impacting CBBE [4]. Brand associations can be explained through three essential components: brand attributes, brand benefits, and brand attitudes.

Brand attributes consider the service’s features, representing everything the customers perceive as being involved in the service offering [6]. They can range from service-related (e.g., features of the service) to non-service-related (e.g., display of information about the service). Brand benefits, on the other hand, relate to the added value each customer associates with the service. Finally, brand attitudes are the evaluation of the service, and they are often what drives consumer behavior [4]. Furthermore, brand image can also be defined as the thoughts, feelings, and attitudes of a customer for a specific brand [3]. Brand image differentiates a brand from its competitors, which helps customers understand their needs and wants regarding the brand. Consequently, a more positive brand image leads to more favorable customer attitudes towards the brand [10].

The significance of brand image in customer decision-making cannot be overstated. Customers often lean towards services with established and positive brand image because it is easier to trust a known brand [11]. Hence, brand image creates value for the customer experience [5]. This said, it is no surprise that multiple authors, including Keller [4], identify brand image as a cornerstone of customer-based brand equity due to its influence on customers’ perceptions of the brand. Thus, we can hypothesize that it also applies to the aviation industry, with the following relation:

H1a: 

Brand Image positively impacts Customer-Based Brand Equity in the aviation sector.

Nonetheless, brand image extends beyond CBBE. As the business environment became more competitive, brand image became a critical success factor, especially in service industries [12] such as the airline industry. A superior brand image allows the company to hold and/or improve its market positioning, helping to attract and retain customers [13]. Brand image has been shown to influence customers’ attitudes towards the brand; it has proven to impact key performance indicators such as satisfaction, loyalty, and trust [14], which help reduce perceived purchase risks.

Aghekyan-Simonian et al. [10] also contend that brand image positively influences purchase intentions by reducing the perceived risk and positively affecting customers’ impressions of service attributes, leading to higher price perception. Many authors have studied brand image’s impact on purchase intention with different purposes and sectors in mind, making this relation a well-studied one [10,14,15,16]. Therefore, if brand image positively influences purchase intention and can lead to higher price perceptions in service industries, we can hypothesize that brand image positively influences willingness to pay premium, specifically to the airline industry, studied in this study.

H1b: 

Brand Image positively influences Willingness to Pay Premium in the aviation industry.

Service attributes incorporate a broad spectrum of elements defining the service experience. These attributes include everything that a service provider offers to its customers, whether tangible or intangible, that contributes to the overall perception of the service. Tangible attributes are related to physical elements such as facilities appearance, personnel, and the overall service environment [17]. These tangible attributes can be observed by the customer and play an important role when it comes to first impressions. On the other hand, intangible attributes are not related to the physical aspect of the service. They involve other factors such as the perceived quality of service, guarantees, after-sales services and price [5]. Intangible service attributes are usually tied to emotional and psychological aspects of the experience, enabling the creation of long-lasting impressions. In sum, service attributes cover every feature of the service that customers encounter or engage with, both tangible and intangible. The impact of these attributes significantly shapes customer perceptions, impacting their behavior. Some specific service attributes studied for transportation industries include travel times, convenience, reliability, and comfort [18]. When studying public transportation service quality, described route characteristics, promotion (which relates to the information about the service available), and complaints management are mentioned as important service attributes [18].

Each day, passengers have a greater pool of airline companies to choose from, making them more experienced and in search of higher levels of service. This translates into clients being more willing to switch airlines if they are not satisfied with the provided service [19]. Service attributes are important to create high levels of service: if the airline has the right service attributes and they are well-executed, service levels increase. If passengers demand higher service levels, it means they value these service attributes, making this variable important for an airline company to build brand value. This brings us back to the concept of CBBE. As already argued by Mourad et al. [5] for the higher education industry, service attributes can be seen to influence brand equity. Thus, we propose that service attributes can be interpreted as an independent variable when influencing CBBE and WPP, due to their high value for service industries, and that their influence also applies to the airline industry, following the proposed hypothesis:

H2a: 

Service Attributes positively impact Customer-Based Brand Equity in the aviation sector.

Understanding consumers’ willingness to pay for improved services contributes to devising effective strategies, developing new services, and pricing them competitively in the market. A key aspect in delivering enhanced service lies in the realm of service attributes, as they play a role in shaping customers’ perceptions of a brand, particularly through emotional interpretation [20]. Service attributes are built upon the foundation of perceived service quality, a factor that has been consistently linked to positively influencing behavioral intentions such purchase decisions [21,22].

By prioritizing the enhancement of service attributes, airline companies engage in a competitive quest for overall superiority over their competitors, with the ultimate goal of justifying premium pricing strategies. Consequently, this model introduces the following hypothesis to investigate whether the strategy of focusing on service attributes to command premium prices offers strategic advantages to airline companies:

H2b: 

Service Attributes positively impact Willingness to Pay Premium in the aviation sector.

Brand awareness represents the extent to which a potential customer can recognize a brand as part of its specific service category with enough detail to purchase it [23]. Keller [4] divided brand awareness into two parameters: brand recall and brand recognition, both relating to the strength a brand has in customers’ memory, and the tendency for a brand name to come to mind.

Diving into brand recognition, this term refers to customers’ recognition of the brand when exposed to it. It requires customers to recognize correctly that they have heard about the brand [4]. Brand recall, however, requires customers to generate the brand name themselves. It requires a customer to remember the brand when given a cue, such as the category or the need it fulfils [3,4]. The importance of these two concepts is relative, and it depends on multiple factors, one of which is the purchase decision process. Specific to the airline industry, we can focus on two situations: if the choice of flight is made through online flight search websites or travel agencies, brand recognition might have a higher impact, since it is crucial that the customer recognizes the airline company when the name shows up. However, brand recall has a more significant influence if the choice is made on specific airline websites since it implies the customer knows the airline name during the search, due to the need to look for a specific airline website [5,6].

Brand awareness is an essential step in building brand value, because to choose a brand, a customer must first be aware of it and its services [17,24]. It is influential in building customer-based brand equity since only if customers are aware of the brand and the category in which it operates, can they attribute value to it. Thus, we propose the following hypothesis:

H3a: 

Brand Awareness positively impacts Customer-Based Brand Equity in the aviation sector.

Brand awareness holds particular significance in high-involvement purchases, such as airline tickets, as customers tend to engage in more extensive research and informed decision-making processes. When customers demonstrate a high level of brand awareness, it means they are familiar with the brand, ultimately reducing their perception of risk associated with the purchase. This improved brand awareness empowers customers to make confident decisions, especially in scenarios where objective assessments offer limited guidance [25].

Research by Radder and Huang [26] highlights that brands with higher recall or recognition are more likely to be preferred or chosen by consumers. The well-established relationship between brand awareness and purchase intention is evident. However, investigating its connection with willingness to pay a premium adds a new dimension. Thus, within the context of the airline industry, we propose the following hypothesis:

H3b: 

Brand Awareness positively impacts Willingness to Pay Premium in the aviation sector.

Purchase intention is one of the most studied variables regarding consumers’ purchase behaviors, and it represents the likelihood of the customer purchasing the service [16]. It does not translate directly into sales but rather into the intention of buying. The greater the purchase intention is, the lower the chance of the customer changing to a competitor service [14]. Despite purchase intention being the most studied, other vital variables predict purchase-related behaviors, such as repurchase intention and willingness to pay premium. Repurchase intention represents the future intention of purchasing from the same brand/company again [27]; it represents customers’ intentions of engaging in further purchases, and so it is an excellent measure of customers’ satisfaction with the brand, which makes it essential for marketeers to study. Regardless of the importance of purchase intention and repurchase intentions, for this study, we chose to focus on willingness to pay premium, given its importance and relation with customer-based brand equity.

The ability to charge a premium price is one that every brand pursues. Willingness to pay premium represents a client’s disposition to pay a premium price for a brand’s service [28]; it represents the strength of a brand in the industry [29]. A brand scores high on willingness to pay premium when its customers are willing to pay more for their service than for a similar one [29]. It is essential to understand that willingness to pay premium is a relative measure, meaning that it is relevant even for low-cost brands, since customers can be willing to pay more for a low-cost brand than others [30].

This variable is thought to have a central place in branding theories [30]. Some researchers demonstrated that consumers are willing to pay higher prices for brands they perceive as being of higher value [31] and that brand equity influences willingness to pay a premium price for a brand [32], while others stated that customers are willing to pay a price premium if a brand offers unique benefits [33]. Thus, willingness to pay premium was the chosen variable to incorporate in this research, as there seems to be a connection between the model variables and willingness to pay premium as the outcome. Considering everything stated in the literature review above, we can also view CBBE as a mediator between the initial variables and willingness to pay premium. Hence, we can hypothesize the following:

H4: 

Customer-Based Brand Equity positively impacts Willingness to Pay Premium in the aviation sector.

H5: 

Customer-Based Brand Equity serves as a mediator between Brand Image and Willingness to Pay Premium.

H6: 

Customer-Based Brand Equity serves as a mediator between Service Attributes and Willingness to Pay Premium.

H7: 

Customer-Based Brand Equity serves as a mediator between Brand Awareness and Willingness to Pay Premium.

For a long time, competition in the marketplace has been defined based on the notion of brand price tiers [34]. The aviation industry is no different: it is built for different clients, with different budgets, looking for different service levels. Brand price tiers do not only relate to price differences. Brands in different price tiers also have different quality levels, they can differ in terms of marketing strategies and cost elements [35]. For airline companies, it can translate into the service experience offered, such as the service attributes included. We can clearly define two price tiers in this market: low-cost airlines and flag airlines. In the lower tier, we place airlines with typically lower prices and fewer service attributes, where the focus is on offering the central service, the flight, and not the experience and additional commodities of flying. For the higher tier, flag airlines, the focus is not only on the flight itself but also on everything else involved in the service, which translates into a higher ticket price.

The nature of inter-tier competition has become an exciting area for researchers; however, we must remember that different tiers also compete against themselves in unique ways and are compared among similar substitutes [35]. As price is a central variable when defining price tiers, and price sensibility changes for different tiers, we begin to question whether the study variables will have the same effect on willingness to pay premium in low-cost airlines and flag airlines. If we think of the study variables, how they are perceived by the customer can also change when thinking of airlines in different price tiers. The value given to a service can alternate according to the price tier in which the airline is placed. Therefore, in this study, we decided first to study the model for the total market to understand how it behaves for the industry, then focus on understanding if there are differences between the models for both price tiers and how it changes management strategies applied to each of them.

Figure 1 summarizes the conceptual model with customer-based brand equity as a central variable. The model studies the antecedents of CBBE, and willingness to pay premium as one of its favorable outcomes. The conceptual model is drawn based on the Resource-Based View (RBV) theory as a foundational framework for our model. The RBV suggests that a firm’s unique resources and capabilities, including intangible assets like brand equity, contribute to its competitive advantage and superior performance. In our model, brand image, brand awareness, and service attributes represent resources that contribute to building a strong CBBE, which is a valuable intangible asset. A strong CBBE, in turn, enables airlines to achieve a higher WPP, leading to increased revenue and profitability. This integration of RBV theory provides a robust theoretical foundation for our empirical model and strengthens the understanding of how brand resources contribute to firm performance in the airline industry.

3. Materials and Methods

3.1. Data Collection and Sample

The target population was people who travelled by plane in the last 24 months, selected in order to collect opinions about airline companies from clients who use the service more regularly. The sample for the study was obtained through convenience sampling, a non-probability sampling method [6,10,36]. A convenience sampling method was employed for this study due to its practicality for accessing the target population of recent airline passengers. This non-probability sampling technique allowed for efficient data collection through online questionnaires disseminated via social media channels. The online questionnaire (Appendix A) was disseminated through social media profiles and groups related to travelling. In the interest of developing and disseminating the questionnaire, a three-step approach was used. First, the questionnaire was constructed based on the literature review with validated scales. Then, it was reviewed by the study supervisor to ensure it met the initial goals of the study. Finally, it was pilot tested by close respondents to clarify any doubts the questions might leave in the target population. It was later disseminated to the general population. To ensure compliance with ethical requirements, informed consent was obtained from all participants prior to their involvement. The online survey maintained anonymity, preventing direct contact between researchers and participants and ensuring no identifying information was collected. Researchers only had access to anonymized data, safeguarding participant privacy.

To ensure only answers from the mentioned target were taken into consideration, the first question excluded people who had not travelled by plane in the last 24 months. Thus, from a sample of 577 respondents, 489 were taken into consideration for this research, translating into an 84.7% effective response rate. Amongst valid responses, 79.8% were female, and more than 80% were from the age groups 25–44 and 45–65, each of them with more than 40% of the responses. When it comes to education, the majority completed a bachelor’s degree (52.6%), and 36.6% had incomes between EUR 10,000 and EUR 20,000.

3.2. Variables

All variables of this study were measured through existing and validated scales (Appendix B), except for service attributes, which, due to the airline industry’s unique specifications, were constructed based on exploratory research. For the service attributes variable, we focused on the specificities of the airline industry and on the attributes that allow us to distinguish between different airline companies and could give us insights into differences between low-cost and flag airlines. Thus, we proceeded to stipulate the following measure items based on the corresponding service attributes in the literature.

As for the remaining variables, brand image and CBBE were measured through a scale of six and three validated items, respectively, adapted from Chatzipanagiotou et al., [7]. Brand awareness was measured with four items adapted from Loureiro [37]. As for willingness to pay premium, three items were adapted from Chatzipanagiotou et al. [7]. Finally, for service attributes, a mix of service attributes described for the transportation sector [5,38] were used to create six items specific for the airline industry (

Table 1. Service attributes measure items.

Measure Item	Service Attributes
This airline company allocates seats together	Comfort [18]
The flight from this company includes a travel suitcase	Convenience [18]
This airline company provides in-flight meals	Comfort [18]
This airline company allows for online check-in without problems	Convenience [18]
This airline company has a good selection of flight timelines	Route characteristics ([5]
This airline company offers after-sales customer service	After-sales service [5]

). Brand image, CBBE and willingness to pay premium were all measured through an agreement seven-point Likert-type scale with ‘strongly disagree’ at the minimum (1) and ‘strongly agree’ at the maximum (7). For brand awareness, an agreement five-point Likert-type scale was used, with ‘strongly disagree’ (1) and ‘strongly agree’ (5) as the borders. Service attributes, however, were measured through a frequency five-point Likert-type scale with ‘never’ (1) and ‘always’ (5) as the extremes.

4. Results

Our conceptual model was tested using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method through SmartPLS 3 software [39]. This approach enables the estimation of cause-effect relationships between latent variables in path models. Initially, we assessed the validity and reliability of the analysis, focusing on the relationship between latent variables and their measures (measurement model), followed by evaluating the relationships between latent variables (structural model).

In examining the measurement model’s validity and reliability, we scrutinized individual indicators for reliability, internal consistency, convergent validity, and discriminant validity of loadings [40]. All loadings exhibited standardized factors above 0.6, with a minimum value of 0.698, except for service attributes 1 and 2, which were excluded due to not meeting the 0.6 threshold. Loadings with standardized factors exceeding 0.6 were statistically significant (p < 0.001), meeting criteria for individual reliability indicators as per Hair et al. [40].

To confirm convergent validity, three factors had to be verified. First, all factors were identified as positive and significant (individual indicators of reliability). Then, we observed all CR (composite reliability) are higher than 0.7 (

Table 2. Cronbach alpha, composite reliability, average variance extracted, correlations, and discriminant validity checks.

Latent Variables	Cronbach’s Alpha	CR	AVE	1	2	3	4	5
(1) Brand_Awar	0.789	0.864	0.617	0.7855	0.1665	0.1697	0.0745	0.1447
(2) Brand_Im	0.855	0.892	0.5810	0.1284	0.7622	0.6810	0.7014	0.6767
(3) CBBE	0.866	0.918	0.7890	0.1430	0.5903	0.8883	0.5922	0.8228
(4) Service_Att	0.764	0.849	0.5850	−0.0034	0.5881	0.4899	0.7649	0.6110
(5) WPP	0.813	0.891	0.7330	0.1164	0.5628	0.6941	0.4942	0.8562

Note: CR—composite reliability; AVE—average variance extracted; data in bold are the square roots of AVE. Above the bolded diagonal are HTMT ratios and below it are the correlations between constructs.

) [40]. Last, as Table 2 shows, average variance extracted (AVE) for all constructs is higher than 0.5. Thus, convergent validity was confirmed. We can also confirm internal consistency reliability by looking at the values of Cronbach alpha and CR (composite reliability), which are all above the value of 0.7 [40] in Table 2.

Lastly, to confirm discriminant validity, two approaches were followed. First, according to Fornell and Larcker’s criteria, the square root of AVE (placed in the diagonal of Table 2 in bold) for every construct should be bigger than its correlation with any other construct [41], which we can see is met. Then, following heterotrait–monotrait ratio (HTMT) criteria, we see ratios below the conservative threshold of 0.85 (Table 2, above the diagonal values) [40], proving this criterion is followed, and thus, discriminant validity is confirmed.

With the measurement model evaluated, we can proceed to the analysis of the structural model. However, first we need to check for collinearity by verifying if VIF (variance inflation factor) values are lower than 5 [40]. For the model in question, VIF values are between 1 and 1.9, so we can state the values indicate no collinearity and proceed to the structural model analysis.

We can now focus on the assessment of magnitude and significance of the structural path coefficients by analyzing the R2 magnitude and the Stone-Geisser Q2 of the endogenous variables. Looking at the magnitude of R2 for the endogenous variables (determination coefficient), both CBBE and willingness to pay premium have higher than 10% values [42], respectively 38.7% and 52.9%, predicting the model’s accuracy. If we look at Stone-Geisser’s Q2, which measures the relevance of the endogenous variables, we have values above zero [40] for both CBBE and willingness to pay premium (0.375 and 0.350, respectively), predicting the model’s relevance. We can now believe the model and respective variables are of quality and proceed to test the hypothesis.

To test the hypothesis, we need to look at both the structural model assessment (

Table 3. Structural model assessment.

	Original Sample (β)	Standard Deviation	T Statistics	p Values
Brand_Aware_ → CBBE	0.087	0.037	2.341	0.019
Brand_Aware_ → WPP	0.020	0.034	0.601	0.548
Brand_Im → CBBE	0.445	0.044	10.077	0.000
Brand_Im → WPP	0.170	0.047	3.605	0.000
CBBE → WPP	0.523	0.037	14.184	0.000
Service_Att_ → CBBE	0.229	0.046	5.023	0.000
Service_Att_ → WPP	0.138	0.039	3.525	0.000

) and the bootstrap results for indirect effects (

Table 4. Bootstrap results for indirect effects.

	Original Sample ( β )	Standard Deviation	T Statistics	p Values
Brand_Aware_ → CBBE → WPP	0.045	0.020	2.293	0.022
Brand_Im → CBBE → WPP	0.233	0.029	7.999	0.000
Service_Att_ → CBBE → WPP	0.120	0.025	4.767	0.000

). Through the data in these two tables, we will later use p values to understand if the model hypothesis can be proven to have significant impact according to this study. Furthermore, an importance–performance map analysis (Figure 2) will also be performed with the goal of gaining more insights into the model’s behavior and its managerial implications. This analysis enables us to understand which variables perform better in the model, and which are of highest importance when it comes to impacting WPP.

For the second quantitative analysis, the goal is to understand whether the differences in the model’s behavior are significant between price tiers, and for that, PLS-MGA was performed. In order to interpret the multi-group analysis results, we first tested the validity and reliability of the measurement model for the two groups: low-cost airlines and flag airlines. As for MGA results, the hypothesis tests performed to understand whether there are significant differences in terms of behavior in the model for the two price tiers studied are compiled in

Table 5. PLS-MGA path coefficient bootstrap MGA.

	Difference (Flag–Low)	One-Tailed (Flag vs. Low) p-Value	Two-Tailed (Flag vs. Low) p-Value
Brand_Aware_ → CBBE	0.346	1.000	0.000
Brand_Aware_ → WPP	0.154	1.000	0.000
Brand_Im → CBBE	0.326	1.000	0.000
Brand_Im → WPP	−0.031	1.000	0.000
CBBE → WPP	−0.032	1.000	0.000
Service_Att_ → CBBE	−0.194	1.000	0.000
Service_Att_ → WPP	0.054	1.000	0.000

and

Table 6. PLS-MGA total indirect effects bootstrap MGA.

	Difference (Flag–Low)	One-Tailed (Flag vs. Low) p-Value	Two-Tailed (Flag vs. Low) p-Value
Brand_Aware_ → CBBE → WPP	0.221	1.000	0.000
Brand_Im → CBBE → WPP	0.187	1.000	0.000
Service_Att_ → CBBE → WPP	−0.122	1.000	0.000

. Through the last column of two-tailed p-values, in which we can see hypotheses are performing significantly differently between low-cost and flag airlines (p < 0.001 for all hypotheses), we can draw conclusions on the difference’s significance.

Furthermore, as it is not enough to understand the model behaves differently between price tiers, deeper research was conducted to understand how the model performs in each of the price tier groups. In order to make management decisions based on airline price tiers, using the data in

Table 7. Structural assessment for flag airlines.

	Original Sample (β)	Standard Deviation	T Statistics	p-Values
Brand_Aware_ → CBBE	0.186	0.044	4.248	0.000
Brand_Aware_ → WPP	0.031	0.042	0.750	0.454
Brand_Im → CBBE	0.605	0.060	10.061	0.000
Brand_Im → WPP	0.231	0.074	3.110	0.002
CBBE → WPP	0.516	0.055	9.340	0.000
Service_Att_ → CBBE	0.027	0.063	0.420	0.675
Service_Att_ → WPP	0.054	0.060	0.900	0.368

and

Table 8. Bootstrap results for indirect effects for flag airlines.

	Original Sample (β)	Standard Deviation	T Statistics	p-Values
Brand_Aware_ → CBBE → WPP	0.096	0.025	3.902	0.000
Brand_Im → CBBE → WPP	0.313	0.044	7.100	0.000
Service_Att_ → CBBE → WPP	0.014	0.033	0.417	0.677

we can evaluate the model hypothesis for flag airlines, and from

Table 9. Structural model assessment for low-cost airlines.

	Original Sample (β)	Standard Deviation	T Statistics	p-Values
Brand_Aware_ → CBBE	−0.096	0.085	1.125	0.261
Brand_Aware_ → WPP	−0.115	0.073	1.574	0.116
Brand_Im → CBBE	0.374	0.073	5.093	0.000
Brand_Im → WPP	0.173	0.076	2.270	0.023
CBBE → WPP	0.498	0.056	8.848	0.000
Service_Att_ → CBBE	0.105	0.090	1.161	0.246
Service_Att_ → WPP	0.053	0.075	0.713	0.476

and

Table 10. Bootstrap results for indirect effects for low-cost airlines.

	Original Sample (β)	Standard Deviation	T Statistics	p-Values
Brand_Aware_ → CBBE → WPP	−0.048	0.043	1.101	0.271
Brand_Im → CBBE → WPP	0.186	0.042	4.480	0.000
Service_Att_ → CBBE → WPP	0.052	0.046	1.136	0.256

we can evaluate the model hypothesis for low-cost airlines.

Lastly, but just as important, we also conducted an importance–performance map analysis both for flag airlines and low-cost airlines. In both cases, we performed the analysis for the constructs and for the questionnaire items. We can analyze IPMA for flag airline constructs from the data shown Figure 3. For low-cost airlines, IPMA for constructs is shown in Figure 4.

The study findings indicate that brand awareness, brand image, and service attributes positively and significantly impact customer-based brand equity (CBBE) in the aviation sector. Brand image and service attributes also directly influence willingness to pay premium (WPP), while brand awareness does not show a significant impact on WPP. CBBE was found to mediate the relationship between the initial variables and WPP, with varying levels of significance. The study also utilized the importance–performance map analysis (IPMA) to assess the significance and performance of the constructs in influencing WPP for both low-cost and flag airlines.

The analysis revealed significant differences between low-cost airlines and flag airlines in terms of the impact of brand awareness, brand image, service attributes, and CBBE on CBBE and WPP. Brand awareness was more important for flag airlines, while brand image and CBBE were important for both segments. CBBE acted as a mediator between the initial variables and WPP, with different performances between low-cost and flag airlines. For flag airlines, brand image had the most impact on CBBE, while CBBE had the highest influence on WPP. In contrast, low-cost airlines showed support for brand image’s impact on CBBE and WPP, with CBBE being the most influential on WPP.

The IPMA analysis highlighted the importance of CBBE and brand image for both low-cost and flag airlines, with CBBE being more important for low-cost airlines. For flag airlines, brand image was identified as the most important construct, followed by CBBE. In contrast, for low-cost airlines, CBBE was the most important, followed by brand image. Service attributes were also important for low-cost airlines, while brand awareness showed negative importance for this segment.

5. Discussion

Having in mind the data already analyzed, it is now important to draw conclusions from that data. Starting with brand awareness, a significant and positive relationship was identified between brand awareness and customer-based brand equity in the aviation industry. This association had already been modelled by many authors who studied brand awareness as a central variable in building CBBE [7]. This positive relationship also goes in line with the research of Gartner and Ruzzier [24], who studied brand awareness impact on brand equity for tourism destinations. However, this study tested the implications of this relation focusing on airline companies. If we look at brand awareness’ effect on willingness to pay premium, findings from the study in the airline industry were not enough to conclude a significant and positive effect; thus, contrary to what was proposed based on the literature review, deeper studies on this relationship would have to be performed to prove its relevance for the airline industry. Furthermore, a significant relationship was also identified between service attributes and CBBE, and between service attributes and willingness to pay premium. This factor had not yet been studied as an independent variable when building a CBBE model; however, its importance and meaning has been well-studied by many researchers [5,19], including in transportation sectors [18]. As for its influence on WPP, it goes in line with some other researchers’ studies of service attributes’ influence on variables such as purchase intention [21,22]. Thus, this study builds on evidence from previous research and adds service attributes as an important variable when building CBBE and WPP for airline companies. Additionally, the relationship between CBBE and WPP was also found to be significantly positive, proving that CBBE influences the price a customer is willing to pay for an airplane ticket. The influence of CBBE on other purchase-related variables such as purchase intention, (re)purchase intention, and acceptance of a price premium, had already been established [7]. As purchase intention is the most studied variable of those mentioned, it was important to put other variables into perspective, adding value with this study by proving that CBBE also influences WPP, specifically for airline companies.

Looking now at the different price tiers defined for this study (flag and low-cost airlines), we can also draw some interesting conclusions. Many authors have stated that competition is defined based on different price tiers and that marketing strategies differ between price tiers [34,35]. One of the goals for this study was to understand if the model behaved differently between price tiers, which was proven. If the models behave differently, it means that marketeers need to be careful when constructing marketing strategies. This study proves that a marketing or management activity can behave differently and generate different results depending on whether it is applied to a low-cost or a flag airline, adding value to existing research on this subject. If we take the example of brand awareness’ relationship with CBBE in both groups, even though it was proven to influence CBBE positively and significantly for flag airlines, the same does not apply for low-cost airlines, since its significant influence could not be proven with this study. This may indicate that for flag airlines, it is more important to invest in brand awareness than for low-cost airlines. This is also visible through IPMA results. However, this does not mean that brand awareness will not affect CBBE for low-cost airlines, since it was proven significant for the general model. It serves only as an indication of the variations that can exist between both price tiers. Moreover, this study also identified that for flag airlines, brand image was the most influential factor for increasing passenger’s willingness to pay more for a ticket; however, for low-cost airlines, it is CBBE which has the biggest influence. When it comes to performance, there is coherence between price tiers, given that for both, CBBE shows a lower performance level than brand image. CBBE’s lower performance levels represent an investment opportunity for airlines, either low-cost or flag, with the goal of increasing willingness to pay more for a ticket.

5.1. Brand Image’s Decisive Impact on CBBE and WPP for the Airline Industry

Brand image is a well-studied variable, whose positive influence on CBBE was already studied by many authors [4]. Furthermore, its impact on purchase-related variables is also a very well-studied research topic [6,10,14,15,16]. Our study has reaffirmed this positive, and robust, relationship between brand image, CBBE, and WPP, firmly establishing its relevance within the airline industry. This finding brings out a critical point for airline companies: dedicating resources to improving brand image will translate into visible effects on the value consumers place in the service and, thus, on the price they are willing to pay for the ticket, both for low-cost and flag airlines.

Upon reviewing this analysis within the context of two distinct price tiers, it is observable that brand image was the only variable from the model, apart from CBBE, the impact of which was proven to be significantly positive on CBBE and WPP for both low-cost and flag airlines. This imposes brand image as a crucial variable in this model, specifically for the airline industry. If we look at the importance–performance map analysis for the general model (Figure 2), we see brand image as having the most importance for the model, apart from CBBE, as well as having the second highest performance level, meaning that increasing brand image performance, although not easy due to its already high value, is one of the most valuable actions, since it will have one of the biggest impacts on WPP. If we look at the individual IPMA for flag and for low-cost airlines, the brand image construct maintains its major importance for both, even having a bigger impact on WPP than CBBE for flag companies. This proves brand image’s crucial nature, and it translates into a necessity for airlines to build on their image, creating a relationship with their customers. This will allow them to charge a higher price for the same service. The more positive the airline brand image is, the more prone the customer will be to accept a higher price and still want to purchase that airline ticket over other options.

In essence, this study supports existing evidence from different authors, placing brand image as a decisive construct when it comes to both customer-based brand equity and willingness to pay premium, across different price tiers, while extending this understanding to the airline industry.

5.2. The Mediating Role of CBBE

Customer-based brand equity is the central variable of this study, since it is theorized that brand image, brand awareness and service attributes influence willingness to pay premium through the mechanism of CBBE. Confirming this, CBBE has not only been demonstrated to exert a direct positive impact on willingness to pay a premium [30,32], but also to operate as a mediator.

However, when considering the two distinct price tier groups established in this study, a nuanced picture emerges. We could not find significant evidence to describe CBBE as a mediator between service attributes and WPP for either group. This aligns with our previous findings, where the influence of service attributes on both CBBE and willingness to pay a premium was not statistically confirmed in either tier. Similarly, the evidence did not convincingly support CBBE acting as a mediator between brand awareness and willingness to pay a premium for low-cost airlines. This is consistent with earlier discussions which found that brand awareness did not significantly impact CBBE or WPP for this price tier.

Although the full impact of CBBE as a mediator could not be proven through price tiers, if we look at the initial study, we can see this variable can make a significant difference for airline companies. The same applies when studying the model for either price tier, as CBBE is the most important in impacting WPP for low-cost airlines, and second most important for flag companies, making it a crucial variable to pay attention to and invest in. It serves not only as a mediator, but also has a direct effect for airlines in the price they can apply to their tickets. Willingness to pay premium is a crucial variable for managers to take into consideration, and as we can see through this model, CBBE is a very import variable to take into consideration when willingness to pay premium serves as a key performance indicator for management.

6. Conclusions

6.1. Theoretical Implications

The present study contributes to the existing literature in many ways, bringing some exciting new areas of investigation. First, it contributes to the existing literature on customer-based brand equity [7], bringing a new influence variable of service attributes to light and creating more evidence for brand image and brand awareness’ influence. Furthermore, it extends the boundaries of research into CBBE by venturing into relatively unexplored territory, service industries, especially for the airline sector.

Moreover, this study also adds to the current literature by studying a model of CBBE with willingness to pay premium as the outcome. Some other purchase-related variables have already been studied as an outcome of the study variables [7,14,15,16]; however, willingness to pay premium is a less-studied area, particularly when applied to service sectors, and even more when segmenting to the airline sector. This research seeks to bridge this gap.

In addition, the present study also shares new insights into how different price tiers have inequalities when it comes to the CBBE model’s behavior. This opens existing research to further enrichment on these differences and their implications for management. Many authors have studied brand price tiers [34,35], theorizing about the differences between competing inside and outside a price tier. This study proves that different price tiers should invest in different aspects to have better return, and that has an impact on how they compete inside and outside their price tier. Thus, the study builds on the existing research by proving that price tiers should be considered when it comes to the constructs’ influence on WPP, as they should impact management strategies. It builds upon these foundational insights to clarify the need for tailored management strategies, by investing in different aspects of the brand.

6.2. Managerial Implications

The model developed for this study is of high value for making managerial decisions for airline companies. Each airline company certainly has its own key performance indicators and business models; however, being able to translate the high value of a ticket to the consumer is no doubt a concern to them all. With this in mind, the study model brings substantial insights to help airline company managers make business and marketing decisions, by enlightening them on where to invest for different airlines.

To begin with, the model provides a visual representation of the importance and the impact of brand awareness, service attributes and brand image on customer-based brand equity and willingness to pay a premium price, which were proved by the analysis. This implies that investing in improving customers’ perception of the airline regarding brand awareness, brand image, service attributes, and customer-based brand equity will impact their perception of the airline’s value and, consequently, their willingness to pay a premium price for a flight ticket. Thus, the study defines key areas for airlines to invest in in order to increase willingness to pay premium for a ticket. WPP is a crucial key performance indicator, and to keep its good performance, it is necessary to pay close attention to the mentioned variables, as they are essential but also very volatile.

Another important managerial take on this study is that brand awareness, brand image, service attributes and customer-based brand equity are all important in enhancing willingness to pay a premium. According to the analysis disregarding price tiers, CBBE is the variable for which an increase in performance will have the most significant impact on WPP. Besides, due to its low performance value, it is a key area for management to invest in. When looking just at flag airlines, we see brand image as the construct with most impact on WPP, followed by CBBE. For low-cost airlines, CBBE is the most important, followed by brand image, as in the initial model. We can see that there was a switch in the importance parameter. Still, this means that CBBE is one of the best areas to invest in, along with brand image, and that price tiers should be taken into consideration when choosing where to invest. As we already saw, the model behaves differently between flag and low-cost airlines. There is a clear differentiation for management when pondering where to invest. Each airline should invest in areas where the expected return is higher.

Moreover, brand awareness is the study variable with the lowest impact on WPP, having however the highest performance. This means that increasing customers’ perception of the airline’s brand awareness will result in the lowest outcome for WPP from the study variables, if we disregard the price tiers. Besides, since it already shows a high performance, investing in increasing it would not be easy. Although all variables of the model are crucial to invest in, as they all proved their importance to the model, CBBE and brand image should be set as priorities.

In conclusion, this model highlights areas for management to invest in to increase willingness to pay a premium price for an airplane ticket. It also shows some differences between the flag and low-cost airlines, allowing management to make better decisions.

6.3. Limitations and Future Research

This study has certain limitations. First, the use of convenience sampling, while offering efficiency, may limit the generalizability of findings to the broader population of airline passengers. Future research could employ probability sampling techniques to address this limitation and enhance the representativeness of the sample. Second, although the model demonstrated significant differences between the two price tiers, some hypothesized relationships could not be fully confirmed within each tier. This limitation highlights the need for further investigation into the specific behaviors of CBBE and its impact on WPP within different price tiers in the airline industry and other service sectors. Finally, the service attribute measures were specifically tailored to the airline industry, potentially limiting their applicability to other service sectors. Future research could refine these measures for broader applicability and explore additional service attributes relevant to various service industries.