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What drives loyalty in the airline industry?
A focus on Generation Y in the Dutch market
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What drives loyalty in the airline industry?
A focus on Generation Y in the Dutch market
Sander Matthijs Ossenkoppele University of Groningen Faculty of Economics & Business Master thesis Marketing Management
January 10, 2016 Maasstraat 99-2, 1078 HG Amsterdam Tel: +31(0)634503464 E-mail: sanderossenkoppele@gmail.com Student number: S1892401 Supervisors University of Groningen First supervisor: Dr. J.T. Bouma Second supervisor: Dr. Ir. M.J. Gijsenberg
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Management summary
The airline industry has changed significantly overtime and has become a difficult and highly competitive market. The traditional European full service network carriers (FSNC) experience major competitive pressure from low cost carriers who have common denominators such as efficiency, productivity and cost leadership which lead to inexpensive fares. In addition, airlines from the Middle East such as the Emirates, Etihad Airways and Qatar Airways are able to deliver high service quality for competitive prices. With high competitive pressures it is of great importance that FSNC’s airline managers make the right strategic choices. A customer-centric approach allows these airline managers to identify the customers that add most value to the airline. Since customer loyalty has been found to be a major driver of customer profitability, determining the drivers of customer loyalty may be of great importance for airline managers. Our research has been performed with a FSNC in the Dutch airline industry and has focused on generation Y (persons born between 1980 and 1994). Since generation Y has been identified as the travelers of the future, determining the drivers of loyalty for this generation can be of great importance for airline managers. The research question we answer is: “What drives loyalty in the airline industry?” – A focus on generation Y in the Dutch market.
In defining and operationalizing customer loyalty two schools of thought emerge; attitudinal and behavioral loyalty. Attitudinal loyalty can be defined as a customer predisposition towards a brand which is a function of the psychological process. Attitudinal loyalty can be measured through measures such as the net promoter score (NPS), satisfaction and repurchase intention. Behavioral loyalty is based on the assumption that repeat purchasing can capture loyalty of a consumer. Behavioral loyalty can be measured through a customer’s share of wallet. Based on earlier academic research and data from a FSNC in the Netherlands, we have selected eight airline attributes to be potential drivers of loyalty; boarding, check-in, comfort and cabin, crew, food and beverages, in-flight entertainment, post-flight and value for money.
4 the previously identified strongest drivers of attitudinal loyalty; crew, post-flight and value for money on behavioral loyalty.
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Preface
As it turned out to be, I managed to complete my Master’s thesis. After years of studying in Groningen, I can conclude that completing this final step comes with relief.
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Table of contents
Management summary ... 3
Preface ... 5
1. Introduction ... 8
1.1 Why customers matter ... 8
1.2 Airlines ... 10
1.3 Focus ... 11
1.4 Research questions... 12
2. Literature review ... 14
2.1 Customer loyalty ... 14
2.2 Drivers of airline loyalty... 16
2.3 Generation Y ... 17 2.4 Hypotheses ... 19 2.4.1 Ground activities ... 19 2.4.2 In-flight activities ... 21 3. Research design ... 24 3.1 Data collection ... 24
3.2 Measurement and scaling ... 25
3.2.1 Dependent variable ... 25 3.2.2 Independent variables ... 25 3.2.3 Mediator variables ... 26 3.2.4 Control variable: ... 26 3.3 Mediation analysis ... 27 4. Results ... 29 4.1 Sample characteristics ... 29 4.2 Normality assumptions ... 29 4.3 Control variable ... 31 4.4 Mediation results ... 32 4.4.1 Route-c ... 32 4.4.2 Route-a ... 32
4.4.3 Route-b and route-c’ ... 34
4.4.4 Bootstrapping ... 35
4.5 Discussion ... 36
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4.5.2 Route-a ... 37
4.5.3 Route-b and route-c’ ... 38
4.5.5 Mediation ... 39
5. Conclusions ... 41
5.1 Academic relevance... 41
5.2 Managerial implications ... 42
5.3 Limitations and future research ... 43
6. References ... 44
7. Appendices ... 53
7.1 E-Score questionnaire ... 53
7.2 Factor analysis independent variables ... 54
7.3 Results MEDIATE-NPS ... 58
7.4 Results MEDIATE-Satisfaction ... 61
7.5 Results MEDIATE-Repurchase intention ... 64
7.6 NPS regression results model ... 67
7.7 Satisfaction regression results model ... 68
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1. Introduction
1.1 Why customers matter
The marketing function has undergone dramatic shifts in the past years (Sheth, Sisodia and Sharma 2000). The product-centric approach has made place for the customer-centric approach. It is relationship oriented and the basic philosophy is that all decisions start with the customer (Shah et al. 2006). Within the customer-centric approach, concepts and metrics such as customer equity (Gupta et al. 2006), customer satisfaction (Heskett et al. 2008), perceived quality (Boulding et al. 1993) and share of wallet (Du, Kamakura and Mela 2007) have been used to drive marketing management. The emphasis on short-term transactions has made place for a focus on the development of long-term customer relationships (Storbacka, Strandvik and Gronroos 1994). It is relevant because strong customer relationships contribute to achieving a competitive advantage and are found to increase customer loyalty and market share (Chen and Hu 2013).
Innovation has played a big part in the development of the customer-centric approach. Due to the information technology (IT) revolution in the latter part of the 20th century, collecting, storing, analyzing, and transmitting large amounts of information has become more accessible. The IT-revolution has created an opportunity for companies to invest in technology to manage customer relationships (Shah et al. 2006). Companies started investing in customer relationship management (CRM) systems software in order to create a continuing dialogue with the customer (Reinartz and Kumar 2002; Verhoef 2003). CRM can be defined as building and maintaining profitable customer relationships by delivering superior value and satisfaction. It deals with all the aspects of acquiring, keeping and growing customers (Kotler and Keller 2005). Proper use of CRM offers managers the possibility to identify the factors of their services that satisfy customers (Chen 2008). The positive relationship of customer satisfaction with company profitability has been discussed and proved in Anderson, Fornell and Lehmann (1994), Heskett et al. (2008) and Storbacka, Strandvik and Gronroos (1994).
9 create an indication of the profitability of the individual customer (Gupta et al. 2006). CLV measures how changes in customer behavior (e.g. increased purchase, retention) could influence the customers’ future profits, or their profitability to the firm (Zhang, Dixit and Friedmann 2010). CE and CLV acknowledge the fact that customers are heterogeneous and have different values to the profitability of a company. After companies have identified the values of their individual customers, marketing actions can be made accordingly. So knowing the CLV of individual customers enables a firm to improve its customer selection, customer segmentation and marketing resource allocation. Therefore, CLV allows identifying the most valuable customers of the company, which is important, since customers function as the company’s most important asset (Rust, Lemon and Zeithaml 2000) or as the most reliable source of future revenues and profits (Lemon, Rust and Zeithaml 2001).
In research by Chen (2008), Ellinger, Daugherty and Plair (1999), and Heskett et al. (2008), customer profitability has been found to be driven by customer loyalty. In addition, Cooil et al. (2007) and Wirtz, Matilla and Lwin (2007) found that customer loyalty can for companies be a key indicator for competitive advantage. Customer loyalty has been defined by Oliver (1999) as a deeply held commitment to rebuy or re-patronize a preferred service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior. Since acquiring new customers is more expensive than retaining current customers (Buttle and Ahmad 2002), companies try to retain their customers through tactics such as customer loyalty programs. According to Beck, Chapman and Palmatier (2015), loyalty programs have relationship building as a goal, they typically include a variety of marketing initiatives to positively influence the customer’s attitude towards the brand. For competitive global markets loyalty programs are key differentiators. Although effective, most customers are “polygamous” or loyal to a portfolio of brands and only a few customers are “monogamous” or “promiscuous” (Uncles, Dowling and Hammond 2003).
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1.2 Airlines
In 1903 the first successful flight was made by the Wright brothers, 112 years later; aviation has become an essential part of our global infrastructure. Providing rapid connections between the world’s cities by air has enabled the globalization that has shaped modern business and the experiences of individuals. Airlines and the wider air transport supply chain clearly create substantial value for consumers and the broader economy (Tyler 2014). 3.3 billion passengers were carried by the aviation industry in 2014 and the number is expected to increase towards 7.7 billion passengers in 2034 (International Air Transport Association 2015). Taking this forecast into account, the airline industry is a growth market, but the industry is difficult and highly competitive. Over the past 30-40 years the airline industry has generated one of the lowest returns on invested capital among all industries (International Air Transport Association 2015). Difficulty has risen due to the impact of the bursting of the technology bubble, the 9-11 terrorist attack and SARS. In addition, the global financial crisis of 2008 followed by the deepest economic downturn since the 1930s (International Air Transport Association 2015).
Over time the competitive landscape has changed tremendously with a rapid growth of low cost carriers (LCCs), high-speed railways, rising fuel costs, fluctuating demand, and tighter security, safety and quality requirements (Vlachos and Lin 2014). The traditional airline business model was based on a “full service network carrier” (FSNC), also known as a “legacy airline”. A FSNC is an airline that focuses on providing a wide range of pre-flight and onboard services, including different service classes and connecting flights. Since most FSNCs operate a hub-and-spoke model, this group of airlines are usually also referred to as hub-and-spoke airlines (International Air Transport Association 2015). Carriers that follow this hub-and-spoke model in Europe are e.g. Air France-KLM and British Airways.
11 FSNCs. For European FSNCs competitive pressure also comes from countries such as Qatar and the United Arabic Emirates. From these countries; legacy airlines such as Etihad Airways, Emirates and Qatar Airways are becoming more and more dominant between Europe and Asia. With a focus on very high quality service they receive large amounts of money from their governments and are able to ask competitive prices (Het Financieel Dagblad 2015).
It appears that carriers have chosen to either focus on cost leadership (e.g. LCC) or on differentiation (e.g. FSNC). According to Porter (1980), on an industry level, these two strategies are indeed profitable but when there is no clear strategy a company becomes stuck in the middle which can result in a large competitive disadvantage. Whether European FSNCs are indeed becoming stuck in the middle is a question not to be answered in this research. What must be acknowledged is that pressures are high and a solid strategy is necessary for the future. Whereas the importance of customer loyalty has been discussed, this study aims to extend the literature on customer loyalty in the airline industry as proposed by Chang and Hung (2013). In specific, Vlachos and Lin (2014) suggested more research on drivers of airline loyalty in different contexts and countries. Since the Dutch market remains relatively underexplored, this research on customer loyalty in the airline industry will focus on the Dutch market.
1.3 Focus
Schiphol airport is in the European airline industry one of the biggest airports and located in the Netherlands. The airline industry in the Netherlands has been valued at 5.6 Billion dollars in 2015 which is 3.9% percent of the total European airline industry value. Marketline (2015) has reported that in the Netherlands the fixed costs associated with an airline from the aircraft, fuel, skilled and unskilled staff, insurance, to airport fees, duties and taxes, ensures rivalry remains heated as airlines fight to protect their profit margins while maintaining and growing revenues. Leading airlines in the Dutch market are Air France-KLM, EasyJet, Ryanair and the TUI group. Marketline (2015) has concluded that in the Netherlands, low-cost switching facilitated by price comparison websites and mobile apps, and a general lack of differentiation (primarily in regards to coach class) also fuels rivalry as buyers switch from airline to airline on the basis of price.
12 common purchasing and consumption behavior (Howe and Strauss 2000). On the basis of this assumption consumer segmentation can be used (Gurau 2012; Kumar and Lin 2008; Noble, Haytko and Phillips 2009). Several generations such as the traditionalists, the babyboomers, generation Y, generation X and generation Z have been identified. For reasons that will be explained in a further section of the research paper we focus on Generation Y.
Generation Y has been defined as the persons born between 1980 and 1994 (Central Bureau of Statistics 2015). Researching this generation can be specifically important for marketers since generation Y has substantial discretionary buying power relative to their incomes (Gurau 2012) and reaching young people at an early age often forms the basis for a relationship upon which other services may be cross-sold over time as a deeper and a more profitable relationship is engendered (Twaites and Vere 1995). Martin and Turley (2004) observe that little is known about consumption patterns and market place behaviors of generation Y. In the Netherlands, generation Y accounts for 3.108.000 persons which is on a total population of 16.748.000 persons more than 18% (Statline 2014). Key features of generation Y as travelers include the following: they travel more often, explore more destinations, spend more on travel, book more over the internet, are hungry for experience, hungry for information, are intrepid travelers and want to get a lot out of their travel (Leask, Fyall and Barron 2014; Richards 2007). With regard to customer loyalty, the academic researchers have not found a general consensus, therefore even more interesting to research.
1.4 Research questions
13 players in the Dutch airline industry. Specifically, it will seek to answer the following research question: What drives airline loyalty for generation Y in the Dutch market?
Sub questions:
- Are there differences in attitudinal and behavioral loyalty? - How to measure loyalty?
- What drives loyalty for generation Y? - What drives airline loyalty?
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2. Literature review
2.1 Customer loyalty
Customer loyalty as a driver for company profits has been extensively researched in Beck, Chapman and Palmatier (2015), Chen (2008), Dick and Basu (1994), Ellinger, Daugherty and Plair (1999), Heskett et al. (2008), Loveman (1998) and in Kumar and Shah (2004). In defining and operationalizing customer loyalty, two schools of thought emerge; the first school of authors has defined loyalty based on the behavioral perspective (Uncles, Dowling and Hammond 2003). In this work, a behavioral measure is used in order to operationalize loyalty. It is based on the assumption that repeat purchasing can capture loyalty of a customer. Within the behavioral perspective, researchers have made conclusions based on proportion of purchases such as share of wallet (Cooil et al. 2007; Du, Kamakura and Mela 2007) while others have focused on the purchase sequence (Bandyopadhyay and Martell 2006). Behavioral loyalty is the observable outcome of attitudinal loyalty (Bandyopadhyay and Martell 2006) and without a knowledge and understanding of the attitude towards the act of buying the brand it is difficult to design marketing programs to modify behavioral loyalty (Bennet and Rundle-Thiele 2002). Therefore, identifying the drivers of attitudinal loyalty can help properly allocate resources among marketing tactics (Seetharaman 2004) and enable the creation of customized marketing programs for maximum effectiveness (Liu-Tompkins and Tam 2013).
The second school of authors has defined loyalty based on an attitudinal approach. It can be defined as a customer predisposition towards a brand which is a function of the psychological process (Jacoby and Chestnut 1978). Attitudinal loyalty concepts infer that consumers engage in extensive problem-solving behavior involving brand and attribute comparisons leading to strong brand preferences (Oliver 1999; Rundle-Thiele 2005). Oliver (1999) stresses that a consumer can become loyal at four different attitudinal phases. In his four stage loyalty model the first phase a consumer goes through is cognitive loyalty followed by affective loyalty, conative loyalty and eventually leads to action loyalty. Especially in order to find drivers of loyalty attitudinal loyalty seems to be the most appropriate choice. Jacoby and Chestnut (1978) suggest a combination of attitudinal and behavioral loyalty in order to create a complete understanding of customer loyalty.
15 consumption fulfillment including levels of under- and over fulfillment. In other words, satisfaction can be seen as an attitude towards the brand that has been developed based on the cumulative satisfying usage occasions; a form of affective loyalty as defined in Oliver (1999). In Oliver (1999), conative loyalty implies a brand-specific commitment to repurchase which is often operationalized by the measures from Zeithaml, Berry and Parasuraman (1996); repurchase intention and word-of-mouth intention. Repurchase intention and word-of-word-of-mouth intention have different consequences that both lead to positive firm performance (Vlachos and Lin 2014). Due to the fact that customer loyalty measures, such as satisfaction, repurchase intention and word-of-mouth intention, combined are of limited value and since there is no form of loyalty that constantly predicts all the different loyalty outcomes (East et al. 2005), the three measures have to be treated separately. In Vlachos and Lin (2014), attitudinal loyalty for has been measured through word-of-mouth intention, satisfaction and repurchase intention.
In order to measure behavioral loyalty, a common problem for companies is the fact that firms lack individual-level, industrywide customer data; they seldom have information about their customers’ relationships with competitors. In general it is infeasible to obtain transaction records from competing firms but firms can obtain external relationship data for a small sample of their customers through customer surveys or other secondary sources (Du, Kamakura and Mela 2007). In this research, the principle share of wallet has been used as a measure for behavioral loyalty. Share of wallet is the percentage of a customer’s spending within a category that’s captured by a given firm
(Keiningham et al. 2011). It is relevant because it measures company versus industry expenditure and gains insights on the loyalty with the company. In the airline industry, share of wallet has been measured in research by Voorhees et al. (2015) and Wirtz, Matilla and Lwin (2007).
For measuring attitudinal loyalty, satisfaction and repurchase intention as discussed in Vlachos and Lin (2014) have been used. In addition, a form of of-mouth intention will be used. Since word-of-mouth intention deals with the potential recommendation of a product/service; it can be seen as a form of recommendation intention. Since the net promoter score (NPS) measures the intention of recommendation the measure can be used as a measure for attitudinal loyalty. Based on the literature discussed above, I propose the following hypotheses:
H1a. Net promoter score has a positive effect on share of wallet.
H1b. Satisfaction has a positive effect on share of wallet
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2.2 Drivers of airline loyalty
To measure drivers of attitudinal loyalty, researchers have used cognitive evaluations of key attributes of airline services (Chang and Hung 2013; Dolnicar et al. 2010; Vlachos and Lin 2014). Cognitive evaluations of key attributes of airline services can be seen as consumers’ fulfillment responses on an attribute-level and thus as attribute-level satisfaction (Oliver 1997). Attribute-level analysis provides higher specificity than a global evaluation approach (Mittal, Kumar and Tsiros 1999), therefore the attribute-level approach may provide more useful information to the managers making investment decisions (Chao 2008). Attribute-level analyses on customer satisfaction and customer loyalty in the airline industry have focused on perceived airline service quality (Chen 2008; International Air Transport Association 2015; Ostrowski, O’Brien and Gordon 1993; Zeithaml, Berry and Parasuraman 1996). For attribute analyses in the airline industry, several frameworks have been used. The frameworks for measuring airline loyalty will be discussed including: Airs@t, SERVQUAL, the Kano model and the travel experience as a service process model.
The International Air Transport Association (2015) has developed Airs@t, an attribute-level passenger satisfaction benchmarking survey designed specifically for airlines. This in-depth research covers all travel service aspects of the pre-flight, in-flight and post-flight passenger travel experience over more than 50 measured attributes.
In the academic literature, different attribute-level frameworks have been developed. Parasuraman et al. (1988) has developed the multiple-item scale SERVQUAL. Based on five dimensions; tangibles, reliability, responsiveness, assurance and empathy, the model measures the expectations of the service quality compared to the perception of the actual service received. The SERVQUAL has been one of the most widely used and applied scales for the measurement of perceived service quality in recent years (Humnekar and Phadrtare 2011). In the airline industry, the SERVQUAL scale has been extensively used (Chen 2008; Chen and Hu 2013; Huang 2009), where airline service attributes have been used as service quality dimensions.
17 satisfied the customer and conversely less functionality results in more dissatisfaction (Shenin and Zairi 2009). Attractive (exciting or delighter) requirements are requirements beyond customer’s expectations. Kano, Takahashi and Tsuji (1984) refer to this type of quality as surprising quality and it is associated with emotion.
In research by Gustafsson, Ekdahl and Edvardsson (1999) on customer focused service development on Scandinavian Airlines, a model has been proposed based on the entire travel process of a passenger or as it will be called, the travel experience. In this study the service process categorizes passengers’ encounters with the airline on either ground services or in-flight services. From a travel experience perspective, three types of phases have been identified: ground pre-flight, in-flight, and post-flight. During these phases the tasks that customers had to perform were categorized alongside three parts; procedural, personal and planning and preparation activities. Procedural activities consist of the mandatory and highly prescribed activities that passengers must perform during the phases of the travel experience. While procedural activities are induced by system requirements, personal activities are instead necessary because everyday chores must be attended to. In the travel experience model as a service process the following encounters have been identified: check-in, lounge, gate, in-flight and baggage claim.
This research will make use of the model as proposed by Gustafsson, Ekdahl and Edvardsson (1999) focusing on generation Y in the Dutch market. Before further development of hypotheses, I will review generation Y as consumers and as travelers.
2.3 Generation Y
Generation Y has been defined in multiple ways. The Australian Bureau of Statistics (2015) classify generation Y as a person born in any year between 1983 and 2000 whereas in the UK and US it refers to a person born in any year between 1980 and the 1990s (Census United States Bureau 2015; Office for National Statistics 2014). The Dutch Central bureau of Statistics (2015) defines generation Y as the persons born between 1980 and 1994. Since the research will be conducted in the Netherlands among the Dutch generation Y, the definition as provided by the Central Bureau of Statistics (2015) will be used. In Aruna and Santhi (2015) and Paul (2001) this definition has been used as well. While researchers have split up generation Y based on age, Leask, Fyall and Baroon (2014) found that as travelers, generation Y can and should be viewed as a single market segment.
18 educated and most culturally diverse generation in history (Pokrywczynski and Wolburg 2001), a combination which others believe has made this generation exceedingly tolerant and open-minded toward different lifestyles such as homosexuality, single parent house-holds etc. (Morton 2002; Paul 2001). People in this age group have been accustomed to using computers from an early age (Allerton 2001) and therefore make intensive use of electronic media (Murray 2000). With regard to purchasing power, this generation has been reared in a consumption-driven society and has more money at their disposal than any teen group in history (Morton 2002).
However, Noble, Haytko and Phillips (2009) have concluded that for generation Y a lack of understanding exists regarding the consumption patterns. Limited findings seem to paint a portrait of a generation that is media- and technology-savvy and worldly enough to see through many advertising tactics. Although generation Y as a consumer seems underexplored, generation Y travels more often, explores more destinations and spends more on travel than any other generation (Leask, Fyall and Barron 2014). While traveling more often, Meng and Uysal (2008) found that finances required for travel influenced travel behavior. As a consumer group Heaney (2007) has indeed concluded that generation Y is very astute and very well informed and on the lookout for bargains. Phillips’ (2007) findings outline that generation Y considers themselves as rationally-oriented consumers for which price and product features are more important than brand names. In Gurau (2012) this finding is strengthened by that fact that although generation Y spends more than previous generations, their brand loyalty levels are lower due to a focus on price and product features.
19 Bruce Tulgan (2009) states: “Generation Y can be very loyal. But they don’t exhibit the kind of loyalty you find in a kingdom: blind loyalty.”
So based on the previous research we can conclude that for generation Y to become loyal, high demands must be answered and mistakes will be punished. In conclusion, generation Y is price sensitive but there lacks a general consensus on whether generation Y can be classified as being loyal or not. Therefore, it is even more interesting to research customer loyalty in the airline industry for generation Y. As a result of this literature review hypotheses have been formed based on the most important attributes, selected and divided along the categories as proposed by Gustafsson, Ekdahl and Edvardsson (1999).
2.4 Hypotheses
The entire travel process of a passenger, or as it will be called the travel experience, has been used in this research as proposed by Gustafsson, Ekdahl and Edvardsson (1999). Based on the division of ground- and in-flight services several customer-airline encounters have been selected. Firstly, the ground activities will be discussed, followed by the in-flight activities. Based on these different encounters customers experience during their trip, the hypotheses have been formed.
2.4.1 Ground activities
Ground activities can be split up into pre-flight activities and post-flight activities. Pre-flight activities that have been identified by Gustafsson, Ekdahl and Edvardsson (1999) are: check-in, lounge and gate, while the post-flight activity deals with the baggage claim.
20 Airs@t has used these three touch-points for their benchmark analyses extensively (International Air Transport Association 2015). Check-in, boarding and post-flight experience can be seen as procedural activities since they are mandatory and highly described activities that a passenger must perform in the travel experience. Procedural activities can therefore be seen as operational factors of an airline as defined in Vlachos and Lin (2014). In spite of the essential nature of these activities, they are often the least understood by the passengers (Gustafsson, Ekdahl and Edvardsson 1999). They are similar to the must-be attributes defined by Kano, Takahashi and Tsuji (1984) whereas these elements of the service are crucial in order to be even considered as a supplier. These elements are often so basic that customers would not state them unless the service sector fails to perform them (Cheng Lim, Tang and Jackson 1999); but their absence is very dissatisfying (Shahin and Zairi 2009).
Fields (2008) has reported that level of service provided for Generation Y is of utmost importance; in addition, they also look to have all these aspects encapsulated in some sort of ‘experience’ rather than just engaging in a basic and rather mundane purchase transaction. Therefore, I hypothesize:
H2a. Generation Y’s perception of an airline’s check-in has a positive effect on share of wallet.
H2b. Generation Y’s perception of an airline’s check-in has a positive effect on NPS.
H2c. Generation Y’s perception of an airline’s check-in has a positive effect on satisfaction.
H2d. Generation Y’s perception of an airline’s check-in has a positive effect on repurchase intention.
H3a. Generation Y’s perception of an airline’s boarding has a positive effect on share of wallet.
H3b. Generation Y’s perception of an airline’s boarding has a positive effect on NPS.
H3c. Generation Y’s perception of an airline’s boarding has a positive effect on satisfaction.
H3d. Generation Y’s perception of an airline’s boarding has a positive effect on repurchase intention.
H4a. Generation Y’s perception of an airline’s post-flight experience has a positive effect on share of wallet.
H4b. Generation Y’s perception of an airline’s post-flight experience has a positive effect on NPS.
H4c. Generation Y’s perception of an airline’s post-flight experience has a positive effect on satisfaction.
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2.4.2 In-flight activities
In between the pre-flight and post-flight services, the in-flight services have been identified by Gustafsson, Ekdahl and Edvardsson (1999). Whereas Gustafsson, Ekdahl and Edvardsson (1999) encapsulates the flight experience on the variable in-flight, this research will elaborate on the several aspects a passenger experiences during the flight. It has been argued that the comfort of the flight is one of the basic requirements of an airline (Gustafsson, Ekdahl and Edvardsson 1999). Vlachos and Lin (2014) argue that a comfortable flight is one of the operational factors of an airline. The Airs@t survey has used comfort in combination with the cabin of the airplane (e.g. comfort and cabin of flight). Therefore, I hypothesize that:
H5a. Generation Y’s perception of an airline’s comfort and cabin has a positive effect on share of wallet.
H5b. Generation Y’s perception of an airline’s comfort and cabin has a positive effect on NPS.
H5c. Generation Y’s perception of an airline’s comfort and cabin has a positive effect on satisfaction.
H5d. Generation Y’s perception of an airline’s comfort and cabin has a positive effect on repurchase intention.
One of the main factors identified by the Airs@t benchmark survey is the experience with the cabin crew. In academic research the passengers’ perception of the courtesy and the responsiveness of the flight attendants have been significant drivers of airline loyalty (Vlachos and Lin 2014). In addition, Shahin and Zairi (2009) have defined in-flight staff service as an important aspect of the flight. Generation Y loves interaction and loves to voice their opinions and be actively engaged in coming up with solutions (Manpower 2014). Therefore I hypothesize that:
H6a. Generation Y’s perception of an airline’s in-flight crew has a positive effect on share of wallet.
H6b. Generation Y’s perception of an airline’s in-flight crew has a positive effect on NPS.
H6c. Generation Y’s perception of an airline’s in-flight crew has a positive effect on satisfaction.
H6d. Generation Y’s perception of an airline’s in-flight crew has a positive effect on repurchase intention.
22 as in-flight newspaper, books, quality of food and drinks and in-flight entertainment. Shahin and Zairi (2009) defined that in-flight staff service and in-flight food and drinks were attractive requirements. Generation Y focuses on the importance of a healthy lifestyle. The group believes that organic food is important to being healthy and shoppers tend to be consumers of organic foods and beverages (NMI and Nielsen Health report 2015). In addition, generation Y makes intensive use of electronic media (Murray 2000), and has become fully accustomed to being online and having technological opportunities at hand at all times. Therefore I hypothesize that:
H7a. Generation Y’s perception of an airline’s in-flight food and beverages has a positive effect on share of wallet.
H7b. Generation Y’s perception of an airline’s in-flight food and beverages has a positive effect on NPS.
H7c. Generation Y’s perception of an airline’s in-flight food and beverages has a positive effect on satisfaction.
H7d. Generation Y’s perception of an airline’s in-flight food and beverages has a positive effect on repurchase intention.
H8a. Generation Y’s perception of an airline’s in-flight entertainment service has a positive effect on share of wallet.
H8b. Generation Y’s perception of an airline’s in-flight entertainment service has a positive effect on NPS.
H8c. Generation Y’s perception of an airline’s in-flight entertainment service has a positive effect on satisfaction.
H8d. Generation Y’s perception of an airline’s in-flight entertainment service has a positive effect on repurchase intention.
23 Generation Y considers themselves as rationally-oriented consumers for which price and product features are more important than brand names. Therefore I hypothesize that:
H9a. Generation Y’s perception of an airline’s value for money has a positive effect on share of wallet.
H9b. Generation Y’s perception of an airline’s value for money has a positive effect on NPS.
H9c. Generation Y’s perception of an airline’s value for money has a positive effect on satisfaction.
H9d. Generation Y’s perception of an airline’s value for money has a positive effect on repurchase intention.
Based on the literature as discussed and the hypotheses formed, I propose the following conceptual framework in figure 1.
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3. Research design
3.1 Data collection
In order to test the hypotheses and therefore examine the relationships between the identified variables a dataset from a FSNC in the Netherlands has been used. This form of research has been identified as descriptive conclusive research (Malhotra 2009) and the outcome of the analysis might be used as input into decision making. For gathering the data the FSNC randomly selects a proportion of the passengers that have travelled the day before and invites these passengers to fill in an online survey. The survey method is based on the questioning of respondents with regard to e.g. behavior, intentions and attitudes (Malhotra 2009) and the FSNC mainly uses fixed-alternative questions that require respondents to choose from a set of predetermined answers which are treated with full anonymity. After the passenger has received the request to fill in the questionnaire, one reminder invitation is sent to increase the number of responses. Passengers that have received a request to fill in the survey in the last twelve months will be excluded from the sampling making sure that each passenger can fill in the survey only once a year. Different versions of the questionnaire are being used by the FSNC and this way the questionnaire length is kept under control while at the same time getting all the detailed information needed (when combining all the questionnaires and versions in the database). Each month, over 100.000 passengers of the FSNC fill in the online questionnaire. The dataset that has been used in this research covers the period of July 2013 to October 2015 and because it has involved the collection of information from a sample of population elements only once, it has been classified as a single cross-sectional design (Malhotra 2009). Since this research focuses on generation Y in the Dutch leisure market, only Dutch leisure passengers born in the period 1980-1994 have been included resulting in a sample size of 21.164 respondents.
25 logically inconsistent or have extreme values (Malhotra 2009). These alterations to the dataset have resulted in a sample size with a total of 391 respondents which is sufficient to perform mediation analysis (Warner 2012). Other studies have used similar sample sizes for their mediation analysis (Durand et al. 2013) which is a consideration for appropriate sample sizes according to Malhotra (2009). The dataset consists of multiple variables that have been used in the research including; one dependent variable, eight independent variables and three proposed mediators.
3.2 Measurement and scaling
The questionnaire that has been used for the research can be found in 7.1 of the appendices. The following part of the research paper explains the measurement and scaling of the variables that have been used in the analysis based on the conceptual model as displayed in figure 1.
3.2.1 Dependent variable
The dependent variable share of wallet has been used as the measure of behavioral loyalty. Share of wallet is the percentage of a customer’s spending within a category that’s captured by a given firm
(Keiningham et al. 2011). The variable has been based on two questions: “How many trips by air did you take in the last 12 months excluding this trip?” and “How many of these trips were with the airline?” Since the direct ratio of the two questions excludes the latest trip the following formula adjusts the ratio with regard that the latest trip has been included. For measurement the following formula has been used: Share of wallet = (Number of trips airline + 1) / (Aviation trips per year + 1). The percentage has been treated as an interval scale ranging from 0-100%.
3.2.2 Independent variables
In the theory section eight independent variables have been identified; boarding, check-in, crew, comfort and cabin, food and beverages, in-flight entertainment, post-flight and value for money. The independent variables represent the rating a passenger gives on service quality on attribute-level. For each of the attributes the passenger has been asked to rate the experience of their last flight on an itemized ordinal Likert rating scale ranging from 1 (= poor) to 5 (=excellent).
26 total variance which is the cut-off point that has been used. The rotated factor loadings (Varimax) confirm the initial proposed division of in-flight variables and ground variables. The variables; check-in, boarding and post-flight are combined in a factor and named factor ground, whereas food and beverages, comfort and cabin and value for money are combined in a factor and named factor in-flight. Reliability analysis confirms the two constructs with a Cronbach’s alpha of 0.602 for factor ground, and factor in-flight with a Cronbach’s alpha of 0.796, both exceeding the critical level of 0.6 (Cortina 1993). The factor analysis has been performed to confirm the initial set up as suggested in the literature. It can therefore be concluded that the initial division of variables based on Gustafsson, Ekdahl and Edvardsson (1999) has been confirmed by factor analysis. Due to the fact that we want to investigate the airline attributes independently, we do not continue with the factors; the analysis functions as control.
3.2.3 Mediator variables
Attitudinal loyalty has been proposed as the mediating variable. As suggested by Vlachos and Lin (2014), attitudinal loyalty has been measured on NPS, satisfaction and repurchase intention. NPS has been measured on a Likert scale ranging from 1 (= definitely not) to 10 (= definitely). Satisfaction has been measured on a Likert scale ranging from 1 (= poor) to 5 (=excellent) and repurchase intention has been measured on a Likert scale ranging from 1 (= definitely not) to 5 (= definitely). Likert scales are a type of ordinal scale and since the three measures have each been selected as attitudinal loyalty measures, treating them independently allows for comparison.
3.2.4 Control variable:
As a control variable, the dummy variable gender (0 = male, 1 = female) of the respondent has been used. In order to find out whether gender has an effect on either the dependent variable or on the mediators, the different types of gender have been treated as independent samples.
An independent-samples t-test has been conducted to compare the dependent variable share of wallet for male and female respondents. Since share of wallet has been measured on an interval scale, an independent-sample t-test is the appropriate statistical tool to test differences between groups (McCrum-Gardner 2008). Firstly, the variables have been tested on normality since it is one of the assumptions for conducting an independent-samples t-test. Secondly, Levene’s test for equality of variances has been performed; it tests the homogeneity of variances which means that the test assumes that the variances are the same in both samples. Thirdly, the t-test has been tested to compare the sample means for male and female respondents.
27 two independent samples, and the variable is measured on an ordinal scale, the Mann-Whitney U test can be used (Malhotra 2009). The hypothesis tests whether the two medians are equal and when the p-value of the mediator is significant, the hypothesis that the groups have equal medians is rejected and therefore it can be concluded that the two groups have different medians.
3.3 Mediation analysis
After preparation of the data as suggested by Malhotra (2009), basic analyses have been performed. The insights that have been gained through basic analysis are valuable in their own, but have guided for multivariate analysis. The hypotheses have been tested with the usage of a multiple regression procedure in the SPSS statistical software program.
28 (Shrout and Bolger 2002). The second point of critique deals with the requirement that “when route-a, and route-b are controlled, a previously significant relation between the independent X and dependent Y variable is no longer significant” (Krause et al. 2010). For these reasons, alternative testing methods such as bootstrapping and the M-test, have been suggested for testing mediation (Hayes and Preacher 2014; Mackinnon et al. 2002).
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4. Results
4.1 Sample characteristics
Data collection has resulted in a sample size of 391 whereas 197 (50.4%) of the respondents are male versus 194 (49.6%) female respondents. The descriptives to the dependent variable, independent variables, control variable and proposed mediators can be found in table 1.
Table 1: Descriptives of the variables.
(N = 391)
Control variable # %
Male 197 50.4
Female 194 49.6
Dependent variable Minimum Maximum Mean Std. Deviation
Share of wallet 2,38% 92,86% 47,75% 20,49
Independent variables Minimum Maximum Mean Std. Deviation
Boarding 1 5 3,13 0,913
Check-In 1 5 3,46 1,039
Comfort and cabin 1 5 2,95 0,937
Crew 1 5 3,52 1,064
Food and beverages 1 5 3,08 1,048
In-flight entertainment 1 5 3,19 1,090
Post-flight 1 5 3,19 0,892
Value for money 1 5 3,06 0,912
Proposed Mediators Minimum Maximum Mean Std. Deviation
NPS 1 10 8,06 1,605
Repurchase intention 1 5 3,53 1,042
Satisfaction 1 5 3,46 0,921
4.2 Normality assumptions
30 approximately normally distributed errors, as did the normal P-P plot of standardized residuals in graph 2 which showed points that were not completely on the line, but close (Field 2009).
Graph 1: Histogram share of wallet Graph 2: Normal P-Plot share of wallet
Graph 3: Scatterplot share of wallet.
31 variance = 420.08, value for money, variance = 0.832). Based on the checks we assume that the data is normal and therefore t-tests and multiple regression can be performed
4.3 Control variable
An independent-samples t-test has been conducted to compare the dependent variable share of wallet for male (N = 197) and female respondents (N = 194). Since share of wallet has been measured on an interval scale; an independent-sample t-test is the appropriate statistical tool to test differences between groups (McCrum-Gardner 2008). Male respondents were associated with a mean share of wallet of 46.94 and a standard deviation of 20.641 while females were associated with a mean share of wallet of 48.59 and a standard deviation of 20.366. To test the hypothesis that male and female respondents were associated with statistically significantly different share of wallet scores, an independent samples t-test has been performed. As can be seen in table 2, the female and male distributions were sufficiently normal for the purposes of conducting a t-test (i.e., skewness < 2.0, and kurtosis < 9.0; Schmider et al. 2010). Additionally, the assumption of homogeneity of variances has been tested and satisfied with Levene’s test for equality of variances, F (389) = 0.059, p = 0.809. The independent samples t-test was associated with a statistically non-significant effect, t (389) = -0.79, p =.426. Thus, the male respondents were not associated with statistically significantly different mean share of wallet scores than the female respondents. For the dependent variable share of wallet differences between males and females have not been found.
Table 2: Share of wallet distributions
(N = 391)
Variable Gender Mean Std. Deviation Kurtosis Skewness Share of wallet Male (197) 46.937 20.641 -.675 .125
Female (194) 48.589 20.366 -.964 -.022
32 statistically significantly different medians than female respondents; for NPS, satisfaction and repurchase intention, differences between males and females have not been found.
4.4 Mediation results
The following part of the results chapter covers the mediation analyses. We have tested the significance of the indirect effects using bootstrapping procedures. Unstandardized indirect effects were computed for each of 5,000 bootstrapped samples and the 95% confidence interval has been computed by determining the indirect effects at the 2.5th and 97.5th percentiles. The SPSS output of the mediation analyses can be found in the appendices 7.3 – 7.5. Using the Baron and Kenny causal steps approach (1986) we first will present the results of the total effect of the airline attributes on behavioral loyalty (route-c). Second, we will present the effect of the airline attributes on attitudinal loyalty (route-a). Third, we will present the results of the effect of attitudinal loyalty and the airline attributes on behavioral loyalty (route-b and route-c’). Fourth, we will present bootstrapping results. We refer to Figure 1 for the proposed hypotheses of the mediation analysis.
4.4.1 Route-c
A multiple linear regression analysis was performed to predict share of wallet based on the airline attributes; boarding, check-in, comfort and cabin, crew, food and beverages, in-flight entertainment, post-flight and value for money. Using the enter method an insignificant regression equation was found (F (8, 382) = 1.585, NS), with an R2 of .0321 and an adjusted R2 of .0119. We therefore conclude that the airline attributes; boarding, check-in, comfort and cabin, crew, food and beverages, in-flight entertainment, post-flight and value for money do not predict share of wallet for generation Y in the Dutch market. For this reason we reject hypotheses H2a-H9a. The results of the multiple linear regression analysis can be found in appendix 7.3 – 7.5.
4.4.2 Route-a
Using A. F. Hayes’ MEDIATE-macro, a multiple linear regression analysis was performed to predict attitudinal loyalty based on the airline attributes; boarding, check-in, comfort and cabin, crew, food and beverages, in-flight entertainment, post-flight and value for money. For each measure of attitudinal loyalty (NPS, satisfaction, and repurchase intention), the regression results will be presented.
33 to have a statistically significant positive effect on NPS. Thus hypotheses H4b, H6b, H8b and H9b have been accepted. The airline attributes; boarding, check-in, comfort and cabin and food and beverages were not found to statistically significant predict NPS. Therefore, we reject hypotheses H2b, H3b, H5b and H7b.
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Table 3: Results overview route-a
Route-a NPS (b) Satisfaction (c) Repurchase intention (d)
Ground
H2. Check-in NS 0.09** NS
H3. Boarding NS 0.12** NS
H4. Post-flight 0.18* 0.12** 0.12* In-flight
H5. Comfort and cabin NS NS NS
H6. Crew 0.38** 0.22** 0.20**
H7. F&B NS 0.13** NS
H8. IFE 0.16* NS 0.08**
H9. Value for money 0.65** 0.32** 0,51**
Standard regression coefficients are reported. *Significant at p < 0.05.
**Significant at p < 0.01. NS = Not significant.
4.4.3 Route-b and route-c’
This part of the results chapter answers the question whether share of wallet is affected by attitudinal loyalty and the airline attributes. It was tested whether the hypothesized mediating variables (NPS, satisfaction and repurchase intention) do have an effect on behavioral loyalty (route-b) and whether the airline attributes have an effect on behavioral loyalty while including the hypothesized mediating variables (route-c’). The MEDIATE-macro SPSS output can be found in appendix 7.3-7.5.
For predicting behavioral loyalty while controlling for NPS we did not find statistically significant results (F (9, 381) = 1.665, NS), with a R2 of .0378 and an adjusted R2 of .0151. We therefore can conclude that share of wallet is not affected by NPS and the airline attributes. Since we do not find evidence for NPS having an effect on share of wallet, we reject hypothesis H1a.
35 comfort and cabin (B = 3.1145, t (381) = 2.0427, p < .05), and for value for money (B = -3.4472, t (381) = -2.1786, p < 0.05) on behavioral loyalty while controlling for repurchase intention. Table 4 represents an overview of the findings concerning the effect of attitudinal loyalty and the airline attributes on behavioral loyalty.
Table 4: Results overview route-b and route-c’
Route-b Share of wallet
NPS NS
Satisfaction NS Repurchase intention 4.20*
Route-c’ Share of wallet
(NPS) Share of wallet (Satisfaction) Share of wallet (Repurchase intention) Ground Check-in NS NS NS Boarding NS NS NS Post-flight NS NS NS In-flight
Comfort and cabin NS NS 3.11*
Crew NS NS NS
F&B NS NS NS
IFE NS NS NS
Value for money NS NS -3.45*
Standard regression coefficients are reported. *Significant at p < 0.05.
**Significant at p < 0.01. NS = Not significant.
4.4.4 Bootstrapping
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4.5 Discussion
Based on the order of the presented results in mediation results section 4.4 we will discuss the implications of the results in the following section. Due to the fact that many hypotheses have been formed in the literature review and have been presented in the results section, we firstly present table 5 with an overview of the results of the proposed hypotheses. In appendix 7.6-7.8 the results of the proposed hypotheses have been presented in the form of the conceptual model as presented in figure 1.
Table 5: Summary of hypotheses test results
Hypotheses Share of Wallet
H1a. NPS Behavioral loyalty NS H1b. Satisfaction Behavioral loyalty NS H1c. Repurchase intention Behavioral loyalty 4.20**
Hypotheses Share of wallet (a) NPS (b) Satisfaction (c) Repurchase intention (d) Ground H2. Check-in loyalty NS NS 0.09** NS H3. Boarding loyalty NS NS 0.12** NS H4. Post-flight loyalty NS 0.18* 0.12** 0.12* In-flight NS
H5. Comfort and cabin loyalty NS NS NS NS
H6. Crew loyalty NS 0.38** 0.22** 0.20**
H7. F&B loyalty NS NS 0.13** NS
H8. IFE loyalty NS 0.16* NS 0.08**
H9. Value for money loyalty NS 0.65** 0.32** 0,51**
Standard regression coefficients are reported. *Significant at p < 0.05.
**Significant at p < 0.01. NS = Not supported.
4.5.1 Route-c
37 loyalty (Dolnicar et al. 2010; Gustafsson, Ekdahl and Edvardsson 1999; Shahin and Zairi 2009; Vlachos and Lin 2014) for generation Y in the Dutch market we did not find the airline attributes to be predicting behavioral loyalty in the form of share of wallet.
4.5.2 Route-a
Ground service’ post-flight and in-flight services; crew and value for money are significantly related to the three attitudinal loyalty measures. Thus these three airline attributes can be considered the top factors driving generation Y’s attitudinal loyalty for FSNC in the Netherlands.
Firstly, post-flight strongly predicts NPS, satisfaction, repurchase intention and thus attitudinal loyalty. This finding is in line with the presence of post-flight perception in the Airs@t benchmark airline survey. In addition, the post-flight experience has been characterized as a procedural activity (Gustafsson, Ekdahl and Edvardsson 1999) where passengers can collect their luggage and finish their flight at the arrival airport. Our results indicate that for generation Y in the Dutch market, post-flight is a strong contributor to attitudinal loyalty. Secondly, crew has a positive effect on all three measures of attitudinal loyalty, a finding that is in line with Shahin and Zairi (2009) and Vlachos and Lin (2014), thus supporting the importance of the staff-customer interaction as highlighted in the SERVQUAL model (Parasaruman, Zeithaml and Berry 1988) and as one of the main factors of the Airs@t benchmark survey. In contrast, Dolnicar et al. (2010) do not report crew as one of the key airline loyalty drivers. Our results indicate that for generation Y in the Dutch market, crew does contribute to predicting attitudinal loyalty. Thirdly, value for money; an airline attribute that takes the price that has been paid for the specific flight into account has a strong positive effect on attitudinal loyalty. The importance of price in the airline industry has been discussed in Dolnicar et al. (2010), Hess, Adler and Polak (2007) and Suzuki (2007). For generation Y in the Dutch airline industry, value for money does indeed have a positive effect on attitudinal passenger loyalty. This finding is in line with the findings of Heaney (2007) and Phillips (2007) where it was concluded that generation Y takes price strongly in consideration when making consumer decisions.
38 airline attributes rather well can be addressed to the fact that cognitive evaluations of key attributes of airline services can be seen as consumers’ fulfillment responses on an attribute-level and thus as attribute-level satisfaction (Oliver 1997). The contact points check-in and boarding have been classified as a procedural activity by Gustafsson, Ekdahl and Edvardsson (1999) and are widely represented in the Airs@t benchmark survey (2015). These contact points are procedural activities and poor performance will lead to dissatisfaction. However, when an airline performs as expected passengers take the experience for granted, hence their moderate level of satisfaction (Kano, Takahashi and Tsuji 1984; Shahin and Zairi 2009; Vlachos and Lin 2014).For generation Y in the Dutch market, food and beverages does have a positive effect on satisfaction but no effect exists on NPS or on repurchase intention. The weak positive effect on attitudinal loyalty is a finding that contradicts Huang’s (2009) proposed food and beverages as a SERVQUAL’s key tangible in the airline industry. Our finding is also in contrast with the finding by Shahin and Zairi (2009) where food and beverages are an attractive requirement in the airline industry.
We find no evidence to support any of the hypotheses regarding the effect of comfort and cabin on attitudinal loyalty. Vlachos and Lin (2014) found for business travelers that the aircraft type is a significant contributor to airline loyalty but for generation Y in the Dutch airline industry no such evidence is found. Comfort and cabin has been argued to be a basic requirement of an airline (Gustafsson, Ekdahl and Edvardsson 1999)and therefore it can be considered to be similar to check-in and boarding, in respect that it is taken for granted when airlines perform as expected.
4.5.3 Route-b and route-c’
The empirical results show that repurchase intention is the only attitudinal loyalty measure that is a predictor of share of wallet. Thus repurchase intention can be considered the top attitudinal loyalty measure to predict share of wallet (behavioral loyalty) for generation Y in the Dutch airline industry. Repurchase intention explains the degree to which a passenger thinks he/she is going to use the airline in the future and can therefore be seen as a prediction of their own behavior. Our finding of linkage between repurchase intention and behavior is in line with Bemmaor (1995), Chandon, Morwitz and Reinartz (2005), Keiningham et al. (2008) and Morrison (1979).
39 Keiningham et al. (2008) who found that for gauging behavioral loyalty NPS is definitely not the ultimate question to ask customers. Although many researchers have used NPS as a measure of attitudinal loyalty, our research finds that it does not predict behavioral loyalty for generation Y in the Dutch airline market.
Satisfaction is a form of affective loyalty as defined in Oliver (1999) and has been found as a driver of customer loyalty in Heskett et al. (2008). Cooil et al. (2007) have found relationships between satisfaction and share of wallet. In addition, de Haan, Verhoef and Wiesel (2015) state that customer satisfaction is useful as a key metric for customer management purposes in the airline industry. However, we do not find a relationship of satisfaction with share of wallet. Therefore our finding is in line with Mittal and Kamakura (2008) where they concluded that although satisfaction ratings theoretically are linked to repurchase behavior, few attempts can be found that relate satisfaction ratings to actual repurchase behavior. Thus our research shows that for generation Y in the Dutch airline industry, levels of satisfaction do not predict behavioral loyalty in the form of share of wallet.
4.5.5 Mediation
Results from the MEDIATE-macro, developed by Dr. A. Hayes show that NPS and satisfaction do not predict share of wallet and therefore the route-b is insignificant and thus mediation does not occur. However, we find that repurchase intention positively predicts share of wallet (route-b). In combination with significant effects of the airline attributes on repurchase intention (route-a), bootstrapping results show mediation effects. We have found mediation effects for the variables; crew, in-flight entertainment, post-flight and value for money.
For the variables; crew, in-flight entertainment and post-flight counts the fact that no significant total effect was measured on forehand but bootstrapping shows that repurchase intention does mediate the relationship with behavioral loyalty. Zhao, Lynch and Chen (2010) have identified this form of mediation as “indirect-only mediation”. Improving the perception of these airline attributes does indirectly result in higher share of wallet through repurchase intention.
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5. Conclusions
Last years, the pressure on the FSNC industry in the Netherlands has been increasing. With pressures coming from gulfstream carriers (e.g. Emirates and Etihad Airways) and from LCCs (e.g. EasyJet and Ryanair), one of the strategic tasks for airline managers is to determine the drivers of customer loyalty. Customer loyalty is a major driver for customer profitability (Cooil et al. 2007) and therefore finding drivers of customer loyalty allows marketing managers to focus on the right strategies to build relationships with the customer. Our study was conducted in the Dutch market and specifically focused on generation Y. We applied a synthesized framework of eight airline attributes to identify their influence on three attitudinal loyalty measures (NPS, satisfaction and repurchase intention) and on one behavioral loyalty measure (share of wallet). Findings reveal that the airline attributes did not predict behavioral loyalty directly. However, the airline attributes managed to predict attitudinal loyalty on all three measures very well. We found that for predicting attitudinal loyalty the airline attributes; crew, post-flight and value for money are the strongest drivers. Findings also reveal that predicting behavioral loyalty based on attitudinal loyalty seems difficult; we did not find any evidence of effects for NPS and satisfaction to predict share of wallet. However, repurchase intention did predict share of wallet relatively well. In addition, for generation Y in the Dutch market repurchase intention was found to mediate the relationship of the previously identified strongest drivers of attitudinal loyalty; post-flight, crew and value for money on behavioral loyalty.
5.1 Academic relevance
Our study constitutes an important step towards better understanding of the drivers of customer loyalty in the airline industry as it remains relatively underexplored (Vlachos and Lin 2014). Whereas Dolnicar et al. (2010) concludes that drivers are different for different market segments, our study contributes to the extent that it focuses on generation Y in the Dutch market; a relatively underexplored market with a generation of which no general consent with regard to loyalty exists. It contributes to the academic research to the extent that it uses a composite approach to develop different measures of loyalty as suggested by Vlachos and Lin (2014).
42 on customer loyalty. Researchers should take these findings into account when researching and interpreting customer loyalty.
Because we find that repurchase intention is the only predictor of behavioral loyalty, our research helps academics to understand how valuable each attitudinal loyalty measure is for predicting behavioral loyalty for generation Y in the Dutch airline industry. The findings on attitudinal and behavioral loyalty have implications for the academic research. For generation Y we do not find evidence that high (low) satisfaction levels or high (low) NPS-scores lead to higher (lower) shares of wallet, opposing to the existing research of de Haan, Verhoef and Wiesel (2015) and Reichheld (2003). Therefore, we have to be aware on our finding that generation Y might be extremely attitudinal loyal but they might still behave otherwise. This extends the finding of Morton (2002) to the degree that generation Y is fickle in their brand loyalties.
5.2 Managerial implications
43 competitor and actively communicate their firm’s positive actions and performance to passengers and other stakeholders.
