THE IMPACT OF ONLINE SERVICE QUALITY ON ONLINE CUSTOMER
SATISFACTION
Wilbert van der Woude
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The impact of online service quality on online customer satisfaction
Wilbert van der WoudeDepartment of Marketing Faculty of Economics and Business
Rijksuniversiteit Groningen Master thesis
16 July 2012
Adress: Ferdinand Bolstraat 27 Phone number: 0031 648787331
E-mail adress: wilbertvanderwoude@gmail.com Student number: 1555774
Supervisor: Hans Risselada Second supervisor: Jelle T. Bouma External supervisor: Maaike van der Horn
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Management Summary
This paper focuses on the relationship between online service quality and online customer satisfaction in the airline industry. The goal of this paper is to provide insights in these relationships which could help airline website managers optimize online customer satisfaction.
According to Frambach et al. (2007) the customer buying process is divided in three stages: the pre-purchasing, the purchasing and the post-purchasing stage. In the pre-purchasing stage the quality of the online services ‘destination information’, ‘time schedule’, ‘special offers flights’ and ‘special offers other than flights’ are found to have a positive and significant impact on online customer satisfaction. In the purchasing stage, the quality of the online services ‘booking process’ has a positive and significant impact on online customer satisfaction. In the post-purchasing stage the quality of the online services ‘check-in process’, ‘travel related information’, ‘check existing booking’, ‘changing or cancelling a booking’, ‘loyalty program information’, ‘loyalty program information (small and medium enterprises)’ have a positive and significant impact on online customer satisfaction. Only the online service ‘up-to-date departure and arrival times’ does not have a significant influence on online customer satisfaction. In general the online service which’ quality has the most impact is ‘booking process’. To provide deeper insights into the relationship between online service quality and online customer satisfaction, we take a look into the nature of the relationship. To check if we can categorize the different online services according to the customer satisfaction theory of Kano (1984). The online services ‘special offer flights’, ‘destination information’, ‘time schedule’, ‘booking process’, ‘travel-related information’, and ‘check-in process’ can be categorized as performance factors. The online services ‘special offer other than flights’, ‘loyalty program information (general)’, ‘check existing booking’ and ‘change/cancel existing booking’ can be categorized as basic factors. And only the online services ‘loyalty program information (Small & Medium Entreprises)’ can be categorized as an excitement factor.
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Preface
In the airline industry, the importance of a companies’ online offering has grown enormously over the last past years. Reports now predict that by the end of 2012, travelers will book one third of the world’s travel sales online, with online leisure travel bookings growing even twice as fast as the total market (PhoCusWright Global Online Travel Overview 2011).
Due to this, the importance of an airline companies’ online offering has become a service aspect of major importance. These developments ask for a more detailed insight in the effect that online services have on major performance indicators like customer satisfaction. The growing complexity of online offerings has made it harder for companies to evaluate them, and the need for specific analyses is growing. It is because of that that I decided to investigate the quality of online services and especially the impact they have on online customer satisfaction. This thesis investigates a companies’ online offering as a combination of different and specific services, which all contribute to the total online experience of a customer in their own way. How this works, is subject of this thesis.
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Table of contents
Management Summary ... 5 Preface ... 7 Table of contents ... 9 Introduction ... 11 Theoretical framework ... 14Different impacts in different purchasing stages ... 14
The asymmetry of the relationships ... 15
Constructs used ... 17
Online service quality ... 17
Online customer satisfaction ... 20
Research design ... 23 Procedure ... 23 Sample ... 23 Measurement ... 25 Data transformation ... 25 Results ... 27
Different impacts in different purchasing stages ... 27
The asymmetry of the relationships ... 30
Performance factors ... 30
Basic factors ... 31
Excitement factors ... 32
Atypical factors ... 33
Collinearity checks ... 34
Differences between specific groups ... 35
Conclusions and recommendations ... 36
Scenario analysis ... 39
References ... 42
Appendix 1: Measurement items ... 50
Appendix 2: Outcomes linear regression with dummy coding ... 51
Appendix 3: Information criteria three models ... 53
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Introduction
During the past years, online sales of travel-related products have grown steadily. Even while the travel industry suffered from a 13 percent decline in sales due to the recession in 2009, the proportion of online sales has only grown. Reports now predict that by the end of 2012, travelers will book one third of the world’s travel sales online, with online leisure travel bookings growing even twice as fast as the total market (PhoCusWright Global Online Travel Overview 2011).
In order to profit from this proposed channel shift, managers in the airline industry have spent much of their attention and resources on the virtual business environment. Due to this, their online airline proposition has evolved from only a sales channel into a service aspect of major importance. Where at first it was only possible to book a ticket online, a customer can now also check-in online, book complementary services like a car or hotel, get inspired to visit new destinations, look for departure and arrival times and even collect loyalty program points all via the same company-owned website. An airline company’s website now inhibits many services which all can be crucial in satisfying and keeping a customer.
However, despite of the fact that the importance of the online airline proposition increased rapidly over the past years, still little is known about how this online proposition can best be designed. Not much is known about the importance of the specific online services to customers in the airline industry. Does the quality of these specific services have an impact on online customer satisfaction? And if so, what kind of impact do these services have? Are these effects linear or non-linear? And how can website managers best design their website to benefit from this?
We know that several kinds of online services exist, all with a different impacts on online customer satisfaction. However, there is not much research done in this field considering the airline or comparable industries. Since the online service range of an airline website is quite specific, we therefore cannot state grounded hypotheses for the kind of impact we suspect an online service to have on the online customer satisfaction in the airline industry.
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relevance. A prescribing agreement as how companies can best design their online services to achieve the highest customer satisfaction (Cho and Menor 2010). This absence is mainly because existing literature in this field provides frameworks that are too generic to apply in specific industries and complex online environments. Existing literature mostly focuses on generic website quality dimensions like website speed, information ability and aesthetics (Lee and Lin 2005; Lociacono et al. 2000; Madu and Madu 2002; Malhotra 2002; Parasuraman 2005; Zeithaml, Parasuraman, and Santos 2003). However, these studies often treat the diverse range of online services of a company as one. They for instance find that website speed, information ability and aesthetics are important drivers of online customer satisfaction. This while website’s nowadays offer many different services which all fulfill specific customer needs. Different online services can be used for different purposes in different stages of the customers’ purchasing process (Frambach et al. 2007). Because benefits sought in services differ per purchasing stage (Mittal, Kumar, & Tsiros, 1999; Hogarth & Einhorn, 1992; Gardial, Clemons, Woodruff, Schumann, & Burns, 1994), it not likely that a generic framework for online quality applies to all different online services adequately. We therefore bundle online services according to the service typology of Frambach et al. (2007) into services in the pre- purchasing, purchasing or post- purchasing stage. Online service quality is then defined not in terms of website speed, information ability and aesthetics, but as the perceived quality of these different services.
Contrary to other industries like the banking sector (Joseph et al. 1999; Juna and Cai, 2001; Kanyama and Black 2000), the broker sector (Balasubramanian, 2003), the portal services sector (van Riel et al., 2001) and the retail environment (Fan and Belanger 2006; Flavián, Guinalíu and Gurrea 2005; Kim & Stoel 2004; Schnaupp, Hausman & Siekpe 2008; Wang, Hernandez and Minor 2010; Wolfinbarger and Gilly 2003), the subject of online service quality has not yet been investigated in the airline industry yet. Therefore together with a major European airline the eleven most important online services are identified. These services are: destination information, special flight offers, special offers other than flights, time schedule, booking process, travel related information (baggage, seats, etcetera), checking a booking that is already made, changing or canceling a booking, checking in, up-to-date departure and arrival times and loyalty program information.
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Kano’s (1984) model who states that services can be classified into three categories: 1. basic factors, which are minimum requirements that cause dissatisfaction if not fulfilled but do not lead to customer satisfaction if fulfilled or exceeded 2. performance factors, which are the normal, linear and symmetric factor and 3. excitement factors, that increase customer satisfaction if delivered but do not cause dissatisfaction if they are not delivered (Anderson & Mittal, 2000; Gale, 1994; Johnston, 1995; Matzler & Sauerwein, 2002; Matzler et al. 2003; Oliver, 1997; Vavra, 1997). In this paper we will investigate how the relationship between online service quality and online customer satisfaction works and consequently in which of the three categories an airlines’ online service would fall.
This research uses data gathered via an online questionnaire rolled out on the website of a major European airline. The questionnaire is held on the website of this airline in eight different countries: Germany, the Netherlands, France, Ireland, Italy, Sweden, the United Kingdom and the United States. Participants were randomly invited to participate in the questionnaire. In total, 21598 respondents participated in the study in the period from September 2011 up and until January 2012. The questionnaire was tailored to let the participant evaluate his website experience as a whole and the specific online services he or she used. Using a regression analysis the relationship between the quality of the online services under investigation and online customer satisfaction is investigated. Also the nature of this relationship is investigated to check if we can categorize the different online services according to the customer satisfaction theory of Kano (1984).
In this paper we investigate the relationship between the quality of online services and online customer satisfaction in the three stages of the customer buying process of Frambach (2007): the pre-purchasing, the purchasing and the post-purchasing stage. To provide deeper insights into this relationship, then check if we can categorize the different online services as performance factors, basic factors or excitement factors, according to the customer satisfaction theory of Kano (1984).
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Theoretical framework
This paper focuses on the relationship between online service quality and online customer satisfaction in the airline industry. Next to investigating the near-obvious hypothesis that online service quality has a positive influence on online customer satisfaction, there are two characteristics of this relationship on which this paper mainly focuses. First of all the different impact of online service quality on online customer satisfaction per purchasing stage. Second the asymmetry of these relationships.
Different impacts in different purchasing stages
An airline website provides online services that are relevant to customers in different stages of their purchasing process. Considering that a customer’s perceived value of a service will depend upon his or her intent (Woodruff, 1997), service quality evaluations are likely to differ across alternative usage situations (Balasubramanian et al., 2005). These situations most importantly relate to the different stages through which a consumer progresses when obtaining a product (Frambach et al. 2007) This consumer purchasing process can be described as a five-stage linear process (Blackwell et al., 2006; Hawkins et al., 2001) where stage one covers need recognition, stage two the information search, stage three the evaluation of alternatives, stage four the purchase decision and stage five the post-purchase behavior. These five stages can be combined into a simpler model from Frambach et al. (2007), which is used in this paper. This model contains three stages: (i) the pre-purchasing stage which is the information gathering stage in which the consumer familiarizes himself with an offering, (ii) the purchasing stage, in which a purchase decision is made and the transaction completed, and (iii) the post-purchasing stage, when the decision on continued use of the offering is made and repeat purchases can take place (Frambach et al. 2007). Research shows that consumers seek to satisfy specific goals by choosing an product or service offering that, given the configuration of attributes that it provides, is capable of doing so (Gutman, 1982; Zeithaml, 1988). Therefore, in each of the three decisional stages, consumers will also evaluate each service on its ability to satisfy the benefits they seek (Keeney, 1999). As we know from previous research that benefits sought differ across the purchasing stages (Mittal, Kumar, & Tsiros, 1999; Hogarth & Einhorn, 1992; Gardial, Clemons, Woodruff, Schumann, & Burns, 1994), it is important to check how different online services perform in addressing the benefits sought at each stage.
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purchasing stage and online services related to the post- purchasing stage. The online services in the pre- purchasing stage are ‘destination information’, ‘time schedule’, ‘special offers flights’ and ‘special offers other than flights’. The online service in the purchasing stage is the ‘booking process’. The online service in the post- purchasing stage are ‘check-in process’, ‘travel related information’, ‘check existing booking’, ‘changing or cancelling a booking’, ‘up-to-date departure and arrival times’, ‘loyalty program information’, ‘loyalty program information (small and medium enterprises)’. This way we can compare the effect of the quality of online services on online customer satisfaction per purchasing stage and determine which service is most effective in increasing customer satisfaction per purchasing stage. This leads to the following hypotheses:
H1a. In the pre-purchasing stage of a customers’ purchasing process, the quality of the services ‘destination information’, ‘time schedule’, ‘special offers flights’ and ‘special offers other than flights’ has a positive impact on online customer satisfaction.
H2a. In the purchasing stage of a customers’ purchasing process, the quality of the service ‘booking process’ has a positive impact on online customer satisfaction.
H3a. In the post-purchasing stage of a customers’ purchasing process, the quality of the services ‘check-in process’, ‘travel related information’, ‘check existing booking’, ‘changing or cancelling a booking’, ‘up-to-date departure and arrival times’, ‘loyalty program information’, ‘loyalty program information (small and medium enterprises)’ has a positive impact on online customer satisfaction.
The asymmetry of the relationships
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(Anderson & Mittal, 2000; Gale, 1994; Johnston, 1995; Matzler & Hinterhuber, 1998; Matzler, Hinterhuber, Bailom, & Sauerwein, 1996; Oliver, 1997). In his model of customer satisfaction, Kano (1984) elaborates on these three quality attribute categories with a different impact on customer satisfaction. Basic factors (dissatisfiers) are minimum requirements that cause dissatisfaction if not fulfilled but do not lead to customer satisfaction if fulfilled or exceeded. Negative performance on these attributes has a greater impact on overall satisfaction than positive performance. The fulfillment of basic requirements is a necessary, but not sufficient condition for satisfaction. The customer regards basic factors as prerequisites. Excitement factors (satisfiers) are the factors that increase customer satisfaction if delivered but do not cause dissatisfaction if they are not delivered. Positive performance on these attributes has a greater impact on overall satisfaction than negative performance. Excitement factors surprise the customer and generate a certain form of excitement. Performance factors are the normal, linear and symmetric factor category. They lead to satisfaction if performance is high and to dissatisfaction if performance is low. The basic idea of this model has been well adopted in current research (Anderson & Mittal, 2000; Gale, 1994; Johnston, 1995; Matzler & Sauerwein, 2002; Matzler et al. 2003; Oliver, 1997; Vavra, 1997).
In this paper the relationship between online service quality and online customer satisfaction is investigated with a specific focus on the nature of this relationship. We know that several kinds of online services exist, all with a different impacts on online customer satisfaction. However, there is not much research done in this field considering the airline or comparable industries. Since the online service range of an airline website is quite specific, we cannot state grounded hypotheses for the kind of impact we suspect an online service to have on the online customer satisfaction in the airline industry. Therefore, we hypothesize in the most neutral form by stating that we expect all online services to have a normal linear and symmetrical effect on online service quality. The outcomes of this paper should be a classification of the online services as basic, performance or excitement factors. This classification can provide valuable insights for optimizing online customer satisfaction. Combining this classification issue with the before mentioned clustering of online services per purchasing stage this leads to the following hypotheses:
H1b. In the pre-purchasing stage of a customers’ purchasing process the relationship between the services ‘destination information’, ‘time schedule’, ‘special offers flights’ and ‘special offers other than flights’ and online customer satisfaction is symmetrical.
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H3b. In the post-purchasing stage of a customers’ purchasing process, the relationship between the services ‘check-in process’, ‘travel related information’, ‘check existing booking’, ‘changing or cancelling a booking’, ‘up-to-date departure and arrival times’, ‘loyalty program information’, ‘loyalty program information (small and medium enterprises)’ and online customer satisfaction is symmetrical.
Constructs used
In this paper several constructs from the customer behavior of business literature are used. The most important ones are online service quality and online customer satisfaction. Why these constructs are chosen, what the relevant academic literature is telling about them and how they are used is discussed below.
Online service quality
As (McDougall & Levesque, 2000) found that perceived value was an important drivers of customer satisfaction, we take customers’ perceived value of the offered online services and investigate the relationship between perceived online service quality and online customer satisfaction in the airline industry. In this paper we define online service quality as the extent to which companies’ key online services are rated by their customers. A list of these key tasks and their associated online services are selected together with a major European airline. These eleven services are proposed to be a comprehensive summary of what airline website customers can look for in an airline website. In this chapter we further elaborate on the concept of online service quality, as it is used in this paper.
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(1985) further define a quality service encounter by specifying reliability, responsiveness, assurances, empathy, and tangibility as most important determinants of service quality.
In this study we define online service quality as the extent to which companies’ key online services are rated by their customers, and follow the literature stream of Grönroos's (1982; 1984). The first reason for choosing this measure is that it is equally applicable in all industries. Perceived quality takes the internal evaluation of customers into account (Grönroos's, 1982; Grönroos, 1984) and is therefore not limited to specifically defined determinants of online quality, which’ importance can differ per industry. That is also one of the main reasons not to choose a more specific service quality measurement metric like SERVQUAL in this paper. The importance of its’ defined determinants of service quality: reliability, responsiveness, assurances, empathy, and tangibility, can differ per industry. As far back as the early 1990s’, Carman (1990) stated caution was recommended while using generic metrics like SERVQUAL in specific industries, since each service industry might have its own unique dimensions. Hence: in specific industries a generic metric for measuring online service quality does not seem enough to highlight the main factors of importance. A couple of targeted studies identified key dimensions of online service quality for specific businesses, such as banks (Joseph et al. 1999; Kanyama and Black 2000), brokers (Balasubramanian, 2003), portal services (Juna and Cai, 2001), travel agencies (van Riel et al., 2001) and retail stores (Wolfinbarger and Gilly 2003). These industry specific studies provide different service quality metrics which are different from the generic metrics like SERVQUAL, but which are very relevant in the subject industry. Unfortunately, not all industries have been subject of a specific study into the drivers of online service quality. There also seems to be a research gap in the field subject of this paper: airline online services. Therefore using the perceived quality measure, which is more generically applicable, seems suitable for this paper.
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service might be defined by entirely different criteria then another online service (Lee and Kozar 2005; Agarwal and Venkatesh 2002; Zhang and von Dran 2002; Jarvenpaa 1997; Kim and Stoel 2004. For instance it is likely for a service that provides inspiring product content to have a great empathy, while a service which includes payment options is likely to have a greater focus on reliability. Using a generic online service quality metric might therefore cause misinterpretation of an online service’s quality. That is also one of the main reasons not to choose a more specific service quality measurement metric like SERVQUAL in this paper
Using the perceived quality measure of Grönroos's (1982; 1984) therefore provides a metric which is generically applicable a) in different industries and b) on different online services. In this paper we define online service quality as the extent to which a companies’ key online website services are rated. Together with a major European airline a list of these services is put together. These eleven services are proposed to be a comprehensive summary of what airline website customers can look for in an airline website. For reasons of data collection efficiency, a unidimensional five point scale measure of perceived service quality relating to an evaluation of the online services was used (Strandvik and Liljander, 1994). The quality ratings that customers give on each of these online services will determine the websites overall quality. These services are:
1. Destination information 2. Special flight offers
3. Special offers other than flights 4. Time schedule,
5. Booking process
6. Travel related information (baggage, seats, etcetera) 7. Checking an existing booking
8. Changing or canceling a booking 9. Check-in process
10. Up-to-date departure and arrival times 11. Loyalty program information (general)
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Online customer satisfaction
The reason that optimizing online customer satisfaction is believed to be worthwhile is that it drives behavioral outcomes like customer’ loyalty (Horppu 2008; Evanschitzky et al. 2004; Anderson and Srinivasan 2003) which on his turn reinforces satisfaction, a phenomenon which is stronger online than offline (Shankar et al. 2003). Understanding the specific drivers of satisfaction is therefore particularly useful for managers in effectively allocating resources (Zeithaml 2000). In this paper we investigate the relationship between online service quality and online customer satisfaction (Taylor and Baker 1994; Tian-Cole et al. 2002; Caruana 2002; Montoya-Weiss 2003). Online customer satisfaction is defined as customer satisfaction with a companies’ online offering. In this chapter we further elaborate in the concept of online customer satisfaction as used in this paper.
In general, there are two main ways adopted in literature to define customer satisfaction. Customer satisfaction as an outcome of a consumption experience or customer satisfaction as a process (Parker & Mathews, 2001). Currently the most widely accepted definition of customer satisfaction is that of a process: an evaluation of what was received and what was expected (Oliver, 1977, 1981; Olson and Dover, 1979; Tse and Wilton, 1988). By looking at customer satisfaction as a process, this stream of research focuses more on the antecedents of satisfaction then to satisfaction itself. The origins of this research stream come from the discrepancy theory of Porter (1961), who based satisfaction on customers’ expectations and product evaluations and was followed up by the contrast theory (Cardozo, 1965; Howard and Sheth, 1969), the assimilation-contrast theory (Anderson, 1973; Olshavsky and Miller, 1972; Olson and Dover, 1979) and the discrepancy paradigm (Oliver, 1977, 1981). Then the problem that consumers could also be satisfied by aspects for which expectations never existed (Yi, 1990) was tackled by the value-percept disparity theory (Westbrook and Reilly, 1983) which defined satisfaction as an emotional response triggered by a cognitive-evaluative process. Then several scholars (Fisk and Young, 1985; Swan and Oliver, 1985; Yi, 1990) also added intra-personal comparisons to the equation, by defining customer satisfaction as the amount of discrepancy between a customers’ expectations and evaluations compared to others.
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satisfaction by comparing academic definitions with consumers’ interpretations. They found that most people cited several definitions of satisfaction, illustrating that it is a multifaceted concept. The most common interpretations were that satisfaction is a feeling which results from a process of evaluating what was received against that expected (Parker & Mathews, 2001).
In this paper we adopt the most widely accepted definition of customer satisfaction (Oliver, 1977, 1981; Olson and Dover, 1979; Tse and Wilton, 1988) as being an evaluation of what was received and what was expected. Applying this to the subject at hand the definition of online customer satisfaction would be a customers’ evaluation of a companies’ online offering which was received compared to what was expected. This definition will be used to investigate the relationship between online service quality and online customer satisfaction.
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Online Customer Satisfaction
Online service quality
Check existing booking Loyalty program information (general) Destination information Changing or cancelling existing booking Up-to-date departure and/or arrival times Check-in process Travel related information Loyalty program information (SME)
Pre-purchasing Purchasing Post-purchasing
Special flight offers Time schedule Offers other than flights Booking process H1a & H1b H2a & H2b H3a & H3b
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Research design
Procedure
A survey was conducted by means of an online questionnaire rolled out on the website of a major European airline. The questionnaire is held on the website of this airline in eight different countries: Germany, the Netherlands, France, Ireland, Italy, Sweden, the United Kingdom and the United States. A proportion of the website visitors received a pop-up screen while entering the website. In the language of their preference, this pop-up asked if they wanted to participate in the questionnaire. If this question was answered positively, they were asked to proceed with their doings on the website while the pop-up screen ran in the background. After the participant finished his website visit, the pop-up screen appeared again and the questionnaire was started. The questionnaire then could be used to evaluate the visit website visit which had just taken place.
Sample
As stated above, the data used in this paper was collected by means of an online questionnaire rolled out on the website of a major European airline. A proportion of the website visitors was randomly invited to participate in the questionnaire. They were asked to evaluate the services they used on the airline website. These services are categorized three groups, according to the purchasing process stages of Frambach et al. (2007).
(1) Online services that are generally used in the pre-purchasing stage: destination information, time schedule, special offers flights and special offers other than flights.
(2) Online services that are generally used in the purchasing stage: booking process.
(3) Online services that are generally used in the post-purchasing stage: check-in process, travel related information, check existing booking, changing or cancelling a booking, up-to-date departure and arrival times, loyalty program information (general), loyalty program information (small and medium enterprises.
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September 2011 up and until January 2012. Participant characteristics are shown in Table 1. There can be seen that 50 percent of the sample was male and 40 percent female. With most of the respondents (52 percent) between 45 and 64 years old the sample could also be described as mostly middle-aged. The country that is most represented in the sample in The Netherlands with 48 percent of the respondents. The Netherlands are followed by the United Kingdom (20 percent) and Germany and the United States (both 9 percent). Most of the respondents can be described as frequent visitors of the airline website, with 46 percent stating that they have visited the website more than 10 times last year.
CHARACTERISTIC NUMBER OF RESPONDENTS PERCENTAGE (n=21598) Gender Male 13054 60% Female 8544 40% Age 18-24 yo 824 4% 25-34 yo 2628 12% 35-44 yo 4228 20% 45-54 yo 5910 27% 55-64 yo 5462 25% 65 > yo 2545 12% Country The Netherlands 10277 48% Germany 2017 9% United Kingdom 4281 20% Ireland 279 1% Sweden 553 3% France 1297 6% Italy 1023 5% United States 1872 9%
Visit frequency (last year)
First visit (ever) 2049 10% First visit (this year) 761 4% Visited 2 to 5 times 4606 21% Visited 5 to 10 times 4333 20% Visited more than 10 times 9850 46%
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Measurement
The questionnaire was tailored to let the participant evaluate his website experience as a whole and the specific online services he or she used separately. Respondents were asked how satisfied they were with their overall website experience and were also asked to rate their experience with each of the available online services they used. Both variables were measured on a 5-point Likert-type scales ranging from poor to excellent. The scales used are shown in Appendix 1. Likert scales were used because they are believed to be more stable than other measures (Evans and Heath, 1995). In this paper unbalanced Likert scales are used because unbalanced scales are believed to have a higher reliability than balanced scales (Evans and Heath, 1995). Also unbalanced Likert scales can be used to minimize social desirability bias, arising from respondents' desires to please the interviewer or appear helpful or not be seen to give what they perceive to be a socially unacceptable answer (Garland, 1991). Table 2 shows the descriptive statistics of the scales used and their correlations
Data transformation
Online Customer
Satisfaction Online services
OCS SO-F SO-OF BP TS D/A TI DI LP-G Check B Change B CI LP-SME
Min 1 1 1 1 1 1 1 1 1 1 1 1 1
Max 5 5 5 5 5 5 5 5 5 5 5 5 5
Mean 3,37 3,86 4,33 3,96 4,01 4,18 3,73 4,49 3,82 4,06 4,39 4,11 5,12 Standard deviation 1,03 1,57 1,67 1,36 1,25 1,31 1,29 1,51 1,47 1,36 1,72 1,37 1,45 Special offer flights 0,559 1
Special offer other than flights 0,565 0,848 1
Booking process 0,664 0,579 0,590 1
Time schedule 0,521 0,560 0,568 0,597 1
Up-to-date departure and/or arrival times 0,523 0,550 0,557 0,591 0,791 1
Travel-related info 0,571 0,562 0,599 0,598 0,561 0,563 1
Destination information 0,514 0,616 0,664 0,563 0,595 0,595 0,653 1
Loyalty program information (general) 0,575 0,552 0,580 0,539 0,512 0,511 0,568 0,557 1
Check existing booking 0,601 0,526 0,554 0,642 0,565 0,587 0,619 0,554 0,580 1
Change/cancel existing booking 0,586 0,576 0,606 0,596 0,520 0,542 0,598 0,566 0,552 0,669 1
Check-in process 0,571 0,478 0,504 0,618 0,519 0,532 0,570 0,500 0,496 0,662 0,558 1,000
Loyalty program information (SME) 0,546 0,616 0,671 0,572 0,555 0,567 0,596 0,653 0,657 0,577 0,631 0,538 1,000 * The hyphenated abbrevations in the 2nd row stand for the variables Online Customer Satisfaction, Special Offer Flights, Special Offer Other than Flights, Booking Process, Time Schedule, Up-to-date Departure and/or
Arrival Times, Travel Related Information, Destination Information, Loyalty Program Information (General), Check Existing Booking, Change/cancel Existing Booking, Check In Process and Loyalty Program Information
(Small & Medium Enterprises)
The first four rows of numbers are univariate statistics on the variables measured, the other numbers are correlations of each variable with the others For example, the underlined number (0,563) says that correlation between the variables Destination Information and Booking Process is 0,563. Online Customer Satisfaction means are relatively high. However, each satisfaction level*variable combination contained at least 76 observations.
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Results
This paper focuses on the relationship between online service quality and online customer satisfaction in the airline industry. In this chapter the results of this investigation are discussed. First we will present the results regarding the direction of these relationships, with specific attention for the different purchasing stages. Second we will discuss the asymmetry of the relationships and check if we can categorize the online services as basic, performance or excitement factors (Kano, 1984).
Different impacts in different purchasing stages
This paper implies that the quality of online services influences online customer satisfaction. We measure the impact of this relationship in three stages of the customers purchasing process: the pre-purchasing stage, the pre-purchasing stage and the post-pre-purchasing stage. Therefore we estimated three models, each for every purchasing stage.
The results as displayed in Appendix 2 show that in the pre-purchasing stage the quality of the online services ‘destination information’, ‘time schedule’, ‘special offers flights’ and ‘special offers other than flights’ all have a positive and significant impact on online customer satisfaction. The significance level of these factors all lies below the critical level of 0,05. By looking at the standardized beta coefficient of the dummy variable for the highest quality ratings (excellent (5)), we can investigate the maximum impact a quality enhancement of that specific online service can have on online customer satisfaction. Standardized beta coefficients correct for differences in scaling and measure the effect of the independent variable on the dependant variable. In this model the online service ‘destination information’ has the highest standardized beta coefficient and thus the most potential impact, and therefore it’s quality can be classified as the strongest predictor of online customer satisfaction in this model. Also the model as a whole is significant and the adjusted R-square value of 0,450 which takes the number of variables in the model into account, indicates that the quality of the four before mentioned online services are valuable predictors of online customer satisfaction. Therefore H1a can be accepted.
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independent variable in this model ‘booking process’ has a large standardized beta coefficient and thus a lot of potential impact, and therefore it’s quality can be classified as a very strong predictor of online customer satisfaction. Also the model as a whole is significant and the adjusted R-square value of 0,549, which takes the number of variables in the model into account, indicates that the quality of the four before mentioned online services are valuable predictors of online customer satisfaction. Therefore H2a can be accepted.
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By comparing the standardized beta coefficients of the dummy variable for the highest quality ratings (excellent (5)) of all three models, we can compare the different potential impacts the quality of these online service can have on online customer satisfaction. We can compare these impacts because in all three models the independent variables are all measured the same way using the same 5-point scale and because all three models use the same dependant variable. Table 3 shows that the online service ‘destination information’ has the most potential impact in the pre-purchasing stage, that the online service ‘booking process’ has a major impact in the purchasing stage and that the online service ‘check-in process)’ has the most potential impact in the post-purchasing stage of the consumer buying process. Comparing these three models, we can see that the independent variable ‘booking process’ has the largest standardized beta coefficient and thus the most potential impact, and therefore it’s quality can be classified as the strongest predictor of online customer satisfaction. For determining which of the three models would fit the data best, we also took a closer look at the information criteria which describe the tradeoff between bias and variance of the models (Table 4). We used the Aikake Information Criterion (AIC) for measuring this relative model fit. The AIC gives the purchasing model as the most accurate one. This could be expected, since the post-purchasing model uses the most variables and the AIC does not have a strong penalty for that. When looking at information criterions that penalize for this more, like the Bayesian Information Criterion (BIC) and the Consistent Aikake Information Criterion (CAIC), the purchasing model has the lowest values, hence is the most accurate model. This is in line with the Adjusted R2 outcomes, which are
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also most favorable for the purchasing model. Since this model only inhibits one variable, this variable ‘booking process’ is supposed to be a very strong predictor of online customer satisfaction. This is in line with the large standardized beta coefficient found for this variable, as discussed above.
The asymmetry of the relationships
In this paper we use Kano’s theory of customer satisfaction (1984) as a starting point to categorize online services. In this theory three quality attribute categories with each a different impact on customer satisfaction are distinguished: performance, basic and excitement factors. Knowing which online services are symmetrical performance factors and which can be classified as basic or excitement factor can be valuable information for optimizing customer satisfaction. To provide these insights for online service quality in the airline industry three models with dummy variables are estimated, each for every purchasing stage. With these dummy variables the relative impact on online customer satisfaction of the four highest quality ratings with respect to the lowest quality rating can be investigated. This relative impact can then be used to examine the asymmetrical nature of this relationship and to categorize the online services. The outcomes of estimating the three linear regression models with dummy variables are also shown in Appendix 2.
Performance factors
According to Kano (1984) performance factors are the normal, linear and symmetric category of customer satisfaction influencers. They lead to satisfaction if performance is high and to dissatisfaction if performance is low. With the linear regression model with dummy variables the relative impact on online customer satisfaction of the four highest quality ratings with respect to the lowest quality rating is investigated. This relative impact can then be used to examine the asymmetrical nature of this relationship and to categorize the online services.
Pre-purchasing model Purchasing model Post-purchasing model
Adjusted R2 0,45 0,549 0,501
AIC 7838,4825 7169,291 7073,9927
BIC 8045,3081 7099,0923 7228,4328
CAIC 8067,3489 7169,9725 7274,3494
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As shown in Table 5, the online services ‘special offer flights’, ‘destination information’, ‘booking process’, ‘travel-related information’, and ‘check-in process’ can be categorized as performance factors. There is a (quite) linear relationship between the customers’ quality of the online service and customers’ online customer satisfaction.
Basic factors
According to Kano (1984) basic factors (dissatisfiers) are minimum requirements that cause dissatisfaction if not fulfilled but do not lead to customer satisfaction if fulfilled or exceeded. Negative performance on these attributes has a greater impact on overall satisfaction than positive performance. The fulfillment of basic requirements is a necessary, but not sufficient condition for customer satisfaction. The customer could regard basic factors as prerequisites.
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As shown in Table 6, the online services ‘check existing booking’ and ‘loyalty program information (general)’ can be categorized as basic factors. Having the quality of these online services at a certain threshold level is important, when the quality of an online service drops below that the negative impact on online customer satisfaction is large. However, above this threshold level the positive effect of enhancing online service quality is not that large.
Excitement factors
According to Kano (1984) excitement factors (satisfiers) are the factors that increase customer satisfaction if delivered but do not cause dissatisfaction if they are not delivered. Positive performance on these attributes has a greater impact on overall satisfaction than negative performance. Excitement factors surprise the customer and generate a certain form of excitement.
As shown in Table 7, only the online services ‘loyalty program information (Small & Medium Entreprises)’ can be categorized as an excitement factor. When the quality of this online service is low, the negative impact on online customer satisfaction is low. But when the online quality does get above a certain threshold, the positive impact of enhancing online service quality gets much larger.
Table 7: Excitement factors: Selection of online services which quality level has a asymmetrical impact on online customer satisfaction
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Therefore can be concluded that H1b and H3b must be rejected; not all online services in the pre- and post-purchasing stage have a symmetrical relationship with online customer satisfaction. H2b however can be accepted, since the only online service in the purchasing stage, ‘booking process’ is symmetrical.
Atypical factors
There are some online services found who could not entirely be categorized using Kano’s theory of customer satisfaction (1984). These special cases are the online services ‘time schedule’, ‘special offers other than flights’ and ‘change/cancel existing booking’.
Table 8 shows that enhancing online service quality of the online service ‘time schedule’ from poor (1) to fair (2) impacts online customer satisfaction, but enhancing it from fair (2) to good (3) does not. Therefore could be stated that ‘time schedule’ is a basic factor which causes dissatisfaction if not fulfilled but does not lead to customer satisfaction if fulfilled or exceeded. However, enhancing the quality level from good (3) to very good (4) or excellent (5) does affect online customer satisfaction, which could indicate a performance factor. Therefore we could state that the online service ‘time schedule’ cannot fully be categorized only as a performance, basic or excitement factor.
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Table 8: Atypical factors
Collinearity checks
In this paper a lot of dummy variables are using to provide insights in the nature of the relationship between online service quality and online customer satisfaction. Using this much dummy variables could account for high correlation between variables and consequently high multicollinearity. We measured this by looking at the VIF-values of the dummy variables. Generally a VIF (variance inflation factor) value above 10 indicates the presence of multicollinearity (O’Brien, 2007).
As can be seen in Appendix 2, there are several VIF-values in the pre- and post-purchasing phase model which are higher than 10, thus indicating multicollinearity. This is as expected, since these models contain a lot or correlated dummy variables. A solution for this problem would be dropping one of the variables (Leeflang, 2000). Since the goals of this research is to understand the
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estimates did not change much. This indicates that the parameter estimates can be used (Farrar & Glauber 1967).
To also investigate the collinearity between the different independent variables without the dummy coding effect, we ran the multiple regression analysis without the dummy variables. These outcomes are not in line with the research design, but shown in Appendix 4 to investigate the suspected collinearity in the three models. As can be seen, no VIF-values higher that 2,153 are found in the pre-purchasing, the purchasing and the post-purchasing model . This does not indicate multicollinearity in the model. Therefore, and because model adjustments are not desirable from a practical
perspective, the model is decided to remain the same.
Differences between specific groups
As can be seen in Table 1 the data set used in this paper consists of participants of different age, gender, nationality and website experience. To know if the results as discussed above hold for all different groups we controlled for their differences. No significant differences can be found when estimating the results gender, age and website experience specific. Therefore can be stated that the results as discussed above hold for men and women, for young and old people and for experienced airline website users and non-experienced airline website users the same. However, there are some differences found between countries. In general, The United Kingdom, the United Stated and Ireland, all countries with English as a native language, seemed to have a higher online customer satisfaction than other countries. This is in line with previous research into survey response styles, where these countries were positively associated with more extreme response styles (Johnson et al., 2005). Also
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Conclusions and recommendations
This paper focuses on the relationship between online service quality and online customer satisfaction in the airline industry. The goal was to provide insights in these relationships which could help airline website managers to use online services to optimize online customer satisfaction.
Table 9: Maximum impact of online service quality on online customer satisfaction
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Also in the last, post-purchasing stage of the consumer buying process the quality of the online services ‘check-in process’, ‘travel related information’, ‘check existing booking’, ‘changing or cancelling a booking’, ‘loyalty program information’, ‘loyalty program information (small and medium enterprises)’ have a positive and significant impact on online customer satisfaction. Only the online service ‘up-to-date departure and arrival times’ does not have a significant influence on online customer satisfaction. In this purchasing stage the online service ‘check-in process’ has the most potential impact. Therefore the recommendation for airline website managers would be, depending on their current online service quality level, to invest in the quality of ‘check-in process’ to optimize online customer satisfaction in the post-purchasing stage of the consumer buying process.
To provide deeper insights into the relationship between online service quality and online customer satisfaction, a look into the looked at the symmetry of the relationships is taken. With information about the nature of these relationships check if we can categorize the online services as basic, performance or excitement factors according to the customer satisfaction theory of Kano (1984).
Performance factors Basic factors Excitement factors Atypical factors
Special offer flights Check existing booking
Loyalty program info.
(SME) Time schedule
Destination information Loyalty program info.(general) Special offers other than flights
Check-in process Change/cancel existing booking
Booking process
Travel-related information
Table 10: Classification of online services according to the customer satisfaction theory of Kano (1984).
The online services ‘special offer flights’, ‘destination information’, ‘time schedule’, ‘booking process’, ‘travel-related information’, and ‘check-in process’ can be categorized as performance factors. There is a (quite) linear relationship between the quality of the online service and customers’ online customer satisfaction. Therefore the message for airline website managers would be, that investing in the quality of these online services is worthwhile for optimizing online customer satisfaction. This holds regardless of their current quality level, and more for online services with a high potential impact.
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the quality of these online services until that certain threshold point is reached. Investing more would not affect online customer satisfaction much, but investing less can have a significant negative impact.
Only the online services ‘loyalty program information (Small & Medium Entreprises)’ can be categorized as an excitement factor. When the quality of this online service is not very high, the negative impact on online customer satisfaction is not that large. But when the online quality does get above average (score 3 on a five-point scale), the positive impact of enhancing online service quality gets much larger. Therefore the recommendation for an airline website manager would be that the most ideal situation to invest in this online service is when the online service quality is at this ‘average’ threshold. Off course this holds more for online services with a high potential impact.
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Table 9: Example: impact of the quality of the online service ‘change/cancel existing booking’ on online customer satisfaction.
Scenario analysis
To get more practical and managerial relevancy, the outcomes of this paper were used to perform a scenario analysis within the researched company. Different data sources were combined to provide a scenario analysis tool which can calculate how much extra revenue could be generated per year by improving the quality of a specific online service.
The quality-satisfaction-curves (table 5 - 8) in this paper were combined with the actual quality levels of the companies’ online services. This way we could identify the current slope of the
quality-satisfaction-curve (Table 5 – 8) and investigate what would be the impact on online customer satisfaction when improving online service quality at this moment. To get more practical and financial relevance from these figures, we also investigated the relationship between online customer satisfaction and conversion within the company. We found that, the higher the online customer satisfaction, the higher the probability a website visitor would buy. Assuming a companies’ customer base is the same, a higher conversion level on the same amount of customers means a higher yearly revenue level. Hence, by improving online service quality we can improve online customer satisfaction, improve average conversion rate and thus improve yearly revenue.
This way, different data sources were combined to provide a scenario analysis tool which can calculate how much extra revenue could be generated per year by improving the quality of a specific online service. Specific for the researched company we then investigated what a small and
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business reasons the exact figures cannot be published in this paper, but the proportional differences in revenue impact between the online services are shown in Table 10. We already knew that
improving the quality of the booking process was the most effective way of boosting online customer satisfaction. But when combining these findings with company specific financials and current quality levels of online services, we can now see how effective improving the quality of the booking process is in terms of generating revenue. We can see in Table 10 that the effect on revenue of improving the quality of the booking process is even 60 times higher than the effect of improving the service special offers other than flights. These findings could help managers within the researched company to make grounded investment decisions.
Online services Service quality improvement (5-point scale) Impact on yearly revenue*
Booking process 0,05 € 120
Special offer flights 0,05 € 46
Time schedule 0,05 € 32
Check existing booking 0,05 € 30
Loyalty program information (general) 0,05 € 27
Check-in process 0,05 € 14
Travel-related information 0,05 € 13
Destination information 0,05 € 4
Change/cancel existing booking 0,05 € 3
Loyalty program information (SME) 0,05 € 2
Special offer other than flights 0,05 € 2
Table 10: Proportional differences in revenue impact between different online services
*Figures are manipulated. This information can only be used to get insights in the proportional differences in revenue impact between different online services
An important limitation of this scenario analysis is that it can only calculate the proposed revenue effects of improving the quality of an online service. What the costs of improving such a service would be, and if they also would vary between online services, is out of scope for this paper.
Another important limitation of this paper is the fact that these findings hold for a specific point in time. For instance when the online airline industry evolves the range of online services of importance in this industry can change. Also making use of the perceived quality metric makes these findings time specific; people’s preferences can evolve over time. Continuous monitoring of these quality and satisfaction measures is therefore needed to keep these findings relevant.
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