DIFFERENCES IN ONLINE DISTRIBUTION
CHANNEL CHOICE FOR HOMOGENEOUS GOODS
The case for airline tickets
Author Roeland Kat Student-ID 6046991 Course Master Thesis
Study MSc. Business Administration – Digital Business track Institution University of Amsterdam
Date 23-06-2017 Supervisor R. de Bliek
Statement of originality
This document is written by Roeland Kat who declares to take full responsibility for the contents of this document. I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract
Airline tickets included in a multichannel strategy of an airline are homogeneous. The average price of airline tickets at online travel agencies (OTAs) are higher due to the cost of the partnership between the OTA and the airline. However, despite the higher price of the homogeneous product at OTAs and the tear down of information barriers by the Internet, more airline tickets are sold through online travel agencies than airline websites. This is a remarkable case. Even more so considering research stating leisure air travellers are strongly influenced by price. This study aims to (1) explain the case of online distribution channel choice where consumers purchase a higher priced homogeneous good (airline ticket) despite the Internet tearing down information barriers and (2) explain consumer purchase intention between online distribution channels with and without price comparison functionality (OTAs and airline websites). Based on existing literature, the main predictors of online airline ticket purchase intention are habit, risk aversion, price-saving orientation and performance expectancy. Data is collected with a survey, whereby sixty respondents completed the questionnaire. The findings indicate that habit positively affects the purchase intention of airline websites, whereas risk aversion and price-saving orientation negatively affect the intention to purchase. Furthermore, this study indicates risk aversion and price-saving orientation are both strong predictors of the purchase intention for online travel agencies. Performance expectancy does not significantly affect the purchase intention of both airline websites and online travel agencies. The results imply consumers purchase a higher priced homogeneous good despite the Internet tearing down information barriers, while consumers believe to have taken less risk using price comparison sites and obtained a price saving.
Contents 1. Introduction ... 1 2. Theoretical framework ... 4 2.1 Purchase intention ... 4 2.2 Consumer differences ... 6 2.3 Channel differences ... 8 2.4 Conceptual model ... 10 3. Methodology ... 11 3.1 Survey ... 11 3.2 Sample ... 12 3.3 Measures ... 13 3.4 Statistical procedure ... 16 4. Results ... 16 4.1 Descriptive statistics ... 16 4.2 Purchase intention ... 19 5. Discussion ... 22 5.1 Theoretical implications ... 22 5.2 Practical implications ... 24
5.3 Limitations and further research ... 25
6. References ... 27
7. Appendices ... 33
Tables and figures
Figure 2.1 Conceptual Model ... 10
Table 2.1 Hypotheses overview ... 11
Table 3.1 Questionnaire adoptions ... 15
Table 4.1 Sample ... 17
Table 4.2 Reliability analysis ... 18
Table 4.3 Means, standard deviations and correlations ... 19
1. Introduction
Although homogeneous goods are identical in terms of features, benefits and quality,
price dispersion is present even for homogeneous goods (Stigler, 1961; Lach, 2002). In case of price dispersion of homogeneous goods among sellers and availability of price information to buyers, economic theory suggests a buyer will choose the lowest priced product to maximize wealth (Lach, 2002). However, there is a remarkable case in online channel choice for homogeneous goods: airline tickets.
The Internet became a new distribution channel for airline tickets and has led to significant changes in the airline industry and, specifically, within its e-business processes (Bigné et al., 2010). Internet as a purchase channel tears down the barriers to the availability of competitive airline ticket price information, intensifies market transparency and gives more power to the e-customer (Ruiz-Mafe et al 2009). Current research explains why consumers switched from offline purchase channels to the Internet for buying their airline tickets. The Internet offers a distribution channel that enables customers to book airline tickets rapidly, conveniently and with potential price savings due to the availability of competitive ticket price information (Ruiz-Mafé et al., 2009).
As the Internet removed the need for customers to have physical contact with the vendor, a new trend emerged, the development of online travel agencies, abbreviated OTAs (Button, 2002; Venkateshwara & Smith, 2006). OTAs offer customers a broad selection of tickets from numerous airlines through their websites. Most airlines have a multichannel strategy by selling their tickets through both OTAs and their own websites (Koo et al., 2011). Although working with OTAs is costlier, as a result of fees and commissions that need to be paid over transactions, this party provides a larger consumer base for airlines and potentially higher sales (O’Connor, 1999).
Airline tickets included in a multichannel strategy of an airline are homogeneous. The airline ticket of a specific airline is no different on the OTA website than on the airline’s own website. For example, flight times, travel time and airports involved are identical. The average price of airline tickets at OTAs are higher due to the cost of the partnership between the online travel agency and the airline. However, despite the higher price of the homogeneous product at OTAs and the tear down of information barriers by the Internet, more airline tickets are sold through online travel agencies than airline websites (Phocuswright, 2016). This is a remarkable case. Even more so considering research stating leisure air travellers are strongly influenced by price (Dolnicar et al., 2011). Where does this difference in online distribution choice come from? As heterogeneity exists between buyers and sellers (Carlson and McAfee, 1983; Escobar-Rodríguez and Carvajal-Trujillo, 2014), key drivers relating to differences between consumers and between online distribution channels are studied.
The main predictors of online purchase intention for airline tickets, regarding differences between consumers, are habit and risk aversion (Escobar-Rodríguez and Carvajal-Trujillo, 2014). It could be that consumers have developed an automatic purchase behaviour for online travel agencies while this habit simplifies the buying process due to a perception of being sufficiently informed in offerings and prices of different airlines to make a purchase decision. Also, it could be that OTAs attract more risk aversive consumers due to a broader offering of brands, on which this type of consumer likes to rely (Matzner et al, 2008). Furthermore, as OTAs offer a comparison between airlines, this may create an illusion for consumers to have taken less risk.
The main predictors of online purchase intention for airline tickets, regarding perceived channel differences, are price-saving orientation and performance expectancy. As online travel agencies consist of price comparison websites, which influences online shoppers’ perceptions of internal reference prices (Jung et al., 2014), it can be assumed that consumers perceive a
price saving when purchasing from online travel agencies. Also, as OTAs offer a comparison functionality between airlines, consumers may find their websites more useful and time saving in the purchasing process than airline websites that are limited to their specific airline tickets. Therefore, it can be assumed that consumers expect the performance of online travel agencies to be better.
Although the online purchase intention for airline tickets has been explored, no research has been done (to the best of my knowledge) explaining the mentioned remarkable case of online distribution choice for homogeneous goods. This study aims to contribute to literature by (1) explaining the case of online distribution choice for homogeneous goods where consumers purchase a higher priced airline ticket despite the Internet tearing down information barriers and (2) explaining consumer purchase intention between online distribution channels with and without price comparison functionality. The research question is: what are the key drivers for consumers for choosing OTAs versus the airlines’ online distribution channel?
This research will be done using a survey questionnaire. The sample consist of sixty consumers who bought at least one airline ticket online for the purpose of vacation in the last two years. As the mentioned case of homogeneous goods applies to leisure (holiday) travellers, and research shows there are differences in behaviour with business travellers (Danica et al., 2011), this research is only focused on the leisure segment.
This study is organized as follows. The next section proceeds with the theoretical framework and the formulation of the hypotheses. In the subsequent section, the methodology followed in this study is set out. The results are then presented and in the final section implications, limitations and further research recommendations are given.
2. Theoretical framework 2.1 Purchase intention
Key drivers for online distribution choice are studied in the context of this research (airline tickets) to prevent inconsistencies. For example, research shows that loyalty is an important key driver for online shopping (Llach et al., 2013). However, research regarding online purchase intention of airline tickets shows loyalty is less of a factor for leisure travellers and that they are more influenced by price (Dolnicar et al., 2011). Another example, it is controversially discussed whether individuals are generally averse to all types of risk, regardless of context (Mandrik and Bao, 2005). Multiple studies have shown that consumers perceive differences in the level of uncertainty and negative consequences and therefore risk-reducing behaviour depends on the context (Gemünden, 1985).
The online purchase intention of airline tickets has been analysed using various different theoretical models. Escobar-Rodríguez and Carvajal-Trujillo (2014) state the prominent among these are the Technology Acceptance Model (TAM) (Davis, 1989), the Theory of Planned Behavior (TPB) (Schifter & Ajzen, 1985), and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003). In the field of tourism, these models have been applied to explain users’ actual adoption and use intention of e-commerce websites in relation to airlines (Bigné et al., 2010; Kim et al., 2009; Escobar-Rodríguez and Carvajal-Trujillo, 2013; Ruiz-Mafé et al, 2013).
Escobar-Rodríguez and Carvajal-Trujillo (2013) indicate that the main predictors of online purchase intention for airline tickets are, in order of relevance, habit, price-saving orientation and performance expectancy. Also, they indicate there is no significant impact of effort expectancy, social influence from referents and hedonic motivation on the online purchase intention of airline tickets. Escobar-Rodríguez and Carvajal-Trujillo (2014) indicate that the key determinants for purchasing airline tickets are trust, habit, cost-saving orientation
and performance expectancy. In both studies of Escobar-Rodríguez and Carvajal-Trujillo (2013, 2014) the variables were adopted from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2).
Ruiz-Mafé et al (2013) studied, using the Theory of Planned Behavior, the effect of attitude, subjective norm and perceived control on airline ticket purchase intention and found only attitude had a significant impact. Attitude is a positive or negative evaluation of individual behaviour and refers to global predisposition to develop this behaviour (Ruiz-Made et al, 2013). Bigné et al (2010) used the Technology Acceptance Model and found attitude has a direct influence on airline ticket purchase intention. Also, risk, trust and perceived behavioural control were found to influence purchase intention through attitude.
Risk has been regarded as an influential and wide-ranging variable to explain consumer behaviour, because for consumers it is often more important to avoid mistakes than to maximize utility in purchase decisions (Mitchell, 1999; Wang et al., 2005). Marketing literature argues that the greater the perceived risk in future choice making, the greater the tendency of individuals to diminish choices and participate in relational market behaviour will be (Sheth and Parvatiyar, 1995). Matzler et al (2008) identified an individual’s risk aversion as an important antecedent of brand trust and brand affect. Ruiz-Mafé et al (2009) showed perceived purchase risk has a direct effect on airline ticket purchasing intentions. Psychological risk, performance risk and privacy risk are the main perceived risk dimensions in airline tickets purchase. As an individual difference variable, this basic attitude toward risk is called risk aversion (Matzler et al, 2008).
Price and frequent flyer programs have been identified as key factors in airline choice and loyalty studies (Espino et al., 2008; Hess et al., 2007; Nako, 1992; Suzuki, 2007). Dolnicar et al (2011) found that drivers of behavioural airline loyalty depend on market segment. They explained loyalty programs are strongly associated with behavioural loyalty for business
travellers, but not for leisure travellers. This finding is in line with previous studies into airline choice, for example Hess et al (2007) stated that frequent flyer programs mattered less to holiday makers as well. This may be explained by the fact that frequent flyer privileges can generally only be achieved by people who also fly for business and therefore making it an unattractive proposition for leisure travellers (Hess et al, 2007).
In conclusion, the main predictors of online airline ticket purchasing are habit, risk aversion, performance expectancy and price-saving orientation. Carlson and McAfee (1983) stated heterogeneity may exist between buyers and sellers. Consumers differ in habits (Escobar-Rodríguez and Carvajal-Trujillo, 2013) and with respect to the amount of risk they are willing to incur in a given situation (Mandrik and Bao, 2005). Furthermore, consumers may have different perceptions of online distribution channels in price-saving orientation and performance expectancy due to presence or absence of the price comparison functionality (Escobar-Rodríguez and Carvajal-Trujillo, 2013; Jung et al., 2014).
2.2 Consumer differences
Limayem et al. (2007) defines habit as the extent to which people tend to perform behaviors automatically because of learning. Habit has two distinctions with experience (Venkatesh et al., 2012). First, experience is a necessary but not a sufficient condition for the development of habit. Second, the passing of time can result in the development of different levels of habit depending on the extent of interaction and familiarity that is formed with a certain technology (Venkatesh et al., 2012).
In highly competitive markets consumers can be confronted with a burst of similar offerings to choose from and overwhelmed by conflicting marketing messages (Ernst & Young, 1996). In the market for airline tickets information searching can be costly (Abayi & Khoshtinat, 2016), consumers may simplify their purchase decision strategy by not searching for new information (Zeithaml, 1988). If a company helps the individuals to get more mentally
engaged with a product, by providing supplementary online information, the likely results is an increasing shopping tendency while it seems unlikely for the individual not to purchase (Abayi & Khoshtinat, 2016). Escobar-Rodríguez and Carvajal-Trujillo (2014), show that consumers can develop a habit of using only certain websites in their purchasing process.
It could be that consumers have developed an automatically purchase behaviour for online travel agencies while this habit simplifies the buying process due to a perception of being sufficiently informed in offerings and prices of different airlines to make a purchase decision. Also, consumers could simplify their buying process to prevent being flooded with similar or conflicting marketing messages from airlines.
H1. Habit positively affects the purchase intention for online travel agencies, but not for airline websites.
Risk aversion reflects one’s general tendency to avoid uncertainty (Hofstede, 1980). In general, the notion of risk comprises two components: the uncertainty of an outcome and the importance of negative consequences associated with the outcome of a choice (Rousseau et al., 1998). Risk-averse consumers feel threatened by ambiguous and novel situations and are reluctant to try new products. They perceive them as risky because the performance of these products is rather uncertain and unknown compared to established products and brands (Steenkamp et al., 1999). Hence, risk-averse consumers may refrain from trying new products and brands and they tend to stay with the well-established brands to avoid possible losses of trying unknown brands. It has also been found that risk-averse consumers reduce risk by choosing higher-priced brands (Zhou et al., 2002). Consumers with higher levels of risk-aversion tend to rely more heavily on trustworthy brands (Matzler et al., 2008). In the context of e-commerce, Ernst & Young (1996) highlighted that the increased perceived risk of online transactions heightens the risk reducing effect of a well-known brand. Hence, highly risk-averse consumers might react stronger to brands, brands will give them more pleasure and risk-averse consumers will
generally feel better when they use brands. Consumers are motivated to reduce perceived risks and thus seek cues to simplify their buying decisions (Matzler et al., 2008). Escobar-Rodríguez and Carvajal-Trujillo (2014) state that the quality of information (e.g. on fares, availability, timetables, etc.) is a strong antecedent of consumer trust in e-commerce in the context of online purchasing of airline tickets.
It could be that OTAs attract more risk aversive consumers due to the offering of airline tickets from different airlines, while these customers like to rely on well-established brands (Matzler et al., 2008) and have more trust in the channel due to a higher quality of information provided (Escobar-Rodríguez and Carvajal-Trujillo, 2014). Furthermore, as OTAs offer a comparison functionality between airline offerings, this may create an illusion for consumers to have taken less risk. As airline websites don’t offer multiple brands, risk aversive consumers may neglect this purchase channel.
H2. Risk aversion positively affects the purchase intention for online travel agencies, but not for airline websites.
2.3 Channel differences
Jung et al (2014) stated customers' perceptions of price differ in the online environment due to the presence of price comparison sites. They indicated that price information provided by an online price comparison site influences online shoppers’ perceptions of internal reference prices. Therefore, an assumption can be made that there will be differences in key drivers between OTAs, which offer price comparison functionality between airline companies, and airline websites, who lack this functionality. The influence of a price comparison site on value perception differs according to product type, while these sites increase both transaction and acquisition value perceptions for the non-and-feel product category, but not for the look-and-feel product category (Jung et al., 2014). Furthermore, research suggest that the maturation
of Internet markets may lead to lower prices and price dispersion, but with variations dependent on type of product and retailer (Bock et al., 2007).
As online travel agencies are price comparison websites, which influences online shoppers’ perceptions of internal reference prices (Jung et al., 2014), it can be assumed that consumers perceive a price saving when purchasing from online travel agencies. Therefore, price-saving orientation may positively affect the purchase intention for online travel agencies. As airline websites don’t have this functionality in their online environment, it can be assumed that price-saving orientation doesn’t positively affect their purchase intention.
H3. Price-saving orientation positively affects the purchase intention for online travel agencies, but not for airline websites.
Escobar-Rodríguez and Carvajal-Trujillo (2013) define performance expectancy as the degree to which using a website will provide benefits to consumers in performing a purchase of an airline ticket. Performance expectancy has proved to be the strongest predictor of use intention of a technology, for example an e-commerce website (Escobar-Rodríguez and Carvajal-Trujillo, 2013; Venkatesh et al., 2003). Escobar-Rodríguez and Carvajal-Trujillo (2014) state the strength of the factor performance expectancy indicates that utilitarian attributes positively affect the online purchase intention.
As OTAs offer a comparison functionality between airline offerings, consumers may find their websites more useful and time saving in the purchasing process than airline websites that are limited to their own airline tickets. This means consumers may have more fulfilling, gratifying and facile interactions with online travel agencies than airline websites. As Escobar-Rodríguez and Carvajal-Trujillo (2014) state utilitarian attributes positively affect the online purchase intention, it can be assumed that performance expectancy positively affects the purchase intention for online travel agencies, and while airline websites lack the attribute of price comparison, it will not positively affect this online distribution channel.
H4. Performance expectancy positively affects the purchase intention for online travel agencies, but not for airline websites.
2.4 Conceptual model
Current research regarding the online purchase intention of airline tickets have studied the movement from offline to online (Ruiz-Mafé et al., 2009; Yoon et al., 2006), the effect of consumer behaviour between online and offline channels (Bigné, 2010; Forgas et al., 2012), and purchase behaviour in general (Bigné, 2010; Bukhari et al., 2013; Ruiz-Mafé et al., 2013; Escobar-Rodriguez and Carvajal-Trujilo, 2013). However, to the best of my knowledge no research has been done explaining the remarkable case of airline tickets concerning online distribution choice for homogeneous goods. The research question is: what are the key drivers for consumers for choosing OTAs versus the airlines’ online distribution channel?
The purchase intention for airline websites and online travel agencies are the dependent variables. The variables identified as the main predictors of online purchase intention (habit, risk aversion, price-saving orientation and performance expectancy) are the independent variables. Figure 2.1 shows the conceptual model of this research with the variables of interest.
Table 2.1 provides an overview of the four hypotheses that will be tested. The first two hypotheses relate to differences between consumers and the latter two relate to perceived differences between distribution channels.
Table 2.1 Hypotheses overview
H1. Habit positively affects the purchase intention for online travel agencies, but not for airline websites.
H2. Risk aversion positively affects the purchase intention for online travel agencies, but not for airline websites.
H3. Price-saving orientation positively affects the purchase intention for online travel agencies, but not for airline websites.
H4. Performance expectancy positively affects the purchase intention for online travel agencies, but not for airline websites.
3. Methodology 3.1 Survey
To test the hypotheses an online survey is conducted. The survey will function as the methodological link between the philosophy and subsequent choice of methods to collect data (Denzin and Lincoln, 2011). A survey strategy is usually associated with a deductive research approach and is frequently used to answer ‘what’ questions. This fits with descriptive research. The survey strategy allows to collect quantitative data which can be analysed quantitatively using descriptive and inferential statistics. In addition, data collected using a survey strategy can be used to suggest possible reasons for particular relationships between variables and to produce models of the relationships. Furthermore, the survey strategy is perceived as authoritative by people in general and comparatively easy both to explain and to understand (Saunders et al, 2016). The time horizon is cross-sectional, involving the study of a particular phenomenon at a particular time. This is due to time constraints. As the data is specifically collected for this research, the data source is primary data.
The questionnaire as distributed is disclosed in Appendix 7.1. All variables are measured by the questionnaire (additional explanation in section 3.3 Measures). The data source type is self-completed while the respondent records their own answers. One version of the survey is distributed online by sharing the hyperlink of the questionnaire on the social media platform Facebook. By clicking on the hyperlink, respondents can complete the survey through their web browser on the website of Qualtrics. This program is used while the website is accessible by computer, smartphone and tablet, and the collected data can be directly exported into SPSS. The survey questions adopted (explained in section 3.3 Measures) are in the English language, as the chosen distribution channel of the survey (Facebook) reaches potential respondents all sufficiently comprehending this language, a Dutch translation is unnecessary. 3.2 Sample
The population of interest to this study are consumers who bought at least one airline ticket online in the last two years for the purpose of vacation. As this population is large and a complete list of all cases does not exist, the sampling frame is unknown. This means a probability sample cannot be selected and the sample must be selected in another way. Non-probability sampling provides a range of alternative techniques to select samples. As one version of the questionnaire is distributed through Facebook, convenience sampling is used. Despite criticism on the credibility of convenience samples, Saunders (2012) points out that samples chosen for convenience often meet purposive sample selection criteria that are relevant to the research aim. Sue and Ritter (2007) state the sample size should be at least 10 times larger than the number of variables being studied. This means, as there are two dependent and four independent variables studied in this research, the aim is to have a sample size of sixty individuals.
3.3 Measures
The identified key drivers in the theoretical framework are habit, risk aversion, price-saving orientation and performance expectancy, these are independent variables. The dependent variables are purchase intention airline websites and purchase intention online travel agencies. Gender, age and annual income are control variables. These are used to assess similarity of the sample and to see whether there are significant differences in effect of gender, age and annual income on the dependent variables.
The questions concerning the opinion and attitude data of the independent and dependent variables are adapted from questionnaires (explained in the following paragraphs) and specified to this research context. Table 3.1 provides an overview of all the adopted questions including the original items and the items specified to this research context. The questions adopted use rating questions consisting of a five-point Likert scale (1= strongly disagree, 5 = strongly agree) for measurement. Furthermore, all adoptions have Cronbach’s Alpha larger than .7, which is an acceptable value (Field, 2014). The Cronbach’s Alpha is provided to give an indication of the internal consistency. The Cronbach’s Alpha is used to measure the consistency of responses to a set of questions (scale items) that are combined as a scale to measure a particular concept (Saunders et al, 2016).
The variable habit is measured with four items derived from the original scale of Escobar-Rodríguez and Carvajal-Trujillo (2013) and specified to this research context. The original scale has a Cronbach’s Alpha of 0.910, which indicates excellent internal consistency. An example of a question used in the questionnaire is “Using one particular website for purchasing airline tickets has become natural to me”. The variable risk aversion is measured with three items derived from the original scale of Bao, Zhou and Su (2003) and specified to this research context. The original scale has a Cronbach’s Alpha of 0.760, which indicates an acceptable internal consistency. An example of a question used in the questionnaire is “I’m
cautious in purchasing from new or different websites”. The variable price-saving orientation is measured with three items derived from the original scale of Escobar-Rodríguez and Carvajal-Trujillo (2013) and specified to this research context. The original scale has a Cronbach’s Alpha of 0.827, which indicates a good internal consistency. An example of a question used in the questionnaire is “I can save money by examining the prices of different websites”. The variable performance expectancy is measured with four items derived from the original scale of Escobar-Rodríguez and Carvajal-Trujillo (2013) and specified to this research context. The original scale has a Cronbach’s Alpha of 0.919, which indicates an excellent internal consistency. An example of a question used in the questionnaire is “I want to save time when I use a particular website in the purchasing process”.
The dependent variables purchase intention airline websites and purchase intention online travel agencies are measured with one item derived from the original scale of Escobar-Rodríguez and Carvajal-Trujillo (2013) and specified to this research context. The original scale has a Cronbach’s Alpha of 0.941, which indicates excellent internal consistency. An example of a question used in the questionnaire is “I intent to use airline websites to purchase airline tickets in the future”.
The questionnaire will ask the respondents demographic facts about gender, age and annual income. Gender is treated as a nominal variable. Age is treated as an ordinal variable while respondents fill in the age category: 18-24, 25-34, 35-44, 45 and over. Annual Income is treated as an ordinal variable while there are five categories: <€10.000, €10.000–€30.000, €30.000–€50.000, >€50.000 and ‘I prefer not to answer’. The chosen age and annual income categories are common in this research context (Escobar-Rodríguez and Carvajal-Trujillo, 2013).
Table 3.1 Questionnaire adoptions Habit (HT)
Original scale from Escobar-Rodríguez and Carvajal-Trujillo (2013), α = 0.910: 1. The use of airline company e-commerce websites has become a habit for me. 2. I am addicted to using airline company e-commerce websites.
3. I must use airline company e-commerce websites.
4. Using airline company e-commerce websites has become natural to me. Scale specified to my research context:
1. The use of one particular website has become a habit for me to purchase airline tickets. 2. I am keen to using one particular website to purchase airline tickets.
3. I must use one particular website to purchase airline tickets.
4. Using one particular website for purchasing airline tickets has become natural to me. Risk aversion (RA)
Original scale from Bao, Zhou and Su (2003), α = 0.760: 1. I am cautious in trying new/different products.
2. I would rather stick with a brand I usually buy than try something I am not very sure of. 3. I never buy something I don’t know about at the risk of making a mistake.
Scale specified to my research context:
1. I am cautious in purchasing from new or different websites.
2. I would rather stick with a website I usually buy than try something I am not very sure of. 3. I never buy from a website I don’t know about at the risk of making a mistake.
Price-saving orientation (PO)
Original scale from Escobar-Rodríguez and Carvajal-Trujillo (2013), α = 0.827:
1. I can save money by examining the prices of different airline company e-commerce websites. 2. I like to search for cheap travel deals in different airline company e-commerce websites. 3. Airline company e-commerce websites offer better value for my money.
Scale specified to my research context:
1. I can save money by examining the prices of different websites. 2. I like to search for cheap travel deals on different websites. 3. Some websites offers better value for my money than others. Performance expectancy (PE)
Original scale from Escobar-Rodríguez and Carvajal-Trujillo (2013), α = 0.919: 1. I find airline company e-commerce websites very useful in the purchasing process. 2. My interaction with airline company e-commerce websites is clear and understandable. 3. I find airline company e-commerce websites easy to use.
4. I can save time when I use airline company e-commerce websites in the purchasing process. Scale specified to my research context:
1. A website needs to be very useful in the purchasing process.
2. My interaction with a website needs to be clear and understandable. 3. A website needs to be easy to use.
4. I want to save time when I use a particular website in the purchasing process. Purchase intention (PI)
Original scale from Escobar-Rodríguez and Carvajal-Trujillo (2013), α = 0.941:
1. I intend to use airline company e-commerce websites to purchase tickets in the future. Scale specified to my research context:
1. I intend to use airline websites to purchase airline tickets in the future. 2. I intend to use online travel agencies to purchase airline tickets in the future.
3.4 Statistical procedure
Using descriptive statistics, the characteristics of the sample is stated. Subsequently, the reliability of each scale is tested to ensure the scales are sufficient measures. A Pearson Correlation test was utilized to test the presence and strength of associations between variables. After this test, a multiple linear regression analysis will be conducted to illustrate whether habit, risk aversion, price-saving orientation and performance expectancy may predict the purchase intention of airline websites and online travel agencies. The R Square is reported to illustrate how much of the variance is explained by the independent variables.
4. Results
4.1 Descriptive statistics Sample
The survey provided useful data of 60 respondents. Table 4.1 provides a frequency overview per characteristic of the respondents. There are more male (55 percent) respondents than female (45 percent). The respondents are most frequently in the age categories 18-24 (30 percent) and 25-34 (30 percent) and least frequent in the age category 35-44 (17 percent). None of the respondents preferred not to share their annual income category. The income category with the highest frequency of respondents is €10.000-€30.000 (32 percent), closely followed by the category <€10.000 (30 percent). The least frequent income category of respondents is >€50.000.
Reliability of scales
The independent variables of this study are habit, risk aversion, price-saving orientation and performance expectancy. These variables were measured with three to four items. To test whether the items can be used to combine into one specific variable, the reliability of the scales need to be tested. As explained in section 3.3 Measures, this is examined by the use of Cronbach’s Alpha. A value larger than or equal to .7 is an acceptable value for Cronbach’s Alpha (Field, 2014). The dependent variables are both measured with one item and therefore excluded from the reliability analysis.
The Cronbach’s Alpha for the independent variables are listed in Table 4.2. The variable habit was measured with four items and has a Cronbach’s Alpha of .732, indicating acceptable internal consistency. The variable risk aversion was measured with three items and has a Cronbach’s Alpha value of .875, indicating good internal consistency. The variable price-saving orientation was measured with three items and has a Cronbach’s Alpha of .888, indicating good internal consistency. The variable performance expectancy was measured with four items and has a Cronbach’s Alpha of .757, indicating acceptable internal consistency.
All the variables have a Cronbach’s Alpha value larger than .7. Therefore, all scales used in this study to measure the variables are reliable.
Correlations
To investigate whether two variables are associated with each other, the Pearson correlation coefficient is used. Means, standard deviations and correlations for the independent, dependent and control variables are shown in Table 4.3.
The correlation matrix is a useful method to check for multicollinearity in the data during a preliminary stage, where correlations of >.9 indicate multicollinearity (Field, 2014). Given the information in Table 4.3 all correlation values are below the threshold of >.9. Furthermore, multicollinearity tests were carried out using linear regression diagnostics. The VIF values for the predictor variables habit and performance expectancy are close to 1 and for price-saving orientation and risk aversion below 5 (Bowerman and O’Connell, 1990; Myers, 1990). The tolerance value for habit and performance expectancy are close to 0.9 and for price-saving orientation and risk aversion above 0.2 (Menard, 1995). Therefore, all predictors are included in the final regression model.
The results show purchase intention airline websites (PI-AW) and OTAs (PI-OTA) are significantly negatively correlated (r = -.456, p<0.01). Risk aversion (RA) and price-saving orientation (PO) are a significantly positively correlated with purchase intention online travel agencies (r = .681, p<0.01 and r = .685, p<0.01). In contrast, risk aversion and price-saving orientation are significantly negatively correlated with purchase intention for airline websites (r = -.707, p<0.01 and r = -.719, p<0.01). Price-saving orientation is significantly positively correlated with risk aversion. Habit (HT) is significantly positively correlated with purchase intention airline websites (r = .281, p<0.05), there is no significant correlation with purchase
intention online travel agencies. Performance expectancy (PE) is not significantly correlated with purchase intention of either channels. Performance expectancy is significantly negatively correlated with risk aversion and price-saving orientation.
Gender is not significantly correlated to any of the variables. Age is significantly positively correlated with purchase intention airline websites and significantly negatively correlated with price-saving orientation. Annual Income is significantly positively correlated with purchase intention airline websites and significantly negatively correlated with online travel agencies. Annual income is also significantly negatively correlated with price-saving orientation and significantly positively correlated with age.
4.2 Purchase intention
Regression analysis can be described as a way of predicting an outcome variable from one or several predictor variables (Field, 2014). As there are four independent variables multiple regression was performed. As there are two dependent variables, this statistical process was performed twice. The variables gender, age and annual income were added in the regression as control variables. The first model contains only control variables and the second model contains both control and independent variables. However, no significant relations between the control and dependent variables are found.
Table 4.4 shows the regression analysis of purchase intention of airline websites (PI-AW) and purchase intention for online travel agencies (PI-OTA). The R Square of the first
model PI-AW is 0.081, indicating that around 8 percent of the variance of PI-AW is explained by the control variables. In the second model of PI-AW the R Square is .611, indicating that around 60 percent is of the variance of PI-AW is explained by the control and independent variables. The R Square Change of .530. R Square of the first model of PI-OTA is .072 indicating that around 7 percent of the variance of PI-OTA is explained by the control variables. In the second model of PI-OTA the R Square is .508, indicating that around 50 percent of the variance of PI-OTA is explained by the control and independent variables. The R Square Change of .436.
Hypothesis 1: Habit
The unstandardized coefficient (B) of PI-AW of the independent variable habit is positive and significant at the 0.01 level (b = .392, p < 0.01, β = .255). This implies that an increase in habit may lead to an increase in purchase intention at airline websites. The unstandardized coefficient (B) of PI-OTA of the independent variable habit is positive but not significant (b = .072, p > 0.05, β = .045).
Hypothesis 1 suggests habit positively affects the purchase intention at online travel agencies and not airline websites. However, the results of the regression analyses show habit actually has a positive significant relation with the purchase intention of airline websites and, although positive, does not have a significant relation with the purchase intention of online travel agencies. Therefore, hypothesis 1 is not supported.
Hypothesis 2: Risk aversion
The unstandardized coefficient (B) of PI-AW of the independent variable risk aversion is negative and significant at the 0.05 level (b = -.306, p < 0.05, β = -.325). This implies an increase in risk aversion may lead to a decrease in purchase intention at airline websites. This is in line with the prediction. The unstandardized coefficient (B) of PI-OTA of the independent variable risk aversion is positive and significant at 0.05 level (b = .362, p < 0.05, β = .372).
This implies an increase in risk aversion may lead to an increase in purchase intention at online travel agencies. This is in line with the prediction.
Hypothesis 2 suggests risk aversion positively affects the purchase intention at online travel agencies and not airline websites. The results of the regression analyses show there is a negative significant relation of risk aversion with the purchase intention of airline websites and a positive significant relation with purchase intention of online travel agencies. Therefore, hypothesis 2 is supported.
Hypothesis 3: Price-saving orientation
The unstandardized coefficient (B) of PI-AW of the independent variable price-saving orientation is negative and significant at the 0.05 level (b = -.364, p < 0.05, β = -.441). This implies an increase in price-saving orientation may lead to a decrease in purchase intention at airline websites. This is in line with the prediction. The unstandardized coefficient (B) of PI-OTA of the independent variable price-saving orientation is positive and significant at the 0.05 level (b = .311, p < 0.05, β = .360). This implies an increase in price-saving orientation may lead to an increase in purchase intention at online travel agencies.
Hypothesis 3 suggests price-saving orientation positively affects the purchase intention at online travel agencies and not airline websites. The results of the regression analyses show there is a negative significant relation of price-saving orientation with the purchase intention of airline websites and a positive significant relation with purchase intention of online travel agencies. Therefore, hypothesis 3 is supported.
Hypothesis 4: Performance expectancy
The unstandardized coefficient (B) of PI-AW of the independent variable performance expectancy is negative and not significant (b = -.101, p > 0.05, β = -.065). The unstandardized coefficient (B) of PI-OTA of the independent variable performance expectancy is negative and not significant (b = -.042, p > 0.05, β = -.026).
Hypothesis 4 suggests performance expectancy positively affects the purchase intention at online travel agencies and not airline websites. However, the regression analyses show performance expectancy has no significant relations with the purchase intention for both airline websites and online travel agencies. Therefore, hypothesis 4 is not supported.
5. Discussion
5.1 Theoretical implications
The remarkable case of airline tickets concerning online distribution choice for homogeneous goods was studied. This research aimed to explain this case be examining the relationship between four key drivers and the purchase intention for airline websites and online travel agencies. Based on existing literature, it was hypothesised the key drivers will positively affect the purchase intention for online travel agencies and not for airline websites. The hypotheses were partially confirmed. Multiple important conclusions can be derived from this study.
First, the key driver habit was not significantly related to the purchase intention of online travel agencies, however it was significant and positively related to the purchase intention of airline websites. This was not in line with the prediction. This means consumers don’t purchase at online travel agencies out of habit. In contrast, consumers of airline websites do have a habit of purchasing at airline websites. A possible explanation could be that some customers simplify their purchase decision behaviour by not searching for new information (Zeithaml, 1988), this seems to be the case for airline website consumers. Consumers may
choose to simplify their purchase decision while information search can be costly (Abayi & Khoshtinat, 2016) or to prevent being flooded with similar or conflicting marketing messages (Ernst & Young, 1996). Furthermore, it could be airline companies help individuals to get more mentally engaged with their websites and as a result their shopping tendency increases (Abayi & Khoshtinat, 2016).
Second, the key driver risk aversion was significant negatively related to the purchase intention of airline websites and significant positively related to the purchase intention of online travel agencies. This was in line with the prediction based on existing literature. As risk averse individuals tend to avoid uncertainty (Hofstede, 1980), a possible explanation could be OTAs attract risk averse individuals, as they offer a comparison functionality between airline offerings, this may create an illusion for consumers to have taken less risk. Furthermore, OTAs could also attract risk averse individuals because these consumers tend to rely on well-established brands (Matzler et al., 2008; Steenkamp et al., 1999) and OTAs offer multiple brands. It could also be the case that consumers perceive OTAs to provide a higher quality of information and therefor have more trust in the channel (Escobar-Rodríguez and Carvajal-Trujillo, 2014). As airline websites don’t offer multiple brands and a comparison functionality, risk averse individuals are not attracted to this distribution channel.
Third, the key driver price-saving orientation was significant negatively related to the purchase intention of airline websites and significant positively related to the purchase intention of online travel agencies. This was in line with the prediction based on existing literature. An explanation could be customers’ perceptions of price differ in the online environment due to the presence of price comparison sites. It is indicated that price information provided by an online price comparison site influences online shoppers’ perceptions of internal reference prices (Jung et al., 2014). Therefore, price-saving orientation may positively affect the purchase intention for online travel agencies. As airline websites don’t have this
functionality in their online environment, it can be assumed that price-saving orientation doesn’t positively affect their purchase intention.
Fourth, the key driver performance expectancy was not significantly related to the purchase intention of both airline websites and online travel agencies. This result was not in line with the prediction. An explanation could be both channels don’t offer the level of utilitarian attributes required to positively affect the online purchase intention of airline tickets (Escobar-Rodríguez and Carvajal-Trujillo, 2014).
To conclude, despite the Internet tearing down information barriers, consumers buy a higher-priced homogeneous good (airline ticket) at a price comparison site while they are risk averse, and feel to have taken less risk, and their price-saving orientation is positively influenced. With these insights, this research contributes to the academic field of online distribution choice of homogeneous goods.
5.2. Practical implications
The findings of this study have important practical implications for airline companies and online travel agencies.
As the findings indicate habit is the only significant predictor of purchase intention for airline websites, it can be suggested airline companies should formulate marketing strategies focusing on creating a habit for consumers regarding their purchase behaviour. Airline companies could promote the versatility for which their website could be used, by emphasizing on different events and interests to purchase an airline ticket for. Examples of different events could be a summer holiday, city trip, winter sports or family trip. It could also emphasize on interest, for example a romantic, beach or cultural vacation. By doing this consumer may visits their online channel more often and feels the airline’s products can be used for numerous purposes, creating a habit that could increase purchase intention.
As the findings indicate risk aversion and price-saving orientation are the significant predictors of purchase intention for online travel agencies, it can be suggested OTAs should formulate marketing strategies focusing on price saving and avoiding uncertainty. As a consequence, a consumer will feel a purchase at an OTA implies a price saving, meaning an individual obtained a ticket at low cost or obtained a ticket with extras, for example included travel insurance, additional luggage space or legroom. Furthermore, customers should feel to avoid uncertainty by purchasing at online travel agencies. This can be achieved by providing accurate and complete information during the purchase decision making process of the customer, providing a secure online environment and assuring customer information will not be shared with third parties without their permission.
5.3. Limitations and further research
There are several limitations considering this study. Two of the four hypotheses were found supported. The lack of support of the hypotheses may be influenced by the respondents of the survey. Assessing the demographic characteristics gender, age and annual income,
respondents were not fully balanced between the categories of the demographic characteristics.
Also, the research did not make a distinction between the nationalities of the
respondents. Regardless of the nationality, respondents who purchased an airline ticket in the last two years for the purpose of vacation, could participate in the survey. The national and cultural diversity may cause different results, for example Hofstede (1980) indicates uncertainty avoidance can be different between countries. Interesting further research may extend on literature by analysing possible cross-cultural differences in the key drivers that influence consumer purchase intention of airline websites and OTAs.
Another reason that might cause the rejection of the hypotheses was the sample size. The sample size that was used in this study consisted of sixty respondents, which could be
perceived as small and less representative than a larger sample size. Furthermore, the dependent variables purchase intention airline websites and purchase intention online travel agencies were both measured with one item. Therefore, the reliability could be questioned (Field, 2014).
Future studies could also examine if the findings replicate on other websites in the tourism field for different kinds of products and services such as train tickets, travel packages and accommodation (Escobar-Rodríguez and Carvajal-Trujillo, 2014). Lastly, considering the cross-sectional design of the research, this paper examined consumer purchase intention between airline websites and online travel agencies at a single point in time. It would be helpful to conduct a longitudinal analysis to determine how these variables change over time.
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7. Appendices
7.1 Appendix: Survey Block 1: Introduction Dear participator,
for my Master Thesis at the University of Amsterdam, I do research about online distribution channel choice for homogeneous goods. My thesis is focused on the case of airline travel tickets.
This survey is intended for individuals who bought at least one airline ticket online in the last two years for the purpose of going on a vacation.
An airline ticket can be bought online from two parties: an online travel agency (OTA) or from an airlines’ website. Online travel agencies offer a broad selection of tickets from numerous airlines, where as an airlines’ website offer only tickets from that particular airline itself.
Airline website examples:
• www.klm.nl
• www.easyjet.nl
• www.ryanair.nl
Online travel agency examples:
• www.skyscanner.nl
• www.cheaptickets.nl
• www.travix.nl
On the following page questions need to be answered on a scale of 1 (strongly disagree) to 5 (strongly agree). Choose the answer that fits your opinion best. Please keep in mind
- there are no right or wrong answers,
- only your own opinion is important, not that of others,
- it is important that you also answer in difficult cases, please don’t skip questions. The answers to the questions will be held confidential and will not be shared with third parties. The survey takes about three to five minutes to complete. Thank you in advance! Roeland Kat
Master student ‘Business Administration – Digital Business track’ at the University of Amsterdam
Block 2: Questions
The use of one particular website has become a habit for me to purchase airline tickets. I am keen to using one particular website to purchase airline tickets.
I must use one particular website to purchase airline tickets.
Using one particular website for purchasing airline tickets has become natural to me. I am cautious in purchasing from new/different websites.
I would rather stick with a website I usually buy than try something I am not very sure of. I never buy from a website I don’t know about at the risk of making a mistake.
I like to search for cheap travel deals on different websites. Some websites offer better value for my money than others. A website needs to be very useful in the purchasing process.
My interaction with a website needs to be clear and understandable. A website needs to be easy to use.
I want to save time when I use a particular website in the purchasing process. I intend to use airline websites to purchase airline tickets in the future.
I intend to use online travel agencies to purchase airline tickets in the future. What is your gender?
o Male o Female
In what age group are you? o 18 - 24
o 25 - 34 o 35 - 44 o 45 +
In what income group are you? o < €10.000
o €10.000 – €30.000 o €30.000 – €50.000 o > €50.000
o I prefer not to answer Block 3: Closing remark
Thank you for your participation! If you have any questions please feel free to contact me on roeland.kat@student.uva.nl.
