The influence of low-fare airlines on vacation choices of students: Results of a stated portfolio choice experiment

Tourism Management 33 (2012) 1174e1184

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The influence of low-fare airlines on vacation choices of students: Results of a stated portfolio choice experiment Anna B. Grigolon*, Astrid D.A.M. Kemperman 1, Harry J.P. Timmermans 2 Department of the Built Environment, Urban Planning Group, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 December 2010 Accepted 9 November 2011

This paper reports the results of a portfolio model of vacation choices of students. The portfolio model concerns the combined choice of destination type, transport mode, duration, accommodation, and travel party for vacations. In addition to usual transport modes such as airline, train, bus and car, a distinction was made between low-fare airlines, as these may be especially appealing to students, and regular airlines. Stated choice data were used to estimate the model. The attributes of the transport modes were systematically varied in the experiment, while respondents were faced with free options for the other choice facets. Estimation results indicated that the developed model of portfolio choice performs satisfactory. In substantive terms, it seems that transport mode predominantly influences the portfolio choices. The attributes that are significant tend to amplify the specific role of transport modes in general and low-fare airlines in particular. Ó 2011 Elsevier Ltd. All rights reserved.

Keywords: Vacation choices Students Stated choice experiment Travel behaviour Portfolio choices Low-fare airlines

1. Introduction Occasionally, the airfare from Eindhoven to London, a 50 min flight is 10 Euros, plus taxes (not always charged due to promotions). During the last five years, the fares for this flight have been typically less than the cost of a 7 km taxi ride from the airport to the university, located in the city centre of Eindhoven, the Netherlands. This example suggests that the appearance of low-fare airlines on the travel scene with their no thrills-very low price strategy has caused fierce competition with regular airlines, extending travellers’ choice sets (e.g., Fleischer, Peleg, & Rivlin, 2011). Low-fare airlines companies emerged in the United States in the mid 1970s and in Europe in the 1990s with the specific aim of operating with a lower cost structure than traditional operators in order to create lower fares (Francis, Dennis, Ison, & Humphreys, 2007; Francis, Humphreys, & Ison, 2004; Pels, Njegovan, & Behrens, 2009). The tourism market is the main target of these companies, although they have also stimulated the demand of business travellers (Brons, Pels, Nijkamp, & Rietveld, 2002). In an effort to compete with the low-fare airlines, some of the major airlines have responded by lowering prices, simplifying their price

* Corresponding author. Tel.: þ31 40 247 2861; fax: þ31 40 243 8488. E-mail addresses: [email protected] (A.B. Grigolon), a.d.a.m.kemperman@tue. nl (A.D.A.M. Kemperman), [email protected] (H.J.P. Timmermans). 1 Tel.: þ31 40 247 3291; fax: þ31 40 243 8488. 2 Tel.: þ31 40 247 2274; fax: þ31 40 243 8488. 0261-5177/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2011.11.013

structure and improving internet sales facilities. Few if any have been truly successful in combining these different business models. In the Netherlands, the study area of the present analysis, the appearance of low-fare airlines coincided with an increase of leisure time spent away from home. Data from SCP (2001) showed that although the average amount of leisure time in the Netherlands has remained constant from 1975 to 1995, the way it is been spent has substantially changed. A larger proportion of the free time of Dutch people is spent away from home, and mobility in off-work hours has increased. Over one-third of the leisure time spent away from home is used for transport and leisure activities. According to Harms (2006), showing evidence of a continuation of this trend, 38% of the trips in The Netherlands are made for leisure activities, 22% relate to shopping activities, and only 17% of the trips are to and from work. According to Bargeman and van der Poel (2006), Dutch vacationers belong to the most active travellers for pleasure in Europe and the world. One may expect that low-fare airlines are especially appealing to those segments of the travelling population that are price-sensitive and have ample time (Castillo-Manzano, López-Valpuesta, & González-Laxe, 2011; Martínez-Garcia & Raya, 2008). The segment of students satisfies these criteria and is thus a major market segment for low-fare airlines. Low-fare airlines, however, do not always ask the lowest price (usually there is a high ticket price variation and when booked a few days before travelling the price may be higher than the price of regular airlines), often offer just one or two flights per day and often their departure time may be less convenient. Moreover, they tend to use less accessible

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airports, increasing the hassle of getting there and possibly also the total travel time (access, flight plus regress time) and travel costs. Low-fare airlines usually do not allow for choice of seats and onboard services, if any, need to be paid. In that sense, travellers in general face a trade-off between costs, convenience, and service when choosing between regular and low-fare airlines. In Europe, in addition to the airlines network, there is an extensive network of train and bus lines serving virtually all major cities and tourist areas. There is a choice between low-fare and more service options, especially for buses. Thus, travellers face quite complex choice sets, with many different options, including different types of airlines, trains, busses and car. To better understand the role of low-fare airlines in this context, it is paramount to analyse the portfolio behaviour of its potential market. The vacation trip decision involves the combined choice of destination, travel party, duration, accommodation, and transport mode. Unfortunately, relatively little is known in the literature on how especially students make decisions among these various choice options and choice facets. Therefore, the goal of this study is to analyse factors influencing transport mode choice behaviour of students in the content of their vacation behaviour. More specifically, the following research questions are addressed. To what extent do low-fare airlines compete with regular airlines on the one hand and transport modes such as the train, bus, and car on the other? To what extent does this competition depend on other choice facets such as duration, destination, travel party and accommodation? What is the relative importance of context and attributes on portfolio decisions? The paper is organised as follows. First, background literature will be discussed, followed by a description of the experiment for portfolio choices including the experimental task that was designed. Next, we will report the data collection and sample characteristics. This is followed by a discussion of the estimated coefficients of the portfolio choice model, in terms of main and interaction effects. The paper is concluded with a summary of the results and a discussion of some managerial implications and limitations of the research project. 2. Literature review 2.1. Portfolio travel choices Most previous studies on travel and vacation choices investigated a single choice facet. Most studies focussed on destination choice and tried to predict visitor volumes and the economic impact to a particular destination (Jeng & Fesenmaier, 2002). Some recent examples are Lyons, Mayor, and Tol (2009) who addressed destination choices of Irish households and a study by MartínezGarcia and Raya (2008) who investigated length of stay of lowfare travellers in Spain. However, vacation choices typically involve several travel choice components that are often interrelated (e.g., Bargeman & van der Poel, 2006; Decrop, 2006; Hyde & Laesser, 2009; Jeng & Fesenmaier, 2002; Woodside & MacDonald, 1994). Only few studies investigated multiple or also called portfolio decisions made by tourists. Dellaert, Ettema, and Lindh (1998) indicated that tourists first have to decide whether they would like to make a trip or not. Subsequently, if they want to make a trip the most important decisions they have to decide upon are: trip destination, travel mode, trip duration, travel party, when to make the trip, and accommodation. In their study, they concluded that potential benefits for marketing may exist in bundling several aspects of travel choices. Oppermann (1995) also concluded that when focussing on vacation travel data such as destination(s), mode of transportation, accommodation type, travel party, and length of

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stay are needed. Nicolau and Más (2008) modelled a multi-stage vacation decision making process including first the choice of going or not going on vacation and next the choice of type of destination. Finally, Tay, McCarthy, and Fletcher (1996) specifically developed a portfolio choice model of the demand for recreational trips. They presented consumers with a set of trip portfolios which are made up of alternative destinations, trip frequencies, and durations. Their model was applied to recreational fishing trips. They used survey data as input to their model. 2.2. Low-fare airlines Although vacation choice behaviour includes several choices, in this paper specific attention is paid to the role of low-fare airlines within this context. There is a large number of studies that analysed air travel choice behaviour and also the number of studies that have dealt with the development of low-fare airlines is steadily increasing (e.g., Castillo-Manzano et al., 2011; Hess, Adler, & Polak, 2007; Rose, Hensher, & Greene, 2005). However, research on the role of low-fare airlines in vacation choice behaviour is still rather limited. Most studies within the tourism context addressed the managerial or economic implications. For example, Kim and Lee (2011) investigated the relative influence of perceived service quality by low-fare airlines passengers on customer satisfaction and the relation between customer satisfaction and behavioural intentions. Warnock-Smith and Potter (2005) conducted an exploratory survey of eight European low-fare airlines. They found that demand for low-fare services is the most important choice factor. Additionally, they concluded that passengers are willing to travel further to access cheaper flights. Castillo-Manzano et al. (2011) discussed the contribution that the introduction of low-fare airlines has made to the various tourism industries. They concluded that low-fare airlines have resulted in new and induced demand from passengers, attracting new users who previously could not afford to travel by airplane and in an increase in the travel frequency of current passengers. Martínez-Garcia and Raya (2008) provided some characteristics of the low-fare airline tourist: they are more accustomed than those who use traditional airlines to using new information and communication technologies (internet) for researching, reserving, and paying for the trip. They are also more autonomous with regard to organising the trip and are greater users of free accommodation. These characteristics typically match the students segment, making them an interesting potential market for the low-fare airlines. 2.3. Students In general, the literature on vacation behaviour of students is relatively scarce and we could not find any literature on the choice of transport mode for this segment at all. The existing literature seems to focus on other issues. For example, Oppermann (1995) examined differences in travel patterns for various cohorts. He found that the younger generation appears to be travelling more frequently and farther than previous generations of the same age. Carr (1999) discussed gender differences in leisure activities of students and suggested that if there is a difference in the behaviour of young men and women tourists then it may be displayed in their leisure activities, because of differences in their socio-cultural norms and values. Another study by Carr (2002) provided a comparative analysis of the behaviour of young tourists on international and domestic trips, suggesting that young and single British tourists on domestic vacations behave in a more passive and hedonistic manner compared to those holidaying at the

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international level. Ross (1993) investigated the destination evaluations, revisitation intentions, vacation preferences, and socio demographic characteristics of budget travellers to the wet tropics region of Northern Australia and found that this segment is mostly young and often well educated. Finally, Sung (2004) clustered adventure travellers and found one group that was labelled ‘budget youngsters’. A typical traveller of this type was between 19 and 34 years of age, with a relatively low income and therefore rather price-sensitive. They prefer to arrange trips by themselves. Rather than travelling alone they prefer to take trips with friends. 2.4. Stated or revealed choice behaviour A portfolio model of travel behaviour can be based on observed actual choices (revealed choice) or on stated choice under controlled laboratory conditions in which respondents express their choices for systematically varied hypothetical choice situations. Because the latter approach has the main advantage that the researcher has control over the factors assumed to influence the choice process, the stated choice approach was adopted in this study. It allows better disentangling the effects of context and attributes on preferences and choices. Hess et al. (2007) discussed these advantages of using stated preference data in their study on airline choice behaviour. However, stated choice experiments may lack in realism. It is important therefore to carefully define the attributes and the levels that are used to describe the alternatives presented to respondents (Hensher, Rose, & Greene, 2005). The quality of the collected data may also be affected if tasks are too difficult or too long (Carson et al., 1994). Portfolio choices (i.e. the choice of multiple facets making up a choice alternative) do present a challenge in this context. Standard experimental designs typically involve singlefaceted profiles. Portfolios increase the number of possible combinations of attribute levels exponentially and therefore the construction of a feasible, realistic and not too demanding experimental design offers a challenge. Moreover, when a feasible design has been constructed, the number of possible choices is extremely large, while not all choices will be observed. Consequently, the estimation of the parameters of the portfolio choice model offers a challenge. 3. The portfolio choice experiment 3.1. Terminology Before explaining the design of this study, it may be useful to define the terms that will be used in this study. A portfolio choice is defined as the choice of a combination of choice alternatives. Examples would be the choice of appetiser, main course, and dessert, or the choice of different newspapers and magazines. In the present study, the portfolio concerns the combined choice of destination, transport mode, travel party, accommodation, and duration of a vacation trip. Note that traditional choice experiments are typically concerned with single choices, such as the choice of a brand, or the choice of a transport mode. In this paper, we refer to the distinct choice options that make up the portfolio as choice facets. Thus, destination, transport mode, accommodation etc. are the choice facets. In principle, each of these choice facets or choice options can be characterised in terms of a bundle of attributes. Because choice experiments are based on discrete options, each attribute is defined in terms of a set of discrete attributes levels. If the attributes are categorical, the levels are a priori defined. Continuous attributes on the other hand need to be discretionized.

3.2. Approach In general, the design of a portfolio experiment follows the same stages as the design of standard choice experiments as they have been applied or suggested in tourism research (e.g., Louviere & Timmermans, 1990). Classical choice experiments, derived from Lancaster’s Theory of Value (Lancaster, 1966) and Random Utility Theory (Thurstone, 1927) are concerned with a single choice facet (e.g., choice of transport mode). In contrast, portfolio choices deal with a combination of multiple choice facets. First, the researcher needs to identify the set of facets that make up the portfolio choice under investigation. Next, for each of the attributes of these facets, a set of appropriate attribute levels needs to be defined. Then, these attribute levels need to be combined into attribute profiles and placed into choice sets such that the assumed choice model can be estimated and the experimental task is feasible and not too demanding. In the present study, the identification of the facets that influence individuals’ vacation behaviour was based on a literature survey of tourist choice behaviour and choice of airline (e.g., Collins, Hess, & Rose, 2007; Decrop, 2006; Dellaert, Borgers, & Timmermans, 1995, 1997; Hensher, Stopher, & Louviere, 2001; Hess et al., 2007; Lyons et al., 2009; Oppermann, 1995; Pearce & Lee, 2005; Van Middelkoop, 2001). Based on this literature review, the following choice facets were selected: destination type, transport mode, accommodation, duration, and travel party. Each of these choice facets involved 4 alternatives. For example, duration was varied as 1 day, 1 to 3 nights, 4 to 8 nights or more than 9 nights. The total number of possible combinations generated by these 5 choice facets would be 1.024 (5 choice facets with 4 alternatives each, thus 45 ¼ 1.024). Portfolio choice problems can be formulated in different ways. For example, respondents may be asked to make choices between a set of pre-defined combinations of alternatives, meaning that they have to choose the combination they like most from the choice sets that are presented to them (e.g., Dellaert et al., 1995, 1997; Louviere & Street, 2000). It implies that the researcher constructs a set of portfolio options, consisting of combinations of attribute levels, defined across all choice facets. This is similar in nature to standard choice experiments. Alternatively, also known as “free format choices”, respondents can be asked to choose any combination of options specified for different choice facets (e.g., Wiley & Timmermans, 2009). The approach applied in this study is a mixture of these two approaches: the attribute profiles of one choice facet were explicitly varied in the experiment, while respondents were faced with free choice alternatives for all remaining choice facets. Attributes were only systematically varied for the transport mode alternatives: low-fare airlines, regular airlines, train/bus, and car. For each of these transport modes, a set of three level attributes was systematically varied: costs, travel time, time of the day, and time to get to the station/airport/pick-up point. All the other choice facets, destination, duration, travel party, and accommodation were each described by four alternatives. The primary goal of the analysis was to understand transport mode choice decisions embedded in other facets such as destination, duration of the trip, travel party and choice of accommodation. In addition, one may expect that these facets may have interactions between them. In the present study, the interactions between transport mode and destination type, between accommodation and travel party and between destination and duration of the trip were chosen to be analysed, even though it is known that many other interactions could be explored. We believe that the interaction between transport mode and destination may be very strong. For example, it is not possible to go from Europe to Australia by car. Another strong interaction may be between travel party and

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accommodation, as for example it is not common that a couple with a young child will stay in a hostel. It is also expected that choosing a geographically close or far destination may impact the decision whether to take only a short break or a longer trip. Therefore, the interdependency between destination and duration of the trip was also explored. The present study will analyse the main and these selected interaction effects of portfolio vacation choices of students. Fig. 1 gives a schematic overview of the experiment. We will now discuss the choice facets, the alternatives and the experimental task in more detail. 3.3. Choices and alternatives 3.3.1. Destination choice In the portfolio choice experiment, respondents were asked to choose the destination of their trip from a list of 14 cities. This list was constructed based on the criterion that the destinations had to be within Europe and be reachable by low-fare airlines, regular airlines, train, bus or car from the Netherlands. Although in principle, destination-specific models can be estimated if the sample is large enough, in the present study the destinations were grouped according to some underlying dimension. Admittedly, such grouping may lead to biases in the sense that we implicitly assume that respondents are familiar with all destinations and would cognitively group them in the same manner. In the context of a wider study, additional information on cognitive representations of destinations, including awareness, should preferably be collected and included in the analysis. Keeping this in mind, for this paper, we classified the 14 destinations into four groups based on their geographical proximity. The first group includes Barcelona, Madrid, Alicante, and Porto. Rome, Milan, Marseille, and Pisa are part of the second group. London, Stockholm, and Dublin are in the third group while Budapest, Prague, and Katowice (Krakow) are in the fourth. It should be noted that consequently the estimated utilities for destination type depend on the classificatory principle used to group the destinations (by distance, or travel time, or culture, or image etc.). It is important to highlight that this classification was arbitrary and could be replaced by other classifications in future analyses. 3.3.2. Choice of trip duration Based on official classifications of the Central Bureau of Statistics (CBS, 2010), the following definitions and classifications were used:  Long Holidays: Absence from home for leisure or recreation purposes for at least four consecutive nights. Staying at the homes of relatives or friends abroad is included.  Short holidays: Absence from home for leisure or recreation purposes with a minimum of one and a maximum of three

CHOICE FACETS

Portfolio Choice Experiment

Stated Choice Experiment

1177

consecutive nights. Staying at the homes of relatives or friends abroad is included.  Day trip: A recreational activity for which a person is away from the usual environment for at least 2 h, without an overnight stay. Using these definitions, duration was varied in the experiment in terms of four levels: one day (without overnight stay); 1 to 3 nights, 4 to 8 nights, more than 9 nights. 3.3.3. Travel party choice Travel party is the person/people with whom the trip is made. In this experiment, the following options could be chosen: alone, with partner, with family or with friend(s). 3.3.4. Accommodation The choice of accommodation was varied in terms of hotel/ rented apartment, hostel, camping, and at friend’s/relative’s house. 3.3.5. Transport mode choice e the stated choice experiment The transport mode alternatives consist of low-fare airlines, regular airlines, train/bus and car. The transport mode alternative specific attributes considered were costs, travel time, time to get the airport/train or bus station and time of day, and were determined as follows:  The costs of the transport modes included in the experiment were shown to respondents as a monetary value (in Euros) based on prices of a return ticket, advertised in websites of airline, train or bus companies, and fuel expenses in the case of car. The cost attribute was destination-specific and varied in three levels: the middle level was assumed as the price searched in the internet, and the low and high levels were varied differently for each transport mode. For low-fare airlines, the first and third levels were defined as respectively 60% and þ60% of the middle level. For regular airlines, and train/bus, this variation was 20% and þ20%. The variation used for car was also 20% and þ20%, and the price of the trip was calculated on the basis of average fuel costs in European cities (AAIreland, 2010). The variation of prices for low-fare airlines is bigger than for the other transport modes, which was reflected in the choice of attribute levels. This operational decision means that estimated coefficients for the price attributes relate to differences. Moreover, this means that in this analysis, we have implicitly assumed that students exhibit the same preferences for these price differences. In future research, different models relaxing this assumption, can be estimated.  Travel time is the time spent on travelling during a one-way trip. The travel time was calculated based on the distance from The Netherlands and an average speed. The low level represents 10% less than the medium (calculated) level, while

ALTERNATIVES ATTRIBUTES

Destination

4 alternatives

Transport mode

4 alternatives

Duration

4 alternatives

Travel party

4 alternatives

Accommodation

4 alternatives

4 attributes

Fig. 1. Conceptual framework of the portfolio choice experiment.

LEVELS

3 levels

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the high level is 10% higher than the medium level. Travel time is the same for low-fare and regular airlines.  Time to get to airport/station is the time required to reach the airport in the case of using regular or low-fare airlines, and the time to reach the train or bus station for the train/bus alternative. This attribute is not relevant for the car alternative because it is assumed that the starting point of a trip by car is the traveller’s house. The levels varied in the experiment were respectively 60, 90 and 120 min. The range would cover all main and regional airports, train stations and international bus travel stations in The Netherlands.  Time of day is the time of the day (morning, afternoon, evening) of the beginning of the trip by plane, train or bus. This attribute was not varied for the car as travellers do not face any limitations when choosing this alternative. 3.4. Experimental task The task of the portfolio experiment required respondents to plan their next holiday(s). First, they were asked to decide on the destination, from a list of 14 cities. Based on the selected destination, 9 different screens (choice sets) were presented from which respondents were asked to indicate their choice of transport mode, the characteristics of which were varied in terms of cost, travel time, time of the day, and time to get the station/airport. Fig. 2 shows an example. Respondents also had the option of not travelling if the presented attributes of transport modes were not attractive to them. In addition, they were asked to choose

accommodation, travel party, and duration of the trip. This approach was chosen because most websites of airlines also start with a choice of destination. Moreover, the destination needs to be known first to derive the destination-specific costs and travel times. Because the effect of attributes may vary for the transport mode alternatives, transport-mode specific attributes were specified. All 13 attributes were varied according to three levels, resulting in a 313 full factorial design for this choice facet only. Thus, the total number of attributes across transport modes is 13, noting that travel time was the same for low-fare and regular airlines. An orthogonal fraction of this full factorial design, consisting of 27 attribute profiles was constructed, enabling the estimation of all attribute main effects plus some interactions. Table 1 lists the choice facets, choice alternatives, attributes, and attribute levels. The choice of an orthogonal fraction across the transport modes implies that attribute levels were varied independently both between and within transport modes. Choice sets were therefore automatically created and respondents were invited to choose the transport mode and other facets of their portfolio they like best for the chosen destination. 3.5. Administration The experiment was conducted in March 2010 using an internet-based survey. Dedicated software developed by and for our research group was used for creating and administrating the survey, which included the portfolio choice experiment. A web-

Fig. 2. Example of the choice of transport mode in the experimental task.

A.B. Grigolon et al. / Tourism Management 33 (2012) 1174e1184 Table 1 The choices, alternatives, attributes, their levels, and coding. Choice facets

Alternatives

Attributes

Levels

Destination

Group 1: Barcelona, Madrid, Alicante, Porto Group 2: Rome, Milan, Marseille, Pisa Group 3: London, Stockholm, Dublin Group 4: Budapest, Prague, Katowice Transport Regular airlines (1) Cost Destination specific mode (low, medium, high) Destination specific (2)a Travel time (low, medium, high) (3) Time of the day morning, afternoon, evening (4) Time to get 60, 90, 120 min Low-fare (5) Cost Destination specific airlines (low, medium, high) Destination specific (2)a Travel time (low, medium, high) (6) Time of the day morning, afternoon, evening (7) Time to get 60, 90, 120 min Train/Bus (8) Cost Destination specific (low, medium, high) Destination specific (9)a Travel time (low, medium, high) (10) Time of the day morning, afternoon, evening (11) Time to get 60, 90, 120 min Car (12) Cost Destination specific (low, medium, high) (13) Travel time Destination specific (low, medium, high) Duration of 1 day the trip 1e3 nights 4e8 nights 9 nights or more Travel party Alone With partner With family With friend(s) Accommodation Hotel/rented apartment Hostel Camping Friend’s/relative’s house a

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universities in the province of Brabant, The Netherlands. In addition, invitation cards were personally given to students at train stations and in city centres. They were briefly explained about the aim of the project and if interested guided to a website for the survey and the experiment. Five 100 euro travel vouchers were raffled among participants. The response rate for the invitation sent by email was 14% and 6% for the cards. We also posted the link to the survey in newsletters of universities and schools and on social network websites (e.g., Facebook), but the response rate was much lower (respectively 2% and 0.01%). Grigolon, Kemperman, and Timmermans (2010) provide more details about the recruitment through social network websites. The profile of the respondents is presented in Table 2. It shows an almost equal mix of women and men. Almost fifty percent is in the 19e24 age group, while another thirty five percent is between 25 and 30 years of age. They belong to households mostly consisting of one to three people. The largest group of students has a high education level, and almost seventy percent has less than V1000 per month to spend on their activities including leisure. Most respondents spend between 26 and 40 h per week studying and most of them do not work or only spend up to 10 h per week on some kind of work activity. 5. Analysis and results The results reported in this article are based on a Multinomial Logit (MNL) Model. For each observed portfolio choice (the combination of the alternatives chosen for the 5 choice facets) a choice set was created, consisting of the chosen portfolio choice, the non-chosen transport mode alternatives keeping all other choice facets the same, and a randomly selected portfolio choice. As it is well known, a problem of choice experiments is that the responses of the same respondent are treated as independent observations in the estimation. Consequently, asymptotic t-statistics are likely inflated (e.g., Louviere & Woodworth, 1983; Ortúzar & Willumsen, 2001).

Note for both Regular and Low-fare airlines, the travel time is the same.

based survey was used for the following main reasons: (i) it allows a dynamic sequence of questions depending on previous answers (ii) it allows the possibility of randomisation of choice sets, as we presented each respondent with only 9 of 27 attribute profiles for the transport mode alternatives (iii) answers can be automatically downloaded by means of drop-down lists, avoiding the data entry process, and thus, saving costs and eliminating human errors (e.g., Cobanoglu, Warde, & Moreo, 2001; Cole, 2005; Ilieva, Baron, & Healey, 2002; Verhoeven, Arentze, Timmermans, & van der Waerden, 2008). 4. Sample A total of 141 respondents participated in the experiment. Respondents had the possibility to plan up to 4 different holidays. The purpose was to analyse whether students were able to plan in advance their main holidays of the year, giving the fact that almost all student’s holidays are fixed due to school breaks. It was assumed that the choices made for subsequent trips, if any, were independent from one another, which may be too rigorous as an assumption. From the 141 respondents who participated on the experiment, 62 of them planned two recreational holidays. It means that the results of the present study are based on 203 planned trips. Because the study was targeted at students, invitations were sent by email to students from a sample of schools and

Table 2 Sample characteristics. Variables

Levels

%

Gender

Female Male 14e18 years 19e24 years 25e30 years 31e36 years Low Medium High Not informed Less than V1000 V1001eV2000 More than V2001 Not informed 1e3 4e6 More than 7 1e10 11e25 26e40 More than 41 Not working 1e10 11e25 26e40 More than 41

46.1 53.9 9.7 48.1 35.1 7.1 0 5.8 92.8 1.4 69.5 24 3.9 2.6 70.8 25.3 3.9 4.9 9.9 64.5 20.6 47.4 35.7 8.4 7.8 0.6

Age

Education level

Income

Number of people in household

Hours spending studying per week

Hours spending working per week

N ¼ 141.

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Table 3 presents a comparison of model performance for a model with main effects only, a model with main effects and selected interaction effects, and a model with main effects and only the statistically significant interaction effects. If the log likelihood of an estimated model (LL[B]) can be shown to be a statistical improvement over the log likelihood of the base model (LL[0]) (i.e. statistically closer to zero), then the model may be thought of as being statistically significant overall (e.g., Hensher et al., 2005). The test to compare the LL function of an estimated model against the LL of its related base model is called the LL ratiotest (Theil, 1971). The test statistic is c2 ¼ 2(LL[0]  LL[B]). This test can also be used more generally to compare the goodness-of-fit of hierarchically related models, where the degrees of freedom is equal to the difference in the number of estimated coefficients. If the c2 value exceeds the critical chi-square value, then the specified model performs better than the comparison model. If on the other hand, the c2 is less than the critical chisquare value, it is not possible to conclude that the specified model is better than the base model. First the model with all interactions was compared to the model with main effects only. This model did not outperform the model with main effects only. Then the model with only the significant interactions included was compared to the main effects only model. The difference in log likelihood between these models was statistically significant. Therefore, we choose to report the results of the model with the main effects and significant interaction effects. In order to organise the discussion of the estimated parameters for our model, we will first discuss the estimated parameters for the alternatives of the choice facets (destination, accommodation, transport mode, duration, and travel party) as shown in Table 4. Then, we continue with a discussion of the attributes varied for the transport mode alternatives (Table 5), and finally report the estimated interaction effects between destination and transport mode, and between accommodation and travel party (Table 6). 5.1. Estimated parameters for the choice facets Table 4 displays the estimated parameters for alternatives of the choice facets considered in the portfolio choice experiment e destination, duration of the trip, accommodation, travel party, and transport mode. To interpret the results, it is important to realise that the attribute levels were effect-coded (see Table 1). This means for example that for every three level attribute, two indicator variables were constructed. The first of these, coded as (1,0) is associated with the first attribute level. The second indicator variable, coded as (0,1) is associated with the second attribute level. The third attribute level is coded (1, 1) on these two indicator variables. Consequently, the estimated utilities for each attribute sum to zero across the levels of that attribute. The t-statistics of each part-worth utility indicate any significant differences against the mean utility of that attribute. Table 4 indicates that the most preferable group of destinations is Barcelona, Madrid, Alicante and Porto, with significant effects at the 5% probability level. The lowest part-worth utility is estimated for the Eastern European countries, indicative of relatively low choice probabilities. This finding may reflect the traditional low

Table 4 Parameter estimates and their significance e portfolio choice variables. Choice facets

Attribute levels

Destination

Barcelona, Madrid, Alicante, Porto Rome, Milan, Marseille, Pisa London, Stockholm, Dublin Budapest, Prague, Krakow Duration of 1 day the trip 1e3 nights 4e8 nights 9 nights or more Accommodation Hotel/rented apartment Hostel Camping Friend’s/relative’s house Travel party Alone With partner With family With friend(s) Transport Regular airlines mode Low-cost airlines (constants) Train/bus Car No trip

Estimates t-statistics Importance (rank) 0.420

3.669

0.086

0.649

0.030

0.215

0.363

1.834

2.219 0.554 1.346 0.318 0.207 0.439 0.072 0.304 0.034 0.522 0.868 0.312 0.266 2.254 1.558 0.962 0

9.039 4.232 10.119 1.293 1.867 3.336 0.408 1.545 0.202 4.108 4.802 1.767 3.643 37.637 12.42 8.089

0.783 (4)

3.565 (2)

0.743 (5)

1.389 (3)

3.812 (1)

Note: the t-statistics for the base level of the attributes were calculated by changing reference levels.

awareness of touristic qualifications of Eastern European cities as in general these cities never had a strong market position for older cohorts due to linguistic and cultural differences. Only the first group of destinations resulted in significant estimated coefficients. As for duration, estimated utilities tend to increase with longer duration. However, for long trips (9 nights of more), there is evidence of decrease in the curve of marginal utility, meaning that students of this sample prefer trips with duration between 2 and 8 nights. All the estimated coefficients are significant at conventional levels. The third portfolio facet is accommodation. Consistent with the literature (e.g., Murphy & Pearce, 1995), Table 4 shows that students prefer staying at a friend or relative’s house, followed by hotel or rented apartment. The results, except for camping, are significant for the current sample size. As for travel party, the estimated parameters are low for respectively travelling alone and travelling with family. Parameters are higher for travelling with friends and especially for travelling with a partner. The estimated parameters are significant at conventional levels. The main focus of the present study is concerned with the transport modes. The estimated parameters for the transport modes suggest that students prefer low-fare airlines, followed by regular airlines, car, and train/bus for their vacations. Another interesting issue is the relative importance of the choice facets. Relative importance is measured as the range in the estimated parameters for the alternatives of one choice facet compared to the ranges of the other facets. In Table 4 the column “importance” shows that transport mode is the most important facet, followed by

Table 3 Model comparisons. Model

Log-likelihood

Number of parameters

Degrees of freedom

McFadden’s Rho-square (c2)

Dif c2 function

Critical c2

Null model LL(0) Main effects only þ sign. interactions þ all interactions

1850.799 1586.588 1515.95 1454.224

e 41 52 137

e e 11 96

e 0.1428 0.1809 0.2143

e e 141.276 106.390

e e 19.68 119.87

A.B. Grigolon et al. / Tourism Management 33 (2012) 1174e1184 Table 5 Parameter estimates and their significance e stated choice experiment for transport mode. Transport mode

Attributes

Regular airlines

Cost

Time of the day

Time to get the airport Travel time

Low-fare airlines

Cost

Time of the day

Time to get the airport Travel time

Train/bus

Cost

Time of the day

Time to get the train/bus station Travel time

Car

Cost

Travel time

Level Low Medium High Morning Afternoon Evening 60 min 90 min 120 min Low Medium High Low Medium High Morning Afternoon Evening 60 min 90 min 120 min Low Medium High Low Medium High Morning Afternoon Evening 60 min 90 min 120 min Low Medium High Low Medium High Low Medium High

Estimates 0.497 0.008 0.489 0.112 0.129 0.017 0.092 0.308 0.216 0.011 0.103 0.091 0.873 0.073 0.800 0.116 0.011 0.082 0.004 0.003 0.001 0.011 0.103 0.091 0.260 0.231 0.029 0.168 0.127 0.041 0.248 0.302 0.549 0.042 0.082 0.040 0.121 0.065 0.056 0.063 0.045 0.018

t-statistics 4.771 0.075 3.674 1.045 1.173 0.387 0.858 2.89 1.310 0.129 1.147 0.440 9.423 0.939 9.776 1.194 0.141 1.044 0.056 0.037 0.339 0.129 1.147 0.440 1.22 1.011 0.198 0.760 0.582 0.227 1.143 1.409 2.744 0.196 0.374 0.149 0.721 0.395 0.233 0.370 0.268 0.433

Importance (rank) 0.987 (1)

0.241 (3)

0.525 (2)

0.194 (4)

1.674 (1)

0.198 (2)

0.007 (4)

0.194 (3)

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Table 6 Parameter estimates and their significance, for the statistically significant interaction effects. Choice facets

Interaction

Estimates

t-statistics

Travel party * Accommodation

Alone/Hostel Partner/Hotel Partner/Camping Family/Hostel No significant interaction effect Barcelona, Madrid, Alicante, Porto/Low-fare airlines, Cost low Rome, Milan, Marseille, Pisa/Low-fare airlines, Cost low Rome, Milan, Marseille, Pisa/Air mode, Travel time low Rome, Milan, Marseille, Pisa/Car, Cost medium Rome, Milan, Marseille, Pisa/Car, Travel time medium London, Stockholm, Dublin/Regular airlines, Cost low London, Stockholm, Dublin/Low-fare airlines, Cost low

0.554 1.566 0.862 0.361

4.058 7.018 3.554 1.825

0.245

2.236

0.536

3.959

0.162

1.342

0.543

2.2

0.544

2.186

0.334

2.375

0.711

6.036

Destination * Duration Destination * Transport mode

0.491 (2)

0.294 (3)

0.851 (1)

0.124 (4)

0.186 (1)

0.109 (2)

Note: the t-statistics for the base level of the attributes were calculated by changing reference levels.

duration of the trip, travel party, destination, and accommodation. It suggests that for the students in our sample cost is the major factor influencing their vacation decision, reflected in the price paid for transportation. The low relative importance for accommodation is in line with results discussed before, where students prefer to stay at friend’s or relative’s house. As for destination type, the low relative importance may suggest that students still have a long wish list. In that sense, it does not seem to matter much where they go.

car, on the other hand, estimated part-worth utilities nonlinearly decrease with increasing cost levels. Estimated “time of day” parameters are not significant. However, remarkable findings were obtained for the “time to get to the airport, train or bus station” attribute. Sensitivity to this attribute is very low in case of low-fare airlines. However, it seems that there is a preference for the closest airport. On the other hand, in case of regular airlines, there is a sharp decline in estimated utility from 90 min to 2 h. Surprisingly and unexpectedly, the preference for a relatively close airport is also negative. This could be caused by specific data points or simply because the variation did not influence too much the response patterns. In the case of train/bus, students seem to be rather insensitive to travel times up to 90 min to reach a major train/bus station but after that, estimated partworth utility drops. Sensitivity to travel time and time of the day was low for all travel modes. Moreover, the estimated parameters are not significant at conventional levels. Regarding the importance of the attributes, Table 5 shows that cost is the most important variable determining students’ choices for all transport modes, except for the train/bus alternative. The time to get to a train/bus station or airport is the first in importance for train/bus, second for regular airlines and fourth for low-fare airlines, indicating that students seem to be more sensitive to the time they spend to reach a train or bus station than an airport.

5.2. Estimated parameters for transport mode attributes 5.3. Estimated interaction effects It is interesting to examine and interpret the estimated parameters for the attribute levels of the transport modes, shown in Table 5. Both in case of regular and low-fare airlines, utility almost linearly decreases with increasing costs levels. However, the decrease is steeper for low-fare airlines than for regular airlines. Hence, students tend to be more sensitive to the costs/airfares of low-fare airlines. Considering the estimated parameters for train/ bus and car, Table 5 shows that students are less sensitive to the costs of bus/train and especially car. In case of bus/train, estimated part-worth utilities tend to decrease with increasing costs, except for high costs, although differences are not significant. In case of the

Interaction effects occur when a combination of attributes gives an extra positive or negative effect to an alternative’s utility. To examine interdependencies in the components making up the portfolio, three interaction effects were explored: the relationship between travel party and accommodation, between destination type and transport mode, and between destination and duration of the trip. Starting with the interaction between travel party and accommodation, Table 6 shows that there is a significant positive effect between travelling alone and staying in a hostel, or travelling with

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a partner and staying in a camping, indicating that this combination of facets seems attractive for the students in this sample. The combinations that were not interesting for the present sample were between travelling with a partner and hotel as accommodation, and travelling with family and staying in a hostel. None of the estimated interaction effects between destination and duration of the trip were significant at conventional levels. The interaction effects between destination type and transport mode show a significant positive relationship between travelling to Barcelona, Madrid, Alicante or Porto and the lowest price range of the low-fare airlines. Travelling to Rome, Milan, Marseille or Pisa using low-fare airlines with the lowest price range or car with the medium price range shows positive effects for students in this sample. However, the preference is a bit higher for the case of low-fare airlines, making this combination very attractive among students. Results for travelling to London, Stockholm or Dublin indicate there are negative effects when using either low-fare or regular airlines with a low airfare. This does not necessarily indicate that students do not prefer lower costs for this group, but it can be an indication that the cost curve is less steep compared to the other destination groups. 6. Conclusions and discussion The purpose of this study has been to gain more insight into the influence of low-fare airlines on the portfolio of vacation travel decisions of students. When making travel choice decisions for leisure/vacation, how do they combine destination, transport mode, duration, travel party and accommodation decisions and within this context what is the role and significance of low-fare airlines? It is known from the literature that travel-related decisions for tourists, in general, are multi-faceted and not only related to the destination itself (e.g., Bargeman, Joh, & Timmermans, 1999; Dellaert et al., 1998; Jeng & Fesenmaier, 1997; Van Middelkoop, 2001; Woodside & MacDonald, 1994). The present study confirms these earlier finding for students, taking into consideration the specific choice portfolio that was defined. More specifically, it was found that transport mode is the most important facet to influence their portfolio decisions. Using the response patterns to a portfolio choice experiment, a main plus interaction effects model was estimated. Students’ choices seem primarily driven by transport mode, followed by duration of the trip, travel party, destination and last, accommodation. As transport mode is the most important choice, we may conclude that students’ travel choices are primarily driven by costs paid for transportation. Low-fare airlines and costs considerations in general played a major role influencing their portfolio decisions. It seems that the advent of low-fare airlines is changing students’ vacation planning, and this process is likely to start with the search for low-priced tickets. Because they are mostly adventurous and quite flexible tourists, even if they look for variety when seeking for new places they have never visited before, costs seem to drive them when calculating the transportation costs involved. One may argue that the importance of transport mode is an artefact of the constructed design in the sense that price, which might be the main driver of student choices, was not explicitly varied in other choice facets such as accommodation. In that context, one may expect that accommodation would be more influential to utility. Results do show that students prefer to stay as a guest at a friend or family’s house, but other budget accommodations (camping and hostel) have a lower estimated part-worth utility. It may be that these accommodations are not considered appropriate for the envisioned trips and destinations, but it may also be because monetary consequences were not specified. This issue should be addressed in future research. Given the complexity of the design, task difficulty and model specification, we decided in this study to estimate the basic

multinomial logit model and vary attributes only for one choice facet. Although the estimated model performs satisfactory, the attributes articulating the specific profile of the transport mode, the main focus of this study, were mostly statistically significant, as were most attribute levels of other choice facets. Nevertheless this study has some limitations that should be addressed in future research. First, some parameters were not significant. At least, three possible reasons might explain this finding. The first reason is this is a true result, suggesting that costs considerations and preferences for particular transport modes dictate the choices of students. It may also be that estimated parameters, which are based on the overall sample, hide taste variation, either inter-personal or intertemporary. Future research should therefore include personal characteristics or allow for heterogeneity (random effects or latent classes). Another reason is that the non-significance of parameters may be simply due to the relatively small sample size. Further analyses may be conducted to identify the sample size at which results become significant. Second, because the sampling of the study was restricted by the practical circumstance that we wanted to recruit students familiar with the regional airport, sample size was small. To the extent that the sample is representative of the larger population, results should be robust and estimated effects should have substantial meaning. However, a small sample size will result in larger standard errors, which in turn will imply less significant effects. This should be kept in mind when interpreting the results. Larger samples are preferred in similar future research. Third, the results of the present analyses are based on an a priori grouping of destinations. This may result in biases if this grouping does not reflect student grouping of similar alternatives. Future research should therefore collect additional information on this issue and explore the impact of alternative groupings. If sample size permits, destination-specific model could be estimated. Fourth, in the experiment price attributes were only varied for transport mode. Because no attributes were varied to other choice facets, heterogeneity may have been introduced because student choices are now based on their own cognitive representation of the destinations and choice facets. While it could be argued that this is unavoidable in any stated choice experiment by not making explicit these attributes, students were not triggered by the same set of explicit attribute levels. Technically, it is possible to include attributes for all choice facets. Of course, it would further complicate the design of the experiments and the respondent task. Effects of information overload may be the result. Again, future research could address this issue. The issue of the effects of explicit monetary values attached to all relevant choice facets could be embedded in this line of research. Fifth, allowing for multiple choices, we implicitly have assumed in the present analysis that such multiple choices by the same respondent are independent. To relax this assumption, a model allowing the estimation of variety-seeking behaviour and/or repetitive behaviour or temporal interdependencies in general should be estimated. Finally, by starting with the choice of destination, the suggested approach may suffer from endogeneity issues. Asking respondents to choose a destination and then based on their choice, generate other aspects of the portfolio, may induce a form of self-selection bias and correlated error terms. Future research should consider these issues. 7. Managerial implications This study has examined the portfolio decisions of students’ market segment, especially in the context of low-fare airlines. This segment is of managerial interest in the sense that the transition

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into adulthood is associated with changes and events, accompanied by developments in young people’s social status and consequently, by some changes in travel and transport use. Their views and needs seems to be key in understanding how services should be developed as they are current and future users of public and other modes of transport (Taylor, Barnard, White, & Lewis, 2007). Besides that, semester breaks and other holidays gives them relatively large time blocks free from school commitments and/or work (Babin & Kim, 2001), making them quite flexible and consequently, a major target group for the low-fare airlines. Moreover, they seem to have a broader range of options and may be more experienced tourists at an earlier age compared to the students of previous generations (Gibson & Yiannakis, 2002). Keeping this in mind, the main managerial implication of this study for the transport companies and especially the low-fare airlines is that they should not change their strategy of offering (very) low airfares if they wish to further service this segment. Costs are the main driver of the students’ vacation planning process. One of the main findings of the present study is that the price sensitivity for low-fare airlines is higher than for the other modes. It means that low-fare airlines should keep the focus in offering and advertising their low-fare tickets, because price variation seems to affect students’ decisions to go or not to go on vacation. For regular airlines, the gain would be offering most efficient departure times e as indicated by the variable “time of day”, or focussing on facilitating access to the airport, enhancing the trade-off between costs, time to get the airport and time of day in the vacation-planning of students. Acknowledgement The study was conducted as part of the project “The value of recreation: Now, and in a completely different future”, which is part of the DBR (Duurzame Bereikbaarheid van de Randstad e Sustainable Accessibility of the Randstad) programme. It was financially supported by the Netherlands Organization for Scientific Research (NWO). References AAIreland. (2010). International fuel price comparisons. Available online at: http:// www.aaireland.ie/petrolprices/. Babin, B. J., & Kim, K. (2001). International students’ travel behavior: a model of the travel-related consumer/dissatisfaction process. Journal of Travel & Tourism Marketing, 10(1), 93e106. Bargeman, B., Joh, C. H., & Timmermans, H. J. P. (1999). Correlates of tourist vacation behaviour: a combination of CHAID and loglinear analysis. Tourism Analysis, 4, 83e93. Bargeman, B., & van der Poel, H. (2006). The role of routines in the vacation decision-making process of Dutch vacationers. Tourism Management, 27(4), 707e720. Brons, M., Pels, E., Nijkamp, P., & Rietveld, P. (2002). Price elasticities of demand for passenger air travel: a meta-analysis. Journal of Air Transport Management, 8(3), 165e175. Carr, N. (1999). A study of gender differences: young tourist behaviour in a UK coastal resort. Tourism Management, 20(2), 223e228. Carr, N. (2002). A comparative analysis of the behaviour of domestic and international young tourists. Tourism Management, 23(3), 321e325. Carson, R. T., Louviere, J. J., Anderson, D. A., Arabie, P., Bunch, D. S., Hensher, D. A., et al. (1994). Experimental analysis of choice. Marketing Letters, 5(4), 351e368. Castillo-Manzano, J. I., López-Valpuesta, L., & González-Laxe, F. (2011). The effects of the LCC boom on urban tourism fabric: the viewpoint of tourism managers. Tourism Management, 32(5), 1085e1095. CBS. (2010). Statistics Netherlands. Available online at: http://www.cbs.nl. Cobanoglu, C., Warde, B., & Moreo, P. (2001). A comparison of mail, fax, and webbased survey methods. International Journal of Marketing Research, 43(4), 441e452. Cole, S. T. (2005). Comparing mail and web-based survey distribution methods: results of surveys to leisure travel retailers. Journal of Travel Research, 43, 422e430. Collins, A., Hess, S., & Rose, J. (2007). Stated Preference survey design in air travel choice behaviour modelling. In European Transport Conference Proceedings. The Netherlands: Leeuwenhorst Conference Centre. Decrop, A. (2006). Vacation decision making. Oxford, England: CAB International.

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