- Email: [email protected]
Testing prospect theory in airline demand
Journal of Air Transport Management 17 (2011) 241e243
Contents lists available at ScienceDirect
Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
Note
Testing prospect theory in airline demand Juan Luis Nicolau* Financial Economics, Accounting and Marketing, University of Alicante, PO Box 99, 03080 Alicante, Spain
a b s t r a c t Keywords: Prospect theory Loss aversion Diminishing sensitivity Airfare determination
The article tests for the existence of reference dependence, loss aversion and diminishing sensitivity in airline demand, in the context of price responsiveness amongst low cost, regular and charter airlines. We incorporate the reference-dependent model into a mixed model to control for heterogeneity. The application finds considerable differences between reference and actual prices in decision-making, confirming that reference dependence exists. People react more strongly to price increases than to decreases relative to their reference price supporting the loss aversion phenomenon and that there is diminishing sensitivity for losses only. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction
2. The model
In competitive airline markets where carriers seek to gain new business as well as and retaining current passengers it is strategically important for them to know their relative competitiveness. Also, as firms become more dependent on their relationships with their customers, understanding travelers’ carrier choice behaviors gives airline managers the opportunity to maximize customer satisfaction. In this regard, the ticket price is generally the main factor influencing both competitiveness and people’s decisions. Malighetti et al. (2010) suggest, the increasing complexity and dynamism of the air transport industry further enhance the role of pricing and, consequently, if airlines can learn how travelers’ choices are affected by fares, they can use the information to make decisions on marketing strategies, yield management, and pricing. We test prospect theory in the context of airfare responsiveness by investigating reference dependence whereby people compare economic outcomes to relevant reference points. Changes from reference points may be valued differently depending on whether they are gains or losses with the marginal impact of a gain or a loss contingent upon the distance from the reference point. We thus analyze reference dependence, loss aversion and diminishing sensitivity in airline demand, where airfares play an important role as a decision criterion. The empirical work focuses on individual passenger choice behavior in the context of low cost, regular, and charter carriers.
To analyze reference dependence, loss aversion and diminishing sensitivity properties in airline demand, we based on Kahneman and Tversky (1979) and Tversky and Kahneman (1991). Kahneman and Tversky replace the notion of a utility function with value function v(x) for an attribute x. The value function is defined in terms of gains and losses, which represent deviations from a reference point. That is, the value function depends on gains and losses relative to a reference point or status quo and not on final wealth positions as in expected utility theory. It is steeper for losses than for gains [v(x) < v(x), x > 0], i.e. the aggravation that one experiences in losing a sum of money appears to be greater than the pleasure associated with gaining the same amount, implying existence of loss aversion, and is concave for gains [v00 (x) < 0, x > 0] and convex for losses [v00 (x) > 0, x < 0]. The value function has a typical S-shape. It brings about diminishing sensitivity, in such a way that the marginal value of both gains and losses decreases with their magnitude. To incorporate these characteristics into the model, and following Tversky and Kahneman, given an alternative i, an attribute x, an attribute utility ui, and a reference point r, there is a reference function R(x) that represents a GAIN or a LOSS, such that
* Tel./fax: þ34 965903621. E-mail address: [email protected]. 0969-6997/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2010.11.001
Ri ðxÞ ¼ ui ðxÞ ui ðrÞ
if xi ri
Ri ðxÞ ¼ l½ui ðxÞ ui ðrÞ
if xi < ri :
(1) (2)
The utility of alternative i, evaluated from reference point r is captured by this reference function. If l > 1 then the individual is loss averse. Note that Tversky and Kahneman present Prospect Theory as an explanation of a body of pre-existing evidence and,
242
J.L. Nicolau / Journal of Air Transport Management 17 (2011) 241e243
unlike Expected Utility Theory, the sole aim of Prospect Theory is to describe behavior, not to characterize optimal behavior. Thus, in order to implement the theory in an empirical setting, it is necessary to specify the utility structure in such a way that it depends on gains and losses relative to a reference point. Following Bell and Lattin (2000) and Klapper et al. (2005) we operationalize Prospect Theory so that the utility function Uint for alternative i and individual n on occasion t is expressed as
Uint ¼ ai þ bn ðGAINint þ ln LOSSint Þ þ 3int
Table 1 Effects of gains and losses on airlines decisions (standard errors in parenthesis).
Independent variables
(4)
Uint ¼ ai þ bn GAINint þ j1n GAIN2int þ gn LOSSint þj2n LOSS2int þ 3int
(5)
We assume that 3int is a random term that is independent and identically distributed and allows us to use the mixed model, in line with Hess (2008). As Bell and Lattin (2000) show a model without heterogeneity may provide an upwardly biased estimate for some parameters, and in particular, the loss aversion parameter, we estimate the mixed model because it explicitly models price response heterogeneity and, in line with Klapper et al. (2005), accounts for heterogeneity to the fullest possible extent. As it leads coefficients q to vary over decision makers with density f(q) and q is not observable, the probability Pnt(i) of an individual n choosing alternative i on occasion t is the integral of Pnt(i/q) over all the possible values of q (Train, 2009).
Z Pnt ðiÞ ¼
expfUint g fðqjb; WÞdq exp Ujnt j¼1
(6)
PJ q
where J is the number of alternatives and f is the density function of q, assuming that q is normally-distributed with average b and variance W. 3. Data We use information on passenger choice behavior on the AlicanteeGatwick route where about 70% of international passengers arriving at Alicante Airport are British (AU Report, 2005). The route allows us to deal with low cost, regular, and charter carriers; it is the
Baseline model
SD of b
b
Gain 0.009 (0.018) 0.106 (0.100) GAIN2 Loss 0.108*** (0.044) 0.425** (0.165) LOSS2 Constant LCC 3.983*** (1.718) (Low-cost carrier) Constant RC 4.534*** (1.861) (Regular carrier) ML 76.49 SIC 85.07 AIC 84.49
(3)
where, RPnt is the reference price for individual n on occasion t and PRICEit is the actual price of alternative i on occasion t, and GAINint and LOSSint are defined as GAINint ¼ (RPnt PRICEit)D1, where D1 ¼ 1 if RPnt PRICEit > 0 and D1 ¼ 0 otherwise. LOSSint ¼ (RPnt PRICEit) D2, where D2 ¼ 1 if RPnt PRICEit < 0 and D2 ¼ 0 otherwise. The prices of alternatives are compared to a common reference price RPnt for each individual, with each person having a single reference point for all alternatives. ai, gn, bn, ln are coefficients to be estimated and 3int is a random term. Reference dependence is observed if the model explains the outcome better than the baseline model with the PRICEit only and some of the parameters associated with GAINint and LOSSint are significant. Loss aversion will be detected if ln > 1 or gn/bn > 1; i.e. if the parameter associated with losses is greater than that related to gains. And diminishing sensitivity exists if one of the squares of GAINint and LOSSint has significant parameters.1 Parameters j1n and j2n will capture these squared effects
Model 2
Reference-dependence model
Price
Rearranging and specifying gn ¼ bnln
Uint ¼ ai þ bn GAINint þ gn LOSSint þ 3int
Model 1
*
Prob<01%;
**
prob<1%;
***
prob<5%;
b
SD of b
0.014* (0.004)
0.017* (0.004)
0.002 (0.004) 0.050**** (0.026) 0.891 (0.602) 1.408* (0.529) 118.53 122.82 122.53 ****
prob<10%.
only route between Alicante and the UK that has the three airline types. Alicante Airport it is the busiest international route in terms of passenger numbers (the second most important route if the domestic AlicanteeMadrid route is included) with Gatwick Airport, Alicante’s primary international partner (Instituto de Estudios Turísticos, 2009). The airport is the sixth busiest of 48 in Spain in terms of annual passenger numbers (Aena, 2009) and fourth in terms of passengers arriving with low-cost carriers (Instituto de Estudios Turísticos, 2009). Use is made of information on passenger choice behavior from structured personal interviews at Alicante Airport carried out in July 2005 and directed at over 18 years olds. The sampling is by quotas defined in terms of type of carrier and consists of 139 individuals. The survey allows use of information on individual passenger choice behavior in terms of carrier type preferred. This is relevant because of the significant impact of low-cost carriers on fares and passenger numbers. Estimation of the carrier choice model makes use of the following variables. Dependent variables. To represent alternatives carrier types three dummy variables are used; low cost, which takes a value of unity when this type of carrier is chosen and zero if not; regular, where a value of unity shows that this kind of airline has been selected and zero if not; and charter, which takes a value of one when chosen and zero if not. Independent Variables. Prices. We use the actual airfares to represent the price of the type of carrier selected by an individual. As respondents do not normally have good memories of the fares of airlines not chosen (Suzuki, 2004), we employ the averaged airfares of each of the other carrier types to capture the effect of these other carriers’ prices on the individual decision. Reference prices. We use an external reference price, or stimulus-based reference price, in which the comparison standard is based on the current distribution of prices observed in the shopping environment. We take the average price of the available alternatives (Moon et al., 2006), as individuals may observe a stand-out price in comparison with other product prices. 4. Results
1
GAIN2int
The expectation is a negative parameter for producing a concave function above the reference point and a positive parameter for LOSS2int resulting in a convex function below it.
The application of mixed models allows testing for the existence of reference dependence, loss aversion and diminishing sensitivity
J.L. Nicolau / Journal of Air Transport Management 17 (2011) 241e243
by estimating the effect of gain and loss parameters; the last with random coefficients to avoid the upward bias found for loss aversion by Bell and Lattin and Klapper et al. Table 1 presents the parameter estimates for a the reference-dependent model (Model 1) derived from prospect theory and designed to examine the effects of gain and loss on individual decisions, plus the square variables for gains and losses, and a baseline model (Model 2) with only the price variable. Model 1 provides the better fit to the data implying that the reference price-based variables explain something in tourist choice that the price variable by itself does not. In particular, it shows that passengers tend to use reference prices, and therefore the difference between reference and actual prices, to make their decisions supporting reference dependence. Focusing on the first model, we observe that the parameter associated with gains is not significantly different from zero and that related to losses is significantly positive. The fact that the loss parameter is greater than the gain parameter supports the idea that tourists react more strongly to price increases than to price decreases relative to the reference price; evidence in favor of loss aversion in line with Hess (2008). This means that when individuals encounter actual prices above their reference prices they opt for a cheaper alternative. According to the way the loss variable is defined [(RPnt PRICEit) in such a way that RPnt PRICEit < 0], it has a negative sign for alternative i. Given that its associated parameter is positive, the effect on the choice of alternative i is negative, reducing its value and therefore, increasing the probability of another alternative j with a lower price being chosen. Also, the significance of the loss parameters supports the idea that a random parameter logit model is appropriate when estimating loss aversion to avoid biased estimates, in line with Bell and Lattin. Concerning, GAIN2 and LOSS2, we find the former is not significantly different from zero and the latter is significantly positive indicating that there is diminishing sensitivity for losses with two nuances: first, because this parameter is greater than that of the loss, this diminishing sensitivity rises to an asymptote and then disappears; and second, the standard deviation of the parameter is
243
significant, suggesting that diminishing sensitivity has different values among individuals, implying that their threshold may occur at different points in the range. 5. Conclusions We sought the existence of reference dependence, loss aversion and diminishing sensitivity in airline demand. By incorporating the reference-dependent model into a mixed model that controls for heterogeneity, the empirical application shows that passengers use reference prices rather than absolute prices to make their decisions, react more strongly to price increases than to price decreases relative to the reference price, and diminishing sensitivity only exists for losses. References Aena, 2009. Informe Anual. www.aena.es. AU Report, 2005. Análisis del impacto de las compañías de bajo coste en el perfil del pasajero del Aeropuerto de Alicante. Alicante University Report. Bell, D.R., Lattin, J.M., 2000. Looking for loss aversion in scanner panel data: the confounding effect of price response heterogeneity. Marketing Science 19, 185e200. Hess, S., 2008. Treatment of reference alternatives in stated choice surveys for air travel choice behaviour. Journal of Air Transport Management 14, 275e279. Instituto de Estudios Turísticos, 2009. Informe Anual “Compañías Aéreas de Bajo Coste”. Instituto de Estudios Turísticos. www.iet.tourspain.es. Kahneman, D., Tversky, A., 1979. Prospect theory: and analysis of decision under risk. Econometrica 47, 263e291. Klapper, D., Ebling, C., Temme, J., 2005. Another look at loss aversion in brand choice data: can we characterize the loss averse consumer? International Journal of Research in Marketing 22, 239e254. Malighetti, P., Paleari, S., Redondi, R., 2010. Has Ryanair’s pricing strategy changes over time? An empirical analysis of its 2006e2007 flights. Tourism Management 31, 36e44. Moon, S., Russell, G.J., Duvvuri, S.D., 2006. Profiling the reference price consumer. Journal of Retailing 82, 1e11. Suzuki, Y., 2004. The Impact of airline service failures on travelers’ carrier choice: a case study of central Iowa. Transportation Journal 43, 26e36. Train, K.E., 2009. Discrete Choice Methods with Simulation. Cambridge University Press, New York. Tversky, A., Kahneman, D., 1991. Loss aversion in riskless choice: a referencedependent model. The Quarterly Journal of Economics 106, 1039e1061.
Contents lists available at ScienceDirect
Journal of Air Transport Management journal homepage: www.elsevier.com/locate/jairtraman
Note
Testing prospect theory in airline demand Juan Luis Nicolau* Financial Economics, Accounting and Marketing, University of Alicante, PO Box 99, 03080 Alicante, Spain
a b s t r a c t Keywords: Prospect theory Loss aversion Diminishing sensitivity Airfare determination
The article tests for the existence of reference dependence, loss aversion and diminishing sensitivity in airline demand, in the context of price responsiveness amongst low cost, regular and charter airlines. We incorporate the reference-dependent model into a mixed model to control for heterogeneity. The application finds considerable differences between reference and actual prices in decision-making, confirming that reference dependence exists. People react more strongly to price increases than to decreases relative to their reference price supporting the loss aversion phenomenon and that there is diminishing sensitivity for losses only. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction
2. The model
In competitive airline markets where carriers seek to gain new business as well as and retaining current passengers it is strategically important for them to know their relative competitiveness. Also, as firms become more dependent on their relationships with their customers, understanding travelers’ carrier choice behaviors gives airline managers the opportunity to maximize customer satisfaction. In this regard, the ticket price is generally the main factor influencing both competitiveness and people’s decisions. Malighetti et al. (2010) suggest, the increasing complexity and dynamism of the air transport industry further enhance the role of pricing and, consequently, if airlines can learn how travelers’ choices are affected by fares, they can use the information to make decisions on marketing strategies, yield management, and pricing. We test prospect theory in the context of airfare responsiveness by investigating reference dependence whereby people compare economic outcomes to relevant reference points. Changes from reference points may be valued differently depending on whether they are gains or losses with the marginal impact of a gain or a loss contingent upon the distance from the reference point. We thus analyze reference dependence, loss aversion and diminishing sensitivity in airline demand, where airfares play an important role as a decision criterion. The empirical work focuses on individual passenger choice behavior in the context of low cost, regular, and charter carriers.
To analyze reference dependence, loss aversion and diminishing sensitivity properties in airline demand, we based on Kahneman and Tversky (1979) and Tversky and Kahneman (1991). Kahneman and Tversky replace the notion of a utility function with value function v(x) for an attribute x. The value function is defined in terms of gains and losses, which represent deviations from a reference point. That is, the value function depends on gains and losses relative to a reference point or status quo and not on final wealth positions as in expected utility theory. It is steeper for losses than for gains [v(x) < v(x), x > 0], i.e. the aggravation that one experiences in losing a sum of money appears to be greater than the pleasure associated with gaining the same amount, implying existence of loss aversion, and is concave for gains [v00 (x) < 0, x > 0] and convex for losses [v00 (x) > 0, x < 0]. The value function has a typical S-shape. It brings about diminishing sensitivity, in such a way that the marginal value of both gains and losses decreases with their magnitude. To incorporate these characteristics into the model, and following Tversky and Kahneman, given an alternative i, an attribute x, an attribute utility ui, and a reference point r, there is a reference function R(x) that represents a GAIN or a LOSS, such that
* Tel./fax: þ34 965903621. E-mail address: [email protected]. 0969-6997/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.jairtraman.2010.11.001
Ri ðxÞ ¼ ui ðxÞ ui ðrÞ
if xi ri
Ri ðxÞ ¼ l½ui ðxÞ ui ðrÞ
if xi < ri :
(1) (2)
The utility of alternative i, evaluated from reference point r is captured by this reference function. If l > 1 then the individual is loss averse. Note that Tversky and Kahneman present Prospect Theory as an explanation of a body of pre-existing evidence and,
242
J.L. Nicolau / Journal of Air Transport Management 17 (2011) 241e243
unlike Expected Utility Theory, the sole aim of Prospect Theory is to describe behavior, not to characterize optimal behavior. Thus, in order to implement the theory in an empirical setting, it is necessary to specify the utility structure in such a way that it depends on gains and losses relative to a reference point. Following Bell and Lattin (2000) and Klapper et al. (2005) we operationalize Prospect Theory so that the utility function Uint for alternative i and individual n on occasion t is expressed as
Uint ¼ ai þ bn ðGAINint þ ln LOSSint Þ þ 3int
Table 1 Effects of gains and losses on airlines decisions (standard errors in parenthesis).
Independent variables
(4)
Uint ¼ ai þ bn GAINint þ j1n GAIN2int þ gn LOSSint þj2n LOSS2int þ 3int
(5)
We assume that 3int is a random term that is independent and identically distributed and allows us to use the mixed model, in line with Hess (2008). As Bell and Lattin (2000) show a model without heterogeneity may provide an upwardly biased estimate for some parameters, and in particular, the loss aversion parameter, we estimate the mixed model because it explicitly models price response heterogeneity and, in line with Klapper et al. (2005), accounts for heterogeneity to the fullest possible extent. As it leads coefficients q to vary over decision makers with density f(q) and q is not observable, the probability Pnt(i) of an individual n choosing alternative i on occasion t is the integral of Pnt(i/q) over all the possible values of q (Train, 2009).
Z Pnt ðiÞ ¼
expfUint g fðqjb; WÞdq exp Ujnt j¼1
(6)
PJ q
where J is the number of alternatives and f is the density function of q, assuming that q is normally-distributed with average b and variance W. 3. Data We use information on passenger choice behavior on the AlicanteeGatwick route where about 70% of international passengers arriving at Alicante Airport are British (AU Report, 2005). The route allows us to deal with low cost, regular, and charter carriers; it is the
Baseline model
SD of b
b
Gain 0.009 (0.018) 0.106 (0.100) GAIN2 Loss 0.108*** (0.044) 0.425** (0.165) LOSS2 Constant LCC 3.983*** (1.718) (Low-cost carrier) Constant RC 4.534*** (1.861) (Regular carrier) ML 76.49 SIC 85.07 AIC 84.49
(3)
where, RPnt is the reference price for individual n on occasion t and PRICEit is the actual price of alternative i on occasion t, and GAINint and LOSSint are defined as GAINint ¼ (RPnt PRICEit)D1, where D1 ¼ 1 if RPnt PRICEit > 0 and D1 ¼ 0 otherwise. LOSSint ¼ (RPnt PRICEit) D2, where D2 ¼ 1 if RPnt PRICEit < 0 and D2 ¼ 0 otherwise. The prices of alternatives are compared to a common reference price RPnt for each individual, with each person having a single reference point for all alternatives. ai, gn, bn, ln are coefficients to be estimated and 3int is a random term. Reference dependence is observed if the model explains the outcome better than the baseline model with the PRICEit only and some of the parameters associated with GAINint and LOSSint are significant. Loss aversion will be detected if ln > 1 or gn/bn > 1; i.e. if the parameter associated with losses is greater than that related to gains. And diminishing sensitivity exists if one of the squares of GAINint and LOSSint has significant parameters.1 Parameters j1n and j2n will capture these squared effects
Model 2
Reference-dependence model
Price
Rearranging and specifying gn ¼ bnln
Uint ¼ ai þ bn GAINint þ gn LOSSint þ 3int
Model 1
*
Prob<01%;
**
prob<1%;
***
prob<5%;
b
SD of b
0.014* (0.004)
0.017* (0.004)
0.002 (0.004) 0.050**** (0.026) 0.891 (0.602) 1.408* (0.529) 118.53 122.82 122.53 ****
prob<10%.
only route between Alicante and the UK that has the three airline types. Alicante Airport it is the busiest international route in terms of passenger numbers (the second most important route if the domestic AlicanteeMadrid route is included) with Gatwick Airport, Alicante’s primary international partner (Instituto de Estudios Turísticos, 2009). The airport is the sixth busiest of 48 in Spain in terms of annual passenger numbers (Aena, 2009) and fourth in terms of passengers arriving with low-cost carriers (Instituto de Estudios Turísticos, 2009). Use is made of information on passenger choice behavior from structured personal interviews at Alicante Airport carried out in July 2005 and directed at over 18 years olds. The sampling is by quotas defined in terms of type of carrier and consists of 139 individuals. The survey allows use of information on individual passenger choice behavior in terms of carrier type preferred. This is relevant because of the significant impact of low-cost carriers on fares and passenger numbers. Estimation of the carrier choice model makes use of the following variables. Dependent variables. To represent alternatives carrier types three dummy variables are used; low cost, which takes a value of unity when this type of carrier is chosen and zero if not; regular, where a value of unity shows that this kind of airline has been selected and zero if not; and charter, which takes a value of one when chosen and zero if not. Independent Variables. Prices. We use the actual airfares to represent the price of the type of carrier selected by an individual. As respondents do not normally have good memories of the fares of airlines not chosen (Suzuki, 2004), we employ the averaged airfares of each of the other carrier types to capture the effect of these other carriers’ prices on the individual decision. Reference prices. We use an external reference price, or stimulus-based reference price, in which the comparison standard is based on the current distribution of prices observed in the shopping environment. We take the average price of the available alternatives (Moon et al., 2006), as individuals may observe a stand-out price in comparison with other product prices. 4. Results
1
GAIN2int
The expectation is a negative parameter for producing a concave function above the reference point and a positive parameter for LOSS2int resulting in a convex function below it.
The application of mixed models allows testing for the existence of reference dependence, loss aversion and diminishing sensitivity
J.L. Nicolau / Journal of Air Transport Management 17 (2011) 241e243
by estimating the effect of gain and loss parameters; the last with random coefficients to avoid the upward bias found for loss aversion by Bell and Lattin and Klapper et al. Table 1 presents the parameter estimates for a the reference-dependent model (Model 1) derived from prospect theory and designed to examine the effects of gain and loss on individual decisions, plus the square variables for gains and losses, and a baseline model (Model 2) with only the price variable. Model 1 provides the better fit to the data implying that the reference price-based variables explain something in tourist choice that the price variable by itself does not. In particular, it shows that passengers tend to use reference prices, and therefore the difference between reference and actual prices, to make their decisions supporting reference dependence. Focusing on the first model, we observe that the parameter associated with gains is not significantly different from zero and that related to losses is significantly positive. The fact that the loss parameter is greater than the gain parameter supports the idea that tourists react more strongly to price increases than to price decreases relative to the reference price; evidence in favor of loss aversion in line with Hess (2008). This means that when individuals encounter actual prices above their reference prices they opt for a cheaper alternative. According to the way the loss variable is defined [(RPnt PRICEit) in such a way that RPnt PRICEit < 0], it has a negative sign for alternative i. Given that its associated parameter is positive, the effect on the choice of alternative i is negative, reducing its value and therefore, increasing the probability of another alternative j with a lower price being chosen. Also, the significance of the loss parameters supports the idea that a random parameter logit model is appropriate when estimating loss aversion to avoid biased estimates, in line with Bell and Lattin. Concerning, GAIN2 and LOSS2, we find the former is not significantly different from zero and the latter is significantly positive indicating that there is diminishing sensitivity for losses with two nuances: first, because this parameter is greater than that of the loss, this diminishing sensitivity rises to an asymptote and then disappears; and second, the standard deviation of the parameter is
243
significant, suggesting that diminishing sensitivity has different values among individuals, implying that their threshold may occur at different points in the range. 5. Conclusions We sought the existence of reference dependence, loss aversion and diminishing sensitivity in airline demand. By incorporating the reference-dependent model into a mixed model that controls for heterogeneity, the empirical application shows that passengers use reference prices rather than absolute prices to make their decisions, react more strongly to price increases than to price decreases relative to the reference price, and diminishing sensitivity only exists for losses. References Aena, 2009. Informe Anual. www.aena.es. AU Report, 2005. Análisis del impacto de las compañías de bajo coste en el perfil del pasajero del Aeropuerto de Alicante. Alicante University Report. Bell, D.R., Lattin, J.M., 2000. Looking for loss aversion in scanner panel data: the confounding effect of price response heterogeneity. Marketing Science 19, 185e200. Hess, S., 2008. Treatment of reference alternatives in stated choice surveys for air travel choice behaviour. Journal of Air Transport Management 14, 275e279. Instituto de Estudios Turísticos, 2009. Informe Anual “Compañías Aéreas de Bajo Coste”. Instituto de Estudios Turísticos. www.iet.tourspain.es. Kahneman, D., Tversky, A., 1979. Prospect theory: and analysis of decision under risk. Econometrica 47, 263e291. Klapper, D., Ebling, C., Temme, J., 2005. Another look at loss aversion in brand choice data: can we characterize the loss averse consumer? International Journal of Research in Marketing 22, 239e254. Malighetti, P., Paleari, S., Redondi, R., 2010. Has Ryanair’s pricing strategy changes over time? An empirical analysis of its 2006e2007 flights. Tourism Management 31, 36e44. Moon, S., Russell, G.J., Duvvuri, S.D., 2006. Profiling the reference price consumer. Journal of Retailing 82, 1e11. Suzuki, Y., 2004. The Impact of airline service failures on travelers’ carrier choice: a case study of central Iowa. Transportation Journal 43, 26e36. Train, K.E., 2009. Discrete Choice Methods with Simulation. Cambridge University Press, New York. Tversky, A., Kahneman, D., 1991. Loss aversion in riskless choice: a referencedependent model. The Quarterly Journal of Economics 106, 1039e1061.