When gratitude is felt toward the organization, the more relevant argument is based on the fact that most of these organizations provide services that are not provided by the public or private sector, and the organizations offer solutions to problems for people in situations of some complexity, helping them feel connected to the organization as a show of gratitude for the support received.

In addition, this type of link may have a particular impact on NPOs and as suggested by Meyer and Allen (1997), commitment characterized by a sense of obligation might be a better predictor of employment outcomes in collective contexts where social ties and regulatory obligations are most relevant.

Then, we propose a concept as a three-dimensional model which, depending on the different connotations attributed by the authors, can have each of its dimensions broken down into two sub-dimensions (**Figure 2**).

Affective commitment has been linked to increased involvement in organizational activities, involvement in organizational activities, a strong willingness to contribute to achieving organizational goals and a strong desire to remain with the organization (Walumbwa et al., 2010; Idris and Manganaro, 2017). Continuance and normative dimensions of commitment have been critiqued for their inconsistencies with affective commitment (Chordiya et al., 2017). Normative commitment is usually strongly linked to affective commitment (Guerrero and Herrbach, 2009) and is linked to individuals' sense of obligation to stay in the organization (Wang et al., 2017).

### MATERIALS AND METHODS

As stated previously, there is a question regarding the link between the individual and the organization and, although most empirical studies are based on the one-dimensional Porter et al. (1974) model or the three-dimensional Allen and Meyer (1990) model, in our case we decided to rethink the links and try to show the existence of links that have been overlooked or have had lesser attention in the body of literature on organizational commitment. The main reason for this decision was that this study was undertaken in a particular environment within which the relationships between members of the organization and the organization itself showed the characteristics we have already mentioned. Because of the complexity of the construction and due to the lack of works in the Spanish language that are adapted to Third Sector organizations, we decided to develop an ad hoc measurement tool that would allow us to achieve our goals. Hence, we developed a measurement scale based on the main previous works, in order to obtain a reliable measurement tool that would enable us to assess most accurately the level of commitment to an organization. First, we defined the dimensions and later allocated the items (**Table 3**) to the different connotations set out. We obtained a 20-item scale from experimental analysis, although we decided to add a control item that enabled us to gather the perceptions of individuals regarding their level of commitment to the organization (Sánchez TABLE 3 | Definition of organizational commitment scale.

OCQ, Organizational Commitment Questionnaire (Mowday et al., 1979). AMM, Allen and Meyer Measurement (Allen and Meyer, 1990).

and Sarabia, 1999). We used a Likert scale of 0–10 for measurement.

The analysis was performed using IBM SPSS 24 for the exploratory factor analysis and using AMOS 24 for the confirmatory factor analysis. Validation of the tool was undertaken by following considerations provided by work on testing sociometric properties required by the scales of measure, an issue that enjoys broad endorsement in the body of marketing and organization literature (Lévy and Varela, 2006; Camisón and Cruz, 2008; Hair et al., 2010).

Exploratory factor analysis was applied to the principal axis factoring method with Varimax rotation in order to compare the underlying structure of empirical data with the theoretical structure of the resulting models from the literature review and which are presented in the above. In the first extraction, the result obtained for the analyzed sample was five dimensions, which were automatically determined. But, as we wanted to test the adequacy with respect to different theoretical proposals, we decided to obtain the factor analysis for models with 3, 4, and 6 dimensions (**Table 4**). Following the recommendations of Hair (Hair et al., 2010) to develop the most suitable, commitment model, and in order to facilitate our work on the confirmatory factor analysis, we proposed comparisons among the four structures with 3, 4, 5, and 6 latent dimensions as a rigorousness test, by comparing competing models (Bentler and Bonett, 1980; Hair et al., 2010).

Parameters (standardized factor loadings) for the confirmatory factor analysis construct elements were obtained through structural equation systems. To carry out the estimation of model parameters, we used the original data matrix instead of the correlation matrix as input, because of the information available to us and because our desire is to explain the nature of the latent construct. Moreover, taking into account the lack of multivariate normality, we decided to use the Asymptotically Distribution Free (ADF) method of estimating function—guided by the considerations presented by Hair et al. (2010). One of the main drawbacks of the method is its higher demand with regard to sample size (Hair et al., 2010). In order to minimize the number of model parameters and to increase the degrees of freedom, we decided to group the observed measures of the same latent variable, the dimensions of commitment, in a composite score, the arithmetic mean, depending on the results of the exploratory factor analysis.

#### Sample

The information was obtained from 14 non-profit organizations that operate in various fields of activity, but all of them are characterized as direct services. The areas of activity are services for children, mental disability, and the promotion of employment, physical disability, and a volunteer organization. The samples were obtained by the directors of the entities. The directors distributed the questionnaires and they committed themselves to respecting the anonymity of those respondents wishing to undertake the completion of the questionnaires.

The final sample consists of 235 questionnaires, of which 156 (66.38%) pertain to workers and 79 (33.61%) pertain to volunteers. The average seniority in the organization is 4.51 years with a standard deviation of 4.67 and a maximum of 26 years and a minimum of 6 months. Sixteen percent of the sample consisted of people who stayed less than a year in the organization, 46.6%

TABLE 4 | Definition of dimensions.

were there for a period of 1–5 years, 27.7% for 5–10 years, and only 9.7% spent over 10 years in the organization. As for the level of education, the largest proportion of the sample comprised people with a mid-level university education (34.7%) followed by graduate degrees (25.4%), and only 10.9% have a level of basic studies.

#### RESULTS

The first step in the analysis was the validation of the measurement tool for the configuration proposed in **Figure 1**. The Bartlett sphericity test (χ 2 = 2828.781; p < 0.001) and the Kaiser–Meyer–Olkin statistic (KMO = 0.861) report that the matrix of correlations for the exploratory factorial analysis factors proves to be very good, hence, appropriate to describing the data structure (p < 0.001; KMO > 0.8; Hair et al., 2010; Lévy and Varela, 2006; Camisón and Cruz, 2008). For reliability and validity of the scale, we calculated Cronbach's α of the composite reliability and the extracted variance (Camisón and Cruz, 2008). Cronbach's α-value obtained was 0.906 (Cronbach's α > 0.7; Lévy and Varela, 2006; Camisón and Cruz, 2008; Hair et al., 2010), which is high enough to believe that our scale is reliable and the factors account for 59.6% (3 dimensions), 65.9% (4 dimensions), 71.7% (5 dimensions), and 75.8% (6 dimensions) of the variance in the original data.

Once it was verified that the requirements for using factor analysis (Hair et al., 2010) were fulfilled (**Table 5**), we performed exploratory factor analysis using the extraction method of

Principal Component Analysis with scale items that reflected the different connotations regarding the individual-organization links as previously defined.

Exploratory factor analysis showed us the different configurations in models of 3, 4, 5, and 6 dimensions (**Table 6**).

Once the factors were extracted and the models to be evaluated were defined, the confirmatory factor analysis was conducted. Before examining the estimated parameters, model adjustments were checked (**Table 7**).

As can be seen, the data show that all models have an adequate level of measurement reliability (composite reliability > 0.7; AVE

TABLE 5 | Exploratory factor analysis.

> 0.4; Cronbach's α > 0.7; Lévy and Varela, 2006; Camisón and Cruz, 2008; Hair et al., 2010). In relation to absolute fit, we can see that the 3-D model has a Chi-square value equal to 0, meaning that this would be the model that enables us to ensure the ability to reproduce the observed matrix, although this feature is not useful for us due to the difficult generalization of the models that were identified as having no degrees of freedom (Hair et al., 2010). The 4-D model also has quite a high level of global adjustment (GFI > 0.9; Chi-square sig > 0.05; Lévy and Varela, 2006; Hair et al., 2010), followed by the 5-D model which does not allow us to accept the null hypothesis of equality between the observed and reproduced matrices (Chi-square sig < 0.05), although the value of the Chi-square standard indicator shows an acceptable value. The 3-D model provides a good fit although the RMSEA value behaves with values above those indicated as appropriate (RMSEA < 0.08; Hair et al., 2010; Lévy and Varela, 2006). The indicators of incremental and Parsimony adjustment (AGFI > 0.9 and 1 < Normalized Chi-square < 5; Hair et al., 2010; Lévy and Varela, 2006) of the 4-D model are the best (AGFI = 0.996; Normalized Chi-square = 0.163) followed by those of the 5-D (AGFI = 0.92; Normalized Chisquare = 2.524) model which are also acceptable. The 6-D model does not present a good fit in any of the analyzed adjustment dimensions.

In **Table 8**, the results of confirmatory factor analysis of the presented models can be seen. The 3-D model shows two main dimensions of organizational commitment, the size of links of Affective and Regulatory type (desire of belonging and sense of obligation), both of which have Standardized Regression Weights (SRW) = 0.795, while the continuous dimension (duty to stay) is not confirmed (SRW < 0.7; Lévy and Varela, 2006; Hair et al., 2010). This model is that which is usually considered and it has been compiled from the contributions of previous work (Allen and Meyer, 1990; Meyer et al., 2002). We aimed at collecting different interpretations offered in accordance with the context to apply, although in our case the results do not confirm the continuity dimension in the way they confirm the normative dimension. In the case of the 4-D model, the Moral Obligation (SRW = 0.723) and affection dimensions (SRW = 0.719) are confirmed, followed by values close to 0.7 of the Gratitude dimension (SRW = 0.616), and again the continuity dimension (SRW < 0.7; Lévy and Varela, 2006; Hair et al., 2010) does not show a high enough value. In the following model, continuing with the breakdown of the affective dimension, we find that both dimensions are confirmed, leaving all other dimensions with standard load values below the minimum (0.7), the Continuity Factor being that with the lower value (close to 0.5).

Finally, the 6-D model shows Commitment factors as the Pride dimension, followed by the Lack of Alternatives, the Costs of Abandonment and we could accept as confirmed both Identification (0.679) and Moral Obligation (0.655), although this model cannot be considered valid and replicable due to the adjustment problems it presents.

### DISCUSSION

The results of this research make theoretical contributions to understanding the underlying nature of links between individuals and Non-profit Organizations (NPOs) and the reason why people (workers and volunteers) become involved in an organization, and managerial implications to improve the human resources management in organizations, especially in NPOs.

#### Theoretical Contributions

One of the main drawbacks in the organizational commitment study is the use of different measurement scales, and the problems inherent to cultural questions, and the linguistic adaptation of questionnaires to specific contexts. In this sense this work analyzes the meaning of the construct identifying different structures and comparing among them. From the comparative analysis of structural models, we can highlight several aspects.

First, in relation to the interpretation of the composition of the latent variable Organizational Commitment, it consists mainly of contributions from the individual's emotional bonds with respect to the organization, followed by normative and yet, the Continuity dimension values are not enough to ensure that this type of relationship creates a relevant link with respect

#### TABLE 6 | Rotated component matrix.

The bold values mean the factor loads on the factor to which they are assigned after the factorial analysis performed.

to the entity. Therefore, we cannot accept the existence of the Continuity dimension as reflected in other studies (Meyer et al., 1990, 2002; Somers, 1993; Dunham et al., 1994; Hackett et al., 1994; Iverson and Buttigieg, 1999; Powell and Meyer, 2004), but it confirms, to the best of our knowledge, the results obtained in previous research relating to the scope of voluntary activities (Liao-Troth, 2001; Stephens et al., 2004; Dawley et al., 2005).

According to previous studies (i.e., Meyer and Allen, 1984, 1991; O'Reilly and Chatman, 1986; Jaros et al., 1993; Boezeman and Ellemers, 2008) the breakdown of the Affective dimension is confirmed and we obtain empirical evidence of the difference between the links generated by feelings of Identification and those generated by feelings of Pride of ownership.

The Commitment configuration to reflect upon is related to the breakdowns made in the normative dimension. This dimension is an important motivational force that has been overlooked and underutilized (McCormick and Donohue, 2016; Meyer and Parfyonova, 2010) and as McCormick and Donohue (2016) point out, they have been the object of conceptual reconfiguration over time (Wayne et al., 2009). In recent years, it has become a moral obligation (Meyer and Parfyonova, 2010). The second theoretical implication is that feelings promoted by

aThe Chi-square text (p < 0.05) for df = 2 its value is 5.991; df = 3 is 7.815, and df = 4 is 9.488 (Malhotra, 2008).

TABLE 8 | Confirmatory factor analysis.

\*\*\*p < 0.001. C.R., Critical Ratio; SRW, Standardized Regression Weights.

the need for internal consistency and meeting responsibilities acquired (Moral Obligation dimension), based on cognitive dissonance theory of Festinger (1957), are stronger links with respect to the entity than those that may be generated by feelings of gratitude toward the organization, feelings that are reflected in the Gratitude dimension and based on the social exchange theory of Blau (1964), which does not show a sufficient level of variance and hence reliability.

Finally, in this study we have aimed at adapting organizational commitment to a specific field, and one which is as peculiar as voluntary organizations. The main theoretical contribution is, regarding NPOs, the most significant links to people who work in an NPO are those that are related to affective ties. In conclusion, this theoretical implication is aligned with motivational theory on intrinsic/extrinsic motivational factors and contribute to demonstrate how intrinsic motivations are more relevant than extrinsic motivations.

#### Practical Implications

Research on commitment in NPOs underlines the role that management and human resources practices can play in fostering employee commitment (Cunningham, 2001; Alatrista and Arrowsmith, 2004). To undertake such research it is essential to identify the factors that promote the commitment and retention of NPO employees (McCormick and Donohue, 2016).

The first managerial implication is the individual's emotional bonds to the organization, defined as affective commitment, which are of great importance to NPOs. Employees and volunteers of NPOs are highly sensitive to the organization's mission and values and they strongly identify with the organization's social mission. NPO internal consumers need these affective bonds to feel committed to the organization and to bring the best to the organization (Ohana and Meyer, 2016) and these bonds influence motivation (Somers, 2010; Cohen, 2011). In considering individual links, this commitment is key to attitudes and behaviors, including higher performance, organizational citizenship behavior, as well as lower levels of turnover and absenteeism (Cunningham, 2001; Meyer et al., 2002; Ridder and McCandless, 2010). Strong employee engagement with values, missions, and goals is therefore essential to organizational success and organizational survival (Ridder and McCandless, 2010). Incorporated into the stated mission, organizational values provide guidance and justification for the decisions and behavior of members of the organization (O'Reilly et al., 1991; Lawrence and Lawrence, 2009). In this sense, in accordance with our results, the main implication for managers is that they have to declare publicly and clearly the organization's mission and values, and this declaration is a key factor in addressing the following:

Regarding volunteer engagement, this is the main aspect with which to attract and retain motivated volunteers because those volunteers feel that they identify with the organizational mission and values. Often, volunteers are motivated to join organizations on the basis of the compatibility of their individual beliefs and values with the organizational values that are adopted (Amos and Weathington, 2008; Van Vuuren et al., 2008).

Motivational aspect for employees: Non-profit employees often feel they are underpaid (Light, 2003; Kim and Lee, 2007; Handy et al., 2008) but they may be willing to sacrifice some money in order to serve a cause or specific social mission (Ohana and Meyer, 2016). In this way, in a context of scarce financial resources, our second recommendation is that human resources managers should consider the definition of job position and must establish mechanisms to promote appropriate activities in considering how they contribute to the mission of the organization. This is more effective than other retention systems such as reward systems (continuance commitment).

It is thus crucial to stress those work experiences that contribute to the feeling of belonging, and to develop an organizational culture based on common values and goals rather than on economic rewards, which can be impractical and unusual in most organizations. There is also evidence of links

#### REFERENCES

related to feelings of obligation and normative commitment, which have come to be associated with an accountability and a greater control of one's own activity that promotes long-lasting behaviors, although concerning this type of relationship there is little empirical support that backs previous results of our work. Therefore, we consider it important to deepen the knowledge of elements linking people with the organization in the context of NPOs and the relationship among them.

#### Limitations and Future Research Lines

Although this work contributes to a better understanding of the nature of links between individuals and NPOs, it has also some limitations. The main limitation of this research is the size of the sample. In future works, it should be interesting to use larger and representative samples to deepen research into the differences between employees and volunteers. Although, the internal meaning of each dimension is still an open debate, and is necessary to clarify the relation between Affective and Normative dimensions, another future research could aim to analyze the relations of each dimension with other variables (e.g., satisfaction, social performance, organization culture) and their effects on them over time. We hope new works will explore these future research lines because are essential to develop mutually beneficial and satisfactory relationships between organization and person.

### AUTHOR CONTRIBUTIONS

The three authors have equally participated in literature review, data analysis and writing of the paper. All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewers JL, SG and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.

Copyright © 2017 Juaneda-Ayensa, Clavel San Emeterio and González-Menorca. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Consumer Behavior in the Choice of Mode of Transport: A Case Study in the Toledo-Madrid Corridor

Ana I. Muro-Rodríguez 1 \*, Israel R. Perez-Jiménez 1 and Santiago Gutiérrez-Broncano2

1 Econometrics Area, Department of Spanish and International Economy, Econometrics, History and Economic Institutions, University of Castilla-La Mancha, Cobertizo San Pedro Mártir, Toledo, Spain, 2 Business Administration Department, University of Castilla-La Mancha, Talavera de la Reina, Toledo, Spain

Within the context of the consumption of goods or services the decisions made by individuals involve the choice between a set of discrete alternatives, such as the choice of mode of transport. The methodology for analyzing the consumer behavior are the models of discrete choice based on the Theory of Random Utility. These models are based on the definition of preferences through a utility function that is maximized. These models also denominated of disaggregated demand derived from the decision of a set of individuals, who are formalized by the application of probabilistic models. The objective of this study is to determine the behavior of the consumer in the choice of a service, namely of transport services and in a short-distance corridor, such as Toledo-Madrid. The Toledo-Madrid corridor is characterized by being short distance, with high speed train available within the choice options to get the airport, along with the bus and the car. And where offers of HST and aircraft services can be proposed as complementary modes. By applying disaggregated transport models with revealed preference survey data and declared preferences, one can determine the most important variables involved in the choice and determine the arrangements for payment of individuals. These payment provisions may condition the use of certain transport policies to promote the use of efficient transportation.

Keywords: consumer behavior, choice of service, transportation, modeling, discrete choice, logit, willingness to pay

## INTRODUCTION

Transport is one of the most important services of a developed society. The growing need for mobility of people motivated mainly by the spatial differences of the locations to which people need access to makes transport modeling a matter of spatial importance for any developed country. One of the main problems to be analyzed is how individuals move or what their mobility patterns are. This issue is fundamental to the proper planning of the transport system. An efficient transport system must serve the mobility needs of individuals, for this it must use the necessary tools to be able to plan this mobility.

The individual faces, decisions daily between different alternatives of choice, whether goods or services, conditioned by the qualities or attributes of the different options available

#### Edited by:

Alicia Izquierdo-Yusta, University of Burgos, Spain

#### Reviewed by:

Cristina Peñasco, Consejo Superior de Investigaciones Científicas (CSIC), Spain Victor Martin, Complutense University of Madrid, Spain

\*Correspondence:

Ana I. Muro-Rodríguez<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology

Received: 25 March 2017 Accepted: 01 June 2017 Published: 20 June 2017

#### Citation:

Muro-Rodríguez AI, Perez-Jiménez IR and Gutiérrez-Broncano S (2017) Consumer Behavior in the Choice of Mode of Transport: A Case Study in the Toledo-Madrid Corridor. Front. Psychol. 8:1011. doi: 10.3389/fpsyg.2017.01011 (McFadden, 2001b; Ye et al., 2007; Chowdhury and Ceder, 2016; Houdek, 2016). In order to determine the variables that affect their choice and to determine the probability of choosing between different available options, disaggregated demand models are used (McFadden, 1981; Schakenbos et al., 2016).

Disaggregated demand models are also called discrete choice models, due to the characteristics variable of choice that can be the mode of transport; this variable is discrete, due to the qualitative nature of the individual's response and also a finite number of responses are shown (Ben-Akiva and Lerman, 1985; Train, 2003; Navarette and Ortuzar, 2013). These models have the ability to predict individual decisions and joint decisions and thus serve as a basis for policy planning (St-Louis et al., 2014).

In order to try to predict the behavior of users regarding modal choice, we will focus on the disaggregated demand models based on the Theory of Choice, in which the traveler maximizes its utility (McFadden, 2001a; Train, 2003). These disaggregated demand models are based on the theory of discrete choice, to determine the probability of choosing the different alternatives which the individual counts with (Martín et al., 2011).

Most of the models used for travel behavior applications are based on utility theory (McFadden, 1974; Domencich and McFadden, 1975; Manski, 1977; Williams, 1977; de Dios Ortúza and Willumsen, 2001), which assumes that the preference of choice of an alternative is captured by a value, called utility, and decision-making selects the alternative in the set of choices most satisfactory (Taniguchi et al., 2014).

This concept, used by the microeconomic theory of the consumer, presents strong limitations for practical applications, since the complexity of human behavior suggests that the decision rule must include a probabilistic dimension (Simon, 1957; Sandoval, 1994; McFadden, 2001b).

Some models assume that the decision rule is intrinsically probabilistic, and even a complete understanding of the problem does not surpass uncertainty (Thurstone, 1927; Quandt, 1956; Luce, 1959; Tversky, 1969; McFadden, 1981). Others consider that the decision rules of individuals are deterministic and motivate uncertainty to the analyst's limited ability to observe and grasp all the dimensions of the election process because of its complexity (Aguado Franco, 2012).

A fundamental assumption in the choice process is that decision making is assumed to have "rational behavior" and implies that a decision maker is a "maximize" of utility and, in the same circumstances, will repeat the same choice, in addition to the transitivity of preferences (Domencich and McFadden, 1975; McFadden, 2001b; de Dios Ortúza and Willumsen, 2001; Train, 2003).

For the estimation of disaggregated demand models, survey data are required. Two types of surveys, revealed preferences, and declared preference surveys can be differentiated. The revealed preference surveys, which try to reflect the current behavior of individuals in their travel decisions and the declared preference surveys, provide us with data that try to reflect how travelers share a certain hypothetical situation (Espino, 2003).

The Toledo-Madrid corridor has HST, bus and car, as alternative modes of transport. In addition, the completion of the Atocha-Chamartín-Airport railway corridor will allow the connection between the Spanish high-speed network and Madrid's international airport. This will favor the cooperation of operators to offer better transport services to travelers (Muro, 2012): new figures of transport tickets, shorter connection times, integrated baggage management, information visualization, etc. (Eurocontrol, 2005b).

In this context, the research work on the "Toledo case" analyzes the current state of mobility in the Toledo-Madrid corridor and, on the other hand, changes the situation in a scenario of new transport infrastructures. This makes it possible to formulate policies at the level of passenger mobility to mitigate the most harmful effects of transport, such as congestion, environmental damage (INFRAS IWW, 1995, 2000, 2004; Intergovernmental Panel on Climate Change (IPCC), 1999; Aviation Environment Federation (AEF), 2000; Whitelegg and Williams, 2001; IATA, 2003; Steer Davies Gleave, 2006; CE Delft, 2008, 2011), by promoting complementarity between modes (Watkiss et al., 2001). Specifically with the promotion of intermodality between aircraft and train, the European Commission aims to address some of the negative effects of transport (Eurocontrol, 2004b).

## THE INDIVIDUAL BEHAVIOR IN THE CHOICE OF A SERVICE

One of the main objectives of social research is to determine the behavior of the consumer in the decision-making process and this is especially relevant when we are talking about services (McFadden, 2001b), whose supply is neither storable nor cumulative such as transport (Domencich and McFadden, 1975; de Dios Ortúza and Willumsen, 2001).

According to Ben-Akiva and Lerman (1985), an election can be considered as the result of a sequential decision-making process, which includes the following steps: definition of the problem of choice, generation of alternatives, evaluation of the attributes of each alternative, choice, and application of choice.

A specific theory of choice is therefore a collection of procedures and elements based on some general hypotheses. The elements that define the process of decision making are the decision maker and its characteristics, the alternatives or the determination of the options available for decision making, attributes, and decision rules, which describes the process used to choose a decision alternative (McFadden, 2001b; Ye et al., 2007; Martín et al., 2011; Chowdhury and Ceder, 2016; Houdek, 2016).

The decision-making or decision-making unit can be a person or a group of people, such as a family or an organization. If one considers a group of people as a decision maker we can ignore all the internal interactions within the group, and consider only the decisions of the group as a whole. In transport modeling, it has been customary to use aggregate models, which are calibrated with data that has been grouped or aggregated in some form (e.g., using average income per zone), although in the last decades a more disaggregated focus has been considered taking into account the decision unit, the individual (Domencich and McFadden, 1975). In this case, to explain the heterogeneity of preferences among decision makers at the individual level, a disaggregated model should include the socioeconomic characteristics of each decision maker, such as age, sex, and income (Collins and Chambers, 2005).

The analysis of individual decision-making requires not only the knowledge of what has been chosen, but also of what has not been chosen. Therefore, it is necessary to make hypotheses about the available options, or alternatives, that an individual considers during the election process. The set of considered alternatives is called the set of choice. We can differentiate two types of set of choice: continuous and discrete choice sets (Ben-Akiva and Lerman, 1985). The first case defines the set of choice taking into account all possible combinations of goods and services available to the individual1 . On the contrary, a discrete choice set contains a finite number of mutually exclusive alternatives that can be specifically enumerated. The choice of a means of transport is a typical example of a choice of a discrete choice set2 (Taniguchi et al., 2014).

The identification of the list of alternatives is a complex process, since not all alternatives will be available all the time for all decision makers. Therefore, it is necessary to differentiate between the universal set of choice, which contains all possible alternatives and the set of choice. The latter is a subset of the universal set of choice available to a particular individual, since alternatives to the universal choice set not available to the individual are excluded.

Normally, to determine these alternatives, deterministic criteria of availability of alternatives are used (for example, having a driving license determines the availability of the alternative of car). In addition to availability, knowledge of the existence of an alternative is a very important factor for the choice made. In this sense, Ben-Akiva and Lerman define that the causes of an alternative is feasible depending on availability in the market, budgetary constraints, available time or other informal factors such as knowledge of the service of a given alternative (Ben-Akiva and Lerman, 1985; Chowdhury et al., 2015; Schakenbos et al., 2016).

Behavior aspects sow uncertainty in the production of alternatives and motivate the use of probabilistic models of choice generation that allow us to obtain the probability of each alternative in the universal set (Swait, 1984).

An attribute is a trait that characterizes an alternative, with a certain value for individuals. The neoclassical economic theory considers that the individuals choose an alternative, from the different amounts of the goods that are included in them. In contrast, the discrete choice theory considers each alternative as a set of attributes and in function of these the attraction of an alternative is expressed by an attribute vector, called the utility function (Ben-Akiva and Lerman, 1985). Moreover, each alternative has its own attributes, and these can be generic for all alternatives or specific for a specific alternative. An attribute is not necessarily an objective quantity that can be measured, but can derive from a subjective measurement from perceptions. Just as the characteristics of the decision maker are taken into account, the analyst must include the attributes of each alternative (Sandoval, 1994).

In decision making, the last of the elements is the decision rule, defined as the process used in decision making, to evaluate the information available on the attributes of each alternative, in the set of choice and to determine a single choice. There are numerous decision rules in the literature that can be grouped into the following four categories (Ben-Akiva and Lerman, 1985):

These four decision rules, at the same time can be grouped into two types of behavior: compensatory or non-compensatory behavior. The compensatory type takes into account the set of all the attributes, so that changes in one or more attributes can be compensated by others, and in case of a decrease in one attribute can be compensated for an improvement in another, as is the case of the utility rule. In non-compensatory behaviors, rules or levels are defined to restrict the choice of some alternatives. Among these are the first three decision rules described (McFadden, 2001b).

The classical (neoclassical) and discrete-choice theories analyze individual consumer behavior (Ben-Akiva and Lerman, 1985), from a utility function that individuals must maximize, based on the analysis of revealed preferences, although with important differences between them, in the definition of decision rules and alternatives.

Most transport choice models have been based on this behavior (utility maximization), although other decision rules have been tried (Cantillo and Ortúzar, 2005), especially for the case of the declared preferences, where the choices that do not follow the principles of maximum utility are measured (Sælensminde, 2002; Rouwendal and Blaeij, 2004).

The mobility of people is a complex phenomenon, due to the large number of factors that influence the decisions

1Based on the neoclassical theory of consumer behavior.

2From this set of decision we derive discrete choice models, which we will see below.

of individuals. For this reason, it is necessary to analyze the displacements according to three interacting aspects a spatial approach, a social approach and a perspective approach (Rodríguez, 1991).

The spatial approach focuses on the different land use and the distribution of activities in space. This creates the necessity of the individual of the individual to move, depending on the location of the activities you want to perform.

The social approach refers to the fact that an individual's movements are exclusive of their characteristics and are therefore the result of the socioeconomic characteristics of the individual who performs it. Because of this it is necessary to have very present variables like the sex, the age, cultural level, income, health, etc.; for the analysis of the mobility.

Finally, we can speak of a perceptive approach, which refers to the image that the individual has formed. This image varies in each individual or group of people, leading to different assessments of the same data in decision making.

In the classic model a sequence of decisions of the individuals is done in four stages which are the generation of trips, zonal distribution, modal distribution, and assignment, but another sub-model has recently been included that is the last one mentioned, the election of the hour that gives place to the models of time distribution.

At present, this method is recognized to be too strict, since the decisions of the individuals are not taken following this sequence, but each sub-model depends on the type of the function of utility assumed to explain all these choices of trip. There are some current approaches that differ from this four-step sequential methodology and simultaneously address the stages of choice of frequency, destination, and mode of travel, but they are still at a level of research and have not been implemented. In addition, we must highlight models based on the activities of families or decision-making centers, which take into account constraints on budget and time choice (de Dios Ortúza and Willumsen, 2001) stand out.

### METHODOLOGICAL FRAMEWORK FOR MODELING CONSUMER CHOICES

From an economic perspective, consumer theory is the economic modeling of the behavior of an economic agent that consumes goods and services. This theory relates preferences, indifference curves and budget constraints to consumer demand curves.

The fundamentals of the individual choice theory of the neoclassical model of consumer behavior are based on the fact that the individual chooses an amount of goods, which form their basket of goods, in order to maximize their utility which translates into their level of satisfaction, subject to their income restriction (Torres, 2008). Therefore, given a consumer whose preferences fulfill these assumptions, there is a deterministic utility function, U, which represents these preferences that will be ordered so that the individual will choose the alternative that gives him greater satisfaction3 .

Given that consumer economic theory has been developed without taking into account the nature of the alternatives, some extensions have been made such as Strotz (1957, 1959), which analyzes consumer behavior by introducing the concept of "utility tree" (Strotz, 1957, p. 270). Muth (1966) proposes that the goods and services of the market are considered by the consumer as inputs of a domestic production function, whose production is the satisfaction of the consumer needs derived from consuming certain goods or Becker (1965) that extends the traditional theoretical formulation by adding the time constraint, in addition to the budget, to the production function.

Finally, one of the most important extensions is that one made by Lancaster (1966). This author considers three assumptions that break with the traditional approach and states. That (1) goods per se do not contribute to usefulness, but are the characteristics they possess, which provide usefulness to the consumer, (2) a good has more than one characteristic, and many of these characteristics can be shared by more than one good; (3) and through the combination of different goods different characteristics can be obtained from those corresponding to the separate goods. These assumptions represent the definition of utility in terms of the attributes of the goods.

Taking into account all these assumptions, consumer economic theory has important limitations to explain the consumer behavior, which justifies models based on Discrete Choice Theory. This reformulation of the behavior of the consumer will not occur until 1981, with the inclusion of consumer goods of a discrete nature (McFadden, 1981), with which the Theory of Discrete Choice begins, whose characteristics are that:

The problem that arises when using Discrete Choice Theory is that it represents a mechanism of deterministic choice and these deterministic election mechanisms do not fit the analysis of choice problems in real situations (Block and Marschak, 1960). In this line of study, this approach has been widely criticized, both in the field of psychology by Thurstone (1927), Luce (1959), and Tversky (1969), as in the field of economics by Quandt (1956) and McFadden (1981). These authors justify the inclusion of uncertainty in modeling, being the source of randomness determinant when specifying the different models.

Therefore probabilistic mechanisms of choice are required in order to analyze individual options. In order to do this, we use Probabilistic Election Theory, specifically to the Theory of Random Utility formalized by McFadden (1974, 1981), Domencich and McFadden (1975), and Manski (1977) and whose most recent developments are by (de Dios Ortúza and Willumsen, 2001).

The Random Utility Approach assumes that the individual always selects the most useful alternative, although the utilities are not known to the analyst and are treated as random variables. In mathematical terms, this is expressed by separating the total

3This is possible under the assumption of rational consumer behavior, namely perfect rationality (Simon, 1957).

utility Uin into a deterministic component Vin, called Systematic Utility and a random component εi that captures uncertainty, called Random Perturbation:

$$U\_{jq} = V\_{jq} + \varepsilon\_{jq}$$

where:

Vjq is the systematic component that represents an appreciable part of the utility of the individual q to choose alternative j. Systematic utility depends on the attributes of the alternative and the socioeconomic characteristics of the person.

εjq is the random term that accounts for unobserved factors.

The individual chooses the most useful alternative. Alternative i is chosen, if and only if:

$$P\_{iq} = \operatorname{Prob}\left(U\_{iq} > \left. U\_{jq} \right|\right) \forall j \neq i$$

The analyst can only obtain the probability of choosing alternative i as:

$$\begin{aligned} P\_{i\dot{q}} &= \mathit{Prob}(V\_{i\dot{q}} + \varepsilon\_{i\dot{q}} > V\_{j\dot{q}} + \varepsilon\_{j\dot{q}} \forall j \neq i) \\ P\_{i\dot{q}} &= \mathit{Prob}(\varepsilon\_{j\dot{q}} - \varepsilon\_{i\dot{q}} < V\_{i\dot{q}} - V\_{j\dot{q}} \forall j \neq i) \end{aligned}$$

This is the probability that each random term εnj – εni, is less than an observed quantity Vni − Vnj, so it is a cumulative distribution function. From the density function f(εn) we can calculate this probability as:

$$P\_{ni} = \operatorname{Prob} \left( \varepsilon\_{nj} - \varepsilon\_{ni} < V\_{ni} - V\_{nj} \,\forall \, j \neq i \right)$$

$$= \int\_{\varepsilon} I \left( \varepsilon\_{nj} - \varepsilon\_{ni} < V\_{ni} - V\_{nj} \,\forall \, j \neq i \right) f \left( \varepsilon\_{n} \right) d\varepsilon\_{n}$$

Being I (·) a function that has a value of one if the term given in brackets is true (if the individual has chosen alternative i) and zero in another case. According to the different hypotheses formulated about the distribution, εnj will have a closed value of this integral (Simple Logit or Hierarchical Logit) or it will have to be evaluated numerically by simulation (Probit or Mixed Logit), and therefore the different Models of discrete choice (Train, 2003).

Defined the set of choice, we can proceed to define the utility function that will give rise to different types of models. The typology of this type of model is broad and can be classified (Medina, 2003 according to the number of alternatives of the endogenous variable (dichotomous response models and multiple response models) or according to the type of function to estimate the probability (Linear Probability Model, Logit Model, and Probit Model): Whether the alternatives are exclusive or incorporate ordinal information (models with non-ordered data and models with ordered data) and whether the regressors refer to aspects of individuals or to alternatives in non-ordered models (multinomial and conditional models).

For the theoretical justification of the models of discrete choice based on the Theory of Random Utility, we can find three types of models of discrete choice depending on the different hypotheses that are taken for the distribution of the random term. First the linear probability model, assuming a uniform distribution, then the Probit Model, assuming a normal distribution and in third place the Logit Model, assuming a logistic distribution.

The Linear Probability Model is the linear fit regression model that is applied to a binary dependent variable. This model is estimated by ordinary minimums to the square and is easy to estimate and interpret. The estimated parameters measure the predicted change in probability of success, vs. a unit increment of Xi . Although there are problems in the estimation of the regression model when the endogenous variable is binary. The specific problems encountered with this model are heteroskedasticity of the perturbation term, the predicted probabilities are inconsistent, since it cannot be guaranteed that they are bounded between zero and one, the non-normality of the perturbation and the coefficient of Determination is not appropriate. Due to these problems, what is interesting is a model that reproduces properly the behavior of a probability function and the alternatives we find the Logit model and the Probit model, similar numerically. When the normal distribution is used as a probability function, the so-called Probit model is obtained, while the use of the logistic distribution provides the Logit model.

The main advantage of the Probit model is its ability to capture all correlations between alternatives, but its main problem is the complexity of its formulation, so there are very few applications that have been developed (Daganzo, 1979) and more than three alternatives are used for the calculation Simulation procedures (Train, 2003).

The logistic regression model, also called Logit models, is much more popular thanks to its analytical flexibility. Thus, the possible hypothesis for deriving some Logit models from the operation of individual choice is analyzed here: the most general case of interest, the Logit Multinomial model (McFadden, 1974), in its particular case, beyond binary situations; The Logit Hierarchical or Nested model (Williams, 1977) and the Logit Mixed model (McFadden and Train, 2000).

### MODELING THE DEMAND FOR SERVICES: APPLICATION TO THE CHOICE OF TRANSPORT SERVICES

Transport contributes significantly to the economic development and allows the market to function in a global way. It should be noted that most modes of transport do not affect society only in a positive ways, there are negative side effects such as congestion, noise and air pollution.

The promotion of intermodality using airplanes and trains is intended to solve some negative effects of transport, such as the impact of congestion on the environment, economy, safety and passengers (Eurocontrol, 2004b). For example, regarding to the access to the airport with the promotion of intermodality, beneficial effects are expected on the economy, especially in the regional economy, and on the environment are expected (Watkiss et al., 2001; Li and Hensher, 2012).

The estimation and internalization of external costs of transport have been important issues for research in the transport sector in the European Union. A Concern for the environment has grown over the last decade and the European Commission has raised the issue of internalization in several of its directives, such as the 1995 Green Paper (European Commission, 1995), the White Paper on 2001 (European Commission, 2001), and the Mid-Term Review in 2006 (European Commission, 2006). The latter two, underline the need for equitable and efficient pricing in terms of external costs. On the other hand, although the impact on the environment or congestion are indisputable, in the literature, there are some reports that try to quantify them, some of them are: (Intergovernmental Panel on Climate Change (IPCC), 1999; Aviation Environment Federation (AEF), 2000; INFRAS IWW, 2000, 2004; Whitelegg and Williams, 2001; IATA, 2003; Steer Davies Gleave, 2006; CE Delft, 2008) and (CE Delft, 2011).

In general, all of these investigations seek to establish pricing systems that capture the external costs associated with transport, so as to reduce negative impacts, in order to improve the efficiency of the transport system, to ensure fairness between modes and improve safety and sustainability.

In recent years, considerable progress has been made in the construction of transport demand models based on the theoretical principles of choice. Within random utility theory, it can be shown that the structure of the models depends on the perceived similarity between discrete choice alternatives. Moreover, this aspect can be interpreted mathematically in terms of the correlation between the components of random utility functions. The use of Logit-type disaggregated demand models allows us to determine the likelihood of choosing one way or another depending on the previously defined attributes, as well as quantifying the willingness to pay of these subjects based on the different characteristics previously given.

To do this, we must first determine which are the parameters that measure the utility of the services or that define the attributes of the service (Martín et al., 2011; Navarette and Ortuzar, 2013; Chowdhury et al., 2015; Schakenbos et al., 2016; Ettema et al., 2017). In order to select the mode of transport, the factors influencing the choice of mode can be classified into three groups (Eurocontrol, 2004a) by the characteristics of the mode of transport (vehicle availability and/or ownership, driving license, home structure, income), the trip characteristics (the mode of choice is heavily influenced by the purpose of the trip, time of travel), and the characteristics of transport infrastructures and services (relative travel time, relative monetary cost, comfort, and convenience, reliability and regularity, protection, and safety).

#### Data Sources

The information necessary for the estimation of these models is obtained through surveys of the mobility of the different individuals at a given moment (Martínez and Muro, 2011). Two types of surveys can be differentiated into revealed preferences and declared preferences.

Revealed preferences (RP) show data on the current behavior of individuals and give us information about their travel decisions. These provide us with descriptive information about the characteristics of the traveler on a particular route. Until the mid-1980s this type of data was the most used to model the transport demand. The main drawbacks to the use of this data in the modeling are the costs of the sample and its limited capacity to understand the behavior of the traveler, the observations of the actual elections may not have sufficient variability for the construction of good models. Because of this it is difficult to be able to detect the relative importance of certain factors on behavior; (de Dios Ortúza and Willumsen, 2001; Espino, 2003; Espino et al., 2006). For the Toledo-Madrid case, information is obtained on the type of mobility of travelers in the current situation with a socio-economic profile and mobility parameters (Martínez and Muro, 2014).

The stated preferences (SP) give information on the behavior of the individual before certain hypothetical situations raised by the researcher (Navarette and Ortuzar, 2013). Unlike RP data, which give information about the trips that an individual habitually makes, they inform about the trips that the individual would realize in certain conditions. The SPs started in the field of market studies and began to be applied to the field of transport demand analysis in the late 1970s. The possibility of designing SP experiments allows, theoretically, to solve the problems they present The RP (de Dios Ortúza and Willumsen, 2001). One of the problems presented by these models is that the analyst cannot assure that the individual performs what he has answered in an SP survey, so it is important to construct realistic alternatives and design comprehensible exercises to present the individual.

In an SP exercise, three elements can be mainly distinguished (Espino, 2003). The first are the situation in which the individual finds himself to declare his preferences which can be a real situation (a journey that is carried out at that moment) or hypothetical (a journey that would take place in the future given a series of conditions), and constitutes the context of the decision. Second, the alternatives that are usually hypothetical are selected, although some of them might exist today, they are presented in the exercise as a function of a set of attributes. Third, it defines how individuals can state their preferences. The most frequent techniques are the hierarchy of the answer, the punctuation of the alternatives and the choice between alternatives.

In the case of Toledo-Madrid, the different alternatives that the user has to carry out this journey are traveling by public transport, either by High Speed Train (HST) or by regular bus (BUS), or by private transport, Private vehicle (CAR).

The basic methodology for the presentation of the options of the declared preferences (SP) is the realization of an efficient design of the scenarios. Specifically, the declared preference questionnaire is designed following a time and price reduction scheme, in three blocks or scenarios, to determine the variation of the individuals' choice. The SP surveys are structured with two approaches: a first comparative approach between the bus and the HST and a second approach comparing the car and the HST. The scenarios are conformed with the variables of trip price, travel time, comfort and cost of parking, this last variable referred only to the case of the car.

The SP survey proposes a simulation exercise is exposed of alternative scenarios that layout the mobility in the future. For the elaboration of these scenarios the combination of price and travel time variables are used. Prior to the establishment of the options, a characterization of the services and infrastructures currently available for each mode of transport is made. This current quote will serve as a basis for calculating the different alternatives for the arrival to Madrid Airport from Toledo and choose the best.

Thus, the methodology followed was to calculate the price and average travel time of each mode of transport from the origin, which in this case is Toledo, to the destination in the current situation and compare it with the different hypothetical scenarios. Therefore, the scenarios that are presented in the SP questionnaires are calculated based on the average prices and times calculated in that route, and compared in each pair of transport modes (Muro, 2012).

Consequently, three route scenarios are established:

The chosen bus and bus (CAR) optimum alternatives are fixed and compared in blocks of three, according to the three scenarios raised for the HST. The prices and travel times for the mode of transport bus and private vehicle are constant for all scenarios and are based on current values. The prices of the bus are calculated according to ticket prices and the prices of the private vehicle are calculated by applying a cost per kilometer journey that incorporates the maintenance of the vehicle, insurance, etc., adding to the cost of the toll. This price does not include parking costs since it is presented as a different variable measured in euros per day of parking.

In summary, for the calculation of these values of time and price, the following hypotheses have been taken into account:

From the econometric point of view, the main difference of both types of data (RP and SP) are the types of error that each present. The RP data suffer from errors in the measurement of independent variables and those of SP present errors in the dependent variable, motivated by the doubt about whether the individual will actually perform what he is declaring. The joint estimation of both types of data is based on the different errors mentioned and can be specified if we consider error terms with different variance. Ben-Akiva and Morikawa formulate mixed data estimates that combine revealed preferences and stated preferences with the aim of avoiding their disadvantages and taking advantage of different sources of information (Ben-Akiva and Morikawa, 1990).

#### Specification and Estimation

In order to explain the mathematical approach of the models of discrete choice applied to the case of transport choice, one can start with the assumption based on the choice of the individual between two available alternatives (binary response models), and then extend this assumption to the j alternatives available (multiple response models). The dichotomous response models have two categories and these usually indicate that an event has occurred, that some feature is present or that an option is chosen.

In the modeling of the choice of transport, the objective will be to analyze the choice of the different alternatives that the user has to make this journey, and these are for example to travel by public transport, either in High Speed Train (HST) or by regular bus (BUS), or by private transport, in a private vehicle (CAR).

As this study focuses on the probability of choosing a mode of transport, the available alternatives will be the high-speed train against that the bus and the car. This allows specifying and estimating binary logistic regression models that will serve as the basis for a first analysis of the goodness of the database. Thus, two models are specified that will have a functional form of binomial logistic regression model, whose alternatives will be HST-BUS and HST-CAR, respectively.

The functional form of the binomial logistic regression models with the HST-BUS / HST-CAR alternatives will be represented by:

$$P(a) = P(U\_a \ge U\_r) = \frac{e^{\alpha V\_a}}{e^{\alpha V\_r} + e^{\alpha V\_a}}$$

Where U is the total utilities of the high-speed train (a) and bus/car (r), respectively.

If the deterministic utility component, V, is assumed to be linear with regard to the parameters, as in most disaggregated model approaches, then:

$$P(a) = \frac{e^{\alpha \bar{\beta} \cdot \bar{\chi}\_a}}{e^{\alpha \bar{\beta} \cdot \bar{\chi}\_r} + e^{\alpha \bar{\beta} \cdot \bar{\chi}\_a}}$$

where:

β¯, is the vector of coefficients.

X¯ , is the vector of independent variables.

So if we have two alternatives, for example the binomial Logit model will be represented

$$Prob(Y\_i = 1) = \frac{1}{1 + e^{-(\alpha + \beta\_k X\_{ki})}} = \frac{e^{\alpha + \beta\_k X\_{ki}}}{1 + e^{\alpha + \beta\_k X\_{ki}}}$$

$$Prob(Y\_i = 1) = \frac{1}{1 + e^{-\alpha - \beta\_1 X\_{1i} - \beta\_2 X\_{2i}}} = \frac{e^{\alpha + \beta\_1 X\_{1i} + \beta\_2 X\_{2i}}}{1 + e^{\alpha + \beta\_1 X\_{1i} + \beta\_2 X\_{2i}}}$$

If we suppose that α = 1, since in the case linear utilities with regard to the parameters, the parameter α cannot be distinguished from the general scale of the β.

$$P\left(a\right) = \frac{e^{\bar{\beta}\overline{X\_a}}}{e^{\bar{\beta}\overline{X\_r}} + e^{\bar{\beta}\overline{X\_a}}}$$

Taking the logarithm on both sides, from the equation above, we obtain a formulation that is suitable for applying linear regression techniques:

$$\ln \frac{P(a)}{1 - P(a)} = \overline{\beta} \left( \overline{X\_a} - \overline{X\_r} \right).$$

Since the probability of choosing the alternative r is given by: P (r) = 1 − P (a)

The final functional form of the model with an additive linear utility function is as follows:

$$\ln \frac{P(a)}{P(r)} = \overline{\beta} \left( \overline{X\_a} - \overline{X\_r} \right)$$

The main explanatory variables that are considered in the transport demand analysis are, differential between the attributes and the socioeconomic variables (de Dios Ortúza and Willumsen, 2001; Cokasova, 2003; Espino, 2003; Eurocontrol, 2005a; Steer Davies Gleave, 2006; Muro, 2012) which are:

#### (1) Attribute variables:

The price variable price measured in euros will be the price paid for each of the means of transport used as public transport, and for the private vehicle, the cost of gasoline based on the kilometers traveled, including the cost of the amortization of the vehicle (insurance, maintenance, etc.). The travel cost variable represent the price paid, in euros, for the route according to the form of transport chosen. In addition to the private car, the price of the car is different, so the total cost of the car will be the sum of the cost of the trip plus the cost of parking.

The total travel time is one of the most important factors that the traveler takes into account for choosing a mode of transport (Steer Davies Gleave, 2006). It is therefore a major factor to increase the demand, as travel time is reduced (IATA, 2003). The variable of travel time measured in minutes can be represented as the total time of the trip or by the time dedicated to each of the stages of the journey, such as waiting time, transfer, etc. The variable time considered in this study is the total time spent on the trip done by the chosen mode of transport.

This variable measures the level of service and is represented by dummy variables, which include values whose coding contains the different levels of service perceived by the individual. In this case, comfort tries to measure how the individual perceives the transshipments that are presented as hypothetical scenarios of the future services of the HST to Madrid airport (for the rest of the alternatives they would be the current travel options). Comfort is the dummy variable defined as the number of transfers chosen for the alternative train. So that in the first scenario, with no transfers, the comfort is 1; in the second scenario, with one trans for, the comfort is 2; and the third scenario, with two transfers the comfort is 3. If you do not choose the HST option the comfort level will be 0.

#### (2) Socioeconomic variables:

The variable sex is included as a dichotomous dummy variable, to analyze the influence of them in the decisions

This variable is expressed in four ranges: the value 1 being from 18 to 33 years; 2 from 34 to 49 years; 3 from 50 to 64 years, and 4 over 65 years. So we are in the specification of three dummy variables that represent value one when it meets that range and zero otherwise.

The utility specification is expressed as a Logit Binomial model, to measure the choice of mode of transport between the HST/BUS (MNL1) alternatives, in the Toledo-Madrid corridor (Madrid Airport):

Utility HST1: UHST1 = ASCHST1 + θPPHST1 + θTTHST1

Utility BUS: UBUS = ASCbus + θPPbus + θTTbus + PθCj C Being,

ASCHST1 and ASCbus, the specific constants of each alternative, in which ASCHST1 is fixed.

θP, θT, and θCj are the parameters associated to each of the explanatory variables. In this specification, the two most important variables (cost, θP and time, θT), where C, refers to all socioeconomic variables and characteristics of travel such as income, sex, age, motive travel, and comfort.

Cj, is a generic variable that represents each and every one of the variables not explicitly collected but mentioned above, where j refers to S, sex; ING to income; and COM, comfort depending on the transfers.

PHST, Pbus, is the price of alternatives HST1 and Bus, respectively.

THST, Tbus, is the travel time of the alternatives HST1 and Bus, respectively.

The specification of the utilities of the two alternatives of the HST-CAR Model (MNL2) will be:

Utility HST2: UHST2 = ASCHST2 + θPPHST2 + θTTHST2

P Utility CAR: UCAR = ASCcar +θPPcar +θTTcar +θPA PAcar + θCjCj

Being, ASCHST2; ASCcar, the specific constants of each alternative.

θP; θT; θPA, and θCj are the parameters associated to each of the explanatory variables.

Cj, is a generic variable that represents each and every one of the variables not explicitly collected but mentioned previously.

PHST, Pcar, is the price of alternatives HST2 and CAR, respectively.

THST, Tcar, is the travel time of alternatives HST2 and CAR, respectively.

The results of the binomial model estimates are shown in **Tables 1, 2** (Bierlaire, 2003) 4 .

The MNL1 models **Table 1** are the result of the estimation of the data of the alternatives HST1 and BUS. The MNL1.1, MNL1.2, MNL1.3, MNL1.4, MNL1.5, MNL1.6, and MNL1.7 models are variants of specified models with different explanatory variables. In general, these variables are statistically significant, as it shows in **Table 1**, and with correct signs (negative values for its inverse relationship).

The results of the first binomial models show low determination coefficients, which are a reflection of the inadequate fit of the Logit Binomial model at around 1% in the case of the HST-BUS alternatives (MNL1.1: 0.057; MNL1.2: 0.080; MNL1.6: 0.81). This can be caused by the lower amount of people who chose the BUS option vs. the HST option.

The MNL2 models **Table 2** are the results of the estimation of the data of the alternatives HST1 and BUS. The MNL1.1, MNL1.2, MNL1.3, MNL1.4, MNL1.5, MNL1.6, and MNL1.7 models are variants of specified models with different explanatory variables. In general, these variables are statistically significant, as it is shown in **Table 2**, and with correct signs (negative values for its inverse relationship), except in the MNL2.2 where we can find the travel cost with positive values. For this reason this model is nullified and in the final models only the cost of the total trip is included.

The MNL2 models (**Table 2**) are the results of the estimation with the data of the alternatives HST2 and CAR. The MNL2.1, MNL2.2, MNL2.3, MNL2.4, MNL2.5, MNL2.6, and MNL2.7 models are the same as the previous variants of the original. These models show determination coefficients around 53% (MNL2.1: 0.522, MNL2.3: 0.522; MNL2.7: 0.531).

From the results of the estimated models we proceed to calculate the payment arrangements of the users. These payment arrangements are calculated as the quotient of the parameters estimated for time and price, so they are interpreted as the willingness to pay or waiting to save in travel time (Espino et al., 2006).

The provisions for the payment of the binomial HST-BUS, fluctuate between 0.397 and 0.431, which means that individuals would be willing to pay from 23 to 25 € to save an hour of travel.

In the case of Logit Binomial HST-CAR models, **Table 2**, the results are very high being 600 € per hour what is saved in travel time. This may be motivated by individuals who answered in a lexicographic form, in which regardless the options, the users have chosen car, although this was but in all the options and by the difficulty of capturing the usual users of the car.

Next, if we assume that there are three available alternatives of choice for the individual in this path, a model is proposed with three alternatives, which are HST, bus and car, whose functional form will be that of a multinomial logistic regression model.

The specification is based on the approach of four alternatives, two from each of the sub-databases:

Utility HST1: UHST1 = ASCHST1 + θPPHST1 + θTTHST1 Utility BUS: UBUS = ASCBUS + θPPBUS + θTTBUS

Utility HST2: UHST = ASCHST2 + θPPHST2 + θTTHST2 Utility CAR: UCAR = ASCCAR + θPPCAR + θTTCAR + θPA PACAR + PθCjCj

The joint treatment of both databases requires, in the specification of the multinomial model, a specific treatment and equal to the problematic applicable to Mixed data (RP and SP).

Thus, the Logit Multinomial (MNL3) model with the HST-BUS-CAR alternatives will have the form:

The probability of choosing each of the alternatives will be:

$$Prob(Y\_i = 1) = \frac{1}{1 + e^{\alpha\_2 + \beta\_{12}X\_{1i} + \beta\_{22}X\_{2i}} + e^{\alpha\_3 + \beta\_{13}X\_{1i} + \beta\_{23}X\_{2i}}}$$

$$Prob(Y\_i = 2) = \frac{e^{\alpha\_2 + \beta\_{12}X\_{1i} + \beta\_{22}X\_{2i}}}{1 + e^{\alpha\_2 + \beta\_{12}X\_{1i} + \beta\_{22}X\_{2i}} + e^{\alpha\_3 + \beta\_{13}X\_{1i} + \beta\_{23}X\_{2i}}}$$

$$Prob(Y\_i = 3) = \frac{e^{\alpha\_3 + \beta\_{13}X\_{1i} + \beta\_{23}X\_{2i}}}{1 + e^{\alpha\_2 + \beta\_{12}X\_{1i} + \beta\_{22}X\_{2i}} + e^{\alpha\_3 + \beta\_{13}X\_{1i} + \beta\_{23}X\_{2i}}}$$

Therefore, a multinomial Logit model (MNL3) is specified, with sub-bases 1 and 2, so that a specification is made with the mixed data processing with different errors and therefore variances, so that ε is the stochastic error Of the HST-BUS data (Base 1) and η the data of the base HST-CAR (Base 2). It can be expressed as: σ 2 ε = µ 2 . σ 2 η , where µ is an unknown parameter.

So the utilities of both databases are expressed

$$\begin{aligned} U\_j^{B1} &= \, ^t V\_j^{B1} + \, \_j \boldsymbol{\varepsilon} = \, ^t \boldsymbol{\alpha} \, ^t \boldsymbol{Y}\_j^{B1} + \, \_j \boldsymbol{\varepsilon}\_j \\ \mu \boldsymbol{U}\_j^{B2} &= \, ^t \mu (\boldsymbol{V}\_j^{B2} + \boldsymbol{\eta}\_j) = \, \_t \boldsymbol{\alpha} \, (\boldsymbol{\theta} \, \boldsymbol{X}\_j^{B2} + \boldsymbol{\omega} \boldsymbol{Z}\_j^{B2} + \boldsymbol{\eta}\_j) \end{aligned}$$

where:

θ, α, and w are the parameters to be estimated.

X BI j and X B2 j are common attributes of alternative j for databases 1 and 2, respectively, while Y B1 j and Z B2 j are noncommon attributes of alternative j for each data set.

By multiplying the utility function of the Base 2 data by the unknown parameter µ, what is obtained is that the stochastic error of this type of data has the same variance as the data of Base 1.

Taking into account these utility functions we are allowed to homogenize the type of error homogenized, due to the multiplication of the SP parameters where by multiplying the utility function of the sub-base data 2 by the unknown parameter µ, we ensure that the stochastic error of this type of data has the same variance as the data of sub-base 1.

In mixed data, it is assumed that SP data should have more noise than RP data. If this is the case, the value of µ, that is known as the scale coefficient of the model, will be between 0 and 1. If the value is >1 it would indicate that the data with the highest noise level are those of RP. In our case and due to the errors detected, we assume that base 2 (HST-CAR) will have more noise (Ben-Akiva and Morikawa, 1990) 5 .

4BIOGEME. http://biogeme.epfl.ch/

5Assuming that the errors distribute as Gumbel with zero mean and different variance.

#### TABLE 1 | Estimated logit binomial model results MNL1 (HST1-BUS).

Value is the estimated coefficient for each of the explanatory variables included in the model. T-test: \* 90%, \*\* 95%, and \*\*\* 99% level of significance. It is the statistic that allows to contrast the null hypothesis of individual non-significance of each of the variables included in the model, (the contrast is performed through the level of significance associated with that statistic, so that if the level of significance is greater than 0.05 the variable is not statistically significant).The MNL1 Models are the results of the estimation with the data of the alternatives HST1 and BUS. The MNL1.1, MNL1.2, MNL1.3, MNL1.4, MNL1.5, MNL1.6, and MNL1.7 models are variants of specified models with different explanatory variables and the different specifications represent a robustness check.

TABLE 2 | Estimated binomial model results MNL2 (HST2-CAR).

Value is the estimated coefficient for each of the explanatory variables included in the model. T-test: \* 90%, \*\* 95% and \*\*\* 99% level of significance. It is the statistic that allows to contrast the null hypothesis of individual non-significance of each of the variables included in the model, (the contrast is performed through the level of significance associated with that statistic, so that if the level of significance is greater than 0.05 the variable is not statistically significant).The MNL2 Models are the results of the estimation with the data of the alternatives HST2 and CAR. The MNL2.1, MNL2.2, MNL2.3, MNL2.4, MNL2.5, MNL2.6, and MNL2.7 models are the same as the previous variants of the original and the different specifications represent a robustness check.

Taking into account the mixed data (MNL3.1) the specification of the model would be: UHST1 = OP ∗ (ASCHST1 + BETA1 ∗PHST1 + BETA2 ∗THST1) + (1-OP) ∗µ ∗ (ASCHST2 + BETA1 ∗PHST2 + BETA2 ∗THST2) UBUS = OP ∗ (ASCBUS + BETA1 ∗ PBUS + BETA2 ∗TBUS) + (1-OP)∗µ ∗ (ASCBUS + BETA1 ∗PBUS + BETA2 ∗TBUS) UHST2 = OP ∗ (ASCHST1 + BETA1 ∗PHST1 BETA2 ∗THST1) + (1-OP) ∗µ ∗ (ASCHST2 + BETA1 ∗PHST2 BETA2 ∗THST2) UCAR =OP ∗ (ASCCAR + BETA1 ∗ CTCAR + BETA2 ∗TCAR) + (1-OP) ∗µ ∗ (ASCCAR + BETA1 ∗ PTCAR + BETA2 ∗TCAR)

We proceeded to estimate multinomial models with three and four alternatives. The results of the estimation of the models are shown in **Tables 3, 4** (Bierlaire, 2003), for the multinomial simple models (MNL) with four and three alternatives, respectively.

Subsequently, models that combine the two databases have been estimated applying the specific problem of mixed data (Ben-Akiva and Morikawa, 1990), estimating Multinomial Logit models, with three and four alternatives. Once detected that the scale value of mixed data is significant, which means that there are differences in errors in the two databases, we start scaling the data, verifying that the results improve with the scaled data. From this moment on when estimating the Multinomial Logit models, the data of the sub-base 2 are scaled by this calculated coefficient in the model MNL3.1.

In **Table 3** it can be verified that the variable Theta (µ) can be verified to be statistically significant. For this reason, the database 1 must be scaled in order to be able to establish alternatives HST1 and HST2 as a single HST alternative. Thus, the three estimated alternatives models will have the scaled data. (**Tables 4, 6**).

In **Table 4** the estimated model is shown with three alternatives. From the MNL4.1 model with three alternatives without scaled data we can verify how in subsequent models with scaled data the results are improved in terms of Pseudo-R2 (ρ2). Therefore, from the scaled data the proposed specifications are

Value is the estimated coefficient for each of the explanatory variables included in the model. T-test: \* 90%, \*\* 95% and \*\*\* 99% level of significance. It is the statistic that allows to contrast the null hypothesis of individual non-significance of each of the variables included in the model, (the contrast is performed through the level of significance associated with that statistic, so that if the level of significance is greater than 0.05 the variable is not statistically significant).

estimated with three alternatives of Multinomial Logit and Mixed Logit models. From each of these results shown the provisions to be paid are calculated as the quotient of the estimated parameters for time and price.

Finally, the specification of a Logit Mixed model (ML), of fixed parameters, is carried out. The Logit Mixed model with error

Value is the estimated coefficient for each of the explanatory variables included in the model. T-test: \* 90%, \*\* 95%, and \*\*\* 99% level of significance. It is the statistic that allows to contrast the null hypothesis of individual non-significance of each of the variables included in the model, (the contrast is performed through the level of significance associated with that statistic, so that if the level of significance is greater than 0.05 the variable is not statistically significant).

TABLE 4 | Results of MNL models estimated with three alternatives and with scaled data.

Value is the estimated coefficient for each of the explanatory variables included in the model. T-test: \* 90%, \*\* 95% and \*\*\* 99% level of significance. It is the statistic that allows to contrast the null hypothesis of individual non-significance of each of the variables included in the model, (the contrast is performed through the level of significance associated with that statistic, so that if the level of significance is greater than 0.05 the variable is not statistically significant). Estimated model with three alternatives without scaled data that serves to verify how successive models with scaled data improve the results.

Value is the estimated coefficient for each of the explanatory variables included in the model. T-test: \* 90%, \*\* 95%, and \*\*\* 99% level of significance. It is the statistic that allows to contrast the null hypothesis of individual non-significance of each of the variables included in the model, (the contrast is performed through the level of significance associated with that statistic, so that if the level of significance is greater than 0.05 the variable is not statistically significant).

components is used when analyzing arbitrary substitutability or correlation patterns. In this case the utility of the alternative i is specified as:

$$U\_{iq} = \alpha' \mathfrak{x}\_{iq} + \mu'\_{q} z\_{iq} + \varepsilon\_{iq}$$

where:

$$\boldsymbol{x}\_{iq} \text{ and } \boldsymbol{z}\_{iq}, \text{ are vectors of observed variables.}$$

α, is a vector of fixed coefficients and represents the parameter vector of the explanatory variables of alternative i.

µq and εiq, are independent, being µq, is a vector of random coefficients with mean 0 and covariance W y εiq, are random variables, iid Gumbel(0,β).

With this formulation the random term is:

$$
\eta\_{iq} = \mu'\_q z\_{iq} + \varepsilon\_{iq}
$$

It can be verified that with this formulation there is correlation between any pair of alternatives:

$$\begin{aligned} \text{cov}(\eta\_{iq}, \eta\_{iq}) &= E \left[ \mu' \_{q} z\_{iq} + \varepsilon\_{iq} \right] E \left[ \mu' \_{q} z\_{jq} + \varepsilon\_{jq} \right] \\ &= z'\_{iq} W z\_{iq} + \sigma\_{\varepsilon}^{2} I \neq 0 \text{ siz}\_{iq} \neq 0 \end{aligned}$$

The probability of choice can be obtained in a similar way to the previous case, conditioned by the value of µq. Due to the fact that there is no guarantee of being able to solve the integral, the probability is obtained by simulation.

Both models (of random parameters and error components) are equivalent when the vector parameter β breaks clown into its mean α plus the deviations µq (βq = α + µq). Reciprocally, if ziq = xiq the error component model is equivalent to a random parameter model: and if ziq = xiq, we would obtain a random parameter model with fixed coefficients for xiq and random coefficients with zero mean for ziq.

The specification of a Logit Mixed (ML) model, with a panel effect to analyze the correlation between variables, using a fixed effects model, where four alternatives are specified first and further on three are specified.

First, a Logit Mixed model (ML5.1) is proposed to determine the panel effect, with mixed data and with the same error component for the two databases, so that the model will be:

$$\begin{aligned} \text{U}\_{\text{HST1}} &= \text{OP}^\* \left\{ \text{ASC}\_{\text{HST1}} + \text{BETA1} \, ^\* \text{P}\_{\text{HST1}} + \text{BETA2} \, ^\* \text{T}\_{\text{HST1}} \right\} \\ &+ (\text{1-OP}) \, ^\* \mu \text{\text{ (ASC}\_{\text{HST2}} + \text{BETA1} \, ^\* \text{P}\_{\text{HST2}} + \\ &\text{BETA2} \, ^\* \text{T}\_{\text{HST2}}) \\ \text{U}\_{\text{BUS}} &= \text{OP}^\* \left\{ \text{ASC}\_{\text{BUS}} + \text{BETA1} \, ^\* \right\} \end{aligned}$$

$$\begin{aligned} \text{P\_{BUS}} &+ \text{BETA2} \, ^\*\text{T}\_{\text{BUS}} \text{(}+ \text{ZERO[} \text{SIGMA1]+ }\text{)}\\ \text{(I-OP)} &\, ^\*\text{(ASC}\_{\text{BUS}} + \text{BETA1} \, ^\*\text{P\_{BUS}} + \text{BETA2} \, ^\*\text{)}\\ \text{T}\_{\text{BUS}} &+ \text{ZERO[} \, \text{SIGMA1]} \, \end{aligned}$$

$$\begin{aligned} \text{U}\_{\text{HST2}} &= \text{OP}^\* \left( \text{ASC}\_{\text{HST1}} + \text{BETA1} \, ^\* \text{P\_{HST1} \, BETA2} \, ^\* \text{T}\_{\text{HST1}} \right) \\ &+ (\text{1-OP}) \, ^\* \mu \text{ (ASC}\_{\text{HST2}} + \text{BETA1} \, ^\* \text{P\_{HST2}} \\ &+ \text{BETA2} \, ^\* \text{T}\_{\text{HST2}} \end{aligned}$$

UCAR = OP ∗ (ASCCAR + BETA1 ∗ CTCAR + BETA2 ∗TCAR +ZERO[ SIGMA1] ) + (1-OP) ∗µ ∗ (ASCCAR + BETA1 ∗ PTCAR + BETA2 ∗TCAR + ZERO[ SIGMA1] )

Being: OP = 1 with values of sub-base 1 and 0 for values of sub-base 2 (OP-1 = Base 2)

And later it is specified with different errors for each base (ZERO\_SIGMA1 and ZERO\_SIGMA2)

UHST1 = OP ∗ (ASCHST1 + BETA1 ∗PHST1 + BETA2 ∗THST1) UBUS = OP ∗ (ASCBUS + BETA1 ∗ PBUS + BETA2 ∗TBUS) + ZERO[ SIGMA1] ) UHST2 = (1-OP) ∗µ ∗ (ASCHST2 + BETA1 ∗PHST2 + BETA2 ∗THST2) UCAR = (1-OP) ∗µ ∗ (ASCCAR + BETA1 ∗ PTCAR

+ BETA2 ∗TCAR + ZERO[ SIGMA2] )

The specifications of the three alternatives are performed, first without scaling the data, ML 6.2:

$$\begin{aligned} \text{U}\_{\text{HST}} &= \text{ASC}\_{\text{HST}} + \text{BETA1} \, ^\ast \text{P}\_{\text{HST}} + \text{BETA2} \, ^\ast \text{T}\_{\text{HST}}\\ \text{U}\_{\text{BUS}} &= \text{ASC}\_{\text{BUS}} + \text{BETA1} \, ^\ast \text{P}\_{\text{BUS}} + \text{BETA2} \, ^\ast \text{T}\_{\text{BUS}}\\ &+ \text{ZERO} [\text{SIGMA}]\\ \text{U}\_{\text{CAR}} &= \text{ASC}\_{\text{CAR}} + \text{BETA1} \, ^\ast \text{PT}\_{\text{CAR}} + \text{BETA2} \, ^\ast \text{T}\_{\text{CAR}}\\ &+ \text{ZERO} [\text{SIGMA}] \end{aligned}$$

And with the Base 2 data scaled, with the scale factor obtained in the MNL3.1 estimate (µ = 0.0830), the model is specified with three alternatives such that ML.6.4:

$$\begin{aligned} \text{U\_{HST}} &= \text{ASC\_{HSTE}} + \text{BETA1} \, ^\ast \text{P\_{HSTE}} + \text{BETA2} \, ^\ast \text{T\_{HSTE}}\\ \text{U\_{BUS}} &= \text{ASC\_{BUS}} + \text{BETA1} \, ^\ast \text{P\_{BUS}} + \text{BETA2} \, ^\ast \text{T\_{BUS}}\\ &+ \text{ZERO} [\text{SIGMA}]\\ \text{U\_{CAR}} &= \text{ASC\_{CARE}} + \text{BETA1} \, ^\ast \text{PT\_{CARE}} + \text{BETA2} \, ^\ast \text{T\_{CARE}}\\ &+ \text{ZERO} [\text{SIGMA}] \end{aligned}$$

**Tables 5, 6** show the results for the Logit Mixto models, with three and four alternatives as well (Bierlaire, 2003).

The results of the Multinomial Logit models with three choice alternatives (HST-CAR and BUS) improve in terms of the coefficient of determination, showing more realistic payment arrangements. Even in the case of when we include the comfort variable (measured according to the number of transfers), together with the total price of the trip (which separately includes the cost of the trip and the cost of parking for the car, is significant. As well as that obtained from models whose variables are not significant and with signs of time not expected, the results improve in a substantial way.

### CONCLUSIONS AND RECOMMENDATIONS

This article shows an application of the demand models or discreet choice of Logit type to obtain the provisions for the payment of the consumers of a transport service, specifically in the corridor Toledo-Madrid/Madrid Airport. In this case it is analyzed what will be the choice of the travelers that make this journey, considering that they have three alternatives at present, the high speed train, car and the bus.

The theoretical basis of these emodels starts from the theory of random utility specifically in the theory of the maximization of utility. This model is based on the existence of a rational consumer that represents the average behavior of the set of consumers, based on a series of parameters that measure that utility. In the case of transport, these variables are usually the travel time, the cost or price of the service and the convenience of the trip, as well as different socioeconomic variables of the individual.

The model data are obtained through a survey of declared preferences. In it an exercise of simulation of alternative scenarios is proposed that configure mobility in the future. This mobility is based on the new infrastructure of connection with the airport. So there are different service options on the route which are two transfers, one transfer or no transfer.

In general, of the results presented above, we highlight the variables of the equation and the measures of goodness of the model.

Logit models have been estimated that measure the probability of choosing the HST in front of the bus or the car independently and according to the socioeconomic characteristics of the subject and the attributes of the different modes of transport. The variables included in each of the models are: time, price, sex and income. Thus, the attributes for each of the transport modes are (1) HST, price and travel time, (2) bus, price and travel time and (3) car taking into account price, parking cost and travel time.

Specifically, the results show that the willingness to pay HST-Car binomial models is too large (600 € per hour saved on the trip), motivated by individuals who have responded in a lexicographic way and motivated by the smaller number of people who have chosen option bus in front of the HST option. The provisions for the payment of the binomial HST-Bus, are more reasonable and oscillate between 23 and 25 € per hour.

In multinomial models the provisions for payment are more realistic, except when we include the comfort variable, together with the total price of the trip, with which models are obtained whose variables are not significant and even with the signs of the time not expected, estimating The model without independent terms with which you get a result of 26.3 € per hour.

From the results of the modeling we can analyze how the consumer is willing to pay 26.3 € for each hour of travel saved which gives us information about the importance of travel time over the price paid.

In conclusion, based on this information, a sustainable transport policy focusing on HST-airplane intermodal transport is recommended in order to facilitate the reduction of travel times, by encouraging the use of efficient public transport as the high-speed train to reach the airport.

A transport policy focused on replacing private transport with public transport (HST), benefits, on the one hand, users who will have shorter travel times and in general, the transport system to reduce the congestion of access to Madrid and to the Madrid Airport (Li and Hensher, 2012).

The main variables to encourage the integration of air and high-speed modes of transport are the visibility of the offer, integrated management of the reservation and boarding passes, check-in and baggage control, full travel responsibility, the management of passenger loyalty programs, ease of connection in intermodal mode, travel time and price (Muro, 2012). As for this offer, and above all regarding the connections between Toledo and Madrid airport, a new line of direct buses to the airport has emerged (Muro and Pérez, 2016) and recently, in February 2017, the offer of combined train and flying tickets (Train and Fly)6 from the city of Toledo, which indicates improvements in air-rail intermodality (air-rail intermodality).

### AUTHOR CONTRIBUTIONS

AM: modeling and coordination. IP: methodological framework. SG: introduction and conceptual framework.

#### FUNDING

This paper has been funded by Aid Research Groups at the University of Castilla-La Mancha 2017. Research Group

6http://www.renfe.com/viajeros/trenmasavion/index\_ES.html

### REFERENCES

"Observatorio de la Innovación en Distribución Comercial". This paper has been funded by The Ministry of Economy and Competitivity (Spain), Research Project with reference: ECO2014-59688-R, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016. And the revision of the translation has been financed with the collaboration of the contract-program of the Faculty of Juridical and Social Sciences of Toledo.

#### ACKNOWLEDGMENTS

We are grateful to our teacher, Professor Timoteo Martínez Aguado, for the collaboration and help he has always given us and, in particular, for his contributions in this work.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Muro-Rodríguez, Perez-Jiménez and Gutiérrez-Broncano. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# New Strategies in the New Millennium: Servant Leadership As Enhancer of Service Climate and Customer Service Performance

Jorge Linuesa-Langreo, Pablo Ruiz-Palomino\* and Dioni Elche-Hortelano

Department of Business Administration Department, University of Castilla-La Mancha, Cuenca, Spain

In a world in which customers are increasingly looking for solutions to their own concerns on how to make a better globalized world, new organizational strategies are emerging to approach the customer in the current third millennium. Servant leadership, which involves putting employees' needs first and serving the broader society, is emerging as a new strategic mechanism to approach the customer in line with the new social valuesdriven Marketing 3.0 era. Yet research has ignored the role and the various mechanisms servant leadership might utilize to improve customer service performance of their service units. Spanning 185 hotels located in Spain, a sample of 247 service units –in close contact with customers– was used to investigate whether servant leadership enhances customer service performance through shaping a service climate within the service unit. Results revealed that service climate mediates the positive influence of servant leadership on customer service performance. Managers can use these findings to note the value of leading the service unit in a servant friendly direction, which is better aligned with the new aspirations of customers today.

Keywords: social-aware customers, servant leadership, service climate, customer service performance, Marketing 3.0

## INTRODUCTION

Recent marketing literature has begun to nurture from a new, very incipient perspective, the social values-driven Marketing 3.0 paradigm (Kotler et al., 2010), which proffers the idea that customers are increasingly seeking solutions to their own concerns and are interested in building a better world.  In other words, in this new millennium, driven by the Marketing 3.0. paradigm, which entails a more human-centric perspective, customers look to products and services to meet their own needs in parallel with fulfilling spiritual, social and moral values (Kotler et al., 2010). The extent to which a product or service provides freedom of choice, independence as well as benevolence, social justice, equality and environmental responsibility is becoming more and more crucial for customers when making purchase choices (Martínez-Cañas et al., 2016), especially in a developed world, where consumption appears to have become an end in itself, through which customers find a voice to promote a better society (Vrontis and Thrassou, 2007). Furthermore, customers are increasingly showing concerns about the effects of their purchase

#### Edited by:

Ana I. Jiménez-Zarco, Open University of Catalonia, Spain

#### Reviewed by:

Isabel Llodrà-Riera, Illes Balears Innovacio Tecnologia, Spain Mauro Capestro, University of Salento, Italy

> \*Correspondence: Pablo Ruiz-Palomino<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Organizational Psychology, a section of the journal Frontiers in Psychology

Received: 24 March 2017 Accepted: 28 April 2017 Published: 16 May 2017

#### Citation:

Linuesa-Langreo J, Ruiz-Palomino P and Elche-Hortelano D (2017) New Strategies in the New Millennium: Servant Leadership As Enhancer of Service Climate and Customer Service Performance. Front. Psychol. 8:786. doi: 10.3389/fpsyg.2017.00786

choices not only for themselves, but also for broader society (Harrison, 2005; González and Fernández, 2016), which represents a strong embracement of transcendent motives in their actions. In other words, in addition to making purchase decisions with an eye on external benefits gained (i.e., extrinsic motivation) or the pleasure acquired from the purchase decision itself (i.e., intrinsic motivation), customers are more and more concerned about whether their purchase decisions contribute to solving the problems of someone else (i.e., transcendent motives). In effect, in the current millennium, customers are more and more worried about whether others, both known or unknown, meet human good, such as truth, beauty, work, friendship, life, and dignity, such that their purchase decisions' impact on others is carefully calculated (Martínez-Cañas et al., 2016).

In accordance with the described scenario, in which customers are demanding that businesses act in a socially responsible manner (Vrontis and Thrassou, 2007), it is of no surprise that managers are beginning to think of new customer-focused strategies to engage with the modern customer today (González and Fernández, 2016). One of these interesting strategies to appeal to the customer in the current social values-driven Marketing 3.0 era is the development of servant leadership in service units –work units which are in close contact with customers–. While leadership is deemed a central aspect to orientate service units' mission and values toward a greater involvement and co-responsibility with broader society, servant leadership is the unique leadership approach which, as captured by its name, focuses on serving others (Liden et al., 2014), including the least privileged in society (Greenleaf, 1977). Servant leadership's concerns extend beyond the organization itself to meet the well-being of followers, customers, other stakeholders, and society in a wider sense (Greenleaf, 1977; Barbuto and Wheeler, 2006). Their profound responsibility to serve others and contribute to the larger society is central in undertaking such leadership approach (Liden et al., 2014). In addition it purports to show followers how to fulfill the business mission of serving broader society, (Graham, 1991). Indeed, according to Greenleaf (1977), the first to coin the term after reading the Journey to the East by Herman Hesse, one of the central tenets of the servant leadership approach is that serving others entails encouraging others to do the same, so that they become servant leaders.

It is of no surprise then that this leadership approach results in an appropriate strategy to improve customer service (Brownell, 2010; Wu et al., 2013). When servant leadership is present in service units, employees of such work units are more likely to provide genuine care to customers and, in turn, authentic, high-quality customer service (Brownell, 2010). This process is possible because it is highly characteristic of servant leaders to fuel a cycle of service within their service units; servant leaders are role-models of servant behavior which, in turn, is mirrored by followers (Hunter et al., 2013). Therefore, under the influence of servant leaders a service climate, i.e., workers' perception that internal practices, procedures, and behaviors support the provision of quality service– is likely to emerge. Furthermore, by shaping service climates, servant leaders should contribute to the enhancement of customer service performance, i.e., the workers' proficiency in undertaking the core parts of their service role to provide high-quality customer service. This finds support in service-linkage research (Wiley, 1996; Pugh et al., 2002; Schneider et al., 2005), which is concerned with finding the links between employees and customers in service firms -where the boundaries between both agents are fairly permeable-. According to this perspective there must be specific drivers which link employees' perceptions of various inter-organizational practices to customer perceptions, and some studies have argued that service climate is the bridge, the missing link between what happens inside –procedures, practices–, and what happens outside –customers' perceptions– (Schneider et al., 2002; Hong et al., 2013). In fact, prior research has linked service climate to perceptions of service quality (Gracia et al., 2010), and service performance (Liao and Chuang, 2007).

It seems then that servant leaders in the new millennium, which depicts a scenario where customers are more concerned about how to contribute to build a better world, might play a role in improving customer service performance; however, such influence might occur by fostering and shaping service climates within their service units. To the best of our knowledge, this particular point has not yet been addressed in existing research. Therefore, this study pursues two main objectives. First, we will investigate whether servant leadership might be a suitable leadership strategy to improve customer service performance, as a measure which captures service units' emphasis on service quality. Second, and more importantly, we examine the mediating effect of service climate between servant leadership and customer service performance. These relationships will be analyzed at the work unit level via spanning service units. The work unit, rather than the individual, is the building block of organizations, today (West and Markiewicz, 2004) and permits managers to work closely with followers on a daily basis. Service units in service, tourism firms, i.e., hotels, represent suitable units of analysis for uncovering the influence of servant leadership on service climate and service customer performance.

### THEORY AND HYPOTHESES DEVELOPMENT

### The Nature of Servant Leadership

With its strong, unique focus on serving others (Wu et al., 2013; Liden et al., 2014), servant leadership offers unique aspects which can enhance the quality of service provided to customers. Greenleaf (1977, p. 27) coined the concept of servant leadership, though he failed to give a formal definition, and described the phenomenon of servant leadership as:

The servant-leader is servant first. It begins with the natural feeling that one wants to serve, to serve first. Then conscious choice brings one to aspire to lead. The difference manifests itself in the care taken by the servant-first to make sure that other people's highest priority needs are being served. The best test, and difficult to administer, is this: Do those served grow as

persons? Do they, while being served, become healthier, wiser, freer, more autonomous, more likely themselves to become servants? And, what is the effect on the least privileged in society? Will they benefit or at least not be further deprived?

Such description clarifies two core aspects of the servant leadership strategy. First, servant leadership extend their service approach to the various stakeholders, including employees, customers, and society in general (Graham, 1991; Wu et al., 2013); these leaders even raise strong concerns focused on improving the well-being of the least privileged in society. As such, because the servant leader's area of concern extends beyond the business organization and includes the broader social environment (Brownell, 2010), these leaders demonstrate a high level of social responsibility for the well-being of society in general (Reinke, 2004). Second, servant leadership prioritizes the fulfillment of others' needs above their own personal needs (Greenleaf, 1977), and inspire followers to develop intelligently, be creative and self-manage in order to serve others (Liden et al., 2008). As a result, because servant leaders practice this "service" mindset in all aspects of their lives (Liden et al., 2008), the principle of serving others above serving oneself is unlikely not to radiate out toward followers' mindsets, attitudes, and behaviors. In effect, servant leaders encourage their followers to develop servant behaviors that will benefit all stakeholders (Liden et al., 2008; Sendjaya et al., 2008), thus ensuring that the strategies and decisions they opt for will offer a positive legacy to society (Barbuto and Wheeler, 2006). Because servant leaders are constantly searching out benefits to society (Searle and Barbuto, 2011), they are likely to inspire related servant attitudes in their followers to do their best to the benefit of all stakeholders, including customers. Thus, by developing a deep level of identification with the behavior carried out by their servant leaders (Zhang et al., 2012), employees should take personal responsibility for providing assistance and worth to customers, and thus provide services which are authentic and of superior quality (Brownell, 2010).

### Servant Leaders and Customer Service Performance

One of the key characteristics that sets service companies apart from those which produce goods is the simultaneous nature of service production and consumption, which, in many cases, results in consumer participation in the co-creation of the service (Bowen and Schneider, 1988). As such, the experience provided to the consumer upon receiving services is as important as, or even more important than, the product offered to the consumer (Bowen and Waldman, 1999). This has led to a paradigm shift for service companies when defining (customer service) performance, moving away from a focus on behavior evaluation to achieve organizational objectives (Campbell et al., 1993), toward a greater focus on behavior developed by the worker himself/herself, which is geared toward serving and helping the customer with the goal of providing high-quality service (Liao and Chuang, 2004).

This change of perspective means that the worker maintains direct and ongoing contact with the customer, thus increasing uncertainty as to how to interact with the customer, as in many cases the customer demands an immediate solution (Skaggs and Galli-Debicella, 2012). In these circumstances, the development of leaders who offer their workers both guidance and common sense, and who consistently cover all of his workers' needs, is vital for improving workers' performance (McGrath, 1962; Morgeson et al., 2010). As such, given that servant leadership develops social responsibility when serving both workers and customers (Mahembe and Engelbrecht, 2014), servant leadership in managerial roles appears to be a key element to generate a higher level of customer service performance (Chen et al., 2015). In effect, servant leaders possess impressive conceptual abilities for offering workers direction, support and clarity in solving day-to-day problems (van Dierendonck, 2011). These aspects help workers cultivate a precise understanding of their changing environment and develop individual and group skills (Hu and Liden, 2011). Some of these skills are, for example, more creative performance (Neubert et al., 2008), independence and self-confidence (Liden et al., 2008), which facilitates behavior which is spontaneous and useful in meeting customer demands without needing any type of supervision (Chen et al., 2015).

The positive relationship between servant leadership and customer service performance can be explained using the social exchange theory (SET, Blau, 1964; Cropanzano and Mitchell, 2005). SET theory indicates that social relationships are based on norms of reciprocity (Gouldner, 1960), where people look to maintain psychological balance in their social interactions, returning "favors" to those who have demonstrated proactive and positive tendencies toward them. Accordingly, when servant leaders in service units put their workers first, and display a service attitude and sincere concern for covering workers' needs of personal and professional growth (Greenleaf, 1977), they create a psychological imbalance in workers' relationships with these leaders. In a bid to benefit these leaders, workers might engage in service behaviors, directed to benefit the service unit, by superbly attending, for example, to customers' needs. These behaviors, might also be impregnated with servant leaders' strong emphasis in meeting the well-being of others, including customers and broader society, as these workers should become servants to an even greater extent according to Greenleaf (1977). As such, the display of such behaviors in encounters with customers should result in high-quality services, particularly in the present environment, in which customers are more and more needful of signals showing that the products and services they consume contribute to building a better society. Various studies support this relationship. While Netemeyer et al. (2005) recognize the better the leader–worker relationship, the stronger the impact is on the relationships maintained between workers and customers, other studies confirm a positive relationship between servant leaders and workers, which, in turn, translates into better worker– customer relationships and higher quality of service provided to customers (Jaramillo et al., 2009; Chen et al., 2015; Ling et al., 2016). Formally:

H1: Servant leadership is directly, positively related to customer service performance.

### Servant Leaders and Service Climate

Leadership is one of the most important factors in the process of climatic formation (Kozlowski and Doherty, 1989). Supervisors who model their leadership approach to all of their workers represent significant influences in forming the climate and providing it with content (Mayer et al., 2007). As such, servant leaders, by maintaining a service attitude oriented to meet both workers and customers' needs (Wu et al., 2013), are active agents in forming a service climate within the work units to which these leaders belong (Walumbwa et al., 2010). In effect, service climate, defined as "employee perceptions of the practices, procedures, and behaviors that get rewarded, supported, and expected with regard to customer service and customer service quality" (Schneider et al., 1998, p. 151), is likely to emerge in service units which are led by servant leaders, as explained by social learning theory (SLT, Bandura, 1977, 1986).

Social learning theory contends that individuals learn the appropriate behavior by observing and emulating values, attitudes and behaviors of attractive, credible role models (Bandura, 1977, 1986). Managers become these influential referents (Mayer et al., 2012) because they embody proximity, frequent social interaction, and formal authority (Merton, 1957; Sutherland and Cressey, 1970), which makes it easier for them to garner attention and convey attractive information. As such, workers, through observing supervisors' behaviors in a day-today work setting, are likely to engage in imitative behaviors (Hall and Lord, 1995), which can be further intensified whenever workers perceive their leaders to be in possession of qualities they consider to be attractive (Neubert et al., 2008; Mayer et al., 2012).

Servant leaders capture such attractiveness as they offer guidance and direction to workers and, by being humble, loving, empathetic, and servant (Sun, 2013), manifests sincere concern for satisfying the needs of both workers and customers (Wu et al., 2013). Imitative behavioral trends are highly probable in workers who are led by servant leaders, who will manifest behaviors that are similar to those of their leaders (Hunter et al., 2013), behaviors which are oriented to serve broader society by developing people committed, in turn, to serve society (Sims, 2005). This service-oriented behavior, developed by workers, results in a phenomenon of contagion (Bono and Ilies, 2006) among all members of the service unit which inspires a continuing service cycle (Hunter et al., 2013), a service-oriented culture (Liden et al., 2014) and, overall, a greater service climate (Walumbwa et al., 2010). The result is the creation of a work environment where members of the service unit share social behavioral norms, aimed first and foremost at offering high-quality service (Schneider et al., 2002). Accordingly, we propose:

H2: Servant leadership is directly, positively related to service climate.

### Service Climate and Customer Service Performance

Organizational theorists emphasize the importance of the organizational climate in determining employees' attitudes and behaviors (Schneider et al., 2013). In accordance with the social information processing theory (SIP, Salancik and Pfeffer, 1978), employees collect the various messages released by their work units and utilize this information for decision making issues. Employees usually tend to adapt their feelings, attitudes, and behaviors according to what they perceive in their immediate work environment (Van Dyne et al., 1995). Therefore, the organizational climate, understood as the shared perceptions regarding policies, procedures, and practices (Schneider, 1990) which signal how things ought to be done and what behaviors are proper in the work environment (Liao and Chuang, 2007), represents an important influence on employees' performance (Schneider and Barbera, 2014). In fact, depending on the specific dimension the organizational climate emphasizes, a number of studies have revealed its significant influence on specific behavioral outcomes regarding areas such as safety (i.e., Zohar and Luria, 2004), ethics (i.e., Deshpande, 1996), innovation (Anderson and West, 1998) or service (i.e., Schneider et al., 2009). This is because such specific climates represent the best source of cues to interpret events, undertake proper attitudes, and understand behavioral expectations concerning the different areas or dimensions emphasized.

In the particular case of service climate, it helps employees internalize that excellent service is expected, desired, and rewarded; it also represents a strong motivational force to deliver the best service in day-to-day activities (Liao and Chuang, 2007; Liao et al., 2009). Such perception is important in the service context, where services are produced and delivered in real time by unit employees (Ehrhart et al., 2011); in such contexts, the more customers perceive service quality is central for employees, the better their service experience (Schneider and White, 2004). This is not surprising as this specific climate emphasizes service quality to a great extent, so as a result, it should have a direct impact on service outcomes (Schneider, 1990) such as customer service performance. Indeed, employees highly engaged and sharing common perceptions about providing good quality of service to customers should perform well with customers (Salanova et al., 2005). This should occur because of social learning mechanisms (Bandura, 1977, 1986) as well as because perceptions that a high value for service is the tone, which should provide meaning to work and make employees enjoy their jobs to a greater extent (Hong et al., 2013). Earlier empirical research has consistently revealed that service climate enhances customer service performance (Borucki and Burke, 1999; Liao and Chuang, 2004, 2007; Salanova et al., 2005; Ehrhart et al., 2011; Hong et al., 2013). Thus, we predict:

H3: Service climate is directly, positively related to customer service performance.

### Servant Leaders-Customer Service Performance: The Mediation of Service Climate

According to linkage research (i.e., Wiley, 1996; Schneider et al., 1998; Wiley and Brooks, 2000; Pugh et al., 2002), there are internal elements of the work environment which can be strongly linked to critical external performance outcomes. Service climate is one of these internal elements which recent research

has identified as the bridge between the work environment as perceived by employees- and critical, external performance success factors oriented to the customer (e.g., customer service performance, Hong et al., 2013). This is important because it permits managers to apply an indirect approach, by focusing on more easily manageable internal aspects (e.g., leadership), to encourage customer service-minded behavior (Solnet, 2007), which can be conducive of better customer service perfomance.

Looking into internal elements, servant leadership implies an environmental stimulus (Hu and Liden, 2011) which is built upon service values based on genuine concern for and loving care of others (van Dierendonck, 2011; Hunter et al., 2013). Such stimulus should germinate, grow and propagate within the collective, because drawing on SLT (Bandura, 1977, 1986) workers feel attracted to imitate such attractive leaders by engaging in similar servant behaviors (Liden et al., 2014). Hence, servant leadership within service units should be associated with the shared perception that interpersonal relationships rest upon such service values, which should help shape a climate fostering helpful behavior oriented to offer high quality service (i.e., Walumbwa et al., 2010). Servant leaders foster climates which send clear messages that egotistical behavior is not tolerated (Liden et al., 2014), and service spirit is strongly encouraged (Liden et al., 2008).

Looking at external performance outcomes, various studies have revealed clear positive effects of service climate on customers (Bowen and Schneider, 2014), such as, for example, customer service (e.g., Schneider and Bowen, 1985), customer satisfaction (e.g., Schneider et al., 1996), and customer loyalty (e.g., Salanova et al., 2005). This is because in scenarios where service climate is perceived, workers share the understanding that the behavioral norms and expectations are to prioritize the needs of others (Liden et al., 2014), specifically, customers, which encourages employees' strong engagement in high-quality service behavior directed to the customer (Liao and Chuang, 2007). Such a service climate is ignited through a spillover process spreading service attitudes and behaviors which should be noted in employee–customer interactions. With customers perceiving employees to be warm, in a good mood, and willing to dedicate time to understand their needs, customers get a good feeling about the service received. Such good feelings would also be nurtured as long as these influenced servant workers show strong concern for building a better society –which is an increasing concern of customers, today (Vrontis and Thrassou, 2007)-. In other words, by observing workers who behave in this way, customers should enjoy such an awesome experience that they should feel that a high quality service has been received (Hong et al., 2013).

Overall, by combining both internal and external perspectives, we contend that because servant leaders enhance service climate within their service units (Walumbwa et al., 2010), these leaders improve the quality of service that workers offer to customers (Ling et al., 2016). In other words, by drawing on the broad existing body of linkage research (Wiley, 1996; Schneider et al., 1998; Wiley and Brooks, 2000; Pugh et al., 2002), we contend that service climate is the bridge between servant leadership and customer service performance. Thus, we propose:

#### H4: Service climate mediates the relationship between servant leadership and customer service performance.

This last hypothesis combined with the previous ones make it possible to summarize our research model as displayed in **Figure 1**.

### METHOD

#### Sample and Procedure

In order to test these relationships, we conducted surveys to gather data in Spain's hospitality industry, which is likely to attract managers who are servant leaders. Customer service of the utmost quality is key for success of companies in this sector, so servant leadership strategies could make an important difference and be common in these circles (Wu et al., 2013).

In an effort to minimize common method bias (CMB), and social desirability bias (SDB), we selected the most fitting participants for all our study constructs (Podsakoff et al., 2003). Firstly, workers were selected to measure the extent to which their supervisors can be portrayed as using a servant leadership strategy. Secondly, service climate was measured by using both workers' ratings and supervisors' ratings, as it allows us to minimize the same-source bias problem (Ostroff et al., 2002) and thus have a more objective indicator of the phenomena. Finally, customer service performance was assessed by the hotel

general manager, not by service units' supervisors who might give biases responses. Accordingly, we designed three different questionnaires for each target respondent (i.e., hotel general manager, service unit supervisor, service unit worker). We pilot tested each questionnaire with a convenience sample of 3 general managers, 10 supervisors, and 25 workers in 3 hotels, respectively, which confirmed the clarity, comprehension, readability, and suitability of the items included. Surveys' cover letters for each target respondent indicated absolute anonymity, and noted that only aggregated data would be utilized for research purposes.

Once consent was gained from the general managers at 185 hotels, each located in a distinct Spanish historical site, data from 247 service units (three members per unit at a minimum) which were in close contact with customers (e.g., reception desk, restaurant) could be gathered. Both supervisor and workers' responses were collected per each service unit; in total 840 responses were received –the response rate was high, around 77%-. As to collection of data concerning each unit's customer service performance, hotel general managers were also surveyed; in total 185 responses were obtained. Data were collected at specific sites in each establishment, which helped us ensure that each survey was paired with its corresponding service unit and hotel.

As further countermeasures for the CMB and SDB issues, the design of the survey was based on recommendations raised by Podsakoff et al. (2003) and Conway and Lance (2010). For example, the survey's cover letter highlighted the fact that there were no correct or incorrect answers, thanked participants in advance for being honest, and noted that all responses would remain anonymous. Participants did not have to share their names, job titles or employers' names in the survey. Furthermore, the cover letter clearly stated that the results were for academic purposes only, thus reducing SDB (Nancarrow et al., 2001). Lastly, when designing the survey (Podsakoff et al., 2003), we worked to ensure a psychological separation between predictors and outcome variables, to keep them from seeming related. We also used distractor elements and utilized items that were simple, focused and concise.

#### Measures

The survey was conducted in Spanish. Brislin's (1980) backtranslation procedure was conducted to our mediator and independent variable, and no meaningful differences between the two translations from and to English were noticed. An exhaustive analysis, according to MacKenzie et al.'s (2005) criteria, showed that all our measures contained highly correlated indicators; in other words, our survey included reflective measures in all cases.

#### Servant Leadership

Service unit workers used Ehrhart's (2004) reliable 14-item scale to rate servant leadership of their service unit supervisors. The scale used a seven-point response format (1 = "strongly disagree," 7 = "strongly agree"). Sample items were, "My supervisor spends the time to form quality relationships with service unit employees" and "My supervisor emphasizes the importance of giving back to the community." Because we were interested in overall patterns of servant leadership behavior within the service unit, we averaged employees' ratings within each service unit. To confirm that this aggregation of individual scores to the unit level was appropriate, we calculated the within-service unit agreement score (rwg, James et al., 1984) and two intraclass correlations: ICC(1), or the proportion of variance in ratings due to service unit membership, and ICC(2), or the reliability of service unit mean differences (Bliese, 2000). The average rwg value was 0.83, and the ICC(1) and ICC(2), were, respectively, 0.65 and 0.86, which met acceptable cutoffs (Bliese, 1998). In addition, we performed a one-way analysis of variance (ANOVA), which indicated significant differences across the service units in the average scores (F = 11.07, p < 0.01). Therefore, we consider the aggregation justified (Bliese, 1998).

#### Service Climate

All service unit members, including both workers and supervisors, completed on a seven-point response format (1 = "strongly disagree," 7 = "strongly agree"), Carrasco et al.'s (2012) 4-item service climate scale, which is an adaption to the Spanish context of the Schneider et al.'s (1998) global service climate scale. Sample items were, "Employees in our service unit have knowledge of the job and the skills to deliver superior quality work and service," and "The overall quality of service provided by our service unit to customers is excellent." The ANOVA indicated significant differences (F = 5.48, p < 0.01), the median rwg value was 0.83, and the ICC(1) and ICC(2) score were 0.27 and 0.56, respectively; so within-unit agreement and between-unit differentiation supported the aggregation of respondents' scores to the service unit level.

#### Customer Service Performance

Hotel general managers were asked to rate the performance of service units surveyed compared to the average of other work units in the hotel. We decided to ask hotel general managers instead of service units' supervisors because general managers should offer more accurate, far less biased responses. Specifically, hotel managers had to respond on a 7-point response format ("very poor," 1, to "excellent," 5) to four items adapted from Oh et al. (2004) to measure the extent to which the service unit in question provided high quality service to the customer. Sample items include "service unit's quality of work" and "service unit's overall performance."

#### Control Variables

In the present study, we introduced two control variables. We included service unit education to control for confounding effects because the educational level has been recognized in the past to influence helping behavior (Van Dyne and LePine, 1998) and successful service performance (Davidoff, 1994). Service unit education was measured by the average education level of participants in each service unit surveyed; individual responses on a six-point format response (1 = primary studies; 2 = secondary studies; 3 = lower level professional education; 4 = higher level professional education; 5 = bachelor degree; 6 = postgraduate degree) were averaged within each service unit. Also, we controlled for service unit size effects because it can affect group dynamics (e.g., interpersonal contacts, synergies)

and performance, positively (Brewer and Kramer, 1986; Smith et al., 1994). We measured service unit size by the number of workers who participated in each service unit we surveyed.

### Data Analysis

We utilized partial least squares via Smart PLS 3.2.6 (Ringle et al., 2015) to test our hypotheses. Such an impressive and potent statistical procedure (Chin et al., 2003) makes the causal analyses of complex situations possible (Henseler et al., 2009). As a structural equation modeling (SEM) approach, it is also suitable for testing mediation hypotheses (James et al., 2006). Also, PLS does not make it necessary to demand assumptions concerning the distribution of the variables (Henseler et al., 2009). As recommended, our PLS analysis used 5,000 subsamples to generate standard errors and bootstrap t-statistics with n−1 degrees of freedom (where n is the number of subsamples) to evaluate statistical significance of path coefficients (Henseler et al., 2009).

We tested the hypotheses at the unit level of analysis, using a sample of 247 service units (n = 247). To run our SEM model at the unit level, our servant leadership variable was formed by ratings provided by service unit workers which were averaged to yield service unit-level scores. Likewise, our service climate variable was constructed using ratings provided by the supervisor and workers of each service unit, that were averaged together to obtain unit-level scores. Finally, our criterion variable, customer service performance, was created based on ratings provided by the general manager of the hotel to which each service unit belonged to.

#### Measurement Model