Title: Factors influencing passenger loyalty towards public transport services: does public transport providers’ commitment to environmental sustainability matter?

Journal Pre-proofs Title: Factors influencing passenger loyalty towards public transport services: does public transport providers’ commitment to environmental sustainability matter? Paula Vicente, Ana Sampaio, Elizabeth Reis PII: DOI: Reference:

S2213-624X(20)30014-6 https://doi.org/10.1016/j.cstp.2020.02.004 CSTP 426

To appear in:

Case Studies on Transport Policy

Received Date: Revised Date: Accepted Date:

23 April 2019 11 December 2019 19 February 2020

Please cite this article as: P. Vicente, A. Sampaio, E. Reis, Title: Factors influencing passenger loyalty towards public transport services: does public transport providers’ commitment to environmental sustainability matter?, Case Studies on Transport Policy (2020), doi: https://doi.org/10.1016/j.cstp.2020.02.004

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2020 Published by Elsevier Ltd on behalf of World Conference on Transport Research Society.

Title: Factors influencing passenger loyalty towards public transport services: does public transport providers’ commitment to environmental sustainability matter?

Authors: Paula Vicente(1), Ana Sampaio(2), Elizabeth Reis(1) (1)

Instituto Universitário de Lisboa (ISCTE-IUL), Business Research Unit (BRU-IUL), Lisboa,

Portugal (2) Universidade

de Évora

e-mail: [email protected] e-mail: [email protected] e-mail: [email protected]

Corresponding author: Paula Vicente Postal address: ISCTE-IUL Av. Forças Armadas 1649-026 Lisboa Portugal Telephone: +351 21 7650207

1

Abstract Public transport providers are constantly looking for ways to increase their ridership, either by gaining new or maintaining current passengers. In recent years, societies’ increasing concern about climate change and preserving the environment has led public transport providers in many countries to become more oriented towards environmental sustainability. The decision of transit agencies to become “greener” benefits the environment and may also benefit the business because companies that are able to visibly demonstrate their ethics and show a commitment to the environment are more likely to have a stronger reputation and attract customers who care deeply about what a business stands for. This study explores the impact of several factors on passenger loyalty towards public transport services. We use structural equation models (SEM) to explore the relationship among various constructs using data collected by means of a survey on public transit users in the Metropolitan Area of Lisbon (Portugal). In particular, we introduce the concept of Commitment to Environmental Sustainability (CES), which represents the contribution public transport providers make towards sustainable development and a cleaner environment and hypothesize that CES positively affects transit passengers’ loyalty. By comparing various SEM models, we find that the public transport providers’ commitment to environmental sustainability does have a direct positive effect on passenger loyalty and an indirect positive effect on loyalty when mediated by satisfaction. The managerial implications of the findings for the public transport service are addressed. Key words: Public transportation; Passenger loyalty; Structural Equation Models; Lisbon

Highlights 1) commitment to environmental sustainability has a direct effect on passenger satisfaction. 2) commitment to environmental sustainability has a direct effect and an indirect effect on passenger loyalty. 3) commitment to environmental sustainability is positively correlated with service quality. 2

Acknowledgements This work received financial support from Fundação para a Ciência e Tecnologia (Science and Technology Foundation) through the UID/GES/00315/2019 project. This article is part of the project entitled Estudo de Satisfação dos Utilizadores dos Transportes Públicos da Área Metropolitana de Lisboa 2013, a joint project of the Metropolitan Transport Authority of Lisbon and Instituto Universitário de Lisboa (ISCTE-IUL).

1. Introduction Transport policies that seek to encourage public transport ridership while reducing car dependency have long been identified as an important aspect in the development of socially, environmentally, and economically sustainable cities (European Commission 2017). Developing and maintaining passenger loyalty is a strategy that works in favor of increased ridership since loyal passengers will continue using a public transport service without seeking or shifting to alternative options and are likely to recommend the service to potential new users (Webb 2010). In order for practitioners and policy makers to develop comprehensive strategies aimed at attaining and sustaining passenger loyalty, it is necessary to understand and identify which aspects of public transport influence loyalty. The recent meta-analysis by van Lierop et al. (2018) is a comprehensive review of public transport literature over 15 years and systematizes the predictors of loyalty in public transport. The results elucidate, on the one hand, that loyalty is associated with users’ perceptions of value for money, on-board safety and cleanliness, interactions with personnel and the image and commitment to public transport that users feel. On the other hand, the concept of loyalty is best defined based on users’ intentions to continue using the service, their willingness to recommend it to others, and their image of and involvement with public transport. The concept of image towards public transport is based on how passengers view public transport as contributing not only to their own welfare, but to society at large. Public transport users who have a positive image of the transit agency and consider public transport an integral component 3

of city life are more likely to demonstrate loyalty and act as ambassadors for public transport agencies (Minser and Webb 2010, Şimşekoğlu et al. 2015, Zhao et al. 2014). Public transport agencies can take advantage of the association between image of and loyalty to public transport to develop communication strategies that influence users’ emotional attachment to public transport and thus stimulate loyalty (Lai and Chen 2011). Recent opinion polls reveal that people believe companies have a duty to offer sustainable rather than unsustainable products/services (GlobeScan 2011) and are less likely to do business with companies that are perceived as environmentally irresponsible (Cone Communications 2013). Moreover, people increasingly avoid specific products or services due to environmental concerns and are willing to choose environmentally friendly solutions even if they cost more (European Commission 2008, Kennell 2016, Peycheva et al. 2014, TNS Opinion 2015). This presents an unprecedented opportunity for companies to show their commitment to environmental sustainability because, as Kennell (2016) suggests, the investment that companies make to become “green” generates a higher return if they communicate their pro-environmental positioning effectively. Many transit agencies have already included eco-friendly goals in their business plans (see for example, OneNYC 2016, Sustainable Sidney 2017, TfL 2017, TMB 2017). While these goals are primarily targeted at reducing costs, increasing efficiency and ensuring future sustainability (ELTIS 2014), it is the environmental awareness of passengers that may drive transit agencies further in their efforts to secure transit users’ loyalty because sustainable business practices are among the most important factors driving brand loyalty today (King 2011). This study explores the impact of several factors that affect passenger loyalty towards public transport services. In particular, we introduce the concept of Commitment to Environmental Sustainability, which represents the contribution public transport agencies give to a cleaner environment and sustainable development in big cities. If a significant effect is found, marketing actions communicating pro-environmental initiatives should become part of public transport providers’ strategy because companies that are able to visibly demonstrate their ethics and show a

4

commitment to the environment are more likely to have a stronger reputation and attract customers who care deeply about what a business stands for (Wenzel 2017). Data from a survey involving public transport users conducted in the Metropolitan Area of Lisbon, at the request of the Metropolitan Transport Authority of Lisbon, is used to apply Structural Equation Modelling (SEM) and to explore a set of hypotheses involving the relationships between service quality, passenger satisfaction, public transport providers’ commitment to environmental sustainability and passenger loyalty. The Metropolitan Area of Lisbon (MAL) is the largest metropolitan area in Portugal, with 2.8 million inhabitants spread over nearly 3000 km2 (Statistics Portugal 2011). It encompasses 18 municipalities to the north and south of the River Tagus (Figure A.1, Appendix). The municipality of Lisbon is the geographic center of MAL. The public transport service in this area includes road, rail (train, tram and subway) and river transport. The bus is the most used mode of travel – more than 55% of commuting trips are made by bus  followed by the train (23%) (Statistics Portugal 2017). The Portuguese Strategic Plan for Transport and Infrastructures “Portugal 2020” foresees funding for private and public operators to renew their fleets with environmentally friendly vehicles using either natural gas or electricity. Presently, approximately 90% of buses and trams that circulate in the municipality of Lisbon are environmentally friendly and a pilot-project is now underway to test the transport efficiency of a 100% electric and silent bus fleet within the municipality of Lisbon (ECO 2016). However, in the MAL’s other municipalities the investment in eco-friendly vehicles is less significant and public transport users realize that  a study on perceptions about transport service in the MAL reveal that the bus is regarded by transit users as the most polluting transport mode (Ramos et al. 2019). The remainder of the paper is structured as follows: Section 2 provides a brief overview of the related literature, before proposing research hypotheses. Section 3 introduces the study context, data used and methodological approach. Section 4 presents the results from a Structural Equation Modelling exercise aimed at explaining passenger loyalty towards public transport services. Section 5

5 discusses the key findings and implications for transport policy-making, before drawing some tentative conclusions and possible future research in Section 6.

2. Theoretical background and research hypotheses From the existing studies on customer loyalty and transit ridership, the authors identified several relevant variables to consider in exploring the relationship between different aspects of public transport service and loyalty (e.g. Allen et al. 2019, Imaz et al. 2015, Lai and Chen 2011, van Lierop and El-Geneidy 2016). This study adds a new construct  public transport providers’ commitment to environmental sustainability – to help further the understanding of the complexities of factors influencing transit loyalty. The meanings of the variables included in the empirical study and the hypotheses to be evaluated are explained below.

2.1 Loyalty Loyalty indicates the extent to which customers are devoted to a particular service provider and how strong is their tendency to select one company over the competition (Butcher et al. 2001). While measuring loyalty means measuring the strength of this devotion (Ranade 2012, Skačkauskienė et al. 2016), there is no consensus on how to measure customer loyalty. Earlier theories base loyalty measurement on customer behaviors, hence loyalty was measured by the quantity of purchases made, or purchase probability, or a continuing pattern in the buying behavior (e.g. Flavian et al. 2001, Hellier et al. 2003). Later, a two-dimensional concept of loyalty appeared that suggested loyalty should be measured according to behavioral and attitudinal criteria. With attitudinal measurements, researchers focus on psychology, and customer loyalty is reflected by: (a) willingness to recommend the service to others, (b) intended future use of the service, (c) likelihood of purchasing other products/services from the same company, (d) believing the services used are superior to others offered in the marketplace, and (e) not actively seeking alternative providers of the same service (Caruana 2002, TaghiPourian and Bakhsh 2015). 6

In recent years, transit researchers have been focusing on developing their understanding of loyalty, as ridership is more likely to be boosted if passenger loyalty is increased (Lai and Chen 2011, Reichheld and Teal 2001). Most customer loyalty studies in public transportation adopt a twodimensional concept of loyalty, suggesting that passenger loyalty can be divided into: (a) a passenger’s continuous behaviour to use a specific mode of transport, and (b) a passenger’s attitudes and emotions towards public transport in general, or towards a specific mode of transport, on an ongoing basis (Lai and Chen 2011, Minser and Webb 2010, Reichheld 2003, Zhao et al. 2014). For this study, we follow previous studies on loyalty towards public transport (e.g. van Lierop and El-Geneidy 2016, Wen et al. 2005) and measure loyalty by considering the likelihood of a passenger continuing to use the service in the future and recommending it to others.

2.2. Service quality Service quality is more difficult to describe than product quality as most services cannot be counted, measured, inventoried, tested, or verified in advance of sales to assure. Quality evaluations are not made solely on the outcome of a service; they also involve evaluations of the process of service delivery (Parasuraman et al. 1985). Process quality refers to the level of services, as determined by customers during the service process and is, therefore, a subjective view of customers. On the other hand, outcome quality is the measurement of customers regarding service results. Basically, service quality is a conscious, yet intangible feeling determined by subjective assessors (Fitzsimmons and Fitzsimmons 2008). Three decades ago, Parasuraman et al. (1988) defined service quality as an organization’s ability to meet or exceed customer expectations. Expectations are defined as beliefs prior to the service, which function as the standard or reference in evaluating the performance of the service. The factors contributing to the formation of expectations, be they positive or negative, include the communication established among consumers (word-of-mouth communication), past consumer experience of the service and service provider, external communication promoted by the service provider (promises of high-quality service usually raise

7

customer expectations) and the personal wishes of customers. Service quality perceptions result from a comparison of consumer expectations with actual service performance  customer dissatisfaction occurs if customers’ expectations are greater than their perceptions of the service delivered by the suppliers (Brady and Cronin 2001). Although service quality has been defined as a multidimensional construct (e.g. Kang and James 2004), there is no consensus on its dimensionality (number and type). The EN 13816 Standard, constituted by the European Committee for Standardization (CEN) in 2002, is a guidance to define, target and measure the quality of service in public passenger transport. The overall quality of public passenger transport contains many criteria. These criteria represent the passenger view of the service provided and, for this standard, they are divided into eight categories: availability (extent of the service offered in terms of geography, time, frequency and transport mode); accessibility (access to public transport system including interface with other transport modes); information (systematic provision of knowledge about the public transport system to assist the planning and execution of journeys); time (aspects of time relevant to the planning and execution of journeys); customer care (service elements introduced to effect the closest practical match between the standard service and the requirements of any individual passenger); comfort (service elements introduced for the purpose of making public transport journeys relaxing and pleasurable), security (sense of personal protection experienced by passengers); and environmental impact (effect on the environment resulting from the provision of public transport service) (CEN 2002). From empirical research, varying dimensions for service quality are found: Wen et al. (2005) mention four dimensions, namely on-board amenity, staff attitude, station performance and operational performance; Eboli and Mazzulla (2007) suggest three dimensions (service planning and reliability, comfort, safety and cleanliness); Perez et al. (2007) propose five dimensions (tangibility, reliability, receptivity, assurance and empathy); Prasad and Shekhar (2010) suggest eight dimensions (assurance, empathy, reliability, responsiveness, tangibility, comfort, connection and convenience); Lai and Chen (2011) propose two dimensions (core services and physical environment); and d’Ovidio et al. (2014) suggest six dimensions (comfort and

8

cleanliness, accessibility, organization, behavior of ticket inspectors, behavior of the staff, and costs of the service). The importance of service quality for business performance has been recognized in the literature through the direct effect on customer satisfaction: service quality is an antecedent to customer satisfaction (Carrillat et al. 2007, Farrell et al. 2001, Zeithaml et al. 2008). Nowadays all companies realize that the key to sustainable competitive advantage lies in delivering high quality service that will, in turn, result in satisfied customers. In the particular context of public transport research, the relationship between service quality and passenger satisfaction has been widely investigated and it is generally acknowledged that service quality has a positive effect on passenger satisfaction (e.g. Cantwell et al. 2009, Eboli and Mazzulla 2007, Fonseca et al. 2010, Machado et al. 2016, van Lierop et al. 2018). Therefore, we hypothesize: H1: Service quality has a direct and positive effect on passenger satisfaction. 2.3. Satisfaction Customer satisfaction is a complex concept comprising multiple components and is recognized as one of the most important success factors of companies (Yeung et al. 2002). The most consensual definition of customer satisfaction is associated with a post-consumer evaluative judgment of a particular service and is described as a feeling of pleasure or disappointment resulting from the comparison between the perceived performance of the service and the consumer’s expectations. In other words, when the performance is perceived as meeting or exceeding expectations, the customer is satisfied; but when the opposite is true, the customer is not fully satisfied and reacts negatively to the experience (Kotler 2003). Customer satisfaction is closely linked to customer loyalty. In fact, many studies attested that satisfied customers are more likely to buy the same product or service again and can attract more customers through suggestions and recommendations to friends, family, acquaintances, or through positive word-of-mouth (Gagić et al. 2013, Yeung et al. 2002). The ultimate purpose of a customer satisfaction study is, therefore, to build and enhance customer loyalty, thus influencing their 9

purchasing decisions (Bou-Llusar et al. 2001, Dimitriades 2006). While various studies have identified a positive effect of service quality on passenger loyalty towards public transport mediated by satisfaction (Kamaruddin et al. 2012, Shiftan et al. 2015, van Lierop and El-Geneidy 2016, Wen et al. 2005), others have proved that loyalty in the public transport sector is a direct consequence of satisfaction (Eboli and Mazzulla 2007, van Lierop and E-Geneidy 2016, Wen et al. 2005). Therefore, we hypothesize: H2: Passenger satisfaction has a direct and positive effect on passenger loyalty. 2.4 Commitment to environmental sustainability Concern for environmental protection has increased since nations became aware that global warming, damage to the ozone layer and acid rains are problems that seriously affect people’s quality of life. Since then, governments, organizations, companies and citizens have become increasingly more conscious of their political, social and individual responsibility towards environmental sustainability. The UN Climate Summit in 2014 highlighted the wide-ranging commitment already made by the public transport sector to environmental sustainability – “The public transport sector is committed to be a climate leader” and “The public transport sector is working consciously and innovatively to improve its already excellent carbon performance and enhance urban transport networks for years to come.” (IAPT 2014, p. 6). These statements are intended to inspire public transport agencies to do more and better for the environment. The decision of transit agencies to include eco-friendly goals in their business strategies (see for example, GMPTA 2007, MALT 2014, TfL 2011) is their acknowledgement that the public transport sector plays an important role in fighting climate change, as well as recognition that “being green” is crucial to creating long-term value and to fostering company longevity. Although sustainability goals are an important step in transit agencies’ strategy, effectively communicating commitment to customers is equally important (UNEP 2005) because more and more customers expect and appreciate this kind of commitment from the companies they buy from (Davis 2016).

10

Lai and Chen (2011) suggested that it is important for public transit agencies to focus on developing strategies that aim to motivate passengers to strongly identify with public transit. The commitment of transit agencies to environmental sustainability is likely to contribute to such purpose because passengers are increasingly environmentally conscious and educated and value sustainability. Moreover, in recent years, researchers have begun to explore how users’ views and opinions about public transit influence their satisfaction and loyalty. For example, Minser and Webb (2010) revealed that users who have a positive image of public transit tend to be more satisfied. Furthermore, other researchers have found that having a positive image of public transit also strongly influences passenger loyalty (Lai and Chen 2011, Minser and Webb 2010, Şimşekoğlu et al. 2015, van Lierop and El-Geneidy 2017, Zhao et al. 2014). With this in mind, we propose the concept of Commitment to Environmental Sustainability (CES) which intends to signify transit agencies’ contributions to environmental protection and sustainability through research, innovation or service design. CES can work in favor of a positive image of transit agencies and consequently contribute to increased passenger satisfaction, stimulate public transport use and increase loyalty towards public transport. Given the novelty of this concept in public transport loyalty models, we decided to explore its impact by considering direct and indirect effects. As such, we hypothesize: H3: Commitment to environmental sustainability by public transport providers has a direct and positive effect on passenger satisfaction. H4: Commitment to environmental sustainability by public transport providers has a direct and positive effect on passenger loyalty. H5: Commitment to environmental sustainability by public transport providers and service quality are positively correlated.

Based on the discussion above, we propose three alternative conceptual models  A, B and C (Figures 1, 2 and 3, respectively)  to examine the relationships between service quality (SQ),

11

passenger satisfaction (SAT), passenger loyalty towards public transport (LOY) and the new construct  public transport providers’ commitment to environmental sustainability (CES): - Model A: CES is an antecedent exogenous construct, which impacts LOY both directly and indirectly, with SAT acting as a mediator variable; - Model B: CES is an antecedent exogenous construct, which impacts LOY solely directly; - Model C: CES is an antecedent exogenous construct, which impacts LOY solely indirectly, with SAT acting as a mediator variable.

Figure 1: Model A-CES impacts loyalty both directly and indirectly

Figure 2: Model B-CES impacts loyalty solely directly

12

Figure 3: Model C-CES impacts loyalty solely indirectly

The three models will be estimated and the one that performs best overall will be considered the one that best explains passenger loyalty, and best describes the effect of commitment to environmental sustainability on passenger loyalty.

3. Methodology 3.1 Sample and questionnaire A survey of public transport users aged 18 or older, living in the Metropolitan Area of Lisbon (MAL) was conducted with the aim of describing the travel behavior of public transport users and evaluating their satisfaction with and loyalty to the public transport service and operators. The sample was allocated proportionally to the 18 municipalities of the MAL. In each municipality, quotas of sex and age were set in accordance with the most recent census data (Statistics Portugal 2011) to improve sample representativeness. Within each municipality, sampling areas were chosen to guarantee adequate geographical coverage of the municipality. Interviewers were assigned to sampling areas to look for respondents and administer the questionnaire. Respondents were selected on the street, and interviewed in-person after confirming municipality, sex and age quotas. Fieldwork took place between the 19th and 31st March 2014, including weekdays and weekends and covered a broad time-range (between 8 a.m. and 10 p.m.). Interviews lasted an average of 10 minutes. Reaching the sex and age quotas was more difficult to achieve in some municipalities but the overall distribution of the observed sample by municipality, sex and age was not significantly different from 13

the predicted sample, which was a positive sign of sample representativeness (ISCTE-IUL 2014a). A total of 1166 valid questionnaires were obtained. To assist the questionnaire design, 6 focus group interviews were conducted with regular and occasional users of public transport; this enabled us to gain insights into travel behavior and customer perceptions of the public transport service and service providers in MAL (ISCTE-IUL 2014b). The survey questionnaire asked respondents about: (1) service quality of public transport, (2) satisfaction with the public transport service, (3) loyalty to public transport, and (4) the environmental commitment of public transport providers. Perceptions of service quality were measured by means of a set of 18 items associated with the multifaceted pattern of the construct and measured on a ten-point Likert-type scale (1=totally dissatisfied to 10=totally satisfied): “punctuality”, “speed on route”, “adequacy of routes offered”, “timetables”, “frequency of vehicles on weekdays”, “frequency of vehicles on weekends”, “comfort of vehicles”, “number of seats”, “safety of persons and property”, “ease of entering/exiting the vehicles/stations”, “distance to the stop/station/terminal”, “frequency of strikes”, “alternative transport in strike period”, “rules of purchase and use of tickets and passes”, “inspection of transport tickets”, “price compared to alternative transport”, “intermodal coordination” and “staff behavior”. Satisfaction with the public transport service was measured by means of 3 items: “This operator offers a service that meets my quality expectations”, “This operator offers a service that meets my personal needs”, and “My overall satisfaction with the service after experiencing the public transport service”. These were then rated using a ten-point Likert-type scale (1=totally dissatisfied to 10=totally satisfied). Loyalty to public transport was measured by means of 2 items: “I would recommend this operator to family and friends” and “I intend to remain a client of this operator”; these items were rated on a ten-point Likert-type scale (1=totally disagree to 10=totally agree). Finally, commitment to environmental sustainability was measured by asking respondents to rate their level of agreement (1=totally disagree to 10=totally agree) with 2 items about the public transport operator they use most frequently: “This operator is concerned about energy and environment” and “This operator is innovative and forward looking”. The questionnaire items were

14

constructed using the results of the focus groups, the European Customer Satisfaction Index (ECSI 1998) and the literature review on transit ridership (Del Castillo and Benitez 2012, Eboli and Mazzulla 2007, Fellesson and Friman 2008, Friman and Gärling 2001, Hensher et al. 2003, Iman 2014, Mokonyama and Venter 2013, Vilares et al. 2005).

3.2 Data analysis approach Structural Equation Modeling (SEM) is used to estimate the structural paths and test the associated hypotheses, using AMOS 24.0. Generally, a SEM model comprises two conceptually interrelated but distinct models: a confirmatory measurement model (CFA) and a structural model. While the CFA confirms the existence of latent dimensions, it does not describe the relationship between dimensions. It is the structural model, based on a theoretical framework, that determines the significance of the relationship between variables. In the context of public transportation, CFA and SEM approaches have been used to investigate relationships between passenger satisfaction, service quality and passenger loyalty (e.g. Changa and Chen 2007, Eboli and Mazulla 2007, 2011, Karlaftis et al. 2001, Kim and Lee 2011, Muhammad et al. 2011, Ngatia et al. 2010, Stuart et al. 2000, Weistein 2000). Although most studies on transit ridership use SEM to infer relationships in an entire transit population, some combine SEM with a market segmentation analysis. While on the one hand this is a strategy to validate the outcomes of a global model, at the same time it allows for a more in-depth analysis of the different groups within the studied transit market. In the context of the transit market Krizek and El-Geneidy (2007) and van Lierop and El-Geneidy (2016) used segments based on car access and income. In our study we segment transit users into two groups: passengers without car (i.e., transit users who do not have a choice and are therefore captive to public transport) and passengers with car (i.e. passengers who have an alternative to public transport). The analysis follows four stages: i) Data dimensionality reduction by means of exploratory principal component analysis (PCA) conducted on the 18 items of service quality. The number of components to retain is decided based

15

on eigenvalues over one and at least 60% of variance explained. Varimax rotation is adopted. A Cronbach’s alpha above 0.7 is adopted as the criterion for the reliability of dimensions, and item retention is based on factor loadings above 0.5 (Hair et al. 2014); the PCA is performed on a randomly chosen sub-sample with half of the dimension of the original sample (583 cases). ii) Validation of the multidimensional structure of service quality was revealed by PCA through confirmatory factor analysis (CFA) using the other half of the sample (583 cases) (Byrne 2009). Several goodness-of-fit measures are used to assess how well the model fits the data: the goodnessof-fit index (GFI), the comparative fix index (CFI), the normed fit index (NFI), the incremental fit index (IFI) and the Tucker-Lewis index (TLI); these indexes reveal a good fit for values greater than 0.9. The root mean square error of approximation (RMSEA) and the chi-square divided by the degrees of freedom are complements to the other measures – the acceptable range for RMSEA is 0.08 or lower, and a value of the chi-square/df smaller than 5 is an indicator of a good fit. An average variance extracted (AVE) above 0.5 is adopted as the criterion for the convergence of items into the proposed factors (Bentler 2007, Kline 2010). iii) Model estimation using structural equation modeling (SEM). Goodness of fit measures previously mentioned in ii) are used to choose the best of the three estimated models. The Akaike Information Criterion (AIC) is also measured; the smaller the values of AIC the better the fit (Bollen 1989). iv)Model estimation in two segments  passengers without car and passengers with car.

4. Results 4.1. Respondents’ characteristics Most respondents are female (57.8%) and 36.8% are under 34 years old. Most of the respondents (36.4%) have completed basic education (9 years of schooling), are employed (either self-employed or employed by a third party) (62.2%) and 31% live in a two-person household (Table 1).

16

Table 1: Socio-demographic characteristics of respondents and the socio-demographic characteristics of the population of MAL based on Census 2011

Characteristic

Sample (%)

Census 2011 (%)✝

57.8

52.7

15-24

17.9

12.4

25-34

18.9

17.0

35-44

16.4

18.4

45-64

26.4

30.7

 65

20.4

21.6

Basic (9 years)

44.2

57.0

Secondary (12 years)

27.2

20.7

University

28.5

22.3

Working

62.2

50.9

Not working

25.7

41.4

Unemployed

11.7

7.7

1

18.2

25.5

2

31.0

32.8

3-5

49.0

39.9

6

2.1

1.7

Gender (female) Age

Education

Occupation

Household size

✝ Source:

Statistics Portugal (2011).

A comparison of the sample profile with Census data reveals the biggest differences are found in occupation. Specifically, the sample over-represents working people and under-represents nonworking people, which is a likely consequence of respondents being selected and interviewed in the street. Additionally, most of the respondents have a car (53%). Nearly ¾ are regular users of public transport (i.e., use public transport at least 5 days a week). The most frequently used modes of public transport are the bus (77.9%) and the subway (43.9%). When asked about the public transport modes used on their last local trip (more than one mode could be mentioned), most of the respondents 17

referred to the bus (42.2%), 19.9% used the train, 19.1% the subway, 1.9% the boat. Additionally, the car was used by 23.0% of the respondents (figures not presented in Table 1).

4.2. Service quality model The exploratory principal component analysis performed on the 18 items of service quality allowed four first-order dimensions to be found (Table 2). The four components account for 60.4% of the initial variance and the Cronbach’s α values > 0.75 confirm that the dimensions show good internal consistency (Hair et al. 2014).

18

Table 2: Results of exploratory factor analysis of public transport service quality

Dimensions of service quality

Factor Loadings

Cronbach’s 

Operational performance

0.892

Adequacy of routes offered

+0.811

Timetables

+0.790

Punctuality/waiting time

+0.755

Speed on route

+0.744

Frequency of vehicles on weekdays

+0.700

Comfort and safety

0.825

Safety of persons and property

+0.771

Number of seats

+0.758

Ease of entering/exiting vehicles/stations

+0.706

Comfort of vehicles

+0.612

Attractiveness of service

0.776

Prices compared to alternative transport

+0.724

Intermodal coordination

+0.713

Inspection of transport tickets

+0.673

Staff behavior✝

+0.449

Distance to stop/station/terminal✝

+0.404

Guarantee of service

0.760

Frequency of strikes

+0.883

Alternative transport in strike period

+0.762

Rules of purchase and use of tickets and passes

+0.597

Efficient response to complaints

+0.591

Note: Kaiser-Meyer-Olkin measure = 0.914; Bartlett’s test p-value <0.001. ✝ This

item was not relevant to name the dimension “Attractiveness of service” due to loading <0.5.

In accordance with the higher loadings (above 0.5) of the items in each dimension, the four dimensions were named as: Operational Performance, Comfort and Safety, Attractiveness of Service, and Guarantee of Service. Despite having a moderate loading (above 0.5 but less than 0.6), the item “rules of purchase and use of tickets and passes” helps to describe whether or not the public

19

transport user can count on the service. In the MAL there are many types of tickets and passes – some allow the use of only one transport mode, others allow the use of several modes but only within a limited area of MAL, others are specific to certain age groups (under 12 years or over 65 years) (MALT 2014), which does not favor flexibility and causes users to feel that the public transport network does not guarantee full mobility. Similarly, the item “efficient response to complaints” has a moderate loading but helps to describe the guarantee of service. In the MAL, the complaints about public transport are mostly about delays, and the suppression of buses or trains (MTA 2016). If the operators deal efficiently with these complaints, then the “guarantee of service” is accomplished. Table 3 presents the results of the confirmatory factor analysis for the relationships among the latent and observed variables of the service quality measurement model. The assessment of model fit for the first-order configuration of service quality was satisfactory since all goodness-of-fit measures were acceptable (GFI=0.95; CFI=0.96; NFI= 0.95; IFI=0.96; RMSEA=0.05; 2(125)/df=3.856). Additionally, the standardized estimates are above 0.5 (the only exception is the estimate for Frequency of strikes), which indicates that each item is significantly correlated to the respective construct; all factors show adequate convergent validity, as the average variance extracted (AVE) is above 0.5 for all factors.

20

Table 3: Results of the confirmatory factor analysis of public transport service quality Dimensions of service quality

Unstandardized estimates (a)

S.E.

Standardized estimates (a)

Operational performance

0.618

Adequacy of routes offered

+0.951

0.037

+0.794

Timetables

+0.938

0.039

+0.745

Punctuality/waiting time

+1.000



+0.735

Speed on route

+0.894

0.036

+0.776

Frequency of vehicles on weekdays

+0.849

0.039

+0.684

Comfort and safety

0.542

Safety of persons and property

+1.138

0.043

+0.818

Number of seats

+1.000



+0.762

Ease of entering/exiting vehicles/stations

+0.841

0.037

+0.672

Comfort of vehicles

+1.034

0.040

+0.806 0.514

Attractiveness of service✝ Intermodal coordination

+1.286

0.073

+0.695

Inspection of transport tickets

+1.109

0.076

+0.529

Staff behavior

+1.181

0.069

+0.653

Distance to stop/station/terminal

+1.000



+0.583

Guarantee of service

(a)

AVE

0.620

Frequency of strikes

+0.823

0.059

+0.486

Alternative transport in strike period

+0.917

0.057

+0.567

Rules of purchase and use of tickets and passes

+0.923

0.050

+0.675

Efficient response to complaints

+1.000



+0.700

All the estimated coefficients are significantly different from zero at any level of significance (p<0.001).

✝

The item Prices compared to alternative transport was intentionally excluded from the CFA configuration to improve goodness of fit.

4.3 Passenger loyalty models Table 4 presents the estimation results for the three models. Model A has a better fit than both model B and model C since it has the lowest value for AIC measure (AIC=1224.97). Additionally, the superiority of model A is supported by the chi-squared difference tests (2diff (A,B)=22.21; 2diff (A,C)=97.96), both significant at p<0.001 (2(1;0.95)=3.84) because, as stated by Werner and Schermelleh-Engel (2010), “when the χ2 diff-value is significant, the larger model with more freely 21

estimated parameters fits the data better than the smaller models”. Model A is therefore chosen as the most suitable of the three to explain passenger loyalty towards public transport and is retained for subsequent analysis. The estimation results of the path analysis for model A confirm the five proposed hypotheses. The largest path coefficient among direct links to passenger loyalty is the link which measures the effect of satisfaction on loyalty, through a positive and significant effect (+0.508). Public transport providers’ commitment to environmental sustainability has a significant direct and positive effect both on satisfaction (+0.195) and on loyalty (+0.433). The indirect effect of public transport providers' commitment to environmental sustainability on passenger loyalty via satisfaction is also significant. Additionally, service quality has a significant positive indirect effect on loyalty through the mediator role of satisfaction. As hypothesized, public transport providers’ commitment to environmental sustainability is positively correlated to service quality (+0.712). The goodness-of-fit measures suggest model A fits the data satisfactorily.

22

Table 4: Structural path estimates for Model A, Model B and Model C Model A(a) Hypothesis and structural paths

Unstandardized estimate

H1: SQ  SAT

Model B(b)

S.E.

Standardized estimate

Unstandardized estimate

+0.752***

0.052

+0.737***

H2: SAT  LOY

+0.676***

0.065

H3: CES  SAT

+0.190***

H4: CES  LOY H5: CES  SQ

Model C(c)

S.E.

Standardized estimate

Unstandardized estimate

S.E.

Standardized estimate

+0.927***

0.047

+0.895***

+0.676***

0.049

+0.663***

+0.508***

+0.658***

0.059

+0.499***

+1.180***

0.060

+0.872***

0.040

+0.195***







+0.311***

0.040

+0.319***

+0.561***

0.063

+0.433***

+0.582***

0.058

+0.454***







+1.980***

0.088

+0.712***

+1.137***

0.090

+0.740***

+1.108***

0.070

+0.720***

*** Significantly different from zero at p<0.001; * Significantly different from zero at p<0.05. (a) GFI=0.92, CFI=0.93, NFI=0.92, IFI=0.93, TLI=0.92, RMSEA=0.06, 2 2 (215)=1102.97;  (215)/df=5.13; AIC=1224.97. (b) GFI=0.92, CFI=0.93, NFI=0.92, IFI=0.93, TLI=0.92, RMSEA=0.06, 2 2 =1125.18;  (216) (216)/df=5.21; AIC=1245.18. (c) GFI=0.92, CFI=0.93, NFI=0.91, IFI=0.93, TLI=0.91, RMSEA=0.06, 2 2 =1200.93;  (216) (216)/df=5.56; AIC=1320.93.

23

4.3.1 Loyalty model for passengers with car and passengers without car Table 5 presents the structural path estimates of model A for each segment of public transport users – passengers with car (n1=618) and passengers without car (n2=548). The goodness-of-fit indexes are satisfactory in both segments, thus suggesting that the hypothesized structural path configuration is adequate to explain passenger loyalty to public transport both for passengers with car and passengers without car.

Table 5: Structural path estimates of Model A for passengers with car and passengers without car Passengers with car(a) Hypothesis and structural paths

Passengers without car(b)

Unstandardized estimate

S.E.

Standardized estimate

Unstandardized estimate

S.E.

Standardized estimate

H1: SQ  SAT

+0.782***

0.069

+0.785***

+0.742***

0.080

+0.707***

H2: SAT  LOY

+0.741***

0.094

+0.535***

+0.625***

0.091

+0.490***

H3: CES SAT

+0.140***

0.040

+0.165***

+0.240***

0.072

+0.208***

H4: CES  LOY

+0.478***

0.063

+0.406***

+0.657***

0.109

+0.448***

H5: CES  SQ

+1.208***

0.122

+0.737***

+0.970***

0.126

+0.689***

*** Significantly different from zero at p<0.001; * Significantly different from zero at p<0.05. (a) (b)

GFI= 0.91, CFI=0.92, NFI=0.89, IFI=0.92, TLI=0.91, RMSEA=0.07, 2(215)/df=3.78. GFI=0.91, CFI=0.92, NFI=0.89, IFI=0.92, TLI=0.91, RMSEA=0.06, 2(215)/df=3.07.

The estimation results for model A in both segments reveal that relationships among latent constructs are significant, replicating the structural configuration results obtained from the overall sample and reinforcing the validity of model A to explain passenger loyalty. In these two subsamples, public transport providers' commitment to environmental sustainability has a significant direct and positive effect both on satisfaction and on loyalty. Among passengers without car the effect of CES on satisfaction and loyalty is stronger. In the two segments, public transport providers' commitment to environmental sustainability has a significant indirect and positive effect on passenger loyalty when moderated by satisfaction. Additionally, public transport providers' commitment to environmental sustainability is positively correlated to service quality. 24

5. Discussion and implications The research undertaken can be summarized as follows: (a) a survey of public transport users in the Metropolitan Area of Lisbon was conducted; (b) dimensions of service quality were identified based on public transport users’ experiences with the transport service; (c) SEM was used to explore both the direct and indirect effects on passenger loyalty of public transport providers' commitment to environmental sustainability; and (d) the loyalty model was estimated in two segments of public transport users: passengers with car and passengers without car. From the Principal Component Analysis, we identified 4 components to describe service quality – operational performance, comfort and safety, attractiveness of service and guarantee of service. As the literature demonstrates, there is no single way to measure service quality in passenger transport. Such diversity is accounted for by the geographic scope of the study (Fellesson and Friman 2008, Fiorio et al. 2011), the population under study (Charbatzadeh et al. 2016, Gutiérrez and Miravet 2016), the specific transport mode or transport service under study (e.g. de Oña et al. 2013, de Oña et al. 2015, Wen et al. 2005) or the specific features of the service being evaluated (Allen et al. 2018, Allen et al. 2019, Friman and Gärling 2001). In our study, we covered a wide geographical area and different kinds of transport (road, railway and boat), which led to us having dimensions that describe the characteristics of the service globally rather than in much detail. Even so the dimensions revealed by our analysis are in line with other studies: “Operational performance” is pointed out by Efthymiou and Antoniou (2017) and Chica-Olmo et al. (2018); “Comfort and safety” is a dimension found in the investigation by Chowdhury and Ceder (2016), Lois et al. (2018), Ngoc et al. (2017) and Şimşekoğlu, et al. (2015); “Attractiveness of service” is pointed out in the investigation by Chowdhury and Ceder (2016), and “Guarantee of service” is found in Efthymiou and Antoniou (2017). The results obtained with the preferred model are also in line with similar studies in the area of public transport (Lai and Chen 2011, Machado et al. 2016, Wen et al. 2005). Model A was the best for describing passenger loyalty towards public transport and confirmed the research hypotheses. Unsurprisingly, we found a significant effect of service quality on 25

satisfaction and a significant effect of satisfaction on loyalty, which are two relationships well documented in the literature (e. g. van Lierop and El-Geneidy 2016). The hypotheses regarding the new construct Commitment to environmental sustainability were all supported – CES has a direct and positive effect on passenger loyalty and passenger satisfaction, CES is positively correlated to service quality, and CES has an indirect positive effect on passenger loyalty, when satisfaction has a mediator role. These outcomes reveal that the attention transit companies pay to sustainability is not overlooked by transport users and is valued by them  transit users recognize that commitment to environmental sustainability is part of the quality of a transportation service (H5) and positively affects their satisfaction (H3) and loyalty towards public transport (H4). Even though extant literature in the public transport sector broadly explores loyalty issues, there is no study investigating the concept of commitment to environmental sustainability by public transport agencies. By integrating this concept into a model of passenger loyalty, this study shows the relevance commitment to environmental sustainability has in developing loyalty towards public transport and, as well, its relevance with regard to passenger satisfaction with public transit service. Additionally, the results presented confirm the relationship structure of the overall loyalty model in the two segments – passengers without car and passengers with car. This means that passengers without car, despite using public transport because they have no alternative, do not differ from passengers with car as far as the determinants of transit loyalty are concerned. Specifically, both groups of passengers recognize that a positive perception about public transport providers’ commitment to environmental sustainability positively affects their satisfaction and intention to remain loyal to public transport. Although the loyalty of passengers without car is not a “free-of-choice” loyalty (van Lierop and El-Geneidy 2016), these passengers appreciate the transit agencies’ commitment to the environment, which is a sign that environmental awareness is implemented as a long-term issue in all sections of society. These results reveal the relevance of environmental issues in today’s business context and the need for transit agencies to include them in their management strategies and policies. The findings suggest that sustainability

26

issues are a key strategic tool, given its essential role in building not only passenger loyalty but also passenger satisfaction. Public transit agency managers ought to design strategies to raise perception of commitment to environmental sustainability and to help passengers to develop greater levels of satisfaction to build enduring relationships with companies. To obtain these outcomes, public transport companies could implement visible environmental practices such as pollution reduction initiatives – switch transit fleet from fossil fuel systems to cleaner alternative solutions, such as biodiesel and natural gas , reduce energy consumption, obtain environmental certifications (e.g. E-Mark, Certification for Sustainable Transportation, …), among others. Moreover, as passengers’ perceptions of commitment to environmental sustainability and satisfaction might be affected largely by transit agency communications related to sustainability issues, the public transport sector should effectively communicate these initiatives to explain the goals of sustainability strategies. Consequently, transit managers should emphasize the importance of environmental issues by promoting sustainability campaigns to build up the overall sustainability image of public transport. With the growing awareness of the importance of environmental preservation, companies that are environmentally friendly can gain the trust of like-minded consumers if they make their “go green” activities part of their communication strategy (Green Business Bureau 2014). According to a global poll conducted across several countries (Ipsos Mori 2014), most consumers believe that companies do not pay enough attention to the environment. This suggests either that companies need to do more, or that they are not communicating their environmental initiatives well enough. Green marketing may result in increased demand from environmentally sensitive passengers because the ecological characteristics of services are likely to be appreciated by these “green “passengers (Polonsky and Rosenberger 2001). To effectively promote commitment to environmental sustainability, it is highly recommended that transit providers develop an integrated communication strategy with multiple information channels to show the characteristics of a green transit mode. Given its inherent marketing

27

potential, the Internet has become the preferred means for companies that wish to publicize their sustainability goals and actions on a regular basis. Publishing sustainability goals and achievements on company websites and an increasing presence in social networks (e.g. Facebook, Twitter, YouTube, LinkedIn, …) (Kietzmann et al. 2011, Koleva 2014), are effective channels for communicating with passengers and keeping them abreast of agencies’ green initiatives and their impact on environmental sustainability. For passengers without car, marketing actions to communicate transit agencies’ green initiatives should transmit both recognition of and positive reinforcement for having a sustainable mobility pattern. For the remaining passengers, marketing actions must highlight the benefits of being green for each individual and for society and, by doing so, stimulate the increasing adoption of public transport.

6. Conclusions and future research To sum up, the present research suggests the relevance to transit companies of taking into account their commitment to environmental sustainability to achieve increased passenger loyalty towards public transport. In the authors’ opinion, green marketing should be an integral part of public transport companies’ strategy. By implementing marketing actions, focused on spreading the company's green image and explaining environmental initiatives, public transport agencies can achieve higher levels of passenger satisfaction, stimulate public transport adoption and increase loyalty towards public transport. Additionally, green marketing may also result in increased demand from environmentally sensitive people because ecological characteristics of the service are likely to be appreciated by these green passengers. It is important to note, however, that nobody chooses a “green” transport service only because it is “green”; the service must fulfil people’s needs. Therefore, a green strategy must be accompanied by changes in the service to accommodate the levels of service required by passengers (Vicente and Reis 2018). Although this research has revealed encouraging empirical results for public transport agencies, the outcomes are general and future research can be improved if different domains are 28

considered. For instance, while the Metropolitan Area of Lisbon has many features in common with other metropolitan areas, such as high population density, heavy traffic and a commitment to making public transport smoother and sustainable, similar studies should nevertheless be replicated in other countries or areas so there can be a deeper examination of the robustness of the passenger loyalty hypotheses with respect to the transit agencies’ commitment to sustainability. Additionally, research could be extended by stratifying the analysis by transport mode to examine whether there are differences in passenger loyalty in the various modes. Public transport modes differ in their environmental impact  the bus is potentially the least environmentally friendly mode if propelled by fuel  which may affect the importance passengers attach to transit agencies’ commitment to sustainability. Moreover, evaluating the impact on loyalty of transit agencies’ commitment to sustainability under different forms of governance (state, private or both) is another interesting avenue for research. Public transport policy is defined by States or Governments and seeks to optimize several conflicting aspirations such as service efficiency, social inclusion goals and also environmental concerns. In turn, public transport service may be provided exclusively by state-owned operators (e.g. Transports Metropolitans de Barcelona) or by coexistent state-owned and private operators (e.g. Transport for London). In the latter case, it is not guaranteed that both share the same objectives  for private operators making profit is a driving goal  and building consensus or obtaining the acquiescence necessary to carry out a strategy aiming to increase transit ridership can be more challenging. Finally, future research should assess whether the relationships found between transit agencies’ commitment to sustainability and the other constructs  namely service quality, passenger satisfaction and passenger loyalty  change over time.

29

Acknowledgements This work received financial support from Fundação para a Ciência e Tecnologia (Science and Technology Foundation) through the UID/GES/00315/2019 project. This article is part of the project entitled Estudo de Satisfação dos Utilizadores dos Transportes Públicos da Área Metropolitana de Lisboa 2013, a joint project of the Metropolitan Transport Authority of Lisbon and Instituto Universitário de Lisboa (ISCTE-IUL).

Appendix

Figure A.1: Map of Portugal with the location of the Metropolitan Area of Lisbon (in red) Source: https://en.wikipedia.org/wiki/Lisbon_metropolitan_area

30

References Allen, J., Eboli, L., Forciniti, C., Mazzulla, G., Ortúzar, J. (2019). The role of critical incidents and involvement in transit satisfaction and loyalty. Transport Policy, 75(C), 57-69. Allen, J., Eboli, L., Mazzulla, G., Ortúzar, J. (2018). Effect of critical incidents on public transport satisfaction and loyalty: an Ordinal Probit SEM-MIMIC approach. Transportation. https://doi/org/10.1007/s11116-018-9921-4 Bentler, P. (2007). On tests and indices for evaluating structural models. Personality and Individual Differences, 42, 825-829. Bollen, K. (1989). Structural equations with latent variables. Wiley: New York. Bou-Llusar, J., Camisón-Zornoza, C., Escrig-Tena, A. (2001). Measuring the relationship between firm perceived quality and customer satisfaction and its influence on purchase intentions. Total Quality Management, 12(6), 719–734. Brady, M., Cronin, J. (2001). Some New Thoughts on Conceptualizing Perceived Service Quality: A Hierarchical Approach. Journal of Marketing, 65(3), 34-49. Butcher, K., Sparks, B., O’Callaghan, F. (2001). Evaluative and Relational Influences on Service Loyalty. International Journal of Service Industry Management, 12, 310–327. Byrne, B. (2009). Structural equation modeling with AMOS: Basic concepts, applications, and programming. CRC Press. Cantwell, M., Caulfield, B., O’Mahony, M. 2009. Examining the factors that impact public transport commuting satisfaction. Journal of Public Transportation, 12 (2), 1-21. Carrillat, F., Jaramillo, F., Mulki, J. (2007). The validity of the SERVQUAL and SERVPERF scales: a meta-analytic view of 17 years of research across five continents. International Journal of Service Industry Management, 18(5), 472-490 Caruana, A. (2002). Service loyalty: The effects of service quality and the mediating role of customer satisfaction. European Journal of Marketing, 36(7/8), 811–828. CEN (2002). EN 13816 - Transportation - Logistics and Services - Public Passenger Transport Service Quality Definition, Targeting and Measurement. European Committee for Standardization (CEN), Brüksel. Changa, Y., Chen, F. (2007). Relational benefits, switching barriers and loyalty: A study of airline customer in Taiwan. Journal of Air Transport Management, 13, 104-109

31

Charbatzadeh, F., Ojiako, U., Chipulu, M., Marshall, A. (2016). Determinants of satisfaction with campus transportation services: Implications for service quality. Journal of Transport and Supply Chain Management, 10(1), 1-14. Chica-Olmo, J., Gachs-Sánchez, G., Lizarraga, C. (2018). Route effect on the perception of public transport services quality. Transport Policy, 67(C), 40-48. Chowdhury, S., Ceder, A. (2016). Users’ willingness to ride an integrated public-transport service: A literature review. Transport Policy, 48, 183–195. Cone

Communications

(2013).

Echo

Global

CSR

Study.

http://www.conecomm.com/news-blog/2013-global-csr-study-release.

Available Accessed

at: 13th

November 2018. d’Ovidio, F., Leogrande, D., Mancarella, R., Viola, D. (2014). The quality of public transport services: a statistical analysis. Paper presented in VIth International Scientific Conference on Economic Policy and EU integration, 96-104, Durrës, Albania. Davis, C. (2016). Communicating sustainability effectively in the business sector. Enviro-Mark Solutions,

Issue

41.

Available

at:

https://www.enviro-mark.com/news-and-

events/newsletter/issue-41-newsletter/communicating-sustainability-effectively-in-thebusiness-sector. Accessed 6th August 2019. de Oña, J., de Oña, R., Eboli, L., Mazzulla, G. (2013). Perceived service quality in bus transit service: A structural equation approach. Transport Policy, 29, 219-226. de Oña, J., de Oña, R., Eboli, L., Mazzulla, G. (2015). Heterogeneity in Perceptions of Service Quality among Groups of Railway Passengers. International Journal of Sustainable Transportation, 9(8), 612-626. Del Castillo, J., Benitez, F. (2012). A methodology for modelling and identifying users satisfaction issues in public transport systems based on users surveys, Procedia - Social and Behavioral Sciences, 54, 1104-1114. doi: 10.1016/j.sbspro.2012.09.815 Dimitriades, Z. (2006). Customer satisfaction, loyalty and commitment in service organizations - Some evidence from Greece. Management Research News, 29(12), 782– 800. Eboli, L., Mazzula, G. (2007). Service quality attributes affecting customer satisfaction for bus transit. Journal of Public Transport, 10(3), 21-34.

32

Eboli, L., Mazzula, G. (2011). A methodology for evaluating transit service quality based on subjective and objective measures from the passenger’s point of view. Transport Policy, 18, 172–181. ECO (2016) Carris testa autocarro totalmente elétrico e silencioso em duas carreiras. ECO 2016 Swipe News SA. Available at: https://eco.pt/2016/09/22/carris-testa-autocarro-totalmenteeletrico-e-silencioso-em-duas-carreiras/. Accessed 21st October 2016. ECSI Technical Committee (1998). European Customer Satisfaction Index Foundation and Structure for Harmonized National Pilot Projects. ECSI. Efthymiou, D., Antoniou, C. (2017). Understanding the effects of economic crisis on public transport users’ satisfaction and demand. Transport Policy, 53, 89-97. ELTIS (2014). The urban mobility observatory. Available at: http://www.eltis.org/. Accessed 25th October 2016. European Commission (2008) Special Eurobarometer 295: Attitudes of European citizens towards the environment. European Commission: Brussels. Available at: http://ec.europa.eu/public_opinion/archives/ebs/ebs_295_en.pdf European Commission (2017). Clean transport, Urban transport. Available at: http://ec.europa.eu/transport/themes/urban/urban_mobility/urban_mobility_actions/public_tra nsport_en. Accessed 27th January 2017. Farrell, A., Souchon, A., Durden, G. (2001). Service Encounter Conceptualization: employees‟ service behaviours and customers‟ service quality perceptions. Journal of Marketing Management, 17, 577-93. Fellesson, M., Friman, M. (2008). Perceived satisfaction with public transport service in nine European cities. Journal of Transportation Research Forum, 47(3), 93-103. Fiorio, C., Florio, M., Perucca, G. (2011). Consumers’ Satisfaction and Regulation of Local Public Transport: Evidence from European Cities. Proceedings of the XXIII Conferenzia Societá Italiana di Economia Pubblica. Fitzsimmons, J., Fitzsimmons, M. (2008). Service Management: Operations, Strategy, Information Technology. McGraw-Hill/Irwin, 537, Boston. Flavian, C., Martinez, E., Polo, Y. (2001). Loyalty to grocery stores in the Spanish market of the 1990s. Journal of Retailing and Consumer Services, 8, 85-93. Fonseca, F., Pinto, S., Brito, C. (2010). Service Quality and Customer Satisfaction in Public Transports. International Journal for Quality Research, 4(2), 125-130. 33

Friman, M., Gärling, T. (2001). Frequency of negative critical incidents and satisfaction with public transport services. II, Journal of Retailing and Consumer Services, 8, 105-114. Gagić, S., Tešanović, D., Jovičić, A. (2013). The vital components of restaurant quality that affect guest satisfaction. TURIZAM, 17, 166–176. GlobeScan (2011) Survey on Sustainable Consumption. Available at: http://www.globescan.com/news_archives/tss_growth/The_Sustainability_Survey_2011_Sust ainable_Consumption.pdf. Accessed 30th January 2017. GMPTA (Greater Manchester Passenger Transport Authority) (2007). Commitment to Service. Available at: http://www.tfgm.com/Corporate/Documents/Environment/Enviro_report0607.pdf. Accessed 11th October 2016. Green Business Bureau (2014). Green Business Certification. Available at: http://www.gbb.org/product-tour/. Accessed 6th February 2017. Gutiérrez, A., Miravet, D. (2016). The Determinants of Tourist Use of Public Transport at the Destination. Sustainability, 8, 908. Hair, Jr, J., Black, W., Babin, B., Anderson, R. (2014). Multivariate data analysis: a global perspective (7th Edition). London: Pearson Education Limited. Hellier, P., Geursen, G., Carr, R., Rickard, J. (2003). Customer repurchase intention: a general structural equation model. European Journal of Marketing, 37(11), 1762-1800. Hensher, D., Stopher, P., Bullock, P. (2003). Service quality – developing a service quality index in the provision of commercial bus contracts, Transportation Research Part A, 37, 499-517. doi: 1016/S0965-8564(02)00075-7. IAPT (2014) Climate action and public transport: analysis of planned actions. UITP Advancing Public Transport: Brussels. Iman, R. (2014). Measuring Public Transport Satisfaction from User Surveys, International Journal of Business and Management, 9(6), 106-114. Imaz, A., Habib, K., Shalaby, A., Idris, A. (2015). Investigating the factors affecting transit user loyalty. Public Transportation, 7, 39-60. Ipsos Mori (2014). Global trends 2014-Environment. Available at: http://www.ipsosglobaltrends.com/environment.html. Accessed 5th February 2017.

34

ISCTE-IUL (2014a). AMTL-Estudo de Satisfação dos Utilizadores dos Transportes Públicos– Final Report of Users’ Survey. Unpublished Report. ISCTE-IUL (2014b). AMTL-Estudo de Satisfação dos Utilizadores dos Transportes Públicos– Final Report of Focus Groups. Unpublished Report. Kamaruddin, R., Osman, I., Pei, C. (2012). Customer expectations and its relationship towards public transport in Klang Valley. Journal of Asian Behavioural Studies, 2(5), 2938. Kang, G., James, J. (2004). Service quality dimensions: an examination of Grönroos’s service quality model, Managing Service Quality, 14 (4), 266–277. Karlaftis, M., Golias, J., Papadimitriou E. (2001). Transit Quality as an Integrated Traffic Management Strategy: Measuring Perceived Service. Journal of Public Transportation, 4(1), 13-26. Kennell, B. (2016) Environmental Concern Empowers the People. Available at: http://www.huffingtonpost.com/brian-kennell/environmental-concern-emp_b_8105580.html. Accessed 18th October 2016. Kietzmann, J., Hermkens, K., McCarthy, I., Silvestre, B. (2011). Social media? Get serious!. Understanding the functional building blocks of social media. Business Horizons, 54, 241-251. Kim, Y., Lee, H. (2011). Customer satisfaction using low cost carriers. Tourism Management, 32(2), 235-243. King, B. (2011). Green Consumerism, Brand Loyalty Are on the Rise. Available at: https://www.sustainablebrands.com/news_and_views/articles/green-consumerism-brandloyalty-are-rise. Accessed 13th November 2018. Kline, R. (2010). Principles and practice of structural equation modeling. 3rd edition. New York: The Guilford Press. Koleva, P. (2014). Transport and sustainable development, in communication and in practice: A comparative analysis of the strategies of three French firms. Journal of Economics and Business, 17(1), 19-37. Kotler, P. (2003). Marketing management (11th ed.). Upper Sadle River, NJ: Prentice Hall. Krizek, K., El‐Geneidy, A. (2007). Segmenting preferences and habits of transit users and non‐users. Journal of Public Transportation, 10(3), 71-94.

35

Lai, W., Chen, C. (2011). Behavioral intentions of public transit passengers – the role of service quality, perceived value, satisfaction and involvement, Transport Policy, 18 (2), 318–325. Lois, D., Monzón, A., Hernández, S. (2017). Analysis of satisfaction factors at urban transport interchanges: Measuring travellers’ attitudes to information, security and waiting. Transport Policy, 67, 49-56. Machado, J., Oña, R., Oña, J. (2016). The role of involvement in regards to public transit riders’ perceptions of the service. Transport Policy, 48, 34-44. http://www.sciencedirect.com/science/article/pii/S0967070X16300646. MALT (Metropolitan Authority of Lisbon Transport) (2014). Organization, tariffs and financing of public transport system of metropolitan area of Lisbon. Unpublished report. Minser, J., Webb, V. (2010). Quantifying the benefits: application of customer loyalty modeling in public transportation context. Transportation Research Record: Journal of the Transportation Research Board, 2144, 111-120. Mokonyama, M., Venter, C. (2013). Incorporation of customer satisfaction in public transport contracts – A preliminary analysis, Research in Transportation Economics, 39, 58-65. doi: 10.1016/j.retrec.2012.05.024 MTA (Mobility and Transport Authority) (2016). Relatório sobre Reclamações no Mercado da Mobilidade e dos Transportes. Available at: http://www.amtautoridade.pt/media/1205/relatorio-amt_reclama%C3%A7%C3%B5es-no-mercado-damobilidade-e-dos-transportes_v3.pdf. Accessed 15th July 2018. Muhammad, I., Daisy, M., Saman, S. (2011). Service Quality in Rail Transport of Pakistan: A Passenger Perspective. In Proceedings of 3rd SAICON: International Conference on Management, Business Ethics and Economics (ICMBEE), December 28-29, Lahore, Pakistan. Ngatia G., Okamura T., Nakamura F. (2010) The Structure of Users’ Satisfaction on Urban Public Transport Service in Developing Country: the Case of Nairobi. The Journal of the Eastern Asia Society for Transport Studies, 8, 1288-1300. Ngoc, A., Hung, K., Tuan, V. (2017). Towards the Development of Quality Standards for Public Transport Service in Developing Countries: Analysis of Public Transport Users’ Behavior. Transportation Research Procedia, 25, 4560–4579.

36

OneNYC (2016) New York City Department of Transportation - Strategic Plan 2016. Available at: https://www.nycdotplan.nyc/PDF/Strategic-plan-2016.pdf. Accessed 3rd August 2019. Parasuraman, A., Zeithaml, V., Berry, L. (1985). A Conceptual Model of Service Quality and Its Implications for Future Research. The Journal of Marketing, 49, 41-50 Parasuraman, A., Zeithaml, V., Berry, L. (1988). SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64 (1), 12-40. Perez, M., Abad, J., Carrillo, G., Fernández, R. (2007). Effects of service quality dimensions on behavioural purchase intentions: A study in public‐sector transport. Managing Service Quality: An International Journal, 17(2), 134-151. Peycheva, D., Pötzschke, J., Hall, T., Rattinger, H. (2014) Attitudes Towards environmental issues: empirical evidence in Europe and the United States. Transworld, Working Paper 31, 1-56. Polonsky, M., Rosenberger, P. (2001). Re-evaluating green marketing: strategic approach. Business Horizons, 44(5), 21-30. Prasad, D., Shekhar, R. (2010) Impact of Service Quality Management (SQM) Practices on Indian Railways - A Study of South Central Railways. International Journal of Business and Management, 5(9), 139-146. Ramos, S., Vicente, P., Passos, A., Costa, P., Reis, E. (2019). Perceptions of the Public Transport Service as a Barrier to the Adoption of Public Transport: A Qualitative Study. Social Sciences, 8, 150. Ranade, K. (2012) Customer Loyalty – What is it? How Can You Measure and Manage It?. Available at: http://www.loyaltyresearch.com/insights/customer-loyalty-what-is-it-how-canyou-measure-and-manage-it/. Accessed 3rd October 2016. Reichheld, F. (2003). The one number you need to grow. Harvard Business Review, 81(12), 46‐55. Reichheld, F., Teal, T. (2001) The Loyalty Effect: The Hidden Force Behind Growth, Profits, and Lasting Value. Harvard Business School Press: Boston. Shiftan, Y., Barlach, Y., Shefer, D. (2015). Measuring passenger loyalty to public transport modes. Journal of Public Transportation, 18(1), 1-16.

37

Şimşekoğlu, Ö., Nordfjaer, T., Rundmo, T. (2015). The role of attitudes, transport priorities, and car use habit for travel mode use and intentions to use public transportation in an urban Norwegian public. Transport Policy, 42, 113-120. Skačkauskienė, I., Vilkaitė-Vaitonė, N., Raudeliũnienė, J., Davidavičienė, V. (2016). A model for measuring passenger loyalty. Transport, 31(1), 100-107. Statistics Portugal (2011). Population aged 15 or older by place of residence. Census Population Report. INE, Portugal. Statistics Portugal (2017). Inquérito à Mobilidade nas Áreas Metropolitanas do Porto e de Lisboa. INE, Portugal. Stuart, K., Mednick, M., Bockman, J. (2000) Structural equation model of customer satisfaction for the New York City subway system. Transportation Research Record, 1735, 133-137. Sustainable Sydney (2017). Sustainable Sydney 2030 - Community Strategic Plan 2017–2021. Available at: https://www.cityofsydney.nsw.gov.au/__data/assets/pdf_file/0011/288173/AdoptedSustainable-Sydney-2030_Accessible-Version.pdf. Accessed 3rd August 2019. TaghiPourian, M., Bakhsh, M. (2015). Loyalty: From Single-Stage Loyalty to Four-Stage Loyalty. International Journal of New Technology and Research, 1(6), 48-51 TfL (Transport for London) (2011). Transport for London: Business Plan 2011/12 – 2014/15. Available at: http://content.tfl.gov.uk/tfls-business-plan-2011-12-to-2014-15.pdf. Accessed 26th October 2016. TfL (Transport for London) (2017). Transport for London Business Plan 2018/19 to 2022/23. Available at: http://content.tfl.gov.uk/tfl-business-plan-december-2017-.pdf. Accessed 17th January 2018. TMB (Transports Metropolitans de Barcelona) (2017). Transports Metropolitans de Barcelona. Available: https://www.tmb.cat/documents/20182/111197/Presentacio_corporativa_2017_EN_ACC.pdf/ 6db9e57d-6419-4c10-994a-42b554e8faad. Accessed 17th January 2018. TNS Opinion (2015) From farm to folk. Available at: https://www.foeeurope.org/sites/default/files/publications/from-farm-to-folk_1.pdf. Accessed 30th January 2017. 38

UNEP (United Nations Environment Programme) (2005). Communicating Sustainability-How to produce effective public campaigns. Available at: http://www.unep.fr/shared/publications/pdf/DTIx0679xPA-CommunicatingEN.pdf. Accessed 6th August 2019. van Lierop, D., Badami, M., El-Geneidy, A. (2018). What influences satisfaction and loyalty in public transit? A critical review of the literature. Transport Reviews, 38(1), 52-72. van Lierop, D., El-Geneidy, A. (2016). Enjoying loyalty: The relationship between service quality, customer satisfaction, and behavioral intentions in public transit. Research in Transportation Economics, 59, 50-59. van Lierop, D., El-Geneidy, A. (2017). Public image: determining the effect of users’ image of public transit loyalty. Paper presented at 96th Transportation Research Board Annual Meeting. Washington D.C., United States. Vicente, P., Reis, E. (2018). Ex-regular Users of Public Transport: Their Reasons for Leaving and Returning. Journal of Public Transportation, 21 (2), 101-116. Vilares, M., Coelho, P., Cadilhe, M. (2005). Satisfação e lealdade do cliente: Metodologias de avaliação, gestão e análise. Escolar Editora: Lisboa. Webb, V. (2010). Customer loyalty in the public transportation context. Master Thesis, MIT: Cambridge. Weinstein, A. (2000). Customer satisfaction among transit riders: how customers rank the relative importance of various service attributes. Transportation Research Record: Journal of the Transportation Research Board, 1735, 124-132. Wen, C., Lan, L., Chang, H. (2005). Structural equation modeling to determine passenger loyalty toward intercity bus services. Transportation Research Record: Journal of the Transportation Research Board, 1927, 249-255. Wenzel, M. (2017) How Sustainable Businesses Drive Customer Loyalty. Available at: https://www.reviewtrackers.com/customer-experience-loyalty/. Accessed 13th November 2018. Werner, C., Schermelleh-Engel, K. (2010). Introduction to Structural Equation Modeling with LISREL - Deciding Between Competing Models: Chi-Square Difference Tests. Available at: https://www.psychologie.uzh.ch/dam/jcr:ffffffff-b371-2797-000000000fda8f29/chisquare_diff_en.pdf. Accessed 12th December 2018. 39

Yeung, M., Ging, L., Ennew, C. (2002). Customer satisfaction and profitability: A reappraisal of the nature of the relationship. Journal of Targeting, Measurement and Analysis for Marketing, 11(1), 24–33. Zeithaml, V., Bitner, M., Gremler, D. (2008). Services Marketing: Integrating Customer Focus across the Firm, 5th Ed., McGraw-Hill, Boston, MA. Zhao, J., Webb, V., Shah, P. (2014). Customer loyalty differences between captive and choice transit riders. Transportation Research Record: Journal of the Transportation Research Board, 2415, 80-88.

Highlights 1) commitment to environmental sustainability has a direct effect on passenger satisfaction. 2) commitment to environmental sustainability has a direct effect and an indirect effect on passenger loyalty. 3) commitment to environmental sustainability is positively correlated with service quality.

40