Passenger satisfaction: a multi-group analysis

Antonucci, Laura; Crocetta, Corrado; D’Ovidio, Francesco Domenico; Toma, Ernesto

doi:10.1007/s11135-012-9771-7

Cited by 13 publications

(9 citation statements)

References 3 publications

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“…According to (Oña et al, 2013) and (Antonucci et al, 2014), passenger satisfaction with public transport can be grouped into a number of latent variables consisting service organization, safety, and reliability, human resources and comfort, and cleanliness.…”

Section: Public Transport Passenger Satisfactionmentioning

confidence: 99%

Designing the Appropriate Data Collection Method for Public Transport Passenger Satisfaction Analysis

2018

IJTTE

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Abstract:Incessantly increasing urbanization is a threat to mobility, environment and ultimately to the quality of life. Promoting public transport in the major cities seems to be a promising solution to this issue which can be achieved by moving toward attracting more satisfied public transport passengers (PTP). The first step in PTP satisfaction analysis is collecting the reliable data which is equally suitable for all passenger groups (i.e. handicapped, visually impaired, etc.) in order to limelight the most important concerns of the public transport users. We assume that a satisfied customer would be willing to purchase the product again and will stay loyal to the provider as long as the gap between his perceived quality and expected quality is less. Among various methods of data collection, Customer satisfaction survey (CSS) suits best to know about the extent of users' satisfaction. In the current paper, at first, the relevant data collection methods have been reviewed and then a passenger satisfaction questionnaire has been designed which rates the extent of Budapest PTP satisfaction and dissatisfaction on a Likert chart. It should be underlined that different set of individual characteristics (age, gender, level of education, overall passenger health condition, etc.), as well as different service attributes (price, frequency, ease of boarding and alighting, etc.), are considered in the aforementioned questionnaire for further cluster analysis of the collected data.

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Section: Public Transport Passenger Satisfactionmentioning

confidence: 99%

Designing the Appropriate Data Collection Method for Public Transport Passenger Satisfaction Analysis

2018

IJTTE

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“…Antonucci et al (2014) propose a model that assumes that customer satisfaction, measured starting from the variable ''overall satisfaction'', is determine by many unobservable latent factors, such as service organization, safety and reliability, comfort and cleanliness, and human resources. Satisfaction in this study is regarded as comprising two parts-satisfaction with facilities and service satisfaction.…”

Section: Satisfaction With Facilitiesmentioning

confidence: 99%

Causal complexity for passengers’ intentions to re-ride

Lin

2016

Qual Quant

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This study explores the relationship among service range, ride convenience, motivation to ride, satisfaction with facilities, service satisfaction, and intention to re-ride. The conveyance of interest is the Puyuma Express because its transport service last year carries out the overall electrified era in eastern Taiwan and its vast passenger capacity breaks the record over the past 128 years. The very high passenger capacity includes passengers who are on their first ride but, to a much greater extent, passengers who are reriding. This fact motivates this research. This work involves a carefully designed questionnaire, and uses both web-based and paper-based questionnaire surveys to collect data. The number of valid samples thus obtained was 228. Multiple regression analysis (MRA) and fuzzy set qualitative comparative analysis (fsQCA) are utilized to test proposed research framework. The two methods yield completely different results. MRA reveals that satisfaction with facilities, service satisfaction, ride convenience, and service range are together responsible for the strong intentions of passengers to re-ride. FsQCA identifies seven complex antecedent paths that are responsible for strong intentions to re-ride. The latter explains outcome more comprehensively than the former. The motivation to ride is an important condition in fsQCA and should not be eliminated as MRA suggests. This empirical study elucidates the causal complexity for passengers' intentions to re-ride, and contributes to theory and practice in the domains of both transportation marketing and consumer behavior.

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“…In de Oña, de Oña, Eboli and Mazzulla (2013) and also in Antonucci, L. et al (2014) there was the method of structural equation modelling applied in order to measure passengers' satisfaction with public city transport services and in order to verify how much some service characteristics could influence the perceived quality. All these authors claim that factors affecting customer satisfaction with public city transport can be grouped into latent variables consisting service organisation, safety and reliability, human resources and comfort and cleanliness.…”

Section: Theoretical Background Of the Users´ Satisfaction Measurementmentioning

confidence: 99%

“…It is important to note that these results are useful only for Spanish public transport services because the respondents of this research were only passengers of the bus transit services in Spain (de Oña, de Oña, Eboli & Mazzulla, 2013). Antonucci, L. et al (2014) applied in their study an explorative factorial analysis and a structural equation model and they found that the Italian passengers are very sensitive to the level of the service organization and to the way the service is delivered. It means the punctuality and regularity and short waiting time are very important factors determining the customer satisfaction.…”

Section: Theoretical Background Of the Users´ Satisfaction Measurementmentioning

confidence: 99%

“…That is why the methods of factor analysis and structural equation modelling were used in this paper. According to de Oña, de Oña, Eboli and Mazzulla (2013) and also Antonucci, L. et al (2014) the method of structural equation modelling is appropriate for describing a complex phenomenon like transit passenger perception of the used service.…”

Section: Theoretical Background Of the Users´ Satisfaction Measurementmentioning

confidence: 99%

See 1 more Smart Citation

The Factors Influencing Satisfaction with Public City Transport: A Structural Equation Modelling Approach

Pawlasová¹

2015

JOC

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Satisfaction is one of the key factors which influences customer loyalty. We assume that the satisfied customer will be willing to use the ssame service provider again. The overall passengers´ satisfaction with public city transport may be affected by the overall service quality. Frequency, punctuality, cleanliness in the vehicle, proximity, speed, fare, accessibility and safety of transport, information and other factors can influence passengers´ satisfaction. The aim of this paper is to quantify factors and identify the most important factors influencing customer satisfaction with public city transport within conditions of the Czech Republic. Two methods of analysis are applied in order to fulfil the aim. The method of factor analysis and the method Varimax were used in order to categorize variables according to their mutual relations. The method of structural equation modelling was used to evaluate the factors and validate the model. Then, the optimal model was found. The logistic parameters, including service continuity and frequency, and service, including information rate, station proximity and vehicle cleanliness, are the factors influencing passengers´ satisfaction on a large scale.

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Passenger satisfaction: a multi-group analysis

Cited by 13 publications

References 3 publications

Designing the Appropriate Data Collection Method for Public Transport Passenger Satisfaction Analysis

Designing the Appropriate Data Collection Method for Public Transport Passenger Satisfaction Analysis

Causal complexity for passengers’ intentions to re-ride

The Factors Influencing Satisfaction with Public City Transport: A Structural Equation Modelling Approach

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