Guidebook for Conducting Airport User Surveys

Biggs, D C; Bol, M A; Baker, J L; Gosling, Geoffrey D.; Franz, J D; Cripwell, J Paul

doi:10.17226/14333

Cited by 4 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this method, the first respondent is selected randomly from among the first n individuals. Systematic sampling is recognized as a good technique for airport surveys and is considered to be equivalent to simple random sampling [60]. IKIA is the largest airport in Iran which currently serves only international flights with a total annual capacity of 6.5 million passengers [61].…”

Section: Methodsmentioning

confidence: 99%

The Role of Personality Traits through Habit and Intention on Determining Future Preferences of Public Transport Use

Yazdanpanah

Hosseinlou

2017

Behavioral Sciences

View full text Add to dashboard Cite

A complex set of factors may affect transportation mode choice. While earlier studies have often considered objective factors in determining preferences of public transport use as a sustainable transportation, subjective factors such as personality traits are underexplored. Therefore, this study aimed to investigate the influence of personality traits on the number of future public transport use. Additionally, “car habit” and “intention toward using public modes” were considered to be important. For this purpose, a case study from departure passengers at Imam Khomeini International Airport (IKIA, Tehran, Iran) was conducted between January and February 2015 at IKIA. Results of structural equation modeling (SEM) shows that only neuroticism and extraversion personality traits were significant in determining future public transportation mode choice. However, the model indicates that these traits indirectly influence intention and car habit. Neuroticism was found to have a total effect of −0.022 on future public transport use, which represents a negative association with public transport use, while extraversion positively influenced future public transport use with a total effect of 0.031. Moreover, the results found interestingly that car access had a better fit to the data than the number of cars in household (NCH); both had significant positive effect on car habit, but only car access had a significant influence on intention. Furthermore, the effect of socio-demographic variables such as age, gender, educational level, income level, and body mass index (BMI) were determined to be significant in identifying choice of future transport mode to airports, which is explained in the discussion section of this paper.

show abstract

Section: Methodsmentioning

confidence: 99%

The Role of Personality Traits through Habit and Intention on Determining Future Preferences of Public Transport Use

Yazdanpanah

Hosseinlou

2017

Behavioral Sciences

View full text Add to dashboard Cite

show abstract

“…According to Cripwell et al (2009), an intercept survey is performed by survey staff at a particular location by approaching potential participants when they enter or exit that particular location.…”

Section: Intercept Surveymentioning

confidence: 99%

Modelling Flyover Induced Travel Demand in Dhaka, Bangladesh

Rahman¹

View full text Add to dashboard Cite

Model for measuring airport innovation

Caetano

Alves

2021

Rev. Bras. Inov.

View full text Add to dashboard Cite

Identifying and measuring innovation at airports becomes necessary not only to point out theelements of its management or infrastructure to be improved, but also to raise opportunities for innovation, with the aim to collaborate for the best efficiency of air transport. In this sense, this study concerns innovation management applied to airports given the limitations in the state of the art related to measuring innovation in this type of organization. The integrated perception of different airport stakeholders has been collected from a survey with 70 Brazilian professionals specialized in the subject, namely researchers, airports, airlines, and the aeronautics industry managers, in a proposal to measure the level of airport innovation from different indicators. The results proposed an algorithm for an Airport Integrated Innovation Index (AI3), composed by 38 measurable variables related to innovation practices, procedures, and infrastructure elements such as new biometric passenger identification systems, real-time passenger flight tracking systems, new types of runway pavement, and actions to reduce airport operational restrictions. This model can be used in airports of different categories (national or international) and sizes (small, medium, or large). It may be adapted for different countries and contexts according to their markets and organizational cultures.

show abstract

Guidebook for Conducting Airport User Surveys

Cited by 4 publications

References 6 publications

The Role of Personality Traits through Habit and Intention on Determining Future Preferences of Public Transport Use

The Role of Personality Traits through Habit and Intention on Determining Future Preferences of Public Transport Use

Modelling Flyover Induced Travel Demand in Dhaka, Bangladesh

Model for measuring airport innovation

Contact Info

Product

Resources

About