Analysis of Airline Passengers’ Demand for Stopover Airport Choice

2021 ◽  
Vol 31 (6) ◽  
pp. 83-95
Author(s):  
Jae-Hong Park ◽  
Jong-Chul Kim
Keyword(s):  
Airline Passengers

scholarly journals Key Determinants of Airline Loyalty Modeling in Thailand

2020 ◽  
Vol 12 (10) ◽  
pp. 4165 ◽  
Author(s):  
Dissakoon Chonsalasin ◽  
Sajjakaj Jomnonkwao ◽  
Vatanavongs Ratanavaraha
Keyword(s):  
Airline Industry ◽  
Structural Equation ◽  
Statistical Significance ◽  
Perceived Quality ◽  
Equation Modeling ◽  
Structural Factors ◽  
Proposed Model ◽  
Airline Passengers ◽  
Domestic Airline

The airline industry in Thailand has grown enormously over the past decade. Competition among airline companies to reach market share and profit has been intense, requiring strong strategic abilities. To increase the service quality of such companies, identifying factors related to the context of airlines is important for policymakers. Thus, this study aims to present empirical data on structural factors related to the loyalty of domestic airline passengers. Structural equation modeling was used to confirm the proposed model. The questionnaire was used to survey and collect data from 1600 airline passengers. The results indicate that satisfaction, trust, perceived quality, relationship, and image of airlines positively influenced loyalty with a statistical significance of α = 0.05. Moreover, the study found that expectation and perceived quality indirectly influenced loyalty. The findings provide a reference for airline operators to clearly understand the factors that motivate passenger loyalty, which can be used to develop the sustainability of marketing strategies and support competitiveness.

scholarly journals Data Analytics for Monitoring the Satisfactory Parameters of Airline Passengers using Machine Learning Algorithms in Python

2020 ◽  
Vol 9 (3) ◽  
pp. 1231-1235
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Complex Information ◽  
Huge Data ◽  
Gradient Boosting Machine ◽  
Airline Passengers ◽  
Effective Representation

An effective representation by machine learning algorithms is to obtain the results especially in Big Data, there are numerous applications can produce outcome, whereas a Random Forest Algorithm (RF) Gradient Boosting Machine (GBM), Decision tree (DT) in Python will able to give the higher accuracy in regard with classifying various parameters of Airliner Passengers satisfactory levels. The complex information of airline passengers has provided huge data for interpretation through different parameters of satisfaction that contains large information in quantity wise. An algorithm has to support in classifying these data’s with accuracies. As a result some of the methods may provide less precision and there is an opportunity of information cancellation and furthermore information missing utilizing conventional techniques. Subsequently RF and GBM used to conquer the unpredictability and exactness about the information provided. The aim of this study is to identify an Algorithm which is suitable for classifying the satisfactory level of airline passengers with data analytics using python by knowing the output. The optimization and Implementation of independent variables by training and testing for accuracy in python platform determined the variation between the each parameters and also recognized RF and GBM as a better algorithm in comparison with other classifying algorithms.

scholarly journals Airline passengers’ perceptions of service quality: themes in online reviews

2019 ◽  
Vol 31 (2) ◽  
pp. 855-873 ◽  
Author(s):  
Ana Brochado ◽  
Paulo Rita ◽  
Cristina Oliveira ◽  
Fernando Oliveira
Keyword(s):  
Social Media ◽  
Design Methodology ◽  
Online Reviews ◽  
Value For Money ◽  
Content Type ◽  
The Core ◽  
Content Analyses ◽  
Airline Passengers ◽  
Media Platform ◽  
Post Purchase

PurposeThis paper aims to identify the main themes shared in online reviews by airline travellers, as well as which of these themes were linked with higher and lower value for money ratings.Design/methodology/approachThe research used mixed content analyses (i.e. quantitative and qualitative) to examine 1,200 reviews of six airline companies shared by airline travellers in a social media platform.FindingsThe analyses revealed nine themes in descriptions of airline travel experiences. These are the core services during “flights”, “airport” operations, crew and ground “staff”, ticket “classes”, “seats”, inflight “services”, “entertainment”, overall experiences of “airlines” and post-purchase recommendations of with which companies to “fly”. Low value for money ratings are linked with the “airport” and “flights” themes.Originality/valueThe results offer useful insights into airline travellers’ overall experiences based on social media information and facilitate the identification of the main themes linked with different value for money ratings.

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