Customer Satisfaction of Vietnam Airline Domestic Services

Khanh, Giao Ha Nam

doi:10.31219/osf.io/dyze3

Cited by 1 publication

(2 citation statements)

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“…The basic idea is to use the position x i of i-th particle to represent a candidate solution to the problem, and use a fitness function value to evaluate the superiority of positions. All particles update their velocity through (4) and position through (5) by sharing the individual and global position with others, and will finally gather in the extremum area after multiple iterations.…”

Section: A Design Of the Basic Modelmentioning

confidence: 99%

“…The existing literature about the consumption willingness of airline additional services mostly uses the questionnaire method [1], [2] or scenario hypothesis method [3], [4] to collect data and adopt the theory of consumer psychology to build traditional statistical models. The literature [5], [6] mines customer opinion survey data to analyze the factors affecting customer satisfaction by methods such as exploratory factor analysis and classifiers such as Parsimonious Bayes. The real passenger travel data which is subjective and limited, however, has not been used in any form of study.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Airline Additional Services Consumption Willingness Based on High-Dimensional Incomplete Data

2022

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Prediction of the purchase willingness of passengers has great benefits for airlines to promote auxiliary services, however, the datasets stored in passenger travel information systems are often high-dimensional and incomplete. This study develops a prediction method of airline additional service consumption willingness based on high-dimensional and incomplete datasets with a triple-layer hybrid PSO-XGBoost model, which consists of an incomplete data processing layer, a high-dimensional data processing layer, and a predicting layer. The raw dataset is converted into a complete and low-dimensional dataset through the first two layers and inputted into the predicting layer to train and optimize the XGBoost model together with the PSO algorithm and 10-fold cross-validation. The experimental results show that the proposed method outperforms other traditional machine learning models, presenting the highest prediction score with 0.9879 in terms of AUC. The findings help predict airline additional services consumption intentions of passengers and are beneficial to efficient and low-cost precise marketing for airlines.

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Section: A Design Of the Basic Modelmentioning

confidence: 99%