Creating a prediction model of passenger preference between low cost and legacy airlines. (December 2019)

• Record Type: Journal Article
• Authors: Mehta, Rian; Rice, Stephen; Deaton, John; Winter, Scott R.
• Is Part Of: Transportation research interdisciplinary perspectives. Volume 3(2019)
• Publication Date: 2019-12
• Subjects: Legacy carriers -- Low-cost carriers -- Passenger preference -- Prediction model; Transportation -- Periodicals; 388.05
• DOI: 10.1016/j.trip.2019.100075
• Languages: English
• ISSNs: 2590-1982

Abstract

Abstract: The airline industry is highly competitive, and in order to increase profits, airlines are always looking to better target key customers. Increased understanding of customers also helps improve the product from the customer's perspective. This study aims to determine which factors predict airline passengers' preference between legacy and low-cost carriers. The correlational design used creates a prediction model for passengers from the United States. Through two stages of this study, 936 participants (379 females) from the US were utilized for the linear multiple regression analyses to build the model. In the first stage, the regression analysis was used to generate a regression model of passenger preference, which was then tested in the second stage, thereby validating the prediction model. Samples for each stage were independent and subjected to a backward stepwise regression analysis. To determine the influencers of passenger preference, nine potential predictors were surveyed. The predictors were age of the participant, gender of the participant, yearly income of the participant, education level of the participant, seat type, purpose of travel, frequency of travel in a year, category of frequent flier program, and risk-taking tendencies of the participant. The results of the data analysis showed frequency of travel in a year, yearly income of the participant, seat type, and education level of the participant as significant predictors of passengers' preference between legacy and low-cost carriers. This research has practical implications for the airline industry in better understanding the consumer base, which could lead to increased profitability for the carriers. Highlights: This study aims to determine which factors predict airline passengers' preference between legacy and low-cost carriers. A correlational design was used to create a prediction model for passengers from the United States. 936 participants (379 females) from the US were utilized for the linear multiple regression analyses to build the model. Nine potential predictors were surveyed. The predictors were the participants' age, gender, income, education level, seat type, purpose of travel, frequency of travel, category of frequent flier program, and risk-taking tendencies. The results of the data analysis showed frequency of travel in a year, yearly income of the participant, seat type, and education level of the participant as significant predictors of passengers' preference.