Document Type
Article
Publication Date
1-23-2019
Publication Title
Journal of Tourism & Hospitality
Volume
8
Issue
1
First page number:
1
Last page number:
9
Abstract
This study attempts to determine the overall satisfaction factors from airline passengers at the San Francisco International Airport (SFO), using the classification method of random forest. The analysis is based on the 2014 annual survey conducted by SFO that collects data on passenger demographics and satisfaction with airport facilities and services. Results of this study indicate that some service attributes are more important than others for passengers’ overall satisfaction at SFO. Study results are expected to provide practical insights to the airport industry. This study, in addition, introduces the machine learning method of random forest to tourism research.
Keywords
Airport; Customer Satisfaction; Predictive Modeling; Random Forest; Service Attributes
Disciplines
Business | Hospitality Administration and Management
File Format
File Size
776 KB
Language
English
Rights
IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Singh, A. K.,
Yoo, M.,
Dalpatadu, R.
(2019).
Determinants of Customer Satisfaction at the San Francisco International Airport.
Journal of Tourism & Hospitality, 8(1),
1-9.
http://dx.doi.org/10.35248/2167-0269.19.8.398
