Title: Factors predicting customer satisfaction in online hotel booking using machine learning technique: evidence from developing countries

Authors: Mehnaz; Jiahua Jin; Wasim Ahmad; Azhar Hussain

Addresses: School of Economics and Management, University of Science and Technology Beijing, 30-Xueyuan Road, Haidian District, Beijing, 100083, China ' School of Economics and Management, University of Science and Technology Beijing, 30-Xueyuan Road, Haidian District, Beijing, 100083, China ' UCSI Graduate Business School, UCSI University, Kuala Lumpur Campus (South Wing), No. 1, Jalan Menara Gading, UCSI Heights, 56000, Cheras, Kuala Lumpur, Malaysia ' Sha Ping Jie Sichuan Institute of Technology Yi Bin, Cuiping District, Yibin, 644001, China

Abstract: This paper predicts and documents the determinants of customer satisfaction in online hotel booking for the foreign tourists in developing countries. The data was taken from the customer web-based reviews and comments. The study forecasts customer satisfaction by comparing logistic model with artificial neural network (ANN) in terms of prediction accuracy. In case of both datasets, i.e., training and testing, ANN outperformed the logistic regression model in terms of prediction. In other words, ANN is more robust in terms of prediction as compared to logistic regression model. Furthermore, empirical results depict that rental price, staff performance, location, services quality, and rating are the significant tools to maximise customer satisfaction. Hotel authorities in developing countries need to focus on these factors where customer feedback may play a significant role implementing the best services of hotels. These incentives will help to increase the booking incentives and ensure sufficient revenues for hotel industry of developing nations.

Keywords: online hotel booking; customer satisfaction; location; price; service quality; artificial neural network; ANN.

DOI: 10.1504/IJADS.2023.134200

International Journal of Applied Decision Sciences, 2023 Vol.16 No.6, pp.741 - 759

Received: 06 Apr 2022
Accepted: 20 Jun 2022
Published online: 13 Oct 2023 *

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