Predictors of Hotel Clients’ Satisfaction in the Cape Verde Islands

The impact of the COVID-19 pandemic on airlines' passenger satisfaction

This study aims to understand airline passengers' satisfaction trends by analyzing the most influential factors on satisfaction before and during the COVID-19 pandemic. The sample consists of a dataset with 9745 passenger reviews published on airlinequality.com. The reviews were analyzed with a sentiment analysis tool calibrated for the aviation industry for accuracy. Machine learning algorithms were then implemented to predict review sentiment based on airline company, travelers' type and class, and country of origin. Findings show passengers were unhappy before the pandemic, aggravated after the COVID-19 outbreak. The staff's behavior is the main factor influencing passengers' satisfaction. Predictive modeling showed that it is possible to predict negative review sentiments with satisfactory performance rather than positive reviews. The main takeaway is that passengers, after the pandemic, are most worried about refunds and aircraft cabin cleanliness. From a managerial standpoint, airline companies can benefit from the created knowledge to adjust their strategies in agreement and meet their customers' expectations.

Distribution Differentiation and Influencing Factors of the High-Quality Development of the Hotel Industry from the Perspective of Customer Satisfaction: A Case Study of Sanya

Achieving customer satisfaction is an important goal of the high-quality development (HQD) of the hotel industry. The purpose of this study is to summarize the spatial distribution characteristics and influencing factors of the HQD of the hotel industry to better help improve hotel customer satisfaction and realize the HQD of the hotel industry. Taking Sanya as an example, this study applied kernel density analysis, grid analysis and a geographically weighted regression (GWR) model to reveal the distribution characteristics and influencing factors of the HQD of the hotel industry. The research results show that (1) from 2010 to 2020, both budget hotels and luxury hotels showed an increasing trend year by year and the degree of spatial agglomeration was continuously strengthened. (2) The overall HQD of the hotel industry in Sanya is at a medium to high level, but the development between different regions is unbalanced. The HQD level of the hotel industry in the eastern part of the city is better than that in the western region. (3) There are significant differences in the HQD level and its spatial distribution characteristics of budget hotels and luxury hotels. (4) Hardware facilities, price levels, market popularity and traffic conditions have a positive impact on the HQD level of the hotel industry, while hotel scale and business prosperity have a negative impact on the HQD level of the hotel industry. The public service level does not pass the significance test. The conclusions of this study can provide theoretical reference for the decision-making of HQD of urban tourism.