The Addition of Images to eWOM in the Travel Industry: An Examination of Hotels, Cruise Ships and Fast Food Reviews

Investigating the dynamic mechanism of user willingness to actively publish travel-related Electronic Word-of-Mouth (eWOM) on tourism platforms

This study aims to uncover the dynamic mechanism of users' willingness to actively publish Electronic word-of-Mouth (eWOM) on tourism platform. For this, a model with system dynamics and structural equation methods were constructed and validated. It was found that perceived usefulness, utilizing attitude, participatory, social identity, tourism experience, and platform agenda settings all had significant and positive effects on eWOM users' willingness to actively publish eWOM on tourism platforms. However, perceived ease of use showed no effect. This study provides a reference paradigm for future studies on willingness to actively publish eWOM. The results bear implications for the management practices of tourism platforms operators and tourism destination operators. It also helps platform operators to develop relevant strategies for recovering the tourism industry in the post-COVID-19 era.

Understanding the Customer Experience and Satisfaction of Casino Hotels in Busan through Online User-Generated Content

Casinos contribute huge sums of tax revenues to local government, and influence the local economy greatly. Customer satisfaction of casino hotels is a key factor that affects overnight customers, when evaluating the casino as a whole. To find out the determinants of customer satisfaction, along with the relationship between the key factors, this study is based on 2897 reviews, focusing on casino hotels in the Busan area, and performs a series analysis. First, text mining techniques are utilized, in order to elucidate which words were mentioned most often in online reviews. Furthermore, the semantic network method as well as factor and regression analysis were conducted. According to the findings, the top 70 ranked keywords are grouped into four clusters: “Entertainment”, “Service”, “Facilities”, and “Atmosphere”. The results of exploratory factor analysis are grouped in five factors: “Location”, “Outdoor Facilities”, ”Indoor Facilities”, “Services”, and “Entertainment”. Within these five factors, “Location” and “Outdoor Facilities” showed significantly positive impact on customer satisfaction, while “Indoor Facilities” and “Entertainment” have a significant negative influence on customer satisfaction. As a result of these findings, theoretical suggestions and practical recommendations have been made, for helping to launch the future marketing strategies of Busan casino hotels.

Determining the factors affecting customer satisfaction using an extraction-based feature selection approach

The coronavirus disease 2019 (COVID-19) causes tremendous damages to the world, including threats to human's health and daily activities. Most industries have been affected by this pandemic, particularly the tourism industry. The online travel agencies (OTAs) have suffered from the global tourism market crisis by air travel lockdown in many countries. How online travel agencies can survive at stake and prepare for the post-COVID-19 future has emerged as an urgent issue. This study aims to examine the critical factors of customers' satisfaction to OTAs during the COVID-19 pandemic. A text mining method for feature selection, namely LASSO, was used to deal with online customer reviews and to extract factors that shape customers' satisfaction to OTAs. Results showed that refunds, promptness, easiness and assurance were ranked as the most competitive factors of customers' satisfaction, followed by bad reviews & cheap and excellent service & comparison. New factors to customers' satisfaction were revealed during the global tourism recession. Findings provide OTAs guidelines to reset services priorities during the pandemic crisis.

Responding to Negative Electronic Word of Mouth to Improve Purchase Intention

Retailers have little control over what their customers say about their products and services online. Review platforms (e.g., Yelp and Travelocity) are rife with negativity, from both real customers with bad experiences and from fake reviews created by competitors. These negative reviews have been shown to influence the purchasing behavior of future consumers. Many platforms do afford companies some control by including them in the online conversation about their products or services. Crafting a response to a poor review which appeals to future consumers may mitigate some of the negative outcomes associated with that review. This study advances our knowledge of responding to negative reviews by adding to the growing body of research, using a simulation-based experiment to test the influence of three elements of a review response on purchase intention (i.e., an apology, an explanation and a pledge to correct the problem identified in the review). In doing so, the data show that purchase intention increases only when a response contains all three elements. Implications for e-commerce researchers and review platform developers are discussed.