Due to the COVID-19 pandemic, the sales of fast-food businesses have dropped sharply. Customer satisfaction has always been one of the key factors for the sustainable development of enterprises. However, in the fast-food restaurant business, gaining the knowledge of customer satisfaction is one of the critical tasks. Moreover, text reviews in social media have become one of important reference sources for customers’ decisions in buying services and products. Therefore, the main purpose of this study is to explore whether customer voices from social media reviews are different during the COVID-19 outbreak and to propose a new method to reduce interpersonal contact when collecting data. A text mining scheme which includes least absolute shrinkage and selection operator (LASSO) and decision trees (DT) are presented to discover the essential factors for customers to increase their satisfaction from unstructured online customer reviews. Finally, three real world review sets were employed to validate the effectiveness of the presented text mining scheme. Experimental results can help companies to properly adapt to similar epidemic situations in the future and facilitate their sustainable development.

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.

Facing crisis market situations, customers' satisfaction is important to keep customers loyal. This study aims to measure the service quality key factors to customer satisfaction in the airline industry. A feature selection approach was applied to measure the service quality key factors for influencing customer satisfaction. Support vector machines (SVM) was employed to evaluate the feature selection algorithms' performance. Findings revealed that responsiveness was the most important factor of airline customers' satisfaction. This research provides paths to airlines' managers on how to assure the services making customers feeling satisfied.

Global e-commerce is growing rapidly during the COVID pandemic. Previous research on customers' online shopping decisions rarely considered social distancing. To investigate customers' continued intention toward online purchases while socially isolated, we propose a framework based on the UTAUT model. A survey of 330 valid samples was collected through an online survey among internet users during a period of social distancing in Indonesia. Hypotheses were validated using a structural equation modeling approach. The results showed that social contingency is the most influential factor on customers' intention to repurchase online under social restriction conditions, followed by customer perceived value and other significant factors. The findings contribute to providing a new understanding of customers' online repurchase intentions when they are in a contingency situation.