Search results

With the rapid development of cloud computing, most software firms face the significant choice of whether they should change the versioning strategy of enterprise software from releasing the on-premise version to the software-as-a-service (SaaS) version. Data being generated and hosted on SaaS vendors' servers brings multiple effects. It enables customers to enjoy the flexibility of accessing data and using the software remotely, named the “portability” effect. However, on the other hand, the cumulative data resources on the cloud also provide a clear target for external attacks, leading to the concern of information security. Considering these, the authors hope to offer insights for software firms by exploring the strategy selection problem.

Taking the portability effect and security risks of the SaaS licensing model into account, the authors develop a two-period model to figure out the market segmentation and identify the feasible conditions for employing three alternative strategies. Comparative statics analyses are conducted to explore the influencing mechanism of exogenous factors on strategy selection. The authors also discuss the strategy selection in the presence of the network effect and the security loss faced by users of on-premise software.

One significant finding is that the on-premise strategy can be excluded when the potential loss from security risks is small. Under this circumstance, the dual version strategy is optimal provided that the increase of customer valuation caused by portability effect is below a threshold. Otherwise, the SaaS strategy generates the highest profit. When the potential loss from security risks turns large, the on-premise strategy, the dual version strategy and the SaaS strategy are the optimal options in order as the portability effect on customer valuation gets stronger.

Previous literature has insufficiently addressed the versioning issue of enterprise software. In this paper, the distinctive features of the SaaS model are considered, and differentiated results compared with previous work are obtained. The research results provide guidelines for software firms in deciding their product releases in the future.

The purpose of this study is to provide better service to hotel customers during the COVID-19 era. Specifically, this study focuses on understanding the changes in hotel customer satisfaction during the epidemic and formulating effective marketing strategies to satisfy and attract guests.

As the first victim of the COVID-19 virus, China’s hotel industry has been profoundly affected and customer satisfaction and needs have also changed. Taking 105,635 hotel reviews obtained from Tripadvisor.com in Beijing and Shanghai as samples, this study explores the changes in consumer satisfaction by using text-mining methods.

The results suggest that there are significant differences in overall ratings, spatial distribution and ratings of different traveller types before and after the epidemic. Generally, customers have higher “tolerance” and are more inclined to give higher ratings and pay more attention to hotel prevention and control measures to reduce health risks after the COVID-19.

This paper proves the changes in customer satisfaction before and after the COVID-19 at the theoretical level and reveals the changes in customer attention through the topic model and provides a basis for guiding hotel managers to reduce the impact of the COVID-19 crisis.

Empirical findings would provide useful insights into tourism management and improve hotel service quality during the COVID-19 epidemic era.

This research explores the hotel customer satisfaction in the field of hotel management before COVID-19 and after COVID-19, by using text mining to analyse mandarin online reviews. The results of this study will suggest that the hotel industry should continuously adjust its products and services based on the effective information obtained from customer reviews, so as to realize the activation and revitalization of the hotel industry in the epidemic era.

The authors investigate how logistics digitalization affects two types of third-party logistics (3PL) performance: financial performance and service performance. In particular, the authors explore the mediating role of customer collaboration between logistics digitalization and firm performance based on organizational information processing theory and examine the moderating role of government support.

The authors use an SPSS macro program (PROCESS regression analysis) to analyze survey data from 235 3PL firms in China. The mediation model, moderation model and moderated mediation model are tested.

The empirical results show that in the new age of digitalization transformation, logistics digitalization positively affects 3PL's financial performance and service performance by strengthening customer collaboration. Additionally, government support amplifies the positive effect of customer collaboration on service performance but not financial performance. The moderated mediation test further indicates that government support strengthens the positive indirect effect of digitalization on service performance through customer collaboration.

This study offers empirical insights into the growing body of 3PL literature, and the findings contribute to the theoretical and practical understanding of the emerging research topic of digital transformation (DT) and sustainability issues in 3PL firms.