This study aimed to identify the interaction actors, experiences, and results of tourists during their travels. Latent Dirichlet Allocation theme analysis was used to identify different themes in 9254 TripAdvisor items of user-generated content (UGC) and 15600 items of Sina Weibo UGC. A content analysis of 2000 UGC items was conducted using NVivo 11 software to uncover the details of tourist interactions. The study results showed that tourists preferred to share reviews on TripAdvisor and emotions on Sina Weibo. Seven types of tourist interaction actors were found: city, attractions, natural environment, companions, other tourists, residents, and service personnel. Seven emotions were generated in positive or negative interactions: joy, happiness, missing, awe, belonging, disappointment, and anger. Our findings provide insights for tourism managers and marketeers.

In China, dockless bike-sharing programs (DBSPs) have changed people’s travel modes, alleviated urban traffic congestion, and reduced carbon emissions. However, a number of DBSPs have experienced financial crises since 2018. This means that research on DBSPs must be considered not only in terms of the environment and technology, but also in the operation of the program. In this paper, we modeled the DBSP operations in a certain area using a system dynamics simulation approach. The main purpose was to explore the dynamics of the program’s operation process and evaluate possible improvement strategies for maximizing the revenue of the overall DBSP. Specifically, the analysis focused on the economic profits of DBSPs in an environment of competition and government regulation. The research findings revealed that the dockless bike-sharing industry has great economic profits, but in the current environment, the market needs to be regulated by the local government. If a DBSP does not introduce new technologies or find new profit channels, it will be difficult to develop sustainably by only relying on riding profits. In addition, we provide a case study of Mobike’s operations in Beijing to support these findings and validate the developed model. Finally, we discuss Mobike’s possible improvement strategies.

Dockless bike-sharing programs (DBSPs) are typical representatives of the new era of the internet economy. These schemes alleviate urban traffic congestion, reduce carbon emissions, and change people’s preferred travel modes, but they also suffer from a series of problems related to unsustainability. The sustainability performance of a DBSP is closely related to its influencing factors, which can be used to understand the main challenges of the program and consider what useful measures should be taken to overcome these issues. Therefore, it is essential to investigate the related factors that are critical to the sustainability of DBSPs from a systems-level perspective. In this paper, we explored publicly available data sources (i.e., literature, institutional reports, and announcements) to determine the intrinsic influencing factors and understand their interactions and feedback relationships. A taxonomy of the key challenges with respect to the fundamental pillars of DBSP sustainability (i.e., environment, society, and economy) was provided. System dynamics methodology was employed to map the critical factors and links in order to analyze the key challenges in facilitating the sustainability of DBSPs. Based on this approach, we put forward proposals to mitigate the severe challenges for governments and DBSP operators. The findings provide a theoretical basis and guiding role to develop improvement strategies for DBSP sustainability.

In this paper, we build a causal interaction diagram between the factors that may influence the sales and profits of online stores. An online store’s real operation data were used to help determine the causal relationship between variables. Finally, we proposed a system dynamics model and conducted a simulation of the operation of an online store. In this model, we focused on the impact of promotion and positive/negative electronic word of mouth (e-WOM) on the sales and profits of the online stores. The simulation results showed a similar trend to the real data and the main research finding showed that promotion is not a long-term measure for the sustainable development of online stores. Excessive promotion effort may lead to consumers’ dissatisfaction leading the increase of negative e-WOM. The systematic simulation can help us understand better the long-term effect of promotion and e-WOM on the operation of online stores. Finally, we gave some management suggestions for online stores’ sustainable operations.

The number of consumers playing virtual reality games is booming. To speed up product iteration, the user experience team needs to collect and analyze unsatisfying experiences in time. In this paper, we aim to detect the unsatisfying experiences hidden in online reviews of virtual reality exergames using a deep learning method and find out the unmet psychological needs of users based on self-determination theory. Convolutional neural networks for sentence classification (textCNN) are used in this study to classify online reviews with unsatisfying experiences. For comparison, we set eXtreme gradient boosting (XGBoost) with lexical features as the baseline of machine learning. Term frequency-inverse document frequency (TF-IDF) is used to extract keywords from every set of classified reviews. The micro-F1 score of textCNN classifier is 90.00, which is better than 82.69 of XGBoost. The top 10 keywords of every set of reviews reflect relevant topics of unmet psychological needs. This paper explores the potential problems causing unsatisfying experiences and unmet psychological needs in virtual reality exergames through text mining and makes a supplement for experimental studies about virtual reality exergames.