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This paper investigates the usage and trust of Chinese social media in the travel planning process (pre-trip, during-trip and post-trip) of Chinese tourists.

Through a combination of structured online survey (n = 406) and follow-up interviews, the research identifies the diversification of the demand-and-supply patterns of social media users in China, as well as the allocation of functions of social media as tools before, during and after travel.

Social media users are diverse in terms of their adoption of social media, use behaviour and scope; the levels of trust and influence; and their ultimate travel decisions and actions. Correlations between the level of trust, influence of social media and the intended changes in travel decisions are observed. Destination marketers and tourism industries should observe and adapt to the needs of social media users and potential tourist markets by understanding more about user segmentation between platforms or apps and conducting marketing campaigns on social media platforms to attract a higher number of visitors.

This paper demonstrated the case of social media usage in mainland China, which has been regarded as one of the fastest growing and influential tourist-generating markets and social media expansions in the world. This study further addressed the knowledge gap by correlating social media usage and travel planning process of Chinese tourists. The research findings suggested diversification of the demand-and-supply pattern of social media users in China, as well as the use of social media as tools before, during and after travel. Users were diversified in terms of their adoption of social media, use behaviour, scope, the levels of trust, influence and the ultimate travel decisions.

Destination marketing organizations should note that some overseas social media platforms that are not accessible in China like TripAdvisor, Yelp, Facebook and Instagram are still valued by some Chinese tourists, especially during-trip period in journeys to Western countries. Some tactics for specific user segments should be carefully observed. When promoting specific tourism products to Chinese tourists, it is necessary to understand the user segmentation between platforms or apps.

Social media is a powerful tool for tourism development and sustainability in creating smart tourists and destinations worldwide. In China, the use of social media has stimulated the development of both information and communication technology and tourism.

Civil service workers are valuable resources for any nation and play a crucial role in driving their country’s economic development. Per the supervisor, this research examines the impact of mindfulness, proactive personality, and career competencies on employee job performance. The study also analyzes the effects of career adaptability and identity on this aspect.

To test the model of this study, questionnaires were administered to a sample of 500 civil service employees whose career-based knowledge and skills were measured in various cities in the province of Punjab, Pakistan.

Mindfulness and career competencies significantly impact supervisor-rated task performance, whereas a proactive personality does not substantially relate to supervisor-rated task performance. Research indicated that the two hypotheses about mediation were accepted. However, career adaptability does not play a significant role in the link between mindfulness and how well a supervisor rates task performance. Regarding moderation, career identity did not significantly moderate the relation between proactive personality and supervisor-rated task performance. However, the other two moderate hypotheses have been proven to be significant.

The findings offer compelling support for career construction theory (CCT) in this study area by analyzing the connections related to career adaptability and identity within the framework. In the future, researchers can build on this model by adding theories like conservation of resources (COR), looking into possible moderators that might change specific pathways in this network of relationships and using longitudinal designs to find stronger causal relationships.

Considering the evolving workplace due to the COVID-19 pandemic, the study offers fresh perspectives on the post-COVID situation, understanding and integrating various variables. For future studies, more variables can be explored in this model with the expansion of sample size and change of context.

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.