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As the current Coronavirus 2019 pandemic eases, international tourism, which was greatly affected by the outbreak, is gradually recovering. The attraction of countries to overseas tourists is related to their overall performance in the pandemic. This research integrates the data of vaccination of different countries, border control policy and holidays to explore their differential impacts on the overseas tourists’ intention during the pandemic. This is crucial for destinations to built their tourism resilience. It will also help countries and industry organizations to promote their own destinations to foreign tourism enterprises.

This study proposes an analysis based on panel data for ten countries over 1,388 days. The coefficient of variation is used to measure monthly differences of Chinese tourists’ intention to visit overseas country destinations.

Results show that, for tourist intention of going abroad: border control of the destination country has a significant negative impact; daily new cases in the destination country have a significant negative impact; domestic daily new cases have a significant positive impact; holidays have significant negative impact; daily vaccination of the destination countries has significant positive impact; and domestic daily vaccination have negative significant impact.

First, there is a large uncertainty in studying consumers’ willingness to travel abroad in this particular period because of unnecessary travel abroad caused by the control of the epidemic. Second, there are limitations in studying only Chinese tourists, and future research should be geared toward a broader range of research pairs.

First, from the government perspective, a humane response can earn the respect and trust of tourists. Second, for tourism industry, to encourage the public take vaccine would be beneficial for both the tourism destination and foreign tourism companies. The same effect can be achieved by helping tourists who are troubled by border control.

First, this research provides suggestions for the government and the tourism industry to deal with such a crisis in the future. Second, this study found that vaccination has a direct impact on tourism. This provides a basis for improving people’s willingness to vaccinate. Thirdly, this study proves suggestion for the destinations to build tourism resilience.

This study analyzes the unique control measures and vaccination in different countries during the pandemic, then provides suggestions for the tourism industry to prepare for the upcoming postpandemic tourism recovery. This study is valuable for improving the economic resilience of tourism destinations. Additionally, it helps to analyze the advantages and disadvantages of different restrain policies around the world.

Demand planning (DP) is a key element of supply chain management (SCM) and is widely regarded as an important catalyst for improving supply chain performance. Regarding the availability of technology to process large amounts of data, artificial intelligence (AI) has received increasing attention in the DP literature in recent years, but there are no reviews of studies on the application of AI in supply chain DP. Given the importance and value of this research area, we aimed to review the current body of knowledge on the application of AI in DP to improve SCM performance.

Using a systematic literature review approach, we identified 141 peer-reviewed articles and conducted content analysis to examine the body of knowledge on AI in DP in the academic literature published from 2012 to 2023.

We found that AI in DP is still in its early stages of development. The literature is dominated by modelling studies. We identified three knowledge clusters for AI in DP: AI tools and techniques, AI applications for supply chain functions and the impact of AI on digital SCM. The three knowledge domains are conceptualised in a framework to demonstrate how AI can be deployed in DP to improve SCM performance. However, challenges remain. We identify gaps in the literature that make suggestions for further research in this area.

This study makes a theoretical contribution by identifying the key elements in applying AI in DP for SCM. The proposed conceptual framework can be used to help guide further empirical research and can help companies to implement AI in DP.

Online learning is increasingly popular, and educational platforms provide a wealth of courses. Improving course sales is the key to promoting sustainable development of online course platforms. However, limited research has explored the marketing of online courses. We study how to drive online course sales by leveraging teacher information.

We performed an empirical study. We collected data through a crawler and image recognition from Tencent classroom.

Our results show that providing teacher information and profile images helps promote online course sales. However, detailed course descriptions weaken the positive impact of teachers' profile images on online course sales. Furthermore, our study shows an inverted U-shaped relationship between the intensity of smiling in teacher profile photos and online course sales, and teacher descriptions negatively moderate this relationship.

Our study contributes to the research on online course sales and extends the context of the research on smiling as well as the studies of visual and textual information.

The results have practical implications for online course sellers and platforms.

Existing scholarly efforts have explored online courses mainly from an education perspective. More research is needed to advance the understanding of online course sales. Our study advances research in the marketing of online courses.