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The purpose of this paper is to develop initial evidence about the nature and features of crowdfunding in China, given it is largely unregulated regulatory frameworks.

The paper used extensive desk research using data collected from the public and private sectors, after which the data was analyzed parallel to existing academic literature, that is, institutional context by Bruton et al. (2014). This paper uncovered patterns of development, profiling crowdfunding platforms, examining the regulatory landscape and providing antecedents of successful crowdfunding projects in China.

When the traditional financial markets are hard to reach, micro, small and medium enterprises (MSMEs) were starved for capital. Crowdfunding can play a major role in funding and risk sharing. It is an innovative and dynamic vehicle for MSMEs as well as enthusiastic investors in China. Since its initial introduction to China in 2009, crowdfunding has gained substantial popularity in a relatively short period. Currently, there is still not an identifiable guideline on how to delineate the significance of the crowdfunding platform. The development of crowdfunding in China faces a few unresolved key issues. As researchers exploring this phenomenon in new ways, crowdfunding platforms can be enhanced in a manner that benefits the capital seeker, investors and society as a whole.

There is a dearth of information on start-up crowdfunding in Asia. With little data available to analyze, so this paper hopes to contribute to knowledge and provide valuable information to researchers and industry representations. Crowdfunding represents a potentially disruptive change in the way that new ventures are funded. This paper represents an initial analysis in the study of new ventures in China. Finally, the authors provide recommendations for entrepreneurs, investors and policymakers as well as researchers and practitioners with suggestions about yet unexplored avenues of research.

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.