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This paper aims to provide a driving behavior scoring model to decide the personalized automobile premium for each driver.

Driving behavior scoring model.

The driving behavior scoring model could effectively reflect the risk level of driver’s safe driving.

A driving behavior scoring model for UBI.

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.

The diffusion of electronic commerce has a notable impact on the economy's prosperity. This paper embraces complexity theory principles to examine the factors affecting Internet users' acceptance and use of electronic retailers. It is essential for the sustainability of electronic retailers to understand the motivations impacting online consumer behaviour. Symmetrical and asymmetrical methods are combined to examine the relationship between perceived ease of use, perceived enjoyment, web characteristics, online consumer reviews (OCRs) and online purchase intention. Further, symmetry and differences between males and females were examined.

Data collected from 425 online consumers using an online structured survey was analysed using structural equation modelling (SEM) and fuzzy set qualitative comparative analysis (fsQCA). The net effects and causal configurations of the four proposed variables and online purchase intention were examined.

The SEM findings confirmed the significance of perceived enjoyment, website characteristics and OCRs on online purchase intention. Perceived enjoyment mediated the relationship between perceived ease of use and online purchase intention. The multi-group analysis confirmed the difference in antecedent impacts between males and females. The fsQCA findings revealed that multiple recipes lead to the occurrence of online purchase intention; in addition, the recipes leading to its absence do not mirror the previous ones.

The present study embraces complexity theory concepts in understanding online purchase intention using fsQCA methodology; further, the role of gender in online consumer behaviour was highlighted in the result discussion.