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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.

From the theoretical perspectives of both multi-agent systems and smart business networks, empirical studies analyzing agent-based inter-organizational systems (ABIOS) in a real-life business setting are rare. The purpose of this paper is to investigate the impact of ABIOS on the performance of business networks.

This study presents a theoretical conceptual model portraying the influence of ABIOS on clients’ coordination structure and information architecture; and the impact of those structural alterations on business network performance in terms of the coordination, agility, and informational performances. To validate the model, a cross-case analysis was conducted in three logistics cases, namely, warehousing, freight forwarding, and intermodal transportation.

The application of ABIOS requires adjustments to the information architecture or the coordination structure, or both. Subsequently, those structural adjustments will stimulate improvements in the coordination, agility, and informational performances.

The assessment of the clients’ performance improvement is done at the company level not at an aggregate network level. Moreover, the study only covers cases from the logistics sector.

This study explains the structural consequences of ABIOS applications. The adoption of an inter-organizational system is a strategic decision that requires support from multi-stakeholders. While the applications of ABIOS can offer performance improvement opportunities, adjustments must be made to the existing coordination structure or the information architecture, or both.

This study contributes to the smart business network literature and the ABIOS literature by presenting a validated conceptual model explaining the interplay among ABIOS, the coordination structure, informational structure, and business network performance, namely, the coordination, agility, and informational performances.