In recent years, many countries have been actively promoting medical tourism to stimulate economic growth. However, the expansion of medical tourism has potentially detrimental effects for the welfare of host countries. In particular, a decline in workers’ productivity could arise as a result of a reduction in public health care provision due to the expansion of the medical tourism sector. By addressing the crowding-out effect on labor productivity, this paper sheds light on the economic impacts of medical tourism on host countries. Our empirical analysis confirms that medical tourism, on average, has a positive effect on host economies’ output growth, particularly in non-OECD countries. Nonetheless, the output contribution of medical tourism is overestimated by an average of 26.8% if the unfavorable indirect productivity effect is not taken into account.

Understanding the relationships between cryptocurrencies is important for making informed investment decisions in this financial market. Our study utilises Bayesian networks to examine the causal interrelationships among six major cryptocurrencies: Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether. Beyond understanding the connectedness, we also investigate whether these relationships evolve over time. This understanding is crucial for developing profitable investment strategies and forecasting methods. Therefore, we introduce an approach to investigate the dynamic nature of these relationships. Our observations reveal that Tether, a stablecoin, behaves distinctly compared to mining-based cryptocurrencies and stands isolated from the others. Furthermore, our findings indicate that Bitcoin and Ethereum significantly influence the price fluctuations of the other coins, except for Tether. This highlights their key roles in the cryptocurrency ecosystem. Additionally, we conduct diagnostic analyses on constructed Bayesian networks, emphasising that cryptocurrencies generally follow the same market direction as extra evidence for interconnectedness. Moreover, our approach reveals the dynamic and evolving nature of these relationships over time, offering insights into the ever-changing dynamics of the cryptocurrency market.

Time control plays a critical role within the online mastery learning (OML) approach. This paper examines the two commonly implemented mastery learning strategies – personalised system of instructions and learning for mastery (LFM) – by focusing on what occurs when there is an instructional time constraint. Using a large data set from a postgraduate finance course offered at an Australian university, we explore students' online quiz-completion patterns, then empirically investigate whether the imposition of an instructional time constraint in the OML approach has an impact on their final-examination performance. Our results suggest that the LFM strategy with an instructional time constraint has a positive impact on students' learning behaviour and contributes to better overall academic performance. Further, our findings suggest that facilitators should be encouraged to implement an instructional time constraint when adopting an OML approach.