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This research endeavours to delve into the potential of Artificial Intelligence (AI) in bolstering Project Resource Management (PRM), discerning the principal challenges inherent in project resource planning, acquisition, and human as well as physical resource management, while also appraising the utilization of AI tools by project team members (PTM) and their efficacy in daily tasks. This research contributes a theoretical basis for what lies ahead studies in PM and unveils the benefits of AI in task monitoring, scheduling, team assignment, cost estimation, and the monitoring of physical project resource availability. This study employs a mixed-method approach, commencing with an initial literature review to identify project challenges. Data collection is facilitated through the administration of questionnaire surveys and interviews with project managers, encompassing both closed-ended and semi-structured questions. The research reveals that PTM readily embraces the utilization of AI for daily tasks and Project Resource Management can be enhanced through AI.

This research aims to analyze the influence of exchange rate, exports, and  imports on Indonesia's foreign exchange reserves in the period of 1992-2021. The dependent variable is foreign exchange reserves, whereas the independent variables consist of exchange rate, exports, and imports. The research uses secondary data in time series obtained from Indonesia’s World Bank Data. Data are analyzed with VAR (Vector Auto Regression), those analyzed use E-Views 10 apss by including analysis method, which includes stationary tests, stability tests, optimum lag tests, cointegration tests, VAR estimation tests, Engle Granger causality tests, Impulse Response Function (IRF) tests, and Variance Decomposition (VD) tests. The results of the study show that exchange rate variables have a large influence on foreign exchange reserves, while export and import variables have a small effect on foreign exchange reserves.

This study aims to determine the integration level of Southeast Asian stock market with the world stock market using an international capital asset pricing model. The countries that were sampled in this study were Indonesia, Malaysia, Philippines, Singapore and Thailand. The sample period starts from January 2000 until August 2016. This study uses an partially-segmented international capital asset pricing model by including the assumption of residual that were correlated between asset pricing equations. Because of this assumption, the seemingly unrelated regression (SUR) estimation method is more appropriately compared to the ordinary least square (OLS) method. The results of this study indicate that the integration level of Southeast Asian stock market varies. The Singapore and Thailand stock markets are fully integrated with the world stock market, the Indonesian and Malaysian stock markets are partially integrated with the world stock market, while the Philippine stock market is segmented with the world stock market. This finding shows the difference in the effectiveness of the stock market liberalization process in Southeast Asia