The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

The golden visa is a regulation designed to facilitate foreign nationals through a residence permit scheme with an emphasis on investment and citizenship. This research aims to look at the development of the golden visa as an innovation policy, and find out how its implications for the flow of foreign investment into Indonesia. This research uses online research methods (ORM) to discover new facts, information and conditions through technology and internet searches. The aspects used to conduct analysis in this descriptive qualitative research are using innovation policy instruments which include regulatory, economic, financial, and soft instruments. The research findings show that the golden visa as an innovation policy has great potential to support national development through investment in priority sectors. However, its implementation needs to be done carefully with strict supervision and inclusive regulations so as to mitigate risks such as money laundering and property price inflation. That way, golden visas can encourage sustainable and inclusive economic growth through the smooth flow of incoming foreign investment.

There is no denying that the COVID-19 pandemic resulted in significant stress worldwide and impacted practically every aspect of human activity. The impacts of this deadly virus on education are not seen as gaining much-needed focus from the scientific research community. The majority of educational institutions globally switched to online instruction during the COVID-19 pandemic. However, there were considerable differences in the technical readiness of various nations. In this regard, the study’s attempt to provide a way forward for how the educational sector ought to manage the challenges brought on by COVID-19 issues in support of online educational activities. Since some of the consequences that resulted have an impact on the educational sector, the answers presumably also should have included innovations that would improve scientific research to lessen its effects. Particularly, it appears there is still much that has to be done about the impact of the COVID-19 pandemic on the educational sector. Hence, this perspective review study aims to explore the potential relationship between the COVID-19 pandemic and the educational sector while suggesting a way forward.

To evaluate the efficiency of decision-making units, researchers continually develop models simulating the production process of organizations. This study formulates a network model integrating undesirable outputs to measure the efficiency of Vietnam’s banking industry. Employing methodologies from the data envelopment analysis (DEA) approach, the efficiency scores for these banks are subsequently computed and comparatively analyzed. The empirical results indicate that the incorporation of undesirable output variables in the efficiency evaluation model leads to significantly lower efficiency scores compared to the conventional DEA model. In practical terms, the study unveils a deterioration in the efficiency of banking operations in Vietnam during the post-Covid era, primarily attributed to deficiencies in credit risk management. These findings contribute to heightening awareness among bank managers regarding the pivotal importance of credit management activities.

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.

This study explores project-based learning in science teaching models. Firstly, the theoretical basis of project-based learning is analyzed, the existing science teaching mode is evaluated, and the construction and implementation strategy of the science teaching mode based on project-based learning is proposed. Then, through empirical research, this study found that this model can effectively improve students' academic performance, enhance students' interest in learning, and improve students' hands-on ability. However, the implementation of this model requires teachers to have a high level of professionalism and adequate teaching resources. Finally, this study concludes that the project-based learning science teaching model is a potential teaching model that deserves further exploration and practice.