This article is devoted to studying the principles of the relationship between democracy and demoethics as tools for transforming the sustainable development of society. The study is based on the assumption that the effective functioning of democracy is associated with such social phenomena as elections and electoral behavior. The study examined electoral behavior and surveyed members of society about the qualities of candidates to which they pay special attention. An analysis of qualitative and quantitative data demonstrating the democratic foundations of elections of members of society was conducted, and an analysis of the choice of voters in the extraordinary elections of the President of the Republic of Kazakhstan by region was conducted. In this study, Bayesian network modeling is experimentally applied to formalize the problem of identifying and analyzing the behavior of virtuous personality traits. A sociological survey of public opinion was conducted using the questionnaire method with the participation of 826 people from all regions of Kazakhstan from May to June 2023. A questionnaire was used to collect data, the main purpose of which was to compare attitudes and find out what values are considered important for people, what norms of behavior are considered acceptable, and to understand what values and norms prevail in society. It is concluded that the concept of demoethics promotes a positive transformation of humanity and helps to form a new leader of virtue, a ruler of the city, capable of making ethical rational decisions that can ensure a balance between the economic, social, and environmental needs of humanity.

The purpose of this study is to explore new financial product’s impact on the behaviour of individual investors. To analyze investors’ risk and return expectations, this article investigates trading volumes before and after the introduction of financial product innovation. An event research technique was used to gather data from the National Stock Exchange. Data was analyzed using descriptive statistics and the Sharpe ratio approach, which were provided by different investors. The research results highlight that individual investors’ overreaction behaviour is brought out by financial product innovation. Furthermore, the study’s results imply that rising trading volumes are not entirely explained by updated risk-adjusted returns and that new financial products lead to excessive trading by investors and lowering returns. Higher trading volumes are not explained by better risk-adjusted returns. Young investors often respond irrationally to information offered by financial advisors, resulting in short-term gains at the expense of long-term gains. The study demonstrates that the development of innovative financial products does not always result in investors’ long-term prosperity. Worse outcomes and excessive trading could follow from it. The paper concludes by providing various real-world implications that the benefits and drawbacks of innovative financial products should be spelled out in detail by financial institutions and representatives. his research contributes to the implementation of individual investors’ overreaction behaviour that is brought out by financial product innovation. It highlights that higher trading volumes are not explained by better risk-adjusted returns.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

This study aims to use dialectical thinking to explore the impacts and responses of Artificial Intelligence (AI) empowerment on students’ personalized learning. The effect of AI empowerment on student personalization is dissected through a literature review and empirical cases. The study finds that AI plays a significant role in promoting personalized learning by enhancing students’ learning effectiveness through intelligent recommendation, automated feedback, improving students’ independent learning ability, and optimizing learning paths, however, the wide application of AI also brings problems such as technological dependence, cheating in exams, weakening of critical thinking ability, educational fairness, and data privacy protection to students. The study proposes recommendations to strengthen technology regulation, enhance the synergy between teachers and AI, and optimize the personalized learning model. AI-enabled personalized learning is expected to play a greater role in improving learning efficiency and educational fairness.

The process of internationalization and innovation (IPI) in the urban road passenger transport (URPT) sector is driven by the need to provide cities with efficient and sustainable mobility solutions. The objective of this study is to understand the perceptions of URPT employees in relation to PII, based on a comprehensive case study. By exploring how these two concepts interrelate and influence each other, the study seeks to provide valuable information that can help improve strategic planning and policy formulation in the urban transport sector. The research, based on semi-structured interviews with 20 employees, reveals significant gaps in internal communication, with only about half of the participants aware of ongoing national and international projects. Information was often limited to those directly involved, indicating a need for improved dissemination strategies. Despite these communication issues, employees positively view the company’s presence at international events and recognize the importance of involvement in European organizations, particularly for knowledge acquisition and networking. Challenges identified include inadequate internal communication and insufficient investment in international projects. However, there was strong agreement on the value of internationalization and innovation process (IIP) for both professional development and organizational growth. To enhance the company’s international presence and return on investment (ROI), the study recommends better coordination, improved information sharing, and strategic planning. These findings emphasize the critical role of effective communication and active participation in international initiatives for the sustainable growth of the organization.

This research analyzes the relationship between political stability, renewable energy utilization, economic progress, and tourism in Indonesia from 1990 to 2020. We employ advanced econometric techniques, including the Fourier Bootstrap Autoregressive Distributed Lag (ARDL) approach and Fourier Toda-Yamamoto causality testing, to ensure the robustness of our results while accounting for smooth structural changes in the data. The analysis uncovers a long-term equilibrium relationship between tourism and its fundamental determinants. Our research reveals significant positive impacts of political stability and renewable energy consumption on tourism in Indonesia. A stable political environment creates a favorable climate for tourism development, instilling confidence in both domestic and international tourists. Promoting renewable energy usage aligns with sustainable tourism practices, attracting environmentally conscious travelers. Furthermore, our findings demonstrate a bi-directional causal relationship between these variables over time. Changes in political stability, renewable energy consumption, and economic growth profoundly influence the tourism sector, while the growth of tourism itself can also stimulate economic development and foster political stability. Our findings underscore the need for environmentally sustainable and politically stable tourism policies. Indonesia’s tourism sector can grow sustainably with renewable energy and stability. Policymakers can develop strategies with tourism, political stability, renewable energy, and economic prosperity in mind.