This research aims to determine the strategy of the Jakarta Provincial Government in increasing the resilience and growth of small and medium enterprises (SMEs) within a collaborative governance framework post-COVID-19. This study explores the effectiveness of SMEs and facilities in accessing financing and fostering collaborative partnerships between SMEs, government agencies, and financial institutions by utilizing USAID’s Theory of Change (TOC). This research uses a qualitative approach supported by in-depth interviews and Focus Group Discussions to enrich the insights of SME stakeholders, large companies, and SME actors and assess the impact of their roles. The results of this research highlight the critical role of SME Cooperative Banks (SCB) in improving SMEs’ access to credit and financial services, including collaborative governance frameworks and partnerships between SMEs, government agencies, and banks, which were identified as necessary to improve policy coherence and encourage conducive SME business environment conditions. The main findings of this research underscore the importance of the SCB model, demonstrating its potential to improve SME resilience and economic sustainability. This SCB model enriches the TOC indicators introduced by USAID. The study identifies gaps in digital infrastructure and market access that hinder SME growth and recommends targeted interventions to address these challenges. This study shows that SCB offers a promising pathway to increase the resilience and growth of SMEs in Indonesia, especially if accompanied by effective collaborative governance strategies. These initiatives can encourage inclusive economic development and strengthen the role of SMEs as drivers of the local economy. Recommendations include expanding the SCB model to other regions, encouraging digitalization, facilitating market access, advocating for a supportive policy framework, and integrating these strategies to advance the principles of USAID’s Theory of Change, fostering sustainable SME development and economic resilience.

Bibliometric analysis is a commonly used tool to assess scientific collaborations within the researchers, community, institution, regions and countries. The analysis of publication records can provide a wealth of information about scientific collaboration, including the number of publications, the impact of the publications, and the areas of research where collaborations are most common. By providing detailed information on the patterns and trends in scientific collaboration, these tools can help to inform policy decisions and promote the development of effective strategies to support and enhance scientific collaborations between countries. This study aimed to analyze and visualize the scientific collaboration between Japan and Russia, using bibliometric analysis of collaborative publications from the Web of Science (WoS) database. The analysis utilized the bibliometrix package within the R statistical program. The analysis covered a period of two decades, from 2000 to 2021. The results showed a slight decrease in co-authored publications, with an annual growth rate of −1.26%. The keywords and thematic trends analysis confirmed that physics is the most co-authored field between the two countries. The study also analyzed the collaboration network and research funding sources. Overall, the study provides valuable insights into the current state of scientific collaboration between Japan and Russia. The study also highlights the importance of research funding sources in promoting and sustaining scientific cooperation between countries. The analysis suggests that more efforts in government funding are needed to increase collaboration between the two countries in various fields.

This paper analyzes the characteristics and influence mechanisms of financial support for China’s strategic emerging industries. Using a sample of 356 listed companies across nine major industries, we conduct an in-depth analysis of the efficiency of financial support and its influencing factors. In addition, this paper analyzes the influence mechanism of financial support for strategic emerging industries based on the relevant theory of financial support for industry development. It clarifies the internal and external influencing factors. Based on the theoretical analysis, a two-stage empirical investigation was conducted: The data of 356 listed companies in strategic emerging industries from 2010 to 2022 were selected as a sample, and the data envelopment analysis (DEA) method was applied to measure efficiency. The influencing factors were then analyzed using a Tobit regression and an intermediate effects test.

The Malaysian dilemma presents a complex challenge in the wake of the COVID-19 pandemic, requiring a comprehensive statistical analysis for the formulation of a sustainable economic framework. This study delves into the multifaceted aspects of reconstructing Malaysia’s economy post-COVID-19, employing a data-driven approach to navigate the intricacies of the nation’s economic landscape. The research focuses on key statistical indicators, including GDP growth, unemployment rates, and inflation, to assess the immediate and long-term impacts of the pandemic. Additionally, it examines the effectiveness of government interventions and stimulus packages in mitigating economic downturns and fostering recovery. A comparative analysis with pre-pandemic data provides valuable insights into the extent of economic resilience and identifies sectors that require targeted support for sustained growth. Furthermore, the study explores the role of technology and digital transformation in building a resilient economy, considering the accelerated shift towards remote work and digital transactions during the pandemic. The analysis incorporates data on technological adoption rates, digital infrastructure development, and innovation ecosystems to gauge their contributions to economic sustainability. Addressing the Malaysian Dilemma also involves an examination of social and environmental dimensions. The study investigates the impact of economic policies on income distribution, social equity, and environmental sustainability, aiming to achieve sustainable economic growth. The study contributes a nuanced analysis to guide policymakers and stakeholders in constructing a sustainable post-COVID-19 economy in Malaysia.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.

Virtual environments like the Metaverse have been gaining popularity in recent years. Live streaming has gained popularity as a favorite way to entertain among social network users, thanks to its real-time authenticity. This study will utilize the Extended Unified Theory of Acceptability and Use of Technology (UTAUT2) to examine the factors influencing the adoption of live streaming in the Metaverse, a new platform with greater immersion, among citizens in Vietnam. The research used a quantitative approach, collected data from a sample of participants through a structured questionnaire including Performance Expectancy (PEE), Effort Expectancy (EEF), Social Influence (SCI), Hedonic Motivation (HEM), and Experience (EXP). Additionally, technological Self-Efficacy (TSE) as an extended alternative is thought to influence that relationship as well. Results from the PLS-SEM technique was used to examine perception, acceptance, and adoption differences among demographic groups. Remarkably, the results show experience has a remarkable impact on the relationship between behavioral intention and the adoption use Metaverse for livestreaming. This study contributes theoretical value for investors and researchers on the entertainment and technology sectors due to the abilities of the live-streaming industry and the advanced features of metaverse in this digital world.