Sustainable tourism is known as an effort to prevent the negative impacts of tourism development by considering economic, social and environmental aspects. Sustainable tourism development is very important to be used as a reference in managing tourism destinations. The large tourism potential in the Bali Province has been inventoried by the Provincial Government of Bali by establishing a tourism area. However, in its development, several designated tourism areas cannot develop optimally and tend to experience a decrease in the quality and quantity of tourist visits. This paper presents transformation pathways toward sustainable tourism development in tourism areas. The general objective is to determine strategies that can be pursued in the development of tourism areas. The specific objective is to develop the best policies and scenarios in the development of tourism areas. This research was conducted in the Soka Tourism Area in Bali Province, Indonesia, which is the only strategic tourism area in Tabanan Regency, which has great potential but has not been able to develop optimally. The data obtained from the focus group discussion, as a data collection method will be analyzed using the MULTIPOL method. The results of the research show that the development of sustainable tourism areas can be carried out by preparing a clear and measurable framework. Promotion, preparation of cross-sector programs and tourism management training activities are needed as priority programs. On the policy, it is necessary to carry out effective planning as an optimal policy involving all stakeholders. In addition, the “Progressive Transformation” and “Integrated Transformation” scenarios can be considered to achieve sustainable tourism development. This study can be an important input for stakeholders in determining policies for the development of tourism areas in the research locations and can be applied in other areas that have similar characteristics.

The paper investigates performance persistence in both Islamic focused (IFFs) and conventional focused fund families (CFFs) in four emerging markets (Saudi Arabia, Malaysia, Indonesia, and Pakistan) from 2007 to 2021 using contingency tables. In the overall sample, we find that positive (negative) persistence in IFFs (CFFs) exists only in the short-run (one-month). At the country level, IFFs demonstrate positive persistence up to one month in Saudi Arabia and Indonesia and six months in Malaysia. For CFFs, negative persistence appears up to one month in Malaysia and six months in Indonesia. Surprisingly, the positive persistence of Indonesian IFFs changes to negative persistence in six months, before disappearing completely later. This result confirms the existence of short-run persistence for both IFFs and CFFs. This study presents new evidence for the persistent performance of fund families in emerging markets.

Logistics’ significance in international trade is being noted more and more frequently. This study was conducted to analyze the influence of logistics performance on trade volume between the Republic of Korea (ROK) and member states of the Association of Southeast Asian Nations (ASEAN) in order to identify the areas of the Indonesian logistics industry that require improvement to increase trade volume between Indonesia and the ROK. This study focuses on Indonesia, Vietnam, Malaysia, Thailand, and the Philippines, which are actively responding to the reconfiguration of the global value chain (GVC). The report also includes Cambodia, Laos, and Myanmar, which can be viewed as potential GVC competitors of Indonesia due to their considerable manufacturing growth potential. Based on the gravity model, which explains trade volume between regions, this study looked into the effect of the logistics performance index (LPI) of these ASEAN nations on trade with the ROK by analyzing panel data. This study utilized previously published (secondary) data to derive new outcomes. Most of the statistical data were extracted from the World Bank database, IHS Markit, and Euromonitor. The results show that an improvement of LPI can lead to growth in the trade volume between ROK and ASEAN Nations including Indonesia. The study’s insights suggest which logistical areas Indonesia should focus on developing in order to boost trade with ROK and obtain a competitive edge in the GVC reconfiguration.

The research aimed to know the influence of long-term relationships, information sharing, Cooperation, and integration process in partial on the supply chain effectiveness of the EPC Steam Power Plant project in the Province of North Sulawesi. It was also to know whether enterprise risk management moderates the influence, long-term relationship, information sharing, Cooperation, and integration process toward supply chain effectiveness. The employees who became the sample in the supply chain activities of the steam power plant project in North Sulawesi were 250 people, 149 of whom were proportionally from the project owner. The research uses the data analysis technique using Structural Equation Modelling-Partial Least Square. The result of the study indicated that long-term relationships, information sharing, Cooperation, and integration process partially have a positive and significant influence on supply chain effectiveness. In addition, enterprise risk management proved to moderate the impact of information sharing. Still, it needed to moderate the effect of a long-term relationship, Cooperation, and integration process on the supply chain effectiveness of the EPC Steam Power Plant Sulut-3 project.

It is admissible that fuzzy numbers (FNs) are apt for representing imprecise or vague data in real-world problems. While using FNs in decision-making problems, selecting the best alternative among available alternatives is challenging, and therefore, ranking FNs is essential. We can find different studies in the literature, but to our knowledge, no one attempted to rank FNs using the concept of volume. This paper proposes a new method for ranking generalized fuzzy numbers (GFNs) using the volume of the solid obtained by revolving its membership function (MF) about the x-axis. We calculate the volumes of positive and negative sides along with the centroid of a generalized fuzzy number(GFN) to define the fuzzy number(FN) score. This score represents the defuzzified value of FN, is used to select the best alternative, and overcomes the limitations in some existing methods like ranking FNs having the same centroid, crisp numbers, symmetric fuzzy numbers, and FNs with the same core.

In today's rapidly changing economic landscape, financial resilience has become increasingly important especially for public sector organizations. This study investigates the impact of e-government adoption and social support on individuals' financial resilience in Indonesia, with a focus on the mediating role of financial management skills. A quantitative research methodology was employed, and 348 complete and suitable questionnaires from individuals in the financial department in local government in Indonesia were analyzed using SmartPLS 4.0 software. The results indicate a significant relationship between e-government adoption and financial management skills, suggesting that digitizing government services contributes to improved financial resilience. Additionally, social support was found to have a positive impact on financial management skills, supporting the notion that social networks provide resources and support for financial well-being. Financial management skills were also found to be significantly associated with financial resilience, indicating that individuals with strong financial management skills are better equipped to adapt to changing circumstances. While the mediating effect of financial management skills between e-government adoption and financial resilience was not significant, it was significant in the relationship between social support and financial resilience. These findings provide insights into the factors that enhance financial resilience in an increasingly digitized society and inform strategies to promote financial well-being in Indonesia.

Several Multivariate Adaptive Regression Spline (MARS) approaches are available to model categorical and numerical (especially continuous) data. Currently, there are other numerical data types—discrete or count data—that call for specific consideration in modeling. Additionally, spatially correlated count data is frequently observed. This has been seen in the case of health data, for example, the number of newborn fatalities, tuberculosis patients, hospital visitors, etc. However, currently no structurally consistent nonparametric regression and MARS model for count data incorporating spatial lag autocorrelation. The SAR-MAGPRS estimator (Spatial Autoregressive - Multivariate Adaptive Generalized Poisson Regression Spline) is developed to fill this gap. Although it can be applied to different count distributions, the estimator was developed in this study under the assumption of a Generalized Poisson distribution. This paper provides an information-theoretic framework for incorporating knowledge of the spatial structure and non-parametric regression models, especially MARS for the count data types. Moreover, the proposed method can assist in modeling the number of diseases while health policies are being developed. The framework presents an application of the Penalized Least Square (PLS) method to estimate the SAR – MAGPRS model.

The proliferation of artificial intelligence (AI) has emerged as a critical metric for assessing a country's technological advancement, but also for regional economic coordination and high-quality development in China. Based on panel data collected from 31 provinces between 2006 and 2021, this study employs the DEA-Malmquist index model and panel Tobit model to examine the scale, distributional attributes, and influencing factors of AI resource allocation. Results indicate that China's AI resource allocation efficiency has generally increased, with technical efficiency generating a “pull effect” that propels total factor productivity growth rates higher than those attributable to technological progress. Furthermore, AI efficiency in non-coastal regions outstrips that in coastal areas, with total factor productivity growth arising from a substantial increase in technological progress rates. Regional economic development, labor demand, openness to foreign participation, and human capital level exert pivotal roles in enhancing AI resource allocation efficiency. Based on these findings, we suggest a set of strategies aimed at enhancing China's AI resource allocation efficiency, including amplifying government guidance, increasing R&D investments, upgrading economic development levels, fostering the development and strengthening of tangible economy, and attracting and nurturing high-quality scientific research talent.

This study aims to identify and evaluate the influence of the factors affecting the implementation of occupational health and safety of employees and employers and its impact on occupational health and safety and legalize risk assessment in small and medium-sized manufacturing enterprises in central Vietnam through the survey among 246 business representatives and data processing through the software SPSS 20 and AMOS 20. The results show that there are 3 factors affecting the implementation of occupational health, safety and hygiene. In order: (1) Safety regulations and instructions; (2) Occupational health, safety policy and (3) Occupational health and safety training. There is no relationship between the impacts on the implementation of occupational health and safety of the employer. In addition, a very interesting finding about the relationship of factors implementing occupational health and safety of employers was a positive influence on the legalization of risk assessment activities. On the basis of these results, employees, business owners and state management agencies will have grounds to offer useful solutions in risk assessment in order to better perform safety work and occupational health.

As a capital market investment, stocks have risks that must be managed. Therefore, investors should consider the returns and risks of investment products. This study aims to estimate the risk of insurance companies' loss when investing. The method used to estimate the level of risk is Value at Risk (VaR) based on Extreme Value Theory (EVT). The data used is secondary data in the form of daily stock closing prices from two insurance companies, AXA General Insurance and BRI Insurance, from January 2016 to January 2022. The data were used to estimate the risk value according to the EVT principle. As a result, Insurance AXA General Insurance, with 5.91% liquidity, has the lowest VaR value with a 99% confidence level, while BRI Insurance has 5.04%. We concluded from these results that AXA General Insurance has a lower investment risk. It means that each company has a different risk value. Therefore, investors should know these risk factors when choosing a company.