This study aims to analyze the influence of physical environmental factors (geosite) and cultural environmental factors (culturesite) on the work motivation of traditional oil miners in Wonocolo Village, Bojonegoro Regency in 2025. This research is motivated by the unique characteristics of Wonocolo traditional oil mining which is still managed traditionally and is a historical legacy from the Dutch era. In addition, the issue of developing geosites, geoparks, and sustainable economic transformation is currently an important concern in local resource-based regional development. The research subjects were traditional oil miners who were still actively working and directly involved in the production process. The study used a descriptive quantitative approach with data collection techniques through closed questionnaires using a Likert scale. The research sample was determined using the snowball sampling technique considering the informal characteristics of workers. Data analysis was carried out using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) method with the help of WarpPLS software. The results of the study show that geosites have a negative and significant effect on work motivation, which indicates that increasing public awareness of the geological value and potential for regional development encourages reduced dependence on mining activities as the main livelihood. However, this condition does not necessarily make people leave their jobs as miners due to limited alternative jobs and household economic considerations. Meanwhile, culturesite does not have a significant effect on work motivation, which shows that cultural values and mining traditions have not become a dominant factor in people's decisions to work as miners. These findings indicate that economic motives are still the primary consideration for people in maintaining employment in the traditional mining sector. This research contributes to the development of geosite and culturesite-based development economic studies, and has important implications for the formulation of Wonocolo area management policies towards a more sustainable local economic transformation through the development of geotourism and alternative economies based on local potential.
In the dynamic and volatile capital market, understanding the factors that influence stock prices is essential for making informed investment decisions. Stock price movements are influenced by rational, fundamental information, irrational investor behavior, and sentiment. This study aims to analyze the influence of rational and irrational factors on stock prices in the Indonesian banking sector during the 2020-2024 period. The rational variables include price-to-book value (PBV), financial literacy, technology adoption, and interest rates, while irrational influence is represented by investor sentiment or mood. Using a dynamic panel data approach with the Generalized Method of Moments (GMM), this study examines four major Indonesian banks: Bank Mandiri, BNI, BRI, and BCA. The results show that PBV, financial literacy, and technology adoption significantly affect stock prices, while interest rates have a significant negative effect. Additionally, investor sentiment has a considerable positive impact, indicating that emotional and psychological factors can drive stock price movements beyond fundamental valuation. This research presents a novel analytical model that integrates rational and irrational perspectives, offering a comprehensive understanding of stock price volatility in emerging markets. It suggests that investors, companies, and policymakers must consider both fundamental indicators and market sentiment simultaneously to make accurate, timely investment decisions.
Increased crime is frequently assumed to be an unavoidable consequence of economic growth and development. The impact of criminal activity creates new challenges within the community. The purpose of this research is to investigate how criminal activities affect Indonesia's inclusive economic development. Panel data using the Least Squares Dummy Variable (LSDV) method covering 34 Indonesian provinces from 2018 to 2022 were used in this research. The findings indicate that the specific types of crime, namely crimes against decency, crimes against freedom of the person, and crimes against public order, notably influence inclusive economic development. The findings indicate that crimes against life, crimes including embezzlement, fraud, and corruption; crimes against property, both violent and non-violent; crimes related to narcotics; and crimes against physical bodies do not exert a significant influence on inclusive economic development. This study underscores the necessity of a comprehensive strategy that prioritizes crime-prevention tactics and involves a variety of stakeholders to address the underlying causes of criminal activity.
This study analyzes the influence of household age structure on carbon emissions in Indonesia using a quantitative approach based on micro-level survey data. Employing an Ordinary Least Squares (OLS) regression model, it introduces the average age of household members and its squared term to capture potential non-linear effects. The results reveal a non-linear, inverted U-shaped relationship between household age and per capita carbon emissions. The turning point occurs at approximately 36 years, suggesting that households with members around this average age tend to have the highest emissions, which then decline as the average age increases further. This finding aligns with the Life-Cycle Hypothesis, which posits that households in their productive years exhibit more energy-intensive consumption patterns. In addition, the study explores variations in emissions across different household age compositions those dominated by children, adults, and elderly structures. By incorporating these classifications, along with relevant socio-economic and physical household characteristics, the analysis provides deeper insights into how demographic profiles shape energy use and emissions. These findings offer valuable input for developing more targeted and equitable low-carbon policy interventions.
This study aims to determine the determinants of unemployment rates in upper-middle-income Asian countries from 2000 to 2023. This study uses the Autoregressive Distributed Lag (ARDL) method. The analysis results show that in the long run, economic growth, foreign direct investment, income openness, and government consumption expenditure have a significant negative effect on unemployment. Conversely, urban population growth has a significant positive effect on unemployment. Meanwhile, in the short run, only urban population growth can have a significant negative impact on unemployment. Meanwhile, the one-year lag coefficient of foreign investment is proven to be positive and significant on unemployment. These findings imply that poverty reduction is primarily determined by long-term structural policies that encourage economic growth, foreign direct investment, trade openness, and the effectiveness of government spending. The impact of urbanization on increasing poverty emphasizes the need for integrated urban planning with formal job creation. Meanwhile, the weak short-term effect reflects the delay in labor market adjustment that requires supporting policies during the transition phase.
