Religious conflict remains a pressing issue in plural societies, particularly where political and social tensions intersect with religious identity. In Indonesia, one of the world’s largest Muslim-majority countries, understanding the drivers of religious conflict at the local level is vital for fostering social cohesion. This study investigates the role of social capital—specifically bonding, bridging, and linking—in influencing religious conflict in Pasuruan Regency, East Java, with particular attention to the mediating roles of political and social conflict. Using a quantitative approach, data were collected through structured face-to-face interviews with 400 respondents, selected using multi-stage sampling. Structural Equation Modeling (SEM) via SmartPLS was employed to analyze both direct and indirect relationships between the constructs. The results reveal that bonding and bridging significantly reduce political and social conflict, which in turn significantly reduce religious conflict. However, their direct effects on religious conflict are not significant. Linking was found to significantly reduce social conflict but did not show a significant influence on political or religious conflict directly. Indirect effect analysis confirmed that bonding, bridging, and linking can mitigate religious conflict through their effects on political and social tensions. This study contributes to the growing literature on social capital and conflict by highlighting the importance of indirect pathways. Practically, the findings suggest that policies aiming to reduce religious conflict should strengthen both horizontal (bonding and bridging) and vertical (linking) social ties, while also addressing underlying political and social grievances. The study underscores the need for integrated, community-based conflict prevention strategies.

This paper develops a comprehensive evaluation index system designed to measure the performance of scientific and technological achievements transformation in colleges and universities. Recognizing the inherent fuzziness and uncertainty in performance indicators, the study introduces a novel evaluation approach that integrates fuzzy logic with the TOPSIS decision-making method, enhanced through the IVPFOWA (Interval-Valued Pythagorean Fuzzy Ordered Weighted Averaging) operator. The IVPFOWA operator is first defined to establish the theoretical foundation for handling fuzzy attribute data. Building on this, the paper outlines the detailed steps of the TOPSIS evaluation decision process when combined with the IVPFOWA operator, demonstrating how the method can effectively address ambiguity in performance assessment. To validate the practicality and effectiveness of the proposed framework, Jiangsu University of Science and Technology is selected as a case study. The results confirm that the method provides a more reliable and adaptable tool for evaluating transformation performance under complex and uncertain conditions.

This research examines Saudi Arabia's stock market performance from 1991 to 2021 while exploring the long-term and immediate effects of economic factors. The study employs Johansen cointegration tests alongside the Vector Error Correction Model (VECM) to analyse the influence of Gross Domestic Product (GDP), inflation, Foreign Direct Investment (FDI), trade balance and government expenditure on stock market performance. The research shows that macroeconomic variables affect market capitalisation results since GDP growth and trade openness lead to positive stock market performance; however, FDI and inflation create inconsistent effects. Results indicate the stock market maintains a long-term equilibrium between variables, leading to long-term correction of market deviations. The present study extends the discussion in the literature by offering a comprehensive and empirical analysis of the Saudi Equity Market as related to the economic diversification process, with a focus on the Saudi Arabia Vision 2030. This research calls for a solid and pro-active fiscal framework to stem inflation, attract good quality foreign capital and enhance the financial institutions. They are very useful in the formulation of strategic development plans that would foster long-term sustainable growth and increase investor confidence. The findings of this study are useful for policymakers, investors, and other analytical experts interested in the relationship between economic determinants and capital market returns, specifically within the context of Saudi Arabia, a young economy in the group of commodity-exporting countries. This study contributes original empirical evidence on the long- and short-run effects of macroeconomic indicators on stock market performance in Saudi Arabia. By applying the VECM approach across a 31-year period, it offers valuable insights for policymakers and investors in managing financial markets during economic diversification under Vision 2030.

This study investigates the role of Xiaohongshu in shaping Chinese international students’ purchase intentions (PI) toward Malaysian traditional food. Drawing on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the research examines the direct effects of information quality, electronic word of mouth (eWOM), perceived usefulness (PU), and perceived enjoyment (PE) on PI, as well as the mediating roles of attitude and trust and the moderating role of food neophobia. A quantitative research design was employed, and data were collected through a structured questionnaire distributed among Chinese international students studying in Malaysia. A total of 380 valid responses were obtained, with established scales adapted from prior research to measure the constructs. The data were analyzed using SPSS, including reliability testing, correlation analysis, regression analysis, and mediation and moderation tests. The results revealed that all four independent variables significantly influenced PI, with both attitude and trust mediating these relationships. Furthermore, food neophobia was found to moderate the effects of attitude and trust on PI, weakening these positive associations among students with higher levels of neophobia. These findings confirm the importance of cognitive and affective factors in food-related consumer behavior within a cross-cultural context. This study extends TAM and TPB by applying them to the context of social media–driven cross-cultural food consumption. It provides theoretical insights into the role of trust, attitude, and individual differences in shaping consumer behavior, while also offering practical recommendations for marketers and cultural promoters seeking to enhance the acceptance of Malaysian traditional food among international students.

This article explores the impact of human capital on the process of economic expansion of Peru’s central region between 2009 and 2022, using data from INEI and MEF. A non-experimental design with panel data was applied, along with two econometric approaches: The Feasible Generalized Least Squares estimation method (FGLS) as well as the Generalized Least Squares (GLS) estimation, which was used to analyze how human capital stock and investment influence real GDP. The analyzed dimensions were education and health, measured through institutional indicators, teaching staff, health coverage, and public expenditure. The results reveal positive and significant effects, with the models explaining 82% and 78% of GDP variability, highlighting public spending on education as the main determinant. In summary, human capital—both in terms of stock and investment—constitutes an essential factor in regional economic growth, reinforcing the need to strengthen public and economic policies in Peru’s central macro-region.

The study aims to develop a model for replacing human labor with industrial robots in the industrial production sector to promote sustainable growth. A mixed-methods approach was employed, combining qualitative interviews with nine experts and quantitative analysis based on survey data from 500 industrial enterprises. Structural Equation Modeling (SEM) confirmed that government policies, industrial Readiness, integration cooperation, and production potential are key determinants of successful robot adoption. Government policy exerted the most substantial direct influence on industrial Readiness and integration cooperation, which, in turn, positively affected production potential. The results emphasize the importance of collaborative strategies between public institutions, educational organizations, and private enterprises to enhance workforce capability and technological preparedness. The proposed Automated Robot–Human Labor Industrial Replacement (ARHLIR) Model offers both theoretical and practical implications for policymakers and industry leaders. The integration of policy support, organizational Readiness, and cooperative networks ensures that automation drives competitiveness, innovation, and long-term sustainability.

This study introduces a novel mathematical framework for the optimal placement of cross-docking facilities within international logistics networks. The model employs an extended K-means clustering algorithm integrated with Geographic Information Systems (GIS) to enhance spatial decision-making. A comprehensive review of the existing literature highlights that transportation and warehousing costs represent the most substantial components of overall logistics expenditures. In international land transportation, direct point-to-point delivery is often impractical, thereby necessitating intermediate transshipment through cross-docking facilities. Inefficient selection of these intermediary nodes can result in elevated storage and transportation expenses. Accurate identification of optimal cross-docking locations, therefore, has significant potential to reduce both transport distances and associated costs in global logistics operations. To examine this premise, two comparative scenarios were developed. The first assumes that cross-docking operations are conducted at national borders prior to international shipment, while the second applies the proposed extended K-means clustering algorithm integrated with GIS to determine optimal cross-docking points beyond the border within the broader international supply chain network. Both scenarios were subjected to numerical simulations and analytical assessments to evaluate their relative performance in minimizing transportation distances. The results reveal that the GIS-supported extended K-means approach produces substantially shorter international transport routes compared with the border-based cross-docking strategy. These findings emphasize the strategic importance of accurately locating cross-docking facilities in international logistics planning. By optimizing cross-docking placement, logistics managers can enhance transportation efficiency, reduce operational costs, and strengthen organizational competitiveness in the increasingly dynamic global marketplace.

Point-of-sale (POS) transactions are considered one of the fundamental components of retail financial systems. The performance of the POS service network relies heavily on a reliable and secure e-banking infrastructure driven by usage patterns. In this study, we perform a structural break analysis to identify all points in the POS transaction time series where major structural behavioral shifts have occurred. We then investigated the potential sources of these shifts in the data. The analysis revealed four major shifts between 2019 and 2023. The corresponding events, such as the introduction of Apple Pay, the broader rollout of MADA’s contactless and mobile payment infrastructure, COVID-19, SAMA’s regulatory sandbox and open banking pilot projects, and structural changes in banking technology and regulation, are evident as potential drivers of these changes. These results are useful for planners doing digital infrastructure management, expansion, and investment planning, as well as performing policy formulation and responses.

This study explores how financial literacy affects students’ financial well-being by looking at the psychological factors that play a role—specifically self-control, mental budgeting, and mental accounting—as mediating variables. The research aims to better understand how cognitive and psychological resources help young people achieve financial well-being in a university setting. Using a quantitative, survey-based approach, the study gathered data from 781 students across four Indonesian cities. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine both the direct and indirect relationships between the variables. The results show that financial literacy has a significant impact on financial well-being, both directly and indirectly, through the influence of self-control, mental budgeting, and mental accounting. Based on the Conservation of Resources Theory, financial literacy serves as a cognitive resource that helps students protect and manage their financial assets more effectively. The findings suggest that universities and policymakers should develop financial literacy programs that also address psychological aspects such as self-control and budgeting habits. Integrating these elements can help students feel more financially secure and maintain better life balance. Overall, this study offers both theoretical and practical insights by connecting financial literacy with the management of psychological resources, and it points to new directions for developing psychology-based financial education to enhance young people’s financial well-being.

This study investigates how personal traits (PT), family background (FB), and the university ecosystem (UE) shape the entrepreneurial mindset (EM) and, in turn, entrepreneurial intention (EI) among university students in Vietnam. Drawing on the Theory of Planned Behavior (TPB), the study integrates individual, familial, institutional, attitudinal, normative, and control factors into a unified framework. By conceptualizing EI as a behavioral decision under uncertainty, the study positions EM as a cognitive mechanism that mediates how students form and enact entrepreneurial decisions. Using PLS-SEM on survey data from 412 students, the results confirm that PT and UE are the strongest drivers of EM. At the same time, EM, attitude (AT), subjective norms (SN), and perceived behavioral control (PBC) exert significant direct effects on EI. The model explains 49.0% of the variance in EM and 56.0% of the variance in EI, demonstrating robust explanatory power. These findings contribute to the decision sciences by demonstrating how cognitive readiness (EM) and TPB constructs influence entrepreneurial decision-making, thereby offering implications for universities and policymakers to design decision-making environments that foster entrepreneurship among students.