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.

Restaurants currently receive numerous government incentives for opening their businesses, as they can absorb labor and contribute to generating substantial taxes. The success of a restaurant business is achieved through product innovation and service processes provided to customers. The study aims to investigate the relationship between chef competence and restaurant performance, focusing on knowledge communication, restaurant menus, and process innovation. The results of data collection in the provinces of the Special Region of Yogyakarta, Central Java, and East Java amounted to 115 restaurant businesses. Researchers collected data by direct distribution and using Google Forms. Data processing was conducted using SmartPLS 4 to address all research hypotheses. The results showed that chef competency has a significant impact on process innovation, product innovation, and communication of knowledge. Restaurants have implemented process innovations that have a significant impact on increasing product innovation by 0.357, knowledge communication by 0.316, and restaurant performance by 0.218. Restaurant innovation of product occurs, which cannot have a significant impact on communication of knowledge, but has a significant impact on restaurant performance by 0.322. The chef's ability to effectively communicate knowledge can have a significant impact on restaurant performance. The research results provide practical contributions for restaurant managers to maintain an environment that facilitates knowledge sharing between senior and junior chefs, thereby promoting menu and process innovation that meets restaurant standards.

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.

The COVID-19 phenomenon led to an increase in the digitalization of the tourism sector, reducing the demand for services and affecting business performance. There is no doubt that leadership plays a fundamental role in the management of organizations. Therefore, it is necessary to delve deeper into the study of the collaborative style to build social capital and measure the impact it can generate on the performance of tourism companies. The influence of collaborative leadership, in its dimensions of resources and work environment, as well as the mobilization of interest groups, on financial and non-financial performance was analyzed, in addition to the mediation of social capital in this relationship. It was carried out under a quantitative approach, not experimenting in the design, taking the data only once, the sample was made up of 782 representatives of Peruvian tourism companies, using self-administered questionnaires and SEM for the analysis. The results indicate that resources and the work environment positively impact financial performance, but not social capital. Furthermore, stakeholder management influences both non-financial performance and social capital. Likewise, it is confirmed that social capital positively affects both dimensions of organizational performance. A partial mediation of social capital was found, as stakeholder management was associated with non-financial performance, with no mediation in the relationship between resources and work environment on financial performance. These findings highlight the need to strengthen collaborative leadership to improve the performance levels of companies that provide tourism services.

This study investigates the impact of digital human resource management (HRM) practices on employee experience and organizational commitment. A quantitative research approach, utilizing surveys with 229 samples, was employed to gather insights from leading telecommunication companies in Vietnam. By analyzing data using PLS-SEM, the findings suggest that digital training and performance management positively influence Employee experience, which in turn demonstrates mediating effect on the relationship of these HR practices and Organizational commitment. Meanwhile digital recruitment and compensation and benefits practices show insignificant impact. We also highlight the importance of a balanced approach to digital HRM, combining technological advancements with human-centric practices to enhance key factors to retain employees.

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.

Variable selection is an important step in data envelopment analysis (DEA) when the number of decision making units (DMUs) is insufficient. This research thus proposes a two-stage variable selection method integrating a well-known supervised machine learning technique, the random forest algorithm. In the first stage, a baseline DEA model with full input and output variables is implemented and each DMU can be determined as being efficient or inefficient. In the second stage, a random forest is trained to learn how to classify efficient or inefficient DMUs well in high dimensions. Accordingly, the importance of each variable can be calculated based on permutation importance indices in random forest. This paper further discusses two issues about data augmentation in order to improve the robustness of permutation importance when the number of DMUs is quite small.

The security of smart grids is seriously threatened by false data injection (FDI) attacks. Falsified data is maliciously injected into the grid's measurement and control systems as part of these attacks, which might seriously disrupt the power supply and jeopardize system integrity. In the context of smart grids, it is also imperative to address the issue of consumer privacy and the protection of their sensitive data. The main objective of this work is to provide a collaborative framework based on federated learning to detect various FDI dangers while protecting SG's resources and privacy. We have implemented several technologies that provide a good solution in order to accomplish this goal. Using a dataset designed to replicate attacks on the power system environment, we used federated learning to locally train models using the data stored on the sensors. The best model should then be chosen by comparing the outcomes. These outcomes demonstrate the potential of our framework, which has used mixed models to repel attacks, short-circuit faults, and maintain lines with a 98% accuracy rate during the federated learning phase.

This document addresses the Nurse Scheduling Problem (NSP) and presents a dynamic multi-criteria optimization model for its solution considering a predefined time horizon. The purpose is to maximize the level of "work well-being" of nurses formulated as the minimization of "aversion" which translates into costs or penalties for certain undesirable work shifts. For this, a series of criteria are defined to estimate the preference structure of nurses according to the hospital center specifications by assigning costs for undesirable shift assignments. The proposed methodology involves developing a heuristic to decompose the global problem into daily subproblems for which a dynamic algorithm is implemented that considers a cost accumulation process for all criteria and all nurses. Daily models are dynamically solved by modifying the coefficients of the well-being function to achieve equity throughout the planning period by updating and accumulating different averages. This methodology has shown satisfactory results for scheduling work shifts for doctors, paramedics, security guards, and drivers in numerous hospital centers in Colombia.

This study aims at exploring the effect of marketing 5.0 as a whole construct on marketing performance and the moderating role of customer resources between these two variables. Moreover, the study aims at examining the effects of marketing 5.0 dimensions, i.e., predictive marketing, contextual marketing, augmented marketing, and agile marketing on marketing performance as well as the moderating role of customer resources in the effect of each dimension on marketing performance. Collecting data by a closed-end questionnaire from a sample consisting of 186 managers and sales persons in clothing shops, the results pointed out that there is a statistically significant effect of marketing 5.0 on marketing performance and there is a statistically significant moderating effect of customer resources between marketing 5.0 and marketing performance. Furthermore, the results revealed that three dimensions of marketing 5.0, i.e., predictive marketing, contextual marketing, and augmented marketing, exerted significant effects on marketing performance. As well, customer resources significantly moderated the effects of predictive marketing and augmented marketing on marketing performance. Such results contribute to marketing performance literature through highlighting the importance of both marketing 5.0 and customer resources together in enhancing marketing performance.