This study analyzes the influence of community-based business management, focused on vicuña conservation, on socioeconomic development in a National Reserve in Peru. Using a quantitative approach and Partial Least Squares Structural Equation Modeling (PLS-SEM), a model integrating operations management (Oa), resource management (Ro), and environmental impact (Ia) was evaluated as determinants of business performance (De) and social welfare (Sc). The results reveal that the model has high explanatory power, with an R² of 0.63 for performance and 0.59 for socioeconomic development. Environmental impact was identified as the strongest predictor of community welfare (β = 0.420, t = 8.115), followed by operations management on organizational performance (β = 0.350, t = 7.778). All hypotheses were accepted with significance levels of p < 0.001, demonstrating that efficiency in vicuña fiber harvesting (chaccu) and sound environmental governance reduce the economic vulnerability of Andean populations. It is concluded that vicuña conservation is not only an ecological imperative but also a strategic asset that, under an organized community-based business structure, allows for poverty alleviation and the sustainability of livelihoods. The study provides empirical evidence for the design of public policies that harmonize biodiversity preservation with economic growth.

This paper examined the impact of Business Intelligence Systems (BIS) on decision-making quality among top-level management in the Jordanian banking sector, with Artificial Intelligence Capability as a moderating variable, to enhance understanding of the link between business intelligence and decision-making quality. A questionnaire was used to gather data from 297 employees of Jordanian Commercial Banks. The research hypotheses were developed and tested using partial least squares structural equation modeling (PLS-SEM). The results revealed a positive relationship between system quality, data quality, and decision-making quality, with service quality showing a particularly strong impact. Effective system quality techniques were found to enhance decision-making quality, underscoring the importance of BI resource management. The findings also indicated a significant effect of data quality on strategic decision-making quality, while data visualization did not show a statistically significant impact. Furthermore, the results confirmed the moderating role of Artificial Intelligence Capability in the relationships between system quality, data quality, data visualization, and decision-making quality. Overall, the findings of this study contribute valuable insights to the literature on business intelligence and strategic decision-making.

Achieving a more conscious Peruvian citizenry and strengthening prosocial values that allow for the construction of a more ethical and sustainable consumer culture in the country seems increasingly distant, but diagnosing the problem could be a first step. In this regard, this study carried out the process of translating and validating the ethical beliefs scale, which, after undergoing a restructuring process, consisted of 16 items and 3 dimensions. The participation of 500 Peruvian consumers was obtained. After statistical processing, an adequate model fit was found (CMIN/DIF = 3.85; CFI = 0.936; SRMR = 0.076; RMSEA = 0.076). The aim is to gain a better understanding of ethical consumption through the diagnosis of this variable. The results could serve as a basis for transforming the worldview of companies, government, and society in order to achieve fair consumption.

This study examines how customer incivility, career opportunities, emotional exhaustion, and schedule i-deals influence job satisfaction and turnover intention among frontline sales and collection employees in Indonesia’s financing (multifinance) industry. Grounded in Job Demands–Resources (JD-R) and Equity Theory, the model positions customer incivility as a key social demand, career opportunities and schedule i-deals as resources, emotional exhaustion as a strain outcome, job satisfaction as the core attitudinal response, and turnover intention as the proximate withdrawal cognition. Data were collected through a cross-sectional survey and analyzed using covariance-based structural equation modeling (CB-SEM) with LISREL 8.7 on responses from 438 employees across seven financing companies. The findings indicate that customer incivility and emotional exhaustion undermine job satisfaction and increase turnover intention, whereas career opportunities and schedule i-deals enhance job satisfaction; job satisfaction, in turn, reduces turnover intention and mediates the effects of key demands and resources on turnover intention. Practically, financing firms should prioritize customer-mistreatment controls, exhaustion prevention, credible career pathways, and negotiated schedule flexibility to stabilize frontline retention.

The objective of this research was to analyze the mediating role of academic motivation in the relationship between academic tutoring and academic performance among students at a private university in Huancayo. The study adopted a quantitative, explanatory, and non-experimental cross-sectional design. The population consisted of 10,000 students, from which a stratified probability sample of 370 participants was obtained. Data collection was carried out using Likert-type questionnaires validated by expert judgment and with adequate internal consistency (α > 0.84). The analysis was performed using Partial Least Squares Structural Equation Modeling (PLS-SEM), applying bootstrapping of 5,000 subsamples to evaluate the statistical significance of the coefficients. The results showed that academic guidance (β = 0.401, p < 0.001) and pedagogical support (β = 0.385, p < 0.001) significantly influence academic motivation, while the latter directly and significantly impacts academic performance (β = 0.540, p < 0.001). Furthermore, the mediation effect of academic motivation was found to be partial, given that the dimensions of tutoring also had significant direct effects on performance. The structural model explained 58% of the variance in academic performance (R² = 0.58), indicating a substantial explanatory capacity of the set of variables included. It is concluded that academic tutoring is most effective when it strengthens students' internal motivational processes, consolidating an integrative approach to improving university performance.

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.

This study aims to analyze the strategic leadership model, business model innovation, and product innovation to improve performance of State-Owned Business Enterprise. This study employed a quantitative research method and reported the results of a survey using a questionnaire with a population of 28 SOEs in Indonesia engaged in the food and fertilizer industry. Analysis was carried out using SmartPLS software, which consists of descriptive statistics, measurement model evaluation, structural model evaluation, and hypothesis testing. Results of this study indicate that influence of strategic leadership is significant on business model innovation. The influence of business model innovation is significant on product innovation and on company performance. However, influences of strategic leadership on company performance and product innovation are not significant, and influence of product innovation on company performance is also not significant. Strategic leadership has a significant impact on the decision-making process, thus it can facilitate organizations to implement effective strategies designed to achieve optimal performance. Companies that implement strategic leadership have potential to improve performance through the implementation of good planning, motivating, directing, developing employee capabilities in product innovation and business model innovation, as well as communicating company's long-term vision or goals.

Retaining new-generation knowledge employees is a strategic human capital challenge for firms operating under rapid technological change. Ambidextrous leadership, which flexibly combines transformational and transactional behaviors, may reduce turnover intention, yet its micro-level mechanisms remain insufficiently integrated in transitional economies. Drawing on commitment-to-change theory and trait activation logic, this study tests whether ambidextrous leadership lowers turnover intention directly and indirectly via (i) commitment to change (affective, continuance, and normative) and (ii) trait-relevant tendencies expressed at work indicated by Big Five trait measures. We surveyed 445 degree-holding employees (born in or after 1990) working in enterprises in Guangdong Province, China, and applied confirmatory factor analysis and structural equation modeling with 5000-resample bootstrapped indirect effects. Ambidextrous leadership was associated with lower turnover intention, with significant indirect effects through stronger commitment to change and more constructive trait-relevant work tendencies. These findings extend ambidextrous leadership research beyond innovation outcomes to a core HRM outcome and highlight actionable leadership practices that combine vision and support with role clarity, monitoring, and contingent reinforcement to stabilize a mobile knowledge workforce in China.

In Peru, micro and small enterprises (MSEs) form the backbone of the economy. However, the reality is that this sector is characterized by informality, especially in more remote areas. Faced with this reality and the advancement of digital transformation, various digital financial services now exist that effectively contribute to the inclusion of these MSEs within the financial system through the payment and receipt of transfers, the issuance of electronic receipts and invoices, and the payment of tax obligations. The main objective of this research is to analyze the effect of digital financial inclusion and the adoption of electronic invoicing on the level of formalization of MSEs in the Junín region. This research is quantitative, non-experimental, and cross-sectional, with an explanatory design. Multiple linear regression was used for statistical analysis, with a sample of 381 formally registered micro and small enterprises (MSEs) in the provinces of Huancayo, Tarma, Satipo, and Chanchamayo. Data was collected using a structured questionnaire and analyzed using standard econometric techniques The results obtained demonstrate that the adoption of electronic invoicing in SMEs has positive and significant effects on their level of formalization. It was also observed that the adoption of both tools generates a complementary effect greater than that of either tool alone. These results highlight the importance of integrating digital innovation policies, financial education, and tax simplification as a strategy to promote formalization. This research contributes new knowledge to previous studies on business formalization, as it is analyzed using multivariate evidence within a specific context and offers results that can be used to improve and strengthen formal businesses in areas with high levels of informality.

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.