School leadership is a strategic factor in strengthening educational quality due to its influence on teachers' professional performance and the achievement of institutional objectives. This study aimed to analyze the explanatory model of school leadership and its influence on teacher performance in regular basic education institutions in the Junín region of Peru. A quantitative, non-experimental, explanatory, cross-sectional study was conducted. The sample consisted of 358 teachers, selected through stratified sampling. Structural Equation Modeling using Partial Least Squares (SEM- PLS ) was employed to analyze the relationships between the variables. The results showed that institutional planning exerts a positive and significant influence on task performance (β = 0.62; p < 0.001), followed by pedagogical support (β = 0.56; p < 0.001) and decision-making (β = 0.31; p < 0.001). Likewise, pedagogical support (β = 0.51; p < 0.001) and decision-making (β = 0.45; p < 0.001) showed significant effects on organizational civic behaviors. The model demonstrated substantial explanatory power, reaching R² values of 0.64 for task performance and 0.59 for organizational civic behaviors. The effect sizes indicated a moderate to high contribution from the dimensions of managerial leadership. It is concluded that managerial leadership constitutes a key mechanism to strengthen teacher performance since the findings support instructional and transformational leadership approaches; while, in practical terms, they suggest strengthening institutional planning, pedagogical support and teacher participation in decision-making to promote continuous improvement and educational quality.

This study investigates the growing importance of new energy vehicles (NEVs) in relation to the broader agenda of sustainable development and emphasizes the impact of social media marketing (SMM) activities on consumers' purchasing decisions. The impact of SMM on consumer responses has been the subject of existing literature. However, the joint mediating roles of green consumption values and green trust within sustainable consumption contexts have received limited empirical attention. This research aims to address this lacuna by examining the mechanisms by which SMM influences the purchase decisions of NEVs among consumers in Guangdong Province, China. Data were collected from 450 valid respondents using a quantitative approach. The investigation concentrates on five dimensions of SMM activities: interactivity, informativeness, personalization, trendiness, and electronic word of mouth (eWOM). The outcome variable is the purchase decision, while green trust and green consumption values are treated as mediating variables. The hypothesized relationships were investigated using descriptive statistics and structural equation modeling. The findings enhance the implementation of the Stimulus Organism Response theory (SOR) in the areas of SMM and NEV consumption. In particular, the results indicate that consumers' internal psychological states can be influenced by digital marketing stimuli, which in turn affect their purchase decisions. Consequently, SMM should not be regarded solely as a promotional or communication tool. Rather, it can serve as a strategic approach to reinforce consumers' environmental values, enhance their trust in green products, and establish more robust relationships between consumers and green products. NEV firms are encouraged to improve their SMM practices by utilizing interactivity, information quality, personalized communication, trend-related content, and eWOM. From a managerial perspective, these measures may have beneficial implications for the optimization of NEV marketing strategies in Guangdong Province, China. The proposed model may be further validated in future research by incorporating larger samples, cross-regional data, longitudinal methods, or real-world application scenarios.

Youth disengagement from agriculture poses a critical challenge to the sustainability of food systems and rural livelihoods in many developing countries, including Indonesia. As the agricultural workforce continues to age, understanding the drivers of youth participation and its implications for household welfare has become increasingly urgent. This study aims to analyze the factors influencing youth participation in the agricultural sector and to assess the impact of youth involvement in agriculture on household food security among farming households in rural Indonesia. The study employs primary cross-sectional household survey data and applies a two-stage quantitative approach. A Probit regression model is used to identify the socio-economic and structural determinants of youth participation in agriculture, while Propensity Score Matching is applied to estimate the causal impact of youth participation on household food security outcomes, measured using the Food Consumption Score and the Food Insecurity Experience Scale. The results show that youth participation in agriculture is primarily shaped by structural and economic factors rather than basic demographic characteristics. Higher educational attainment significantly reduces the likelihood of youth engagement in agriculture, reflecting higher opportunity costs and alternative employment prospects. In contrast, the availability of youth labor within the household and access to productive assets, particularly land, significantly increase the probability of youth participation, while access to agricultural credit plays a supportive but weaker role. The impact analysis further reveals that households with active youth participation exhibit significantly better food security outcomes, characterized by higher food consumption scores and lower levels of food insecurity compared to comparable households without youth involvement. These findings suggest that youth participation contributes to improved household resilience and food access. The study highlights the importance of addressing structural constraints, such as land access and financial inclusion, to promote sustainable youth engagement in agriculture. Strengthening youth participation is therefore not only essential for farmer regeneration but also for enhancing household food security and long-term rural development.

This study empirically investigates the influence of AI-enabled accounting intelligence (AIAI) on capital market performance (CMP) of publicly listed firms on the Amman Stock Exchange, examining the mediating roles of disclosure quality (DQ) and financial information comparability (FIC). This study used a balanced panel of 215 firms listed on the Amman stock exchange that operate in a number of sectors, including banking, financial services, technology, industrial, telecommunications, and energy, from 2017 to 2024 (resulting in 1,720 firm-year observations). We employ sophisticated econometric procedures and techniques such as pooled OLS, fixed-effects, and random-effects estimation, as well as several tests such as Hausman specification testing, Breusch-Pagan LM diagnostics, panel unit root tests, heteroskedasticity- and autocorrelation-robust inferences, stepwise mediation analysis with Sobel tests, and two-step system GMM estimation to address endogeneity concerns. The empirical findings document a positive and statistically significant association between AI-enabled accounting intelligence and capital market performance (β = 0.412, p < 0.01). Moreover, AIAI exerts a robust positive influence on disclosure quality (β = 0.487, p < 0.01) and financial information comparability (β = 0.453, p < 0.01), while both DQ (β = 0.318, p < 0.01) and FIC (β = 0.276, p < 0.01) significantly enhance CMP. Mediation tests confirm that disclosure quality and financial information comparability partially mediate the AIAI–CMP nexus, with Sobel z-statistics of 6.142 and 5.873, respectively. The results remain robust after lagged specifications, alternative dependent variables, and GMM estimation. Drawing on information asymmetry, agency, signaling, efficient market, and resource-based view theories, the study contributes to the emerging discourse on digital accounting transformation by demonstrating that AI-driven accounting infrastructures function as strategic intangible resources that lower informational frictions, strengthen reporting credibility, and ultimately translate into superior valuation outcomes in capital markets.

Water quality instability is a major constraint in giant freshwater prawn farming, particularly in land-based systems where fluctuations in environmental conditions can reduce survival and productivity. This study applied an integrated CRITIC–TOPSIS framework to evaluate alternative water quality management strategies for giant freshwater prawn farming under multiple operational and production criteria. Four alternatives, representing different levels of management sophistication from conventional monitoring to a full IoT-based real-time monitoring system, were assessed using five criteria: Cost, Labor, Energy, Survival, and Yield. The CRITIC method was used to determine objective criterion weights, while TOPSIS was employed to rank the alternatives according to their relative closeness to the ideal solution. The CRITIC results indicated that Cost (0.2523) and Energy (0.2506) were the most influential criteria, followed by Labor (0.1681), whereas Survival and Yield each received a weight of 0.1645. The TOPSIS results showed that Alternative 4 was the most suitable option, with the highest closeness coefficient (0.5534), followed by Alternative 3 (0.5084), Alternative 2 (0.4583), and Alternative 1 (0.4466). Alternative 4 also achieved the best production performance, with 93% survival, 4.65 kg yield, and the lowest labor requirement (17 h), although it required the highest cost (13,746 Baht) and energy consumption (35 kWh). Compared with the conventional strategy, Alternative 4 improved survival by 11 percentage points, increased yield by 0.55 kg, and reduced labor by 18 h. The findings demonstrate that the integrated CRITIC–TOPSIS framework can serve as an effective decision-support tool for selecting water quality management strategies in freshwater prawn farming by balancing resource requirements and production outcomes. Under the present evaluation framework, the IoT-based real-time monitoring strategy was identified as the most suitable alternative for land-based giant freshwater prawn farming.

Understanding how individuals make decisions regarding participation in employment social security programs has become increasingly important in digitally connected societies. This study develops a decision-making model that explains employment social security participation among Generation Z in Indonesia by integrating social media engagement, institutional credibility, digital trust, and online awareness. Drawing upon the Stimulus–Organism–Response (SOR) Theory, the study conceptualizes social media engagement and institutional credibility as decision stimuli, while digital trust and online awareness represent cognitive mechanisms that shape participation decisions. Data were collected from 412 Generation Z respondents in Banten Province, Indonesia, using purposive sampling. The proposed model was evaluated using covariance-based structural equation modeling (CB-SEM) with IBM SPSS AMOS. The results demonstrate that social media engagement and institutional credibility significantly influence digital trust, online awareness, and participation decisions. Digital trust and online awareness positively affect employment social security participation, although the relationship between digital trust and online awareness is not statistically significant. Mediation analysis confirms that digital trust and online awareness serve as important decision mechanisms linking digital communication and institutional factors to participation outcomes. The findings contribute to decision science literature by offering a data-driven behavioral decision model that explains how digital information environments and institutional credibility influence social security participation among younger generations. The proposed model may assist policymakers and social security institutions in designing more effective digital engagement strategies to improve participation rates.

The public banks in the financial services sector are in the process of transformation into a digital model and thus they need to offer an omnichannel experience that is integrated, accessible and centered to the client. The purpose of this study is to analyze the influence that the experience of omnichannel has on the satisfaction of information of clients in the public banking sector of Peru by means of a multivariate analysis. This research is quantitative, non-experimental, cross-sectional and of an explanatory type. 384 users of the services of digital banking were investigated through a questionnaire of Likert type, structured. The variables of experience of omnichannel and of satisfaction of information of clients were analyzed. By means of descriptive statistics, correlation analysis and of regression analysis of type multiple linear, the variables studied were analyzed. The results show correlation positive and high between all the dimensions of the experience of omnichannel and the satisfaction of information of clients. The proposed regression model has a high explanatory power (adjusted R-Squared = 0.8902). Among the variables analyzed, Ease of use is followed by Accessibility, Convenience, Content quality and Interface design. Customers value very much digital banking platforms that are simple, accessible, efficient and that have information of interest well organized. The results of the study support that customer satisfaction in the public banking sector of Peru depends significantly on the integrated quality of the omnichannel experience offered by public banking institutions. Thus, usability, accessibility and functional efficiency should be strategic elements in order to improve the quality of the digital services and to promote financial inclusion.

Digital transformation has become a critical strategic priority in the banking sector; however, its effectiveness in improving branch-level performance depends not only on technology adoption but also on organizational readiness, leadership capability, and ecosystem support. Despite increasing investments in digital banking infrastructure, limited empirical research explains how these strategic factors collectively influence branch performance through internal capability development mechanisms, particularly in emerging banking contexts such as Indonesia. Addressing this gap, this study examines the effects of digital transformation, digital banking risk, leadership capability, and the digital banking ecosystem on strategic capabilities, strategy execution, and branch performance. Using a quantitative approach, Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to data from 204 bank branch respondents implementing digital services. The findings indicate that digital transformation, leadership capability, and the digital banking ecosystem significantly strengthen strategic capabilities and strategy execution, while digital banking risk demonstrates a comparatively moderate influence. Strategic capabilities and strategy execution also serve as key mediating mechanisms translating digital initiatives into improved branch performance. This study extends the Dynamic Capabilities perspective and provides practical insights for banking institutions seeking to optimize branch performance through leadership strengthening, ecosystem collaboration, and effective strategy execution alignment.

The global construction industry is undergoing rapid digital transformation, driven by the adoption of advanced technologies such as artificial intelligence (AI), geographic information systems (GIS), the Internet of Things (IoT), and City Information Modeling (CIM). Among these innovations, CIM has been increasingly recognized as a promising approach for enhancing the effectiveness of urban planning management. Despite its potential, research examining the determinants of CIM implementation remains limited. Existing studies have predominantly addressed individual factors, such as technological integration, regulatory challenges, or resource availability, while the interrelationships among these factors have received insufficient attention. This study aims to identify a comprehensive set of factors influencing CIM implementation and to evaluate their relative importance in the context of urban planning management. The results identify five key determinants: the availability and quality of data required for CIM, the adequacy of implementation resources, the accuracy of CIM outputs, the economic benefits and operational effectiveness of CIM adoption, and the contribution of CIM to the development of smart and sustainable cities. Furthermore, significant divergences in objectives and perceptions were observed between management units and execution units. By systematically analyzing the interactions among influencing factors, this study addresses a critical gap in the existing literature on CIM implementation. The findings provide practical implications for government agencies in designing policies to facilitate CIM adoption and offer strategic guidance for enterprises planning future CIM implementation in urban planning management.

Drying-system selection is a critical decision in germinated brown rice processing because drying performance affects storage stability, microbial safety, product appearance, and commercial quality. However, drying systems often exhibit conflicting performance across multiple quality indicators, making single-criterion evaluation insufficient. This study proposes an integrated Shannon–Gibbs entropy framework for multi-criteria ranking of drying systems for germinated brown rice processing. Three drying alternatives were evaluated: a tray dryer, a stand-alone electric dryer, and a hybrid dryer integrating electric heating with solar air heating. Five product-quality criteria were considered: moisture content, water activity, lightness (L*), redness/greenness (a*), and yellowness/blueness (b*). Shannon entropy was used to determine objective criterion weights from the normalized decision matrix, while an updated Gibbs entropy ranking method was applied to obtain entropy-adjusted performance scores. In the proposed Gibbs entropy formulation, the Shannon-normalized matrix was directly used to construct the weighted normalized performance matrix, the Gibbs constant was defined as the reciprocal of the row sum of weighted normalized values, and the final ranking was derived from normalized entropy-adjusted scores. The Shannon entropy results showed that b* was the most influential criterion, with a weight of 0.544916, followed by a* at 0.198931, moisture content at 0.122783, L* at 0.082475, and water activity at 0.050896. The updated Gibbs entropy ranking identified the hybrid dryer as the best alternative (Si*=1.000000), followed by the stand-alone dryer (Si*=0.997008) and the tray dryer (Si*=0.822019). The ranking was consistent with WASPAS, COPRAS, and TOPSIS, confirming the robustness of the proposed framework. The findings demonstrate that the integrated Shannon–Gibbs entropy approach provides a transparent, data-driven, and uncertainty-aware decision-support tool for selecting drying technologies in value-added rice processing.