Jazmín Pérez-Salazar

Circular-economy research in hospitality has expanded from isolated waste-reduction practices to questions of procurement, digital measurement, stakeholder participation, and organizational change. Yet the available evidence remains difficult for hotel managers to convert into a governed investment portfolio: published reviews classify practices and barriers, while implementation decisions require owners, baselines, decision gates, auditable indicators, and feedback routines. An integrative review was therefore conducted to connect circular-hospitality evidence with management and governance mechanisms. The synthesis began from an established bibliometric map of 71 hospitality publications (2018–2024), audited its principal references and claims against publisher and DOI metadata, and incorporated peer-reviewed studies published through August 2026 that materially extended evidence on food systems, furniture and procurement, digital leadership, institutional pressures, and human participation. Sources were coded by operational domain, decision level, actor, information requirement, barrier, and outcome. Four mutually dependent governance functions emerged: strategic direction, knowledge mobilization, process control, and portfolio integration. On this basis, the article develops a five-stage 5D framework—Diagnose, Design, Deploy, Digitize, and Demonstrate—and specifies deliverables, decision gates, responsibilities, and denominator-aligned indicators for each stage. The resulting model shifts circularity from a catalogue of environmental initiatives to a repeatable management system. Its contribution is conceptual and translational rather than causal: it offers a testable architecture for hotel groups, independent properties, and destination partnerships, while identifying where longitudinal validation, common measurement rules, and evidence from emerging economies remain necessary.

The persistent financial exclusion of micro-enterprises is fundamentally driven by information asymmetry, as traditional credit scoring models rely heavily on audited financial statements that small entities rarely possess. To address this “thin-file” challenge, this study proposes a shift from asset-based valuation to behavioral algorithmic profiling, hypothesizing that high-frequency operational risk patterns can serve as informative proxies for solvency compared to static liquidity ratios. Using an Extreme Gradient Boosting (XGBoost) architecture on a synthetic dataset of 5000 micro-enterprise transaction logs, we develop a predictive framework that extracts latent features such as supply chain latency, inventory turnover consistency, and digital footprint intensity. The proposed model achieves an Area Under the Curve (AUC) of 0.94, outperforming traditional linear baselines and achieving performance levels above those commonly reported in micro-enterprise solvency prediction studies. The results indicate that operational stability emerges as a strong indicator of repayment capacity within the evaluated context, outperforming static liquidity-based measures. These findings suggest that computational intelligence approaches grounded in high-frequency operational data may contribute to mitigating information asymmetries in micro-enterprise credit assessment, particularly in environments characterized by limited financial disclosure, although further empirical validation is required prior to large-scale deployment.

This study analyzes the impact of emotional intelligence (EI) on the productive performance of micro-entrepreneurs in Zone 5 of Ecuador, which includes the provinces of Guayas, Santa Elena, and Los Ríos. EI, defined as the ability to recognize, understand, and manage one’s own and others' emotions, is crucial for business success, especially in contexts with limited resources and high uncertainty. Since microenterprises represent over 80% of employment in Ecuador, EI is essential for overcoming challenges such as limited access to financing, informality, and deficiencies in management skills. Using a quantitative approach, 200 micro-entrepreneurs from the commercial, industrial, and service sectors were surveyed, employing validated instruments such as the Bar-On Emotional Intelligence Inventory and the Mayer-Salovey-Caruso Emotional Intelligence Test. The results showed that EI significantly influences productive performance (r = 0.501), with empathy and social skills being the most influential dimensions. Micro-entrepreneurs in the service sector achieved higher EI and performance scores, while no significant gender differences were found, although women excelled in customer satisfaction. The study suggests implementing EI workshops, creating support networks, and promoting self-assessment tools to strengthen business management and ensure the sustainability of microenterprises in the region.