Small and micro enterprises are pivotal in national economic and social development. To foster their growth, managing their credit risks scientifically is crucial. This study starts by examining the credit information of these enterprises. We use imbalanced sample processing algorithms to ensure a balanced representation of minority-class samples. Then, a machine learning classifier is employed to identify key factors contributing to these enterprises' low credibility. Based on these factors, an XGBoost scoring card model is developed. The study reveals: firstly, the integration of the SMOTE algorithm with the XGBoost model exhibits certain performance advantages in handling imbalanced datasets; secondly, trustworthy financial information remains at the heart of crucial risk determinants; thirdly, the XGBoost scoring card model based on significant features effectively enhances the accuracy of credit risk assessment. These insights provide both theoretical references and practical tools for enhancing the robustness of small and micro enterprises, facilitating early warnings on credit risks, and refining financing efficiency.

This study, through the use of company data, offers an in-depth analysis of the current situation and future prospects of biogas plants and how they can promote economic advantages and environmental benefits. The geographical context of the Piana del Sele (Salerno, Italy) was chosen on the basis of objective criteria, including its relevance in the agro-industrial sector and the availability of accurate and reliable company data that was collected through IT systems, including software business performance monitoring and digital data analysis. This choice was fundamental to guaranteeing the validity and representativeness of our analyses and results. From a methodological point of view, mathematical formulas and algorithms were used, which allowed the calculation of the biogas producibility indices by type of biomass as well as the quantification of the effluents produced to then estimate the quantification of the biogas and the interconnected economic savings. Furthermore, this study uses the SWOT (strengths, weaknesses, opportunities, and threats) approach in an innovative way by integrating renewable energy communities into it. The aim is to demonstrate the potential of biogas plants and how energy communities can transform weaknesses and threats into opportunities. The results show that the integration of biogas plants into the farm environment offers considerable potential, and although it is a challenge for small and medium-sized farms, it could bring energy self-sufficiency and economic surplus. Furthermore, the integration of renewable energy communities would be able to promote the diversification of energy supply and transform weaknesses and threats into opportunities.

Technology-based small and micro enterprises play a crucial role in national economic and social development. Managing their credit risk effectively is key to ensuring their healthy growth. This study is based on corporate credit management theory and Wu’s three-dimensional credit theory. It clarifies the credit concept and measurement logic of these enterprises, considering their unique development characteristics in China. A credit evaluation system is constructed, and an innovative method combining machine learning with comprehensive evaluation is proposed. This approach aims to assess the credit status of technology-based small and micro enterprises in a thorough and objective manner. The study finds that, first, the credit level of these enterprises is currently moderate, with little variation. Second, financial information remains a key factor in credit evaluation. Third, the ML-AHP (Machine Learning-Analytic Hierarchy Process) combined weighting method effectively integrates subjective experience with objective data, providing a more rational assessment. The findings provide theoretical references and practical guidance for the healthy development of technology-based small and micro enterprises, early credit risk warning, and improved financing efficiency.

Grasping the concerns of customers is paramount, serving as a foundation for both attracting and retaining a loyal customer base. While customer satisfaction has been extensively explored across diverse industries, there remains a dearth of insights into how distinct rural bed and breakfasts (RB&Bs) can effectively cater to the specific needs of their target audience. This research utilized latent semantic analysis and text regression techniques on online reviews, uncovering previously unrecognized factors contributing to RB&B customer satisfaction. Furthermore, the study demonstrates that certain factors wield distinct impacts on guest satisfaction within varying RB&B market segments. The implications of these findings extend to empowering RB&B owners with actionable insights to enhance the overall customer experience.

Social media has become a key venue where PhD graduates seek career information, compare experiences, and negotiate uncertainty. Drawing on information behavior and sense-making perspectives, this study examines how returnee PhDs from non-core study destinations discuss employment challenges in China’s academic labor market when credential signals are contested. Using Korean-trained PhDs as a theoretically motivated exemplary case, we collected 1149 publicly available posts from Xiaohongshu, a Chinese social media platform, and applied BERTopic to identify latent themes, followed by qualitative close reading of representative posts to interpret discourse functions. The model yielded ten topics, and semantic association analysis indicates substantial overlap among high-frequency topics, suggesting intertwined concerns rather than neatly separated issue domains. The four most prevalent topics account for 72.06% of the corpus, centering on credential recognition, job-search pathways, informal screening rules, and intersecting age- and gender-related pressures. Qualitative readings further reveal recurring discursive moves, including exposing tacit hiring heuristics, contesting stigmatizing labels (e.g., “water PhD,” a derogatory term implying low-quality credentials), and exchanging actionable strategies across regions and career tracks. Overall, the findings point to discursive convergence under evaluation uncertainty: when formal criteria are ambiguous and institutional signals are unreliable, participants turn to social media to stabilize expectations by triangulating cases and iteratively refining shared interpretations of the job market. This study contributes empirical evidence on uncertainty-driven information practices in highly educated labor markets and demonstrates the value of combining topic modeling with qualitative interpretation to capture online collective sense-making.