In the field of management psychology, the matching of employees and leaders can help improve enterprise performance. Then, in the field of sustainable development, can human aspects enhance green product development performance? Does the match between leaders and employees help to advance this improvement? A survey questionnaire was used to collect data for this study; 52 leaders and their 214 employees were contacted. The findings of this study revealed that green transformational leadership is positively related to green product development performance. Besides, both green transformational leadership and individual green values have positive influences on green psychological climate, which leads to the promotion of green product development performance in the organization. Further, when green transformational leadership matches employees with green values, it is more conducive to create green psychological climate in the organization, thereby improving the green products development performance. The implications for practitioners are discussed on the basis of the empirical findings.

In the process of educational practice in the digital age, the higher education system has shifted to the online model, and the training of college students’ deep learning has become the core issue. While online teaching offers great possibilities for education, its inherent lack of interaction has always been a major limiting factor in its effectiveness. To address this challenge, interactive pedagogy is an interaction-based teaching model in which students learn knowledge more effectively through communication and interaction with teachers and classmates, which helps to improve students’ learning abilities. This study, rooted in the theoretical framework of self-determination theory and utilizing structural equation modeling for empirical analysis, seeks to investigate the influence of online interactive teaching on deep learning among university students. The research reveals that interactive teaching significantly and directly contributes to deep learning, while the fulfillment of university students’ three fundamental psychological needs indirectly fosters deep learning by fostering intrinsic motivation. This study uncovers novel dimensions of factors influencing deep learning and underscores the vital role of interactive teaching in fostering deep learning among university students. Moreover, this not only deepens the application of self-determination theory in education but also provides valuable theoretical support for online education practices.

We propose a dynamic monitoring and precision fertilization decision system for agricultural soil nutrients, integrating UAV remote sensing and GIS technologies to address the limitations of traditional soil nutrient assessment methods. The proposed method combines multi-source data fusion, including hyperspectral and multispectral UAV imagery with ground sensor data, to achieve high-resolution spatial and spectral analysis of soil nutrients. Real-time data processing algorithms enable rapid updates of soil nutrient status, while a time-series dynamic model captures seasonal variations and crop growth stage influences, improving prediction accuracy (RMSE reductions of 43–70% for nitrogen, phosphorus, and potassium compared to conventional laboratory-based methods and satellite NDVI approaches). The experimental validation compared the proposed system against two conventional approaches: (1) laboratory soil testing with standardized fertilization recommendations and (2) satellite NDVI-based fertilization. Field trials across three distinct agroecological zones demonstrated that the proposed system reduced fertilizer inputs by 18–27% while increasing crop yields by 4–11%, outperforming both conventional methods. Furthermore, an intelligent fertilization decision model generates tailored fertilization plans by analyzing real-time soil conditions, crop demands, and climate factors, with continuous learning enhancing its precision over time. The system also incorporates GIS-based visualization tools, providing intuitive spatial representations of nutrient distributions and interactive functionalities for detailed insights. Our approach significantly advances precision agriculture by automating the entire workflow from data collection to decision-making, reducing resource waste and optimizing crop yields. The integration of UAV remote sensing, dynamic modeling, and machine learning distinguishes this work from conventional static systems, offering a scalable and adaptive framework for sustainable farming practices.

With the popularity of online education, multiple technology-based educational tools are gradually being introduced into online learning. The role of gamification in online education has been of interest to researchers. Based on learners’ visual, auditory, and kinesthetic (VAK) learning styles, this study uses an empirical research method to investigate the behavioral intention of students to participate in online gamified classrooms in selected universities located in Guangdong province and Macao. The main contributions of this study are to focus on the impact that differences in learning styles may have on the behavioral intentions of learners and to include the “perceived learning task” as an external variable in the theoretical framework. The main research findings are: perceived usefulness and enjoyment are partially mediated between VAK learning styles and the intention to participate in online gamified classrooms; and perceived learning tasks are partially mediated between perceived usefulness and the intention to participate in online gamified classrooms. According to the findings and the Technology Acceptance Model (TAM), this study constructs the Technology-Learning Behavior Acceptance Model (T-LBAM) to explore the intrinsic influencing factors of students’ intention to participate in gamified online classes and makes suggestions for future online gamification teaching.

Purpose: The purpose of this paper is to explore the knowledge infrastructure of destination food research based on a systematic literature review. This bibliometric analysis involves authors, institutions, countries and co-citation networks and discloses trending developments in the field.Design/methodology/approach: This study is based on 176 articles published in hospitality and tourism journals during the period 2000–2018 and bibliometric data downloaded from the ISI Web of Science. This review applies bibliometric analysis, citation network analysis and content analysis to examine the structure and networks of destination food knowledge so as to provide destination food researchers with a reference guide to the context, methods and focus of previous studies.Findings: The findings demonstrate the development and trends of destination food research. This is the first study to address the limitations of prior systematic literature reviews by applying network analysis to reveal the interrelated structures and properties of the destination food research domain.Practical implications: Practitioners can also discover trends in the design of development strategies, as well as identify extended themes that may be integrated with research in the field of destination food at present.Originality/value: As a summary of key characteristics, this report is a useful reference guide to previous studies for researchers. This study presents core content about the destination food field and identifies development trends within destination food research. Based on the results, further research directions are provided.

In the context of global economic digital transformation and technological innovation, the application of AI Technology has a profound impact on corporate innovation and development. Existing research has primarily focused on the direct effect of AI Technology on Corporate Innovation Performance, while there is limited exploration of its interaction with organizational learning mechanisms. Based on the Dynamic Capabilities Theory, this study constructs a framework of “Technology—Individual Learning Capability—Team Learning Capability—Innovation Performance”, analyzing how AI Technology enhances learning capabilities to drive improvements in innovation performance and explores the moderating role of Organizational Learning Capability. Through empirical analysis of data from Specialized, Refined, Unique, and Innovative Enterprises in China, the study finds that AI Technology significantly enhances Corporate Innovation Performance, with Organizational Learning Capability playing a critical moderating role. Additionally, heterogeneity analysis indicates that factors such as production factors, industry characteristics, and firm size significantly influence the effectiveness of AI Technology in enhancing innovation performance. This research reveals the pathway through which AI Technology optimizes organizational learning mechanisms to improve innovation performance, offering both theoretical support and practical guidance for corporate strategic decision-making.