Small and medium-sized enterprises (SMEs) play a critical role in achieving environmental sustainability, particularly in developing economies where regulatory enforcement and resource constraints remain significant challenges. Drawing on Institutional Theory, this study examines how green leadership influences environmental performance in Ghanaian SMEs, with digital innovation as a mediating variable and environmental culture as a moderating variable. Institutional Theory provides the conceptual foundation for explaining how normative pressures embedded in leadership values and organizational culture, alongside mimetic pressures associated with digital innovation adoption, shape firms’ environmental outcomes. Using survey data collected from SMEs in Ghana and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results revealed that green leadership has a significant positive effect on both digital innovation and environmental performance. Digital innovation also significantly enhances environmental performance and partially mediates the relationship between green leadership and environmental performance. Notably, the findings demonstrated that environmental culture significantly moderates the relationship between digital innovation and environmental performance, with the effect stronger in organizations with a well-developed environmental culture. This indicates that internalized environmental values amplify the effectiveness of digital innovation initiatives. The study contributes to the sustainability and organizational literature by extending Institutional Theory to the SME context in a developing economy and by clarifying the conditional role of environmental culture in translating digital innovation into superior environmental performance. Practically, the findings suggest that SME leaders and policymakers should promote environmentally oriented leadership, invest in digital innovation, and cultivate strong environmental cultures to enhance sustainability outcomes.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.

The aim was to examine the relationships between selected demographic and psychographic factors and consumers' willingness to accept content generated by advanced technological innovations (AIGC) in social infrastructure. The sample consisted of 1,308 respondents. Spearman's correlation coefficient was used to examine the relationships between ordinal variables. To assess the differences between groups of respondents, a one-way analysis of variance was used, during which multiple linear regression analysis was used to confirm the predictive power of awareness and experience in relation to AI-generated content in relation to the tendency to accept such content. The study confirmed a statistically significant but weak negative relationship between the age of respondents and their willingness to accept AIGC, with younger age groups showing a slightly higher rate of acceptance. Respondents' attitudes toward the use of personal data through AI and their overall awareness of technological trends had a more significant impact on acceptance. The findings show that respondents who are open to data collection through AI technologies show a significantly higher level of acceptance of automatically generated content. Similarly, respondents who positively evaluate the current quality of AIGC have higher expectations for the future transformation of marketing strategies and media practices. The decisive factors in the social infrastructure for the acceptance of AIGC are not so much the age of the respondents, but rather their awareness, technological literacy, and level of trust in the technology itself. The study therefore recommends increasing transparency and public awareness about the use of AI in marketing and media practices in order to strengthen consumer confidence in automated content.