Although most existing studies have considered entrepreneurial bricolage as a means to overcome resource constraints in new ventures, few have explored the direct effects of entrepreneurial bricolage on employee creativity, particularly in the context of entrepreneurial internet firms. Drawing from multiple theories, this study proposes a cross-level mediation model for the trickle-down effects of entrepreneurial bricolage and business model innovation on employee creativity. By using a 2-wave longitudinal design, survey data were collected from multiple sources, including 49 leaders and 336 (...) employees from entrepreneurial internet firms in China. Multilevel structural equation modeling was applied to analyze the cross-level mediation model. The results show that both entrepreneurial bricolage and business model innovation failed to significantly and positively direct employee creativity. Furthermore, entrepreneurial bricolage exerted a cross-level influence on employee creativity that was sequentially transmitted through between-level business model innovation and within-level creative self-efficacy. The theoretical and managerial implications of these findings are also discussed. (shrink)

Highlighting the implications of entrepreneurship education, this study examines the effects of entrepreneurship education in predicting the entrepreneurial intention of university students. The study also explores the mediating role of opportunity recognition and the moderating role of entrepreneurial learning in this process. To test our multilevel-moderated mediation model, based on a dataset containing 1,150 university students from 55 universities in the Guangdong-Hong Kong-Macao Greater Bay Area of China, hierarchical linear modeling is utilized to test the research hypotheses. The findings reveal (...) that entrepreneurship education can promote the entrepreneurial intention of students through opportunity recognition. Furthermore, entrepreneurial learning plays a moderating role in the link between entrepreneurship education and opportunity recognition. Implications for the design and delivery of entrepreneurship education are discussed. (shrink)

The purpose of this study was to apply deep learning to music perception education. Music perception therapy for autistic children using gesture interactive robots based on the concept of educational psychology and deep learning technology is proposed. First, the experimental problems are defined and explained based on the relevant theories of pedagogy. Next, gesture interactive robots and music perception education classrooms are studied based on recurrent neural networks. Then, autistic children are treated by music perception, and an electroencephalogram is used (...) to collect the music perception effect and disease diagnosis results of children. Due to significant advantages of signal feature extraction and classification, RNN is used to analyze the EEG of autistic children receiving different music perception treatments to improve classification accuracy. The experimental results are as follows. The analysis of EEG signals proves that different people have different perceptions of music, but this difference fluctuates in a certain range. The classification accuracy of the designed model is about 72–94%, and the average classification accuracy is about 85%. The average accuracy of the model for EEG classification of autistic children is 85%, and that of healthy children is 84%. The test results with similar models also prove the excellent performance of the design model. This exploration provides a reference for applying the artificial intelligence technology in music perception education to diagnose and treat autistic children. (shrink)