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Student engagement is of critical importance because it can help students gain more knowledge from the course. This paper investigates how to improve student engagement of graduate students in seminar teaching, and the results show that student self-efficacy, classmate support, and teacher support have significant positive effects on student engagement. These findings highlight the value of self-efficacy, classmate support, and teacher support. Therefore, college should take measures to promote self-efficacy, classmate support, and teacher support.

Employee innovative behavior is crucial for organizations to engage in innovative activities and gain competitive advantages in the context of digital transformation. Despite many studies having focused on the relationship between leadership and employee innovative behavior, the role of digital leadership and the underlying mechanisms for employee innovative behavior remain unclear. Using the cognitive–affective processing system framework, the study investigated the dual mediating role of psychological empowerment and affective commitment between digital leadership and employee innovative behavior and the moderating role of a proactive personality in such relationships. Employing data from 359 employees, the study conducted structure equation modeling to examine the hypotheses. The results show that digital leadership influences employee innovative behavior through psychological empowerment but not affective commitment. Furthermore, a proactive personality does not moderate the direct effect of digital leadership on psychological empowerment and affective commitment or the indirect effect of digital leadership on employee innovative behavior. Theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

Although previous studies have predominantly dealt with innovation ambidexterity, they have only focused on a single innovation activity and overlooked the interaction of innovation activities. Drawing on organizational ambidexterity theory, this study established four types of innovation configurations: dual exploration (technology exploration and business model exploration), business model leveraging (technology exploration and business model exploitation), technology leveraging (technology exploitation and business model exploration), and dual exploitation (technology exploitation and business model exploitation). Using the panel data of 613 listed manufacturing firms in China, this study examined whether and how configurations of ambidextrous innovation affect firm performance in the context of digital transformation. Empirical results provide evidence that a dual exploration and technology leveraging strategy has a positive impact on firm performance, while a dual exploitation and business model leveraging strategy has the opposite effect and is subject to the moderating influence of the level of digitalization. Under high levels of digitalization, the positive effect of the dual exploration strategy on firm performance becomes more significant, while the effects of others are weakened. This study contributes to the organizational ambidexterity literature by providing a finer-grained understanding of the effect of ambidextrous innovation from a configurational perspective. This study also contributes to the digitalization transformation literature by revealing the moderating role of digitalization. [ABSTRACT FROM AUTHOR]

In recent years, the relationship between digital transformation and innovation became very popular topics, attracting extensive attention, and inspiring a number of documents. Although much literature discusses the intersection of both fields, most works offer neither a complete nor a truly objective overview of the current state of research. Therefore, there is a need for a comprehensive and objective review of research themes to analyze the intersection. For this purpose, based on the literature collected from the Web of Science (WoS) database published between 1994 and 2021, co-word analysis was carried out to explore research themes and identify the most salient themes in digital transformation and innovation research. The results of scientific output show that digital transformation and innovation is attracting increasing academic interest of scholars from many countries and different fields. The distribution of high-frequency keywords shows that the research in this field is multidisciplinary, including not only many economic and management fields, but also many classical theories and research methods. The clustering results of keywords reveal five clusters of themes: diffusion and adoption of technology and innovation, digital innovation management, digital transformation management, digital platform and ecosystem, and digital entrepreneurship and economy. According to the results of strategic diagram and performance analysis, digital innovation management and digital transformation management are the mainstream of research, while digital platform and ecosystem and digital entrepreneurship and economy have strong development potential. This study provides a snapshot of the thematic development of digital transformation and innovation research, enabling researchers to better master the current situation and suggesting the development trend in the future. [ABSTRACT FROM AUTHOR]

Purpose – This paper aims to unravel the technological innovation pattern in China’s aerospace industry. The technological innovation pattern of China’s aerospace industry is identified and its theoretical foundation, structure, philosophy, formation and effects on the development of China’s aerospace industry are explored. Design/methodology/approach – First, the theoretical foundation of synergy innovation of China’s aerospace industry is reviewed to further identify the technological innovation pattern. Second, Chinese ancient philosophy (dialectical thinking) is used to explain the structure and process of synergy innovation in China’s aerospace industry. Third, the formation process of synergy innovation is introduced, and, finally, the effects of synergy innovation are discussed. Findings – The technological innovation pattern of China’s aerospace industry has undergone an evolutionary process. During this process, China’s aerospace firms have formed a unique technological innovation pattern, synergy innovation, under China’s special political and economic background. The synergy innovation has three characteristics, including original, integrated and application-based. The synergy innovation pattern application is one of the most important reasons behind the great achievements of China’s aerospace industry. Originality/value – A unique technological innovation pattern, synergy innovation, is proposed for the first time. A new perspective for understanding innovation is provided by applying the Chinese dialectical thinking to decipher the philosophy of the technological innovation pattern. Based on this, this paper suggests that China’s aerospace industry should follow the situation and apply the synergy innovation pattern to achieve development and growth. This paper also illustrates a multi-method approach and emphasizes the different levels of organizing for innovation. [ABSTRACT FROM AUTHOR]

In China, SMEs are facing financing difficulties, and commercial banks and financial institutions are the main financing channels for SMEs. Thus, a reasonable and efficient credit risk assessment system is important for credit markets. Based on traditional statistical methods and AI technology, a soft voting fusion model, which incorporates logistic regression, support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), is constructed to improve the predictive accuracy of SMEs' credit risk. To verify the feasibility and effectiveness of the proposed model, we use data from 123 SMEs nationwide that worked with a Chinese bank from 2016 to 2020, including financial information and default records. The results show that the accuracy of the soft voting fusion model is higher than that of a single machine learning (ML) algorithm, which provides a theoretical basis for the government to control credit risk in the future and offers important references for banks to make credit decisions. [ABSTRACT FROM AUTHOR]