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.

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.

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.

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.

As globalization deepens and the digital economy rapidly develops, cross-border e-commerce, especially live-streaming e-commerce, has emerged as a significant driver of international trade growth. However, the highly unpredictable sales demand in this sector and external factors such as the COVID-19 pandemic and Brexit have posed significant challenges in accurately forecasting sales within the UK live-streaming e-commerce market. To address these challenges, we propose a novel sales forecasting framework utilizing the Temporal Fusion Transformer (TFT) model. Our multimodal approach integrates diverse time series data, including historical sales, key opinion leader (KOL) influence, and seasonal patterns. The Temporal Fusion Transformer (TFT) model demonstrated consistently lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE) across all forecasting horizons compared to other machine learning approaches, including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Gated Recurrent Unit(GPU)-accelerated architectures. Furthermore, it exhibited significantly superior performance over traditional time-series methods such as the Autoregressive Integrated Moving Average (ARIMA) model. This research proposes a phased framework for short-term, medium-term, and long-term forecasting, providing a fresh perspective for product forecasting studies and offering significant theoretical support for cross-border e-commerce enterprises in product life cycle management.

Supported by government policies, the innovation of China’s solar photovoltaic industry has been triggered greatly. As an important part of public policy, various talent policies have been issued to increase support for innovation. Therefore, it is essential to study the relationship between talent policy and firm innovation of Chinese solar photovoltaic industry. However, there is less empirical research on this topic. In this context, this study examined whether talent policy influences firm innovation. To examine the underlying causal mechanism, this study explored the role of human capital quality structure and human capital function structure in the relationship between talent policy and firm innovation. Based on the buffering and bridging mechanism of resource dependence theory, an empirical model of talent policy, human capital structure and firm innovation were conceptualized. Using an unbalanced panel data of 101 listed firms of the solar photovoltaic industry in China from 2008 to 2021, the random effect GLS regression was employed to empirically test the impact of talent policy on firm innovation, the mediating effects of human capital quality structure and human capital function structure, and examined the heterogeneous impact of nature of property rights and R&D intensity on the above relationships. The results show that talent policy can significantly promote firm innovation. Human capital structure plays mediating effects on the relationship between talent policy and firm innovation. Moreover, the talent policy and human capital structure’s effects on firm innovation are more significant in non-state-owned firms and firms with low R&D intensity. This study provides an important reference for promoting firm innovation through human capital structure in the construction of the talent policy.