In the context of intensifying global competition, productive entrepreneurship plays an important role in industrial upgrading and sustainable economic development. This study explores how the institutional environment affects productive entrepreneurship through different allocation paths. By integrating multiple institutional logics and using PD-QCA (Panel Data fsQCA) methods, we examined data from 76 countries for the period 2012–2022. The results show that institutional complexity provides entrepreneurs with diversified opportunities and resources, but also brings decision-making challenges due to the conflict of institutional factors. We identified three main models: (1) Government-Market Collaborative Model: driven by government support and property rights protection. (2) Market-Led Model: financial support is crucial. (3) Three-Wheel Drive Model: combines property rights protection, market opening, and social norms that promote entrepreneurship. This study contributes to the literature by revealing the multidimensional effects of institutional complexity and provides practical implications for policymakers seeking to promote high-quality entrepreneurial activity. Limitations include the static nature of the data and the need to further explore dynamic institutional interactions over time.

Rising costs, complex supply chain management, and stringent regulations have created significant financial burdens on business sustainability, calling for new and rapid strategies to help enterprises transform. Supply chain digitalization (SCD) has emerged as a promising approach in the context of digitalization and globalization, with the potential to reduce an enterprise’s debt costs. Developing a strategic framework for SCD that effectively reduces the cost of debt (CoD) has become a key academic challenge, critical for ensuring business sustainability. To this end, under the perspective of four flows, SCD is deconstructed into four distinct features: logistics flow digitalization (LFD), product flow digitalization (PFD), information flow digitalization (IFD), and capital flow digitalization (CFD). To precisely measure the four SCD features and the dependent variable, COD, publicly available data from Chinese listed manufacturing enterprises such as annual report texts and financial statement data are collected, and various data mining technologies are also used to conduct data measurement and data processing. To comprehensively investigate the impact pattern of SCD on CoD, we employed the explainable machine learning methodology for data analysis. This methodology involved in-depth data discussions, cross-validation utilizing a series of machine learning models, and the utilization of Shapley additive explanations (SHAP) to explain the results generated by the models. To conduct sensitivity analysis, permutation feature importance (PFI) and partial dependence plots (PDPs) were also incorporated as supplementary explanatory methods, providing additional insights into the model’s explainability. Through the aforementioned research processes, the following findings are obtained: SCD can play a role in reducing CoD, but the effects of different SCD features are not exactly the same. Among the four SCD features, LFD, PFD, and IFD have the potential to significantly reduce CoD, with PFD having the most substantial impact, followed by LFD and IFD. In contrast, CFD has a relatively weak impact, and its role is challenging to discern. These findings provide significant guidance for enterprises in furthering their digitalization and supply chain development, helping them optimize SCD strategies more accurately to reduce CoD.

Purpose: The purpose of this paper is to explore the differences in the impact mechanism of different types of enterprises’ digital transformation on green innovation performance. Design/methodology/approach: From the perspective of production and operation, this study deconstructs enterprise digital transformation into six characteristics and measures these characteristics using data from listed industrial companies between 2016 and 2020. Various machine learning methods, such as cluster analysis and Bayesian networks, are applied to explore the complex impact mechanism of digital transformation characteristics on green innovation performance. Findings: The findings are as follows: (1) Enterprises with different digital transformation modes exhibit distinct characteristic differences. (2) Different digital transformation modes have diverse impact mechanisms on green innovation performance, and higher levels of digital transformation correspond to more complex impact relationships. (3) Emphasizing digital transformation innovation activities within enterprises has a positive ripple effect on green innovation performance. (4) Digital asset input has a limited influence on green innovation performance and exhibits varied effects on enterprises at different levels of digital transformation. Originality/value: The conclusions of this research will help enterprises understand their digital competitiveness and guide their digital transformation practices to enhance green innovation performance. These findings will also assist governments in formulating policies that promote the development of green innovation in the digital economy era.

The rapid advancement of digitalization is reshaping multiple aspects of firms and transforming the nature of innovation and entrepreneurship. However, a mature solution to accurately measure the digitalization maturity of enterprises is lacking. To help firms better understand their digitalization competitiveness, this study examined Chinese listed companies and, drawing on publicly available multi-dimensional heterogeneous data, employed the skyline algorithm and entropy weight method to construct a scientific measure and evaluation of digitalization maturity. To assess the reliability and practical feasibility of the measurement, this study further conducted heterogeneity analyses across industries and regions in China based on the evaluation results. The findings indicated that firms with superior digitalization maturity were predominantly concentrated in industries that were highly sensitive to digital technologies, as well as in regions characterized by stronger resource endowments and more frequent knowledge exchanges. In contrast, the digital transformation of traditional industries, such as the real estate sector, and of the regions where these industries are concentrated remained relatively weak and required further strengthening. These findings provide significant implications for both policymakers and industry stakeholders.