The traditional Jun porcelain industry has long suffered from a systemic disconnect among design, craftsmanship, and market feedback, which hinders its digital and intelligent transformation. This study proposes an enterprise information system innovation framework driven by digital twin and artificial intelligence-generated content. The system constructs a multi-scale digital twin spanning the kiln, form, and glaze, enabling predictable and interactive control of the kiln transformation process. The firing success probability is embedded as a soft prompt into a low-rank adaptation-fine-tuned Stable Diffusion model, integrating creativity with process constraints. A crowdsourced evaluation and reinforcement learning mechanism enables a “perception-generation-feedback” closed loop. Field tests in Yuzhou enterprises show the system significantly enhances process transparency, design freedom, and market agility. It offers a scalable information system paradigm for the digital transformation of intangible cultural heritage enterprises.
The digital economy (DIG) has transformed traditional industries and advanced agricultural development. Using panel data from 30 Chinese provinces between 2012 and 2021, this study developed evaluation index systems to measure the level of DIG development and agricultural development. The entropy weighting method quantified these indices, while a two-way fixed effects model and spatial Durbin model assessed the direct and spatial effects of the DIG on the quality of agricultural development. The results showed significant regional disparities in DIG advancement and agricultural development. The DIG significantly boosted agricultural quality, with a greater influence in more urbanized provinces. Additionally, it demonstrated a positive spatial spillover effect; improvements in one region benefitted neighboring areas. These findings deepened the understanding of the DIG’s impact on agriculture and provided a basis for regionally coordinated policy for agricultural modernization.
This study utilizes panel data of worldwide listed companies from 2013 to 2022 and employs a fixed-effects model to empirically examine the impact of artificial intelligence (AI) on corporate sustainable development and its underlying mechanisms. The results indicate that AI significantly enhances corporate sustainable development capabilities. Heterogeneity analysis reveals that the effect of AI on sustainable development varies across firm characteristics such as firm size, market competition intensity, and firm age. Specifically, AI has a more pronounced impact on firms with larger asset scales compared to those with smaller scales, firms with fewer employees compared to those with more employees, firms in highly competitive markets compared to those in less competitive markets, and mature firms compared to younger firms. Mechanism analysis shows that AI facilitates corporate sustainable development by reducing information asymmetry, enhancing internal financial efficiency through real-time cash flow monitoring and predictive analytics, and alleviating financing constraints by improving access to diversified financial services. Furthermore, AI supports sustainability by optimizing environmental and social performance, thereby enhancing ESG ratings and advancing sustainable development goals. This study establishes a theoretical framework linking artificial intelligence and corporate sustainability based on the Resource-Based View (RBV). By further integrating perspectives on financing, collaboration, and ESG performance, the research offers valuable insights for firms and policymakers aiming to enhance sustainable competitiveness.
This study utilizes panel data of worldwide listed companies from 2013 to 2022 and employs a fixed-effects model to empirically examine the impact of artificial intelligence (AI) on corporate sustainable development and its underlying mechanisms. The results indicate that AI significantly enhances corporate sustainable development capabilities. Heterogeneity analysis reveals that the effect of AI on sustainable development varies across firm characteristics such as firm size, market competition intensity, and firm age. Specifically, AI has a more pronounced impact on firms with larger asset scales compared to those with smaller scales, firms with fewer employees compared to those with more employees, firms in highly competitive markets compared to those in less competitive markets, and mature firms compared to younger firms. Mechanism analysis shows that AI facilitates corporate sustainable development by reducing information asymmetry, enhancing internal financial efficiency through real-time cash flow monitoring and predictive analytics, and alleviating financing constraints by improving access to diversified financial services. Furthermore, AI supports sustainability by optimizing environmental and social performance, thereby enhancing ESG ratings and advancing sustainable development goals. This study establishes a theoretical framework linking artificial intelligence and corporate sustainability based on the Resource-Based View (RBV). By further integrating perspectives on financing, collaboration, and ESG performance, the research offers valuable insights for firms and policymakers aiming to enhance sustainable competitiveness.
As the uncertainty of business environment and competition strength increases, a large number of enterprises implement cloud ERP so as to overcome these adverse changes. The introduction of cloud ERP does not merely mean the introduction of an online information system; it comprises the absorption of management process which is inherent in cloud ERP package and the formation of a new organization structure accompanied by transformation process. The main propose of this research is to examine the performances in cloud ERP introduced enterprises by utilizing balanced scorecard. To test the model, structural equation modeling is employed to analyze data collected from 155 enterprises which have introduced cloud ERP for more than one year. Based upon the empirical results, it is found that the cloud ERP can significantly enhance the operational efficiency and decision-making ability of enterprises. This research contributes to the understanding of cloud ERP and provides practical guidance for enterprises to optimize their operations through the adoption of cloud ERP systems.
