In the era of artificial intelligence, human resource management has undergone significant changes compared to traditional approaches regarding value creation methods and influencing factors. This research aims to utilize grounded theory to comprehensively explore the influencing factors of value co-creation in enterprise human resource management within the context of artificial intelligence. Additionally, this research seeks to capture the dynamic relationships, causal links, and evolutionary patterns among the various elements within the system by constructing a system dynamics model. The results indicated that (1) Environmental factors primarily play a regulatory role, organizational factors serve a supportive role, and participant factors act as the driving force in influencing value co-creation in human resource management. (2) In the context of artificial intelligence, both hardware infrastructure and software capabilities can significantly impact value co-creation in human resource management. This research complements current research on the influencing factors of value co-creation in enterprise human resource management. It offers new perspectives and frameworks for the theoretical development and practical application of value co-creation in this area, supporting companies in effectively managing and developing value co-creation in human resource management.

The popularization of bridge employment is conducive to the realization of active aging, which requires not only the subjective initiative of retirees but also the active cooperation of local governments and relevant enterprises. This study combined evolutionary game theory with system dynamics to model and simulate the behavior of local government and enterprises on bridge employment, aiming to highlight the importance of government behavior and enterprise actions in the process of active aging and to analyze the effectiveness of different incentive and punishment mechanisms in promoting bridge employment. Results show that the system with dynamic incentive and punishment is easier to keep stable than the system with static incentive and punishment; specifically, the dynamic mechanism with low incentive intensity and high punishment intensity is better than other mechanisms. Finally, this study emphasized the importance of government policy to bridge employment and put forward relevant management implications.

The effectiveness of enterprise human resource management is an important topic of human resource management in academic studies, and it is the key for enterprises to achieve long-term sustainable development. Up to now, fruitful research achievements have been achieved, but there is still a lack of a systematic literature review. Based on 218 research results collected in the CNKI and SSCI databases of the WOS from 1975 to 2021, this paper systematically reviews the enterprise human resource management effectiveness from the aspects of evolution, topic overview, and content sorting by using bibliometrics with Citespace software and a systematic literature review. This paper expounds the concept of enterprise human resource management effectiveness, analyzes the existing research results, and finds out where the research gap is. Based on this, this paper puts forward the prospect of future research topics from the aspects of measurement methods, influencing factors, and results, which not only helps to systematically reveal the overview of the existing research in the field of enterprise human resource management effectiveness, but also provides direction for further research.

In the context of globalization and rapid technological advancement, the introduction of Artificial Intelligence (AI) has brought new opportunities and challenges to Human Resource Management (HRM). This study constructs an evolutionary game model to explore the strategy choices and evolutionary paths of enterprises and employees in HRM value co-creation with AI involvement. We numerically simulated the dynamic evolution of strategies under different scenarios, revealing the equilibrium characteristics of strategic interactions between enterprises and employees in the AI context. The study finds that, first, the evolutionary game system between enterprises and employees converges to two equilibrium points: {cooperation, active} and {non-cooperation, passive}. Overall, the probability of the former is 2.39 times greater than that of the latter. Second, higher initial probabilities of cooperation and active involvement, along with lower costs for cooperation and active involvement, facilitate the system’s evolution towards the {cooperation, active} equilibrium. Third, enterprises are more sensitive to the benefit distribution ratio than employees. This study provides theoretical support for effectively conducting HRM practices in the AI era through systematic analysis of HRM value co-creation behavior, along with practical policy recommendations.