In the big data era, artificial intelligence (AI) is transforming international economics and business by enhancing human capabilities, speeding up tasks, and improving efficiency. AI's key characteristics - malleability, generativity, and continuous learning - enable ongoing innovation improvements through data collection. This has led to AI reshaping innovation processes and management strategies. While 80% of major companies have integrated AI into their core operations recently, there is limited understanding of its practical impact on organizational innovation. Although conceptual frameworks exist describing AI's potential influence on company innovation practices, real-world evidence remains scarce. In practical applications, AI supports various employee tasks but has limitations. Creative problem-solving and innovation remain predominantly human domains. The effectiveness of human-AI collaboration depends on employees' internal motivation to innovate. However, there are contrasting effects: while AI can enhance work processes, it may also negatively impact employees' psychological well-being and lead to avoidance behaviors, potentially hampering innovation efforts. Given these opposing outcomes, it is likely that AI's impact on employee innovation behavior includes both positive and negative aspects. Understanding the conditions under which AI can foster employee innovation and improve organizational efficiency becomes crucial. This research aims to investigate AI's influence on both organizational innovation processes and employee innovation behaviors.

With the rise of online communities, research has focused on how to manage the communities and foster members’ knowledge-sharing intentions. While prior studies have emphasized the importance of shared norms and values among community members, there lacks a unified conceptualization and measurement. In response, this study bridges insights from both online community research and traditional organizational culture literature to introduce the concept of online community culture. It further develops a measurement scale consisting of aggressive and supportive cultural elements, comprising nine items from the community member’s viewpoint. Through a series of studies involving several survey administrations, the scale demonstrates robust convergent, discriminant, and predictive validity. These findings have implications not only for advancing our understanding of the determinants of knowledge sharing in online communities, but also for assessing community culture and member composition, thereby facilitating sustainable online community operations.