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Abstract
As a core driving force of the most recent round of industrial transformation, artificial intelligence has triggered significant changes in the world economic structure, profoundly changed our life and way of thinking, and achieved an overall leap in social productivity. This paper aims to examine the effect of knowledge transfer performance on the artificial intelligence industry innovation network and the path artificial intelligence enterprises can take to promote sustainable development through knowledge transfer in the above context. First, we construct a theoretical hypothesis and conceptual model of the innovation network knowledge transfer mechanism within the artificial intelligence industry. Then, we collect data from questionnaires distributed to Chinese artificial intelligence enterprises that participate in the innovation network. Moreover, we empirically analyze the impact of innovation network characteristics, organizational distance, knowledge transfer characteristics, and knowledge receiver characteristics on knowledge transfer performance and verify the hypotheses proposed in the conceptual model. The results indicate that innovation network centrality and organizational culture distance have a significant effect on knowledge transfer performance, with influencing factors including network scale, implicit knowledge transfer, receiver's willingness to receive, and receiver's capacity to absorb knowledge. For sustainable knowledge transfer performance on promoting Chinese artificial intelligence enterprises innovation, this paper finally delivers valuable insights and suggestions.
Highlights
Artificial Intelligence (AI) is a strategic technology leading the future
In the context of the global economic downturn, a new round of scientific and technological revolution, industrial transformation, and social progress driven by AI has been emerging, which has rekindled hope for the future development of the world economy
The knowledge transfer occurring in the AI industry innovation network can promote the experience if sharing and technology exchange among all participants and help all parties meet common challenges, avoid potential risks, and solve practical problems
Summary
Artificial Intelligence (AI) is a strategic technology leading the future. Most developed countries regard the development of AI as a major strategy for enhancing national competitiveness and safeguarding national security. The characteristics of the AI industry innovation network are positively related to the knowledge transfer performance of Chinese AI enterprises. AI industry innovation network scale is positively related to the knowledge transfer performance of Chinese AI enterprises. The classified regression analysis results showed that network centrality and network scale had a significant effect on knowledge transfer performance, while relationship strength, stability, and reciprocity were negatively related to knowledge transfer performance and failed to pass the significance test (see Table 12). Classified regression analysis on the organizational distance between the network subjects and knowledge transfer performance in AI industry innovation network. In the proposed conceptual model, we divided the 11 explanatory variables into four categories: innovation network characteristics, organizational distance, characteristics of transferred knowledge, and knowledge receiver factors.
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