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Artificial intelligence (AI) innovation offers promising opportunities for underperforming firms but entails risks of failure. Whether such firms should proactively increase or conservatively decrease their AI engagement remains a controversial but important issue. This paper combines the attention-based view and resource orchestration theory to investigate this issue using panel data on Chinese A-share listed companies from 2008 to 2022. Our results reveal that performance shortfalls significantly inhibit AI innovation. The mechanism analysis confirms that performance shortfalls negatively affect AI innovation by reducing executive attention to AI. The moderation analysis indicates that government support for AI can mitigate this inhibitory effect. Heterogeneity tests reveal that the inhibitory effect is weaker when firms have more abundant external AI resources and stronger risk-taking internal governance. Further analysis shows that performance shortfalls negatively affect various R&D models for AI innovation and different types of AI technologies. Nevertheless, underperforming firms can enhance their long-term profitability by continuing AI innovation. These findings provide theoretical and practical guidance for underperforming firms in making AI innovation decisions.
Fan et al. (Thu,) studied this question.