Responsible Artificial Intelligence Attention and Firm Innovation: An Attention-Based View

Academic Summary
This article draws on the attention-based view (ABV) to examine whether, how, and under what conditions top management team (TMT) attention to responsible artificial intelligence (AI) influences firm innovation. We developed a 480-word responsible AI dictionary grounded in 155 academic sources and 527 corporate case descriptions, and applied it to 2452 S&P 500 earnings call transcripts (2011–2021) using natural language processing (NLP) and large language model (LLM) techniques, yielding 2670 firm-year observations. Linking these measures to US patent data, we find that greater responsible AI attention predicts more and higher-impact patents. The effect is stronger in low-technology industries and under short-term investor pressure, while the presence of a chief technology officer (CTO) does not amplify it. Mechanism analyses reveal that responsible AI attention fosters innovation by increasing investment in AI-relevant human capital and mitigating innovation risk. Theoretically, this article enriches the AI and innovation management literature by positioning responsible AI attention as a dynamic strategic asset that mobilizes resources, reduces risk, and enables contextual adaptation. Practically, findings suggest that firms can strengthen innovation by prioritizing managerial attention to responsible AI, distributing responsibility beyond technical specialists, balancing ethical safeguards with strategic flexibility, and aligning governance with investor and industry conditions.

Managerial Summary
This article examines how managerial attention to responsible artificial intelligence (AI) can enhance firm innovation. Using text analytics on 2452 earnings call transcripts from S&P 500 firms (2011–2021) and a panel of 2670 firm-year observations linked to patent outcomes, we show that firms whose top management teams (TMT) devote greater attention to responsible AI produce more and higher-impact patents. This effect is stronger in low-technology industries and when firms face short-term investor pressure; it is not amplified by having a chief technology officer (CTO). In practice, sustained attention to responsible AI tends to build AI-related skills and reduce project risk, thereby supporting a more reliable innovation pipeline. Executives should treat responsible AI as a strategic priority rather than a compliance task by establishing cross-functional governance, investing in role-based governance training, and sharing accountability across the C-suite. Innovation managers can embed ethics checkpoints (bias audits, design reviews) into project workflows to enhance stability and organizational learning. Policymakers can reinforce responsible innovation by providing clear regulatory frameworks and incentives that align ethical safeguards with competitiveness. Together, these actions can help build more durable organizational capability for responsible innovation and support long-term performance and adaptation to ongoing technological change.