Demand for Long-Term-Oriented Human Capital as a Strategic Response to Innovation Aspiration Gaps: An Analysis of Online Job Postings by Firms

Against the backdrop of the innovation aspiration gap (IAG), how firms strategically adjust their human capital structure to address challenges remains a critical issue that has not been fully explored in academia. Integrating behavioral theory and human capital theory, this study explores how firms respond to the innovation aspiration gap by recruiting long-term oriented (LTO) talent. Utilizing web crawling technology, this study collected job posting data from recruitment platforms for listed companies in China's high-tech industry from 2014 to 2024, and employed the Qwen-2.5 large language model to perform semantic analysis on job descriptions to identify positions requiring LTO talent. The findings reveal that IAG positively influences corporate demand for LTO talent. Drawing on the process perspective of performance feedback, this study examines the contextual factors influencing this relationship. In the performance evaluation stage, IAG persistence negatively moderates this positive relationship. In the attribution stage, AI technology adoption exerts a positive moderating effect. In the search and change stage, executive risk preference similarly exerts a positive moderating effect. These results were validated through a series of endogeneity and robustness checks. Further analysis indicates that recruiting LTO talent effectively narrows the firm's innovation aspiration gap. Methodologically, this study extends the measurement paradigm for complex managerial traits by utilizing large language models. Theoretically, it bridges the logical disconnect between performance feedback and strategic execution from a human capital perspective, and deepens research on the boundary conditions of the innovation aspiration gap based on a process perspective.