This study offers a new perspective on the depth-versus-breadth debate in innovation strategy, by modeling inventive search within dynamic collective knowledge systems, and underscoring the importance of timing for technological impact. Using frontier machine learning to project patent citation networks in hyperbolic space, we analyze 4.9 million U.S. patents to examine how search strategies give rise to distinct temporal patterns in impact accumulation. We find that inventions based on deep search, which relies on a specialized understanding of complex recombination structures, drive higher short-term impact through early adoption within specialized communities, but face diminishing returns as innovations become "locked-in" with limited diffusion potential. Conversely, when inventions are grounded in broad search that spans disparate domains, they encounter initial resistance but achieve wider diffusion and greater long-term impact by reaching cognitively diverse audiences. Individual inventions require both depth and breadth for stable impact. Organizations can strategically balance approaches across multiple inventions: using depth to build reliable technological infrastructure while pursuing breadth to expand applications. We advance innovation theory by demonstrating how deep and broad search strategies distinctly shape the timing and trajectory of technological impact, and how individual inventors and organizations can leverage these mechanisms to balance exploitation and exploration.

翻译：本研究通过建模动态集体知识系统中的发明搜索，并强调技术影响时机的重要性，为创新战略中的深度与广度之争提供了新视角。利用前沿机器学习在双曲空间中投影专利引用网络，我们分析了490万项美国专利以考察搜索策略如何引发影响积累的差异化时间模式。研究发现：基于深度搜索的发明——依赖于对复杂重组结构的专业化理解——通过专业社区内的早期采用产生更高的短期影响，但随着创新被"锁定"且扩散潜力有限而面临收益递减；相反，当发明建立在跨越不同领域的广度搜索基础上时，虽会遭遇初始阻力，但通过触达认知多元的受众能实现更广泛的扩散和更大的长期影响。个体发明需要深度与广度的结合才能获得稳定影响。组织可在多项发明中战略性地平衡两种路径：利用深度构建可靠的技术基础设施，同时通过广度拓展应用场景。我们通过论证深度与广度搜索策略如何差异化地形塑技术影响的时机与轨迹，以及个体发明者和组织如何利用这些机制平衡利用式与探索式创新，推动了创新理论的发展。