Econ Papers Daily / 期刊论文
驾驭崎岖数据景观：数据外推技术对知识生产的影响
Navigating the Rugged Data Landscape: The Impact of Data-Extrapolation Technologies on Knowledge Production
摘要
Data-extrapolation technologies enable progress by importing and applying empirical patterns from one domain to another. Their reliance on empirical patterns, however, is also their limitation: these tools can only be applied where data are abundant. Using the setting of structural biology, I investigate the impact of a data-extrapolation technology on innovation. I exploit the variation in prior data availability to compare areas in which the tool was usable to those in which it was not. I find that the tool increased the quantity of research in data-rich areas: the resulting output yielded limited biological insights but was methodologically rigorous and contributed to downstream research. This paper was accepted by Toby Stuart, entrepreneurship and innovation. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.04115 .
中文摘要
数据外推技术通过将某一领域的经验模式引入并应用于另一领域来推动进步。然而，其对经验模式的依赖也构成了局限：这些工具只能应用于数据丰富的领域。本文以结构生物学为背景，研究一种数据外推技术对创新的影响。我利用先前数据可得性的差异，将可使用该工具的区域与无法使用的区域进行比较。研究发现，该工具增加了数据丰富区域的研究数量：由此产生的成果所获得的生物学洞见有限，但在方法上严谨，并对下游研究有所贡献。本文由创业与创新领域编辑Toby Stuart接收。补充材料：在线附录和数据文件可在https://doi.org/10.1287/mnsc.2023.04115获取。