| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w29767 |
| 来源ID | Working Paper 29767 |
| Could Machine Learning be a General Purpose Technology? A Comparison of Emerging Technologies Using Data from Online Job Postings | |
| Avi Goldfarb; Bledi Taska; Florenta Teodoridis | |
| 发表日期 | 2022-02-21 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | General purpose technologies (GPTs) push out the production possibility frontier and are of strategic importance to managers and policymakers. While theoretical models that explain the characteristics, benefits, and approaches to create and capture value from GPTs have advanced significantly, empirical methods to identify GPTs are lagging. The handful of available attempts are typically context specific and rely on hindsight. For managers deciding on technology strategy, it means that the classification, when available, comes too late. We propose a more universal approach of assessing the GPT likelihood of emerging technologies using data from online job postings. We benchmark our approach against prevailing empirical GPT methods that exploit patent data and provide an application on a set of emerging technologies. Our application exercise suggests that a cluster of technologies comprised of machine learning and related data science technologies is relatively likely to be GPT. |
| 主题 | Development and Growth ; Innovation and R& ; D |
| URL | https://www.nber.org/papers/w29767 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/587441 |
| 推荐引用方式 GB/T 7714 | Avi Goldfarb,Bledi Taska,Florenta Teodoridis. Could Machine Learning be a General Purpose Technology? A Comparison of Emerging Technologies Using Data from Online Job Postings. 2022. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w29767.pdf（743KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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