Feature Distributions of Technologies

IF 2.3 4区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
Systems Pub Date : 2024-07-26 DOI:10.3390/systems12080268
Jiannan Zhu, Chao Deng, Jiaofeng Pan, Fu Gu, Jianfeng Guo
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Abstract

In this study, we propose a big data-based method for characterizing the feature distributions of multiple technologies within a specific domain. Traditional approaches, such as Gartner’s hype cycle or S-curve model, portray the developmental trajectory of individual technologies. However, these approaches are insufficient to encapsulate the aggregate characteristic distribution of multiple technologies within a specific domain. Thus, this study proposes an innovative method in terms of four proposed features, namely versatility, significance, commerciality, and disruptiveness, to characterize the technologies within a given domain. The research methodology involves that the features of technologies are quantitively portrayed using the representative keywords and volumes of returned search results from Google and Google Scholar in two-dimensional analytical spaces of technique and application. We demonstrate the applicability of this method using 452 technologies in the domain of intelligent robotics. The results of our assessment indicate that the versatility values are normally distributed, while the values of significance, commerciality, and disruptiveness follow power-law distributions, in which few technologies possess higher feature values. We also show that significant technologies are more likely to be commercialized or cause potential disruption, as such technologies have higher scores in these features. Further, we validly prove the robustness of our approach by comparing historical trends with the literature and characterizing technologies in reduced analytical spaces. Our method can be widely applied in analyzing feature distributions of technologies in different domains, and it can potentially be exploited in decisions like investment, trade, and science policy.
技术的特征分布
在本研究中,我们提出了一种基于大数据的方法,用于描述特定领域内多种技术的特征分布。传统方法,如 Gartner 的炒作周期或 S 曲线模型,描绘了单项技术的发展轨迹。然而,这些方法不足以概括特定领域内多种技术的总体特征分布。因此,本研究提出了一种创新方法,即通过四个拟议特征(即通用性、重要性、商业性和颠覆性)来描述特定领域内的技术特征。研究方法包括在技术和应用的二维分析空间中,利用谷歌和谷歌学术搜索结果中的代表性关键词和返回量,对技术特征进行量化描述。我们使用智能机器人领域的 452 项技术演示了这一方法的适用性。我们的评估结果表明,通用性值呈正态分布,而重要性、商业性和破坏性值则呈幂律分布,其中很少有技术拥有较高的特征值。我们还表明,重要技术更有可能商业化或造成潜在破坏,因为这类技术在这些特征上的得分更高。此外,我们还将历史趋势与文献进行了比较,并在缩小的分析空间中对技术进行了特征描述,从而有效证明了我们方法的稳健性。我们的方法可广泛应用于分析不同领域技术的特征分布,并有可能在投资、贸易和科学政策等决策中加以利用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Systems
Systems Decision Sciences-Information Systems and Management
CiteScore
2.80
自引率
15.80%
发文量
204
审稿时长
11 weeks
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