作者：Fefie Dotsika, Andrew Watkins
年份：2017
期刊：Technological Forecasting & Social Change
研究内容：通过词共现生成词网络，一系列的词语代表一个技术点。由词网路各种中心性的对比变化分析技术的破坏性趋势。
¶关键术语含义
有价值技术：In an ever-changing technological landscape where innovation is a crucial driver for economic growth and survival, it is desirable to be able to predict which technologies, when established, have the potential to revolutionise an industry, create new markets, and increase accessibility and affordability.
Disruptive innovation定义：Disruptive innovation is defined as the process of transforming a product or service that historically has been accessible at the top of a market access (i.e. for a high price or specialised skill-set) to become accessible to a new and larger population of consumers at the bottom of that market
Maturing trends were found to share influential common topics identified by high degree, betweenness and closeness centrality scores.Niche and potentiallyemerging trends within groupswere detected by means of eccentricity and farness metrics.
¶使用数据
- WOS文献数据
- Forrester,Frost & Sullivan, Gartner, IDC and Ovum.五种Business reports
¶分析方法
¶1.分析各个领域的数量分布。
结论：没有明显证据表明学术出版物总是超前于商业出版物。
¶2.网络结构与特征分析。
网络的节点是词，连线表示两个词在同一个出版物中共现的次数总和，使用UCNET等工具制作。
- 网络结构指标，包括network size，density，diameter，average degree等。
- 聚类与子网络指标，包括coefficient，Erdös number，average embeddedness，modularity等。 结论：
- 所有网络呈现low density和high clustering coefficients，与随机网络差异明显
- Positive modularity values
- Erdös number is low
¶3.节点位置分析（找出已经是disruptive 的技术）
- Degree centrality，指标越高，语义重要性越中心。
- Eigenvector centrality，扩展了Degree centrality中心性的概念，与上类似。
- Betweenness centrality，起到桥梁作用的次数越多，在信息流中更有影响力。
- Closeness centrality，与网络中其他所有点的距离和。
- Eccentricity，距离最远点的距离。 提取degree、eigenvector、betweenness 、closeness四项中最高的两个词作为main keyword，通过可视化验证了这些词确实处于central的位置。
根据不同的中心性组合得到不同的disruptive技术类型。 如High degree- Low betweenness，表示Popular mature keyword.
¶4.成熟前位置分析（找出可能成为disruptive 的技术）
有high closeness 和 low degree 的技术最有成为disruptive 的技术的趋势。
列举Twitter`s popularity等一系列网络例子来证明这种趋势的存在。
借鉴意义：
- 网络形成后的趋势分析方法。
- 例证方式。
Identifying potentially disruptive trends by means of keyword network analysis