Author
Listed:
- Dotsika, Fefie
- Watkins, Andrew
Abstract
Identifying potentially disruptive technologies is crucial to safeguarding competitive advantage by enabling stakeholders to assign resources in a manner that increases the chances of exploiting the disruption and/or mitigating the ensuing risks. However, disruptive technologies and emergent trends within known disruptive domains are mostly identified ex-post. This paper contributes to the ex-ante prediction of emergent technologies within disruptive domains by proposing a literature-driven method for the forecasting of potentially disruptive technological trends. It adopts a keyword network analysis and visualisation approach for uncovering emergent thematic, structural and temporal developments within publications and applies it as a forecasting tool to an empirical study of seven disruptive domains: 3D Printing, Big Data, Bitcoin, Cloud Technologies, Internet of Things, MOOCs and Social Media. Maturing trends were found to share influential common topics identified by high degree, betweenness and closeness centrality scores. Niche and potentially emerging trends within groups were detected by means of eccentricity and farness metrics. Visualisation techniques were found effective for further clarification and trend identification. Finally, potentially disruptive trends within domains were found to be associated with high closeness paired with low degree centrality. The findings were distilled into a framework for assisting the forecasting of potentially disruptive trends.
Suggested Citation
Dotsika, Fefie & Watkins, Andrew, 2017.
"Identifying potentially disruptive trends by means of keyword network analysis,"
Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 114-127.
Handle:
RePEc:eee:tefoso:v:119:y:2017:i:c:p:114-127
DOI: 10.1016/j.techfore.2017.03.020
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:119:y:2017:i:c:p:114-127. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.