Economics at your fingertips  

Identification of the unique attributes and topics within Smart Things Open Innovation Communities

M. Olmedilla, H. Send and S.L. Toral

Technological Forecasting and Social Change, 2019, vol. 146, issue C, 133-147

Abstract: One of the main challenges of open innovation communities is how to create value from shared content either by selecting those ideas that are worthy of pursuit and implementation or by identifying the users' preferences and needs. These tasks can be done manually when there is an overseeable amount of content or by using computational tools when there are massive amounts of data. However, previous studies on text mining have not dealt with the identification of unique attributes, which can be defined as those contributions that are inextricably linked with a specific tag or category within open innovation websites. The uniqueness of these ideas means that they can only be obtained through a selection of one choice among several alternatives. To obtain such unique ideas and thus to also obtain innovations, this paper proposes a novel methodology called co-occurrence differential analysis. The proposed methodology combines traditional co-occurrence analysis with additional statistical processing to obtain the unique attributes and topics associated with different alternatives. The identification of unique content provides valuable information that can reveal the strengths and weaknesses of several options in a comparative fashion.

Keywords: Text mining; Unique innovations; Open communities; Unique attributes; Co-occurrence analysis; Open innovation (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.1016/j.techfore.2019.05.004

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Haili He ().

Page updated 2020-10-03
Handle: RePEc:eee:tefoso:v:146:y:2019:i:c:p:133-147