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A scientometric exploration of crowdsourcing: research clusters and applications

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A scientometric exploration of crowdsourcing: research clusters and applications. / Ozcan, Sercan; Boye, David Oluwatobilob Adjetey; Arsenyan, Jbid; Trott, Paul.

In: IEEE Transactions on Engineering Management, Vol. 0, 0, 20.10.2020, p. 0.

Research output: Contribution to journalArticlepeer-review

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@article{377837dfcdfd497289ceb126e46d475b,
title = "A scientometric exploration of crowdsourcing: research clusters and applications",
abstract = "Crowdsourcing is a multidisciplinary research area, it represents a rapidly expanding field where new applications are constantly emerging. Research in this area has investigated its use for citizen science in data gathering for research and crowdsourcing for industrial innovation. Previous studies have reviewed and categorised crowdsourcing research using qualitative methods. This has led to the limited coverage of the entire field, using smaller discrete parts of the literature and mostly reviewing the industrial aspects of crowdsourcing. This study uses a scientometric analysis of 7,059 publications over the period 2006 - 2019 to map crowdsourcing research to identify clusters and applications. Our results are the first in the literature to map crowdsourcing research holistically. We classify its usage in the three domains of innovation, engineering, and science where 11 categories and 26 sub-categories are further developed. The results of this study reveal that the most active scientific clusters where crowdsourcing is used are Environmental Sciences and Ecology. For the engineering domain, it is Computer Science, Telecommunication and Operations Research. In innovation, idea crowdsourcing, crowdfunding and crowd creation are the most frequent areas. The findings of this study map crowdsourcing usage across different fields and illustrate emerging crowdsourcing application",
keywords = "Crowdsourcing, Scientometric, Text Mining, Emerging Clusters, Research trends",
author = "Sercan Ozcan and Boye, {David Oluwatobilob Adjetey} and Jbid Arsenyan and Paul Trott",
year = "2020",
month = oct,
day = "20",
doi = "10.1109/TEM.2020.3027973",
language = "English",
volume = "0",
pages = "0",
journal = "IEEE Transactions on Engineering Management",
issn = "0018-9391",
publisher = "IEEE/ IAPR",

}

RIS

TY - JOUR

T1 - A scientometric exploration of crowdsourcing: research clusters and applications

AU - Ozcan, Sercan

AU - Boye, David Oluwatobilob Adjetey

AU - Arsenyan, Jbid

AU - Trott, Paul

PY - 2020/10/20

Y1 - 2020/10/20

N2 - Crowdsourcing is a multidisciplinary research area, it represents a rapidly expanding field where new applications are constantly emerging. Research in this area has investigated its use for citizen science in data gathering for research and crowdsourcing for industrial innovation. Previous studies have reviewed and categorised crowdsourcing research using qualitative methods. This has led to the limited coverage of the entire field, using smaller discrete parts of the literature and mostly reviewing the industrial aspects of crowdsourcing. This study uses a scientometric analysis of 7,059 publications over the period 2006 - 2019 to map crowdsourcing research to identify clusters and applications. Our results are the first in the literature to map crowdsourcing research holistically. We classify its usage in the three domains of innovation, engineering, and science where 11 categories and 26 sub-categories are further developed. The results of this study reveal that the most active scientific clusters where crowdsourcing is used are Environmental Sciences and Ecology. For the engineering domain, it is Computer Science, Telecommunication and Operations Research. In innovation, idea crowdsourcing, crowdfunding and crowd creation are the most frequent areas. The findings of this study map crowdsourcing usage across different fields and illustrate emerging crowdsourcing application

AB - Crowdsourcing is a multidisciplinary research area, it represents a rapidly expanding field where new applications are constantly emerging. Research in this area has investigated its use for citizen science in data gathering for research and crowdsourcing for industrial innovation. Previous studies have reviewed and categorised crowdsourcing research using qualitative methods. This has led to the limited coverage of the entire field, using smaller discrete parts of the literature and mostly reviewing the industrial aspects of crowdsourcing. This study uses a scientometric analysis of 7,059 publications over the period 2006 - 2019 to map crowdsourcing research to identify clusters and applications. Our results are the first in the literature to map crowdsourcing research holistically. We classify its usage in the three domains of innovation, engineering, and science where 11 categories and 26 sub-categories are further developed. The results of this study reveal that the most active scientific clusters where crowdsourcing is used are Environmental Sciences and Ecology. For the engineering domain, it is Computer Science, Telecommunication and Operations Research. In innovation, idea crowdsourcing, crowdfunding and crowd creation are the most frequent areas. The findings of this study map crowdsourcing usage across different fields and illustrate emerging crowdsourcing application

KW - Crowdsourcing

KW - Scientometric

KW - Text Mining

KW - Emerging Clusters

KW - Research trends

U2 - 10.1109/TEM.2020.3027973

DO - 10.1109/TEM.2020.3027973

M3 - Article

VL - 0

SP - 0

JO - IEEE Transactions on Engineering Management

JF - IEEE Transactions on Engineering Management

SN - 0018-9391

M1 - 0

ER -

ID: 22778483