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 applications.

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

“…Nosratabadi et al [26] extracted feature terms from titles and abstracts based on lexicality, filtered them, calculated feature term frequencies to characterize the documents as feature vectors, and classified them using association rules. Ozcan et al [10] constructed a literature topic matrix and a topic-feature term matrix based on the titles and abstracts of patent documents to identify topics for technological innovations. In addition, several researchers have attempted to construct features from external resources to improve classification accuracy.…”

“…The processed explicit and implicit features are added to the feature term embedding array D as shown in Equation (10).…”

“…At the same time, it helps academic institutions and scientific literature management platforms analyze the development direction of disciplines [6], facilitates the exploration of knowledge production and dissemination, and accelerates the rapid development of scientific research [7,8]. Acknowledging the advantages of scientometric analysis, it has been widely used to evaluate leading scientific researchers or publications [9], examine the structure of a scientific field's network [10,11], reveal emerging issues [12], and help researchers study the development of research fields and disciplines by categorizing documents along multiple dimensions [4].…”

“…Moreover, the data elements used by existing scientific literature classification systems are primarily derived from explicit scientometric information such as titles, abstracts, and keywords. Nevertheless, data elements may also have implicit relationships such as journal names, authors, research institution names, and research content and method [6,10,18,19]. The same journal, author, or research institution are more likely to focus on certain research content and methods, even if there is no direct relationship between them.…”

A Data Envelopment Analysis Evaluation Study of Urban Crowd Sourcing Competitiveness Based on Evidence From 21 Chinese Cities

“…Good development of protection and information security management. In addition, local governments in China have also issued a number of laws and regulations related to service outsourcing, aiming to promote the transformation and upgrading of the service outsourcing industry and achieve leapfrog development (Ozcan et al, 2020).…”