Crowdsourcing: The Trend of Prior Studies

Zakariah, Zazaleena; Janom, Norjansalika; Arshad, Noor Habibah; Salleh, Siti Salwa; Aris, Siti Armiza Mohd

doi:10.1109/icaiet.2014.30

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…These factors are vital as it can be used as a road map to prepare an action plan for a successful crowdsourcing implementation for low income community. The questionaires are develop based on the conceptual framework of crowdsourcing business model for low income community [34][35]. The conceptual framework consists of three main part which are crowd worker, job provider and platform.…”

Section: Methodsmentioning

confidence: 99%

Low Income Community as Crowd Worker for Crowdsourcing: Issues, Challenges and Future Direction

Zakariah

Janom²,

Arshad³

2018

IJET

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World are now facing flood of low income community responding to current global issue regarding employment. Considering the advantages of crowdsourcing, it has been identified as one potential solution to the issue. Responding to that, the concept has been implemented in many developing countries that have high rate of low income community such as India, Nepal and Philippines. Through crowdsourcing, business and individual can achieve greater and higher productivity in faster and shorter period. Crowdsourcing offer flexibility in terms of time, locations and durations of doing tasks and the most important is crowdsourcing will give additional income to complement existing income that will help to uplift one’s lifestyle hence reduce the rate of low income community. However, focusing on low income community as crowd worker for crowdsourcing require depth understanding on varies factors such as crowd education levels, skills, attitudes, language and etcetera. Visibly, there are numerous issues and challenges in the implementation process. Thus, this study aims to discuss the issues and challenges when dealing with low income community as crowd worker. This study presents seven vital factors that need to be taken into deliberation when dealing with low income community. The results and discussion from this study can be used as a road map to prepare an action plan for a successful crowdsourcing implementation for low income community.

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Section: Methodsmentioning

confidence: 99%

Low Income Community as Crowd Worker for Crowdsourcing: Issues, Challenges and Future Direction

Zakariah

Janom²,

Arshad³

2018

IJET

Self Cite

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“…Swedish counties (la¨n) covered: The counties that are recorded in these books are as follows. Gotland, Gävleborg, Norrbotten, Västerbotten, Västernorrland, Västmanland, Ä lvsborg, 4 and Ö rebro (see Fig. 3).…”

Section: Structure Of Shibrmentioning

confidence: 99%

“…crowd). It is often characterised by small and short-term deals [4]. In a recent study, crowdsourcing, when combined with contemporary technology, is shown to deliver far more complete and validated data than automated processes alone could produce [5].…”

Section: Introductionmentioning

confidence: 99%

SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

Cheddad

Kusetoğulları

Hilmkil³

et al. 2021

Neural Comput & Applic

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This paper presents a digital image dataset of historical handwritten birth records stored in the archives of several parishes across Sweden, together with the corresponding metadata that supports the evaluation of document analysis algorithms’ performance. The dataset is called SHIBR (the Swedish Historical Birth Records). The contribution of this paper is twofold. First, we believe it is the first and the largest Swedish dataset of its kind provided as open access (15,000 high-resolution colour images of the era between 1800 and 1840). We also perform some data mining of the dataset to uncover some statistics and facts that might be of interest and use to genealogists. Second, we provide a comprehensive survey of contemporary datasets in the field that are open to the public along with a compact review of word spotting techniques. The word transcription file contains 17 columns of information pertaining to each image (e.g., child’s first name, birth date, date of baptism, father's first/last name, mother’s first/last name, death records, town, job title of the father/mother, etc.). Moreover, we evaluate some deep learning models, pre-trained on two other renowned datasets, for word spotting in SHIBR. However, our dataset proved challenging due to the unique handwriting style. Therefore, the dataset could also be used for competitions dedicated to a large set of document analysis problems, including word spotting.

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“…Whereas these previous studies identify crowdsourcing research [1,[7][8][9][10], they lack a holistic approach where all crowdsourcing scientific domains are mapped and categorised. We have identified only one quantitative study on the topic [7] with a dataset of 346 studies, research being specific to the innovation management field.…”

Section: Introductionmentioning

confidence: 99%

A Scientometric Exploration of Crowdsourcing: Research Clusters and Applications

Ozcan

Boye

Arsenyan

et al. 2022

IEEE Trans. Eng. Manage.

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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.

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Crowdsourcing: The Trend of Prior Studies

Cited by 11 publications

References 11 publications

Low Income Community as Crowd Worker for Crowdsourcing: Issues, Challenges and Future Direction

Low Income Community as Crowd Worker for Crowdsourcing: Issues, Challenges and Future Direction

SHIBR—The Swedish Historical Birth Records: a semi-annotated dataset

A Scientometric Exploration of Crowdsourcing: Research Clusters and Applications

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