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Composing multi-relations association rules from crowdsourcing remuneration data

In crowdsourcing, requesters are companies that require external workers to execute specific tasks, whereas a platform acts as a mediator to match and allocate the tasks to digital workers. To assign it to a worker, the platform must first identify the types of tasks and match them to the appropriat...

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Main Authors: Siti Salwa Salleh,, Nurhayati Zakaria,, Norjansalika Janom,, Syaripah Ruzaini Syed Aris,, Noor Habibah Arshad,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2022
Online Access:http://journalarticle.ukm.my/19424/1/02.pdf
http://journalarticle.ukm.my/19424/
https://www.ukm.my/apjitm/articles-issues
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spelling my-ukm.journal.194242022-08-18T08:09:08Z http://journalarticle.ukm.my/19424/ Composing multi-relations association rules from crowdsourcing remuneration data Siti Salwa Salleh, Nurhayati Zakaria, Norjansalika Janom, Syaripah Ruzaini Syed Aris, Noor Habibah Arshad, In crowdsourcing, requesters are companies that require external workers to execute specific tasks, whereas a platform acts as a mediator to match and allocate the tasks to digital workers. To assign it to a worker, the platform must first identify the types of tasks and match them to the appropriate workers based on their level of competency. Each worker has different ICT competencies which affect work quality and remuneration. However, general practise frequently assumes a single level of worker’s capability for all tasks, hence the categorisation of difficulty of tasks is unclear and inconsistent. Apart from causing dissatisfaction among workers, this also implies an absence of incentive standardisation. Therefore, this study explores this matter and which aims to identify and visualise the parameters that affect remuneration determination. To gather the data, focus group discussions and interviews with crowdsourcing players have been conducted. The data comprise a lot of redundancies, therefore an apriori algorithm is used to normalise it by removing redundancies and then extracting significant patterns. Next, an association rule is used to uncover correlations between parameters. To gain a more understandable insight, the data relationship is visualised using an alluvial chart that manages to illustrate the flow. Findings show that task type, outcome variation, and competency requirements demonstrate a degree of interdependence. It is suggested that there is a significant pattern showing that the remuneration scheme is determined by five levels of DW, which are expert, advanced, intermediate, novice, and basic. Advance workers are most likely to participate in the crowdsourcing, and the remuneration scale is suggested to be wider compared to others. The study's findings provide input for remuneration strategy in future work. Penerbit Universiti Kebangsaan Malaysia 2022-06 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/19424/1/02.pdf Siti Salwa Salleh, and Nurhayati Zakaria, and Norjansalika Janom, and Syaripah Ruzaini Syed Aris, and Noor Habibah Arshad, (2022) Composing multi-relations association rules from crowdsourcing remuneration data. Asia-Pacific Journal of Information Technology and Multimedia, 11 (1). pp. 12-25. ISSN 2289-2192 https://www.ukm.my/apjitm/articles-issues
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description In crowdsourcing, requesters are companies that require external workers to execute specific tasks, whereas a platform acts as a mediator to match and allocate the tasks to digital workers. To assign it to a worker, the platform must first identify the types of tasks and match them to the appropriate workers based on their level of competency. Each worker has different ICT competencies which affect work quality and remuneration. However, general practise frequently assumes a single level of worker’s capability for all tasks, hence the categorisation of difficulty of tasks is unclear and inconsistent. Apart from causing dissatisfaction among workers, this also implies an absence of incentive standardisation. Therefore, this study explores this matter and which aims to identify and visualise the parameters that affect remuneration determination. To gather the data, focus group discussions and interviews with crowdsourcing players have been conducted. The data comprise a lot of redundancies, therefore an apriori algorithm is used to normalise it by removing redundancies and then extracting significant patterns. Next, an association rule is used to uncover correlations between parameters. To gain a more understandable insight, the data relationship is visualised using an alluvial chart that manages to illustrate the flow. Findings show that task type, outcome variation, and competency requirements demonstrate a degree of interdependence. It is suggested that there is a significant pattern showing that the remuneration scheme is determined by five levels of DW, which are expert, advanced, intermediate, novice, and basic. Advance workers are most likely to participate in the crowdsourcing, and the remuneration scale is suggested to be wider compared to others. The study's findings provide input for remuneration strategy in future work.
format Article
author Siti Salwa Salleh,
Nurhayati Zakaria,
Norjansalika Janom,
Syaripah Ruzaini Syed Aris,
Noor Habibah Arshad,
spellingShingle Siti Salwa Salleh,
Nurhayati Zakaria,
Norjansalika Janom,
Syaripah Ruzaini Syed Aris,
Noor Habibah Arshad,
Composing multi-relations association rules from crowdsourcing remuneration data
author_facet Siti Salwa Salleh,
Nurhayati Zakaria,
Norjansalika Janom,
Syaripah Ruzaini Syed Aris,
Noor Habibah Arshad,
author_sort Siti Salwa Salleh,
title Composing multi-relations association rules from crowdsourcing remuneration data
title_short Composing multi-relations association rules from crowdsourcing remuneration data
title_full Composing multi-relations association rules from crowdsourcing remuneration data
title_fullStr Composing multi-relations association rules from crowdsourcing remuneration data
title_full_unstemmed Composing multi-relations association rules from crowdsourcing remuneration data
title_sort composing multi-relations association rules from crowdsourcing remuneration data
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2022
url http://journalarticle.ukm.my/19424/1/02.pdf
http://journalarticle.ukm.my/19424/
https://www.ukm.my/apjitm/articles-issues
_version_ 1743107998779179008
score 13.211869

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