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2022 | OriginalPaper | Chapter

Exploring Freelancer Attributes with Peer Endorsements

Authors : Sambit Tripathi, Amit Deokar, Prasanna Karhade

Published in: From Grand Challenges to Great Solutions: Digital Transformation in the Age of COVID-19

Publisher: Springer International Publishing

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Abstract

Online freelancing markets connect buyers with workers globally to assign various categories of tasks. A worker’s quality on the platform could be assessed by their past performance (reputation systems), skills, and experience. However, these methods cannot provide insights about the worker quality in case of newly acquired skills or new workers participating on the platform. An endorsement system can be particularly instrumental in such cases by gathering and sharing endorsements of skills given to a worker by other workers. We investigate how endorsements received by a worker from their peers relate to their attributes. Drawing on social value orientation theory, we determine worker category from the past endorsement activity. Next, we apply the decision tree induction method to extract the worker attributes that are related to endorsement decisions across all worker categories. Finally, we frame our propositions based on the extracted rules.
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Metadata
Title
Exploring Freelancer Attributes with Peer Endorsements
Authors
Sambit Tripathi
Amit Deokar
Prasanna Karhade
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-031-04126-6_3

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