Doing good for others or self: a study of crowdsourced translation on digital labor platforms

• Main Authors: Lee, Chei Sian, Yang, Yuhang, Low, Koon Yen, Chia, Hong Kit, Ma, Long
• Other Authors: Wee Kim Wee School of Communication and Information
• Format: Article
• Language: English
• Published: 2024
• Subjects: Computer and Information Science; Self-determination theory; Public services motivation
• Online Access: https://hdl.handle.net/10356/175750
• Institution: Nanyang Technological University

Purpose: Over the past decade, digital labor platforms (DLP) have emerged as a new trend and are major components of the platform economy. Crowdsourcing is being used in an innovative way to allow firms to outsource tasks to individuals with access to DLP. Apart from paid crowdsourcing, unpaid crowdsourcing has also emerged, where crowdsourced volunteers are recruited to contribute to crowdsourcing tasks available on the platform. Integrating perspectives from the public services motivation, uses and gratification theory and self-determination theory, this paper investigates the motives and underlying needs behind participation in the crowdsourcing of translation tasks. Design/methodology/approach: Interviews were conducted with 21 volunteer crowdsourced volunteers from a voluntary global crowdsourcing community to understand the factors driving them to spend time and effort contributing to the community. Content analysis methods were utilized to analyze the text data. Findings: The results indicated that the autonomy, competence and relatedness needs were the underlying needs of the crowdsourced volunteers. Furthermore, we found diverse motives consisting of both intrinsic and extrinsic motivations driving users to contribute voluntarily. Originality: While prior research efforts have clarified many issues related to participation in DLP, the reasons behind volunteers' participation in DLP are still unexplored. The findings of the present study contribute to a better understanding of the motives underlying volunteers’ participation in the crowdsourcing tasks on DLP.