ENHANCING SKILL TRANSFER IN CROWSOURCED DESIGN: A CONCEPTUAL AND COMPUTATIONAL FRAMEWORK
DOI:
https://doi.org/10.55197/qjssh.v6i3.770Keywords:
crowdsourcing, asynchronous skill transfer, social learning theory, collaborative learning, engagementAbstract
Crowdsourcing is a common approach to finding creative solutions through distributed contributor networks. Threadless, Top Coder, and InnoCentive platforms use open contests to support innovation, but they do not focus on long-term skill improvement. The restricted system reduces the available chances for knowledge exchange and prolonged participant engagement. The current crowdsourcing systems do not provide organized approaches to asynchronous skill transfer, producing knowledge-sharing, mentoring, and collaborative learning deficits. Research has mainly focused on real-time coordination instead of skill acquisition processes through decentralized collaboration. The research develops a conceptual model that combines Social Learning Theory with Communities of Practice to build better skill transfer capabilities in creative crowdsourcing projects. The proposed framework utilizes observational learning, peer modeling, self-efficacy development, and structured community participation to establish an interactive learning environment that sustains continuous engagement and expertise advancement. The research utilizes qualitative methods through crowdsourcing experiments and computational approaches to discover successful ways of skill dissemination. The research produces theoretical and practical advancements by discovering methods to boost participant retention and knowledge acquisition and sustain crowdsourcing platforms. Research results will generate practical solutions to develop skill-based crowdsourcing platforms that increase creativity and maintain participant retention.
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Copyright (c) 2025 RAINAL HIDAYAT WARDI, MAITHREYI SUBRAMANIAM, RUSMAWATI GHAZALI, RAHMAN ROSMAN
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