Abstract
Algorithms have evolved from rule‐based tools into sophisticated, data‐driven engines that underpin decision‐making across domains—yet research on human–AI collaboration remains confined to task‐level contexts. This study bridges that gap by proposing a firm‐level framework, semi‐parallel learning, which foregrounds continuous human–AI partnership through a blended matching process. In this model, algorithmic systems and human intermediaries operate concurrently, informing and refining each other in real time to steer organizational strategy, adapt to market dynamics, and foster long‐term competitiveness.This research draws on a three‐year qualitative, longitudinal single‐case study of Kuaishou, a Chinese live‐streaming commerce platform. Guided by a post‐dualistic, practice‐theory philosophy, data were collected via 236 semi‐structured interviews, focus groups, extensive participant observation, archival documents, and informal dialogues.
This study reframes human–AI interaction as a strategic, firm‐level partnership rather than isolated, task‐level collaboration. Semi‐parallel learning highlights how blended matching creates continuous feedback loops, enabling organizations to balance match quality, ecosystem diversity, and strategic value creation. This research underscores the importance of embedding real‐time human insights into algorithmic workflows to translate AI pilots into enterprise‐wide value, driving sustainable growth in digital platform contexts.
| Date of Award | 15 Oct 2025 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Jun Luo (Supervisor), Martin Liu (Supervisor) & Jimmy Huang (Supervisor) |