Unraveling the path to user continuance intention in AIGC platforms: insights from NCA and fsQCA analyses
Li Yu et al.
What the paper says
Purpose As information technology rapidly advances, artificial-intelligence-generated content (AIGC) is progressively transforming how we live, learn and work. Individuals’ continuance intentions determine the commercial viability and scalable adoption of these technologies. To elucidate the complex causal mechanisms underlying user continuance intention and inform platform optimization strategies, this study develops a comprehensive analytical framework grounded in information ecology theory and integrated with the Unified Theory of Acceptance and Use of Technology (UTAUT), encompassing four dimensions: technological, user, informational and environmental. Design/methodology/approach Using survey data from 512 AIGC platform users, this research adopts necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA) to transcend the “net effect” paradigm and elucidate equifinal pathways leading to high user continuance intention. In this study, NCA identifies the necessary conditions influencing users’ continuance intention, whereas fsQCA uncovers multiple conjunctural causal paths, resulting in high continuance intention. Findings Three distinct equifinal configurations associated with high continuance intention are identified: (1) a socially driven model emphasizing community dynamics, (2) an efficiency-centric model prioritizing functional optimization and (3) an advantage-sensitive model dominated by perceived superiority. No single antecedent constitutes a necessary condition, revealing the inherent conjunctural nature of user retention mechanisms. Originality/value This investigation advocates differentiated platform governance strategies that synergistically address heterogeneous user needs through technical refinement, community engagement and usability enhancements. This research advances continuance intention theory within emerging AI contexts while providing insights into multidimensional platform optimization.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.