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Research on Digital Transformation and Sustainable Mechanisms of Community Services Based on AI

Yuan Wang

International Journal of Information Systems in the Service Sector2026https://doi.org/10.4018/ijisss.403997article
AJG 1
Weight
0.50

What the paper says

This study explores the sustainability bottlenecks such as “data islands”, lack of user feedback mechanisms, and service homogeneity that are common when artificial intelligence is applied in community service information systems. This study conducts empirical analysis based on the 2023 operation data of community service platforms in three typical Chinese cities and usage logs from over 1,200 residents. A “multi-loop branched closed loop mechanism” collaborative optimization mechanism was proposed and piloted, and the service performance was significantly improved through institutionalized data fusion architecture, dynamic classification of service nodes, and human-machine collaborative feedback closed-loop (user satisfaction increased by 15.7%, and the average response time was shortened by 34.1%). The approach is not only applicable to smart community scenarios, but its core logic-embedding technology into institutionalized collaborative processes to enable sustainable smart services-provides a migratable methodological contribution to a wider range of service information systems.

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https://doi.org/https://doi.org/10.4018/ijisss.403997

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@article{yuan2026,
  title        = {{Research on Digital Transformation and Sustainable Mechanisms of Community Services Based on AI}},
  author       = {Yuan Wang},
  journal      = {International Journal of Information Systems in the Service Sector},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijisss.403997},
}

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Research on Digital Transformation and Sustainable Mechanisms of Community Services Based on AI

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 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.