Framework for Enhancing Park Service Effectiveness Leveraging Emerging Technologies

Effectively enhancing park service effectiveness is a pivotal pathway for advancing the construction of Park Cities. However, it has currently witnessed issues such as the misalignment between park management research and the actual deficiencies, a lack of connections from evaluation results to deficiency diagnosis and optimization recommendations, and the insufficiency in timeliness and continuity of research outcomes, which collectively fail to adequately support for park managers’ decision-making in service effectiveness enhancement. This study, addressing the entire workflow, proposes an “evaluation-diagnosis-decision-making” framework for the enhancement of park service effectiveness underpinned by new technologies. Initially, by leveraging multi-sourced data to expand the investigation dimensions of park service effectiveness, it conducts indicator evaluations with intelligent algorithms; subsequently, constructing knowledge graphs to achieve precise deficiency diagnosis and optimization recommendations; and finally, utilizing templates and intelligent technologies for automatic report generation, and introducing an intelligent question-and-answer service supported by the large language model, thereby offering refined evaluation, diagnosis, and decision-making assistance for enhancing park service effectiveness. With an empirical case study on the evaluation of park service effectiveness in Beijing, the article demonstrates the application effectiveness of this framework, which can provide a new perspective and an operational tool for the improvement of urban park management services.