一种用于评估开放政府数据门户的集成可用性框架：欧盟与海湾阿拉伯国家合作委员会国家的比较分析
An Integrated Usability Framework for Evaluating Open Government Data Portals: Comparative Analysis of EU and GCC Countries
AI总结：
本研究构建了适配用户多样性、评估协作参与度与数据可理解性的开放政府数据门户集成可用性评估框架，通过对欧盟和GCC共33个国家级门户的验证排名，揭示实践优劣与改进方向，助力打造用户友好的可持续OGD门户。
摘要：
本研究探讨了开放政府数据（OGD）门户在促进不同利益相关者之间的透明度与协作方面的关键作用。针对可用性、面向多元群体的沟通以及战略价值创造方面的挑战，本文构建了一个评估OGD门户效能的集成框架，该框架可适配用户多样性（无论其数据素养和语言水平如何），评估协作与参与度，以及用户探索和理解门户所提供数据的能力。研究通过将该框架应用于欧盟及海湾阿拉伯国家合作委员会（GCC）国家的33个国家级门户对其进行验证，据此对OGD门户进行排名，识别出表现欠佳的门户可借鉴的良好实践以及普遍存在的不足。值得注意的是，本研究揭示了GCC国家OGD门户的竞争力与创新性，明确了多语言支持、数据可理解性等具体改进方向。研究结果凸显了公开数据质量指标的日益增长的趋势，并主张加强用户与门户运营方之间的双向沟通渠道。总体而言，本研究在填补以往研究空白的同时，为推动发展用户友好、协作性强且可持续的OGD门户做出了贡献。
英文摘要：
This study explores the critical role of open government data (OGD) portals in fostering transparency and collaboration between diverse stakeholders. Recognizing the challenges of usability, communication with diverse populations, and strategic value creation, this paper develops an integrated framework for evaluating OGD portal effectiveness that accommodates user diversity (regardless of their data literacy and language), evaluates collaboration and participation, and the ability of users to explore and understand the data provided through them. The framework is validated by applying it to 33 national portals across European Union and Gulf Cooperation Council (GCC) countries, as a result of which we rank OGD portals, identify some good practices that lower-performing portals can learn from, and common shortcomings. Notably, the study unveils the competitive and innovative nature of GCC OGD portals, pinpointing specific improvement areas such as multilingual support and data understandability. The findings underscore the growing trend of exposing data quality metrics and advocate for enhanced two-way communication channels between users and portal representatives. Overall, the study contributes to accelerating the development of user-friendly, collaborative, and sustainable OGD portals while addressing gaps identified in previous research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
