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Evolving Deep Multiple Kernel Learning Networks Through Genetic Algorithms
IEEE Transactions on Industrial Informatics ( IF 11.7 ) Pub Date : 9-15-2022 , DOI: 10.1109/tii.2022.3206817
Wangbo Shen 1 , Weiwei Lin 1 , Yulei Wu 2 , Fang Shi 1 , Wentai Wu 3 , Keqin Li 4
Affiliation  

As social media becomes embedded in our daily lives, having online presences and activities on social media is no longer an option but a requirement for private and public sectors. While businesses have successfully leveraged social media to gain a competitive advantage, the lack of literature on government social media success stories has drawn academic attention. This article aims to consolidate the existing body of knowledge through a literature review by describing the state of government and social media use and conceptualizing an integrative model for governments. In total, 56 relevant existing studies were carefully analyzed and discussed, specifically relating to trends, foci, methodologies, contexts, samples, and theoretical foundations. This article then develops an integrative model that explains the role of technological, governmental, and individual-based factors on government social media use and its impact. Our finding shows that platform quality, content quality, and government service quality are the best predictors of citizen platform use. At the same time, perceived individual benefits mediate the relationship, while perceived behavioral control moderates it. We discuss the findings and highlight the direction for future research. From a theoretical perspective, this article offers an in-depth overview of government social media use from an integrated perspective. It contributes to the field by conceptualizing a government social media use model. To government leaders and policymakers, the article presents avenues to understand better the factors influencing citizens’ decisions to use and engage with government social media pages and design adequate strategies to maximize the benefits of delivering public services to citizens through social media. We then conclude this article by noting its limitation.

中文翻译:


通过遗传算法发展深度多核学习网络



随着社交媒体融入我们的日常生活,在社交媒体上进行在线展示和活动不再是私营和公共部门的一种选择,而是一种要求。尽管企业已成功利用社交媒体获得竞争优势,但政府社交媒体成功案例文献的缺乏引起了学术界的关注。本文旨在通过文献回顾来巩固现有的知识体系,描述政府和社交媒体的使用状况,并概念化政府的综合模型。总共对 56 项相关的现有研究进行了仔细分析和讨论,具体涉及趋势、焦点、方法、背景、样本和理论基础。然后,本文开发了一个综合模型,解释技术、政府和个人因素对政府社交媒体使用的作用及其影响。我们的研究结果表明,平台质量、内容质量和政府服务质量是公民平台使用的最佳预测因素。与此同时,感知到的个人利益调节了这种关系,而感知到的行为控制则调节了这种关系。我们讨论研究结果并强调未来研究的方向。本文从理论角度,从综合角度对政府社交媒体的使用进行了深入概述。它通过概念化政府社交媒体使用模型为该领域做出了贡献。本文为政府领导人和政策制定者提供了更好地了解影响公民使用和参与政府社交媒体页面决策的因素的途径,并设计适当的策略,以最大限度地发挥通过社交媒体向公民提供公共服务的好处。 然后,我们通过指出其局限性来结束本文。
更新日期:2024-08-26
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