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竞争性游戏化在基于众包的背景感知推者系统中的竞争性游戏化
1Department of Management Information Systems, National Chengchi University, No. 64, Sec. 2, ZhiNan Rd., Wenshan District, Taipei City 11605, Taiwan.
概括
基于众包的上下文感知推系统 (CARS) 在流行病期间有效支持餐厅的决策. 混合游戏化促进了参与,而自我竞争鼓励了这些系统中的任务多样性.
科学领域:
- 人与计算机的交互
- 信息系统信息系统信息系统
- 流行病学 流行病学
背景情况:
- 基于众包的背景感知推系统 (CARS) 在COVID-19等大流行期间对于无接触决策至关重要.
- 了解游戏化对用户参与众包实时数据收集的影响至关重要.
研究的目的:
- 调查基于众包的CARS在疫情期间支持用户决策方面的有效性.
- 分析不同的游戏设计如何影响用户参与餐厅推众包任务.
主要方法:
- 在COVID-19期间为餐厅推开发基于众包的CARS.
- 这是一项为期两周的实地研究,对68名用户进行了四种条件:控制,自我竞争,社会竞争和混合游戏化.
- 该系统根据餐厅的流行病状况提供了实时建议.
主要成果:
- 众包对于在流行病期间收集实时信息以提出建议是可行的.
- 混合竞争性游戏化增加了高性能和低性能用户的参与度.
- 自我竞争的游戏设计激励用户承担更广泛的任务.
结论:
- 基于众包的CARS可以在健康危机期间提供及时,上下文意识的建议.
- 游戏化策略,特别是混合竞争和自我竞争,可以在众包系统中显著提高用户参与度和任务多样性.
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