信息质量视角下算法分发与编辑分发的对比研究
A Comparative Study of Algorithm-Based Distribution and Editor-Based Distribution from the Perspective of Information Quality
刘坤锋 1李静雨 1李广1
作者信息
- 1. 郑州航空工业管理学院信息管理学院,河南 郑州 450046
- 折叠
摘要
[目的]了解用户当前对算法分发与编辑分发的信息质量感知及差异,为相关理论研究和实践活动提供借鉴.[方法]参考已有文献,设计包含算法分发的信息质量和编辑分发的信息质量两个潜在变量的测量量表.采用问卷调查法,获取273份有效样本,利用描述性统计和配对样本t-检验方法进行数据分析.[结果]相比于编辑分发,用户当前整体上更认可算法分发具备的信息质量.具体而言,用户认为在全面性、准确性、新颖性和符合需求等信息质量维度上,算法分发均优于编辑分发;算法分发在准确性维度的优势不显著,在其他3个维度的优势均显著.[结论]算法分发用户应注重提升算法素养,编辑分发亟须提升显示度和影响力.
Abstract
[Purposes]This article aims to understand users'current perception of information quality of algorithm-based distribution and editor-based distribution,as well as their differences,and then provide insights for relevant theoretical research and practical activities.[Methods]Based on existing literature,a measurement scale including two latent variables was designed,namely information quality of algorithm-based distribution and information quality of editor-based distribution.A questionnaire sur-vey was conducted to collect 273 valid samples,and descriptive statistics and paired sample t-tests were employed for data analysis.[Findings]Compared to editor-based distribution,users currently generally recognize that algorithm-based distribution has higher information quality.Specifically,in terms of com-prehensiveness,accuracy,novelty,and meeting user needs,users believe that algorithm-based distribu-tion is superior to editor-based distribution;algorithm-based distribution doesn't have significant advan-tages in terms of accuracy,but have significant advantages in the other three dimensions.[Conclusions]This article suggests that the users of algorithm-based distribution should focus on improving their algo-rithm literacy,while editor-based distribution urgently needs to increase visibility and influence.
关键词
算法分发/编辑分发/信息质量/信息分发/信息用户Key words
algorithm-based distribution/editor-based distribution/information quality/information dis-tribution/information user引用本文复制引用
基金项目
河南省软科学研究计划(222400410364)
国家社会科学基金青年项目(22CTQ038)
出版年
2024
NETL
NSTL
万方数据