| 质量差异点击诱饵对用户满意度的影响 |
| Impact of quality-difference clickbait on user satisfaction |
| 摘要点击 86 全文点击 0 投稿时间：2025-08-29 修订日期：2026-04-09 |
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| 中文关键词 点击诱饵; 内容平台; 质量差异; 用户满意度; 参照点 |
| 英文关键词 clickbait; content platform; quality-difference; user satisfaction; reference point |
| 基金项目 国家自然科学基金项目（面上项目，重点项目，重大项目） |
| 投稿方向 平台经济，实证研究 |
| 作者 | 单位 | 邮编 | | 杨丽园 | 天津大学管理学院 | 300072 | | 刘畅 | 天津大学管理学院 | | | 南国芳* | 海南大学国际商学院 | 570228 |
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| 中文摘要 |
| 针对内容平台普遍存在的点击诱饵问题, 本文突破现有研究采用二分法定义的局限, 通过推荐项目的内容性和曝光性特征的质量差异实现点击诱饵连续化度量, 根据两类特征质量差异的符号定义正向与逆向质量差异点击诱饵. 运用固定效应模型分析REASONER推荐数据集, 研究质量差异点击诱饵对用户满意度的影响. 研究发现, 正向质量差异点击诱饵使用户满意度提高, 而逆向质量差异点击诱饵使用户满意度降低. 曝光性特征质量对上述影响呈现负向调节作用, 而内容性特征质量则呈现正向调节作用. 这一结论是反常识的, 通常认为优质内容能够缓解质量差异的负面影响, 而结果表明内容性特征质量的提高会强化而非缓解逆向质量差异点击诱饵的负面影响. 结合参照点理论与注意力机制, 可进一步解释特征质量调节作用的合理性. 此外, 质量差异点击诱饵的影响在性别、职业与地区属性上存在显著用户异质性. 研究结论为内容平台点击诱饵的识别与治理提供了理论依据与实践启示. |
| 英文摘要 |
| To address the prevalent issue of clickbait on content platforms, this study overcomes the limitations of the dichotomous definition used in existing research. It achieves continuous quantification of clickbait via quality differences between the content and exposure characteristics of recommended items, and defines positive and negative quality-difference clickbait based on the signs of these differences. Using a fixed-effects model to analyze the REASONER recommendation dataset, this research examines the impact of quality-difference clickbait on user satisfaction. Results show that positive quality-difference clickbait enhances user satisfaction, while negative quality-difference clickbait reduces it. Exposure characteristic quality exerts a negative moderating effect on this impact, whereas content characteristic quality exerts a positive one. Counterintuitively, our results indicate that improved content quality strengthens rather than alleviates the negative effect of such clickbait. The theoretical rationale for this moderating effect is further explained using reference point theory and attention mechanisms. Moreover, the impact of quality-difference clickbait exhibits significant user heterogeneity across gender, career, and address. These findings provide theoretical and practical implications for clickbait identification and governance on content platforms. |
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