不容易“点赞”：认知负荷如何影响在线评论中的用户参与度？

IF 6.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Decision Support Systems Pub Date : 2025-03-17 DOI:10.1016/j.dss.2025.114436

Yuqiu Wang , Kai Li

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引用次数: 0

摘要

用户参与（例如，点赞、分享和评论）被广泛认为是商业成功的关键。尽管已有研究探索了用户粘性的决定因素，但对认知负荷的关注相对较少。本研究基于认知负荷理论、期望确认理论和应激-应变-结果框架，考察了研究1中内在、外在和相关认知负荷对用户参与的异质性效应，研究1中内在、外在和相关认知负荷方差（即从一篇综述加工到另一篇综述引起的认知负荷变化）的调节作用，以及研究2中它们的潜在机制。结果表明：(1)内外认知负荷负向影响用户参与，关联认知负荷正向影响；(2)内外认知负荷变异强化内外认知负荷负向作用，关联认知负荷变异强化关联认知负荷正向作用；(3)感知疲劳负向调节内因认知负荷和外因认知负荷，正向调节相关认知负荷。我们的发现对用户参与的文献有贡献，并为优化在线评论系统提供了实用的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Not easy to “like”: How does cognitive load influence user engagement in online reviews?

User engagement (e.g., likes, shares, and comments) is widely recognized as critical to business success. Although existing studies have explored the determinants of user engagement, relatively little attention has been paid to cognitive load. This study, based on cognitive load theory, expectation confirmation theory, and the stressor-strain-outcome framework, examines the heterogeneous effects of intrinsic, extraneous, and germane cognitive load on user engagement in Study 1, the moderating effects of intrinsic, extraneous, and germane cognitive load variance (i.e., described as cognitive load changes induced by processing from one review to another review) in Study 1, and their underlying mechanisms in Study 2. Results indicate that: (1) intrinsic and extraneous cognitive load negatively influence user engagement, while germane cognitive load has a positive effect, (2) intrinsic/extraneous cognitive load variance accentuates the negative effect of intrinsic/extraneous cognitive load, while germane cognitive load variance strengthens the positive effect of germane cognitive load, and (3) perceived fatigue negatively mediates the effects of intrinsic and extraneous cognitive load but positively mediates the effect of germane cognitive load. Our findings contribute to the literature on user engagement and offer practical insights for optimizing online review systems.

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来源期刊

Decision Support Systems 工程技术-计算机：人工智能

CiteScore

14.70

自引率

6.70%

发文量

119

审稿时长

13 months

期刊介绍： The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).