Research on the Influence of User-based Fram on Recommendation Acceptance

Abstract

Abstract: More and more network platforms provide users with algorithm-generated active recommendations based on big data. The core of active recommendation is to persuade users to accept new content and improve the conversion rate between the content. However, inappropriate recommendation mode will cause users to have psychological resistance, resulting in subsequent negative behavior. At present, scholars have paid limited attention to the recommendation framework, and have not explained the question of why users more willing to adopt recommendations under the relation-based recommendation framework. Therefore, this study the theory of emotional adaptation and the theory of relationship communication construct a dynamic decision-making model of“stimulus—emotion—motivation—adoption” under the framework of relation-based recommendation.
To test the hypothesis, this studycollect 569 valid questionnaires on Credamo platform, and the questionnaire data The findings are as follows: irstly, recommendation adoption is a dynamic decision-making process, and the strength of the relationship between the recommended users has a positive correlation with the trust of the recommended users Secondly, trust further improve the user's inference of manipulation intention of the recommended content, and ultimately improve their willingness to accept recommendations. hirdly, users self-construal has a moderating effect on this effect, that is, for users with dependent self-construal, trust has a greater effect on reducing the judgment of manipulation intentionfor users of independent self-construal
The conclusion of this study provides rich practical implications for the precision marketing innovation ofinternet platform in the era of big data, and provides new research paradigm for the follow-up scholars to study active recommendation Firstly, based on the relational communication theory, this study explorers the user perception factors under the recommendation framework, and proposes that the extent of interest matching is the main factor affecting the user's information adoption motivation esides, this study further explain the user's relationship motivation from the perspective of relationship strength Secondly, this study introduces trust to construct the recommended users' relationship and emotion model, and the two important pre-factors of trust formation are analyzed, which are relationship intensity and interest matching degree. Thirdly, the influence of manipulative intention inference, which is the motive reaction of emotion and intention in decision-making model, is deeply explored, which plays a positive role in the cross-domain combination of psychological resistance theory and relational communication theory Finally, this study the moderator effect of self-construal on the relationship-based user recommendation adoption model, is the first time self-construal as a moderator to study the effects of psychological resistance and relationship communication, which provides a new perspective to characterize the portrait of online users.

Key words: Recommendation Acceptance, Recommendation Framing, Emotional Adaptation, Psychological Resistance, the Manipulation Intention

摘要： 基于用户对主动式推荐的心理抗拒背景,结合情感适应理论与关系传播理论,对用户感知因素进行梳理,构建了用户在基于关系的推荐框架下对推荐采用的决策模型。为验证假设,收集569份有效问卷,运用Amos进行实证分析。结果显示:推荐采用是一个动态决策过程,被推荐用户与推荐用户的关系强度与兴趣匹配度正向影响其对推荐过程的信任感;信任感进一步改善了被推荐用户对推荐内容的操纵意图推断,最终影响其推荐采用意愿;被推荐用户的自我建构会调节信任感对操纵意图推断的影响,当用户为依存型自我建构时,信任感会减少操纵意图推断,当用户为独立型自我建构时,信任感对于减少操纵意图推断的影响变弱。

CLC Number:

F713.55

HUANG Yuanhao, LI Jingyi, LI Xianguo , XU Anxin. Research on the Influence of User-based Fram on Recommendation Acceptance[J]. Collected Essays on Finance and Economics, 2022, 38(9): 91-101.

黄元豪, 黎静仪, 李先国, 许安心. 基于关系的推荐框架对用户推荐采用意愿的影响机制研究[J]. 财经论丛, 2022, 38(9): 91-101.

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