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<p>In an era dominated by big data and artificial intelligence, algorithms serve as facilitators across various application scenarios, creating complex layers of interaction. This research develops a theoretical framework based on social exchange theory, social information processing theory, and the stereotype content model to explore user responses to the accuracy and interpretability of algorithmic recommendations and to identify the mediating factors influencing their willingness to engage in value co-creation. The study’s findings reveal that: (1) In high-accuracy scenarios (vs. low-accuracy), users perceive algorithmic recommendations as more competent and warmer, which enhances their willingness to engage in value co-creation, with perceived competence and warmth serving as mediators. (2) In scenarios with strong interpretability (vs. low interpretability), users similarly perceive recommendations as more competent and warmer, increasing their willingness to participate in value co-creation, with perceived competence and warmth acting as mediators. This research provides a practical framework for enterprises offering algorithmic recommendation services, guiding them in fostering greater user participation in value co-creation.</p>

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Unlocking the power of algorithmic recommendations: the effect of recommendation characteristics on users’ willingness to value co-creation

  • 期刊论文

  • 发表日期：

    2025年1月9日

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  • Jinsong Chen,
  • Yuexin Zhang,
  • Zhaoxia Liu

摘要

In an era dominated by big data and artificial intelligence, algorithms serve as facilitators across various application scenarios, creating complex layers of interaction. This research develops a theoretical framework based on social exchange theory, social information processing theory, and the stereotype content model to explore user responses to the accuracy and interpretability of algorithmic recommendations and to identify the mediating factors influencing their willingness to engage in value co-creation. The study’s findings reveal that: (1) In high-accuracy scenarios (vs. low-accuracy), users perceive algorithmic recommendations as more competent and warmer, which enhances their willingness to engage in value co-creation, with perceived competence and warmth serving as mediators. (2) In scenarios with strong interpretability (vs. low interpretability), users similarly perceive recommendations as more competent and warmer, increasing their willingness to participate in value co-creation, with perceived competence and warmth acting as mediators. This research provides a practical framework for enterprises offering algorithmic recommendation services, guiding them in fostering greater user participation in value co-creation.