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<p>Widespread greenwashing and information asymmetry significantly undermine consumer trust in sustainable markets. This research investigates the psychological processes through which Responsible AI signals influence green shopping behavior. Grounded in signaling theory, the study utilizes a quantitative design with a sample of 442 digital commerce customers in Vietnam. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results reveal that transparency, unbiasedness, accountability, explanation quality, and accuracy constitute five conceptually distinct Responsible AI signals that enhance perceived diagnosticity and trust. These factors subsequently drive green purchase intentions and actual behaviors. A significant finding is the hyperopia paradox, where a strong orientation toward the future negatively moderates the relationship between intention and behavior. This indicates that excessive self control can lead consumers to delay sustainable purchases. Theoretically, the study shows that responsible AI serves as a verification mechanism to reduce perceived risk. Practically, the findings suggest that platforms should implement transparent AI models, while regulators should develop ethical frameworks to support Sustainable Development Goals.</p>

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From ethical algorithms to sustainable actions: Responsible AI recommendation signals in green consumer decision-making

  • 期刊论文

  • 发表日期：

    2026年7月5日

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  • Thong Minh Phan,
  • Ngoc Bao Mai Pham

摘要

Widespread greenwashing and information asymmetry significantly undermine consumer trust in sustainable markets. This research investigates the psychological processes through which Responsible AI signals influence green shopping behavior. Grounded in signaling theory, the study utilizes a quantitative design with a sample of 442 digital commerce customers in Vietnam. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results reveal that transparency, unbiasedness, accountability, explanation quality, and accuracy constitute five conceptually distinct Responsible AI signals that enhance perceived diagnosticity and trust. These factors subsequently drive green purchase intentions and actual behaviors. A significant finding is the hyperopia paradox, where a strong orientation toward the future negatively moderates the relationship between intention and behavior. This indicates that excessive self control can lead consumers to delay sustainable purchases. Theoretically, the study shows that responsible AI serves as a verification mechanism to reduce perceived risk. Practically, the findings suggest that platforms should implement transparent AI models, while regulators should develop ethical frameworks to support Sustainable Development Goals.