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<p>High reliability organizations (HROs) in healthcare, oil extraction, and aviation require adaptability to changing conditions where appropriate responses can prevent catastrophic failures resulting in casualties and environmental damage for society as a whole and not just for the single organizations involved. Integrating artificial intelligence (AI) systems can enhance HRO teamwork efficiency when properly implemented. This study investigated how individuals’ <i>history</i> of interaction with AI or human partners influences their choices between these partners in collaborative work contexts. Using a behavior analytic framework that considers individual <i>history</i> as one of the factors affecting current choice behavior, we examined whether participants’ history—measured by the Implicit Relational Assessment Procedure (IRAP) and Q methodology—could predict partner selection and responsiveness to errors. Through a simulated healthcare task, participants made choices between AI and human partners for assistance in a recurring manner. Findings demonstrated that IRAP and Q tools successfully identified aspects of participants’ history that corresponded with their partner choice behavior. Participants showed lower switching rates after errors from their preferred partner type, indicating persistent choice patterns despite partner’s performance errors, hence providing value-added guidance for HROs seeking to optimize human-AI collaboration. Moreover, the methodology utilized for investigating participant history via IRAP and Q contributed to the experimental analysis of contextual factors that may mediate the influence of cultural environment.</p>

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Implicit Relational Assessment Procedure and Q Methodology: Measurement of Choice Behavior and Sensitivity to Partner Error in an Analog Work Setting

  • 期刊论文

  • 发表日期：

    2025年12月7日

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  • Rita Olla,
  • Ramona Houmanfar,
  • Elisabetta Cherchi,
  • Diane Montgomery,
  • Yueran Yang

摘要

High reliability organizations (HROs) in healthcare, oil extraction, and aviation require adaptability to changing conditions where appropriate responses can prevent catastrophic failures resulting in casualties and environmental damage for society as a whole and not just for the single organizations involved. Integrating artificial intelligence (AI) systems can enhance HRO teamwork efficiency when properly implemented. This study investigated how individuals’ history of interaction with AI or human partners influences their choices between these partners in collaborative work contexts. Using a behavior analytic framework that considers individual history as one of the factors affecting current choice behavior, we examined whether participants’ history—measured by the Implicit Relational Assessment Procedure (IRAP) and Q methodology—could predict partner selection and responsiveness to errors. Through a simulated healthcare task, participants made choices between AI and human partners for assistance in a recurring manner. Findings demonstrated that IRAP and Q tools successfully identified aspects of participants’ history that corresponded with their partner choice behavior. Participants showed lower switching rates after errors from their preferred partner type, indicating persistent choice patterns despite partner’s performance errors, hence providing value-added guidance for HROs seeking to optimize human-AI collaboration. Moreover, the methodology utilized for investigating participant history via IRAP and Q contributed to the experimental analysis of contextual factors that may mediate the influence of cultural environment.