Dataset for: The More Competent, the Better? The Effects of Perceived Competencies on Disclosure Towards Conversational Artificial Intelligence

Conversational AI (e.g., Google Assistant or Amazon Alexa) is present in many people’s everyday life and, at the same time, becomes more and more capable of solving more complex tasks. However, it is unclear how the growing capabilities of conversational AI affect people’s disclosure towards the system as previous research has revealed mixed effects of technology competence. To address this research question, we propose a framework systematically disentangling conversational AI competencies along the lines of the dimensions of human competencies suggested by the action regulation theory. Across two correlational studies and three experiments (N total = 1453), we investigated how these competencies differentially affect users’ and non-users’ disclosure towards conversational AI. Results indicate that intellectual competencies (e.g., planning actions and anticipating problems) in a conversational AI heighten users’ willingness to disclose and reduce their privacy concerns. In contrast, meta-cognitive heuristics (e.g., deriving universal strategies based on previous interactions) raise privacy concerns for users and, even more so, for non-users but reduce willingness to disclose only for non-users. Thus, the present research suggests that not all competencies of a conversational AI are seen as merely positive, and the proposed differentiation of competencies is informative to explain effects on disclosure. Dataset for: Gieselmann, M., & Sassenberg, K. (2022). The More Competent, the Better? The Effects of Perceived Competencies on Disclosure Towards Conversational Artificial Intelligence. Social Science Computer Review. https://doi.org/10.1177/08944393221142787 unknown

对话式人工智能（Conversational AI，例如谷歌助手（Google Assistant）与亚马逊Alexa（Amazon Alexa））已广泛融入大众日常生活，其解决复杂任务的能力也在持续提升。然而，由于过往研究关于技术能力的影响结论存在分歧，目前尚不明确对话式人工智能不断增强的能力会如何影响用户向该系统披露个人信息的行为。为解答这一研究问题，本研究基于行动调节理论（action regulation theory）提出的人类能力维度框架，构建了一套用于系统拆解对话式人工智能能力的研究体系。本研究通过两项相关研究与三项实验（总样本量N=1453），探究了不同维度的人工智能能力如何分别影响用户与非用户向对话式人工智能披露个人信息的行为。研究结果显示，对话式人工智能的智力能力（例如规划行动、预判问题）能够提升用户的信息披露意愿，并降低其隐私顾虑；与之相对的是，元认知启发式策略（meta-cognitive heuristics，例如基于过往交互推导通用策略）会提升用户的隐私顾虑，对非用户的影响更为显著，但仅会降低非用户的信息披露意愿。因此，本研究表明，对话式人工智能并非所有能力都会被视为正向影响因素，而本研究提出的能力维度划分方法，可为解释个人信息披露行为的相关影响提供有效依据。本数据集关联论文：Gieselmann, M. 与 Sassenberg, K.（2022）。《能力越强越好？感知能力对对话式人工智能信息披露行为的影响（The More Competent, the Better? The Effects of Perceived Competencies on Disclosure Towards Conversational Artificial Intelligence）》，发表于《社会科学计算机评论（Social Science Computer Review）》，DOI: 10.1177/08944393221142787，原文标注unknown