Purpose: Generative artificial intelligence (GenAI) has progressed in its ability and has seen explosive growth in adoption. However, the consumer's perspective on its use, particularly in specific scenarios such as financial advice, is unclear. This research develops a model of how to build trust in the advice given by GenAI when answering financial questions. Design/methodology/approach: The model is tested with survey data using structural equation modelling (SEM) and multi-group analysis (MGA). The MGA compares two scenarios, one where the consumer makes a specific question and one where a vague question is made. Findings: This research identifies that building trust for consumers is different when they ask a specific financial question in comparison to a vague one. Humanness has a different effect in the two scenarios. When a financial question is specific, human-like interaction does not strengthen trust, while (1) when a question is vague, humanness builds trust. The four ways to build trust in both scenarios are (2) human oversight and being in the loop, (3) transparency and control, (4) accuracy and usefulness and finally (5) ease of use and support. Originality/value: This research contributes to a better understanding of the consumer's perspective when using GenAI for financial questions and highlights the importance of understanding GenAI in specific contexts from specific stakeholders.

翻译：目的：生成式人工智能（GenAI）的能力已取得进展，其应用呈现爆炸式增长。然而，消费者对其使用的看法，特别是在金融咨询等特定场景中，尚不明确。本研究构建了一个模型，旨在探究如何建立用户对GenAI回答金融问题时提供建议的信任。设计/方法论/方法：该模型通过结构方程模型（SEM）和多群组分析（MGA）结合调查数据进行检验。MGA比较了两种场景：一种是消费者提出具体问题，另一种是提出模糊问题。研究发现：本研究表明，当消费者提出具体金融问题与提出模糊问题时，建立信任的机制存在差异。人性化特质在两种场景中具有不同影响。当金融问题具体时，类人交互并不会增强信任；而（1）当问题模糊时，人性化特质有助于建立信任。在两种场景中建立信任的四种途径包括：（2）人工监督与参与循环，（3）透明度与控制，（4）准确性与实用性，以及（5）易用性与支持。原创性/价值：本研究有助于更好地理解消费者使用GenAI处理金融问题时的视角，并强调了从特定利益相关者角度理解特定情境中GenAI应用的重要性。