How Emotional Expressiveness Affects Trust Formation in a Conversational Decision Support System

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Trust is a fundamental component in human-AI relationships, serving as a critical element of user acceptance and satisfaction, particularly within the realm of Decision Support Systems (DSS). The technological advances in conversational user interfaces (CUIs) such as ChatGPT and digital assistants (e.g., Alexa) allow laypeople to interact with DSS without knowing the mechanisms behind them.
Extensive research has explored the benefits of CUIs and strives to improve their usability and adoption rate. However, while interacting with such CUIs, how to facilitate proper user trust for decision support is still under-explored. To address the research gap, we aim to test the impact of emotional expressiveness in CUIs on building user trust.

To analyze the impact of emotional expressiveness in CUIs to build user trust and whether voice-based CUI is more efficient in building user trust compared to text-based CUI. We implemented a conversational interface with varying emotional expressiveness that can serve six conditions: two text-based and four voice-based. Text-based CUIs are differentiated by lexical expressiveness. Voice-based CUIs are varying in both lexical expressiveness and prosodic expressiveness. Regardless of the modality and emotional expressiveness, each CUI serves as an interactive medium for users with the DSS, which supports them to find a suitable house given a scenario.

Through an empirical study (N = 151), the experimental results are insufficient to conclude the impact of prosodic expressiveness and lexical expressiveness on user trust and usability in CUIs. In addition, we did not find any statistically significant difference between text-based and voice-based CUIs in trust or perceived usability.

Our findings can potentially be explained by the uncanniness effect [46]: initially, increased emotional expressiveness in a chatbot could positively influence user trust, but over time this could turn into a negative impact. These results offer a potential way to explain the complex dynamics of trust in conversational DSS and some implications in chatbot design within the context of DSS. Our findings
can benefit the future design and development of conversational agents-based DSS by considering emotional expressiveness.