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.

Biochemical oxidation and reduction are the principle of biological water and wastewater treatment, in which electron donor and/or acceptor shall be provided. Elemental sulfur (S⁰) as a non-toxic and easily available material with low price, possesses both reductive and oxidative characteristics, suggesting that it is a suitable material for water and wastewater treatment. Recent advanced understanding of S⁰-respiring microorganisms and their metabolism further stimulated the development of S⁰-based technologies. As such, S⁰-based biotechnologies have emerged as cost-effective and attractive alternatives to conventional biological methods for water and wastewater treatment. For instance, S⁰-driven autotrophic denitrification substantially lower the operational cost for nitrogen removal from water and wastewater, compared to the conventional process with exogenous carbon source supplementation. The introduction of S⁰ can also avoid secondary pollution commonly caused by overdose of organic carbon. S⁰ reduction processes cost-effectively mineralize organic matter with low sludge production. Biological sulfide production using S⁰ as electron acceptor is also an attractive technology for metal-laden wastewater treatment, e.g. acid mine drainage. This paper outlines an overview of the fundamentals, characteristics and advances of the S⁰-based biotechnologies and highlights the functional S⁰-related microorganisms. In particular, the mechanisms of microorganisms accessing insoluble S⁰ and feasibility to improve S⁰ bio-utilization efficiency are critically discussed. Additionally, the research knowledge gaps, current process limitations, and required further developments are identified and discussed.

@en