Chatbots as Advisers: the Effects of Response Variability and Reply Suggestion Buttons
Authors
As chatbots gain popularity across a variety of applications, from investment to health, they employ an increasing number of features that can influence the perception of the system. Since chatbots often provide advice or guidance, we ask: do these aspects affect the user’s decision to follow their advice? We focus on two chatbot features that can influence user perception: 1) response variability in answers and delays and 2) reply suggestion buttons. We report on a between-subject study where participants made investment decisions on a simulated social trading platform by interacting with a chatbot providing advice. Performance-based study incentives made the consequences of following the advice tangible to participants. We measured how often and to what extent participants followed the chatbot’s advice compared to an alternative source of information. Results indicate that both response variability and reply suggestion buttons significantly increased the inclination to follow the advice of the chatbot.
Research Questions / Practical Problems
Question signals indexed for this paper.
Research Questions
3- How do response variability (dynamic delays and varied replies) and reply suggestion buttons respectively affect users' acceptance of chatbot recommendations?Category: Trust, Transparency, and Response Latency DesignSimilar questionsarrow_forward
- In diverse task scenarios, how can chatbot design features such as reply suggestion buttons improve user task efficiency and accuracy?Category: Trust, Transparency, and Response Latency DesignSimilar questionsarrow_forward
- Do chatbot design features such as response variability induce cognitive biases in users?Category: Trust, Transparency, and Response Latency DesignSimilar questionsarrow_forward
Practical Problems
1- Users may distrust chatbot recommendations and experience low efficiency in multitask scenarios.Category: Trust, Transparency, and Response Latency DesignSimilar questionsarrow_forward
- 100%
Towards an optimal dialog strategy for information retrieval using both open-ended and close-ended questions
IUI '18· Conversational Chatbots +1
- 67%
Examining AI Methods for Micro-Coaching Dialogs
CHI '22· Conversational Chatbots +1
- 67%
Unified Conversational Models with System-Initiated Transitions between Chit-Chat and Task-Oriented Dialogues
CUI '23· Conversational Chatbots +2
- 67%
Exploring User Engagement Through an Interaction Lens: What Textual Cues Can Tell Us about Human-Chatbot Interactions
CUI '24· Conversational Chatbots +2
Based on Jaccard similarity of research subtopics & professions (≥60%)