# Polite But Boring? Trade-offs Between Engagement and Psychological Reactance to Chatbot Feedback Styles

arXiv: https://arxiv.org/abs/2601.20683 · HTML: https://mlsanity.com/paper/2601.20683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 132 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 18 of 18)

- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Does Writing with Language Models Reduce Content Diversity?](https://mlsanity.com/paper/2309.05196/) (2023)
- [How should my chatbot interact? A survey on human-chatbot interaction design](https://mlsanity.com/paper/1904.02743/) (2019)
- [Conceptual Metaphors Impact Perceptions of Human-AI Collaboration](https://mlsanity.com/paper/2008.02311/) (2020)
- [What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents](https://mlsanity.com/paper/1901.06525/) (2019)
- [Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies](https://mlsanity.com/paper/2001.04564/) (2020)
- [Customizing Emotional Support: How Do Individuals Construct and Interact With LLM-Powered Chatbots](https://mlsanity.com/paper/2504.12943/) (2025)
- [Directed Diversity: Leveraging Language Embedding Distances for Collective Creativity in Crowd Ideation](https://mlsanity.com/paper/2101.06030/) (2021)
- [Computers as Bad Social Actors: Dark Patterns and Anti-Patterns in Interfaces that Act Socially](https://mlsanity.com/paper/2302.04720/) (2023)
- [Designing Effective Interview Chatbots: Automatic Chatbot Profiling and Design Suggestion Generation for Chatbot Debugging](https://mlsanity.com/paper/2104.04842/) (2021)
- [Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptions](https://mlsanity.com/paper/2308.04879/) (2023)
- [Do Expressions Change Decisions? Exploring the Impact of AI's Explanation Tone on Decision-Making](https://mlsanity.com/paper/2502.19730/) (2025)
- ["I've talked to ChatGPT about my issues last night.": Examining Mental Health Conversations with Large Language Models through Reddit Analysis](https://mlsanity.com/paper/2504.20320/) (2025)
- [Scrolling in the Deep: Analysing Contextual Influences on Intervention Effectiveness during Infinite Scrolling on Social Media](https://mlsanity.com/paper/2501.11814/) (2025)
- [Polite or Direct? Conversation Design of a Smart Display for Older Adults Based on Politeness Theory](https://mlsanity.com/paper/2203.15767/) (2022)
- [Can a Funny Chatbot Make a Difference? Infusing Humor into Conversational Agent for Behavioral Intervention](https://mlsanity.com/paper/2403.00365/) (2024)
- [GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text](https://mlsanity.com/paper/2407.11827/) (2024)
- [Understanding How Psychological Distance Influences User Preferences in Conversational Versus Web Search](https://mlsanity.com/paper/2409.19982/) (2024)

## Built on by (most cited first, 1 of 1)

- [Synergy: A Next-Generation General-Purpose Agent for Open Agentic Web](https://mlsanity.com/paper/2603.28428/) (2026)

## Abstract (arXiv)

As conversational agents become increasingly common in behaviour change interventions, understanding optimal feedback delivery mechanisms becomes increasingly important. However, choosing a style that both lessens psychological reactance (perceived threats to freedom) while simultaneously eliciting feelings of surprise and engagement represents a complex design problem. We explored how three different feedback styles: 'Direct', 'Politeness', and 'Verbal Leakage' (slips or disfluencies to reveal a desired behaviour) affect user perceptions and behavioural intentions. Matching expectations from literature, the 'Direct' chatbot led to lower behavioural intentions and higher reactance, while the 'Politeness' chatbot evoked higher behavioural intentions and lower reactance. However, 'Politeness' was also seen as unsurprising and unengaging by participants. In contrast, 'Verbal Leakage' evoked reactance, yet also elicited higher feelings of surprise, engagement, and humour. These findings highlight that effective feedback requires navigating trade-offs between user reactance and engagement, with novel approaches such as 'Verbal Leakage' offering promising alternative design opportunities.