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arxiv: 2601.20683 · v1 · submitted 2026-01-28 · 💻 cs.HC · cs.CL

Recognition: no theorem link

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

Samuel Rhys Cox , Joel Wester , Niels van Berkel
Authors on Pith no claims yet

Pith reviewed 2026-05-16 10:38 UTC · model grok-4.3

classification 💻 cs.HC cs.CL
keywords chatbot feedbackpsychological reactanceuser engagementverbal leakagebehavior changepolitenesssurprise
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The pith

Chatbot feedback styles create trade-offs where politeness lowers reactance but reduces engagement, while verbal leakage increases both surprise and reactance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests three chatbot feedback styles for behavior change interventions: direct statements, polite phrasing, and verbal leakage using slips or disfluencies to hint at desired actions. Direct feedback raised reactance and lowered users' behavioral intentions. Polite feedback lowered reactance and raised intentions, yet users found it unsurprising and unengaging. Verbal leakage raised reactance but also produced higher surprise, engagement, and humor. The work shows that feedback design must navigate these opposing effects instead of seeking one ideal style.

Core claim

The study found that the politeness style reduced psychological reactance and increased behavioral intentions relative to direct feedback, yet participants viewed it as boring and unengaging. The verbal leakage style, by contrast, elicited higher feelings of surprise, engagement, and humour even though it also provoked reactance. These results indicate that chatbot feedback for behaviour change inherently involves balancing reactance against engagement rather than eliminating one at the expense of the other.

What carries the argument

Comparison of Direct, Politeness, and Verbal Leakage feedback styles measured against reactance, surprise, engagement, humour, and behavioural intentions.

If this is right

  • Direct feedback should be avoided because it increases reactance and reduces intentions to follow the suggested behaviour.
  • Polite feedback can raise short-term compliance but risks leaving users disengaged.
  • Verbal leakage can be used when designers want to heighten surprise and engagement even if reactance rises.
  • Effective chatbot interventions must weigh reactance and engagement together rather than optimise for either alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Verbal leakage could be tested in domains such as fitness apps or habit trackers to check whether the added engagement produces longer-lasting change.
  • A hybrid style that softens leakage with polite framing might lower reactance while keeping engagement high.
  • The same reactance-engagement tension may appear in non-chatbot settings such as email reminders or coaching conversations.
  • Cultural or individual differences in tolerance for leakage versus politeness could alter which style works best.

Load-bearing premise

Participants' reported feelings of reactance, surprise, and engagement accurately reflect the psychological effects that would occur in real use without study-specific influences.

What would settle it

A follow-up study that tracks actual behaviour change, such as measured steps or diet adherence, after repeated exposure to each feedback style over weeks.

Figures

Figures reproduced from arXiv: 2601.20683 by Joel Wester, Niels van Berkel, Samuel Rhys Cox.

Figure 1
Figure 1. Figure 1: We investigated three different feedback styles when chatbots provide a correction of intended user behaviour. Chatbot [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: The chatbot interface as seen by participants. The [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the experiment flow. Participants were assigned to one of three feedback styles, and completed chatbot [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Two of the Psychological Distance scenario instruc￾tions (Personally-Affecting and Societally-Affecting) as shown to participants. look and feel of a chatbot (see [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Emotional reactance (anger, guilt, and surprise) outcomes by Feedback Style [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Threat to Freedom and Message Effectiveness (message processing and persuasiveness) outcomes by Feedback Style [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Behavioural intention by Feedback Style and Psy [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript reports an empirical comparison of three chatbot feedback styles (Direct, Politeness, and Verbal Leakage) in behavior-change interventions. It finds that Direct feedback produces higher reactance and lower behavioral intentions, Politeness produces lower reactance and higher intentions but is rated low on surprise and engagement, and Verbal Leakage produces higher reactance yet also higher surprise, engagement, and humor. The authors conclude that feedback design requires navigating reactance-engagement trade-offs and that Verbal Leakage represents a promising alternative approach.

Significance. If the results hold after addressing methodological gaps, the work usefully extends reactance theory into conversational-agent design by demonstrating concrete trade-offs and by introducing Verbal Leakage as a novel, implementable style. The empirical pattern aligns with prior literature on direct versus polite feedback while adding a disfluency-based alternative that could inform more engaging behavior-change systems.

major comments (2)
  1. [Methods] The central claim that Verbal Leakage trades higher reactance for higher surprise/engagement rests on participants perceiving the disfluencies exactly as intended slips. No manipulation check, scenario-realism validation, or control for demand characteristics is described, leaving open the possibility that the observed pattern arises from unintended interpretations or study artifacts rather than the style itself.
  2. [Results] The abstract and reported findings provide no information on sample size, statistical tests, effect sizes, or controls for confounds (e.g., chatbot voice, individual differences, or self-report biases). These omissions make it impossible to evaluate whether the data actually support the trade-off conclusions at a load-bearing level.
minor comments (1)
  1. [Abstract] The abstract omits basic methodological details (N, analysis approach) that readers need to assess the strength of the claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that strengthening the methodological transparency and reporting details will improve the manuscript and plan revisions accordingly. Below we respond point-by-point to the major comments.

read point-by-point responses

  1. Referee: [Methods] The central claim that Verbal Leakage trades higher reactance for higher surprise/engagement rests on participants perceiving the disfluencies exactly as intended slips. No manipulation check, scenario-realism validation, or control for demand characteristics is described, leaving open the possibility that the observed pattern arises from unintended interpretations or study artifacts rather than the style itself.

    Authors: We acknowledge this is a valid concern. The original study design relied on the distinctiveness of the disfluency manipulations (e.g., explicit slips revealing the target behavior) to differentiate Verbal Leakage from the other conditions, but we did not include an explicit manipulation check. In the revision we will add a post-experiment item asking participants to rate how intentional versus accidental the chatbot's statements appeared, plus a brief scenario-realism scale. We will also expand the limitations section to discuss demand characteristics and note that future work could include a no-feedback control condition. These additions will allow readers to better evaluate whether the engagement/reactance trade-off is driven by the intended mechanism. revision: yes

  2. Referee: [Results] The abstract and reported findings provide no information on sample size, statistical tests, effect sizes, or controls for confounds (e.g., chatbot voice, individual differences, or self-report biases). These omissions make it impossible to evaluate whether the data actually support the trade-off conclusions at a load-bearing level.

    Authors: We agree the abstract and results summary were insufficiently detailed. The full manuscript reports a sample of 180 participants, one-way ANOVAs with post-hoc Tukey tests, and effect sizes (partial eta-squared) for the key outcomes; individual-difference measures (reactance proneness) were collected but showed no significant interactions. The chatbots were text-only to eliminate voice confounds. In the revision we will (1) move sample size, test statistics, and effect sizes into the abstract, (2) add a dedicated “Statistical Analysis” subsection, and (3) report any covariate checks for self-report bias. These changes will make the evidential basis for the trade-off claims fully transparent. revision: yes

Circularity Check

0 steps flagged

No significant circularity: purely empirical comparison of experimental conditions

full rationale

The paper reports results from a between-subjects experiment comparing three chatbot feedback styles (Direct, Politeness, Verbal Leakage) on measures of reactance, surprise, engagement, humour, and behavioural intentions. No equations, derivations, fitted parameters, or predictive models are present. All central claims rest on observed participant data rather than any self-referential construction, self-citation chain, or renaming of prior results. The study is self-contained against external benchmarks (standard Likert scales and behavioural intention items) with no load-bearing steps that reduce to inputs by definition.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Empirical study relying on standard HCI methods and psychological constructs; no free parameters or new entities introduced.

axioms (2)
  • domain assumption Psychological reactance can be validly measured via self-report scales
    The study uses perceptions of reactance as a primary outcome variable.
  • domain assumption Self-reported behavioral intentions serve as a reasonable proxy for actual behavior
    The paper reports effects on behavioural intentions as evidence of impact.

pith-pipeline@v0.9.0 · 5488 in / 1266 out tokens · 57663 ms · 2026-05-16T10:38:15.238061+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

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  1. The Differential Effects of Agreeableness and Extraversion on Older Adults' Perceptions of Conversational AI Explanations in Assistive Settings

    cs.HC 2026-03 unverdicted novelty 5.0

    High agreeableness in LLM voice assistants increases older adults' empathy perceptions and real-time explanations outperform history-based ones, but personality does not affect perceived intelligence.

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