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Would I regret being different? The influence of social norms on attitudes toward AI usage

T0 review · 2 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read Choosing AI over a human raises regret, and an AI-favoring social norm only softens—never reverses—that aversion.

desk verdict The main effects are solid and honestly reported, but the paper's novel norm-source result is an uninterpretable null without a manipulation check. read the letter →

arxiv 2509.04241 v1 pith:ULW3KC3U submitted 2025-09-04 cs.HC

Jaroslaw Kornowicz , Maurice Pape , Kirsten Thommes This is my paper · ORCID
classification cs.HC
keywords socialnormsalgorithmaversionregrettheoryhuman-AIinteractiondecisionsupportsystemsvignetteexperimentAIadoptionorganizationalbehavior
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper asks why people hesitate to use AI in the workplace even when it may perform well, and whether social norms can change that hesitation. In a vignette experiment, participants imagined being a new employee choosing between a senior analyst and an AI tool; the study varied which choice was the prevailing norm, whether the employee followed it, and whether the norm was set by a supervisor or by colleagues. The central finding is that choosing AI raises regret more than any other factor—roughly three times the effect of breaking the norm—while an AI-favoring norm softens but does not eliminate that regret. The source of the norm made no significant difference, and qualitative answers suggest people blame humans more than technology, which is part of why AI choices sting. For organizations, the paper implies that descriptive norms are a partial, cheap intervention for AI adoption, and that regret aversion is a real emotional cost paid by early adopters.

What carries the argument

Regret aversion is the mechanism: people report more regret when a bad outcome follows a choice that deviates from what is typical or expected. The experimental engine is a 2x2x2 between-subjects vignette in which a new employee observes a descriptive norm (prefer the AI tool or prefer a senior analyst, established by a supervisor or by colleagues), makes a choice, then receives negative client feedback; regret is measured with the validated five-item Decision Regret Scale. The hinge of the argument is a pair of linear regressions—one isolating counter-normative status, AI use, and norm source, the other testing the interaction between AI use and an AI-favoring norm.

What would settle it

A replication that adds a manipulation check on norm source would settle H5: if participants rate superior- and colleague-set norms as equally authoritative, the null is an artifact. For the main claim, the decisive test is an incentivized experiment in which participants make a real advisor choice with real payoffs and report regret after feedback; if the AI-versus-human regret gap (β ≈ 0.89) does not persist, or the AI × AI-norm interaction vanishes, the vignette results would not generalize to consequential decisions.

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Extended reading notes

Core claim

Drawing on Regret Theory, the paper posits that people anticipate regret when choices deviate from the norm, and that this shapes AI adoption. In an online vignette experiment, 245 participants imagined being a new analyst choosing between a senior colleague and an AI tool; the vignette varied which behavior was the norm, whether the employee followed it, and whether the norm came from a supervisor or colleagues. Choosing AI raised regret sharply (β = 0.89) versus choosing a human, and defying the norm added less regret (β = 0.25)—the opposite ordering from what the authors hypothesized. An AI-favoring norm reduced the regret from AI use via a significant interaction (β = −0.52): attenuation

Load-bearing premise

The null result on norm source presumes the vignette made 'superior' and 'colleagues' feel genuinely different to participants; the paper reports no manipulation check, and if the two sources were perceived as interchangeable, the non-result for H5 says nothing about whether norm source matters.

Editorial extensions

If this is right

  • Because counter-normative choices carry a regret penalty, imitation is self-reinforcing: the first people to use AI pay an emotional cost that later users, who follow an established AI norm, do not.
  • An AI-favoring descriptive norm is a real but partial intervention: it weakens the regret from choosing AI (interaction β = −0.52) without flipping aversion into appreciation.
  • For organizations, who sets the norm—a supervisor or colleagues—appears interchangeable for regret, so either route can plausibly seed an AI norm.
  • Because blame is attributed more to humans than to technology, regret from AI use is partly a blame-avoidance story: choosing a human offers a blame target that an AI does not.
  • Regret-based interventions should pair norm framing with other levers, such as transparency, explainability, or user control, since norms alone do not close the AI regret gap.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The regret asymmetry implies a diffusion dynamic the cross-sectional design cannot observe directly: if early AI adopters pay a regret penalty that later adopters do not, adoption may need to clear a threshold before it becomes self-sustaining—a prediction testable with panel or field data.
  • The null result on norm source is interpretable only if the manipulation worked; the paper reports no manipulation check and concedes the two sources 'may have appeared too similar within the vignette,' so a replication with a sharper status contrast (pay, rank, or sanctioning power) could still find a source effect.
  • The blame-shifting pattern in the qualitative data suggests a concrete extension: experimentally varying whether an AI or a human is available as a blame target, or making responsibility explicit, should shift both regret and advisor choice in predictable ways.
  • Because regret is retrospective self-report in a hypothetical scenario, the effect sizes bound what happens under real stakes; an incentivized design with actual outcomes would test whether the AI-versus-human regret gap survives real consequences.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper reports a preregistered online vignette experiment (final N = 245) in which participants imagine being a new consultant choosing between asking a senior analyst and using an AI tool, after observing either their superior or their colleagues using one of the options as the prevailing norm. Regret is measured with the validated Decision Regret Scale. Five hypotheses are tested: counter-normative choices increase regret (supported), AI choices increase regret (supported), counter-normative behavior has a stronger effect than AI use (contradicted; the AI effect is larger), an AI-favoring norm reduces AI regret (supported via an interaction), and a superior as norm source increases regret (null). Open-ended justifications are coded inductively. The paper concludes that regret aversion, embedded in social norms, drives imitation in AI-related decision-making, and that the source of the norm—superior vs. colleagues—did not significantly affect regret.

Significance. If the central claims hold, the paper offers credible experimental evidence that social norms moderate algorithm aversion, and it explicitly takes up the question of how AI-favoring norms become established, going beyond Bogard and Shu (2022). The strengths are substantial: the study is preregistered, a power analysis with multiple-testing correction was conducted, a validated regret scale was used, comprehension and attention checks were enforced, and the data and analysis code are openly available. The reporting is transparent, including a contradicted hypothesis (H3) and a null effect (H5). The main effects and the UsedAI × NormAI interaction are statistically well supported. However, the paper's distinctive contribution—whether the source of the norm matters—rests on a null result that is difficult to interpret without a manipulation check, and the authors themselves concede that the two sources may have appeared too similar. The practical significance for organizations is therefore weaker than the abstract suggests.

major comments (2)
  1. [§3.1, §3.2.4, Table 3 (H5)] The null result for H5 (β_SourceSuperior = 0.16, p = 0.106) is used in the Abstract and Discussion to conclude that the norm source does not matter. This inference requires that participants perceived the 'superior' as higher-status/more authoritative than 'colleagues.' No manipulation check is reported; the attention check verifies who established the norm and what the norm was, but not whether the source was perceived as intended. The manipulation changes only the noun phrase, and the authors concede in the Discussion that the two sources 'may have appeared too similar within the vignette.' If the manipulation failed, the null is uninformative rather than evidence of equivalence. Because the paper's novel contribution over Bogard and Shu (2022) is precisely about norm establishment, this is load-bearing. Please add a manipulation check (or report one if it was collected), or explicitly
  2. [§5 (H5)] Even if the manipulation were effective, a non-significant p-value is not evidence of absence. The statement that the origin of the norm 'does not have a significant impact' needs support from an equivalence test, a confidence interval, or a Bayesian analysis. Reporting only β = 0.16, p = 0.106 leaves open the possibility of a small but real effect that the study was underpowered to detect. Please report the confidence interval for β_SourceSuperior and, ideally, a pre-specified equivalence bound; otherwise the conclusion of comparability between superior- and colleague-sourced norms is not supported.
minor comments (5)
  1. [Abstract; §5] Typos: 'choosing an human' should be 'choosing a human'; 'an wide range' should be 'a wide range'; 'explanability' should be 'explainability'; 'investiagte' should be 'investigate.'
  2. [§3.3 vs. §3.4] Section 3.3 is an empty heading ('Participants'), and the participant description appears entirely under §3.4. Please merge or renumber.
  3. [§4.2, Figures 1–2] The qualitative analysis is presented as inductive coding, but no inter-coder reliability measures or coding-process details are reported. The figures show percentages without denominators or statistical tests. As exploratory evidence this is acceptable, but the Discussion treats some patterns (e.g., blame-shifting in specific treatments) as substantive; please add reliability information or soften the claims.
  4. [Appendix A.4, Table 6] Several typos appear in the quoted example answers: 'cpetence,' 'initaative,' 'aand,' 'soiught.' Please proofread the appendix.
  5. [Abstract] The phrase 'Both peer and supervisor influence emerged as relevant factors' is vague in light of the null H5 result. Consider rephrasing to reflect that the source manipulation showed no significant effect, with the caveat that this may be due to manipulation weakness.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the paper's claims are tested empirically against preregistered experimental data.

full rationale

The paper is an experimental vignette study. Its central claims (H1–H5) are operationalized as regression coefficients in Equations (1) and (2), and the reported effects (e.g., βCounterNorm = 0.25, βUsedAI = 0.89, βUsedAI×NormAI = −0.52) are estimates from collected data. There is no step in which a parameter is fitted to a subset of the data and then relabeled as a prediction, nor is any construct defined in terms of the outcome it is used to explain. The hypotheses are derived from external theory (Regret Theory, norm theory) and prior empirical work, but these are background motivations rather than inputs that force the reported results. The one self-citation (Kornowicz and Thommes 2025) appears in the introduction as a general reference on algorithm aversion and is not load-bearing for any of the paper's derivations. The authors' own admission in the Discussion that the superior-versus-colleagues manipulation 'may have appeared too similar within the vignette' is a construct-validity limitation affecting the interpretability of the H5 null result; it does not indicate that any claim reduces to its own inputs by construction. The preregistration, published data, and analysis code also make the empirical chain transparent rather than circular. Accordingly, no circularity is present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on standard experimental assumptions and on the validity of the norm-source manipulation. No new entities or fitted parameters are introduced; the only 'free' design choices are the power analysis parameters and the exclusion threshold, both reported.

assumptions (4)
  • domain assumption Participants validly adopt the perspective of the new employee in the vignette and report genuine regret.
    The entire method depends on participants' ability to imagine themselves in the scenario and to report their emotional response as instructed (Section 3.2).
  • domain assumption Regret Theory (Bell, 1982; Loomes and Sugden, 1982) applies to hypothetical vignette choices.
    The hypotheses are derived from regret theory and norm theory, and the study tests them in a hypothetical scenario rather than with real decisions (Sections 1-2).
  • domain assumption The norm source manipulation (superior vs. colleagues) is perceived as intended by participants.
    No manipulation check is reported; the authors acknowledge the sources may have appeared too similar (Discussion). This is load-bearing for the null H5 result.
  • domain assumption Comprehension and attention checks sufficiently ensure data quality.
    Participants who failed comprehension or attention checks were excluded, but the sensitivity of results to these exclusions is not analyzed (Section 3.2).

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Cite this review

Pith. "Pith review of Would I regret being different? The influence of social norms on attitudes toward AI usage." pith.science (2026). https://pith.science/paper/ULW3KC3U

@misc{pith2026250904241,
  author       = {Pith},
  title        = {Pith review of: Would I regret being different? The influence of social norms on attitudes toward AI usage},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ULW3KC3U}},
  note         = {Machine review of arXiv:2509.04241}
}
read the original abstract

Prior research shows that social norms can reduce algorithm aversion, but little is known about how such norms become established. Most accounts emphasize technological and individual determinants, yet AI adoption unfolds within organizational social contexts shaped by peers and supervisors. We ask whether the source of the norm-peers or supervisors-shapes AI usage behavior. This question is practically relevant for organizations seeking to promote effective AI adoption. We conducted an online vignette experiment, complemented by qualitative data on participants' feelings and justifications after (counter-)normative behavior. In line with the theory, counter-normative choices elicited higher regret than norm-adherent choices. On average, choosing AI increased regret compared to choosing an human. This aversion was weaker when AI use was presented as the prevailing norm, indicating a statistically significant interaction between AI use and an AI-favoring norm. Participants also attributed less blame to technology than to humans, which increased regret when AI was chosen over human expertise. Both peer and supervisor influence emerged as relevant factors, though contrary to expectations they did not significantly affect regret. Our findings suggest that regret aversion, embedded in social norms, is a central mechanism driving imitation in AI-related decision-making.

Figures

Figures reproduced from arXiv: 2509.04241 by the authors.

Figure 1
Figure 1. Themes regarding the question why participants regret their decision. [PITH_FULL_IMAGE:figures/full_fig_p013_1.png] view at source ↗
Figure 2
Figure 2. Themes regarding the justification of their choice. [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. Screenshot of the vignette for the treatment combination: Norm Adherence: [PITH_FULL_IMAGE:figures/full_fig_p026_3.png] view at source ↗

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.