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REVIEW 5 major objections 5 minor 1 cited by

Adoption of AI-Assisted E-Scooters: The Role of Perceived Trust, Safety, and Demographic Drivers

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

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Adoption of AI-assisted e-scooters hinges on perceived AI safety and trust, not prior riding experience.

desk verdict A useful first dataset on AI-assisted e-scooter acceptance, but the SEM's key path weights are untrustworthy because the central constructs are empirically indistinguishable. read the letter →

arxiv 2502.05117 v1 pith:TQX3B6DA submitted 2025-02-07 cs.HC

Amit Kumar , Arman Hosseini , Arghavan Azarbayjani , Arsalan Heydarian , Omidreza Shoghli This is my paper · ORCID
classification cs.HC
keywords e-scooteradoptionAI-assistedperceivedsafetytruststructuralequationmodeldecisiontreeanalysismicromobilitysurveyresearch
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

This paper asks what makes people willing to ride e-scooters equipped with AI driving assistance, using a structured survey of 405 U.S. road users. It claims that the strongest direct drivers of willingness to use AI-assisted e-scooters are the perception that AI technology is safe and trust in AI-enabled e-scooters, with standardized path weights of 1.040 and 0.935, both p<0.001. It also claims that ethnicity, income, and age split preferences between AI-assisted and regular e-scooters, while gender, prior micromobility frequency, and past crash experience do not significantly move willingness. If these relationships hold, boosting adoption means building confidence in AI safety and trust rather than recruiting experienced riders or addressing crash-related hesitation.

What carries the argument

The load-bearing machinery is a full-scale Structural Equation Model (SEM), a statistical technique that estimates paths among unobserved latent constructs from survey items, paired with a decision tree classifier that segments respondents by demographics. The SEM has five latent constructs: frequency of regular micromobility use (C1), perceived safety of road users around e-scooters (C2), perception of safety in AI-enabled technology (C3), trust in AI-enabled e-scooters (C4), and willingness to use AI-enabled e-scooters (C5). It tests eight hypothesized paths, H1–H8, and produces the standardized regression weights and p-values that identify C3 and C4 as the strongest predictors of C5. The decision tree uses demographic features to classify each respondent as preferring an AI-assisted e-scooter or not, and its splits reveal ethnicity, income, and age as the most informative demographic variables.

What would settle it

A confirmatory factor analysis on the same 16 questionnaire items, comparing a one-factor model in which all items load on a single 'AI attitude' dimension against the proposed three-factor model, would settle whether the constructs are distinct; if the one-factor model fits equally well or better, the reported path weights are artifacts of near-collinearity.

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Extended reading notes

Core claim

The central claim is that willingness to use an AI-assisted e-scooter is governed mainly by two psychological beliefs—perceived safety of AI-enabled technology and trust in AI-enabled e-scooters—rather than by riding habits or demographic background. In the structural equation model, both beliefs predict willingness to use strongly (standardized weights 1.040 and 0.935, p<0.001), and perceived AI safety also strengthens trust in the AI e-scooter (1.075, p<0.001). Perceived safety of road users around regular e-scooters has small positive effects on AI safety perception and trust, but frequency of regular micromobility use and crash experience have no significant direct effect on willingness. The companion decision tree analysis finds that Middle Eastern and Asian respondents, and holders of advanced degrees, tend to prefer AI-assisted e-scooters, while some younger and lower-income respondents prefer regular ones, with gender not emerging as a deciding factor.

Load-bearing premise

The path coefficients can be read as separate effects only if the survey items measure three genuinely distinct attitudes—AI safety perception, AI trust, and willingness to use—but the paper's own correlation table reports correlations of 0.952 to 0.982 among them.

Editorial extensions

If this is right

  • Public and industry efforts to increase AI-assisted e-scooter adoption should focus on communicating AI safety performance and building trust, since these are the only strong direct levers on willingness to use in the model.
  • Prior micromobility use and past crash involvement do not significantly predict willingness, so campaigns aimed at experienced riders or crash victims would not shift adoption intentions on their own.
  • Demographic segmentation can identify likely early adopters—Middle Eastern and Asian respondents and advanced-degree holders lean toward AI-assisted e-scooters—while some younger, lower-income groups prefer regular e-scooters.
  • Because perceived safety around regular e-scooters has small positive effects on AI safety perception and trust, improving general e-scooter road safety may indirectly support adoption of AI-assisted models.
  • The absence of a gender effect in the decision tree suggests AI assistance could help close the gender gap seen in regular e-scooter use, though the study does not directly measure this.

Reading between the lines

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

  • The paper's own correlation and validity tables show C3, C4, and C5 correlating at 0.952–0.982, so the three 'distinct' constructs may actually be one general attitude toward AI e-scooters; if so, the large path weights overstate the separability of safety and trust as levers.
  • A randomized experiment that gives one group safety-focused information about AI e-scooters and another group trust-focused information, then measures willingness to use, would directly test the model's prediction that both levers move adoption with safety having the larger effect.
  • The decision tree's modest accuracy of about 65% suggests demographic variables leave much of the preference unexplained, meaning perceived safety and trust likely operate within demographic groups as well as between them.
  • A behavioral follow-up, such as letting participants ride an AI-assisted e-scooter in a virtual-reality simulator, could reveal whether stated willingness translates into actual use and whether trust and safety perceptions measured before the ride predict behavior.
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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

5 major / 5 minor

Summary. The paper reports a survey-based study of willingness to use AI-assisted e-scooters. Data from 405 U.S. respondents are analyzed with two methods: a decision tree classifier that relates sociodemographic variables to stated preference between AI-assisted and regular e-scooters, and a structural equation model (SEM) with five latent constructs (frequency of micromobility use, perceived safety around e-scooters, perceived safety in AI technology, trust in AI-enabled e-scooters, and willingness to use them). The headline findings are that ethnicity, income, and age influence preference, and that perceived safety in AI technology and trust in AI-enabled e-scooters are the strongest predictors of willingness to use, with standardized path coefficients H6 = 1.075, H7 = 1.040, and H8 = 0.935. The manuscript includes reliability and validity tables, hypothesis test results, and a discussion of implications for urban mobility and policy.

Significance. If the SEM results were valid, the conclusion that safety and trust perceptions dominate prior usage experience and demographics would be a practically useful input for designing AI-assisted micromobility systems and for public communication campaigns. The survey addresses a genuinely underexplored adoption context, and the manuscript is transparent in reporting many diagnostics and in acknowledging that some items were removed post hoc. However, the central quantitative evidence as reported is not internally consistent: standardized path coefficients exceed 1, the reported inter-construct correlations violate the Fornell-Larcker criterion, and one reported correlation exceeds 1. These problems affect the headline path estimates directly. The decision-tree component also lacks a statistical basis for the word "significantly." The paper is therefore not yet publishable in its current form, but the underlying question and data collection effort are suitable for a major revision if the measurement model is respecified and the analyses are corrected.

major comments (5)
  1. [Table 3 and Section 5.2] The discriminant validity of constructs C3, C4, and C5 is not established. The inter-construct correlations in Table 3 are C3–C4 = 0.982, C3–C5 = 0.967, and C4–C5 = 0.952, while the square roots of the average variance extracted on the diagonal are 0.566, 0.738, and 0.768, respectively. Every one of these correlations exceeds the corresponding diagonal elements, violating the Fornell-Larcker criterion. The text in Section 5.2 additionally states a correlation of 1.037 between safety and use, which is not a valid correlation. Because the constructs are empirically indistinguishable, the path coefficients H6–H8 cannot be interpreted as independent effects, and the central claim in Section 5.3 that C3 and C4 are separate drivers of C5 is not supported by the reported measurement model.
  2. [Table 5 and Section 5.3] The standardized regression weights for H6 (1.075) and H7 (1.040) exceed 1.0, which is inadmissible in a properly identified standardized solution and is a typical symptom of severe multicollinearity or a Heywood case. The paper reports these as strong and highly significant effects, but such estimates cannot be interpreted as valid path coefficients. The authors should report unstandardized estimates, standard errors, and error variances; reconsider the specification that treats C3, C4, and C5 as distinct latent variables; and re-estimate the model before any conclusion about the relative strength of these predictors is drawn.
  3. [Section 4.5.1] The data-cleaning and item-deletion decisions are not sufficiently quantified. The text states that questions were eliminated for failing discriminant validity, composite reliability, AVE, or factor-loading criteria, and that responses from unengaged participants and multivariate outliers were removed, but no counts are given for excluded items or excluded respondents, and the original full item set is not provided in an appendix. The passage beginning "One can wonder why the measuring model did not use every statement…" acknowledges the issue, but without the number of dropped items, the reasons per item, and the final sample size after exclusions, the measurement model cannot be reconstructed or independently checked.
  4. [Table 2 and Section 4.5.1] Construct C1 (frequency of regular micromobility use) has Cronbach's alpha = 0.45, composite reliability = 0.46, and AVE = 0.23, all below conventional thresholds. The statement that this is acceptable "in this case, given the context" is not supported by the cited source, and the low reliability of a construct that appears in the structural model affects the interpretation of the non-significant H1–H3 results. The measurement model should be respecified or the low reliability explicitly justified with a domain-specific reference.
  5. [Section 4.4 and Section 5.1] The decision tree analysis does not provide evidence for the claim that ethnicity, income, and age "significantly influence" preferences. The reported test-set accuracy is 65.3%, but no baseline accuracy, confidence interval, or statistical test is given, and the descriptive node-by-node interpretation in Section 5.1 may reflect overfitting. A permutation test or comparison against a majority-class baseline is needed before demographic features can be described as significant determinants.
minor comments (5)
  1. [Section 5.2] The text reports a correlation of 1.037 between C3 and C5, while Table 3 reports 0.967; please correct the inconsistency and verify that all reported correlations are between -1 and 1.
  2. [Table 4] The HTMT values for C3–C4 (0.711) and C4–C5 (0.707) are below the stated thresholds, but they are difficult to reconcile with the very high inter-construct correlations in Table 3; please clarify how the two matrices were computed.
  3. [Section 5.4] The text reports p = 0.621 for the effect of C6 on C5, while Table 6 reports p = 0.737 for the same path; please correct the discrepancy.
  4. [Tables 2 and 6] The composite reliability values for the same constructs differ between Table 2 (e.g., C1 = 0.46, C5 = 0.74) and the values reported in Section 5.4 (C1 = 0.48, C5 = 0.53); please clarify which model these values belong to and ensure the reporting is consistent.
  5. [Throughout] There are numerous typographical errors, including "incluing," "respone," "micormobility," "F uture W ork," "estimaed," and "Furtehrmore." A careful proofreading pass is needed.

Circularity Check

1 steps flagged · score 6.0 of 10

Central SEM claim is partially circular: the willingness construct's main item restates the safety/trust predictors, and the reported near-unit correlations make C3-C5 empirically indistinguishable.

  1. self definitional [Section 4.1/Table 2 (items Q11, Q12, Q14, Q15); Section 5.2/Table 3 and text]
    "W Q11: Compared to regular e-scooters, the AI-assisted features will reduce the likelihood of accidents. ... U Q12: I trust this system for a safer ride. ... V Q15: Compared to regular e-scooters, I feel more confident using the AI-assisted e-scooter in various traffic conditions because of its AI capabilities. ... safety and use (1.037) show an unexpectedly high correlation, suggesting potential multicollinearity or measurement issues."

    Q15 (loading 0.84) asks whether the respondent would feel more confident using the AI-assisted e-scooter because of its AI capabilities. This shares the comparison frame and safety/confidence content of C3's Q11 and C4's Q12/Q14. The C5 latent therefore partly restates the predictors rather than measuring an independent behavioral intention. Table 3 reports correlations 0.982 (C3-C4), 0.967 (C3-C5), and 0.952 (C4-C5), all exceeding the diagonal sqrt-AVE values, and the text concedes 'potential multicollinearity or measurement issues.' Standardized weights H6=1.075 and H7=1.040 exceed 1, a Heywood/multicollinearity symptom. The 'strongest predictors' claim is therefore partly an artifact of overlapping item wording, not an independent causal test.

full rationale

The paper's headline result is that perception of AI safety (C3) and trust in AI-enabled e-scooters (C4) are the strongest predictors of willingness to use (C5), with H7=1.040 and H8=0.935. This result is not a formal identity, but it is partially circular at the measurement level: C5 is identified mainly by Q15, an item about confidence in the AI-assisted e-scooter because of its AI capabilities, which is essentially the same belief probed by the C3 and C4 items. The near-unit inter-construct correlations (0.952-0.982) and standardized coefficients above 1 show that the latent variables are not empirically separable, so the path estimates do not test independent causal effects. The paper's own text acknowledges 'potential multicollinearity or measurement issues.' The decision-tree demographic analysis and the raw survey content are independent, and the self-citations (e.g., Azarbayjani [40]) are background rather than load-bearing. Because the central claim is contaminated by construction rather than fully independent, the circularity score is 6 rather than 0-2.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim rests on fitted SEM coefficients, self-report measurement assumptions, and the separability of three nearly collinear constructs. No code or data are shipped, so the fitted values cannot be independently checked.

free parameters (4)
  • SEM standardized path coefficient C3 to C5 (H7) = 1.040
    Fitted to the survey data; this is the paper's main evidence that AI safety perception drives willingness to use.
  • SEM standardized path coefficient C4 to C5 (H8) = 0.935
    Fitted to the survey data; supports the claim that trust drives willingness.
  • SEM standardized path coefficient C3 to C4 (H6) = 1.075
    Fitted to the survey data; values above 1 indicate multicollinearity or suppression and make path interpretation unstable.
  • Outlier and unengaged-response exclusion thresholds = mean 0.5 to 4.5; SD at least 0.25; Mahalanobis threshold 9.488
    Hand-chosen screening rules in Section 4.5.1; the number of removed responses is not reported, so their influence on results is unknown.
assumptions (3)
  • domain assumption Self-reported Likert ratings are valid proxies for perceived safety, trust, and willingness to use AI e-scooters.
    All constructs in the SEM are measured by self-report (Table 2); there is no behavioral or objective outcome.
  • domain assumption The latent constructs C3, C4, and C5 are empirically distinct despite inter-construct correlations near 1.
    Section 5.2 and Table 3 treat them as separate; near-collinearity (0.952 to 0.982) threatens this premise.
  • domain assumption Cross-sectional survey data can support the proposed unidirectional causal ordering.
    Section 3 hypothesizes that perceptions cause willingness; Section 8 concedes SEM cannot handle feedback loops, so causality is not established.

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Cite this review

Pith. "Pith review of Adoption of AI-Assisted E-Scooters: The Role of Perceived Trust, Safety, and Demographic Drivers." pith.science (2026). https://pith.science/paper/TQX3B6DA

@misc{pith2026250205117,
  author       = {Pith},
  title        = {Pith review of: Adoption of AI-Assisted E-Scooters: The Role of Perceived Trust, Safety, and Demographic Drivers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TQX3B6DA}},
  note         = {Machine review of arXiv:2502.05117}
}
read the original abstract

E-scooters have become a more dominant mode of transport in recent years. However, the rise in their usage has been accompanied by an increase in injuries, affecting the trust and perceived safety of both users and non-users. Artificial intelligence (AI), as a cutting-edge and widely applied technology, has demonstrated potential to enhance transportation safety, particularly in driver assistance systems. The integration of AI into e-scooters presents a promising approach to addressing these safety concerns. This study aims to explore the factors influencing individuals willingness to use AI-assisted e-scooters. Data were collected using a structured questionnaire, capturing responses from 405 participants. The questionnaire gathered information on demographic characteristics, micromobility usage frequency, road users' perception of safety around e-scooters, perceptions of safety in AI-enabled technology, trust in AI-enabled e-scooters, and involvement in e-scooter crash incidents. To examine the impact of demographic factors on participants' preferences between AI-assisted and regular e-scooters, decision tree analysis is employed, indicating that ethnicity, income, and age significantly influence preferences. To analyze the impact of other factors on the willingness to use AI-enabled e-scooters, a full-scale Structural Equation Model (SEM) is applied, revealing that the perception of safety in AI enabled technology and the level of trust in AI-enabled e-scooters are the strongest predictors.

Figures

Figures reproduced from arXiv: 2502.05117 by the authors.

Figure 1
Figure 1. The proposed model to assess willingness to use AI enabled driving assistance technology [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Classification tree for demographic analysis of AI-assisted vs regular e-scooter choice [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. The proposed model to assess willingness to use AI enabled e-scooter [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Structure Equation Modeling Full Scale Model [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Persona Migration and Expectation Recalibration in Generative AI Adoption: A Longitudinal Study at a State Department of Transportation

    cs.CY 2026-07 conditional novelty 6.0 of 10

    In an eight-week state DOT pilot, employees' perceived usefulness of Microsoft 365 Copilot dropped significantly after hands-on use, while most initial champions became less enthusiastic.

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Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.