Ethics and Artificial Intelligence Adoption

Carlos J. Costa; Martim Veiga

REVIEW 5 major objections 5 minor 24 references

Ethics and Artificial Intelligence Adoption

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that an attitude toward ethical behavior is a significant predictor of AI use and that seven of the nine hypotheses in its ethics-anchored adoption model were validated.

desk verdict A plausible but unverified ethics-to-use path in a small PLS-SEM study; deserves review but needs data, instrument, and a common-method-bias check. read the letter →

arxiv 2412.00330 v1 pith:7BE7MRIL submitted 2024-11-30 cs.CY

Martim Veiga , Carlos J. Costa This is my paper · ORCID

classification cs.CY

keywords artificialintelligenceethicstechnologyadoptionbehavioralintentionattitudetowardethicalbehaviorstructuralequationmodelingnetbenefitsUTAUT

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 sets out to show that AI can be adopted in society without setting ethics aside, and that a person's attitude toward ethical behavior is one measurable driver of whether they actually use AI. Using partial least squares structural equation modeling on 237 questionnaire responses, mostly from IT professionals, the authors report that seven of their nine hypotheses held: performance expectations, facilitating conditions, and behavioral intention behave as technology-acceptance theories predict, and the Attitude Towards Ethical Behavior construct has a significant positive path to self-reported AI use (H5, $\beta = 0.207$, $p < 0.05$). The two rejected paths both involve social influence. If the result is right, ethics functions as a support for adoption rather than a brake on it, which gives organizations and regulators a concrete lever: cultivating ethical attitudes may increase AI uptake.

What carries the argument

The carrying mechanism is a partial least squares structural equation model built by joining technology-acceptance constructs (performance expectation, facilitating conditions, social influence, behavioral intention, use) with the net-benefits dimension of the information-systems success model and with an adapted construct, Attitude Towards Ethical Behavior (AFCE), drawn from Internet-ethics research. Each research hypothesis is a path in this model, and the reported betas, p-values, F-squared effect sizes, and R-squared coefficients decide which paths survive. The argument rests on the AFCE-to-Use path (H5), because that is the route by which the conclusion claims ethics changes adoption behavior.

What would settle it

Collect objective AI-usage logs from the same 237 respondents and re-estimate the model with logged use replacing self-reported use; if the AFCE-to-Use path (H5) is no longer significant after controlling for behavioral intention and facilitating conditions, the paper's central claim fails.

Watch

Extended reading notes

Core claim

The central claim is that an ethics-grounded adoption model is empirically viable: the Attitude Towards Ethical Behavior (AFCE) construct, defined as an individual's view of the fundamental ethical principles of Internet use applied to AI, directly and positively influences the Use of AI, with a reported standardized path of $\beta = 0.207$ and $p < 0.05$. The authors interpret this as evidence that ethics 'impacts' people's decisions and that it is possible to adopt AI in society using AFCE as the model's mainstay. The same estimation supports the standard acceptance chain — performance expectation and facilitating conditions feed behavioral intention, behavioral intention feeds use, and use feeds net benefits — while social influence shows no significant association with either intention or use in this sample.

Load-bearing premise

The load-bearing premise is that one-time, self-reported survey answers from 237 mostly IT respondents can establish a causal direction in which ethical attitude drives AI use, rather than merely correlating with it.

Editorial extensions

If this is right

Organizations can treat ethical attitude as an adoption lever: strengthening data-privacy norms and ethical-behavior expectations may increase AI use, not merely satisfy compliance.
Performance expectation is the largest driver of intention in the model, so users who believe AI improves their work are more likely to intend to use it.
Social influence plays no visible role in this sample, meaning the validated model does not depend on peer pressure or supervisor pressure to predict use.
Use strongly predicts net benefits, so adoption itself is positioned as the route to personal and organizational gains.
An ethics-anchored model can be empirically estimated and mostly supported, which the authors take as evidence that ethical AI adoption is feasible today.

Reading between the lines

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

The causal language of the conclusion goes beyond what the cross-sectional, single-source survey design can establish; a longitudinal study that measures AFCE before any AI use and then logs actual usage would test the claimed direction.
Because the sample is mostly IT professionals, the null social-influence finding may not generalize; in less technical populations, social influence could resurface as a meaningful path.
AFCE is adapted from Internet-ethics items, so an AI-specific ethics scale covering bias, autonomy, and transparency might change the size of the H5 effect; that is a direct testable extension.
A common-method-bias check, such as a single-factor or marker-variable test, would clarify how much of the AFCE–use correlation is shared questionnaire variance rather than a real behavioral link.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a standard UTAUT/Delone-McLean hybrid with an ethics-attitude construct added, tested on 237 self-reports. The headline claim—that attitude toward ethical behavior predicts AI use (H5, beta = 0.207)—is plausible but not verified. The measurement instrument and data are not in the manuscript, there is no common-method-bias test, and a few internal inconsistencies (nine vs eight hypotheses; VIF text vs Table II) need cleaning up. I would send it to review, but I would not accept it as is.

What's actually new: the paper recombines established models into a specific path from ethical attitude (AFCE) to use, and it collects a fresh dataset. It does the usual PLS-SEM checks—Cronbach's alpha, composite reliability, AVE, Fornell-Larcker—and reports nonsignificant hypotheses as nonsignificant rather than burying them. That is honest work as far as it goes.

The soft spots are real but not fatal to the idea. The biggest one is that all constructs come from a single online questionnaire on 1–7 Likert scales, so common method variance could inflate the H5 path. Without the item wording and data, you cannot test whether AFCE is measuring something distinct from general positivity or a conditional use intention. The paper should have run Harman's single-factor test or used a marker variable. Second, the causal language in the conclusion ('proven') outruns the cross-sectional design; this can only support associative claims. Third, the instrument, cross-loadings, and AVE tables are referenced as Annexes but not present, so the validity evidence cannot be audited. The nine-versus-eight hypothesis count and the Inner VIF contradiction (text says 0.768–0.921; Table II shows 1.284+) are small but embarrassing.

The citation pattern is mostly standard, with several self-citations; that is not itself a flaw, but it does not add evidence for the new path.

For a reader, this is a workable starting point for a survey-based AI adoption study. I would not cite it in my own work this year, because the central result is not yet falsifiable from the manuscript. But the research question is relevant and the model is testable, so it deserves a serious referee. My recommendation: send to peer review with a strong request for the measurement instrument, the data (or a public repository), a common-method-bias diagnostic, and careful revision of the causal wording. If the authors can show the H5 path survives those checks, it becomes a modest but useful empirical contribution.

Referee Report

5 major / 5 minor

Summary. The paper proposes a model of AI adoption that joins UTAUT, DeLone–McLean, and Theory of Planned Behavior constructs, adding a new dimension, Attitude Towards Ethical Behavior (AFCE), adapted from Internet ethics to AI. The model is tested with PLS-SEM on 237 online self-report responses, mainly from IT professionals. The authors report that seven of the (variously stated) eight or nine hypotheses are supported, with H5 (AFCE directly influences Use) being central to the conclusion that ethics can serve as the 'mainstay' of AI adoption. Social Influence hypotheses H3a and H3b are not supported. The paper concludes that ethical AI adoption is possible and that ethics impacts people's decisions.

Significance. If the empirical claims were fully supported, the main contribution would be a modest extension of technology-acceptance models by inserting an ethics attitude construct, with a concrete estimate that ethics attitudes positively predict self-reported AI use. The paper has some strengths: it uses standard PLS-SEM validation procedures (Cronbach's alpha, composite reliability, AVE, Fornell–Larcker), retains nonsignificant hypotheses as nonsignificant, and grounds the model in established theories with explicit citations. However, the central claim rests on a single cross-sectional, self-report survey, and the manuscript omits the measurement instrument, the common method bias diagnostics, and the collinearity table that would be required to substantiate the path estimates. The significance is therefore conditional on additional evidence that is not currently in the paper.

major comments (5)

[Section V, Table II and accompanying text] The collinearity assessment is contradicted by the reported numbers. The text states that 'All Inner VIF values vary between 0.768 and 0.921,' but Table II lists values such as 1.000, 1.284, 1.429, 1.369, 1.320, 1.185, and 1.482. The stated range matches the diagonal of the Fornell–Larcker table (Table III), not the Inner VIF values. As a result, the paper does not actually report the Inner VIF results, and the claim that there are no collinearity problems is unsupported. This matters because collinearity is a precondition for interpreting the individual path coefficients, including the central H5 estimate.
[Section V, H5 and Section VI] No common method bias diagnostic is reported, yet all constructs were measured with the same 1–7 Likert-scale online questionnaire. The path from AFCE to Use (beta = 0.207, p reported as 0) is the key evidence for the conclusion that ethics 'impacts' AI adoption. Without a test such as Harman's single-factor, a common latent factor, or a marker variable, the observed correlation could be inflated by response style, social desirability, or item wording. The absence of this test is a load-bearing gap for the central claim, not a mere reporting detail.
[Section III, AFCE construct and Annexes 2 and 4] The measurement instrument for AFCE is not available. The paper cites reference [17] for Internet ethics and states that AFCE is an adaptation to AI, but the actual item wordings, cross-loadings, and AVE tables are referenced as 'Annex 2' and 'Annex 4' that are not included in the manuscript. This omission prevents the reader from checking whether the AFCE items inadvertently capture conditional use intention (for example, willingness to use AI only if it is ethical) or positive affect, either of which would make the significant H5 path tautological. It also prevents verification of the claimed content validity of the adaptation. This is directly relevant to the paper's headline result.
[Section V and Section VI, causal language] The paper uses causal language ('directly affects', 'positively impacts', 'Ethics affects people's decisions') while the data are a single cross-sectional, self-report survey from a convenience sample of 237 respondents, mainly from the Information Technology area. Temporal precedence is not established, and the sample is not representative of the 'society' to which the conclusion generalizes. At minimum, the discussion should reframe the findings as associations and acknowledge the limitation that causality cannot be inferred from these data; otherwise the central conclusion is overreaching.
[Abstract and Section V, hypothesis count] The paper states repeatedly that 'seven of the nine investigation hypotheses' were validated, but only eight hypotheses are actually listed (H1, H2a, H2b, H3a, H3b, H4, H5, H6). Section VI even says 'seven research hypotheses' while describing the model. This inconsistency affects the headline quantitative claim and indicates that the hypothesis list or the summary count is in error. The authors should correct the count and ensure all hypotheses are enumerated consistently.

minor comments (5)

[Section V, Table V] The p-values for supported hypotheses are reported as '0'; considering the typical reporting conventions, the authors should report exact values (e.g., p < 0.001) rather than 0, since p is never exactly zero.
[Section III, H6 wording] Hypothesis H6 is stated as 'There is a correlation between the Use of Artificial Intelligence mechanisms and the Net Benefits dimension,' which is a correlational statement, while H1–H5 use 'directly affects' or 'positively impacts.' The model results are then interpreted causally in the conclusion; aligning the hypothesis wording with the analysis would clarify the intended claim.
[Section IV, method description] The method section does not describe how the questionnaire was distributed, the sampling procedure, the response rate, or how missing data were handled. Adding these details would improve reproducibility and help readers assess sample representativeness.
[Throughout] There are several typographical and formatting errors, for example 'recen t years' in the abstract, 'Hipothesys' in Table V, mixed use of 'Behavioral Intent' and 'Behavioral Intention', and inconsistent use of commas and periods as decimal separators (e.g., Table II uses '1,284' while the text uses '0.768'). A careful proofreading pass is needed.
[References] Several references are to the authors' own prior work (e.g., [1], [2], [12], [13], [20], [22], [23]). While self-citation is not inappropriate, the literature review could benefit from a broader set of sources on AI ethics and technology adoption to ensure the framing is not overly reliant on the authors' own papers.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: H5 is an estimated SEM path from newly collected questionnaire data, not a quantity defined by its own inputs.

full rationale

The paper's derivation chain is a standard empirical SEM workflow: constructs are assembled from external acceptance and information-systems theories (UTAUT [7], DeLone & McLean [9,10], TPB via [17]), a questionnaire was administered to 237 respondents, and hypotheses are evaluated from estimated coefficients (Table V). The central claim, that Attitude Towards Ethical Behavior predicts self-reported Use (H5, beta = 0.207, p < 0.05), is an estimated path coefficient from the data, not a restatement of a defining equation or a fitted parameter renamed as a finding. The AFCE construct is defined as an individual's view about Internet-related ethical principles applied to AI ([17]) and 'directly influences the Use of Artificial Intelligence' is a substantive hypothesis that could have failed; in fact two social-influence hypotheses did fail in the same analysis, showing the test is not forced. The authors' self-citations appear in the literature review and method discussion but do not carry the inference for H5, which rests on external foundations ([17], [24]) and newly collected data. Missing annexes and common-method concerns bear on validity and verification, not on circularity, and no specific reduction shows any hypothesis is equivalent to its input by construction.

Assumptions & free parameters 8 free parameters · 5 assumptions · 1 invented entities

The central claim rests on standard social science measurement assumptions: that the cited acceptance theories transfer to AI, that Likert self-reports capture true attitudes and use, that the convenience sample represents the population, and that common method bias is absent. These are domain assumptions, not derived facts. The structural path coefficients are fitted parameters estimated from the 237 responses.

free parameters (8)

Path coefficient H1 (EP -> IC) = 0.602
Estimated via PLS-SEM from 237 responses; used to test H1.
Path coefficient H2a (CF -> IC) = 0.225
Estimated via PLS-SEM; supports H2a.
Path coefficient H2b (CF -> IS) = 0.442
Estimated via PLS-SEM; supports H2b.
Path coefficient H3a (IS -> IC) = 0.040
Estimated via PLS-SEM; not significant, hypothesis rejected.
Path coefficient H3b (IS -> Use) = 0.016
Estimated via PLS-SEM; not significant, hypothesis rejected.
Path coefficient H4 (IC -> Use) = 0.701
Estimated via PLS-SEM; supports H4.
Path coefficient H5 (AFCE -> Use) = 0.207
Estimated via PLS-SEM; central claim of the paper.
Path coefficient H6 (Use -> BL) = 0.706
Estimated via PLS-SEM; supports H6.

assumptions (5)

domain assumption The UTAUT, TAM, and DeLone-McLean models apply to AI adoption in society.
The model in Section III borrows constructs and causal paths from these theories without re-deriving them for the AI context.
domain assumption Self-reported Likert responses accurately measure attitudes, intentions, and use behavior.
Section IV: questionnaire relies on 7-point Likert self-reports; no objective use data or social desirability control.
domain assumption The sample of 237 mostly IT respondents represents the broader population (society).
Section IV describes convenience sample of employees with emphasis on IT; conclusions generalize to 'society'.
domain assumption Common method bias does not materially inflate path coefficients.
Single cross-sectional questionnaire; no common method variance test reported in Section V.
domain assumption Reflective measurement specification for all constructs.
Section V analyzes outer loadings and AVE assuming reflective indicators; no justification for formative vs reflective.

invented entities (1)

Attitude Towards Ethical Behavior (AFCE) adapted to AI
purpose: Explanatory antecedent of AI use; introduced as a novel construct in the model (Section III, Table I).
Measured via unpublished questionnaire items; no external validation or falsifiable prediction outside this sample.

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Cite this review

Pith. "Pith review of Ethics and Artificial Intelligence Adoption." pith.science (2026). https://pith.science/paper/7BE7MRIL

@misc{pith2026241200330,
  author       = {Pith},
  title        = {Pith review of: Ethics and Artificial Intelligence Adoption},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7BE7MRIL}},
  note         = {Machine review of arXiv:2412.00330}
}

read the original abstract

In recent years, we have witnessed a marked development and growth in Artificial Intelligence. The growth of the data volume generated by sensors and machines, combined with the information flow resulting from the user actions on the Internet, with high investments of the governments and the companies in this area, provided the practice and developed the algorithms of the Artificial Intelligence However, the people, in general, started to feel a particular fear regarding the security and privacy of their data and the theme of the Artificial Intelligence Ethics began to be discussed more regularly. The investigation aim of this work is to understand the possibility of adopting Artificial Intelligence nowadays in our society, having, as a mandatory assumption, Ethics and respect towards data and people's privacy. With that purpose in mind, a model has been created, mainly supported by the theories that were used to create the model. The suggested model has been tested and validated through Structural equation modeling based on data taken back from the respondents' answers to the questionnaire online: 237 answers, mainly from the Investigation Technologies area. The results obtained enabled the validation of seven of the nine investigation hypotheses of the proposed model. It was impossible to confirm any association between the Social Influence construct and the variables of Behavioral Intention and the Use of Artificial Intelligence. The aim of this work was accomplished once the investigation theme was validated and proved that it is possible to adopt Artificial Intelligence in our society, using the Attitude Towards Ethical Behavioral construct as the mainstay of the model.

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Reference graph

Works this paper leans on

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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What drives students’ Internet ethical behaviour: an integrated model of the theory of planned behaviour, personality, and Internet ethics education,

Y.-Y. Wang, Y. -S. Wang, and Y. -M. Wang, “What drives students’ Internet ethical behaviour: an integrated model of the theory of planned behaviour, personality, and Internet ethics education,” Behav. Inf. Technol., vol. 41, no. 3, pp. 588 –610, Feb. 2022, doi: 10.1080/0144929X.2020.1829053

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doi:10.3390/app13159015 2023

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Predicting Bitcoin prices : The effect of interest rate, search on the internet, and energy prices,

J. T. Aparicio, M. Romao, and C. J. Costa, “Predicting Bitcoin prices : The effect of interest rate, search on the internet, and energy prices,” in 2022 17th Iberian Conference on Information Systems and Technologies (CISTI), Jun. 2022, pp. 1–5. doi: 10.23919/CISTI54924.2022.9820085

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work page 1995

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F. D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Q. , vol. 13, no. 3, pp. 319–340, 1989, doi: 10.2307/249008

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M. Aparicio and C. J. Costa, “Collaborative systems: characteristics and features,” in Proceedings of the 30th ACM international conference on Design of communication , in SIGDOC ’12. New York, NY, USA: Association for Computing Machinery, Oct. 2012, pp. 141 –146. doi: 10.1145/2379057.2379087

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V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User Acceptance of Information Technology: Toward a Unified View,” MIS Q., vol. 27, no. 3, pp. 425–478, 2003, doi: 10.2307/30036540

doi:10.2307/30036540 2003

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Determinants adoption of computer-assisted auditing tools (CAATs),

I. Pedrosa, C. J. Costa, and M. Aparicio, “Determinants adoption of computer-assisted auditing tools (CAATs),” Cogn. Technol. Work, vol. 22, no. 3, pp. 565 –583, Aug. 2020, doi: 10.1007/s10111-019-00581-4

work page doi:10.1007/s10111-019-00581-4 2020