PONE-D-25-41907The Autonomy Paradox in AI-Generated Content Adoption: Extending the TAM Model in China’s Micro-Short Drama IndustryPLOS ONE

Dear Dr. Tang,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. The reviewers pointed issues with the manuscript, especially with its empirical part. You need to properly address these issues. Additionally, I can recommend ANATRASIRICHAI, N.; BULL, D. Artificial intelligence in the creative industries: a review. Artificial Intelligence Review, n. 55, p. 589-656, 2022 and HERTZMANN, A. Can computers create art? Arts, v. 7, n. 18, 2018. . It would help to situate better the use of AI in the creative process of microdramas. It should be noted that there must be a "quantitative" side to it. The proliferation of AI-generated "slop" has been hurting the arts in many sectors, not to mention the costs. It would be helpful if you expanded the concerns of AI usage in the meaning of the arts, how do creators deal with this issue?

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Reviewers' comments:

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Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Dear Authors,

Thanks for the submission. The topic is timely and the mixed-methods angle is promising. That said, there are a few core issues that need attention before the work is methodologically solid and ready for PLOS ONE.

Brief summary

You propose the Creative Industries Technology Acceptance Model (CITAM) to explain AIGC adoption among micro–short-drama creators (N=607), emphasizing innovation compatibility (IC) and a moderator you call creative autonomy retention (CAR). You combine CB-SEM with interviews and report support for all six hypotheses, including small but significant negative moderations.

What needs fixing (major)

Fit indices don’t line up.

You label the CFA fit “excellent” while reporting TLI = 0.85 (χ²(98)=179.729, CFI=0.988, RMSEA=0.037, SRMR=0.030). Later, the structural model shows TLI = 0.985. Please reconcile these numbers (likely 0.985 vs 0.85), report fit separately for measurement and structural models, and correct the interpretation.

The moderator’s identity shifts.

CAR is introduced as Creative Autonomy Retention, but a table note calls it Control and Autonomy Reactance. Those are not the same idea. Pick one definition and name, keep it consistent across text/tables/figures/appendix, clarify the direction of scoring (higher = what?), and state how any reverse-coded items were handled. Confirm unidimensionality.

Effect-size claims are off.

You cite f² ≈ 0.006 for the interaction(s) but say the moderation adds “~5% variance.” That doesn’t match. Please report the actual ΔR² from adding the interaction term, and tone down the claim if the gain is small. Also note whether predictors were centered and how the interaction was built (product term vs latent interaction).

Sampling description vs. achieved sample.

You describe purposive recruitment followed by stratified random sampling (with role targets like 40/30/20/10), yet the final role mix doesn’t match those targets. There’s also a “69% have 3+ years” statement that doesn’t align with the listed breakdown. Please clarify the sampling frame and execution, justify any representativeness claims (or drop them), and either weight post-hoc or be precise about the limits.

Data availability likely non-compliant.

PLOS ONE typically expects de-identified survey data in a public repository (with a DOI). Interviews can remain controlled-access with safeguards. As written, your “available on request via an ethics contact” approach for the survey dataset is unlikely to meet policy. Please deposit the de-identified survey data and codebook (e.g., OSF/Zenodo) or provide a policy-consistent exception.

Design label is inconsistent.

You call the design “explanatory sequential” and then describe a two-phase exploratory sequential (qual → quant). Choose the correct design, justify it, and align the narrative.

Positioning vs. TAM needs honesty.

You say you “extend TAM” but you drop PU/PEOU “for parsimony.” That reads less like an extension and more like an alternative tailored to creative contexts. Either reframe it as an alternative or include PU/PEOU as controls to show incremental value.

R² values drift.

R²(AUB) appears as 0.330 in one place and 0.335 elsewhere. Please make all R² and CI values consistent across text, tables, and figures.

Measurement transparency.

Keep HTMT, but please also share the Fornell–Larcker matrix and inter-construct correlations in the supplement, list all item loadings (flag any < .50 and how you handled them), and provide descriptive stats (M, SD, skew, kurtosis). If assumptions are borderline, consider robust ML (e.g., MLR).

Moderation/mediation reporting.

State clearly if you mean-centered variables, how the interaction was constructed, and whether you estimated latent interactions. Include simple-slope plots with CIs. For mediation, report the indirect effects with bootstrapped CIs and the number of draws, all in one consolidated results section.

Also fix (moderate/minor)

Terminology hygiene: Standardize the moderator’s name/abbreviation everywhere (text, tables, figure captions).

Causal tone: With cross-sectional data, avoid “influences/affects”; use “is associated with” or “predicts.”

Appendix completeness: Provide full item wording for IC, CAR, AI, and AUB, indicating any reverse-coded items; confirm CAR’s unidimensionality.

Common method bias: Harman’s single-factor test is not sufficient. Add a latent method factor or a marker-variable check and report the impact.

Tables/figures: Ensure the Table 1 note matches the construct names, and add a table of descriptive stats + correlations for all constructs.

Policy references: Where you mention policy implications (e.g., EU AI Act), add precise citations in the reference list.

Strengths worth keeping

Topical and practical contribution for creators and managers.

Mixed-methods design that helps interpret the small moderation effects.

Bottom line

Recommendation: Major revision. If you:

(1) fix the fit-index inconsistencies and right-size the effect-size claims,

(2) lock down the moderator’s definition/measurement,

(3) clean up the sampling story and any representativeness language, and

(4) bring the data-availability statement into line with PLOS ONE policy,

the paper will be much stronger and on firmer methodological ground.

Happy to take another look once these are addressed.

Reviewer #2: I appreciate the time and effort put into this research. From the article it is evident that this research has been carried out with high rigour, ensuring key concepts and hypotheses have been clarified, and supported with substantial literature review.

By saying that, I also find this paper to be too ambitious at moments, attempting to cover too much at once drawing from way far too many theories that only get partially explained. This is especially pronounced in Theoretical Framework and Hypotheses section (p.200 onwards) where many complex theories such as SDT, notion such as 'enhanced cognitive fit' (p.218), both asking for more explanation and evidence.

Same goes for 'creative identity threat theory [38] and the concept of psychological ownership [34], mentioned in p258.

This is again repeated in 'This defensive mechanism is also expected to impact actual usage. Drawing on

cognitive load theory [40] and identity protection mechanisms [41] (p270) - these concepts need more explanation, it is not enough to just say that the research is drawing upon them and cite one article. These are complex theories asking for substantial explanation. One approach could be to consider expanding on these theories through the final discussion in the paper, once the empirical data has been showed.

I believe that by expanding on these areas rather than just listing them, will make this research even stronger.

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Reviewer #1: Yes: Mohammed Salah,PhD

Reviewer #2: No

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The autonomy paradox in AI-generated content adoption: creative-specific alternative to TAM model in China's micro-short drama industry

PONE-D-25-41907R1

Dear Dr. Tang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Rafael Galvão de Almeida, PhD.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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Reviewers' comments: