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The reviewer seems satisfied about the recent changes and therefore I can recommend this article for acceptance.
[# PeerJ Staff Note - this decision was reviewed and approved by Claudio Ardagna, a PeerJ Section Editor covering this Section #]
Approved. Requested Changes have been taken care of.
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**PeerJ Staff Note:** Please ensure that all review, editorial, and staff comments are addressed in a response letter and that any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate.
Authors in this article use a professional and clear English. Figures support the text.
There is sufficient background provided in the literature review and references.
Structure of the article is correct starting with background, literature review, methodology proposal and empirical support, ending with conclusions. Conclusions however are limited.
Formal results include clear definitions and detailed proofs of hypothesis.
Research questions are well defined. Authors want to propose new method of CCI calculation which is valuable for the research area and fills knowledge gap. Methods are sufficiently detailed described and possible to replicate.
Performed investigation however is limited from empirical perspective.
1. There is no rationale explained why "onion price" was selected as a measure.
2. Survey conducted on 600 respondents does not explain the sampling method and representativeness justification.
Benefit of such new method CCI calculation is clearly stated. Data have been provided and publicly available. Statistically soundness is however limited. The choice of variables are not fully justified.
conclusions are very limited. Would suggest to elaborate more about results and how they benefit to literature. Practical side should also be emphasized.
no further comments, all mentioned above.
Language Quality: Overall, the manuscript is written in clear, professional English. However, some sections (especially those involving complex equations and derivations) could benefit from rewording for enhanced clarity.
Structure and Style: The paper follows a logical structure (Abstract, Introduction, Related Work, Methodology, Experiment, Conclusion) and includes well-structured tables and figures.
Figures and Data Presentation: All 12 figures are relevant, well-labeled, and contextual.
References: Comprehensive and current.
*Cross-check reference formatting and inclusion of DOIs where missing.
Originality: The use of online behavioral data and the introduction of the S-CCI algorithm for consumer confidence estimation is a novel contribution.
Research Questions: Well stated and clearly addresses a gap in traditional CCI methods (slow, coarse-grained).
Methodological Rigor: Strong mathematical modeling (both univariate and multivariate models). Use of actual datasets (stock prices, onion prices, simulation data) adds credibility.
Reproducibility: Equations and processes are detailed, but ensure all parameters (exmp: in the 3D surface modeling) are fully explained or clarified for reproducibility.
Statistical Validity: Includes performance metrics (Precision, Recall, F1-score, Accuracy).
Empirical validation via survey results (Likert scale, SPSS analysis) further strengthens credibility.
Conclusions: Well-linked to data and clearly support the hypothesis that S-CCI performs better.
*Avoid making any overstated claims about generalizability beyond tested datasets.
Strengths:
Comprehensive integration of behavioral economics and data science.
The multivariate model is well-founded in theory and practical utility.
Clear evidence of algorithmic superiority.
Suggestions:
Improve the flow and explanation of complex equations in Sections 4 and 5.
Add more discussion on limitations (exmp: potential bias in online behavioral data, platform dependency).
Consider including more visual explanation (exmp: diagrammatic representations of the 3D surfaces).
Discuss potential real-time applications or implementation.
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