Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript investigates how enterprises can simultaneously promote data asset trading and new qualitative productive forces through the Technology – Organization - Environment (TOE) framework.

Using a dataset of Chinese A-share listed companies (2020–2024), it integrates Dynamic Qualitative Comparative Analysis (QCA) and regression modeling to identify multiple configuration pathways driving data asset trading and its mediating role in enhancing enterprise-level productive forces.

Five configurations are identified and grouped into three models:

1. Technology-Organization- Environment synergy;
2. Data Technology- Environmental Ecology dual drive;
3. Environmental Constraint–Technological Compensation.

The study is theoretically innovative and provides practical insights for corporate and policy strategies in the digital economy.

General Assessment:

This paper provides a solid contribution to the emerging literature on the data economy and enterprise-level productivity. The integration of TOE theory with configurational and empirical analysis is a strong point. However, several areas- particularly conceptual clarification, methodological transparency, and English expression - need improvement before publication.

Major Comments:

1. Conceptual and Theoretical Clarity
• Strengthen the theoretical explanation of how data asset trading affects new qualitative productive forces.
• Clarify whether the mediating mechanism is unidirectional or reciprocal.
• Expand on how the TOE framework extends previous research on digital transformation or resource-based theory.
2. Measurement Validity
• The innovative keyword-frequency approach (e.g., for data asset trading, fintech, and data elements) needs stronger justification. Explain why text frequency from annual reports is an appropriate proxy for actual firm behavior, and if possible, validate it against real operational indicators.
3. Methodological Transparency
• Provide detailed information on QCA calibration (thresholds, consistency, coverage) and how cases were assigned to configurations.
• Include a clear diagram of the TOE-based configurational model and mediating effects.
4. Interpretation of Results
• The negative relationships (S1b, S2, S3) between certain configurations and new productive forces are theoretically interesting. Discuss why some configurations inhibit productivity - perhaps due to over-financialization, policy distortions, or weak organizational adaptation.
5. Language and Readability
• The paper would benefit from professional English editing to improve clarity and conciseness.
• Simplify long sentences, unify terminology (“new-quality productive forces” vs. “new qualitative productive forces”), and correct minor typographical issues.

Minor Comments: 

• Ensure all citations (e.g., placeholders like “[30]”) are complete and properly formatted.
• Consolidate repetitive robustness tables (Tables 5–7) into an appendix.
• Improve figure/table captions for self-contained interpretation.
• Add a visual representation of the TOE framework.
• Define all abbreviations (e.g., PRI, QCA) on first appearance.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

While this study offers meaningful contributions to understanding the interplay between enterprise data asset trading and new qualitative productive forces under the TOE framework, several limitations temper its implications. The authors successfully identify multiple configurations for achieving high-level data asset trading and demonstrate that technological, organizational, and environmental factors jointly shape enterprise outcomes. However, the analysis relies heavily on secondary data from Chinese A-share listed companies, which may restrict the generalizability of the findings to other economic or institutional contexts.

Moreover, although the configurational approach provides valuable insights into complex causal relationships, the study could further clarify how dynamic interactions among the TOE dimensions evolve over time and how they influence the sustainability of digital transformation. The proposed models — particularly the “technology-organization-environment synergy” and “data technology-environmental ecosystem” models — remain largely conceptual and may benefit from deeper empirical validation or cross-industry comparison.

In conclusion, the paper advances the theoretical discourse on enterprise digitalization and data asset utilization, yet future research should adopt longitudinal and cross-national perspectives, integrate qualitative evidence, and examine governance or ethical dimensions of data trading to strengthen both its explanatory and practical relevance.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript tackles a highly timely and significant topic: the relationship between enterprise data asset trading and the development of "new qualitative productive forces" in China, using the TOE framework. The ambition to combine Qualitative Comparative Analysis (QCA) with regression analysis to build a "complex mediation model" is commendable, as this mixed-method approach has the potential to yield nuanced, pathway-based insights. However, the manuscript, in its current form, suffers from several fundamental conceptual and methodological flaws that prevent its publication. These major issues must be thoroughly addressed before the paper can be reconsidered. 

1. The concept of "new qualitative productive forces" (NQPF) is presented as the main outcome variable, but it is treated as a given, well-established academic construct. It is not.

   • The term originates from a 2024 policy document (Central Economic Work Conference). It is a high-level political and economic concept, not a defined and operationalized construct from existing academic literature. The paper fails to provide any rigorous theoretical foundation for NQPF. What is it, academically? How does it differ from established concepts like Total Factor Productivity (TFP), innovation capacity, or digital transformation? The introduction is insufficient in this regard.

   • Section 3.2.3 states that NQPF is measured "based on the two-factor theory of productivity, focusing on the core dimensions of labor force and labor tools" using an entropy method. This is a black box. The authors provide no citation for this "two-factor theory" and no justification for why these specific inputs constitute this new, complex concept. This operationalization seems to simply re-label a standard productivity measure (or a component of one) as NQPF. This is a severe threat to construct validity.

   • The concept of NQPF was introduced in 2024 (or late 2023). The paper uses data from 2020-2024. How can the authors measure a concept's presence and development (the outcome) in 2020, 2021, 2022, and 2023, before the concept itself was even defined? This is a fundamental logical and temporal contradiction. The authors appear to be retroactively applying a new political buzzword to old data without justification.

2. The measurement of key variables in the model is highly problematic and creates tautological relationships.

   • The independent variable "enterprise's data element level" (X1) is measured by word frequency in annual reports (Section 3.2.1). The mediating variable "enterprise data asset trading" (Mediator) is also measured by word frequency in annual reports (Section 3.2.2). The study is therefore, in large part, examining whether "talking about data" (X1) predicts "talking about data trading" (Mediator). This is not a meaningful finding.

   • Using word frequency from an annual report is a weak proxy for actual data asset trading. It is a measure of corporate disclosure, signaling, or discussion of the topic, which is not the same as the economic act of trading. This is a critical limitation that fatally undermines the mediation analysis. The authors must find a more valid proxy for actual trading or, at minimum, explicitly reframe their entire paper as a study of "corporate disclosure of data trading," not trading itself.

     • The abstract claims to use "dynamic Qualitative Comparative Analysis." However, the analysis presented in Table 4 appears to be a standard, (at best) pooled, cross-sectional QCA. "Dynamic QCA" is not a standard term, and if the authors mean a temporal QCA (TQCA) or some other panel-data QCA method, they have not described or implemented it. The panel nature of the data (4,275 observations from 2020-2024) seems to have been ignored by pooling all observations, which is a significant methodological weakness that washes out all temporal effects. The "dynamic" claim must be substantiated or removed.

     • The analysis (Table 4) and subsequent discussion (Section 4.1.2) present Configuration S3 as the "Environmental Constraints-Technological Compensation" driven model. However, its raw coverage is 0.005. This configuration explains only 0.5% of the cases. This is not a "path" or a "model"; it is statistical noise. Discussing this configuration as a meaningful finding is a significant over-interpretation of the results and undermines the credibility of the entire QCA. This configuration should be discarded as trivial.

3. Language, Clarity, and Originality:

   • The quality of the English needs significant improvement. The text is often dense with jargon ("value-multiplying effect," "extensive radiating and driving effects") which obscures meaning.

   • The iThenticate report shows a 24% similarity index. This is high and suggests that large portions of the text, likely the introduction and literature review, may be too close to the original sources. This requires a thorough check and careful paraphrasing.

To be reconsidered, the authors must:

1. Provide a rigorous, academic definition of "new qualitative productive forces," clearly differentiating it from TFP and other concepts.

2. Either find a valid, established measure for NQPF (which is unlikely given its novelty) or explicitly defend the current "black box" measure from Section 3.2.3 with detailed theoretical and methodological justification. The temporal paradox (measuring a 2024 concept in 2020) must be resolved. A more intellectually-honest approach may be to use TFP as the outcome and then discuss NQPF in the conclusion as a potential policy implication.

3. The tautology between X1 and the mediator is unacceptable. A different, valid proxy for actual data asset trading must be found. Using text analysis for both is a fatal flaw.

4. Explain precisely what "dynamic" QCA means here. Justify pooling panel data for a QCA or (preferably) conduct a proper panel-data QCA or a year-by-year analysis to actually observe "dynamic" changes.

5. Remove the trivial S3 configuration from the analysis and discussion.

6. The entire manuscript requires professional copyediting for clarity, and the 24% similarity index must be addressed through significant re-writing and paraphrasing.

The topic is interesting, but the current execution is not at a publishable standard due to these major conceptual and methodological failures.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

Thank you for your revisions and for the detailed point-by-point responses. I appreciate the effort invested in addressing the concerns raised in the first-round review. However, after carefully examining the revised manuscript and your replies, I must conclude that substantial issues remain unresolved, and major revision is still necessary before the manuscript can be considered for publication.

You have expanded the definition and contextualization of “New Quality Productive Forces” (NQPF), and the discussion is now more detailed. However, the concept is still introduced and operationalized as if it were an established academic construct. The theoretical justification remains policy-driven rather than scholarly grounded, and the distinction from TFP, although elaborated, is still not embedded in a coherent theoretical framework.

Despite expanding the description of the entropy-based measurement, the index continues to function as a black box. It is unclear why the selected indicators collectively represent NQPF, nor is there any empirical or conceptual validation of this structure. The underlying issue from Round 1 therefore remains unresolved.

Although you attempted to differentiate the keyword sets for the “data element level” and the “data asset transaction disclosure,” both variables are still measured via text mining of annual reports, using conceptually and linguistically overlapping corpora. The methodological problem of shared method variance, semantic proximity, and endogeneity persists.
This continues to undermine the credibility of the mediation analysis.

You have added descriptive temporal discussion and a figure, but the method remains a cross-sectional QCA with year-by-year commentary, not a true dynamic or panel-QCA approach. The manuscript should either implement an established dynamic QCA methodology or refrain from using the term altogether.

Even in the revised version, configuration S3 has extremely low coverage, and its high consistency alone does not justify substantive interpretation. The arguments offered in your response do not adequately support including S3 as a meaningful pathway.

Some key theoretical and methodological sources remain missing. The literature review continues to rely disproportionately on policy documents and recent local studies, rather than on foundational academic work.

Although some wording has improved, the manuscript remains overly verbose, repetitive, and occasionally unclear. The similarity index still requires substantial rewriting to ensure academic originality and clarity

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 3

Reviewer 3 Report

Comments and Suggestions for Authors

After carefully reviewing the revised version of the manuscript, I find that the authors have adequately addressed all previously raised comments and concerns. The revisions have significantly improved the clarity, quality, and scientific rigor of the paper. In its current form, the manuscript meets the standards of the journal, and I therefore recommend it for publication.