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Dear authors, I have assessed for myself the quality of your rebuttal to the revisions, and believe that the current version of your manuscript is now in good shape for being published. Congrats.
[# PeerJ Staff Note - this decision was reviewed and approved by Arkaitz Zubiaga, a PeerJ Section Editor covering this Section #]
The manuscript presents a very relevant, timely and technically sound study on the impact of executives' digital background for AI adoption in enterprises. But the authors should address the two reviewer's concerns, such as:
- information on how key variables, such as the AI Utilization Level metric are constructed. and more importantly,
- enhance the study’s impact, comparing to similar international research on leadership in digital transformation
The manuscript is generally well-written in clear and professional English. However, some sentences could be restructured to improve fluency and readability. The phrasing in the abstract and introduction is somewhat dense, and simplifying complex sentence structures would enhance clarity. The introduction provides sufficient background and context, emphasizing the importance of AI in enterprises and the role of executives with a digital background. However, the justification could be more direct in explaining how the study fills a specific research gap. The literature review includes relevant and recent studies, with citations from 2023 and 2024, and is well-structured. However, there is a strong focus on research related to Chinese enterprises. To enhance the study’s impact, incorporating more international research, particularly on leadership in digital transformation and AI adoption, is recommended. Additionally, integrating key studies on technology management, innovation, and foundational research in AI and digital transformation could help frame the study within a broader international context. The conclusions align with the results and offer valuable insights. The research questions are well-structured and thoroughly addressed, and the discussion of practical implications is a strong point. However, the conclusions could be more concise and focus on the practical implications for companies aiming to enhance AI adoption.
The study appears to be conducted with high technical and ethical standards. The dataset, consisting of Chinese A-share listed companies from 2012 to 2022, is robust, and no ethical concerns were identified regarding data collection or analysis. The methodology is well-structured and detailed, supporting replication. The statistical analysis includes robustness checks, such as instrumental variables and the Heckman two-stage model, which strengthen the validity of the findings. However, additional information is needed on how key variables, such as the AI Utilization Level metric, were constructed. Clarifying how text-mining metrics and AI-related keywords were selected and processed—whether manually or automatically—would improve transparency and replicability. The research methods are thoroughly documented, and the econometric models used, including OLS, Tobit, Logit, instrumental variable models, and Heckman corrections, are appropriate for the analysis. The inclusion of robustness tests further enhances the reliability of the findings. Additional clarity regarding how executives' digital backgrounds were classified, particularly if external validations were used beyond self-reported resumes, would also be beneficial.
The statistical methods used in the study are rigorous and suitable for the dataset. The regression analyses include necessary control variables and dummy variables, ensuring that the results account for potential confounding factors. The moderation and mediation analyses add depth to the study by examining indirect and interactive effects. Endogeneity concerns are well-addressed using instrumental variable techniques and the Heckman two-stage method. The findings are statistically sound, with clearly reported significance levels and robustness checks. The manuscript also includes several tables that support the empirical analysis, with figures and tables clearly labeled and described. However, the study would benefit from acknowledging potential limitations, such as whether the results can be generalized to companies outside of China. Additionally, addressing self-reporting biases in executive backgrounds would improve the study's credibility. Expanding the theoretical discussion with more references to international AI adoption studies would further strengthen the paper. A brief section discussing potential biases and future research directions would also enhance transparency. Overall, the study is methodologically strong, but greater contextualization within global AI research and a clearer discussion of limitations would improve the validity of the findings.
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This paper presents a timely and relevant study on the impact of executives' digital backgrounds on AI adoption in enterprises. The research is well-structured, employing a robust dataset spanning a decade (2012-2022) and rigorous statistical methods, including robustness checks and instrumental variable approaches. The findings provide valuable insights into how digital leadership enhances AI integration, with notable contributions in examining moderating (Total Factor Productivity) and mediating (Digital Technology Application Capabilities) effects. The heterogeneity analysis across enterprise types (private vs. state-owned) and regions further strengthens the practical implications.
However, the study's theoretical foundation could be expanded by incorporating frameworks such as Dynamic Capabilities Theory, the Technology-Organization-Environment (TOE) framework, and Upper Echelons Theory to better contextualize the role of digital leadership in AI adoption. Additionally, external factors such as regulatory influences, industry variations, and cultural aspects remain underexplored. While the paper effectively focuses on Chinese firms, a discussion on global AI adoption trends would enhance its broader applicability.
To improve clarity, the conceptual framework should be visually outlined, and some hypotheses could be restructured for better readability. Strengthening the literature review with more high-impact references would provide a stronger foundation. Additionally, simplifying complex sentence structures would improve readability. Addressing these areas would significantly enhance the study’s contribution and its potential for publication in a high-impact journal.
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