Stupid to Smart: The Sustainability Map of AI in Organization

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

I think the paper has improved.

 

 

 

Author Response

Responses to Reviewer 1

 

Comment: I think the paper has improved.

Response: We thank the reviewer for the feedback.

Reviewer 2 Report (Previous Reviewer 3)

Comments and Suggestions for Authors

Each of the reviewer’s concerns has been addressed, and the manuscript revisions (as described) appear to adequately resolve the issues. The revisions have substantially strengthened the manuscript. The added case studies are particularly helpful in illustrating the OAIAM model’s practical application, and the condensed discussion of white- vs blue-collar AI removes previous redundancies. The model is now clearer and more actionable, especially with the expanded dynamic open-system interpretation. The paper fills a clear theoretical gap in the literature, moving beyond static pre-adoption frameworks and providing a more nuanced understanding of ongoing and evolving AI integration in organizational contexts.

The revised manuscript demonstrates notable improvements in English language quality, with clearer sentence structure and more concise phrasing that enhance overall readability and better convey the authors’ ideas.

In conclusion, the work represents a meaningful and timely contribution to AI management research and provides both theoretical clarity and practical guidance. It will be of value to researchers, practitioners, and policymakers seeking to understand or manage AI transformation within organizations.

I recommend of accepting it in the present form.

Comments for author File: Comments.pdf

Author Response

Responses to Reviewer 2

 

Comment 1: Each of the reviewer's concerns has been addressed, and the manuscript revisions appear to adequately resolve the issues.The revised manuscript demonstrates notable improvements in English language quality.The work represents a meaningful and timely contribution to AI management research and provides both theoretical clarity and practical guidance.

Response: We are grateful for the reviewer’s acknowledgment of our revisions.

Reviewer 3 Report (Previous Reviewer 4)

Comments and Suggestions for Authors

Please see the attachment.

Comments for author File: Comments.pdf

Author Response

please see the attachment

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report (Previous Reviewer 4)

Comments and Suggestions for Authors

No comments.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The proposed quantification through textual analysis of annual reports requires further elaboration regarding its methodological robustness. The scientific writing is overly extensive in certain sections, which may undermine the objectivity and conciseness expected of review articles. A textual revision is recommended to enhance linguistic economy, along with a more synthetic use of figures to facilitate the visualization of the model and its dimensions.

The article offers an original and relevant theoretical contribution, with potential academic and practical impact. However, the absence of empirical validation, the excessive length of some sections, and the need to improve textual clarity are my minor suggestions to be addressed prior to publication.

Reviewer 2 Report

Comments and Suggestions for Authors

Why do we need a model of an organization's AI adoption level?  The reason should be made clearer in the paper. 

In my opinion, the model is too general and vague to be of any practical value.  How could the model be used in practice?  Perhaps a comparison of a low adoption organization and a high adoption organization would be useful.

In the limitations and future outlook section, the authors do not give a clear path forward.  If data limitations make it impossible to apply the model then that significantly diminishes the value of the model.

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript entitled “Assessing the Level of Continuous AI Adoption in Digital Enterprises: An Organizational AI Adoption Maturity Framework” aims to address the critical challenge of how digital enterprises can continuously adopt artificial intelligence (AI) to maximize organizational effectiveness. It introduces the Organizational AI Adoption Maturity Model, a framework designed to assess and categorize the stages of continuous AI adoption within organizations. The goal is to unify existing research, provide diagnostic tools to evaluate an organization's current AI adoption level, and guide future development strategies. Ultimately, the study seeks to establish a comprehensive theoretical and practical foundation for understanding and advancing organizational AI adoption maturity.

The strengths of the manuscript can be so summarized:

• Integrates existing theoretical models (ISCM, DOI theory, TAM, UTAUT) with an original two-dimensional maturity framework (AI adoption depth and width).
• Classifies adoption stages and pathways in a way that is both theoretically grounded and practically relevant.
• Provides industry-specific strategies, increasing applicability.
• Literature review is rich, up-to-date, and well-connected to the proposed model.

The manuscript is strong in its current form but would benefit from small clarifications and enhancements However, the manuscript could furtherly be improved by the below suggestions:

1. Page 12, Lines 498–504: While the method for AI adoption width is well-defined, the depth evaluation remains qualitative. Include preliminary examples or a scoring rubric to facilitate consistent assessment.
2. Section 6, Page 14 onwards: Adding at least one brief case study or practical application example would strengthen the practical utility of the framework.
3. The discussion on white-collar vs. blue-collar AI is repeated and could be shortened without loss of clarity (e.g., Page 6, Lines 234 - 260; Page 18, Lines 767 - 792).
4. Page 16–17, Lines 737–744: Since the model is presented as a dynamic system, include guidance or a template for organizations to measure changes over time in both depth and width dimensions.
5. Ensure smoother flow between conceptual definitions and guidelines. For instance, the shift from Section 4 (Regions I–IV) to Section 5 (Assessment) could use a brief linking paragraph summarizing how the classification leads naturally into measurement approaches.

Overall, the work is insightful and makes a meaningful contribution. With minor refinements, it can serve as a strong reference framework for academic and practitioner audiences.

The English in the manuscript is clear, fluent, and does not require improvement for comprehension or publication.

The overall recommendation is to accept after minor revisions.

Reviewer 4 Report

Comments and Suggestions for Authors

1. Introduction
- The introduction clearly contextualizes the importance of AI adoption in digital enterprises.
- The problem statement is relevant and timely.

Suggestions:
- Provide a sharper articulation of the research gap compared to existing maturity models.
- The objectives could be more explicitly aligned with the structure of the article.

2. Literature Review
- The literature review covers a wide range of frameworks (TOE, TAM, UTAUT, ISCM).
- Demonstrates strong theoretical grounding.

Suggestions:
- Some sections could be more concise, avoiding redundancy.
- Include more international sources to expand beyond the Chinese research context.

3. Model Development
- The OAIAM framework is well-structured, combining 'AI adoption depth' and 'AI adoption width'.
- Figures and conceptual diagrams clarify the model.

Suggestions:
- Create a figure that illustrates the methodological steps followed in the research as carried out in 10.1007/s10668-024-05348-0 and 10.1108/JHOM-05-2023-0136.
- Provide empirical validation or case studies to strengthen claims.
- Discuss potential limitations of the model in non-digital enterprises.

4. Practical Guidelines
- Offers actionable steps for managers adopting AI.
- Good practical contribution by linking theory and managerial application.

Suggestions:
- Clarify the transition from theoretical discussion to guidelines.
- Add specific examples of how managers can implement the proposed steps.

5. Conclusions
- Summarizes the contributions effectively.
- Highlights both theoretical and managerial implications.

Suggestions:
- Expand on limitations and outline clearer directions for future research.
- Consider emphasizing more strongly the novelty of the framework.