Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

(1) The paper does not relate to the Journal's scope and topics.

(2) Overall, it is far too long. Several paragraphs and figures are redundant, such as Fig 2 and 6 or the background on Industrial revolutions 1 to 4.0

(3) The english writing style is sometimes hard to understand. 

(4) The chapter research motivation (1.2) is far too long and contains too many personal reflections of the authors regarding the paper and process

(5) Lots of information is irrelevant to the reader, such as 3.2, especially figure 10

(6) overall, the literature study contains more than 60% of the paper - 25 out of appr. 35 pages. 

(7) How the model is derived from the literature review in chapter 3.2 is unclear

(8) here is no example case, validation or other tangible deduction from the theoretical model

(9) The conclusion is surprisingly short for the overall paper length

Comments on the Quality of English Language

You are prifcient in English, but some sentences are so complex that I as a reader cannot follow. E.g. "digital twinning future trends futureness evaluation framework" (line 236) is difficult to wrap my head around. 

Author Response

In the attached file!

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The literature review conducted in this paper is solid, and the future trends dataset compiled is a highly valuable outcome. To further enhance the rigor and forward-looking nature of the research, the following two core revisions are suggested:

There is an issue of inconsistent logical hierarchy within the current categorization (Pages 18-19, Tables 2 & 3). For instance, "Artificial Intelligence" (a superordinate concept) is listed as a peer to "Machine Learning" (its subordinate technology) in the trend items, which undermines the scientific validity of the classification. It is recommended to restructure the current flat list of 16 categories into a clear, multi-level taxonomy. For example, establish "1. AI and Cognitive Technology" as a top-level category, and then subdivide it into second-level subcategories such as "1.1 Machine Learning" and "1.2 Natural Language Processing." This would eliminate the problematic conceptual inclusion relationships.

The listed future directions (e.g., enriching the dataset, enhancing clustering mechanisms) represent common (Page 28, Section 5 "Conclusions"), generic steps for literature reviews and fail to highlight the unique findings and urgent needs identified by this specific study. It is recommended to analyze the interrelationships between the proposed directions and identify which are the pivotal ones requiring intensified research efforts. This analysis should prioritize directions that directly build upon the distinctive trends or gaps revealed by your dataset, moving beyond standard procedural recommendations.

Author Response

In the attached file!

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The manuscript presents a substantial and ambitious study that aggregates a large body of publications and introduces an original approach to identifying future trends in the field of Digital Twinning. The work has strong potential, particularly for researchers in AI, Digital Twins, and systems engineering. However, the manuscript is overly long, methodologically not fully transparent, and visually complex.

The introduction is pervasive and unnecessarily encyclopedic. Not all citations are clearly linked to the statements they support. I recommend shortening and streamlining this section.

The research employs a systematic literature review based on PRISMA, ProKnow-C, and InOrdinatio. Despite the declared use of PRISMA, the process is not presented using a standard PRISMA flow diagram but instead through a UML sequence diagram. The grouping of trends into 16 categories appears subjective and insufficiently validated.

The methods are rich but not fully transparent. I recommend providing explicit search rules and query parameters to enhance reproducibility.

The results are clear but require reduction, simplification of visualizations, and more precise structuring. Many figures are overcrowded and difficult to read. Several images contain tiny text and lack unambiguous captions. Table 3 is excessively long. In addition, the results section partially overlaps with the methodology and discussion.

The conclusions logically follow from the systematic analysis. The hypotheses RH1 and RH2 are meaningfully supported, and the authors clearly articulate the study’s limitations. However, the practical contribution of the work is not sufficiently explained. Some statements regarding “future applications” are overly general and not directly connected to the presented analysis.

Author Response

In the attached file!

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The manuscript demonstrates a solid and methodologically sound approach to analyzing future trends in Digital Twinning, utilizing systematic literature review and expert evaluation. These are important and reliable methods for synthesizing current academic and practical knowledge. The presentation of their findings is clear, well-organized, and contributes valuable insights to the field.

However, it is important to note that the future trends analysis does not fully leverage all established futures research approaches. Specifically, more advanced methodologies like scenario planning, Delphi studies, participatory foresight, or AI-driven predictive analytics—common in futures studies—are not comprehensively integrated.
Please mention that these approaches are not taken into account (deliberately).
Incorporating these diverse futures research techniques in future papers could enrich the analysis by providing broader perspectives, deeper anticipation of disruptive innovations, and stronger validation of trend persistence and impacts. Therefore, while the foundation of the future trends analysis is credible and useful, expanding the methodological toolkit to include a wider array of futures research frameworks would enhance the completeness and robustness of the assessment. Such an extension would support a more holistic and forward-looking evaluation, aligning well with the rapidly evolving Digital Twin ecosystem.

Overall, the study is a valuable contribution with a strong methodological basis, and addressing this aspect would further strengthen its impact and utility for academia and industry alike.

Author Response

In the attached file!

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Paper has been improved significantly after the last feedback round

Comments on the Quality of English Language

Paper has been improved significantly after the last feedback round

Author Response

In the attached file.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

The revised manuscript shows a substantial improvement in methodological transparency, structural clarity, and argumentation. The newly added explanations concerning the IdE_SLRM methodology, the dual-case validation approach, and the expanded discussion significantly strengthen the scientific contribution.
However, several important aspects still require refinement. The introduction remains overly long and should be further streamlined. The methods section, although more detailed, still lacks reproducible elements such as the exact search syntax and a clearer description of the semantic reduction procedure. Several figures and tables remain visually dense, and Table 2 should be moved to the Supplementary Materials.
Overall, the manuscript is considerably improved, but additional condensation and clarification are recommended before acceptance.

Author Response

In the attached file!

Author Response File: Author Response.pdf