Regulations on Non-Financial Disclosure in Corporate Reporting: A Thematic Review
Round 1
Reviewer 1 Report
Dear Author(s),
Please find below my concerns and recommendations regarding your manuscript proposal entitled "Regulations on Non-financial Disclosure in Corporate Reporting: A Thematic Review" sent to Sustainability MDPI Journal.
The Introduction is very dense, but it needs some improvements. Thus, I recommend you to clearly define and describe the following important aspects:
- the research gap: based on the previous literature, present the gap covered by your research proposal;
- the research goal;
- the research question.
Also, at the end of the Introduction, shortly present the structure of the rest of the article (main sections of the manuscript).
The Figure 1 (rows 179-180) is not very clear. Each diamond should have at least one entrance and at least one exit, because it is a decision-making structure, in accordance with the formalism related to the logical scheme. Please revise and correct the figure.
Under figure 4, please specify the source of the image.
The chpater "2. Research Background and Research Question" should be improved by using the following resources: https://doi.org/10.3390/jrfm13120313, http://www.ecoforumjournal.ro/index.php/eco/article/view/884, https://doi.org/10.1108/JFRA-09-2020-0261, http://www.transformations.knf.vu.lt/47a/article/amod. By including these references, you will widen the research context for the readers of your paper. Also, it is important to define at least one research hypothesis, because your paper it is supposed to be a scientific article.
At row 312, the section "4.2.1. Fiduciary Duties of Directors" starts with a figure. Please avoid this approach and insert a descriptive text before the figure.
The same remark for the sections "4.2.3 Disclosure Approaches" and "4.2.4 Stakeholders’ Engagement" and "4.2.5 The Effectiveness of Regulatory Interventions" and "4.2.6 The Impacts of Regulations" and "4.2.7 Directive 2014/95/EU" and "4.2.8 The Role of Different Actors".
In the Conclusions chapter, I recommend you to also include some aspects regarding the managerial implications of your research results. This way, you will highlight your contribution to the field of knowledge.
Dear Author(s),
Please consider all the above remarks as being constructive recommendations in order to improve the general quality of your manuscript proposal.
Kind Regards!
Author Response
Dear Reviewer 1,
Please see the attachment.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
I like the manuscript.
My only remark is to connect the review authors made with the literature on prediction models.
You write: "There is a growing call globally for corporations to improve transparency in corporate reporting, along with the surge of enhancing disclosure of non-financial information."
- This is true, but this information can be used in at least one very relevant economic exercise not mentioned by the authors - which is firm-level prediction, of both negative and positive outcomes.
- For the negative outcomes, non-financial information can be used most notably for predicting firm bankruptcy and default. There are at least two notable studies in this topic.
- Literature starts with Altman (1968) and this same author recently highlights the default prediction models can be improved by including non-financial information (Altman et al., 2022). In fact, previous study also calculates how much more banks or suppliers could save from using models including also non-financial information.
- On the positive side, studies predicting high growth firms have been much less successful. Recent applications of machine learning and long lists of financial variables have yielded modest success, which is why adding non-financial information could improve prediction (Coad & Srhoj, 2020).
Good luck with your paper.
Literature
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609.
Altman, E. I., et al. (2022). Revisiting SME default predictors: The Omega Score. Journal of Small Business Management, 1-35.
Coad, A., & Srhoj, S. (2020). Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms. Small Business Economics, 55(3), 541-565.
Author Response
Dear Reviewer 2,
Please see the attachment.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Dear Author(s),
I have read and analyzed the revised version of the manuscript and I have one minor recommendation: in the section "5. Discussion and Future Studies" please also present the limitations of your research.
Kind Regards!
Author Response
Dear Reviewer 1,
Please see the attachment.
Author Response File: Author Response.pdf
