https://orcid.org/0000-0001-9863-1457
Peer Review History
| Original SubmissionMarch 4, 2024 |
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PONE-D-24-08785Research Transparency on Contentious Topics: Exploring the Connection between Diversity and InnovationPLOS ONE Dear Dr. Wojan, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== Please respond to all comments and highlight in the ms. ============================== Please submit your revised manuscript by Jul 25 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript:
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You also have the option of uploading the data as Supporting Information files, but we would recommend depositing data directly to a data repository if possible. We will update your Data Availability statement on your behalf to reflect the information you provide. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Yes Reviewer #3: Partly ********** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No Reviewer #2: Yes Reviewer #3: I Don't Know ********** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ********** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: Yes ********** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: Research Transparency on Contentious Topics: Exploring the Connection between Diversity and Innovation The statistical framing of this article in the abstract does not seem problematic but it is not new or a contribution. Similarly, the discussion in the introduction about the credibility crisis in applied research is also unrelated to the contribution of this article. Following best practices (or well known practices) is a necessary condition of publication, but not sufficient and not the points to lead with. I would remove both. The contribution seems to be about the causal effect of diversity of ownership teams on business innovation. I suggest leading with that. The paper claims that “The first step in reducing the set of estimates is to select a single measure that satisfies the requisite axioms discussed below“, but why is a single measure required? Why can’t we study multiple dimensions of this multidimensional concept? Having a single measure is convenient, but what’s the reason for this? If the separate dimensions (your axioms and other features) are correlated, you’ll only learn that if you study the dimension separately, at least at first. If you do have one dimension, and we try to satisfy the (reasonable) axioms proposed, then the authors claim that they have a measure proposed satisfies them. This is merely stated. It seems true, but it needs to be proven. But more importantly, is this the unique measure that satisfies the axioms? A proof that this is unique would be useful. (A somewhat similar example of a uniqueness proof, done by building on entropy, is in doi:10.1093/pan/mpl011). If not, then do the empirical results vary depending on which measure (among those that fit the axioms) is used? The 2018 *annual* business survey seems like a useful dataset, but why stop in 2018? The article has the laudable training/test framework, and so why not -- following that setup -- use a purely out of sample test set with one of the subsequent years of data? A causal identification assumption is required to make Eq 1 useful. This needs to be clearly stated and justified. I’m not sure that is possible here, but it at least needs to be addressed. It is the central point of the entire paper. Eq 1 also makes big homogeneity assumptions (such as beta1 not varying by the fixed effects). These should be tested and explored. I would suggest that the authors first run their analysis separately within different firm size and industry classes and then only put them together into one equation if the data support that. More likely there are different types of effects in different industries or types of firms, but this will be apparent if you look rather than assume. The “registered report” framework here is hurting the analysis. The authors are not learning from the data and so the readers are not learning from the authors. I would jettison that report and just do a proper model-free statistical exploration. Reviewer #2: The manuscript demonstrates a robust and technically sound methodology, utilizing both frequentist and Bayesian approaches to enhance the reliability of findings. The use of a split-sample/dual-method protocol is a novel approach that enhances research transparency and addresses issues of reproducibility.The study addresses a highly relevant and contentious topic with significant implications for diversity and innovation policies.However, there are still few comments and suggestions for improvement as follow: (1) Complexity of Methodological Explanation: The explanation of the dual-method approach and the statistical techniques used, including Bayesian methods and corrections for multiple comparisons, is highly technical and may be challenging for readers without a strong statistical background. Therefore, it is advisable to Simplify the methodological sections by providing a more straightforward summary or an appendix with detailed explanations and step-by-step guides. Including visual aids such as diagrams to illustrate the research design and statistical methods could also be beneficial. (2) More Discussion on Practical Implications is needed: The manuscript provides an extensive methodological discussion but lacks depth in discussing the practical implications of the findings for business practices and policy-making, authors are advised to explain further how findings could influence business strategies, diversity policies, and innovation practices. (3) Proofreading Needs, a final proofread to catch minor typographical errors and ensure consistency in terminology and formatting would improve the manuscript's polish. (4) Two Self-Citation were found, While self-citations are acceptable, ensuring they do not dominate the reference list Reviewer #3: Dear authors, this is a very interesting paper pursuing several ambitious and relevant goals. I absolutely agree with the need for increased research transparency in social sciences/economics and appreciate the authors’ engagement with these issues and the proposed ideas and protocol. However, considering the different aims and contributions made, the paper would benefit from a clearer focus. This includes, on the one hand, engaging a little more with the existing literature on innovative effects of diversity and, on the other hand, ensuring that the proposed protocol is accessible for the intended audience. Major points 1. I understand that the conceptual and empirical background on diversity effects is not the focus of this paper and is therefore kept short. However, as an application addressing (in the authors’ words) a “contentious” issue, and certainly one attracting both theoretical, empirical and policy attention, I find the engagement with the literature superficial. I broadly agree with the theoretical reasoning for both innovative advantages and hinderances but would suggest to acknowledge the existing literature on diversity effects a bit more explicitly. This applies especially to the section “Diversity Findings Vulnerable to False Discovery” but also to the sections discussing the results. Some suggestions: o for an overview of the literature: Ozgen, C. (2021). The economics of diversity: Innovation, productivity and the labour market. Journal of Economic Surveys, 35(4), 1168–1216. https://doi.org/10.1111/joes.12433 o for a specific application of team diversity to firm innovation: Brixy, U., Brunow, S., & D’Ambrosio, A. (2020). The unlikely encounter: Is ethnic diversity in start-ups associated with innovation? Research Policy, 49(4), 103950. https://doi.org/10.1016/j.respol.2020.103950 o as a seminal reference for the existence of diversity effects: Page, S. E. (2007). The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton Univ. Press. o for a review of different measures of diversity: : Nijkamp, P., & Poot, J. (2015). Cultural diversity: A matter of measurement. In The Economics of Cultural Diversity (pp. 17–51). Edward Elgar Publishing. 2. I have some concerns regarding the fractionalisation measure proposed here. I think it is important to realise that fractionalisation is a pretty standard indicator in research on diversity generally. There are a few variants (mostly different decompositions) but they are often based on Herfindahl-Hirschmann concentration measures. Considering that the aim here is to reduce risks of false discovery, I am not quite sure why you propose your own improvement of established fractionalisation. This is not to say that I disagree with implementing new measures (to the contrary) but wouldn’t it improve comparability to previous results if you actually used the same measures? In contrast, if the intention is to contribute to the literature on measuring diversity, the proposed indicators would need to be theoretically grounded in the literature, the conceptual ideas of diversity and also analysed and documented vis-a-vis its empirical behaviour (e.g., as a first step, also descriptively). Without this, the diversity analysis here seems to be more of an empirical exercise while it could be a very relevant contribution to the literature on diversity effects in its own right. I would suggest that the authors reflect on their intended contribution and whether, given space constraints, a combination of a new methodological approach and a new indicator can be achieved here without short-changing either of these aims. 3. Following on from the previous point, I have some more concrete concerns with the proposed diversity measures. a) It took me a few reads to understand Axiom 3. I would suggest that you re-write it for clarity. If I understand correctly, you are referring to the issue that team size affects the possible amount of diversity, i.e. it is harder for a small team to be diverse than for a large one. b) If I understand your Axiom 3 correctly, then the fact that fractionalisation is “invariant to team (population) size by design” is an issue that is acknowledged in studies using fractionalisation or other indices (see e.g. Brixy et al., 2020). It is usually addressed by explicitly controlling for team size. Why is this not an option (or not sufficient) in your case here? c) As far as I can see, the proposed OF index here only differs from fractionalisation because it raises the shares to the power of n (unique groups) rather than just to the power of 2, as in a HHI. Perhaps I am misunderstanding this, but it is not clear to me how this change addresses Axiom 3, since team size is not reflected in the OF equation, only number of groups. A fully diverse team of three people (i.e. three different groups) is still going to have a lower OF than a fully diverse team of four people. Could you please explain how your modification improves upon the issue of team size and how it compares to the established fractionalisation indices? d) The notion of a composite diversity indicator is very interesting and could open interesting avenues from a conceptual perspective. However, here, almost no details on the construction of this indicator is provided, and its theoretical foundations, advantages or drawbacks are not discussed. Also, isn’t the COF just an average across fractionalisation indices of different dimensions? What does this imply for composite diversity as a concept? 4. A table showing the considered levels of the various diversity dimensions would be helpful (i.e. how many educational specialisations do you consider?) both for interpretation and also for transparency reasons (since different aggregation levels for the groups could affect the results). Also, the number and categories of control fixed effects (i.e. industry and firm size controls) would be helpful. 5. I understand that you cannot identify family-owned businesses in the data. But since this characteristic is emphasised as an explanation throughout the text, I am wondering if the authors could provide a sense of the proportion of family-owned businesses. How common are family-owned businesses in the US and in which industries? Can the authors provide any evidence supporting the claim that family-owned businesses are more likely to show higher degrees of diversity in age or gender than other types of businesses? And finally, since the authors explain the negative effects of gender and age diversity by referring to their relatively high prevalence among family-owned businesses, is it also established that these family-owned businesses are less innovative? In principle, a negative effect of gender or age diversity could be theorised: if innovation is supported by the combination of different and novel experiences, perhaps the experiences among different age groups or genders are not distinct enough to provide innovative impulses? This would of course need to be investigated further and the imprecision in the estimates remains an issue but I am wondering whether this perhaps deserves a little more detail in interpretation. 6. Depending on the intended audience, you may want to provide more detail on how the Bayesian method is implemented. The authors write (p.5) “What we used from the exploratory stage and how we arrived at reasonable values for the missing input should assist those with little experience with Bayesian estimation.” and I see that some effort is made in that respect, but I still feel that the details of the procedures could be clarified (e.g. here some (probably) trivial questions showing my ignorance, but why are the priors set with standard deviation 10? What does the traceplot look like here? Why do you use the Geweke and Heidelberger-Welch test and not the other ones?). The results are mostly clearer to follow but if this paper is indeed intended to show a protocol that can be applied rather easily, I would consider some more details on how to do that. 7. Since PLOS ONE addresses an international audience, the country of analysis should be stated clearly at least once. At the moment, the article speaks of “the Census Bureau’s Federal Statistical Research Data Center” (p. 2) and “the 2018 Annual Business Survey” but never actually specifies that we are talking about the US context here. Also, please consider whether more information on the used survey may be required (e.g. what is the coverage or sampling of this survey?) for readers unfamiliar with this dataset. Minor points: 1. p.8: “These results, provided in Supplementary Materials, are qualitatively very similar to the de novo confirmatory estimates but represent final estimates of the effect of team diversity on innovation.” This sentence is not clear to me – what does it mean when you say that those are “final” estimates? 2. There are a few strong statements that would need to be qualified or supported with sources: o p. 4 “Finally, previous research finds that diversity and homophily are positively associated with innovation….” needs a reference. o p. 3 “A composite ownership fractionalization measure, which combines two or more diversity measures, represents how diversity is experienced”- I am doubtful that any quantitative measure could ever capture the actual experience of diversity. 3. In the notes for Table 1 and Table 2, the category M is missing, i.e. the only one not defined (by exclusion it seems to be education specialisation?). 4. p.8. When referring to tables 1 and 2 in the text, it would be helpful to remind the reader what the condition of inclusion in these tables was, i.e. that they show the most and least precise estimates. ********** 6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). 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| Revision 1 |
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PONE-D-24-08785R1Research Transparency on Contentious Topics: Exploring the Connection between Diversity and InnovationPLOS ONE Dear Dr. Wojan, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. ============================== Thank you for your valuable submission. 1) Please double-check grammar (e.g. punctuation and verb tense); 2) Please double-check refs (e.g. abbreviations, page numbers, and sentence case need to be corrected); 3) Although the title is not concerning, I believe the authors could improve by focusing on a specific aspect of the innovation. For example, Bayesian estimation is correct, but for a broad audience, it won't be as strong as "novel framework" or even "Robust methods for...". These are only simple examples requesting authors to make it more 'punchy' yet important and clear for researchers too; 4) Consider refining the Abstract, particularly in terms of language and the presentation of information (e.g. "Bayesian estimation replaces traditional [...] in the confirmatory stage" seems a little bit more direct and strong statement). The authors are strongly encouraged to make the Abstract more engaging, emphasising the novelty and relevance of the research; - Complex terms such as 'priors' and 'posteriors' could perhaps be introduced in the Introduction rather than the Abstract to make the latter more accessible; - If the options for Title are not particularly engaging, consider rephrasing terms like 'Bayesian methods' to make it engaging for both experts and the broad audience. This also applies to the wording in the Abstract; - Please work on a strong introductory line that emphasises the relevance of the study; - Provide consistent results based on your study with its parameters; - Additionally, strengthen the Abstract reinforcing the importance of the findings, and make the practical implications of the study more explicit; 5) Simplify sections where the focus shifts towards stats and use clearer language to explain complex concepts; 6) The Introduction needs to provide a clearer explanation of how the chosen diversity measure aligns with the stated axioms and why it was selected over alternatives; 7) The absence of a Methods section or, at the very least, an in-depth explanation of search, data interpretation and presentation focussing on stats is a little bit worrying. I'd highly encourage the authors to reshape the sections preceding the Tables, focusing on explaining how the data was gathered and how it will be displayed in terms of stats parameters. A sup section could also be important; 8) The presentation of OF, EF, and COF is commendable, though the text could benefit from further refinement. Specifically, it could elaborate on: a) how the indices interact with the different dimensions; and b) a clearer and more direct argument (with data from both simulations and practical examples) on how Bayesian methods can help mitigate power loss in frequentist models; - The introduction of the OF and COF indices is innovative, but it would benefit from a more explicit explanation of how they compare to traditional diversity indices beyond the tables. For instance, explaining why the exploratory stage mitigates concerns about subjective priors could help readers; 9) Consider tidying the Tables; 10) Overall, the Tables are dense with numerous parameters. The abbreviations are unclear without constant reference to the footnotes; - Not all readers require both Frequentist and Bayesian estimates to be presented simultaneously. Streamlining the Tables prioritising the most significant data or presenting detailed Bayesian diagnostics separately would enhance clarity; - For the Frequentist model, providing R-squared or adjusted R-squared values would help evaluate how well the model fits the data; - It is unclear at a glance which estimates are meaningful, given the numerous values and parameters presented; - While it may be implicit, the use of Forest Plots could be particularly useful in simplifying the density and presentation of the data; - The same for presentation of priors - or mention before the Tables; 11) The authors are encouraged to use more references while debating and presenting the arguments in Discussion; - It'd be valuable to include more refs and explanations on the prevalence of HARKing, claims regarding irreproducible studies, and in-depth references for specific information, such as in line 526: "climate change, public health, diversity"; - What 'Registered Report'?; - Are other studies or meta-analyses relating to diversity and innovation? If so, they should be mentioned and explored; In addition, consider updating refs, including those by Nosek, Baltagi, or Vehtari, for example. Also, strengthen your argument by citing relevant literature, and provide a clearer summary of the changes made to the ms. Wishing you success with the study. ============================== Please submit your revised manuscript by Oct 21 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript:
If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Thiago P. Fernandes, PhD Academic Editor PLOS ONE [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #3: (No Response) Reviewer #4: All comments have been addressed Reviewer #5: All comments have been addressed ********** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #3: Partly Reviewer #4: Yes Reviewer #5: No ********** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #3: Yes Reviewer #4: No Reviewer #5: No ********** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #3: Yes Reviewer #4: (No Response) Reviewer #5: No ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #3: Yes Reviewer #4: Yes Reviewer #5: No ********** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #3: Dear Authors, Thank you for the opportunity to read the revised version of this manuscript. I believe that the paper has much improved and I appreciate the work that the authors have invested. In particularly, the justification for the analysis and the detail of the method description are both much clearer now. However, I still have concerns with the proposed measure of ownership fractionalisation (OF). While the justification for the axioms is clearer now, there is still no actual engagement with the conceptual meaning of this indicator or even proof that it satisfies the axioms. The “simple modifications” suggested to ensure that the axioms are satisfied are not conceptually explained or justified and seem to be derived purely mathematically, whereas the EF does have a conceptual grounding. This leads to a strange comparison of a well-known and conceptually valuable index with an almost arbitrary-seeming mathematical construct. The authors instead provide two tables that are supposed to compare the OF with the established fractionalisation indicator based on an HHI. I see two issues with these tables: 1. An easy to fix but important problem is that the rows and columns in table 2 and table 3 seem to be mislabeled (or flipped). It says that the rows are number of owners and columns are unique groups but this does not correspond to the content of the tables. As it is presented now, the table should show OFs for two owners (row 1) across two, three and four unique groups, which would mean that owners can be in more than one group simultaneously. While this is of course possible theoretically, it falls outside the proposed OF indicator (and is rather an example of the COF or other multidimensional measures). In fact, when tracing the numbers in table 2, it is clear that the numbers in the last column (4-equally distributed) correspond to 1 – 4*((1/4)^2) = 0.75, 1 – 4*((1/4)^3) = 0.9375 and 1- 4*((1/4)^4)=0.9844. According to equation 1, the exponent n identifies the number of groups, not the number of owners as the table now suggests. It seems that the labels have been flipped in the tables without adjusting the numbers. 2. I also do not agree with the calculations in table 3. Clearly, the EF does not have a “number of owners” dimension but is only calculated for the number of groups. I understand that this is what you are trying to show by repeating the same value in the table but I think that this is an oversimplification and the values in the table are actually not entirely correct or not clearly presented. Because the EF is calculated based on the relative prevalence of the unique groups (not based on ownership shares) changing the number of owners while keeping the number of groups constant would in fact change the EF value in some cases. Consider the case of 2 unique groups: it is true that the value for two owners and four owners with equal distribution would both be 0.5. But for three owners, the EF would actually be 1-((1/3)^2+(2/3)^2)=0.6667 because the distribution across groups would necessarily be unequal. Since the EF depends on the relative proportions of the groups as determined by both the number of groups and the number of owners, it is actually not entirely “invariant” to team size. The same applies for three unique groups and three versus four owners. Moreover, the columns using “concentrated” ownership do not really makes sense for the EF since they are not formally captured. Of course, we can just use ownership shares analogously to how we would use population shares here but then the EF-value for some constellations still depends on the distribution across groups. For instance, a team with two unique groups and three owners and concentrated shares could either have an EF of 1 – (0.01^2+0.99^2)=0.0198 (just like a team with two owners) or of 1-(0.02^2+0.98^2)=0.0392. Without more information on the concentration across the two groups, either of these values could occur. In essence: the example comparisons the authors present in table 3 do not actually capture the behaviour of the EF as it is not as static as suggested here but does in fact respond to changes in team size as long as these have an effect on relative prevalence of the groups. Thus, the empirical demonstration that was meant to clarify the behaviour of the OF relative to the EF thus does not help to clarify the added value and/or distinction between the two indices yet but rather highlights the complexity of these measures. I would suggest either revising the tables to show the behaviour of the two indices more comprehensively and correctly (perhaps with a less abstract example?) or perhaps also consider other methods of comparison. For instance, I wonder (just out of curiosity) about the simple correlations between the EF and OF measures in your data: Do they tend in the same direction on average? How different are these two indices really? Reviewer #4: "I am pleased to announce that all the issues have been thoroughly addressed as desired, and the work is now in excellent shape, ready for publication. Through careful revisions and attention to detail, all necessary enhancements have been implemented, ensuring the quality and accuracy of the content. The result reflects a refined and polished piece, meeting the highest standards of excellence. I am confident that this publication will contribute significantly to the field and resonate well with the target audience. I'm excited to share this finalized work and look forward to the positive impact it will have." Reviewer #5: After a careful study of the article, reviewers' comments and corrections, I have come to the conclusion that the quality of the manuscript has not improved and fundamental problems including the theoretical contribution of the research can be seen in the literature. ********** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #3: No Reviewer #4: Yes: Muhammad Zunnurain Hussain Reviewer #5: No ********** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. 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| Revision 2 |
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A Novel Framework for Increasing Research Research Transparency: Exploring the Connection between Diversity and Innovation PONE-D-24-08785R2 Dear Dr. Wojan, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Thiago P. Fernandes, PhD Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #3: All comments have been addressed ********** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #3: Yes ********** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #3: Yes ********** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #3: (No Response) ********** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #3: Yes ********** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #3: (No Response) ********** 7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy. Reviewer #3: No ********** |
| Formally Accepted |
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PONE-D-24-08785R2 PLOS ONE Dear Dr. Wojan, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Thiago P. Fernandes Academic Editor PLOS ONE |
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