Data asset valuation model based on generative artificial intelligence

Yungang Tang; Yaoqian Liu; Daxin Liu

doi:10.1371/journal.pone.0328926

Peer Review History

Original SubmissionNovember 9, 2024
17 Apr 2025 Decision Letter - Fu-Sheng Tsai, Editor Dear Dr. Tang, 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 submit your revised manuscript by Jun 01 2025 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. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled '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 . 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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? Reviewer #1: Partly Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes Reviewer #2: No ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ****** Reviewer #1: This article builds a generative artificial intelligence-based data asset valuation model based on existing literature, which integrates data feature extraction, generative value generation algorithm and market adaptive valuation to dynamically assess the business value of data assets. The conclusions of the article show that the model can address the shortcomings of traditional valuation methods in data asset valuation. Overall, the article is well written, the analysis is solid and the conclusions are challenging and bring a lot of new information to the field. However, there are certain issues, such as the sample and analysis methods of the study, that deserve in-depth discussion. There are four issues that require further research. 1.What is the basis for the composition of the three dimensions of the model? What are the criteria for dimension selection?Please elaborate.Please elaborate. 2.Do metrics such as user data, market data, and operational data provide a comprehensive picture of the data assets reported by the companies? A data asset is an asset that is formed from data elements by adding value, not just a collection of data.Please elaborate. 3.How do companies with sample databases determine that their assets include data assets? What percentage of data assets?Please elaborate. 4.In assessing the valuation of data assets of listed companies in China from 2015-2023, the final valuation results were not seen using the data asset valuation model created based on the productive artificial intelligence model created in this thesis. Please add. Reviewer #2: The article highlights the dynamic nature of data assets and the need for more adaptive valuation models in the digital economy. The proposed framework (integrating data feature extraction, value generation algorithms, and market adaptability assessment), offers a comprehensive solution for more precise and responsive valuation. Additionally, the discussion on how Generative AI enhances data valuation, through synthetic data generation, improved model training, and market adaptability, provides valuable insights into its broader implications. The introduction of a systematic framework for dynamic pricing, along with its potential applications for businesses and policymakers, makes this study a relevant contribution to the field of digital transformation and data asset management. This article offers important perspectives on leveraging data as a strategic business asset. However, the empirical data collection process is not clearly explained, and the data itself is not yet accessible. It is only mentioned "The sample primarily originated from Chinese 165 A-share listed companies, covering the period from 2015 to 2023." (line 164-165) and "The calculation of data asset value was based on corporate financial statements and industry analysis reports" (Line 182). A more detailed explanation of the data collection process is necessary to ensure transparency and enhance the credibility of the study. While the article references Chinese A-share listed companies as the dataset, it does not specify the exact sources of this data and other listed variable. It would be beneficial to specify the exact source of the A-share listed companies used in the study. Providing this information, along with access to the data, would offer greater transparency and allow for a more thorough evaluation of the model’s robustness. Additionally, making the data available would enhance the study’s replicability and reproducibility, further strengthening its impact. Clarifying the origin of the dataset would help readers assess the reliability and scope of the data used in model validation. ****** 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 #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0328926.r001
Revision 1
25 Apr 2025 Author Response Response to Reviewers We responded to each issue raised by the reviewers in a bold and underlined manner. Reviewers' comments: 1. Is the manuscript technically sound, and does 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 Response: For Reviewer 1’s response of “Partly”, we summarized what has been conducted. The data asset valuation model is constructed using a Generative AI algorithm based on real data. To construct a robust and practical model, widely adopted machine learning models are used to pick influential predictive variables. The constructed model is tested using validation and test data sets. Also, it is shown that fusion models are more capable than single models. ________________________________________ 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Response: No responses are required ________________________________________ 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 exceptions (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: No Response: For Reviewer 2’s response of “No”, we provide the link for the data set: https://www.hkex.com.hk/Mutual-Market/Stock-Connect/Eligible-Stocks/A-share-Lookup-Tools?sc_lang=en. However, we do not share the collected and cleansed data set in the article, instead the data set was shared in China macroeconomic database. ________________________________________ 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 Response: No responses are required. ________________________________________ 5. Review Comments to the Author Reviewer #1: This article builds a generative artificial intelligence-based data asset valuation model based on existing literature, which integrates data feature extraction, a generative value generation algorithm, and market adaptive valuation to dynamically assess the business value of data assets. The conclusions of the article show that the model can address the shortcomings of traditional valuation methods in data asset valuation. Overall, the article is well written, the analysis is solid and the conclusions are challenging and bring a lot of new information to the field. However, there are certain issues, such as the sample and analysis methods of the study, that deserve in-depth discussion. There are four issues that require further research. 1. What is the basis for the composition of the three dimensions of the model? What are the criteria for dimension selection? Please elaborate. Please elaborate. Response: All three dimensions are critically significant in the construction data asset valuation model. Each has a crucial role in refining the contracted data asset valuation model. We do not use any criteria in dimension selection. However, the related literature gives us insight into the dimensions of the model. 2. Do metrics such as user data, market data, and operational data provide a comprehensive picture of the data assets reported by the companies? A data asset is an asset that is formed from data elements by adding value, not just a collection of data. Please elaborate. Response: Yes, user data, market data, and operational data provide a picture for data assets, however, their role is complimentary. Their collection process and accessibility to them are also a part of the whole process. 3. How do companies with sample databases determine that their assets include data assets? What percentage of data assets? Please elaborate. Response: Actually, companies keep these records for some regularity or obligatory conditions with no attention to their value of them. However, the currently held databases are a part of the data assets. How much portion of the data asset is not computed for the sample data? 4. In assessing the valuation of data assets of listed companies in China from 2015-2023, the final valuation results were not seen using the data asset valuation model created based on the productive artificial intelligence model created in this thesis. Please add. Response: Significant variables are found that play a critical role in the data asset valuation model. Return on Assets (ROA), Return on Equity (ROE), and Operating Cash Flow (OCF) are found. Besides, in the conclusion section, these findings are stated in 2) verbally. Reviewer #2: The article highlights the dynamic nature of data assets and the need for more adaptive valuation models in the digital economy. The proposed framework (integrating data feature extraction, value generation algorithms, and market adaptability assessment), offers a comprehensive solution for more precise and responsive valuation. Additionally, the discussion on how Generative AI enhances data valuation, through synthetic data generation, improved model training, and market adaptability, provides valuable insights into its broader implications. The introduction of a systematic framework for dynamic pricing, along with its potential applications for businesses and policymakers, makes this study a relevant contribution to the field of digital transformation and data asset management. This article offers important perspectives on leveraging data as a strategic business asset. However, the empirical data collection process is not clearly explained, and the data itself is not yet accessible. It is only mentioned that "The sample primarily originated from Chinese 165 A-share listed companies, covering the period from 2015 to 2023" (lines 164-165) and "The calculation of data asset value was based on corporate financial statements and industry analysis reports" (Line 182). A more detailed explanation of the data collection process is necessary to ensure transparency and enhance the credibility of the study. While the article references Chinese A-share listed companies as the dataset, it does not specify the exact sources of this data and other listed variables. It would be beneficial to specify the exact source of the A-share listed companies used in the study. Providing this information, along with access to the data, would offer greater transparency and allow for a more thorough evaluation of the model’s robustness. Additionally, making the data available would enhance the study’s replicability and reproducibility, further strengthening its impact. Clarifying the origin of the dataset would help readers assess the reliability and scope of the data used in model validation. Response: We provide the link for the data set: https://www.hkex.com.hk/Mutual-Market/Stock-Connect/Eligible-Stocks/A-share-Lookup-Tools?sc_lang=en. However, we do not share the collected and cleansed data set in the article, instead the data set was shared in China macroeconomic database. ________________________________________ 6. 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 #1: No Reviewer #2: No Response: No responses are required. Attachments Attachment Submitted filename: MH25041901-Response Letter(1).docx https://doi.org/10.1371/journal.pone.0328926.r002
12 Jun 2025 Decision Letter - Fu-Sheng Tsai, Editor Dear Dr. Tang, 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 submit your revised manuscript by Jul 27 2025 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. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled '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, Fu-Sheng Tsai Academic Editor PLOS ONE [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes Reviewer #2: No ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ****** Reviewer #1: (No Response) Reviewer #2: My recommendation is minor: the manuscript should include detailed information about the data source and the specific steps taken in data collection and preparation, including any filters applied and the data cleansing process. While the cleaned dataset itself does not need to be shared, a clear explanation of the procedures followed is necessary. In line 179, the authors mention that the data are acquired from Chinese A-share listed companies. However, the explanation is very minimal. Since the data is publicly available and the source has been stated in the response letter (https://www.hkex.com.hk/Mutual-Market/Stock-Connect/Eligible-Stocks/A-share-Lookup-Tools?sc_lang=en), I recommend that the authors also include this citation in the manuscript. This addition is important for clarity, transparency, and reproducibility of the study. ****** 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 #1: No Reviewer #2: Yes: Muhamad Prabu Wibowo ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0328926.r003
Revision 2
18 Jun 2025 Author Response Response to Reviewers 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 #1: All comments have been addressed Reviewer #2: 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 #1: Yes Reviewer #2: Yes 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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 exceptions (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 in 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: No Response to Reviewer #2: We provide the link for the implemented dataset in the manuscript. Also, we provide a brief explanation for how the total dataset is reduced to the final form to be used in the modeling process. However, we do not share the preprocessed dataset. If requested by readers via email to the corresponding author, we can email it in unprocessed form, since preprocessing is an important part of the modeling process and a problem-specific aspect. 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 #1: Yes Reviewer #2: 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 #1: (No Response) Reviewer #2: My recommendation is minor: the manuscript should include detailed information about the data source and the specific steps taken in data collection and preparation, including any filters applied and the data cleansing process. While the cleaned dataset itself does not need to be shared, a clear explanation of the procedures followed is necessary. In line 179, the authors mention that the data are acquired from Chinese A-share listed companies. However, the explanation is very minimal. Since the data is publicly available and the source has been stated in the response letter (https://www.hkex.com.hk/Mutual-Market/Stock-Connect/Eligible-Stocks/A-share-Lookup-Tools?sc_lang=en), I recommend that the authors also include this citation in the manuscript. This addition is important for clarity, transparency, and reproducibility of the study. Response: Lines 163-167 include how the dataset is filtered out. Since we apply strict rules to eliminate problematic data, for instance, missing observations and outliers are out completely, the final dataset obtained is composed of directly usable data in the modeling process. The second sentence is also added to the text. 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 #1: No Reviewer #2: Yes: Muhamad Prabu Wibowo Attachments Attachment Submitted filename: MH250616-Response Letter 0615.docx https://doi.org/10.1371/journal.pone.0328926.r004
9 Jul 2025 Decision Letter - Fu-Sheng Tsai, Editor Data Asset Valuation Model Based on Generative Artificial Intelligence PONE-D-24-51382R2 Dear Dr. Tang, 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, Fu-Sheng Tsai Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #1: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: Yes ****** Reviewer #1: (No Response) Reviewer #2: (No Response) ****** 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 #1: No Reviewer #2: Yes: Muhamad Prabu Wibowo ******** https://doi.org/10.1371/journal.pone.0328926.r005
Formally Accepted
Acceptance Letter - Fu-Sheng Tsai, Editor PONE-D-24-51382R2 PLOS ONE Dear Dr. Tang, 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 You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days 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. You will receive an invoice from PLOS for your publication fee after your manuscript has reached the completed accept phase. If you receive an email requesting payment before acceptance or for any other service, this may be a phishing scheme. Learn how to identify phishing emails and protect your accounts at https://explore.plos.org/phishing. 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 Professor Fu-Sheng Tsai Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0328926.r006

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