PONE-D-20-02847

Innovative artificial intelligence approach and evaluation campaign for predicting the subjective feeling of work-life balance among employees

PLOS ONE

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PLOS ONE

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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: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

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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: No

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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

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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: Dear authors,

I want to congratulate you for your present work and interest in publication. It will be a valuable source of knowledge for interested researchers on the field.

In my professional opinion, your text is relevant and suitable but needs a few improvements to achieve the expected quality for the journal. I will go through them in order of appearance in your manuscript:

[Abstract]

Dear authors. Please give more insights about the methodology, models and the results obtained on your abstract, this section must summarize the project as possible, Have in mind that could be the only part of your work that could be read in a quick search for external researchers.

[Introduction]

Artificial intelligence is a wide knowledge field. I strongly suggest to narrow your terms and use “machine learning” or “deep learning” instead. This should be applied even to the title of the article.

Line 57: when you mention the group of 800 employees as “exceptionally large”, which are your references for this comparison? Maybe other studies on similar cases? The sampling of the population on the determined geographical area? Please clarify.

Also, at this point will be useful for the readers to make clear about the geographical and demographic limitations of your research. Please clarify.

[Materials and Methods]

Line 121: Please make a further description on the three chosen variables for the experiments. As they are written “time balance”, “involvement balance” and “satisfaction balance” are quite ambiguous and hard to measure for the readers.

After reading the section is clear that there is no consensus on the definition or how to measure the WLB. So, it’s important to specify which of the exposed will be the definition used specifically for this research. Or, if you’re introducing your own definition, please make it clear.

[State of the art]

As show in the State of the art, the innovation value of your research seems to be only comparable with projects on Poland. Is the state of the art limited for the country or aren’t examples available outside it?

In the state of the art you review models based on Fuzzy Cognitive maps and ANN for similar research topics. This reinforces my idea that you should be clearer on the real innovative value of the research. Please try to describe the major differences and improvements in comparison of these projects.

[Dataset description]

Link to reference (25) is broken. Please find an alternate one.

Line 237: After reading the referenced paper on (5) I assume that the 16 branches of the economy are the “16 sections of the Polish Classification of Business Activities” but as a foreign reader it was difficult to me to realize that. Please make it clear on this text.

[Architecture of the AI tool]

What was your criteria for selecting that architecture for the ANN?

Did you make other experiments with alternative configurations?

Even though the results were favorable, I’m concerned about the model optimization process. The architecture appears to be an arbitrary choice. That’s not necessarily wrong, but it will be better if you explain your decision.

[Data preprocessing and the experimental pipeline]

In order to improve the readability and structure of the manuscript, I recommend you to move the details of the feature selection techniques used on the experiments from the line 390 to 394 to this section. Please give a further description of the 10 selected features and try to justify the usage of the SelectKBest algorithm over other alternatives.

[Results]

Was your training dataset balanced? I couldn’t find anywhere the ratio of observations for each of the 4 categories. This could be decisive when calculating the accuracy of the predictions. Please clarify this or attach the classification report including other metrics like f1, precision and recall, ROC AUC, etc. I’m afraid that under this circumstances accuracy won’t be enough to answer the Research Question 1.

The selected method for

[Conclusions]

I would like to read here your conclusions and personal appreciations about the work done using ANN models. Do you conclude it was a good approach? Could you compare with other possibilities?

Please, be specific here on the findings on which were the more meaningful variables for the prediction. This information could be extracted from the results of the feature selection algorithm.

Don’t hesitate to tell us detailed technical conclusions of your work. It may be important for the researchers to know about the troubles you had to face, your thoughts and considerations over the data extraction and preprocessing, the used model and the validation and evaluation of the results.

Reviewer #2: This is a well written paper and an innovative approach. Please see my comments on the attached file. I strongly encourage the changes that i recommended be implemented before acceptance for publication

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Reviewer #1: Yes: Ariel Ortiz Beltrán

Reviewer #2: No

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Submitted filename: Innovative artificial intelligence approach and evaluation campaign for predicting the subjective feeling of work.docx

Innovative machine learning approach and evaluation campaign for predicting the subjective feeling of work-life balance among employees

PONE-D-20-02847R1

Dear Dr. Pawlicka,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Sergio A. Useche, Ph.D.

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 #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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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: (No Response)

Reviewer #2: Yes

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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 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

**********

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: Dear authors,

I'm glad to observe all the previous comments properly addressed.

At this point, I consider your text is ready for publication.

Congratulations for the work done!

Reviewer #2: This is an interesting article and innovative. The authors addressed the comments. I recommend accepting the paper for publication following the journal recommended format.

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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: Yes: Ariel Ortiz Beltrán

Reviewer #2: No