Productivity Equation and the m Distributions of Information Processing in Workflows
Round 1
Reviewer 1 Report
Overall this appears to be an interesting paper worthy of publication, however this Reviewer feels the need to request the Introduction and Conclusions to be considerably expanded, in light of the broad audience of the journal.
It is essential to more comprehensively set the scene for the work, introducing all key concepts and ideas succinctly. For example, how is a hybrid organization defined?
In the conclusions, the author must spell out explicitly and clearly, ideally with examples, the main implications of the result obtained.
The above will substantially enhance the interest in the work.
Author Response
Thank you very much for the review.
I understood your comments as very important for article improvement.
Concerning all coments:
Introduction was expanded.
Conclusion was added.
More descriptive logical conclusion were described, also including hybrid organizations concept.
Implications of this research were given theoretically for future researches concerning human, organisms, machines interactions with nonlinear environments.
I hope it is better than the previous article.
Thanks for your time,
Reviewer 2 Report
The article deals with production equation and distribution of information processing in workflows. The introduction fails to introduce the issue. After reading the introduction, I have no idea what the article is about. The introduction should introduce the issue that the article deals with. In addition, it should include what makes this issue important, how the article content helps or develops the issue, etc. The language level is also at a very bad level. Many sentences and definitions cannot be understood because of bad grammar. In addition, the article lacks more detailed state of the art and discussion.
Author Response
Thank you very much for the review.
I understood your comments as very important for article improvement.
Concerning all coments:
Introduction was expanded.
More descriptive logical conclusions were described in all sections.
Discussion was expanded.
Implications of this research were given theoretically for future researches concerning human, organisms, machines interactions with nonlinear environments.
I hope it is better than the previous article.
Thanks for your time,
Reviewer 3 Report
The premise for this research work is not clear at all. The abstract and introduction are highly convoluted. There are major flaws in English language, due to bad grammar, spelling mistakes and complex statements, its very difficult to understand author’s proposal. The key terms are not defined properly, like organism-object interactions, organism-object environments, hybrid organization etc. Its not clear what is the relevance of the problem, how this work addresses unresolved issues, contribution of this work etc. Mathematical formulation is also very unclear. I suggest using Latex or other tools for writing mathematical formulas. Explain the variables and assumptions clearly, I will encourage author to read some good mathematical journal articles to get sense of writing mathematical formulas more thoroughly and show derivations clearly. The definitions are not clear, for example what is the difference between m distributions and m variations. How equations 1 and 2 holds? Its not proven clearly, lot of assumptions are made without any basis.
Author Response
Thank you very much for the review.
I understood your comments as very important for article improvement.
Concerning all coments:
Introduction was expanded.
More descriptive statements were described.
Implications of this research were given theoretically for future researches concerning human, organisms, machines interactions with nonlinear environments.
Sentences in general were revised and corrected.
Misunderstood words were corrected regarding organism-object interactions..
Mathematical conclusions were in part added and also expanded.
m distributions and m variations were wrongly written and were corrected.
I hope it is better than the previous article.
Thanks for your time,
Round 2
Reviewer 2 Report
Article quality has improved significantly. Added sections and figures improved the overall readability and understandability of the article. The formulas in Figure 2 are not readable.
Author Response
Thank you very much for your review dear reviewer!
- I identified at least 7 sentences that were not enough well written. Mainly in the introduction. They were badly described. I correct these ones.
- Regarding figure 2, I gonna talk with editor because, when article is formatted they can raise figure dimension for better visualization. I left the figure in small proportion within the article just to not occupy to much space.
- Methods section were updated and 2 paragraphs were rewritten to better introduce the main equation and its arguments.
- Results section descriptions were very briefly modified.
All modifications done are in red, marked by Microsoft Word tool for revision.
Thanks again for your time dear reviewer, very appreciated.
Reviewer 3 Report
This paper proposes a mathematical model to analyze information processing in workflows
The structure of the paper has improved significantly.
The definitions and mathematical terms have more clarity now.
The abstract and introduction are more lucid and coherent.
Problem statement is more clear now.
Improvements needed:
Clarify random variables, events and time expressions used in lines 97 through 100. These terms are still not very clear from probability stand point.
In definition 1 explain what is i.d.d or its a typo?
Explain how steady state is achieved to analyze the system. Its not very clear.
Figure 1, bigger fonts for figure labels.
Figure 2 , is not at all clear, if derivation is given then why do we need this flow diagram.
Spelling corrections in lines 33, 52, 101
Author Response
Thank you very much for your review dear reviewer,
All modifications done are marked in red by Microsoft Word tool for revision.
- At least 7 sentences were rewritten because they were really hard to understand. I already corrected them, mainly in the introduction section.
- In definition 1 explain what is i.d.d or its a typo?
Sorry, my mistake. I corrected. It is i.i.d.
- Figure 1, I will talk with editor if it goes to formatting process in order to raise figure dimensions. I left it small just to not occupy space.
- Spelling corrections in lines 33, 52, 101
done!
- Figure 2 , is not at all clear, if derivation is given then why do we need this flow diagram.
I added an explanation about figure 2. This paragraph explain the importance of showing derivations with flowchart in order to present the dynamics of iterations or interactions flows. Several inputs and outputs of information can be possible to achieve and not exactly a specific flow directions as it was intuitivelly indicated in the derivations equations page.
- Clarify random variables, events and time expressions used in lines 97 through 100. These terms are still not very clear from probability stand point.
Methods section was briefly rewritten. Specifically two parts were modified to give a better introducing for the main equation and its concepts. Then, random variables, events and time expressions were better introduced in terms of probability stand point.
- Explain how steady state is achieved to analyze the system. Its not very clear.
New paragraph was added in discussion section. This comment was very important also to make more visible this feature of the system. The steady state can be defined only by the performance range desired. Controller might be the possible trigger for delimitating range of outputs. I believe it could be called a steady state due to system starting condition and nature.
Considering a steady state in the sense of very stacionary output can be reached depending mainly on the information and processing states, but even if very precise, like 100% precision, imagine an office worker executing an activity..., for every day, the same activity, that is product of its cognitive performance, hardly will express exactly the very 100% features of time aspects, processing path and even precision. Maybe for this reason we popularly say human commit mistakes, machines dont.
