Exploring Patterns of Evolution for Successful Global Brands: A Data-Mining Approach

Round 1

Reviewer 1 Report

The topic of the paper is interesting and very up-to-date. The literature basis is good and the research methodology is well described. The paper is well written and readable.

The paper itself has some flaws worth improving:

• First describe the research gap and then on the basis of it give the aim of the study and research questions. They are in paper but after research gap. Describe the links between the research gap and the goal of the paper and research question.
• Write why the paper is important. What is the main contribution of the paper to the field?
• Please improve literature analysis adding more information about data mining.
• Add in the discussion interlinks to literature analysis. Describe the links between research and theory and other similar researches.

Author Response

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Reviewer 2 Report

The topic is highly relevant and definitely original.

However, the performed research and methodologic processing are broad and shallow instead of being focussed and deep.

Each well-known brand is particular and different, each industry has its special IP (especially TMs) features. I do not understand how combining many brands from many industries and assessing them in the stated manner could lead to academically robust conclusions. I do not reject data mining, but this should be done while narrowing the focus, not by trying to catch "everything" - this is futile and not feasible.

I believe this paper needs to be re-drafted and the authors should drop the idea to deal with so many different brands in a general manner.

This is confusing and misleading. Instead, they should pick e.g. one industry or two industries and truly explore their brands. Regarding timing, again, the authors need to make a choice - longitudinal study or comparative study?

Finally, I do not see any proper theoretical background. Indeed, the authors use only few references and obviously their kind of over-broad data mining brings results which they are not able to properly assessed and argued.

Consequently, provided conclusions are weak, speculative and under-developed.

Author Response

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Reviewer 3 Report

The paper under review is not clearly linked to the main topic of the publication, as it does not analyse the performance of the brands in the ranking based on sustainability/responsibility issues, but from a general view.

From a formal view, an overview by an English native/expert would be advisable. Furthermore, the conclusions depicted at the end of the introductory section are not conclusions, but a new description of the paper structure (similarly or complementarily to the description in the previous paragraph), or the first paragraph under the subheading 2.3 (Data Analysis) does not make sense there. Additionally, the utility –and clarity– of some figures is questionable (see specifically Figure 4).

Regarding the content, consideration of bibliographical references in the most recent period (2017 onwards) would add value to the paper, and a clear a concrete reason for choosing the Interbrand’s Best Global Brand ranking as a reference should have been provided when mentioning the best well-known brand rankings (we have to wait until sub-heading 3.1 to read it was chosen due to the fact Interbrand was the consulting firm that before began to provide this kind of rankings –and so it seems it was not a question about the quality of the ranking provided–).

And even much more relevant, the analytical methodology appears as appropriate, but the utility of the prediction for the 2017-2020 period –beyond testing the validity of the model– could be questionable, as rankings for these years (i.e., real data) have also been published. In addition (and as even mentioned by authors), the COVID-19 pandemic has caused a substantial change in the way brands are perceived, and then in their position in rankings in this 2021 and future years, sustainability issues (which are not specifically –or, directly, are not–considered in the paper) being decisive.

Author Response

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Reviewer 4 Report

The authors should correct some grammar and punctuation errors (see row 11,107,132,134, etc.)

Author Response

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Reviewer 5 Report

I cannot recommend this paper for publishing. In my opinion, the contribution of this paper to the theory and practice related to sustainability is too little. The authors have simply carried out data analysis for the biggest brands. In my opinion, the results presented in the paper have the limited benefits for companies related to the mentioned brands .

I think that the paper could be improved through developing the aspect of predicting trends in more precise manner.  

Moreover, the presentation of the results should also be improved. The results shown in Table 1 and 2 are also described in the text (lines 267-271 and 274-279). In addition, Table 7 is not mentioned in the text and the symbols in column “Prediction” (x, o, ∆) are not clarified.

Author Response

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Round 2

Reviewer 1 Report

Authors have implemented my remarks.

Author Response

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Reviewer 2 Report

This version is a true improvement, i.e. the authors have engaged in a significant re-drafting and have considered all proposed recommendations. My objections are overcome, just a touch of proofreading should be done and perhaps more materials considered and references added.

Author Response

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Reviewer 3 Report

The paper has been clearly improved based on provided comments and suggestions. Some links were provided to issues as sustainability and equity (even when the analysis continues dealing not only or specifically with such issues, but it is conducted from a general view), and the quality of writing has been also improved. Some contents have been improved or relocated and some bibliographical references in the last years has been added.

However, and even when the analytical methodology appears as interesting and appropriate, a clear(er) contrast between predicted and real results in the period 2018-2020 would be clearly advisable, in order to provide a clear indication on the validation of the methodology. In other words, when reading the paper, we are just expecting a kind of table or similar just including the ‘predicted’ and the ‘real’ results in the ranking for 2018-2020 in order to see the accurateness (or not) of the predictions obtained from the suggested AP clustering algorithm.

Author Response

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Reviewer 5 Report

I have no more comments.

Author Response

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