Knowledge transfer

The CyTRAP BlogRank Knowledge Transfer Score (you are here) is a comment-analysis algorithm that assigns a numerical weighting to how knowledge transfer happens with a blog.

One way how knowledge sharing happens on blogs is in communicating knowledge to blog or website visitors. Research indicates that the transferring of knowledge is completed through either links from blog to blog, research papers or via search engines.

The BlogRank Knowledge Transfer Score ranges from 0 (worst) through 100 (best). In this context, knowledge transfer score describes the level of offering readers and visitors aadditional know-how providing insights by giving access to such information through links to other material (e.g., another blog, white paper, FAQ, checklist, etc.) 

The CyTRAP BlogRank Knowledge Transfer Score  is part of what makes up the CyTRAP BlogRank.

The CyTRAP BlogRank is calculated by using SIX  indices: Headline Score, Text Complexity Score, First Impressions Score, Knowledge Transfer Score (you are here),  Online Word of Mouth Marketing (WOMMA) made up of the  Ripple Score (Facebook, LinkedIn AND Twitter) and the Engagement/Social Interaction Score.

In short, we calculate the z-score for each of the indicators explained below. Thereafter these numbers (i.e. z-scores) are then used in the following way: Sum[(A x 30) +  (B x .70) ] = Knowledge Transfer Score that ranges from about -2 to + 2 (PS. A negative z–score means that the original score was below the mean).

This score is then re-scaled in order to rank your blog compared to the others (1 through 100 for the best).

The CyTRAP BlogRank Knowledge Transfer Score we can then use to rank your blog compared to others. We calculate these numbers over a 90 days – 3 months period for each blog and chart the performance.

A – 1st Level Knowledge Transfer Score

This score addresses the number of URLs in a blog post to material hosted on the same domain

First Level Knowledge Transfer Score = SUM [Number of links or URLs to same domain materials  for this period / # of words this period (Minimum used here is 400 words per blog post) ] (e.g., over 3 months or about 90 days).

Hence, if one blog post has no links to other materials on the same domain but others have many, things even out over the period of 90 days.

B – 2nd Level Knowledge Transfer Score

This index calculates to how many external sites the blog posts link to such as another white paper, a research paper at a university and so forth. Hence, such links give readers’ access to knowledge elsewhere and may even empower them launching of new knowledge.

2nd Level Knowledge Transfer Score = SUM [Number of links or URLS to materials on other domains for this period /  # of words this period (Minimum used here is 400 words per blog post) ) (e.g., over 3 months or about 90 days) ]

You can find more information about knowledge transfer in: Zhao Xueqin Zhao Xueqin (December 2008). Research on the knowledge transfer in academic blog. Second International Symposium on Intelligent Information Technology Application Volume: 2, Publisher: IEEE, pp: 351-354. DOI: 10.1109/IITA.2008.446

How do we calculate the final score

In short, we first calculate the raw score for the above two indicators. If a blog has no URLs in its published blog posts, the blog gets a zero for the Knowlege Transfer  Score (see equation below). If it has 1 link to another of its blog posts (section A above) and is very short (i.e. we use default of 400 words), its raw score will be 0.0025.

Using the raw scores we calculate the z-score for each of the above indicators. Thereafter these numbers (i.e. z-scores) are then used in the following way: Sum[(A x 30) +  (B x .70) ] = Knowledge Transfer Score that ranges from about -2 to + 2 (PS. A negative z–score means that the original score was below the mean).

Based on statistical logic, for a composite standardised (z) score, individual variables (that comprise this composite) must be transformed before forming the composite.

The z-scores for the above indicators are added up to get an overall z-score.  This information is calculated into an overall score. Click here to find out how we process the raw data.

The actual CyTRAP BlogRank Knowledge Transfer number is used in the CyTRAP BlogRank algorithm to help determine the CyTRAP BlogRank of the blog, website or other social media effort being benchmarked.

At this point, the overall scores are compared and rescaled using 100 as the top score.