Some of these problems are due to a lack of adequate contractual transparency and oversight. During the negotiation process, you don`t have immediate access to your contract library, as you can`t see prior agreements to get an idea of what a fair offer is. At this stage, the evaluation of the attribute agreement should be applied and the detailed results of the review should provide a good set of information in order to understand how best to organize the evaluation. As soon as your company experiences a more agile analysis of the contract and the results are displayed in your KPI, you can feel safer when it comes to making new business commitments. Setting up new agreements will be much easier if you have your entire library on hand during the negotiation. The point here is that many samples are needed to detect differences in an attribute analysis, and if the number of samples is doubled from 50 to 100, the test does not become much more sensitive. Of course, the difference that needs to be identified depends on the situation and the level of risk that the analyst is willing to assume in his decision, but the reality is that, in 50 scenarios, it will be difficult for an analyst to think that there is a statistical difference in the reproducibility of two evaluators with match rates of 96% and 86%. With 100 scenarios, the analyst will hardly see a difference between 96 and 88%. We find that in the second case, it shows a greater similarity between A and B than in the first.
This is because, although the percentage of concordance is the same, the percentage of concordance that would occur “by chance” is significantly higher in the first case (0.54 versus 0.46). The reasons why agreements (consistencies) have been weak can be: contract analyses can add value to your company`s end result, as contracts are the basis of any business cooperation. Think of them as the software for business. We all know that “errors” in software cause machines to run inefficiently. In the same way, contracts can contain errors affecting the profits of their business. These errors include: as with any measurement system, the accuracy and precision of the database must be understood before the information is used (or at least used during use) to make decisions. At first glance, it would seem that the apparent starting point is an attribute analysis (or the measurement of R&R attributes). But it may not be such a good idea. Nevertheless, significant guidelines have appeared in the literature.
Perhaps the first Landis and Koch, the values < 0 were not compliant and 0-0.20 as low, 0.21-0.40 as fair, 0.41-0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1 almost perfect. However, these guidelines are not universally recognized; Landis and Koch did not provide evidence to support this, but supported them on personal opinions. It was found that these guidelines could be more harmful than useful.  Fleiss`s:218 equally arbitrary guidelines characterize Kappas above 0.75 as excellent, 0.40 to 0.75 as just right, and below 0.40 as bad. . . .