Intellectual Data Analysis Method for Evaluation of Virtual Teams

Sandra Strigūnaitė, Dalia Krikščiūnienė

Abstract


The purpose of the article is to present a method for virtual team performance evaluation based on intelligent team member collaboration data analysis. The motivation for the research is based on the ability to create an evaluation method that is similar to ambiguous expert evaluations. The concept of the hierarchical fuzzy rule based method aims to evaluate the data from virtual team interaction instances related to implementation of project tasks.
The suggested method is designed for project managers or virtual team leaders to help in virtual teamwork evaluation that is based on captured data analysis. The main point of the method is the ability to repeat human thinking and expert valuation process for data analysis by applying fuzzy logic: fuzzy sets, fuzzy signatures and fuzzy rules.
The fuzzy set principle used in the method allows evaluation criteria numerical values to transform into linguistic terms and use it in constructing fuzzy rules. Using a fuzzy signature is possible in constructing a hierarchical criteria structure. This structure helps to solve the problem of exponential increase of fuzzy rules including more input variables.
The suggested method is aimed to be applied in the virtual collaboration software as a real time teamwork evaluation tool. The research shows that by applying fuzzy logic for team collaboration data analysis it is possible to get evaluations equal to expert insights. The method includes virtual team, project task and team collaboration data analysis.
The advantage of the suggested method is the possibility to use variables gained from virtual collaboration systems as fuzzy rules inputs. Information on fuzzy logic based virtual teamwork collaboration evaluation has evidence that can be investigated in the future. Also the method can be seen as the next virtual collaboration software development step.

Keywords


fuzzy logic; virtual teamwork evaluation; teamwork collaboration data analysis

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"Social Technologies" ISSN online 2029-7564