Knowledge Management
Mostafa Pahlevanzadeh; Nadjla Hariri; Dariush Matlabi; Fahimeh Babalhavaeji
Abstract
The aim of the current research is to design a performance evaluation model of the knowledge management system in the software industry using a neural network. First of all, qualitative method using library studies and review of backgrounds in evaluating the performance of knowledge management has been ...
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The aim of the current research is to design a performance evaluation model of the knowledge management system in the software industry using a neural network. First of all, qualitative method using library studies and review of backgrounds in evaluating the performance of knowledge management has been discussed in relation to the extraction of various indicators and reviewed in past studies. In the second stage, based on the data collected from the qualitative stage, a quantitative study was conducted to confirm and confirm the findings obtained from the qualitative stage. For exploratory study and extraction of categories related to evaluation factors, meta combination method was used. With the fuzzy Delphi method, the validation and presentation of the final indicators were done with the help of a questionnaire and a survey of experts. 7 main categories including individual factors, organizational factors, technology and infrastructure, functional factors, knowledge management tools, economic factors, knowledge management tools and 29 sub-categories were identified. The sample size was selected by simple random sampling with Cochran's formula of 186 people. In order to collect quantitative data, a researcher-made questionnaire was used, the items of which were taken from the results of meta-composite analysis in the first stage. The innovation of the research is building a model using neural network algorithms, which is the ability to predict the performance evaluation index of the knowledge management system and the impact of each of the indicators using neural network in the field of software.
