عنوان مقاله [English]
Nowadays knowledge management has been recognized as one of the most important weapons of strategy, which increase the competitive advantages of the organization (Choi and Lee 2006), so that organizations and companies today are bound to use KM approaches to increase and improving their performance. (Chou 2007), among the experts such as (Unaka and Tike Uchihi 1995) (Davenport and Prusak, 1998), suggested that knowledge management has attracted considerable attention among academics and professional practitioners in recent years (Quoted by Kalakan 2010). Skim calls knowledge management a systematic and explicit management of knowledge associated with the processes of creating, collecting, organizing, disseminating and applying knowledge (Azadbakht 2008). From Wallace's (2008) perspective, knowledge management is a process that facilitates the capture, distribution, creation and application of knowledge for decision making.
Knowledge Management (KM) is a key solution to create a continuous competitive advantage for the organization. (Archioli and Van Yeun, 2009). To establish knowledge management, it is necessary to provide intellectual, cultural, educational and educational platforms, knowledge centers, and the provision ofa technology platforms, and paying attention to knowledge infrastructures is important (Hasanzadeh, 2007: 43).
The results of research conducted by Khosravi (2015), show that human resource variables have the greatest impact on knowledge management maturity. Leadership, infrastructure, knowledge management process and organizational culture are also at the next priority. The results of research by Hashmi (2011) show that the development of a definition of knowledge from the point of view of tacit knowledge and the use of IT, adaptation to cultural complexity, attention to human resources, the creation of new organizational structures and coping with increasing competition, are the most important challenges In front of today's organizations in this regards.
The current research is applied in terms of purpose and in terms of collecting data descriptive of survey type. The sample consisted of 89 personnel working in Dorood Education Department. Participants in the study completed a researcher-made questionnaire on knowledge management deployment. To answer the questions, the five-choice Likert spectrum questionnaire, which included very few, low, somewhat, very high, and very much, with a numerical value of 1 to 5, was used.
Bayes networks represent a common probability model among given variables. Each variable is represented by a node in a graph. The direct dependence between the respective nodes and conditional probabilities for each variable is stored at the site associated with the dependent node. By using the Bundle model, the effects of the variables may be recognized in the reverse direction of the dependent variables of their predecessors.
Artificial Neural Networks are widely used in many types of applications in various fields. Although the principles of work and the code of rules of the artificial neurons seem to be simple, the obvious feature is the potential power and calculation of this model. The use of artificial neural networks is simple and complexity can only be caused by the growth of several constitutions.
To generate rules using educational data we use the following three algorithms: A) Genetic Algorithm B) Johnson algorithm C) Holts algorithm. The results from the implementation of each algorithm are as follows: A) Using the genetic algorithm 4100, the law is produced, figure 6, which we are the laws that have the most impact. We list 10 valid laws that have the most impact.
The results, which were analyzed using data mining techniques, showed that the prediction accuracy using the 0.88, decision tree of 0.94, Bayes ’Theory, artificial neural networks is 1, so Bayes ’Theory and networks Artificial neurons have the highest predictive accuracy.
In addition, after extracting the rules with 3 genetic algorithms, Johnson and Holts Rough sets and rules derived from the decision tree and eliminating low-impact rules, the remaining rules are combined and after extracting the seven final rules, the results of the genetic algorithm are also the finite artifacts and the rules have less amount of variables. According to the coefficients derived from genetic algorithm, variable 1 (interaction and trust between managers and staff), variable 3 (creating learning processes by managers), variable 22 (organizational reward for knowledge sharing), and variables 32 (existence of knowledge bank from employee knowledge) have the least effect On the outcome.
Also variable 16 (the amount of use of related and non-related learning), has a reverse effect as a result. The seventh law will be deleted and the six remaining rules are referred as the final rules.
The First Final Rule: If the knowledge sharing unit becomes partly more visible to staff, and managers are more or less sensitive to retaining a college student, then there is the possibility of deploying knowledge management.
The Second Final Rule: If the Faculty facilitates the sharing of knowledge for staff, and managers are very much aware of the views and outcomes of the staff, then there is the possibility of deploying knowledge management.
The Third Final Rule: If managers have a little sensitivity to keep their knowledgeable staff at a low level and have a low level of knowledge sharing for employees, then there is no possibility of deploying knowledge management.
The Fourth Final Rule: If managers are reluctant to keep their college staff at a certain level, and facilitate the sharing of knowledge sharing units for employees, then there is no possibility of deploying knowledge management.
The Fifth Final Rule: If managers have a high degree of conscientiousness to retain their staff members and facilitate the knowledge sharing unit to some extent for employees, then there is the possibility of deploying knowledge management.
The Sixth Final Rule: If managers are reluctant to keep their college staff at a certain level, and facilitate the sharing of knowledge sharing units for employees, then there is possibility of deploying knowledge management.
In general, based on the findings of this study, key factors that play a significant role in the deployment of knowledge management are knowledge sharing, employee retention and the use of staff comments and suggestions. The final factor, the use of comments and suggestions, should be more and more sought after by managers. If there is less and less factor in maintaining the scholarly staff and the factor in sharing knowledge in the organization, then it will not be possible to establish knowledge management in that organization or management.