Designing a Model of Neural-Adaptive Fuzzy Inference System (ANFIS) to Evaluate and Predict Organizational Knowledge Management Level with Innovation Focus.

Document Type : Original Article

Authors

1 Department of Industrial Management, Faculty of Management, Central Tehran, Islamic Azad University, Tehran.

2 Professor, Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.

Abstract

In recent years, knowledge management has become a vital issue in all organizations. Knowledge management is one of the effective factors in creating and expanding innovation. With innovation, the long-term advantages of the organization are maintained in the competitive arena. Evaluating and predicting the level of knowledge management for managers is very important. Among the new methods of modeling, fuzzy systems have a special place in different fields of science. The purpose of this research is applied and according to data collection method is survey. The Neural-Adaptive Fuzzy Inference System (ANFIS) is a good method for solving nonlinear problems. This method combines fuzzy inference method and artificial neural network which benefits from both methods. In this study, five main components were selected as inputs for fuzzy inference system to measure and predict the level of knowledge management of the organization. For evaluating the model performance, the parameters of mean square error of error (RMSE), percentage of relative error (ε), mean absolute error (MAE) and coefficient of explanation (R2) were used. Their values are 0.12, 0.0152%, 0.036 and 0.995. This indicates the accuracy and reliability of the model. The output of this study is the neural-adaptive fuzzy inference system (ANFIS).

Keywords

References

[1]    Afrazeh ,A ., Knowledge management (concepts, models, measurement and implementation). Tehran: Amirkabir University of Technology Press. 2005, p.1.
[2]    Taleghani ,Gh,. Anvari ,A., Eftekhari ,L., The Relationship between Knowledge Management and Organizational Innovation in an Insurance Company, Insurance Research Journal, Year 27, Issue 1, 2012, pp. 151-171.
[3]    Dargahi, H. Asadi ,S. Ahmadi ,B.  Mahmoudi ,M., A Study of the Relationship between Knowledge Management with Creativity and Organizational Innovation in Employees of Educational Hospitals, Tehran University of Medical Sciences, Volume 17, Number 1, 2018 , pp. 97-108.
[4]    Safari ,H. Ejli ,M. Ghasemian ,A., Determining the position of strategic position of an educational institution in the life curve of the organization with a fuzzy approach, Journal of New Research in Decision Making 1, No. 2, 2016.pp 117-138.
[5]    Molaei ,S. Shakeri ,R. Yaghoubi ,M., The Impact of Personal Knowledge Management on Culture and Innovative Performance in Knowledge-Based Companies, Journal of Management Research in Iran, Volume 22, Number 4. 2016 pp 130-150
[6]    Vincent ,B. Laure ,M. N'Doli ,A. Mauricio ,C., Evaluating innovative processes in French firms: Methodological proposition for firm innovation capacity assessment.Contents lists available at ScienceDirect.elsevier, Research Policy, 43, 2014 pp 608– 622.
[7]    Choi, Y., An empirical study of factors affecting successful implementation of knowledge management. ETD collection for the University of Nebraska - Lincoln. AAI9991981. 2008.
[8]    Bakhtiari, H. ., The Necessity and Importance of Knowledge Management in the Information Age, First Executive Management Conference, Tehran. 2009.
[9]    Drucker, P., Managing in Time Of Great Change, penguin Putnam, NY,1998.
[10] Ahn J-H.Chang,S-G., Assessing the contribution of knowledge to business performance: the KP3 methodology, Decision Support System, 36, 2004, pp.403-416.
[11] Tseng S-M, Knowledge Management System performance Measure Index, to be published in Expert System With Applications.
[12] Nonaka, I,. Takeuchi H, The Knowledge Creating Company: How Japanese Companies Create the Dynamics of Innovation, Oxford University, Press, Oxford, UK. 1995,
[13] Lee K.C, Lee S, Kang I.W, “KMPI: Measuring knowledge Management performance” Information & Management, 42, pp. 469-482.
[14] Davenport T.H, Prusak ,L. ,. Working knowledge, Harvard Business School Press, Boston, Massachusetts, USA. 1998. P149.
[15] Danport and Prosak., Knowledge Management. Translation: Rahman Seresht. Volume One. Tehran: Sapco Publishing, 2000, pp. 124-126.
[16] Skyrme, J. Amidon,M., Creating the Knowledge-Based Business, London: Business Intelligence. 1997.
[17]. Sanjaghi, M,I. Joneydi ,J,Y., Approaches to Improving the Management of Knowledge Management in Organizations. Defense Strategy Quarterly. Fifth year. Number Sixteen:, 2007, pp 85-119.
[18]. Babagheibi Azghandi, A., Assessing and reviewing the status of knowledge management in organizations (review of the Deputy of Information Technology and Communication Technology of the Police Force of the Islamic Republic of Iran). Police Human Development. No. 39:, 2011., pp. 73-106.
[19] Afrazeh, A. Knowledge management (concepts, models, measurement and implementation). Tehran: Amirkabir University of Technology Press., 2005, pp.37-67
[20]. Davenport, T. Prosak, L., Knowledge Management. Translated by Mohammad Rahman Pakseresht. Tehran: Sapco., 2000, p.21.
[21]. Alwani, M. Shahqolian, K., Designing a Model for Assessing the Level of Knowledge Management in Iranian Industrial Organizations (Research in the Automotive Industry). Quarterly Journal of Management Studies, No. 52, 2006, pp. 1-16.
[22] Hashemi, S., Assessing the level of knowledge management at the University of Law Enforcement Sciences. Quarterly Journal of Law Enforcement Management Studies. No. 5, 2010, pp.183-214.
[23] Samimi ,Y. Aghaie A., Presenting a Framework for Evaluating the Performance of Knowledge Management Systems, Allameh Tabatabaei University Industrial Management Quarterly, Volume 3 No. 10, 2005, pp. 1-23
[24] Ahmadi ,A., Salehi ,A. Knowledge Management, Payame Noor Publications, 2011, pp. 314-316
[25] Ata, R. and Kocyigit, Y. , An adaptive neuro-fuzzy inference system approach for prediction of tip speed ratio in wind turbines. Expert Systems with Applications, 37 (7):, 2010, pp5454-5460.
[26] Azar, A. Alipour Z., Danaeefard H., Fuzzy framework on the perception of fairness theory of justice within Mellat Bank, Managment. Res. Iran, TarbiatModarres University., 68 (3):, 2010, pp 61–89.
[27] Nardershahi ,M. Safi ,A. Tavakoli ,R., Development of Neural Network Decision Based on Genetic Algorithm for Assessing Preferences in Multi-Purpose Decision Making Problems, Journal of New Research in Decision Making, Volume 4, Number 3. 2019, pp 127-153

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