Iranian Journal of Information Processing and Management

Iranian Journal of Information Processing and Management

Designing the adaptive fuzzy-neural inference system to measure the benefits of knowledge management in the organization

Authors
Hossein Yekkeh
Seyed Mohammadbagher Jafari
Seyed Mohammad Mahmoudi
Mehdi ShamiZanjani
10.52547/jipm.37.1.288
Abstract
In recent years, knowledge management has become a fundamental principle in the field of management. Since the introduction of knowledge management, many institutions have tried to measure the benefits of using this concept. Success in implementing knowledge management and continuing its usage largely depends on measuring knowledge management benefits. However, few studies were conducted on this issue. This study, by using the adaptive neural fuzzy inference method (ANFIS) via Matlab 2017 software, tried to provide a predictive model to measure the benefits of knowledge management in the organization. The study population consists of scientists and experts working in 15 branches of the Social Security Organization, with a minimum of five years of experience in knowledge management related tasks. Based on the results, the degree of compatibility of the estimates with the actual results and the predictability and accuracy of the results were discussed, and at the end, based on the results, guidelines were provided to the studied organization.
Keywords
Knowledge Management
Knowledge Management Benefits
Adaptive Neural Fuzzy Inference System
ANFIS
  1. جعفری، سید محمدباقر، مهدی شامی زنجانی، سید محمد محمودی، و حسین یکه. 1398. ارائه چارچوب شناسایی منافع مدیریت دانش در سازمان با استفاده از روش فراترکیب. فصلنامه علمی-پژوهشی فرایند مدیریت و توسعه ۳۲ (۴): ۱۱۹-۱۵۲.
  2. جعفری، سید محمدباقر، مهدی شامی زنجانی، سید محمد محمودی، و حسین یکه. 1398. ارائه چارچوب شناسایی منافع مدیریت دانش در سازمان با استفاده از روش فراترکیب. فصلنامه علمی-پژوهشی فرایند مدیریت و توسعه ۳۲ (۴): ۱۱۹-۱۵۲.
  3. رئیسی وانانی، ایمان، محمدرضا تقوا، و دلنیا امیرشاهی. 1397. طراحی سیستم استنتاج فازی برای ارزیابی عملکرد سیستم مدیریت دانش در صنعت توسعه نرم‌افزار. فصلنامه مطالعات مدیریت کسب‌وکار هوشمند 24 (6): 5-36.
  4. رئیسی وانانی، ایمان، محمدرضا تقوا، و دلنیا امیرشاهی. 1397. طراحی سیستم استنتاج فازی برای ارزیابی عملکرد سیستم مدیریت دانش در صنعت توسعه نرم‌افزار. فصلنامه مطالعات مدیریت کسب‌وکار هوشمند 24 (6): 5-36.
  5. عالی‌نژاد، امیرحمزه، عادل آذر، و محمدابراهیم پورزرندی. 1399. طراحی مدل پیشبینی و ارزیابی ظرفیت نوآوری شرکت‌های دانش‌بنیان با رویکرد استنتاج فازی-عصبی تطبیقی (ANFIS). پژوهش‌های مدیریت عمومی 47 (13): 55-84.
  6. عالی‌نژاد، امیرحمزه، عادل آذر، و محمدابراهیم پورزرندی. 1399. طراحی مدل پیشبینی و ارزیابی ظرفیت نوآوری شرکت‌های دانش‌بنیان با رویکرد استنتاج فازی-عصبی تطبیقی (ANFIS). پژوهش‌های مدیریت عمومی 47 (13): 55-84.
  7. یحیی‌پور، شیوا. 1392. ارائه سیستمی فازی برای ارزیابی منافع حاصل از مدیریت دانش در شرکت فولاد خوزستان. پایان‌نامه کارشناسی ارشد.دانشکده مدیریت، دانشگاه تهران.
  8. یحیی‌پور، شیوا. 1392. ارائه سیستمی فازی برای ارزیابی منافع حاصل از مدیریت دانش در شرکت فولاد خوزستان. پایان‌نامه کارشناسی ارشد.دانشکده مدیریت، دانشگاه تهران.
  9. Al-Hmouz,A., J. Shen, R. Al-Hmouz, and J. Yan. 2012. Modeling and Simulation of an Adaptive Neuro -Fuzzy Inference System (ANFIS) for Mobile Learning. IEEE Transactions on Learning Technologies 5 (3): 226-237. [DOI:10.1109/TLT.2011.36]
  10. Al-Hmouz,A., J. Shen, R. Al-Hmouz, and J. Yan. 2012. Modeling and Simulation of an Adaptive Neuro -Fuzzy Inference System (ANFIS) for Mobile Learning. IEEE Transactions on Learning Technologies 5 (3): 226-237. [DOI:10.1109/TLT.2011.36]
  11. An, H. K., and A. N. Abdalla. 2019. Prediction of queuing length at metering roundabout using adaptive neuro fuzzy inference system. Measurement and Control 52 (5-6): 432-440. [DOI:10.1177/0020294019839415]
  12. An, H. K., and A. N. Abdalla. 2019. Prediction of queuing length at metering roundabout using adaptive neuro fuzzy inference system. Measurement and Control 52 (5-6): 432-440. [DOI:10.1177/0020294019839415]
  13. Anantatmula, Vittal, and Shivraj Kanungo. 2006. Structuring the underlying relations among the knowledge management outcomes. Journal of Knowledge Management 10 (4): 25- 42. [DOI:10.1108/13673270610679345]
  14. Anantatmula, Vittal, and Shivraj Kanungo. 2006. Structuring the underlying relations among the knowledge management outcomes. Journal of Knowledge Management 10 (4): 25- 42. [DOI:10.1108/13673270610679345]
  15. Cabrilo, S., and S. Dahms. 2018. How strategic knowledge management drives intellectual capital to superior innovation and market performance, Journal of Knowledge Management 22 (3): 621-648. [DOI:10.1108/JKM-07-2017-0309]
  16. Cabrilo, S., and S. Dahms. 2018. How strategic knowledge management drives intellectual capital to superior innovation and market performance, Journal of Knowledge Management 22 (3): 621-648. [DOI:10.1108/JKM-07-2017-0309]
  17. Cebi, F., O. F. Aydin & S. Gozlu. 2010. Benefits of Knowledge Management in Banking. Journal of Transnational Management 15 (4): 308-321. [DOI:10.1080/15475778.2010.525486]
  18. Cebi, F., O. F. Aydin & S. Gozlu. 2010. Benefits of Knowledge Management in Banking. Journal of Transnational Management 15 (4): 308-321. [DOI:10.1080/15475778.2010.525486]
  19. Chen, L., and S. Mohamed. 2007. Empirical study of interactions between knowledge management activities. Engineering, Construction and Architectural Management 14 (3): 242-260. [DOI:10.1108/09699980710744890]
  20. Chen, L., and S. Mohamed. 2007. Empirical study of interactions between knowledge management activities. Engineering, Construction and Architectural Management 14 (3): 242-260. [DOI:10.1108/09699980710744890]
  21. Chen, M. Y, M. J. Huang, & Y.C. Cheng. 2009. Measuring knowledge management performance using a competitive perspective: An empirical study. Expert Systems with Applications 36 (4): 8449-8459. [DOI:10.1016/j.eswa.2008.10.067]
  22. Chen, M. Y, M. J. Huang, & Y.C. Cheng. 2009. Measuring knowledge management performance using a competitive perspective: An empirical study. Expert Systems with Applications 36 (4): 8449-8459. [DOI:10.1016/j.eswa.2008.10.067]
  23. Chua, Alton Y. K., and D. H. Goh. 2008. Untying the knot of knowledge management measurement: a study of six public service agencies in Singapore. Journal of Information Science 34 (3): 259-274. [DOI:10.1177/0165551507084139]
  24. Chua, Alton Y. K., and D. H. Goh. 2008. Untying the knot of knowledge management measurement: a study of six public service agencies in Singapore. Journal of Information Science 34 (3): 259-274. [DOI:10.1177/0165551507084139]
  25. Choy, C. S., W. K. Yew, and B. Lin. 2006. Criteria for measuring KM performance outcomes in organisations. Industrial Management & Data Systems 106 (7): 917-936. [DOI:10.1108/02635570610688850]
  26. Choy, C. S., W. K. Yew, and B. Lin. 2006. Criteria for measuring KM performance outcomes in organisations. Industrial Management & Data Systems 106 (7): 917-936. [DOI:10.1108/02635570610688850]
  27. Dayan, Rony, P. Heisig, and F. Matos. 2017. Knowledge management as a factor for the formulation and implementation of organization strategy. Journal of Knowledge Management 21 (2): 308-329. [DOI:10.1108/JKM-02-2016-0068]
  28. Dayan, Rony, P. Heisig, and F. Matos. 2017. Knowledge management as a factor for the formulation and implementation of organization strategy. Journal of Knowledge Management 21 (2): 308-329. [DOI:10.1108/JKM-02-2016-0068]
  29. de Gooijer, J. 2000. Designing a knowledge management performance framework. Journal of Knowledge Management 4 (4): 303-310. [DOI:10.1108/13673270010379858]
  30. de Gooijer, J. 2000. Designing a knowledge management performance framework. Journal of Knowledge Management 4 (4): 303-310. [DOI:10.1108/13673270010379858]
  31. Dženopoljac, V., S. Janoševi, and N. Bontis. 2016. Intellectual capital and financial performance in the Serbian ICT industry. Journal of Intellectual Capital 17 (2): 373-396. [DOI:10.1108/JIC-07-2015-0068]
  32. Dženopoljac, V., S. Janoševi, and N. Bontis. 2016. Intellectual capital and financial performance in the Serbian ICT industry. Journal of Intellectual Capital 17 (2): 373-396. [DOI:10.1108/JIC-07-2015-0068]
  33. Gupta, V., and M. Chopra. 2018. Gauging the impact of knowledge management practices on organizational performance - a balanced scorecard perspective. VINE Journal of Information and Knowledge Management Systems 48 (1): 21-46. [DOI:10.1108/VJIKMS-07-2016-0038]
  34. Gupta, V., and M. Chopra. 2018. Gauging the impact of knowledge management practices on organizational performance - a balanced scorecard perspective. VINE Journal of Information and Knowledge Management Systems 48 (1): 21-46. [DOI:10.1108/VJIKMS-07-2016-0038]
  35. Jahangoshai Rezaee, M., M. Dadkhah, & M. Falahinia. 2019. Integrating neuro fuzzy system and evolutionary optimization algorithms for short-term power generation forecasting. International Journal of Energy Sector Management 13 (4): 828-845. [DOI:10.1108/IJESM-09-2018-0015]
  36. Jahangoshai Rezaee, M., M. Dadkhah, & M. Falahinia. 2019. Integrating neuro fuzzy system and evolutionary optimization algorithms for short-term power generation forecasting. International Journal of Energy Sector Management 13 (4): 828-845. [DOI:10.1108/IJESM-09-2018-0015]
  37. Kaur, H., S. K. Sood. 2019. Adaptive Neuro Fuzzy Inference System (ANFIS) based wildfire risk assessment. Journal of Experimental & Theoretical Artificial Intelligence 31 (4): 599-619. [DOI:10.1080/0952813X.2019.1591523]
  38. Kaur, H., S. K. Sood. 2019. Adaptive Neuro Fuzzy Inference System (ANFIS) based wildfire risk assessment. Journal of Experimental & Theoretical Artificial Intelligence 31 (4): 599-619. [DOI:10.1080/0952813X.2019.1591523]
  39. Lee, C.S., K.Y. Wong. 2015. Development and validation of knowledge management performance measurement constructs for small and medium enterprises. Journal of Knowledge Management 19 (4): 711-734. [DOI:10.1108/JKM-10-2014-0398]
  40. Lee, C.S., K.Y. Wong. 2015. Development and validation of knowledge management performance measurement constructs for small and medium enterprises. Journal of Knowledge Management 19 (4): 711-734. [DOI:10.1108/JKM-10-2014-0398]
  41. Lo, K., and K. Chin. 2009. User-satisfaction-based knowledge management performance measurement, International Journal of Quality & Reliability Management 26 (5): 449-468. [DOI:10.1108/02656710910956184]
  42. Lo, K., and K. Chin. 2009. User-satisfaction-based knowledge management performance measurement, International Journal of Quality & Reliability Management 26 (5): 449-468. [DOI:10.1108/02656710910956184]
  43. McIver, D., D. A. Lepisto. 2017. Effects of knowledge management on unit performance: examining the moderating role of tacitness and learnability. Journal of Knowledge Management 21 (4): 796-816. [DOI:10.1108/JKM-08-2016-0347]
  44. McIver, D., D. A. Lepisto. 2017. Effects of knowledge management on unit performance: examining the moderating role of tacitness and learnability. Journal of Knowledge Management 21 (4): 796-816. [DOI:10.1108/JKM-08-2016-0347]
  45. Masrur A., & A. A. S. M. Ali Shah. 2017. Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. Journal of King Saud University - Engineering Sciences. 29: 237-243. [DOI:10.1016/j.jksues.2015.02.001]
  46. Masrur A., & A. A. S. M. Ali Shah. 2017. Application of adaptive neuro-fuzzy inference system (ANFIS) to estimate the biochemical oxygen demand (BOD) of Surma River. Journal of King Saud University - Engineering Sciences. 29: 237-243. [DOI:10.1016/j.jksues.2015.02.001]
  47. Mousavizadeh, M., S. Ryan, G. Harden & J. Windsor. 2015. Knowledge Management and the Creation of Business Value. Journal of Computer Information Systems 55 (4): 35-45. [DOI:10.1080/08874417.2015.11645785]
  48. Mousavizadeh, M., S. Ryan, G. Harden & J. Windsor. 2015. Knowledge Management and the Creation of Business Value. Journal of Computer Information Systems 55 (4): 35-45. [DOI:10.1080/08874417.2015.11645785]
  49. Neshat, M., A. A. Pourahmad, and M. R. Hasani. 2016. Designing an Adaptive Neuro Fuzzy Inference System for Prediction of Customers Satisfaction. Journal of Information & Knowledge Management. 15 (4): 15 (4): 1-21. [DOI:10.1142/S0219649216500374]
  50. Neshat, M., A. A. Pourahmad, and M. R. Hasani. 2016. Designing an Adaptive Neuro Fuzzy Inference System for Prediction of Customers Satisfaction. Journal of Information & Knowledge Management. 15 (4): 15 (4): 1-21. [DOI:10.1142/S0219649216500374]
  51. Perez-Soltero, A., H. Galvez-Leon, M. Barcelo-Valenzuela, and G. Sanchez-Schmitz. 2016. A methodological proposal to benefit from team knowledge: An experience in a Mexican SME dedicated to the design of electromechanical devices. VINE Journal of Information and Knowledge Management Systems 46 (3): 298-318. [DOI:10.1108/VJIKMS-08-2015-0043]
  52. Perez-Soltero, A., H. Galvez-Leon, M. Barcelo-Valenzuela, and G. Sanchez-Schmitz. 2016. A methodological proposal to benefit from team knowledge: An experience in a Mexican SME dedicated to the design of electromechanical devices. VINE Journal of Information and Knowledge Management Systems 46 (3): 298-318. [DOI:10.1108/VJIKMS-08-2015-0043]
  53. Pina, P., M. Romão, and M. Oliveira. 2013. Using benefits management to link knowledge management to business objectives. VINE 43 (1): 22 - 38. [DOI:10.1108/03055721311302124]
  54. Pina, P., M. Romão, and M. Oliveira. 2013. Using benefits management to link knowledge management to business objectives. VINE 43 (1): 22 - 38. [DOI:10.1108/03055721311302124]
  55. Shamizanjani, M., S. Yahyapour, & M. Mosakhani. 2015. A conceptual breakdown structure for knowledge management benefits using meta-synthesis method. Journal of Knowledge Management 19 (6): 1295 - 1309. [DOI:10.1108/JKM-05-2015-0166]
  56. Shamizanjani, M., S. Yahyapour, & M. Mosakhani. 2015. A conceptual breakdown structure for knowledge management benefits using meta-synthesis method. Journal of Knowledge Management 19 (6): 1295 - 1309. [DOI:10.1108/JKM-05-2015-0166]
  57. Shams Nateri, A., E. Hasanlou, & A. Hajipour. 2019. Using adaptive neuro-fuzzy and genetic algorithm for simultaneously estimating the dye and AgNP concentrations of treated silk fabrics with nanosilver. Pigment & Resin Technology 48 (1): 20-28. [DOI:10.1108/PRT-11-2017-0096]
  58. Shams Nateri, A., E. Hasanlou, & A. Hajipour. 2019. Using adaptive neuro-fuzzy and genetic algorithm for simultaneously estimating the dye and AgNP concentrations of treated silk fabrics with nanosilver. Pigment & Resin Technology 48 (1): 20-28. [DOI:10.1108/PRT-11-2017-0096]
  59. Shehabeldeen, T. A., M. A. Elaziz., A. H. Elsheikh, &J. Zhou. 2019. Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with harris hawks optimizer. Journal of Materials and Technology 8 (6): 5882-5892. [DOI:10.1016/j.jmrt.2019.09.060]
  60. Shehabeldeen, T. A., M. A. Elaziz., A. H. Elsheikh, &J. Zhou. 2019. Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with harris hawks optimizer. Journal of Materials and Technology 8 (6): 5882-5892. [DOI:10.1016/j.jmrt.2019.09.060]
  61. Vestal, W. 2002. Measuring Knowledge management. American Productivity Quality Center (APQC), Houston,TX Yan, H., Z. Zou, and H. Wang. 2010. Adaptive neuro fuzzy inference system for classification of water quality status‖. Journal of Environmental Sciences 22 (12): 1891-1896. [DOI:10.1016/S1001-0742(09)60335-1]
  62. Vestal, W. 2002. Measuring Knowledge management. American Productivity Quality Center (APQC), Houston,TX Yan, H., Z. Zou, and H. Wang. 2010. Adaptive neuro fuzzy inference system for classification of water quality status‖. Journal of Environmental Sciences 22 (12): 1891-1896. [DOI:10.1016/S1001-0742(09)60335-1]
  63. Zarei, M. J., F. Gholizadeh, S. Sabbaghi, & P. Keshavarz. 2018. Estimation of CO2 mass transfer rate into various types of Nanofluids in hollow Fiber membrane and packed bed column using adaptive neuro-fuzzy inference system. International Communications in Heat and Mass Transfer 96: 90-97. [DOI:10.1016/j.icheatmasstransfer.2018.05.022]
  64. Zarei, M. J., F. Gholizadeh, S. Sabbaghi, & P. Keshavarz. 2018. Estimation of CO2 mass transfer rate into various types of Nanofluids in hollow Fiber membrane and packed bed column using adaptive neuro-fuzzy inference system. International Communications in Heat and Mass Transfer 96: 90-97. [DOI:10.1016/j.icheatmasstransfer.2018.05.022]
  65. Zyngier, S., & F. Burstein. 2012. Knowledge Management Governance: The Road to Continuous Benefits Realization. Journal of Information Technology 27: 140-155 [DOI:10.1057/jit.2011.31]
  66. Zyngier, S., & F. Burstein. 2012. Knowledge Management Governance: The Road to Continuous Benefits Realization. Journal of Information Technology 27: 140-155 [DOI:10.1057/jit.2011.31]

  • Receive Date 10 December 2022