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  • طراحی مدل موفقیت پیاده سازی مدیریت دانش در سطح سازمانی با رویکرد مدلسازی ساختاری تفسیری (ISM) مورد مطالعه: بانک تجارت

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آدرس مقاله


کد مقاله : IMJ-1808-1095 (R4) بازدید : 84 صفحه: 47 - 62

نوع مقاله: پژوهشی

طراحی مدل موفقیت پیاده سازی مدیریت دانش در سطح سازمانی با رویکرد مدلسازی ساختاری تفسیری (ISM) مورد مطالعه: بانک تجارت

محورهای موضوعی : مدیریت صنعتی

Behzad Keshanchi 1 (Head of organizing and banking regulation of the Tejarat bank, M.A in Banking)
davod khosroanjom 2 (دانشجوی دکترا روزانه مدیریت صنعتی دانشگاه تربیت مدرس)
Davod Pahlavani 3 (Deputy Director of organizing and banking regulation of the Tejarat bank, M.A in Financing Management)
Amir Poorgholi 4 (Expert of organizing and banking regulation of the Tejarat bank, M.A in Accounting)

تاریخ دریافت : 1397/05/30 تاریخ پذیرش : 1397/07/17 تاریخ انتشار : 1397/06/01

کلید واژه: مدلسازی ساختاری تفسیری, مدیریت دانش, حمایت مدیریت ارشد, استراتژی سازمانی,

چکیده مقاله :

ادبیات گسترده ای در زمینه پیاده سازی مدیریت دانش در سازمان ها ارائه شده است و هر یک از محققان این حوزه به یکی از ابعاد پیاده سازی مدیریت دانش اشاره کرده‌اند، ولی تاکنون یک مدل جامع و فراگیر از ابعاد و شاخص های پیاده سازی مدیریت دانش و ارتباط بین آنها ارائه نشده است. در این تحقیق، ابتدا با بررسی ادبیات در زمینه پیاده سازی مدیریت دانش، ابعاد و شاخص های آن شناسایی و سپس با استفاده از یک متدلوژی تحلیلی نوین تحت عنوان مدلسازی ساختاری تفسیری (ISM) روابط بین ابعاد و شاخص های پیاده سازی مدیریت دانش تعیین و بصورت یکپارچه مورد تحلیل قرار گرفته است. نتیجه منجر به طراحی مدل پیاده سازی مدیریت دانش در بانک تجارت شده است. پرسشنامه تحقیق، توسط 16 نفر از خبرگان بانکی پر شده است که نتایج تحقیق نشان می دهد مدل یکپارچه پیاده سازی مدیریت دانش شامل استراتژی سازمانی، حمایت مدیریت ارشد، فرهنگ سازمانی، مهندسی مجدد فرآیند کسب و کار، معماری دانش، فناوری اطلاعات و شبکه های خبرگی است که این ابعاد با یکدیگر در ارتباط هستند و بعد حمایت مدیریت ارشد بعنوان اساس مدل پیاده سازی مدیریت دانش می باشد.

چکیده انگلیسی:

Extensive literature on the implementation of knowledge management in organizations has been presented and each of the researchers in this area referred to one dimension of Knowledge Management implementation, but so far a comprehensive model of dimensions and indicators of implementing Knowledge Management and the relationship between them is not provided. First, in this research, the literature on implementation of Knowledge Management, its dimensions and indicators were identified and then, using a new analytical methodology called Interpretative Structural Modeling (ISM), the relationships between dimensions and indicators of implementing Knowledge management has been determined and integrated in an integrated way. The result has led to designing a Knowledge Management implementation model in the Tejarat bank. The research questionnaire was filled by 16 banking experts whose results indicate that the integrated model of Knowledge Management implementation includes organizational strategy, senior management support, organizational culture, business process reengineering, knowledge architecture, information technology and Experienced networks, so that these dimensions are interconnected with each other, and senior management support is the basis of implementing Knowledge Management model.

منابع و مأخذ:


  1. Bouzon, M., Govindan, K., Manue, C., & Rodriguez, T. (2015). Reducing the extraction of minerals:Reverse logistics in the machinery manufacturing industry sector in Brazil using ISM approach. Resources Policy, 46(1), 27–36.
    2.
  2. Chua, A., & Lam, W. (2005). Why KM projects fail: A multi-case analysis. Journal of Knowledge Management, 9(3), 6–17.
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  3. Davenport, T. H., De Long, W., & Beers, M. C. (1998). Successful knowledge management projects. Sloan Management Review, 39(3), 43–57.
    4.
  4. De la Huerga, R, Silvera, V., & Turoff, M. (2015). A CIA–ISM scenario approach for analyzing complex cascading effects in Operational Risk Management. Engineering Applications of Artificial Intelligence, 46(4), 289–302.
    5.
  5. Dulipovici, A., & Robey, D. (2013). Strategic alignment and misalignment of knowledge management systems. Journal of Management Information Systems, 29(4),103–126.
    6.
  6. Guiral, A., Rodgers, W., Ruiz-Barbadillo, E., & Gonzalo, J. A. (2015). Can expertise mitigate auditors' unintentional biases? Journal of International Accounting, Auditing and Taxation, 24(2), 105-117.
    7.
  7. Kannana,G., Pokharel, S., & Kumarc, P.S. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling, 54(4), 28-36.
    8.
  8. Kumar, A., & Dixit, G. (2018). An analysis of barriers affecting the implementation of e-waste management practices in India: A novel ISM-DEMATEL approach. Sustainable Production and Consumption, 14(2), 36 –52.
    9.
  9. Martinsonsa, M., Davisonb,R., & Huangc, Q. (2017). Strategic knowledge management failures in small professional service firms in China. International Journal of Information Management, 37(3), 327–338.
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  10. Mathiyazhagan,K.,Govindan,K.,NoorulHaq,A.,Geng,Y.,2013.An ISM approach for the barrier analysis inimplementing green supply chain management.Clean. Prod. 47(2), 283–297.
    11.
  11. Mertins , K., Heisig, P. and Vorbeck, J. (2003), Knowledge Management: Conceps and Practices, Springer, New York, NY.
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  12. Moskaliuk, J., Bokhorst, F., & Cress, U. (2016). Learning from others' experiences: How patterns foster interpersonal transfer of knowledge-in-use. Computers in Human Behavior, 55(2), 69-75.
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  18. Shahizan, H. & Laith, A. Y. AL-H. (2011). The relationships among critical success factors of knowledge management, innovation and organizational performance: A Conceptual Framework. International Conference on Management and Artificial Intelligence, IACSIT Press, 6(1), 94-103.
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  20. Thakkar, J.( 2007). Development of a balanced scorecard An integrated approach of Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP). International Journal of Productivity and Performance Management, 56(1), 25-59.
    21.
  21. Tortoriello, M., Reagans, R., & McEvily, B. (2012). Bridging the knowledge gap: The influence of strong ties, network cohesion, and network range on the transfer of knowledge between organizational units. Organization Science, 23(4),1024-1039.
    22.
  22. Wang, L., Cao, Q., & Zhou, Z. (2018). Research on the influencing factors in coal mine production safety based on the combination of DEMATEL and ISM. Safety Science, 103(4), 51– 61.
    23.
  23. Wang, Y., & Wang, Y. (2016). Determinants of firms' knowledge management system implementation: An empirical study. Computers in Human Behavior, 64(2), 829-884.
    24.
  24. Wei, C., Lin, W., Chen, H., An, W., & Yeh, Wei (2015). Finding experts in online forums for enhancing knowledge sharing and accessibility. Computers in Human Behavior, 51(1), 325-335.
    25.
  25. Wong, K.Y. (2005), Critical Success Factors for Implementing Knowledge Management in Small and Medium Enterprises, Industrial Management and Data Systems, 105(3), 261-279.
    26.
  26. Wu, L. L., & Lin, J. (2006). The quality of consumers' decision-making in the environment of e-commerce. Psychology and Marketing, 23(2), 297-238.
    27.
_||_

  1. Bouzon, M., Govindan, K., Manue, C., & Rodriguez, T. (2015). Reducing the extraction of minerals:Reverse logistics in the machinery manufacturing industry sector in Brazil using ISM approach. Resources Policy, 46(1), 27–36.
    2.
  2. Chua, A., & Lam, W. (2005). Why KM projects fail: A multi-case analysis. Journal of Knowledge Management, 9(3), 6–17.
    3.
  3. Davenport, T. H., De Long, W., & Beers, M. C. (1998). Successful knowledge management projects. Sloan Management Review, 39(3), 43–57.
    4.
  4. De la Huerga, R, Silvera, V., & Turoff, M. (2015). A CIA–ISM scenario approach for analyzing complex cascading effects in Operational Risk Management. Engineering Applications of Artificial Intelligence, 46(4), 289–302.
    5.
  5. Dulipovici, A., & Robey, D. (2013). Strategic alignment and misalignment of knowledge management systems. Journal of Management Information Systems, 29(4),103–126.
    6.
  6. Guiral, A., Rodgers, W., Ruiz-Barbadillo, E., & Gonzalo, J. A. (2015). Can expertise mitigate auditors' unintentional biases? Journal of International Accounting, Auditing and Taxation, 24(2), 105-117.
    7.
  7. Kannana,G., Pokharel, S., & Kumarc, P.S. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, Conservation and Recycling, 54(4), 28-36.
    8.
  8. Kumar, A., & Dixit, G. (2018). An analysis of barriers affecting the implementation of e-waste management practices in India: A novel ISM-DEMATEL approach. Sustainable Production and Consumption, 14(2), 36 –52.
    9.
  9. Martinsonsa, M., Davisonb,R., & Huangc, Q. (2017). Strategic knowledge management failures in small professional service firms in China. International Journal of Information Management, 37(3), 327–338.
    10.
  10. Mathiyazhagan,K.,Govindan,K.,NoorulHaq,A.,Geng,Y.,2013.An ISM approach for the barrier analysis inimplementing green supply chain management.Clean. Prod. 47(2), 283–297.
    11.
  11. Mertins , K., Heisig, P. and Vorbeck, J. (2003), Knowledge Management: Conceps and Practices, Springer, New York, NY.
    12.
  12. Moskaliuk, J., Bokhorst, F., & Cress, U. (2016). Learning from others' experiences: How patterns foster interpersonal transfer of knowledge-in-use. Computers in Human Behavior, 55(2), 69-75.
    13.
  13. Muthusamy, S. K., & White, M. A. (2005). Learning and knowledge transfer in strategic alliances: A social exchange view. Organization Studies, 26(3), 415-441.
    14.
  14. Navimipour, N., & Charband, Y. (2016). Knowledge sharing mechanisms and techniques in project teams: Literature review, classification, and current trends. Computers in Human Behavior, 62(3), 730-742.
    15.
  15. Ou, C. X. J., Davison, R. M., & Wong, L. H. M. (2016). Using interactive systems for knowledge sharing: The impact of individual contextual preferences in China. Information & Management, 53(2), 145–156.
    16.
  16. Rodgers, W., & Negash, S. (2007). The effects of web-based technologies on knowledge transfer. Communications of the ACM, 50(1), 117-122.
    17.
  17. Rodgers, W., Seoderbom, A., & Guiral, A. (2014). Corporate social responsibility enhanced control systems reducing the likelihood of fraud. Journal of Business Ethics, 91(1), 151-166.
    18.
  18. Shahizan, H. & Laith, A. Y. AL-H. (2011). The relationships among critical success factors of knowledge management, innovation and organizational performance: A Conceptual Framework. International Conference on Management and Artificial Intelligence, IACSIT Press, 6(1), 94-103.
    19.
  19. Stewart, S.A. & Abidi, S.S.R.(2012). An infobutton for web 2.0 clinical discussions: the knowledge linkage framework, IEEE Trans. Inf. Technol. Biomed. 16 (1),129–135 .
    20.
  20. Thakkar, J.( 2007). Development of a balanced scorecard An integrated approach of Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP). International Journal of Productivity and Performance Management, 56(1), 25-59.
    21.
  21. Tortoriello, M., Reagans, R., & McEvily, B. (2012). Bridging the knowledge gap: The influence of strong ties, network cohesion, and network range on the transfer of knowledge between organizational units. Organization Science, 23(4),1024-1039.
    22.
  22. Wang, L., Cao, Q., & Zhou, Z. (2018). Research on the influencing factors in coal mine production safety based on the combination of DEMATEL and ISM. Safety Science, 103(4), 51– 61.
    23.
  23. Wang, Y., & Wang, Y. (2016). Determinants of firms' knowledge management system implementation: An empirical study. Computers in Human Behavior, 64(2), 829-884.
    24.
  24. Wei, C., Lin, W., Chen, H., An, W., & Yeh, Wei (2015). Finding experts in online forums for enhancing knowledge sharing and accessibility. Computers in Human Behavior, 51(1), 325-335.
    25.
  25. Wong, K.Y. (2005), Critical Success Factors for Implementing Knowledge Management in Small and Medium Enterprises, Industrial Management and Data Systems, 105(3), 261-279.
    26.
  26. Wu, L. L., & Lin, J. (2006). The quality of consumers' decision-making in the environment of e-commerce. Psychology and Marketing, 23(2), 297-238.
    27.

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