بررسی میزان بهره‌وری مدل فرآیند خدمات مددکاری و توانمندسازی با استفاده از تکنیک‌های داده‌کاوی
محورهای موضوعی : مدیریت بازرگانی
مهرداد محمدزاده علمداری
1
,
منصور اسماعیل پور
2
,
علیرضا اسلامبولچی
3
,
فرهاد سلیمانیان قره چیق
4
1 - دانشجوی دکترای مدیریت فناوری اطلاعات،گروه مدیریت، واحدهمدان،دانشگاه آزاداسلامی،همدان،ایران
2 - دانشیار گروه مهندسی کامپیوتر،واحد همدان،دانشگاه آزاد اسلامی،همدان،ایران
3 - استادیار گروه مدیریت،واحد همدان،دانشگاه آزاد اسلامی،همدان،ایران
4 - استادیار گروه مهندسی کامپیوتر،واحد ارومیه،دانشگاه آزاد اسلامی،ارومیه،ایران
کلید واژه: توانمندسازی, داده کاوی, کمیته امداد, خدمات مددکاری, سازمان‌های مردم نهادغیردولتی,
چکیده مقاله :
فرآیند خدمات مددکاری و توانمندسازی در نهادهای حمایتی از جمله کمیته امداد امام خمینی(ره) جامع و گسترده است و با تحقیق، پذیرش و ارائه خدمات مددکاری برای نیازمندان شروع و بعد از حمایت هدفمند و زماندار، با آموزش و توانمندسازی مددجویان و خروج آنان از چتر حمایتی به پایان می رسد. از این رو بهره گیری از تکنیک های نوین جهت بررسی میزان بهره وری مدل پیاده سازی شده رورت دارد تا اهداف سازمانی از جمله اصلاح فرآیندهای جاری برای رعایت عدالت اجتماعی در حمایت از نیازمندان واقعی و خروج از چرخه حمایت مددجویان توانمند، تأمین شود. در این مطالعه پژوهشگران به دنبال پاسخ به این سؤال هستند که آیا مدل فرایند خدمات مددکاری و توانمندسازی بهره وری لازم جهت نیل به اهداف سازمانی را دارا بوده یا نیاز به بازطراحی دارد؟ در این راستا داده های مربوط به میزان تحقق اهداف کاربردی بعد از پیاده سازی مدل فرآیند خدمات مددکاری و توانمندسازی از جنبه شناسایی، راهنمایی، پذیرش، نیازسنجی، اولویت بندی، میزان خدمات عمومی و تخصصی قابل ارائه به نیازمندان واجد شرایط با رویکرد توانمندسازی، با استفاده از پرسشنامه جمع آوری و با استفاده از متدولوژی کریسپ-داده کاوی[1]مراحل مختلف مبتنی بر داده کاوی انجام گردید. در نهایت جمع بندی نتایج حاصل از داده کاوی با استفاده از رافست[2] 0.86، درخت تصمیم گیری[3] 0.80، تئوری بیز[4] 0.70، شبکه های عصبی مصنوعی[5]0.85 میزان دقت مدل را پیش بینی نمود. لذا مدل موجود بهره وری لازم جهت تحقق اهداف سازمانی را در حدمطلوب دارا بوده و با بازطراحی مجدد مدل موجود توسط پژوهشگران آتی با بهره گیری از نتایج پژوهش حاضر، امکان ارائه مدل بهینه جهت الگوبرداری سایر سازمان های مردم نهاد غیردولتی[6]در داخل و خارج از کشور فراهم خواهد شد.
The pervasive and comprehensive nature of social work and empowerment process in supportive institutions such as Imam Khomeini Relief Committee commences with the identification, acceptance and provision of supportive social work for the deprived destitute and, through purposeful timely training and empowering, helps the clients develop the ability to earn a living. Hence it is absolutely vital to explore the extent of process productivity by employing innovative techniques that can rectify and reform current processes and do more social justice to the impoverished helping the empowered ones withdraw the support cycle and stand on their own feet. The researchers in the present study were, thus, concerned with the question whether the process model of social work delivery and empowerment in Imam Khomeini Relief Committee was sufficiently productive to achieve the organizational goals or needed to be redesigned. To this end, a questionnaire was administered to collect the research data concerning the extent to which the goals of the model were achieved after its implementation with regard to identification, guidance, acceptance, needs analysis, prioritization, and provision of general and specific services for the needy clients in line with the overall empowerment approach. The data were further incrementally analyzed via the Crisp-Data mining at different stages of data mining. Finally, the compilation of data mining results could predict the accuracy of the model through Rough stood 0.86, Decision Tree 0.80, Bayes theory 0.70 and Artificial Neural Networks 0.85. The proposed model, hence, was proved to enjoy the desired productivity for achieving organizational goals. The findings might be employed by prospective researchers to redesign and optimize productivity models in other Non-Government organizations in line with organizational goals.
Chan, c., & Lewis, B. (2002), A basic primer on data mining, Information system Management, 4(19), 56-60.
Chapman, P., & Clinton, J., & Kerber, R., & Khabaza, T., & Reinartz, T., & Shearer, C. (2000), 100step-by-step data mining guide.Technical Report, CRISP-DM Consortium.
Gupta, G. K. (2006). Introduction to Data Mining With case studies, India,Prentic-Hall Pvt, 1-476.
Guo, X.; Duff A, Hair, M. (2008). "Service Qulity Measurement in the Chiese Corporate Banking Market", International Journal of Bank Marketing,26(5),305-327.
Han, J.; Kamber. M; Pei J. (2006), Data mining: Concepts and Techniques, Morgan Kaufmann Publishers is an imprint of Elsevier, 12-14.
Mirshekar, A., & Shokraei, M., & Keshavarzian,M. M. (2011). Designing a conceptual model of empowerment in the Relief Committee with an emphasis on the role of the institution in realizing the Islamic-Iranian model of progress,National Conference on empowerment with an economic jahad approach in Imam Khomeini Relie Committee, 359-376.[In Persian]
Moghimi, S. M. Z., & Hamid Rezaei, R. (2011). An interactive model of empowerment in the Imam Khomeini Relief Committee (RA) with a neighborhood-centered jihadi approach, Selected Articles of the Conference on National Empowerment with the Economic Jihad Approach at Imam Khomeini Relief Committee, Tehran,295-324.[In Persian]
Moradi, G., & Gasemi,V. (2012). Data mining techniques and their application in social studies, Journal of Social Sciences Faculty of Literature and Human Sciences Ferdowsi University of Mashhad, 157-178.[In Persian]
Myles,A.J., & Brown, S. D. (2003), Induction of decision trees using fuzzy partitions, Journal of chemometrics, 17(10), 531-536.
Omid,M. (2011), Empowerment Model for Creating Employment Opportunities for Clients covered by Relief Committee, National Conference on Empowerment with Economic Approach in Imam Khomeini Relief Committee.[In Persian]
Rafiee, S. (2009), Empowerment (Guidelines for safe and healthy society), Tehran: Shahr Press.[In Persian]
Rahimi,G. (2015), Investigating the Role of Diversity of Relief Committee Services on Empowerment of Patients Case Study of Isfahan Relief Committee (In Persian)
Ranjbar,A. (2015), The effect of self-sufficiency and job creation loans on the empowerment
of borrower families in Mazandaran Province Relief Committee.[In Persian]
Sohrabi, B., & Raisii,I., & Talebian,M. (2016), A Model for Analyzing the Behavior of Social Networking Users by Using Data Mining Methods: A Social Network in Iran, Human Resource Research, 6(4).[In Persian]
Suzkuki ,k. (2011), Artificial Neural Networks - Methodological Advances and Biomedical Applications.
Ning. Tan,P; Steinbach, M; Kumar V.(2005), Introduction to Data Mining.Pearson education, Pearson Addison-Wesley, 68-70.
Tang,Z. M., & Maclennan,J. (2005), Data Mining with SQL Server, Wiley Publishing Inc.Indianapolis,Indiana.
Timo,K., & Noble, J. M. (2009), Bayesian Networks An Introduction, A John Wiley and Sons Ltd Publication, 1-3.
Vander Alast, W. M. (2011), Process Mining: Discovery, Conformance and Enhancement of Business, Published by Springer, 1-352.
Yuan,Y., & shaw. M. J. (1995), Fuzzy Sets and Induction of fuzzy decision trees, Fuzzy Sets and Systems, 69, 125-139.
Zoe,Y. Z., & Leonid,C., & Frada,B., & Ken,S .(2009), Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners,European Journal of Operational Research, 195(3),662-675.
_||_
Chan, c., & Lewis, B. (2002), A basic primer on data mining, Information system Management, 4(19), 56-60.
Chapman, P., & Clinton, J., & Kerber, R., & Khabaza, T., & Reinartz, T., & Shearer, C. (2000), 100step-by-step data mining guide.Technical Report, CRISP-DM Consortium.
Gupta, G. K. (2006). Introduction to Data Mining With case studies, India,Prentic-Hall Pvt, 1-476.
Guo, X.; Duff A, Hair, M. (2008). "Service Qulity Measurement in the Chiese Corporate Banking Market", International Journal of Bank Marketing,26(5),305-327.
Han, J.; Kamber. M; Pei J. (2006), Data mining: Concepts and Techniques, Morgan Kaufmann Publishers is an imprint of Elsevier, 12-14.
Mirshekar, A., & Shokraei, M., & Keshavarzian,M. M. (2011). Designing a conceptual model of empowerment in the Relief Committee with an emphasis on the role of the institution in realizing the Islamic-Iranian model of progress,National Conference on empowerment with an economic jahad approach in Imam Khomeini Relie Committee, 359-376.[In Persian]
Moghimi, S. M. Z., & Hamid Rezaei, R. (2011). An interactive model of empowerment in the Imam Khomeini Relief Committee (RA) with a neighborhood-centered jihadi approach, Selected Articles of the Conference on National Empowerment with the Economic Jihad Approach at Imam Khomeini Relief Committee, Tehran,295-324.[In Persian]
Moradi, G., & Gasemi,V. (2012). Data mining techniques and their application in social studies, Journal of Social Sciences Faculty of Literature and Human Sciences Ferdowsi University of Mashhad, 157-178.[In Persian]
Myles,A.J., & Brown, S. D. (2003), Induction of decision trees using fuzzy partitions, Journal of chemometrics, 17(10), 531-536.
Omid,M. (2011), Empowerment Model for Creating Employment Opportunities for Clients covered by Relief Committee, National Conference on Empowerment with Economic Approach in Imam Khomeini Relief Committee.[In Persian]
Rafiee, S. (2009), Empowerment (Guidelines for safe and healthy society), Tehran: Shahr Press.[In Persian]
Rahimi,G. (2015), Investigating the Role of Diversity of Relief Committee Services on Empowerment of Patients Case Study of Isfahan Relief Committee (In Persian)
Ranjbar,A. (2015), The effect of self-sufficiency and job creation loans on the empowerment
of borrower families in Mazandaran Province Relief Committee.[In Persian]
Sohrabi, B., & Raisii,I., & Talebian,M. (2016), A Model for Analyzing the Behavior of Social Networking Users by Using Data Mining Methods: A Social Network in Iran, Human Resource Research, 6(4).[In Persian]
Suzkuki ,k. (2011), Artificial Neural Networks - Methodological Advances and Biomedical Applications.
Ning. Tan,P; Steinbach, M; Kumar V.(2005), Introduction to Data Mining.Pearson education, Pearson Addison-Wesley, 68-70.
Tang,Z. M., & Maclennan,J. (2005), Data Mining with SQL Server, Wiley Publishing Inc.Indianapolis,Indiana.
Timo,K., & Noble, J. M. (2009), Bayesian Networks An Introduction, A John Wiley and Sons Ltd Publication, 1-3.
Vander Alast, W. M. (2011), Process Mining: Discovery, Conformance and Enhancement of Business, Published by Springer, 1-352.
Yuan,Y., & shaw. M. J. (1995), Fuzzy Sets and Induction of fuzzy decision trees, Fuzzy Sets and Systems, 69, 125-139.
Zoe,Y. Z., & Leonid,C., & Frada,B., & Ken,S .(2009), Combining data mining and case-based reasoning for intelligent decision support for pathology ordering by general practitioners,European Journal of Operational Research, 195(3),662-675.
