Investigating the Productivity of Process Model of Social Work Services and Empowerment Using Data Mining Techniques

Subject Areas : Business Management

Mehrdad Mohammadzadeh Alamdary ¹ , Mansour Esmaeilpour ² , Alireza Slambolchi ³ , Farhad Soleimanian Gharehchigh ⁴

1 - Ph.D. Candidate in Information Technology Management, Department of Management, Hamadan Branch, Islamic Azad University, Hamadan, Iran
2 - Associate Professor, Department Computer Engineering, Hamadan Branch, Islamic Azad University, Hamadan, Iran
3 - Assistant Professor, Department of Management, Hamadan Branch,Islamic Azad University,Hamadan, Iran
4 - Assistant Professor, Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

Received: 2018-07-26 Accepted : 2019-06-02 Published : 2020-08-22

Keywords: Data mining, empowerment, NGO, Relief Committee, Social work services,

Abstract :

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.

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