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Modeling Barriers of Theorizing in Knowledge and Information Science from the Perspective of Faculty Members Based on Grounded Theory

Document Type : Research Paper

Authors

1 Master of Information Science and Knowledge, Tarbiat Modares University/Economics and Managemen Dept, Tehran, Iran

2 Full Professor, Information Science and Knowledge Dept, Faculty of Economics and Management, Tarbiat Modares University, Tehran, Iran

3 Assistant Professor, Department of Information Science and Knowledge, Tarbiat Modares University, Tehran, Iran

Abstract

Despite the breadth and richness of the subject and the variety of topics in the field of knowledge and information science facing researchers, the field suffers from theoretical poverty. The purpose of this study is to identify the theoretical barriers in information science and knowledge. This is a qualitative research. Semi-structured interviews were used to collect data. Members of the faculty of knowledge and information science at Iranian universities formed the statistical population of this study. Theoretical sampling method was used for sample selection. In total, 18 faculty members of Birjand, Tarbiat Modarres, Tabriz, Shahid Bahona and Shahid Chamran universities interviewed. The data were coded based on the grounded theory in three steps. MAX QDA software was used for data encoding. Finally 10 core categories were obtained. Among the 10 core categories, the categories of "shaky personality" and "belief system failure" were selected as the central classes. According to Glazer’s recommendation, the belief system failure was referred to the shaky personality class. In other words, the shaky personality class was identified as the central class. For the first time in Iran and outside Iran, the barriers of theorizing in knowledge and information science have been examined with a qualitative approach. Therefore, on the one hand, the subject under study and adopting a qualitative approach on the other hand, demonstrate its value and authenticity.

Keywords

References
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Knowledge Retrieval and Semantic Systems
Volume 8, Issue 27
July 2021
Pages 1-38
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