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Managing Old and Obsolete Knowledge: Exploring the Pattern of Unlearning Actions Based on Different Knowledge Types, The Case of Iranian Software Industry

Rezazade Mehrizi, Mohammad Hossein | 2011

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 41802 (44)
  4. University: Sharif University of Technology
  5. Department: Management and Economics
  6. Advisor(s): Ghasemzadeh, Fereidoun; Kermanshah, Ali
  7. Abstract:
  8. The rapid technological changes have faced organizations with a new challenge: old and obsolete knowledge when it hampers organizational change or creates some kind of possible negative impact. Hence, in many cases, companies need to actively deal with such old and obsolete knowledge and their possible negative impacts in order to survive in highly competitive market. Defining unlearning as the intentional process of managing the old and obsolete knowledge in order to reduce its possible negative impacts, this study focuses on how organizations manage their old and obsolete knowledge and how the pattern of unlearning actions varies based on different types of knowledge.
    Analyzing the literature, we identify an initial list of five categories of unlearning actions. We typify them as unlearning approaches. However, we use this list as a starting point and keep our exploration open to further developments of this list. On the other hand, we rely on the Blackler’s taxonomy of knowledge types (embrained, embodied, embedded, encoded, and encultured). Having these two dimensions, unlearning approaches and knowledge types, we develop the theoretical framework of this study. Through an interpretative qualitative research, we adopt an exploratory comparative multiple cases study design in order to get deep understanding of the unlearning approaches and how they are differently applied to different types of knowledge. Focusing on software sector, characterized by deep and rapid regime of knowledge changes, we selected four software companies and we collected data on unlearning actions that companies implemented in three technological shifts. Using exploratory thematic analysis at latent level, the first finding of this study is to expand the range of unlearning approaches to seven major approaches and their associated sub-approaches. We specify the knowledge taxonomy at a more detailed level in order to better show the influence of knowledge types on the implementation of unlearning approaches. Using seven unlearning approaches (with 32 detailed sub-approaches) and five knowledge types (with 14 sub-types), we could run a confirmatory thematic analysis in order to map unlearning actions to unlearning approaches and knowledge types. Analyzing the pattern of using unlearning approaches for different knowledge types at the aggregate level and at the level of each company, we could find insights on how the characteristics of knowledge can influence the application of unlearning approaches. Overall, organizations tend to use unlearning approaches for knowledge types that are less sensitive in terms of human and organizational aspects. Moreover, the results of our analysis show that not only the characteristics of each knowledge type, but also the features of its container plays an influential role in implementing unlearning approaches. Comparing different companies, we could find how the characteristics of companies such as size and the level of structural specialization can influence the pattern of using unlearning approaches. Our study not only strongly confirmed the importance of unlearning as a distinct managerial action, but also identified a wide range of approaches that companies can use in order to deal with their old and obsolete knowledge. Our findings stresses that the application of these approaches should be done with regard to the underling knowledge types. Theoretically, the insights accrued from this study in terms of how the characteristics of knowledge can influence the application of unlearning approaches can be a basis for developing theoretical hypothesis and further studies in order to test them
  9. Keywords:
  10. Knowledge Management ; Software ; Organizational Learning ; Old and Obsolete Knowledges ; Active Forgetting

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