KNOWLEDGE MANAGEMENT IN TAX ADMINISTRATION: A CASE STUDY IN INDONESIA - Subroto, Gatot (Scientax, 2020)
Knowledge Management (KM) development in the public sector is relatively lagging behind compared to that in the business. Tax administration is no exception. This is contrary to the fact regarding the abundance of knowledge in the process of taxation, as well as the magnitude of the potential benefits KM may bring in advancing tax administration. The literature review in this paper identifies various types of knowledge in tax administration and classifies at least three major benefits of KM in the taxation process, which are related to the improvement of tax administration, tax compliance, and the prevention of maladministration and misappropriation in the tax administration and around the taxation process. Unfortunately, KM for tax administration is less attractive for both practitioners and academics. Trying to fill the gap, this research investigates the KM implementation in the Indonesian tax administration, Directorate General of Taxes (DGT). Data is collected through interviews, document analysis, and observations. This research finds that the KM initiative is still relatively in its infancy in terms of awareness and readiness. In the midst of low support from the top management and despite various developed and deployed IT system, the KM program tends to implement personalization strategy. The research also measures the maturity level using General KM Maturity Model (G-KMMM) developed by Pee & Kankanhalli (2009) and finds that the overall level of maturity is still at level 2 (aware). A generic KM Model for tax administration is proposed. This model combines various types of knowledge in each cluster of tax administration business process, which is then consulted with the components that form knowledge definition by Davenport & Prusak (1998) and the shapes of knowledge by Nonaka & Takeuchi (1995). The model provides insights regarding system development strategy and reveals some obstacles to be overcome. In addition to accommodating explicit knowledge in codification strategy, technology should also be developed to support personalization.