User's Segmentation on Continued Knowledge Management System Use in the Public Sector

Abstract

Knowledge management systems (KMS) can help an organization support knowledge management activities and thereby increase organizational performance. This study extends the expectation-confirmation model for predicting mandatory continued KMS use in the public sector. The models are assessed using data from a sample of 627 employees of the Kaohsiung City government in Taiwan and analyzed using the finite mixture partial least squares (FIMIX-PLS) method. The results of this study indicate that (1) data heterogeneity (i.e., educational level) segments two specific groups that show different perceptions toward continued KMS use; (2) the results of aggregate-based data analysis are different from the results of group-specific data analysis; (3) compatibility, relative to confirmation, has larger impact on perceived usefulness regardless of groups; (4) the effect of user satisfaction on continued usage behavior is significant different between the two groups; (5) cognition-driven continued use and emotion-driven continued use are identified in the two groups.