IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

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

User's Segmentation on Continued Knowledge Management System Use in the Public Sector
View Sample PDF
Author(s): Chi-Cheng Huang (Aletheia University, Taipei, Taiwan)
Copyright: 2020
Volume: 32
Issue: 1
Pages: 22
Source title: Journal of Organizational and End User Computing (JOEUC)
Editor(s)-in-Chief: Sangbing (Jason) Tsai (International Engineering and Technology Institute (IETI), Hong Kong)and Wei Liu (Qingdao University, China)
DOI: 10.4018/JOEUC.2020010102

Purchase

View User's Segmentation on Continued Knowledge Management System Use in the Public Sector on the publisher's website for pricing and purchasing information.

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.

Related Content

Xiaoye Ma, Yanyan Li, Muhammad Asif. © 2024. 29 pages.
Weihui Han, Tianshuo Zhang, Jamal Khan, Lujian Wang, Chao Tu. © 2024. 22 pages.
Ke Zheng, Zhou Li. © 2024. 21 pages.
Chen Quan, Baoli Lu. © 2024. 22 pages.
Xiangqian Wang, Haifeng Hu, Yuyao Wang, Zhaoyu Wang. © 2024. 25 pages.
Wanwan Li, Ying Cai, Mohd Hizam Hanafiah, Zhenwei Liao. © 2024. 16 pages.
Rong Liu, Vinay Vakharia. © 2024. 25 pages.
Body Bottom