• Knowledge Management Research
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• v.9 no.3
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• pp.43-57
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• 2008

This study examines the impact of knowledge processes (KP) on the performance of knowledge management systems (KMS). It posits that task needs and available functionality of technology existing in an organization could influence the usability of KP and the KMS performance. A firm-level structural model was developed based on data collected from corporate KM users. Survey-based research was carried out to test this model. Following questionnaire development, validation, and pretest with a pilot study, data were collected from 886 knowledge management (KM) users including directors, managers, and workers in a South Korea-based company, Korea Asset Management Corporation (KAMCO), to measure the task needs and available functionality of technology to improve the KMS performance. Results show that the matching between the two factors-technology and task-had a significant influence on the usability of KP and the KMS performance, and a better usability of KP has positive impact on the KMS performance. Implications on KM practices and KMS designs are also discussed.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.423-430
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• 2023

One recent notable method for real-time elimination of ionospheric errors in geodetic applications is the Predicted Global Ionosphere Map (PGIM). This study analyzes the level of accuracy achievable when applying the PGIM provided by the Center for Orbit Determination of Europe (CODE) to the Korean Peninsula region. First, an examination of the types and lead times of PGIMs provided by the International GNSS Service (IGS) Analysis Center revealed that CODE's two-day prediction model, C2PG, is available approximately eight hours before midnight. This suggests higher real-time usability compared to the one-day prediction model, C1PG. When evaluating the accuracy of PGIM by assuming the final output of the Global Ionosphere Map (GIM) as a reference, it was found that on days with low solar activity, the error is within ~2 TECU, and on days with high solar activity, the error reaches ~3 TECU. A comparison of the errors introduced when using PGIM and three solar activity indices-Kp index, F10.7, and sunspot number-revealed that F10.7 exhibits a relatively high correlation coefficient compared to Kp-index and sunspot number, confirming the effectiveness of the prediction model.