• Journal of the Korea Safety Management & Science
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• v.9 no.6
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• pp.193-196
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• 2007

This paper presents real examples of quality statistics for users to easily understand the concept and purpose for obtaining the degree of freedom. Moreover degree of freedom by Satterwaite can be used for linear combinations of unbiased variance. Finally effective degree of freedom by Welch-Satterthwaite is applicable to obtain expanded uncertainty considering type A and type B uncertainty.

• Korean Journal of Veterinary Research
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• v.47 no.2
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• pp.139-145
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• 2007

Measurement uncertainty could play an important role in the assessment of test results in laboratories and industries. We investigated measurement uncertainties possibly included in determination of flubendazole, a benzimidazole anthelmintic, in pork by HPLC. The concentration of flubendazole was 62.69 ng/g in a sample of pork. Uncertainty was estimated in the analytical procedure of flubendazole. A model equation was made for determination of flubendazole in pork. The four uncertainty components such as weight of sample, volume of sample, calibration curve, and recovery were selected to estimate measurement uncertainties. Standard uncertainty was calculated for each component and all the standard uncertainties were combined. The combined standard uncertainty was expanded to a sample population as an expanded uncertainty. The expanded uncertainty was calculated using k value on Student's t-table and effective degrees of freedom from Welch-Satterthwaite formula. The expanded uncertainty was calculated as 3.45 with the combined standard uncertainty, 1.584 6 and the k value, 2.18. The final expression can be ($62.69{\pm}3.45$) ng/g (confidence level 95%, k = 2.18). The uncertainty value might be estimated differently depending on the selection of the uncertainty components. It is difficult to estimate all the uncertainty factors. Therefore, it is better to take several big effecting components instead of many small effecting components.

• Journal of Korea Trade
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• v.27 no.4
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• pp.77-100
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• 2023

Purpose - Using the Model of Goal-Directed Behavior (MGB), this study identifies the critical factors that influence consumer intention to purchase an electric vehicle (EV). This study also provides differentiated policy implications to the Korean and Chinese governments and EV-related companies for the expansion of the EV market in both countries by comparing consumers' perceptions of EV purchase intentions. Design/methodology - Our extended MGB model adds to the standard model consideration of financial incentives, perceived risks, and environmental concerns. An online survey was conducted of Korean and Chinese consumers. Based on the collected responses, all tested hypotheses were verified using PLS-SEM (Partial Least Squares-Structural Equation Modeling). Differences in the path analysis results between Korea and China were compared and verified using Henseler's MGA (multi-group analysis), the parametric test, and the Welch-Satterthwaite test. Findings - The most critical factor that influences the intent to purchase an EV in consumers from both countries is personal desire. PBC and SN were identified as the critical factors that respectively increase personal desire in Korea and China. In addition, in Korea, among the three factors EC, FIP, and PR, environmental concerns were found to have the most significant impact on attitudes and purchase intention. In contrast, in China, economic factors (specifically financial incentives) had greater importance than environmental issues. Originality/value - This study has academic contributions in that it presents a new research model that includes financial incentive policies, environmental concerns, and perceived risk variables based on the MGB to explore consumers' purchase intentions. This study can also make a practical contribution in that it provides some meaningful implications to the governments and EV-related companies of both countries based on the differences in the analysis results of the Korean and Chinese markets.