A Study on Organizational Performance and Performance Evaluation Based on Principal Component Analysis and Structural Equation Model
- Authors
- Park, Seongchul; Jeong, Miyoung; Kim, Juntae
- Issue Date
- Nov-2016
- Publisher
- AMER SCIENTIFIC PUBLISHERS
- Keywords
- Structural Equation Model; Principal Component Analysis; SEM; PCA; Performance Evaluation; Organizational Performance
- Citation
- ADVANCED SCIENCE LETTERS, v.22, no.11, pp 3700 - 3703
- Pages
- 4
- Indexed
- SCOPUS
- Journal Title
- ADVANCED SCIENCE LETTERS
- Volume
- 22
- Number
- 11
- Start Page
- 3700
- End Page
- 3703
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/14947
- DOI
- 10.1166/asl.2016.7954
- ISSN
- 1936-6612
1936-7317
- Abstract
- Globalization of market is gradually leading to more intensified competition among companies. As a means of countermeasure against intensified competition, companies need to raise organizational efficiency and reinforce competitiveness. For the efficiency and reinforced competitiveness of their organization, companies develop and utilize organizational performance management system. This study analyzed through principal component analysis that employees were sensitive in performance evaluation, and examined the effects of the efficiency of performance evaluation of organizational performance management system on the organizational performance through the structural equation model. The suitability of structural equation model was verified excellent through the experiment; the effective path of verified study model showed that performance evaluation significantly influenced organizational commitment and job satisfaction to enhance the work atmosphere, and had a greater effect on organizational performance compared to other paths. However, the factor that influenced organizational performance in the most significant degree was the work performance, which varies largely by individual's abilities.
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Collections - College of Advanced Convergence Engineering > Department of Computer Science and Artificial Intelligence > 1. Journal Articles

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