Information System (IS) Success in Manufacturing Companies : A Case of Kulim Hi-Tech Park
Nowadays, there are a lot of massive Information Systems (IS) investments at organizations all over the world. Improvement of evaluation and selection methods for is has been provoked due to the above mentioned trend and the given resources scarcity at the organizations. However, the failure rate a...
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| Format: | Dissertation |
| Language: | English English |
| Published: |
2003
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| Online Access: | https://etd.uum.edu.my/1078/1/NOR_MAZIZAH_BT._MOHD._HAFIR.pdf https://etd.uum.edu.my/1078/2/1.NOR_MAZIZAH_BT._MOHD._HAFIR.pdf https://etd.uum.edu.my/1078/ |
| Abstract | Abstract here |
| Summary: | Nowadays, there are a lot of massive Information Systems (IS) investments at organizations all over the world. Improvement of evaluation and selection methods for is
has been provoked due to the above mentioned trend and the given resources scarcity at the organizations. However, the failure rate at the implementation stage is still high that one could have thought. Therefore, it seems to be clear that other neglected factors should be taken into consideration in these methods, in order to have a better understanding of IS design and implementation processes.
Seddon and Kiew (1996) critically examines the meaning of four IS success constructs and the evidence of relationship between them in the Department Accounting System. The purpose of this study is to examine four IS Success in the manufacturing industries such as system quality, information quality, user satisfaction, usefulness and importance of the system. This study will also examine the meaning of four of these IS Success
factors and the evidence of relationship between them. Finally, the study provides results from empirical tests of these relationships. Tests are conducted using conventional ordinary least squares regression path analysis.
The empirical results of our study can provide support for the Seddon and Kiew’s model. The overall results are supported for seven of the hypotheses in Figure 2. The correlation coefficients obtained are highly significant that we have contidencc to say that the null hypotheses can be rejected for all hypotheses.
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