Abstract
This study aims to evaluated the user satisfaction of retrieval services
concerning to universities thesis and dissertations in university dissertation
unit of Baghdad Library for achieving the following objectives:
1- Evaluating the performance of this unit (thesis unit of Baghdad University
Library) regarding to users opinion.
2- Recognizing the reasons in this unit behind the case of non satisfaction of
its users and trying to find the suitable solutions.
To achieve those two objectives, the questionnaire tool was performed
and determined the user's satisfaction level by using a sample survey. 1118
graduated students were subjected to this experiment. The following main
results were appeared:
§ The satisfaction level of the service was 77% for Ph.D. students, and 82%
for M.Sc students, those two percentage rates were considered are good
for this service.
§ The non satisfaction reasons for this service could be assigned into
following :
- Non conformance the language of users and the system language.
- There are many thesis and dissertations which related to users need but
did not retrieved by the system.
This study instructed the following:
v Improving the retrieval system particularly which it is related to the subject
analysis to make the system language mach with the user language.
v Data Base should be established for all of those university thesis and
dissertations and it must be accessible on the internet.
Weibull distribution is considered as one of the most widely distribution applied in real life, Its similar to normal distribution in the way of applications, it's also considered as one of the distributions that can applied in many fields such as industrial engineering to represent replaced and manufacturing time ,weather forecasting, and other scientific uses in reliability studies and survival function in medical and communication engineering fields.
In this paper, The scale parameter has been estimated for weibull distribution using Bayesian method based on Jeffery prior information as a first method , then enhanced by improving Jeffery prior information and then used as a se
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