TITLE

The Effect of Strategic Learning Systems and Organizational Structure on e-Government Performance: A Survey in the Public Sector by Means of an Artificial Neural Network

AUTHOR(S)
Zorlu, Kursad
PUB. DATE
January 2011
SOURCE
Proceedings of the International Conference on Intellectual Capi;2011, p626
SOURCE TYPE
Conference Proceeding
DOC. TYPE
Article
ABSTRACT
The present study aims to measure the e-Government performance of public institutions and organizations in Turkey and to determine the strategic learning system (SL) and organizational structure (OS) of Turkish e-Government performance. The population of the study consists of the public institutions and organizations which operate through Turkey's e-Government portal. Accordingly, a poll was conducted on the employees in the data processing and/or technical departments of these organizations. During the application, 150 questionnaire forms were distributed within or sent to 8 organizations where the required permission was obtained and 105 usable forms were returned. The data obtained from the questionnaire forms, which employed a 5 point Likert scale, were produced by Cascade Forward Back Propagation (CFBP) Artificial Neural Networks (ANNs) in the MATLAB program. Three different scales and 52 statements were included in the poll in addition to demographic variables. Within the scope of the findings obtained from the study is that the performance of the organizations operating through the e-Government portal in Turkey is not "very good", though it remains slightly above the average. Additionally, organizational structure was shown to affect strategic learning levels in a positive and powerful manner and in turn influence the e-Government performance by a few degrees. However, organizational structure did not seem to have an effect on to-and-fro data flow and e-Government performance. Though organizational structure showed a direct and positive effect on e-Government performance, it is determined that such an effect is quite behind that effect on the learning system.
ACCESSION #
69713447

 

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