| dc.creator |
Albeni, M |
|
| dc.creator |
Karaoz, M |
|
| dc.date |
2005-09-01T00:00:00Z |
|
| dc.date.accessioned |
2021-12-03T11:29:00Z |
|
| dc.date.available |
2021-12-03T11:29:00Z |
|
| dc.identifier |
579cd29d-c837-41d0-a8f7-681ef68d5868 |
|
| dc.identifier |
10.1016/j.techfore.2004.09.005 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/579cd29d-c837-41d0-a8f7-681ef68d5868/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/91966 |
|
| dc.description |
Efficient development of industries requires a broad range of technological capabilities which can be acquired only by a long process of learning. Continuous measuring and monitoring of the ever-changing technological learning would be useful for building technological capability and managing technological policies. Nevertheless, research on how to measure the technological learning over time at macro levels remains largely untouched. In this paper, by adding the experience curve into the multifactor productivity part of Neoclassical production function, we will develop a model which will allow one to estimate the technological learning levels over long periods. This model would allow a user to both estimate the past learning experiences and forecast its future path on a time varying basis. The model has been used and tested in the estimation of the annual technological learning values for 28 Turkish manufacturing industries from 1981 to 2000. (c) 2004 Elsevier Inc. All rights reserved. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Dynamic technological learning trends in Turkish manufacturing industries |
|
| dc.type |
info:eu-repo/semantics/article |
|