ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Publication Date: 2019
    Description: 〈p〉Publication date: September 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 Energy Policy, Volume 132〈/p〉 〈p〉Author(s): Ekundayo Shittu, Bruno G. Kamdem, Carmen Weigelt〈/p〉 〈div xml:lang="en"〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉While the role of organizational learning in improving firm performance is well documented, there are still questions on what drives technological learning. This is evident in the electricity industry where the growth of renewable energy technologies has been pervasive. Vicarious learning contributes to the adoption of emerging technologies through successful inter-firm knowledge sharing and transfer. However, there is hesitation to adoption that characterizes vicarious learning especially in the context of intra-firm learning. This paper investigates the differences in knowledge acquisition within and across electricity firms in the U.S. The learning curve model is applied to a longitudinal study of 5573 plants belonging to 1542 U.S. electricity firms between 1998 and 2010. This study finds: (i) The capacity growth of the solar photovoltaic technology is positively associated with intra-firm knowledge acquisition; (ii) The effect of financial incentives on the adoption of solar and wind technologies is higher under inter-firm learning; (iii) The higher the stringency of policy mandates, the more varied is the progress on technological change across technologies; (iv) Knowledge sharing between firms are higher for wind technology than for solar technology. These findings combine to show disparities in the learning trends of technologies across and within firms’ boundaries.〈/p〉〈/div〉 〈/div〉
    Print ISSN: 0301-4215
    Electronic ISSN: 1873-6777
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Political Science
    Published by Elsevier
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...