Developing an improved reverse engineering adoption model towards the improvement of performance in metal engineering industries

Yichalewal Goshime, Daniel Kitaw, Frank Ebinger and Kassu Jilcha

African Journal of Science, Technology, Innovation and Development, 2023, vol. 15, issue 1, 89-106

Abstract: Purpose: This research aims to develop an improved model that promotes reverse engineering /RE/ practice in metal engineering industries (MEIs). RE is one cost-effective technology transfer /TT/ and innovation method that improves organizational performance. However, only a few scholars have written on the adoption of RE models, and no one has contextualized TT and innovation models as a means to adopt RE practice. Methodology: Primarily, the study conducted a systematic literature review based on previous works to develop a conceptual model for RE adoption. To do this, the researchers conducted an intensive literature review and identified factors contributing to the model. Contextualizing TT and innovation factors and models within the new RE adoption model is also a significant part of the work. After a comparative analysis, the researchers developed the improved RE adoption model that enhances the performance of MEIs. Finding: The majority of previous related literature focuses on RE hardware, with only a few authors acknowledging the soft aspects, i.e., managerial and legal issues of RE practice. Besides, no authors contextualized factors of TT and innovation within the RE adoption. In this study, the researchers identified and clustered RE adoption factors as organizational, technological, managerial, and resource-based from previous RE adoption models and contextualization of TT and innovation adoption factors. Originality: To the best of the writers’ knowledge, no previous authors have contextualized TT and innovation models within the adoption of RE. However, such models have a substantial impact on adopting the practice. Hence, the researchers developed an improved model by examining and contextualizing the existing models that can impact MEI performance through improving product, process, and technological capabilities.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/20421338.2022.2037178 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:rajsxx:v:15:y:2023:i:1:p:89-106

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rajs20

DOI: 10.1080/20421338.2022.2037178

Access Statistics for this article

African Journal of Science, Technology, Innovation and Development is currently edited by None

More articles in African Journal of Science, Technology, Innovation and Development from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().