
Additive manufacturing technology is often considered as a key technology with disruptive potential. This article researches the relationship between the use of different additive technology applications (rapid prototyping and additive manufacturing) and product innovation. More specifically, we focus on the question to what extent characteristics of the firm's prior knowledge support the adoption of additive manufacturing technologies for product innovation. Theoretically, the depth and scope of firms' prior knowledge including prior experience, readiness, resources, the nature and scale of the organization, the amount invested, and external environmental aspects, represent a strategic resource for the successful adoption of additive technology. Utilizing a large-scale quantitative dataset of 3,180 European manufacturing firms from 2015, descriptive and multivariate analyses confirm the positive relationship between firms' adoption of additive technology and product innovation. This study enriches knowledge of how firms' strategic resource endowment affects the adoption of additive technology for product innovation. Our findings underline the relevance to build and prepare the firm with prior knowledge relevant for technology adoption of additive manufacturing technology.
+ ID der Publikation: hslu_98631 + Art des Beitrages: Präsentation Konferenzpapier/Tagungsbeitrag + Sprache: Englisch + Letzte Aktualisierung: 2024-04-17 14:14:01
knowledge, digital innovation, 3D printing, additive manufacturing, digitalization, innovation
knowledge, digital innovation, 3D printing, additive manufacturing, digitalization, innovation
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
