Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/317669 
Erscheinungsjahr: 
2024
Quellenangabe: 
[Journal:] Journal of Business Economics and Management (JBEM) [ISSN:] 2029-4433 [Volume:] 25 [Issue:] 1 [Year:] 2024 [Pages:] 66-84
Verlag: 
Vilnius Gediminas Technical University, Vilnius
Zusammenfassung: 
In today's highly competitive business environments, manufacturers face stiff competition. As digital technologies have become more pervasive, many businesses in the manufacturing sector have begun to tap into the potential of big data analytics to gain an edge in their markets. Companies in the manufacturing sector can gain a significant competitive advantage by strategically utilizing big data analytics to uncover profound insights that have the potential to significantly enhance their capabilities in product innovation. This research delves into communication's role as a go-between for big data analytics and product innovations' success at manufacturing firms. The validity and reliability of the measurement scales were first thoroughly examined in this study. The research model was then tested using structural equation modeling and process macro analysis. The analytical findings unveil those big data analytics exert a pronounced, positive, and statistically significant impact on product innovation performance and information-sharing dynamics. Furthermore, it is discerned that information-sharing exerts a substantial and affirmative influence on the capacity for product innovation. Additionally, it is established that the impact of big data analytics on product innovation performance undergoes moderation by the information-sharing mechanism.
Schlagwörter: 
analytics-driven innovation
big data analytics
data analytics in manufacturing
information sharing
innovation performance
product innovation
JEL: 
M00
O31
D83
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe
484.01 kB





Publikationen in EconStor sind urheberrechtlich geschützt.