Developing green innovation performance by fostering of organizational knowledge and coopetitive relations
Gema Albort-Morant (),
Antonio Leal-Millán (),
Gabriel Cepeda-Carrion () and
Jörg Henseler ()
Additional contact information
Gema Albort-Morant: Universidad de Sevilla
Antonio Leal-Millán: Universidad de Sevilla
Gabriel Cepeda-Carrion: Universidad de Sevilla
Jörg Henseler: University of Twente
Review of Managerial Science, 2018, vol. 12, issue 2, No 6, 499-517
Abstract:
Abstract This study explores the links between knowledge base, relationship learning, and green innovation performance within a coopetitive framework. We posit that green innovation is directly influenced by a broad and deep knowledge base. We also hypothesize that the knowledge base–green innovation performance link is positively mediated by relationship learning (indirect effect). These hypotheses were empirically tested using consistent partial least squares path modeling. A sample of 112 firms from the Spanish automotive components manufacturing sector was used. The mediating effect of relationship learning on the knowledge base–green innovation performance link was observed to be positive and significant. Therefore, managers should build strong relations with stakeholders to assimilate, transfer, and adapt new knowledge and thus enhance green innovation performance.
Keywords: Knowledge base; Relationship learning; Green innovation performance; Coopetition; Partial least squares (search for similar items in EconPapers)
JEL-codes: M1 Q01 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)
Downloads: (external link)
http://link.springer.com/10.1007/s11846-017-0270-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.
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:spr:rvmgts:v:12:y:2018:i:2:d:10.1007_s11846-017-0270-z
Ordering information: This journal article can be ordered from
http://www.springer.com/business/journal/11846
DOI: 10.1007/s11846-017-0270-z
Access Statistics for this article
Review of Managerial Science is currently edited by R. Ewert and W. Kürsten
More articles in Review of Managerial Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().