Absorptive capacity and relationship learning mechanisms as complementary drivers of green innovation performance

Purpose: This paper aims to explore in depth how internal and external knowledge-based drivers actually affect the firms’ green innovation performance. Subsequently, this study analyzes the relationships between absorptive capacity (internal knowledge-based driver), relationship learning (external knowledge-based driver) and green innovation performance. Design/methodology/approach: This study relies on a sample of 112 firms belonging to the Spanish automotive components manufacturing sector (ACMS) and uses partial least squares path modeling to test the hypotheses proposed. Findings: The empirical results show that both absorptive capacity and relationship learning exert a significant positive effect on the dependent variable and that relationship learning moderates the link between absorptive capacity and green innovation performance. Research limitations/implications: This paper presents some limitations with respect to the particular sector (i.e. the ACMS) and geographical context (Spain). For this reason, researchers must be thoughtful while generalizing these results to distinct scenarios. Practical implications: Managers should devote more time and resources to reinforce their absorptive capacity as an important strategic tool to generate new knowledge and hence foster green innovation performance in manufacturing industries. Social implications: The paper shows the importance of encouraging decision-makers to cultivate and rely on relationship learning mechanisms with their main stakeholders and to acquire the necessary information and knowledge that might be valuable in the maturity of green innovations. Originality/value: This study proposes that relationship learning plays a moderating role in the relationship between absorptive capacity and green innovation performance.