Understanding the Role of Culture in Eco-Innovation Adoption – An Empirical Cross-Country Comparison
Start Date
12-17-2013
Description
In this paper we merge research approaches from information systems, social and environmental psychology, as well as innovation diffusion to investigate the effect of cultural factors on the adoption of eco-innovations. Specifically, we conduct an empirical study based on the Decomposed Theory of Planned Behavior and the Value Belief Norm Theory to estimate how culture influences the intention to adopt electric vehicles as a surrogate for eco-innovations. In our study we find evidence that there exist major differences in adoption behavior of eco-innovations between Germans and Chinese. Furthermore we were able to show that in contrast to prior findings on innovation adoption, primary sources’ influence was the most important predictor of the intention to adopt electric vehicles.
Recommended Citation
Busse, Sebastian; El Khatib, Vujdan; Brandt, Tobias; Kranz, Johann; and Kolbe, Lutz, "Understanding the Role of Culture in Eco-Innovation Adoption – An Empirical Cross-Country Comparison" (2013). ICIS 2013 Proceedings. 6.
https://aisel.aisnet.org/icis2013/proceedings/GlobalIssues/6
Understanding the Role of Culture in Eco-Innovation Adoption – An Empirical Cross-Country Comparison
In this paper we merge research approaches from information systems, social and environmental psychology, as well as innovation diffusion to investigate the effect of cultural factors on the adoption of eco-innovations. Specifically, we conduct an empirical study based on the Decomposed Theory of Planned Behavior and the Value Belief Norm Theory to estimate how culture influences the intention to adopt electric vehicles as a surrogate for eco-innovations. In our study we find evidence that there exist major differences in adoption behavior of eco-innovations between Germans and Chinese. Furthermore we were able to show that in contrast to prior findings on innovation adoption, primary sources’ influence was the most important predictor of the intention to adopt electric vehicles.