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Int J Environ Res Public Health. 2019 Jul 26;16(15). pii: E2678. doi: 10.3390/ijerph16152678.

How Do Anticipated and Self Regulations and Information Sourcing Openness Drive Firms to Implement Eco-Innovation? Evidence from Korean Manufacturing Firms.

Author information

1
Department of International Commerce, Finance, and Investment, Kyung Hee University, Seoul 02447, Korea.
2
Narendra Paul Loomba Department of Management, Zicklin School of Business, Baruch College, City University of New York, New York, NY 10010, USA.
3
Department of International Business and Trade, Kyung Hee University, Seoul 02447, Korea. rusiahys@khu.ac.kr.

Abstract

Building upon institutional theory and the concept of openness to external sources in terms of breadth and depth, this study investigates the following three understudied drivers of eco-innovation in terms of external and internal factors: Anticipated regulation and self-regulation as external drivers, and information sourcing openness comprised of breadth and importance as internal drivers. Toward this end, this study employs a sample of 1824 Korean manufacturing firms collected from the Korean Innovation Survey 2010, which is compatible with the Oslo Manual and the Eurostat Community Innovation Survey (CIS). The current research adopts a multivariate probit model for the nine binary outcome variables and a zero-inflated negative binomial (ZINB) regression model for a count variable. It is found that, both anticipated regulation and self-regulation positively affect eco-process innovation and eco-product innovation across all of the nine eco-innovation types. The empirical findings on the effects of the breadth of external sources and the importance of used information acquired from external sources for innovative activities indicate that both the breadth and the importance have positive impacts on the number of types of eco-innovation with which a firm is engaged.

KEYWORDS:

anticipated regulation; eco-innovation; information sourcing openness; institutional theory; multivariate probit model; self-regulation; zero inflated negative binomial model

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