Preprint Article Version 1 This version is not peer-reviewed

A Study on the Determinants of Eco-Innovation of Korean Manufacturing Firms

Version 1 : Received: 17 April 2019 / Approved: 19 April 2019 / Online: 19 April 2019 (11:25:06 CEST)

How to cite: Yu, C.; Hwang, Y.S. A Study on the Determinants of Eco-Innovation of Korean Manufacturing Firms. Preprints 2019, 2019040218 (doi: 10.20944/preprints201904.0218.v1). Yu, C.; Hwang, Y.S. A Study on the Determinants of Eco-Innovation of Korean Manufacturing Firms. Preprints 2019, 2019040218 (doi: 10.20944/preprints201904.0218.v1).

Abstract

The move to a low carbon economy is very important for enhancing international competitiveness. The eco-innovation is the critical factor of the green paradigm. This study is designed to investigate deeply the determinants of eco-innovation of manufacturing firms in Korea by suggesting anticipated regulations, self-regulations, and industry-specific characteristics as external factors and open information sources as internal factors. The data used in the analysis is 1946 sample firms from Korean Innovation Survey 2010 based on the Oslo Manual. Using the multi-variate probit analysis and the zero-inflated negative binomial (ZINB) regression analysis, we have found out that the anticipated regulations and self-regulations have significant influences both on eco-process innovation and eco-product innovation, while industrial characteristics have no effects. The empirical results also show that the breadth of information sources has a positive effect on businesses in implementing eco-innovations. Our findings show that the Korean government should provide a good platform where firms can better understand the future trends of environmental policies, particularly policies on anticipated and self-regulations. At the same time, Korean firms should establish a voluntary system to control environmental activities so that they can improve eco-innovations through integrating external information.

Subject Areas

eco-innovation; anticipated regulation; self-regulation; industry-specific characteristics; information sourcing openness; multivariate probit model; zero inflated negative binomial model

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