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Predicting the Effect of Environment, Social and Governance Practices on Green Innovation: An Artificial Neural Network Approach

Few studies have been conducted to investigate whether the Environment, Social and Governance (ESG) practices could influence green innovation in small and medium enterprises (SMEs). Therefore, the purpose of this study is to predict the effect of Environment, Social, and Governance (ESG) practices...

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Main Authors: Mukhtar, B., Shad, M.K., Woon, L.F.
Format: Article
Published: 2023
Online Access:http://scholars.utp.edu.my/id/eprint/34156/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142698757&doi=10.1007%2f978-3-031-16865-9_42&partnerID=40&md5=cd687eaab00285c1e140fd95452fa756
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spelling oai:scholars.utp.edu.my:341562023-01-04T02:46:19Z http://scholars.utp.edu.my/id/eprint/34156/ Predicting the Effect of Environment, Social and Governance Practices on Green Innovation: An Artificial Neural Network Approach Mukhtar, B. Shad, M.K. Woon, L.F. Few studies have been conducted to investigate whether the Environment, Social and Governance (ESG) practices could influence green innovation in small and medium enterprises (SMEs). Therefore, the purpose of this study is to predict the effect of Environment, Social, and Governance (ESG) practices on green innovation in SMEs. In this study, green innovation is segmented into two dimensions which are sustainable product innovation and sustainable process innovation. The data was collected through a questionnaire from medium-level IT firms and was analyzed using the Artificial Neural Network (ANN) approach. The findings indicated the different impactful factors of ESG practices to enhance green innovation. The results indicate that social and political contribution is the most impactful factor to enhance sustainable product innovation followed by pollution & waste and emission reduction. In addition, the findings of this study shows that pollution & waste is the most impactful factor to enhance sustainable process innovation followed by anti-competitive behavior and emission reduction. This study will provide insights on ESG practices as an important consideration to enhance green innovation among business, operations especially in SMEs. The findings of this paper are useful for regulators, legislators, shareholders, creditors, and practitioners in pursuing ESG practices that will not only improve financial performance but will also enhance green innovation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. 2023 Article NonPeerReviewed Mukhtar, B. and Shad, M.K. and Woon, L.F. (2023) Predicting the Effect of Environment, Social and Governance Practices on Green Innovation: An Artificial Neural Network Approach. Lecture Notes in Networks and Systems, 550 LN. pp. 527-539. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142698757&doi=10.1007%2f978-3-031-16865-9_42&partnerID=40&md5=cd687eaab00285c1e140fd95452fa756 10.1007/978-3-031-16865-9₄₂ 10.1007/978-3-031-16865-9₄₂
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Few studies have been conducted to investigate whether the Environment, Social and Governance (ESG) practices could influence green innovation in small and medium enterprises (SMEs). Therefore, the purpose of this study is to predict the effect of Environment, Social, and Governance (ESG) practices on green innovation in SMEs. In this study, green innovation is segmented into two dimensions which are sustainable product innovation and sustainable process innovation. The data was collected through a questionnaire from medium-level IT firms and was analyzed using the Artificial Neural Network (ANN) approach. The findings indicated the different impactful factors of ESG practices to enhance green innovation. The results indicate that social and political contribution is the most impactful factor to enhance sustainable product innovation followed by pollution & waste and emission reduction. In addition, the findings of this study shows that pollution & waste is the most impactful factor to enhance sustainable process innovation followed by anti-competitive behavior and emission reduction. This study will provide insights on ESG practices as an important consideration to enhance green innovation among business, operations especially in SMEs. The findings of this paper are useful for regulators, legislators, shareholders, creditors, and practitioners in pursuing ESG practices that will not only improve financial performance but will also enhance green innovation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
format Article
author Mukhtar, B.
Shad, M.K.
Woon, L.F.
spellingShingle Mukhtar, B.
Shad, M.K.
Woon, L.F.
Predicting the Effect of Environment, Social and Governance Practices on Green Innovation: An Artificial Neural Network Approach
author_facet Mukhtar, B.
Shad, M.K.
Woon, L.F.
author_sort Mukhtar, B.
title Predicting the Effect of Environment, Social and Governance Practices on Green Innovation: An Artificial Neural Network Approach
title_short Predicting the Effect of Environment, Social and Governance Practices on Green Innovation: An Artificial Neural Network Approach
title_full Predicting the Effect of Environment, Social and Governance Practices on Green Innovation: An Artificial Neural Network Approach
title_fullStr Predicting the Effect of Environment, Social and Governance Practices on Green Innovation: An Artificial Neural Network Approach
title_full_unstemmed Predicting the Effect of Environment, Social and Governance Practices on Green Innovation: An Artificial Neural Network Approach
title_sort predicting the effect of environment, social and governance practices on green innovation: an artificial neural network approach
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/34156/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142698757&doi=10.1007%2f978-3-031-16865-9_42&partnerID=40&md5=cd687eaab00285c1e140fd95452fa756
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score 13.211869

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