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Assessing the industrial readiness for adoption of industry 4.0 in Nepal: A structural equation model analysis()

Industry 4.0 (I4.0) mainly focuses on manufacturing technology and processes that comprise a cyber-physical system (CPS), Internet of things (IoT), Industrial Internet of things (IIOT), Cognitive Computing and Artificial intelligence as innovation towards the computerization and exchange of informat...

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Autores principales: Rajbhandari, Sharad, Devkota, Niranjan, Khanal, Ghanashyam, Mahato, Surendra, Paudel, Udaya Raj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866891/
https://www.ncbi.nlm.nih.gov/pubmed/35243054
http://dx.doi.org/10.1016/j.heliyon.2022.e08919
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author Rajbhandari, Sharad
Devkota, Niranjan
Khanal, Ghanashyam
Mahato, Surendra
Paudel, Udaya Raj
author_facet Rajbhandari, Sharad
Devkota, Niranjan
Khanal, Ghanashyam
Mahato, Surendra
Paudel, Udaya Raj
author_sort Rajbhandari, Sharad
collection PubMed
description Industry 4.0 (I4.0) mainly focuses on manufacturing technology and processes that comprise a cyber-physical system (CPS), Internet of things (IoT), Industrial Internet of things (IIOT), Cognitive Computing and Artificial intelligence as innovation towards the computerization and exchange of information. Though it has been an emerging issue in the industrial and business world, very few studies has been undertaken in the Nepalese case. In this context, the study accesses the industrial readiness for the adoption of I4.0 in Kathmandu valley, Nepal. Explanatory Exploratory research design was adopted to examine if variables chosen actually affect industrial readiness and data was collected through structured questionnaires using both descriptive and inferential statistics via Structural Equation Modeling (SEM). Adopting the census method to collect data, a total of 287 industries located in three industrial estates were taken as a population. Collected data were analyzed using SPSS and AMOS. The study found that the major problem while adopting I4.0 is the lack of skilled manpower in the industrial sector. In addition, we found people, customers, and culture, strategy and leadership, governance and operations have a significant effect on technology innovation decision making. Similarly, government intervention plays a significant mediation role between dependent and independent variables. Furthermore, this study found industries in Nepal are not ready for I4.0 as they are not implementing any enabling technology that enables industry 4.0. These results will support managers/policymakers in recognizing the strategic actions that can be embraced in order to improve the company's readiness level to seek the optimum benefits from the adoption of I4.0 paradigms.
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spelling pubmed-88668912022-03-02 Assessing the industrial readiness for adoption of industry 4.0 in Nepal: A structural equation model analysis() Rajbhandari, Sharad Devkota, Niranjan Khanal, Ghanashyam Mahato, Surendra Paudel, Udaya Raj Heliyon Research Article Industry 4.0 (I4.0) mainly focuses on manufacturing technology and processes that comprise a cyber-physical system (CPS), Internet of things (IoT), Industrial Internet of things (IIOT), Cognitive Computing and Artificial intelligence as innovation towards the computerization and exchange of information. Though it has been an emerging issue in the industrial and business world, very few studies has been undertaken in the Nepalese case. In this context, the study accesses the industrial readiness for the adoption of I4.0 in Kathmandu valley, Nepal. Explanatory Exploratory research design was adopted to examine if variables chosen actually affect industrial readiness and data was collected through structured questionnaires using both descriptive and inferential statistics via Structural Equation Modeling (SEM). Adopting the census method to collect data, a total of 287 industries located in three industrial estates were taken as a population. Collected data were analyzed using SPSS and AMOS. The study found that the major problem while adopting I4.0 is the lack of skilled manpower in the industrial sector. In addition, we found people, customers, and culture, strategy and leadership, governance and operations have a significant effect on technology innovation decision making. Similarly, government intervention plays a significant mediation role between dependent and independent variables. Furthermore, this study found industries in Nepal are not ready for I4.0 as they are not implementing any enabling technology that enables industry 4.0. These results will support managers/policymakers in recognizing the strategic actions that can be embraced in order to improve the company's readiness level to seek the optimum benefits from the adoption of I4.0 paradigms. Elsevier 2022-02-21 /pmc/articles/PMC8866891/ /pubmed/35243054 http://dx.doi.org/10.1016/j.heliyon.2022.e08919 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research Article
Rajbhandari, Sharad
Devkota, Niranjan
Khanal, Ghanashyam
Mahato, Surendra
Paudel, Udaya Raj
Assessing the industrial readiness for adoption of industry 4.0 in Nepal: A structural equation model analysis()
title Assessing the industrial readiness for adoption of industry 4.0 in Nepal: A structural equation model analysis()
title_full Assessing the industrial readiness for adoption of industry 4.0 in Nepal: A structural equation model analysis()
title_fullStr Assessing the industrial readiness for adoption of industry 4.0 in Nepal: A structural equation model analysis()
title_full_unstemmed Assessing the industrial readiness for adoption of industry 4.0 in Nepal: A structural equation model analysis()
title_short Assessing the industrial readiness for adoption of industry 4.0 in Nepal: A structural equation model analysis()
title_sort assessing the industrial readiness for adoption of industry 4.0 in nepal: a structural equation model analysis()
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8866891/
https://www.ncbi.nlm.nih.gov/pubmed/35243054
http://dx.doi.org/10.1016/j.heliyon.2022.e08919
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