Barriers to adoption of blockchain technology in green supply chain management
- Authors: Bag, Surajit , Viktorovich, Dmitriev Aleksandr , Sahu, Atul Kumar , Sahu, Anoop Kumar
- Date: 2020
- Subjects: Barriers , Blockchain technology , Green supply chain management
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/478520 , uj:43249 , DOI: 10.1108/JGOSS-06-2020-0027 , Citation: Bag, S. et al. 2020. Barriers to adoption of blockchain technology in green supply chain management.
- Description: Abstract: Purpose – The purpose of this study is to identify the barriers to the adoption of blockchain technology in green supply chain management (GSCM) and further analyze the cause and effect relationship to prioritize the barriers for making strategic decisions. Design/methodology/approach – The study examines 15 potential barriers related to the adoption of blockchain in GSCM which is identified from the literature review and finalized after subsequent discussions with industry professionals. Integrated Fuzzy-Decision-Making Trial and Evaluation Laboratory approach is used to analyze cause and effect relationships and prioritize the barriers. Fuzzy set theory is used to handle the uncertainty and vagueness associated with the personnel biases and data deficiency problems. Three small to medium enterprises’ (SMEs’) are considered for gathering data and further analyzing the crucial barriers that are impeding the adoption of blockchain technology in GSCM. Findings – The findings reveal that “lack of management vision” and “cultural differences among supply chain partners” are the most influencing barriers, whereas; “collaboration challenges” and “hesitation and workforce obsolescence” are the most influential barriers in the adoption of blockchain in GSCM. Research limitations/implications – The study is developed based on 15 selected barriers which were further tested using data from three SMEs’ in the emerging economy of India. The adoption of blockchain technology in GSCM is at a nascent stage and more research studies are necessary to extend the knowledge base. Practical implications – Managers need to eliminate the barriers and extend the blockchain technology application in GSCM. Managers need to develop the mission and vision of the company by doing proper alignment of blockchain technology with GSCM goals. Second, managers need to make strong collaborations and remove the hesitation and workforce obsolescence barrier by providing the right education and pieces of training.
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Role of technological dimensions of green supply chain management practices on firm performance
- Authors: Bag, Surajit , Gupta, Shivam , Kumar, Sameer , Sivarajah, Uthayasankar
- Date: 2020
- Subjects: Green Supply Chain Management , Artificial Intelligence , Firm Performance
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/478501 , uj:43247 , DOI 10.1108/JEIM-10-2019-0324 , Citation: Bag, S. et al. 2020. Role of technological dimensions of green supply chain management practices on firm performance.
- Description: Abstract: Purpose: The research study aims to investigate green supply chain management (GSCM) elements as part of a complete system. It aims to understand the special properties of GSCM system under the moderating effects of product complexity and purchasing structure. Methodology: A thorough literature review led to the building of the conceptual framework. Six constructs were identified using systems theory. These constructs include green supply chain technological dimensions (particularly, Artificial Intelligence (AI) based), green supply chain strategy, green supply chain process, product complexity, purchasing structure, and firm performance. The instrument was scientifically developed for gathering survey responses using Dillman’s (2007) complete design test methods. The conceptual model was eventually tested based on survey data collected from 250 automotive component and allied manufacturers in the emerging economy of South Africa. Findings: The results indicate that GSCM technological dimensions (AI-based) positively influence GSCM strategy. Further, GSCM strategy was found to positively influence GSCM process. The GSCM processes have significant effects on environmental performance, social performance and financial performance. The product complexity has a significant moderation effect on the paths GSCM strategy and GSCM process. Originality: The findings from multivariate data analysis provide a better understanding of GSCM system dynamics and are helpful to key decision makers. This unique model has elevated GSCM theory to a new level. There are limited studies available in the existing GSCM literature using systems theory. This study will offer an advanced/comprehensive understanding to readers in this relatively new concept.
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An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance
- Authors: Bag, Surajit , Gupta, Shivam , Kumar, Ajay , Sivarajah, Uthayasankar
- Date: 2020
- Subjects: Artificial intelligence , Big data , Knowledge management
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/478511 , uj:43248 , DOI: https://doi.org/10.1016/j.indmarman.2020.12.001 , Citation: Bag, S. et al. 2020. An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance.
- Description: Abstract: This study examines the effect of big data powered artificial intelligence on customer knowledge creation, user knowledge creation and external market knowledge creation to better understand its impact on B2B marketing rational decision making to influence firm performance. The theoretical model is grounded in Knowledge Management Theory (KMT) and the primary data was collected from B2B companies functioning in the South African mining industry. Findings point out that big data powered artificial intelligence and the path customer knowledge creation is significant. Secondly, big data powered artificial intelligence and the path user knowledge creation is significant. Thirdly, big data powered artificial intelligence and the path external market knowledge creation is significant. It was observed that customer knowledge creation, user knowledge creation and external market knowledge creation have significant effect on the B2B marketing-rational decision making. Finally, the path B2B marketing rational decision making has a significant effect on firm performance.
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Big data analytics powered artificial intelligence to enhance sustainable manufacturing and circular economic capabilities
- Authors: Bag, Surajit
- Date: 2020
- Language: English
- Type: Doctoral (Thesis)
- Identifier: http://hdl.handle.net/10210/479419 , uj:43364
- Description: Abstract: he global manufacturing environment is undergoing a major shift with the advent of Industry 4.0 (I4.0) technologies. Worldwide different countries are taking I4.0 initiatives. The Government of Germany and the European Union have designed I4.0 programs to accomplish digital goals. The Italian Government has implemented the I4.0 National digital plan to increase the capital investment on I4.0 technologies and research for providing a competitive edge to the manufacturing firms. Similarly, Hungary has launched I4.0 National technology platform to embrace I4.0. The Austrian Government has adopted a National level “Plattform Industrie 4.0” that works as an advice-giving body for I4.0 promotion. In the United States of America (USA) the digital programme is named “Smart manufacturing leadership coalition”. The Government of India has undertaken a “Made in India” initiative and digital strategy with special focus on the manufacturing sector. Furthermore, a National policy framework including I4.0 is under formulation to advance the growth of modern manufacturing using advanced materials... , Ph.D. (Engineering Management)
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An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance
- Authors: Bag, Surajit , Gupta, Shivam , Kumar, Ajay , Sivarajah, Uthayasankar
- Date: 2020
- Subjects: Artificial Intelligence , Big Data , Knowledge Management
- Type: Article
- Identifier: http://hdl.handle.net/10210/483793 , uj:43919 , Citation: Bag, S. et al. 2021. An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance.
- Description: Abstract: This study examines the effect of big data powered artificial intelligence on customer knowledge creation, user knowledge creation and external market knowledge creation to better understand its impact on B2B marketing rational decision making to influence firm performance. The theoretical model is grounded in Knowledge Management Theory (KMT) and the primary data was collected from B2B companies functioning in the South African mining industry. Findings point out that big data powered artificial intelligence and the path customer knowledge creation is significant. Secondly, big data powered artificial intelligence and the path user knowledge creation is significant. Thirdly, big data powered artificial intelligence and the path external knowledge creation is significant. It was observed that customer knowledge creation, user knowledge creation and external knowledge creation have significant effect on the B2B marketing-rational decision making. Finally, the path B2B marketing rational decision making has a significant effect on firm performance.
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Role of technological dimensions of green supply chain management practices on firm performance
- Authors: Bag, Surajit , Gupta, Shivam , Kumar, Sameer , Sivarajah, Uthayasankar
- Date: 2021
- Subjects: Green Supply Chain Management , Artificial Intelligence , Firm Performance
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/483810 , uj:43921 , Citation: Bag, S., Gupta, S., Kumar, S. and Sivarajah, U. (2020), "Role of technological dimensions of green supply chain management practices on firm performance", Journal of Enterprise Information Management, Vol. 34 No. 1, pp. 1-27. https://doi.org/10.1108/JEIM-10-2019-0324
- Description: Abstract: Purpose: The research study aims to investigate green supply chain management (GSCM) elements as part of a complete system. It aims to understand the special properties of GSCM system under the moderating effects of product complexity and purchasing structure. Methodology: A thorough literature review led to the building of the conceptual framework. Six constructs were identified using systems theory. These constructs include green supply chain technological dimensions (particularly, Artificial Intelligence (AI) based), green supply chain strategy, green supply chain process, product complexity, purchasing structure, and firm performance. The instrument was scientifically developed for gathering survey responses using Dillman’s (2007) complete design test methods. The conceptual model was eventually tested based on survey data collected from 250 automotive component and allied manufacturers in the emerging economy of South Africa. Findings: The results indicate that GSCM technological dimensions (AI-based) positively influence GSCM strategy. Further, GSCM strategy was found to positively influence GSCM process. The GSCM processes have significant effects on environmental performance, social performance and financial performance. The product complexity has a significant moderation effect on the paths GSCM strategy and GSCM process. Originality: The findings from multivariate data analysis provide a better understanding of GSCM system dynamics and are helpful to key decision makers. This unique model has elevated GSCM theory to a new level. There are limited studies available in the existing GSCM literature using systems theory. This study will offer an advanced/comprehensive understanding to readers in this relatively new concept. Keywords:
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Key success factors for supply chain sustainability in COVID-19 pandemic: an ISM approach
- Authors: Bag, Surajit , Kilbourn, Peter , Pisa, Noleen , Giannakis, Mihalis
- Date: 2021
- Subjects: COVID-19 , Pandemic, critical success factors , Critical success factors
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/483826 , uj:43923 , Citation: Bag, S., Kilbourn, P., Pisa, N. & Giannakis, M. 2021. Key success factors for supply chain sustainability in COVID-19 pandemic: an ISM approach.
- Description: Abstract: The COVID-19 pandemic has resulted in major disruptions to busi-nesses, supply chains and economies alike. The negative effects of the pandemic are yet to be fully realised. In this study, we aimed to reflect on and explore strat-egies for supply chain sustainability in the face of business downturn caused by the COVID-19 pandemic. The focus of this study is the heavy engineering indus-try in South Africa as it relies on a global supply chain network. The paper begins with a brief introduction of negative effects of COVID-19 on supply chains fol-lowed by the research questions that drives this study. We used a literature review to select the critical success factors which were further refined using experts’ opinion. These factors subsequently, were used as input to an interpretive struc-tural modeling (ISM) technique. The ISM model yielded some interesting find-ings that can aid organizations in building resilient supply chains that are sustain-able in nature. We conclude that organizations need to develop a culture of col-laboration; since greater collaboration among value chain members is required to create a more resilient supply chain.
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Role of technological dimensions of green supply chain management practices on firm performance
- Authors: Bag, Surajit , Gupta, Shivam , Sivarajah, Uthayasankar , Kumar, Sameer
- Date: 2020
- Subjects: Green Supply Chain Management , Artificial Intelligence , Firm Performance
- Language: English
- Type: Article
- Identifier: http://hdl.handle.net/10210/481443 , uj:43624 , Citation: Bag, S., Gupta, S., Kumar, S. and Sivarajah, U. (2020), "Role of technological dimensions of green supply chain management practices on firm performance", Journal of Enterprise Information Management, Vol. 34 No. 1, pp. 1-27. https://doi.org/10.1108/JEIM-10-2019-0324 Publisher: Emerald Publishing Limited Copyright © 2020, Emerald Publishing Limited
- Description: Abstract: Purpose: The research study aims to investigate green supply chain management (GSCM) elements as part of a complete system. It aims to understand the special properties of GSCM system under the moderating effects of product complexity and purchasing structure. Methodology: A thorough literature review led to the building of the conceptual framework. Six constructs were identified using systems theory. These constructs include green supply chain technological dimensions (particularly, Artificial Intelligence (AI) based), green supply chain strategy, green supply chain process, product complexity, purchasing structure, and firm performance. The instrument was scientifically developed for gathering survey responses using Dillman’s (2007) complete design test methods. The conceptual model was eventually tested based on survey data collected from 250 automotive component and allied manufacturers in the emerging economy of South Africa. Findings: The results indicate that GSCM technological dimensions (AI-based) positively influence GSCM strategy. Further, GSCM strategy was found to positively influence GSCM process. The GSCM processes have significant effects on environmental performance, social performance and financial performance. The product complexity has a significant moderation effect on the paths GSCM strategy and GSCM process. Originality: The findings from multivariate data analysis provide a better understanding of GSCM system dynamics and are helpful to key decision makers. This unique model has elevated GSCM theory to a new level. There are limited studies available in the existing GSCM literature using systems theory. This study will offer an advanced/comprehensive understanding to readers in this relatively new concept.
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