Decision-making framework for identifying regions vulnerable to transmission of COVID-19 pandemic

Gupta, Renu; Rathore, Bhawana; Srivastava, Abhishek; Biswas, Baidyanath

doi:10.1016/j.cie.2022.108207

Cited by 23 publications

(13 citation statements)

References 144 publications

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“…These approaches have some drawbacks-for example, ISM and TISM only provide qualitative assessment; quantitative components are absent from these methods [45]. Additionally, Gupta et al [30] claim that DEMATEL has the ability to assess the degree of interaction between the barriers. As a result, it is preferable to employ DEMATEL to investigate the causal relationships between the finalized determinants.…”

Section: Methodsmentioning

confidence: 99%

“…In the CE, resources are ideally circulated repeatedly in the resource loops to extend the useful life of products, components, and materials [30]. The objective of the CE is to increase the resource efficiency of the system through the systematic use of repair, reuse, upcycling, remanufacturing, and cascading materials recycling, and eventually reduce the requirement for new products, components, and virgin raw material [23].…”

Section: A Brief Overview On Cementioning

confidence: 99%

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Determinants of Remanufacturing Adoption for Circular Economy: A Causal Relationship Evaluation Framework

Khan

Ali

Singh

2022

ASI

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Organizations are transforming their linear models into circular models in order to become more sustainable. Remanufacturing is an essential element of the circular model; thus, there is an urgent need to adopt remanufacturing. It can offer organizations economic and environmental advantages and facilitate the transition to a circular economy (CE). Several aspects are crucial to the use of remanufacturing methods in order to transition to the CE. Therefore, in this study, we aimed to develop a framework for investigating the causal relationship among determinants of adopting remanufacturing processes for the circular economy. Through an integrated approach comprising a literature review and the Modified Delphi Method, we identified ten remanufacturing adoption determinants. The causal relationship among these determinants was established using the DEMATEL method. Furthermore, we classified these determinants into cause and effect groups. Five determinants, “consumer preferences”, “remanufacturing adoption framework”, “market opportunities”, “management commitment”, and “preferential tax policies”, belong to the cause group, and the remaining five belong to the effect group based on the effect score. To implement remanufacturing processes and transition to a circular economy, it is necessary to pay greater attention to these identified determinants, especially those that belong to the cause group. The outcomes of this study may aid management and policy makers in formulating strategies for effectively implementing remanufacturing methods within their organizations.

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Section: Methodsmentioning

confidence: 99%

Section: A Brief Overview On Cementioning

confidence: 99%

Determinants of Remanufacturing Adoption for Circular Economy: A Causal Relationship Evaluation Framework

Khan

Ali

Singh

2022

ASI

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“…However, these techniques consume more time and have high chances of generating exploratory findings (Phellas et al, 2011). In contrast, FDM addresses the vagueness and ambiguity of experts' judgements with the help of fuzzy set theory (Gupta et al, 2022). In this manner, it addresses the situations in which humans cannot precisely conclude (Rathore, 2021;Rathore and Gupta, 2021).…”

Section: Background Work On Multi-criteria Decision-making (Mcdm) Tec...mentioning

confidence: 99%

“…, 2011). In contrast, FDM addresses the vagueness and ambiguity of experts' judgements with the help of fuzzy set theory (Gupta et al. , 2022).…”

Section: Literature Review and Theoretical Foundationmentioning

confidence: 99%

Identification and analysis of adoption barriers of disruptive technologies in the logistics industry

Rathore

Gupta²,

Biswas³

et al. 2022

IJLM

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PurposeRecently, disruptive technologies (DTs) have proposed several innovative applications in managing logistics and promise to transform the entire logistics sector drastically. Often, this transformation is not successful due to the existence of adoption barriers to DTs. This study aims to identify the significant barriers that impede the successful adoption of DTs in the logistics sector and examine the interrelationships amongst them.Design/methodology/approachInitially, 12 critical barriers were identified through an extensive literature review on disruptive logistics management, and the barriers were screened to ten relevant barriers with the help of Fuzzy Delphi Method (FDM). Further, an Interpretive Structural Modelling (ISM) approach was built with the inputs from logistics experts working in the various departments of warehouses, inventory control, transportation, freight management and customer service management. ISM approach was then used to generate and examine the interrelationships amongst the critical barriers. Matrics d’Impacts Croises-Multiplication Applique a Classement (MICMAC) analysed the barriers based on the barriers' driving and dependence power.FindingsResults from the ISM-based technique reveal that the lack of top management support (B6) was a critical barrier that can influence the adoption of DTs. Other significant barriers, such as legal and regulatory frameworks (B1), infrastructure (B3) and resistance to change (B2), were identified as the driving barriers, and industries need to pay more attention to them for the successful adoption of DTs in logistics. The MICMAC analysis shows that the legal and regulatory framework and lack of top management support have the highest driving powers. In contrast, lack of trust, reliability and privacy/security emerge as barriers with high dependence powers.Research limitations/implicationsThe authors' study has several implications in the light of DT substitution. First, this study successfully analyses the seven DTs using Adner and Kapoor's framework (2016a, b) and the Theory of Disruptive Innovation (Christensen, 1997; Christensen et al., 2011) based on the two parameters as follows: emergence challenge of new technology and extension opportunity of old technology. Second, this study categorises these seven DTs into four quadrants from the framework. Third, this study proposes the recommended paths that DTs might want to follow to be adopted quickly.Practical implications The authors' study has several managerial implications in light of the adoption of DTs. First, the authors' study identified no autonomous barriers to adopting DTs. Second, other barriers belonging to any lower level of the ISM model can influence the dependent barriers. Third, the linkage barriers are unstable, and any preventive action involving linkage barriers would subsequently affect linkage barriers and other barriers. Fourth, the independent barriers have high influencing powers over other barriers.Originality/valueThe contributions of this study are four-fold. First, the study identifies the different DTs in the logistics sector. Second, the study applies the theory of disruptive innovations and the ecosystems framework to rationalise the choice of these seven DTs. Third, the study identifies and critically assesses the barriers to the successful adoption of these DTs through a strategic evaluation procedure with the help of a framework built with inputs from logistics experts. Fourth, the study recognises DTs adoption barriers in logistics management and provides a foundation for future research to eliminate those barriers.

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“…The current COVID-19 outbreak or SARS-CoV-2 which has been recorded for the first time in Wuhan City in China (Shawaqfah and Almomani 2021 ; Dubey et al 2022 ; Kulshreshtha and Sharma 2022 ) represents one of the most difficult public health concerns the world has ever seen. On January 30, 2020, the World Health Organization (WHO) declared COVID-19 a global health emergency (Sohrabi et al 2020 ; Gupta et al 2022 ; Mujwar, 2021 ; Bruce and Liang 2020 ). According to the WHO, more than 517 and a half million confirmed cases and more than 6 million fatalities occurred globally during the second week of May 2022.…”

Section: Introductionmentioning

confidence: 99%

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

Meskher

Belhaouari

Thakur

et al. 2022

Environ Sci Pollut Res

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Coronavirus disease 2019 (COVID-19) has delayed global economic growth, which has affected the economic life globally. On the one hand, numerous elements in the environment impact the transmission of this new coronavirus. Every country in the Middle East and North Africa (MENA) area has a different population density, air quality and contaminants, and water- and land-related conditions, all of which influence coronavirus transmission. The World Health Organization (WHO) has advocated fast evaluations to guide policymakers with timely evidence to respond to the situation. This review makes four unique contributions. One, many data about the transmission of the new coronavirus in various sorts of settings to provide clear answers to the current dispute over the virus’s transmission were reviewed. Two, highlight the most significant application of machine learning to forecast and diagnose severe acute respiratory syndrome coronavirus (SARS-CoV-2). Three, our insights provide timely and accurate information along with compelling suggestions and methodical directions for investigators. Four, the present study provides decision-makers and community leaders with information on the effectiveness of environmental controls for COVID-19 dissemination. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11356-022-23392-z.

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Decision-making framework for identifying regions vulnerable to transmission of COVID-19 pandemic

Cited by 23 publications

References 144 publications

Determinants of Remanufacturing Adoption for Circular Economy: A Causal Relationship Evaluation Framework

Determinants of Remanufacturing Adoption for Circular Economy: A Causal Relationship Evaluation Framework

Identification and analysis of adoption barriers of disruptive technologies in the logistics industry

A review about COVID-19 in the MENA region: environmental concerns and machine learning applications

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