Evaluation of the smart city: Applying the dematel technique

PurposeAmid the outbreak of coronavirus disease 2019 (COVID-19), supply chains have faltered. This has influenced operational and financial performance and lead to uncertainty in supply and distribution. Therefore, systems measuring supply chain risk and disruption management performance have gained interest. This study explores barriers to supply chain performance measurement during disruptions such as COVID-19.Design/methodology/approachBased on a survey and literature review, the authors formalise the barriers and rank them using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) fuzzy Višekriterijumsko kompromisno rangiranje (VIKOR) methodology. A total of 14 experts in 11 countries were surveyed. Performance measurement is based on a balanced scorecard.FindingsFifteen barriers are identified. Based on DEMATEL analysis, the cause–effect relationships among the four scorecard aspects are explored. The customer axis is revealed as the cause, while the financial, internal business and learning/development are identified as the effect of the supply chain performance measurement. Fuzzy VIKOR calculations show that uncertainty of investment, disrupted cash flows and the bullwhip effect are the most critical barriers to measure supply chain performance during outbreaks such as the COVID-19 pandemic.Research limitations/implicationsThe study identifies and ranks general barriers; additional research is required to differentiate barriers in specific industrial sectors.Practical implicationsThe findings may help develop proactive, resilient supply chain performance strategies to overcome disruptions.Social implicationsPolicy-makers and decision-makers in industrial and service firms can explore these findings to inform strategies for robust supply chains that can resist disruption in risky environments.Originality/valueThis research addresses a knowledge gap in barriers to measure supply chain performance in post-pandemic areas. It is unclear how far firms will measure supply chain performance in terms of learning from disruption patterns, managing financial and customer demand processes in light of COVID-19. This study contributes by explaining the influences among the barriers and exploring them, offering insights from multiple stakeholders.

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“…, 2021). It creates a structural model to analyse the interaction amongst various factors (Koca et al. , 2021).…”

Section: Methodsmentioning

confidence: 99%

“…Author's own creation/work 3.1 DEMATEL DEMATEL was developed by Fontela and Gabus between 1972 and 1976 at the Battelle Memorial Institute (Braga et al, 2021). It creates a structural model to analyse the interaction amongst various factors (Koca et al, 2021). The computations are made through the following steps (Zhao et al, 2021):…”

Section: Source(s)mentioning

confidence: 99%

Barriers to supply chain performance measurement during disruptions such as the COVID-19 pandemic

Fares

Lloret

2023

IJQRM

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“…The influential network-relation map (INRM) produced by this method is helpful for decision-makers to understand the mutual influence relationship between the criteria and further identify key factors ( 11 , 30 ). Therefore, this method has been widely used in various fields, such as food insecurity ( 31 ), leanness assessment ( 32 ), tourism ( 33 ), and smart cities ( 34 ). For a detailed explanation of this method, refer to previous studies ( 30 , 35 , 36 ).…”

Section: Methodsmentioning

confidence: 99%

Exploring the Key Factors of Shared Decision-Making Through an Influential Network Relation Map: The Orthopedic Nurse's Perspective

et al. 2022

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BackgroundFew studies have used quantitative methods to explore the key factors affecting shared decision-making (SDM) in nursing decision-making from the perspective of orthopedic nurses.PurposeTo understand the intercorrelations among shared decision-making questionnaire–nurse (SDM-Q-NUR) factors and identify key factors for clinical nursing care decisions in orthopedics.MethodsIn May 2021, this study investigated the interdependence of the SDM-Q-NUR scale and developed an influential network-relation map (INRM) from the clinical experience of 13 trained orthopedic nurses using the Decision-making Trial and Evaluation Laboratory method.ResultsThe INRM results showed that the nine criteria corresponded to three stages: preparation, discussion, and decision. “I helped my patient or patient's family understand all the information” (C5) and “I wanted to know from my patient or patient's family how they want to be involved in making the nursing care decision” (C2) are the main key factors for the beginning of nursing decision. In the discussion and decision stages, the corresponding key factors are “I made it clear to my patient or patient's family that a nursing care decision needs to be made” (C1) and “I asked my patient or patient's family which nursing care option they prefer” (C6). The result's statistical significance confidence and gap error were 98.106% and 1.894%, respectively.ConclusionsWhen making nursing decisions with patients, orthopedic nurses need to have detailed information about how patients are involved in SDM and all relevant information. Nurses should also inform patients and their families regarding the purpose of the discussion, namely, to help one understand the content, advantages, and disadvantages of the nursing care options, and finally, make a decision.

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“…In the studies using MCDA, the widely employed methods include the Best-Worst Method (BWM) [43][44][45], Analytic Hierarchy Process (AHP) [46][47][48], Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)-I and II [49][50][51][52], VlseKriterijumska Optimizacija I KOmpromisno Resenje (VIKOR) [53][54][55], Elimination and Choice Translating Reality (ELECTRE) [56,57], Decision making trial and evaluation laboratory (DEMATEL) [58][59][60], and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [61][62][63]. Further, the studies that deal with qualitative attributes utilize fuzzified MCDA approaches such as Fuzzy AHP [64,65], Fuzzy TOPSIS [66,67], and many others.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessment of Clean Energy Transition Potential in Major Power-Producing States of India Using Multi-Criteria Decision Analysis

Indrajayanthan¹,

Mohanty²

2022

Sustainability

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India has an ambitious target to promote clean energy penetration, but as of 2021, the electricity mix of India is dominated by coal to about 71%. Therefore, analyzing the clean energy potential and the ability of the individual states to entrench energy transition in the upcoming years will be supportive for policymakers. This study is propounded to assess the clean energy transition potential with a focused analysis on seven major power-producing states of India. These states include Maharashtra, Gujarat, Tamil Nadu, Uttar Pradesh, Karnataka, Madhya Pradesh, and Andhra Pradesh. The clean energy transition potential assessment is performed by utilizing multi-criteria decision analysis methodologies such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Optimization Method by Ratio Analysis (MOORA). Further, the analysis is performed against four major criteria that include high carbon energy resource dependency, low carbon energy resource dependency, clean energy potential, and policy support. Altogether, the assessment criteria include four primary level criteria and fourteen secondary level parameters. In order to reflect the significance of each parameter and criterion to the characteristics of clean energy transition potential, appropriate weightage is provided using the Fuzzy Analytic Hierarchy Process (AHP). The results indicate that Gujarat has the highest clean energy transition potential in both the multi-criteria decision analysis methods. On the other hand, Uttar Pradesh exhibited the least performance, and a complete energy transition to clean energy resources is less likely in this state. The rest of the states obtained intermediate ranking, and a comparative analysis between the two methods was also accomplished. This study suggests that India should focus on the clean energy policy with vigorous efforts on top-performing states which will effectively accelerate the power sector decarbonization.

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Evaluation of the smart city: Applying the dematel technique

Cited by 43 publications

References 24 publications

Barriers to supply chain performance measurement during disruptions such as the COVID-19 pandemic

Barriers to supply chain performance measurement during disruptions such as the COVID-19 pandemic

Exploring the Key Factors of Shared Decision-Making Through an Influential Network Relation Map: The Orthopedic Nurse's Perspective

Assessment of Clean Energy Transition Potential in Major Power-Producing States of India Using Multi-Criteria Decision Analysis

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