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Purpose - The recent pandemic caused by coronavirus disease 2019 (COVID-19) has significantly impacted the operational performances of pharmaceutical supply chains (SCs), especially in emerging economies that are critically vulnerable due to their inadequate resources. Finding the possible barriers that continue to impede the sustainable performance of SCs in the post-COVID-19 era has become essential. This study aims to investigate and analyze the barriers to achieving sustainability in the pharmaceutical SC of an emerging economy in a bid to help decision-makers recognize the most influential barriers. Design/methodology/approach - To achieve the goals, two decision-making tools are integrated to analyze the most critical barriers: interpretive structural modeling (ISM) and the matrix of cross-impact multiplications applied to classification (MICMAC). In contrast to other multi-criteria decision-making (MCDM) approaches, ISM develops a hierarchical decision tool for decision-makers and cluster analysis of the barriers using the MICMAC method based on their driving and dependency powers. Findings - The findings reveal that the major barriers are in a four-level hierarchical relationship where "Insufficient SC strategic plans to ensure agility during crisis" acts as the most critical barrier, followed by "Poor information structure among SC contributors," and "Inadequate risk management policy under pandemic." Finally, the MICMAC analysis validates the findings from the ISM approach. Originality/value - This study provides meaningful insights into barriers to achieving sustainability in pharmaceutical SCs in the post-COVID-19 era. The study can help pharmaceutical SC practitioners to better understand what can go wrong in post-COVID-19, and develop actionable strategies to ensure sustainability and resilience in practitioners' SCs.
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The recent unprecedented situations like the COVID-19 pandemic and the Russia-Ukraine war have severely impacted food security and grain production in emerging economies. These countries can try to import grains to enhance secure food security, but this will strain their dollar reserve and endanger their financial stability. Under such circumstances, the adoption of sustainable grain storage practices is essential to reducing the unusual gap between grain production and grain availability. This research, therefore, explores the key factors that may affect the stability of stored grains to promote agricultural sustainability and food security in emerging economies. First, the study identifies the significant factors that influence the stability of stored grains from an emerging economy perspective. Then, the study employs an integrated approach consisting of Pareto analysis, fuzzy-based Total Interpretive Structural Modeling (TISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. Based on the literature review and expert feedback, nineteen factors were initially identified. After employing Pareto analysis, the top thirteen factors have been further analyzed using fuzzy TISM- fuzzy MICMAC to examine their interrelationships. The study findings indicate that "Proper training on advanced storage operations” is the most significant factor influencing sustainable grain storage operations. The study insights can help practitioners to focus more on the crucial aspects of the grain storage operation and can assist the policymakers and industry leaders of emerging economies in strategic decision-making to achieve agricultural sustainability and thus improve food security.
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In a closed-loop supply chain (CLSC), acquiring end-of-life vehicles (ELVs) and their components from both primary and secondary markets has posed a huge uncertainty and risk. Moreover, the constant supply of ELV components with minimization of cost and exploitation of natural resources is another pressing challenge. To address the issues, the present study has developed a risk simulation framework to study market uncertainty/risk in a CLSC. In the first phase of the framework, a total of 12 important variables are identified from the existing studies. The total interpretive structural model (TISM) is used to develop a causal relationship network among the variables. Then, Matriced Impacts Cruoses Multiplication Applique a un Classement is used for determining the nature of relationships (i.e., driving or dependence power). In the second phase, the relationship of TISM is used to derive a Bayesian belief network model for determining the level of risks (i.e., high, medium, and low) associated with the CLSC through the generation of conditional probabilities across 1) multi-, 2) single-, and 3) without-parent nodes. The study findings will help decision-makers in adopting strategic and operational interventions to increase the effectiveness and resiliency of the network. Furthermore, it will help practitioners to make decisions on change management implementation for stakeholders'performance audits on the attributes of the ELV recovery program and developing resilience in the CLSC network. Overall, the present study holistically contributes to a broader investigation of the implications of strategic decisions in automobile manufacturers and resellers. IEEE
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The COVID-19 global pandemic, over the last year and a half, has managed to create massive disruptions in global supply chains and exposed their vulnerabilities, thereby reemphasizing the importance of resiliency. The current study aims to identify and prioritize, through the quantitative decision-making technique of Interpretive Structural Modelling (ISM), a set of barriers to resiliency for the pharmaceutical supply chain in India. The rationale behind choosing the Indian pharmaceutical supply chain was that the pharmaceutical sector in India supplies over half of the global demand for vaccines and generic drugs, and the trajectory of growth is indicated around US$100 billion by the year 2025, along with exporting pharmaceutical products to nearly 200 destination countries. The findings of the current study are expected to aid the decision-makers in evaluating the relative criticality and the interrelationship between the potential (and critical) barriers to supply chain resiliency, and in turn to develop strategic plans. This, in turn, can help to combat unforeseen supply chain disruptors such as COVID-19. This methodology and the findings of the study can be generalized for other supply chains. © 2023 K.J. Somaiya Institute of Management Studies and Research.
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Interpretive structural modeling (ISM) is widely used to understand the complex connections between different components. This study presents a bibliometric overview of ISM research, with a focus on its linkages to the Sustainable Development Goals (SDGs) and the impact of COVID-19. The study analyzed 1988 publications on ISM published between 2012 and 2021, of which 1202 were directly mapped to the SDGs and 59 were related to COVID-19. The study identified key authors, institutions, countries, and journals involved in the research and their linkages to the SDGs. The results showed that ISM research is strongly linked to SDG 12 (on responsible consumption and production) and SDG 9 (on industry, innovation, and infrastructure). We also identified influential SDGs on the basis of centrality measures such as betweenness and eigenvector. The top four countries contributing to ISM publications were India, China, the United Kingdom, and the United States. The most frequently cited journals were Benchmarking: An International Journal, Sustainability, the Journal of Modelling in Management, and the Journal of Cleaner Production. Four main clusters were identified in the ISM research, including (1) integration with AHP and fuzzy logic for promoting sustainability alignment, (2) ISM-based strategy development for various stakeholders, (3) ISM-based decision-making in various fields, and (4) ISM-based risk evaluation. For the first time, studies that used the ISM approach to understand the epidemiological characteristics of COVID-19 were identified, and their key findings were discussed. The study also identified several emerging topics for future ISM research, such as blockchain and IoT, environmental management systems, climate change adaptation, smart cities, and humanitarian logistics and their potential linkages to the SDGs.
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The way an organization operates has a pattern to it. A knowledge-based way of understanding these patterns and implementing according to them retains the competitive advantage of the organizations. Thus, identifying factors is important because, if successful, it results in shared intellectual capital. Changing the core of the pattern upon which the organization works creates several problems in retaining an organization's competitiveness. This research focuses on identifying the elements which have a significant influence on an organization's operations due to the remote working of employees during situations like the COVID-19 pandemic. Further, the relationships of factors among each other have been explored from the available research. Based on the study of various organizations it has been found that not much work has been done to identify such factors even though several organizations have suddenly opted for their workforce to work remotely due to the COVID-19 pandemic. This has resulted in lost productivity and opportunities, organizational dis-balances, and a slower rate of development. The generated model may help organizations to understand the weak notes of remote working and implement structural changes accordingly to improve the productivity in remote working and tackle the productivity and opportunity loss due to remote workforce. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The COVID-19 pandemic and trade frictions impact the continuity of supply chain (SC) operations. In the volatile environment, big data analytics (BDA), a key technology for storing data and predictive analytics, has become an important tool for mitigating SC vulnerability. Based on the literature review, this paper identifies six influencing factors and four vulnerability drivers for mitigating vulnerability, and employs Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to a Classification (MICMAC) to explore the influence pathways that BDA mitigates SC vulnerability. The findings show that BDA can influence knowledge acquisition and strategy formulation by improving the forecasting capability of enterprises, which facilitates strategy implementation and ultimately mitigates vulnerability. Furthermore, with the support of BDA, resource redundancy addresses vulnerability from supply-side, higher production level and efficiency reduce vulnerability from demand-side, and rational SC design alleviates vulnerability from operation-side. © 2022 IEEE.
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In the past two years, coronavirus pandemic has severely impacted global industries and altered market dynamics. The present study compares the challenges facing Indian textile and apparel industry before and after the coronavirus pandemic. The context of our study focuses on handloom industry, as the primary financial risk for handloom micro entrepreneurs lies in capital requirements for raw materials, equipment and their lack of formal management structures to tackle the pressure of uncertainty. Thus, studying and mitigating internal and external barriers of the traditional manufacturing micro entrepreneurs during and post pandemic remains crucial to frame policy decisions for sustainability of this vulnerable sector. We have employed a two-phase (before and after the onset of pandemic) successive exploratory mixed method, starting with the Delphi technique (qualitative phase) and concluding with multi-criteria decision-making. In Phase 2 analysis, seventeen key critical barriers identified in Phase 1reduced to twelve. Phase 1 modelling suggests that lack of effective government policies, demonetization, and tax policy implementation are the most significant barriers. Further, Phase 2 identifies the absence of effective government policies as the most significant obstacle to the performance of Indian handloom industry, especially after the pandemic. Additionally, lack of branding was found to be most critically linked between independent and dependent barriers.
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The way an organization operates has a pattern to it. A knowledge-based way of understanding these patterns and implementing according to them retains the competitive advantage of the organizations. Thus, identifying factors is important because, if successful, it results in shared intellectual capital. Changing the core of the pattern upon which the organization works creates several problems in retaining an organization's competitiveness. This research focuses on identifying the elements which have a significant influence on an organization's operations due to the remote working of employees during situations like the COVID-19 pandemic. Further, the relationships of factors among each other have been explored from the available research. Based on the study of various organizations it has been found that not much work has been done to identify such factors even though several organizations have suddenly opted for their workforce to work remotely due to the COVID-19 pandemic. This has resulted in lost productivity and opportunities, organizational dis-balances, and a slower rate of development. The generated model may help organizations to understand the weak notes of remote working and implement structural changes accordingly to improve the productivity in remote working and tackle the productivity and opportunity loss due to remote workforce. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Online shopping has gained much popularity over the past decade. Indeed, in a post-COVID world, online shopping is the only medium of shopping for many. A great deal of research effort has been devoted to understanding the factors that positively or negatively influence online shopping behavior of consumers. However, most of these influence relationships have been studied individually, and not how such factors interrelate with each other and thus the underlying complex driving and dependence relationships among those factors are unknown. Moreover, these underlying driving and dependence relationships among online shopping behavior factors can be highly dependent on the cultural context of the consumers. In this research we identify the key factors that have been shown to have influence on online shopping behavior from a rigorous review of literature. We then apply an Interpretive Structural Modelling (ISM) technique to find the underlying complex hierarchical relations of factors related to Australian and Chinese culture. We apply MICMAC analysis to find the driving and dependence power of these factors in context of these two cultures. We finally explain the differences and similarities found for Australian and Chinese culture with reference to Hofstede’s Cross Culture theory. Prominent findings include timeliness of delivery and order accuracy is considered having high dependence and driving power in the Australian context but has low driving and dependence power in Chinese context. Our findings will be beneficial for including better cultural context factors into future online shopping platform design. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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The pandemic outbreak has dramatically changed every sector and walk of life. Specifically, the developing countries with scarce resources are facing unprecedented crises that further jeopardize efforts to achieve sustainable life. Considering the case of a developing country, Pakistan, this study empirically identifies the most important strategies to reduce the socio-economic and health challenges during COVID-19. Initially, the study identified 14 key strategies from the prior literature. Later, these strategies were determined with the help of the interpretive structural modeling (ISM) approach through expert suggestions. The ISM model represents seven levels of pandemic containment strategies based on their significance level. The strategies existing at the top level of ISM model are the least important, while the strategies at the bottom of hierarchy levels are highly significant. Therefore, the study results demonstrated that "strong leadership and control" and "awareness on social media" play significant roles in reducing pandemic challenges, while "promoting online purchase behavior" and "online education" are the least important strategies in tackling pandemic crisis. This study will benefit government authorities and policymakers, enabling them to focus more on significant measures in battling this ongoing crisis.
Subject(s)
COVID-19 , Social Media , Humans , COVID-19/epidemiology , Pandemics/prevention & control , SARS-CoV-2 , Socioeconomic FactorsABSTRACT
With the amount of medical waste rapidly increasing since the corona virus disease 2019 (COVID-19) pandemic, medical waste treatment risk evaluation has become an important task. The transportation of medical waste is an essential process of medical waste treatment. This paper aims to develop an integrated model to evaluate COVID-19 medical waste transportation risk by integrating an extended type-2 fuzzy total interpretive structural model (TISM) with a Bayesian network (BN). First, an interval type-2 fuzzy based transportation risk rating scale is introduced to help experts express uncertain evaluation information, in which a new double alpha-cut method is developed for the defuzzification of the interval type-2 fuzzy numbers (IT2FNs). Second, TISM is combined with IT2FNs to construct a hierarchical structural model of COVID-19 medical waste transportation risk factors under a high uncertain environment; a new bidirectional extraction method is proposed to describe the hierarchy of risk factors more reasonably and accurately. Third, the BN is integrated with IT2FNs to make a comprehensive medical waste transportation risk evaluation, including identifying the sensitive factors and diagnosing the event's causation. Then, a case study of COVID-19 medical waste transportation is displayed to demonstrate the effectiveness of the proposed model. Further, a comparison of the proposed model with the traditional TISM and BN model is conducted to stress the advantages of the proposed model.
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Purpose: Global construction has been affected by COVID-19 unprecedently. The construction sectors in the least developed countries are considered as vulnerable, but the covid made the countries experience the worst situation ever. To minimize the losses by effective measures, there needs to assess the COVID-19 impacts on the construction sector. So, the aim of this study is to investigate the most critical impacts of COVID-19 on construction in the least developed countries by considering the case study of Bangladesh. Design/methodology/approach: The authors adopted multistep research methods, including (1) literature analysis and discussion with experts to establish a comprehensive list of COVID-19 impacts;(2) through a questionnaire survey, data were collected from 217 construction professionals by email, Google Form and Skype for quantifying the significance of covid impacts;(3) reliability of the survey checked by the Cronbach Alpha test;(4) Relative Importance Index (RII) to determine the ranks of the impacts based on their significance;(5) Interpretive Structural Model (ISM) to explore the corelations and the hierarchical structure;and (6) cross-impact matrix multiplication applied to classification (MICMAC) analysis to classify the COVID-19 impacts. Findings: The study identified a total of 18 COVID-19 impacts on the construction sector. Among them, the job cuts, schedule delays, project suspension, cost overrun and effects on mental health are more influential and significant than others. Further, this study found that unpaid leave and job cuts are the two most fundamental impacts which influence other succeeding significant impacts. And ultimately all the impacts lead to hampering the national economy and development. Finally, MICMAC analysis suggested that unpaid leave and job cuts should be addressed first to resolve and effects on the national economy and development should be later. Research limitations/implications: This study does not consider all the COVID-19 impacts due to the relevant context and simplicity of the ISM method. Also, the respondent's attitude might be slightly different during the post-mass vaccination period. Practical implications: This study will help the company's management, employees and government to develop effective strategies to understand the insight of their interrelations and ultimately overcome the identified covid effects. This will must contribute to the industry, its employees, the government and society by ensuring the national economy and development, construction operations, investment, employment and social security. Originality/value: This study will contribute to the knowledge body (practitioners and researchers) by providing the list of significant covid impacts and insight into their interrelations for further deep analysis of the pandemic effects. This will also help the authorities and stakeholders in developing policies and strategies to minimize or avoid these effects and avoid future consequences due to any pandemic like covid. © 2022, Emerald Publishing Limited.
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Blockchain (BLC) and the Internet of Things (IoT) are two emerging technologies that have become popular among practitioners for improving the transparency, adaptability, and safety of any industry. This is especially critical for food security, as COVID-19 highlighted the vulnerability of food supply chain (FSC). However, Indian organizations are experiencing problems in implementing the integrated form of BLC-IoT due to limited knowledge and insufficient research. The current study aims to propose a conceptual framework to reduce the impact of adoption barriers against BLC-IoT in FSC. Thirteen key barriers were identified after a thorough literature review and consultation with experts. The relationship among barriers was established using Interpretive structural modeling (ISM) and Decision-making trial and evaluation laboratory (DEMATEL) methods. The analysis shows that the lack of government regulation and workers' low competency significantly influence BLC-IoT adoption. The results also indicate the intricacy of decision-making by demonstrating that 9 of the 13 barriers were a part of the linkage cluster. The study outcome will help practitioners in developing and planning strategies for effective adoption of BLC-IoT in FSC.
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During the new COVID-19 outbreak, companies are looking to sourcing leaders to assist them diversify their supply base and prepare for a number of situations. In recent days, the role of emerging paradigms, including lean, agile, resilient, green, and sustainability (LARGS) in highly competitive supply chains, has been gaining momentum. However, there is no research on the LARGS paradigm for sustainable supplier selection in the literature. The aim of this paper is to identify important criteria for supplier selection in the LARGS paradigm and to develop the hierarchical relationship between the criteria. This research has identified 22 key criteria for supplier selection in the LARGS paradigm. Data were collected from 12 experts and analysed by interpretive structural modeling (ISM). From the ISM model, it is observed that geographic location is placed at the bottom of the hierarchy, showing high driving power and the most important criteria while selecting any supplier. Lead time also indicates high driving power and organizations must focus on the suppliers' lead time to improve product performance and introduce new products faster into the markets. The findings will help the practitioners and policymakers to formulate supply chain robustness and resilience strategies to diminish supply chain risks imposed by the COVID-19 pandemic. The presented model can be assessed as a strategic tool to select a supplier who considers lean, agile, resilient, green, and sustainable criteria simultaneously to increase supply chain efficiency and effectiveness. The study is first of its kind to identify supplier selection criteria in LARGS paradigm and develop hierarchical relationships between them using ISM approach.
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Background: The complexities of the workplace environment in the downstream oil and gas industry contain several safety-risk factors. In particular, instituting stringent safety standards and management procedures are considered insufficient to address workplace safety risks. Most accident cases attribute to unsafe actions and human behaviors on the job, which raises serious concerns for safety professionals from physical to psychological particularly when the world is facing a life-threatening Pandemic situation, i.e., COVID-19. It is imperative to re-examine the safety management of facilities and employees' well-being in the downstream oil and gas production sector to establish a sustainable governance system. Understanding the inherent factors better that contribute to safety behavior management could significantly improve workplace safety features. Objective: This study investigates employees' safety behavior management model for the downstream oil and gas industry to consolidate the safety, health and wellbeing of employees in times of COVID-19. Methods: Nominal Group Technique (NGT) was first employed to screen primary behavioral factors from 10 workplace health and safety experts from Malaysia's downstream oil and gas industry. Consequently, 18 significant factors were identified for further inquiry. Next, the interpretive structural modeling technique was used to ascertain the complex interrelationships between these factors and proposed a Safety Behavioral Management Model for cleaner production. Results: This model shows that management commitment, employee knowledge and training, leadership, and regulations contribute significantly to several latent factors. Our findings support the Social Cognitive Theory, where employees, their environment, and their behaviors are related reciprocally. Conclusion: It is postulated that identifying safety factors and utilizing the proposed model guides various stakeholder groups in this industry, including practitioners and policymakers, for achieving long-term sustainability.
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Supply chain processes (SCM) encompass a wide spectrum of functions that facilitate the flow of a raw material till its finished product stage. The most vital aim of SCM lies in establishing a link between all the facilities of a company such as manufacturing, transporting, channelizing and delivering goods and enhancing business processes by making them more flexible, more agile and, consequently, more competitive. This unpleasant coronavirus pandemic has adversely affected almost all supply chain networks around the globe and has highlighted the shortcomings of the existing supply chains. This research study makes an attempt to find the disruptions in supply chain caused by COVID-19 by using Interpretive Structural Modeling (ISM) and provides a few solutions for the same. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Purpose>This paper aims to focus on developing a theoretical framework for the analysis of factors influencing additive manufacturing (AM) in the health-care domain.Design/methodology/approach>A total of 18 factors are considered through extensive literature review and the relationship between each factor is studied using total interpretive structural modeling (TISM) and the model is logically developed. TISM model is developed using appropriate expert inputs. In addition, cross-impact matrix multiplication applied to classification (MICMAC) analysis is conducted to group the factors.Findings>It was found that “ease of design” and “research and development” are the two most important factors with the highest driving power and dependencies. Through MICMAC analysis, the significance of factors is studied.Practical implications>The study has been done based on inputs from academic experts and industry practitioners. The inferences from the study have practical relevance.Originality/value>The development of a structural model for the analysis of factors influencing AM in the health-care domain is the original contribution of the authors.
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Purpose The recent pandemic of COVID-19 has posed challenges for delivering essential and desirable health-care services for the masses. Digital health-care services initiated by several hospitals and health practitioners promise efficient and safe health care in the new normal post-COVID era but need a supportive enabling ecosystem. Therefore, this study aims toward identifying and modeling the key enabling factors for digital health-care services. Design/methodology/approach A total of nine factors were identified from the literature review and verified by the domain experts which can enable the wider acceptance of digital health-care services. The identified factors were then modeled with the help of the total interpretive structural modeling (TISM) approach and fuzzy Matrices d'Impacts Croises Multiplication Appliquee a un Classement (MICMAC) and a meaningful contextual relationship were developed for the factors. Findings This study reflects that the trust of patients is required for the acceptance of digital health care. Quality of patient care and affordability cum accessibility of online services will define mass engagement. Hospital staff resilience, hospital care service capacity, strategic partnerships and collaborations supported by technology and regulatory structure are the major factors defining the enabling ecosystem. Originality/value This study has its uniqueness in the way the TISM approach and fuzzy MICMAC are used for modeling the enabling factors toward growth and acceptance of digital health-care services in the days to come in developing nations. The focus of this study can be considered as relevant for the study interested in investigating the role of cognitive dimensions in influencing actors' behaviors and decisions.
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Purpose The purpose of this study is two-fold. First, to identify and encapsulate the enablers that can facilitate technology integration in higher education and second, to understand and analyze the interplay between technology agility enablers. Design/methodology/approach The study used the Total Interpretive Structural Modeling (TISM) approach to construct a theoretical model of the technology agility enablers in higher education and MICMAC analysis for ranking and segregating the enablers based on their dependence power into four categories: Autonomous, Dependent, Linkage and Independent. Findings The study helped identify eight technology agility enablers, with the Covid-19 pandemic as the most significant enabler. The Covid-19 pandemic has catalyzed the diffusion of technology across the education sector in India, including tertiary higher education. The study revealed government initiatives and institutional commitment as other enablers that can promote technology agility in higher education. Practical implications The results of this study would assist the policymakers and management of universities and colleges in understanding the important enablers that can facilitate technology integration in higher education. Originality/value Research in the past on technology adoption in higher education has looked into each enabler in isolation. This research provides a comprehensive view of the enablers and has attempted to establish a multidirectional interplay between the enablers.