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PurposeThis research aims to ascertain the various operational maturity challenges faced by the online food ordering and delivery enterprises (OFODE), their nature and their interactive relationships. In particular, this paper aims to (a) identify the most relevant operational maturity challenges faced by the OFODE during the COVID-19 lockdown in Oman, (b) explore and establish any likely structural relationship among these challenges and (c) put them into logical clusters. Design/methodology/approachExperts helped to reduce the 18 initially identified maturity challenges to 13 most pressing ones. Mutual relationships, dominance of interactions and their classifications were explored using fuzzy interpretive structural modeling (FISM) and fuzzy MICMAC analysis. FindingsThe study of situation-specific operational maturity challenges convinced the authors to propose a distinct FISM model that depicts the relationship among these challenges. Keeping commissions and fees reasonable emerges as the challenge which all other challenges seemingly culminate into. One of the most important situation-specific challenges (i.e. customer confidence about infection free delivery) emerges as a linkage challenge which aggravates as well as is aggravated by certain challenges. Research limitations/implicationsBesides enriching literature, the proposed model has implications for practitioners particularly when the similar lethal waves are experienced anywhere. The number of respondents, subjective approach, specific context as well as the geographical area coverage are the key limitations. Originality/valueTo the best of the authors' knowledge, this study is the first known scientific effort which attempts to model the operational maturity challenges faced by the OFODE during COVID-19 lockdown period. The authors used the FISM modeling approach to forge these interrelated challenges into a structural model.
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PurposeMobile-based payment is increasing exponentially but in the developing country like India, consumers' perception is highly positive in daily cash transaction. The purpose of this research is to identify and examine the important challenges for mobile wallet (m-wallet) implementation in India. In the wake of COVID-19, one of the transmission mechanisms of this virus has been the coins and paper money passed between a buyer and a seller. As such m-wallet considered as a convenience of payment has become a necessity in light of the pandemic.Design/methodology/approachThe authors explored 19 unique sets of challenges selected from the literature and collected data from 14 experts from private sector, multinational corporations and mixed private and public partnership who have significant knowledge and experience of mobile payment implementation and use in their respective organisations. Also, the authors have used Interpretive Structural Modelling (ISM) methodology in developing a hierarchal model for the identified challenges. The authors implemented Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis to classify the identified challenges.FindingsThe ISM-based framework is divided into nine different hierarchical levels. "Lack of strong regulatory compliance (Ch6)” has been recognised as the most important challenge, which inhibited the mobile wallet implementation, whereas "Perception of customers about the value of using mobile wallets (Ch11)” is the most dependent critical challenge. There are seven hierarchical layers in between the top and the bottom level with the varied number of challenges based on their driving and dependence power.Originality/valueThis is the first research to the best of our knowledge that has not only comprehensively reviewed the m-wallet literature but also employed a unique ISM-MICMAC-based approach to develop a framework of challenges for the m-wallet implementation.
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The study aims to assess a sustainable green financial environment by exploring the underlying structure of monetary seismic aftershocks of the COVID-19 pandemic. This study is qualitative and uses a review of literature, primary data collection methods, and qualitative analysis techniques as the study's overall design. The data is collected by one-to-one interview using a matrix style questionnaire from a panel of experts based on the purposive sampling technique. Interpretive structural modeling (ISM) combined with Matrices' Impacts Cruise's Multiplication Appliquée a UN Classement (MICMAC) is used for assessment, modeling, and analysis of data. The monetary aftershocks, namely, "more cash in hand required," "decreased travel costs," "shift to more certain or fixed revenue streams," "lower rent costs," "more zealous monitoring of cash collection cycle," and "decreased entertainment costs," occupy level I (top of the model being least critical shocks), and "tedious regulations" occupy level VIII (bottom of the model being the most vital). Other aftershocks form the middle of the model being moderate critical. Analysis of MICMAC shows that monetary seismic aftershocks high fees for assistance regarding SOPs, tedious regulations, and more downtime due to pandemic alerts are independent. This study addresses the core issue from within the aftermath of the COVID-19 pandemic. It provides new important information regarding the structure of a sustainable green financial environment that is useful for economists, financial analysts, commercial and central bankers, accountants and finance managers from the organization's public/and private sectors, local and international community, and researchers of the domain. It provides an informative structural model and classification of critical aftershocks. There are specific data/methodological/resource-related limitations of the study. The study's data are collected from a focus group; the study's methodology is qualitative and indicates relations among variables that do not quantify the associations. The study is a typical initiative of academic researchers with limited financial/physical resources; therefore, the generalizability of the study results is accordingly limited. The study is based on original, essential data and innovatively and creatively approaches the problem. It provides a unique model of an unprecedented phenomenon for reverberating the sustainable green financial environment.
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The study aims to assess and analyze the social outcomes of the COVID-19 pandemic. The study uses the discourse of comprehensive literature review to identify the outcomes, Interpretive Structural Modeling (ISM) for developing a structural model and Matrices' Impacts Cruise's Multiplication Appliquée a UN Classement (MICMAC) for analysis, classification of societal outcomes, and corroboration of results of ISM. Data from fifteen experts was collected through a survey questionnaire. As a result of the literature review, a list of sixteen outcomes was generated and verified by a panel of experts. Results of ISM revealed that the outcomes, namely, "emotional instability," "mental health self-harm," loneliness reduced recreational activities, obesity, and "increased screen time" come at the top of the model; therefore, they are less vital outcomes, whereas the most significant outcome which is at the bottom of the model is "employment instability"; hence it has a major impact on the society. The remaining outcomes fall in the middle of the model, so they have a moderate-severe impact. Results of MICMAC validate the findings of ISM. Overall findings of the study reveal that "employment instability" is the crucial social outcome of the COVID-19 pandemic. It is an original attempt based on real-time data, which is helpful for society at large, researchers, the international community, and policymakers because this study provides a lot of new information about the phenomenon. The study includes understanding society at large, policymakers, and researchers by illustrating the complex relations and simplifying the connections among a wide range of social outcomes of COVID-19.
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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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This article seeks to systematically identify and model antecedents of entrepreneurial bootstrapping and bricolage to determine and interpret the relationships and hierarchy between them. Entrepreneurial bootstrapping and bricolage are key dynamic capabilities that help entrepreneurs access, accumulate and enhance resources to adapt to scarce business environments. The article employs a modified total interpretive structural modelling analysis to determine hierarchical inter-relationships between the antecedents and a Matrice d' Impacts Croises Multiplication Applique An Classment analysis to understand their driving and dependence powers. The results highlight that founder characteristics and human capital are placed at the lower levels, making them critical driving elements of the model along with environmental hostility and resource constraints. Entrepreneurial orientation, slack, external financial capital and entrepreneurial frugality are dependent variables, with social capital as a linkage variable. This study will guide entrepreneurs trying to implement resourcefulness behaviours to respond to the coronavirus disease-2019 crisis by prioritising driving antecedents to impact the dependent factors further. © 2023 Entrepreneurship Development Institute of India.
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PurposeSuppliers and marketers have started planning toward postpandemic scenarios where logistics and retail will happen in a new way with the help of technological advances. This shift means new challenges for manufacturers, suppliers and retailers, and there is a need for strategic sourcing decisions for a robust supply chain system, logistics and on-time delivery system, as consumers have shown a positive change in online buying behavior. Furthermore, with digital transformation, customers are expected to not return to traditional buying. Hence, it becomes essential to identify the factors acting as enablers of online purchase behavior for sustainable digital business. This study aims to analyze the positive shifts in online purchasing by consumers, identify and model the enablers of positive transformations in online purchasing by consumers.Design/methodology/approachThe interpretative structural modeling (ISM) technique is used to draw the interrelationships among the variables and their impact on online buying. A context-oriented relationship among the factors has been set up through the expert opinion technique. A total of 40 specialists have been approached for this. ISM with Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) analysis was used to prioritize these drivers, identify the most critical factors and establish a driver-dependence relationship among these drivers.FindingsSeveral significant categories of enablers like health, trust, convenience, work from home, referral buying, panic purchase and overstocking possess a strong influence on the shift to online due to the pandemic. The results will help the policymakers, suppliers, retailers, managers and practitioners with insights to plan, prepare for challenges and make decisions toward preparation and shifting to the emergent digital world. In addition, the study provides academicians scope for further research in the related area.Research limitations/implicationsConsumer behavior significantly impacts retail and supply chain business, as it is an interface with the customer and links between a manufacturer and a customer. This study provides an insight into the shift in purchase behavior which can help suppliers in this transition phase to be better prepared for tomorrow to achieve sustainable competitive advantage.Originality/valueThis study assists practitioners and researchers in understanding the interrelationships among the factors using ISM-MICMAC analysis in a realistic way rather than daydreaming with overambitious goals.
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PurposeThis article aims to model the challenges of implementing artificial intelligence and machine earning (AI-ML) for moderating the impacts of COVID-19, considering the agricultural supply chain (ASC) in the Indian context.Design/methodology/approach20 critical challenges were modeled based on a comprehensive literature review and consultation with experts. The hybrid approach of "Delphi interpretive structural modeling (ISM)-Fuzzy Matrice d' Impacts Croises Multiplication Applique'e à un Classement (MICMAC) − analytical network process (ANP)” was used.FindingsThe study's outcome indicates that "lack of central and state regulations and rules” and "lack of data security and privacy” are the crucial challenges of AI-ML implementation in the ASC. Furthermore, AI-ML in the ASC is a powerful enabler of accurate prediction to minimize uncertainties.Research limitations/implicationsThis study will help stakeholders, policymakers, government and service providers understand and formulate appropriate strategies to enhance AI-ML implementation in ASCs. Also, it provides valuable insights into the COVID-19 impacts from an ASC perspective. Besides, as the study was conducted in India, decision-makers and practitioners from other geographies and economies must extrapolate the results with due care.Originality/valueThis study is one of the first that investigates the potential of AI-ML in the ASC during COVID-19 by employing a hybrid approach using Delphi-ISM-Fuzzy-MICMAC-ANP.
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The spike in internet users led healthcare companies to confer their agile presence on various digital platforms and engage customers online to increase their viability amid the rising competition. Online customer engagement takes place through branded content, i.e., the content provided by the brand or the company. Healthcare companies can leverage customer engagement by focusing on various crucial enablers. Therefore, this study explores the enablers of customer engagement for branded content in healthcare and devises a model depicting interrelationships among them. The enablers were ascertained using the literature review and validated by experts. Further, the interrelationship among the enablers was analyzed using TISM (Total Interpretive Structural Modeling) approach, and Fuzzy MICMAC (Cross-impact matrix multiplication) classified the enablers into different clusters. Results exhibited that informativeness is the most significant enabler, deriving other enablers. In contrast, shareability and co-creation of content are the most dependent and strategic enablers in the model hierarchy. The outcomes of this research will aid healthcare companies in knowing and prioritizing the enabler's contribution in engaging customers towards branded content.
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PurposeThis study aims to examine which organisational and other factors can facilitate the adoption of artificial intelligence (AI) in Indian management institutes and their interrelationship.Design/methodology/approachTo determine the factors influencing AI adoption, a synthesis-based examination of the literature was used. The interpretative structural modelling (ISM) method is used to determine the most effective factors among the identified ones and the inter-relationship among the factors, while the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to analyse the cause-and-effect relationships among the factors in a quantitative manner. The approaches used in the analysis aid in understanding the relationship among the factors affecting AI adoption in management institutes of India.FindingsThis study concludes that leadership support plays the most significant role in the adoption of AI in Indian management institutes. The results from the DEMATEL analysis also confirmed the findings from the ISM and Matrice d' Impacts croises- multiplication applique and classment (MICMAC) analyses. Remarkably, no linkage factor (unstable one) was reported in the research. Leadership support, technological context, financial consideration, organizational context and human resource readiness are reported as independent factors.Practical implicationsThis study provides a listing of the important factors affecting the adoption of AI in Indian management institutes with their structural relationships. The findings provide a deeper insight about AI adoption. The study's societal implications include the delivery of better outcomes by Indian management institutes.Originality/valueAccording to the authors, this study is a one-of-a-kind effort that involves the synthesis of several validated models and frameworks and uncovers the key elements and their connections in the adoption of AI in Indian management institutes.
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To ensure business continuity in the post-COVID-19 era, decision-makers should reconfigure their traditional supply chain (SC) networks, assisted by the development and implementation of cutting-edge technologies. Recently, Industry 5.0 (I5.0) has gained increasing attention as a paradigm offering salient features for the creation of resilient and inclusive operations by ensuring long-standing SC sustainability. However, there has been insufficient analysis of the challenges of implementing I5.0 in SCs. Therefore, this study aims to understand the challenges of implementing I5.0 when managing the impact of SC disruptions due to the COVID-19 pandemic in an emerging economy. Both qualitative and quantitative methods were used for this research. First, the challenges to I5.0 implementation were identified through a literature review and experts' feedback. Those challenges were examined and prioritised using the Best-Worst Method (BWM). Second, the contextual relationships amongst these challenges were analysed using interpretive structural modelling (ISM) with cross-impact matrix multiplication applied to classification (MICMAC) analysis. Findings showed that to adopt I5.0 initiatives successfully in order to manage the post-COVID-19 impact on SC sustainability, the active involvement of senior managers is required in the execution process. Findings also showed that financial support and funding (e.g., tax reduction, short loans, etc.) from investors and the government play a pivotal role in enabling sustainability in SCs. Finally, the challenges were classified using MICMAC analysis to provide valuable insights for managing future disruptions. This study is expected to help managers and decision-makers successfully overcome the challenges of implementing I5.0 in SCs and thus improve SC sustainability.
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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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PurposeOpen innovation has attracted the attention of experts and business entities for the sustainable survivability of firms, especially in the post-COVID-19 era. The food and beverage industry has been facing sustainable survivability problems. It is important to identify and evaluate the factors of open innovation from the perspectives of the food and beverage industry. This study serves that purpose by identifying and evaluating the factors of open innovation in the post-COVID-19 era with a special reference to Pakistan's economy.Design/methodology/approachThe present study integrates the Fuzzy Delphi Method (FDM), Interpretive Structural Modelling (ISM) and Matrice d' Impacts Croises Multiplication Applique a Classement (MICMAC) methods to analyze the factors involved in the adoption of open innovation in the food and beverage industry in Pakistan. Firstly, based on an extensive literature review of the most relevant studies, the factors affecting open innovation have been identified and finalized using FDM and experts' opinions. Secondly, the hierarchical framework has also been prepared by implementing the ISM approach. Thirdly, the MICMAC approach was employed to evaluate the factors to examine the driving and dependence powers of the factors of open innovation adoption.FindingsThe study identified 17 factors of open innovation adoption in Pakistan's food and beverage industry and 16 factors were finalized using FDM. The ISM-MICMAC matrix unveiled that awareness seminars and training, along with a lack of executive commitments, were strong factors with high driving power, but these factors proved to be weakly dependent powers regarding the other factors. Moreover, a lack of innovation strategy, R&D and non-supportive organizational culture exhibited low driving power but strong dependent power.Practical implicationsThe findings of the study could help firms and business entities understand the driving and dependent factors involved in open innovation for the sustainable survivability of the food and beverage industry. The study provides strong reasons to believe that an open innovation strategy, along with stakeholder collaboration, the adoption of rules and regulations and managerial commitment, could stimulate open innovation. Moreover, governments should promote the business sector, especially the food and beverage industry, to facilitate the sector while also providing awareness seminars and training, creating environments conducive to reducing innovation costs.Originality/valueSome previous studies have analyzed the factors involved in green innovation from the perspective of the manufacturing industry and environmental protection. The present study is a pioneer study to examine the factors involved in the adoption of open innovation in the food and beverage industry in Pakistan from the perspective of the post-COVID-19 era. For this purpose, the present study uses an integrated Fuzzy Delphi-ISM-MICMAC approach for the analysis.
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Emerging economies are struggling with proper and efficient management of waste due to their constrained resources and weak management. In recent days, this crisis has worsened due to the outbreak of the highly contagious COVID-19 pandemic. To avoid building up stockpiles and contaminating communities with potentially contagious medical waste (MW), and to ensure sustainability in the current and post-COVID-19 era, it is a dire need to develop and implement a safe and efficient medical waste management (MWM) system. This research, thereby, aims to identify, assess, and prioritize the key challenges to efficient and sustainable MWM to mitigate the impacts of the disruptions caused by situations like the pandemic in emerging economies. An integrated approach consisting of the Best-Worst Method (BWM), Interpretive Structural Modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) has been proposed to achieve the objectives. Based on the literature review and expert feedback, a total of seventeen challenges were identified and later prioritized by using BWM. The top twelve challenges have been further analyzed using ISM-MICMAC to examine their interrelationships. This study reveals that lack of proper law enforcement and insufficient financial support from investors and the government are two crucial challenges for efficient MWM implementation. The research insights can assist healthcare facility administrators, practitioners, and city managers in identifying the associated challenges and shaping strategic decisions for establishing and managing efficient MWM systems to ensure sustainable development in the post-COVID-19 era.
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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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Purpose>The purpose of this study is to identify, analyse and model the post-processing barriers of 3D-printed medical models (3DPMM) printed by fused deposition modelling to overcome these barriers for improved operational efficiency in the Indian context.Design/methodology/approach>The methodology used interpretive structural modelling (ISM), cross-impact matrix multiplication applied to classification (MICMAC) analysis and decision-making trial and evaluation laboratory (DEMATEL) to understand the hierarchical and contextual relations among the barriers of the post-processing.Findings>A total of 11 post-processing barriers were identified in this study using ISM, literature review and experts' input. The MICMAC analysis identified support material removal, surface finishing, cleaning, inspection and issues with quality consistency as significant driving barriers for post-processing. MICMAC also identified linkage barriers as well as dependent barriers. The ISM digraph model was developed using a final reachability matrix, which would help practitioners specifically tackle post-processing barriers. Further, the DEMATEL method allows practitioners to emphasize the causal effects of post-processing barriers and guides them in overcoming these barriers.Research limitations/implications>There may have been a few post-processing barriers that were overlooked by the Indian experts, which might have been important for other country's perspective.Practical implications>The presented ISM model and DEMATEL provide directions for operation managers in planning operational strategies for overcoming post-processing issues in the medical 3D-printing industry. Also, managers may formulate operational strategies based on the driving and dependence power of post-processing barriers as well as the causal effects relationships of the barriers.Originality/value>This study contributes to identifying, analyzing and modelling the post-processing barriers of 3DPMM through a combined ISM and DEMATEL methodology, which has not yet been reviewed. This study also contributes to decision makers developing suitable strategies to overcome the post-processing barriers for improved operational efficiency.
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Indonesian steel manufacturing sector has proven able to survive and even thrive during the COVID-19 pandemic. Its post-pandemic potential is also very large, considering that Indonesia's steel consumption per capita is the lowest in ASEAN. However, the highly volatile business situation and competition mean that it needs to reduce process costs, reduce processing time, and increase productivity;all of which could potentially be achieved by digital transformation. However, there is very limited research on digital transformation in Indonesian steel manufacturing. This research aims to map the readiness, potential benefit, and barriers to digital transformation in Indonesian steel manufacturing using MICMAC matrix method, with validation and verification carried out directly by experts involved in decision-making process on several steel manufacturing entities in Indonesia. Result of this study shown that to prepare digital transformation readiness, Indonesian steel manufacturing should emphasize its effort on preparing strategy and leadership, investment for industry 4.0, innovation policy, competency development, connectivity, and cyber security. The main benefits that should be pursued first are the capability of monitoring in real time and assisting in communication and data flow;whereas the main barrier that should be taken care of first is preparing its organizational change. © 2022 ACM.
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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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Purpose - Due to country-wise lockdown and state-wise curfews in COVID-19, people were not able to make offline payments (i.e. cash payments) during purchases in India. So, people are switching their payment behavior from offline to online mode. But, as per the central bank report, the rate of adoption through mobile payments is still slow. The paper focuses on identifying critical barriers to mobile payment systems (MPSs) adoption in India. Innovation resistance theory (IRT) has been used as a base model for barriers, despite the wide range of choices of barriers avail- able in the MPSs context. Additionally, three external variables which are out of the wider coverage of IRT constructs were incorporated in this paper. The study, on the other hand, adds to innova-tion resistance theory in the frame of reference of MPSs from a theoretical perspective. Interpretive structural modeling (ISM), together with MICMAC analysis is brought into play to analyse the direct and indirect relationship amongst the barriers.Research methodology - ISM approach has been used to establish the relationship among the eight (08) identified barriers, through literature and expert opinions. The key barriers to high driving power are then identified with the help of MICMAC analysis. Findings - The results reveal that value barrier (b2), image barrier (b5) and visibility barrier (b7) are the most significant variables. Interestingly, IRTs' risk barrier (b3) and privacy barrier (b6) from the literature fall in the lowest level of the ISM model. The majority of the barriers fall under quadrant III of MICMAC analysis, indicating the high driving and dependence power.Research limitations - The developed ISM model is based on the sentiments of five (05) experts, which could be biased and influence the structural model's final output. Due to COVID-19, data has been collected through online video conferencing mode, this may vary if data will be collected through an offline or face-to-face interview. The proposed model's key findings aim to assist in explaining the barriers that exist during MPS adoption.Originality/Value - This study is the first attempt to use the ISM approach in conjunction with IRT to detect barriers within MPSs. The result of this paper will guide and motivate the researcher to analyse more critical barriers with IRT to contribute to the theoretical development.
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The pandemic of COVID-19 has altered the world canvas forever. The education sector, too, has been impacted by the same. There has been a phenomenal rise in e-platforms for teaching, learning, and evaluation. Teachers and students had to train themselves overnight to embrace the changing dynamics of the education sector. The change has been marked with challenges. In this new education landscape, online exams have occupied center stage. While the idea of giving exams from any part of the world welcomes freedom, it also raises concerns among faculty and students about academic integrity. Thus, as online studies and online assessment continue to expand, the paper aims to identify the factors responsible for unethical practices in online assessment. The paper further identifies the association between the identified factors. The paper proposes a four-level model that focuses on the lack of training for both faculty and students, interpersonal barriers, technological barriers, time management, personal ethics, and design of assessment as underlying reasons for unethical behavior in online assessments. The paper further explains the linkages using fuzzy MICMAC analysis. The results have both practical and social implications. Understanding the factors and their relationship with each other can help the instructors and administrators in their decision-making process regarding online evaluations and formulate policies that would instill strong ethical values, such as academic integrity and honesty, in their students throughout their academic journey.