Modelling of factors influencing organizational readiness for change in start-ups during the COVID-19 emergency

PurposeThe purpose of this paper is to "identify”, "analyze” and "construct” a framework to quantify the relationships between several determinants of organizational preparedness for change in the start-ups during the COVID-19 emergencies.Design/methodology/approachTotal interpretive structural modelling (TISM) is used to find characteristics that assist in analyzing the readiness or preparedness level before initiating a change deployment process in start-ups. A cross-impact matrix multiplication applied to classification (MICMAC) analysis is performed to determine the driving and dependent elements of change in start-ups.FindingsFrom literature research and an expert interview, this study selected ten variables of change preparedness to explore inner interconnections and comprehend the inner connections factors. The findings depict that clarity of mission and goals, reward system, technological advancement and motivational readiness have been considered the most important readiness factor for deploying organizational change in start-ups during the COVID-19 emergencies.Practical implicationsThis research will aid the management and researchers gain a better understanding of the factors that influence change preparedness. Constant observation of current changes in the start-ups and the external environment will aid in improving the quality of products or services provided by the start-ups during the COVID-19. The start-ups can use these criteria linked to change readiness. The priority of each element is determined using MICMAC analysis and ranking using the TISM technique, which assists start-ups in ordering the enablers from highest to lowest priority.Originality/valueThere is no research regarding factors influencing organizational readiness for change in start-ups during the COVID-19 emergencies. This research gap is filled by analyzing aspects linked to organizational readiness for change in start-ups. This gap inspired the present study, which uses the "Total Interpretive Structural Modelling (TISM)” technique to uncover change determinants and investigate hierarchical interconnections among factors influencing organizational readiness to change in start-ups during the COVID-19 emergencies.

Analysing the Antecedents of Entrepreneurial Bootstrapping and Bricolage: A Modified Total Interpretive Structural Modelling and MICMAC Approach

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.

Readiness for sustainable-resilience in healthcare organisations during Covid-19 era

Purpose: Using total interpretive structural modelling (TISM), this paper aims to "identify”, "analyse” and "categorise” the sustainable-resilience readiness factors for healthcare during the Covid-19 pandemic. Design/methodology/approach: To obtain the data, a closed-ended questionnaire was used in addition to a scheduled interview with each respondent. To identify how the factors interact, the TISM approach was employed and the cross-impact matrix multiplication applied to a classification method was used to rank and categorise the sustainable-resilience readiness factors. Findings: This study identified ten sustainable-resilience readiness factors for healthcare during the Covid-19 pandemic. The study states that the major factors are environmental scanning, awareness and preparedness, team empowerment and working, transparent communication system, learning culture, ability to respond and monitor, organisational culture, resilience engineering, personal and professional resources and technology capability. Research limitations/implications: The study focused primarily on sustainable-resilience readiness characteristics for the healthcare sector. Practical implications: This research will aid key stakeholders and academics in better understanding the factors that contribute to sustainable-resilience in healthcare. Originality/value: This study proposes the TISM technique for healthcare, which is a novel attempt in the subject of readiness for sustainable-resilience in this sector. The paper proposes a framework including a mixture of factors for sustainability and resilience in the healthcare sector for operations. © 2020, Emerald Publishing Limited.

Readiness for lean-sustainability in start-ups during the COVID-19 era

Purpose: This study aims to "identify”, "analyse” and "categorise” the lean-sustainability enablers for start-ups during the COVID-19 epidemic using total interpretive structural modelling (TISM). Design/methodology/approach: A closed-ended questionnaire was used to collect data in addition to the scheduled interview. The TISM methodology is used to determine how the variables interact, and the matrice d'Impacts croises-multiplication applique´ a classement (MICMAC) method is used to rank and categorise the lean-sustainability enablers. Findings: This study identified ten lean-sustainability enablers for start-ups during the COVID-19 pandemic. The study says that the key factors are leadership and managerial commitment, implementation of employee skills and abilities, strategic need, personnel engagement and financial ability. Research limitations/implications: The study focused primarily on lean-sustainability characteristics for start-ups. Practical implications: This research will aid key stakeholders and academics in better understanding the factors that contribute to lean-sustainability in start-ups. Originality/value: This study proposes the TISM technique for start-ups, which is a novel attempt in the subject of lean-sustainability in this industry. © 2022, Emerald Publishing Limited.

Readiness for lean-sustainability in start-ups during the COVID-19 era

Purpose>This study aims to "identify”, "analyse” and "categorise” the lean-sustainability enablers for start-ups during the COVID-19 epidemic using total interpretive structural modelling (TISM).Design/methodology/approach>A closed-ended questionnaire was used to collect data in addition to the scheduled interview. The TISM methodology is used to determine how the variables interact, and the matrice d'Impacts croises-multiplication applique´ a classement (MICMAC) method is used to rank and categorise the lean-sustainability enablers.Findings>This study identified ten lean-sustainability enablers for start-ups during the COVID-19 pandemic. The study says that the key factors are leadership and managerial commitment, implementation of employee skills and abilities, strategic need, personnel engagement and financial ability.Research limitations/implications>The study focused primarily on lean-sustainability characteristics for start-ups.Practical implications>This research will aid key stakeholders and academics in better understanding the factors that contribute to lean-sustainability in start-ups.Originality/value>This study proposes the TISM technique for start-ups, which is a novel attempt in the subject of lean-sustainability in this industry.

Readiness for sustainable-resilience in healthcare organisations during Covid-19 era

Purpose>Using total interpretive structural modelling (TISM), this paper aims to "identify”, "analyse” and "categorise” the sustainable-resilience readiness factors for healthcare during the Covid-19 pandemic.Design/methodology/approach>To obtain the data, a closed-ended questionnaire was used in addition to a scheduled interview with each respondent. To identify how the factors interact, the TISM approach was employed and the cross-impact matrix multiplication applied to a classification method was used to rank and categorise the sustainable-resilience readiness factors.Findings>This study identified ten sustainable-resilience readiness factors for healthcare during the Covid-19 pandemic. The study states that the major factors are environmental scanning, awareness and preparedness, team empowerment and working, transparent communication system, learning culture, ability to respond and monitor, organisational culture, resilience engineering, personal and professional resources and technology capability.Research limitations/implications>The study focused primarily on sustainable-resilience readiness characteristics for the healthcare sector.Practical implications>This research will aid key stakeholders and academics in better understanding the factors that contribute to sustainable-resilience in healthcare.Originality/value>This study proposes the TISM technique for healthcare, which is a novel attempt in the subject of readiness for sustainable-resilience in this sector. The paper proposes a framework including a mixture of factors for sustainability and resilience in the healthcare sector for operations.

Assessing supply chain resilience to the outbreak of COVID-19 in Indian manufacturing firms

As the world has seen the impact of COVID-19, development of resilient supply chain strategies has emerged as top priority. The inconsistent demands, product consumption and the shorter lifecycle of products during the pandemic needs appropriate planning and designing to make the supply chain more resilient. In this study, an analytical model is proposed to assess the resilience of supply chain to overcome the effect of the disruption impacts. The supply chain risks will depend on the nature of the business and therefore, besides literature review on supply chain resilience the inputs from experts were required. The interdependency among the indicators was analysed by employing Interpretive Structural Modelling (ISM) and demonstrated with the help of a framework. The strength of the interdependence is assessed using Bayesian Network approach. BN transformed the qualitative expert inputs to quantitative assessment by utilising the principles of conditional probability. Three cases from Indian manufacturing industries were used to demonstrate and assess the critical supply chain resilience indicators using integrated ISM-BN approach. The cases showed that the proposed approach can assist decision makers in identifying the critical indicators to be focused towards improving the supply chain resilience to overcome the outbreak of Covid-19 pandemic. A comparative analysis of the supply chain risk indicators has also been performed, thereby extending the practical implication of supply chain resilience.

Identifying Consumer Resistance of Mobile Payment during Covid-19: An Interpretive Structural Modeling (Ism) Approach

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.

A TISM and MICMAC Analysis of Factors During the COVID-19 Pandemic in the Indian Apparel Supply Chain

The main aim of this study is to explore the challenges faced by the Indian apparel supply chain in the wake of COVID-19 to identify the factors that are being affected and build a multilevel hierarchy model to prioritize the factors and understand their inter-relationships. An intensive literature review was conducted, and many experts from apparel supply chain were consulted. The study was conducted by the help of a survey sent to these experts from different echelons in the apparel industry. The data was then analysed using total interpretive structural modelling (TISM). A multi-level hierarchy TISM model and MICMAC (matrice d'impacts croises multiplication appliquee a un classment) analysis were used to establish a relationship between the identified factors. The "difficulty in export order fulfilment" factor is found to be the most sensitive factor, which means that it is present in the TISM model hierarchy in a place that it is affected by most of the factors and in-turn impacts factors like operational cost, change in marketing strategy, change in consumer buying pattern, which impact profitability and cut-off in employment. "Cut-off in employment" is found to be most impacted by all other factors in the TISM model.

Modelling of factors influencing organizational readiness for change in start-ups during the COVID-19 emergency

Purpose: The purpose of this paper is to “identify”, “analyze” and “construct” a framework to quantify the relationships between several determinants of organizational preparedness for change in the start-ups during the COVID-19 emergencies. Design/methodology/approach: Total interpretive structural modelling (TISM) is used to find characteristics that assist in analyzing the readiness or preparedness level before initiating a change deployment process in start-ups. A cross-impact matrix multiplication applied to classification (MICMAC) analysis is performed to determine the driving and dependent elements of change in start-ups. Findings: From literature research and an expert interview, this study selected ten variables of change preparedness to explore inner interconnections and comprehend the inner connections factors. The findings depict that clarity of mission and goals, reward system, technological advancement and motivational readiness have been considered the most important readiness factor for deploying organizational change in start-ups during the COVID-19 emergencies. Practical implications: This research will aid the management and researchers gain a better understanding of the factors that influence change preparedness. Constant observation of current changes in the start-ups and the external environment will aid in improving the quality of products or services provided by the start-ups during the COVID-19. The start-ups can use these criteria linked to change readiness. The priority of each element is determined using MICMAC analysis and ranking using the TISM technique, which assists start-ups in ordering the enablers from highest to lowest priority. Originality/value: There is no research regarding factors influencing organizational readiness for change in start-ups during the COVID-19 emergencies. This research gap is filled by analyzing aspects linked to organizational readiness for change in start-ups. This gap inspired the present study, which uses the “Total Interpretive Structural Modelling (TISM)” technique to uncover change determinants and investigate hierarchical interconnections among factors influencing organizational readiness to change in start-ups during the COVID-19 emergencies. © 2022, Emerald Publishing Limited.

Analysis of Influencing Factors of Multi-value Chain Collaborative Operation Efficiency in Power Manufacturing Industry Based on FISM-ANP

Affected by the COVID-19, the global manufacturing industry has been greatly impacted. In order to adapt to the current new normal of economy, the multi-value chain collaborative operation mode of power manufacturing industry has come into being. In order to deeply study the influencing factors of multi-value chain collaborative operation efficiency in power manufacturing industry, this paper constructs an influencing factors system in terms of management level, technology level and policy level, combines fuzzy interpretative structural model (FISM) with analytic network process (ANP) to develop an analysis model from both qualitative and quantitative perspectives. Accordingly, it is suggested that: power manufacturing enterprises should promote the construction of R&D-production-sales-logistics-services multi-chain collaboration;promote the construction of data space to realize the sharing of data and information;accelerate the development of digital operation mode under Industry 4.0;and build third-party platform to efficiently integrate upstream and downstream resources. © 2022 IEEE.

Readiness for lean-sustainability in start-ups during the COVID-19 era

Purpose This study aims to "identify", "analyse" and "categorise" the lean-sustainability enablers for start-ups during the COVID-19 epidemic using total interpretive structural modelling (TISM). Design/methodology/approach A closed-ended questionnaire was used to collect data in addition to the scheduled interview. The TISM methodology is used to determine how the variables interact, and the matrice d'Impacts croises-multiplication applique ' a classement (MICMAC) method is used to rank and categorise the lean-sustainability enablers. Findings This study identified ten lean-sustainability enablers for start-ups during the COVID-19 pandemic. The study says that the key factors are leadership and managerial commitment, implementation of employee skills and abilities, strategic need, personnel engagement and financial ability. Research limitations/implications The study focused primarily on lean-sustainability characteristics for start-ups. Practical implications This research will aid key stakeholders and academics in better understanding the factors that contribute to lean-sustainability in start-ups. Originality/value This study proposes the TISM technique for start-ups, which is a novel attempt in the subject of lean-sustainability in this industry.

Analysing the impact of artificial intelligence on the competitiveness of tourism firms: a modified total interpretive structural modeling (m-TISM) approach

Purpose>This study explores the variables that drive the impact of artificial intelligence (AI) on the competitiveness of a tourism firm. The relationship between the variables is established using the modified total interpretive structural modelling (m-TISM) methodology. The factors are identified through literature review and expert opinion. This study investigates the hierarchical relationship between these variables.Design/methodology/approach>The modified total interpretive structural modelling (m-TISM) method is used to develop a hierarchical interrelationship among variables that display direct and indirect impact. The competitiveness of a tourism firm is measured by investigating the effect of variables on the firm's financial performance.Findings>The study identifies ten key factors essential for analysing the impact of AI on a firm's competitiveness. The m-TISM methodology gave us the hierarchical relationship between the factors and their interpretation. A theoretical TISM model has been constructed based on the hierarchy and relationship of the elements. The elements that fall in Level V are “AI Skilled Workforce”, “Infrastructure” and “Policies and Regulations”. Level IV includes the elements “AI Readiness”, “AI-Enabled Technologies” and “Digital Platforms”. Elements that fall under Level III are “Productivity” and “AI Innovation”. Level II and Level I comprise “Tourist Satisfaction” and “Financial Performance”, respectively. The levels indicate the elements' hierarchical level, with Level I the highest and Level V the lowest.Research limitations/implications>Tourism and AI scholars can analyse the given variables by including the transitive links and incorporate new variables depending upon future research. The m-TISM model constructed from literature review and expert opinion can act as a theoretical base for future studies to be conducted by researchers.Practical implications>Management/Practitioners can focus on the available characteristics and capitalise on them while working on the factors lacking in their organisation to enhance their competitiveness. Entrepreneurs starting their own business can utilise the elements in understanding the ecosystem of strengthening a firm's competitiveness. They can work to improve on the aspects which are crucial and trigger the impact on competitiveness. The government and management can devise policies and strategies that encompass the essential factors that positively impact the competitiveness of the firms. The approach can then be looked at with a holistic approach to cater to the other related components of the tourism industry.Originality/value>This study is the first of its kind to use the modified TISM methodology to understand the impact of AI on the competitiveness of tourism firms.

Readiness for sustainable-resilience in healthcare organisations during Covid-19 era

Purpose Using total interpretive structural modelling (TISM), this paper aims to "identify", "analyse" and "categorise" the sustainable-resilience readiness factors for healthcare during the Covid-19 pandemic. Design/methodology/approach To obtain the data, a closed-ended questionnaire was used in addition to a scheduled interview with each respondent. To identify how the factors interact, the TISM approach was employed and the cross-impact matrix multiplication applied to a classification method was used to rank and categorise the sustainable-resilience readiness factors. Findings This study identified ten sustainable-resilience readiness factors for healthcare during the Covid-19 pandemic. The study states that the major factors are environmental scanning, awareness and preparedness, team empowerment and working, transparent communication system, learning culture, ability to respond and monitor, organisational culture, resilience engineering, personal and professional resources and technology capability. Research limitations/implications The study focused primarily on sustainable-resilience readiness characteristics for the healthcare sector. Practical implications This research will aid key stakeholders and academics in better understanding the factors that contribute to sustainable-resilience in healthcare. Originality/value This study proposes the TISM technique for healthcare, which is a novel attempt in the subject of readiness for sustainable-resilience in this sector. The paper proposes a framework including a mixture of factors for sustainability and resilience in the healthcare sector for operations.

Analysis of challenges for blockchain adoption within the Indian public sector: an interpretive structural modelling approach

Purpose>Blockchain is one of the most significant emerging technologies that is set to transform many aspects of industry and society. However, it has several major technical, social, legal, environmental and ethical complexities that offer significant challenges for mainstream use within the public sector. The coronavirus disease 2019 (COVID-19) pandemic has compelled many public sector employees to work remotely, highlighting a number of challenges to blockchain adoption within the Indian context signifying the pertinence of this research topic in the post-pandemic era. This study offers insight to researchers and policymakers alike on how such challenges are interdependent within this important subject.Design/methodology/approach>We explored 16 unique sets of challenges selected from the literature and gathered data from nine experts from government settings, healthcare and education sectors and academia who have significant knowledge and experience of blockchain implementation and use in their respective organisations. The implementation of Interpretive Structural Modelling (ISM) and Matriced' Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) provided a precise set of driving, linkage and dependent challenges that were used to formulate the framework.Findings>The developed ISM framework is split into six different levels. The results suggest that the bottom level consists of challenges such as “Lack of standards (C9)” and “Lack of validation (C10)” form the foundation of the hierarchical structure of blockchain adoption. However, the topmost level consists of a highly dependent challenge termed “adoption of blockchain in the public sector (C16)”. The research filters the selected set of five challenges to develop a parsimonious model and formulated six propositions to examine the impact of “lack of standard (C9)”, “lack of validation (C10)” on “security issues (C3)” and “privacy concerns (C2)”, which eventually determine individuals' “reluctance to use blockchain technology (C12)”.Originality/value>This research fills a key gap in exiting research by exploring the key challenges in blockchain adoption within the public sector by developing a valuable framework to model this important topic. To the best of our knowledge, this is the first paper to address these challenges and develop a parsimonious model for challenges of blockchain adoption in the public sector settings.

Smart circular supply chains to achieving SDGs for post-pandemic preparedness

PurposeThe coronavirus disease 2019 (COVID-19) pandemic created heavy pressure on firms, by increasing the challenges and disruptions that they have to deal with on being sustainable. For this purpose, it is aimed to reveal the role of the smart circular supply chain (SCSC) and its enablers towards achieving Sustainable Development Goals (SDGs) for post-pandemic preparedness.Design/methodology/approachTotal interpretive structural modelling and Matrice d'Impacts Croises Multipication Applique' a un Classement (MICMAC) have been applied to analyse the SCSC enablers which are supported by the natural-based resource view in Turkey's food industry. In this context, industry experts working in the food supply chain (meat sector) and academics came together to interpret the result and discuss the enablers that the supply chain experienced during the pandemic for creating a realistic framework for post-pandemic preparedness.FindingsThe results of this study show that “governmental support” and “top management involvement” are the enablers that have the most driving power on other enablers, however, none of them depend on any other enablers.Originality/valueThe identification of the impact and role of enablers in achieving SDGs by combining smart and circular capabilities in the supply chain for the post-pandemic.

Barriers to the Adoption of Urban Living Labs for NBS Implementation: A Systemic Perspective

Urban Living Labs (ULLs) are widely believed to provide a safe environment for experimentation, co-creation and evaluation of innovations in real-life settings. A growing number of cities have been adopting ULLs to co-create and test Nature-Based Solutions (NBS). However, many of these cities have been facing major barriers in trying to adopt the ULL approach for implementing NBS. In this study, we seek to identify these barriers and provide a systemic understanding. Barriers are identified by means of workshops and interviews. Subsequently, interpretive structural modelling serves to identify the interdependencies among the barriers, resulting in a structural model of barriers in adopting ULLs for NBS. Our results show that political and institutional barriers are significantly limiting the adoption of ULLs. Moreover, knowledge brokers and other intermediaries, as well as cross-sectoral collaboration, play a key role in getting ULLs adopted. The findings from this study can help cities to develop strategies that overcome the main barriers for ULL adoption in the context of nature-based solutions.

Predicting the zoonotic capacity of mammals to transmit SARS-CoV-2.

Back and forth transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between humans and animals will establish wild reservoirs of virus that endanger long-term efforts to control COVID-19 in people and to protect vulnerable animal populations. Better targeting surveillance and laboratory experiments to validate zoonotic potential requires predicting high-risk host species. A major bottleneck to this effort is the few species with available sequences for angiotensin-converting enzyme 2 receptor, a key receptor required for viral cell entry. We overcome this bottleneck by combining species' ecological and biological traits with three-dimensional modelling of host-virus protein-protein interactions using machine learning. This approach enables predictions about the zoonotic capacity of SARS-CoV-2 for greater than 5000 mammals-an order of magnitude more species than previously possible. Our predictions are strongly corroborated by in vivo studies. The predicted zoonotic capacity and proximity to humans suggest enhanced transmission risk from several common mammals, and priority areas of geographic overlap between these species and global COVID-19 hotspots. With molecular data available for only a small fraction of potential animal hosts, linking data across biological scales offers a conceptual advance that may expand our predictive modelling capacity for zoonotic viruses with similarly unknown host ranges.