Modelling the resilience of start-ups during COVID-19 pandemic

PurposeResilience, the ability of start-ups to deal with anticipated instabilities and probable disruptions, is becoming an important success element during coronavirus disease 2019 (Covid-19). To survive in this pandemic situation, resilience is an important concept for start-ups. The present paper aims to “identify”, “analyse” and “categorize” the resilience factors for start-ups during the Covid-19 pandemic using total interpretive structural modelling (TISM).Design/methodology/approachThe resilience elements of start-ups during Covid-19 were identified and shortlisted during the first phase, which included literature analysis and extensive interaction with experts. TISM was used in the second phase to investigate or to determine how the factors interplayed between the resilience factors of start-ups during Covid-19. The Matrice d'impacts Croises Multiplication Appliquee a un Classment (MICMAC) method is used to rank and categorize the factors. Closed-ended questionnaire with the scheduled interview was conducted to collect the data.FindingsThe first part of the study found ten resilience elements in total. The TISM digraph was constructed in the second step to show why one resilience component led to another. The MICMAC analysis divided these factors into four groups: autonomous, linkage, dependent and independent. These groups represented resilience variables based on their driving and dependent power, which assist executives and managers in proactively addressing them while using the TISM digraph as a guide.Research limitations/implicationsDuring the Covid-19 epidemic, this study focused primarily on resilience characteristics for Indian start-ups.Practical implicationsThis study will help key stakeholders and scholars to better understand the elements that contribute to start-up's resilience.Originality/valueThe TISM method for start-up's resilience is suggested in this paper, which is a novel attempt in the field of resilience in this industry.

Lean Enhancement by Application of Total Interpretive Structural Modelling

Prioritizing of factors for effective lean manufacturing poses a challenge to management due to complexities in interrelationships. Diligent understanding of measures of lean manufacturing assumes great importance. Essential manufacturing flexibilities take care of uncertainties driven by dynamics of the market. Interrelationship between factors of manufacturing flexibility and lean manufacturing adds to complexity. Judicious analysis of these factors is imperative to understand their effect on lean manufacturing. Total interpretive structural modeling methodology is used for establishing relationships among the factors affecting lean performance. Case studies have been carried out and TISM is applied to understand the dynamism of factors. Study brings out how the organization of the companies and level of automation help in understating the driving and dependence power. The study helps in understanding the influence of hierarchy and level of factors identified by TISM technique on lean performance as also the factors which merit attention of top management to achieve better results

A TISM and MICMAC analysis of factors during COVID-19 pandemic in Indian Apparel Supply Chain

The objective 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). 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 TISM model.

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

PurposeThis 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/approachThe 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.FindingsThe 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/implicationsTourism 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 implicationsManagement/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/valueThis 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.

Enablers facilitating industry-academia, transnational education and university-community partnering agility in higher education

PurposeThe study has a two-fold purpose: first, to identify the enablers of partnering agility in higher education, and, second, to analyze the interplay between the enablers.Design/methodology/approachTotal interpretive structural modelling (TISM) was used to construct a theoretical model of partnering agility enablers, and cross-impact matrix multiplication applied to classification (MICMAC) analysis was used to rank and segregate the enablers into independent, autonomous, dependent and linkage zones on the basis of their driving and dependence power.FindingsThe study helped in identifying eight enablers that can be instrumental in driving partnering agility in higher education. According to the TISM model, clarity on roles and responsibilities of partners was found to be the most crucial and vital enabler followed by resource sharing.Practical implicationsThe conceptual model provides a new direction on how to develop and strengthen higher education partnerships. The model has prioritized all the crucial enablers that the management can work around in order to drive partnering agility in higher education institutions.Originality/valueStudies in the past have majorly focused on academia–industry partnerships. This research has tried to provide a comprehensive view of the enablers and the multidirectional interplay between the enablers that can facilitate partnerships between academia and industry, Indian and international universities, and academia and community.

Antecedents and enablers of supply chain reconfigurability and its effect on performance

The reconfiguration of supply chain is becoming a crucial concept used to deal with market disruptions and changes such as COVID 19 pandemic, demand uncertainty, new technologies, etc. It can be defined as the ability of the supply chain to change its structure and functions in order to adapt to new changes. Its assessment requires an understanding of its quantitative factors to provide indicators that are easy to interpret. Effective reconfigurability assessment can be achieved by measuring quantitatively its six characteristics (modularity, integrability, convertibility, diagnosability, scalability and customization). This paper aims at identifying the quantitative factors of each characteristic and their inter-relationships by using Total Interpretive Structural Modelling (TISM). The structural model obtained by TISM is applied to understand the dependency quantitative factors. Based on TISM results, a classification of quantitative factors is determined using « Matrice d'Impacts Croisés, Multiplication Appliquée à un Classement » (MICMAC) analysis. This paper may be helpful to understand the previously mentioned characteristics of reconfigurable supply chain in order to facilitate the measuring and the assessment of reconfigurability.

Modelling the Enablers for Branded Content as a Strategic Marketing Tool in the COVID-19 Era

This study aims towards identifying and modelling the significant factors which act as enablers for the branded content to be used strategically by marketers as a marketing tool in the COVID-19 era. A qualitative approach was adopted for this study, and significant factors associated with branded content were identified from the literature review and primary survey. The factors were then verified by the experts in the area of branding and digital marketing. Total interpretive structural modelling (TISM) and Decision-making Trial and Evaluation Laboratory (DEMATEL) techniques were used to model the factors as per their contextual relationships. As per the model outcomes from TISM and DEMATEL approaches, branded content is an efficient marketing tool that promises value delivery to stakeholders. This, in turn, depends on the authenticity and transparency in content development and distribution. The most significant driving enablers for the system suggest efficient measurement and evaluation strategies and the customer as co-creator for the branded content.

What drives organisational performance: strategic thinking, technological change, strategic risks? A modified total interpretive structural modelling approach and MICMAC analysis

Purpose The purpose of this study is to explain the interrelationships between the elements of strategic thinking, technological change and strategic risks. The main objective of this research is to identify the hierarchy for the elements of thinking, technological change and strategic risk and also to identify the driving powers of these elements. Design/methodology/approach The methodology used in this study is modified total interpretive structural modelling and MICMAC analysis which gives the interrelationships and also the driving powers of the elements by analysing the relationships between the elements from the existing literature. This method helps us in answering/understanding the “what”, “how” and “why” of the research. Modified total Interpretive structural modeling is considered in this study, which helps in doing both the paired comparisons and transitivity checks simultaneously. A digraph is constructed at the end of the analysis, which shows the links between the elements, and a driver dependence matrix is constructed, which shows the driving powers. Findings This study gives an understanding of the role of the elements, the relationships between them and the hierarchy of addressing these elements, and also the driving and dependence power. Findings of this research give us an understanding of how strategic thinking/technological change/strategic drives the performance of the firm. Research limitations/implications This study is conducted with the help of existing literature; this can be further extended by considering the expert opinion. Practical implications The model explains the direct and transitive links of the elements and the strength of the relation between them, which helps the researchers and the practitioners to understand the driving power and importance of these constructs. It also helps us to understand the role of these elements and, if implemented in an organisation, which elements need to be prioritised for enhancing the performance of the firm. Originality/value Research done in the past has individually analysed the elements effecting strategic thinking; this study identifies the relationships between the elements of all three constructs and helps in understanding the levels of hierarchy.