Effect of coronavirus pandemic in changing the performance barriers for textile and apparel industry in an emerging market

Abstract

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.

1. Introduction

The coronavirus pandemic (COVID-19) was declared a global emergency on January 30, 2020, by the World Health Organization (WHO).Despite rigorous global containment (government-inflicted restrictive boundary travel), lockdowns, and quarantine attempts, the frequency of COVID-19 ceased to decrease, with 180,817,269 lab-confirmed cases and over 3,923,238 deaths around the world (as of June 29, 2021). These events fuelled uncertainties of an impending recession and economic depression. This changing business environment has brought about newer threats to micro, small, and medium enterprises (MSMEs), which do not have "deep pockets" to overcome this turmoil (Bortamuly et al., 2013). Among Indian micro entrepreneurs, Indian handloom industry entrepreneurs produce traditional textiles using ancient hand-operated looms. The entire process of production takes place in rural areas, and few products could even find their way into the international market. Today, India is a major handloom producer in the world, accounting for 85 per cent of the total production globally (YOJANA, 2019., p. 14). However, demand for handloom goods decreased by 72 percent during COVID-19 pandemic (Sannith, 2020). Being a traditional household industry, the handloom MSMEs were directly related to the livelihood of 40–50 millions of people engaged in other activities like processing and trading (YOJANA, 2019., p. 14) who suffered the wrath of COVID-19 financially. Consequently, this industry offers an appropriate context for the present study.

The government and stakeholders have taken multiple steps to help the MSMEs in this uncertain time (Karuppiah et al., 2020). However, amidst these deteriorating scenario of COVID-19, the challenges faced by handloom MSMEs are also changing. This would imply that the government and stakeholders need to quickly address this changing time and adopt policies that will be more relevant post-COVID. The above lack of understanding implicates the importance of exploration and comparison of the challenges in Indian textile and apparel economy during and after the post-coronavirus pandemic. However, the extant literature needs to focus on the changing dynamics of the performance barriers for handloom MSMEs in the context of COVID during and after the pandemic, leading to a research gap. This gives us our following research questions (RQ).

• RQ1: What are the critical performance barriers of the handloom industry in the emerging market during and after the COVID-19 situation?

As performance barriers of handloom MSMEs, researchers have identified lack of working capital (Seth et al., 2020), rising input costs (Bortamuly et al., 2013), demand uncertainty (Prasad and Tata (2010a), lack of effective level of integration (Thakkar et al., 2008), negative impact of the handloom sector on the environment and society (Fieldson and Rai, 2009; Jia et al., 2020), and skilled labour unavailability (Venkatesh et al., 2015) among others. However, the contextual relationships and relative importance of these barriers have not been studied in the existing literature. Such exploration is essential for proper policy decisions and strategic initiatives by the government, MSMEs, and handloom stakeholders. Moreover, the time-varying nature of these importance scores and their contextual inter-relationships are also essential, especially in the pre-and post-COVID-19 period when the market is abruptly dynamic and uncertain.

• RQ2: How are the contextual relationship and the interdependence level among these key barriers during and after COVID-19?

To answer the questions above, we have chosen India as the emerging market to gain insights into both generic and contextual barriers and their changes. We have employed a multi-criteria decision-making (MCDM) approach to model the change in MSMEs’ critical performance barriers under COVID-19 pandemic and distinguish the most dominant barrier/s that have been influencing MSME growth. Further, we use interpretative structural modelling (ISM) that depicts the contextual relationship between the barriers. Notably, this technique is more effective and efficient than other MCDM techniques like TOPSIS (Technique for Order Performance by Similarity to the Ideal Solution), ANP (Analytic Network Process), and AHP (Analytic Hierarchy Process). It does not require the level of dominance of barriers to determine their interrelationships (Diabat et al., 2014). This technique also minimises expert response bias, for which a more reliable representative model of the stated problem could be developed (Darbari et al., 2018). In phase 1 (i.e., before the pandemic), Delphi analysis led to the identification of 17 barriers (Phase 1), which was later reduced to 12 key barriers (Phase 2). From the second round of Delphi analysis after the onset of the pandemic, it is evident that the lack of effective governmental policies, imitational threat, raw material shortage, and tax policies implementation are among top four barriers that have affected industry performance thus far. Moreover, in Phase 1, the MCDM technique differentiates the most driving barriers, which are noted at the bottom, including lack of effective government policies, demonetization, and tax policies implementation, which could influence all other factors but remained unaffected by others. After ranking these barriers in the post-pandemic scenario, it was found that lack of effective government policies, followed by tax policies implementation is the most significant barrier during MCDM analysis.

This paper is structured as follows: Section 2 highlights the relevant literature. The research methodology is depicted in Section 3; findings and results are discussed in Section 4. Theoretical contributions are presented in Section 5, followed by a conclusion in Section 6. Finally, the paper ends with limitations and future scope.

2. Literature review

2.1. Background of the indian handloom industry

The performance barriers for MSME sectors will vary based on the context. Some barriers are expected to be generalizable, and some will be industry-specific. Therefore, it is essential to choose a context that is important in terms of the country where the study is being done. In this study, we have chosen Indian handloom industry as the context of our study. This industry serves as one of the most extensive and unorganised economic industries after agriculture and constitutes an integral part of the larger MSME canvas in India (Ministry of Textiles, 2016). In India, 87% of weavers belongs to rural, while the rest 13% are urban (NCAER, 2010). Presently, there are 36 million units of MSME operating in India, which employ 80 million people (Maiti, 2018). Due to its traditional heritage and employment generation capacities, the handloom industry is unique to India's MSME sector (Maiti, 2018). These serve rural India and have a more pronounced impact in urban areas. This handloom, a part of the textile industry at large, has not been an exception to the massive disruptions caused by the pandemic. Both global and domestic supply chains have been disrupted for almost six months, further bleeding the Indian economy (Das, 2020; Gössling et al., 2020). The Indian textile and apparel industry is projected to lose more than $3 billion in freight.

Additionally, in March 2020, the rate of exports fell by about 32.2 percent compared to last year (Preetha, 2020). Specifically, apparel exports, which in 2018–19 was about $16.1 billion, fell by almost 4 percent to $15.4 billion, with the March exports dropping by nearly 35% as compared to the same month last year (Ravi, 2020). The potential revenue loss was estimated to be US $8–10 billion (Ravi, 2020). Importantly, this industry was already faced with myriad problems before the onset of the pandemic, but COVID-19 has worsened the situation. Along with external barriers (COVID-19, demonetization, inefficient government laws, the imitational threat, and more), raw material procurement and escalated production costs have been among the significant barriers to the growth of handloom weavers (Hazarika et al., 2016). Therefore, the study of these barriers affecting handloom industry becomes essential. Although many studies have focused on potential barriers to sustainable performance for MSMEs, the contextual focus on handloom MSMEs has remained under-explored. Moreover, to the best of our knowledge, the changing dynamics of the performance barriers pre- and post-COVID have not been studied till now in the handloom industry or any MSME context. This further substantiates the existing literature gap, which the current study tries to cover.

2.2. COVID-19 and the new norms

The impact of pandemic is disastrous for manufacturing, trade, and MSME enterprises. Sahoo and Ashwani (2020) indicated that a decline in the manufacturing sector would shrink in a range of 5.5–20 percent (best-case scenario to worst-case scenario) and MSME net value-added would shrink to a range of 2.1–5.7 percent in 2020. Around 30 percent of MSMEs expects to lay off 50 percent or more of their workforce by 2020 (OECD Secretary-General, 2020). MSMEs are highly vulnerable to changes due to their small size and liabilities of their newness (Upadhye et al., 2010). Further, MSMEs mostly depend on "per day earnings" to survive and will continue to be the worst hit owing to the lockdown and a slump in demand. Almost 50 percent of MSMEs have less than one month's savings, or just a month's reserve, in their cash reserves. According to a survey conducted by the International Labour Organisation (ILO), due to the sudden closure of around 93 percent of Indian MSME, the poverty rate increased by 15 percent and nearly 20 percent in rural and urban areas, respectively (Azim Premji University, 2021). The identified barriers to microentrepreneurs' sustenance include short-run shocks such as demonetization and the implementation of tax policy (Goods and Services Tax); medium-run factors include increasing financial instability; and long-run factors include insufficiently broad-based domestic demand as well as debt-financed consumption (Azim Premji University, 2021). Thus, to gauge the impact of these barriers and to mitigate them, it is imperative to note the shift in barriers under conditions of uncertainty such as wars, pandemics, and calamities. Although existing literature has focused on such aspects, little attention has been given to the handloom sector (Bortamuly et al., 2013b; Nicola et al.), 2020, whose importance has been described above. In this paper, we attempt to fill this gap in the literature.

2.3. Barriers affecting the performance of indian MSME with a particular focus on the handloom sector

COVID-19 epidemic has significantly damaged microbusinesses as well as the global economy (Eggers, 2020). The socioeconomic impacts of COVID-19 on several facets of the global economy were summarised by Nicola et al., in 2020. Eggers (2020) carried out a study of the literature on 69 papers that looked at SMEs during prior crises and made recommendations on how to deal with economic downturns in the areas of finance, strategy, and the institutional environment. There was much information on the difficulties, possibilities, and suggested future approaches. In order to understand problems, expectations, and plans for resurrection after the pandemic, Berkel (2020) evaluated 85 ″micro" businesses. The analysis shows that several industries had a loss in operations prior to the lockdown as a result of numerous obstacles and shocks. COVID-19 has made matters worse and exacerbated a few obstacles, such as a shortage of working capital and growing input costs. This concurs with Mishra as well (2020).

In their case-based study on micro-entrepreneurs, Prasad and Tata (2010a) compiled a list of the most important internal and external factors which influences the output and supply chain management of Indian handloom micro-entrepreneurs. They took notice of the challenges with the handloom industry's knowledge on economic resources, connections, market orientation, social and cultural contexts, unpredictability, and political and legal frameworks. In their study, Zhu et al. (2011) came to the conclusion that for some textile manufacturing firms, a lack of human resource skills and difficult-to-enter main markets for eco-friendly clothing operate as hurdles to producing sustainable clothing. Twenty-six barriers to the implementation of green supply chain management methods for small and medium-sized businesses were highlighted by Mathiyazhagan et al., 2013. Using MCDM methodologies, they also discovered their contextual link and came to the conclusion that supply obstacles are the most dominant factors.

According to Bortamuly et al. (2013), this study identifies and examines the factors that influence the adoption of technology in the Assamese handloom sector using primary data from 500 respondents. The findings showed that industry owners' yearly income and level of education both significantly influence how quickly they embrace new technologies. Additionally, Hazarika et al. (2016) mentioned how this business typically lacks competence and material training. According to Prajwal (2018), after new tax policies were put into place in 2017, the cost of raw materials increased significantly as a result of the 5% surcharge tax, which was previously exempted in order to support the development of the handloom industry. This caused an increase in the overall price of the finished goods. Gardas et al. (2018) investigated the cause-and-effect connections between industrial obstacles and the textile and apparel sectors. Based on a survey of the literature and the opinions of experts, they identified fourteen impediments and came to the conclusion that inadequate government policies and subpar infrastructure were among the most important ones. They also emphasised the negative effects of demonetization, which resulted in a 50% decline in wholesale demand overall and significant financial losses. The interrelationships between 17 obstacles discovered in the textile and clothing business were examined by Raut et al., in 2019. According to the investigation's findings, demonetization, insufficient infrastructure, a lack of effective integration, limited foreign investment, and ineffective government policies are the biggest obstacles facing the supply chains for clothing and textiles. A localised water deficit results from the handloom industry's need for a huge water supply at various stages of manufacturing (Fieldson and Rai, 2009). A crucial point is that, because steady R&D is not practised, handloom micro-enterprises are frequently ignorant of the harm caused by chemical discharges in their nearest water source or drainage system (Alkaya and Demirer, 2014; Jia et al., 2020).

Despite what existing literature has suggested, the interrelationships and relative relevance of the barriers to the sustainable performance of handloom MSMEs have not been examined in existing literature. Such research is necessary for the government, MSMEs, and handloom stakeholders to make wise policy decisions and implement strategic actions. Additionally, it is crucial to consider how the significance scores change over time as well as how context-specific linkages are related, particularly in the period before and after COVID-19 when the market is most volatile and unpredictable. The present work closes the aforementioned knowledge gaps. Through a review of the literature and expert opinion, we originally selected 17 barriers from the body of existing research that have an impact on the performance of the Indian handloom sector through a review of the literature and expert opinion. These barriers are summarised in Table 1 and the description can be located in appendix section A.

Table 1.

Critical barriers to performance of Handloom Industry with their dimension of sustainability.

Barriers	References	ENV	ECO	SC
Imitational threat	Prasad and Tata (2010a)	✓	✓	✓
The paucity of new designs	Industry experts		✓	✓
Lack of branding	Eggers et al. (2013); Prasad and Tata (2010a)		✓
Lack of skill education	Hazarika et al. (2016)	✓	✓	✓
Poor Infrastructure	Gardas et al. (2018)	✓	✓	✓
Lack of R&D	Prasad and Tata (2010b)	✓	✓	✓
Demonetization	Gardas et al. (2018); Raut et al. (2019)	✓	✓	✓
The negative impact of the handloom sector on the environment and society	Fieldson and Rai (2009); Gardas et al. (2018); Jia et al. (2020)	✓		✓
Complex supply chain	Majumdar & Sinha, (2019); Mishra, (2020);	✓	✓	✓
Lack of working capital	Bortamuly et al. (2013); Raut et al. (2019)	✓	✓	✓
Rising input costs	Bortamuly et al. (2013); Industry experts	✓	✓	✓
Skilled labor unavailability	Bortamuly et al. (2013); industry experts	✓	✓	✓
Demand uncertainty	Prasad and Tata (2010a)		✓	✓
Raw Material Shortage	Hazarika et al. (2016); Prasad and Tata (2010a)	✓	✓	✓
Lack of effective governmental policies	Gardas et al. (2018)	✓	✓	✓
Lack of effective level of integration	Prasad and Tata (2010a); Thakkar et al. (2008)	✓	✓	✓
Tax policy implementation	Industry experts	✓	✓	✓

2.4. Research gap

Although a number of studies have studied the drivers and barriers to sustainable performance of MSMEs and a few have focused on textiles, only a few studies have depicted the overall situation of various sectors during the COVID-19 pandemic (Bortamuly et al., 2013b; Nicola et al., 2020). However, no study thus far has lent any insight into the changing situation of handloom MSMEs. This leads to a gap in the research. As discussed earlier, textiles are a very important sector for India, and handloom governs a huge majority of the textile and MSME sectors. Therefore, exploration of the changing barriers in MSME is important. This study provides the same, focusing on the mitigation strategies that handloom MSMEs have adopted in conjunction with the Indian government to combat the COVID-19 crisis.

Additionally, to the best of our knowledge, there has been no study comparing the change in performance barriers before and after the onset of the pandemic. Such a comparative analysis is important so that stakeholders can adapt quickly to the dynamics of COVID-19 and similar calamities the onset of the pandemic. Such a comparative analysis is important so that stakeholders can adapt quickly to the dynamics of COVID-19 and similar calamities. The lack of such studies creates a gap in the literature. Notably, this study was conducted in two phases: phase 1 was conducted before the pandemic, while phase 2 was conducted during the pandemic. This helps us better clarify the changes in the performance barriers for the MSME sector at large. Importantly, this study has chosen the Indian handloom sector as its research domain. This area in itself is unique, as although in the past decade, just 187 studies have been conducted for the textile industry, negligible studies have been done specifically for the Indian handloom industry, which effectively forms a significant part of the textile sector (Bortamuly et al., 2013).

No paper has previously used time as a dimension in the MCDM analysis and investigated the time-varying nature of the decision-making frameworks. However, a dynamic economic environment suggests that many barriers and drivers of performance are not constant. The lack of such studies creates a gap in the literature. This is one of the first papers to focus on the MCDM method's application in the time dimension. Most of the applications of MCDM in the extant literature have focused on only one time period (Purohit et al., 2016a; Sarmah and Rahman, 2018; Darbari et al., 2019; Tan et al., 2019a), while we analysed two periods (during and after the COVID-19 pandemic), thus bridging this gap.

2.5. Problem statement

The handloom industry is the country's largest cottage industry, with 2.377 million (YOJANA, 2019). It is also the second-largest employment provider in the rural region employing more than 3 million people in direct and allied activities. During COVID-9 pandemic the demand for handloom goods decreased by 72 percent (Sannith, 2020). Given the present difficulties, it is nearly impossible for the handloom sector to remove every barrier at once and boost its performance in the Indian MSME sector. In order to minimise, if not completely eliminate, these barriers, we must analyse their contextual relationship and nature. To mitigate the impact of such uncertainties such as pandemic, wars and calamities it becomes necessary to note the change in performance internal and external barriers. In order to bridge this gap, this study with the help of qualitative and quantitative approach analysed two periods (during and after the COVID-19 pandemic) and notes the shift in change in performance barriers. The findings of the study can guide policymakers and other stakeholders in understanding the impact of internal and external barriers while developing strategies and policies during uncertain times to take better informed decision for policy making.

3. Research methodology

This study develops an integrated sequential approach for modelling and ranking changes in critical performance barriers within the Indian textile sector as COVID-19 hits the emerging market. The overall analysis was done in two phases: pre-COVID-19 and the onset of COVID-19. In phase 1, a qualitative technique known as the "Delphi method" has been applied to identify the vital critical barriers affecting the said sector. Further, a quantitative method is followed using a multi-criteria decision-making technique to establish the contextual relationship among these barriers. In phase II, the Delphi method is used again to rank the importance of the barriers found in round 1 interviews. This is followed by an MCDM analysis. Refer to Fig. 1 for the conceptual model for the research methodology.

Fig. 1.

Research methodology conceptual model.

Panel members with over 20 years of handloom expertise have been selected based on three broad categories: 1) Master weavers and 2) government agencies, including primary cooperative societies and apex cooperative societies. Their consent to participate was verbally obtained. We conducted two interviews with the panel to identify performance barriers and get their consent and unanimity on the critical barriers. Specifically, our panel comprised 31 experts. The first round of face-to-face interviews with semi-structured questions was recorded and reviewed with their permission. Based on this phase, wherein we collected the interview responses, we matched them with our findings from the existing literature review and noted the key barriers common to both the existing literature and the interviews. Then we proceeded to the MCDM analysis with seven respondents.

In the second phase or round, we discussed the "common barriers" before the panel again. We discussed their importance while measuring it on a five-point Likert scale, ranging from unimportant to extremely important. Notably, coding and indexing were used to capture the key barriers affecting the handloom industry's sustainability in phase 1, followed by result analysis in phase 2 with the help of the Content Validity Ratio (CVR). Refer to Appendix (Section B) for the formula and scaling. Further, post-COVID-19 contextual barrier analysis was done with seven respondents.

3.1. Delphi method

The critical barriers affecting the performance of the traditional manufacturing MSME were identified in Phase 1. Before the Delphi method, a systematic literature review (SLR) was conducted. The literature review has been done in a structured process that includes the definition and testing of search terms based on research questions, the literature search and selection, coding, and reporting of results (Durach et al., 2017).

The Delphi technique supports a productive discussion that leads to consent over several iterations (Loo, 2002). Following Delbari et al. (2016), due care was taken in planning, organising, and executing the current study for conducting Delphi rounds. To the best of our knowledge, the exploration of MSME performance barriers in the event of external uncertainty, such as the COVID-19 pandemic, specifically regarding the MSME sector has yet to be noted in the literature.

3.2. Multi-criteria decision-making

The identified barriers' rankings and interrelationships have been established with interpretative structural modelling (ISM) and Matrices Impacts cross-multiplication applique and classmate (MICMAC) analysis. The ISM approach is interpretive, as in judgments of the gatherings, choosing whether the factors are connected and how they relate to contextual relationships (Jia et al., 2015). ISM analysis is used when a problem concept is unclear and unexplored; it helps in articulating mental models with several elements that can then be visualised into a hierarchical model (Thakkar et al., 2005), and elements are grouped into an extensive and precise model based on their degree of similarity (Warfield, 1974). As shown in Table 3 , these barriers were further classified into four clusters based on MICMAC analysis (Ali et al., 2020; Purohit et al., 2016a; Tan et al., 2019a). Several authors have used the ISM method in different contexts, such as in construction (Tan et al., 2019b), footwear (Purohit et al., 2016b), hotels (Sarmah and Rahman, 2018), and food supply chains (Darbari et al., 2019), among others.

Table 3.

Matrices Impacts cross-multiplication applique and classmate (MICMAC) clusters.

Clusters	Description
Autonomous Cluster	When the segregated factors have weak driving and dependence power.
Dependent	Elements falling in this cluster have a high reliance power but a weak driving power.
Linkage	This factor contributes to high driving and dependence power.
Driving	Factors having high driving power but a weak dependence power are characterised in this zone.

4. Findings and results

4.1. Finding of delphi analysis

• RQ1: What are the critical performance barriers of the MSME sector in the emerging market both during and after the COVID-19 situation?

The critical performance barriers of the Indian textile and apparel (handloom) industry were identified via the Delphi technique. For the initial round of interviews for the Delphi analysis, a total of 56 experts were approached before the COVID-19 pandemic. However, only 31 of the contacted experts were approved to be involved in semi-structured face-to-face interviews. The average duration of these interviews was kept to a minimum of 30 min. Round 1 of the interviews was conducted before the COVID-19 pandemic hit the Indian economy, identifying critical barriers affecting the performance of the MSME sector, specifically the handloom industry. Finally, 17 barriers were selected.

Table 1 depicts the identified key barriers affecting the performance of the handloom industry by the expert panel and literature review in the first round. After the onset of the pandemic, the second round of interviews was conducted, wherein we requested the panellists to chalk out the importance level of these barriers on a five-point Likert scale ranging from 1 to 5 (5 being extremely important and 1 being not essential) to reach a consensus and determine the importance level among the barriers found in the interviews under round 1. Barriers with a CVR of at least 0.35 (the number of panellists was 31) were selected as the primary barriers responsible for affecting the performance of the said industry.

Table 2 also depicts the outcome of round 2 responses, including the acceptance or rejection status after the second round of Delphi post-COVID-19. Barriers with a CVR value of 0.355 or higher were accepted, while the rest were rejected. Out of the 17 barriers obtained in round 1, five were rejected, and only 12 were taken for further analysis, as indicated in Table 2.

Table 2.

Results of delphi round 2.

Barriers	Performance drivers	ne	CVR	Result
LEI	Lack of effective level of integration	21	0.355	Accepted
PI	Poor infrastructure	24	0.548	Accepted
PND	Paucity of new designs	15	0.032	Rejected
LWC	Lack of working capital (credit access)	23	0.484	Accepted
CSC	Complex supply chains	22	0.419	Accepted
LSE	Lack of skill education	16	0.032	Rejected
LRD	Lack of research and development	17	0.097	Rejected
RIC	Rising input costs	25	0.613	Accepted
LB	Lack of branding	21	0.355	Accepted
SLU	Skilled labour unavailability	21	0.355	Accepted
DU	Demand uncertainty	22	0.871	Accepted
IT	Imitational Threat	29	0.419	Accepted
RMS	Raw material shortage	27	0.742	Accepted
LEG	Lack of effective governmental policies	30	0.935	Accepted
DEM	Demonetization	22	0.030	Rejected
TPI	Tax policies implementation	26	0.677	Accepted
NES	The negative impact of the handloom sector on the environment and society	19	0.226	Rejected

From round 2 of the interviews, it is evident that the lack of effective governmental policies, imitational threat, raw material shortage, and tax policies implementation are the top four barriers affecting the sustainable performance of the handloom industry. Besides, eight barriers (i.e., lack of effective level of integration, poor infrastructure, lack of working capital (credit access), complex supply chains, rising input costs, lack of branding, skilled labour unavailability, and demand uncertainty) were captured. Table 2 also shows the identified barriers, which have been assigned reference codes for MCDM analysis.

4.2. Finding of MCDM analysis

• RQ2: How are the contextual relationship and the interdependence level among these key barriers during and after COVID-19?

The contextual relationship and the interdependence level among these key barriers, both before and during COVID-19, were identified via ISM modeling. For ISM, the same group of 31 experts was consulted via email. Following our second round of discussions, we received seven responses in both the pre- and post-COVID-19 phases. The contextual relationship was obtained through the brainstorming method. The "yes" and "no" questions were asked to establish the interrelationship between the barriers (Dubey et al., 2020). If the number of identified handloom barriers were n, then ⁿC2 would represent the total number of paired comparisons. All the mathematical steps of ISM are summarised in Appendix (Section B).

4.2.1. Results from the ISM model

Then, we formed a structural model from the final reachability matrix and level partitioning. A structural model is a directed graph with levels of nodes (barriers). Fig. 2 depicts the hierarchical distributions of all 17 critical barriers affecting the sector before the COVID-19 pandemic. Fig. 2 depicts the hierarchical distributions of all 12 critical barriers selected by a Delphi panel of experts during the pandemic.

Fig. 2.

Pre-COVID-19 ISM modeling.

Importantly, in phase 1 (i.e., before the pandemic), the independent barriers in the ISM model were noted at the bottom: lack of effective government policies (level IX), demonetization (level VIII), tax policies implementation (level VII), imitational threat (level VI), and raw material shortage (level VI). Interestingly, these five barriers influence the rest, but in themselves, they are unaffected by others. The highly influenced barriers include skilled labour unavailability ((level II), complex supply chain (level I), lack of effective level of integration (level II), paucity of new designs (level II), a lack of branding (level I), and the negative impact of the handloom sector on the environment and society (level I); these were noted at the top of the ISM model. The barriers in the middle have the potential to influence the ones above and below them, making them highly unstable. They are known as ‘linkage factors’; they include lack of skill education (level III), lack of research and development (level III), poor infrastructure (level III), rising input costs (level IV), lack of working capital (credit access) (level IV), and finally demand uncertainty at level V.

In phase 2, after the COVID-19 pandemic, the bottom-level factors, namely, the lack of effective government policies (level VI) and tax policies implementation (level V) influence all other factors but are unaffected by others. The second group is the top-level indicators that influence the rest of the indicators: lack of effective integration (level 1), poor infrastructure (level 1), complex supply chains (level 1), and skilled labour unavailability (level 1). The third group is the intermediate factors driven by bottom-level barriers that influence the above barriers, namely, lack of working capital (level III), lack of branding (level II), rising input costs (level III), imitational threat (level IV), raw material shortage (level IV), and demand uncertainty (level III). The digraph depicted in Fig. 2, Fig. 3 shows that there are barriers that show unidirectional and bidirectional relationships.

Fig. 3.

Post-COVID-19 ISM modeling.

4.2.2. Results from MICMAC analysis

To find out how policymakers should look to mitigate these barriers for MSMEs, the MICMAC approach was used. The final reachability matrix determines driving and dependence power, as shown in Fig. 4, Fig. 5 . The summary of cluster analysis before and after the onset of a pandemic is given in Table 4 .

Fig. 4.

MICMAC analysis of identified barriers for the Indian MSME (handloom) industry pre-COVID-19.

Fig. 5.

MICMAC analysis of identified barriers for the Indian MSME (handloom) industry after COVID-19.

Table 4.

Summary of Cluster analysis before and after the onset of the pandemic (MICMAC analysis).

Cluster	The onset of COVID-19 (12 barriers)	Pre-COVID-19 (17 barriers)
Driving Cluster	Lack of effective governmental policies; Tax policies implementation; Imitational threat. (Total 3)	Lack of effective government policies; demonetization; Tax policies implementation; and imitational threat. (Total: 4)
Linkage Cluster	Lack of effective level of integration; Poor infrastructure; Lack of working capital (credit access); Complex supply chains;	Raw material shortage; poor infrastructure; rising input cost; Lack of working capital (credit access); demand uncertainty; Lack of effective level of integration; complex supply chain. (Total: 7)
	Rising input costs;
	Skilled labour unavailability;
	Demand uncertainty; Raw material shortage.
	(Total:8)
Dependence Cluster	Lack of branding (Total 1)	Lack of research and development; Skilled labour unavailability; paucity in new designs; lack of branding; Lack of skill education; the negative impact of the handloom sector on the environment and society. (Total: 6)
Total barriers	12	17

Although researchers (Mishra and Bhattacharjee, 2017; Prasad and Tata, 2010b) have identified various barriers to the sustainable performance of the Indian handloom industry, they have failed to prioritise the dominant barriers, which are liable for the poor condition of the weaver populace. The current study prioritises these barriers and defines a contextual interrelationship between them using the ISM approach. The MICMAC analysis provides insight into the relative importance and contextual relationship among the selected indicators or barriers.

The barriers of low dependence and driving power are characterised as "autonomous clusters." Phase 1 suggests that no barriers fall under this cluster, indicating thereby the importance of all 12 barriers. Six barriers (i.e., paucity of new designs, lack of skill education, lack of research and development, lack of branding, skilled labour unavailability, and the negative impact of the handloom sector on the environment and society) have low driving and high dependence on power and are categorised under cluster II. Further, seven critical barriers (i.e., lack of effective level of integration, poor infrastructure, lack of working capital (credit access), complex supply chains, rising input cost, demand uncertainty, and a raw material shortage) have high driving and high dependence on power. They are thus grouped under the linkage cluster. Finally, cluster IV comprises the four most significant barriers (i.e., imitational threat, Tax policies implementation, demonetization, and lack of effective governmental policies); these have low dependence but high driving power.

Phase 2 analysis suggests no barriers fall under the autonomous cluster (Cluster I). Lack of branding is categorised in cluster II (dependence cluster). The weak driving power and strong dependence indicate that these barriers create little pressure on other barriers; however, they show strong dependence. Further, we segregate eight critical barriers (i.e., lack of effective level of integration, poor infrastructure, lack of working capital or (credit access), complex supply chains, rising input cost, skilled labour unavailability, demand uncertainty, and a raw material shortage) based on their high driving and high dependence powers under the linkage cluster. Notably, these barriers are highly unstable; any action on one would affect the others within this cluster.

Further, any of these could severely affect the production line, resulting in an imbalance in the demand-supply flow, thereby threatening business performance, growth, and a long-term competitive advantage. Thus, these barriers tend to inter-relate long-term commitments and, importantly, are interdependent. Hence, we suggest that these barriers should be looked into more minutely.

Finally, cluster IV comprises the three most significant barriers, which have low dependence, but high driving power; they include a lack of effective government policies, Tax policies implementation, and imitational threat. On July 1, 2017, when the Indian government implemented the Goods and Service Tax (GST), it caused an inflation in the price of raw materials due to high transportation costs coupled with an additional 5 percent tax based on the latest tax reforms, which earlier was subsidised for handloom development; this resulted in high-priced final products (Prajwal, 2018b). On the one hand, government policies have increased the handloom prices, and on the other, they have handicapped this sector from selling its products.

Before COVID-19, we may have suggested that decision-makers adopt policies and take measures to overcome challenges, such as raw material shortage, poor loom infrastructure, credit access, the imitational threat from power looms, and the mill sector. However, in these uncertain times, the decision-makers should intensely focus on issues such as liquidity, labour, demand uncertainty, infrastructure, rising input cost, and tax.

4.3. Managerial insights

The findings can be extended beyond COVID-19 to other global calamities too. The paper also helps policymakers and think tanks create strategies and policies for MSMEs by giving them an idea about the interdependence of critical factors. Policymakers should note that demonetization, the negative impact of the handloom industry on society, the paucity of new designs, a lack of skill education, and a lack of R&D are perceived as less significant barriers post-COVID. This indicates that due to the pandemic, the MSMEs mainly focused on their survival by cutting down on food intake, borrowing from relatives, friends, and moneylenders, and selling assets (Azim Premji University, 2021). There is a shift seen between barriers during ISM modelling and MICMAC for pre-post-COVID-19 analysis. Two barriers, namely, imitational threat and raw material shortage, shifted from independent barriers to linkage barriers due to the noted change in their levels and directionality. Lack of branding barriers observed an increase in their levels, and from dependent barriers (level 1) to linkage barriers (level 2), implying the barrier's increasing importance. From MICMAC cluster analysis it can be noted the skill labour unavailability due to increase in its driving power shifted from dependence cluster to linkage cluster, making it highly unstable. Due to an increase in the driving power of both barriers, policymakers are advised to look minutely into the lack of branding and skill labour unavailability issues for handloom stakeholders. In India, for the most part, the publicity is just through shows and fairs with restricted outlets (Prasad and Tata, 2010a). This does generate a greater need for brand development, promotions, and financial support. Also, handloom micro entrepreneurs do need to adopt new technology to market and sell their products, such as e-commerce platforms, as they are flexible and cost-effective and, most importantly, provide a primary tool that could both be adopted and implemented (Liguori and Pittz, 2020). Due to the limited number of customers in the market after the pandemic, online communities on social media are a way for enterprises to reconnect with their customers (Ratten, 2020).

MSME stakeholders (micro entrepreneurs and policymakers) should also focus on the issues related to labour migration and take measures to simplify the supply chain in these unprecedented times. Moreover, better coordination between handloom stakeholders and online marketing platforms could reduce the "middlemen," which would help generate better profits (Wong et al., 2015). The development of strong inter-organizational connections between microbusiness owners that cross racial, religious, and caste boundaries, access to financial resources like credit; and promoting participation in the political and legal system through voter registration and election turnout are all things that NGOs could play a significant role in. Change agents could also encourage the formation of cooperatives to boost the purchasing power within the supply chain.

It was found that imitational threat and raw material shortage barriers also saw a shift in their directionality, as in phase 1, the barriers were independent and unidirectional. However, in phase 2, due to an increase in interdependence, the barriers became bidirectional. This indicates that the imitation threat and raw material barriers are now inextricably linked, as a shortage of raw materials plagued the industry during the pandemic, leading to an increase in the use of polyester-quality yarn and posing a threat to original yarn.

According to the study, the two key constant dominant barriers that occupy the highest levels in their respective levels, such as levels 8 and 9 during phase 1 and levels 5 and 6 during phase 2 (refer to Fig. 2, Fig. 3), are tax policies implementation and the lack of effective governmental policies.Policymakers should focus on encouraging the ease of doing business; however, complicated tax processes such as mandatory NTP registration in both online and offline modes of business discourage micro-entrepreneurs from switching to online markets and deviating from India's vision of having a digitally powered economy (Kumar, 2022).

5. Conclusion, limitations, and future scope

Policymakers and stakeholders in the handloom industry have been increasingly emphasising understanding the barriers to enhancing the overall supply chain performance, especially in the middle of this global pandemic. This paper uniquely investigates the barriers and analyses the interaction among them. Developing a contextual relationship among MSME barriers would enable policymakers to form a unified approach and help in informed decision-making, especially in these uncertain times. This study contributes to the existing literature by identifying key performance barriers, supported by practical insights from industry experts. The research framework may help analyse barriers to performance due to both upstream and downstream internal and external factors. Notably, it is evident that lack of effective governmental policies, imitational threat, raw material shortage, and tax policy implementation are the top four barriers that have affected industry performance, and lack of branding serves as the critical barrier in cases of colossal market uncertainty. Two-phased (before and after the onset of COVID-19), successive exploratory hybrid methods, starting with the Delphi technique (qualitative phase) and concluding with interpretive structural modeling—MICMAC analysis (quantitative phase), are used for analysis. First, the qualitative Delphi method is applied to identify critical barriers, followed by a quantitative method to obtain the contextual relationship among these key barriers in phases 1 and 2. Second, their contextual relationship is derived via the MCDM method.

This paper depends on the ISM method, which is bound by a few inherent restrictions. Moreover, this study limits itself to only 17 barriers that affect the performance of the MSME industry in India. Additional barriers could be considered in the future as per the relevant scenario. Second, the ISM method depends on the decisions of industry experts; hence, conclusions could be biased. Further cross-country research is needed to confidently generalise the model.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

CRediT authorship contribution statement

Tarunima Mishra: Conceptualization, Data curation, Methodology, Writing – original draft, preparation. Swagato Chatterjee: Methodology, Supervision, Writing – review & editing. Jitesh J. Thakkar: Supervision.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Handling Editor: Cecilia Maria Villas Bôas de Almeida

Appendix.

Section A

Table A1.

Critical barriers to performance of Handloom Industry with their dimension of sustainability. (Dimensions of sustainability: Environmental: ENV; Economic: ECO; Social: SC)

Barriers	Description	References	ENV	ECO	SC
Imitational threat	The fastest handloom can make a sari in one week; however, a power-loom can make two saris within a single day with a similar pattern with the help of machines Possessing huge threat to Handloom industry	Prasad and Tata (2010a)	✓	✓	✓
The paucity of new designs	Majority weavers of the handloom industry only produce traditional products with limited range; moreover, few measures have been taken for innovating and adopting new patterns.	Industry experts		✓	✓
Lack of branding	Indian handloom industry has low brand image in terms of advancement and publicizing its products when contrasted with the material segment. Micro entrepreneurs generally sticks to practices of local branding and markets.	Eggers et al. (2013); Prasad and Tata (2010a)		✓
Lack of skill education	Weavers employ a traditional method for producing and outlining due to a lack of presentation, attentiveness, and learning. This sector lacks expertise and training.	Hazarika et al. (2016)	✓	✓	✓
Poor Infrastructure	Poor infrastructure, such as deteriorating ports, roads, phone networks, and electricity shortages, adds to MSME costs. The quality of handloom items depends on modern handloom technology and high-grade raw materials. Outdated equipment leads to poor competency and bad-quality products. Lack of IT infrastructure hinders sector growth.	Gardas et al. (2018)	✓	✓	✓
Lack of R&D	Weavers can't do all the inventive activities needed to realise an innovation on their own due to their small size and resource limitations.	Prasad and Tata (2010b)	✓	✓	✓
Demonetization	In 2016, India adopted 'demonetization' to prevent undeclared money. Most small stores and wholesalers closed in the first week of implementation. Banning 'rolling' money hampered financial flow for months, which impacted the handloom business. Wholesale demand for T&A fell by 50% during this era, causing substantial financial losses.	Gardas et al. (2018); Raut et al. (2019)	✓	✓	✓
The negative impact of the handloom sector on the environment and society	Handlooms are socially and environmentally more inclusive than power-looms. Handloom manufacturing takes a lot of water at different stages, causing localised water shortages. As there is no steady R&D, handloom micro-enterprises are ignorant of chemical discharges in their water supply or drainage. Thus, there is indeed an urgent need to create awareness among weavers to adopt green practices to achieve sustainability.	Fieldson and Rai (2009); Gardas et al. (2018); Jia et al. (2020)	✓		✓
Complex supply chain	Before the COVID-19 epidemic, the typical lead time to create and deliver a handloom product was 45–60 days, frequently 80 days. This lead time delay has been exacerbated by lockdowns and social distancing conventions, since raw material is often purchased from another state, making the supply chain complicated, fragile, and one of the most important barriers.	Majumdar & Sinha, (2019); Mishra (2020);	✓	✓	✓
Lack of working capital	The epidemic has exacerbated market liquidity concerns owing to limited exports, imports, and a national lockdown. Previous literature has discussed official vs informal sources, hard versus soft loans, owners' qualities, financing variables, etc.	Bortamuly et al. (2013); Raut et al. (2019)	✓	✓	✓
Rising input costs	The build-up inventory would be a strain, as they would first need to maintain and clear out the old/waste supplies, adding to their business costs in this shaky market. Yarn prices have also risen. Handloom weavers have poor net margins due to depreciation, increased inventory, transportation expenses, and production quality issues.	Bortamuly et al. (2013); Nicola et al. (2020); Industry experts	✓	✓	✓
Skilled labor unavailability	COVID-19 has caused widespread lockdowns, and social alienation has driven MSMEs to temporarily stop their looms/factories, causing 30–70% of temporary labour to return home due to loss of income and insecurity. These looms/factories must retain/recall trained workers throughout and after the pandemic.	Bortamuly et al. (2013)	✓	✓	✓
Demand uncertainty	Both purchasing and selling are limited to essential items, which directly affects non-essential handloom MSME products. Previous literature stated that uncertainty in handloom MSME includes a) replacement commodities, generating a downward demand trend; b) weather circumstances; and c) socio-cultural norms, such as rigorous wedding and festival limitations.	Prasad and Tata (2010a)		✓	✓
Raw Material Shortage	In the handloom business, raw materials are often purchased through intermediaries, and three-quarters of weavers buy from outside sources. Fluctuating costs and raw material availability lead to poor production and resultant losses, making raw material scarcity one of the main hurdles identified in the study.	Hazarika et al. (2016); Prasad and Tata (2010a)	✓	✓	✓
Lack of effective governmental policies	As the case industry has a skill shortage, strong norms should be set for training the workforce. Environmental policies and waste management must be created. Tax relaxation initiatives and T&A infrastructure upgrades are also needed to strengthen the case industry.	Gardas et al. (2018)	✓	✓	✓
Lack of effective level of integration	Due to fewer consumers, each link in the wider supply chain would aim to maximise its profit without considering any other semi-adjacent/adjacent participant, rendering the entire supply chain useless. These families' producers are horizontally and vertically spread, with a similar cast trying to increase customers. After the epidemic, the consumer base is skewed, preventing 'direct' customer collaboration.	Prasad and Tata (2010a); Thakkar et al. (2008)	✓	✓	✓
Tax policy implementation	The Indian government mandated 'Goods and Services Tax' (GST) on July 1, 2017. Most MSMEs don't qualify for GST's minimal registration limit. Also keeping out of the government's way reduces costs. Even being out of the loop has impacted some MSMEs terribly; on the other side, the government can't map out the micro businesses to give relief subsidies. Higher transportation costs and a 5% surcharge tax (GST), which was subsidised for handloom development, have raised the costs of raw materials and finished goods.	Industry experts	✓	✓	✓

Section B

Delphi Analysis

The following formula was applied in calculating CVR for each driver.

$$C\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{V}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{d}\mathrm{i}\mathrm{t}\mathrm{y}\mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}(\mathrm{C}\mathrm{V}\mathrm{R})=\frac{ne-\frac{N}{2}}{\raisebox{1ex}{$N$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}$$

Here, ne indicates the number of experts present in the study, indicating 'essential', whereas N depicts the total number of experts participating in the study (Lawshe, 1975). According to Lawshe (1975), responses should be given on a five-point Likert scale under three categories, such as 'essential,' 'useful but not essential,' and 'not necessary.' Subsequently, 'extremely important (=5)' and 'very important (=4)' of the Likert scale were merged into the 'essential' option; 'moderately important (=3)' was considered under 'useful but not essential' option; while 'slightly important (=2)' and 'not important (=1)' were clubbed under 'not necessary’ (Delbari et al., 2016).

The steps of ISM are as follows

Structural Self-Interaction Matrix (SSIM).

SSIM is developed based on the contextual relationship established between barriers represented as i and j. According to Sage (1977), the relationship between any two variables, in our case barriers, can be represented by four standard symbols (V, X, A, O), which would help in giving a direction to the flow of the relationship. This representation is depicted in Table B1.

Table B1.

Conversion algorithms for SSIM to initial reachability matrix

Representative symbols	i → j	j → i	(i,j) th entry	(j,i) th entry
V	✓	✗	1	0
A	✗	✓	0	1
X	✓	✓	1	1
O	✗	✗	0	0

Once SSIM is developed, it should be further discussed with opinion experts so that the result of SSIM is validated. Table B2 and Table B3 depict the SSIM of the said sector pre-pandemic and after the onset of the pandemic, respectively.

Table B2.

Structural self-interaction matrix (SSIM) (Pre COVID-19)

Barriers	NES	TPI	DEM	LEG	RMS	IT	DU	SLU	LB	RIC	LRD	LSE	CSC	LWC	PND	PI	LEI
Lack of effective level of integration (LEI)	V	A	A	A	A	A	X	O	O	V	O	O	A	A	O	O	–
Poor Infrastructure (PI)	V	A	A	A	X	A	O	O	O	X	O	O	A	A	O	–
Paucity in Designs (PND)	O	O	O	A	A	A	O	O	O	A	A	A	V	A	–
Lack of working capital (LWC)	V	O	A	A	A	O	X	O	V	X	V	O	O	–
Complex supply chain (CSC)	V	A	A	A	A	A	O	X	X	A	A	A	–
Lack of skill education (LSE)	V	O	O	A	A	O	O	A	O	O	O	–
Lack of research and development (LRD)	V	O	O	A	A	O	A	O	O	O	–
Rising input costs (RIC)	V	A	A	A	A	A	X	V	O	–
Lack of branding (LB)	A	A	A	A	A	A	O	O	–
Skilled labour unavailability (SLU)	V	A	A	A	A	O	O	–
Demand Uncertainty (DU)	V	A	A	A	A	O	–
Imitational threat (IT)	O	A	A	A	A	–
Raw material shortage (RMS)	V	A	A	A	–
Lack of effective governmental policies (LEG)	V	V	V	–
Demonetization (DEM)	V	V	–
Tax policies implementation (TPI)	V	–
The negative impact of the handloom sector on the environment and society(NES)	–

Table B3.

Structural self-interaction matrix (SSIM) (Post COVID-19)

Barriers	TPI	LEG	RMS	IT	DU	SLU	LB	RIC	CSC	LWC	PI	LEI
Lack of effective level of integration (LEI)	A	A	A	A	X	O	O	V	A	A	O	–
Poor infrastructure (PI)	A	A	X	A	O	O	O	X	A	A	–
Lack of working capital (LWC)	O	A	A	O	X	O	V	X	O	–
Complex supply chain (CSC)	A	A	A	A	O	X	X	A	–
Rising input cost (RIC)	A	A	A	A	X	V	O	–
Lack of branding (LB)	A	A	A	A	O	O	–
Skilled labor unavailability (SLU)	A	A	A	O	V	–
Demand Uncertainty (DU)	A	A	A	O	–
Imitational Threat (IT)	A	A	A	–
Raw material shortage (RMS)	A	A	–
Lack of effective governmental policies (LEG)	V	–
Tax policies implementation (TPI)	–

Table B4.

Initial reachability matrix (Pre COVID-19)

Barriers	LEI	PI	PND	LWC	CSC	LSE	LRD	RIC	LB	SLU	DU	IT	RMS	LEG	DEM	TPI	NES
Lack of effective level of integration (LEI)	1	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1
Poor Infrastructure (PI)	0	1	0	0	0	0	0	1	0	0	0	0	1	0	0	0	1
Paucity in Designs (PND)	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0
Lack of working capital (LWC)	1	1	1	1	0	0	1	1	1	0	1	0	0	0	0	0	1
Complex supply chain (CSC)	1	1	0	0	1	0	0	0	1	1	0	0	0	0	0	0	1
Lack of skill education (LSE)	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0	1
Lack of research and development (LRD)	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0	1
Rising input costs (RIC)	0	1	1	1	1	0	0	1	0	1	1	0	0	0	0	0	1
Lack of branding (LB)	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0
Skilled labor unavailability (SLU)	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0	0	1
Demand Uncertainty (DU)	1	0	0	1	0	0	1	1	0	0	1	0	0	0	0	0	1
Imitational threat (IT)	1	1	1	0	1	0	0	1	1	0	0	1	0	0	0	0	0
Raw material shortage (RMS)	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	1
Lack of effective governmental policies (LEG)	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Demonetization (DEM)	1	1	0	1	1	0	0	1	1	1	1	1	1	0	1	1	1
Tax policies implementation (TPI)	1	1	0	0	1	0	0	1	1	1	1	1	1	0	0	1	1
The negative impact of handloom sector on the environment and society(NES)	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1

Table B5.

Initial reachability matrix (after COVID-19)

Barriers	LEI	PI	LWC	CSC	RIC	LB	SLU	DU	IT	RMS	LEG	TPI
Lack of effective level of integration (LEI)	1	0	0	0	1	0	0	1	0	0	0	0
Poor infrastructure (PI)	0	1	0	0	1	0	0	0	0	1	0	0
Lack of working capital (LWC)	1	1	1	0	1	1	0	1	0	0	0	0
Complex supply chain (CSC)	1	1	0	1	0	1	1	0	0	0	0	0
Rising input cost (RIC)	0	1	1	1	1	0	1	1	0	0	0	0
Lack of branding (LB)	0	0	0	1	0	1	0	0	0	0	0	0
Skilled labour unavailability (SLU)	0	0	0	1	0	0	1	0	0	0	0	0
Demand Uncertainty (DU)	1	0	1	0	1	0	0	1	0	0	0	0
Imitational Threat (IT)	1	1	0	1	1	1	0	0	1	0	0	0
Raw material shortage (RMS)	1	1	1	1	1	1	1	1	1	1	0	0
Lack of effective governmental policies (LEG)	1	1	1	1	1	1	1	1	1	1	1	1
Tax policies implementation (TPI)	1	1	0	1	1	1	1	1	1	1	0	1

Table B6.

Final reachability matrix (Pre COVID-19)

Barriers	LEI	PI	PND	LWC	CSC	LSE	LRD	RIC	LB	SLU	DU	IT	RMS	LEG	DEM	TPI	NES
Lack of effective level of integration (LEI)	1	1*	1*	1*	1*	0	1*	1	1*	1*	1	0	0	0	0	0	1
Poor Infrastructure (PI)	1*	1	1*	1*	1*	1*	1*	1	1*	1*	1*	1*	1	0	0	0	1
Paucity in Designs (PND)	1*	1*	1	0	1	0	0	0	1*	1*	0	0	0	0	0	0	1*
Lack of working capital (LWC)	1	1	1	1	1*	0	1	1	1	1*	1	0	1*	0	0	0	1
Complex supply chain (CSC)	1	1	0	0	1	1*	0	1*	1	1	1*	0	1*	0	0	0	1
Lack of skill education (LSE)	1*	1*	1	0	1	1	0	0	1*	1*	0	0	0	0	0	0	1
Lack of research and development (LRD)	1*	1*	1	0	1	0	1	0	1*	1*	0	0	0	0	0	0	1
Rising input costs (RIC)	1*	1	1	1	1	1*	1*	1	1*	1	1	0	1*	0	0	0	1
Lack of branding (LB)	1*	1*	0	0	1	0	0	0	1	1*	0	0	0	0	0	0	1*
Skilled labour unavailability (SLU)	1*	1*	1*	0	1	1	0	0	1*	1	0	0	0	0	0	0	1
Demand Uncertainty (DU)	1	1*	1*	1	1*	0	1	1	1*	1*	1	0	0	0	0	0	1
Imitational threat (IT)	1	1	1	1*	1	0	0	1	1	1*	1*	1	1*	0	0	0	1*
Raw material shortage (RMS)	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	1
Lack of effective governmental policies (LEG)	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
Demonetization (DEM)	1	1	1*	1	1	1*	1*	1	1	1	1	1	1	0	1	1	1
Tax policies implementation (TPI)	1	1	1*	1*	1	1*	1*	1	1	1	1	1	1	0	0	1	1
The negative impact of handloom sector on the environment and society(NES)	0	0	0	0	1*	0	0	0	1	0	0	0	0	0	0	0	1

Table B7.

Final reachability matrix (after COVID-19)

Barriers	LEI	PI	LWC	CSC	RIC	LB	SLU	DU	IT	RMS	LEG	TPI
Lack of effective level of integration (LEI)	1	1*	1*	1*	1	0	1*	1	0	0	0	0
Poor infrastructure (PI)	1*	1	1*	1*	1	1*	1*	1*	1*	1	0	0
Lack of working capital (LWC)	1	1	1	1*	1	1	1*	1	0	1*	0	0
Complex supply chain (CSC)	1	1	0	1	1*	1	1	1*	0	1*	0	0
Rising input cost (RIC)	1*	1	1	1	1	1*	1	1	0	1*	0	0
Lack of branding (LB)	1*	1*	0	1	0	1	1	0	0	0	0	0
Skilled labour unavailability (SLU)	1*	1*	1*	1	1*	1*	1	1	0	0	0	0
Demand Uncertainty (DU)	1	1*	1	1*	1	1*	1	1	0	0	0	0
Imitational Threat (IT)	1	1	1*	1	1	1	1*	1*	1	1*	0	0
Raw material shortage (RMS)	1	1	1	1	1	1	1	1	1	1	0	0
Lack of effective governmental policies (LEG)	1	1	1	1	1	1	1	1	1	1	1	1
Tax policies implementation (TPI)	1	1	1*	1	1	1	1	1	1	1	1*	1

Representation of SSIM to 1s and 0s, which were earlier represented by as V, A, X, and O is known as initial reachability Matrix. Steps to be followed for the conversion of the matrix represented in Table B1. The initial matrix is formed after substituting the barriers; the final matrix is formed after the transitivity analysis. Table B4, Table B5 depict an initial reachability matrix pre and post-pandemic, respectively. The final reachability matrix is iterated based on transitivity conditions. It states that if variable A is related to variable B, and variable B is related to variable C, then variable A is indeed related to variable C. Table B6, and B7 illustrate the final reachability matrix along with the driving power and dependence power of barriers, pre and post-pandemic respectively.

Level Partitions

After forming the final reachability set, Reachability Set, Antecedent Set, and Intersection Set are produced in accordance. After completion of this table, a consecutive iteration is done so that barriers having common reachability set at an intersection set are omitted, which effectively decides the level as the first elimination barrier, given the top priority in ISM. This process continues until all barriers are assigned to their levels in the ISM model, which develops the digraph. Table B8 and B9 depict the level partitioning before and after the onset of the COVID-19 pandemic.

Table B8.

Level partitioning (Pre COVID-19)

Barriers	Reachability set	Antecedent Set	Intersection Set	Levels
Lack of effective level of integration (LEI)	1,2,3,4,5,7,8,9,10,11,17	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16	1,2,3,4,5,7,8,9,10,11	II
Poor Infrastructure (PI)	1,2,3,4,5,6,7,8,9,10,11,12,13,17	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16	1,2,3,4,5,6,7,8,9,10,11,12,13	III
Paucity in Designs (PND)	1,2,3,5,9,10,17	1,2,3,4,6,7,8,10,11,12,13,14,15,16,17	1,2,3,10	II
Lack of working capital (LWC)	1,2,3,4,5,7,8,9,10,11,13,17	1,2,4,8,11,12,13,14,15,16	1,2,4,8,11,13	IV
Complex supply chain (CSC)	1,2,5,6,8,9,10,11,13,17	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17	1,2,5,6,8,9,10,11,13,17	I
Lack of skill education (LSE)	1,2,3,5,6,9,10,17	2,5,6,8,10, 13,14,15,16	2,5,6,10	III
Lack of research and development (LRD)	1,2,3,5,7,9,10,17	1,2,4,7,8,11,13,14,15,16	1,2,7	III
Rising input costs (RIC)	1,2,3,4,5,6,7,8,9,10,11,13,17	1,2,4,5,7,8,11,13,14,15,16	1,2,4,5,8,11,13,17	IV
Lack of branding (LB)	1,2,5,9,10,17	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17	1,2,5,9,10,17	I
Skilled labour unavailability (SLU)	1,2,3,5,6,9,10,17	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16	1,2,3,5,6,9,10	II
Demand Uncertainty (DU)	1,2,3,4,5,7,8,9,10,11,17	1,2,4,5,8,11,12,13,14,15,16	1,2,4,5,6,11	V
Imitational threat (IT)	1,2,3,4,5,8,9,10,11,12,13,17	2,12,13,14,15,16	2,12,13	VI
Raw material shortage (RMS)	1,2,3,4,5,6,7,8,9,10,11,12,13,17	2,4,5,8,12,13,14,15,16	2,4,5,8,12,13	VI
Lack of effective governmental policies (LEG)	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17	14	14	IX
Demonetization (DEM)	1,2,3,4,5,6,7,8,9,10,11,12,13,15,16,17	14,15	15	VIII
Tax policies implementation (TPI)	1,2,3,4,5,6,7,8,9,10,11,12,13,16,17	14,15,16	16	VII
The negative impact of handloom sector on the environment and society (NES)	5,8,17	1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16	5,8,17	I

Table B9.

Level partitioning (onset of COVID-19)

Barriers	Reachability Set	Antecedent Set	Intersection Set	Levels
Lack of effective level of integration (LEI)	1,2,3,4,5,7,8	1,2,3,4,5,6,7,8,9,10,11,12	1,2,3,4,5,7,8	I
Poor infrastructure (PI)	1,2,3,4,5,6,7,8,9,10	1,2,3,4,5,6,7,8,9,10,11,12	1,2,3,4,5,6,7,8,9,10	I
Lack of working capital (LWC)	1,2,3,4,5,6,7,8,10	1,2,3,5,8,9,10,11,12	1,2,3,5,8,10	III
Complex supply chain (CSC)	1,2,4,5,6,7,8,10	1,2,3,4,5,6,7,8,9,10,11,12	1,2,4,5,6,7,8,10	I
Rising input cost (RIC)	1,2,3,4,5,6,7,8,10	1,2,3,4,5,8,9,10,11,12	1,2,3,4,5,8,10	III
Lack of branding (LB)	1,2,4,6,7	2,3,4,5,6,7,8,9,10,11,12	2,4,6,7	II
Skilled labour unavailability (SLU)	1,2,4,6,7	1,2,3,4,5,6,7,8,9,10,11,12	1,2,4,6,7	I
Demand Uncertainty (DU)	1,2,3,4,5,6,7,8	1,2,3,4,5,8,9,10,11,12	1,2,3,4,5,8	III
Imitational Threat (IT)	1,2,3,4,5,6,7,8,9,10	2,9,10,11,12	2,9,10	IV
Raw material shortage (RMS)	1,2,3,4,5,6,7,8,9,10	2,3,4,5,9,10,11,12	2,3,4,5,9,10	IV
Lack of effective governmental policies (LEG)	11,12	11	11	VI
Tax policies implementation (TPI)	12	11,12	12	V

Data availability

Data will be made available on request.