1 Department of Operations, GITAM School of Business, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India
2 Indira Gandhi National Open University, Delhi, India
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed.
COVID-19 (COV) pandemic has brought many misfortunes to the economies of the country. Industries were shut down due to lockdown; production was stopped, which affected their profit margins. Many Micro, Small, and Medium Enterprises (MSME) had to close their operations permanently due to lack of funds and lack of labor force. This research is being conducted to study the MSME’s problems due to this pandemic crisis. Five problems have been identified from the available literature. These five problems are lack of transportation facilities, non-availability of raw materials, deficiency of cash flow, lack of manpower, and local law enforcement. The data is collected in the MSME sector of the country. The target population are the employees working in those industries. After collecting data, we have used exploratory factor analysis and structural equation modeling using the software SPSS 22.0 and AMOS 20.0. The result showed that all the proposed hypotheses got accepted. The model fit parameters are within the threshold level. This study also provides recommendations for the MSME sector to revive the COV pandemic situation.
COVID-19, MSME, problems, survey, lockdown
The outbreak of the infection, COVID-19 (COV), had brought a worldwide misfortune for living souls and different industrial and service sectors such as manufacturing, supply chain (SC) and logistics, automobile industry, hospitality industry, travel industry, oil industry, construction industry, telecom sector, food industry, and medical care industry (Ahmed et al., 2021; Chamola et al., 2020; Hobbs, 2020; Ivanov & Dolgui, 2020; Iyengar et al., 2020; Mahmud et al., 2021; Queiroz et al., 2020; Remko, 2020; Rodrigues et al., 2021; Rowan & Galanakis, 2020; Roy et al., 2020; Zutshi et al., 2021). The first case of COV was found in the city of Wuhan in the Hubei Province, China, in December 2019 (De Vito & Gómez, 2020). Further, noticing its impact like unusual and uncontrolled contamination with more than 118,000 cases found in the 114 nations worldwide, the WHO named this global pandemic a COV pandemic on March 11, 2020.
The Micro, Small, and Medium Enterprises (MSME) sector has emerged as one of the critical sectors in the Indian economy. MSME contributes to the employment generation both in rural and urban areas. MSME accounts for 45% of India’s total industrial production and 40% of its total exports. Manufacturing MSME contributes approximately 7.09% to the GDP of the country, and service MSME contributes approximately 30.50% to the country’s GDP. But Indian MSME faces many problems such as lack of innovation, proper strategies to handle the crisis, financial issues, and labor issues (Baral et al., 2021). When the COV pandemic started increasing in the country, the Indian government had to go for a lockdown in the whole country, which resulted in the shutdown of the industries, shops, blockage, restrictions in travelling, etc. (Kumar et al., 2021).
Due to the global lockdown, people’s daily lives, family businesses, start-ups, and other firm typologies such as MSME have suffered drastic exponential economic consequences. Adverse changes in the labor market and consumption have occurred due to the transformation of people’s daily lives (Rodrigues et al., 2021; Verma & Gustafsson, 2020). As a result, MSME must face several challenges, such as meeting the requirements of protective sanitary measures, adjusting production to demand, and addressing other issues. The MSME sector is financially weak and has less experience in crisis management. This article aims to study the problems that are being faced by the Indian MSME post-COV pandemic. Problems faced by the MSME are lack of transportation facilities (TRF), non-availability of raw material (RM), deficiency of cash flows (DCF), lack of manpower (MP), and local law enforcement (EN).
Government of India imposed lockdown on 29 March 2020, but emergency services were open. The milk and article SCs were likewise permitted to work. Synthetic substances, cars, gadgets, and different ventures were closed down because of supply disturbance and limitation of coordination’s/shipment (Sarkis, 2020). The nation is experiencing a downturn in the second from last quarter of the financial year (FY) 2020. The economic effect from the COV in India has generally been troublesome. According to the Ministry of Statistics, India’s development in the fourth quarter of the FY 2020 went down to 3.1%. Nationally, economic activity was effectively halted as a result of the lockdown. The COV pandemic had a significant impact on the manufacturing and service sectors. Due to the nationwide lockdown, agricultural products were the least affected by the MSME sector. During the lockdown, 60% of the economic activities in India’s MSME sector were frozen. A majority of commercial and industrial outlets of small business enterprises were shut down during the nationwide lockdown in India. In the aftermath of the shutdown, the SC was disrupted, making it difficult for MSMEs to obtain raw materials at affordable prices.
In India, manufacturing industries are ultimately hampered because of an absence of labor, coordination, and SC because of lockdown limitations. Although numerous organizations are leaning more towards Internet-based shopping exercises and broad conclusion of multiple showrooms totally by attempting to meet vehicle purchaser needs (Chowdhury et al., 2021). Changes in action plans and the utilization of innovative practices and advances likewise lead to changes in existing SC structures and connections (Chowdhury et al., 2020). The Government of India declared a nationwide lockdown to limit the spread of coronavirus. The consequences of a lockdown, however, were catastrophic for the city. As a result, the Indian MSME sector was severely affected. MSMEs suffered from a lack of manpower, for example, when the majority of workers returned home. A large number of people moved from rural to urban areas in search of better job prospects. Laborers from various regions in India have returned in enormous numbers to the places where they grew up. In the past, few companies were manufacturing or assembling ventilators (Morgan et al., 2021). As a result, managers of MSMEs in rural regions found it difficult to persuade these workers to return to their former workplaces. The disturbance of the progression of materials and merchandize has negative ramifications on different parts of the business, precisely an unexpected finish to approaching incomes and the movement of the labor force across all aptitude levels (Adel & Kotb, 2020).
The SC boundaries are the absence of top administration responsibility and backing, an indistinct hierarchical goal, worker strengthening and preparing, deficient assets, poor corporate culture, doubt among representatives and SC accomplices, absence of schooling and training to representatives and providers (Donthu & Gustafsson, 2020), helpless data and correspondence innovation foundation, reluctance to actualize SC rehearses (Ebersberger & Kuckertz, 2021), lack of joining among SC accomplices, absence of coordinated effort among SC accomplices, absence of responsiveness, absence of consumer loyalty list, and so forth (Karmaker et al., 2021; Sharma et al., 2021). These hindrances are intricate, and in this manner, leaders need to comprehend them well to reduce the obstructions. It has been seen that SC is constantly impacted by certain obstacles (Jamwal et al., 2020). Presently in India, COV has disturbed the production network in the assembling areas. The hindrances for the Indian SC brought about by the COV are discovered from the conversations with educational specialists and mechanical specialists. They figure out numerous obstructions of SC in the assembling areas such as an absence of labor, absence of crude materials, inaccessibility of imported products, a bottleneck in last-mile conveyance, lack of transportation, sluggish developments of merchandize, limitation on abroad transportation, absence of purchasers, absence of income, slow credit stream from the monetary areas, and nearby laws authorization (Queiroz et al., 2020; Wu & Olson, 2020). The five hindrances are the absence of labor, nearby laws authorization, absence of transportation, shortage of crude materials, and lack of income on the lookout, discovered as essential in the SCs in India. Table 1 shows the hypothesis and the details of the five problems faced by the MSME during COV. Figure 1 shows the proposed research model for the study.
Figure 1. Proposed Theoretical Model for the Study
Source: The authors.
The data for the study was collected from parts of the country using a simple random sampling method. A structured questionnaire was prepared with the help of the academician and industry person (Baral & Verma, 2021). The questionnaire consisted of two parts: the first part comprised of the organizational details of the MSME sectors, and the second part consists of the MSME problems due to the COV. An online survey was being conducted by identifying the target population from the MSME. Before going to the final survey, a pilot survey was conducted by taking a sample size of 52. The pilot survey results were satisfactory, and the Cronbach’s alpha (α) value was more significant than 0.70 for the variables (Nunnally, 1978). Since the pilot survey was satisfactory, we went for the final survey. The questionnaire was sent through email to the respondents. Online mode of the survey was selected (Mukherjee & Chittipaka, 2021). The target population was the employees working in MSME in India. The respondents were mainly plant managers, directors, and the owners of the MSME. The number of questionnaires sent to the respondents was 653, out of which only 316 questionnaire data were taken for the study. The rest of the questionnaire was rejected because the questionnaire was half-filled, not adequately filled, and some respondents did not send us back the questionnaires (Pal et al., 2021).
Table 1. Details of the Five Problems Faced by the MSME During COVID-19
Source: From the literature review.
Table 2. Demographics of the Respondents
Source: The authors.
Table 2 shows the demographics of the respondents. A questionnaire method was used. The firm in which the total number of employees was in the range 51–100, the respondents’ percentage was 18%, which is the highest. It was followed by the firm in which the total number of employees were in the range 151–250; the respondent’s percentage here was 17%. The rest of the firms where the total number of employees was in the range of 26–50, the respondent’s percentage was 16%; in the range of 1–9, the respondent’s percentage was 15%; in the range of 101–150, the respondent’s percentage was 14%; in the range of 10–25, the respondent’s percentage was 12%; and in the range of 251 and above, the respondent’s percentage was 8%. The percentage of the respondents who are plant managers is 41%, which is the highest. It is followed by the percentage of the respondents who are directors, which is 35%, and the percentage of the respondents who are owners, which is 23%. The percentage of the respondents from the types of firms which are medium enterprises is 39%, which is the highest. It is followed by the percentages of the respondents from the types of firms which are microenterprises (31%) and small enterprises (30%).
After the collection of the data, we had to check whether the collected data was biased or not. For this, we used Harman single factor test in the software SPSS. The result showed that the first factor explained a maximum variance of 36.729%, which is below the recommended value of 50% (Podsakoff et al., 2003).
Reliability and Validity
Cronbach’s Alpha (α).A measure of internal consistency, Cronbach’s alpha, measures how closely related a group of items are to each other. It is regarded as a scale reliability metric. A “high” alpha value does not imply that the measure is one dimensional (Wu & Little, 2011). Additional analyses can be performed if you want to provide evidence that the scale in question is unidimensional in addition to measuring internal consistency. One method for determining dimensionality is exploratory factor analysis (EFA) (Watkins, 2018). Cronbach’s alpha is not a statistical test; instead, it is a reliability coefficient (or consistency). Cronbach’s alpha values of 0.7 or higher indicate acceptable internal consistency. Table 3 shows the values of Cronbach’s alpha for the constructs. The values are more significant than 0.70, which is acceptable (Netemeyer et al., 2003).
Composite Reliability (CR).As with Cronbach’s alpha, construct reliability (or CR) measures the internal consistency of scale items (Netemeyer et al., 2003). As a result, it can be viewed as equal to the total variance of accurate score compared to scale score variance. CR that achieved 0.70 or above means the scale has good reliability (Heeler & Ray, 1972). Table 3 shows the values of CR for the constructs. The values are more significant than 0.70, which is the acceptable value.
Table 3. Results of Cronbach’s Alpha (α) and Composite Reliability
Source: The authors (output obtained from SPSS 20.0).
Exploratory Factor Analysis
The next step is to perform EFA for the collected data. Factor analysis is a procedure for establishing relationships between measured variables in a dataset and latent factors that explain the covariation between these measured variables (Allen, 2017; Olkin & Sampson, 2001; Watkins, 2018). As a result, a group of variables can be identified and analyzed to see how they relate to each other. Factor analysis is often used to determine the hidden dimensions or constructs that may not be apparent from the direct analysis (Keith et al., 1995; Kline, 2000). EFA helps in grouping the variables in a meaningful way. Principal axis factoring and Promax rotation have been selected to perform the EFA in SPSS software. All the 18 indicators were grouped into five variables. Table 4 shows the rotated component matrix.
Table 4. Rotated Component Matrix
Source: The authors (output obtained from SPSS 20.0).
Construct Validity (CV)
CV is used to determine how well a test measures what it is supposed to measure. Does the test successfully test what it claims to test Comparing the test to other tests that measure similar qualities helps determine the correlation between the two measures (Olkin & Sampson, 2001; Wu & Little, 2011). If the results of a test are identical to those found on other widely accepted measures of cognitive aptitude, it shows that the test’s construct validity is high.
Average variance explained (AVE) is the average variance in indicator variables which is explained by a given construct or theory (Keith et al., 1995; Kline, 1999). It is possible to calculate the AVE for each construct by dividing the total factor loadings by the square root of the sum of the factor loadings. All the latent and indicator variables are scaled to have unit variance in the completely standard solution. Table 5 shows the values of construct correlation and AVE.
Table 5. Construct Correlation and AVE
Source: The authors (output obtained from AMOS 22.0).
Structural Equation Modeling
Structural equation modeling (SEM) is a set of statistical techniques used to measure and analyze the relationships of observed and latent variables (Hu et al., 2009; Kaplan, 2001). To test the proposed hypothesis for the study, SEM is being used. The software used is AMOS 22.0. The latent variables along with its indicators are ‘lack of transportation facilities’ (TRF): it has four indicators—TRF1, TRF2, TRF3, and TRF4; non-availability of raw materials (RM): it has four indicators—RM1, RM2, RM3, and RM4; deficiency of cash flow (DCF): it has three indicators—DCF1, DCF2, and DCF3; lack of manpower (MP): it has three indicators—MP1, MP2, and MP3; and local law enforcement (EN): it has three indicators—EN1, EN2, and EN3. One dependent variable is problems faced by the MSME during COV (PMSME): it has four indicators—PMSME1, PMSME2, PMSME3, and PMSME4. Figure 2 shows the final model for the study.
Table 6 shows the results of the path analysis performed in AMOS 22.0. This indicates that all the proposed hypotheses are accepted. The squared correlation for the model is 67% of PMSME.
Table 6. Path Analysis for the Hypothesis
Source: The authors (output obtained from AMOS 22.0).
The component non-availability of raw materials (RM) means a shortage of raw materials in the industries and manufacturing units due to the lockdown implemented because of the COV pandemic (Das et al., 2020; Roy et al., 2020). Without raw materials, enterprises cannot produce their products, which will directly affect their profit margins. There were many reasons for the non-availability of raw materials, such as disruptions in SC and various states borders were shut down, which restricted the movement of the vehicles (Remko, 2020). MSME sector in India is suffering from a lack of relief measures. Managers need to identify the effects of this shortage. As a result of the non-disbursement of appropriate relief, key business activities such as trade and procurement of raw materials were disrupted (Suresh Lal et al., 2020). According to a new report, a pandemic-related loss of 20%–25% was suffered by nearly 50% of Indian MSME units during the COV outbreak. As a result of the lack of appropriate relief measures, manufacturers of non-essential and essential goods and services have suffered losses equivalent to 50% of their annual revenue (Mishra & Rampal, 2020).
Figure 2. Final Model
Source: The authors (output obtained from AMOS 22.0).
The component local law enforcement (EN) means the local authorities of the states made a different kind of law after consulting with the central government (Jamwal et al., 2020). The contaminated zones were identified, and complete lockdown was implemented in those zones. The component lack of transportation facilities (TRF) means that due to lockdown, transportation was shut in many areas, and states borders were closed for the vehicle movement (Mahendra Dev & Sengupta, 2020). The component lack of manpower (MP) means non-availability of the labor forces in the companies. This situation arose after the lockdown (Biswas & Das, 2020). Due to lockdown, many persons were jobless. They started returning to their hometown, which created a crisis in the labor force in the industries as the companies faced many problems in getting back the labor force (Sipahi, 2020). The component deficiency of cash flow (DCF) means the companies were out of production: could not produce nor sell their products; there was a deficiency in cash flow in the markets, making the companies significantly lose (Dohale et al., 2021).
In the case of a company with an annual turnover of more than R25 crores, there was a decline in the net sales by –25.3%. During the lockdown, a large number of small businesses were shut down for a variety of reasons. The first factor was the lack of effective demand for goods and services (Mahmud et al., 2021). Another reason was a high mortality rate among employees. In April 2020, it was predicted that 255 of the total number of firms operating in the MSME sector would close due to a lockdown that would last for more than eight weeks. During the COV pandemic, a nationwide lockdown was imposed. As a result, workers were unable to perform their regular duties. During the crisis, they lacked the necessary expertise and skills to manage business activities effectively. The managers/owners were also concerned about gaining government support (Mittal & Raman, 2021). Their aspirations were for better financial assistance and tax reductions. The government has already begun implementing some of these measures. However, qualifying requirements and the institutions’ slow response have sluggishness among MSME owners and managers (Roy et al., 2020).
Some of the recommendations that the MSME sector needs to implement in order to come out from the COV pandemic situation are as follows.
This research was done to identify the problems that the Indian MSME faces due to the COV pandemic. As we all know, due to the pandemic, industries have been affected very severely; their revenue generation has decreased, decreasing their profit margins. MSME plays a significant role in the economy of the country. Indian MSME faced many challenges, such as lack of raw materials, labor force, and good funds. For this research, five problems were identified for the available literature: lack of transportation facilities, non-availability of raw materials, deficiency of cash flow, lack of manpower, and local law enforcement. A questionnaire was developed for survey-based research. The target population was mainly owners, directors, and plant managers of the MSME. Five hypotheses were formulated for the study. Five independent variables had three or four indicators, and one dependent variable had four indicators. EFA was performed, followed by SEM. A model was developed using five independent variables and one dependent variable. The model that was developed showed a good fit, and all the hypotheses were accepted. This research will benefit both the managers and the government organizations to develop a solution for the problems faced by the MSME in the current scenario for the pandemic.
Further, some study limitations, like a small sample, were taken for the research; a large sample would have shown better results. This research could be extended to other sectors also where the pandemic had hit the most. Further, this research could extend to other countries also to develop as a comparative study. At last, we need to find out the solution to the problems that were found out from this research.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The authors received no financial support for the research, authorship, and/or publication of this article.
Adel, R., & Kotb, I. (2020). Smart retailing in COVID-19 world : Insights from Egypt. European Journal of Marketing and Economics, 3(2), 132–156. https://doi.org/10.26417/794nsm56u
Ahmed, F., Syed, A. A., Kamal, M. A., López-garcía, M., de las N., Ramos-requena, J. P., & Gupta, S. (2021). Assessing the impact of COVID-19 pandemic on the stock and commodity markets performance and sustainability: A comparative analysis of south asian countries. Sustainability, 13(10). https://doi.org/10.3390/su13105669
Allen, M. (Ed.). (2017). Factor analysis: Exploratory. In The SAGE encyclopedia of communication research methods. https://doi.org/10.4135/9781483381411.N186
Baral, M. M., Singh, R. K., & Kazanço?lu, Y. (2021). Analysis of factors impacting survivability of sustainable supply chain during COVID-19 pandemic: An empirical study in the context of SMEs. International Journal of Logistics Management. https://doi.org/10.1108/IJLM-04-2021-0198
Baral, M. M., & Verma, A. (2021). Cloud computing adoption for healthcare: An empirical study using SEM approach. FIIB Business Review, 10(3), 255–275. https://doi.org/10.1177/23197145211012505
Biswas, T. K., & Das, M. C. (2020). Selection of the barriers of supply chain management in Indian manufacturing sectors due to COVID-19 impacts. Operational Research in Engineering Sciences: Theory and Applications, 3(3), 1–12. https://doi.org/10.31181/oresta2030301b
Chamola, V., Hassija, V., Gupta, V., & Guizani, M. (2020, April). A comprehensive review of the COVID-19 pandemic and the role of IoT, drones, AI, blockchain, and 5G in managing its impact. IEEE Access, 8, 90225–90265. https://doi.org/10.1109/ACCESS.2020.2992341
Chowdhury, M. T., Sarkar, A., Saha, P. K., & Anik, R. H. (2020). Enhancing supply resilience in the COVID-19 pandemic: A case study on beauty and personal care retailers. Modern Supply Chain Research and Applications, 2(3), 143–159. https://doi.org/10.1108/mscra-07-2020-0018
Chowdhury, P., Kumar Paul, S., Kaisar, S., & Abdul Moktadir, M. (2021). COVID-19 pandemic related supply chain studies: a systematic review. Transportation Research Part E: Logistics and Transportation Review, 148, 102271. https://doi.org/10.1016/j.tre.2021.102271
Das, S., Basak, S., & Das, G. M. (2020). A Study for Understanding the Problems of MSMEs under Current Pandemic Situation with Special Reference to Kolkata. The Management Accountant Journal, 55(12), 65–67. https://doi.org/10.33516/MAJ.V55I12.65-67P
De Vito, A., & Gómez, J. P. (2020). Estimating the COVID-19 cash crunch: Global evidence and policy. Journal of Accounting and Public Policy, 39(2), 106741. https://doi.org/10.1016/J.JACCPUBPOL.2020.106741
Dohale, V., Ambilkar, P., Gunasekaran, A., & Verma, P. (2021). Supply chain risk mitigation strategies during COVID-19: Exploratory cases of “make-to-order” handloom saree apparel industries. International Journal of Physical Distribution and Logistics Management. https://doi.org/10.1108/IJPDLM-12-2020-0450
Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of Business Research, 117, 284–289. https://doi.org/10.1016/j.jbusres.2020.06.008
Ebersberger, B., & Kuckertz, A. (2021). Hop to it! The impact of organization type on innovation response time to the COVID-19 crisis. Journal of Business Research, 124, 126–135. https://doi.org/10.1016/j.jbusres.2020.11.051
Heeler, R. M., & Ray, M. L. (1972). Measure validation in marketing. Journal of Marketing Research, 9(4), 361–370. https://doi.org/10.1177/002224377200900401
Hobbs, J. E. (2020). Food supply chains during the COVID-19 pandemic. Canadian Journal of Agricultural Economics, 68(2), 171–176. https://doi.org/10.1111/cjag.12237
Hu, L., & Bentler, P. M. (2009). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning and Control, 32(9), 1–14. https://doi.org/10.1080/09537287.2020.1768450
Iyengar, K. P., Vaishya, R., Bahl, S., & Vaish, A. (2020). Impact of the coronavirus pandemic on the supply chain in healthcare. British Journal of Health Care Management, 26(6), 1–4. https://doi.org/10.12968/bjhc.2020.0047
Jamwal, A., Bhatnagar, S., & Sharma, P. (2020). Coronavirus disease 2019 (COVID-19): Current literature and status in India. https://doi.org/10.20944/PREPRINTS202004.0189.V1
Kaplan, D. (2001). Structural equation modeling. International Encyclopedia of the Social & Behavioral Sciences, 15215–15222. https://doi.org/10.1016/B0-08-043076-7/00776-2
Karmaker, C. L., Ahmed, T., Ahmed, S., Ali, S. M., Moktadir, M. A., & Kabir, G. (2021). Improving supply chain sustainability in the context of COVID-19 pandemic in an emerging economy: Exploring drivers using an integrated model. Sustainable Production and Consumption, 26, 411–427. https://doi.org/10.1016/j.spc.2020.09.019
Keith, T. Z., Fugate, M. H., Degraff, M., Diamond, C. M., Shadrach, E. A., & Stevens, M. L. (1995). Using multi-sample confirmatory factor analysis to test for construct bias: An example using the K-ABC. Journal of Psychoeducational Assessment, 13(4), 347–364. https://doi.org/10.1177/073428299501300402
Kline, R. B. (1999). Book review: Psychometric theory (3rd ed.). Journal of Psychoeducational Assessment, 17(3), 275–280. https://doi.org/10.1177/073428299901700307
Kline, R. B. (2000). Book review: Measurement and evaluation in psychology and education (6th ed.). Journal of Psychoeducational Assessment, 18(2), 160–166. https://doi.org/10.1177/073428290001800205
Kumar, P., Singh, S. S., Pandey, A. K., Singh, R. K., Srivastava, P. K., Kumar, M., Dubey, S. K., Sah, U., Nandan, R., Singh, S. K., Agrawal, P., Kushwaha, A., Rani, M., Biswas, J. K., & Drews, M. (2021). Multi-level impacts of the COVID-19 lockdown on agricultural systems in India: The case of Uttar Pradesh. Agricultural Systems, 187, 103027. https://doi.org/10.1016/j.agsy.2020.103027
Mahendra Dev, S., & Sengupta, R. (2020). Impact of COVID-19 on the Indian Economy: An Interim Assessment. https://time.com/5818819/imf-coronavirus-economic-collapse/
Mahmud, P., Paul, S. K., Azeem, A., & Chowdhury, P. (2021). Evaluating supply chain collaboration barriers in small- and medium-sized enterprises. Sustainability, 13(13), 7449. https://doi.org/10.3390/SU13137449
Mishra, K., & Rampal, J. (2020). The COVID-19 pandemic and food insecurity: A viewpoint on India. World Development, 135, 105068. https://doi.org/10.1016/J.WORLDDEV.2020.105068
Mittal, V., & Raman, T. V. (2021). Examining the determinants and consequences of financial constraints faced by micro, small and medium enterprises’ owners. World Journal of Entrepreneurship, Management and Sustainable Development. https://doi.org/10.1108/WJEMSD-07-2020-0089
Morgan, A. K., Awafo, B. A., & Quartey, T. (2021). The effects of COVID-19 on global economic output and sustainability: Evidence from around the world and lessons for redress. Sustainability: Science, Practice, and Policy, 17(1), 77–81. https://doi.org/10.1080/15487733.2020.1860345
Mukherjee, S., & Chittipaka, V. (2021). Analysing the adoption of intelligent agent technology in food supply chain management: An empirical evidence. FIIB Business Review. https://doi.org/10.1177/23197145211059243
Netemeyer, R., Bearden, W., & Sharma, S. (2003). Scaling procedures: Issues and applications. https://us.sagepub.com/en-us/nam/scaling-procedures/book10174
Nunnally, J. C. (1978). Psychometric theory. McGraw-Hill.
Olkin, I., & Sampson, A. R. (2001). Multivariate analysis: Overview. International Encyclopedia of the Social & Behavioral Sciences, 10240–10247. https://doi.org/10.1016/B0-08-043076-7/00472-1
Pal, S. K., Mukherjee, S., Baral, M. M., & Aggarwal, S. (2021). Problems of big data adoption in the healthcare industries. Asia Pacific Journal of Health Management, 16(4), 282–287.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879.
Queiroz, M. M., Ivanov, D., Dolgui, A., & Fosso Wamba, S. (2020). Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, 1–38. https://doi.org/10.1007/s10479-020-03685-7
Remko, van H. (2020). Research opportunities for a more resilient post-COVID-19 supply chain: Closing the gap between research findings and industry practice. International Journal of Operations and Production Management, 40(4), 341–355. https://doi.org/10.1108/IJOPM-03-2020-0165
Rodrigues, M., Franco, M., Sousa, N., & Silva, R. (2021). COVID 19 and the business management crisis: An empirical study in SMEs. Sustainability, 13(11), 5912. https://doi.org/10.3390/SU13115912
Rowan, N. J., & Galanakis, C. M. (2020). Unlocking challenges and opportunities presented by COVID-19 pandemic for cross-cutting disruption in agri-food and green deal innovations: Quo Vadis? Science of the Total Environment, 748, 141362. https://doi.org/10.1016/j.scitotenv.2020.141362
Roy, A., Patnaik, B. C. M., & Satpathy, I. (2020). Impact of COVID-19 crisis on Indian MSME sector: A study on remedial measures. Eurasian Chemical Communications, 2(9), 991–1000. https://doi.org/10.22034/ecc.2020.114672
Sahoo, P., & Ashwani. (2020). COVID-19 and Indian economy: Impact on growth, manufacturing, trade and MSME sector. Global Business Review, 21(5), 1159–1183). https://doi.org/10.1177/0972150920945687
Sarkis, J. (2020). Supply chain sustainability: Learning from the COVID-19 pandemic. International Journal of Operations and Production Management, 41(1), 63–73. https://doi.org/10.1108/IJOPM-08-2020-0568
Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2021). Accelerating retail supply chain performance against pandemic disruption: Adopting resilient strategies to mitigate the long-term effects. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-07-2020-0286
Sipahi, E. (2020). COVID 19 and MSMEs: A revival framework. Research Journal in Advanced Humanities, 1(2). https://royalliteglobal.com/advanced-humanities/article/view/146
Suresh Lal, B., Sachdeva, P., & Mittal, T. (2020). Impact of COVID-19 on micro small and medium enterprises (MSMEs): An overview. International Journal of Multidisciplinary Research and Development Online, 7, 2349–5979.
Verma, S., & Gustafsson, A. (2020). Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach. Journal of Business Research, 118, 253–261. https://doi.org/10.1016/j.jbusres.2020.06.057
Watkins, M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807
Wu, D. D., & Olson, D. L. (2020). The effect of COVID-19 on the banking sector (pp. 89–99). Springer. https://doi.org/10.1007/978-3-030-52197-4_8
Wu, W., & Little, T. D. (2011). Quantitative research methods. Encyclopedia of Adolescence (Vol. 1, pp. 287–297). Elsevier. https://doi.org/10.1016/B978-0-12-373951-3.00034-X
Zutshi, A., Mendy, J., Sharma, G. D., Thomas, A., & Sarker, T. (2021). From challenges to creativity: Enhancing SMEs’ resilience in the context of COVID-19. Sustainability, 13(12), 6542. https://doi.org/10.3390/su13126542