Assess the impact of the COVID-19 pandemic and propose solutions for sustainable development for textile enterprises: An integrated data envelopment analysis-binary logistic model approach

Nguyen Han-Khanh; Vu Mai-Nam Article Assess the impact of the COVID-19 pandemic and propose solutions for sustainable development for textile enterprises: An integrated data envelopment analysis-binary logistic model approach Journal of Risk and Financial Management Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Nguyen Han-Khanh; Vu Mai-Nam (2021) : Assess the impact of the COVID-19 pandemic and propose solutions for sustainable development for textile enterprises: An integrated data envelopment analysis-binary logistic model approach, Journal of Risk and Financial Management, ISSN 1911-8074, MDPI, Basel, Vol. 14, Iss. 10, pp. 1-24, https://doi.org/10.3390/jrfm14100465 This Version is available at: https://hdl.handle.net/10419/258569 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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Risk Financial Manag. 2021, 14, 465. https://doi.org/10.3390/jrfm14100465 www.mdpi.com/journal/jrfm Article Assess the Impact of the COVID-19 Pandemic and Propose Solutions for Sustainable Development for Textile Enterprises: An Integrated Data Envelopment Analysis-Binary Logistic Model Approach Han-Khanh Nguyen 1,* and Mai-Nam Vu 2 1 Faculty of Economics, Thu Dau Mot University, Number 6, Tran Van On Street, Phu Hoa Ward, Thu Dau Mot 590000, Vietnam 2 Graduate Training Institute, Thu Dau Mot University, Number 6, Tran Van On Street, Phu Hoa Ward, Thu Dau Mot 590000, Vietnam; vun[email protected] * Correspondence: [email protected]; Tel.: +84-933-727-969 Abstract: The COVID-19 pandemic impacted many socio-economic areas of countries around the world. It has made the production and business situations of enterprises face substantial difficulties. In this study, the authors used data envelopment analysis (DEA) models to assess the impact of the COVID-19 pandemic on Vietnam’s textile and garment enterprises. The authors have used the binary logistic model to determine the factors affecting employees’ decision to change jobs in the textile industry. The research results showed that the COVID-19 pandemic greatly affected the business performance of the textile and garment enterprises in Vietnam. Moreover, the results helped identify the factors affecting employee turnover and proposed solutions to help businesses stabilize their personnel situation and develop sustainable businesses in the post-COVID-19 era. Keywords: COVID-19; economic impact; data envelopment analysis; binary logistic model 1. Introduction In Vietnam, the textile and garment industry has been the second-largest export value industry for many consecutive years. In 2019, the total export amount reached 39.6 billion USD, and the trade surplus was 16.6 billion USD with an increase of 2.25 billion USD, equivalent to 7.55% compared to 2018. Compared to the year 1999, the export turnover in 2019 increased over 22 times from 1.75 billion USD in 1999 to 39 billion USD. The trade surplus in 2019 reached 17.7 billion USD, which was up 106.5 times compared to 185 million USD in 1999 (Nguyen 2019). In 2020, due to the heavy impact of the COVID-19 epidemic, Vietnam’s textile and garment industry faced many challenges, including breaking the supply chain of raw materials. The supply chain, in terms of human resources, was also greatly affected. Orders dropped significantly when Vietnam implemented social distancing. By the end of 2020, the entire Vietnam textile and garment industry achieved an export turnover of 35.29 billion USD, down 10.91% compared to 2019 (show in Figure 1) (Quynh 2021). Statistical results show that the export growth of the textile and garment industry in 2018 reached 16.5%; however, by the end of 2019, the COVID-19 pandemic caused the export growth to decrease to 8%. The prolonged epidemic situation caused this growth to drop deeply in 2020 (show in Figure 2). The export turnover of textiles and garments in the first 11 months of 2020 reached 26.73 billion USD (down 10.5% compared to 2019). Vietnam’s GDP decreased from 7.02% in 2019 to 2.91% in 2020. Citation: Nguyen, Han-Khanh, and Mai-Nam Vu. 2021. Assess the Impact of the COVID-19 Pandemic and Propose Solutions for Sustainable Development for Textile Enterprises: An Integrated Data Envelopment Analysis-Binary Logistic Model Approach. Journal of Risk and Financial Management 14: 465. https://doi.org/10.3390/ j rfm14100465 Academic Editor: Thanasis Stengos Received: 22 August 2021 Accepted: 26 September 2021 Published: 2 October 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses /by/4.0/). J. Risk Financial Manag. 2021, 14, 465 2 of 24 Figure 1. Vietnam’s textile and garment export turnover (Synthetic BSC 2021). Figure 2. Export growth of textile and garment industry and GDP growth of Vietnam (Vietstock Finance 2021). Industry scale: In 2019, Vietnam rose to the 3rd largest in the top 5 largest textile and garment exporting countries in the world. These included China, Bangladesh, Vietnam, India, and Pakistan (shown in Figure 3) (Hai 2019). J. Risk Financial Manag. 2021, 14, 465 3 of 24 Figure 3. Export turnover of textiles 2017–2019 (Unit: million USD) (Hai 2019). In terms of the strong attraction of foreign direct investment (FDI) inflows, he cost of labor and operating costs were low, and FDI inflows were increasingly strong in emerging economies such as Indonesia, Vietnam, and Bangladesh. In Vietnam, by the end of 2017, the total registered and additional capital was 15.89 billion USD, an increase of about 10%. This number jumped to 18.69 billion USD in 2018. The proportion of FDI capital was 70% in Vietnam’s textile and garment industry (Linh 2020). With regard to industry structure, the US, EU, Japan, Korea, and China are the largest export markets for Vietnam’s textile and garment industry (see Figure 4). Figure 4. Export share of Vietnam’s textile and garment industry in 2019 (To 2019). More than three million employees work for nearly 7000 enterprises in Vietnam’s textile and garment industry, with low average salaries compared to other countries (Le 2019). Due to the salary and welfare regimes of textile enterprises, they are not attractive to workers. Many potential workers lack industrial experience and do not meet the needs of the job, but require high wages. Because of this, businesses are facing great difficulties in recruiting skilled workers. Meanwhile, the number of textile enterprises is increasing, which has created even greater competition in the labor market. Unique features of this labor market add to the recruitment problem. The industry employees are low-income, work overtime, and have related health problems. The majority of garment workers are female and require more personal time to take care of their families. Therefore, the turnover in the textile and garment enterprises is continuous. Stabilizing the human resource picture helps textile enterprises to be more proactive J. Risk Financial Manag. 2021, 14, 465 4 of 24 when difficulties arise. At the same time, stabilizing human resources helps companies maintain product quality. For textile enterprises, human resources have a particularly important meaning when developing business strategies. The question is, how does the COVID-19 pandemic affect the production and business situation of Vietnam’s textile and garment enterprises? What factors affect the decision of employees to job- -hop in textile enterprises? What solutions are needed to recruit suitable personnel for employment positions in textile enterprises to stabilize the production and business situation? This study focuses on these issues. 2. Research Development 2.1. Literature Review Tamatam et al. (2019) used DEA models to estimate the relative efficiency of domestic Indian banks for the period 2008–2017. This study evaluated the performance of banks based on their ownership and size and studied their productivity trends. In the same year, Mahdiloo et al. (2014) used DEA models to evaluate supplier performance against benchmarks when several factors played a dual role in both inputs and outputs. This study used the DEA model for the first time to consider factors with dual roles. The results of this study gave a complete ranking of suppliers in the system. Abbas et al. (2015) used DEA models to evaluate efficiency and productivity, in Islamic and conventional banks, to compare their performance over the period 2005–2009. The results showed that, for religious reasons, Islamic banks, with their patronage, have been showing tremendous growth around the world. Lee et al. (2017) used DEA models to measure the comparative performance of 18 Korean commercial banks in the presence of negative observations and examined the difference in performance among these banks. Seth et al. (2021) used DEA models to explore the predictors of working capital management performance and their influence on firm performance. The results of this study evaluated the impact of various exogenous variables on the efficiency of working capital management and the performance of companies. Virginia et al. (2017) used binary logistic models to study substance abuse treatment outcomes. The researchers followed more than 30 subjects, determined relapse rates, and identified factors that could predict the re-use of addictive substances. Sanchez-Varela et al. (2021) used binary logistic models to evaluate position loss predictions on positioning drilling operations. The results demonstrated that position loss is more likely to occur when more generators are used and drilling takes place in shallower water in adverse weather conditions. Omondi-Ochieng (2021) used Binary logistic models to evaluate the financial performance of the United Kingdom’s (UK) national non-profit sports federations. The results of this study predicted the financial performance of the UK’s national non-profit sports federations. DEA models and binary logistic models have been widely used and applied in many fields of science, engineering, and the world economy (Kwon 2014; Wong and Wong 2007; Ashuri et al. 2019; Pourmahmoud and Azad 2021; Aparicio 2016). However, no scientific study has combined DEA and Binary logistic models to assess the impact of the COVID-19 pandemic and to identify sustainable development solutions for textile enterprises. This is the first study that uses combination of these models to assess the impact of the pandemic on the production and business situation of textile and garment enterprises and the factors that have contributed to the decision of employees to change jobs in textile enterprises. 2.2. Research Process The goal is to assess the impact of the COVID-19 pandemic on the production and business situation of textile and garment enterprises in Vietnam and find solutions to help these enterprises recover and develop sustainably in the post-COVID-19 period. The authors have undertaken an extensive literature review to choose the right analysis J. Risk Financial Manag. 2021, 14, 465 5 of 24 methods, models, and tools that are accurate and bring the best results. After reading and studying many modern tools and models, the authors found that DEA models and Binary logistic models are the most suitable for carrying out this study. DEA is a linear programming method for measuring the effectiveness of multiple decision-making units (DMUs) when a production process presents the structure of multiple inputs and outputs. This method relies on the past business data of the enterprise to construct the production boundary in the non-parametric plane (production boundary). A Binary logistic model is used to study the probability of occurrence of a binary-dependent variable (there are only two values: 0 and 1). When the dependent variable is in binary form, we cannot do the analysis with normal linear regression, because the model will violate the regression assumptions and invalidate the system statistics of the tests in the regression, leading to inaccurate results. Binary logistic regression does not necessarily satisfy these assumptions. Therefore, in this study, the authors use the binary logistic model to determine the factors affecting the decision of workers to change jobs in the textile industry. The procedure for carrying out this study is described in detail in Figure 5. Figure 5. Research process. To assess the impact of the COVID-19 pandemic on the production and business situation of textile and garment enterprises in Vietnam, the authors used DEA models, taking the following steps in turn: The authors collected data on the production and business condition of leading enterprises in Vietnam for the period 2016–2020 through the website of the general statistics office of Vietnam (Statistics 2021). From data on the production and business condition of Vietnamese textile and garment enterprises, the author selected five input factors including Total assets (F1), Equity (F2), Cost of goods sold (F3), Selling expenses (F4), Enterprise management cost (F5); and two output factors: Net revenue (F6), Profit after tax (F7) (Unit: 1000 USD). These factors fully reflected on the production and business situation of textile and garment enterprises. After collecting the above data, the authors checked the correlation of input and output factors to ensure that these factors were closely correlated with each other, fol- J. Risk Financial Manag. 2021, 14, 465 6 of 24 lowing the criteria of analysis conditions of the DEA models. If the correlation of the inputs and outputs was not guaranteed, the authors reselected the input and output factors to ensure the conditions of the DEA models. The number of inputs and outputs has no correlation to the number of DMUs, just the correlation of inputs and outputs. After testing the correlation between the input factors and the output factors, the conditions for analysis were ensured. The authors used the super-efficiency model to assess the overview of the production and business situation of Vietnamese textile and garment enterprises in the period 2016–2020. By analyzing the efficiency score of Vietnamese textile and garment enterprises and the ranking score of Vietnamese enterprises in this period, the authors could see the change in the production and business efficiency of the enterprises during the pandemic. After assessing the overview of the production and business situation of Vietnamese textile and garment enterprises in the period 2016–2020, the authors used the malmquist productivity index to analyze and evaluate each criterion of enterprises. Specifically, the authors evaluated the effectiveness of technology investment, the efficiency of technical investment, and the efficiency of production and business activities of Vietnam’s textile and garment enterprises with the period 2017–2020 serving as the basis for the results. This offered a solid basis for analyzing and assessing the impact of the COVID-19 pandemic on the production and business situation of Vietnamese textile and garment enterprises in the period 2016–2020. In fact, human resources determine the success of the business by ensuring the creation of a creative source in the enterprise. Human resources with dynamic, creative, and intellectual activities create useful values in en enterprise’s development strategy. One of the biggest difficulties that Vietnamese textile and garment enterprises often face is the situation of workers’ job hopping. Textile and garment enterprises require qualified and technical human resources in production to create high quality products. When human resources are unstable, it affects the quality of textile products, thereby affecting the production and business efficiency of textile enterprises. Therefore, textile enterprises that want to achieve good business performance need to stabilize human resources to ensure the production situation. To help textile enterprises generate solutions to stabilize the situation of human resources, the authors used a Binary logistic model to consider the factors affecting the decision of employees in textile enterprises to switch jobs. Specifically, in this study, the authors analyzed the following factors: worker’s age (AG), education level (ED), income (IN), working seniority (SE), and distance (DI) to determine the level of impact of these factors on a worker’s ability to switch jobs in textile enterprises. In this study, the authors consider the following hypotheses: Hypothesis 1 (H1). The age of workers is related to the decision about job-hopping. Hypothesis 2 (H2). The education level of workers in the textile and garment industry is related to the decision to switch jobs. Hypothesis 3 (H3). The distance from the employee’s place of residence to the workplace has an impact on the decision to change jobs. Hypothesis 4 (H4). The number of years of experience of an employee is related to the employee’s decision to switch jobs. In the textile and garment industry, stabilizing the personnel situation will stabilize the production situation and help enterprises develop sustainably. In order to determine how these factors affect employees’ job-switching ability in textile and garment enterprises, the authors have built a survey with the above factors described in Table 1: J. Risk Financial Manag. 2021, 14, 465 7 of 24 Table 1. The variables of the regression model. Code Description Kind of Variables PM Possibility to change jobs Dependent AG Worker’s age (age to present) Independent ED Education level (years of schooling) Independent IN Income (monthly income in million VND) Independent SE Working seniority (years) Independent DI Distance (kilometers) Independent The survey was conducted through the association of textile enterprises. The authors processed the obtained results on the software SPSS 20.0. From there, solutions were proposed to recruit personnel compatible with the characteristics of enterprises to improve the personnel situation. This in turn improves the production and business situation and guarantees the quality of products of Vietnamese textile enterprises to help them develop sustainably. 2.3. Data Description In this study, the authors relied on reports of enterprise business results and studies using the DEA model, which have achieved many successes in the field of economics (as Table 2). In this study, the authors used five inputs and two outputs to assess the impact of the COVID-19 pandemic on production and the business situation of textile enterprises in Vietnam. These were: Total assets of an enterprise (F1) were the entire set of assets, finance, valuable papers in cash, and property rights under the legal ownership or right to use the enterprise used for production and business activities of the enterprise. Equity (F2) is the total net assets of the business, owned by shareholders. Equity is made up of charter capital, undistributed profits, and other sources. Cost of goods sold (F3) of an enterprise includes costs of purchasing machinery, raw materials, production costs of goods, labor costs, administrative expenses, etc. The cost of goods sold depends on the cost of goods contract with a supplier or type of business. Selling expenses (F4) are expenses incurred in the process of consuming products, goods, and services of an enterprise. Selling expenses include expenses such as the cost of the sales staff, materials, packaging, tools, supplies, product warranty, outsourced services, and other monetary expenses. Administration expenses (F5) are the expenses that an enterprise uses to operate its operations including all production and business activities of the enterprise that cannot be separated for each specific activity. Net revenue (F6) is the income earned by the enterprise after deducting all deductions such as import and export tax, sales discounts, excise tax, returned sales, and trade discounts. Profit after tax (F7) is the last remaining part of the production and business activities of the enterprise. Profit after tax is the amount obtained after taking total sales revenue minus all expenses for production. The product yield includes corporate income tax. These financial indicators reflect the entire production and business situation of a textile enterprise. J. Risk Financial Manag. 2021, 14, 465 8 of 24 Table 2. Factors used in previous studies. No. Authors Input Factors Output Factors Number of DMUs 1 Seiford and Zhu (1999) Number of employees; Total assets; Equity Turnover; Profi t 55 2 Lin and Chiu (2013) Fixed assets; Operating expense; Capital Non-interest income; Interest income; Profit 30 3 Fujii et al. (2014) Employees; Deposits; Fixed assets Acquired assets; Customer loans; Bad loans 24 4 Wang et al. (2017a) Owner’s equity; Total assets; Cost of goods sold; Total operating expenses Net sales; Profit after tax 16 5 Wang et al. (2017b) Total assets; Cost of sold capital; Financial expenses; General and administration expenses Revenue of sales; Profit after tax 18 6 Han Khanh Nguyen (2020) Total assets; Owners’ equity; Cost of goods sold; Total operating expenses Net sales; Profit after tax 14 7 Le et al. (2020) Total assets; Equity; Cost of goods sold; Total operating cost Sales revenues; Profit after tax 21 8 Han Khanh Nguyen (2021a) Equity; Total asset; Cost of goods sold; General and administrative expenses; Net revenue; Profit after tax; 10 9 Yu et al. (2021) Fixed assets; Labor; Operating expenses Loan; Securities investment; Non-interest income 22 10 Han Khanh Nguyen (2021b) Total assets; Equity; Cost of goods sold; Total Operating Expenses Net sales; Profit after tax corporate income 14 In this study, the actual data source on enterprises from 2016 to 2020 is used to assess the impact of the COVID-19 pandemic on the production and business situation of Vietnamese textile and garment enterprises. To ensure the use conditions of the Super Slacks-Based Model and Malmquist Productivity Index, the sampling process used in this study is carried out based on the following conditions: identification of enterprises whose main products are textiles and garments; large-scale enterprises occupying a large share of the market; enterprises of a similar size; and businesses that have been in the industry for more than 10 years. Small businesses with very little share of the market are not used as samples in this study. Company names have been compiled and coded as shown in Table 3 below: Table 3. List of companies. CODE Name of Company DMU1 VT Corporation DMU2 TTH Textile Garment Joint Stock Company DMU3 SG Manufacturing Trade Joint Stock Company DMU4 TNG Investment and Trading Joint Stock Company DMU5 NB Garment Corporation Joint Stock Company DMU6 PP Textile & Garment Joint Stock Corporation DMU7 HT Textile & Garment Joint Stock Corporation DMU8 G10 Corporation Joint Stock Company DMU9 SH Garment Joint Stock Company DMU10 VN Textile & Garment Group DMU11 VT Garment Corporation DMU12 HY Garment Corporation—Joint Stock Company J. Risk Financial Manag. 2021, 14, 465 15 of 24 of the impact of the COVID-19 pandemic on the production and business situation in the Vietnamese textile and garment enterprises between 2016–2020 was appropriate and highly reliable. The results of Table 11, also obtained from the DEA model, showed that the ranking of Vietnam’s textile and garment enterprises fluctuated substantially in the rankings in the period 2016–2020. Among them, DMU5 was the enterprise with the most stable production and business situation and always maintained its leading position during the research period: Score DMU5 2016 = 2.6509; Score DMU5 2017 = 2.8658; Score DMU5 2018 = 2.6672, Score DMU5 2019 = 2.9824, Score DMU5 2020 = 2.7074. DMU3, furthermore, was able to stabilize the production and business situation of its enterprise during this period: Score DMU3 2016 = 1.3071, Score DMU3 2017 = 1.2385, Score DMU3 2018 = 1.1859, Score DMU3 2019 = 1.4924, Score DMU3 2020 = 1.4681. Some businesses had large fluctuations in their production and business situation, especially those that were greatly affected by the COVID-19 pandemic. This was reflected in the rankings of these businesses moving from the top in the rankings and then falling to the bottom. DMU11: Rank DMU11 2016 = 2; Rank DMU11 2017 = 4; Rank DMU11 2018 = 4, Rank DMU11 2019 = 6, Rank DMU11 2020 = 9. Besides, several businesses were significantly affected by the COVID-19 pandemic. DMU12: Rank DMU12 2016 = 3; Rank DMU12 2017 = 2; Rank DMU12 2018 = 3, Rank DMU12 2019 = 4, Rank DMU12 2020 = 10. The serious impacts of the COVID-19 pandemic on Vietnam’s textile and garment industry included declines in sales due to the large-scale forced closure of stores and delays in the export of goods. Strict social distancing regulations also impacted overall textile sales. Since then, it has become apparent that the COVID-19 pandemic greatly affected the production and business conditions of textile and garment enterprises in Vietnam in the 2016–2020 period. Table 11. Score and rank of all DMUs (2016–2020). 2016 2017 2018 2019 2020 DMU Score Rank Score Rank Score Rank Score Rank Score Rank DMU1 1.0932 11 1.1868 8 1.5797 6 1.3952 8 1.3079 6 DMU2 1.4000 5 1.2904 5 1.2848 8 1.3405 9 1.3003 7 DMU3 1.3071 7 1.2385 7 1.1859 9 1.4924 5 1.4681 5 DMU4 1.2456 8 1.0941 11 1.0559 10 1.0759 10 1.0564 12 DMU5 2.6509 1 2.8658 1 2.6672 1 2.9824 1 2.7074 1 DMU6 1.3390 6 1.2801 6 0.6609 13 0.6171 13 2.1312 2 DMU7 1.1898 9 1.1683 9 1.3040 7 1.4661 7 1.2939 8 DMU8 1.1497 10 1.0658 12 1.0293 11 1.0601 11 1.1540 11 DMU9 1.0383 12 1.1109 10 1.5846 5 2.2394 2 1.8774 3 DMU10 1.0000 13 1.0000 13 1.0000 12 1.0000 12 1.0000 13 DMU11 2.0668 2 1.8304 4 1.8009 4 1.4809 6 1.2858 9 DMU12 2.0120 3 1.8673 2 1.8098 3 1.6929 4 1.2248 10 DMU13 1.7613 4 1.8662 3 2.0096 2 1.8708 3 1.6195 4 4.2. Malmquist Productivity Index Results In this study, the authors use the Malmquist productivity index to evaluate the business performance of Vietnamese textile and garment enterprises in the period 2016–2020. The catch-up index reflected the efficiency of technical investment in the industries. The frontier index reflected the efficiency of technology investment in textile enterprises, The Malmquist productivity index reflected the production and business efficiency of textile enterprises from 2016–2020. The catch-up index results shown in Table 12 reflect the technical efficiency of investment in Vietnam’s textile enterprises in the period 2016–2020. Vietnam’s textile and garment enterprises maintained their technical efficiency during this period and were less affected by the COVID-19 pandemic. In the field of textiles and garments, the tech- J. Risk Financial Manag. 2021, 14, 465 16 of 24 niques of product design, methods of testing and evaluating the quality of textile products, methods of organization, and management of industrial garment production played an important role in the business. However, the research results showed that during this period, textile and garment enterprises in Vietnam maintained their technical efficiency. This was a good signal for Vietnamese textile and garment enterprises because maintaining technical efficiency is the foundation for enterprises to continue to invest in technology in the next period ,to develop products of high quality, and to help businesses develop sustainably in the future. Table 12. Catch-up index results. Catch-Up 2016=>2017 2017=>2018 2018=>2019 2019=>2020 Average DMU1 1.0000 1.0000 1.0000 1.0000 1.0000 DMU2 1.0000 1.0000 1.0000 1.0000 1.0000 DMU3 1.0000 1.0000 1.0000 1.0000 1.0000 DMU4 1.0000 1.0000 1.0000 1.0000 1.0000 DMU5 1.0000 1.0000 1.0000 1.0000 1.0000 DMU6 1.0000 0.9033 0.9680 1.1436 1.0037 DMU7 1.0000 1.0000 1.0000 1.0000 1.0000 DMU8 1.0000 1.0000 1.0000 1.0000 1.0000 DMU9 1.0000 1.0000 1.0000 1.0000 1.0000 DMU10 1.0000 1.0000 1.0000 1.0000 1.0000 DMU11 1.0000 1.0000 1.0000 1.0000 1.0000 DMU12 1.0000 1.0000 1.0000 1.0000 1.0000 DMU13 1.0000 1.0000 1.0000 1.0000 1.0000 Average 1.0000 0.9926 0.9975 1.0110 1.0003 Max 1.0000 1.0000 1.0000 1.1436 1.0037 Min 1.0000 0.9033 0.9680 1.0000 1.0000 SD 0.0000 0.0268 0.0089 0.0398 0.0010 The index reflecting the efficiency of the technology investment in textile and garment enterprises is shown in Table 13. Some enterprises were technologically efficient in the 2016–2020 period such as DMU3, and DMU6. The technology indexes of these enterprises were obtained as follows: FDMU3 (2017–2018) = 1.1953; FDMU3 (2018–2019) = 1.1588; FDMU3 (2019–2020) = 1.0030. FDMU6 (2017–2018) = 1.1790; FDMU6 (2018–2019) = 1.0017; FDMU6 (2019–2020) = 1.1308. However, several textile and garment enterprises were severely affected by the COVID-19 pandemic, making the investment in enterprise technology ineffective. Specifically: DMU4, DMU5, DMU7, DMU9, and DMU11 had inefficient use of technology; The technology index of these enterprises is: FDMU4 (2017–2018) = 1.1130; FDMU4 (2018–2019) = 1.0084; FDMU4 (2019–2020) = 0.7832. FDMU5 (2017–2018) = 1.1445; FDMU5 (2018–2019) = 1.0237; FDMU5 (2019–2020) = 0.7122. FDMU7 (2017–2018) = 1.2378; FDMU7 (2018–2019) = 0.9949; FDMU7 (2019–2020) = 0.7228. FDMU9 (2017–2018) = 1.4965; FDMU9 (2018–2019) = 1.3437; FDMU9 (2019–2020) = 0.4645. FDMU11 (2017–2018) = 1.3901; FDMU11 (2018–2019) = 0.7634; FDMU11 (2019–2020) = 0.6282. Technology played a huge role in helping businesses change production methods, altering the entire value chain of textile products, from product research to product development, which helps reduce production costs, transportation costs, and increases labor productivity, production, and the business efficiency of textile and garment enterprises. However, the results showed that from 2019 to 2020, Vietnam’s textile and garment enterprises were severely affected by the COVID-19 pandemic. Using technology to improve efficiency was ineffective in textile and garment enterprises during this period. J. Risk Financial Manag. 2021, 14, 465 17 of 24 Table 13. Frontier index results. Frontier 2016=>2017 2017=>2018 2018=>2019 2019=>2020 Average DMU1 0.9583 1.5966 0.9555 0.9672 1.1194 DMU2 0.8705 0.9166 1.0243 0.9087 0.9300 DMU3 0.9721 1.1953 1.1588 1.0030 1.0823 DMU4 0.7813 1.1130 1.0084 0.7832 0.9215 DMU5 0.9770 1.1445 1.0237 0.7122 0.9643 DMU6 0.7279 1.1790 1.0017 1.1308 1.0098 DMU7 1.0264 1.2378 0.9949 0.7228 0.9955 DMU8 0.8488 0.9809 1.0203 0.9821 0.9580 DMU9 0.9790 1.4965 1.3437 0.4645 1.0709 DMU10 0.9914 0.9785 1.0000 0.9982 0.9920 DMU11 1.0441 1.3901 0.7634 0.6282 0.9564 DMU12 1.0510 1.2345 0.8794 0.9317 1.0242 DMU13 1.1649 1.0264 0.8890 0.9276 1.0020 Average 0.9533 1.1915 1.0048 0.8585 1.0020 Max 1.1649 1.5966 1.3437 1.1308 1.1194 Min 0.7279 0.9166 0.7634 0.4645 0.9215 SD 0.1187 0.2045 0.1383 0.1844 0.0597 The results of the malmquist productivity index, shown in Table 14 and Figure 6, revealed that during the height of the pandemic, DMU5, DMU7, DMU9, DMU11, DMU12, and DMU13 were seriously distressed, with no signs that production and business situations were going to recover. In particular, the production and business efficiency scores of these enterprises were affected. These scores were MDMU5 (2017–2018) = 1.1445; MDMU5 (2018–2019) = 1.0237; MDMU5 (2019–2020) = 0.7122. This result showed that before the COVID-19 pandemic, business performance was good (M = 1.1445), but at the end of 2019 when the COVID-19 pandemic occurred, business performance had declined to M = 1.0237. Then as COVID-19 began to spread rapidly in Vietnam and around the world, business dropped precipitously to M = 0.7122. The results are also reflected in DMU5’s revenue index in 2019 of 210,217.91 (1000 USD), which decreased to 174,843.00 (1000 USD) in 2020. As a result, revenue decreased by 17%, making profit 42% lower than that of DMU5 prior to the COVID-19 pandemic. Table 14. Malmquist index results. Malmquist 2016=>2017 2017=>2018 2018=>2019 2019=>2020 Average DMU1 0.9583 1.5966 0.9555 0.9672 1.1194 DMU2 0.8705 0.9166 1.0243 0.9087 0.9300 DMU3 0.9721 1.1953 1.1588 1.0030 1.0823 DMU4 0.7813 1.1130 1.0084 0.7832 0.9215 DMU5 0.9770 1.1445 1.0237 0.7122 0.9643 DMU6 0.7279 1.0651 0.9696 1.2932 1.0139 DMU7 1.0264 1.2378 0.9949 0.7228 0.9955 DMU8 0.8488 0.9809 1.0203 0.9821 0.9580 DMU9 0.9790 1.4965 1.3437 0.4645 1.0709 DMU10 0.9914 0.9785 1.0000 0.9982 0.9920 DMU11 1.0441 1.3901 0.7634 0.6282 0.9564 DMU12 1.0510 1.2345 0.8794 0.9317 1.0242 DMU13 1.1649 1.0264 0.8890 0.9276 1.0020 Average 0.9533 1.1827 1.0024 0.8710 1.0023 Max 1.1649 1.5966 1.3437 1.2932 1.1194 J. Risk Financial Manag. 2021, 14, 465 18 of 24 Min 0.7279 0.9166 0.7634 0.4645 0.9215 SD 0.1187 0.2075 0.1386 0.2083 0.0598 Figure 6. Malmquist index results. MDMU7 (2017–2018) = 1.2378; MDMU7 (2018–2019) = 0.9949; MDMU7 (2019–2020) = 0.7228. This result reflects that before the COVID–19 pandemic, business performance was good (M = 1.2378), but at the end of 2019 when COVID-19 had spread in China, business performance decreased (M = 0.9949). As the pandemic spread in Vietnam and the world, business results dropped dramatically (M = 0.7228). The results are also reflected in the revenue index of DMU7 in 2019, which is 184,939.43 (1000 USD), down to 141,576.70 (1000 USD) in 2020. As a result, revenue decreased by 23%, and profit decreased by 47% compared to before the pandemic. MDMU9 (2017–2018) = 1.4965; MDMU9 (2018–2019) = 1.3437; MDMU9 (2019–2020) = 0.4645. This result reflects that before the COVID-19 pandemic, business performance was good (M = 1.4965), but at the end of 2019 when the COVID-19 pandemic began, business performance decreased (M = 1.3437). As the pandemic ran rampant in 2020, business results dropped even further (M = 0.4645). For example, DMU9 was an enterprise that before COVID-19 was well-situated (M = 1.4965). But DMU9 was hard hit by the COVID-19 pandemic (M = 1.4965). The results are also reflected in DMU9’s revenue index in 2019 of 191,793.61 (1000 USD), which decreased to 165,800.39 (1000 USD) in 2020. As a result, revenue decreased by 14% and profit decreased by 48% compared to before the pandemic. Textile enterprises, in particular, faced many difficulties during the pandemic. To maintain production, business activities, and labor participation, textile enterprises had to rearrange production lines and factories, and streamline stages in the production process. Workers left textile and garment enterprises to go to other jobs. Until the COVID-19 epidemic has been brought under control and the market recovers, it will be difficult to entice them back to resume production. To overcome difficulties, textile and garment enterprises have had to actively regulate production and, at the same time, acquire the flexibility to adjust production orders following delivery time and the number of employees on leave to stabilize production and business. J. Risk Financial Manag. 2021, 14, 465 19 of 24 4.3. Binary Logistic Model Results The textile and garment industry depends greatly on the skills of its well-trained workers to improve product quality and productivity. However, workers in these enterprises change jobs often, creating some human resource instability, which has a substantial impact on garment enterprises. In this study, the authors used a Binary logistic model to find the factors affecting the job-hopping phenomenon in Vietnamese textile and garment enterprises. From there, managers in Vietnamese textile and garment enterprises must recruit the right people for positions in the enterprise and have a strong employee retention policy in effect. To learn about the factors affecting the decision of employees to change jobs in textile and garment enterprises, the authors surveyed 686 employees working in textile enterprises (shown in Table 15). The survey participants provided sufficient information and met the requirements of the survey, and no survey participants provided insufficient information. Therefore, this data source was achieved with high reliability and accuracy in the analysis according to the Binary logistic model. Table 15. Case processing summary. Unweighted Cases a N Percent Selected Cases Included in Analysis 686 100.0 Missing Cases 0 0.0 Total 686 100.0 Unselected Cases 0 0.0 Total 686 100.0 a If weight is in effect, see classification table for the total number of cases. The results in Table 16 show that the binary logistic model used in this study is statistically significant because of Sig.Step = 0.000; Sig.Block = 0.000; Sig.Model = 0.000 (<0.05). Therefore, the results from the study are completely reliable to analyze and evaluate the factors affecting the job change of employees in the textile enterprises in Vietnam. Table 16. Omnibus tests of model coefficients. Chi-Square df Sig. Step 1 Step 22.167 5 0.000 Block 22.167 5 0.000 Model 22.167 5 0.000 The 686 people surveyed in this study were divided into two criteria: No job-hopping (0) and job-hopping (1). Of the 423 people who did not change jobs, it is estimated that 387 people in the process of working did not change jobs. The odds of correct prediction are 91.5%. Out of the 263 job-hopping people, the correct prediction rate is 13.8%. The average correct predictive result for both subjects was 61.13% job-hopping and no job-hopping (show in Table 17 and Figure 7). J. Risk Financial Manag. 2021, 14, 465 20 of 24 Table 17. Classification table a. Observed Predicted PM Percentage Correct 0 1 Step 1 PM 0 387 36 91.5 1 227 36 13.7 Overall Percentage 61.7 a The cut value is 0.500. Figure 7. Observed groups predicted probabilities. The results of Table 18 show that, out of the five surveyed factors, the Sig coefficients of four factors are: Sig.AG = 0.004; Sig.ED = 0.014; Sig.IN = 0.049; Sig.SE = 0.045 (<0.05). This result reflects that the factors AG, ED, IN, SE had an impact on the decision to change jobs in the textile enterprises. Coefficient Sig.DI = 0.726 > 0.05. This reflects that it was not reliable enough to confirm that the DI factor had an impact on the decision to change jobs of employees in the textile and garment enterprises in Vietnam. All coefficients VIF < 2, showing that the factors AG, ED, IN, SE, DI used in this study do not have multicollinearity (these variables are not linearly dependent on each other). Table 18. Variables in the equation. B S.E. Wald df Sig. Exp(B) Collinearity Statistics Tolerance VIF Step 1 a AG −0.032 0.011 8.124 1 0.004 0.968 0.996 1.004 ED 0.139 0.057 6.020 1 0.014 1.149 0.993 1.008 IN −0.035 0.018 3.887 1 0.049 0.966 0.996 1.004 SE 0.056 0.028 4.004 1 0.045 1.058 0.994 1.006 DI −0.008 0.021 0.123 1 0.726 0.993 0.996 1.004 Constant −1.172 0.925 1.606 1 0.205 0.310 0.996 1.004 a Variable(s) entered on step 1: AG, ED, IN, SE, DI. J. Risk Financial Manag. 2021, 14, 465 21 of 24 According to the results of Table 18, the authors built a Binary logistic model that reflected the factors that affected the decision to change jobs of employees in the textile and garment enterprises in Vietnam: log 1.172 0.033AG + 0.139ED 0.35IN 0.056SE 1 i e i  Ρ ==−− −+  −Ρ   The binary logistic model showed that two factors, ED and SE, of employees had a positive correlation to the decision to switch jobs. Thus, the more educated the workers were, the more they sought new environments to have the opportunity to practice and develop their capabilities and earn higher incomes. Therefore, the more experienced workers in the textile and garment industry brought a higher risk of turnover, reflecting a real problem in today’s society and economy. The employees with long-term experience in the textile industry are recruited by many other textile enterprises with attractive policies and salaries, which has stimulated their job-hopping. The results show that, for workers with high income and high age, the probability of changing jobs is low. From the results of this study, the author proposes solutions to help textile enterprises achieve stability in the post-pandemic period. Human resources: It is necessary to recruit workers with professional qualifications suitable to the employment positions of enterprises. Develop appropriate salary and remuneration policies to maintain stable human resources, production and business. This gives the company a competitive advantage. Moreover, it is necessary to provide managerial and technical talent in textile enterprises with appropriate knowledge to improve labor productivity. Technology: Research and apply new technologies from developed countries, including environmentally-friendly materials, to create new products that are suitable for the needs of current customers. Apply simulation technologies to manage design, production, and quality of textile and garment products. Focus on automation technology to save time and costs and create a foundation for adapting to the fast-changing trends of the market post-pandemic. Supply chains: It is necessary to link raw material suppliers, textile accessory suppliers, manufacturing enterprises, and product distribution businesses to form a closed-loop supply chain, stabilizing the production and consumption situation. The modeling method used in this study reflected the main factors affecting the production and business results of the textile and garment enterprises in reality. The use of these models can be extended to consider and evaluate other factors in the business activities of textile enterprises to help managers have a multidimensional view and propose optimal solutions for businesses, thereby helping businesses stabilize production and achieve strategic goals. 5. Conclusions and Discussion 5.1. Conclusions In this study, the Malmquist Productivity Index results showed that, from 2016 to 2020, textile and garment enterprises achieved technical efficiency, but their technological efficiency and business results were not very good. The results from the Super Slacks-Based Model show the continuous change in business rankings, indicating that Vietnam’s textile and garment market is a potential market with fierce competition. Therefore, each business needs to focus on the main product to increase its share in the market. In addition, the results from the Binary Logistic Model help managers in textile enterprises to select suitable job positions to stabilize the human resource situation for the business. Due to product characteristics in the textile industry, which are directly processed by workers, the availability of human resources in textile enterprises has a major influence on product quality, production, and the business situation of enterprises. The labor competition among enterprises in the industry and between textile enterprises J. Risk Financial Manag. 2021, 14, 465 22 of 24 and other fields is very fierce. Furthermore, when foreign enterprises invest in the textile and garment industry, they have an advantage because they attract good workers by giving them high salaries, and they have attractive remuneration policies. These include both skilled laborers and middle-level managers such as line leaders and vice presidents, group leaders and deputies. The main reason workers change jobs in the textile industry is for higher incomes, a better working environment, and development opportunities. When faced with these situations, managers in enterprises should invest in more automated machinery to replace the reduced number of workers and, at the same time, train workers to improve their skills and productivity. To prepare for the shortage of labor post-COVID-19, each textile and garment enterprise needs to be innovative in its management and invest in new machinery, equipment, and technology to automate the production process. In addition, managers must implement a more lucrative remuneration policy, creating a stable source of income to attract and maintain employees. 5.2. Discussion Based on the results of this study, managers in textile and garment enterprises can develop recruitment criteria suitable for each job position and have binding conditions to ensure employees work. Having a long-term relationship with the enterprise helps to stabilize the enterprise and improve the efficiency of production and of the business. In addition, government regulatory agencies can use the results of the pandemic impact assessment model in this study to propose solutions that help textile and garment enterprises to restore production and development post-pandemic. The decision to change jobs often depends on many variables such as seasonal factors and psychological factors. The business performance of enterprises also depends on the state’s law and tax policies. Therefore, this study has some limitations. The authors have not considered the seasonal factors, psychological factors, and demographic factors affecting the decision of workers in the textile industry to change jobs. The research methods have not been combined with the tax policy conditions or the legal policies related to the textile industry. In the future, the authors will focus on combining these factors to provide managers with the best solutions to help textile enterprises stabilize their socio-economic situation. Author Contributions: H.-K.N. contributed to analyzed the data and wrote; M.-N.V. contributed to wrote and formatted. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. References Abbas, Muhammad, Rayan S. Hammad, Mohamed Fathy Elshahat, and Toseef Azid. 2015. 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