4.1. Super Slacks-Based Model Results
The Pearson correlation is the condition when using the DEA model that the inputs and outputs must be correlated. The correlation coefficient has a value in the range [−1, 1]. If the value of the correlation coefficient in the range [−1, 0) reflects the negative correlation between the factors, and if the value of the correlation coefficient in the range (0, 1] reflects the positive correlation between the factors, together, a coefficient of zero (0) reflects that the factors have no correlation. The results of the correlation coefficient test in this study are shown in
Table 9 and
Table 10 below:
The results of the correlation coefficient shown in
Table 9 and
Table 10 obtained from the DEA model in this study have positive values (>0). That reflects that the factors used for analysis in this study were positively correlated with each other. That is, when the value of one element increases, the value of the other also increases; when the value of one element decreases, the value of the other also decreases. In particular, the results in the above tables show that the correlation coefficients in this study all have results greater than 0.7 (some results of correlation coefficient equal to 1) that reflect that the factors used in this study are strongly correlated. This means that any change in one factor will affect the change in another. Therefore, the use of these factors in the analysis and assessment 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.
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 techniques 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.
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:
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:
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.
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 M
DMU5 (2017–2018) = 1.1445; M
DMU5 (2018–2019) = 1.0237; M
DMU5 (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.
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.
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.
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.
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).
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).
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:
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.