1. Introduction
Coconut trees with a comprehensive value chain, creating hundreds of handicraft items, not only bring economic, culture and tourism value, but also positively contribute to the process of responding to climate change and environmental protection. Although there have been numerous studies on coconut handicrafts (Arancon, 2009; Medina et al., 1997; Muralidharan & Jayashree, 2011), these studies mainly conduct situation analysis in order to propose solutions and develop strategies. In Vietnam, handicraft industry has become a topic of broad public interest (Tran, 2006; Cao, 2017). In spite of a large number of studies on handicrafts or coconut value chains with diverse research methodologies, there is no independent research paper on coconut handicrafts. Therefore, it is necessary to carry out a more in-depth study of coconut handicraft industry to highlight the position and potential of coconut handicrafts when it is placed next to handicraft products from other materials or other products in the same coconut value chain.
The Mekong River Delta has more than 130, 000 hectares of coconut area, accounting for 74% of the total land area of coconut in Vietnam. Among provinces in the Mekong Delta region, Ben Tre province, which is known as coconut homeland, has the largest coconut planting area with approximately 72, 289 hectares. The estimated production for coconuts in Ben Tre province during the year 2018 was 615, 473 million (Vietnam General Statistics Office, 2019). Therefore, this study chooses Ben Tre province to be the study site, which is representative of the typical coconut handicraft production area in the region. Although coconut handicraft is only considered as a by-product in the coconut value chain, this field has potential for future development (Tran et al., 2011). With the advantages of traditional handicraft industry and abundant materials, coconut handicraft firms have many assets for development. However, the coconut handicrafts production of the majority of microenterprises (MEs) in this area is still characterized by small-scale, limited skilled labor, lack of capital, lack of linkage, lack of market research, not applying modern production techniques, so these manufacturers mainly produce simple and identical handicraft items (Tri, 2015). This negatively impacts the income and performance of these firms. Hence, this study aims to investigate the factors affecting the technical efficiency of the micro-firms operating in the coconut handicraft industry in the Mekong Delta to offer solutions for these firms to operate more effectively, thereby contributing to enhance the brand name of coconut handicrafts of the Mekong Delta.
2. Literature Review
According to the Law on provision of assistance for small- and medium-sized enterprises of Vietnam National Assembly (2017) and Article 6 of Decree 39/2018/ND-CP of the Government of Vietnam (2018) detailing guidelines for law on support for small- and medium-sized enterprises, types of enterprise are determined based on the criteria presented in Table 1.
Table 1: Criteria for Determining the Types of Enterprises
Koopmans (1951) defined technical efficiency as follows: An enterprise achieves a technical efficiency score only if the efficiency score is feasible and no “better” point exists. Farrell (1957) proposed the idea of using Production Possibility Frontier (PPF) as a criterion for measuring the (relative) efficiency between enterprises (referred to as Decision Making Units, DMUs) operating in the same sector or field. In Farrell’s model, the concept of productive efficiency is clarified and concretized into technical efficiency (TE) and allocative efficiency (AE) in which, TE is the ability of a company to maximize outputs from a given amount of inputs and technology conditions. While AE reflects the ability to combine inputs and outputs at the optimal rate under the influence of prices. The productive efficiency reflects the relationship between an organization’s outputs and inputs in comparison to the minimum inputs or maximum outputs that the organization can achieve (Land et al. 1992). So, technical efficiency is the ability to produce a given amount of output from a minimum input or the ability to produce a maximum output from a given amount of input, with a certain level of technology.
This paper, therefore, employs the concept of technical efficiency to measure a firm’s performance, which is defined as the capacity of a firm to produce the maximum possible output from a given bundle of inputs and a given technology (Coelli et al., 2005). This paper uses the terms of technical efficiency, productive efficiency and operational efficiency as a general concept of technical efficiency of enterprises. Many studies have separated technical efficiency obtained from constant returns to scale (CRS) into two parts: the first part is pure technical inefficiency, and second is scale inefficiency. Thus, the measurement of scale efficiency (SE) can be used to determine the quantity by which productivity can be enhanced by changing the scale of production according to a determined optimal production scale (Coelli et al., 2005).
Numerous studies have examined the determinants of technical efficiency of enterprises operating in many different fields. Purmiyati et al. (2018) found that the profits, education, experience, capital, credit amount, government credit program access and credit realization period individually have a positive and significant effect on the technical efficiency level of industrial micro-enterprises, whereas age shows a significantly negative effect on the level of technical efficiency. Hoxha (2009) also proved that firm size and age negatively influence the efficiency of the firms, while educational background of the entrepreneur positively affects the performance of the micro firm. Charoenrat and Harvie (2017) pointed that firm size, firm age, skilled labor, location, type of manufacturing ownerships, cooperatives, foreign investment, and exports are important firm-specific factors contributing to the technical efficiency of Thai manufacturing SMEs. Results of this study were also in line with the previous study conducted by Amornkitvikai et al. (2014) that firm age, medium-sized enterprises compared with small-sized enterprises, firm location in Bangkok, foreign investment, and government assistance are significantly and positively related to firm technical efficiency.
Yegon et al. (2015) reported that education level and occupation have negative impacts, while age and gender have positive impacts on technical inefficiency in soybean production. Yiadom-Boakye et al. (2013) also claimed that, on average, male rice farmers are relatively more technically efficient than their female counterparts. Davidova and Latruffe (2007) provided the first analysis of the relationship between farm financial structure and technical efficiency in Central and Eastern European farming during the transition to a market economy. The results indicated that financial exposure is a source of inefficiency for all livestock farms (individual and corporate) either due to the agency theory argument for individual farms or to the adjustment hardships for corporate farms. However, the empirical results do not support the agency theory approach for the individual crop farms as hypothesized, as the increase in indebtedness is positively related to the technical efficiency scores for those farms.
At the same time, Tian and Estrin (2007) stated that an increase in bank loans lead to an increase in the size of managerial perks and free cash flows and a decrease in corporate efficiency. Mohd-Noor et al. (2020) investigated the impact of bank regulation and supervision on the efficiency of banking sectors on 108 Islamic banks from 26 countries offering Islamic banking and finance products and services. The empirical findings suggested that supervisory power, activity restrictions, and private monitoring positively influence the efficiency of Islamic banks. Masud et al. (2019) indicated that overall productivity progress of life insurance institutions in Malaysia could mainly be attributed to technological change. However, technical efficiency change and pure technical efficiency change have negative impact on total factor productivity.
Vo and Le (2014) revealed that technical efficiency scores are from 52% to 80%, depending on the industries in the assumption of variable returns to scales. This research also found that while firm’s size has positive relationship with the level of technical efficiency, firm’s age impacts negatively on this level of a firm. Besides that, using financial leverage can help to enhance the level of technical efficiency for firms operating in the food and beverages industry and fabricated metal industry. Le (2020) researched the factors affecting retail banking efficiency of Vietcombank branches in the Mekong-Delta region. The results pointed out that bank scale-related factors, capital adequacy, credit quality, time specific and region impact significantly the retail banking efficiency.
Nguyen and Nguyen (2017) indicated that the efficiency of production households in Binh Dinh handicraft village is quite high, the majority of households already had decided reasonable scales to produce. However, there is a large difference in the efficiency index of the households in the villages, the implementation of general development plan of villages from 2006 until now has no positive impact on the efficiency of production and business of households, and the combination with the tourism development also has not brought any improvement in efficiency. Quan (2009) investigated that milled rice firms are more efficient than fishery processing firms in terms of technical, allocation and cost efficiency. Quan (2010) indicated that age, credit, education, type of firm and size of capital are found to be main factors influencing the production efficiency of the fishery firms in the Mekong River Delta. Phuoc and Vo (2019) pointed out six factors affecting the efficiency of small and medium enterprises (SMEs) in Ben Tre province, including: firm-specific characteristics, business owner characteristics, capital, social relations, national incentive policies, innovation activities. Thai (2009) stressed that education level of the household head, loans for investment in rubber production, the number of rubber trees with a mouth to shave, and the technical coefficient of labor positively impact technical and cost efficiency of small holder rubber farms in the province of Kon-Tum, Central Highland. Nguyen and Mai (2011) suggested that access to national incentive policies, educational background of owner, scale of company, social relations, and revenue influence the efficiency of SMEs in Can Tho city, the center of the Mekong River Delta.
Through the comprehensive review of prior studies related to the research topic, most of them employed Data Envelopment Analysis (DEA) method to measure technical efficiency of firms and then Tobit regression to investigate determinant factors affecting technical efficiency. However, there is no independent study on the technical efficiency of coconut handicrafts firms. Hence, it is important to address that the significant difference in this study compared to prior studies is the combined use of DEA and Tobit regression methods to analyze determinant factors of technical efficiency of microfirms in the coconut handicraft industry. Prior studies have suggested several factors influencing technical efficiency such as firm size, firm age, education, experience, gender, capital, credit amount, government credit program access, financial exposure, profit, supervisory power, activity restrictions, skilled labor, application of science and technology, location, type of ownerships, cooperatives, foreign investment and exports.
3. Research Methodology
3.1. Sample Selection
The article uses primary data of 120 MEs that have been operating for two years or more in the field of coconut handicrafts in the Mekong Delta, specifically in Ben Tre province by using convenience sampling technique. Cross-sectional data are collected in 2019. MEs are directly interviewed based on designed questionnaires. The study also collects secondary data from reports, documents, statistical yearbooks, scientific journals, and articles on websites of relevant departments.
3.2. Estimation Method
This study employs DEA method in the first stage of analysis to measure technical efficiency of microfirms operating in the coconut handicraft industry and Tobit regression in the second stage to estimate the impact of determinant factors on technical efficiency.
3.2.1. Estimation of Technical Efficiency
To measure relative technical efficiency, Coelli et al. (1998) proposed two approaches: parametric methods or Stochastic Frontier Analysis (SFA) and non-parametric methods or Data Envelopment Analysis (DEA).
In this study, technical efficiency (TE) is estimated by applying DEA method in the first stage of analysis. DEA is a linear programming method aiming to identify the efficient Production Possibility Frontier (PPF). Accordingly, DMUs that reach the PPF curve is considered to be more efficient, whereas DMUs that do not reach the PPF curve is considered to be less efficient (compared to the other DMUs). The main advantage of DEA method is the flexibility due to its non-parametric nature, i.e., no assumption about the production function is required. The efficiency score takes values from zero to one with a value of one, indicating optimal technical efficiency. Values of less than one mean that the enterprise has not reached the optimal technical efficiency. Besides that, DEA method can be implemented in a narrow range (small sample size) (Quan, 2012).
TE is measured through the estimation of variable returns to scale technical efficiency (VRS TE), constant returns to scale technical efficiency (CRS TE) and scale efficiency (SE) by employing the output-oriented DEA. The output-oriented/output-maximization model is useful when the DMUs can achieve the highest amount of output given fixed amount of input. At this point, technical efficiency is the proportional increase in outputs, while holding input amounts constant.
The output-orientated DEA model under the assumption of CRS (CRS Output-Oriented DEA) can be expressed as follows (Charnes et al., 1978):
\(\begin{array}{ll} \max _{\mu_{k} v_{i}} & \sum_{k=1}^{p} \mu_{k} y_{k 0} \\ \text { s.t. } & \sum_{i=1}^{m} v_{i} x_{i 0}=1 \\ & \sum_{k=1}^{p} \mu_{k} y_{k j}-\sum_{i=1}^{m} v_{i} x_{i j} \leq 0 \\ & \mu_{k} \geq \varepsilon, v_{i} \geq \varepsilon \\ j=1, \ldots, n & k=1, \ldots, p \quad i=1, \ldots, m \end{array}\) (1)
Where ykj is amount of output k from DMU j, xij is amount of input i from DMU j, uk is weight given to output k, vi is weight given to input i, n is number of DMUs, p is number of outputs, m is number of inputs, ε is a “non-Archimedean” infinitesimal, yk0 is amount of output k from the targeted DMU (DMU0), xi0 is amount of input i from DMU0.
However, when several factors such as imperfect competition, financial constraints, and government intervention, possibly prevent DMUs from operating at optimal scale, the assumption of VRS would be more reasonable. Banker et al. (1984) expanded the output-oriented DEA model under the assumption of VRS (VRS Output-Oriented DEA), as follows:
\(\begin{array}{ll} \max _{\mu_{k} v_{i}} & \sum_{k=1}^{p} \mu_{k} y_{k 0}-\mu_{0} \\ \text { s.t. } & \sum_{i=1}^{m} v_{i} x_{i 0}=1 \\ & \sum_{k=1}^{p} \mu_{k} y_{k j}-\sum_{i=1}^{m} v_{i} x_{i j}-\mu_{0} \leq 0 \\ j=1, \ldots, n & \mu_{k} \geq \varepsilon, v_{i} \geq \varepsilon \\ & k=1, \ldots, p \quad i=1, \ldots, m \end{array}\) (2)
Where ykj is amount of output k from DMU j, xij is amount of input i from DMU j, uk is weight given to output k, vi is weight given to input i, n is number of DMUs, p is number of outputs, m is number of inputs, ε is a “non-Archimedean” infinitesimal, yk0 is amount of output k from the targeted DMU (DMU0), xi0 is amount of input i from DMU0, u0 is scalar value being free in sign.
Scale efficiency, a measure of the optimum scale of operations, can be estimated as the ratio of technical efficiency measured under CRS over technical efficiency measured under VRS, which is generalized through the following formula:
\(\mathrm{SE}=\frac{\text { CRSTE }}{\text { VRSTE }}\) (3)
This ratio takes values between zero and one with a value of one, indicating scale efficiency. Values of less than one are either due to decreasing returns to scale (DRS) or increasing returns to scale (IRS), with constant returns to scale (CRS) defining the optimum scale.
The results from DEAP software version 2.1 show the VRS TE score, CRS TE score, and SE index derived from the Output-Oriented DEA model. This study applies revenue as the output variable, in the DEA model. Besides that, three input variables are total costs, labor costs, and fixed assets.
3.2.2. Estimation of Factors Affecting Technical Efficiency
In the second stage of analysis, the estimation result of the average VRS technical efficiency score derived from the output-oriented DEA model is used as the dependent variable in the Tobit regression model to investigate the factors influencing the technical efficiency of the MEs in the coconut handicraft sector. This study uses VRS TE as the dependent variable because when there is no DMU operating at the optimal scale, using TE under the assumption of CRS as the dependent variable will lead to biased estimation of TE due to the impact of scale efficiency (Doan & Do, 2016). Therefore, it is more appropriate to use technical efficiency based on VRS assumptions. Thai (2009); Amornkitvikai et al. (2014) also used the output-oriented TE score under the assumption of VRS as the dependent variable to examine the effects of determinant factors on TE.
Since the estimated technical efficiency scores take value from 0 to 1, the Tobit regression model is considered to be the most appropriate model in which the dependent variable is censored above or below a certain threshold (Gujarati, 2011; Cameron & Trivedi, 2009; Coelli et al., 2005). Based on the results of prior studies and practice in the study area, Tobit regression model is proposed as follows:
\(\begin{aligned} \mathrm{TE}_{i}=\mathrm{TE}^{*}=& \beta_{0}+\beta_{1} \mathrm{AGE}_{i}+\beta_{2} \mathrm{SIZE}_{i}+\beta_{3} \mathrm{EDU}_{i} \\ &+\beta_{4} \mathrm{EXP}_{i}+\beta_{5} \mathrm{MEA}_{i}+\beta_{6} \mathrm{AST}_{i} \\ &+\beta_{7} \mathrm{SEX}_{i}+\beta_{8} \mathrm{COR}_{i}+u_{i} \\ \mathrm{TE}_{i}=& 0 \text { if } \mathrm{TE}^{*} \leq 0 \\ \mathrm{TE}_{i}=& \mathrm{TE}^{*} \text { if } 0<\mathrm{TE}^{*} \leq 1 \\ \mathrm{TE}_{i}=& 1 \text { if } \mathrm{TE}^{*}>1 \end{aligned}\) (4)
Where, TEi is the technical efficiency score derived from VRS Output-Oriented DEA method, β are the estimated coefficients of the Tobit regression model, i is the order of observations, ui is error terms, AGE, SIZE, EDU, EXP, MEA, AST, SEX, COR are the independent variables, respectively. Table 2 shows the variables used in both stage of analysis.
Table 2: Description of Variables in the DEA Model and the Tobit Regression Model
Notes: (1) Yegon et al. (2015); (2) Hoxha (2009); (3) Nguyen and Mai (2011); (4) Purmiyati et al. (2018); (5) Thai (2009); (6) Charoenrat and Harvie (2017); (7) Vo and Le (2014); (8) Phuoc and Vo (2019); (9) Amornkitvikai et al. (2014); (10) Davidova and Latruffe (2007); (11) Quan (2009); (12) Nguyen and Nguyen (2017).
4. Empirical Results and Discussion
4.1. Technical Efficiency of MEs in the Coconut Handicraft Industry in the Mekong Delta
Table 3 illustrates the descriptive statistics of the variables used in the DEA model and the Tobit regression model.
Table 3: Descriptive Statistics of the Variables in the DEA Model and the Tobit Regression Model (Obs. = 120)
One output and three input variables, which are presented in panel A in Table 3, are used in the CRS output-oriented DEA and VRS output-oriented DEA models to estimate CRS TE and VRS TE, respectively, thereby calculating SE of the MEs operating in the coconut handicraft industry. The estimated results of the CRS TE, the VRS TE, and the SE of 120 MEs operating in the coconut handicraft industry are shown in Table 4.
Table 4: Estimated Results of Technical Efficiency Scores (Obs. = 120)
From Table 4, it can be observed that the average levels of VRS TE and CRS TE of micro-firms in our sample are 0.684 and 0.580, respectively. These TE score implies that microenterprises on average reach 68.4% of the optimal output that can be achieved in 2019 under the assumption of VRS, while these MEs only achieve 58.0% of the optimal output under the assumption of CRS. Results from Table 4 also show that the mean scale efficiency of 120 micro-firms in our sample is relatively high with the mean value of 0.873. This suggests that the microfirms in our sample can scale their production more reasonably to improve the efficiency of these enterprises.
Based on the results in Table 5, it can be seen that the scale of most of the MEs operating in the coconut handicraft industry in the Mekong Delta is small. Approximately 61.67% of MEs are operating with increasing returns to scale (IRS), which means that firms can increase their output more quickly than their costs increase; so these firms will expand. Whereas, about 30.83% of MEs are operating with decreasing returns to scale (DRS), so these enterprises will benefit by reducing theirs size. In other words, they need to reduce their production scale to achieve optimal efficiency. Besides that, 7.5% of the surveyed enterprises face constant returns to scale (CRS), which implies that they are operating at optimal scale.
Table 5: Number and Percentage of Microenterprises, Classified by Types of Returns to Scale
4.2. Determinants of Technical Efficiency of MEs Operating in the Coconut Handicraft Industry in the Mekong Delta
Censored Tobit regression using the VRS TE score, a proxy for firm performance, as a dependent variable is employed in the second stage of the analysis. The results derived from Tobit regression are presented in Table 6.
Table 6: Estimated Results of the Tobit Regression Model
Notes: The values in parentheses ( ) are adjusted standard errors. *, ** and *** indicate statistical significance at the 10%, 5%, and 1% level, respectively. The result from Wooldridge test implies that there is no presence of serial correlation with Prob > χ2 = 0.0001. The result from Collin test shows that multicollinearity is no issue in this model with all VIF values being less than 2.
The results from Table 6 show that The Likelihood Ratio (LR) test has a p-value of 0.0001, which is less than the significance level α (0.01). Based on the LR test results, independent variables simultaneously have a significant effect on the level of technical efficiency at the significance level of 1 percent. Separately, firm size (SIZE), membership in economic association (MEA), application of science and technology (AST), and cost-to-revenue ratio (COR) have a positive and statistically significant effect on the technical efficiency level of microenterprises. The impacts of these four independent variables on the technical efficiency can be explained as follows:
As expected, the positive relationship between firm size and technical efficiency exists. Larger firms will be more technical efficient than smaller firms, as smaller firms may face several difficulties: (ⅰ) difficulty in accessing loans for their investments; (ⅱ) lack of effective resources such as human resource; (ⅲ) lack of economies of scale, and (ⅳ) lack of formal contracts with customers and suppliers. This is clearly shown through the research results in Table 6 that the estimated coefficient is positive (β2 = 0.00003) at the significance level of 5 percent. This empirical findings is in accordance with previous studies conducted by Nguyen and Mai (2011); Thai (2009); Charoenrat and Harvie (2017); Vo and Le (2014); Amornkitvikai et al. (2014).
From the estimated results in Table 6, it can be seen that membership in economic association has a positive correlation with technical efficiency with the estimated coefficient (β5 = 0.14117) at the significance level of 1 percent. This result is in line with the original assumption and the prior study of Nguyen and Mai (2011); Charoenrat and Harvie (2017); Phuoc and Vo (2019). Enterprises that participate in local business associations and unions achieve higher technical efficiency than businesses that do not. It is true that joining economic associations, specifically the Coconut Association brings many benefits to micro-firms operating in coconut handicraft industry in the Mekong Delta.
The estimated result in Table 6 shows that application of science and technology positively influence technical efficiency of micro firms in our sample with the positive estimated coefficient (β6 = 0.12686) at the significance level of 0.05. This finding is completely consistent with the study of Phuoc and Vo (2019). Strong investment in the development of science and technology brings about a great and long-term contribution to the improvement of technical efficiency of microfirms operating in coconut handicraft industry.
It can be seen from the results in Table 6 that cost-to-revenue ratio has a positive influence on technical efficiency with the estimated coefficient (β8 = 0.04217) at the significance level of 0.01. This empirical finding is contrary to the original assumptions and previous studies such as Davidova and Latruffe (2007); Tian and Estrin (2007). In fact, when enterprises operating in the coconut handicraft sector have strong financial resource to invest considerably in training skilled-labor, management, machinery, equipment, and synchronous technological innovation, they can improve the utility and sensory value of their products as well as make more unique handicraft products. Hence, the greater the investment in fixed assets, human resource and innovative activities, the higher the cost-to-revenue ratio, which may lead to an increase in the technical efficiency scores of microfirms.
However, the study has not found the impacts of firm age (AGE), education level (EDU), experience (EXP) and gender of business owner (SEX) on the technical efficiency of the MEs in the coconut handicraft sector in the Mekong Delta. The difference in technical efficiency among MEs with different number of years of operation is not statistically significant, possibly because the Mekong Delta region, particularly Ben Tre province, has been well-known as the “capital” of coconut handicrafts for many decades with the average number of operation years of 14.5 (see Table 3). Turning to education level of business owner, the estimated coefficient of this variable is not statistically significant, but has the expected positive effect on technical efficiency. This result implies that education level tends to have no considerable effect on the technical efficiency levels of the micro-firms in the study area. This is because the production activities of coconut handicrafts of most MEs in the Mekong Delta are mainly based on personal experience of business owner and have not applied advanced technologies, therefore, the role of the educational level has not been promoted. In addition, the difference in technical efficiency among MEs with different management experience is not statistically significant since these business owners have many years of management experience with the average value of 12.2 years (see Table 3). This result is consistent with the theory of the experience curve in microeconomics, when the accumulated manufacturing experience is large enough, the experience factor will no longer make a significant difference (Besanko et al., 2014). The results in Table 6 also suggest that gender is not statistically significant to account for the variation of technical efficiency. This result can be explained that the fact that the leader is a man or a woman makes no difference to the technical efficiency of the MEs. Although there were many studies proving that the leadership styles of men and women have many differences, but gender is not the decisive factor affecting technical efficiency of Vietnamese microenterprises in coconut handicraft industry.
5. Conclusion
Based on the primary data of 120 microenterprises operating in the coconut handicraft industry in the Mekong Delta in 2019, the study employs the output-oriented DEA method in the first stage of analysis to estimate technical efficiency of these microfirms and applies the Tobit regression model in the second stage to examine factors affecting technical efficiency under the assumptions of variable returns to scale.
The research results show that on average, variable returns to scale technical efficiency (VRS TE), constant returns to scale technical efficiency (CRS TE) and scale efficiency (SE) are 68.4%, 58.0%, and 87.3%, respectively. However, microenterprises in the study area have not fully utilized their inputs, so they have not reached the optimal level of technical efficiency. These MEs can increase their output given fixed amount of inputs. The empirical findings of the study also imply that firm size, membership in economic association, application of science and technology, and cost-to-revenue ratio positively influence the technical efficiency level of microenterprises.
In order to improve the technical efficiency, firms should have a reasonable scale-up plan, proactively formulate investment plans relative to their capacity, capital, technology and human resources. Besides that, microenterprises should take part in economic associations to form a production chain from the supply of raw materials to the sale of finished products. Enterprises should analyze the current state of technology applied in their production activities as well as consider the need for technology innovation to gain a greater competitive advantage. In addition, coconut handicraft firms should prioritize building strong relationships with local departments and agencies, thereby helping these businesses quickly grasp the changes of legal regulations, national incentive policies such as preferential interest rates, and science and technology supporting policies, as well as foster knowledge and management skills.
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