A study of factors affecting the competitve capacity of “19 forestry joint stock company” in Binh Dinh province

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Competitive capacity of an enterprise is a matter of business that managers are always interested in. This article aims to assess the current real competitiveness of the “19 forestry joint stock company” by analyzing the external and internal competitive factors of the company, thereby determining how strongly these factors affect the company’s competitive capacity.

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Nội dung Text: A study of factors affecting the competitve capacity of “19 forestry joint stock company” in Binh Dinh province

JOURNAL OF SCIENCE, Hue University, Vol. 70, No 1 (2012) pp. 63-70 A STUDY OF FACTORS AFFECTING THE COMPETITVE CAPACITY OF “19 FORESTRY JOINT STOCK COMPANY” IN BINH DINH PROVINCE Tran Van Hoa1, Le Thi The Buu2 1 College of Economics, Hue University 2 Quang Trung University, Binh Dinh Abstract. Competitive capacity of an enterprise is a matter of business that managers are always interested in. This article aims to assess the current real competitiveness of the “19 forestry joint stock company” by analyzing the external and internal competitive factors of the company, thereby determining how strongly these factors affect the company’s competitive capacity. Applying the exploratory factor analysis (EFA) and multiple linear regression, the study has found 7 external factors and 5 internal factors that significantly affect the company’s competitive capacity. These findings are expected to be useful for the company in particular and for the whole sector in general to find out the appropriate strategies to improve their competitiveness. 1. Introduction The wood processing industry of Vietnam has developed sharply during the last 10 years. In fact, Vietnam has currently been ranked second (after Malaysia) in wood products exports in ASEAN [1]. At present, the demand for wood products in the World has risen rapidly with an annual growth of 8% on average. To satisfy this, the wood processing and wood products industries in many countries have changed significantly, especially those in China and in several Asian countries such as Indonesia, Thailand, Malaysia and Vietnam. They all develop quickly in terms of quality and quantity [6]. Therefore, the competition in the wood-processing sector increases more and more among domestic companies as well as between domestic and foreign companies. The “19 Forestry joint stock Company” was established in January 1990 with the name “19 Forestry Company” under the regulations of Ministry of Agriculture and Rural Development. The Company changed its name into the “19 Forestry joint stock Company” in October 2004. It has been one of the multi-sector companies, especially in the wood processing industry for export, in Binh Dinh province. Currently, this company has faced many difficulties resulting from keen competition in domestic as 63 64 A study of factors affecting the competitve capacity of… well as international markets. Therefore, analyzing and assessing its current competitiveness to determine factors that affect this capacity has become urgent and necessary. 2. Research methods The study used secondary data on current business activities of the company and its competitors, on legal regulations of wood products exports and doing a literature review to find out conceptual frameworks. In order to survive and develop, the company has to interact with the macro, micro and internal environments. To measure the influence of the macro factors on the company, the research adopted the P.E.S.T model developed by Michael Porter that include political, economic, socio-cultural and technological factors. On the other hand, the 5 forces model of Porter, with the forces being current competitors, customers, suppliers, potential competitors, and substitution products, was used to measure the microenvironments of the company. In addition, the internal factors that influence the company such as development strategy, capital, labor force and marketing management were also used to measure the competitive capacity of the company [4]. The study also collected primary data from survey for a combined analysis of quantitative and qualitative methods. The questionnaire was constructed and finalized by interviewing experts and managers in the wood-processing sector of Vietnam and by conducting on-site field trip to assess current business activities, external and internal factors of the company. These factors were evaluated on Likert type scale of 5 points. A sample size of 201 for external factors and 201 for internal factors were randomly selected (more than 8 times of 24 observatory variables); however, there were 440 questionnaires being sent to respondents and 402 (accounting for 91%) returned which were valid for data analysis. The respondents of the survey were the workers who work in the wood-processing companies, the managers of the wood-processing companies and the members of the Binh Dinh Wood Processing Associations. The study used SPSS 16.0 tool for descriptive statistical analysis, Exploratory Factor Analysis (EFA) and multiple linear regression. The results of descriptive statistical analysis for survey data indicate that, the male accounts for 58.2% while female only accounts for 41.8%. In terms of age, among the 4 age groups, the age group of 41-50 has the highest proportion (37.3%). The group of 31-40 comes second (accounting for 33.3%) and the last group (12.4%) is 51-60. In terms of educational level, respondents who have Associate and/or Bachelor degrees or higher form the majority with a proportion of 48.8%, followed by people with vocational qualification (33.3%) and high school qualification (17.9%). TRAN VAN HOA, LE THI THE BUU 65 3. Results and discussion 3.1. Company’s external factors - Exploratory Factor Analysis and Cronbach’s Alpha There were 24 observatory variables selected as inputs for Exploratory Factor Analysis (EFA) to determine the scale of the company’s external factors. As Barlett's test of spericity is significant with an observed significant level of 0.000, the null hypothesis that the inter-correlation matrix involving these variables is an identity matrix is rejected. Thus, from the perspective of the Bartlett's test, factor analysis is feasible. As Bartlett's test is almost always significant, a more discriminating index of factor analyzability is the KMO (Kaiser-Meyer-Olkin). For this data set, it is 0.703 (0.5 < KMO < 1), which is very large, so the KMO also supports factor analysis. The results of the EFA discover and determine seven new factors and all these factors have Cronbach’s Alpha indices which are greater than 0.9 and all factor loadings greater than 0.6. These new factors have been named as below (see Table 1): + Factors related to Policy: (1) Policy Factor (PF); + Factors related to Suppliers: (2) Suppliers Factor (SF) + Factors related to Market potentials: (3) Market Factor (MF) + Factors related to Economic environment: (4) Economic Factor (EF) + Factors related to Competitors: (5) Competitors Factor (COF) + Factors related to Customers: (6) Customers Factor (CUF) + Factors related to Technique and Technology: (7) Technology and Technique Factor (TTF) Table 1. Factor analysis of the external factors influencing the competitive capacity of the Company Factors Variables 1 4. Customs policy 0,989 6.Political situations 0,984 5. Incentive policy 0,981 7. System of laws 0,980 2 16. Price input increase 0,934 23. Numbers of input suppliers 0,930 24. Quota of bank credit 0,921 3 4 5 6 7 A study of factors affecting the competitve capacity of… 66 15. Scarce materials 0,647 12. Consumers’ tendency 0,960 13.Wood market potentials 0,945 14. Increase in wood demand 0,941 11. Per capita income 0,645 2. Interest rates 0,975 1. Economic growth rate 0,973 3. Exchange rate fluctuations 0,970 17. Competitors 0,941 19. Number of enterprises 0,924 18. Growth rates of the industry 0,900 22. Customers’ pressure 0,969 21. Transaction costs 0,961 20. Strict requirement from customers 0,936 8. Technological development in industry 0,950 9. Development of IT and telecom 0,944 10. Life cycle of technique 0,882 Coefficient of Cronbach's Alpha 0,996 0,929 0,926 0,993 0,969 0,972 0,951 Eigen value 6,901 4,069 3,152 2,637 1,952 1,835 1,602 (Source: Authors’ analysis of survey data in SPSS). After conducting the EFA, the multiple linear regression was applied to assess the degree of effect that these external factors have on the company’s competitive capacity. The results of the ANOVA test show that with the critical F-value of 502.146 (at an observed significant level of 0.000), the linear regression model is appropriate or we find significant statistical evidence for a linear relationship between the dependent variable and the independent variables as a group. Furthermore, the R square value is equal to 0.948 which means that 94.8% of variation in the dependent variable (that is NLCT) is explained by 7 new factors above (or this high R square value indicates that the data points fall very closely along the best-fit line and that the independent variables are good predictors of the dependent variable). The statistical results in Table 2 show that as all p-values are less than 0.05, at 95% confidence level, we find significant statistical evidence for a relationship between the dependent variable and every independent variable: Policy Factor (PF); Suppliers TRAN VAN HOA, LE THI THE BUU 67 Factor (SF); Market Factor (MF); Economic Factor (EF); Competitors Factor (COF); Customers Factor (CUF); Technology and Technique Factor (TTF). Table 2. Coefficients – Multiple linear regression for effect of company’s external factors Variables Unstandardized Coefficients Standardized Coefficients t Sig. 374.755 0.00 B Std. Error (Constant) 3.771 0.01 PF 0.044 0.01 0.072 4.408 0.00 SF 0.574 0.01 0.934 56.901 0.00 MF 0.061 0.01 0.099 6.050 0.00 EF 0.058 0.01 0.095 5.797 0.00 COF 0.100 0.01 0.163 9.910 0.00 CUF 0.069 0.01 0.112 6.800 0.00 TTF 0.066 0.01 0.108 6.571 0.00 R2 = 0.948 Beta F = 502.146 (Sig. = 0.000) Dependent variable: Competitive capability (COMCAP) (Source: Authors’ analysis of survey data in SPSS). The regression model identifying the cause-effect relationship between external factors and the company’s competitive capacity was formulated as below: COMCAP = 0.072*PF + 0.934*SF + 0.099*MF + 0.095*EF + 0.163*COF + 0.112*CUF + 0.108*TTF The regression equation indicates that Suppliers Factor (SF) affect the company’s competitive capacity (COMCAP) the most (0.934), followed by Competitors Factor (COF) (0.163), Customers Factor (CUF) (0.112), Technology and Technique Factor (TTF) (0.108), Market Factor (MF) (0.099) and Economic Factor (EF) (0.095). 3.2. Company’s Internal Factors – Exploratory Factor Analysis and Cronbach’s Alpha In this study, as the KMO is high (0.727) and the observed significant level of the Barlett’s test is lower than 0.05, it can be concluded that the data are appropriate for factor analysis. In fact, after conducting the EFA, the study identified 5 new factors with initial Eigen values greater than 1 (which is satisfactory). In addition, the Cronbach’s Alpha of these new factors are greater than 0.6 and the cumulative Eigen value (%) indicates that these factors explain 77.587% of total variance (more than 50% which is considered satisfactory).