Performance of SMEs. (PSME)
SMEs performance is SMEs accomplishment that is measured in a certain period. Indicator of accomplishment can be measured from SMEs productivity, ability to get profit or another indicator such as ability to achieve market share target. Therefore SMEs performance is a business accomplishment attained in a particular period that is measured based on comparison of various standards.
THEORETICAL MODELS AND HYPOTHESES
The theoretical framework for this study was developed based on the brief literature reviewed above. The independent variable was Business information services, Management skills, Availability of Managerial experience, Infrastructure services, Macro-Environment Factors, Level Education of Manager and the dependent variable was Performance of Small and Medium Enterprises. The conceptual model is illustrated in Figure 1.
H1: There is a positive and significant relationship between Business information services and performance of SMEs.
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H2: There is a positive and significant relationship between Management skills and performance of SMEs.
H3: There is a positive and significant relationship between Availability of managerial experience and performance of SMEs.
H4: There is a positive and significant relationship between Infrastructure services and performance of SMEs.
H5: There is a positive and significant relationship between Macro-environment factors and performance of SMEs.
H6: There is a positive and significant relationship between Level educations of owner /manager and performance of SMEs.
Business Information Services
Management Skills
Availability of Managerial Experience Performance of Small
and Medium Enterprises
Macro-Environment Factors
Level Education of Manager
Figure 1: Theoretical model. Source: Research results by author
METHODOLOGY
Procedure for data collection
Quantitative research methods are used in this study. Theoretical models have 6 independent concept measured by 28 observed concepts and one dependent concept measured by 4 observed concept. Scale concepts studied in theoretical models are multivariate scale. The observed concepts are measured on a 5-point Likert scale (1: strongly disagree to 5: strongly agree). A survey questionnaire was sent by e-mail to the business managers of 325 small and medium enterprises with labor numbers over 300. Of the 325 questionnaires dispatched, 281 usable responses were received, representing an effective response rate of 86.46%. Statistical analyses were done in two phases; first an explanatory factor analysis was performed and then a linear regression equation to determine which factors affecting the performance of small and medium enterprise. SPSS 22.0 and were used as statistical software for analyses.
Description of the Survey
The data collected from 281 small and medium enterprises in Vietnam with the characteristics are presented in Table 1.
The concept scales of the study are preliminarily assessed and screened by
EFA method and Cronbach‘s Alpha coefficients for each component. Selection criteria are satisfied when concepts have correlation coefficients turn-total >0.40,
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Cronbach‘s alpha coefficients > 0.60; system load factor >0.40; total variance extracted for ≥ 50% (Hair & CTG, 1998).
The results of the EFA, showed 28 variations observed in 6 components of the enterprise performance scale. As KMO coefficient = 0.827, EFA matches the data and the statistical test Chi-quare Bertlett 4901.099 worth 0.000 significance level. Thus, the observed concepts are correlated with each other considering the overall scope. The variance extracted by 68.879 shows that factors derived from 68.879% explained variance of the data, eigenvalues in the system by 1.491. Therefore, the scale draw is acceptable.
Table. 1 Characteristics of the sample small and medium enterprise in Vietnam.
Source: Data analysis of research data by SPSS 22.0
Ownership
|
Quantity
|
Percentage
|
Stock enterprises
|
95
|
33.80
|
Private enterprises
|
186
|
66.20
|
Size
|
Quantity
|
100.00
|
300 < Firm<500 labors
|
115
|
40.92
|
Up 500 labors
|
166
|
59.08
|
Total
|
281
|
100.00
|
Table. 2 The table summarizes the results of scale. Source: Data analysis of research
data by SPSS 22.0
Model
|
|
|
|
Variable
|
Cronbach‘s
|
Variance
|
Value
|
|
|
|
|
s
|
alpha
|
(%)
|
|
|
|
|
|
|
|
Macro-environment factors
|
|
5
|
0,833
|
|
|
|
Management skills
|
|
|
6
|
0,879
|
|
|
|
Business information services
|
|
4
|
0,838
|
|
|
|
Availability
|
of
|
Managerial
|
|
|
|
Satisfactor
|
|
experience
|
|
|
|
4
|
0,882
|
68.879
|
|
|
|
|
y
|
|
Infrastructure services
|
|
|
6
|
0,899
|
|
|
|
Level
|
educations
|
of
|
4
|
0,804
|
|
|
|
owner/manager
|
|
|
|
|
|
|
|
|
|
|
|
|
Performance of SMEs.
|
|
|
4
|
0,736
|
59.880
|
|
|
Analysis of the Correlation Matrix
The first step of conducting linear regression analysis is to consider the linear correlation between all the concepts. That means to consider the overall relationship between each independent variable with the dependent variable and between the independent concepts.
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The estimated results of the multiple regression model indicate a good fit with the data (F = 83.359, p < 0.05; R2 = 63.8%; all VIF < 2.0). The results of testing the effects of marketing on export performance are shown in Table 3.
Table 3 shows that the dependent variable correlates have quite strong linear correlation in the sense α> 0.05 with 6 independent concepts S1, S2, S3, S4, S5 and S6. Since all absolute correlation coefficients between the concepts are in range of 0.669 to 0.969 satisfying -1≥ r ≥+1, all concepts satisfy the rule of multiple linear regressions.
Table. 3 Effects performance of small and medium enterprises. Source: Data analysis
of research data by SPSS 22.0
Coefficientsa
Model
|
Unstandardized
|
Standardized
|
t
|
Sig.
|
Collinearity
|
|
|
Coefficients
|
Coefficients
|
|
|
Statistics
|
|
|
B
|
Std.
|
Beta
|
|
|
Tolerance
|
VIF
|
|
|
|
Error
|
|
|
|
|
|
|
(Constant)
|
.085
|
.180
|
|
.471
|
.638
|
|
|
|
MS
|
.186
|
.031
|
.251
|
6.058
|
.000
|
.750
|
1.334
|
|
BIS
|
.147
|
.035
|
.170
|
4.205
|
.000
|
.789
|
1.267
|
1
|
AME
|
.235
|
.036
|
.286
|
6.508
|
.000
|
.669
|
1.494
|
|
IS
|
.238
|
.036
|
.273
|
6.599
|
.000
|
.756
|
1.323
|
|
MEF
|
.068
|
.028
|
.090
|
2.468
|
.014
|
.969
|
1.032
|
|
LEM
|
.143
|
.038
|
.163
|
3.785
|
.000
|
.694
|
1.441
|
a. Dependent Variable: PSME
Notes: * p < 0.05; ** p < 0.01; ns: non-significant; all VIF < 2.0; R2 (export performance) = 63.8%; F = 83.359, p < 0.001.
The regression equation for the unstandardized coefficient takes the following form: |
