To find which factors most affect the level of e-readiness of large and medium en- terprises in Thai Nguyen province, the author conducted linear regressions where e- readiness level served as the dependent variable. The basic model treats POER and PEER served as independent variables and the extended model used the control vari- ables such as firm size, firm age, industry sector, and type of ownership as dummy variables.
4.5.1 The Basic Model
Table 4.14 displays the ANOVA results of regression analysis using the basic model and Table IV 16 presents the coefficients of the model. As can be seen from Table IV 15 the regression with computed-F value of 31.348 with a significant value of 0.000 shows that the model has statistical significance.
TABLE4.14: The ANOVA of the basic regression model
Model Sum of Squares df. value Mean Sq F value Sig. value
Regression 19.955 2 9.977 31.348 .000b
Residual 31.509 99 0.3180
Total 51.464 101
Table4.15indicates that the POER and PEER variables have computed-t values of 2.014 and 2.983 with significant values of 0.047 and 0.004 respectively. Thus, both variables are statistical significance. Their positive values confirm Molla,2005bfinding that rela- tionship between financial commitment, perceived environmental e-readiness (PEER), technology competence and e-business use is positive, and the relationship between POER and e-business use is indirect.
TABLE4.15: Coefficients of basic model
Model unstand. B Std. Err stand. Beta t Sig. Tolerance VIF
(Constant) 0.824 0.304 2.708 0.008
POER 0.217 0.108 0.264 2.014 0.047 0.36 2.778
PEER 0.39 0.131 0.391 2.983 0.004 0.36 2.778
Furthermore, the VIF values for the two variables are 2.778 and 2.778 bigger than 2.0 indicating that there is no collinearity in the model. This results support the previous findings by Lai et al, 2006 and Shemi, 2012 that in general top management commit- ment and infrastructure and technology have significant impact on SMEs’ e-readiness This also confirms the findings “consumers’ confidence in the security of communi- cations and commercial exchange mechanisms provided by the companies is shown
to encourage purchasing on the Internet” by Rodríguez-Ardura et al.,2008. The POER significant affects to e-readiness level of the firm support the finding by Ramayah et al (2010) that organisational factors including security, trust, and privacy concerns, financial ability, size of the organisation, perceived benefits of ICT, organisational cul- ture affects e-readiness of the firm as well as his finding that technology factors with Internet speed, complexity of technology, payment facility, power supply affects the e-readiness level is confirmed by PEER factors.
The PEER factor affecting the e-readiness was confirmed by Molla, 2005b that envi- ronmental factors like pressure from competitors, customers, or suppliers; the role of government partners’ alliances; technological infrastructure; technology consultants;
image of Internet technology; and users’ expectations affect the e-readiness of the firm.
4.5.2 The Extended Model with Control Variables as Dummy Vari- ables
To find the effect of the control variables on e-readiness, the author conducted series of regression with variables such as firm size, industry sector, business age and types of ownership. Those variables served as dummy variables.
TABLE4.16: Coefficients of regression with firm size as dummy
Model unstand. B Std. Err stand. Beta t Sig. Tolerance VIF
(Constant) -0.305 0.3380 -0.9030 0.3690
POER 0.312 0.12 0.306 2.597 0.011 0.358 2.793
PEER 0.521 0.147 0.422 3.549 0.001 0.351 2.846
Large 0.199 0.1270 0.1120 1.564 0.121 0.969 1.032
Table 4.16 displays the coefficients of the regression with POER and PEER are inde- pendent variables and firm size is the dummy variable. As can be seen from the table, the dummy variable, namely, large has computed-t of 1.564 with a significant value of 0.121 indicating that the dummy variable is not significant fitted in the model. In other
words, the firm size does not significantly affect the e-readiness level of the firm. These results did not support Molla,2005bfinding that firm size has a negative relationship with e-readiness level of the firm.
Table4.17shows the coefficients of the regression using business age, a dummy vari- able. As can be seen from the table, dummy variables namely “less than 2 years”, “2-5 years”, and “5-10 years” have their significant value of 0.460, 0.920 and 0.847 respec- tively. Therefore, the business age dummy variables are not significantly fitted in the model.
TABLE 4.17: Coefficiens of regression with business age as dummy vari- able
Model unstand. B Std. Err stand. Beta t Sig. Tolerance VIF
(Constant) -0.253 0.385 -0.656 0.513
POER 0.293 0.123 0.287 2.39 0.019 0.357 2.802
PEER 0.547 0.156 0.444 3.5 0.001 0.322 3.11
Less than 2 0.266 0.358 0.059 0.741 0.46 0.824 1.213
2-5 years -0.019 0.192 -0.009 -0.101 0.92 0.601 1.664 5-10 years -0.029 0.151 -0.015 -0.194 0.847 0.809 1.236
Table 4.18 presents the regression coefficients of the model with industry sectors as dummy variables. As can be seen from Table4.18, the dummy variables, namely, less than 2 years, 2-5 years, and 5-10 years, have their significant value of 0.460, 0.920 and 0.847 respectively. Therefore, the business age dummy variables are not significantly fitted in the model. According to the table, the variables, namely, Mining, Construc- tion, Transportation, Service, and Wholesale have significant values of 0.592, 0.790, 0.590, 0.973 and 0.747 respectively. Therefore, these variables are not significantly fit- ted in the model. Furthermore, the variable named manufacturing with its significant value of 0.049 seems to be fitted in the model. However, the present of this variable causes the POER variable out of the model due to its significant value of 0.094. Thus, the industry sector dummy variables are not fitted in the model. This does not support
the finding “industry type and ownership of the company significantly influenced e- business adoption” by Lai et al., 2006.
TABLE4.18: Coefficients of regression with industry as dummy
Model unstand. B Std. Err stand. Beta t Sig. Tolerance VIF
(Constant) 0.06 0.444 0.134 0.894
POER 0.192 0.113 0.188 1.694 0.094 0.325 3.074
PEER 0.454 0.141 0.369 3.218 0.002 0.307 3.255
Mining -0.205 0.382 -0.063 -0.537 0.592 0.295 3.385
Construction 0.093 0.349 0.042 0.267 0.79 0.162 6.174
Transportation -0.236 0.436 -0.052 -0.541 0.59 0.436 2.296
Manufacturing 0.681 0.341 0.386 1.999 0.049 0.108 9.274
Service 0.015 0.44 0.003 0.034 0.973 0.427 2.341
Wholesale 0.122 0.378 0.039 0.323 0.747 0.271 3.69
Table4.19 displays the coefficients of the regression with ownership as dummy vari- ables. From table 4.19, it can be seen that the variables, namely, state and collective have significant values of 0.354 and 0.619 respectively. Thus, those variables are not significantly fitted in the model. The FDI variable with its significant value of 0.28 seems fitted in the model. However, the present of this variable causes the POER out of the model as its significant value of 0.070. Therefore, the ownership dummy vari- ables are not significantly fitted in the model. In other words, ownership does not significantly affect the e-readiness of the firm. This result does not support Lai et.al, 2006 result “industry type and ownership of the company significantly influenced e- business adoption”.
4.5.3 The Final Model
From the analysis in the previous sections, the author chose the basic model as the final model for the analysis of factors affecting the e-readiness of large and medium enterprises in Thai Nguyen Province. The final model now can be written as:
TABLE 4.19: Regression coefficients with types of ownership as dummy variables
Model unstand. B Std. Err stand. Beta t Sig. Tolerance VIF
(Constant) 0.183 0.434 0.423 0.674
POER 0.232 0.126 0.227 1.835 0.07 0.321 3.117
PEER 0.451 0.154 0.366 2.923 0.004 0.314 3.182
FDI 0.491 0.22 0.222 2.232 0.028 0.499 2.002
State 0.235 0.253 0.068 0.931 0.354 0.933 1.072
Collective 0.1 0.201 0.037 0.499 0.619 0.907 1.103
E−readiness =0.264∗POER+0.391∗PEER (4.1)
The model shows that both POER and PEER have positive impact to e-readiness. This means enterprise with higher perceived e-readiness has higher level of e-readiness.
Furthermore, the beta value of PEER is higher than the beta of POER shows that per- ceived extended e-readiness has more effect to e-readiness compared to perceived or- ganizational e-readiness. In other word, the external factors affect more to e-readiness than the internal factors. This result supports the findings by Uzoka,2008, Chibelushi, 2009, Kamal, 2006, Yusoff, Ramayah, and Ibrahim, 2010and Shemi,2013. In addition to that, the two beta values of the equation are smaller than 1.0 shows that enterprises have higher perceived e-readiness than their e-readiness which was calculated based on their facts of technology, management, people and process. In other word, although enterprises understand the importance of ICT and ecommerce to their business they did not invest enough in the ICT and e-commerce.
Chapter 5