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H. Discriminant Validity

As suggested by Hair et al. [24], the AVE of each latent construct should be higher than the construct's highest squared correlation with any other latent construct (Fornell-Larcker criterion). An indicator's loading should be higher than all of its cross loadings as shown in Table 1.4.

I. Reliability Analysis and Hypotheses Testing

Cronbach's alpha coefficient is used to assess the inter-item consistency of the measurement item. A composite reliability of 0.70 or greater is acceptable. The

Table 1.2 Result of measurement model

Model construct

Measurement item

Loading

CRa

AVEb

ET

ET_FORMAL

0.787

0.875

0.588

ET_HRDF

0.811

ET_INDOOR

0.771

ET_SKILL

0.585

ET_TR1

0.851

FC

FC_FIRMSIZE

0.799

0.850

0.654

FC_SW01

0.813

FC_SW02

0.815

IA

INNO_NP

0.870

0.864

0.761

INN_EXNP

0.874

TI

TI_01

0.789

0.880

0.595

TI_02

0.766

TI_04

0.734

TI_05

0.848

TI_06

0.716

aComposite reliability (CR) = (square of summation of the factor loading)/{(square of the summation of factor loadings) + (square of the summation of the error variances)}

bAverage variance extracted (AVE) = (square of summation of the factor loading)/{(summation of the square of the factor loadings) + (summation of the error variances)}

Table 1.3 Summary results of the model construct

Model construct

Measurement item

Standard estimate

t-value

ET

ET_FORMAL

0.787

49.758**

ET_HRDF

0.811

53.211**

ET_INDOOR

0.771

44.607**

ET_SKILL

0.585

24.599**

ET_TR1

0.851

80.492**

FC

FC_FIRMSIZE

0.799

46.289**

FC_SW01

0.813

33.518**

FC_SW02

0.815

32.484**

IA

INNO_NP

0.870

34.510**

INN_EXNP

0.874

43.326**

TI

TI_01

0.789

54.027**

TI_02

0.766

45.559**

TI_04

0.734

45.281**

TI_05

0.848

85.472**

TI_06

0.716

37.512**

t-values > 1.96* ( p < 0.05); t-values > 2.58** ( p < 0.01)

Table 1.4 Discriminant validity of construct

Table 1.5 Path coefficients and hypothesis testing

Hypothesis

Relationship

Coefficient

t-value

Supported

H1

ET >TI

0.313

10.111**

Yes

H2

FC >TI

-0.173

6.771**

Yes

H3

IA >TI

-0.208

6.802**

Yes

**p < 0.01; *p < 0.05

result indicates that all alpha values are above 0.6 as suggested by Krieg [22], and the composite factor reliability coefficients of construct ranged from 0.850 to 0.880 (see Table 1.2) which met the standard of 0.70 as suggested by Hulland [26]. As such, the measurements are reliable.

The R2 value was 0.249 suggesting that 24.9 % of the variance in technological

adoption can be explained by employees training, firm characteristic, and innovation activities. Employee training has a direct positive significance (β ¼ 0.313, p < 0.01) to technological adoption, whereas firm characteristic (β ¼ -0.173, p < 0.01) and innovation activities (β ¼ -0.208, p < 0.01) have direct negative significance to technological adoption. In this study, we found that employees' training was the most significant predictor of TI adoption followed by innovation activities and firm characteristic as shown in Table 1.5. The higher the employees training, the higher is TI adoption.

 
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