4.3.1 Analysis of influencing factors
In this study,a SEM model was developed using AMOS 25.0 software for parameter estimation.The results showed(Table 6)that college students' willingness to participate in digital learning was influenced by perceived ease of use, perceived usefulness,behavioral attitudes, and subjective norms, and willingness to participate in learning had a positive and significant effect on participation behavior (Table 6).Behavioral attitude had a significant positive effect on college students' willingness to participate in digital learning with a path coefficient of 0.136, and the H1 hypothesis was verified to hold. Among the five observed variables of the latent variable behavioral attitude,the standardized load coefficients of improving job performance AT3,acquiring business knowledge AT1,promoting career development AT4 and improving employability AT2 were higher at 0.796, 0.781, 0.771 and 0.763, respectively,while the standardized load coefficient of self-esteem awareness AT5 was only 0.737,which was relatively higher than the other observed variables,and this observed variable has the least effect on college students' willingness to participate in digital learning.This indicates that college students attach more importance to practical factors such as job performance, career development and employability when considering whether to participate in digital learning. With the implementation of the Digital China strategy and the rapid development of digital technology, college students with a stronger sense of career development are more willing to participate in training and learn advanced "Internet+" and other technologies to improve their job performance, promote career development, and obtain more opportunities for promotion.
Second,Table 6 shows that college students' perceived usefulness and perceived ease of use of digital training also have a significant positive effect on their willingness to participate in digital training, with path coefficients of 0.146 and 0.233, respectively, and hypotheses H2 and H3 hold. the standardized load coefficients of GZ1, GZ2, and GZ3 are 0.801, 0.845, and 0.776, respectively, indicating that when considering the the role of perceptual digital training, college students will focus on the enhancement of their abilities. The standardized load coefficients of ES1, ES2, and ES3 were 0.817, 0.775, and 0.874, respectively, which indicates that when considering the convenience of digital training, college students pay more attention to whether the operation steps are clear and easy to understand during the process of participating in digital training, followed by whether it is easy to learn the content they need. This result fully proves that due to the incomplete symmetry of training contents and the lack of knowledge of training services, college students judge digital training more by past experience and subjective tendency, and such judgment is easily dominated by the level of expectations of training or services.
Meanwhile,the path coefficient of subjective norms on willingness to participate in training was 0.257,and hypothesis H4 holds.Among the three observed variables of this latent variable,the standardized loadings of CR1,CR2, and CR3 were 0.862,0.733,and 0.714,respectively,indicating that college students are deeply influenced by the perceptions of their family and friends when considering whether to participate in digital training.Family and friends would support college students' participation in digital training because of their improved job performance or career development after participating in digital training.
Finally,college students' participation in training is influenced by their willingness to participate in training.Among the three observed variables of the latent variable of willingness to participate in training,the standardized loading coefficients of training motivation,training opportunity and future expectation are 0.776, 0.801 and 0.687,respectively,indicating that the existence of training opportunity is an important external factor influencing college students' willingness to participate in training,while training motivation and future expectation are internal factors influencing college students' willingness to participate in digital training.Therefore, better quality digital training opportunities should be provided externally,and internally, college students should be guided to establish good training motivation and raise their expectations for their own development,which will help enhance their willingness and behavior to participate in digital learning.
Table 6
Model Path Estimation Results
路径
|
Non standardized path/load factor
|
S.E.
|
C.R.
|
P
|
Standardized path/load factor
|
Willingness to participate in training<- subjective norms
|
0.223
|
0.059
|
3.804
|
***
|
0.257
|
Willingness to participate in training<- Behavioral attitude
|
0.14
|
0.066
|
2.121
|
0.034
|
0.136
|
Willingness to participate in training<- Perceived ease of use
|
0.214
|
0.06
|
3.554
|
***
|
0.233
|
Willingness to participate in training<- perceived usefulness
|
0.134
|
0.06
|
2.247
|
0.025
|
0.146
|
Participation in training behavior<- willingness to participate in training
|
0.351
|
0.073
|
4.832
|
***
|
0.348
|
CR1<- subjective norm
|
1
|
|
|
|
0.862
|
CR2<- subjective norm
|
0.833
|
0.071
|
11.783
|
***
|
0.733
|
CR3<- subjective norm
|
0.838
|
0.072
|
11.608
|
***
|
0.714
|
GZ1<- perceived usefulness
|
1
|
|
|
|
0.801
|
GZ2<- perceived usefulness
|
1.064
|
0.075
|
14.191
|
***
|
0.845
|
GZ3<- perceived usefulness
|
0.971
|
0.071
|
13.69
|
***
|
0.776
|
AT5<- Behavioral attitude
|
1
|
|
|
|
0.737
|
AT4<- Behavioral attitude
|
1.14
|
0.087
|
13.139
|
***
|
0.771
|
AT3<- Behavioral attitude
|
1.09
|
0.08
|
13.542
|
***
|
0.796
|
AT2<- Behavioral attitude
|
1.022
|
0.079
|
13.007
|
***
|
0.763
|
AT1<- Behavioral attitude
|
1.033
|
0.078
|
13.305
|
***
|
0.781
|
SW1<- Willingness to participate in training
|
1
|
|
|
|
0.754
|
SW2<- Willingness to participate in training
|
1.012
|
0.093
|
10.926
|
***
|
0.784
|
SW3<- Willingness to participate in training
|
0.906
|
0.089
|
10.214
|
***
|
0.668
|
BE1<- Participating in training behavior
|
1
|
|
|
|
0.759
|
BE2<- Participating in training behavior
|
1.038
|
0.087
|
11.894
|
***
|
0.8
|
BE3<- Participating in training behavior
|
1.032
|
0.089
|
11.648
|
***
|
0.745
|
ES3<- Perceived ease of use
|
1
|
|
|
|
0.817
|
ES2<- Perceived ease of use
|
0.959
|
0.066
|
14.508
|
***
|
0.775
|
ES1<- Perceived ease of use
|
1.055
|
0.068
|
15.525
|
***
|
0.874
|
4.3.2 Mesomeric effect analysis
On the basis of analyzing the influencing factors of college students' participation in digital training, further explore the willingness mechanism of college students to participate in digital training. According to Hayes' study[31],a non parametric percentile Bootstrap method was used and a 95% confidence interval was set to repeatedly select 5000 random samples for analysis. Mesomeric effect results (Table 7) show that there are four ways to perceive usability - willingness to participate in digital training - behavior and attitude - willingness to participate in digital training - behavior to participate in digital training, perceived usefulness - willingness to participate in digital education - behavior to participate in digital education, subjective norms - willingness to participate in digital training - behavior to participate in digital training.The confidence intervals corresponding to the 95% probability level and the quantile correction percentage are [0.037,0.141],[0.001,0.113],[0.007,0.113] and [0.041,0.153]. The confidence intervals of quantile correction percentage quantile are [0.034,0.136], [0.002,0.109], [0005,0.109] and [0.0380149] respectively. The upper and lower limits of deviation correction and the percentage quantile do not include 0.The results show that the willingness to participate in digital training has a significant mesomeric effect, that is, the willingness to participate in digital training plays a mediating role between ease of use perception and training participation behavior. The willingness to participate in training plays a mediating role between behavioral attitudes and training participation behavior. The willingness to participate in training plays a mediating role between perceived utility and training participation behavior. The willingness to participate in training plays a mediating role between subjective norms and training participation behavior.
Table 7
Test Results of mesomeric effect
Mediation Path
Participating in training behavior ← Willingness to participate in training ← Perceived ease of use
|
Bilateral test for indirect effect coefficient
|
P-value
|
Bias-corrected
|
Percentile
|
Lower
|
Upper
|
Lower
|
Upper
|
Participating in training behavior ← Willingness to participate in training ← Behavioral attitude
|
0.081
|
0.002
|
0.037
|
0.141
|
0.034
|
0.136
|
Participating in training behavior ← Willingness to participate in training ← Perceived usefulness
|
0.047
|
0.011
|
0.001
|
0.113
|
0.002
|
0.109
|
Participating in training behavior ← Willingness to participate in training ← Subjective norms
|
0.051
|
0.005
|
0.007
|
0.113
|
0.005
|
0.109
|
Mediation Path
|
0.089
|
0.001
|
0.041
|
0.153
|
0.038
|
0.149
|