Table 6.
Comparison between students who joined TEL willingly (N = 568) and unwillingly (N = 228).
| Hypotheses | Global | Group: Willingly | Group: Unwillingly | diff.abs | t | df | p | |
|---|---|---|---|---|---|---|---|---|
| H1 # | Techno-overload -> Burnout | −0.01 | 0.18 | −0.08 | 0.26 | 1.83 | 794 | 0.03 |
| H2 | Techno-invasion -> Burnout | 0.02 | 0.07 | 0.02 | 0.05 | 0.27 | 794 | 0.39 |
| H3 | Techno-complexity -> Burnout | 0.25 | 0.29 | 0.23 | 0.06 | 0.49 | 794 | 0.31 |
| H4 | Techno-insecurity -> Burnout | 0.15 | 0.10 | 0.17 | 0.07 | 0.47 | 794 | 0.32 |
| H5 | Techno-uncertainty -> Burnout | 0.31 | 0.19 | 0.33 | 0.14 | 0.96 | 794 | 0.17 |
| H6 | Burnout -> Self-regulation | −0.46 | −0.69 | −0.36 | 0.33 | 2.94 | 794 | 0.00 |
| H7 | Burnout -> Learning agency | −0.32 | −0.64 | −0.18 | 0.46 | 3.75 | 794 | 0.00 |
| H8 | Burnout -> Persistence | −0.35 | −0.67 | −0.22 | 0.45 | 4.12 | 794 | 0.00 |
Note. diff.abs = absolute difference; the bold rows indicate the paths where students who joined TEL willingly significantly differed from those who joined TEL unwillingly; # = As techno-overload did not significantly predict burnout in TEL (path coefficient of −0.03) for the whole sample, the two sub-datasets cannot be regarded as significantly different on the path relationship.