Table 9

Mediation of perceived usefulness in the relationship between technological factors and blockchain adoption

Path(Total β)(X → PU)(PU → BB)IndirectBoot SEBCa 95% CI(β)pMediation
RA → PU → BB+0.309+0.359+0.6120.1920.028[0.140, 0.253]+0.0890.047Partial
CA → PU → BB+0.308+0.298+0.6060.1940.036[0.129, 0.273]+0.127<0.001Partial
CL → PU → BB−0.006+0.009+0.6440.0050.027[−0.050, 0.057]+0.0240.501No mediation
TK → PU → BB+0.188+0.184+0.6300.0980.028[0.047, 0.154]+0.0660.064Full
PFB → PU → BB+0.518+0.463+0.5140.2040.027[0.153, 0.260]+0.321<0.001Partial
IS → PU → BB+0.328+0.432+0.6170.2620.036[0.197, 0.341]+0.0700.061Full

Note(s): Mediation was assessed using bias-corrected bootstrapped confidence intervals based on 5,000 resamples (Hayes, 2018), complemented by the classical Baron and Kenny (1986) logic. Following the typology of Zhao et al. (2010), mediation is classified according to the joint pattern of indirect and direct effects. Indirect-only (full) mediation is inferred when the indirect effect is significant while the direct effect is not. Complementary (partial) mediation is inferred when both the indirect and direct effects are significant and point in the same direction. All classifications reported below follow this bootstrap-based standard rather than relying solely on the causal-steps approach. Indirect-effect significance was assessed using bias-corrected 95% bootstrap confidence intervals based on 5,000 resamples. An indirect effect was considered significant when the confidence interval did not include zero. Direct-effect significance is reported in the p column

Source(s): Authors' own work

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