Does Digital Tool Usage Translate Digital Competence into Workflow Efficiency? Explanatory and Predictive Evidence from AI-Augmented Banking

Abstract

Digital competence is frequently presented as a prerequisite for effective work with artificial-intelligence-enabled systems. Yet competence does not show that technology has been incorporated into task execution. This study examines whether task-embedded digital tool usage translates digital competence into perceived technology-enabled task efficiency. A cross-sectional questionnaire yielded 301 complete responses from a banking-sector sample. The proposed capability-enactment model was evaluated with reflective partial least squares structural equation modelling (PLS-SEM) and 5,000 bootstrap resamples. Its predictive implications were tested through repeated nested cross-validation. Ridge regression and random forests used competence-only, usage-only, and combined item-level feature sets. Digital competence was associated with digital tool usage (beta = 0.388) and perceived task efficiency (beta = 0.261). Tool usage retained an association with efficiency after competence was controlled (beta = 0.303). The indirect association was 0.118 (95% bootstrap CI 0.074-0.171), supporting partial statistical mediation. The combined random forest produced the lowest held-out RMSE (0.835) and highest pooled Q2predict (0.240). It also outperformed competence-only and usage-only forests in paired comparisons. Group permutation analysis assigned more predictive information to usage than to competence, although both were useful. The results identify digital tool usage as a proximal behavioural mechanism between capability and perceived workflow return. They also show why explanatory significance should be complemented by out-of-sample validation when designing or evaluating human-AI work systems.