Mapping Boundary Conditions and Bottlenecks of Learning Analytics Dashboard Acceptance: A Cross-Group and Cross-Design Comparison

Drawing on extended Technology Acceptance Model, this study argues that learner's Learning Analytics Dashboards (LADs) data literacy and LAD cognitive complexity are boundary conditions that shape learner LAD acceptance. Two learner groups, one with LAD data literacy and another without, were examined. Two LAD visualization designs were included: a heat map and a line graph. PLS-SEM and necessary condition analysis (NCA) results show that perceived usefulness (PU) was the most consistent driver and non-compensatory necessary bottleneck of LAD acceptance across all situations, with substantially stronger effects among data-literate learners under line graph ( β = 0.82, d = 0.48, p < 0.001). Emotional value functioned as both a driver and bottleneck for learners without LAD data literacy regardless of the LAD designs ( β = 0.43 and 0.50, d = 0.20 and 0.22, p < 0.001), and as a non-compensatory bottleneck for data-literate learners ( d = 0.54 and 0.37, p < 0.001).