AMO composite scores of perceived generative AI adoption barriers by sample on a 0–3 scale
| AMO component | Wellbeing services | Technical services | t(df) | p (Two-sided) | Hedges’ g |
|---|---|---|---|---|---|
| M (SD) n | M (SD) n | ||||
| All 19 items | 1.71 (0.64) n = 235 | 1.47 (0.57) n = 85 | 3.33 (167.8) | 0.001 | 0.40 |
| Ability (4 items) | 1.53 (0.77) n = 233 | 1.26 (0.68) n = 85 | 3.01 (166.2) | 0.003 | 0.36 |
| Motivation (6 items) | 1.51 (0.82) n = 232 | 1.57 (0.66) n = 85 | −0.70 (182.9) | 0.486 | −0.08 |
| Opportunity (9 items) | 1.92 (0.70) n = 234 | 1.50 (0.71) n = 85 | 4.69 (145.9) | < 0.001 | 0.60 |
| Wellbeing services | Technical services | t(df) | Hedges’ g | ||
|---|---|---|---|---|---|
| M ( | M ( | ||||
| All 19 items | 1.71 (0.64) | 1.47 (0.57) | 3.33 (167.8) | 0.001 | 0.40 |
| Ability (4 items) | 1.53 (0.77) | 1.26 (0.68) | 3.01 (166.2) | 0.003 | 0.36 |
| Motivation (6 items) | 1.51 (0.82) | 1.57 (0.66) | −0.70 (182.9) | 0.486 | −0.08 |
| Opportunity (9 items) | 1.92 (0.70) | 1.50 (0.71) | 4.69 (145.9) | < 0.001 | 0.60 |
Wellbeing services data are from one regional wellbeing services county; technical services data are from civil servants across 104 municipalities. Sectoral comparisons should be interpreted with this design asymmetry in mind
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