No barrier is necessary and no barrier configuration is sufficient for low AI readiness (fsQCA null result)¶

Claim · All claims
Evidence · 1 study (2 entries) · associational r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? No barrier reached the 0.90 necessity consistency threshold; resistance to change showed low necessity (consistency = 0.24), contrary to H3. → V.G. Kremen 2026 q2 i? No barrier configuration met the 0.80 sufficiency threshold; the highest consistency was 0.33, contrary to H4. → V.G. Kremen 2026 (2)

Evidence¶

V.G. Kremen 2026¶

V.G. Kremen, O.M. Spirin, O.I. Liashenko, S.H. Lytvynova, Y.I. Malovanyi, O.P. Pinchuk, O.M. Sokolyuk, S.O. Semerikov. (2026). AI adoption readiness among Ukrainian education managers: Barriers, typologies, and policy implications. Computers and Education: Artificial Intelligence 11 (2026) 100648. https://doi.org/10.1016/j.caeai.2026.100648

q2 · i? · associational · r1

fsQCA necessity analysis of four crisp-set barrier conditions against low AI readiness (calibrated personal readiness) among 395 managers. The authors state H3 is not supported: most low-readiness managers did not report resistance to change.

"Necessity analysis (Table 8) found that no barrier reached the 0.90 consistency threshold for necessity. Low system readiness was the strongest candidate (consistency = 0.84) but still fell short, and resistance to change – contrary to the expectation in H3 – showed low necessity (consistency = 0.24)"

V.G. Kremen 2026 (2)¶

V.G. Kremen, O.M. Spirin, O.I. Liashenko, S.H. Lytvynova, Y.I. Malovanyi, O.P. Pinchuk, O.M. Sokolyuk, S.O. Semerikov. (2026). AI adoption readiness among Ukrainian education managers: Barriers, typologies, and policy implications. Computers and Education: Artificial Intelligence 11 (2026) 100648. https://doi.org/10.1016/j.caeai.2026.100648

q2 · i? · associational · r1

fsQCA sufficiency truth-table analysis of all 16 configurations of the four barriers against low AI readiness. The authors state this null should not be read as equifinality; low readiness appears diffusely rather than configurationally determined.

"No configuration met the 0.80 consistency threshold; the highest consistency was only 0.33 (competency, regulatory, and distrust barriers jointly present). H4 is therefore not supported."

Discussion¶

Related Claims¶

• On a common 0–1 metric, digital-competency adequacy shows the largest readiness gap (42.4%), followed by system readiness (21.3%) — related
• High GAI use is not a necessary condition for high learning engagement, and no single condition is strictly necessary — related
• Multiple sufficient configurations lead to high AI literacy performance (equifinality) — related
• Regulatory absence and digital competency gaps are the most prevalent barriers to AI adoption among Ukrainian education managers — related
• Ukrainian education managers' personal AI readiness significantly exceeds their perceived system readiness (d = 0.73) — related