Measuring AI Leadership: Development and Validation of a Multidimensional Measure for AI-Native Organizations
cs.AI · cs.CY · cs.HC
Quick verdict
Develops a 108-item AI Leadership Battery with solid multi-stage validation; main concerns are an underpowered 51-item CFA and same-source criterion data.
Summary
This preprint develops and validates the AI Leadership Battery, a self-report measure of 36 behaviorally specific leader subdimensions organized into 11 content families (108 items, three per subdimension). Following deductive scale-development procedures, the authors generated 180 candidate items, obtained content-validity ratings from 199 doctoral-credentialed Prolific panel records (96.7% met all three correspondence/distinctiveness criteria), ran family-wise EFAs in 590 working leaders (KMO .884-.958; alpha .752-.941), and tested prespecified correlated-factor CFAs in 601 independent leaders. Fit was good for the 30- and 27-item forms (CFI .95/.98) but weaker for the 17-factor, 51-item form (CFI .89), which estimated 238 parameters from n=202. All 142 subdimension-orbiting-construct pairs met HTMT and adapted Fornell-Larcker criteria (max HTMT .72), and a color-preference marker-variable analysis changed little. Hierarchical regressions against seven same-wave, leader-reported outcomes showed significant delta-R2 in 19/21 content-family models (up to .436, and .522 for subdimensions, for AI adoption/integration), with 5x5-fold cross-validation improving held-out R2 in 18/21 content-family models. Key limitations: split-form administration means the full 36-subdimension structure was never tested jointly; largest within-battery latent correlations reach .85; criterion data are same-source/same-wave self-reports with unadjusted p-values; and item wording remains in 'protected study materials,' limiting reproducibility.
Review complete
Assessment at a glance
5
risk flags identified
● high 1
● medium 3
● low 1
5 strengths · 5 weaknesses · 5 author questions
Levels reflect verification complexity — not a quality score. Evolve never rates your paper.
Key claims under review
- AI leadership is a distinct, behaviorally specific domain - 36 subdimensions in 11 content families - separable from digital leadership, algorithmic management, AI attitudes/literacy, and organizational AI capability.
- The Battery's subdimension scores show adequate reliability (alpha .752-.941) and a confirmable correlated-factor structure in independent samples, though fit for the 51-item form is mixed.
- All 142 subdimension-orbiting-construct comparisons meet HTMT and adapted Fornell-Larcker discriminant criteria (max HTMT .72), supporting distinctiveness from the administered comparison measures.
- AI Leadership scores add significant incremental variance beyond orbiting constructs for most of seven same-wave outcomes (delta-R2 up to .436 for content families and .522 for subdimensions), with cross-validation confirming held-out gains in 18/21 content-family models.
Strengths
Multi-stage validation with genuinely independent samples
Content panels (199 records, doctoral-credentialed raters, fully crossed item-by-definition design following Colquitt et al., 2019), family-wise EFA in 590 leaders, then CFAs in 601 leaders with no overlap with development (Tables 2-6), with prespecified models maintained throughout.
Modern, prespecified psychometric criteria
Parallel analysis for factor retention, robust-ML CFA, HTMT with 1,000 bootstrap resamples plus an adapted Fornell-Larcker criterion across 142 orbiting comparisons (max HTMT .72, max upper CI bound .82), and a marker-variable sensitivity analysis (Stage 3, Table 7).
Criterion work beyond the scale-development norm
Transparent limitation reporting
Clear construct positioning
Weaknesses
Severely underpowered 17-factor CFA
Full Battery never administered jointly
Same-source, same-wave criterion evidence with unadjusted tests
Reproducibility limited by protected materials
Opaque item-retention decisions
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Frequently asked questions
What is the AI Leadership Battery and how many items does it have?
It is a self-report questionnaire measuring 36 specific AI-leadership behaviors (e.g., proactive AI disclosure, ethical boundary setting, psychologically safe AI experimentation) organized into 11 content families. Each subdimension uses three items, for 108 items total. It was developed and validated with online samples of working leaders recruited via Prolific.
Is the AI Leadership Battery a validated measure?
It has initial validation evidence: content ratings by doctoral-level panelists, exploratory and confirmatory factor analyses in independent samples, reliability (Cronbach's alpha .75-.94 across subdimensions), passing HTMT discriminant-validity checks against 11 related constructs, and incremental prediction of seven self-reported work outcomes. Caveats: the 51-item segment's confirmatory fit was weaker (CFI .89) and no participant completed all 36 subdimensions at once, so full-scale structure evidence is still limited.
Where can I get the AI Leadership Battery items or scale?
The preprint states that exact item wording remains in 'protected study materials' and is not published; researchers would need to contact the corresponding author at Elon University. No public data or code repository is mentioned in the manuscript.
How is AI leadership different from digital leadership or algorithmic management?
The paper positions AI leadership as behaviorally specific leader actions in AI-enabled work - calibrating reliance on AI, disclosing and explaining AI use, coaching critical discernment, and enforcing ethical boundaries. Digital-leadership scales target broad transformation readiness, while algorithmic-management measures treat the algorithm itself as the directing and monitoring agent rather than the human leader's behaviors.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Exact item wording, survey instructions, and supplementary loading details are withheld ('protected study materials'), so item quality and content validity could not be independently assessed.
- Figures 1-3 and the supplementary materials are not analyzable from the extracted text; structural and validity judgments rest on the reported tables.
- This is preprint v007 with no stated peer-review status; results and framing may change across versions.
- Some context (e.g., exact orbiting-construct short-form items, screening exclusions detail) resides in codebooks not included in the text.
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