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Measuring AI Leadership: Development and Validation of a Multidimensional Measure for AI-Native Organizations

Mustafa Akben, Leslie Coyne

arXiv:2609.17965v1cs.AIcs.CYcs.HC

TL;DR

Existing measures do not capture the behaviors needed to study leadership in AI-enabled work. The paper develops and validates the AI Leadership Battery, with analyses providing initial evidence for its use and showing added explanatory value across work outcomes.

  • Problem

    Existing measures do not capture the behaviors needed to study leadership in AI-enabled work, where leaders must decide when to rely on AI, challenge it, and remain accountable.

    Source: Measurement Gap and Neighboring Domains · p. 4

  • Method

    The paper develops the Battery through deductive scale development, content review, and exploratory and confirmatory factor analysis, then evaluates its reliability and validity.

    Source: Practitioner Framework · p. 13, Item Generation and Refinement · p. 16, Conclusion · p. 42

  • Results

    Analyses provide initial evidence for the Battery’s use and show AI Leadership scores add explanatory value across work outcomes, particularly AI adoption and integration.

    Source: Overview of Contributions · p. 39, Conclusion · p. 42

  • Takeaways & Limitations

    The Battery offers a behavioral framework for empirically examining how leaders evaluate AI-supported judgment, organize learning and adaptation, communicate AI involvement, and maintain accountability.

    Source: Conclusion · p. 42

    Initial measurement evidence remains to be extended by administering all content-family measures to the same participants, evaluating the 51-item form’s mixed-model fit, and examining discriminant validity among closely related subdimensions.

    Source: Limitations and Future Research · p. 41

Abstract

from arXiv · show

AI is changing what leaders must judge, explain, learn, and coordinate, yet existing measures do not capture these behaviors at the level needed to study leadership in AI-enabled work. We develop the AI Leadership Battery, which organizes 36 behaviorally specific subdimensions into 11 theory-specified content families. Following established scale-development procedures, the research used deductive item generation; content validation of definitional correspondence and definitional distinctiveness; exploratory factor analysis and item reduction; confirmatory factor analysis in independent samples; and tests of internal consistency reliability, convergent validity, discriminant validity, and criterion-related and incremental validity. Across the development and validation studies, the analyses provided evidence for the Battery's content, multidimensional structure, reliability, and distinction from selected orbiting constructs. The Battery also contributed additional information beyond orbiting constructs across organizational growth, decision speed, customer/stakeholder response capability, AI-enabled team performance, AI-enabled work experience, AI security and risk management, and AI adoption and integration. The resulting measure provides researchers with a behavioral framework for examining how leaders in AI-enabled work regulate judgment, learning, adaptation, transparency, and accountability.

Measurement Gap and Neighboring Domains … Social Coordination and Capability Development

The AI Leadership Battery targets behaviorally specific actions through which leaders guide people, judgment, learning, and implementation in AI-enabled work. Its theory-specified domain links recurring AI-work conditions to 11 content families and 36 behavioral subdimensions.

Measurement Gap and Neighboring Domains

  • Prior measures address digital readiness, algorithmic control, AI attitudes or literacy, and organizational capability, whereas this Battery measures leaders’ specific actions in AI-enabled work.Related measures include Abbu et al. (2025), Chang et al. (2026) and Parent-Rocheleau et al. (2024), Liu et al. (2025), Park et al. (2024) and Wang et al. (2023), and Jaturat et al. (2026) and Mikalef & Gupta (2021).Source: Measurement Gap and Neighboring Domains · p. 4

Theoretical Derivation of the Domain

  • Probabilistic AI outputs and rapid knowledge change make expertise provisional; AI also reshapes work and redistributes decision input while leaders remain accountable for implementation and consequences.These three conditions ground the Battery’s 11 content families and 36 behavioral subdimensions.Source: Theoretical Derivation of the Domain · p. 5

From AI-enabled work conditions to theory-specified content families

  • The 36 subdimensions were defined before item writing, refined through development cycles, content-validated, and factor-analyzed within each family to connect theory, definitions, items, and structural evidence.Source: From AI-enabled work conditions to… · p. 6

Epistemic Judgment and Self-directed Adaptation

  • Developmental Stretch covers capability growth, feedback-driven practice updates, and flexibility about expertise; Adaptive Leadership covers provisional AI-use decisions, evidence-based recalibration, and reconfiguration when approaches misfit.Source: Epistemic Judgment and Self-directed… · p. 6
  • Outside-In Learning specifies scanning external AI signals, brokering external perspectives, and translating developments across domains.Source: Epistemic Judgment and Self-directed… · p. 7

Social Coordination and Capability Development

  • Proactive Transparency specifies disclosure of AI involvement, rationale, limitations, or harms; Role & Skill Evolution develops human-AI capabilities, redesigns work toward human value, and coaches critical discernment.Source: Social Coordination and Capability… · p. 7
  • Team Learning Experiences formalizes experimentation, capability review, learning transfer, role modeling, and developmental responses to mistakes; Psychologically Safe Climate supports experimentation, help-seeking, dissent, and vulnerability about AI-driven change.Source: Social Coordination and Capability… · pp. 7–8
  • Purposeful Communication facilitates collective interpretation of AI-related change and preserves human purpose as roles evolve.Source: Social Coordination and Capability… · p. 8

Accountable Implementation … Conceptual Organization of the AI Leadership Battery

The AI Leadership Battery organizes observable leader behaviors for AI-enabled work into 11 content families and related subdimensions. It distinguishes specific choices in calibration, ethical accountability, implementation, and other leadership behaviors from a broad orientation toward technology.

Accountable Implementation

  • Contextual Calibration makes situation-specific reliance observable through checking AI assumptions, task fit, evidence, and local organizational context.Its subdimensions are Context-Sensitive Reliance Calibration, Critical Questioning of AI Assumptions, Deliberative Contextual Fit-Checking, and Integration of Local Organizational Context.Source: Accountable Implementation · p. 8
  • Ethical Guardrails & Accountability covers ethical boundary setting, enforcement despite cost, and leaders’ personal answerability for AI-influenced outcomes.Its subdimensions are Ethical AI Boundary Setting, Costly Ethical Enforcement, and Personal Accountability for AI Outcomes.Source: Accountable Implementation · p. 9
  • Change Discipline addresses pacing implementation to team capacity, governing continuation or change through explicit criteria, and coordinating sustainment.Its subdimensions are Absorptive Pacing and Responsiveness, Implementation Governance and Adaptive Evaluation, and Coordinated Sustainment.Source: Accountable Implementation · p. 9

Contextualization and Construct Novelty

  • The framework contextualizes psychological safety, coaching, ethical leadership, and implementation leadership for AI-related judgment, learning, role change, communication, and implementation.It makes specific leadership choices visible within a shared framework rather than treating AI leadership as a broad orientation toward technology.Source: Contextualization and Construct Novelty · p. 9

Measurement Framework and Terminology

  • Each of the 11 content families contains behavioral subdimensions measured by three-item reflective scales; forms are selected-item sets and Signals are practitioner communication groupings.The 30-item, 51-item, and 27-item labels identify modeled subsets, not the full administered survey lengths.Source: Measurement Framework and Terminology · pp. 9–10

Figure 1

  • Figure 1 organizes the 11 content families and behavioral subdimensions around what leaders do across AI-enabled decisions, relationships, and work practices.The framework treats AI leadership as a behavioral domain, with theoretically related behaviors grouped into content families.Source: Measurement Framework and Terminology · pp. 9–10

Conceptual Organization of the AI Leadership Battery

  • The 30-item form groups Developmental Stretch, Contextual Calibration, and Ethical Guardrails & Accountability; the 51-item form includes the five Outward families, and the 27-item form groups Adaptive Leadership, Outside-In Learning, and Change Discipline.Inner, Outward, and Grounded Signals recombine the families to communicate how leaders orient to AI, guide people through AI-enabled work, and maintain contextual and ethical grounding. The original validation groups had 30, 51, and 27 retained items, respectively; Developmental Stretch and Change Discipline occupy different groupings in the later framework.Source: Conceptual Organization of the AI… · pp. 10–11
  • Development comprised 590 records from 589 unique leaders, while validation comprised 601 unique leaders with no overlap with development.The repeated N = 601 refers to the same three validation samples across analyses; the 199 panel records are analytic records, not a verified unique-person count.Source: Conceptual Organization of the AI… · p. 11

Practitioner Framework · Behavioral Subdimensions and Item Retention

The sections present the AI Leadership Battery as a behavioral framework organized into practitioner-facing groups and describe how candidate items were evaluated and retained. The final measure includes 36 behavioral subdimensions, each represented by three items.

Practitioner Framework

  • The practitioner framework groups the content families into Inner, Outward, and Grounded clusters, addressing leader orientation, social and developmental conditions, and calibrated, bounded, accountable implementation.The families are Developmental Stretch, Adaptive Leadership, Outside-In Learning; Proactive Transparency, Role & Skill Evolution, Team Learning Experiences, Psychologically Safe Climate, Purposeful Communication; and Contextual Calibration, Ethical Guardrails & Accountability, and Change Discipline.Source: Practitioner Framework · p. 12
  • The Battery defines AI leadership as a behavioral domain shaped by recurring demands of AI-enabled work, rather than as a single generalized leadership attribute.It provides initial measurement evidence for 36 behavioral subdimensions, each measured by three items.Source: Practitioner Framework · p. 12
  • The research examined whether candidate items matched prespecified behavioral definitions, formed internally consistent subdimensions, differed from selected orbiting constructs, and added outcome variance beyond them.The questions were addressed sequentially across content-validation, development, and independent confirmation samples.Source: Practitioner Framework · pp. 12–13
  • The deductive development process moved from item-definition correspondence and exploratory item reduction to confirmatory models and tests of external relations and validity.Newly recruited samples were reserved for form-specific confirmatory models and selected external relations; validation samples were reused for descriptive same-wave criterion analyses.Source: Practitioner Framework · p. 13
  • Validity concerns interpretations and uses of scores, not a permanent property of the instrument.The program linked each validity question to the sample, measure, and analysis used to examine it.Source: Practitioner Framework · p. 13

Behavioral Subdimensions and Item Retention

Stage 1: Content Definition and Content-Validity Assessment … Procedure and Analysis

Stage 1 defined 11 content families and 36 subdimensions, generated candidate items, and evaluated their correspondence with intended definitions and distinctiveness from orbiting constructs. The content review informed item revisions, while all candidates advanced to exploratory factor analysis for combined content and structural evaluation.

Item Generation and Refinement

  • The team specified 11 content families and 36 subdimensions, then generated 180 candidate items—five per subdimension—focused on direct leader actions and comprehensive definition coverage.Exact candidate and selected-item wording remains in protected study materials.Source: Item Generation and Refinement · pp. 15–16
  • Content review prompted revisions to AI Use Rationale and Structured AI Experimentation and Learning Routines, improving definitional correspondence or coverage in the revised pools.Newly recruited panels evaluated the revised pools under the same protocol and comparison sets; reported results concern the retained pools.Source: Item Generation and Refinement · p. 16

Panel Design and Participants

  • Twelve surveys assessed content validity; Team Learning Experiences was split across two, while each other family used one, with raters comparing items against intended and orbiting-construct definitions.Each survey included two to five orbiting-construct definitions, and raters evaluated every candidate item against every definition in a fully crossed within-rater design.Source: Panel Design and Participants · p. 16
  • The 12 panels included 199 panel-level records, not verified unique individuals; paid Prolific panelists had doctoral degrees, relevant experience, and specified residence and language eligibility.Data were collected in June and July 2026.Source: Panel Design and Participants · pp. 16–17

Procedure and Analysis

  • Raters used a seven-point scale to judge item-definition fit; the preprint summarizes the task structure, while exact instructions, practice materials, and stimuli remain protected.The approach drew on Hinkin and Tracey (1999) and Colquitt et al. (2019).Source: Procedure and Analysis · p. 17
  • Items had to receive their highest mean for the intended definition, score at least 5.00, and place it alone in the highest repeated-measures Duncan grouping versus published neighboring definitions.Same-family comparisons served as internal diagnostics. The procedure adapted content-adequacy methods.Source: Procedure and Analysis · p. 17
  • All candidates advanced to exploratory factor analysis; retention evaluation combined correspondence, distinctiveness, and later structural performance rather than relying on one content statistic.Candidates were classified according to whether they met all three study-specific criteria.Source: Procedure and Analysis · p. 17

Results · Exploratory and Confirmatory Factor Loadings

Most candidate items met the study-specific content criteria, and all advanced to exploratory factor analysis; retained-item loadings were then summarized across development and independent validation samples. Rater judgments supported both definitional correspondence and distinctiveness.

Results

  • 174 of 180 candidate items (96.7%) met all three study-specific criteria, and each subdimension had at least three qualifying items.Six items did not meet all three criteria.Source: Results · p. 17
  • All 180 candidate items advanced to exploratory factor analysis, with final selection informed by content-validity evidence and factor-analytic results.Source: Results · p. 18

Exploratory and Confirmatory Factor Loadings

Stage 2: Exploratory Factor Analysis, Item Selection, and Internal Consistency … Results

Stage 2 supported the proposed 36-subdimension structure, produced a 108-item Battery, and found that all subdimensions met the prespecified internal-consistency review criteria. These findings provided the empirical basis for independent confirmatory testing.

Participants and Screening

  • Eligible participants were working leaders recruited through Prolific for three development surveys containing 50, 85, and 45 candidate items; eligibility required responsibility for a direct report or working team and tool familiarity.Participants also had to be at least 18 and comfortable completing the survey in English.Source: Participants and Screening · p. 20
  • Prespecified screening and integrity checks excluded 61 records, leaving analytic samples of 194, 203, and 193 (590 records from 589 leaders); median leadership experience was 7–9 years.One retained participant completed both the 85- and 45-item forms, which were analyzed separately.Source: Participants and Screening · p. 21

Measures and Analytic Strategy

  • EFAs were conducted separately within 11 content families using principal axis factoring with oblimin rotation; retention used parallel analysis and scree plots, while item selection weighed loadings, cross-loadings, communalities, content validity, and behavioral coverage.The exploratory stage evaluated the proposed structure and aimed to select three items per subdimension.Source: Measures and Analytic Strategy · pp. 21–22

Results

  • KMO values ranged from .884 to .958, minimum item-level sampling adequacy was .801, and all Bartlett tests were significant at p < .001; parallel analysis and scree plots supported the prespecified factor counts.Source: Results · p. 22
  • The selection process retained 108 items—three for each of 36 subdimensions—and all retained items loaded most strongly on their intended factors; within-family factor correlations ranged from .546 to .788.Selection combined loading, cross-loading, separation, and communality diagnostics with content-validity evidence and behavioral coverage.Source: Results · p. 22
  • All 36 three-item subdimensions met the prespecified internal-consistency criteria, with alpha ranging from .752 to .941, omega total from .754 to .943, and corrected item-total correlations from .565 to .924.The authors interpreted these estimates as showing consistent relationships among items within each subdimension.Source: Results · pp. 22–23

Stage 3: Confirmatory Factor Analysis, Reliability, and Validity Evidence · Participants and Screening · Measures

Stage 3 used three independent validation samples to test prespecified confirmatory factor models, with retained item subsets and measures of orbiting constructs assigned across survey forms. Screening retained 601 leaders; the surveys also included a marker-variable sensitivity check and used unweighted item means without imputation.

Participants and Screening

  • The three surveys contained 50, 85, and 45 candidate items, while confirmatory factor analyses used prespecified subsets of 30, 51, and 27 retained items.Three new working-leader samples were recruited through Prolific using the same eligibility criteria, with each participant completing one validation survey.Source: Participants and Screening · p. 25
  • The three validation surveys began with 220, 225, and 222 respondents; screening excluded 66 and retained 198, 202, and 201, totaling 601 leaders.Mean age ranged from 40.42 to 43.38 years, median leadership experience from 6 to 9 years, and median direct reports from 5 to 7.Source: Participants and Screening · p. 25

Measures

  • The confirmatory models represented 10, 17, and 9 AI Leadership subdimensions, with three forward-keyed items per subdimension on a seven-point agreement scale.Source: Measures · p. 25
  • Eleven orbiting constructs were measured with literature-based three-item scales administered only on their theoretically assigned forms, using the same seven-point agreement format.Analyses examined their relationships with AI Leadership subdimensions measured on the same form.Source: Measures · pp. 25–26
  • The 30-item form assessed Ethical Leadership, Learning Goal Orientation, and Trust in Automation.Source: Measures · p. 25
  • The 51-item form assessed Informational Justice, Managerial Coaching, Meaningful Work, Team Learning Behavior, and Team Psychological Safety.Source: Measures · p. 25
  • The 27-item form assessed Adaptive Leadership Behavior, Boundary Spanning, and Implementation Leadership.Source: Measures · pp. 25–26
  • Three color-preference items served as the marker variable for a common-method-variance sensitivity analysis; subdimension and orbiting-construct scores were unweighted item means, with no responses imputed.Source: Measures · p. 26

Confirmatory Models … Confirmatory Factor Analysis Model Fit

In independent validation samples, the three confirmatory models were admissible, with stronger global fit for the 30- and 27-item forms than the 51-item form. The analyses also supported subdimension reliability and convergence, and all 142 evaluated relationships met the study’s discriminant-validity criteria, including after marker-variable adjustment.

Confirmatory Models

  • Prespecified correlated-factor CFA models retained Stage 2 item assignments and structure for three forms containing 30, 51, and 27 items.Each item loaded on its hypothesized factor, cross-loadings were fixed to zero, factor variances were fixed to one, and factors within each form were allowed to correlate; the independent analyses evaluated 10-, 17-, and 9-factor models, respectively.Source: Confirmatory Models · p. 26

Discriminant Validity Relative to Orbiting Constructs

  • Discriminant validity was evaluated for every AI Leadership subdimension against each orbiting construct administered on the same form, yielding 142 relationships across three samples.The criteria were HTMT below .85, an upper 95% bootstrap confidence-interval bound below .90, and focal-subdimension AVE exceeding its squared observed correlation with the orbiting construct; intervals used 1,000 respondent-level resamples.Source: Discriminant Validity Relative to… · p. 27
  • Marker-variable sensitivity was tested by controlling for the color-preference marker and comparing adjusted with unadjusted results against prespecified criteria.The analysis calculated partial correlations and recalculated HTMT using partial item correlations.Source: Discriminant Validity Relative to… · p. 27

Confirmatory Results

  • The 30- and 27-item forms showed stronger global fit, while the 51-item form showed mixed evidence across fit indices.For the 30-item form, robust CFI = .95, robust TLI = .93, robust RMSEA = .058, 90% CI [.049, .067], and SRMR = .061; for the 51-item form, robust CFI = .89, robust TLI = .87, robust RMSEA = .062, 90% CI [.057, .067], and SRMR = .059; for the 27-item form, robust CFI = .98, robust TLI = .98, robust RMSEA = .033, 90% CI [.014, .046], and SRMR = .042. All three models converged and were admissible.Source: Confirmatory Results · pp. 27–28, Table 6 · p. 28
  • Standardized loadings ranged from .59 to .96, composite reliability from .77 to .94, and average variance extracted from .53 to .83, meeting prespecified benchmarks.These results supported reliable item sets and convergence among indicators representing each behavioral subdimension in the independent validation samples.Source: Confirmatory Results · p. 28

Confirmatory Factor Analysis Model Fit

  • All 142 subdimension–comparison construct pairs met the three prespecified discriminant-validity criteria; maximum HTMT was .72, with highest bootstrap 95% CI upper bound .82; after marker-variable adjustment, maximum HTMT was .71 and all pairs met HTMT criteria.Source: Confirmatory Factor Analysis Model Fit · pp. 28–29

Orbiting Construct with the Highest HTMT for Each AI Leadership Subdimension · Stage 4: Concurrent and Incremental Validity

The table identifies the orbiting construct with the highest HTMT for each AI Leadership subdimension, and the comparisons support distinctiveness relative to the measures administered. However, some learning- and adaptation-related subdimensions share substantial variance, warranting further examination.

Orbiting Construct with the Highest HTMT for Each AI Leadership Subdimension

  • Other subdimensions’ closest constructs included Informational Justice, Team Learning Behavior, Team Learning Experiences, and Implementation Leadership.The listed matches span structured experimentation and learning routines, applied capability review, role redesign, and implementation-related behaviors.Source: Orbiting Construct with the Highest… · pp. 29–30
  • Managerial Coaching was the closest orbiting construct for several learning-related subdimensions, including continuous capability development, role redesign, and developmental handling of AI-related mistakes.Other listed matches include translating AI learning into ongoing work practices; the table reports each subdimension’s highest-HTMT construct.Source: Orbiting Construct with the Highest… · p. 30
  • Team Psychological Safety was the closest orbiting construct for AI help-seeking, safe AI experimentation, and vulnerability about AI-driven change.Managerial Coaching was the closest construct for open AI voice and dissent.Source: Orbiting Construct with the Highest… · p. 30
  • Ethical Leadership was the closest orbiting construct for context-sensitive reliance calibration, critical questioning, deliberative fit-checking, local-context integration, and ethical guardrails.The table also lists Implementation Leadership for absorptive pacing and responsiveness.Source: Orbiting Construct with the Highest… · p. 30
  • The table reports the highest-HTMT orbiting construct for each subdimension; complete latent-correlation and orbiting-construct results are in the supplementary materials.The note defines HTMT as the heterotrait–monotrait ratio and AVE as average variance extracted.Source: Orbiting Construct with the Highest… · pp. 29, 31

Stage 4: Concurrent and Incremental Validity

  • Comparisons with orbiting constructs provide evidence of distinctiveness, although the largest within-battery latent correlations were .85, .81, and .79.These correlations involved learning- and adaptation-related subdimensions; the authors note that future research can further examine their discriminant validity.Source: Orbiting Construct with the Highest… · p. 31

Design and Measures … Panel A. Incremental Variance Explained Beyond Orbiting Constructs

The section tests whether AI Leadership Battery scores explain outcome variation beyond orbiting constructs and whether they improve held-out prediction. Results show broadly significant in-sample increments and more frequent held-out gains for content-family composites than subdimensions.

Design and Measures

  • The analyses used three validation samples and separate models by form, testing seven multi-item outcomes with content-family composites or subdimension scores.Scores were unweighted means; outcomes used seven-point scales, with the attention-check item excluded from organizational growth. Coefficient alpha and omega ranged from .85 to .96 across surveys.Source: Design and Measures · pp. 31–32

Analytic Strategy

  • OLS hierarchical regressions entered orbiting constructs first, then AI Leadership blocks; ΔR² and unadjusted change F-tests evaluated increments beyond the same baseline.Separate composite and subdimension models were supplemented by commonality analysis of unique and shared variance and dominance analysis of predictor importance.Source: Analytic Strategy · p. 32
  • Five-fold cross-validation repeated five times compared orbiting-construct baselines with models adding content-family composites or subdimensions.Mean held-out R² summarized performance on responses excluded from model estimation but drawn from the same validation samples.Source: Analytic Strategy · pp. 32–33

Results

  • For AI adoption and integration in the 51-item sample, content-family and subdimension blocks added .436 and .522 beyond baseline R² .071.For the content-family model, combined R² was .507: .436 was unique to AI Leadership, .015 unique to orbiting constructs, and .057 shared; 85.9% of explained variance was uniquely associated with the AI Leadership block.Source: Results · p. 33
  • Mean held-out R² improved in 18/21 content-family and 14/21 subdimension models, with improvement over baseline more frequent for content-family composites.For AI adoption and integration in the 51-item form, mean held-out R² was .009 for baseline, .430 with content-family composites, and .417 with subdimensions.Source: Results · pp. 33–34
  • In-sample ΔR² ranged from .009 to .436 for content-family blocks and .042 to .522 for subdimension blocks; increments were significant in 19/21 and 18/21 models, respectively.The two nonsignificant content-family increments concerned decision speed in the 30- and 51-item forms.Source: Results · p. 33, Table 8 · p. 34

Panel A. Incremental Variance Explained Beyond Orbiting Constructs

Panel B. Repeated Cross-Validation … 1. Uncertainty in AI output and changing knowledge require leaders to update their

The AI Leadership Battery organizes AI-related leadership behavior into 36 subdimensions and 11 content families, with initial evidence of reliability, validity, and added explanatory value for work outcomes. Cross-validation results indicate that content-family composites and subdimension scores can add explanatory value beyond orbiting constructs, with patterns varying by research form.

Incremental Explained Variance Across Outcomes and Research Forms

  • Incremental-variance analyses compared in-sample ΔR² after adding content-family composites or subdimension scores to orbiting-construct baselines across three research-group panels.Panel labels follow practitioner vocabulary, but estimates retain original research groupings rather than aggregate Signal scores.Source: Incremental Explained Variance Across… · p. 37

Held-Out Model Performance Across Outcomes and Research Forms

  • The 51-item form showed its largest held-out increments for AI adoption and integration, AI-enabled team performance, and AI-enabled work experience; the 27-item form was more even, and the 30-item form yielded smaller increments.Each form used its own sample, behavioral content, and orbiting constructs; together, the findings support concurrent incremental validity for the measured organizational and AI-related outcomes.Source: Held-Out Model Performance Across… · p. 38

Overview of Contributions

  • The Battery measures 36 behavioral subdimensions organized within 11 content families, developed through deductive construct definition and content validation, exploratory factor analysis and item selection, and confirmatory factor analysis in independent samples.The stages provide a theoretical organization of AI-related leadership behavior and initial measurement evidence for organizational research.Source: Overview of Contributions · p. 39
  • Across validation samples, subdimension scores met benchmarks for item loadings, composite reliability, and average variance extracted, showed discriminant validity from administered comparison measures, and added explanatory value across several work outcomes.AI adoption and integration had particularly large increments in the 51-item sample.Source: Overview of Contributions · p. 39

Panel B. Repeated Cross-Validation

Theoretical Contribution

  • The framework organizes AI-enabled leadership behaviors around three recurring demands of AI-enabled work.The supplied passage identifies this organizing principle but does not specify the three demands.Source: Theoretical Contribution · p. 39

1. Uncertainty in AI output and changing knowledge require leaders to update their

  • Uncertain AI outputs and changing knowledge call for leaders to update understanding, calibrate reliance to context, support learning and transparency, uphold ethical standards, and coordinate implementation over time.The Battery supports study of how learning informs calibration, disclosure supports shared interpretation, and ethical boundaries shape implementation decisions; examples include questioning AI assumptions, explaining AI use, and enforcing ethical boundaries.Source: §1 · p. 40

Measurement Contribution · Limitations and Future Research · Conclusion

The AI Leadership Battery provides a behaviorally detailed framework for studying AI leadership, with initial evidence supporting its use in examining leadership practices and organizational and AI-related outcomes. Future research can extend the measurement evidence across designs, populations, and settings.

Measurement Contribution

  • Each of the 36 subdimensions has three items, allowing consistent detail across families and study of specific practices such as revising AI-use decisions or explaining AI-use rationales.Theoretically organized content families provide a broader structure, while subdimension measures preserve detail for examining relationships with constructs and outcomes.Source: Measurement Contribution · pp. 40–41
  • Different held-out performance patterns for content-family composites and subdimension blocks show that measurement detail should be considered in relation to the research question.The composites served as descriptive summaries of the same behavioral content; the comparison is reported in Table 8, Panel B, and Figure 3.Source: Measurement Contribution · p. 41

Limitations and Future Research

  • Future studies can administer all content-family measures to the same participants, further evaluate mixed-model fit of the 51-item form, and examine discriminant validity among closely related subdimensions.These designs would enable evaluation of relationships across the full Battery and comparison of alternative factor structures for different research questions.Source: Limitations and Future Research · p. 41
  • Longitudinal and multi-informant studies can extend cross-sectional, leader-reported findings by examining changes in practices and links to later adoption, team learning, and implementation outcomes.Predictive tests in new samples and comparisons across outcomes could clarify which practices are consistently associated with particular aspects of AI-enabled work.Source: Limitations and Future Research · pp. 41–42
  • Research across occupations, countries, languages, and AI-use levels can extend evidence from English-speaking Prolific leaders, while invariance testing can assess comparability across groups and settings.Repeated measurement could assess test–retest reliability and, in leadership-development studies, sensitivity to change.Source: Limitations and Future Research · p. 42

Conclusion

  • The Battery translates AI-enabled work into 36 leadership-behavior subdimensions organized within 11 content families, with initial evidence supporting empirical study of organizational and AI-related outcomes.The research program included content validation, exploratory factor analysis and item selection, confirmatory factor analysis in independent samples, and concurrent and incremental validity analyses.Source: Conclusion · p. 42
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