Researcher in applied psychometrics: building and validating measures of AI leadership for AI-native organizations
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papers here
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reviewed by Evolve
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first-author
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corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-20) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
No publication-volume data is available in this snapshot, so output is unscored. This axis would measure how many papers the researcher produces over time and how consistent that stream is; one cataloged paper is not yet enough evidence.
Impact—
Citation and influence data are not available, so impact is unscored. This axis would measure citations, downstream uptake, and reach of the work; the cataloged paper has no citation record here yet, so no estimate is made.
Focus100/100
Focus is scored 100 because the field distribution of the cataloged record is entirely concentrated: the one reviewed paper sits in a single field, organizational behavior / applied psychometrics (leadership and management). This single-field concentration suggests a well-defined research identity around leadership measurement and scale development, though it rests on a base of one cataloged paper, so the score describes breadth rather than volume.
Collaboration14/100
Collaboration scores 14, derived from a raw count of one distinct coauthor across the cataloged record; the frequent-coauthor list names Leslie Coyne. The reviewed paper is first-authored (not corresponding). This points to a small, tight working pattern — effectively a two-person collaboration — rather than a large network; the score will likely move as more papers are cataloged.
Activity—
No time-series publication data is available, so activity is unscored. This axis would measure recent output rate and tempo — how actively the researcher is publishing now. The snapshot holds only a 2026 paper year, too little to establish a cadence.
Reproducibility20/100
Reproducibility scores 20, a mean computed from one reviewed paper (reproPapers = 1), and that paper's reproducibility readiness was rated low. Evolve's review identifies concrete reasons: an underpowered 51-item confirmatory factor analysis and criterion data drawn from the same source. The multi-stage validation design is a strength, but sharing data, code, and better-powered analyses would lift this score.
Research summary
Mustafa Akben is a researcher working at the intersection of organizational behavior and applied psychometrics, focused on how leadership is measured in AI-native organizations. The Evolve catalog currently includes one first-authored paper, a 2026 arXiv preprint titled 'Measuring AI Leadership: Development and Validation of a Multidimensional Measure for AI-Native Organizations.' In it, Akben develops a 108-item AI Leadership Battery and subjects it to multi-stage validation, aiming to give AI-native organizations a rigorous instrument for assessing leadership in human-AI collaboration settings. Evolve's review credits the work with solid multi-stage validation while flagging two methodological concerns: an underpowered 51-item confirmatory factor analysis and reliance on same-source criterion data. The bibliometric snapshot reflects a tightly concentrated profile: all cataloged output sits in a single field (organizational behavior / applied psychometrics, leadership and management), giving a focus score of 100, while a single distinct coauthor (Leslie Coyne) yields a low collaboration score of 14. Reproducibility currently scores 20 based on one reviewed paper, consistent with the 'low' reproducibility readiness assigned in review. Output, impact, and activity axes lack sufficient underlying data and are left unscored, making this an early-stage profile that will sharpen as more of Akben's record is cataloged.
Collaboration pattern
The cataloged record shows a compact collaboration footprint: one distinct coauthor, Leslie Coyne, who appears as the frequent collaborator, and a first-authored (non-corresponding) 2026 paper. This pattern suggests close two-person work on scale development rather than a broad multi-team network. Because the snapshot contains a single paper, the low collaboration score (14) reflects sample size as much as working style, and the picture may change as more publications are indexed.
Research keywords
Highlights
A 108-item AI Leadership Battery for AI-native organizations
Solid multi-stage validation, with flagged methodological limits
Timeline
- 2026Publishes a first-authored arXiv preprint, 'Measuring AI Leadership: Development and Validation of a Multidimensional Measure for AI-Native Organizations.'
- 2026Develops and validates a 108-item AI Leadership Battery through multi-stage scale-development procedures.
- 2026Enters the Evolve catalog with a review noting solid validation alongside an underpowered 51-item CFA and same-source criterion data.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Profile is name-resolved from public bibliographic data via ORCID 0000-0001-7382-9184; institution and country are not recorded in the snapshot.
- All bibliometric dimension scores derive from a snapshot containing a single reviewed paper, so they may not represent the researcher's full publication record.
- Output, impact, and activity axes are unscored because the underlying data (publication counts, citations, time series) is not available.
- The reproducibility score is a mean over one paper (reproPapers = 1) and should be read as indicative, not a stable estimate.