Industrial-Organizational Psychology · Psychometric Research
Measuring the science of who thrives in tomorrow's workforce.
GlobaProsLabs.ai (GPL) is the I/O psychometric research arm of the GlobalPros ecosystem.
We produce scientifically rigorous insights on work-style traits, occupational fit, entrepreneurial
potential, and AI-era workforce readiness — grounded in peer-reviewed literature and tens of
thousands of subscriber assessments.
Our findings are grounded in a rigorous three-stage process — combining large-scale measurement data from across the GlobalPros ecosystem with the most current peer-reviewed I/O psychology literature.
01
Large-Scale Measurement
We collect and analyze psychometric assessment data from tens of thousands of GlobalPros ecosystem subscribers — jobseekers, employed professionals, and TA candidates — producing a continuously growing dataset of real-world work-style profiles.
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Scientific Calibration
Our advisory team of PhD I/O psychologists scientifically calibrates and validates measurement instruments against established occupational frameworks — primarily the U.S. Department of Labor's O*NET work-style taxonomy — and peer-reviewed psychometric literature.
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Applied Research Publication
Validated findings are published as original GPL research reports or curated third-party literature reviews, making cutting-edge psychometric insights accessible to practitioners in talent acquisition, organizational development, and workforce planning.
Research Domains
Four Core Areas of Investigation
GPL research is organized around four interconnected domains, each designed to translate psychometric measurement into actionable workforce intelligence.
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O*NET Work-Style Benchmarking
Validating individual psychometric profiles against the U.S. Department of Labor's O*NET occupational database, producing normative benchmarks for jobseekers and employed professionals across hundreds of occupational categories.
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Entrepreneurial Potential Prediction
Identifying the constellation of work-style traits most strongly predictive of entrepreneurial success — including venture survival, team leadership effectiveness, and long-term business growth across diverse industry contexts.
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Work Environment Fit
Assessing how individual trait profiles predict performance and satisfaction across remote, hybrid, collaborative, autonomous, high-pressure, and structured work settings — supporting smarter TA selection and development decisions.
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AI Disruption Readiness
Examining work-style trait predictors of success in roles significantly exposed to AI augmentation or automation, informing workforce planning, upskilling strategies, and candidate selection for future-facing roles.
GPL investigates the science of work-style traits — how they predict occupational fit, entrepreneurial success, environment adaptability, and resilience to AI disruption — across four rigorously defined research domains.
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Scientifically validated
All GPL research instruments are developed and validated by PhD I/O psychometrists against peer-reviewed standards.
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O*NET anchored
Findings are benchmarked against the U.S. Department of Labor's O*NET framework — the gold standard for occupational trait mapping.
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Data-driven at scale
Research draws on psychometric data from 40,000+ GlobalPros ecosystem subscribers, continuously refreshed as the platform grows.
Domain 01
O*NET Work-Style Benchmarking
The U.S. Department of Labor's O*NET database defines 16 core work-style dimensions empirically linked to success across 1,900+ occupations. GPL research validates how individual psychometric profiles map to these dimensions, producing normative benchmarks and fit scores usable in selection, development, and career planning contexts.
Why it matters for selection
Organizations that hire for work-style alignment see lower turnover, faster productivity ramp-up, and higher engagement. Mapping candidates to O*NET benchmarks converts hiring from intuition to predictive science.
Why it matters for professionals
Professionals whose natural work styles align closely with the O*NET profile of their occupation report higher job satisfaction, lower role stress, and stronger long-term career progression.
Methodology
GPL scores each subscriber's TraitDNA™ profile against O*NET occupational templates, producing alignment indices for 1,900+ occupations and identifying high-fit occupational clusters beyond a candidate's current title.
Current findings
Early subscriber data indicates that best-fit O*NET alignment predicts self-reported performance ratings at a statistically significant level across most professional occupational clusters assessed to date.
Domain 02
Entrepreneurial Potential Prediction
Entrepreneurial success cannot be predicted from business acumen alone. GPL research identifies the work-style trait constellations most strongly associated with venture survival, team-building effectiveness, and sustained growth — informing both individual self-assessment and investor-facing talent evaluation.
Beyond risk tolerance
Popular narrative focuses on risk appetite as the defining entrepreneurial trait. GPL research identifies a richer multi-trait model — including initiative, persistence, leadership orientation, and stress tolerance — that more accurately distinguishes sustained founders from short-tenure operators.
Industry-specific profiles
Optimal entrepreneurial trait profiles differ significantly across industry sectors. GPL research maps sector-specific success signatures for technology, professional services, consumer, and social enterprise ventures.
Applications
Findings inform entrepreneurial readiness screening for accelerator programs, professional development planning for aspiring founders, and selection criteria for leadership roles in high-autonomy environments.
Current findings
A six-trait composite model derived from GPL subscriber data demonstrates meaningful separation between self-identified successful founders and non-founders when scored against the TraitDNA™ measurement framework.
Domain 03
Work Environment Fit
Not all professionals thrive in all environments. Remote work, open-plan collaboration, high-autonomy roles, and high-pressure deadline-driven settings each reward different trait profiles. GPL research maps the psychometric signatures of environmental fit to improve placement decisions and reduce costly mismatches.
Remote & hybrid work fit
GPL identifies trait profiles most predictive of sustained high performance in remote and hybrid settings, informing both individual career planning and organizational remote hiring strategy.
Collaborative vs. autonomous roles
Some professionals peak in high-interdependence team environments; others in low-interruption autonomous work. Accurate mapping reduces burnout, improves output quality, and increases retention.
High-pressure & structured settings
Roles in healthcare, emergency services, financial trading, and operations management demand specific stress-management and procedural work-style traits. GPL research calibrates selection benchmarks for these environments.
TA & development applications
Environment-fit scores derived from TraitDNA™ measurement provide talent acquisition teams with a validated, legally defensible supplement to traditional competency-based screening methods.
Domain 04
AI Disruption Readiness
AI is not simply replacing jobs — it is reshaping tasks within jobs at different rates across different roles. GPL research examines which work-style traits predict successful adaptation, redeployment, and performance in AI-augmented or AI-exposed roles, providing a psychometric lens on the future of work.
Task-level exposure mapping
GPL analysis tracks AI-exposed tasks across 19,000+ occupational task definitions, correlating task-level exposure with work-style trait profiles to identify which professionals face the highest displacement risk — and why some adapt while others don't.
Adaptive trait profiles
Research identifies trait profiles associated with successful adaptation to AI-augmented roles — including openness to change, analytical orientation, and learning agility — enabling targeted upskilling and redeployment planning.
Organizational applications
GPL findings inform workforce planning for organizations undergoing AI-driven transformation, providing data-driven guidance on which employee segments are most prepared for role evolution versus retraining requirements.
Current findings
Analysis of 40,000+ assessments shows statistically meaningful trait-level differences between professionals in high-AI-exposure roles who report thriving versus those reporting role stress, with adaptability and achievement orientation emerging as key differentiators.
GPL Publications
Reports & Publications
GPL periodically publishes original research reports and curated reviews of third-party peer-reviewed literature across our four core psychometric research domains.
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Original research reports
GPL primary research drawing on subscriber psychometric data and O*NET occupational benchmarks.
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Literature reviews
Curated summaries of the most relevant recent peer-reviewed I/O psychology publications on work-style assessment.
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Periodic updates
New publications are added as research matures. Reports are available to GlobalPros ecosystem subscribers and research partners.
GlobaProsLabs.ai is the I/O psychometric research arm of the GlobalPros ecosystem — bringing together serial entrepreneurship, decades of applied psychometrics, and cutting-edge organizational science.
About GlobaProsLabs.ai
GlobaProsLabs.ai (GPL) is the I/O psychometric psychological research arm of GlobalProsResearch.org, GlobalProsEdge.ai, and GlobaPros.ai.
GPL provides I/O psychometric research for testing jobseekers, employed professionals, and TA selection and development candidates. Research is based on scientifically evaluating measurement results from tens of thousands of subscribers to the GlobalPros ecosystem in conjunction with the most recent peer-reviewed literature.
GPL periodically publishes reports on its findings as well as third-party publications relevant to work-style assessment, occupational fit, entrepreneurial potential, and AI workforce readiness.
Research methodologyScientifically validated psychometric measurement benchmarked against O*NET occupational standards and peer-reviewed I/O psychology literature.
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Advisory teamFour PhD I/O psychologists authoring, calibrating, and validating the TraitDNA™ Work-Style Measurement instrument.
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Data foundationAssessment data from 40,000+ GlobalPros ecosystem subscribers, continuously refreshed as the subscriber base grows.
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EcosystemResearch arm of GlobalProsResearch.org, GlobalProsEdge.ai, and GlobaPros.ai — the GlobalPros professional intelligence platform.
Leadership & Advisory Team
GPL is led by founder and CEO Steven Seeberg and supported by a distinguished advisory team of I/O psychometric psychologists whose careers span Pearson, Amazon Web Services, the U.S. Air Force, and Montclair State University.
Founder, CEO
Author of the recent research report "AI Recruiting Leads to Regulation and Litigation", as well as numerous other technology, business, and tax publications. Mr. Seeberg is a serial entrepreneur — co-founder of GlobalPros.ai, co-founder of Vadic Corp (pioneer of low-to-medium speed modems, sold to Racal Electronics after reaching $100m in revenue), and founder of Chat 247 Live.
I/O Psychometric Sr. Consultant Advisor
With an M.S. in Educational Psychology (psychometrics focus), Scott has consulted assessment publishers for ten years and formerly served as Sr. Research Analyst and Talent Assessment Developer at Profiles International and Pearson's Talent Assessment Group. He coordinates GPL's advisory team of four PhD I/O psychologists, authoring and validating the TraitDNA™ Work-Style Measurement.
PhD I-O Psychometric Psychologist Advisor
PhD in I-O Psychology (University of South Florida), former Research Director at Pearson's talent assessment team overseeing the Watson-Glaser Critical Thinking Appraisal and Raven's Progressive Matrices. Previously held senior leadership roles in U.S. Air Force recruitment and selection. Currently Program Director, I-O Psychology MA Program, Montclair State University.
PhD I-O Psychometric Psychologist Advisor
PhD in I-O Psychology (Louisiana Tech University), Senior Research Scientist at Amazon Web Services applying organizational network analysis and statistical modeling to talent management and culture research. Published on hybrid work, organizational culture, and high-potential talent identification. Author of The Disconnect (Substack). Active People Analytics community contributor.
