Grounded in theory
Kahn (1990); Schaufeli et al. (2002); May, Gilson & Harter (2004); Rich et al. (2010); Soane et al. (2012).
Behavioral Engagement
How humans invest effort, attention, and meaning—across tasks, teams, and AI-augmented systems.
Explore StudiesModern psychometrics
IIBES framework (independent ↔ interdependent engagement), factor models, reliability, invariance, and fairness.
Applied outcomes
Links to turnover, commitment, performance, and leadership pipelines in real organizations.
Flagship studies
IIBES: Independent & Interdependent Behavioral Engagement
A multidimensional engagement measure distinguishing self-driven, team-driven, and disengaged modes of action. Built with rigorous EFA/CFA, reliability families (α/ω), and subgroup stability checks.
- Construct mapping → item design → scale validation
- Measurement invariance & DIF audits
- Score bands, interpretation guides, operator briefs
Psychological Safety → Growth Mindset → Engagement → Turnover
Serial mediation models showing how safety catalyzes growth-mindedness and engagement, reducing turnover intent in student and employee samples.
- PROCESS-style bootstrapped indirect effects
- Moderated mediation variants (trust calibration)
- Replicable analysis playbooks
Engagement in Human–AI Decision Loops
Experimental paradigms testing how model explanations, uncertainty, and workload shape trust, focus, and task persistence.
- A/B & multivariate designs with guardrail metrics
- Explainable ML (SHAP/ICE) + human measures
- Planned recruitment window: 2026
Methods & measures
Modeling
GLM/MLM, moderation/mediation (incl. serial & moderated mediation), longitudinal sensitivity, survival/attrition risk.
Psychometrics
EFA/CFA, reliability (α/ω/hierarchical ω), IRT (1–3PL, GRM/GPCM), score engineering, invariance & DIF.
Artifacts
Technical appendices, operator briefs, norms & thresholds, decision logs, and model cards for measures.
Findings snapshots
Safety enables growth
Higher psychological safety → higher growth-mindset → higher engagement; indirect path predicts lower turnover intent.
Team vs. solo profiles
Distinct IIBES profiles (independent vs. interdependent) explain unique variance in commitment and performance indicators.
Human–AI cadence
Transparent uncertainty and lightweight explanations improve trust calibration and sustained task focus under load.
Selected references & outputs
Kahn (1990); Schaufeli et al. (2002); May et al. (2004); Rich et al. (2010); Soane et al. (2012); Steiner et al. working papers and conference abstracts on growth-mindset, safety, engagement, and turnover.
Related flagship path
This research most directly supports Organizational & People Analytics work where engagement, retention, and leadership signals need validated measurement and defensible analysis.
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For teams exploring engagement measurement, leadership signals, or human-AI work design, this research can support applied decisions, scoped studies, and evidence-backed advisory work.