The Evidence
The scoring methodology is calibrated against external peer-reviewed behavioral research — Perlow, Mankins, Cross & Grant, Bain, and others — combined with the author’s original diagnostic cohort of leaders. Where you land depends on your role, your organization, and how long the pattern has been running.
The estimated recoverable time per week for a senior leader carrying active behavioral drag. Not time that needs to be created. Time already inside the working week — currently consumed by behavioral friction rather than leadership. The range is wider than most leaders expect. A CFO running a PE-backed company carries a different drag profile than a divisional VP in enterprise manufacturing. Where you land depends on your role, your org size, and which pillar is doing the most damage.
Consistent with findings from Perlow, Hadley & Eun (Harvard Business Review, 2017), which documented that senior executives spend an average of 23 hours per week in meetings — more than double the figure recorded in the 1960s — and that a significant portion produces no decision output. Mankins, Bird & Root (HBR, 2013) similarly found that unnecessary meetings consume 15% of an organization’s collective senior time annually.
Above a score of 70, the dominant pattern is decision retrieval — leaders pulling back decisions they have already delegated. The organization doesn’t see a retrieval. It learns that ownership is conditional. That learning persists long after the behavior stops.
Grounded in Gallup’s State of the American Manager research, which identifies decision retrieval and micromanagement as the primary driver of team disengagement — with cascading costs that compound invisibly across the organization. Bass & Riggio’s work on transformational leadership (2006) establishes the mechanism: leader behavior sets the ownership norms the entire team internalizes.
Four leaders from the author’s original diagnostic cohort targeted a single pillar for ninety days. They dropped between 12 and 25 Index points. Three to five hours recovered per week. Every one of them. The fifth pillar, the methodology, and the full case detail — in the book.
The 90-day window is grounded in Lally et al. (European Journal of Social Psychology, 2010), the definitive empirical study of habit formation, which found behavioral patterns take between 18 and 254 days to shift — with a median of 66 days for simple behaviors and longer for leadership-context behaviors under social reinforcement. McKinsey’s research on decision-making speed (2019) further shows that sustained behavioral change at the senior level requires a minimum consistent practice window of 60–90 days before organizational-level effects become measurable.
Methodology Note
The scoring methodology is calibrated against external peer-reviewed behavioral research — including Perlow, Hadley & Eun (HBR, 2017), Mankins, Bird & Root (HBR, 2013), Cross & Grant (HBR, 2016), Bain & Company time-at-work research, Lally et al. (2010), and others — combined with the author’s original diagnostic cohort of leaders. The 13.5-hour figure is a research-based estimate derived from published findings on senior executive behavioral load; individual figures vary meaningfully by role, industry, and organization size. The 90-day intervention results (12–25 point score reduction, 3–5 hours recovered per week) reflect outcomes from four leaders in the author’s original cohort who completed a structured single-pillar change protocol. Individual results will vary based on role, organization, baseline score, and consistency of practice. The Index score is a behavioral indicator, not a guarantee of outcome. Third-party academic citations are used for contextual benchmarking only and do not independently validate Index-specific findings.
The Measurement Protocol
The 21-day measurement window is not an arbitrary design choice — it is the minimum observation period at which stable behavioral signal emerges from M365 data. Fleeson (2001) established that trait-level patterns require at least 30 behavioral episodes to surface reliably. A senior leader in an active M365 environment generates more than 30 scoreable episodes per pillar across 21 days. Shorter windows produce state-level noise, not trait-level signal. Sonnentag & Zijlstra (2006) further showed that behavioral patterns follow weekly cycles — three complete weekly cycles is the minimum window that captures the natural variance of high-demand and lower-demand periods, so the score reflects pattern rather than a single week’s workload.
The 21-day minimum interval between runs follows the same logic. Consecutive runs that overlap share behavioral data — email threads, meeting series, decision cycles — producing autocorrelated scores that appear more consistent than the underlying behavior warrants. A 21-day interval ensures each run is a genuinely independent observation. This is what makes the Index Δ-trajectory score statistically meaningful rather than just mathematically computed.
“The diagnostic measures a 21-day behavioral window. Running it more frequently than every 21 days would mean measuring the same behavior twice — which tells you nothing new and reduces the reliability of your score. Your next run date is shown in your dashboard.”
The four-run protocol below is calibrated to three independent research findings on behavioral change. The 45-day and 90-day checkpoints are fixed measurement moments, not arbitrary calendar dates — each one captures a complete, non-overlapping 21-day behavioral sample.
| Run | Window | Unlock date | Science basis |
|---|---|---|---|
| Run 1 — Baseline | Days 1–21 | Before intervention | Fleeson (2001): 21 days = trait-level signal threshold |
| Run 2 — Midpoint | Days 43–63 | Day 43 earliest | Lally et al. (2010): median 66-day habit formation; midpoint check captures early trajectory before the critical window closes |
| Run 3 — Outcome | Days 85–105 | Day 85 earliest | Prochaska & DiClemente (1983): 90-day sustained practice window for behavioral consolidation at the senior level |
| Run 4+ — Quarterly | Every 90 days | Rolling | Marlatt & Gordon (1985): reactivation risk is highest in the 60–90 day post-intervention window; quarterly monitoring catches the first reactivation signal |
The leader does not choose when to run. The protocol determines when the next window opens. By spacing measurements 21 days apart and using a 21-day window for each run, each measurement captures a complete, non-overlapping behavioral sample. No signal from Run 1 contaminates Run 2.
The Research Is Growing
The scoring methodology is calibrated against published behavioral research. As more leaders run the diagnostic and contribute anonymous data, the instrument gains role-level and industry-level precision it cannot yet provide at scale. The next stage of the research tells a CFO in a PE-backed company exactly where CFOs in PE-backed companies typically land — what their dominant drag pattern looks like, how long it has typically been running, and what changes first when they do the work. That precision requires a growing dataset. Run your diagnostic and contribute your anonymous profile. You are not doing the research a favor. You are improving the instrument you just used.
Why the average understates what the data can do
The diagnostic asks seven questions before it scores you. Your role. Your organization type and sector. Your org size. How long you have been in senior leadership. Whether this pattern has ever been directly challenged. Those variables are not background detail — they determine what your score actually means and how resistant the pattern is to change. A leader eighteen months into a role and a leader whose pattern has been institutionally reinforced for a decade are in different situations, even at the same score. The cohort already captures this. More submissions let us publish it with the specificity it deserves.
What the data unlocks as the cohort grows
What you contribute — anonymously
Anonymous submission only. No name, no company, no email required. No raw scores are submitted — pillar rankings only. Aggregate findings will be published as the dataset grows. Contributors are acknowledged in future editions of the research.
What you get back immediately
After you submit, you receive one segmented finding from the current cohort specific to your seniority tier — how leaders at your level typically distribute across the four pillars, and which pillar most commonly dominates. Not a generic average. A benchmark that reflects your context.
One Result
She sat with the number for a long time.
At ninety days, her Index score was 59. Twenty-two points lower.
Her team called it the most productive quarter the finance function had ever had.
CFO · PE-Backed Company · 90-Day Result
What her starting score was. What her top pillar was.
What the behavioral rule was that changed everything.
In the book.