Methodology

The three tiers of scientific backing

Every element of our recommendation system falls into one of three tiers, and we label each explicitly:

1. Validated psychometrics — the actual instruments we run (Holland RIASEC, Big-5 BFI-2, MBTI, SRQ-A, Vallerand Passion, VARK, Schwartz Values, ICAR). Peer-reviewed, cited, real.
2. Research-informed direction — the direction each instrument contributes to each axis is defensible from published literature. Effect sizes come from Schmidt & Hunter (1998), Sackett et al. (2017), and instrument-specific meta-analyses.
3. Pragmatic engineering weights — the exact numeric weights (e.g. RIASEC 0.30, Big-5 Agreeableness 0.08) are our engineering defaults, not derived from a meta-analysis. We calibrate them internally and publish the values here so the choice is transparent.

Validated instruments (Tier 1)

• Holland RIASEC — Holland (1997). Career interest inventory. Cronbach's α = 0.80–0.90.
• Big-5 (BFI-2) — John, Naumann & Soto (2008). Personality dimensions. Cronbach's α = 0.84–0.88.
• MBTI — Myers-Briggs. Test-retest reliability 0.60–0.70. Psychometrically contested but widely used.
• SRQ-A — Ryan & Connell (1989). Self-Determination Theory motivation quality.
• Vallerand Passion Scale — Vallerand et al. (2003). Harmonious vs Obsessive passion.
• Schwartz Values — Schwartz (1992). 10 universal human values across 70+ cultures.
• ICAR — Condon & Revelle (2014). Open-source cognitive reasoning; CHC-aligned.
• VARK — Fleming (2001). Learning styles.

The four Ikigai axes

❤️ Love — what you enjoy

Weights: interest_content 0.70 · personality_fit 0.30 · RIASEC 0.30 · Vallerand passion 0.20. Includes SRQ-A multiplier (0.6–1.4 based on Relative Autonomy Index).

💪 Good at — where you excel

Weights: subject_aptitude 0.25 · strength_match 0.15 · follow_through 0.05 · CHC 0.30 · behavioural taste-test 0.25. Weights rebalance when instruments are missing.

💰 Paid for — economic viability

Weights: salary_level 0.50 · salary_consistency 0.20 · education_ROI 0.30. Big-5 Neuroticism tilts consistency weight by ±0.10.

🌍 World needs — contribution

Weights: mission 0.20–0.30 · Schwartz prosocial 0.15 · market_demand 0.22–0.25 · AI_durability 0.22–0.25 · sustainability 0.20–0.21 · Big-5 Agreeableness 0.08.

The composite

The four axes converge through a harmonic mean (deliberately punishing to any single weak axis, faithful to the Ikigai model). Then a piecewise linear calibration maps raw scores 0–10 to a displayed 0–10 range where accurate matches read as 7+.

Calibration formula:

• raw ≤ 4 → displayed = raw × 1.75
• raw > 4 → displayed = 7 + (raw − 4) × 0.5

So raw 4 = displayed 7, raw 5 = displayed 7.5, raw 6 = displayed 8, raw 10 = displayed 10. Purely a UX choice, not a psychometric one.

Two ranker adjustments before calibration

• Authenticity multiplier — decision drivers from the retrospective intake produce a −1..+1 score, applied as 1 + 0.30 × score on Ikigai-seed component weights. Grounded in Sheldon & Elliot (1999) self-concordance model.
• Regret penalty — past-decision regrets keyword-matched to SOC prefixes; matching careers get raw × (1 − penalty). Pragmatic.

What's proprietary vs validated

• The Career DNA quiz, Career Taste Test, and the personalisation questionnaire (Ikigai seed) are proprietary discovery tools — not validated psychometrics. They contribute signal but shouldn't replace Holland / Big-5.
• The Ikigai four-circle Venn diagram is a 2014 Western adaptation by Marc Winn, not authentic traditional Japanese ikigai. Useful framework nonetheless.

Data sources

• Occupation profiles — U.S. Bureau of Labor Statistics (BLS) Occupational Employment Statistics + O*NET Database
• University data — College Scorecard (US Dept. of Education) + NIRF (India) + UCAS (UK) + public university websites
• India-specific salaries — aggregated from public reports (PayScale, Ambition Box, Glassdoor)
• AI-exposure scores — Felten, Raj & Seamans (2023)

Third-party data attributions

WhatTNext AI stands on the shoulders of several publicly-licensed datasets. We disclose each below, per each publisher's licence terms.

• O*NET-SOC (US Department of Labor) — occupation descriptions, skill/knowledge lists, and AI-exposure baselines are sourced from O*NET Online, a service of the US Department of Labor / Employment & Training Administration. Used under the O*NET Data Licence.
• ESCO (European Commission) — modern occupation titles (Data Engineer, ML Engineer, DevOps, etc.) not covered by O*NET are sourced from ESCO v1.2+. This service uses the ESCO classification of the European Commission. ESCO occupations are cross-referenced with O*NET where a crosswalk exists, and inserted as supplementary rows (identified by source="esco") where no O*NET analog exists. We have modified ESCO by generating pseudo-SOC codes (ESC-XXXX) for internal keying and by merging with O*NET; these modifications are indicated in the data itself via the source and esco_uri fields. The Commission does not guarantee the accuracy of ESCO data and shall not be liable for consequences of use.
• Lightcast Open Skills — skill category and taxonomy metadata is sourced from Lightcast Open Skills under CC-BY-4.0. Used for canonical skill naming and category rollups.

McKinsey durability framework

Several surfaces on this platform — the Skills Browser, My Report's Durable Skills Portfolio panel, the Skill Gap Analysis SCI chips (🔻 declining, 🔄 evolving, 🏛️ enduring), the Pivot Planner adjacency count, and the AI Fluency vs Technical AI tagging — are framework informed by McKinsey Global Institute's Agents, robots, and us: Skill partnerships in the age of AI (November 2025). The report is the source of the eight high-prevalence "durable" skills (communication, management, operations, problem-solving, leadership, detail orientation, customer relations, writing), the Skill Change Index (SCI) concept, the AI Fluency vs Technical AI distinction, and the skill-adjacency-count mechanic.

Important honesty caveat: our SCI values, path assignments, and AI-content tags are derived from our own Rubric v2 exposure scores weighted across ESCO occupation-skill mappings — they are not the numbers McKinsey published. We approximate their framework; we do not replicate their methodology (they computed SCI on 3.4M occupation-DWA-skill pairings with GPT-4o). Every derived signal in our data carries sci_source: "derived_v1". When we display or reference these concepts we say "framework informed by McKinsey Global Institute (2025)", never "per McKinsey". Prevalence percentages we cite for the 8 durable skills (99% communication, 94% management, etc.) are drawn directly from the report and used descriptively.

Selected citations

• Schmidt & Hunter (1998). "The validity and utility of selection methods in personnel psychology." Psychological Bulletin.
• Sackett, Zhang, Berry & Lievens (2017). "Revisiting meta-analytic estimates of validity in personnel selection." Journal of Applied Psychology.
• Ryan & Connell (1989). "Perceived locus of causality and internalization." Journal of Personality and Social Psychology.
• Vallerand et al. (2003). "Les passions de l'âme: On obsessive and harmonious passion." J. Pers. Soc. Psychol.
• John, Naumann & Soto (2008). "Paradigm shift to the integrative Big Five trait taxonomy."
• Holland (1997). Making vocational choices: A theory of vocational personalities and work environments.
• Condon & Revelle (2014). "The International Cognitive Ability Resource." Intelligence.
• Schwartz (1992). "Universals in the content and structure of values." Advances in Experimental Social Psychology.
• Sheldon & Elliot (1999). "Goal striving, need satisfaction, and longitudinal well-being."
• Felten, Raj & Seamans (2023). "Occupational, industry, and geographic exposure to AI." Strategic Management Journal. Dataset. Primary source for our AI-exposure scores where SOC coverage exists (~774 US occupations); other rows fall back to our 6-dimension internal rubric.