ARGYRIS, CHRIS & SCHÖN, DONALD
Organizational Learning (1978)
Single-loop vs. double-loop learning. Organizations that adopt AI without questioning their adoption assumptions are single-looping.
ARISTOTLE
Nicomachean Ethics
Phronesis — practical wisdom as the meta-virtue. The episteme/techne/phronesis distinction is the backbone of Pillar 7. Akrasia — the intention-action gap — underpins Pillar 6.
AUTOR, DAVID
Why Are There Still So Many Jobs? (2015)
Task-level analysis of automation. The unit of AI adoption is the task, not the job — foundational to Pillar 4.
BANDURA, ALBERT
Self-Efficacy (1977)
Belief in one's own capability predicts behavior change. The psychological mechanism behind Pillar 1's emphasis on mastery through practice.
BROWN, TIM
Change by Design (2009)
Design thinking and the IDEO methodology. Prototyping as a way of thinking, not just testing — the foundation of Pillar 5.
DAVENPORT, THOMAS & KIRBY, JULIA
Only Humans Need Apply (2016)
Augmentation vs. automation. Five strategies for human-AI partnership. The intellectual ancestor of Pillar 4's people-first sequencing.
DEWEY, JOHN
Experience and Education (1938)
Learning by doing. Experience is primary, theory secondary. The philosophical anchor for Pillar 1: craft over curriculum.
EDMONDSON, AMY
The Fearless Organization (2019)
Psychological safety as a prerequisite for learning and experimentation. Necessary for every pillar — but insufficient without trust calibration.
ERICSSON, ANDERS
Deliberate Practice (1993)
Expertise is built through structured, intentional practice with feedback — not through time served. The learning science behind Pillar 1.
FEYNMAN, RICHARD
The Pleasure of Finding Things Out (1999)
Anti-bullshit, direct contact with reality, curiosity, tinkering, and epistemic honesty. A spirit-book for the entire framework.
FLORIDI, LUCIANO
The Ethics of Artificial Intelligence (2023)
Information ethics and the philosophical infrastructure for AI governance. The academic grounding for Pillar 7's active ethics.
GEBRU, TIMNIT ET AL.
Model Cards for Model Reporting (2019)
Transparency tools for documenting AI systems. The inspiration for Adaptive Adoption's own model card architecture.
GIBBONS, PAUL
The Science of Organizational Change (2015, 2019)
The argument that change management must be rebuilt on behavioral science, complexity, trust, and ethics — not persuasion models from the 1940s.
GOLLWITZER, PETER
Implementation Intentions (1999)
If-then planning — the strongest known intervention for closing the intention-action gap. The mechanism behind Pillar 6's behavioral tools.
HALPERN, DAVID
Inside the Nudge Unit: How Small Changes Can Make a Big Difference (2015)
Applied behavioral science from the UK's Behavioural Insights Team — translating nudge theory into organizational and policy-level decision-making.
HEALY, JAMES
BS at Work
Behavioral science applied to organizational change. Change the environment, change the behaviour.
HEALY, JAMES & GIBBONS, PAUL
Adopting AI: The People-First Approach (2025)
A human-centered approach to AI strategy, adoption, and ethics.
KENNEDY, TRICIA & GIBBONS, PAUL (EDS.)
The Future of Change Management, Vol. 1 (2024)
Contributed chapters on mental health, neuroscience, behavioral tools, trust, and complexity-native change design.
HUME, DAVID
A Treatise of Human Nature (1739)
"Reason is, and ought only to be, the slave of the passions." The philosophical ancestor of behavioral science — motivation precedes rational compliance.
KAHNEMAN, DANIEL
Thinking, Fast and Slow (2011)
Dual-process theory. Trust defaults and automation bias are System 1 failures in a System 2 domain. Central to Pillars 3, 6, and 7.
LAVE, JEAN & WENGER, ETIENNE
Situated Learning (1991)
Legitimate peripheral participation. Learning happens at the edge of practice, not in classrooms. The social theory behind communities of practice.
LEE, JOHN D. & SEE, KATRINA A.
Trust in Automation (2004)
The undertrust/overtrust spectrum. People miscalibrate trust in automated systems predictably — and dangerously.
LITTLE, JASON
Lean Change Management (2014)
Iterative, feedback-driven approach to organizational change.
MANIFESTO FOR AGILE SOFTWARE DEVELOPMENT
agilemanifesto.org (2001)
The original source text: learning by doing, responsiveness over rigid planning, working reality over documentation theater. The spirit ancestor of Pillars 1 and 5.
MEADOWS, DONELLA
Thinking in Systems (2008)
Leverage points and feedback loops. Where you intervene in the system matters more than how hard you push.
MEZA, ROBERT
Aim for Behavior
The argument that change programs should target observable behaviors, not attitudes or awareness. Central to Pillar 6.
MEZA, ROBERT & GIBBONS, PAUL
Behavioral Science Tools for the Change Professional (2024)
The SHIFT method for behavioral diagnosis. Published in The Future of Change Management, Vol. 1.
MICHIE, SUSAN ET AL.
The Behaviour Change Wheel (2014)
COM-B model and 93 behavior change techniques. The most comprehensive taxonomy of behavior change interventions available.
MINTZBERG, HENRY
The Rise and Fall of Strategic Planning (1994)
The clean anti-planning ancestor. AI moves too fast to define a future state; you must design and iterate your way toward it.
NONAKA, IKUJIRO & TAKEUCHI, HIROTAKA
The Knowledge-Creating Company (1995)
Tacit-to-explicit knowledge conversion. How organizations learn from practitioners, not just databases.
PEARL, JUDEA
Causality: Models, Reasoning, and Inference (2000)
The formal framework for causal inference. Why correlation-based AI needs causal reasoning — and why organizations confuse the two.
REST, JAMES
Four-Component Model of Moral Behavior (1986)
Ethical behavior requires sensitivity, judgment, motivation, and character — not just rules. The psychology behind Pillar 7.
RIES, ERIC
The Lean Startup (2011)
Build-measure-learn. Every AI initiative is a prototype until evidence says otherwise. The operational logic of Pillar 5.
SARASVATHY, SARAS
Effectuation (2001)
Entrepreneurial logic under uncertainty — start with what you have, not what you predict. The design principle behind probe-and-iterate.
SCHÖN, DONALD
The Reflective Practitioner (1983)
Reflection-in-action and knowing-in-action. How professionals actually think in practice, not in theory.
SENGE, PETER
The Fifth Discipline (1990)
Systems thinking and the learning organization. The aspiration that organizations can learn — and the evidence that most don't.
SENNETT, RICHARD
The Craftsman (2008)
The dignity and epistemology of working with your hands. You learn by making, not by reading about making. The philosophical backbone of Pillar 1.
SNOWDEN, DAVE
Cynefin Framework (1999)
Domain distinctions — simple, complicated, complex, chaotic. Matching the intervention to the domain. The operating system of Pillar 2.
STACEY, RALPH
Complexity and Management (2001)
Complex responsive processes. Complicated is expert-solvable; complex is emergent and unpredictable. The theoretical foundation for Pillar 2.
SUMMERFIELD, CHRISTOPHER
These Strange New Minds
How AI systems develop cognitive capabilities that are familiar yet alien. The neuroscience-informed perspective on what AI actually does.
THALER, RICHARD & SUNSTEIN, CASS
Nudge (2008)
Choice architecture — designing the environment so the default behavior is the desired behavior. The policy logic behind Pillar 6.
WARDLEY, SIMON
Wardley Mapping (2016)
Situational awareness through value-chain mapping. Understanding where you are before deciding where to go.
WEISBORD, MARVIN
Productive Workplaces (1987, 2012)
People-first, whole-system, dignity-and-meaning OD — without collapsing into stale change-management tropes.
WENGER, ETIENNE
Communities of Practice (1998)
The unit of learning is the community, not the individual. The social architecture behind Pillar 1's peer learning circles.
Gibbons original IP in this framework: Behavioral Science — Diagnostics and Drivers (adapted from COM-B) (Capability, Motivation, Trust, Opportunity). The AI Mastery Matrix (6 dimensions × 4 levels). The RIST Trust Framework™ (Relational, Institutional, Self-Trust, Task Trust). The undertrust/overtrust spectrum applied to AI. Augmentation-first sequencing. Complexity-native adoption design. Prototype-native change design. Ethics as practiced capability. The claim that change management as a discipline has not yet caught up with the demands of the AI era — and the attempt to close that gap. The entire Adaptive Adoption™ structure, naming, and operational content.