Theoretical Propositions

The primary research question is:

How is Cognitive Integrity created, preserved, degraded, and restored in AI-native organizations?

The propositions below are organized by theoretical role. This publication version presents a focused main-text set while preserving traceability to the broader supporting proposition architecture maintained in internal research records.

Antecedent Propositions

P1. Higher Correspondence Preservation is positively associated with higher Cognitive Integrity.

P2. Greater Intent Clarity is positively associated with Cognitive Integrity.

P3. Higher Representation Quality is positively associated with Cognitive Integrity.

Mediating Propositions

P4. Cognitive Synchronization positively mediates the relationship between shared representations and Cognitive Integrity.

P5. Representation Validation positively mediates the relationship between AI-generated representations and Cognitive Integrity.

P6. Human-AI Reconciliation positively mediates the relationship between Human-AI Coordination and Cognitive Integrity (Amershi et al., 2019; Faraj et al., 2018).

Moderating Propositions

P7. The degree of AI autonomy strengthens the negative relationship between weak preservation mechanisms and Cognitive Integrity degradation (Raisch and Krakowski, 2021; Wooldridge, 2009).

P8. Governance maturity weakens the negative effects of representation drift, human-AI divergence, and agent-to-agent divergence on Cognitive Integrity (Floridi and Cowls, 2019; Jobin et al., 2019; National Institute of Standards and Technology, 2023).

Outcome Propositions

P9. Higher Cognitive Integrity is positively associated with greater Adaptive Capacity.

P10. Cognitive Integrity is positively associated with Organizational Adaptation through Adaptive Capacity.

P11. Cognitive Integrity positively influences Decision Quality through improved representational consistency and decision provenance.

P12. Cognitive Integrity is indirectly associated with Organizational Performance through Adaptive Capacity, Organizational Adaptation, Decision Quality, and Execution Quality.

These propositions should be treated as theoretical propositions. Future empirical work should translate them into hypotheses, variables, indicators, and measurement models. The broader supporting proposition catalog remains available for future empirical research, appendices, or dissertation work.