Future Research
The future research agenda has six streams organized as a validation sequence.
| Stage | Primary Question | Minimum Evidence Needed |
|---|---|---|
| 1. Construct validation | Are correspondence properties conceptually clear and distinct from alignment, traceability, information quality, maturity, and performance? | Expert review, construct sorting, discriminant validity arguments, and known-case comparisons. |
| 2. Bounded metric development | Can specific correspondence properties be measured with explicit objects, levels, procedures, and evidence? | Metric specifications, coding protocols, evidence admissibility tests, and inter-rater or procedure reliability checks. |
| 3. Evidence and inference reliability | Do human, AI-assisted, and hybrid procedures produce traceable and reviewable observations? | Source-evidence audits, coding comparison, AI inference labeling, adjudication records, and contestability tests. |
| 4. Temporal validity | Can measurement distinguish state, transition, drift, recovery, and preservation? | Longitudinal cases, procedure-version controls, comparable evidence intervals, and trajectory reconstruction. |
| 5. Profile and aggregation validation | When, if ever, may local results support higher-level profiles, composites, or indices? | Non-compensability analysis, sensitivity testing, robustness checks, invariance evidence, and governance review. |
| 6. AI-assisted measurement assurance | How does AI participation change evidence, inference, uncertainty, and contestability? | Human-AI adjudication studies, model-confidence calibration, provenance checks, and challenge-resolution records. |
First, construct validation should test whether the proposed correspondence construct family is clear, non-redundant, and empirically distinguishable from alignment, traceability, information quality, process conformance, knowledge reuse, governance maturity, and performance.
Second, evidence and observation studies should examine how different evidence types support different correspondence properties. Such studies should compare human coding, AI-assisted extraction, hybrid review, and independent adjudication.
Third, metric and indicator studies should develop candidate metrics for bounded correspondence properties, test reliability and validity, and evaluate interpretation boundaries.
Fourth, temporal studies should examine drift, degradation, recovery, and preservation across organizational change. These studies should distinguish real correspondence change from procedure, evidence, and coding changes.
Fifth, aggregation studies should test when local correspondence results may support higher-level profiles, composite indicators, or indices. These studies should examine non-compensability, bottlenecks, sensitivity, robustness, and invariance before proposing benchmarks.
Sixth, AI-native organization studies should examine how AI participation changes measurement evidence, inference, uncertainty, governance, and contestability. This stream is especially important because AI may both increase measurement capacity and increase representational ambiguity.