External evidence registry

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---
title: "External Evidence Registry"
artifact_type: external_evidence_registry
status: active
registry_created: '2026-08-18'
last_verified: '2026-08-18'
analysis_owner: Empathy product team
source_period: '2011-02-16 to 2026-06-16, plus one undated current guideline'
tags: [market-analysis, external-evidence, claims, launch, provenance]
---

# External Evidence Registry

## What this registry is for

This folder preserves the external research used in the [Launch Claims Register](../launch-claims-register-2026-08-18.md). Each source has its own analysis record showing:

- the question it helps answer;
- the evidence extracted and its exact location;
- the launch claims and products it can support;
- what it does **not** prove;
- safe and unsafe uses of the evidence; and
- enough bibliographic and retrieval information to recover the source later.

The external sources corroborate market problems, methodological requirements, risks, and product boundaries. They do not prove that customers will buy, activate, retain, or renew the Empathy platform.

## Date and provenance rules

Three dates are deliberately kept separate:

| Field | Meaning |
| --- | --- |
| `source_published` | When the publisher says the source was published. `undated` is used when the live source gives no reliable date. |
| `source_accessed` / `last_verified` | When the source and the analysis were checked. |
| `analysis_created` | When this internal analysis record was actually created. |

Only E01 has a proven earlier internal provenance: the Qualtrics report appears in the April 2026 Phase One material. The other analysis records were created on 18 August 2026. Their source dates are retained separately; they have not been backdated.

## How to read evidence weight

| Weight | Meaning |
| --- | --- |
| High | Direct, methodologically relevant evidence from a current benchmark, peer-reviewed study, or recognised standards body. Still not proof of our product outcome. |
| Moderate | Relevant and credible, but vendor-produced, conceptual, older, or adjacent to our exact market/use case. |
| Context only | Practitioner discussion. Useful for language, failure modes, and counterevidence; not representative market measurement. |

## Registry

| ID | Source and report | Published | Evidence class | Claims | Main product relevance | Weight | Recency at analysis |
| --- | --- | --- | --- | --- | --- | --- | --- |
| E01 | [Qualtrics: 2026 Market Research Trends](E01-qualtrics-2026-market-research-trends.md) | 2025-11-14 | Vendor industry survey | C1, C2, C3, C4, C5, C7 | Portfolio, Insight Navigator | Moderate | 9 months; internally documented in April 2026 |
| E02 | [MRS: The Paradox of Qualitative Research at Scale](E02-mrs-qualitative-research-at-scale.md) | 2025-06-05 | Professional methodology paper | C1, C2, C3, C6 | Guided Interviews, Discussion Groups | Moderate | 14 months |
| E03 | [ESOMAR: 20 Questions for Buyers of AI-Based Research Services](E03-esomar-ai-buyers-20-questions.md) | 2024-03-11 | Professional guidance | C2, C3, C6, C7 | AI-enabled products | High for governance criteria | 29 months |
| E04 | [Insights Association: H1 2026 Data Quality Benchmark](E04-insights-association-h1-2026-data-quality-benchmark.md) | 2026-06-16 | Current industry benchmark | C2 | Trust Centre | High for problem prevalence | 2 months |
| E05 | [ESOMAR/GRBN: Online Sample Quality Guideline](E05-esomar-grbn-online-sample-quality.md) | Undated on current page | Professional guideline | C2 | Trust Centre, Quant Surveys | High for control categories | Current page verified; publication date unavailable |
| E06 | [Zhang, Xu and Alvero: Generative AI Meets Open-Ended Survey Responses](E06-zhang-xu-alvero-ai-open-ended-responses.md) | 2025-05-07 | Peer-reviewed original study | C2 | Trust Centre, Quant Surveys | High for the stated setting | 15 months |
| E07 | [Pew Research Center: Writing Survey Questions](E07-pew-writing-survey-questions.md) | 2021-05-26; modified 2024-05-06 | Research-method guidance | C2 | Research Guard, Quant Surveys | High for questionnaire principles | Updated 27 months ago |
| E08 | [Williams et al.: Asynchronous Online Focus Groups](E08-williams-et-al-asynchronous-focus-groups.md) | 2012-07-16 | Peer-reviewed method paper | C1, C2 | Discussion Groups | Moderate | Foundational; 14 years |
| E09 | [Kankanhalli, Lee and Lim: Knowledge Reuse Through Repositories](E09-kankanhalli-et-al-knowledge-reuse.md) | 2011-02-16 | Peer-reviewed field study | C4, C7 | Insight Navigator | Moderate/contextual | Foundational; 15 years |
| E10 | [Batzner et al.: Synthetic Persona Transparency](E10-batzner-et-al-synthetic-personae.md) | 2025-10-15 | Peer-reviewed review of 63 studies | C3, C6, C7 | Segment Lens | High for product boundary | 10 months |
| E11 | [r/UXResearch: Research Repositories](E11-reddit-research-repositories.md) | 2025-12-17 | Practitioner discussion | C4, C7 | Insight Navigator | Context only | 8 months |
| E12 | [r/UXResearch: AI Persona Tools](E12-reddit-ai-persona-tools.md) | 2025-12-23 | Practitioner discussion | C2, C3, C6 | Segment Lens | Context only | 8 months |
| E13 | [r/analytics: Self-Service Reporting Failure Modes](E13-reddit-self-service-reporting.md) | 2025-05-13 | Practitioner discussion | C4, C7 | Insight Navigator, access controls | Context only | 15 months |

## What the combined evidence establishes

- Faster answers, pressure on researcher capacity, and the need to maintain quality are wider market concerns, not ideas invented for this proposition.
- AI-enabled research needs visible validation, provenance, human oversight, and clear limits.
- Survey quality controls and questionnaire design checks address documented methodological risks.
- Asynchronous qualitative work is an established method with real access and timing advantages, but it is not universally superior to live groups.
- Research repositories and self-service access can improve reuse, but adoption, curation, governance, and workflow fit determine whether they become useful.
- Persona-based AI should be grounded in completed human research and must not be positioned as a synthetic substitute for customers.

## What remains unproven

- that an unknown buyer wants this exact seven-product bundle;
- that low-touch acquisition and onboarding will work;
- that the products deliver measurable time or quality improvements;
- that users will return after first use;
- that support costs fit the subscription price; and
- that customers will pay, renew, or expand.

Those are launch-learning questions, not claims the external sources can settle.

## Recovery and maintenance

1. Treat this index as the canonical source ledger and preserve the E01-E13 identifiers in future reports.
2. Use the canonical URL or DOI recorded in each file. Page and section locators are included where available.
3. Do not store full copyrighted papers in this repository unless the licence permits it. The analysis records contain paraphrases and bibliographic fingerprints instead.
4. Recheck all live URLs and source changes at least every six months. The next review is due **18 February 2027**.
5. When a source disappears, recover it by title, authors, publisher, DOI, or article/post identifier before changing the evidence ID.

## Related records

- [Launch Claims Register](../launch-claims-register-2026-08-18.md)
- [Product Evidence Register](../product-evidence-register-2026-08-18.md)
- [Phase One validated pains and positioning evidence](../phase-one-validated-pains-and-positioning-evidence.md)