Products
Manuscript
| Item | Status |
|---|---|
| Draft v1 | Complete — five chapters, 16,759 words, 2026-05-03 |
| Internal peer review | Complete — 2026-05-04, Major Revision |
| Revision v2 | Not started |
| Submission | Not submitted |
The internal review raised four critical, six major, and ten minor concerns. None is fatal, but together they prevent the central claim from being sustained at its current strength. The first critical item changes the study’s dependent variable, so the remaining items cannot be settled ahead of it.
Publication plan
The project is structured as a multi-paper programme, of which the TOE-Fit nonlinear extension is the lead paper and the only one currently drafted. Target journals are ranked by fit with TOE framework extensions, digital transformation context, and mixed quantitative methods, with policy-oriented outlets as a secondary tier. Journals with strict measurement and panel standards are out of reach until the outcome-measure problem is resolved — which is a reason to resolve it rather than a reason to aim lower.
Cover letters to the two highest-ranked targets were written against Draft v1 and will require rewriting, because both lean on the measurement-gap argument that the revision destabilizes.
Disclosure policy for findings
Numerical results are deliberately not published on this site.
The manuscript is unpublished and under revision, and the first item of that revision replaces the dependent variable on which every estimate rests. Publishing coefficients now would put numbers into circulation that are expected to change, in a form that invites citation. They will be released with the published paper.
What is published here is the study’s design, theoretical framework, methodology, and analytical procedure — the parts that are stable and that make the eventual results interpretable.
Data and code
Analysis code is organized as 22 R projects covering the full pipeline. Analytical outputs — tables and figures — are versioned alongside the manuscript. Neither is publicly released, as the underlying data derive from a government programme and are subject to its terms.