Evidence map›Paper›PMID 42571261›Full record

ArticleMethodsX2026

The DSR-PRISMA protocol: Embedding systematic knowledge mapping into design science research for applied technology studies.

Thanh-Cong Truong

Abstract read
In one paragraph

Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Thanh-Cong TruongFaculty of Data Science, University of Finance-Marketing, Ho Chi Minh, Viet Nam.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Design Science Research (DSR) studies in applied technology fields often rest on literature reviews that cannot be traced and stop at artifacts that are never abstracted into design knowledge. Both habits damage reproducibility: readers cannot retrace the path from evidence to requirements to artifact, and evaluations drift away from the claims they should test. The DSR-PRISMA Protocol addresses this gap by embedding PRISMA 2020 into the rigor cycle of DSR. The protocol runs in seven phases: problem framing, systematic knowledge mapping, design requirement derivation, artifact design and development, demonstration, evaluation, and design knowledge abstraction. Six worksheet instruments (T1 to T6) document each phase, and three traceability rules connect them: every requirement traces to evidence, every component traces to a requirement, and every claim traces to an evaluation episode. A worked example in AI solution selection for small and medium-sized enterprises runs all seven phases and fills every template. Two mini-scenarios show that the instruments transfer to cybersecurity training and learning analytics, and a qualitative comparison contrasts the protocol with unstructured DSR practice on four reproducibility criteria. The protocol gives authors a documented chain from evidence to design knowledge, and gives reviewers an instrument to audit that chain.-Seven phases take a study from problem framing to design knowledge abstraction, with PRISMA 2020 embedded at the systematic knowledge mapping phase.-A finding-gap-requirement table converts review findings into traceable design requirements, the bridge missing from both DSRM and PRISMA.-Six worksheet templates and an eleven-item reporting checklist let authors and reviewers audit every phase of a DSR study.

Indexed as

Artifact evaluationDesign requirementsDesign Science ResearchPRISMA 2020Systematic literature review

Identifiers

PMID42571261
PMCPMC13451874

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.