Query to ChatGPT: “draft the next-generation Scopus query matrix + SLR protocol skeleton + PRISMA-ready workflow for this project”
Below is a solid AI-aided systematic mapping / SLR starter package you can adapt into a protocol document.
The design follows the logic in the two papers you attached: AI can support search, screening, and extraction, but the review still needs a rigorous protocol, explicit criteria, transparency, and human oversight. AI is especially useful for the more mechanical stages, while interpretation and final judgments remain researcher-led.
1. Working review type
Recommended format:
AI-aided systematic mapping review with SLR discipline
Why this is the best fit:
your field is terminologically fragmented;
the hidden literature is distributed across CE, supply chains, information systems, interoperability, traceability, DPP, and business-model research;
AI can help with query expansion, screening prioritization, clustering, and structured extraction, but the study still needs protocol-driven rigor.
2. Provisional review title
Data and information infrastructures for circular economy transitions: an AI-aided systematic mapping review with implications for Digital Product Passports, interoperability, and circular business model innovation
Alternative narrower version:
Information infrastructures, interoperability, and lifecycle data in circular economy research: an AI-aided systematic review with a focus on fashion and textile SMEs
3. Review objective
To systematically identify, map, and synthesize how the literature conceptualizes and empirically studies data infrastructures, information infrastructures, information sharing, interoperability, lifecycle data, and related digital mechanisms as enablers of circular economy implementation, with special attention to circular business model innovation and the fashion-textile sector.
4. Core review questions
Use 3–4 maximum.
RQ1. How does the literature conceptualize the role of data or information infrastructures in the circular economy?
RQ2. What mechanisms link information sharing, interoperability, traceability, and lifecycle data to circular practices and circular business model innovation?
RQ3. Which sectors, technologies, and governance arrangements dominate this literature, and where are the main research gaps?
RQ4. What implications does this literature have for Digital Product Passports and for SMEs in fashion and textile value chains?
5. Conceptual scope
Central constructs
circular economy
circular business model innovation
data infrastructure
information infrastructure
lifecycle data
information sharing
interoperability
traceability
product passports / DPP
digital platforms / information systems
governance / data governance
Priority context
fashion and textiles
SMEs
extended value chains
product lifecycle visibility
regulatory-data infrastructures
6. Next-generation Scopus query matrix
Use this as a query family, not as one single search.
Run all queries in Title-Abstract-Keywords first.
Cluster A. Core CE + infrastructure language
These are the most direct but lowest-recall queries.
Q1
TITLE-ABS-KEY((“data infrastructure” OR “information infrastructure”) AND “circular economy”)
Q2
TITLE-ABS-KEY((“data infrastructure” OR “information infrastructure”) AND circular*)
Q3
TITLE-ABS-KEY((“lifecycle data” OR “product data”) AND “circular economy”)
Q4
TITLE-ABS-KEY((“data governance” OR “information governance”) AND “circular economy”)
Cluster B. CE + interoperability / information sharing
These should recover the hidden literature better.
Q5
TITLE-ABS-KEY((“information sharing” OR interoperability OR “data sharing”) AND “circular economy”)
Q6
TITLE-ABS-KEY((“supply chain visibility” OR traceability OR interoperability) AND “circular economy”)
Q7
TITLE-ABS-KEY((traceability OR “material traceability” OR “product traceability”) AND “circular economy”)
Q8
TITLE-ABS-KEY((interoperability OR “semantic interoperability”) AND circular*)
Q9
TITLE-ABS-KEY((“information asymmetry” OR “coordination costs” OR “transaction costs”) AND “circular economy”)
Cluster C. CE + digital systems / platforms
These help capture adjacent IS and operations work.
Q10
TITLE-ABS-KEY((“digital platform*” OR “information system*” OR “digital infrastructure”) AND “circular economy”)
Q11
TITLE-ABS-KEY((“digital twin*” OR IoT OR blockchain OR RFID) AND “circular economy”)
Q12
TITLE-ABS-KEY((“product lifecycle management” OR PLM) AND “circular economy”)
Q13
TITLE-ABS-KEY((“knowledge graph*” OR ontology OR ontologies OR RDF OR “semantic web”) AND “circular economy”)
Cluster D. CE + product passports / DPP
These are essential for your bridge to ESPR and textiles.
Q14
TITLE-ABS-KEY((“digital product passport” OR “product passport” OR “material passport”) AND “circular economy”)
Q15
TITLE-ABS-KEY((“digital product passport” OR DPP) AND (interoperability OR traceability OR “data sharing”))
Q16
TITLE-ABS-KEY((“digital product passport” OR DPP) AND (“business model” OR “circular business model” OR CBMI))
Cluster E. Fashion and textile focus
These are the conference-relevant core queries.
Q17
TITLE-ABS-KEY((“circular economy” AND (fashion OR textile* OR apparel)) AND (“business model” OR “circular business model” OR CBMI))
Q18
TITLE-ABS-KEY(((“information sharing” OR interoperability OR “data infrastructure” OR “information infrastructure” OR traceability) AND (fashion OR textile* OR apparel)))
Q19
TITLE-ABS-KEY(((“digital product passport” OR DPP OR “product passport”) AND (fashion OR textile* OR apparel)))
Q20
TITLE-ABS-KEY(((“data asset*” OR “digital capability” OR “data governance”) AND (“green innovation” OR sustainab*) AND (fashion OR textile* OR apparel)))
Cluster F. SME and governance lens
These make the review more useful for your talk and future paper.
Q21
TITLE-ABS-KEY((SME* OR “small and medium*” OR “small firm*”) AND (“circular economy”) AND (digital* OR data OR information OR traceability))
Q22
TITLE-ABS-KEY((SME* OR “small and medium*”) AND (fashion OR textile* OR apparel) AND (traceability OR interoperability OR “digital product passport” OR “information sharing”))
Q23
TITLE-ABS-KEY((“circular business model innovation” OR CBMI) AND (interoperability OR traceability OR “information sharing” OR “data governance”))
Q24
TITLE-ABS-KEY((“circular business model*” OR CBMI) AND (fashion OR textile* OR apparel) AND (digital* OR data OR traceability OR interoperability))
Optional refinement filters
After initial runs, refine by:
document type: article, review
language: English
subject areas: Business, Management and Accounting; Economics, Econometrics and Finance; Decision Sciences; Social Sciences; Environmental Science; Computer Science, selectively
years: preferably 2000–present, or 2010–present for the core dataset
7. Query strategy logic
Document this explicitly in the protocol.
Stage 1: High-precision seed search
Run Q1, Q5, Q14, Q17, Q18, Q19.
Stage 2: Vocabulary expansion
From the seed set, inspect keywords, abstracts, and cited concepts to identify recurring alternative terms:
lifecycle data
product data
material passports
digital traceability
supply chain visibility
semantic interoperability
digital platforms
ontology / knowledge graph
Stage 3: Expanded retrieval
Run Q2–Q24.
Stage 4: Deduplicate and screen
Merge all exports and remove duplicates.
Stage 5: Backward and forward snowballing
Apply snowballing to the most relevant included papers.
This is aligned with the review-method guidance discussed in the papers you attached, which describe hybrid search strategies combining database searches with citation-based expansion.
8. SLR protocol skeleton
You can paste this into a methods document.
8.1 Review type
This study adopts an AI-aided systematic mapping review with systematic literature review procedures. AI is used to support query development, screening prioritization, clustering, and structured extraction, while final inclusion, exclusion, interpretation, and synthesis remain under human control. This approach is consistent with recent work on AI-enhanced literature reviews, which emphasizes semi-automation rather than replacement of core review methodology.
8.2 Objectives
The review aims to identify and synthesize how research links data and information infrastructures to circular economy implementation, interoperability, traceability, lifecycle visibility, and circular business model innovation, with particular relevance to DPPs and fashion-textile SMEs.
8.3 Databases
Primary database:
Scopus
Optional validation database:
Web of Science
Optional complementary search:
Google Scholar for snowballing only
manual journal scanning for key journals
8.4 Time span
Preferred:
2000 to present
Alternative:
2010 to present for the main analytical sample
pre-2010 retained only if foundational
8.5 Document types
Include:
peer-reviewed journal articles
review articles
Optional:
conference papers only if highly relevant in IS / computer science and clearly influential
Exclude:
editorials
notes
book reviews
non-scholarly reports unless separately justified
8.6 Language
English only
8.7 Inclusion criteria
Include studies that:
explicitly address circular economy, circularity, circular business models, or closely related lifecycle recirculation concepts;
discuss at least one information-related enabling mechanism, such as data infrastructure, information infrastructure, interoperability, information sharing, traceability, lifecycle data, product passports, digital platforms, or data governance;
provide conceptual, empirical, methodological, or review-level insight relevant to CE implementation;
are sufficiently detailed in abstract/full text to code key variables.
8.8 Exclusion criteria
Exclude studies that:
use “circular” in unrelated mathematical or engineering senses;
focus on purely technical computation without relevance to CE implementation or governance;
mention traceability/data only marginally with no substantive role in the argument;
address sustainability generally but not circularity;
are duplicates or inaccessible full texts.
8.9 Screening procedure
Phase 1: Title and abstract screening
screen for conceptual fit with CE + information/data mechanism
code as include / exclude / maybe
Phase 2: Full-text screening
apply full inclusion/exclusion criteria
record reason for exclusion
Phase 3: Snowballing
backward and forward citation screening of included core papers
This reflects the staged review logic described in the AI-review literature: search, screening, extraction, quality assessment, and reporting should remain explicit and documented.
8.10 AI support plan
Document AI use in a dedicated subsection.
Example wording:
AI was used to support search-string expansion, thematic clustering, screening prioritization, and preliminary coding suggestions.
AI was not used to make final inclusion or exclusion decisions.
All final selections and interpretive synthesis were performed by the authors.
This is important because transparency about AI use is explicitly emphasized in both attached papers.
8.11 Data extraction form
Recommended variables:
Bibliographic
author(s)
year
title
journal
country/region if relevant
Review coding
document type
sector
focal supply-chain stage
unit of analysis: product / firm / ecosystem / policy / infrastructure
Core concepts
CE concept used
data/information concept used
interoperability present? Y/N
traceability present? Y/N
DPP/product passport present? Y/N
lifecycle data present? Y/N
governance/data governance present? Y/N
SME relevance? Y/N
textile/fashion relevance? Y/N
CBMI/business-model relevance? Y/N
Mechanisms
how data/information is said to enable CE
barriers
enablers
value-chain coordination role
policy/regulatory role
Evidence
conceptual / empirical / review / methodological
method
dataset type
main findings
Analytical coding
mechanism category:
visibility
verification
coordination
interoperability
trust
compliance
innovation enablement
business-model enablement
quality / confidence note
8.12 Quality assessment
Because this is likely to be a mixed conceptual-empirical corpus, use a fit-for-purpose appraisal rather than one rigid checklist.
Possible quality dimensions:
clarity of concepts
methodological transparency
empirical grounding
relevance to review question
specificity of mechanism linking information/data to CE
usefulness for synthesis
Use a simple 1–3 or 1–5 scale.
8.13 Synthesis strategy
Use a mixed synthesis:
Descriptive mapping
publication trends
journal distribution
sector distribution
method distribution
technology distribution
Thematic synthesis
core mechanism families
barriers/enablers
conceptual clusters
Gap analysis
underused concepts
missing sectors
weak links between CE and information infrastructures
implications for DPPs and textile SMEs
This fits well with the mapping and descriptive strengths of AI-supported review work discussed by Wagner et al.
9. PRISMA-ready workflow
Here is a practical workflow you can follow and later convert into a PRISMA flow diagram.
Identification
Run all Scopus queries in the matrix.
Export all records with full metadata.
Merge datasets into one master file.
Remove duplicates automatically, then manually verify.
Screening
Title/abstract screening using inclusion/exclusion criteria.
Mark records as include / exclude / maybe.
Use AI only for prioritization, clustering, or suggested tags.
Eligibility
Retrieve full texts for included/maybe records.
Conduct full-text screening.
Record explicit exclusion reasons:
not CE-relevant
no substantive data/information mechanism
wrong document type
inaccessible full text
peripheral mention only
Included
Final included studies enter extraction sheet.
Conduct backward and forward snowballing on core papers.
Re-run screening and eligibility for snowballed papers.
Finalize included corpus.
Synthesis
Descriptive bibliometric mapping.
Thematic coding.
Mechanism synthesis.
Gap analysis and implications.
10. Suggested PRISMA reporting fields
Keep a log for:
total records identified per query
total records after merge
duplicates removed
records screened
records excluded in title/abstract
full texts assessed
full texts excluded with reasons
studies included in final synthesis
additional studies identified through snowballing
11. Recommended AI use statement for the paper
You can use this almost verbatim:
This review adopted an AI-aided systematic approach. Artificial intelligence was used to support query expansion, thematic clustering, screening prioritization, and preliminary coding assistance. However, all final inclusion and exclusion decisions, full-text assessments, coding validation, and interpretive synthesis were performed by the authors. This design reflects current best practice in AI-enhanced literature reviews, where AI supports but does not replace protocol-driven review procedures.
12. What this project can become
With this design, you could produce:
Option A. A conference paper on the hidden literature and its implications for DPPs
Option B. A journal-oriented systematic mapping review
Option C. A narrower SLR on fashion/textile SMEs and CBMI
My recommendation is:
first run the mapping review broadly;
then derive a narrower publishable paper focused on textiles, SMEs, and DPP-enabled CBMI.
13. Immediate next step
Do this next, in order:
Run Q1, Q5, Q14, Q17, Q18, Q19.
Export all results.
Build one master spreadsheet.
Add columns for: query source, duplicate status, title/abstract decision, full-text decision, reason for exclusion, thematic tags.
Then expand to Q2–Q24.