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Digital transformation capability maturity model enabling the assessment of industrial manufacturers
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“For resource-constrained SMEs, streamlined and flexible maturity models provide actionable roadmaps while avoiding the diagnostic overload common in enterprise-scale frameworks (Schumacher et al., 2016;Mittal et al., 2018;Gökalp & Martinez, 2021).”
From Digital transformation: a comparative analysis of SME digital maturity models (2025) · cites this paper as Gökalp & Martinez, 2021
In the citing paper
“Secondly, there is a paucity of cross-industry evidence on the performance payoff of progressing through maturity (Teichert, 2019;Gökalp & Martinez, 2021).”
From Digital transformation: a comparative analysis of SME digital maturity models (2025) · cites this paper as Gökalp & Martinez, 2021
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Metadata
- Authors
- Gökalp, E., & Martinez, V.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Gökalp, Ebru
- Venue
- Computers in Industry
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 132
- Pages
- 103522
- ISSN
- 0166-3615 (ISSN-L)
- OpenAlex
- W3184834255
- Cited by
- 200
- References
- 54
- Citations by year
- 2026: 26 · 2025: 38 · 2024: 49 · 2023: 56 · 2022: 29 · 2021: 2
- FWCI
- 17.59
- Type
- journal article
- Language
- en
- Open access
- green
- www.scopus.com
- Licence
- Locations
- Computers in Industry (publishedVersion)
- OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Digital Innovation in Industries
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Keywords
- digital transformation, maturity model, manufacturing companies, gap analysis, digital maturity assessment, improvement roadmap, benchmarking, chemical industry, machine-building industry
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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