Cited by 105Technology Adoption and User BehaviourKnowledge Management and SharingInnovation and Knowledge Management

Enabling digital technologies adoption in industrial districts: The key role of trust and knowledge sharing

Open access
Capestro, Mauro, Rizzo, Cristian, Kliestik, Tomas, Peluso, Alessandro M., Pino, Giovanni·2024·Technological Forecasting and Social Change

Integrity signals

Registry

Crossref · 10.1016/j.techfore.2023.123003

Cites

120 of 120 references verified0 citation errors · 0 not checkable

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Claims the paper makes

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Cites

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Authors
Capestro, M., Rizzo, C., Kliestik, T., Peluso, A. M., & Pino, G.
Affiliations
  • University of Pavia (IT)
  • University of Turin (IT)
  • University of Salento (IT)
  • University of Chieti-Pescara (IT)
Corresponding author
Rizzo, Cristian
Venue
Technological Forecasting and Social Change
Publisher
Elsevier BV
Published
2024-01
Volume
198
Pages
123003
ISSN
0040-1625 (ISSN-L)
OpenAlex
W4388745759
Cited by
105
References
120
Citations by year
2026: 30 · 2025: 51 · 2024: 23 · 2023: 1
FWCI
26.91
Type
journal article
Language
en
Open access
Locations
  • Technological Forecasting and Social Change (publishedVersion)
  • Institutional Research Information System University of Turin (University of Turin) (submittedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
Ministry of Education Science Research and Sport of the Slovak Republic (NFP313010BWN6)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Communication › Knowledge Management and Sharing
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Innovation and Knowledge Management
Keywords
digital technology adoption, industrial districts, cognitive trust, affective trust, tacit knowledge sharing, footwear industry, competitive advantage, organizational factors, technological factors

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.

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