Digital Maturity of SMEs in the EU: Leaders and Laggards of Luxembourg’s Manufacturing Ecosystem [* Cross-Reference *]

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Marko Orošnjak; Sławomir Kędziora; Mickael Desloges, 2026, "Digital Maturity of SMEs in the EU: Leaders and Laggards of Luxembourg’s Manufacturing Ecosystem [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.ZZWKMS, LIST Open Repository, V1
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Description
Digital maturity is increasingly recognised as a determinant of competitiveness for small and medium-sized enterprises (SMEs), yet empirical evidence from advanced economies remains limited. Here, we evaluate a sample of Luxembourgish manufacturing SMEs across six dimensions of the Digital Maturity Assessment Tool (DMAT)—Digital Business Strategy (DBS), Digital Readiness (DR), Human-Centric Digitalisation (HCD), Data Governance/Connectedness (DG), Automation and AI (AAI), and Green Digitalisation (GD)—to quantify their overall maturity. To avoid compositional artefacts, given that we rely on the EU’s DMAT, we introduce leave-one-out correlation (LOOC) to assess the association between DMA score and each focal dimension; within-firm disparities were tested via repeated-measures ANOVA; sample profiles were examined using Principal Component Analysis (PCA) followed by hierarchical clustering (HCPC). Respectively, the results converged across methods: HCD (r = 0.717) and DBS (r = 0.652) exhibited the strongest links to maturity, DG/AAI/GD were moderate contributors (r ≈ 0.50–0.58), and DR was weak (r = 0.298). The ANOVA analysis indicated substantial between-dimension differences (partial η2 ≈ 0.41), with DG and DBS leading and AAI and GD lagging. PCA–HCPC revealed two coherent cluster profiles—Leaders and Laggards—arrayed along a general maturity axis, with the most significant gaps in DBS and HCD. Practically, firms that prioritise DBS and HCD exhibit a higher DMA score, which creates a foundation for industrialising and automatising manufacturing processes. Given the small, single-country, cross-sectional design, longitudinal and adequately powered studies with objective performance outcomes are warranted to validate and generalise these findings. (2025-11-21)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.3390/technologies13120541 for the original and latest version of the publication*** (2026-07-01)
Subject Business and Management; Earth and Environmental Sciences; Engineering; Physics; Social Sciences
Keyword Maturity (psychological), Sample (material), Cluster (spacecraft), Cluster analysis, Capability Maturity Model, Hierarchical clustering, Multilevel model, Automation, Principal component analysis
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