Conceptualising digital transformation in SMEs: an ecosystemic perspective
Bibliographic record
Abstract
Purpose Supported by a service ecosystem that is increasingly immersed into digital transformation, small- and medium-sized enterprises (SMEs) have access to turnkey information technology (IT) applications, which may come free of charge but not free of concerns. The purpose of this paper is to explore a group conceptualisation and associated perceptions of IT issues within an ecosystem that includes three subgroup profiles: entrepreneurs, IT professionals and socioeconomic support professionals. Design/methodology/approach Using group concept mapping, a bottom-up and participatory mixed methods-based approach, a concept map was estimated, based on a list of items, to define seven clusters pertaining to issues and challenges of adoption and use of turnkey IT applications in SMEs of less than 20 employees. Perceptions measures of relative importance and feasibility were obtained by subgroup profiles. Findings The relative importance and relative feasibility measures for the seven clusters indicate significant statistical differences in ratings among the subgroup profiles. A discussion on the importance of relational capital in addressing challenges of digital transformation in SMEs is developed. Originality/value Results highlight signifiant differences concerning key dimensions in the adoption and use of IT from the perspective of three subgroup profiles of actors within the ecosystem. First, the results stress the need to develop a shared understanding of IT challenges. Second, they suggest policymakers could use these conceptual representations to further develop and strengthen the IT-related support agenda for SMEs, especially the smaller ones (e.g. training programs, business support and coaching initiatives, etc.).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".