Orchestrating the Digital Transformation Process through a ‘Strategy-as-Practice’ Lens: A Revelatory Case Study
Bibliographic record
Abstract
Implementing a digital transformation (DT) strategy constitutes an important challenge for most firms. Small and medium-sized enterprises (SMEs) in particular must be helped in enacting and managing their DT process. To do so, we aim to answer two research questions: What are the dimensions that define a DT strategy in a SME context? And how do these dimensions contribute to the enactment of a DT process in this context? Using an information systems (IS) strategy-as-practice theoretical lens, combined with an information technology (IT) asset orchestration perspective, we opt for an interpretive case study of an industrial service SME whose characteristics are conducive to a renewed vision of IS strategy in a DT context. From this study emerges a process model that allows us to describe and better understand, in a concrete manner, how a DT occurs and how it is managed through a coherent DT strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".