Building organizational agility through digital transformation: a configurational approach in SMEs
Bibliographic record
Abstract
Purpose This study adopts a capability-based view to investigate the attainment of organizational agility in manufacturing small and medium-sized enterprises (SMEs). It first explores how these firms mobilize different types of operational and dynamic information technology (IT) capabilities to achieve a high level of agility through their digital transformation. In return, it also reveals opposite paths, i.e. those leading to its absence. Design/methodology/approach The research method involves a qualitative comparative analysis (QCA) of 65 Canadian manufacturing SMEs. Through necessity and sufficiency condition analysis procedures, this approach emphasizes the complexity of the digital transformation processes by revealing different sets (i.e. configurations) of what enable, or inhibit, organizational agility in these firms. Findings The findings indicate that manufacturing SMEs need to align at least one dynamic IT capability (sensing, learning, coordinating or integrating) and one operational IT capability (IT management, IT infrastructure or e-business) to be highly agile. In contrast, other findings indicate that there are suboptimal combinations of IT capabilities, which result in an absence of agility. Originality/value This study offers a deeper understanding of the complex interaction of IT capabilities in SMEs seeking greater agility in their business activities. Its contributions provide insights on the mobilization of a range of capabilities, both complementary and interdependent, to respond to changing conditions in their internal and external environments. These actionable findings can help various actors in the digital development of SMEs to design and implement effective IT strategies and public policies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".