Exploring the scope and depth of digitalisation in times of crisis: Implications for SME resilience
Bibliographic record
Abstract
This article investigates the role of digitalisation in bolstering small- and medium-sized enterprises (SMEs) during crises. Drawing on data from 245 North American SMEs, we examine the impact of digital technology adoption on firm resilience. The findings reveal nuanced relationships: both the depth (Digital Depth) and scope (Digital Scope) of digitalisation exhibit curvilinear associations with resilience (Profit Outlook), suggesting optimal levels for maximum benefits. Moreover, digital depth moderates the relationship between digital scope and resilience, highlighting their interactive role in enhancing adaptive capacities. This research employs a quantitative approach to explore how SMEs can leverage digital tools for resilience. Notably, it highlights the importance of strategic digital investments tailored to industry dynamics for resilience enhancement illustrating the study underscores the practical significance of digital integration for SMEs, indicating its potential to not only restore pre-crisis performance but also foster sustainable growth in the aftermath of disruption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".