Digital business transformation adoption in SMEs and large firms during COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has presented significant challenges for businesses worldwide. Those who recognised the importance of an online presence and transformed their traditional business model into a digital one were better equipped to mitigate the pandemic's negative impacts. However, there is a lack of research on the differences in digital transformation between small and medium-sized enterprises (SMEs) and large firms, as well as the main factors driving this transformation. This paper aims to address this gap by examining the successful digital transformation among these groups (SMEs and Large firms) in the province of New Brunswick, Canada, as a case study. The study uses secondary data from the TechImpact survey to explore the primary factors of this shift for both SMEs and large firms. The study confirms the critical factors identified in the literature, while also revealing new factors specific to each group. For SMEs, these include adopting a digital business model, investing in low-budget social media and e-marketing, recruiting young digital experts, and accessing government grants and subsidies. For large firms, the factors include implementing mass customisation through online channels, providing remote work incentives, using a comprehensive content management system, and prioritising electronic customer relationship management and e-loyalty.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".