Digital business transformation adoption in SMEs and large firms during COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it