Development of a Digital Performance Assessment Model for Quebec Manufacturing SMEs
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 digitalization of industries is at the heart of today’s global economy. However, there seems to be a lack of knowledge about the most effective method for initiating a digital transformation in Small and Medium-Sized Enterprises (SME). In the context globalization and shortages in labors, access to goods, services and skills, the need for SMEs to face the competition becomes a crucial issue. This research attempts to develop a model, based on a literature review and case studies, in order to evaluate digital performance as well as to study the assumption that some parameters of the model, such as Leadership, Culture and organization and Data management for example, have different impacts on the performance of SMEs. A literature review and an 80-hour questionnaire-based methodology and field interviews allowed to evaluate the impact on business performance of the different notions revolving around the topic of digital transformation. The results show that the most significant parameters that tend to augment the digital performance and thus help to foster a digital transformation in SMEs are mainly the management commitment and exemplarity (28%), the acquisition and development of skills (26%), the digital architecture (42%), the automation (42%), the quality of data (42%) and the use of the e-commerce (42%). The purpose of this study is then to target those important elements that have the most effect on the performance of small and medium-sized manufacturing companies, with the aim of guiding efforts and investments both in academia and in the real world.
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.
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 | 0.000 | 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 it