Impact of digital capabilities of countries on the pedagogical transitions in business schools
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
Purpose During the COVID-19 pandemic, the importance of digital infrastructure in higher education surged. This study aims to analyze how a country’s digital capabilities influence pedagogical transitions in business schools and compare the impacts between digitally advanced and advancing countries. Design/methodology/approach The authors applied the job demands–resources model and the IMD World Digital Competition Ranking 2021 to analyze the impact of nations’ digital capabilities on the pedagogical transitions experienced by 121 business faculty members from 20 nations. The countries were categorized into digitally advanced countries and advancing countries. The snowball sampling method was used to gather data through an online survey consisting of 24 items. SPSS was used to statistically analyze the data in two stages using paired t-test and group comparison. Findings Significant shifts between face-to-face and online lectures occurred in both groups. Advanced countries witnessed positive shifts in discussions, presentations, oral assessment, independent learning opportunities, online teaching methods, technical support and faculties’ readiness, whereas advancing countries mainly noted alterations in professional development and communication technologies. Originality/value This study offers insights into optimizing digital capabilities and enhancing business schools’ readiness for effective pedagogical shifts during crises. Both the theoretical contribution and the findings will benefit national education policies, higher education institution leaders, scholars and educators.
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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 | 0.000 | 0.000 |
| 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