Impact of digital capabilities of countries on the pedagogical transitions in business schools
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.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".