Exploring technology attitudes and personal–cultural orientations as student readiness factors for digitalised work
Bibliographic record
Abstract
Purpose Emerging forms of digitalisation are placing new demands on workforce entrants around the globe. This study, catalysed by innovation programs in Ukraine and Latvia, conceptualises, measures and compares key facets of dispositional readiness of university students in two post-Soviet nations for digitalised work. Design/methodology/approach Survey data, addressing technology attitudes and personal–cultural orientations (PCO), were collected by project teams at universities in Ukraine and Latvia and delivered to the authors for analysis. The authors defined three characteristics of digitalised work, conceptually positioned five of the measured constructs as readiness factors and generated readiness profiles for the two national student cohorts. Investigation of significant differences between the groups was conducted using an Independent Samples T-Test. A composite profile was produced for comparing the overall dispositional readiness of both groups for digitalised work. Findings The factor-level profiles showed similar patterns of dispositional alignment and misalignment with digitalised work. For example, technology optimism and learning interest were reported by large percentages of Ukrainians and Latvians and tolerance for unstructured work by small percentages. However, significant differences were found in group levels of technology optimism, technology anxiety, ambiguity intolerance and empowered decision-making. In each case, the Ukrainian profile appeared more strongly aligned with the target. Practical implications The global digitalisation of work requires students, educators, human resource professionals and business leaders to rethink workforce readiness assessment and adapt (re)training programs. Technology enthusiasm and learning interest should be regarded as crucial measurable attitudes motivating technical skills development. Also, cultural orientations should be positioned alongside personality traits and digital skills as factors shaping successful human–computer interaction. Originality/value This study initiates a new sociotechnical and cross-cultural trajectory of technology readiness research from data generated in two post-Soviet contexts. Moreover, it positions several measurable dispositions as factors influencing student readiness for digitalised work.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".