Digital competencies of managers and leaders self-assessment aspects : a design science research approach
Bibliographic record
Abstract
As digital transformation accelerates across industries, managers and leaders are increasingly expected to develop digital competencies that extend beyond technical know-how to include strategic, social, and motivational skills. However, most existing frameworks and tools—such as DigComp or IC3—are designed for general populations and fail to address the specific challenges of digital leadership. This thesis aims to address this gap by designing a self-assessment tool tailored to the digital competencies of managers and leaders. Adopting the Design Science Research (DSR) methodology, this study combines a literature-based framework with iterative design and empirical validation. Four key competency dimensions were identified—technical, managerial, social, and motivational—which serve as the foundation for a perception-based questionnaire using a Likert scale. The tool enables managers to self-evaluate their digital strengths and development needs in a structured and accessible manner. The qualitative evaluation, conducted through interviews with ten managers from the manufacturing sector in Canada, suggested the relevance and usability of the framework. Findings revealed a strong demand for reflective tools that support digital awareness, adaptability, and strategic alignment. Participants also proposed enhancements such as AI-driven personalized feedback, benchmarking features, and broader accessibility for SMEs. This research contributes to both academic literature and professional practice by offering a flexible, leadership-specific self-assessment model that supports digital competence development in managerial contexts. It also opens new avenues for future research on personalization, ethical implications, and long-term impact on leadership behaviors.
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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".