Digital Leadership in the Hybrid Work Era: Its Impact on Employee Innovation and the Mediating Role of Digital Readiness
Bibliographic record
Abstract
The shift to hybrid work models, accelerated by the COVID-19 pandemic, demands a new paradigm of leadership. Digital leadership, defined as a leader’s ability to leverage technology to empower and guide distributed teams, has emerged as a critical competency. This study examines the impact of digital leadership on employee innovative work behavior within hybrid work settings, with a specific focus on the mediating role of employee digital readiness. A cross-sectional research design was employed, and data was collected via an online survey from 208 professionals working in hybrid models across various sectors in Canada. The data was analyzed using correlation and mediation analysis (PROCESS Macro). The findings reveal a statistically significant positive relationship between digital leadership and employee innovation. Furthermore, digital readiness fully mediated this relationship, indicating that digital leadership fosters innovation primarily by enhancing employees’ competence, confidence, and resources to effectively use digital tools. The study concludes that for organizations to thrive in the new normal, investing in developing digital leaders who can cultivate a digitally ready workforce is not merely an IT strategy but a core business imperative for sustaining innovation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".