Digital Overload, Self-care self-efficacy, and Innovation Performance in the Age of AI: The Moderating Role of IT Mindfulness
Bibliographic record
Abstract
This study examines how digital technology–induced overload impairs innovation performance by eroding employees’ self-focused efficacy. Drawing on technology-stress and coping theories, we argue that the pervasive “always-on” digital environment compels employees to shoulder excessive workloads and rapidly master new tools, thereby draining the cognitive resources essential for creative thought. Specifically, technology overload diminishes self-focused efficacy—the confidence in one’s ability to manage work demands—which, in turn, undermines innovative output. Moreover, we investigate IT mindfulness—defined as a heightened awareness of and intentional engagement with digital tools—as a buffering mechanism that enables employees to deploy adaptive coping strategies under high digital pressure. A multi-wave survey of 367 knowledge workers in technology-intensive industries supports our proposed model. Structural equation modelling and hierarchical regression analyses indicate that technology overload directly reduces innovation performance, that this effect is mediated by self-focused efficacy, and that IT mindfulness attenuates the negative impact of overload. These findings advance our understanding of the unintended consequences of digital transformation and provide actionable guidance for creating work environments that promote both employee well-being and 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".