Strategic and organisational factors for advancing knowledge in intelligent automation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores the determinants of intelligent automation implementation within organisations and their implications for strategic value and technological adoption. Grounded in diffusion of innovation theory, this study examines how digital competencies, technology absorptive capacity, and strategic value influence the adoption of intelligent automation. Using a quantitative approach, the findings reveal that digital competencies do not directly impact intelligent automation implementation, but exert an indirect influence through technology absorptive capacity and strategic value. Technology absorptive capacity emerges as a critical enabler facilitating the assimilation and application of the external knowledge necessary for intelligent automation integration, whereas strategic value plays a significant role in aligning intelligent automation adoption with organisational goals. These results emphasise the importance of absorptive and strategic capacities in bridging the gap between digital readiness and intelligent automation. This study highlights that successful intelligent automation adoption requires a multifaceted approach that integrates technological, organisational, and strategic considerations. Although intelligent automation offers substantial potential to improve operational efficiency and competitiveness, its adoption remains resource-intensive, necessitating investments in digital capabilities, training, and stakeholder engagement. This research also underscores the need for a human-centric approach to address employee concerns and align intelligent automation with broader organisational strategies. This study contributes to the literature on digital transformation and automation by providing empirical evidence of the determinants of intelligent automation implementation and their interplay. These findings offer insights for managers, policymakers, and researchers and pave the way for more effective and sustainable adoption strategies for intelligent automation. Future research should explore additional factors that influence the adoption of intelligent automation across diverse sectors and organisational contexts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it