Strategic and organisational factors for advancing knowledge in intelligent automation
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.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".