<scp>ICT</scp> knowledge absorptive capacity: A critical factor for technology integration in schools
Bibliographic record
Abstract
Abstract This study examines whether and how a school's information and communication technology (ICT) knowledge absorptive capacity (ACAP) affects technology integration in schools. In addition, it investigates the influence of various contextual factors on the degree of contingency of ACAP, such as activation triggers, social integration mechanisms and regimes of appropriability. The study is based on a random sample of N = 411 schools representative of Germany. Structural equation modelling and machine learning were employed. The findings indicate that ICT ACAP has a positive impact on technology integration in schools and serves as a mediator in the relationship between external knowledge and technology integration. The impact of ICT ACAP on technology integration is contingent upon the presence and efficacy of knowledge‐sharing mechanisms within the school, as well as the extent to which schools engage in collaborative efforts with competitors (coopetition). The insights of this study have implications for policymakers and educational leaders, who could prioritize building ACAP and fostering collaborative networks to create more adaptable and innovative school environments. Practitioner notes What is already known about this topic For schools, technology integration is considered an important educational innovation. Acquiring, creating and sharing knowledge are essential for an efficient technology integration. Knowledge absorptive capacity (ACAP) is a critical factor in the acquisition of knowledge. What this paper adds Higher information and communication technology (ICT) ACAP is associated with increased technology integration. ICT ACAP mediates between the depth of external knowledge and technology integration. The efficacy of ACAP is contingent upon a number of contextual variables, in particular, knowledge sharing in schools and coopetition. Implications for practice and/or policy Schools need to identify, integrate and exploit relevant ICT knowledge to integrate technology successfully. Schools must develop systematic knowledge management systems to ensure that newly acquired knowledge is used reasonably. Schools must collaborate, even if they compete, to succeed in technology integration.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".