Academic Researchers’ Absorptive Capacity Influence on Collaborative Technologies Acceptance for Research Purpose: Pilot Study
Bibliographic record
Abstract
A wide variety of Collaborative Technologies (CT) emerged to facilitate the collaboration among peers. Despite the extensive literature of CT adoption in various contexts, a massive lack exists in CT adoption by academic researchers. Consequently, this study concerns the CT adoption by academic researchers. The study investigates how academic researchers’ Absorptive Capacity (ACAP) impacts the acceptance of CT for research purpose. The authors have extended Technology Acceptance Model (TAM) to explain how academic researchers’ ACAP of CT impacts the academic researchers’ Behavioral Intention (BI) to accept those technologies for researching purpose. The extended model was empirically evaluated using a survey data collected from 72 researchers in the academic fields from a leading university in Malaysia. The quantitative analysis indicated that the researchers’ differences represented by ACAP influence their behavioral intention towards CT acceptance. Except insignificant impacts of ACAP for understanding and ACAP for assimilating dimensions on Perceived Usefulness (PU), and ACAP for applying on Perceived Ease of Use (PEOU).
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".