How does organisational absorptive capacity matter in the assimilation of enterprise information systems?
Bibliographic record
Abstract
Abstract Extant literature offers two mostly distinct perspectives on enterprise systems assimilation – driven either by internal expertise and learning capability or by external institutional pressures. This study combines the two perspectives and subscribes to the view that organisations’ learning capability moderates their acquiescence to institutional pressures. The study then anchors organisational learning capability to the concept of absorptive capacity and proposes that its two dimensions – potential absorptive capacity (PACAP) and realised absorptive capacity (RACAP) – affect enterprise systems assimilation through different pathways. Our survey‐based empirical study of Enterprise Resource Planning (ERP) systems in the post‐implementation stage reveals that while both PACAP and RACAP have a positive direct impact on assimilation, PACAP positively moderates the impact of mimetic (institutional) pressures, but not normative (institutional) pressures, on assimilation; whereas RACAP positively moderates the impact of normative pressures, but not mimetic pressures, on assimilation. Thus, our theoretical contribution lies in understanding the distinct ways in which PACAP and RACAP moderate the influence of external institutional pressures on enterprise systems assimilation.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".