The Moderating Role of Absorptive Capacity in the Assimilation of Enterprise Information Systems
Bibliographic record
Abstract
We attempt to understand how external institutional forces affecting ERP assimilation within organizations need not impact all organizations uniformly but instead can be moderated by the enterprises’ knowledge-based capabilities. Building on an institutional model of ERP assimilation, we investigate the role of absorptive capacity (ACAP) in ERP assimilation. Specifically we examine how the ACAP of an organization can enhance or retard the effect of institutional forces on the degree of ERP assimilation. Following a recent framework we operationalize ACAP as potential ACAP (PACAP) and realized ACAP (RACAP) and find that both dimensions affect ERP assimilation in different ways. While both, PACAP and RACAP, have a direct positive impact on assimilation, PACAP moderates the impact of mimetic forces on assimilation whereas RACAP moderates the effect of normative pressures. While we find overall a strong support for our hypothesized model, interestingly, we also find that RACAP negatively moderates the effect of mimetic pressures on assimilation. We discuss the contributions of this study to a better understanding of IT assimilation processes.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".