Motivations Underlying the Adoption of ERP Systems in Healthcare Organizations: An Analysis from "Success Stories"
Bibliographic record
Abstract
In order to deal with continuous drastic increases in healthcare spending and major demographic changes, governments in industrialised countries are initiating major reforms aimed at improving the efficiency and the quality of care services provided to their citizens. These reforms along with increasing requirements for accountability tend to "corporatize" these institutions by exposing them to market-like pressures. To face their changing environment, healthcare institutions have invested in ERP systems as their basic technological infrastructure, highlighting a phenomenon that recalls the earlier popularity of the ERP movement with private companies. Based on the analysis of 180 public "success stories, " the main objective of this study is to identify and characterize the sets of motivations that lead to the adoption of ERP systems in healthcare organizations. Our findings indicate that these motivations can be classified into six categories, namely technological, managerial operational, managerial strategic, clinical operational, clinical strategic, and financial. Three clusters of healthcare organizations were identified with regard to these motivations, and labelled as a Business View, a Clinical View, and an Institutional View of ERP systems.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".