Delineating the ERP institutionalization process: go‐live to effectiveness
Bibliographic record
Abstract
Purpose The institutionalization of an organizational innovation, such as an enterprise resource planning (ERP) system, takes place as a continuous adaptation process that includes the development of a support organization, infrastructure, regulations, and norms, as well as the acquired knowledge of the organizational members. This paper aims to provide a structured road map for understanding this complex process and to explain some of the critical issues in institutionalizing ERP in the organization. Design/methodology/approach Multiple case studies were employed as the research approach. A multiphase design was used to introduce structure to the methodology. Findings The paper, using a reasonably representative sample, provides valuable insights into the ERP institutionalization process within organizations. It identifies and documents a number of key challenges that organizations face in the three phases of the institutionalization process. Practical implications A number of findings from the paper may help managers in successfully institutionalizing ERP systems. The paper identifies 15 key activities and several challenges in executing those activities along with coping strategies that firms employed to face these challenges. Originality/value ERP systems mark a major shift in the organizational approach to information systems. The paper uses empirical data from case studies to explore and delineate the ERP institutionalization process in the adopting organizations.
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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 | 0.044 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".