Why ERP Implementations Fail – A Grounded Research Study
Bibliographic record
Abstract
Aim/Purpose: A grounded research study to understand ERP implementation failure. This study was done in a United Nations agency. Background: An organization mid-size ERP system (AGRESSO) was implemented over a period of 6 years in a United Nations agency, under conditions of political pressures and limited budget. Methodology : Observations and quasi-structured interview method was used to collect the data. Contribution: ERP implementation success is still difficult to frame. This study looks at this problem in terms of the causes of failure. Moreover, ERP research studies are relatively few and dispersed, especially for the UN context – which to our knowledge has not been published. Findings: The major finding is that the political nature of the UN fosters a hierarchical culture that is detrimental for Information Systems implementation in general, excluding the end-user from the functional requirements engineering process. There seems to be a lack of vision and strategic direction for ERP implementation in the UN. The context of the UN makes the strategic direction the more difficult of formulate and implement. Recommendations for Practitioners: For the UN, a cultural paradigm shift is necessary whereby the end-user must be included in any information systems development and implementation initiative. End-user development (although not a new approach) needs to be adopted for the UN. Recommendation for Researchers: Information systems development and deployment studies for the UN should take front stage as it represents an underlying stream of high complexity on all research in the field. Understanding ERP implementation in the UN has the potential to enhance its success in all other industries. Impact on Society: Any progress of the UN impacts positively the whole world since 193 countries are members of the UN. As such, ERP implementation is primarily about increasing operational efficiencies, it and promises transparency with regards to the member states financial contributions. Future Research: More ERP implementation studies on the different types of UN organizations. Also studies that address appropriate ERP systems for the various types of UN organization do not exist. The UN provides many research opportunities as it is hardly being studied.
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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 | 0.049 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".