ERP Institutionalisation- A Quantitative Data Analysis Using The Integrative Framework of IS Theories
Bibliographic record
Abstract
There is a wide agreement that IT projects have disappointing success rates and often generate less value than originally promised.In the context of ERP systems, the same statistical reports exist which demonstrate an overwhelming number of failures in ERP implementations.A thorough review of IS literature, however, leads us to believe that organisations that broadly deploy and routinise IT (in particular, ERPs) into their day-today work procedures realise the greatest productivity benefit and business values, and in return perceive to be more successful.The stage wherein ERP is fully assimilated, widely accepted and routinised is also referred to as institutionalised ERP in the extant IS literature of institutional theory.As a result, the authors of this paper believe that studying the influence of various social, environmental, technological and organisational factors on ERP institutionalisation has significant potential in improving the chance of successful ERP projects.In doing so, this paper introduces an integrative framework of IS theories based on an in-depth review of IS literature.The survey instrument is developed to gather data on possible impacts of factors derived from each theory on ERP institutionalisation.The gathered data is then analysed using quantitative data analysis methods to shape the final hypothetical inferences.Finally, based on the data analysis results, this paper proposed valuable suggestions to business and IT managers to improve the chance of ERP success in their organisations.
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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 | 0.034 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".