The Role of Technology, Organization, and Environment Factors in Enterprise Resource Planning Implementation Success in Jordan
Bibliographic record
Abstract
In many ways, the application of Enterprise Resource Planning (ERP) systems is useful. In today’s business arena, ERP is regarded as a necessity. Implementation of ERP is costly and requires a lot of efforts but in Jordanian organizations, its success rate has been unsatisfactory. Hence, this study attempts to identify factors linked to implementation success of ERP in Jordan. The strategic factors are examined and they include technology adoption, web site service, competitive, top management support, change management, business process management, and trust. Questionnaires were distributed to ERP users in Jordanian firms which returned 141 responses which were analyzed. The results show significant linkage between technology adoption, web site service, competitive, top management support, change management, and business process management, and ERP implementation success. Nonetheless, the findings do not support the linkage between business process management and ERP implementation success. The findings show that firms can leverage TOE for improving ERP’s implementation success to gain the anticipated benefits. Also, there is possibility that different critical success factors have different impacts on ERP benefits. Such finding expands the supposition of TOE theory that resources generate competitive advantages. This paper adds to researches on ERP by providing further evidence of the differing impacts of TOE on the successful implementation of ERP.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".