AN EXAMINATION OF INFORMATION TECHNOLOGY ASSETS AND RESOURCES AS ANTECEDENT FACTORS TO ERP SYSYTEM SUCCESS
Bibliographic record
Abstract
Organizations adopt enterprise resource planning (ERP) systems to improve information exchange across the enterprise. Research continues to show that adopting organizations do not achieve the intended objectives with the acquisition of such packages. Studies are needed to understand factors – contingent or otherwise – that may help increase knowledge in the area. This study was designed to contribute to that discourse. We examined the effects of select few information technology (IT) assets and resources, i.e. IT budgets, organizational actors’ IT skills/knowledge, IT function’s value, external expertise, and so forth, on ERP success. While such antecedent factors matter in the discourse, research combining them in order to assess their effects on ERP success is rare. Using a cross-sectional field survey, we collected data from 165 firms in three Nordic countries. Data analysis was performed using the partial least squares (PLS) technique. Statistical support was found for nine (9) out of the fifteen (15) hypotheses formulated. External expertise and organizational IT skills/knowledge were found to have significant, positive effects on ERP success, as did satisfaction with legacy systems, a result that contradicts conventional wisdom in the area. Our data did not indicate that IT function’s value, IT department size and budgets have significant effects on ERP success.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".