AN EXAMINATION OF INFORMATION TECHNOLOGY ASSETS AND RESOURCES AS ANTECEDENT FACTORS TO ERP SYSYTEM SUCCESS
Why this work is in the frame
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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 â contingent or otherwise â 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 actorsâ IT skills/knowledge, IT functionâs 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 functionâs value, IT department size and budgets have significant effects on ERP success.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it