Internal IT Knowledge and Expertise as Antecedents of ERP System Effectiveness: An Empirical Investigation
Bibliographic record
Abstract
The literature shows that contingency factors such as organizational culture and structure, organization size, top management support, external expertise, and internal support are critical for the effectiveness of Enterprise Research Planning (ERP) systems in adopting organizations. Research on the effect of in-house computer and information technology (IT) knowledge and expertise on the success of such packages is rare. The purpose of this study was to explore the influence of computer/IT skills as antecedents of ERP system effectiveness. Using relevant theoretical foundations, a research model was developed to test eight relevant hypotheses. Data was collected in a cross-sectional field survey of 109 firms in two European countries. The partial least squares (PLS) technique was used for data analysis. The PLS results confirmed six out of the eight hypotheses. The study's conceptualization supported the view that in-house computer/IT skills are indeed pertinent to ERP system success in adopting organizations. The research implications for practice and research conclude this study.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".