Interactions between contingency, organizational IT factors, and ERP success
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the impact of some organizational information technology (IT) factors (i.e. IT assets, employees' IT skills, IT resources, and satisfaction with legacy IT systems) and their interacting effects with two contingency factors (i.e. organization's size and structure) on enterprise resource planning (ERP) system success. Design/methodology/approach Surveys were conducted in two European countries. Respondents came from diverse, private, and industrial organizations. Relevant hypotheses were developed and tested using a structural equation modeling technique. Findings The analysis supported – partially or fully – six of the eight hypotheses formulated. For example, the data indicated strong positive relationships between IT assets and IT resources, on the one hand, and ERP success, on the other. Organization's size and structure were also found to be moderators in some of the relationships. Also, the analysis revealed that satisfaction with legacy IT systems increased with ERP success, which was an unexpected finding. Originality/value This study contributes to the literature, being among the few to investigate the effects of organizational IT factors and their interacting effects with relevant contingency factors in the context of ERP system success. Methodologically, the study utilized a “non‐deterministic” model to facilitate deeper insights into the effects of variables.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".