An Empirical Research on the Impacts of organisational decisions’ locus, tasks structure rules, knowledge, and IT function’s value on ERP system success
Bibliographic record
Abstract
This research examined the impacts of organisational decisions’ locus, tasks structure, rules and procedures, organisational actors’ information technology (IT) skills/knowledge and IT department’s or function’s value perceptions on enterprise resource planning (ERP) system success. While such antecedent factors matter in the discourse, research on their impacts on ERP success is rare. To increase understanding in the area, we proposed a research model and developed pertinent hypotheses that included the above-mentioned factors. Using a cross-sectional field survey, we collected data from 165 firms in three European countries. Data analysis was performed using the partial least squares (PLS) technique. Statistical support was found for 11 out of the 17 hypotheses formulated. Organisational design constructs, i.e. tasks structure, rules and procedures, in-house IT personnel skills/knowledge have impacts on ERP success, whereas the perceptions of IT function’s value and business employees’ IT skills/knowledge did not. Contributions and practical implications of the research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".