Revisiting the role of communication quality in ERP project success
Bibliographic record
Abstract
Purpose Communication quality is repeatedly listed among the top success factors to consider when implementing an ERP system. Analysis shows its role is more complex. It helps some aspect of success but has no influence on others. The aim of this paper is to conduct a case study to determine the role of communication quality in the success of an ERP project implementation. Design/methodology/approach A case study was conducted to determine the role of communication quality in the success of an ERP project implementation. Findings Results suggest that different aspects of communication quality impact different dimensions of project success. Some dimensions of project success did not seem influenced by communication quality. Results also show that, for the dimensions of project success that are influenced by communication quality, the form might be as important as the content of communication. Research limitations/implications The literature may be repeating an “accepted truth” without actually testing it. The evaluation of the regularity of the patterns observed will require additional observations. Also, the reasons behind the association between the communication quality attributes and the different components of success will need to be further investigated. Practical implications For managers, the findings highlight that communication is not a silver bullet when conducting ERP projects. Managers should also be aware that the form of the communication efforts will likely have as much impact as the content of the communication process. The results specially emphasized the importance of openness in communication. Originality/value The study considers nine dimensions of communication quality. By examining the separate effects of the communication content and form on the components of ERP project success, the paper provides a deeper understanding of the role of communication in the implementation of ERP systems.
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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 | 0.023 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".