Identification of challenges and their ranking in the implementation of cloud ERP
Bibliographic record
Abstract
Purpose The purpose of this paper is to identify the critical challenges in the implementation of cloud enterprise resource planning (ERP). The challenges identified were customization, organizational change, long-term costs, business complexity, loss of information technology competencies, legal issues, integration, data extraction, monitoring, migration, security, network dependency, limited functionality, awareness, performance, integrity of provider, perception, and subscription costs. Here the small and medium enterprises (SMEs) and large organizations were differentiated with respect to the challenges identified. This paper also suggested ranked lists of challenges both for SMEs and large organizations. Design/methodology/approach An online survey was conducted and data of 93 respondents were analyzed. Exploratory factor analysis and one-way analysis of variance (ANOVA) was used to statistically test the data. Here the SMEs and large organizations were differentiated with respect to the challenges identified. Findings This study shows that SMEs and large organizations differ from each other for most of the challenges except business complexity, integration, monitoring, security, limited functionality, performance, and integrity of provider. Also from the ranked list of challenges in cloud ERP, security was the top concern for both SMEs and large organizations. Originality/value The findings may help organizations to get a broad idea about the challenges which are critical for the implementation of cloud ERP.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".