Identification of challenges and their ranking in the implementation of cloud ERP
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it