Customer Satisfaction as a Critical Success Factor for ERP Design
Bibliographic record
Abstract
Enterprise Resource Planning (ERP) has been an important tool over the last number of years in managing business processes for large corporations. An ERP is a software system that links various departments and allows them to work together through a centralized software system. An example would be a hotel management ERP where multiple departments such as accounting, front desk, housekeeping and human resources share vital business information. However, more research needs to be done on ERP initiatives for small to midsized enterprises (SME) to also help them reach their productivity goals effectively. This is, in part, the motivation behind this thesis. This thesis first looks at various methodologies and metrics that can help inform the design and implementation of an ERP. This thesis also incorporates customer satisfaction as a Critical Success Factor (CSF) and metric for analyzing an SME’s ERP design and implementation. Prior to the design and implementation phase, gathered quantitative customer experience data is used as a guide to inform the design criteria with respect to the implementation of an ERP for an SME. This thesis demonstrates that ERP design and implementation concepts can utilize the sentiment of an SME’s customer base to subsequently help key issues get resolved in the ERP design process which may also lead to a successful ERP implementation for an SME. In this thesis, an ERP is designed and developed that is informed through customer satisfaction as a CSF in addition to other techniques such as the As-Is, To-Be and Balanced Scorecard methodologies [1]. Customer Satisfaction is used quantitatively before and after the design and implementation of an ERP to both inform and evaluate the success of the ERP design and implementation for an SME.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 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 teacher head, 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".