A Study of e-Government Business Model with Customer Relationship Management Driven in Taiwan
Bibliographic record
Abstract
The study examined the relationship among customer relationship management (CRM) readiness, e-government (e-Gov) business model and customer equity to indicate the advisable actions on citizen-centric e-Gov business model. Based on the benchmarking concept, the study used the questionnaire to analyze the influences of CRM readiness (blueprint management, change management, human resource, process management, ICT resource) on e-Gov business model (administration innovation, customer interface, service infrastructure, resources allocation) and the effect of citizen-centric e-Gov business model through customer equity (value, brand, relationship). The results can identify the level of CRM readiness in Taiwanese leading departments/agencies and improve the service process for e-Gov business model with a CRM perspective. The study found that: 1. Governments can enhance the effects of e-Gov business model through CRM readiness examining. 2. The sustainable customer equity can be transformed through e-Gov business model. 3. CRM participation and experience sharing could be broadened through the mechanisms of life-long learning for public servants further.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".