Critical Success Factors in Customer Relationship Management Strategy in the Local Government Authorities in Zimbabwe
Bibliographic record
Abstract
The aim of the study was to identify critical success factors in customer relationship management strategy success in the local government authorities in Zimbabwe. A thorough abridgment of the literature was conducted, mainly to understand the nature and structure of local government authorities in Zimbabwe as well as to identify critical success factors in CRM strategy success. A Meta-analysis methodology was employed and explanatory research approach was adopted by means of a survey strategy. 197 questionnaires have been collected from twenty-one local government authorities in Zimbabwe. The findings of the study revealed that all of the ten critical success factors are significant and positively linked to CRM strategy success. Furthermore, the statistical tests show that success and failure of CRM strategy success are highly dependent on four major critical success factors including Implementation Approach, Change Management, Metrics and Implementation Strategy. However, process design and Buy-in Approach and Adoption have low significance impact in CRM strategy success in local government authorities in Zimbabwe. The results of the data analysis led to the creation of a framework which outlines the critical success factors in CRM strategy success in local government authorities in Zimbabwe and the CRM implementation Index which need to be followed before implementing the CRM strategy. This study has clearly indicated that customer relationship management forms a powerful strategy that local government authorities should apply to manage long-term relationships with their key stakeholders.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".