An examination into the factors that hinder the effective implementation of e-government: the case of Gweru city council
Bibliographic record
Abstract
The main focus of the research was on examination into the factors that hinder the effective implementation of e-Government in Zimbabwe Local authorities using the case study of Gweru City Council. The research was aimed at identifying the factors hindering the effective implementation of e-Government program at Gweru city council, advantages of e-Government implementation, assessing the institutional capacity of Gweru City Council in the adoption of e-Government, and coming up with recommendations to problems and/or challenges faced in implementing e-Government to improve service delivery and citizen participation. The term e-Government was contextualized and conceptualized in this research – that is the provision of the meaning of the term e-Government. The diverse delivery models of e-Government are also taken note of ie delivery models of e-Government as government to citizen (G2C), Government to business (G2B), Government to Government (G2G) and Government to Non Governmental Organizations. The e-Government implementation strategies for Zimbabwe as outlined in the Zim Asset economic blue print document were also discussed. The case studies of successful countries on E-Government such Korea, South Africa, Canada, Denmark and others as examples to overcome the e-Government challenges in Zimbabwe were also discussed in this document. As such the research subscribed to a descriptive approach of doing a study. . Stratified sampling was used to come up with five strata of respondents the citizens, council employees, council management, senior staff of council and the elected councilors. Simple random sampling was then used to pick citizens, council employees, and council management whereas purposive sampling was used to pick up councilors, and top officials of council (Town Clerk, Human resources director and the Town Engineer). Questionnaires with both closed and open ended questions were used for citizens, council employees, councilors and council management and an interview guide was used to get data from the Town Clerk, Human resources director and the town engineer. A total of ninety two respondents provided data for this research, which has been analyzed and presented in tables, charts and qualitatively. The results of the research reflected that there are many challenges militating against the full implementation of e-Government at Gweru city council and the dominant challenge being the financial constraints. The results also revealed that the institutional capacity of Gweru city council for the adoption of e-government is very weak. The council employees are not trained regarding the e-Government program and thus the main reason why the employees are resisting against the change. The council has no e-Government implementation plan or strategy in place and citizen awareness on e-Government implementation has not yet been done or taken seriously. Most councilors feel that they can still perform their duties well even without the use of electronic devices. To this matter Gweru city council is taking chameleon strides in fully implementing e-Government. A manual system of record keeping and management is therefore pre-dominant in doing business at Gweru city council. Recommendations that have been given include that the government of Zimbabwe should take an active role in putting in place a legal agenda for e-Government implementation by breaking up legislative and regulatory obstructions to the delivery of online services, Gweru city council should aim to have a separate budget for e-Government initiatives, Gweru city council should send all relevant council employees as well as elected officials on extensive ICT training workshops in order to boost the ICT confidence and skills in council staff.
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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 | 0.006 | 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 | 0.001 | 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) | 0.001 | 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".