An examination into the factors that hinder the effective implementation of e-government: the case of Gweru city council
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».