Smartphone Applications in Government: Characterizing and Evaluating Municipal Smartphone Applications for Service Requests
Notice bibliographique
Résumé
Using advanced information and communication technologies to deliver information and services, which is referred to as e-government, is trending at different levels of governments. Through the evolution of web technologies from Web 1.0 to Web 2.0 along with the emergence of wireless Internet, e-government has also developed with growing benefits for both governments and citizens. Although a number of previous studies explored promises and challenges of e-government, a majority of them are rhetoric and focus on non-mobile e-government, and there is a gap in understanding actual uses of smartphone applications in the public sector. This research aims to fill the gap by studying 311 mobile applications adopted by some municipalities in Canada. 311 service, which is originally a direct call line that allows citizens to report issues and access non-emergency municipal services, is now offered via multiple platforms such as smartphone applications. These smartphone applications can be seen as a new practice of mobile e-government or m-government. Semi-structured interviews were conducted with six municipalities that provide multiple channels for citizens to make non-emergency service requests. Although smartphone applications have not been adopted for a long time in governments, participating governments are satisfied with the current use and have observed a number of advantages by comparing with other communication channels. The identified advantages imply potentials to promote a more open government by increasing efficiency, transparency and citizen engagement. A detailed characterization of multiple channel was obtained by looking into 311 records in one of the interviewed municipalities – the City of Edmonton. Three years of request data were analyzed by comparing relative share of service request for each channel and by extracting the spatial patterns of the requests. A regression model was also built to explore the relationships between channel use and sociodemographic variables. The analysis results show a shift in channel usage from traditional to Internet-enabled channels, and that specific digital inequalities exist reinforcing distinctions between traditional and Internet-enabled channels. Based on the results obtained, recommendations are provided to governments to further exploit advantages of smartphone applications in delivering government-related services.
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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 | 0,001 | 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) | 0,000 | 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 ».