The Transformative Impact of the Mobile Phone and Electronic Transactions in India: Lessons, Policy Issues and Challenges
Notice bibliographique
Résumé
In emerging economies like India, the mobile phone is often the only available device for accessing the Internet and large variety of associated services. With a teledensity, hugely mobile, of nearly 89% of some 1.2bn population as on December 31, 2016, India has become the second largest mobile phone market in the world. The urban teledensity was 170% whereas the rural was 53%. Further, although licenses have been allocated for broadband services such as 3G, 4G/LTE, their deployment is limited, often to urban areas. Additionally, despite the increasing adoption of smartphones, nearly 60% of mobile users have a feature phone in 2016, such numbers being larger in rural areas. The teledensity, extent of deployments and smartphone adoption gaps show the magnitude of the urban-rural digital divide in India. On another dimension, through its very ambitious Digital India project, the Government of India is trying to promote electronic transactions and payments through a variety of means, to turn India into a “less cash” economy. An aspect that needed to be considered in this initiative was the absence of a nationally recognized identity. In order to respond to this situation, a massive program to provide unique identities, called Aadhar, resulted in the creation of a database of 1.1bn. For digital disbursements and transactions, Aadhar database can be used for real time authentication of identity, thus accelerating adoption of digital transactions. The success of the Digital India program rests on the quality of the underlying infrastructure policies which, besides access devices, include the fiber backbone and the wireless deployments including the mobile and public wi-fi in rural areas, and the policies and processes of enabling institutions involved in Digital India. This paper will focus on the impacts of the widespread penetration and use of mobile networks, smartphones and other intelligent mobile devices in India. It will discuss issues such as: • How does the availability and quality of telecom services influence the design of other enabling and supporting infrastructure and institutions involved in digital transactions, especially for rural areas? • In addition to efficiently allocating and managing the use of the spectrum, what other roles can governments play in enabling the continued growth of mobile services in the context of the digital divide? • Under the Digital India project, what initiatives are governments taking in moving India from a “cash driven” to a “less cash” economy, where electronic transactions become the norm? Policy makers in developing countries like India traditionally deal with conventional issues related to facilitating the growth of mobile communications (e.g. spectrum availability and re-allocation; infrastructure deployment etc.). Moving forward, they will need to formulate policies and regulations, where required, to address new challenges related to the large-scale migration of mobile users from 2G to 3G/4G broadband networks and the use of Internet-based transactional services. How can “Demand Pull” policies complement “Supply Push” initiatives, especially for maintaining the integrity of transactional services against cyber-attacks and cyber-fraud? Methodology: Based on secondary data and in-depth interviews with officials in UIDAI, banking sector, beneficiaries and state governments.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,009 |
| Communication savante | 0,014 | 0,010 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».