Notice bibliographique
Résumé
Mobile electronic commerce (or m-commerce) is generally defined as the set of financial transactions that can be carried out over a wireless mobile network (Pierre, 2003; Varshney, 2001; Varshney, Vetter, & Kalakota, 2000). According to this definition, m-commerce constitutes a subset of all electronic commercial transactions (electronic commerce or e-commerce) from business-to-consumer (B2C) or business-to-business (B2B). Thus, short personal messages such as those from short messaging system (SMS) sent between two individuals do not fall within the category of m-commerce, whereas messages from a service provider to a salesperson or a consumer, or vice versa, do fit this very definition. M-commerce appears an emerging manifestation of Internet electronic commerce which meshes together concepts such as the Internet, mobile computing, and wireless telecommunications in order to provide an array of sophisticated services (m-services) to mobile users (Paurobally, Turner, & Jennings, 2003). Before purchasing a product, clients need services such as those used to search for a product and a merchant who offer the lowest price for this product. Consumers also like to participate in auctions and analyze the quality/price ratio of a product for a certain number of suppliers (Jukic, Sharma, Jukic, & Parameswaran, 2002). Online shopping for a given product is becoming increasingly popular, and electronic purchasing and bargaining consist of looking up and deciphering the contents of electronic catalogues prior to making a decision. To automate this process and to ensure that these documents are comprehensible to computers, they must have a standard format. Such services exist in standard commerce; however, in e-commerce, they require further consideration such as those related to the market dynamics, the variety of platforms, and the languages used by various merchant sites (Itani, & Kayssi, 2003; Lenou, Glitho, & Pierre, 2003). Just as in standard commerce, e-commerce includes an initial step wherein consumers search for products they wish to purchase by virtually visiting several merchants. Once the product is found, negotiation for this possible transaction can take place between the customer and the merchant. If an agreement is reached, the next step is the payment phase. At each step of the process, a number of problems arise, such as transaction security, confidence in the payment protocol, bandwidth limitations, quality of service, shipping delays, and so forth (Paurobally et al., 2003). The peak withdrawal periods have always presented a major challenge for certain types of distributed applications. The advent of m-commerce further highlights this problem. Indeed, in spite of rather optimistic predictions, m-commerce iss plagued by several handicaps which hinder its commercial development. This article exposes some basic concepts, technology and applications related to mobile electronic commerce. The background and key technological requirements needed to deploy m-commerce services and applications are discussed, some prominent applications of m-commerce are summarized, future and emerging trends in m-commerce are outlined, and a conclusion of these topics are presented.
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 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,014 | 0,012 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,213 | 0,237 |
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 ».