Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".