What is Business-to-Business E-Commerce

Cowan et al. (2002) argued that the human cognitive ability to search for information and to evaluate their usefulness is extremely limited in comparison to those of computers. In detail, it’s cumbersome and timeconsuming for a person to search for information from limited resources and to evaluate the information’s usefulness. They further indicated that while people are able to perform several queries in parallel and are good at drawing parallels and analogies between pieces of information, advanced systems that embody ISA architecture are far more effective in terms of calculation power and parallel processing abilities, particularly in the quantities of material they can process (Cowan et al. 2002). According to Bradshaw (1997), information complexity will continue to increase dramatically in the coming decades. He further contended that the dynamic and distributed nature of both data and applications require that software not merely respond to requests for information but intelligently anticipate, adapt, and actively seek ways to support users. E-commerce applications based on agent-oriented e-commerce systems have great potential. Agents can be designed using the latest web-based technologies, such as Java, XML, and HTTP, and can dynamically discover and compose E-services and mediate interactions to handle routine tasks, monitor activities, set up contracts, execute business processes, and find the best services (Shih et al., 2003). The main advantages of using these technologies are their simplicity of usage, ubiquitous nature, and their heterogeneity and platform independence (Begin and Boisvert, 2002). XML will likely become the standard language for agent-oriented E-commerce interactions to encode exchanged messages, documents, invoices, orders, service descriptions, and other information. HTTP, the dominant WWW protocol, can be used to provide many services, such as robust and scalable web servers, firewall access, and levels of security for these E-commerce applications. Agents can be made to work individually, as well as in a collaborative manner to perform more complex tasks (Franklin and Graesser, 1996). For example, to purchase a product on the Internet, a group of agents can exchange messages in a conversation to find the best deal, can bid in an auction for the product, can arrange financing, can select a shipper, and can also track the order. Multi-agent systems (groups of agents collaborating to achieve some purpose) are critical for large-scale e-commerce applications, especially B2B interactions such as service provisioning, supply chain, negotiation, and fulfillment, etc. The grouping of agents can be static or dynamic depending on the specific need (Guttman et al., 1998b). A perfect coordination should be established for the interactions between the agents to achieve a higher-level task, such as requesting, offering and accepting a contract for some services (Guttman et al., 1998a). There are several agent toolkits publicly available which can be used to satisfy the customer requirements and ideally they need to adhere to standards which define multi-party agent interoperability. For example, fuzzy logic based intelligent negotiation agents can be used to interact autonomously and consequently, and save human labor in negotiations. The aim of modeling a negotiation agent is to reach mutual agreement efficiently and intelligently. The negotiation agent should be able to negotiate with other such agents over various sets of issues, and on behalf of the real-world parties they represent, i.e. they should be able to handle multi-issue negotiations at any given time.