, Free University Amsterdam
Pages: pp. 8-10
E-business is the point where economic value creation and information technology come together. The Internet and the World Wide Web in particular have emerged as major driving forces in changing our economy. Business executives are now aware that information technology is a key strategic factor in industry, not just a tool to increase business-as-usual efficiency. This has happened over just a few years, but most of Internet business's socioeconomic impact likely lies ahead.
These developments have led to the quick growth of scientific interest and activities related to e-business. Given these developments' pace and depth, big challenges and opportunities exist for applying intelligent systems technology and methodology. In fact, e-business has several distinct characteristics that make investigating the deployment of intelligent systems technology highly attractive:
Intelligent systems promise better interoperability of humans, systems, and agents in e-business, by helping to handle large-scale connectivity and interactivity on the Internet and Web in a context-sensitive way. One example is ontology and Semantic Web technology that undertakes to understand user context and share associated consensus knowledge through semantic methods. Several IEEE Intelligent Systems special issues have recently reported on advances in this field.
For intelligent systems research in e-business to have real impact, we need to cross the existing borders between computer science and economic and management sciences. Truly interdisciplinary progress in science appears difficult to achieve because of the traditional decomposition into disciplines, which can turn different academic fields into almost separate cultures. Nevertheless, the contributors to this special issue on intelligent e-business hope to show how interdisciplinary thinking has helped to advance e-business.
E-business implies that information technology must prove itself in an interactive and distributed context of economic value creation. Technology push and market pull both play their role in driving e-business forward. So, you could say that three different logics of value4 determine an e-business's success (see Figure 1):
Figure 1 Three logics of value at play in e-business.
Although these three logics concern clearly separate issues, they must work together and be aligned for e-business to succeed. Many dotcoms and Internet businesses have learned these lessons the hard way. We must holistically assess information systems (intelligent or not) with respect to Figure 1's different techno-market-business logic dimensions.
The Internet and Web enable new business forms that exploit greater levels of interactivity for both customers and suppliers, increasing the opportunities for intelligent systems in e-business. Interactivity can (techno-logically) help unite the business and market logics. So, customers can become more directly and actively involved in value production. Different value development processes ensue, however, depending on the customer or supplier's level of interactive input. 6Figure 2 depicts these interactive value processes:
Figure 2 An interactive value development framework. 6
The distributive dimension (value adding and extracting) denotes how the production effort is split between supplier and customer, while the generative dimension (value capturing and creating) indicates the output increase due to combined interactivity—in other words, sharing versus baking the value pie.
This framework helps put new technological capabilities into a sharper economic-value perspective. However, the old economic-value principles are still valid, and you can easily find examples of all four value development processes in the old economy. But the Internet and Web do provide novel means for higher two-way or community interaction and involvement; for reducing the barriers of time, space, connectivity, and individual context; and thus for moving toward value generation. Consequently, we must consider business and customer involvement on a more equal footing. This is a new economic phenomenon that the technology of online interactivity directly causes.
This framework that ties together the business and market logics also yields a characterization of digital strategies based on different degrees of interactive input by both the enterprise and customer (see Table 1). (See Don Tapscott and his colleagues' work for an alternative business writer's perspective on interactive digital strategies. 7) This helps us understand and position the very different strategic roles and functions of varying intelligent systems technologies—including semantic information search, ontology-based product content management, shopbots, data mining, intelligent partnering agents, trading agents and online market design, and knowledge-based groupware supporting virtual communities. The articles in this special issue address some of these subjects.
In this issue, we explore various types of intelligent e-business systems and their associated interactive digital strategies and offer several cross-sections through the techno-market-business space, as depicted in Figure 1.
In "Designing and Evaluating E-Business Models," Jaap Gordijn and Hans Akkermans present ontology-based graphical and scenario methods to analyze e-business models. For e-business, a business model shows why and how value is created, exchanged, and consumed in a network of actors. The study outlines how you can qualitatively and quantitatively tackle strategic partnering issues in innovative value constellations. (An interesting resource on a wide variety of e-business models is North Carolina State University's Web site, Business Models on the Web, http://ecommerce.ncsu.edu/business_models.html.)
In "Extending Equilibrium Markets," Per Carlsson, Fredrik Ygge, and Arne Andersson discuss e-market design based on the general equilibrium theory of economics. They extend this theory by showing how you can handle irregularities and discontinuities in supply and demand curves in very large marketplaces. An intriguing feature of their market algorithm is that it handles irregularities by using the market's large size and complexity to its advantage instead of seeing it as a drawback. On an interdisciplinary note, this approach resembles how fluctuations in quantum mechanics average out in large-scale systems to yield conceptually and computationally much simpler classical models. 8 As an industrial application, the authors study deregulated power markets that naturally show large price fluctuations as well as irregular demand curves on short time scales. 9,10
In "Matching Buyers and Suppliers: An Intelligent Dynamic-Exchange Mode," Sung Ho Ha and Sang Chan Park discuss automated supplier selection in B2B marketplaces. If e-market businesses break up the conventional supply chain, finding the right partner becomes an issue. The authors offer a solution featuring intelligent partnering agents capable of multicriteria decision-making.
In "A Multiagent Framework for Automated Online Bargaining," Fu-ren Lin and Kuang-yi Chang consider adaptive bidding by learning from business-to-consumer marketplaces. They show how intelligent agents can improve the bargaining process by learning online from experiences in previous negotiation rounds and generalizing associated patterns.
In "Do What I Mean: Online Shopping with a Natural Language Search Agent," Barry Silverman, Mintu Bachann, and Khaled Al-Akharas demonstrate how natural language query agents improve meaning understanding and precision in e-catalog search engines for online shopping. The proposed solution has operated continuously since late 2000, and the article presents experimental results on the natural language agents' timing and effectiveness in their search for the right product.
In "Product Data Integration in B2B E-Commerce," Dieter Fensel, Ying Ding, Ellen Schulten, Borys Omelayenko, Guy Botquin, Mike Brown, and Alan Flett survey research issues that result from the heterogeneous information descriptions of products, e-catalogs, and e-business documents. Achieving interoperability is a key issue in e-business content management that calls for the kind of intelligent solutions the Semantic Web hopes to provide.
Finally, in "The E-Commerce Product Classification Challenge" (in this issue), Ellen Schulten, Hans Akkermans, Nicola Guarino, Guy Botquin, Nelson Lopes, Martin Dörr, and Norman Sadeh launch a contest that invites research groups to show how to semiautomatically map a given product description between different e-commerce product classification standards. Other articles in this special issue and in previous Intelligent Systems issues (for example, see Trends and Controversies) discuss related research work. I hope that a contest-like research challenge provides an attractive forum to discuss different intelligent solutions for the same real-life e-commerce problem. After all, intelligent e-business quite simply means confronting the research, business, and technology challenges of today for the benefit of value creation tomorrow.
I thank the reviewers who made a big effort in helping me get this special issue together. I am further indebted to Patrick Sweet and Jaap Gordijn for the many discussions and insights presented in this article.