Ecommerce & Egovernment

B2B E-Business Implementation Approaches

2006-08-10T18:38:00.000-04:00

There are four general B2B implementation approaches in use. The first is independent B2B marketplaces, such as Commerce One, Ariba, and Freemarkets. The second approach discussed is the private B2B approach, such as the one found at Unilever and Cisco. A third commonly encountered B2B implementation approach involves consortiums, as have been formed in the auto, aviation, chemical and petroleum, building-materials, aerospace, and retailing industries. There is a fourth, transitional approach that was implemented by GE (General Electric), for example.
Independent B2B Marketplaces The first approach discussed, which involves an existing company finding an independent B2B marketplace (emarketplace), is a commonly encountered one. Many companies begin the B2B integration process by focusing on the purchasing cycle. Obtaining goods from suppliers using independent B2B marketplaces very often is the fastest and most economical way to acquire B2B capabilities.
This is done by selecting an independent B2B provider, such as Commerce One, Ariba, or Freemarkets, to come in and integrate the company’s internal systems with the selected independent market exchanges (e-marketplace).
An independent B2B marketplace or e-marketplace is an Internet destination where businesses from around the world can come together to buy and sell goods and services in an auction format. The destination and the auction are controlled and managed by the independent B2B provider.
Buyers prepare bidding-project information and post them on the site. Suppliers then download the project information and submit their bids. Buyers evaluate the suppliers’ bids and may negotiate electronically to achieve the best deal. The buyer then accepts the bid of the supplier that best meets their requirements, and the sale is finalized. Purchasers and suppliers can either pay a general fee, a per-transaction fee, or a combination of the two to the B2B provider, otherwise known as the Web host. Each one of these B2B providers has its own software applications and host Web sites.
For example, Commerce One uses its trademarked Enterprise Buyer proprietary software to link companies to all e-marketplaces of the Global Trading Web community on its Web site CommerceOne.net. Commerce One’s Global Trading Web is the world’s largest B2B trading community and provides unprecedented economies of scale for buyer organizations. This software can be purchased and installed by an existing company in order to obtain access to the Global Trading Web community that enables commercial transactions to take place between e-marketplaces.
Private B2B Exchanges The second approach discussed is private B2B exchanges.
A private B2B exchange is an e-marketplace created by a single company to provide e-business capabilities to its business units and preferred trading partners.
In 2000, in the early stages of e-business development, many companies trying to be ahead of the curve jumped into public B2B marketplaces usually run by third parties.
They soon discovered that there were many inherent problems. Although at times they were obtaining better prices, many times the diminished quality and increased rate of defects in the products were hurting their bottomline gain. There were also problems in returning defective items, receiving orders when promised, and maintaining continuity in the supply chain (Prince, 2001).
Today, more and more businesses with the necessary resources are developing their own private exchanges. The e-market focus of some companies, such as Wal-Mart, has turned away from public exchanges because finance, supply chain, purchasing, and IT managers realized that, in many cases, their systems and employees were illequipped to handle the technical and procedural requirements of large public exchanges (Krell, 2002). Wal-Mart has invested in middleware or enterprise application integration (EAI) technology to link its internal applications together and to a few (up to 12) critical suppliers in the supply-chain process. The real value of e-procurement, e-billing, and electronic supply-chain initiatives is realized through real-time, hard-coded integration (Krell).
Other companies, such as Siemens AG, have turned to private exchanges in order to limit access to procurement information (Konicki, 2001). Siemens prefers a private exchange because it does not want its competitors to have access to its production plans. Private exchanges are gaining momentum because, for those companies that have the resources to develop them, they are able to deliver the capabilities many public e-marketplaces promised but have not delivered: the ability to centrally manage procurement across many business units, the ability to enable real-time design collaboration and integration with back-end systems, and the linkage of production-, inventory-, warehouse-, and order-management systems.
Consortium
The third B2B implementation approach discussed is a consortium: a quasipublic online marketplace approach. A consortium is a group of companies within a particular industry establishing an exchange connecting each of them and their suppliers. Today, there is a consortium exchange in almost every industry. Consortium members fund most of these exchanges.
One example of a consortium is found in the auto industry. Ford, General Motors, and DaimlerChrysler together established Covisint.com as a global, independent e-business exchange. Covisint is the central hub where original equipment manufacturers (OEMs) and suppliers come together to do business in a single business environment using the same tools and interface.
Covisint enables companies to compress planning cycles and enhance supply-chain planning (http://www.covisint.com/about/). In February 2002, Covisint was handling 100 million supply-chain procurement transactions per month. These transactions take place between the exchange’s members and more than 2,000 of their suppliers (Krell, 2002). In 2004, however, Covisint experienced some major problems that led to the acquisition of the company by two other firms, Compuware Corp and Freemarkets, Inc. (Sullivan & Dunn, 2004).
Transitional
The fourth approach involves an existing company moving from a private B2B exchange to an independent, external marketplace venture. A good example of this would be General Electric (http://www.gegxs.com/gxs/ about). General Electric, given its vast capital resources and diversity across many industries, decided to develop and establish its own B2B software and private B2B operations. Subsequently, it used this experience to set up its own external, independent B2B exchange (called GE Global eXchange Services) to compete with the likes of Commerce One and Ariba in the B2B provider market. This type of approach would require a large amount of resources and is therefore not practical for many smaller businesses. Even for businesses the size of GE, the resources necessary to maintain such an exchange can become cost prohibitive (Barlas, 2002).
FUTURE TRENDS
B2B e-business experienced an initial boom based on unrealistic projections and expectations, followed by a few years of gloom based on the process of a new technology outgrowing its adolescent phase of development.
Recent projections by Standard & Poor’s, however, indicate that the future of B2B e-business looks bright. The growth of B2B e-business is forecasted to reach $3.6 trillion in 2005, $4.9 trillion in 2006, and $6.4 trillion in 2007 (Kessler, 2004).
One of the more successful B2B implementation approaches for the future seems to be that of large, private exchanges, such as Ariba and Freemarkets. According to Ordanini, Micelli, and Di Maria (2004), large, private B2B exchanges especially represent a promising phenomenon and offer superior capabilities of generating higher turnovers than smaller niche exchanges.
Electronic B2B transactions, as shown earlier, are already improving the competitiveness of enterprises through sinking costs, faster information, and enhanced flexibility, among other benefits. In the future, however, B2B will be not only the application of technologies, but also a motor of change for economic processes and industry structures: B2B applications have an enormous potential for the alteration of economic processes in the direction of the knowledge society (Schedl & Sülzle, 2004).
In the B2B e-business arena, increased activity through mergers and acquisitions is expected to continue into the future, not only in the middle market among small- and medium-sized competitors, but also among the larger B2B exchanges (“M&A Outlook,” 2005).
In the near future, more and more companies, especially finance and investment firms, will be adding multilingual dimensions to their B2B e-business strategies (“IndyMac Bank,” 2004).

Future Trends

2006-08-09T13:47:00.000-04:00

The research firm of IDC expects the ASP market to by 92% over the 5-year period 1999 to 2004 to an estimated $7.8B in 20041. However, this forecast represents a drastic reduction from the earlier estimate of Gartner Research Group, who predicted that the market would be around $22B (Mears, 2003). While there are many different estimates on ASP spending, it should be noted that all of them have revised their estimate downward from the projections made in late 1990s.
A possible reason for the downward revision of ASP spending could be due to the crash of many dot-com firms in early 2000. In late 1990s, many firms entered the ASP market with a lot of fanfare and with the expectation that the premise behind their business model of customers renting software applications over the Internet would hold true. However, the collapse of the dot-com firms in part has lead to the demise of many ASP. Those that do remain can be considered as the survivors. This second generation ASP have altered their business model and have changed their tactics to provide more and high quality offerings and superior service.

Organizational Outcomes and ASP Evaluation

2006-08-07T19:17:00.000-04:00

It is important that periodic evaluation be made both of the organizational outcomes and services and support provided by the ASP. Given the newness of the ASP concept, there exists a dearth of studies that have as their primary focus the evaluation of services and support provided by the ASP (Ma, Pearson, & Tadisina, 2005). There is also no agreement among researchers on the ways to define and operationalize quality (Reeves & Bednar, 1994). However, a good starting point is the study of Ma et al. (2005) who identified seven dimensions (features, availability, reliability, assurance, empathy, conformance, and security)
for evaluating the service quality of ASP. Trust is also an issue that may affect ASP choice and usage (Seltsikas, Currie, & Tebbourne, 2002). The task of a small business decision maker is to ensure that the use of the ASP results in net positive outcomes. In addition to quantitative factors such as performance, productivity and usage, we argue that a small business decision maker must use nonquantifiable measures such as culture, satisfaction (with both the process and outcomes) and trust to assess the impact of the ASP.

ASP Business Models

2006-08-04T21:20:00.000-04:00

A business model specifies how a firm will generate money to sustain its operations and to generate the desired rate of return. Business models usually specify how the firm will generate revenue and the specific tactics it will use to operate within the industry. An ASP generates a revenue stream either through monthly fees or through per user fees. Based on target markets and products, ASP may be classified into Enterprise ASP, Local/ Regional ASP, Specialist ASP, Vertical Market ASP, and Volume Business ASP (ASPnews.com). Enterprise ASP deliver high-end business applications such as enterprise resource planning software, e-commerce applications and supply chain management applications. Local/Regional ASP supply wide variety of application services for smaller businesses in a local area. Specialist ASP provides applications for a specific need, such as Web site services, human resources, customer-relationship management software. Vertical Market ASP provides support to a specific industry, such as healthcare, finance etc. Volume Business ASP supply general small and medium-sized businesses with prepackaged application services in volume.

Firm Resource Base: Why Use ASP?

2006-08-02T19:17:00.000-04:00

Larger businesses have a greater access to capital (money, material, employees, technology, and knowledge) and have greater freedom to engage in new technology adoption.
Larger firms can easily overcome their technical expertise limitations by hiring additional staff, using consultants, or using third-party vendors. Small businesses are different from large businesses in many areas such as capital availability, access to capital markets, technical capabilities and availability of professionals. In small businesses, very few people or possibly an individual may function as the IT staff. Premkumar and Roberts (1999) asserted that firms without the required employee skills may not be aware of new technological innovations or may fear the risk associated with adoption of such technology within their organization.
Lack of access to education and training within small business may affect their willingness to adopt new technology (Fariselli, Oughton, Picory, & Sugden, 1999). Hiring suitably qualified personnel or motivating them to work within a small business setting may be a daunting task for a small business decision maker. For developing a home page or for other less knowledge and/or capitalintensive task, a small business may make use of off the shelf books, software, and tutorials. Small businesses with restricted technical expertise may require the use of external expertise or personnel in the form of consultants, vendor support, outsourcing, or the use of ASP.

Drivers Present in the Business Environment

2006-07-30T23:13:00.000-04:00

Rapid developments in the global marketplace, including technological advances, mushrooming consumer demands, new products, escalating globalization, and the implications of corporate reengineering and the restructuring, have fundamentally transformed the contemporary business climate. To compete in this changing business and technology environment, small businesses are constantly revising strategies to better manage their businesses.
ASP are seen as a way for a small business to overcome its technology, knowledge, and monetary limitations and compete with much larger firms.

Issues In The Use Of Asp By Small Business

2006-07-27T13:59:00.000-04:00

In Figure 1, we present our framework. We use this framework to identify issues of concern to both academics and small business managers. The noteworthy features of our framework are: (a) drivers within the business environment that precipitate the need for the use of ASP, (b) firm resource base, (c) ASP value proposition, (d) ASP business model, (e) ASP selection, and (f) Organizational outcomes and ASP evaluation. Next, we discuss each of these issues in detail.

Future Trends

2006-07-25T18:23:00.000-04:00

New technologies offer to e-commerce sites the possibility of creating enhanced and highly personalized user experiences, increasing the site potential to attract more e-shoppers. One issue that has to be settled in this context is the time to adapt—that is, how much time elapses from the instant that some user behavior is observed to the instant that the system changes the user environment. In the proposed architecture, the environment will change only when a new VR-world is requested. Some users may find this reaction delayed; most users, however, have been found to react negatively to constant changes in their environment (e.g., items disappear to be replaced ones considered to be more interesting to the user (Schneidermann, 1997)). A viable approach to a more timely reaction would be use of navigation clues that will direct users to an interconnection point (where change of environment is expected); navigation clues may appear when the system decides that the user’s environment should be changed. In order to support either form of a more timely reaction, certain portions of adaptability mechanisms have to be executed at the client side, eliminating the requirement for creation and transmission of new content. The use of more sophisticated methods for item selection, with possible integration of AI techniques, is also an interesting research direction. Finally, the possibility of allowing multiple users to simultaneously enter a VR-mall and interact with each other while shopping will be investigated.

Generating Virtual Worlds

2006-07-21T20:56:00.000-04:00

When a user reaches a “VR-world interconnection” point (gate, teleport, etc.), a request is issued to the VR-world generator, which will create the next “portion” of the VRmall to be sent to the user. This request contains the user identity and—possibly—expressly stated user preferences (e.g., “I want a high-quality environment” or “I am interested in velvet textiles”). The VR-world generator additionally retrieves from the user profile database the preferences and information for the specific user (either statically stated or deduced by the VR-mall personalization engine). Afterwards, the VR-world generator accesses the content database to extract the items that will be placed in the new VR-world. The initial list of the items to be placed in the new VR-world is formulated in either of the following two ways: (a) the user selects to visit an “emall department” (e.g., furniture, clothing etc.), in which case the items belonging to the selected department are chosen; or (b) the user requests to see “matching items” to some designated merchandise (items within the shopping cart or some explicitly specified), in which case the items having common properties with the designated merchandise are chosen. The initial item list is sorted in descending order of the sum of scores corresponding to the property-value pairs within each item. The VR-world generator has also an upper limit regarding the number of items that will be placed in a VR-world, and another upper limit pertaining to the download size; if either limit is exceeded, items are removed from the list until the restrictions are met. When the item list has been determined, a proper space element is selected from the content database (one with enough placeholders for the selected items and matching the user preferences), and the items are positioned at the placeholder locations, arranging for “similar” items to be placed in clusters (Lepouras, 2004).
At this point, the VR-world has been fully created and can be sent to the user. Figure 2 presents two screenshots from the VR-mall. The first one illustrates movement along an isle, while in the second screenshot an object has been “grabbed” and is examined.

Personalization and Adaptation

2006-07-20T13:14:00.000-04:00

Central to the proposed architecture is the VR-mall personalization engine, comprising the user profiles database and the user-modeling engine. The user profiles database stores information regarding the profiles of individual users, and associations between user profiles and specific items or item properties. This information is utilized in the process of VR-mall creation to include in the mall the merchandise that most closely matches the interests of the current e-shopper. For each e-shopper, the user profiles database hosts both static and dynamic information.
Static information reflects characteristics that remain constant, at least in the context of the current visit (e.g. preferred language, connection speed, age, etc.). This information is either entered by the user (e.g., a response to a “Language selection” prompt) or deduced by the system (e.g., connection speed is estimated by measuring the download time for an image of known size). Dynamic information pertains to the interaction of the user with the virtual environment and is collected by the user activity recorder. This information describes certain actions that the e-shopper has performed in the VR-mall, including moving close to an item and moving away from it, start and end of item manipulation, resetting activities (probably due to disorientation problems), acquiring and losing visibility for items, requesting specific resources or resource types, and so forth. This information is collected within the user browser, and communicated to the user activity recorder periodically. When the user activity recorder receives a group of event information, it first arranges to combine “activity beginning—activity end” records, to compute the duration of each activity. For events that are instantaneous by nature (e.g., request for an image of a product), only the count of these events is computed. The combined information is inserted into the user profiles database, and the user-modeling engine is invoked to update the profile of the user.
The user-modeling engine is a separate architectural module that examines the user activities observed within the virtual environment, and deduces the preferences of the user towards certain items or item categories. Upon invocation, the user modeling engine extracts from the user profiles database the records that describe activities of the current user and processes them as follows:
1. Items that have come into visibility are assigned a grade in the range of (-5) to (10), depending on the time that they have attracted the visitor’s attention (-5 = not at all, 10 = very long).
2. If for some item some resources have been explicitly requested (e.g., 3D models, detailed text, images), an extra amount is added to the item’s interest grade (1 to 3, depending on the time the extra resource was viewed).
3. Items that have not come into visibility are not assigned any grade, as the user may ignore altogether that the items were present in the VR-world.
The final grade for each item is computed by multiplying the above computed grade with an aging factor, which ranges from 1.0 (for recent activities) and 0.1 (for activities that occurred a long time ago). This step effectively assigns a greater importance to recent activities, allowing for modeling of changing user interests (Kilfoil, Ghorbani, Xing, Lei, Lu, Zhang, et al., 2003).
The last phase of the user profile update procedure is the mapping of the grades computed in the previous step to preferences towards item categories, or—more generally— item properties. To this end, the semantic information associated with the items (in the form of propertyvalue pairs) is extracted from the digital items representation repository. For each property-value pair retrieved, the grades of all items associated with it are summed up to form the score of the specific property-value pair. This information is finally inserted into the user profile database. Similar algorithms are used to deduce user preferences towards specific media types and interaction methods.

Defining the VR-Mall Content

2006-07-17T22:33:00.000-04:00

In the proposed system, creation and maintenance of the adaptive VR-mall is undertaken by two stakeholder groups, namely content creators and domain experts, who perform their tasks using specially crafted content management tools. Content creators provide the digital forms of the VR-mall merchandise in the appropriate form (pictures, 3D models, sounds, videos, etc.). Content creators additionally design the space elements that are used within the VRmall, including rooms, halls, corridors, shelves, display cases, and stands. Space elements generally contain merchandise placeholders, which are replaced by appropriate merchandise items or navigation aids when an instance of the VR-mall is created. For example, shelves may contain placeholders indicating where products will be placed, whereas a placeholder within a corridor may be replaced by a sign listing the merchandise categories along the corridor. All resources can be provided in multiple levels of detail. High levels of detail are used for e-shoppers with broadband connections and when eshoppers explicitly request highly detailed images of items; low levels of detail are used to reduce download times, because the size of the digital representation in low levels of detail is significantly smaller.
To enable the selection of the most suitable items for each user, the adaptive VR-mall system needs to possess certain information regarding each item, such as the item category (e.g., furniture, electric appliance, holiday pack), target age group, shopping season, and so forth. This
information is provided in the form of property-value

Adaptive Virtual Reality Shopping Malls

pairs by domain experts—that is, VR-mall stakeholders who have significant expertise on the merchandise. This information is stored alongside the digital representations of the merchandise and is exploited by the VR-world generator in the process of selecting the items that are considered to best match the interests of the current eshopper.

THE VR-MALL ARCHITECTURE

2006-07-14T22:52:00.000-04:00

The proposed architecture provides a generic framework for building an adaptive VR-mall, undertaking the tasks related to user profiling and monitoring, selection of the items best suited to the user profile, association of the relevant interaction methods, and dynamic formulation of the VR-worlds. In this sense, the tasks that need to be performed by the VR-mall maintainers are limited to the provision of the content—that is, the digital representations of the items merchandised through the VR-mall. The proposed architecture is depicted in Figure 1.

BACKGROUND

2006-07-11T14:23:00.000-04:00

E-commerce sites nowadays expose variable degrees of sophistication, functionality, and complexity. Most ecommerce sites offer lists of available products, usually organized in categories. For each product, a brief description, the price, and possibly an image are made available to e-customers; more information items may be included depending on the e-commerce domain (e.g., customer reviews for books and music). A basic e-commerce site offers the same content to all its visitors.
The first step towards offering services tailored to the user needs is the categorization of users into groups and serving each group with specifically selected content (e.g., Arlitt, Krishnamurthy, & Rolia, 2001). Personalization provides a finer granularity for tailored content delivery, because content formulation is based on the preferences and behavior of individual users, rather than aggregate data from user groups. Preferences may be declared through static profiling (Datta, Dutta, VanderMeer, Ramamritham, & Navathe, 2001) where users declare their preferences through profile definition pages; dynamic profiles extend their static counterparts by incorporating information collected from user activities during the interaction sessions.
On the other hand, 3D objects and VR can greatly enhance user experience within an electronic shop. Since 3D environments are inherently more complex as compared to 2D interfaces, the issue of navigation within such an environment is important. Chittaro and Coppola (2000) discuss the use of animated products as a navigation aid for e-commerce. Hughes et al. (2002) examine the integration of ideal viewing parameters with navigation, to assist the navigation procedure. Park and Woohun (2004) present a prototype augmented reality system, enabling users to “put and feel a product” in order to find the match in the real environment.
Although adaptation and VR technologies seem promising for e-commerce, their adoption is currently hindered due to a number of challenges, mainly related to the technologies themselves. The first major challenge is content creation: for each item in the VR-mall, the respective representations have to be created. The virtual space for the mall must also be designed, and stands and shelves on which items will be placed must be inserted. Finally, interaction methods for each item need to be designed.
These may vary from item to item depending on the type of digital representations (e.g., 3D models may be rotated; videos may be played, paused, and continued; photographs may be only viewed), and the nature and semantics of the item (e.g., for a 3D model of a camera, interaction may be provided to illustrate change of lenses, while a 3D model of a vase can be only rotated). This is a cumbersome, time consuming, and costly process (Lepouras, Charitos, Vassilakis, Charissi, & Halatsi, 2001).
A second major challenge is the overall system complexity, stemming from the diversity of its components, structures, and interactions (European Center for Virtual Reality, 2004). The system must include provisions for user profile modeling (both static and dynamic parts), selection of the items that best fit the current user profile, dynamic creation of virtual worlds (VR-worlds) in which the selected items must be placed, coupled with proper interaction methods.
A final challenge is the overall size of the VR-mall description. VR-worlds tend to be of large size, and thus their download time can be considerable. Constructing thus a single world representing the whole VR-mall will result in long waiting times, which may lead users to navigate away from the VR-mall. A more prominent approach would be the formulation of smaller VR-worlds, each one containing a subset of the VR-mall merchandise.
These worlds may be interconnected using gates, teleports, or any other suitable means (when a user reaches an interconnection item, they are transferred to another VR-world constructed and downloaded at that time; thus waiting times are broken down into small portions). The proposed architecture adopts this approach, which additionally provides the opportunity to populate each VRworld with the merchandise that best fits the user interests, as this can be determined by the user activities observed so far. The details of this process are analyzed in the next section.