Electronic Commerce in Decision Technologies: A Business Cycle Analysis

Hemant K. Bhargava 
Dept. of Systems Management 
Naval Postgraduate School 
bhargava@nps.navy.mil 

Ramayya Krishnan 
The Heinz School 
Carnegie Mellon University 
rk2x@andrew.cmu.edu 

Rudolf Müller 
Institut für Wirtschaftsinformatik 
Humboldt-Universität zu Berlin 
rmueller@wiwi.hu-berlin.de

Abstract:

The phenomenal growth of global information networks such as the World Wide Web has generated considerable interest in electronic commerce in information goods and services. Most work in this area has focused on commerce in static information services such as digital libraries and electronic newstands. In contrast, this paper is concerned with electronic commerce in interactive information services, in particular, software and information technologies that support decision making. We identify distinctive features of decision technologies that influence the functionality required to support the business cycle of consumers and providers in an electronic commerce setting. We then discuss alternative modalities of providing this functionality and discuss the implications of this choice on barriers to entry in the marketplace. We conclude with a discussion of legal and social issues that need to be addressed in electronic markets for decision technologies. 

Introduction

Telecommunication-enabled technologies such as electronic data interchange [Kimberley-1991] and electronic funds transfer [Ahmad-1994,Wolff-1996] make it possible for many steps in commerce to take place electronically. In certain cases of commerce, the goods themselves can be represented electronically, for example in video-on-demand markets and in electronic news-stands. This class of (information) goods presents new opportunities in electronic commerce since the entire transaction—from product enquiry to price negotiation to product delivery—can be conducted electronically. Our focus in this paper is on a particular subset of such goods: interactive information goods that support decision making for their consumers. This category includes data sets, model instances, model schemas, solvers, modeling languages and systems, modeling environments, decision support systems, and computational platforms. 

In [Bhargava-etal-1996a] we called information goods of this type decision technologies, and discussed the potential of transacting with decision technologies as products and services in electronic markets. If we assume that electronic markets for decision technologies are indeed a possibility, and if we purport to play a role in developing such markets, we are faced with certain questions relating both to the process of commerce and to the technologies that facilitate commerce: What is specific about decision technologies in comparison with other goods in electronic markets? What implications do these features have for the way in which decison technologies should be treated in electronic commerce? What is the role of a market infrastructure in the conduct of electronic commerce with decision technologies? How does market infrastructure either inhibit or encourage participation in the market by consumers and providers? What public interest issues such as intellectual property protection need specialized policies and legislation in order for electronic commerce in decision technologies to be successful? 

In attempting to answer these questions, which is the objective of this paper, we observe that electronic commerce in decision technologies has an interesting and unique characteristic. It allows us to treat decision technologies as services obtained on a pay-per-use basis rather than only as products that are owned and licensed [Bhargava-etal-1996a]. This concept has in fact existed for decades though on a very small scale, for example, in timesharing systems or service bureaus that solved complex computational problems (e.g., linear programming). However, modern information technologies have produced qualitative changes in the environment for use-oriented commerce in decision technologies, and make this idea much more technologically feasible and commercially viable; it promises to greatly alleviate several problems currently faced by decision technology consumers (e.g., awareness, accessibility, compatibility) and providers (e.g., advertising, heterogeneity, version management). 

Bhargava et al. [Bhargava-etal-1996a] surveyed 5 research efforts in this area, each involving a Web-based information system for decision technologies. These systems feature, for example, yellow pages of decision technologies, offer detailed information about particular models or modeling methods, allow users to send specific data and remotely execute certain decision technologies, or give users the ability to download decision technology software to their machines. 

We believe that these systems are the early incarnations of what might perhaps evolve into electronic markets for decision technologies. These systems are not yet “markets” not just because they presently do not involve pricing of the products or services that are transacted, but also because they do not fully address the “business cycle” that consumers and providers of decision technologies would engage in. It is this shortcoming of emerging markets for decision technologies that we focus on in this paper. 

The rest of this paper is organized as follows. Section[Electronic Commerce in Decision Technologies Examples] presents an overview of the activity in electronic commerce in decision technologies. In Section[Characteristics of Decision Technologies], we present characteristics, about—and unique to—decision technologies that should be kept in mind in developing electronic commerce in decision technologies. We use this information when we turn to the core purpose of our paper in Section[Analysis of Consumer Operations] and Section[Analysis of Provider Operations] where we discuss specific steps in the business cycle, in the context of electronic commerce, for consumers and providers respectively. We conclude (Section[Discussion]) with a discussion of legal and social issues that need to be addressed in electronic markets for decision technologies. 

Electronic Commerce in Decision Technologies: Examples

 Among the strengths of today’s telecommunication technologies are the wide reach of the Internet, the fact that this connectivity is based on open standards, and the support for hypermedia, distributed computation and universal authorship offered by the World Wide Web [Berners-etal-1994]. Attracted by these strengths, several academic and corporate groups have implemented Internet-based systems in which consumers and providers can engage in transactions involving deployment, distribution and/or use of decision technologies [Beasley-1990,Becker-1995b,Becker-1996,Bhargava-etal-1995a,Bhargava-etal-1995b,Dongarra-Grosse-1987,Gunther-etal-1995,NEOS-1995,Olsen-1996]. 

Broadly, these efforts differ on the extent of formal organization in the market within which they operate. One extreme is unorganized markets such as when decision technology providers create their storefronts on the World Wide Web. On the other extreme are organized markets where, perhaps using Web technologies, market infrastructure—specialized to serve the needs of electronic commerce in decision technologies—is created to host registered providers and consumers. 

In this section, we illustrate the activity in electronic commerce in decision technologies by discussing one commercial provider of decision technologies (Olsen & Associates, a system that is truly in the commercial domain and an example of a storefront in an unorganized market) and one emerging organized market for decision technologies (DecisionNet, our own research program). Broadly, in looking at these two examples the question we ask is: Who wants to sell what to whom by the help of which intermediate infrastructure and in the context of which legal environment? Among other things, we examine the range of goods being offered, who the consumers and providers are, and what incentives are available to them. 

Olsen and Associates

Olsen and Associates, a company founded in 1985, operates a commercial decision support service based in Zürich, Switzerland. It is available on the World Wide Web at http://www.olsen.ch. 

The Olsen site (or storefront) features a complete set of models, library resources and customer support for financial market traders. Of these, the decision technologies offered are mathematical models which can be used to compute directional forecasts and timing indicators and to provide trading support for applications in investment and risk management (see Figure[The Olsen Decision Support Service: Currency Rate Converter]). 

Only Olsen models are featured on the site and all the models have been set up to be executed remotely on Web servers by consumers equipped with Web browsers. It is this executable access to its models that is “sold” by Olsen under a variety of strategies ranging from pay per mouseclick to monthly or yearly subscription arrangements. More generally, Olsen may be viewed as a provider of specialized financial modeling research and development services. 

Olsen makes use of a variety of standard Web infrastructure to inform consumers about its services and to provide executable access to its models. For instance, consumers use standard search engines such as Lycos to locate the Olsen site on the Web. However, other specialized services—such as for pricing and billing— are developed by or for Olsen and likely replicated inhouse by other providers of similar storefronts on the Web. This is both a strength and a shortcoming of unorganized markets. While each provider has the option of developing his own services and technology best suited to his needs, there is considerable reinvention of the wheel and no effort made to leverage economies of scale. Finally, since only Olsen-developed content is featured on the site, there are no legal complications relating to copyright or software licensing. 

DecisionNet

DecisionNet [Bhargava-etal-1995a,Bhargava-etal-1995b] is an organized electronic market for decision technologies; the market infrastructure consists of agents that support consumers and providers in transactions. The decision technologies themselves reside on provider machines distributed across the Internet. DecisionNet is accessible via the World Wide Web, at http://dnet.sm.nps.navy.mil/. Most of the transactions are performed with HTTP, but other Internet application protocols such as FTP and Telnet are integrated transparently. 

Consumers and providers in DecisionNet are from research, education and practice. The technologies in DecisionNet range from data sets to model schemas to modeling environments. Providers in DecisionNet are developers of decision technologies, or integrators who bundle existing decision technology offerings into services. Consumers can get information about technologies, search DecisionNet yellow pages, and access and execute listed technologies. Providers register technologies providing metainformation about their technologies; other agents use this information to enable consumers to perform technology search, technology combination and technology execution. 

DecisionNet distinguishes between technologies that are only registered in the DecisionNet yellow-pages without any further support for execution, called exclusive technologies, and independent technologies, for which agents in DecisionNet provide value-added services at execution time (see also [Bhargava-etal-1996b]). The latter is the primary focus in DecisionNet, since it may lower the barrier to enter electronic markets as a consumer or a provider [Bhargava-etal-1996a]. In future, DecisionNet intends to offer billing and other services necessary for commercial transactions. With that, DecisionNet could reimburse providers for usage of their technologies, without requiring the provider to support these electronic commerce transactions using its own information technology (IT) infrastructure. At the moment, given the state of software and Internet legislation, the legal framework of an environment such as DecisionNet is somewhat vague. For example, is anyone—provider or DecisionNet—breaking any law by allowing public access to decision technologies that run under a spreadsheet or a programming language compiler or a mathematical programming solver obtained under a single-machine license? Or, is DecisionNet or a technology provider liable for consumer miseries arising out of misuse—perhaps due to inadequate instructions—of a complex decision technology? 

Characteristics of Decision Technologies

 An important issue in developing electronic commerce in decision technologies is to understand the phases of the business cycle for consumers and providers. For the general case of electronic commerce, Kalakota and Whinston [Kalakota-Whinston-1996] present mercantile models of electronic commerce that specify steps in the business cycles of consumers and providers. In this paper we focus on those phases where the implementation must take note of features that are specific to decision technlogies. A list of such features is described in this section; it is not meant to be exhaustive nor is it assumed that every item applies only to decision technologies. Rather, it reflects our experience in observing and developing this type of electronic commerce. In Sections Section[Analysis of Consumer Operations] and Section[Analysis of Provider Operations], we analyze those phases of the business cycles that are influenced by these features. 

1  Decision technologies can be described from multiple perspectives. Decision technologies can be described from an application-oriented perspective (important from an end-user’s point of view) as well as a methodological perspective (important to researchers and developers). For example, a model can be described as a financial portfolio allocation model from an application perspective and as a quadratic programming model from a methodological perspective. 
2  Metrics such as numerical precision, speed of execution, and robustness are used to characterize decision technologies. This follows from the fact that decision technologies are computational objects. There is a tradition of compiling these metrics for benchmark problems designed to stress test decision technologies or to make comparisons between them [Kolisch-etal-1995,Reinelt-1991]. 
3  Assessing the potential of a decision technology can be a difficult problem. While decision technolgies such as solution algorithms have a fixed functionality (e.g., optimally solving a linear programming problem instance), other technologies such as model schemas may have applications unforeseen by the developer. Thus, a transportation model can be used to develop a transportation plan, or, with different interpretations of the nodes, flows and arcs in the model, it can be used in the development of a production schedule [Glover-etal-1993]. 
4  The resources required to execute a decision technology depend on the inputs to the computation and on the complexity of the model structure. Certain computational services (e.g., a currency rate converter or mortgage calculator) run in almost fixed time regardless of the specific problem they are run on. But for many other technologies, the computation time, memory, need for external storage, communication bandwidth and other resources cannot be estimated until detailed problem-specific information is obtained from the consumer or, worse, until the computation is actually performed. 
5  Interaction with decision technologies involves exploratory analysis. The goal is not to compute a function or to execute a program once to obtain a set of outputs (say, an objective function value or optimal value of a decision variable) from a set of inputs. Rather, the purpose is to obtain insight into a decision space by perturbing a set of inputs and examining the sensitivity of the outputs to these perturbations. This exploratory interaction with decision technologies (often characterized as what-if analysis) demands the ability to make small changes to a set of inputs and repeated applications of a function such as an optimization algorithm. This type of exploratory and potentially expensive computation is greatly facilitated by storing the state of the computation (i.e., storing inputs and state of program execution). 
6  Interaction with decision technologies usually involves interoperation. Typically, a consumer is interested in a solution to her decision problem and not in the use of a particular decision technology. Decision support usually demands a combination of a variety of decision techonolgies [Mohring-etal-1995a]. Thus, for example, the creation of a portfolio allocation plan requires the optimization of a quadratic programming portfolio allocation model and the visualization of the plan implied by the optimization as a pie chart. This involves interoperating a model, an optimization algorithm and a data visualization tool. 
7  Decision technologies are information goods. That means, first, that the technologies—or rather the results of using them—can be delivered over an electronic network. Second, as with desktop-based decision support systems, decision technologies in electronic commerce can be delivered and formatted in a way that they can be integrated into the information technology infrastructure of the consumer. parHaving discussed these features, we turn our attention to the principal purpose of this paper, which is to examine how consumers and providers would interact with decision technologies in an electronic commerce setting, and how this interaction is influenced by these features. 

Analysis of Consumer Operations

 In their mercantile process model for consumers in electronic commerce, Kalakota and Whinston partition the business cycle into 3 phases: prepurchase interaction, purchase consummation and postpurchase interaction. We discuss each of these in turn, concentrating on aspects where decision technologies raise unique challenges. We use Olsen and DecisionNet (Section[Electronic Commerce in Decision Technologies: Examples]) both to illustrate the state-of-the-art and to add clarity to the discussion. 

PrePurchase Interaction

According to Kalakota and Whinston, the prepurchase interaction for consumers consists of 3 activities: product/service search and discovery, comparison shopping and product selection, and negotiation of terms. We discuss, for each of these, the distinctive elements in the case of decision technologies, and add another activity distinctive to decision technologies: discovering a service as a collection of preexisting services. 

Product/service search and discovery in the information space

In the previous section we noted that decision technologies can be described from application oriented or methodological perspectives. Consider the following examples. 

Example I:  Upon analysis of a project scheduling problem for a client, an analyst decides that she needs an implementation of a min slack heuristic. She turns to DecisionNet and uses terms such as min slack and heuristic to locate several providers of the heuristic method. 
Example II:  A manager of a software project needs to develop a project schedule which minimizes the total duration of the project. He visits DecisionNet and uses terms such as project scheduling and minimum duration to locate the min slack heuristic method. parThese examples illustrate the use of taxonomies—methodology-based in the first example and application-based in the second—to locate a technology. A taxonomy specifies both a collection of terms and the relationships (e.g., synonyms, class-subclass) between them, and these relationships can be used to increase the power of search. For example, consider a consumer interested in a network flow model. If a network flow model is not available in the market, a pure keyword based search would fail. However, if the retrieval engine knew that a network flow model is a specialized type of linear programming model then it might suggest a linear programming model as a substitute for the consumer. Knowledge of these semantic relationships is an important requirement for decision technology information retreival. In some cases, this search and retrieval process may need expert human intervention. 

In an electronic market for decision technologies, who should design and implement taxonomies and information retrieval methods? Should these be implemented as an infrastructural service in the market, or should they be offered as a value added service by independent agents? In DecisionNet—an organized market—these services are provided at the market level. Providers and their technologies are listed in the market’s yellow pages, using standard descriptive information given by providers when they register their technologies with DecisionNet. DecisionNet then presents a comprehensive listing to consumers, and offers them features for browsing as well as doing keyword and index searches (see Figure[An Indexed Search in DecisionNet. Source: DecisionNet]). 

In the case of Olsen, since the market infrastructure is that of the Web, one can only conduct searches using services such as Lycos (at http://www.lycos.com) to locate Olsen since there is neither a standard way of describing or locating decision technologies. Since Olsen features only 7 computational models, no search feature is provided on the Olsen site. 

Discovering a service as a collection of preexisting services

Not present in Kalakota and Whinston’s process model, we believe this step is an important one in electronic markets for decision technologies. Consider a scenario in which a personal investor would like to develop a stock portfolio. His objective is to develop a visual model (see Figure[A Visual Model of Portfolio Allocation. Source: The NEOS Server] for an example) that shows the level of his investment in different stocks. While an electronic market might feature a portfolio allocation model and a visualization tool, neither technology—used individually—meets the consumer’s need. In this situation, the consumer, either manually or through the use of some intermediary technology, should be able to discover—on demand—collections of decision techonologies and the order in which they should be used to meet his requirement. Making such a collection work demands a lot of effort and knowledge about multiple data formats. Given the complexity of this task, providers or the market should provide features to facilitate the interoperable use of decision technologies. 

Currently, neither DecisionNet nor Olsen fully support this feature. DecisionNet permits interoperable execution of decision technologies but does not support their discovery. Olsen does not support interoperable use of its models. 

Comparison shopping and product selection

Since there is an established tradition of characterizing decision technologies using metrics such as numerical precision, robustness and speed of execution on benchmark problems, these form a natural and objective means for comparison shopping of decision technologies. Further, this offers opportunities to integrate work in electronic shopping [Lee-Widemeyer-1986] which applies decision theory to the selection problem. 

In addition to the use of objective measures, consumers can comparison shop in a number of innovative ways. To develop a measure of trust, they might execute a featured component of the service or submit their problem for solution. Thus, for instance, a consumer browsing the portfolio allocation service might check performance data on individual stocks (see Figure[Historical Performance of Coca Cola Stock. Source: NEOS site]) and execute the portfolio allocation service. In the latter case, only part of the solution—the overall expected return from the portfolio—might be returned to the user in order to provide an incentive to complete the commercial transaction. Another interesting way to facilitate comparison shopping involves credits or disounts offered by providers (e.g., Olsen offers 10 Swiss Francs credit to let new consumers try out its computational models). 

Negotiation of terms

Metrics used to characterize decision technologies offer a natural set of terms that might be negotiated in an electronic market for decision technologies. In addition, two other terms are computational resources (CPU and memory) and state maintenance. Recall that state maintenance (i.e., storage of problem inputs and state of computation) is an important requirement to facilitate exploratory computation with decision technologies. In addition to deciding where the state should be maintained (e.g., at the decision technology provider vs. an infrastructural provider of persistent storage services), the length of time for which the state will be maintained needs to be negotiated. If either data preparation for, or computation with, a decision technology is time consuming, then the consumer would be interested in a service that maintains state. 

Purchase Consummation

Kalakota and Whinston’s mercantile model lists 3 activities in the purchase consummation phase: placement of order, authorization of payment, and receipt of product. 

Placement of order

In the case of decision technologies—instantiation, with detailed input data, of the interface definition of a decision technology corresponds to order placement. This instantiation can require considerable effort and should be supported in decision technology markets as discussed below. 

 Input requirements for a decision technology might consist of blocks of data with interdependencies between them (e.g., between a model’s index sets and tables indexed on these sets). These interdependencies and the format for communicating the data may be encoded into instructions sent to the consumer, placing the responsibility on the consumer to set up the data file suitably. Or, these may be encoded into a data preparation service that interacts with the consumer, obtains data over several such interactions, and finally creates the data file. DecisionNet features both approaches (see [Bhargava-etal-1996b]), the former in a technology involving the use of the AMPL modeling environment (implemented as an independent technology), and the latter in a technology involving the use of the GAMS modeling environment (implemented as an exclusive technology). 
 Because of the importance of what-if analysis, it is important to maintain input data for consumer problems. This means that when what-if analysis is conducted, only the changes to the inputs, rather than the entire set of inputs, have to be communicated by the consumer. This saves the consumer time and effort, and is a better use of scarce network resources. parAs with the other steps, the question that needs to be resolved is who should provide these order placement services. Should this feature be built into the market infrastructure, or should it be the decision technology provider who stores the inputs, or a niche provider of value added storage services or should it be the consumer herself? In DecisionNet, for exclusive technologies, order placement is made possible by DecisionNet agents, freeing individual providers from this step. In the case of Olsen, since it functions in an unorganized market, this service is impemented by the provider (Olsen). 

Authorization of payment

Once the terms, particularly the price of using the decision technology service, have been negotiated, this step is usually similar to payment authorization in other markets. The differences occur when bundles of decision technologies are created “on-the-fly.” This raises the need for a method for the reconciliation of payment made by the consumer for use of the bundle. Broadly, the alternatives are to have the consumer interact independently with each decision technology that was executed as part of the bundle, or to set up a procedure that would allow consumers to interact with a single intermediary charged with reconciling payments to the providers. Clearly, this depends on bundling and payment policies pursued by providers. Neither DecisionNet or Olsen address the payment problem when bundles are created—DecisionNet, because it does not feature a billing server, and Olsen because it does not support bundled services. 

Receipt of product

Recall that decision technologies, or the results of executing them, are digital goods, thereby permitting network delivery and, more importantly, the integration of delivered information in the consumers IT infrastructure. For instance, a consumer might use a service on DecisionNet to decide the type of widget he would like to purchase, the terms of purchase, and the supplier. In this case a seamless integration of the result could be an EDI-based purchase order that the consumer could immediately use in an electronic commerce transaction with the widget supplier. 

In general the outputs a decision technology generates could be viewed as a computational object. This object may be stored at the technology provider, for example as a file which may be downloaded via FTP, or as an active object under the control of a CORBA [Orfali-etal-1996] or COM/OLE [Brockschmidt-1995] broker. The result could also be an active document, for example, a Java applet, that is sent to the consumers Web browser. A combination of the latter two would send a Java applet to the consumer that implements a protocol by which the consumer communicates with the broker in order to browse the computed information. In any case, we can assume a significant demand on format conversion services that documents may have to pass before they are received by the consumer. 

PostPurchase Interaction

Here we discuss the single activity called customer service and support in Kalakota and Whinston’s model. 

Customer service and support

 The fact that decision technology bundles can be created on demand also poses new challenges in customer service and dispute resolution. Consider a bundle consisting of a portfolio allocation model, a quadratic programming solution algorithm, and a visualization tool to graphically depict percentage allocations into various instruments. In executing this bundled service, the user notes that upon supplying the inputs, the expected output (i.e., a pie chart depiction of portfolio allocation) is not returned. Who should the consumer turn to address this problem? Should the consumer approach the model provider, the solution algorithm provider or the visualization tool provider? Was the problem created due to errors in the inputs supplied by the consumer or due to problems with the computational models supplied by the providers? 

This sort of problem with bundled services does not arise in Olsen since the only services featured are Olsen services. Presumably, if something goes wrong during computation of an Olsen model, one resolves these problems directly with Olsen. Clearly, this problem does arise in DecisionNet also, but is not addressed beyond supplying the consumer with information about how to contact the concerned provider. As such systems grow more complex, this is clearly an issue in the public interest that needs to be resolved to ensure that the rights of both consumers and providers are protected. 

Analysis of Provider operations

 Kalakota and Whinston partition the business cycle for merchants involved in electronic commerce into 8 activities grouped into 3 phases: presales interaction, product/service production and delivery and postsales interaction. In our discussion, we drop the last phase entirely since for its two activities (order billing and account/payment management, and customer service and support), any aspects distinctive to decision technologies are already covered in the consumer’s business cycle. Overall, we note that many of the provider and consumer activities are tightly related—after all there is a function to be performed that requires both consumers and providers. Therefore, in this section, to avoid repetition, we focus our discussion on provider activities not already covered as part of the consumer’s business cycle. 

Presales interaction

As with the consumer cycle, we add the step of “Developing a service from a collection of preexisting services” to the two steps in Kalakota and Whinston’s model. 

Customer inquiry and order generation

Generating orders and creating consumer interest is a necessary step in all electronic commerce. In addition to the traditional approaches (e.g., purchase email lists of potential consumers, advertise on search engines), what features of decision technologies might be exploited to develop tailored solutions to these problems? 

Given the difficulties in describing decision technologies and assessing their potential, providers or organized markets may wish to publish solved cases (e.g., the NEOS server, a market discussed in [Bhargava-etal-1996a] discusses mathematical programming technology and the portfolio allocation problem) and present overviews of their technologies and the taxonomies used to characterize them. This strategy is pursued by DecisionNet and Olsen. 

In addition, both consumers and providers can exploit the ability to remotely execute decision technologies. Consumers might post sample problems and data sets to secure bulletin boards subscribed to by preferred providers. In this manner providers get to know about consumer problems and consumers might be able to buy research and development expertise which would otherwise have to maintained inhouse. 

Providers can also generate orders by allowing consumers to test drive their software or offer discounts (such as the 10 Swiss Francs by Olsen) to get consumers to try their technlogies. This method of facilitating online access to technologies allows consumers to develop a measure of trust and to assess the potential of the providers decision technology. 

Cost estimation and pricing of product services

This step is fundamental to electronic commerce in decision technologies and is among the most challenging. As with other information services, the large majority of the costs are incurred in developing and making the service available in the market. These are fixed costs. In contrast to information services that evaluate in (near) constant time (e.g., the currency translator from the Olsen site), for certain complex decision technologies (e.g., NP-hard combinatorial optimization problems), usage of computational resources (e.g., CPU, memory and storage) can be quite sensitive to the input parameters corresponding to the decision problem being solved. These variable costs of resources are difficult to estimate and represent the major challenge to cost estimation of decision technologies. 

Several alternative pricing strategies are appropriate in light of these challenges. These include fixed-prices for services, monthly or yearly subscription rates, or usage-senstive pricing. In addition, providers should examine the use of pricing strategies to achieve market segmentation, and distribution of demand over time. For example, Olsen offers two alternatives — a subscription model and a pay-per-use model. In the subscription model, the consumer pays a flat sum (by month or by year) for unlimited use. The pay-per-use model charges per run of the model (by mouseclick). 

Developing a service from a collection of preexisting services

Recall that decision technologies are used in an interoperable manner by consumers. Frequent collective use of a particular set of decision technologies i suggests that a market exists for the integrated service. Then, a provider might develop a bundle of existing services and register the bundle as a distinct service. The bundled service adds value by saving the consumer the effort required in integration. 

Bundling can be facilitated in a marketplace where is there commitment to standards for data exchange such as CORBA [Orfali-etal-1996] and COM/OLE [Brockschmidt-1995]. These standards facilitate distributed computing and allow new service offerings, particularly bundles of existing services, to be created with little effort. Other technology that can facilitate creation of bundles is work on model management which has focussed on supporting topics such as sequenced model execution and integration [Dolk-Kotteman-1993,Geoffrion-1992]. 

Commitment to data standards has important implications from an organizational point of view. In the absence of widely adopted open standards or de facto (e.g., Microsoft ODBC) standards, bundling will either require institutional agreements between providers on proprietary standards or result in vertically integrated offerings in the marketplace. On the other hand, market-wide data standards promote the entry into the market of solution component providers and enables bundling which effectively creates a networked organization [Malone-etal-1987] of such providers. 

Product service, production and delivery

Kalakota and Whinston divide this into four phases: Order receipt and entry, order selection and priorization, order scheduling, and order fullfillment and delivery. Three distinctive features of decision technologies that we have discussed in Section[Characteristics of Decision Technologies] have implication for order processing and service delivery. 

 When bundles are created, either on demand by consumers, or by providers, their execution requires interoperable computation of decision technologies that are part of the bundle. This interoperable computation is either performed through the intervention of consumers or through information technology made available by providers or the market. 
 Order fullfillment with decision technologies does not correspond to a single request for computation but a series of requests over the course of a session. This requires maintenance of the state of the computation (broadly defined to include the inputs to the computation and the computation itself). This service of maintaining state may be supplied by providers or the market. 
 When the results of computation are returned to the user, such results may be further processed. This requires results (the product being delivered) to be formatted for integration into the consumers information technology infrastructure (e.g., spreasdheets, EDI software and so on). par

Discussion

 We have analyzed, in the last two sections, the phases in the business cycles for consumers and providers when transacting with decision technologies in an electronic market. This analysis recommends functionality that should be in place in order to account for features that distinguish decision technologies from other products and services in electronic markets. Some of this functionality could be provided by information technology infrastructure, others raise commercial, legal and social questions. 

In the case that information technology could provide a solution, a general architectural question concerns who should be the provider for the solution—market infrastructure, independent agents, individual consumers, or individual technology providers. If individual providers or consumers had to implement these solutions, it would not only raise the barriers to participate but would also involve much reinvention of the wheel. However, this approach allows speedy implementation of specialized requirements. On the other hand, for the solution to be provided at the market level, there must evolve standards and frameworks that encompass the diversity in decision technologies and in interactions involving them. Once solutions are implemented at the market level (via the market infrastructure or by indepedent agents) they would be available globally to consumers and providers participating in the market. This is the approach taken in DecisionNet. 

The success of electronic commerce in decision technologies also depends on how certain non-technological problems are resolved. Legal issues must figure on the top of such a list. For example, when software is made available over the network such as the Web on a pay-per-use basis, it demands new pricing strategies consistent with the legal framework. The traditional single desktop, LAN-wide or site license model needs to be extended before markets can legally host decision support software. Provider commitments to third parties (like funding agencies, licenses for enabling software) may, in certain cases, exclude commercial usage of research results. 

Another important issue concerns protection of intellectual property. Decision models and algorithms represent intellectual expertise that is not proportional to, say, their size or the time required to execute them. Consider the following scenario. A consumer browses, then selects and executes a model. After using it, he develops a variant of the model and offers it in the market as a provider. How does the original provider get compensated appropriately? This same problem arises when integration or bundling of existing decision technologies takes place in the market. 

In the U. S., the federal government has taken active interest in the “national information infrastructure” and its potential use in future electronic commerce. However, legislation is certainly lagging behind technology, and that is a detriment to serious commerce. Samuelson [Samuelson-1996] discusses two current legislative proposals [NIITF-1995] to protect intellectual property rights in the case of digital goods, and argues that both need significant refinement. Further, even these proposals appear to take the view that digital goods are limited to things such as videos, images, paintings, fictional novels, and so on. Decision technologies, with their interactive and computational characteristics, pose even more complex challenges for developing legislation that would be enforceable, not unduly restrictive, and at the same time balance the interests of consumers, providers, and other entities involved in electronic commerce. 

Third, there are questions of dispute resolution, as mentioned in Section[Customer service and support]. What kind of guarantees can be made to consumers and in which cases can the provider be made responsible for failures. What kind of consumer councils could set-up a reasonable consumer protection? 

A final issue we discuss involves privacy of consumer’s problem-specific data. When a decision technology is executed on a remote machine, consumer data needs to be sent over a network to the providers machine. While encryption can protect the data during transport, there is the possibility that the data might be captured by the provider. While information technology may be used to mask the identity of the consumer (e.g., in DecisionNet, only DecisionNet agents interact with providers on behalf of consumers) or to play the role of certification agencies (e.g., an electronic market analog of the Better Business Bureau), the fundamental question relates to the ownership of the data. Does the submission of data by a consumer for remote execution on a providers machine constitute transferral of ownership of the data or closely related derivatives of the data? 

Similarly, what about information captured about a transaction between a consumer and a decision technology provider—who owns this data? Can a decision technology provider sell information about a consumer to another decision technology provider who might use this information in a targeted email campaign? Can a decision technology provider use information in the aggregate in advertsing campaigns? While the former might call for informed consent from the consumer, the latter is considered reasonable use of the data [Duncan-etal-1993]. 

Solutions to these social and legal questions will need to be developed hand in hand with information technology before electronic commerce in decision technologies can be a reality. We hope that the questions raised in this paper will encourage further research in this area, and lead to the realization of electronic markets for decision technologies. 

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Appendix I: Figures

Figures 1--6

Appendix II: Bios 

Hemant K. Bhargava is an Associate Professor of Information Technology at the Naval Postgraduate School. He received the Ph. D. and M. S. in Decision Sciences from the University of Pennsylvania, an MBA from the Indian Institute of Management, and a B. S. (Honors) in Mathematics from the University of Delhi. His research focus is in the decision sciences, and involves computer-aided mathematical modeling, logic modeling, and artificial intelligence. His current work develops methods for using the Internet for global sharing of computer-based decision technologies. Dr. Bhargava’s research has been applied in organizations such as the U. S. Coast Guard, Army and Navy, and to such problems as fleet mix planning, the “Persian Gulf War syndrome,” force sourcing, flight scheduling, radar coverage optimization, and watchbill scheduling. 

Ramayya Krishnan is Full Professor of Management Science and Information Systems at the H. John Heinz III School of Public Policy and Management. He has a B. Tech from the Indian Institute of Technology, Madras and a Phd in Management Science and Information Systems from the University of Texas at Austin. His current research interests are in Electronic Commerce, Information Networking and its implications for distributed decision support and statistical database security. His work on these topics has been funded by the National Science Foundation and the Army Research Office. 

Rudolf Müller is an Assistant Professor of Information Systems at the Humboldt-University at Berlin. He is there a member of the National Research Center SFB 373 “Quantification and Simulation of Economic Processes”. He has a diploma in Pure Mathematics from the University of Bonn and a Ph. D. in Applied Mathematics from the Technical University of Berlin. His research interests are in combinatorial optimization, information networks and software engineering. His current work focuses on the design of computational services in the Internet, investigating applications for combinatorial optimization, decision support and statistical da ta analysis Dr. Müller’s research is funded by the Deutsche Forschungsgemeinschaft.