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Economics of Web Service Provisioning: Optimal Market Structure and Intermediary Strategies


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ECONOMICS OF WEB SE RVICE PROVISIONING: OPTIMAL MARKET STRUCTURE AND INTERMEDIARY STRATEGIES By QIAN TANG A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2004

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Copyright 2004 by Qian Tang

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To Yanzan and my parents

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iv ACKNOWLEDGMENTS Words do not suffice to express my gratefulness to those who have helped me get through the difficulties and accomplish this dissert ation. First, I give my special thanks to Dr. Hsing Kenny Cheng, chairman of my su pervisory dissertation committee. Without his support, encouragement, and patient gui dance, I could not ha ve finished this dissertation and developed the strong intere sts in research. I also thank Dr. Shubho Bandyopadhyay, Dr. Steven Shugan, and Dr. Asoo Vakharia for serving on my supervisory committee, and for their helpful su pport. I appreciate Dr. Gary Koehler for his valuable suggestions on my research. I learn the art and beauty of research from all the faculty members in my department. To show my thankfulness, I am determined to follow their examples, and be a good professor and scholar. Last, but certainly not least, I wish to dedi cate my thanks to Yanzan and my parents from the bottom of my heart. Although they are physically far away from me, I feel they are with me all the time. It is their unconditional support and love that have made all my accomplishments possible.

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v TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES...........................................................................................................viii LIST OF FIGURES...........................................................................................................ix ABSTRACT....................................................................................................................... xi CHAPTER 1 INTRODUCTION.........................................................................................................1 1.1 Web Services: History and Overview...................................................................2 1.1.1 Software as service.....................................................................................3 1.1.2 Platform independence...............................................................................4 1.1.3 Integration of Web services........................................................................5 1.2 Business Implications of Web Services................................................................6 1.2.1 Reduction of integration cost.....................................................................7 1.2.2 Service-oriented architecture......................................................................7 1.2.3 Web service intermediary...........................................................................8 1.3 Research Issues.....................................................................................................9 1.3.1 Optimal market structure............................................................................9 1.3.2 Optimal location and pricing of an integrated Web service.....................10 1.3.3 Optimal subscription and listing fee charged by a WSI...........................11 1.4 Summary of Major Findings...............................................................................12 2 OPTIMAL WEB SERVICE MARKET STRUCTURE..............................................15 2.1 Related Literature...............................................................................................16 2.2 A General Model................................................................................................18 2.2.1 Independent service vendors....................................................................20 2.2.2 Strategic alliance......................................................................................22 2.2.3 Web service marketplace..........................................................................23 2.3 Analytical Insights from a Simplified Model.....................................................24 2.4 Computational Explorations...............................................................................30

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vi 3 OPTIMAL LOCATION AND PRICING OF AN INTEGRATED WEB SERVICE.36 3.1 Problem Description and Related Literature......................................................37 3.2 The Linear City Model.......................................................................................41 3.2.1 Cost to buy from WSI in linear city..................................................41 3.2.2 Cost to buy from service vendors in linear city................................41 3.2.3 Optimal location and pricing in linear city........................................42 3.3 The Unit Circle Model........................................................................................44 3.3.1 Cost to buy from WSI in unit circle..................................................45 3.3.2 Cost to buy from service vendors in unit circle................................45 3.3.3 Optimal location and pricing in unit circle........................................46 4 Optimal subscription and lising fee of a Web service intermediary............................51 4.1 The Web Service Supply Chain..........................................................................53 4.2 Literature Review...............................................................................................55 4.3 The Model...........................................................................................................59 4.3.1 Consumer’s subscription decision............................................................60 4.3.2 Service vendor’s listing decision..............................................................62 4.4 Optimal Subscription and Listing Fee................................................................63 4.4.1 Network value is le ss than intrinsic value................................................66 4.4.2 Network value is great er than intrinsic value...........................................73 5 CONCLUSIONS AND FUTURE RESEARCH.........................................................81 APPENDIX A PROOFS OF CHAPTER 2.........................................................................................87 A.1 Proof of Lemma 2-1...........................................................................................87 A.2 Proof of Lemma 2-3...........................................................................................88 A.3 Proof of Lemma 2-4...........................................................................................89 A.4 Proof of Proposition 2-6....................................................................................90 A.5 Proof of Proposition 2-7....................................................................................90 A.6 Proof of Proposition 2-8....................................................................................91 A.7 Proof of Proposition 2-9....................................................................................92 B PROOFS OF CHAPTER 3.........................................................................................94 B.1 Proof of Proposition 3-1.....................................................................................94 B.2 Proof of Lemma 3-2...........................................................................................97 B.3 Proof of Lemma 3-4...........................................................................................99 B.4 Proof of Proposition 3-5...................................................................................100 B.5 Proof of Proposition 3-6...................................................................................100

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vii C PROOFS OF CHAPTER 4.......................................................................................102 C.1 Proof of Lemma 4-1.........................................................................................102 C.2 Proof of Lemma 4-3.........................................................................................103 C.3 Proof of Proposition 4-6...................................................................................104 C.4 Proof of Proposition 4-12.................................................................................105 C.5 Proof of Corollary 4-14....................................................................................106 C.6 Proof of Corollary 4-15....................................................................................107 LIST OF REFERENCES.................................................................................................109 BIOGRAPHICAL SKETCH...........................................................................................113

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viii LIST OF TABLES Table page 2-1. Design of numerical experiments.............................................................................31 2-2. Optimal market structure w.r.t 3V ............................................................................33 C-1. Optimal Subscription Fee when v ..................................................................105 C-2. Optimal Subscription Fee when v ..................................................................106

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ix LIST OF FIGURES Figure page 1-1. Example of Web service application.........................................................................4 1-2. Web service integration.............................................................................................6 2-1. Independent service vendors...................................................................................20 2-2. Strategic alliance..................................................................................................... .22 2-3. Web service marketplace.........................................................................................23 2-4. Optimal profit of ISV..............................................................................................26 2-5. Optimal profit of marketplace w.r.t. c .....................................................................27 2-6. Optimal profit of marketplace w.r.t. V3 .................................................................28 2-7. ISV vs. marketplace.................................................................................................28 2-8. SA vs. marketplace (*** 3VVV ).........................................................................29 2-9. SA vs. marketplace (** 3VV ).................................................................................30 2-10. Marketplace is optimal...........................................................................................32 2-11. SA dominates for small integration cost while marketplace dominates for large integration cost.......................................................................................................32 2-12. Strategic alliance is optimal....................................................................................32 3-1. Web service execution model..................................................................................37 3-2. Linear city model.....................................................................................................4 1 3-3. Marginal customer in LC model..............................................................................42 3-4. Unit circle model.....................................................................................................4 5 4-1. Web service supply chain.........................................................................................54

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x 4-2. Model of subs cription and listing fee......................................................................60 4-3. Proportion of Subscribers when v ....................................................................66 4-4. Optimal subscription fee when v .....................................................................71 4-5. Optimal profit when v ......................................................................................71 4-6. Proportion of subscribers when v ....................................................................73 4-7. Optimal subscription fee depends on when 2122 ..................................77 4-8. Optimal subscription fee depends on when 22 ...........................................77

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xi Abstract of Dissertation Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ECONOMICS OF WEB SE RVICE PROVISIONING: OPTIMAL MARKET STRUCTURE AND INTERMEDIARY STRATEGIES By Qian Tang August 2004 Chair: Hsing Kenneth Cheng Major Department: Decision and Information Sciences The Web services technology empowers a se rvice-oriented arch itecture featured by “Just-in-Time” software integration and “on-demand” software provisioning. My objective is to study the impact of this new tech nology on firm strategies. To the best of my knowledge, mine is among the first studies of optimal strategies for offering Web services from an economic perspective. First I address the optimal market st ructure to provide complementary Web services. In particular, three market st ructures are compared: independent service vendors; strategic alliance; and marketplace. The optimal market structure with different integration cost and market settings is derived. The model incorporates the integration cost that was not considered in previous literature on physical product bundling. Results indicate that in the context of Web service integration, a We b service marketplace (which corresponds to a structure of mixed bundling of physical goods) is not necessarily always the best market structure.

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xii Next, I consider the optimal location a nd pricing problem of a Web services intermediary (WSI) that sells an integrated time-sensitive We b service. My study differs from previous research on facility locations in that I solve for the optimal location and price of the integrated Web service simulta neously. Two spatial models are analyzed, first a linear city model and then a unit circle model. I show that the integrated Web service is optimally located between the Web service vendors and the WSI should charge a penetration price if the delay cost is low. In addition, there could be multiple optimal locations for the WSI if the service vendors are located far away. Finally, I analyze the optimal subscripti on and listing fee for a WSI that provides value-added service, such as aggregation services and technica l services. My study extends current research on information inte rmediaries by considering multiple groups of Web service vendors and consumers. Analyses suggest that the intermediary is best off by setting the listing fee such that all servic e vendors list on it. Further, the optimal subscription fee is determined by network in tensity, value of technical services, and properties of Web services.

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1 CHAPTER 1 INTRODUCTION The Web services architecture represents a new computing paradigm that allows for the distribution, discovery, production and consumption of loosely coupled software components over the Internet. The objective of the dissertation is to study the optimal strategies for providing Web services from an economic perspective. In particular, I aim to address three research problems: op timal market structure for providing complementary Web services; optimal location and pricing of an integrated Web service provided by a Web service intermediary; op timal subscription and listing fee charged by a Web services intermediary in a Web service supply chain. The organization of the dissertation is as follows. Chapter 1 first gives a general introduction of the Web serv ices technology, which includ es the evolution of Web services and its technical characteristics. Th en I discuss the business implications of Web services and introduce the research issues. Major findings are summarized at the end of Chapter 1. In Chapter 2, I examine the problem of optimal Web service market structure. Chapter 3 solves the joint decision problem of optimal location and pricing for an integrated Web service with the analysis of two spatial models. Chapter 4 analyzes the optimal subscription and listing fee charged by a Web service intermediary. In each chapter, I provide literature revi ew and discuss the relevance to and distinctions from this research. Managerial insights are interpreted after presenting results from the analytical and numerical studies. Chapte r 5 concludes this dissertatio n with discussion on future research plans.

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2 1.1 Web Services: History and Overview The business world is undergoing a globa lization trend. Firms are expanding their territories into new markets ab road to create growth. Suppl y chains are being established among partners in different geog raphical regions to increase s ourcing efficiency. To reap the full benefit of globalization, a firm oftentimes needs to st andardize or reengineer its business processes, which requires the integrat ion of various information systems built on different platforms at different times. Furt her, the globalization of business requires a distributed computing environment that allo ws companies to take advantage of the computing power at various operational units regardless of geogra phical location and platform. Web services, a recent paradigm of computing, represents the most promising solution to date to addressing the challenge of distributed enterprise computing required by the globalization of business. Hailed as revolutionary, Web Services tec hnology came along an evolutionary path of growth. There has been continuous effort to improve the reusability, flexibility and interoperability of information systems. The advent of object-oriented languages makes it possible to encapsulate functionality in software components called “objects.” Sun Microsystems introduced the platform-indepe ndent, bytecode-based Java language so that programs can be downloaded and run anyw here in the world. Microsoft catered to this componentization trend by providing the Object Linking and Embedding (OLE) technology. As Internet popul arity grows and network tech nology matures, firms began to seek solutions for distribut ed computing. Yet the problem of incompatibility soon gets in the way when it comes to the collaboration and interaction among heterogeneous systems. Sun’s Java Remote Method Invocation (RMI) over Internet Inter-Orb Protocol (IIOP) aims to deliver distributed compu ting capabilities, but it is overwhelmingly

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3 complex and requires of Java end nodes. Likewise, Distributed Common Object Model (DCOM) proposed by Microsoft works only on Windows platforms (Schmelzer 2002). For the first time, Web serv ices technology poses a promis ing solution to the “plugand-play” Holy Grail based on open standa rds and the decomposition of software application. According to the Stencil Group (Sleeper 2001 ), Web services are “loosely coupled, reusable software components th at semantically encapsulate discrete functionality and are distributed and program matically accessible ove r standard Internet protocols.” From a technical perspective, Web services represent a collection of standard protocols for the creation, distribution, discove ry and integration of semantic software components that encapsulate business functio nalities. The key to Web services is dynamic software discovery and just-in-time software service cr eation (ingtegration) through the integration of loosely coupled so ftware components. Central to the Web services architecture are the concepts of soft ware as service and platform independence. 1.1.1 Software as service As opposed to packaged monolithic applicat ions that must be written or licensed, Web services encapsulate specific business f unctionalities that can be “rented” over the Internet. The idea of software as services dates back to the provision of application as services by Application Servi ce Providers (ASP). But the Web services are not merely a newer version of ASP. Traditional ASP usua lly provides complex application systems, like Enterprise Resource Planning (ERP), th rough proprietary connection. Web services decompose business processes into granular components and thus allow customers to select services on an as-needed basis (Sharm a and Gupta 2002). Further, Web services are distributed over the Internet, while trad itional ASPs host their applications on a centrally located server. Fi gure 1-1 shows an example of Web service application for a

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4 travel agency, which invokes several softwa re modules (Web services) through the Internet to complete a vacation-planning process. The Web services involved might be written in different programming languages and distributed on different systems. Figure 1-1. Example of Web service application The service-oriented architecture also opens new business opportunities for firms because it allows firms to sell their so ftware components as Web services over the Internet. For example, CitiBank develope d CitiConnect, a payment-processing Web service that can be plugged into other comp any’s transaction process (Hagel and Brown 2001). The spectrum of Web services spans from personal services, such as stock quote, messaging services; to enterprise-centric serv ices, such as call center control, payroll management, and shipping and logistics. 1.1.2 Platform independence The economic globalization and the continuously cha nging business environment necessitate an interoperable and flexible computing infrastructure. Web services technology can be used to create a pl atform-independent distributed computing environment, since it is built on a set of uni versally agreed upon standards such as XML, WSDL, SOAP, UDDI, and other specifica tions developed by various industrial consortiums. The Extensible Markup Language (XML) protocol allows self-describing Customer Credit Card Web Service Airline Web Service 1 Airline Web Service 2 Car Rental reservation Web service Hotel reservation Web service uses invokes Travel Agency

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5 data to be exchanged independent of pl atform and language. The Web Services Description Language (WSDL) builds on XM L and specifies how Web services can communicate with each other. The XML-ba sed messaging protocol Simple Object Access Protocol (SOAP), recently renamed Services-Oriented Architecture Protocol, supports the invocation of software compone nts over existing netw orks like HTTP and FTP in a fashion similar to Remote Procedur e Call (RPC). The Universal Description, Discovery and Integration (UDDI) specifi cation specifies the mechanism for the description, registration, dynamic lookup and integration of software components. 1.1.3 Integration of Web services In essence, the Web servi ces architecture represents a platform-, language-, and vendor-neutral framework for the interaction and integration of software components via standard networking technologi es. Figure 1-2 shows how the Web service protocols work together to compose two complementary Web services: the customer relationship management (CRM) Web service and the enterprise resource planning (ERP) Web service (Samtani and Sadhwani 2001). In the example, users request information about a particular person. The request is first handled by a UDDI server, which looks up its registry for relevant services that handle user information. Both CRM and ERP services are found. The UDDI server then forwards the location and WSDL information of the two services to an application server, whic h invokes the services to retrieve requested information. All communications between the application server and the two services are based on the SOAP protocol. In the end, the retrieved information is sent back to the user. The location of the services is transp arent to the user who may not even be aware that two services are involved in the process.

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6 Figure1-2. Web service integration 1.2 Business Implications of Web Services Despite the debate over Web services’ advantages and disadvantage, Web services are definitely in production in the software industry. Many software vendors are rushing to reengineer their product offerings a nd provide Web-service-savvy products (Kreger 2003). For example, Amazon.com is provid ing tools to let its sales associates, booksellers and developers develop Web servi ces to take advantage of the services offered by Amazon (such as inventory mana gement, book reviews). GM used a Webservice-based platform to act as a translator between its ol d and new systems and realized a reduction of $1 billion on software support (Welch 2003). ZapThink, a Waltham (MA) consultancy that tracks the growth of Web se rvices market estimates that spending on Web services technology was $1.8 billion in 2002; and projects an over $5 billion investment in year 2003 (Salkever, 2003). The application of Web services varies from cost-cutting projects to establishing service-or iented architecture within an enterprise or between business partners (Ferris and Farrell 200 3). To study the impact of Web services

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7 on firm strategies, let’s first examine the benefits of Web service from a business perspective. 1.2.1 Reduction of integration cost The standardizing of protocols has profound business implications. One of the key benefits of Web services lies in reduced integration cost because of the openness of technology. Firms are relieved of the comple xity of integrating a pplications built on different platforms or located on different ne tworks. The applica tion of Web services often involves the integration of disparate software components, either within an enterprise or among trading partners. Internally, the Web Service architecture ch anges the fundamental cost structure of Enterprise Application Integration (EAI). A firm can decrease development cost and duration dramatically by leveraging existing systems and outsourcing standard modules. Externally, Business-to-Business (B2B) integrat ion or collaboration is made more cost efficient because the firms no longer have to set up a separate inte gration project with each business partner. Thus, business alliances can be created and decoupled on the fly. 1.2.2 Service-oriented architecture The service-oriented architecture (somet imes called e-servi ce) is defined as offering software components as services that can be purchased or rented over a network, such as the Internet. As standardized, self -describing application modules that can be described, published, located and invoked over the Internet, Web services form the foundation of a service-oriented architecture to support universal a pplication integration (Rust and Kannan 2003). For example, Huang and Chung (2003) proposed a framework of application integration based on Web serv ices technology, addressing issues such as security, transaction cont rol, and reliability.

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8 The essence of a service-oriented archit ecture lies in “service on demand” and “just-in-time” service in tegration. Firms benefit from th e service-oriented architecture powered by Web services in many ways. For ex ample, firms have the ability to choose components that best match the company’s bus iness processes, to tailor components to the individual needs of business units, and to incrementally pay for the overall system (Fingar 2000; Sundarra j and Talluri 2003). 1.2.3 Web service intermediary Although Web services are designed to be platform-independent, they leave unspecified the context necessary for servic e integration on the process level. For example, the Web service consumers have to define the order of sequence, control information flow, exceptions handlings, and tran sactional integrity enforcement, just to name a few (Cubera et al. 2003, Little 2003). Furt hermore, a directory service is required if run-time discovery and integr ation are to be materialized. To help realize the “plug-and-play” serv ice-oriented architecture, a new business model, referred to as the Web service interm ediaries (WSI), has seen rapid growth in practice in industry recently. A WSI provide s value-added services including directory and search engine, auditing, quality of se rvice (QoS) assurance and integration and orchestration of Web se rvices. For example, Salcentral.com (which originally called itself “the Napster of Web serv ices”) provides a Web service search engine and tools for developing and integrating We b services. Maintenance of Web services is made more simplified since the WSI hosts the latest versi on of Web services and offers tools so that the Web service producers can manage and c ontrol their Web services. By offering aggregation services and technical support for Web service development, management

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9 and integration, a Web service intermediary be nefits both ends of the Web service supply chain: the Web service vendors and consumers. 1.3 Research Issues With growing adoption of Web services technology, the research landscape in many fields, such as software engineering and management, is about to experience a major shift. For instance, the Web service technology emphasizes on reducing the size of software components while at the same ti me introduces new issues on Web service integration. The execution of Web services over the Internet causes a network delay not associated with traditional standalone systems. Thus, Web services technology could change software development and marketin g strategies, such as software quality, development costs, and pricing. Therefore, it is imperati ve that information systems researchers delve into this new computing pa radigm and provide useful insights to guide business practice. At the same time, it should be noted that the ultimate value of Web services technology is not in itself. Ra ther, it lies in the productivity gain and the creation of new business opportunities due to its reusability, flex ibility and interoperability. My objective is to study the impact of Web services technology on firm strategies and software development with the use of economic models. In particular, I aim to analyze the Web services technology from the following three perspectives. 1.3.1 Optimal market structure By building on standard technologies, Web services technology enables dynamic software integration that is essential to ente rprise application inte gration and business-tobusiness integration. Therefore, a natural star t for Web services research is to examine

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10 the impact of integration cost when co mposing Web services of complementary functionalities. Several research questions arise when We b services are exploited for application integration. First, does a software producer al ways benefit from the interoperability of Web services? Second, how should software producers take advantage of Web service technologies and optimally provide comple mentary Web services to maximize total profit? Finally, should firms offer their soft ware components separately, form a strategic alliance to provide a composite Web service or establish a marketplace that sells both individual and composite Web services? Th e first part of my study answers these questions by analyzing the optimal market structure for providing two complementary Web services. 1.3.2 Optimal location and pricing of an integrated Web service Besides traditional functions such as matchmaking, aggregation of demand and supply, providing trust, and offering market ch aracteristics to supplie rs and consumers as in the electronic in termediaries (Bailey and Bakos 1997, Bakos 1998), a Web service intermediary (WSI) has several unique features worthy of special interest. For instance, unlike traditional electronic intermediaries th at generally offer static information, a WSI can participate activel y in the Web service supply chain by offering dynamic information goods such as Web services. In the second part of the dissertation, I study the optimal strategy of a Web service intermediary that sells an integrated Web service to compete with Web service vendors. As discussed previously, the composition of We b services incurs an integration cost. Therefore, a Web service consumer interested in the composite Web service must take into account the integration co st when deciding whether to buy the integr ated service

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11 from the WSI or integrate by oneself the Web services bought separately from the Web service vendors. On the other hand, the exec ution of Web services on remote servers causes a response delay that consists of pr ogram running time and network delay. As a result, a Web service consumer must factor in the delay cost when deciding whether to “buy” (or execute) the integrated Web service from th e intermediary. Since the network delay is related to the physical distance between the Web service intermediary and the client, the optimal strategy of the WSI requi res solving the joint decision problem of location and pricing of the integrated Web service. Specifically, I seek to address the follo wing research questi ons. What is the optimal pricing strategy for the composite Web service: a penetr ation price or a high price? Where should the WSI in competitio n with two individual service providers host its integrated Web service? Should the WSI be located close to the service providers or stay away from the service providers to avoid competition? 1.3.3 Optimal subscription and listing fee charged by a WSI Depending on technical strengths and busin ess scope, Web service intermediaries can play various roles in linki ng the service vendors and service requestors. For example, Salcentral.com maintains a comprehensive directory of Web services so that service consumers can browse, search for, and a udit particular Web services. Another Web service intermediary, GrandCentral.com provides a centralized Web service network and acts as a trust broker that handles all the issues around message de livery and routing, security, etc. In summary, a WSI provides value-added services to both Web service vendors and Web service requestors, allowing it to charge fees to both sides of the Web service supply chain.

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12 Finally, I focus on studying the optimal st rategy for a Web service intermediary that provides aggregation and technical serv ices to Web service vendors and consumers. The Web service intermediary ch arges fees to both sides of th e Web service supply chain. I address the optimal pricing strategies for a Web services intermediary in a supply chain of complementary Web services. That is, the WSI serves multiple groups of Web service vendors and consumers. The following research questions are explored. First, what are the optimal subscription fee and listing fee? Second, should the intermediary subsidize one side of the market to maximize its profit? If so, which side of the market should the intermediary subsidize? Next, what is the im pact of technical stre ngth and intensity of network effect on the intermediary’s pricing st rategy? Finally, how do the characteristics of Web services affect the optimal strategies of the intermediary? 1.4 Summary of Major Findings To analyze optimal market structures I compare three market structures– independent service vendors (IS V), strategic alliance (SA) a nd Web service marketplace. Analytical results and computational explor ations suggest that Web service vendors benefit from the integration of Web services because the Web service marketplace always dominates the ISV market structure, regardle ss of the integration cost. The optimal market structure is determined by the integr ation cost, and the va luations and market potentials of the individual and composite Web services. As the valuation of the integrated Web service is sm all, the service providers pr efer marketplace to strategic alliance. For larger valuation of the integrat ed software service, service providers prefer marketplace if the integrati on cost is high; while strategic alliance dominates if the integration cost is low. If the valuation of the integrated software service is sufficiently high, strategic alliance becomes th e optimal market structure.

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13 The joint decision problem of optimal loca tion and pricing for an integrated Web service provided by a Web services intermed iary (WSI) is analyzed with two spatial models. In general, optimal location and pr ice depend on integration cost, delay cost and prices of the individual Web se rvices. In a linear city mo del, I show that the midpoint position between the service providers is always the optimal location for the integrated Web service. Furthermore, when the delay cost is quite small, the best pricing strategy is to charge a penetration pric e to capture entire market demand. On the other hand, the WSI should charge a high price to share the ma rket with the service providers when the delay cost is high. Analysis of a unit circle model show s that the WSI is optimally located midway between the service providers and charges a market -covering price when the delay cost is small. In addition, if the distance between the servic e providers is large, there are multiple optimal locations for the integrated Web service. Finally, I find that in the presence of cro ss network externalities between two ends of a Web service supply chain, a WSI always has incentive to subsidize the service vendors by setting a low listing fee that induces all servic e vendors to list their Web services on it. On the other hand, the interm ediary may choose to attract only portion of the Web service consumers, depending on the relationship between the intensity of cross network externalities and consumer’s valu ation of the WSI’s va lue-added technical services. If the consumers va lue the technical services more than the network effect, the optimal subscription fee and profit are incr easing in network effect. Furthermore, the WSI should allow more consumers to subscr ibe to its service as network effect intensifies. The optimal subscription fee is also affected by the nature of Web services provided by the service vendors, such as the pric es of Web services and market potentials

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14 of the composite Web service. For example, if the market potential of the composite Web service is large and the consumers have high valuation for the technical services, the intermediary should never set a low subscriptio n fee to attract all consumers to subscribe when the consumers value the network effect more than the technical services. On the other hand, the WSI should set the subscriptio n to allow all Web service consumers to subscribe if the market potential of the composite service is smaller than the market potentials of the two individual Web serv ices and the network intensity is high.

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15 CHAPTER 2 OPTIMAL WEB SERVICE MARKET STRUCTURE In this chapter, I study the optimal mark et structure for providing complementary Web services when consumers incur an in tegration cost to compose multiple Web services. In particular, I compare three market structures for two software vendors offering two complementary Web services: (1) the independent service vendors (ISV) structure in which the two firms sell Web serv ices separately; (2) the strategic alliance (SA) structure in which the two firms establ ish partnership to offe r an integrated Web service; and (3) the Web services marketplace structure in which the two firms sell both the individual and the composite Web services. One example of complementary Web services is a school calendar scheduling Web service and a weather forecast Web service. The calendar-sch eduling Web service can be integrated with the weather forecast Web serv ice so that the school can schedule certain events (such as such as football games and skiing competitions) in accord with the weather conditions. Another example of Web services that can be integrated is an inventory management system and a purchas ing system. The two systems can work together so that purchasing orders can be au tomatically generated when the inventory is below a certain level and the inventory can be updated in r eal time when purchases are made. This chapter is organized as follows. First, I review lite rature on physical good bundling and integration, followed by discussi on about the uniqueness of Web service integration. Then I present th e models of three market st ructures and derive profit

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16 functions in each market structure. After th at, the optimal market structure is found by comparing the profits of three market structur es with varying integration cost and market conditions. The analysis includes analytical study of a simplified model and numerical explorations of the general model. 2.1 Related Literature The bundling strategy for complementary pr oducts and services has been an active research topic in marketing and economics Economides and Salop (1992) study the optimal market structure in light of the tradeoff between vertical integration of complementary products and horizontal competition among substitutable composite products. They model a market of two co mplementary products, each provided by two firms. A number of market structures, which differ in the degree of competition and integration, exist under different combinations of the four firm s. Their research focuses on comparing the equilibrium price of the composite product under different market structures. Matutues and Regibeau (1992) study the opt imal strategy on product compatibility and bundling with a spatial mode l in which consumers have heterogeneous fit cost (or taste) of different product components. Th ey set up a model of two firms each selling two complementary products. Farrell and Katz (2000) study a market composed of a monopoly offering one product component and se veral companies (pos sibly including the monopoly) offering another complement ary product component. Their study concentrates on the monopoly’s incentive to “squeeze” the producers of the other product component by means of pricing, product i nnovation, or exclusive trading rules. Implications on social welfare are explored.

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17 Venkatesh and Mahajan (1993) examine the optimal pricing stra tegy under three bundling strategies: pure component, pure bundling and mixed bundling with a probabilistic model. Similar bundling stra tegies are analyzed by Chuang and Sirbu (1999) with application to N goods (journal articl es). Both papers (Venkatesh and Mahajan 1993, Chuang and Sirbu 1999) conclude that the mixed bundling strategy yields maximum profit for firms. Several unique features of Web serv ices technology necessitate a different approach to the study of Web service integration. First, Web services are essentially programmable software components. Unlike physical goods that ca n be bundled (or put together) at ease, the integration of two arbitrary software co mponents requires both human expertise and financial resources beca use of the complexity of addressing the platformand language-disparities, such as reco ding of data and/or ap plication interfaces. In essence, the integration of so ftware components introduces an integration cost not considered in all previous research on product or service bundling strate gies to the best of my knowledge. Second, Web services technology provides th e flexibility of selecting software components on an as-needed basis. In ot her words, there are demands for both the individual and the composite Web services. Many previous studies focus on the market structure for the composite product while ignoring the demand for the individual components (Economides and Salop 1992, Matutues and Regibeau 1992, Farrell and Katz 2000). Third, an integrated software application is usually a new software product that is indivisible and has different value and func tion than simply adding the values and

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18 functions individual software components. Product bundles, on the other hand, are put together without change to each component. An analogy of integrated Web service is alloy wheal, which is made from tin and aluminum. Many previous works on product bundling treat the value of the bundled product as the sum of values of the components, which is referred to as the assumption of strict addition. Typically mixed bundling emerges as the profit-maximizing strategy (Venkatesh and Maha jan 1993, Chuang and Sirbu 1999). As I will show later, mixed bundl ing is not necessarily always optimal for Web services. Although Venkatesh and Kamakura (2003) rela x the strict addition assumption and consider contingent valuation for bundling complementary and subs titute products, their result can’t be applied to Web services dire ctly because of two reasons. First, unlike physical goods, the marginal cost of providing one copy of Web service is negligible. Second, since the integrated We b service is indivisible, it is impossible to buy the integrated software and if the consumer is only interested in one component. Likewise, the papers by Bakos and Bryjolfsson (1999, 2000) have to be modified to fit in the context of Web service integration. I addr ess the impact of integration cost on Web service market structure by taking into account different the demand of both the individual and the integrated Web services. 2.2 A General Model Consider two service vendors selling two di stinct but functionally complementary software components (S1 and S2). Component s S1 and S2 can be integrated into a composite service (S3). Correspondingly, the potential buyers of Web services are classified into three groups: the potential buyers of S1, the potential buyers of S2, and

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19 potential buyers of S3. Let the size of the three groups of potential buyers be 1Q, 2Q and 3Q. The buyers in each group have a homoge neous reservation price (valuation) of 1V, 2V and 3V for S1, S2, and S3, respectively. I consider three market structures that can be adopted by two profit-maximizing Web service vendors. In the first market structure, independent service vendors (ISV), the two firms sell the Web services independen tly. Consumers interest in the composite service must integrate S1 and S2 by themselv es and thus incur an integration cost of c. In the second market structure, strategic alli ance (SA), the two firms form an alliance to sell only S3, the integrated Web service. In the third market structure, Web service marketplace (Marketplace), the two firms sell S1, S2, and S3. There is no integration cost for customers in the SA or Marketplace structures. The two service vendors seek the optimal market structure to maximize their profits. Intuitively, the valuations of the thre e services, the sizes of the three groups of customers, and the integration cost will affect the service vendors’ decisions. Of special interest is the impact of the integration co st, since one key benefi t from Web services technology is reduced integra tion cost. I develop an economic model to examine the optimal market structure for the Web service vendors with respect to various integration costs. To reflect industry reality, two assumptions are ma de regarding the integration cost and service valuations. First, I assume that the composite Web service is valued more than any of the individual services alon e. Second, I assume the integration cost can not exceed the values of each Web service. In summary, 30icVV 1,2 i (2.1)

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20 2.2.1 Independent service vendors Under the independent service vendors (ISV) market structure, each service vendor sells its software component at price 1 s P and 2 s P (Figure 2-1). The demand for each of the Web services S1 (or S2) is composed of two groups of buyers – the buyers interested in S1 (or S2) and the buyers who are interested in the composite Web service (S3). Figure 2-1. Indepe ndent service vendors I adopt a linear demand function as in Park er and Alstyne (2001) to calculate the number of buyers of S1, S2 and S3, denoted by 1 s q 2 s q and 3 s q as follows. si s iii iP qQQ V 1,2i (2.2) 12 333 3 ss sPPc qQQ V (2.3) Note that in the ISV market structure, buye rs of the composite Web service have to spend an integration cost of c, see Eq. (2.3). Accordingly, the demands of each service vendor 1 s D and 2 s D are 3 s isisDqq 1,2 i (2.4) Both Web service vendors seek to set the pr ice of their products to maximize profit, which is formulated in the following problems. S1 S2 Web Service Vendor 1 Web Service Vendor 2 2 s P Customers of S1 S2 and S3 1 s P

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21 12 33 3()()max sisiss sisisisisiPii iPPPc PDPQQPQQ VV 1,2 i (2.5) By solving the first order conditions of both service vendors simultaneously, one derives the equilibrium optimal prices of Web services S1 and S2 in Eq. (2.6). Subsequently, the optimal profits of the two service vendors s i can be calculated by plugging the prices in Eq. (2.6) into the profit function. Unfortunately, the complexity of s i prohibits an explicit disp lay of its functional from. 2222 1232331323233323322323 1 1 22 12313232313312 2222 1231332313133313311313 2 2 1231323232222 4443 2222 444s sQQVQQVQQVVcQQVQVVcQVQQVV PV QQVQQVVQQVVQVV QQVQQVQQVVcQQVQVVcQVQQVV P QQVQQVVQQV 2 2 133123 V VQVV (2.6) It should be pointed out, however, that if the integration cost is prohibitively high, there could be no demand of th e integrated product, i.e., 30sq In that case, the service vendors’ profit-maximizing problem reduces to ˆˆ ˆ ˆ max ()sisi s isiii P iP PQQ V ,1,2 i (2.7) By inspection, the optimal prices a nd profits in case of no demand for the composite Web service are *ˆ 2i siV P *ˆ 4ii siVQ 1,2 i (2.8) Summarizing the results of Eqs. (2.6) and (2 .8), one gets the optimal profits for the each of the independent service vendors in the ISV market structure as follows. ***ˆ max{,} s isisi 1,2 i (2.9)

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22 In order to make the three market strate gies comparable, I take a “macro” market view of the independent service vendors. That is, I use the sum of the independent service vendor’s optimal profits as a m easurement of the goodness of ISV market structure. The optimal profit of th e ISV market strategy is defined as *** 12 s ss, where s i (1,2i) are described in Eq. (2.9). 2.2.2 Strategic alliance Under the strategic alliance (SA) market structure, the two Web service vendors form a strategic alliance by integrating the Web services into one composite service S3, see Figure 2-2. To distinguish from the Web services marketplace strategy, I assume that the composite service is not divisible. That is, the potential customers of the strategic alliance are those who are interested in the composite Web service S3. Figure 2-2. Strategic alliance As described in the introduction, one key advantage of the Web services technology lies in the easy integration of soft ware components. Therefore, I assume that the SA incurs a minor one-time integration co st to produce the composite service. In addition, the cost of providing the integrat ed Web service is negligible due to the technological simplicity of Web services, i.e ., the marginal cost of providing one more copy of the composite Web service is assumed to be zero. The strate gic alliance sells the aP Customers of S3 S1 S2 S3 Strategic Alliance Integrate

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23 composite service at price aP. The consumers don’t incur additional integration cost when they buy S3 from the SA. Since the in tegrated Web service is indivisible, the potential buyers of the SA are the buyers of S3. The strategic alli ance seeks to optimally set the price of the composite service (aP) to maximize profit, which is formulated as 33 3()max aa aaPP PQQ V (2.10) Solving the first order conditi on of Eq. (2.10) yields the optimal price and profit of the strategic alliance, summarized as follows. 32aV P 334aVQ (2.11) 2.2.3 Web service marketplace The third market structure, Web service marketplace (Marketplace), can be viewed as a combination of the ISV and SA market st ructures, where three types of services–S1, S2 and S3 are offered, see Figure 2-3. Similar to the strategic alliance, the “sunk” cost of composing Web services by the marketplace is negligible and the marginal costs are assumed to be zero. Figure 2-3. Web service marketplace Under the Web services marketplace mark et structure, the Web service vendors seek to optimally set the prices of the Web services, 1 mP 2 mP and 3 mP to maximize total Customers of S1, S2, S3 Web Services Market p lace S1 S2 S1 S2 integrate S3 123,,mmmPPP

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24 profit. To ensure that the demand for the co mposite Web service S3 is non-negative, the price of the composite Web service sold by th e marketplace cannot ex ceed the total cost of creating S3 by the cons umers themselves, i.e., 312 mmmPPPc Put in math, the optimization problem in the Marketplace structure can be formulated in Eq. (2.12). 123312123 111222333 ,, 123max()()() s.t mmmmmmmmm m mmm PPPPPPcPPP PQQPQQPQQ VVV (2.12) The constrained profit maximization problem defined in Eq. (2.12) can be solved using KKT conditions. The similar approach is used in proving Lemma 2-3 in the next section. The optimal total pr ofit under the Web service mark etplace market structure is described as follows. 312 312 112233() if 2 () 1 () if 42m mVVV c VVV VQVQVQc (2.13) where 222222 121313122123232131323231 132123231 2 123312231312 1321232314() (4444222) 4()mQQVVQQVVQQVVQQVVQQVVQQVV QQVQQVQQV QQQcVcVcVcVVVVVV QQVQQVQQV 2.3 Analytical Insights from a Simplified Model The best profit-maximizing market struct ure for the Web service vendors is found by comparing the service vendors’ total profit in the three market structures. The analysis of the general model in the previous section suggests that the service vendors’ optimal strategy is determined by several fact ors – the size of pot ential buyers in each group, the valuations of the se rvices and the integration cost However, the mathematical

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25 complexity of the general model, especially the optimal total profit of the independent service vendors (see the optimal prices in Eq. (2.6)), makes the analysis of optimal market structure technically intractable. Therefore, the following two simplifying assumptions are made to modify the genera l model in order to gain insights from analytical study. First, I assume that ther e are (approximately) equa l number of potential customers of S1 and S2. Secondly, it is assu med that the potential customers of S1 and S2 have (approximately) equal valuations of the two services. By imposing the assumptions, we can focus on studying the impact of integration cost on optimal market structure first. The assumptions are specified mathematically as follows. 12QQQ and 12VVV (2.14) In the simplified model, I study a symmetr ic market where the two complementary services are valued equally and both Web service vendors enjoy the same market potential. At the same time, the relationship between the integration cost and the valuations of the services, described in Eq. (2.1), still holds in the simplified model. Plugging Eq. (2.14) into the general model, I derive the optimal total profits under the three market structures in the context of a symmetric market, which are summarized in the following lemmas. Lemma 2-1. In a symmetric market, the optim al total profit under the independent service vendors (ISV) market structure is specified in Eq. (2.15). In addition, s is decreasing and convex in the integration cost c. **1 max, 2 s sVQ (2.15) where 22222 33333333333333 2 3332()() (23)sVVQVQcQVQVVQQVQQVVQcVQQcVQ VQVVQ

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26 Figure 2-4. Optimal profit of ISV Lemma 2-2. In a symmetric market, the optim al total profit of the strategic alliance is specified in Eq. (2.11). Proof. Because the strategic alliance sells the composite Web service only, the valuation and market size of the individual Web services won’t affect the strategic alliance’s profit. Q.E.D. Lemma 2-3. In a symmetric market, the optimal total profit of the Web services marketplace is 3 3 332 if 2 2 11 if 242m mVV c VV VQVQc (2.16) where 2 33 33 33(22) 11 244(2)mQQVVc VQVQ QVQV Lemma 2-4. m is increasing and concave in the integration cost c. m is increasing in the valuation (3V) and market potential (3Q) of the composite service. In addition, the total profit of the marketplace is at minimum when 0 c which is described in Eq. (2.17). 2 33 33() (0) 2(2)mVVQQ c QVVQ (2.17) s /2 VQ s Inte g ration cost ( c ) 0

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27 Figure 2-5. Optimal profit of marketplace w.r.t. c Lemma 2-5. In a symmetric market, the op timal profit of the Web services marketplace takes the form of m when 34VV ; the optimal prof it switches from m to 3311 24VQVQ when 324VVV ; the optimal profit takes the form of 3311 24VQVQ if 32VVV Proof. Lemma 2-3 suggests that the op timal profit of the Web services marketplace is bimodal, which de pends on the relationship between V, 3V and c. According to Eq. (2.16), the optimal profit is m if 32 2VV c while the optimal profit is 3311 24VQVQ if 32 2VV c Recall our assumption in Eq (2.1) that the integration cost can’t exceed the values of We b services being integrated, i.e., 0 cV accordingly, there are three possible functi onal forms of the profit with respect to 3V. In particular, if 34VV 32 2VV c is always satisfied; if 32VVV 32 2VV c is always satisfied; if 324VVV either 32 2VV c or 32 2VV c applies. Q.E.D. Lemma 2-1 to 2-4 describes the optimal profits under three market structures. Figures 2-4 and 2-5 plot the behavior of the profit under the ISV and marketplace market Integration cost Profit m 33/2/4 VQVQ

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28 structure with respect to integration cost re spectively. Furthermore, Lemma 2-5 specifies three functional forms of profit of the marketplace under the assumption that 0 cV Correspondingly, Figures 2-6 (a), (b), a nd (c) illustrate th ese three cases. (a) (b) (c) Figure 2-6. Optimal profit of marketplace w.r.t. V3 (a) 34VV (b) 324VVV (c) 32VVV Given the optimal profits under three market structures, one can derive the optimal market structure for the Web service vendors wi th respect to different integration cost (c) and market characteristics (market sizes and va luations of Web services). Propositions 26 to 2-9 summarize our key findings. Proposition 2-6. The service vendors are always be tter off under the Web services marketplaces than staying as independent service providers regardless of the integration cost. Figure 2-7. ISV vs. marketplace s (ISV) /2 VQ Inte g ration cos t Profit 33/2/4 VQVQ m (Marketplace) Profit c V m Profit c V m 3311 24 VQVQ Profit c V 3311 24 VQVQ

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29 Proposition 2-7. When 3VVV Web services marketplace is the dominant market structure, regardless of th e integration cost. Specifically, 33(24)/() VQQQV (2.18) Proposition 2-8. When *** 3VVV, strategic alliance is the optimal market structure if the integration cost is below c; marketplace is the optimal market structure if the integration cost is above c. In particular, *V is defined in Eq. (2.18) and 22 333 3 342 1 22QVVVQQ cVV Q (2.19) 22 333 ** 3484QQQQQQ VV Q (2.20) Proposition 2-9. When ** 3VV, strategic alliance is the optimal market structure, regardless of the integration cost, where **V is defined in Eq. (2.20). Proofs of Propositions 2-6 to 2-9 are relega ted to Appendix A. Figure 2-7 depicts the total profit for the service vendors in th e ISV vs. marketplace structure. We observe that the marketplace yields more profit, rega rdless of the integratio n cost. Fi gures 2-8 and 2-9 give graphical il lustration of Propositions 2-8 and 2-9 respectively. Figure 2-8. SA vs. marketplace (*** 3VVV) *a (SA) 331 4VQ Integration cost Profit *m (Marketplace) c V

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30 Figure 2-9. SA vs. marketplace (** 3VV) 2.4 Computational Explorations From the simplified model, we observe that the integration cost plays a critical role in determining the optimal market structure fo r the Web service vendors. In addition, the optimal market structure also depends on the valuations and the sizes of market potential of the individual and composite Web services in a symmetric market. To complete our analysis on optimal market structure, one should consider the general cases where 12VV and 12QQ. Due to the technical intractability, we resort to numerical experiments to draw insights from the generali zed model in this section. In the numerical experiments, I focus on th e situations where the valuations and the market potentials of the indi vidual services are different. Without loss of generality, experiments are conducted assuming 12VV since one can always exchange Web service 1 and service 2 without changing the results. In addition, we run the experiments under the constraints specified in Eq. 2-1 (i.e., 312max{,}VVV,12min{,}cVV ). Insights from the numerical experiments ar e summarized in Ob servations 2-1 and 2-2. Table 2-1 describes the design of the e xperiments, which classi fies six combination scenarios of iV and iQ (1,2,3 i ). *a (SA) 331 4VQ Integration cost Profit *m (Marketplace) V

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31 Table 2-1. Design of numerical experiments Case 1V vs. 2V 1Q vs. 2Q 3Q vs. 1Q and 2Q 1 12VV 12QQ 312max{,}QQQ 2 12VV 12QQ 312min{,}QQQ 3 12VV 12QQ 132QQQ 4 12VV 12QQ 312max{,}QQQ 5 12VV 12QQ 312min{,}QQQ 6 12VV 12QQ 132QQQ Observation 2-1. Ceteris paribus, the optimal ma rket structure switches from Web service marketplace to strategic alliance as the valuation of the composite service (3V) increases. Further, the Marketplace alwa ys dominates the ISV market structure. Observation 2-1 suggests th at the results from the numerical experiments are consistent with the analytical stud y of the simplified model. When 3V is small, the service vendors are best off by implementing a marketplace, regardless of the integration cost. As the valuation of the composite Web service increases, the optimal market structure turns to a mixture of marketplace and strategic alliance, with the SA market structure as optimal for small integration cost and the Web serv ices marketplace as dominant for large inte gration cost. When 3V is sufficiently high, the Web service vendors always form a strategic alliance, regardless of the integration cost. Figures 2-10, 2-11, and 2-12 plot three examples of th e optimal market structure with varying integration cost and market conditions. Notice th at in all experiments, the marketplace always dominates the ISV, implyi ng that the service vendors always benefit from the interoperability of the Web services.

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32 Figure 2-10. Marketplace is optimal Figure 2-11. SA dominates for small inte gration cost while marketplace dominates for large integration cost Figure 2-12. Strategic alliance is optimal Legend Profit of the marketplace Profit of the SA Profit of the ISV

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33 Observation 2-2. The threshold of 3V for SA to surpass Marketplace is decreasing in potential market size of the composite service (3Q). In addition, the threshold value is smaller in a market where 12VV and 12QQ than in market where 12VV and 12QQ. Table 2-2. Optimal market structure w.r.t 3V Optimal Market Structure Scenario Parameter Values M SA if c for smaller c; M for bigger c SA 1 12 VV 312 QQQ 1.0, 0.6 12 VV 100,50,200 123 QQQ (0.6,3.9) [3.9,5.35] 5.35 1.0, 0.6 12 VV 100,60,40 123 QQQ (0.6,6.6) [6.6,8.4] 8.4 2 12 VV 123QQQ 1.0, 0.6 12 VV 100,60,50 123QQQ (0.6,5.9) [5.9,7.7] 7.7 3 12 132 VV QQQ 1.0, 0.6 12 100,40,60 123 VV QQQ (0.6,5.35) [5.35,7.0] 7.0 4 12 123 VV QQQ 1.0, 0.6 12 50,100,200 123 VV QQQ (0.6,3.8) [3.8,5.25] 5.25 5 12 312 VV QQQ 1.0, 0.6 12 60,100,40 123 VV QQQ (0.6,6.2) [6.2,8.0] 8.0 6 12 132 VV QQQ 1.0, 0.6 12 40,100,60 123 VV QQQ (0.6,4.95) [4.95,6.55] 6.55 Table 2-2 shows an example of optimal market structure with respect to 3V in six scenarios. For example, in the first scenario, we set 11V 20.6V (12VV ) and 1100Q 250Q 3200Q (312QQQ). The integration cost is restricted in the range of 00.6c The marketplace is the optimal ma rket structure regardless of the integration cost if the valuation of the composite service 3(0.6,3.9]V ; if the valuation of

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34 the composite service 3(3.9,5.35]V, the optimal market structure is dependent on the integration cost, with the strategic alliance as optimal for small integration cost and the marketplace as optimal for large integrati on cost; the strategic alliances becomes the dominant strategy for the service vendors if the valuation of th e composite service satisfies 35.35V, regardless of the integration cost. The threshold values of the composite service valuations (3V) in this experiment are 3.9 and 5.35, where the marketplace turns from less dominant to being dominated. Another interesting observation from the numerical experiment is that a more “balanced” market tends to favor the strategic alliance. This can be shown by comparing case 3, a polarized market where service vendor 1 has apparent advantages over service vendor 2 (12VV 12QQ ), against case 6, a balanced market where both service provider 1 a nd service provider 2 has certain market advantage (12VV 12QQ ). With the same market potential of the integrated service (3Q), the Strategic Alliance beats the marketplace if 37.0V in case 3 while the marketplace is dominate d by the strategic alliance if 36.55V in case 6. Observation 2-1 suggests that a higher valuation for the composite component (3V) tends to favor the strategic alliance. Observ ation 2-2 further descri bes how the threshold value is affected by the market potential of th e composite service. At first sight, this observation is somewhat obvi ous since the optimal profit of the strategic alliance is 33/4VQ, which is increasing in 3V and 3Q. However, the marketplace also sells the composite product and its optimal profit is al so increasing in the valuation and market potential of the composite service (see Lemm a 2-4). Observation 2-2 might result from the fact that the profit of the marketplace is restricted in its price setting policy

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35 312mmmPPPc. In fact, it can be proved that the optimal price of the composite software component in the marketplace is less than the optimal price in the strategic alliance, see Appendix A. This suggests that the service vendor s don’t necessarily always benefit from diversif ying their products. If the composite service is highly valuable and has a large market potenti al, the service vendor s are better off by establishing a strategic alliance selling simply the composite service. Another interesting observation from the numerical experiment s suggests that a more “balanced” market seem to favor the stra tegic alliance more than the marketplace. The market is balanced if one service vendor sells a valuable service with small market potential while the other service vendor sells a less valuable service with larger market potential. In other words, if the service vendor each has some advantage in service valuation or market power, th ey are more likely to form a cooperative strategic alliance. On the other hand, if the market is extrem ely asymmetric with one service vendor selling a highly valuable service and enjoying a larg e potential market, the service vendors are more likely to prefer the Web services market place, which gives them a certain degree of autonomy.

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36 CHAPTER 3 OPTIMAL LOCATION AND PRICING OF AN INTEGRATED WEB SERVICE While the Web services technology is a pr omising solution to bridging the platform discrepancy and geographical distance between software co mponents, some constraints may impede the widespread adoption of Web services and the service-oriented architecture. In particular, the performa nce of Web services is refrained by the computing power of local se rvers and the robustness and capabilities of the underlying network through which the Web services are distributed. From technical perspectives, the executi on of Web services follows the traditional Client/Sever paradigm. Figur e 3-1 illustrates an example of Web service consumption. First, the Web service client sends a pr ocessing request to a remote Web service application server. The Web service appli cation server locates the appropriate Web service and transforms (or “serializes”) the request into Web-services-compliant format (SOAP) and then forward to a particular Web serv ice to handle the request. The result is transmitted over the network and finally transformed (or “deserializes”) in a format understood by the end user. Overall, the response delay in Web service execution consists of the computation time at the se rver and the network delay. Sometimes, response time is especially important for time-sensitive applications such as stock quote and instant messaging. As the computing pow er keeps increasing at decreasing cost, in accord with the well-known Moore’s Law of co mputing, it’s not hard to predict that the network latency will become a prominent part of the response delay for applications distributed over the Internet. This chapter studies the impact of network delay on the

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37 pricing and location of an integrated Web service provided by a Web service intermediary (WSI). Figure 3-1. Web service execution model 3.1 Problem Description and Related Literature Consider two independent software vendors offering two complementary Web services denoted by S1 and S2. Let 1P and 2P be the price of S1 a nd S2 respectively. At the same time, a Web service intermediary (WSI) offers a time-sensitive composite Web service denoted by S3, which is developed by integrating S1 and S2. Examples of timesensitive Web services include stock quote, credit ch eck required for payment, and so on. The WSI charges a price of 3P for its integrated Web service S3. Customers who are interested in the inte grated Web service can either buy the composite service from the WSI or buy the two Web services S1 and S2 fr om the service provide rs and integrate by themselves, in which case the customer incurs an integration cost c. In the context of Web services paradi gm, the customers “buy” a Web service by executing the Web service hosted at the servi ce provider’s server. The customers do not own and house the software component. Cons equently, customers experience a response delay resulting from the turnaround ti me of executing the Web service. Web Service A pp lication Server WS Execution End-user request response request response SOAP Messages transpor t Internet

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38 In this chapter, we focus on the demand for the integrated Web service. Suppose the reservation prices of the customers for th e integrated service are sufficiently high so that all customers have one unit demand for the composite Web service and they either buy from the WSI or from the service vendors. A customer evaluate s the total cost to decide whether to buy the integrated Web se rvice from the WSI or buy the individual Web services from the service vendors and then create the integrated Web service by herself. To be more specific, the total co st to “purchase” the integrated Web service includes the price charged by the WSI (or the Web service vendor s), delay cost and integration cost (if the cust omer buys from the service ve ndors). In case a customer incurs the same cost to, the customer will buy the composite Web service from the WSI due to some valued-added services by the WSI. As the response time is a major concern for consumers of time-sensitive integrated Web service, it is important to analyze the composition of response time of applications over the Internet. According to Johansson, et al. (2000), the response time of applications over the Internet is co mposed of two parts local processing time and network response time. Local processing time is determined by the capacity of the local server and the request load. Network response time can be further divided into transmit time, queuing delay and network latency. Transmit time is related to network bandwidth while the queuing delay is determined by capacity of network devices and amount of data transmission jobs. Network latency is the time taken to transport data between two locations on the network and is generally a function of physical dist ance between the two nodes on the network. Johansson (2000) points out that while much research has been done on network design in consideration of network bandwidth and device capacity, the

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39 network latency is usually igno red in previous research. Since the cost of computing is halved roughly every eighteen months and the network infrastructure has seen an accelerating growth recently, th e network latency will become one dominant term of response time of applicat ions over the Internet. In an abstract sense, the Internet is compos ed of two parts, the cores and the edges. Local servers and client machin es are located at the edges, connected by the switches and routers at the core. The network latency fo r exchanging data between the server and the client is a function of number of routers and switches between them which is correlated with the physical distance between the server and the client. To model the impact of network latency on the response time of Web services, let t be the delay cost per unit of distance between the customer and the Web service provider where the distance corresponds to the number of rout ers and switches between them. The choice of optimal location for the WSI bears some resemblance to the facility location problem in previous literature. In formation systems research on network design has traditionally taken an operations resear ch (OR) approach, borrowing methods from classic OR problems such as the facility location problem (E rlenkotter 1977). The facility location problem, usually modeled as a mixed integer programming problem, is defined as choosing the number of facilities and locations to minimize total cost (or maximize profit) subject to capacity constraint s, demand constraints and others. Solution methodologies include developing heuristic s based on dynamic progr amming (Li et al. 1999), Lagrangean relaxation (Liu et al. 2001 ), and so on. Sun (2003) and Sun and Koehler (2003) were among the first to study the location model for Web service intermediaries. Mixed integer programming models are proposed in Sun (2003) and Sun

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40 and Koehler (2003) to decide the best lo cation of a WSI by considering the usage requirement, server’s capacity, and assignmen t constraints. Efficient heuristics were developed to tackle the locat ion problem as the proposed m odel became computationally intractable. Several special characteristics of Web se rvices suggest solving the location and pricing problem of the integrated Web service in a different way from traditional facility location problems. First, while the facility setu p cost is an important factor in the facility location problem, I treat it as sunk cost and focus on the WSI’s revenue from selling the composite Web service. Second, usually the facility location probl em deals with cost minimization and solves the decision pr oblem for one singl e company without consideration of competitions. The WSI, how ever, has to compete with the Web service providers by choosing the right location and the right price for its integrated Web service. Our problem is unique in that we consider the joint decision of location and pricing problem in a market of complementary Web serv ices. Last, as will be illustrated below, the delay cost of accessing Web services fo r a customer in the network has several complex functional forms, which is dependent on the location of the customer. Although in facility location problems one can model the delay cost as a function of distance between computing nodes, it does not provide the flexibility to choose different delay cost functions for customers at different locations. In this chapter, I propose a spatial model to study the join t location and pricing decision problem for the WSI. The optimal loca tion and price is first derived in a linear city model. Then a unit circle model is a pplied to study the joint decision problem in a more general context.

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41 3.2 The Linear City Model We assume the two Web servic e providers offering S1 and S2 are located at the end points of a unit length lin ear city (LC) and let x be the WSI’s location on the linear city (Figure 3-2). Due to the symmetry of this lin ear city model, we consider without loss of generality the case where 01/2x The potential customers of the integrated service are uniformly distributed in th is linear city between 0 and 1. Recall that the customers incur a delay cost t per unit of distance from consuming (or accessing) the Web services. Figure 3-2. Linear city model 3.2.1 Cost to buy from WSI in linear city The customer at location y (01y ) incurs a network delay cost of ty for consuming the Web service S1 and (1)ty for S2. The network delay cost for the same customer to access the integrated We b service offered by the WSI is ||tyx. Then, the total cost for the consumer located at y to buy the integrated service from the WSI is composed of the price of the integrated service (3P) and the delay cost, i.e., 3||Ptyx (3.1) 3.2.2 Cost to buy from service vendors in linear city The total cost for customers between 0 a nd 1 to purchase the Web services S1 and S2 separately and integrate by themselves in cludes the prices of the individual Web services (1P and 2P), the integration cost (c), and the total delay cost which equals to the sum of network delay of accessing each Web service, i.e., (1)tytyt In math, the S1 S2 S3 (Intermediary) Customer 0 1 x y

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42 total cost for customers who choose to create the integrated Web service by themselves is as follows. 12PPct (3.2) 3.2.3 Optimal location and pricing in linear city By evaluating the total costs described in Equations (3.1) and (3.2), each customer decides whether to buy the inte grated service from the WSI or to create the integrated service by purchasing the indivi dual Web services from the se rvice vendors separately. The marginal customers who are indifferent between buying from the WSI and creating the integrated Web service by themselves are described as below, where 1my and 2my denote the distance between the marginal customer and the WSI (Figure 3-3). 1231mPPctPty, 1232mPPctPty (3.3) Figure 3-3. Marginal customer in LC model For customers between the tw o marginal customers at 1my and 2my, the total cost to buy the composite Web service from the WSI is lower than that to buy S1 and S2 separately and then cr eate the composite service by themse lves. Therefore, they will buy from the WSI. However, if the WSI sets price 3P too high, all customers between 0 and 1 would create the integrated service by them selves, leaving no demand for the WSI. On the other hand, if the WSI set the price 3P low enough, all customers between 0 and 1 would buy from the WSI. In summ ary, the demand of the WSI is12Dyy where S1 S2 0 S3 (WSI) Marginal Customer 1 Marginal Customer 2my1my x

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43 0, if 0 if 0 if m i mm iii m iy yyyx x yx 1,2 i (3.4) Accordingly, the joint decision of optimal location and pricing for the integrated Web service by the WSI is formulated as follows 33312 ,() s.t Eq. (3.4)maxPxPDPyy (3.5) Proposition 3-1. In the unit-length linear city model, the optimal price and profit of the integrated Web service are 3121 2 PPPct and 121 2 PPct The mid-point position between the service provider s is the optimal location for the integrated Web service, i.e., *1 2 x Furthermore, the WSI captur es the entire ma rket demand by charging 3P and 3P increases faster with c than t. Proof. The derivation of the op timal profit and location is quite tedious and the details are delegated to the Appendix B. I just provide a sketch of proof here. This joint decision problem is solved in two steps. Firs t, I derive the optimal price and profit for the WSI given any particular position x Then, the optimal location is selected as the one that yields the highest profit obtained in the previous step. Q.E.D. Proposition 3-1 suggests that if all cust omers are located between the service providers, in which case all customers incur the same cost if they integrate the composite Web services by purchasing S1 and S2 from th e service providers, the optimal location of the WSI would be the midpoint position between the service providers. In addition, the WSI is best off by charging a penetration price to capture entire market demand. In

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44 addition, as the delay cost or the integration cost increases, the WSI should increase the price of the composite Web se rvice and obtain more profit. However, integration cost has bigger impact on profit (and pr ice) than the delay cost. It is quite intuitive that the WSI gains more profit when the integrati on cost is higher since more customers will switch to the WSI due to the higher cost of integrating the indi vidual Web services. However, when the delay cost increases, the costs of buying from the WSI and the service providers will both increase. The increased profit of the WSI in the presence of larger delay cost suggests that the negative im pact of delay cost is higher for the service providers than the WSI. In the linear city model, it is assume d that the service ve ndors are located at “extreme points” such that all customers are located between the serv ice providers. As a result, all customers experience equal delay co st if they buy the two Web services from service vendors. In the next section, I shall relax this assumption by considering a unit circle model. 3.3 The Unit Circle Model In this section, I study the more general cas e with a unit circle (UC) model in which not all customers are located between two Web service vendors. Let 0 be the location of the first service provider on the unit circle, and the second serv ice provider is located at distance d clockwise from the first provider. W ithout loss of generality, we restrict our attention to consider the case where 1 0 2 d Suppose the integrated Web service is located at x clockwise (01 x ) on the unit circle. There are N potential customers of the integrated service uniformly distributed along the unit circle.

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45 The unit circle model is depi cted in Figure 3-4, where points A and B correspond to the locations of the two Web service providers and the integrated Web service is located at point E. For the purpose of analysis, we mark points C, D and F, which are diagonal to points A, B and E on the unit circle respectively. Figure 3-4. Unit circle model 3.3.1 Cost to buy from WSI in unit circle One distinctive feature of the unit circle m odel is that the delay cost for a customer along the unit circle is conditiona l on his location, since the s hortest route to reach a node could be traveled either clockwise or counter -clockwise. For example, for a particular customer located at y clockwise from point A, the tota l cost to purchase the integrated service from the WSI (Itc ) is 3 3||, if | |1/2 (1||), if||1/2IPtyxyx tc Ptyxyx (3.6) 3.3.2 Cost to buy from service vendors in unit circle If a customer integrates the services by he rself, the delay cost is the sum of delay costs of accessing the Web services S1 and S2. Unlike the linear city model where all (S1) A ( 0 ) B ( d ) ( S2 ) C (0.5) (0.5d ) D E (WSI) x F (0.5 x )

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46 customers are located between the two servi ce providers and incur the same delay cost when accessing the Web services S1 and S2, customers at different locations experience different response delay due to different s hortest route of access. For example, a customer located between A and B incur a delay cost of td, while a customer located between C and D incur a delay cost of (1) td Customers located within the BC and DA segments incur a delay cost between td and (1) td Equation (3.7) formulates the total cost (NItc ) for a customer at y (01y ) if he (or she) integrate the Web service by oneself, which includes the prices of the Web services S1 and S2, the integration cost, and delay cost. 12 12 12 12, if 0 (2), if 1/2 (1), if 1/21/2 (22), if 1/21NIPPctdyd PPctyddy tc PPctdyd PPctyddy (3.7) 3.3.3 Optimal location and pricing in unit circle Given the total costs Itc and NItc one can find the location of the marginal customer who is indifferent between buying the integrated Web se rvice from the WSI and from the Web service vendors. Conse quently, the demand for the integrated Web service at a particular location on the unit ci rcle can be calculated as a function of the delay cost, integration cost, distance between the individual servi ce providers and their prices. Using the same approach as in th e previous section, one can solve the joint decision problem of optimal location and pr icing for the integrated Web service. However, it is rather tedious since the cost functions of Itc and NItc have conditional format, which in turn leads to complex profit function for the WSI. Therefore, I use a different approach to solve the location and pr icing problem in the uni t circle model. The

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47 following lemmas explain our logic of derivation. Further, re sults from analytical study are presented in Propositions 3-5 and 3-6, followed by inte rpretations of managerial insights. Lemma 3-2. The highest market-covering price for the integrated Web service at x is 121212 12 3 12 12min{, (0.5), ()}, 0 (), 0.5 (0.5), 0.50.5 (1), 0.51 PPctxPPctdPPctdxxd PPctdxdx P PPctdxd PPctxdx (3.8) Proof. The highest market-covering price is the highest price the WSI can charge while still attracting all customers in market. Th is price is calculated in two steps. First, for each customer I calculate the total cost to buy Web services from the service vendors and then create the integrated Web service by himself (or herself). Then, the highest market-covering price is selected as the lowest total costs for all customers in market. Detailed derivation is left to Appendix B. Lemma 3-3. When the WSI charges the highest market-covering price, the WSI achieves maximum profit if the integrated Web service is located between the two Web service providers. Proof. When the WSI captures the entire mark et demand, its profit is constrained by the highest price it can charge, which is conditional on the location of the integrated Web service, as specified by the four cases in Eq. (3.8). By inspection, one gets 312PPPc in the first case while 312PPPc in the rest three cases. In other words, if the integrated Web service is placed between the two service vendors, the intermediary can charge a higher price wh ile still captures entire market demand.

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48 Therefore, the WSI will choos e to locate the integrated We b service between the service providers to maximize profit. Q.E.D. Lemma 3-4. If the WSI raises its market-covering price 3P by 3kt P N where k is a positive integer, it will lose no less than min{, } kN customers. Furthermore, if 3(1) ktkt P NN the WSI loses demand of no less than min{1, } kN Proof. See Appendix B. Proposition 3-5. When 122()tPPc it is optimal to host the integrated Web service between the service providers of S1 and S2 and the optimal price is the highest market-covering price specified in (3.8), i.e., 3121212min{, (0.5), ()} PPPctxPPctdPPctdx (3.9) Proof. Proposition 3-5 is derived from Lemm a 3-2, 3-3 and 3-4. See Appendix B for a detailed proof. Proposition 3-6. When 122()tPPc and 1 3 d there are multiple optimal locations for the WSI. The optimal location and pricing of the integr ated Web service are described in (3.10). If 11 32 d 3121 () 2 PPPctd and *11 2 22 dxd (3.10) On the other hand, when 122()tPPc and 1 0 3 d the optimal location and pricing of the integrated Web serv ice are described in Eq. (3.11). If 1 0 3 d ,* 312/2 PPPct and *2 d x (3.11)

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49 Propositions 3-5 and 3-6 are consistent with the analysis in the linear city model. However, in the linear city model, the two Web service vendors are located at the end points such that the integrated Web service can only be stored between them. In the unit circle model, the WSI can choose the intens ity of competition by locating at different regions on the unit circle. For example, the competition between the WSI and the service providers is maximal if the integrated We b service is located between A and B; In contrast, the competition is minimal in section between C and D; Further, the sections between BC and DA represent region s of moderate competition. Proposition 3-5 suggests that if the delay cost is small, the WSI prefers maximum competition and will set a low penetration price to capture the entire market demand. At first sight, this result is quite “unconventional” in that cla ssic economic theories, such as the theory of Bertrand competition, suggest th at firms prefer lesser competition in order to avoid price war. This unusual result can be explained by two characteristics of the Web service market. First, as an executabl e program distributed over the Internet, the performance of a Web services based platform is greatly constrai ned by the underlying networking infrastructure. In particular, a customer accessing the Web service incurs a delay cost due to network latency, which is associated with the physical di stance between the customer and Web service application server. Second, due to the platform independence and software modularity, a Web services based platform boasts the flexibility of integrating multiple Web services on the fly, across the street or across the ocean. Consequently, in case when multiple Web services at different locations are accessed to compose an integrated Web serv ice, the network delay of accessing the integrated Web service is the sum of netw ork latency of accessing each constituent Web

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50 services. On the other hand, there’s only one single network delay when a customer accesses an integrated Web service from the WSI directly. Therefore, although the WSI faces a stronger competition when the integrat ed Web service is located between the two Web service vendors, it will be compensated since it can charge a higher market-covering price. In fact, this is not a deviant from the results in a Bertrand competition, since the WSI sets a low penetration price to cover the entire market. While the linear city model yields one single optimal location for the WSI, Proposition 3-6 suggests that there could be mu ltiple optimal locations for the integrated Web service in the unit circle model. In fact the result in the linear city model can be viewed as a special case of the unit circle model since the mid-point position is always the optimal location according to Proposition 3-6.

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51 CHAPTER 4 OPTIMAL SUBSCRIPTION AND LI SING FEE OF A WEB SERVICE INTERMEDIARY Depending on technical strengt hs and business scope, the Web service intermediary (WSI) can play various roles in the Web servic e supply chain. In the previous chapter, I focused on studying the optimal strategy for a WS I that provides inte gration service and sells an integrated Web service. In this ch apter, I study another m odel of WSI that does not sell Web services of its own. Instead, the WSI offers aggregation service to match the Web service vendors with the Web service consumers. In addition, the WSI provides value-added services to take the ad vantage of its tec hnical expertise. One example of such WSI is Salcentral.com which maintains a comprehensive directory of Web services so that service consumers can browse, search for and audit particular Web servi ces. Another WSI, GrandCentral.com provides a centralized Web services network and acts as a trust broker that handles issu es of message delivery and routing, security, …, etc. In summary, the WSIs provide certain va lue-added services to both Web service vendors and service requestors, allowing them to charge a fee to both sides of the Web services supply chain. In this chapter, I study the optimal strategies for the WSI in a supply chain of complementary Web services. In particular the WSI serves two groups of web service vendors that provide Web services of co mplementary functionalities. The two complementary Web services can be integrat ed by consumers to create a new composite Web service. Correspondingly, the consumers are divided into three groups – those who

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52 desire the two individual We b services and those for the composite Web service. The WSI charges the Web service vendors a list ing fee and the Web service consumers a subscription fee in order to access the added value provided by the WSI. The added value of the WSI consists of the “intrins ic” value and the “cross network externality” value. The intrinsic value of the WSI refers to the standalone services offered by the WSI such as Web service development tools, maintenance, and security. The cross network externality valu e is proportional to the number of service vendors listed in and the customers subscribing to the WSI. That is, the more service vendors listed on the WSI, the more valuable it is for service consumers to subscribe to the intermediary, and vice versa. The Web services supply chain has severa l unique characteristics. First, unlike traditional supply chain, the object supplied and consumed in the Web services supply chain is not tangible physical goods, but ra ther software components residing at the service provider’s computer server. Hence, most issues considered in traditional supply chain literature such as inventory and orderi ng decisions of the distributor are no longer relevant to the WSI. The major decision f acing the WSI is how to optimally set the listing fee for the Web service providers a nd the subscription fee for the Web service consumers. Second, most supply chain litera ture involves one supp lier and one customer, or one group of suppliers and one group of customers. One of the most appealing features of the Web servic e technology is the ease of creating a new composite web service (i.e., new business functionality) from integrating tw o Web services of complementary functionalities. To account fo r this reality, this model includes two groups of Web service vendors providing comp lementary Web services. The consumers

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53 of the Web service supply chain are thus comprised of three gr oups – those demanding the individual Web services and those in need of the composite Web service. Third, in addition to the intrinsic value offered by the WSI, we study the impact of “cross network externality” effect on the WSI’s optimal stra tegies. The intensity of the cross-network externality effect is found to be a key fact or affecting the WSI’s pricing strategies. The objective of this chapter is to study wh at are the best strategies for the profitmaximizing WSI. Specifically, I address the following research questions. First, what are the optimal subscription fee and listing fe e? Second, is it optimal for the WSI to attract all the Web service provi ders and/or all the Web servi ce customers? Next, what is the impact of the WSI’s intrinsic value and in tensity of network externality effect on its pricing strategies? Finally, how does the nature of the Web services market affect the optimal strategy of the WSI? This chapter is organized as follows. Fi rst, I provide critical background of the Web service supply chain. Next, I review rela ted literature and outli ne the uniqueness of this research. After that, I introduce the analytical model and derive the optimal listing fee and subscription fee, followed by discussion on managerial insights. 4.1 The Web Service Supply Chain A Web service supply chain is composed of three parties, the Web service vendors, the Web service consumers and the Web serv ice intermediary. A Web service vendor develops some Web service with certain business function and makes it accessible through its Web site. To make the Web serv ice interoperable and discoverable, a Web service description file (WSD L), which describes the function of the Web service and specifies the technical signatures such as en try point, transport pr otocol and encoding

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54 style, etc., is uploaded to a registry service, which can be either the public business registry (PBR) or a private registry provided by the WSI. Currently, the PBR is cooperatively operate d by four companies, IBM, Microsoft, SAP and NTT. A Web services registry can be understood as a data base of Web service descriptions (WSDL files).which accepts quer ies on base of multiple criteria, such as functionality, industry, geographical region. Listing and searching via the public business registry is free while a Web service intermediary may charge subscription fee to the service consumers and listing fee to the service vendors. When a Web service request er (consumer) considers us ing a software component offered by other companies (service vendors) instead of developing it in house, she searches for the desired Web services via th e WSI or PBR. After obtaining information from the registry, i.e., retrieving the WSDL file, the service requestor can choose to purchase the Web service. In the context of Web services, the “purchase” of a Web service involves binding the service requesto r’s client application with the remote Web service and then invoking the Web service ho sted on the service vendor’s Web site. Figure 4-1 illustrates the inter actions among the parties of the Web services supply chain. Figure 4-1. Web service supply chain Web service intermediary ( WSI ) Bind/Invocation Find Publish Web service vendor Web service Requester PBR Publish Find

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55 In addition to providing regist ry services, a WSI also provides value-added services to consumers of Web services. For exam ple, the intermediary can help reduce transaction costs among tradi ng partners by providing acc ount management so that consumers only need one account to access multiple Web services (e.g., SalCentral.com ). The WSI may improve Web services manage ment by enforcing contract to guarantee quality of service and security (e.g., FlamencoNetworks.com ). The WSI can also exploit its technical expertise and pr ovide integration services to realize “service on demand” (e.g., GrandCentral.com ). The WSI can set up a networking infrastructure to provide reliable service provisioning (e.g., BlueTitan.com ). At the same time, Web service vendors benefit from publishing their Web serv ices on the WSI since the added values by the WSI help increase the chance of a succes sful transaction with subscribers of the intermediary. 4.2 Literature Review Vast amount of research has been conducted on traditional intermediaries, with applications mostly in financial market, labor market and supply chain markets. Research on Web services in general and Web services intermediaries (WSI) in particular has primarily centered around tec hnical issues in the computer science field, while there is a lack of research on Web services and WSI from the perspective of business management. Prior literature on traditional inte rmediaries can be generally classified into two categories. The first stream of research on traditi onal intermediaries studies the role of intermediaries. According to Spulber (1996), intermediaries can play several roles in a vertical market matching and searching, price sett ing and market cl earing, providing

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56 liquidity and immediacy as well as guaranteei ng of quality. There is ample research on each aspect of the roles play ed by the intermediaries. R ubinstein and Wolinsky (1987) model the interaction between buyers and sellers as a time-consuming bilateral search process and studies how a matchmaking interm ediary can affect pr ofit division between the two parties of transaction. Yavas (1994) studies the im pact of an intermediary on consumer search behavior, while Naert ( 1971) takes the perspec tive of producers and analyzes the producer’s optimal decisions on advertising and markup in an intermediated market. Biglaiser (1993) take s another avenue of research and shows that an expert intermediary can contribute to quality guarantee. The advent of the Internet technologies and the ensuing e-commerce heralds recent research on the functions of electronic intermediaries (Bailey and Bakos 1997, Bakos 1998, Kaplan a nd Sawhney 2000). By observing the that the Internet helps reduce search cost (Bakos 1997), Bailey (1998) sets out to study whether the intermediation via the Internet reduces friction and finds empirically that there is greater pric e dispersion in online intermediaries. The second stream of abundant research on traditional intermediaries examines the optimal strategies of intermediaries. Gehrig (1993) studies the tradeoff between ask-bid spread and cost of delay in private search and finds that a monopoly intermediary will charge a positive spread. In another paper by Wooders (1997), it is shown that a profitmaximizing intermediary may act as a Walrasia n auctioneer by setting bid and ask prices to nearly Walrasian equilibrium prices. Prio r research on intermediaries of traditional physical goods market unfortunately cannot be directly applied to online intermediaries, since most online intermediaries take the role of “matchmakers” instead of “market makers”. That is, they provide value-added se rvices and don’t sell products on their own.

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57 There are no issues of inventor y costs, shipping costs, order quantities and transfer prices that underline the framework of numerous previous research. Recent work on optimal strategi es of information intermediaries is more related to our research on WSI. Baye and Morgan (2001) study how an information gatekeeper, which provides product and price informa tion to consumers, should optimally set subscription fee to consumers and advertising fee to producer s. Baye and Morgan (2001) find that the gatekeeper will set a low subscrip tion fee to attract all customers. There are two key differences between Baye and Morgan (2001) and this research. First, they assume that customers are geographically segmented so that consumers only buy from local firms if they do not subscribe to th e gatekeeper. In contrast, Web services requestors can always search via a public bus iness registry and obt ain an entire list of available service vendors. Second, the advertis ed price is lower than unadvertised price in requestors, while in the context of We b services market, the service vendors and requestors trade directly even if they find the match via the WSI, see Fig. 1. This feature combined with the Web service requestors’ ability to search the entire list of service providers leads to no difference between lis ted and unlisted prices. These two key differences between the information gate keeper and the WSI result in opposite conclusions to those in Baye and Morgan ( 2001). Bhargava and Choudhary (2004) study the product line design (vertical differentiation) of an information intermediary in the presence of aggregation benef its. In their model, consumer s have heterogeneous search costs and producers have heterogeneous ex pectations of gains from joining the intermediary. In this paper, we model th e interaction among the service vendors, the

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58 WSI, and consumers in response to the subs cription fee and listing fee charged by the WSI. Recent work on optimal strategi es of information intermediaries is more related to this research on Web service intermediaries. Baye and Morgan (2001) study how should an information gatekeeper, which provides pr oduct and price information to consumers, optimally set subscription fee to consumers a nd advertising fee to producers. It is shown that the gatekeeper should set a low subscripti on fee to attract all customers. There are several key differences from this research. First, they assume that customers are geographically segmented so that consumers only buy from local firms if they do not subscribe to the gatekeeper. In contrast, as explained in the pr evious section, the requestors of Web services can always search via a public business registry and obtain an entire list of available service vendors. Se cond, they explicitly allow price dispersion, making advertised price lower than unadvertis ed price while in the context of a Web service supply chain, the serv ice vendors and requestors trade directly even if they find the match via an intermediary. This implies that there’s no price difference between listed and unlisted prices. It is worth noti ng that due to the major discrepancies of problem setup, this resear ch leads to opposite conclusi ons from those by Baye and Morgan (2001). Of most relevance to this research is the work by Corbett and Karmarkar (1999), which analyzes optimal subscription fee and listing fee by an intermediary when there exhibit cross network externalities. Their study, however, focuses on one group of sellers and consumers of one product. Since a majo r benefit of Web services technology is the ease of software integration, the WSI we study faces two groups of service vendors

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59 providing complementary functionalities, re sulting in three gr oups of customers namely two groups of consumers of the individual We b services and a third group of consumers of the integrated Web service. Consequently, the problem facing the WSI is much more complicated. 4.3 The Model Following the modeling approach of Corbe tt and Karmarkar (1999), I consider two groups of Web service vendors which provide two complementary Web services, denoted by S1 and S2. The two complementary Web services can be integrated into one composite Web service S3. There are 1N service vendors of S1 and 2N service vendors of S2. The numbers of service providers 1N and 2N are sufficiently large, and the Web service market of S1 and S2 is highly compe titive. The prices of S1 and S2, denoted by 1P and 2P respectively, are thus treated as exogenously given. The Web service consumers consis t of three distinct groups – 1Q consumers interested in S1, 2Q consumers interested in S2, and 3Q consumers interested in the composite Web service S3. In case a cons umer is interested in the individual Web service (S1 or S2) and the composite Web servi ce S3, he is counted as a consumer of S3 since the composite Web service always performs the functions of S1 and S2. A monopolist WSI that provides value-adde d services charges a fixed listing fee L to Web service providers and ch arges a fixed subscription fee F to service consumers. Let i x (1,2 i ) be the proportion of servi ce vendors listed on the WSI and jy (1,2,3j) be the proportion of consumers subscrib ing to the intermediary. Figure 4-2 delineates the basic setup of the model discussed above.

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60 Figure 4-2. Model of subscription and listing fee The sequence of events is described as follows. First, the WSI sets the subscription fee F and listing fee L Observing the listing fee and subscription fee, consumers decide whether to subscribe to the WSI base d on their judgment of expected value from the subscription. At the same time, the service vendors decide whether to list on the WSI, in consideration of the expected num ber of subscribers and vendors joining the intermediary. In equilibrium, service consum ers have rational exp ectation of proportion of listed service vendors, and vice versa. I use backward induction to solve the WS I’s decision of optimal subscription fee and listing fee. First we derive the proportions of We b service consumers (,1,2,3jyj ) and vendors (,1,2ixi ) who decide to join the WS I given the subscription fee F and listing fee L In the next section, I analyze how the WSI optimally sets the subscription fee and listing fee 4.3.1 Consumer’s subscription decision From the service consumer’s point of vi ew, the added value offered by the WSI consists of intrinsic value and cross network externality effect The intrinsic value of the WSI results from the reduction of transacti on cost, enhanced security and management, Web Service Intermediar y Service vendors of S1 Consumers of S1 x1 x2 N1 N2 Q1 Q3 y1 y2 y3 Q2 Service vendors of S2 Consumers of S2 Consumers of S3

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61 and improved reliability, etc. In general, the intrinsic value is related to the technical strength of the WSI and is i ndependent of the number of se rvice vendors listed on it. The consumers are heterogeneous in their judgment of the intrinsic value, denoted by the random variable v uniformly distributed in the interval [0,] v The ceiling of the distribution, v is used as a representative measure of consumers’ valuation of the WSI’s intrinsic value. The second part of the WSI’s added value is network effect which is increasing in the number of Web service vendors listed on the intermediary. Let indicate the intensity of the cross network externality, or consumer’s valuation for the network effect. Then, the network value to a consumer equals multiplied by the number of service vendor s listed on the WSI. Web service consumers decide whether to subscribe to the WSI by evaluating the cost (subscription fee) and value from subscribing to it. For a customer who is interested in S1, the total benefit from subs cribing to the intermediary is 1vx where v is the consumer’s valuation of the WSI’s intrinsic value, and 1 x is the proportion of listed Web service vendors providing S1. Let 01v be the valuation of the marginal consumer of S1 indifferent between subscrib ing and not subscribing to the WSI. Then equation 011vxF holds. Similarly, the marginal cons umer of S2 with intrinsic value of 02v is described by 022vxF The proportion of consumers of S1 (or S2) who subscribe to the WSI corresponds to those consumers who have higher valuation than that of the marginal consumer, specified as follows. Note that if the subscription fee is prohibitively high, all consumers will stay away from the WSI (0 y ), while a sufficiently low subscription fee attracts all consumers to subscribe (1 y ).

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62 1,2()/ if ()1, if (all subscribe) 0, if (nobody subscribe)iii iii i ivFxvxFvx yxFx Fvx (4.1) The consumer who is interested in the composite Web service derives 12vxx benefit from subscribing to the WSI. That is, the network value for the consumers of composite service S3 is increasing in the pr oportion of listed service vendors of S1 and the proportion of listed service vendors of S2. In accordance, the proportion of consumers of S3 who subscribe to the intermediary is as follows. 121212 31212 12()/ if (,)0 if 1 if vFxxvxxFvxx yxxFvxx Fxx (4.2) 4.3.2 Service vendor’s listing decision Subscribers to the WSI search the Web se rvices listed on the intermediary and choose to transact with thos e vendors listed on the WSI, while non-subscribers search through the public business registry (PBR) wh ich contains the entire list of service vendors. Since the price of the same Web serv ice in this competitive market is the same, consumers pick the service vendor with equal likelihood. In other words, listing to the WSI helps the service vendors increase the chance of accomplishing transactions with more consumers (unless none of the consumers subscribe to the WSI) and reduce the competition from peer service vendors (if not all service vendors publish on the intermediary). Given the proportion of consumers s ubscribing to the intermediary (1y, 2y and 3y ), the profits for a service vendor to list (I i ) or not list (NI i ) are formulated in Equations (4.3) and (4.4), re spectively. The subscript i is an index of Web services (S1

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63 or S2). Notice that the de mand of Web service S1 (likewise, S2) comes from both the consumers of S1 (S2) and those of the composite Web service S3. 33 311 ()()I ii iiii iiiiiiyyyy PQPQL NxNNxN ,1,2i (4.3) 3 311NI i iiii iiyy PQPQ NN 1,2 i (4.4) A Web service vendor of S1 will choose to list on the WSI if it gains more profit from listing (INI ) while otherwise it will not list. Hence, one derives the proportion of service vendors listing on the WSI from the equation NII ii (1,2i), which is described below. Similar to the consumer side, the intermediary will attract all service vendors if the listing fee is low enough (1 x ). It is interesting that that although a high subscription fee will drive all consumers away from the intermediary, a high listing fee won’t deter all service vendors. This is b ecause those consumers who subscribe to the intermediary won’t search be yond the WSI, creating a niche ma rket of subscribers for the service vendors listed on the WSI. 3333 3()() if (,) 1 otherwiseiiiiii ii iiPyQyQPyQyQ L LNN xyy 1,2 i (4.5) 4.4 Optimal Subscription and Listing Fee After deriving the response functions of the service consumers and service vendors, the WSI solves the decision problem described as follows. The initial cost to set up for the WSI is sunk and the marginal operational cost incurred by the intermediary is assumed to be negligible.

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64 1122331122 ,max ()() s.t. Eqs. (4.1), (4.2),and (4.5)LFyQyQyQFxNxNL (4.6) Lemma 4-1. The optimal strategy of the WSI is to induce all service vendors to list on the intermediary, i.e., 121 xx Specifically, the WSI will set the listing fee at 12min{,} LLL where 33()iii i iPyQyQ L N (1,2i ). Lemma 4-1 specifies that the listing fee s hould be low enough such that all service vendors list on the WSI (121 xx ). The exact value of the listing f ee cannot be determined until we derive the proportion of the subscribers (jy ’s), which in turn is determined by the subscription fee F. In addition, the minimum of 1L and 2L is dependent on the values of iP iN and iQ Knowing that its optimal strategy is to at tract all service vendor s to list on it, the WSI is naturally confronted with the follo wing question: whether it should attract all customers to subscribe? Lemma 4-2 suggests that this is not the case. Lemma 4-2. The intermediary’s profit is not always maximized when all customers subscribe to its service, i.e., 1231 yyy Proof. I prove Lemma 4-2 by showing an ex ample where the intermediary obtains more profit without full subscr iption from the service consumers. First note that F is the maximum subscription the intermediary ca n charge to induce total subscription. From Lemma 4-1 we know that the intermed iary has incentive to allow all service vendors to list on it. Consequently, the ma ximum profit the interm ediary can achieve when all consumers subscribe to it is 1123113223()()() QQQPQQPQQ Next consider the case when the intermediary charge a subscription fee of '2 F

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65 which induces all customers of S3 to subscr ibe but not all customers of S1 and S2 will subscribe, i.e., 120,1 yy and 31 y Then the intermediary’s profit is 2 where 2313232 QPQPQ is calculated when we plug in 2F 31 y and 120 yy into the profit function. With simple algebra, it’s easy to show that the intermediary obtains more profit if 3121122() QQQPQPQ i.e., 21 Q.E.D. Next we reformulate the optimal deci sion problem faced by the WSI by applying Lemma 4-1 and Lemma 4-2 in Eq. (4.7). 1122331113322233max ()()() s.t. (4.1), (4.2), (4.5)N FyQyQyQFPyQyQPyQyQ (4.7) Note that the functional form of proportions of subscribers (' jys ) is conditional on the value of subscription fee ( F ). Accordingly, the WSI’s profit function takes different format under various subscription fee. In orde r to solve for the optimal subscription fee, we need to compare the profit under each possible combination of jys (1,2,3j). That is, we need to consider internal solutions (01jy ) as well as the boundary solutions (0jy or 1). Furthermore, the proportion of consumers who subscribe to the WSI varies under different relationship of and v. For example, if the intermediary charge a subscription fee of v all consumers of S3 will subscribe to it if v On the other hand, only partial consumers of S3 will subscribe to it if v with subscription fee charged at v In the following two subsections, I solve for the optimal subscription fee *F under different relationships between cons umer’s valuation of the network effect ( ) and the WSI’s intrinsic value ( v).

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66 4.4.1 Network value is less than intrinsic value When consumers value intrinsic value more than network value ( v ), the proportion of subscribers with respect to subscription fee is illustrated in Figure 4-3, which breaks down into the following three subcases. If the subscr iption fee is between and 2 (case I-1), partial consumers of S1 and S2 subscribe to the intermediary while all consumers of S3 subscribe to the intermediary; If the subscription fee is between 2 and v (case I-2), partial consumers of S1, S2 and S3 subscribe to the intermediary; If the subscription fee is between v and 2 v (case I-3), only par tial consumers of S3 subscribe to the intermediary. Obviously, th e intermediary has no incentive to set the subscription fee lower than or higher than 2 v since the market is saturated under subscription fee at and in the latter case no consumer will subscribe. Figure 4-3. Proportion of Subscribers when v I first derive the locally optimal subscrip tion fee and then compare the WSI’s profit in each sub-case. The optimal subscription fee is found as the one that generates the highest profit for the WSI. Case I-1: 2 F If the subscription fee charged by the intermediary is between and 2 all consumers of S3 will subscribe to the interm ediary while only partial customers S1 and S2 will subscribe to the intermediary, i.e., 120,1yy and 31 y Correspondingly, the intermediary’s profit maximization problem is defined in Eq. (4.8). Lemma 4-3 describes 2 v 2v I 1 I-2I-3 F

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67 the optimal subscription fee when the intermedia ry desires to attract all consumers of S3 and partial consumers of S1 and S2. 111233 1,2max ()() s.t 2ii F ivFvF QQQFPQQ vv F (4.8) Lemma 4-3. If v and 2 F the subscription fee that maximizes the profit of the intermediary (* 11F) is specified as follows. a. If 11223 12()() () vPQvPQvQ v QQ 11F ; b. If 1122311223 1212()()()() 3() vPQvPQvQvPQvPQvQ QQQQ 1111FF where 11F is defined as 11223 11 12()() 2() vPQvPQvQ F QQ ; c. If 11223 12()() 0 3() vPQvPQvQ QQ 112 F Proof. Lemma 4-3 is derived from solvi ng the constrained optimization problem defined in (4.8). See Appendi x C for a detailed derivation. Case I-2: 2Fv If the subscription fee charged by the intermediary is between 2 and v part of customers interested in each of the Web se rvices S1, S2 and S3 will subscribe to the intermediary, i.e., 01jy (1,2,3 j ). Correspondingly, the intermediary’s profit maximization problem is defined as follo ws. Lemma 4-4 describes the optimal subscription fee when the intermediary desires to allow all groups of consumers to subscribe.

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68 121233 1,2 22 max ()() s.t2ii F ivFvFvFvF QQQFPQQ vvvv Fv (4.9) Lemma 4-4. If v and 2 Fv the subscription fee that maximizes the profit of the intermediary (* 12F) is specified as follows. a. If 1122123 123()()() 0 332 vPQvPQvPPQ QQQ 1212FF where 12 F is defined as 1122123 12 123()()(2) 2() vPQvPQvPPQ F QQQ ; b. If 1122123 123()()() 332 vPQvPQvPPQ v QQQ 122 F Proof. Lemma 4-4 is drawn from solving the constrained optimization problem defined in (4.9). It is quite similar to proving Lemma 3. So we won’t repeat here. Case I-3: 2 vFv If the subscription fee charged by the intermediary is between v and 2 v no consumers of S1 and S2 will subscribe to the intermediary while partial consumers of S3 will subscribe to the intermediary, i.e., 120yy and 301 y Correspondingly, the intermediary’s profit maximization problem is defined as follows. Lemma 4-5 describes the optimal subscription fee when the intermediary desires to get subscription from proportional consumers of S3 only. 13312322 max () s.t. 2FvFvF QFPPQ vv vFv (4.10) Lemma 4-5. If v and 2 vFv the subscription fee that maximizes the profit of the intermediary is 13Fv

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69 Proof. We derive Lemma 4-5 by solving th e constrained optimization problem and it’s quite similar to proving Lemma 4-3. Detailed proof will be omitted. Proposition 4-6. If the intrinsic va lue of the intermediary is larger than the network value ( v ), the optimal subscription fee charged by the intermediary is specified as follows. a. If 110 the optimal subscription fee is 12F defined in Lemma 4-4. Proportional consumers of S1, S2 and S3 will subscribe to the intermediary, i.e., 01jy ( 1,2,3 j ); b. If 1112 optimal subscription fee is 2 All consumers of S3 will subscribe to the intermediary while only partial consumers of S1 and S2 will subscribe to the intermediary, i.e., 31 y and 120,1yy ; c. If 1213 optimal subscription fee is 11 F defined in Lemma 4-3. All customers of S3 subscribe to the intermed iary while only partial customers of S1 and S2 subscribe to the intermediary, i.e., 31 y and 120,1yy ; d. If 13v optimal subscription fee is All customers of S1, S2 and S3 will subscribe to the intermediary, i.e., 1jy ( 1,2,3 j ), where 1122123 11 123()()() 332 vPQvPQvPPQ QQQ 11223 12 12()() 3() vPQvPQvQ QQ and 13 is defined as 11223 13 12()() vPQvPQvQ QQ One interesting observation from Proposition 46 is that the intermediary has more incentive to serve consumers of the integrat ed Web service than to serve consumers of individual Web services. Unless the network effect is very large, see (d) of Proposition 4-6, the intermediary only desires to allow pa rtial consumers of S1 and S2 to subscribe to. In contrast, the intermediary will set th e subscription fee to attract all consumers of S3 except for very small network eff ect, e.g., see (a) of Proposition 4-6.

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70 Furthermore, Proposition 4-6 suggests th at the optimal subs cription fee is conditional on intensity of the network effect ( ). Note that 111213 this implies that as the intensity of network effect incr eases, or consumer’s valuation of the network effect increases, the intermediary has incentive to allow more and more consumers to subscribe to it, since the proporti on of subscribers increases from (a) to (d). The behavior of optimal subscription fee and profit is summarized in Proposition 4-7. Proposition 4-7. If the intrinsic va lue of the intermediary is larger than the network value ( v ), the optimal subscription fee a nd optimal profit are increasing in network intensity ( ). Proof. By inspection, the subscription fee is increasing in ne twork intensity under each conditions specified in (a)-(d). In addition, note that the subscription fee coincides at each point that delimits successive cases fr om (a) to (d). For example, when 11 the optimal subscription fee 12F under the condition (a) equals 2 which is the optimal subscription fee under the condition (b). Th erefore, the optimal subscription increases with network intensity. The network effect ha s similar effect on the optimal profit for the intermediary. Q.E.D. Figures 4-4 and 4-5 illustrate the behavior of optimal subscription f ee and profit when the network effect is lower than the intrinsic value of the WSI ( v ). The parameters in the shown example are: 1100 Q 2150 Q 390 Q 15 P 22 P 5v, 110.29 121.2 133.6 Note that the optimal subscription fee (or profit) is a kinked one (different functional forms with varying network intensity). But the optimal subscription fee (or profit) is genera lly increasing in network intensity.

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71 Figure 4-4. Optimal subscription fee when v Figure 4-5. Optimal profit when v It’s worthy noting that although Proposition 4-6 describes the optimal subscription fee with respect to different va lues of the network intensity ( ), not all conditions prescribed in Proposition 4-6 can be met in reality. For example, in case when 13v the condition specified in (d) will never be sa tisfied. In other works, the intermediary will never set the subscription fee at which attracts all consumers to subscribe to the intermediary, when the condition in (d) is me t. Corollaries 4-8 and 4-9 present more insights from Proposition 4-6 by studying the im pact of intrinsic value on the optimal subscription fee. Profit 1() Network effect () Subscription Fee 1() F Network effect ()

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72 Corollary 4-8. The intermediary will set the subscription fee to attract all customers of S3 to subscribe if vV where 1122123 123() PQPQPPQ V QQQ Proof. If 110 the condition specified in (a) of Proposition 4-6 will never be satisfied. Therefore, the optimal subscripti on fee charged by the intermediary must be realized in one of the remain ing cases of (b)-(d), which al l suggest a total subscription from consumers of S3, i.e., 31 y Q.E.D. Corollary 4-9. The intermediary won’t serve all Web service consumers if vV, where 1122 3PQPQ V Q Proof. To achieve total subscription from all customers, the intermediary has to set up its subscription fee at According to Proposition 4-6, the subscription fee is optimal only when 13v But if 13v which translates to vV, the condition in d of Proposition 6 can never be satisfied. Q.E.D. Corollary 4-8 specifies the condition under which the WSI will set the subscription to allow all subscribers of S3 to subscribe to it. This relates the intermediary’s optimal strategy with the nature of Web services provided by the service vendors since the threshold value V is increasing in prices of Web services (1P and 2P ). Specifically, the WSI will strategically attract more consumer s with higher value (or price) of the Web services. Corollary 4-9 can be viewed to some exte nt as the opposite ca se of Corollary 8. Instead of answering when the intermediary should allow more consumers to subscribe to it, Corollary 9 specifies the condition under which the intermediary desires to serve less

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73 consumers. Corollary 4-9 also relates the intermediary’s optimal strategy with the nature of Web services provided by the service vendors. Note that the threshold value V is determined by the prices of the Web services (1P and 2P ). According to Corollary 9, if the consumers have a relatively higher valua tion of the value-added services from the WSI compared to prices of Web services the intermediary need not set a low subscription fee to attract all consumers to subscribe to it. 4.4.2 Network value is grea ter than intrinsic value Similar analysis can be applied to the second scenario where consumers place a higher value of the network effect than the intrinsic value ( v ). The proportion of subscribers with respect to subscription fee is illustrated in Figur e 4-6, which breaks down into the following three sub-cases. If the subscription fee is between and v (case II-1), partial consumers of S1 and S2 subscribe to the intermediary while all consumers of S3 subscribe to the intermediary, If the subscription fee is between v and 2 (case II-2), no consumer of S1 and S2 will subscribe while all consumers of S3 will subscribe to the intermediary; If the subscription fee is between 2 and 2 v (case II-3), partial consumers of S3 subscribe to the intermediary and no consumer of S1 and S2 will subscribe. Similar to the previous scenario, we don’t consider the cases when the subscription fee is lower than or higher than 2 v Figure 4-6. Proportion of subscribers when v 2 v 2v II 1 II-2 II-3 F

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74 Case II-1: Fv In Case II-1 the intermediary’s profit function is the same as the one defined in Case I-1. All consumers of S3 will subscr ibe to the intermediary while only partial customers S1 and S2 will subscribe to the intermediary, i.e., 120,1yy and 31 y However, the range of subscrip tion fee is different from that of case I-1. We define the intermediary’s decision problem in Eq. (4.11). Lemma 4-10 specifies the optimal subscription fee when the intermediary desires to attract all consumers of S3 and partial consumers of S1 and S2. 211233 1,2max ()() s.t ii F ivFvF QQQFPQQ vv Fv (4.11) Lemma 4-10. If v and Fv the subscription fee that maximizes the profit of the intermediary (* 21F) is specified as follows. a. If 31122 12()() vQvPQvPQ v QQ 21Fv ; b. If 3112211223 1212()()()() vQvPQvPQvPQvPQvQ QQQQ 2121FF where 21F is defined as 11223 21 12()() 2() vPQvPQvQ F QQ ; c. If 11223 12()() vPQvPQvQ QQ 21F Proof. Lemma 4-10 is proved using the sa me approach in proving Lemma 4-3. Case II-2: 2 vF If the subscription fee is between v and 2 all consumers of S3 will subscribe while no consumers of S1 or S2 will subscribe to the WSI, i.e., 120yy and 31 y

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75 Furthermore, the WSI must set the subscription fee at 2 which is the highest subscription fee the WSI can charge in order to have all consumers of S3 subscribe to it. This is because any subscription fee less than 2 won’t attract more consumers and thus will render less profit for the WSI. Therefore, Case II-2 can be viewed as a special case of Case II-3 to be discussed below. Case II-3: 22 Fv If the intermediary set the subscription fee between 2 and 2 v no consumer of S1 or S2 will subscribe while some cons umers of S3 might subscribe to it, i.e., 120yy and 301 y The profit maximization problem is described in Eq. (4.12). It is same with the one defined in case I-3, except for different constraint the subscription fee. Lemma 4-11 specifies the optimal subscr iption fee when the intermediary desires to get subscription from proportio nal consumers of S3 only. 23312322 max () s.t. 22FvFvF QFPPQ vv Fv (4.12) Lemma 4-11. If v and 22 Fv the subscription fee that maximizes the profit of the intermediary is 23Fv Proof. The proof is similar to that of Lemma 4-5. Proposition 4-12. If consumer evaluate the value from network eff ect more than that of the technical services ( v ), the optimal subscription fee is described as follows, where 31122 21 12()() vQvPQvPQ QQ and 11223 22 12()() vPQvPQvQ QQ

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76 a. If 21v the optimal subscription fee is 2 All customers of S3 subscribe to the intermediary while no custom ers of S1 and S2 subscribe, i.e., 120yy and 31 y ; b. If 2122 the optimal strategy for the intermediary is to set the subscription so that all consumers of S3 subscrib e while some consumers of S1 or S2 subscribe, i.e., 31 y and 120,1yy In particular, the subscription fee is d. 21, if 0 2, if <0F F where 21F is defined in Lemma 4-10 and 2 11223312()()4()() QvPQvPvQvvQQQ c. If 22 the optimal subscription fee and proportions of subscribers are *, if 0 2, if 0 F where 1231122() QQQPQPQ leading to all consumers of S1, S2, and S3 to subscribe if *F or all consumers of S3 to subscribe but none of customers of S1 and S2 when *2 F Proof. See Appendix C. Similar to the previous scenario for v I derive the optimal subscription fee for the WSI by comparing the WSI’s profit in Case II-1 and Case II-3, which is summarized in Proposition 4-12. But this scenario ( v ) is a bit more complex than the case when v since we don’t have conclusive solutions for the optimal subscription fee under conditions (b) and (c), which are determined by functions and respectively. That is, the optimal subscription fee is determined not only by the network intensity but also the nature of the Web services, e.g., prices of individual Web services ( P ’s), market size ( Q ’s), and the intrinsic value of the intermediary’s service v Figures 4-7 and 4-8 draw an example of the behavior of optimal subscription fee with respect to network effect under conditi ons (b) and (c), which are determined by

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77 functions and respectively. The parameters in Figure 4-7 are: 180 Q, 280 Q 3200 Q,11 P, 21 P, 10v. The parameters in Figure 4-8 are: 180 Q, 280 Q 3200 Q, 11 P, 20.5 P, 1v Figure 4-7. Optimal s ubscription fee depends on when 2122 Figure 4-8. Optimal s ubscription fee depends on when 22 Network effect () 2 Network effect () 2 F=2 F=F21 F= F=2

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78 Furthermore, we summarize the characteris tics of the optimal subscription fee with respect to the specific relationship of v P and Q in Corollaries 4-13, 4-14, 4-15 and 416. Corollary 4-13. When the network value is gr eater than the intrinsic value ( v ), the intermediary will never set the subscription fee at if 123QQQ and 1122 312PQPQ v QQQ Proof. According to condition (c) in Proposition 4-12, the WSI has incentive to set the subscription fee at which will allow all consumers subscribe to it, if 0 and 22 However, if 123QQQ, we must have 1122 312PQPQ QQQ so that is positive. Since v it follows that if 1122 312PQPQ v QQQ must be negative. Q.E.D. Corollary 4-14. When the network value is grea ter than the intrinsic value, the optimal subscription fee is 2 if 1232() QQQ and 1122 31222 PQPQ v QQQ Proof. I show that under the conditions in Corollary 4-14, the following inequalities hold: 21v 0 and 0 Detailed proof is relegated to Appendix C. Corollary 4-15. When the network value is grea ter than the intrinsic value, the optimal subscription fee is increasing in network intensity if 1232() QQQ and 1122 31222 PQPQ v QQQ Proof. See Appendix C for detailed proof. We show that th ere are only three possible cases of the optimal subscription fee: (1) 2 for the entire region v ; (2) 21F

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79 for 2122 and then 2 for 22 ; (3) 21F and then 2 for 2122 2 for 22 Corollary 4-16. If 22 and 123QQQ the optimal subscription fee is Proof. By inspection, is positive when 123QQQ under condition c in Proposition 4-12. Q.E.D. Proposition 4-12 specifies three strategies for the intermediary when the network effect is more significant ( v ): charge a low subscription fee to attract all consumers ( ); allow only consumers of S3 to subscribe (2 ); or allow partial consumers of S1 (or S2) and all consumers of S3 to subscribe (21F ). Although the optimal strategy should be determined by the network intensity and functions of and there are cases where the WSI is certain about the op timal subscription without having to evaluate the values of or Corollary 4-13 states the condition when th e intermediary will never charge a low subscription fee to attract all consumers to subscr ibe to its service. It suggests that if the market for the composite service is large and consumers have high valuation of the technical expertise (intrinsic value) of the WSI, it will never offer a low subscription fee to attract all consumers. Corollary 4-14 further states the c ondition under which the intermediary only needs to attrac t consumers of S3 to subscribe to its service. Corollary 4-15 characterizes the behavior of the optim al subscription fee if the market for the composite service is large but the consumer’s valuation of the WSI’s intrinsic value is not as high. It should be noted that if at some point, the optimal subscription fee is 2 the WSI will never set the subscription fee of In other words, if the WSI’s strategy is to

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80 attract consumers of S3 only, it won’t lower the subscription fee to attract all consumers as network effect intensifies. On the other hand, if the market size of the composite Web service S3 is small, Corollary 4-16 suggests that the WSI should set the s ubscription fee to attract all consumers if the network effect is large.

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81 CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH The intense competition and the constant changing nature of the global economy call for an agile information technology infrastr ucture for businesses to stay competitive and adapt to new threats and opportunities. Astute managers face an enormous pressure to cut costs and leverage existing informa tion technology resources, a task complicated by legacy systems built on technologies of different ages. Web services technology, touted as the foundation to an interoperabl e, location transparen t service-oriented architecture has been developed as a promisi ng solution to the challe nge of heterogeneity and change. Despite the enormous hype and doubt in industry, there has been a lack of academic awareness on this new computing paradigm, especially from business angle. The objective of my dissertati on is to study the business implications of Web services on firm strategies. Specifically, I focus on the opt imal strategies to pr ovide Web services for Web service vendors and Web service intermedia ries. The dissertati on is divided into three research topics. The first part of the dissertation deals with the optimal market structure to provide complementary Web services. In particul ar, I compare three market structures– independent service vendors (ISV), strategic alliance (SA), and Web service marketplace. Under the ISV market structure, two Web service vendors offer two complementary Web services separately and it is left to consumers to integrate the two Web services. Under the SA market structure, two Web service vendors form an al liance to sell an integrated

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82 Web service. Under the market structure of Web service marketplace, both individual and composite Web services are provided. Interesting managerial insights have been derived from theoretical analysis of a simplified model and numerical e xplorations on a generalized m odel. First, it is found that the marketplace dominates the ISV mark et structure, implying that Web service vendors can benefit from the in tegration of Web services. Second, the integration cost and the market characteristics of Web servi ces play important roles in determining the optimal market structure. If the valuation of the integrated software service is small, the service vendors prefer marketplace to SA. For larger valuation of the integrated Web service, the marketplace is pref erred if the integration cost is high while SA dominates if the integration cost is low. As the valuati on of the integrated software service becomes sufficiently high, SA turns to be the optimal market structure. In addition, if the integrated service enjoys a larger market pot ential, SA will beat marketplace for a smaller valuation of the integrated service. Fina lly, in a more balanced market, where one service vendor has advantage over software valuation while the other service vendor has advantage over market potential, SA is prefer red to marketplace for a smaller valuation of the integrated service. The second part of the dissertation is intrigued by observing Web service consumption model. In a Web-service-base d computing environment, the Web service consumers no longer need to license or house the software modules (Web services). Instead, the consumption of Web services involves accessing the software component on the remote server of Web service vendors. This causes network latency because of the

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83 turnaround time between Web service vendors and consumers. As a result, the performance of Web services is affected by where the Web services are located. In the second part of the dissertation, I solve the joint decision of location and pricing for a time-sensitive composite Web service provided by a Web service intermediary. I propose two spatial models to solve the joint opt imization problem by taking into account both delay cost and integr ation cost. A linear city model is first presented to study a special cas e where the individual Web se rvice providers are located at the ends of the linear city model. Optimal location and price are derived in the linear city model. Analytical results indicate th at the optimal location for the integrated Web service is midpoint between the service providers. When the delay cost is small, the WSI should set a low penetration price to capture en tire market demand. When the delay cost is high, the best strategy for the WSI is to share the market with the service providers. Furthermore, the optimal price and profit are increasi ng in delay cost and integration cost. To study the general cases wher e not all customers reside be tween the servic e providers, a unit circle model is applied. Analysis in the unit circle model shows that when the delay cost is low, the highest market-coveri ng price is the optimal price and the optimal location is midpoint between the service providers. Interestingly, an alysis of the unit circle model suggests that there exist multiple optimal locations for the integrated Web service if the distance between the two Web service ve ndors is large. Finally, I study the optimal strategies of a Web services interm ediary (WSI) that provides aggregation service and value-added technical services in a supply chain of complementary Web services. The value of th e aggregation service is dependent on the number of participants in the Web service supply chain. Specifi cally, the Web service

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84 consumers appreciate the aggregation service mo re if there are more vendors listed on the WSI. Likewise, the Web serv ice vendors think it is more worthwhile to list the Web services on the WSI if more consumers ar e aware of it. The value-added technical services are network-dependent, such as enforcing quality of service, enhancing security, improving Web service management. The aggregation and value-added services give the WSI the privilege to charge subscription fee to Web service consumers a nd listing fee to Web service vendors. The last part of the dissertation aims to solve fo r the optimal subscription and listing fees. In particular, I consider a WSI that faces service vendors providing two complementary Web services and there are demand for bot h the individual Web services and the composite Web service. Analytical results su ggest that in the presence of inter-network externalities, the optimal strategy for the interm ediary is to set a lis ting fee such that all Web service vendors list their Web services on it. On the other hand, the optimal subscription fee is determined by the rela tionship between the network value and the value of technical services. When the cons umers appreciate the technical services more than the network effect, the optimal subscripti on fee is increasing in network effect and the intermediary will attract more consumers as network effect intens ifies. In addition, the intermediary’s strategy is affected by the natures of Web services being traded, such as the prices of Web services, the market size, and the relationship between consumer’s valuation of technical services by the WSI and the prices of Web services. This dissertation should serve as a ground for extended research in the future. There are many interesting issues worthy of fu ture work. In the study on optimal market structure, I shall consider more market stru ctures. For example, instead of having the

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85 Web service vendors form a marketplace, a th ird-party company could serve the same task of providing both indivi dual and composite Web services In some cases, the two complementary Web services are not of equal im portance. In other wo rds, there could be no demand for a supplementary Web service by its own. Another in teresting issue of Web service market structure is the tradeo ff between customization and integration. While a strategic alliance or marketplace he lps reduce integration cost, the Web service consumer suffers from loss of customization. Therefore, I shall inco rporate the effect of customization for the analysis on optimal market structure in the future. Furthermore, the current research on market structure takes a “macro” view of the problem, ignoring the profit distribution among service vendors. It is interesting to analyze the market structures under different profit division mechanisms. In the analysis of optimal location and pric ing of an integrated Web service, I only derive analytical results for th e case when the delay cost is low in the unit circle model. While analytical derivations of general scenarios for large delay cost become mathematically impracticable to solve, I can adopt other numerical approaches, such as simulations, to draw more insights. The second and third part of the dissertation address diffe rent types of Web service intermediary–a “matchmaker” that provides value-added services and a “market maker” that sells an integrated Web service. A WSI that has the technical capabilities can provide both integration and aggregation serv ices. This means the relationship between the WSI and the Web service vendors could be both cooperative and competitive. It’s worth studying the optimal strategies of su ch Web service intermediaries. Another

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86 avenue of future research is to extend th e monopolistic WSI to consider competition in the duopoly setting.

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87 APPENDIX A PROOFS OF CHAPTER 2 A.1 Proof of Lemma 2-1 In a symmetric market, where the valuat ions and market sizes of the two Web services are equal, the profit maximiza tion problem for the service vendors is 12 33 3()()max sisiss sisisisisiPPPPc PDPQQPQQ VV 1,2i (A.1) By solving the above problems for each Web service vendor simultaneously, one gets the optimal solutions described as follows. 3333 3323siVVQVVQcVQ P QVVQ (A.2) 22222 33333333333333 2 333()() (23)siVVQVQcQVQVVQQVQQVVQcVQQcVQ VQVVQ (A.3) Take the first and s econd derivatives of s i with respect to c. 222 3333333 2 3332[()()()] (23)siVQVQQQVQQVcVQVc cVVQVQ (A.4) 2*2 333 22 3332() (23)siQVQVQV cVQVQV (A.5) By inspection, the first derivative is negative under the assumption that 30 cVV while the second derivative is positive. Therefore, s is decreasing and convex in c, where *** 12 s ss In addition, s is maximized when 0 c Recall that

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88 the optimal total profit under the ISV market structure is **1 max, 2 s sVQ It follows that the optimal total profit is s when 0c since 22 3333333 2 33()(9)8()4 1 (0) 22(23)sQQVVVVQVVVVVQ cVQ QVVQ (A.6) Obviously, when 3VV, we always have *1 (0) 2scVQ. Therefore, the shape of the function s switches from s to 1 2 VQ as the integration cost increases. A.2 Proof of Lemma 2-3 We solve the constrained profit maximiza tion problem by establishing Lagrangean function, see (A.7) and then solving for the KKT conditions in Equations (A.8) to (A.11). 123 12333123 3()()()()()mm mmmmmmPPP LPQQPQQPQQPPcP VVV (A.7) 111 12 0, 0mmm PmPP LQQPL V (A.8) 222 22 0, 0mm PmmPP LQQPL V (A.9) 233 333 32 0, 0mmm PmPP LQQPL V (A.10) 1230, 0mmmLPPcPL (A.11) Obviously, the zero price solution can’t be optimal. So we must have equalities in Equations (A.8) to (A.10). If 0 then according to (A.11) we have 0 L, which implies312 mmmPPPc. Substitute into Equations (A.8 ) to (A.10) and one gets the optimal solutions as follows.

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89 ** 33 12 33(22) 22(2)2mmQVVVc VV PP QVQV (A.12) 3333 3 33(22) 22(2)2mVQVVVcV P QVQV (A.13) 2 33 33 33(22) 11 244(2)mQQVVc VQVQ QVQV (A.14) Note that 0 requires 322 VVc. On the other hand, if 322 VVc, we must have we have0 The corresponding optimal solutions are 1ˆ 2mV P 2ˆ 2mV P 3 3ˆ 2mV P (A.15) 3311 ˆ 24mVQVQ (A.16) In summary, the optimal profit of the mark etplace is specified in Eq. (A.14) if 322 VVc and specified in Eq. (A.16) otherwise. Q.E.D. A.3 Proof of Lemma 2-4 Take the first and sec ond order derivative of m with respect to c 33 33(22) 2mQQVVc cVQVQ 2* 3 2 332 2mQQ cVQVQ (A.17) According to Lemma 2-3, m is valid in the interval 322 VVc. Therefore, m is increasing and concave in c. It is obvious that th e optimal profit when 322 VVc (*ˆm ) is increasing in 3V and 3Q Next we take the derivative of m with respect to 3V and 3Q as follows. *22222222 3333 2 333(222) (2)mQQVQVQcQQVcVQQcVQ VQVQV (A.18)

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90 *222222222 333333 2 333(2) (2)mVVVQQVQcVQVVQQVcQcVQ QQVQV (A.19) Obviously, *m is increasing in 3V since 30mV In addition, under the constraints 322 VVc and cV, we have 30mQ In summary, *m (*m and *ˆm ) is increasing in both 3V and 3Q Q.E.D. A.4 Proof of Proposition 2-6 First we compare the profit under ISV market structure (* s ) vs. that of a marketplace (*m ) with respect to zero integration cost as follows. 322 ** 333 2 3333() (0)(0) (2)(23)msVVQQQ cc QVVQVQQV (A.20) Obviously, *m is always greater than s at 0c From lemma 2-1, we know that the optimal total profit of the independent service vendors is maximized when the integration cost is zero. Further, Lemma 2-3 suggests that the marketplace’s optimal profit minimizes when the integration cost is zero. Therefore, the marketplace is always a better market structure than th e ISV market structure. Q.E.D. A.5 Proof of Proposition 2-7 Proposition 2-6 suggests that the marketpl aces always dominates the ISV market structure. So we focus on comparing the prof its under the market stru cture of SA against marketplace. First note that when 32 VVV we have 322 VVc for all 0c. According to Lemma 2-5, the optim al profit of the marketplace is 3311 24 VQVQ in the interval [0,] cV. This suggests that the strategic a lliance is always dom inated in this

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91 situation since its optimal profit is 331 4 VQ Therefore, when 32 VVV, the marketplace is the optimal st rategy for the service vendors. Next we compare the optimal profit of the marketplace against that of the strategic alliance by analyzing the differe nce of optimal profits under the two market structures when 3 32 2(4) Q VVV Q. Note that when the integration cost is zero, the profit difference is ** 3333 3 33(42) ()(0) 4(2)maVQQVQVQV Vc VQQV (A.21) When 3 32 2(4) Q VVV Q, we have 3333242 QVQVQVQV suggesting that is always positive. Therefore, strategi c alliance is dominated by the Web services marketplace if 3 32 2(4) Q VVV Q. Summarizing the results above, we conclude that the marketplace is the optimal market strate gy for the service vendors regardless of the integration cost when 3 32 (4) Q VVV Q. Q.E.D. A.6 Proof of Proposition 2-8 From the proof of Proposition 2-7, we know that **(0)mac if 3 32 (4) Q VV Q. Note that under the assumption 0 cV we have322 VVc since 3 32 (4) Q VV Q. This implies that the functional form of the marketplace is *m according to Lemma 2-3.

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92 By equating m with a, one gets two roots of the integration cost c, where the marketplace generates the same profit as th e strategic alliance, described as follows 22 333 3 342 1 22 QVVVQQ VV Q (A.22) Since 0cV and 31 2 VVV we have the smaller root as the threshold value c. From lemma 2-3, we know that *m is increasing in the inte gration cost. Therefore, the strategic alliance is superi or to the marketplace when cc while the marketplace is superior to the strategic alliance when cc. A.7 Proof of Proposition 2-9 According to lemma 2-5, the optimal profit of the Web services marketplace takes the form of *m when 34 VV. From lemma 2-4, we note that *m is increasing in integration cost. Theref ore, in the interval 0cV the optimal profit of the marketplace reaches maximum at cV The difference of the optimal profit of the marketplace and the strategic alliance is 22 ** 333333 3 33(2812) ()() 4(2)maQVVQVVQVQQV VcV VQQV (A.23) If 3()0 V we conclude that the profit of the st rategic alliance is greater than that of the marketplace in the interval 0 cV The condition 3()0 V is equivalent to 22 33333(28)120 QVVQVQVVQ (A.24) which is satisfied under the following to conditions 3VV or 3VV (A.25)

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93 where 22 33 384 (4) QQQQQ VV Q 22 33 384 (4) QQQQQ VV Q From proposition 2-8, we know that the strategic alliance dominates the marketplace if 3 32 (4) Q VV Q. Obviously, the second condi tion is not feasible since 32 (4) Q VV Q. Therefore, the strategic allian ce dominates the marketplace under the first condition, i.e., 3VV. Q.E.D.

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94 APPENDIX B PROOFS OF CHAPTER 3 B.1 Proof of Proposition 3-1 First, we derive the WSI’s optimal price and profit at given location x Note that the WSI’s demand is conditional on the price of the integrated service 3P therefore, we need to calculate the WSI’s optimal price a nd profit under different cl assifications of the demand function. Case 1: If 312PPPct, we have 120yy This makes zero profit for the WSI since the price is too high. We will disr egard this situation in the future analysis. Case 2: If 12312(1) PPctxPPPct we have 120,mmyyx. The total demand of the WSI is 12mmDyy The decision problem of the WSI is to optimally set the price 3P to maximize profit, described in (B.1). 3123 3 12312() max =2 s.t. (1)PPPctP P t PPctxPPPct (B.1) By solving the profit maximization problem in (A1), one gets the optimal price and profit as follows. 312(1) PPPctx, 122[(1)] x PPctx (B.2) Case 3: If 12312(1) PPctxPPPctx, we must have 1 myx and 21myx, so the total demand of the WSI is 2 mDxy Similar to the case 1-2, we

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95 solve the profit maximization pr oblem for the WSI. The optimal price and profit in this case are summarized as below. a. If 122() tPPc and 121 32 PPct x t the optimal price and profit are the same as in (B.2). b. If 122() tPPc and 120 3 PPct x t or if 1202() tPPc and 12() 0 tPPc x t the optimal price and profits are 12 3(1) 2 PPctx P 2 12[(1)] 4 PPctx t (B.3) c. If 1202() tPPc and 12()1 2 tPPc x t the price and profits are 312PPPctx, 12PPctx (B.4) Case 4: If 312PPPctx, we have1myx and 21myx. This suggests that the WSI captures the whole market demand. In addition, if the WSI attracts all demand in market, it doesn’t gain more profit if it lowers the price. Therefore, the bounding solution gives the optimal price and profit in this case, which is the same as in Eq. (B.4). By comparing the maximal profits in Cases B-2 to B-4, we can determine the optimal profit for the WSI given any location x Next, given the WSI’s optimal profit at any location x the optimal location of the WSI is selected as the one that yields maximum profit. Since the profit in case 1-3 is conditional on the delay cost t we determine the optimal location and profit for th e WSI under different setting of t First, we look at the scenario when 122() tPPc If 120 3 PPct x t we need to compare the profits

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96 specified in Equations (B.2), (B.3), and (B.4) to derive the optimal price and profit. It can be shown that the optimal profit is describe d in Eq. (B.3). Note that the profit in Eq. (B.3) is increasing in x Therefore, the intermediary wi ll set its location at the upperbound. In summary, the optimal loca tion, price and pr ofit for the range 120 3 PPct x t are 123 PPct x t 12 32() 3 PPct P 2 124() 9 PPct t (B.5) In a similar manner, we compare the prof its in Equations (B.2) and (B.4) for the scenario when 121 32 PPct x t Our analysis shows that the maximum profit is found in (B.2). Consequently, the optimal location, price and profit for the range 121 32 PPct x t are 1 2 x 3120.5 PPPct 120.5 PPct (B.6) By comparing the profits in Equations (B.5) and (B.6), we conclude that *1 2 x is the optimal location for the WS I. This is because when 122() tPPc 22 12120.5()4() 0 9 ttPPcPPc t (B.7) The corresponding optimal price and profit are 3120.5 PPPct, 120.5 PPct (B.8)

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97 Next, we consider the scenario when 1202() tPPc Following a similar solution procedure, we find that the optimal location is *1 2 x and the corresponding price and profit are the sa me as in Eq. (B.8). To summarize the above two scenarios, the optimal location of the WSI is *1 2 x independent of the delay cost. In addition, the optimal price and profit is shown in Eq. (B.8). By inspection, the optimal profits and prices are increasing in delay cost t and integration cost c. In addition, the comparative statics is straightforward since **ct Q.E.D. B.2 Proof of Lemma 3-2 Note that the total cost to buy from the We b service intermediary (WSI) or to create the integrated Web service by buying S1 and S2 from individual service providers (SP) is determined by the customer’s and the WSI’s locations, see Equations. (3.6) and (3.8). Therefore, we need to calculate th e highest market penetration price 3P under four scenarios, i.e., the WSI is located in the AB BC, CD or DA segment. In each scenario, we consider demand of customers in AB, BC, CD and DA. Scenario 1. The WSI is between A and B ( 0 x d ) For customers between AB, the total cost to buy from the SP is 12PPctd while the highest cost to buy from the WSI is 3Ptx if 0 2 d x, or 3() Ptdx if 2 d x d. Thus, the WSI attracts all customers between AB if

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98 31212min{,()} PPPctxPPctdx. Similarly, all customers between CD will buy from the WSI if 3121 () 2 PPPctd. For customers between BC, the cost to buy from SP and WSI are greater than the cost incurred by the customer at location B, but the increased cost is greater if buying from SP. This implies that if customer at point B will buy from WSI, all customers between BC will buy from the WSI too. Li kewise, all customers between DA will buy from the WSI if the customer at location A prefers the WSI. Scenario 2. The WSI is between B and C ( 1 2 dx ) Due to similar argument as in scenario 1, the WSI captures the entire market demand in section AB if 312() PPPctdx while all customers between CD prefer the WSI if 3121 (2) 2 PPPctdx. In addition, all customers between BC will buy from WSI if the customer at location B buys from WSI and all customers between DA will buy from the WSI as long as customer at A prefers to buy from the WSI. Adding the fact that 1 2 dx and 0 dx the highest price that could still capture the whole market is 12() PPctdx Scenario 3. The WSI is between C and D ( 11 22 x d ) Consider the marginal cust omer who is located between AB and diagonal to the WSI. The cost to buy from SP is 12PPctd while the cost to buy from WSI is 3/2 Pt. Therefore, the custom er will buy from WSI if 312(1/2) PPPctd For all other customers, they have a higher or equal cost to buy from SP while a lower cost to

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99 buy from WSI. Thus, they will all buy from WSI if the marginal customer buys from WSI. Scenario 4. The WSI is between D and A ( 1 1 2 dx ) Similar to the argument in previous scen arios, all customers will buy from WSI if the customer at point B buys from the WSI. The highest price the WSI can charge is 12(1) PPctx Q.E.D. B.3 Proof of Lemma 3-4 We prove this lemma by induction. Let N denote the reduced demand when the WSI raises its price above the market-covering price. First consider raising price to 3t P N(1 k ). Since 3P is the highest market penetration price, there is one marginal customer who is indifferent between the WSI and the service providers. This marginal consumer will switch to th e service providers if the WS I raises the price, i.e., 1 N At the same time, the customer next to the marg inal customer but closer to the WSI becomes indifferent between the se rvice providers and the WSI. Suppose we have 1 Nk when 3(1) t Pk N and there is one marginal customer (denoted by 0 ). If we further raise the price by t N the marginal customer at 0 will switch to the service providers. At the same time, the customer next to 0 and closer to or further from the WSI becomes the next marginal customer. The WSI continue s to loose customers in this way until all customers in market switch to the serv ice providers. In summary, we have min{,}NkN when 3kt P N. If 3(1) ktkt P NN it’s similar to the case of

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100 3kt P N except that one more customer switche s to the WSI instead of becoming the kth marginal customer. Q.E.D. B.4 Proof of Proposition 3-5 We first prove that the market-covering price is optimal for the WSI if 122() tPPc When the price of the integrated service is 3P the WSI obtains total profit 3PN. If the WSI sets a price below 3P it will lose profit since reducing price won’t increase the WSI’s demand. On th e other hand, if the WSI raises its price by 3P, the WSI’s profit becomes 33333()()()PPNNPNPNNNP (B.9) Obviously, if 3P is very large, all customers will switch to the service providers (NN) and the WSI has zero profit. We now restri ct our attention when NN Lemma 3-3 suggests that when 3(1) ktkt P NN Nk thus 33NPkP. Further, we have 33() PNNPNkt. According to the functional form of 3P in Lemma 1, we have 3Pt if 122()tPPc. Therefore, the WSI’s profit decreases when it charges a price higher than 3P regardless of its location x Moreover, Lemma 3-2 suggests that if the WSI charges the highest market-covering price 3P it gains maximum profit if it is located between the service provi ders. In summary, the WSI’s total profit is maximized if it is located between the se rvice providers and sets the price at 3P Q.E.D. B.5 Proof of Proposition 3-6 The highest penetration price charged by the WSI when it is located between the Web service vendors can be rewritten as follows.

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101 12 312 121 (1) if 0and 22 1 (2) (), if and 2 22 1111 (3) (), if and 2 2322 d PPctxxxd d PPPctdxxdxd PPctdddxd (B.10) Look at the highest market-cove ring price described in (1 ) to (3) of Eq. (B.10). Note that if 1 3 d, there could be multiple optimal locations ( 11 2 22 dxd ) for the WSI since the price in (3) is lower than in (1) an d (2). In addition, note that price in (1) is increasing in x while the price in (2) is decreasing in x Therefore, the optimal location for the WSI is 2 d for (1) or (2). In cases where there are multiple optimal locations, 2 d is in the range between 1 2 d and 1 2 2 d In summary, 2 d is the optimal location, regardless of the functional form of the optimal price 3P Q.E.D.

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102 APPENDIX C PROOFS OF CHAPTER 4 C.1 Proof of Lemma 4-1 We consider the intermediary’s revenue from subscription and listing separately. First observe from the definition of 'jys in Equations (4.1) to (4.3) that the intermediary’s revenue from collecting subscription fee is non-decreasing in the proportion of listed service vendors ('i x s ). Next we analyze the intermediary’s revenue from charging listing fee to the service vendors. According the definition of x in Eq. (4.5), we can rewrite the interm ediary’s revenue from listing (L ) is defined as follows. 111332223312 1113321212 2223312121 12(1) ()() if max{,} (2) () if L and (3) () if L and (4) LPyQyQPyQyQLLL PyQyQLNLLLL PyQyQLNLLLL LNLN 12 if Lmin{L,L} (C.1) where 11133 1 1() PyQyQ L N and 22233 2 2() PyQyQ L N are defined as the highest listing fee the intermediary can charge in order to induce all service vend ors to list on it. The revenue from listing is described in (1) to (4) of Eq. (C.1), which is a kinked one, depending on the value of the listing fee. Note th at case (1) corresponds to 120,1 xx ; case (2) corresponds to 21 x and 101 x ; case (3) corresponds to 11 x and 201 x ; case (4) corresponds to 121 xx If the intermediary charge a listing fee of 1L all service vendors of S1 will list on the intermediary (11 x ) and correspondingly, the revenue from charging listing fee to

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103 service vendor of S1 is 1111133() x LNPyQyQ If the listing fee is higher than 1 L (' 1LL), then there will be le ss service vendors listing on the intermediary, i.e., 11 x This in turn leads to a lo wer subscription rate, i.e., 11yy and' 33yy. It follows that '' 1113311133()() PyQyQPyQyQ. This means that the intermediary obtains less profit from service vendors of S1. At the same time, the profit from the subscription side is decreasing due to lesser propo rtion of subscribers. The similar argument applies to the case when the listing fee is higher than 2 L ('' 2LL). Finally, the intermediary’s profit will not improve if it charges a listing fee lower than 12min{,} L L since the proportion of listing service vendors and subscribers rema ins unchanged and thus a lower listing will cause lower revenue from collecting listing f ee. In summary, the intermediary should set the listing fee to allow all service vendors list on it. C.2 Proof of Lemma 4-3 Lemma 4-3 is drawn from solving the pr ofit maximization problem defined in Eq. (4.8), which is a constrained optimization pr oblem with quadratic objective function. The approach presented here is equivale nt to applying KKT conditions. Since the optimal solution is either an interior solution or boundary solution, we first solve the unconstrained version problem. Optimal subs cription fee and profit for the unconstrained optimization problem are described as follows. 11223 11 12()() 2() vPQvPQvQ F QQ (C.2) 2 22 11223 1111 12 22 13122321 12()() () 4() 2(2)2(2) 4() QvPQvPvQ F vQQ QQvvvPvPQQvvvPvP vQQ (C.3)

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104 Next we analyze whether 11F is a feasible solution as follows. 11223 11 12()() 2() vPQvPQvQ F QQ (C.4) 11223 11 12(3)(3) 2 2() vPQvPQvQ F QQ (C.5) Finally we calculate and compare the pr ofits at lowerand upper bound as follows. 111122123()()()() PQPQPPQ (C.6) 1122123 11()(2)()(2)(2) (2) vPQvPQvPPQ v (C.7) 11223 1111(2)(2) (2)() vPQvPQvQ v (C.8) Now we conclude: a. If 11223()()0 vPQvPQvQ from Eq. (C.4) we know that 11F and further 1111(2)() according to Eq. (C.8). Therefore, the optimal subscription fee is as specified in condition (a) in Lemma 4-3; b. According to Eqs. (C.4) and. (C.5), if 11223()()0 vPQvPQvQ and 11223(3)(3)0 vPQvPQvQ we must have 112 F Therefore, the optimal subscription fee is 11F which corresponds to condition (b) in Lemma 4-3; c. If 11223(3)(3)0 vPQvPQvQ then according to Eq. (C.5) we know that 2 F and 1111(2)() Therefore, the optimal subscription fee is 2 as presented under the third c ondition of Lemma 4-3. Q.E.D. C.3 Proof of Proposition 4-6 Lemma 4-3, 4-4 and 4-5 specify the optimal strategy if the intermediary desires to achieve different combinations of 'jys(1,2,3 j ), which is summarized in Table C-1. Since the optimal strategy is dependent on the network intensity ( ), we solve for the optimal subscription fee under each of th e four situations in Table C-1.

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105 Table C-1. Optimal Subscription Fee when v 1iF 110 1112 1213 13v 11F 2 2 11F 12F 12 F 2 2 2 13F v v v v The optimal subscription fee can be found as the one of 11F 12F and 13F that maximizes the profit of the WSI. We observe: a. If 110 the optimal subscription fee is 12F as specified in (a) of Proposition 4-6 since 2 1122312 121211 123(3)(3)(2) ()(2)0 4() QvPQvPQvPP F vQQQ (C.9) 2 1122312 121213 123[()()()] ()()0 4() QvPQvPQvPP Fv vQQQ (C.10) b. If 1112 the optimal subscription fee is 2 as specified in (b) of Proposition 4-6 since 1122312 1113()(2)(2)()() (2)()0 vQPQPQvPP v v (C.11) c. If 1213 the optimal subscription fee is 11F as specified in (c) of Proposition 4-6 since 2 11223 111112 12[(3)(3)] ()(2)0 4() QvPQvPvQ F vQQ (C.12) d. If 13v we conclude that the op timal subscription fee is as specified in (d) of Proposition 4-6 by observing the fact that 1111()(2) in the proof of (1) in Lemma 4-3 and 1113(2)() v from Eq. (C.11). Q.E.D. C.4 Proof of Proposition 4-12 Proposition 4-12 can be proved by using th e similar approach when we prove Proposition 4-6. First we summarize the op timal strategy of the intermediary under

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106 different settings of the network intensity ( ) and propositions of subscribers ('jys, 1,2,3j) obtained in discussing Cases II-1 to II-3, see Table C-2. Table C-2. Optimal Subscription Fee when v 2iF 21v 2122 22 21F v 21F 22F 2 2 2 23F 2 2 2 The optimal subscription fee can be found as the one of 11F 12F and 13F that maximizes the profit of the WSI. We observe: a. If 21v the optimal subscription fee is 2 as specified in (a) of Proposition 4-12 since 21233()(2)()0 vQv; b. If 2122 the optimal subscription fee is dependent on the sign of function because 2 11223312 212123 12()()4()() ()(2) 4() QvPQvPvQvvQQQ F vQQ c. If 22 the optimal subscription fee is dependent on the sign of function because 212311223()(2)()() PQPQQ Q.E.D. C.5 Proof of Corollary 4-14 It can be easily checked after simple algebra that under the conditions specified in Corollary 4-14, we must have 21v and 31123(2)(2) vQvPQvPQ. It follows from (a) in Proposition 4-12 that the optimal subscription fee is 2 when 21v As for the optimal subscription fee in the range of 2122 first note that is negative if 21 since 21311223()4[(2)(2)]0 vQvPQvPQvQ (C.13)

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107 Next, we calculate the first derivative of with respect to and evaluate the sign for 22 described in Eqs. (C.14) and (C.15). 1211223()[()()3] QQvPQvPQvQ (C.14) 22123()2()0 vQQQ (C.15) Since is increasing in and it is evaluated negative at 22 it follows that is negative for the interval 2122 Adding the fact that is negative at 21 we conclude that 0 is negative in the interval 2122 According to (b) in Proposition 4-12, we know that th e optimal subscription fee is 2 under the condition that 2122 In addition, the optimal subscription fee will never switch from 2 to 21F with the increase of when 123QQQ Finally, we show that if 22()0 the function is negative in the interval 22 due to two fact s: (1) functions and have same sign when 22 see Eqs. (C.16) and (C.17); (2) is decreasing in when 1232() QQQ 222 2211211322233()4(2) vvQvQQPQQvQPQQvQ (C.16) 222 11211322233 22 122 () vQvQQPQQvQPQQvQ QQ (C.17) In summary, under the conditions specifi ed in Corollary 4-14, the optimal subscription charged by the intermediary is 2 if v Q.E.D. C.6 Proof of Corollary 4-15 First note from the definition of 21 that under the condi tions specified in Corollary 4-15, we have 21v This implies that first condition specified in

PAGE 120

108 Proposition 4-12 will never be satisfied. Theref ore, we only need to consider the optimal subscription fee under conditions (b) and (c), i. e, the optimal subscription fee is either 21F or 2 when 2122 while the optimal subscription fee is either or 2 when 22 Since 21F is found between and 2 v and 21F when 21 we can prove that the optimal subscr iption fee is increasing in if there are only three possible cases for the optimal subscription fee: (a) 2 for the entire region v ; (b) 21F for 2122 ; then 2 for 22 ; (c) 21F and then 2 for 2122 ; 2 for 22 We note that: a. From the proof of Corollary 4-14, we know that when 1232() QQQ, the optimal subscription will never switch from 2 to 21F in the interval 2122 Furthermore, we have shown in the previous Corollary that if the optimal subscription fee is 2 when 2122 i.e., 22()0 we must have 0 for the region22 ; b. If the optimal subscription fee is 21F in the interval 2122 we must have 22()0 Since functions and have same sign, we must have 22()0 which implies that the optimal subscription take the form of at 22 ; c. It is already shown in the previous Corollary that if 22()0 the optimal subscription fee takes the form of 2 in the interval 22 Q.E.D.

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109 LIST OF REFERENCES Bailey, J. P. 1998. Intermediation and electr onic markets: aggregat ion and pricing in Internet commerce. PhD Dissert ation, MIT, Cambridge, MA. http://www.rhsmith.umd.edu/l bpp/jbailey/pub/phdthesis.pdf last accessed June 08, 2004. Bailey, J. P. and Bakos, Y. 1997. An exploratory study of the emerging role of electronic intermediaries. International Journal of Electronic Commerce 1 3 7-20. Bakos, J. Y. 1997. Reducing buyer search costs: implications for el ectronic marketplaces. Management Science 43 12 1676-1692. _________. 1998. The emerging role of electro nic marketplaces on the internet. Communications of the ACM 41 8 35-42. _________, Brynjolfsson, E. 1999. Bundling in formation goods: pricing, profits and efficiency. Management Science 45 12 1613-1630. _________, _________ 2000. Bundling and competition on the internet. Marketing Science 19 1 63-82. Baye, M., Morgan, J. 2001. Informati on gatekeepers on the internet and the competitiveness of homogeneous product markets. The American Economic Review 91 3 454-474. Bhargava, H., Choudhary, V. 2004. Economics of an information intermediary with aggregation benefits. Information Systems Research 15 1 22-36. Biglaiser, G. 1993. Middlemen as experts. The Rand Journal of Economics 24 2 212-223. Chuang, J. C, Sirbu, M. A. 1999. Optimal bundling strategy for digital information goods: network delivery of articles and subscriptions. Information Economics and Policy 11 2 147-177. Corbett, J. C., Karmarkar, U. S. 1999. Optimal pricing strategies for an information intermediary. Working paper, Univer sity of California, Los Anglos. http://personal.anderson.ucla.edu /charles.corbett/papers/99-001.pdf last accessed June 11, 2004. Cubera, F., Khalaf R., Mukhi, N., Tai, S., Weerawarana, S. 2003. The next step in Web services. Communications of the ACM 46 10 29-34.

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110 Economides, N., Salop, S. 1992. Competition and integration among complements, and network market structure. Journal of Industrial Economics 40 1 105-123. Erlenkotter, D. 1977. Facility location with pricesensitive demands: private, public and quasi-public. Management Science 24 4 378-386. Farrell, J., Katz, M. 2000. Innovation, rent extr action and integration in systems markets, Journal of Industrial Economics 48 4 413432. Ferris, C., Farrell, J. 2003. What are Web services? Communications of the ACM 46 6 31. Fingar, P. 2000. Component-based frameworks for e-commerce. Communications of the ACM 43 10 61-66. Gehrig, T. 1993. Intermediation in search markets. Journal of Economics and Management Strategy 2 97-120. Geng, X., Huang, Y., Winston, A. B. 2003. Smart marketplaces: a step beyond web services. Information and e-Business Management 1 1 15-34. Gottschalk, K., Graham, S., Kreger, H., Sne ll, J. 2002. Introduction to web services architecture. IBM Systems Journal 41 2 170-177. Samtani, G., Sadhwani, D. 2001. EAI and Web services: easier enterprise application integration? http://www.webservicesarc hitect.com/content/artic les/samtani01print.asp last accessed June 08, 2004. Hagel, J. III, Brown, J. S. 2001. Your next IT strategy. Harvard Business Review October 105-113. Harter, D. E., Slaughter, S. A. 2003. Quality improvement and infrastructure activity costs in software development: a longitudinal analysis. Management Science 49 6 784-800. Huang, Y., Chung, J-Y. 2003. A web servicebased framework for business integration solutions. Electronic Research and Applications 2 15-26. Irani, R. 2001. Web services intermedia ries: adding value to Web services. http://www.webservicesarchitect.com/content/articles/irani07print.asp last accessed June 11, 2004. Johansson, J. M. 2000. On the impact of ne twork latency on distributed systems design. Information Technology and Management 1 3 183-194. Johansson, J. M., March, S. T., Naumann, J. D. 2000. The effects of parallel processing on update response time in distributed database design. Proceedings of International Conference on Information Systems (ICIS). 187-196.

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111 Kaplan, E., M. Sawhney 2000. E-hubs: the new B2B marketplaces. Harvard Business Review May-June 97-103. Kreger, H. 2003. Fulfilling the Web services promise. Communications of the ACM. 46 6 29-34. Krishnan, M. S., Kriebel, C. H., Kekr e, S., Mukhopadhyay, T. 2000. An empirical analysis of productivity and quality in software products. Management Science 46 6 745-759. Li, B., Golin, M. J., Italiano, G. F., Deng, X. 1999. On the optimal placement of web proxies in the internet. Proceedings of 18th IEEE INFOCOM 1282-1290. Little, M. 2003. Transactions and web services. Communications of the ACM 46 10 4954. Liu, Q., Padmanabham, V. N., Voelker, G. M. 2001. On the placement of web server replicas. Proceedings of 20th IEEE INFOCOM 1587-1596. Lyytinen, K., Mathiassen, L., Ropponen, J. 1998. Attention shaping a nd software risk: a categorical analysis of four clas sical risk management approaches. Information Systems Research 9 3 233-255. Matutes, C., Regibeau, P. 1992. Compatibili ty and bundling of complementary goods in duopoly, Journal of Industrial Economics 40 1 37-54. Naert, P. 1971. Optimizing consumer advertising, intermediary and markup in a vertical market structure. Management Science 18 4 90-101. Newcomer, E. 2002. Understanding Web services: XML, WSDL, SOAP and UDDI. Addison-Wesley. Parker, G. and Van Alstyne, M. 2001. Op ening the code: how open is optimal? Proceedings of 23rd International Conference s on Information Systems (ICIS) 519523. Rubinstein, A., Wolinsky, A. 1987. Middleman. The Quarterly Journal of Economics 102 3 581-594. Rust, R. T., Kannan, P. K. 2003. E-service: a new paradigm for business in the electronic environment. Communications of the ACM 46 6 36-42. Salkever, A. 2003. Slowly weav ing web services together. BusinessWeek June 24, 2003. Schmelzer, R. 2002. XML and Web services unleashed. Sams Publishing Indianapolis, IN.

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112 Sharma, S. K., Gupta, J. N. D. 2002. Application service pr oviders: issues and challenges. Logistics Information Management 15 3 160-169. Sleeper, B. June 2001. Defining Web services : an analysis memo from Stencil Group. http://www.perfectxml.com/Xanalysis/TSG/TSG_DefiningWebServices.pdf last accessed June 09, 2004. Spulber, D. 1996. Market microstructure and intermediation. Journal of Economic Perspectives 10 3 135-152. Sun, Y. 2003. A location model for web servi ces intermediaries. Doctoral Dissertation, Warrington College of Business Administration, University of Florida, Gainesville, FL. _____, Koehler, G 2003. A location model for web services intermediaries, California State University, San Marcos working paper. Sundarraj, R. P., Talluri, S. 2003. A multi-peri od optimization model for the procurement of component-based enterpri se information technologies. European Journal of Operational Research 146 2 339-351. Venkatesh, R., Mahajan, V. 1993. A probabi listic approach to pricing a bundle of products or services. Journal or Marketing Research 30 4 494-508. Venkatesh, R., Kamakura, W. 2003. Optim al bundling and pricing under a monopoly: contrasting complements and substitutes from independently valued products. Journal or Business 76 2 211-231. Welch, D. 2003. Where web services meets the road. BusinessWeek June 24, 2003. Wooders, J. 1997. Equilibrium in a market with intermediation is Walrasian. Review of Economic Design 3 75-89. Yavas, A. 1994. Middleman in bilateral search markets. Journal of Labor Economics 12 3 406-429. Zegura, E. W., Calvert, K., Bhattacharjee, S. 1996. How to model an Internetwork. Proceedings of 16th IEEE INFOCOM 594602.

PAGE 125

113 BIOGRAPHICAL SKETCH Qian Tang is born in Shanghai, China, in 1975. She received her Bachelor of Engineering in Management Information Systems in 1998 from Tongji University, Shanghai. In August 2000 she came to the US for a doctoral program in the Department of Decision and Information Sciences. She ea rned her Master of Science degree in 2003 and is expecting to receive the Ph.D. degree in August 2004. Qian’s research interests are in the analytical modeling of economics of information systems, and the impact of IT on firm strategies. Her teaching interests include e-commerce, information systems, et c. She is a member of Association for Information Systems (AIS), Decision Sciences Institute (DSI), and Institute for Operations Research and Ma nagement Science (INFORMS).


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ECONOMICS OF WEB SERVICE PROVISIONING:
OPTIMAL MARKET STRUCTURE AND INTERMEDIARY STRATEGIES















By

QIAN TANG


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2004

































Copyright 2004

by

Qian Tang



































To Yanzan and my parents















ACKNOWLEDGMENTS

Words do not suffice to express my gratefulness to those who have helped me get

through the difficulties and accomplish this dissertation. First, I give my special thanks

to Dr. Hsing Kenny Cheng, chairman of my supervisory dissertation committee. Without

his support, encouragement, and patient guidance, I could not have finished this

dissertation and developed the strong interests in research. I also thank Dr. Shubho

Bandyopadhyay, Dr. Steven Shugan, and Dr. Asoo Vakharia for serving on my

supervisory committee, and for their helpful support. I appreciate Dr. Gary Koehler for

his valuable suggestions on my research. I learn the art and beauty of research from all

the faculty members in my department. To show my thankfulness, I am determined to

follow their examples, and be a good professor and scholar.

Last, but certainly not least, I wish to dedicate my thanks to Yanzan and my parents

from the bottom of my heart. Although they are physically far away from me, I feel they

are with me all the time. It is their unconditional support and love that have made all my

accomplishments possible.
















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

L IST O F T A B L E S ................. .................................................................. .. viii

LIST OF FIGURES ......... ......................... ...... ........ ............ ix

ABSTRACT .............. .......................................... xi

CHAPTER

1 IN TR O D U C T IO N ....................... .... .............. ..................... ..............

1.1 W eb Services: History and Overview................. ............................................ 2
1.1.1 Softw are as service ................................................ ...... ........ ....... .. .. 3
1.1.2 Platform independence ........................................ .......................... 4
1.1.3 Integration of W eb services............................................... .................. 5
1.2 Business Im plications of W eb Services..................................... .....................6
1.2.1 R education of integration cost.................................... ...... ............... 7
1.2.2 Service-oriented architecture ........................................... ...............7
1.2.3 Web service intermediary................................. 8
1.3 R research Issues ............................................... ...................... 9
1.3.1 O ptim al m market structure....................................................... ..................9
1.3.2 Optimal location and pricing of an integrated Web service...................10
1.3.3 Optimal subscription and listing fee charged by a WSI.........................11
1.4 Sum m ary of M major Findings ......................................................... .................... 12

2 OPTIMAL WEB SERVICE MARKET STRUCTURE............................................15

2 .1 R elated L iteratu re ..................................................................... ................ .. 16
2.2 A General M odel ......................................... ............. .. ............. 18
2.2.1 Independent service vendors ....................................... ............... 20
2 .2 .2 Strategic allian ce ..................................... ........ ................ ..............22
2.2.3 W eb service marketplace........................ ...... ....................... 23
2.3 Analytical Insights from a Simplified Model ............................................. 24
2.4 Com putational Explorations .................................... ......................... .. .......... 30





v









3 OPTIMAL LOCATION AND PRICING OF AN INTEGRATED WEB SERVICE.36

3.1 Problem Description and Related Literature ............... ................... ...........37
3.2 The Linear City M odel ................................................................................ 41
3.2.1 Cost to buy from WSI in linear city ...............................................41
3.2.2 Cost to buy from service vendors in linear city .............................41
3.2.3 Optimal location and pricing in linear city....................................42
3.3 The U nit Circle M odel ................................ ........................................ 44
3.3.1 Cost to buy from WSI in unit circle ...............................................45
3.3.2 Cost to buy from service vendors in unit circle .............................45
3.3.3 Optimal location and pricing in unit circle.....................................46

4 Optimal subscription and lising fee of a Web service intermediary............................51

4.1 The W eb Service Supply Chain...................................... ........................ 53
4.2 L literature R review ..................... ........................... .. .. ...... ............... 55
4 .3 T he M odel .................. ..... ... ... .................................................... 59
4.3.1 Consumer's subscription decision ........................................ .............60
4.3.2 Service vendor's listing decision...........................................................62
4.4 Optimal Subscription and Listing Fee.............................................................63
4.4.1 Network value is less than intrinsic value .........................................66
4.4.2 Network value is greater than intrinsic value................ ..................73

5 CONCLUSIONS AND FUTURE RESEARCH ............................... ...............81

APPENDIX

A PR O O F S O F C H A P TE R 2 .............................................................. .....................87

A Proof of Lem m a 2-1 ..................................................................... 87
A .2 P roof of L em m a 2-3 ............................................................................ ...... 88
A .3 Proof of Lem m a 2-4 ........................................................................ 89
A .4 Proof of Proposition 2-6 ........................... ................. .. .......... ..... 90
A .5 P roof of P proposition 2-7 ....................................................................... ....90
A .6 Proof of Proposition 2-8 ....................................................... ............... 91
A .7 Proof of Proposition 2-9 ....................................................... ............... 92

B PR O O F S O F C H A PTE R 3 .............................................................. .....................94

B .1 Proof of Proposition 3-1 ............................................. ............................ 94
B .2 P roof of L em m a 3-2 ..................................................................................97
B .3 P roof of L em m a 3-4 ..................................................................................99
B .4 P roof of P proposition 3-5 ....................................................................... ..... 100
B .5 Proof of Proposition 3-6.............................................................................. ... 100









C PR O OFS OF CH APTER 4 ............................................... ............................ 102

C .1 Proof of L em m a 4-1 .............. ............... ........ ................................ 102
C .2 P roof of L em m a 4-3 ............ ....................................................................... 103
C.3 Proof of Proposition 4-6 ....... ........ ................. ................. 104
C.4 Proof of Proposition 4-12...... ......... ......... ..................... 105
C .5 P roof of C orollary 4-14......................................... ....................................... 106
C .6 Proof of C orollary 4-15 ........................... ..... .. .................. ............... 107

LIST O F R EFEREN CE S ... .... ............................................................ ............... 109

BIOGRAPHICAL SKETCH ...... ........ ................... ............................ 13
















LIST OF TABLES

Table pge

2-1. Design of num erical experim ents................................ ................... ...... ........ 31

2-2. Optimal market structure w.r.t V3 ......................................................... 33

C- Optim al Subscription Fee when y < ....................................... ............... 105

C-2. Optim al Subscription Fee when y > ....................................... ............... 106
















LIST OF FIGURES

Figure pge

1-1. Example of Web service application................... ......... .....................4

1-2. W eb service integration .................................. .............................. ............... 6

2-1. Independent service vendors .............................................................................20

2-2 Strategic alliance........... ................................................................... ....... .. .... 22

2-3. W eb service m arketplace.............................................................. .....................23

2-4. O ptim al profit of ISV ........................................... ....................................... 26

2-5. Optim al profit of m marketplace w .r.t. c .......................................... ............... 27

2-6. O ptim al profit of m marketplace w .r.t. V3 ........................................ .....................28

2-7. ISV vs. m arketplace......... ................................................................ ... .... ....... 28

2-8. SA vs. m marketplace (V < < V ).. ..................................... ..................... 29

2-9. SA vs. marketplace ( > ) ........................................................... ...... 30

2-10 M marketplace is optim al ........................................ .............................................32

2-11. SA dominates for small integration cost while marketplace dominates for large
integration co st .....................................................................32

2-12. Strategic alliance is optim al........................................................................ .. .... 32

3-1. W eb service execution m odel...................................................................... .. .... 37

3-2. Linear city m odel ............... ................. ...................... ............ 41

3-3. M marginal custom er in LC m odel.................. ......... ........................ ...... ......... 42

3-4. U nit circle m odel ......................... ........ .. .. ..... ............... 45

4-1. Web service supply chain ............ .... ........................... ............... 54









4-2. M odel of subscription and listing fee ........................................... ............... 60

4-3. Proportion of Subscribers when y < ....................................... ...............66

4-4. Optim al subscription fee when y < ........................................ ............... 71

4-5. Optim al profit w hen < ................................. ....................................... 71

4-6. Proportion of subscribers w hen > .......................................................... .....73

4-7. Optimal subscription fee depends on ( when y21 < 2 < y22.................................77

4-8. Optimal subscription fee depends on when y > /22 .......................................77















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

ECONOMICS OF WEB SERVICE PROVISIONING:
OPTIMAL MARKET STRUCTURE AND INTERMEDIARY STRATEGIES

By

Qian Tang

August 2004

Chair: Hsing Kenneth Cheng
Major Department: Decision and Information Sciences

The Web services technology empowers a service-oriented architecture featured by

"Just-in-Time" software integration and "on-demand" software provisioning. My

objective is to study the impact of this new technology on firm strategies. To the best of

my knowledge, mine is among the first studies of optimal strategies for offering Web

services from an economic perspective.

First I address the optimal market structure to provide complementary Web

services. In particular, three market structures are compared: independent service

vendors; strategic alliance; and marketplace. The optimal market structure with different

integration cost and market settings is derived. The model incorporates the integration

cost that was not considered in previous literature on physical product bundling. Results

indicate that in the context of Web service integration, a Web service marketplace (which

corresponds to a structure of mixed bundling of physical goods) is not necessarily always

the best market structure.









Next, I consider the optimal location and pricing problem of a Web services

intermediary (WSI) that sells an integrated time-sensitive Web service. My study differs

from previous research on facility locations in that I solve for the optimal location and

price of the integrated Web service simultaneously. Two spatial models are analyzed,

first a linear city model and then a unit circle model. I show that the integrated Web

service is optimally located between the Web service vendors and the WSI should charge

a penetration price if the delay cost is low. In addition, there could be multiple optimal

locations for the WSI if the service vendors are located far away.

Finally, I analyze the optimal subscription and listing fee for a WSI that provides

value-added service, such as aggregation services and technical services. My study

extends current research on information intermediaries by considering multiple groups of

Web service vendors and consumers. Analyses suggest that the intermediary is best off

by setting the listing fee such that all service vendors list on it. Further, the optimal

subscription fee is determined by network intensity, value of technical services, and

properties of Web services.














CHAPTER 1
INTRODUCTION

The Web services architecture represents a new computing paradigm that allows

for the distribution, discovery, production and consumption of loosely coupled software

components over the Internet. The objective of the dissertation is to study the optimal

strategies for providing Web services from an economic perspective. In particular, I aim

to address three research problems: optimal market structure for providing

complementary Web services; optimal location and pricing of an integrated Web service

provided by a Web service intermediary; optimal subscription and listing fee charged by

a Web services intermediary in a Web service supply chain.

The organization of the dissertation is as follows. Chapter 1 first gives a general

introduction of the Web services technology, which includes the evolution of Web

services and its technical characteristics. Then I discuss the business implications of Web

services and introduce the research issues. Major findings are summarized at the end of

Chapter 1. In Chapter 2, I examine the problem of optimal Web service market structure.

Chapter 3 solves the joint decision problem of optimal location and pricing for an

integrated Web service with the analysis of two spatial models. Chapter 4 analyzes the

optimal subscription and listing fee charged by a Web service intermediary. In each

chapter, I provide literature review and discuss the relevance to and distinctions from this

research. Managerial insights are interpreted after presenting results from the analytical

and numerical studies. Chapter 5 concludes this dissertation with discussion on future

research plans.









1.1 Web Services: History and Overview

The business world is undergoing a globalization trend. Firms are expanding their

territories into new markets abroad to create growth. Supply chains are being established

among partners in different geographical regions to increase sourcing efficiency. To reap

the full benefit of globalization, a firm oftentimes needs to standardize or reengineer its

business processes, which requires the integration of various information systems built on

different platforms at different times. Further, the globalization of business requires a

distributed computing environment that allows companies to take advantage of the

computing power at various operational units, regardless of geographical location and

platform. Web services, a recent paradigm of computing, represents the most promising

solution to date to addressing the challenge of distributed enterprise computing required

by the globalization of business.

Hailed as revolutionary, Web Services technology came along an evolutionary path

of growth. There has been continuous effort to improve the reusability, flexibility and

interoperability of information systems. The advent of object-oriented languages makes

it possible to encapsulate functionality in software components called "objects." Sun

Microsystems introduced the platform-independent, bytecode-based Java language so

that programs can be downloaded and run anywhere in the world. Microsoft catered to

this componentization trend by providing the Object Linking and Embedding (OLE)

technology. As Internet popularity grows and network technology matures, firms began

to seek solutions for distributed computing. Yet the problem of incompatibility soon gets

in the way when it comes to the collaboration and interaction among heterogeneous

systems. Sun's Java Remote Method Invocation (RMI) over Internet Inter-Orb Protocol

(IIOP) aims to deliver distributed computing capabilities, but it is overwhelmingly









complex and requires of Java end nodes. Likewise, Distributed Common Object Model

(DCOM) proposed by Microsoft works only on Windows platforms (Schmelzer 2002).

For the first time, Web services technology poses a promising solution to the "plug-

and-play" Holy Grail based on open standards and the decomposition of software

application. According to the Stencil Group (Sleeper 2001), Web services are "loosely

coupled, reusable software components that semantically encapsulate discrete

functionality and are distributed and programmatically accessible over standard Internet

protocols." From a technical perspective, Web services represent a collection of standard

protocols for the creation, distribution, discovery and integration of semantic software

components that encapsulate business functionalities. The key to Web services is

dynamic software discovery and just-in-time software service creation integrationo)

through the integration of loosely coupled software components. Central to the Web

services architecture are the concepts of software as service and platform independence.

1.1.1 Software as service

As opposed to packaged monolithic applications that must be written or licensed,

Web services encapsulate specific business functionalities that can be "rented" over the

Internet. The idea of software as services dates back to the provision of application as

services by Application Service Providers (ASP). But the Web services are not merely a

newer version of ASP. Traditional ASP usually provides complex application systems,

like Enterprise Resource Planning (ERP), through proprietary connection. Web services

decompose business processes into granular components and thus allow customers to

select services on an as-needed basis (Sharma and Gupta 2002). Further, Web services

are distributed over the Internet, while traditional ASPs host their applications on a

centrally located server. Figure 1-1 shows an example of Web service application for a









travel agency, which invokes several software modules (Web services) through the

Internet to complete a vacation-planning process. The Web services involved might be

written in different programming languages and distributed on different systems.



Airline Web Service 1

Customer ses rave invokes Airline Web Service 2
Agency

'.... Car Rental reservation
Web service




Figure 1-1. Example of Web service application

The service-oriented architecture also opens new business opportunities for firms

because it allows firms to sell their software components as Web services over the

Internet. For example, CitiBank developed CitiConnect, a payment-processing Web

service that can be plugged into other company's transaction process (Hagel and Brown

2001). The spectrum of Web services spans from personal services, such as stock quote,

messaging services; to enterprise-centric services, such as call center control, payroll

management, and shipping and logistics.

1.1.2 Platform independence

The economic globalization and the continuously changing business environment

necessitate an interoperable and flexible computing infrastructure. Web services

technology can be used to create a platform-independent distributed computing

environment, since it is built on a set of universally agreed upon standards such as XML,

WSDL, SOAP, UDDI, and other specifications developed by various industrial

consortium. The Extensible Markup Language (XML) protocol allows self-describing









data to be exchanged independent of platform and language. The Web Services

Description Language (WSDL) builds on XML and specifies how Web services can

communicate with each other. The XML-based messaging protocol Simple Object

Access Protocol (SOAP), recently renamed Services-Oriented Architecture Protocol,

supports the invocation of software components over existing networks like HTTP and

FTP in a fashion similar to Remote Procedure Call (RPC). The Universal Description,

Discovery and Integration (UDDI) specification specifies the mechanism for the

description, registration, dynamic lookup and integration of software components.

1.1.3 Integration of Web services

In essence, the Web services architecture represents a platform-, language-, and

vendor-neutral framework for the interaction and integration of software components via

standard networking technologies. Figure 1-2 shows how the Web service protocols

work together to compose two complementary Web services: the customer relationship

management (CRM) Web service and the enterprise resource planning (ERP) Web

service (Samtani and Sadhwani 2001). In the example, users request information about a

particular person. The request is first handled by a UDDI server, which looks up its

registry for relevant services that handle user information. Both CRM and ERP services

are found. The UDDI server then forwards the location and WSDL information of the

two services to an application server, which invokes the services to retrieve requested

information. All communications between the application server and the two services are

based on the SOAP protocol. In the end, the retrieved information is sent back to the

user. The location of the services is transparent to the user who may not even be aware

that two services are involved in the process.










(Get Location )
a rd Request User
Bl uer Information
Request (2)


UDDL Registry
(Private)
SLebel-CRM Applicatlan
P. ovder I S ternet A users
Appolicatiion I t User j(OAPClent
Personal '61"
Information tSemnd the
Inlormn allin
SoP -Pe to Users .rt
Applicaton 'i-Get User
SAccount FireWall
Information

Figurel-2. Web service integration

1.2 Business Implications of Web Services

Despite the debate over Web services' advantages and disadvantage, Web services

are definitely in production in the software industry. Many software vendors are rushing

to reengineer their product offerings and provide Web-service-savvy products (Kreger

2003). For example, Amazon.com is providing tools to let its sales associates,

booksellers and developers develop Web services to take advantage of the services

offered by Amazon (such as inventory management, book reviews). GM used a Web-

service-based platform to act as a translator between its old and new systems and realized

a reduction of $1 billion on software support (Welch 2003). ZapThink, a Waltham (MA)

consultancy that tracks the growth of Web services market estimates that spending on

Web services technology was $1.8 billion in 2002; and projects an over $5 billion

investment in year 2003 (Salkever, 2003). The application of Web services varies from

cost-cutting projects to establishing service-oriented architecture within an enterprise or

between business partners (Ferris and Farrell 2003). To study the impact of Web services









on firm strategies, let's first examine the benefits of Web service from a business

perspective.

1.2.1 Reduction of integration cost

The standardizing of protocols has profound business implications. One of the key

benefits of Web services lies in reduced integration cost because of the openness of

technology. Firms are relieved of the complexity of integrating applications built on

different platforms or located on different networks. The application of Web services

often involves the integration of disparate software components, either within an

enterprise or among trading partners.

Internally, the Web Service architecture changes the fundamental cost structure of

Enterprise Application Integration (EAI). A firm can decrease development cost and

duration dramatically by leveraging existing systems and outsourcing standard modules.

Externally, Business-to-Business (B2B) integration or collaboration is made more cost

efficient because the firms no longer have to set up a separate integration project with

each business partner. Thus, business alliances can be created and decoupled on the fly.

1.2.2 Service-oriented architecture

The service-oriented architecture (sometimes called e-service) is defined as

offering software components as services that can be purchased or rented over a network,

such as the Internet. As standardized, self-describing application modules that can be

described, published, located and invoked over the Internet, Web services form the

foundation of a service-oriented architecture to support universal application integration

(Rust and Kannan 2003). For example, Huang and Chung (2003) proposed a framework

of application integration based on Web services technology, addressing issues such as

security, transaction control, and reliability.









The essence of a service-oriented architecture lies in "service on demand" and

"just-in-time" service integration. Firms benefit from the service-oriented architecture

powered by Web services in many ways. For example, firms have the ability to choose

components that best match the company's business processes, to tailor components to

the individual needs of business units, and to incrementally pay for the overall system

(Fingar 2000; Sundarraj and Talluri 2003).

1.2.3 Web service intermediary

Although Web services are designed to be platform-independent, they leave

unspecified the context necessary for service integration on the process level. For

example, the Web service consumers have to define the order of sequence, control

information flow, exceptions handling, and transactional integrity enforcement, just to

name a few (Cubera et al. 2003, Little 2003). Furthermore, a directory service is required

if run-time discovery and integration are to be materialized.

To help realize the "plug-and-play" service-oriented architecture, a new business

model, referred to as the Web service intermediaries (WSI), has seen rapid growth in

practice in industry recently. A WSI provides value-added services including directory

and search engine, auditing, quality of service (QoS) assurance and integration and

orchestration of Web services. For example, Salcentral.com (which originally called

itself "the Napster of Web services") provides a Web service search engine and tools for

developing and integrating Web services. Maintenance of Web services is made more

simplified since the WSI hosts the latest version of Web services and offers tools so that

the Web service producers can manage and control their Web services. By offering

aggregation services and technical support for Web service development, management









and integration, a Web service intermediary benefits both ends of the Web service supply

chain: the Web service vendors and consumers.

1.3 Research Issues

With growing adoption of Web services technology, the research landscape in

many fields, such as software engineering and management, is about to experience a

major shift. For instance, the Web service technology emphasizes on reducing the size of

software components while at the same time introduces new issues on Web service

integration. The execution of Web services over the Internet causes a network delay not

associated with traditional standalone systems. Thus, Web services technology could

change software development and marketing strategies, such as software quality,

development costs, and pricing. Therefore, it is imperative that information systems

researchers delve into this new computing paradigm and provide useful insights to guide

business practice.

At the same time, it should be noted that the ultimate value of Web services

technology is not in itself. Rather, it lies in the productivity gain and the creation of new

business opportunities due to its reusability, flexibility and interoperability. My objective

is to study the impact of Web services technology on firm strategies and software

development with the use of economic models. In particular, I aim to analyze the Web

services technology from the following three perspectives.

1.3.1 Optimal market structure

By building on standard technologies, Web services technology enables dynamic

software integration that is essential to enterprise application integration and business-to-

business integration. Therefore, a natural start for Web services research is to examine









the impact of integration cost when composing Web services of complementary

functionalities.

Several research questions arise when Web services are exploited for application

integration. First, does a software producer always benefit from the interoperability of

Web services? Second, how should software producers take advantage of Web service

technologies and optimally provide complementary Web services to maximize total

profit? Finally, should firms offer their software components separately, form a strategic

alliance to provide a composite Web service or establish a marketplace that sells both

individual and composite Web services? The first part of my study answers these

questions by analyzing the optimal market structure for providing two complementary

Web services.

1.3.2 Optimal location and pricing of an integrated Web service

Besides traditional functions such as matchmaking, aggregation of demand and

supply, providing trust, and offering market characteristics to suppliers and consumers as

in the electronic intermediaries (Bailey and Bakos 1997, Bakos 1998), a Web service

intermediary (WSI) has several unique features worthy of special interest. For instance,

unlike traditional electronic intermediaries that generally offer static information, a WSI

can participate actively in the Web service supply chain by offering dynamic information

goods such as Web services.

In the second part of the dissertation, I study the optimal strategy of a Web service

intermediary that sells an integrated Web service to compete with Web service vendors.

As discussed previously, the composition of Web services incurs an integration cost.

Therefore, a Web service consumer interested in the composite Web service must take

into account the integration cost when deciding whether to buy the integrated service









from the WSI or integrate by oneself the Web services bought separately from the Web

service vendors. On the other hand, the execution of Web services on remote servers

causes a response delay that consists of program running time and network delay. As a

result, a Web service consumer must factor in the delay cost when deciding whether to

"buy" (or execute) the integrated Web service from the intermediary. Since the network

delay is related to the physical distance between the Web service intermediary and the

client, the optimal strategy of the WSI requires solving the joint decision problem of

location and pricing of the integrated Web service.

Specifically, I seek to address the following research questions. What is the

optimal pricing strategy for the composite Web service: a penetration price or a high

price? Where should the WSI in competition with two individual service providers host

its integrated Web service? Should the WSI be located close to the service providers or

stay away from the service providers to avoid competition?

1.3.3 Optimal subscription and listing fee charged by a WSI

Depending on technical strengths and business scope, Web service intermediaries

can play various roles in linking the service vendors and service requestors. For example,

Salcentral.com, maintains a comprehensive directory of Web services so that service

consumers can browse, search for, and audit particular Web services. Another Web

service intermediary, GrandCentral.com, provides a centralized Web service network and

acts as a trust broker that handles all the issues around message delivery and routing,

security, etc. In summary, a WSI provides value-added services to both Web service

vendors and Web service requestors, allowing it to charge fees to both sides of the Web

service supply chain.









Finally, I focus on studying the optimal strategy for a Web service intermediary

that provides aggregation and technical services to Web service vendors and consumers.

The Web service intermediary charges fees to both sides of the Web service supply chain.

I address the optimal pricing strategies for a Web services intermediary in a supply chain

of complementary Web services. That is, the WSI serves multiple groups of Web service

vendors and consumers. The following research questions are explored. First, what are

the optimal subscription fee and listing fee? Second, should the intermediary subsidize

one side of the market to maximize its profit? If so, which side of the market should the

intermediary subsidize? Next, what is the impact of technical strength and intensity of

network effect on the intermediary's pricing strategy? Finally, how do the characteristics

of Web services affect the optimal strategies of the intermediary?

1.4 Summary of Major Findings

To analyze optimal market structures, I compare three market structures-

independent service vendors (ISV), strategic alliance (SA) and Web service marketplace.

Analytical results and computational explorations suggest that Web service vendors

benefit from the integration of Web services because the Web service marketplace always

dominates the ISV market structure, regardless of the integration cost. The optimal

market structure is determined by the integration cost, and the valuations and market

potentials of the individual and composite Web services. As the valuation of the

integrated Web service is small, the service providers prefer marketplace to strategic

alliance. For larger valuation of the integrated software service, service providers prefer

marketplace if the integration cost is high; while strategic alliance dominates if the

integration cost is low. If the valuation of the integrated software service is sufficiently

high, strategic alliance becomes the optimal market structure.









The joint decision problem of optimal location and pricing for an integrated Web

service provided by a Web services intermediary (WSI) is analyzed with two spatial

models. In general, optimal location and price depend on integration cost, delay cost and

prices of the individual Web services. In a linear city model, I show that the midpoint

position between the service providers is always the optimal location for the integrated

Web service. Furthermore, when the delay cost is quite small, the best pricing strategy is

to charge a penetration price to capture entire market demand. On the other hand, the

WSI should charge a high price to share the market with the service providers when the

delay cost is high. Analysis of a unit circle model shows that the WSI is optimally

located midway between the service providers and charges a market-covering price when

the delay cost is small. In addition, if the distance between the service providers is large,

there are multiple optimal locations for the integrated Web service.

Finally, I find that in the presence of cross network externalities between two ends

of a Web service supply chain, a WSI always has incentive to subsidize the service

vendors by setting a low listing fee that induces all service vendors to list their Web

services on it. On the other hand, the intermediary may choose to attract only portion of

the Web service consumers, depending on the relationship between the intensity of cross

network externalities and consumer's valuation of the WSI's value-added technical

services. If the consumers value the technical services more than the network effect, the

optimal subscription fee and profit are increasing in network effect. Furthermore, the

WSI should allow more consumers to subscribe to its service as network effect

intensifies. The optimal subscription fee is also affected by the nature of Web services

provided by the service vendors, such as the prices of Web services and market potentials






14


of the composite Web service. For example, if the market potential of the composite

Web service is large and the consumers have high valuation for the technical services, the

intermediary should never set a low subscription fee to attract all consumers to subscribe

when the consumers value the network effect more than the technical services. On the

other hand, the WSI should set the subscription to allow all Web service consumers to

subscribe if the market potential of the composite service is smaller than the market

potentials of the two individual Web services and the network intensity is high.














CHAPTER 2
OPTIMAL WEB SERVICE MARKET STRUCTURE

In this chapter, I study the optimal market structure for providing complementary

Web services when consumers incur an integration cost to compose multiple Web

services. In particular, I compare three market structures for two software vendors

offering two complementary Web services: (1) the independent service vendors (ISV)

structure in which the two firms sell Web services separately; (2) the strategic alliance

(SA) structure in which the two firms establish partnership to offer an integrated Web

service; and (3) the Web services marketplace structure in which the two firms sell both

the individual and the composite Web services.

One example of complementary Web services is a school calendar scheduling Web

service and a weather forecast Web service. The calendar-scheduling Web service can be

integrated with the weather forecast Web service so that the school can schedule certain

events (such as such as football games and skiing competitions) in accord with the

weather conditions. Another example of Web services that can be integrated is an

inventory management system and a purchasing system. The two systems can work

together so that purchasing orders can be automatically generated when the inventory is

below a certain level and the inventory can be updated in real time when purchases are

made.

This chapter is organized as follows. First, I review literature on physical good

bundling and integration, followed by discussion about the uniqueness of Web service

integration. Then I present the models of three market structures and derive profit









functions in each market structure. After that, the optimal market structure is found by

comparing the profits of three market structures with varying integration cost and market

conditions. The analysis includes analytical study of a simplified model and numerical

explorations of the general model.

2.1 Related Literature

The bundling strategy for complementary products and services has been an active

research topic in marketing and economics. Economides and Salop (1992) study the

optimal market structure in light of the tradeoff between vertical integration of

complementary products and horizontal competition among substitutable composite

products. They model a market of two complementary products, each provided by two

firms. A number of market structures, which differ in the degree of competition and

integration, exist under different combinations of the four firms. Their research focuses

on comparing the equilibrium price of the composite product under different market

structures.

Matutues and Regibeau (1992) study the optimal strategy on product compatibility

and bundling with a spatial model in which consumers have heterogeneous fit cost (or

taste) of different product components. They set up a model of two firms each selling

two complementary products. Farrell and Katz (2000) study a market composed of a

monopoly offering one product component and several companies (possibly including the

monopoly) offering another complementary product component. Their study

concentrates on the monopoly's incentive to "squeeze" the producers of the other product

component by means of pricing, product innovation, or exclusive trading rules.

Implications on social welfare are explored.









Venkatesh and Mahajan (1993) examine the optimal pricing strategy under three

bundling strategies: pure component, pure bundling and mixed bundling with a

probabilistic model. Similar bundling strategies are analyzed by Chuang and Sirbu

(1999) with application to N goods (journal articles). Both papers (Venkatesh and

Mahajan 1993, Chuang and Sirbu 1999) conclude that the mixed bundling strategy yields

maximum profit for firms.

Several unique features of Web services technology necessitate a different

approach to the study of Web service integration. First, Web services are essentially

programmable software components. Unlike physical goods that can be bundled (or put

together) at ease, the integration of two arbitrary software components requires both

human expertise and financial resources because of the complexity of addressing the

platform- and language-disparities, such as recoding of data and/or application interfaces.

In essence, the integration of software components introduces an integration cost not

considered in all previous research on product or service bundling strategies to the best of

my knowledge.

Second, Web services technology provides the flexibility of selecting software

components on an as-needed basis. In other words, there are demands for both the

individual and the composite Web services. Many previous studies focus on the market

structure for the composite product while ignoring the demand for the individual

components (Economides and Salop 1992, Matutues and Regibeau 1992, Farrell and

Katz 2000).

Third, an integrated software application is usually a new software product that is

indivisible and has different value and function than simply adding the values and









functions individual software components. Product bundles, on the other hand, are put

together without change to each component. An analogy of integrated Web service is

alloy wheal, which is made from tin and aluminum. Many previous works on product

bundling treat the value of the bundled product as the sum of values of the components,

which is referred to as the assumption of strict addition. Typically mixed bundling

emerges as the profit-maximizing strategy (Venkatesh and Mahajan 1993, Chuang and

Sirbu 1999). As I will show later, mixed bundling is not necessarily always optimal for

Web services.

Although Venkatesh and Kamakura (2003) relax the strict addition assumption and

consider contingent valuation for bundling complementary and substitute products, their

result can't be applied to Web services directly because of two reasons. First, unlike

physical goods, the marginal cost of providing one copy of Web service is negligible.

Second, since the integrated Web service is indivisible, it is impossible to buy the

integrated software and if the consumer is only interested in one component. Likewise,

the papers by Bakos and Bryjolfsson (1999, 2000) have to be modified to fit in the

context of Web service integration. I address the impact of integration cost on Web

service market structure by taking into account different the demand of both the

individual and the integrated Web services.

2.2 A General Model

Consider two service vendors selling two distinct but functionally complementary

software components (S1 and S2). Components S1 and S2 can be integrated into a

composite service (S3). Correspondingly, the potential buyers of Web services are

classified into three groups: the potential buyers of S1, the potential buyers of S2, and









potential buyers of S3. Let the size of the three groups of potential buyers be Q1, Q2 and

Q3. The buyers in each group have a homogeneous reservation price (valuation) of V,

V2 and V3 for S1, S2, and S3, respectively.

I consider three market structures that can be adopted by two profit-maximizing

Web service vendors. In the first market structure, independent service vendors (ISV),

the two firms sell the Web services independently. Consumers interest in the composite

service must integrate S1 and S2 by themselves and thus incur an integration cost of c.

In the second market structure, strategic alliance (SA), the two firms form an alliance to

sell only S3, the integrated Web service. In the third market structure, Web service

marketplace (Marketplace), the two firms sell S1, S2, and S3. There is no integration

cost for customers in the SA or Marketplace structures.

The two service vendors seek the optimal market structure to maximize their

profits. Intuitively, the valuations of the three services, the sizes of the three groups of

customers, and the integration cost will affect the service vendors' decisions. Of special

interest is the impact of the integration cost, since one key benefit from Web services

technology is reduced integration cost. I develop an economic model to examine the

optimal market structure for the Web service vendors with respect to various integration

costs. To reflect industry reality, two assumptions are made regarding the integration

cost and service valuations. First, I assume that the composite Web service is valued

more than any of the individual services alone. Second, I assume the integration cost can

not exceed the values of each Web service. In summary,

O








2.2.1 Independent service vendors

Under the independent service vendors (ISV) market structure, each service vendor

sells its software component at price PI7 and P12 (Figure 2-1). The demand for each of

the Web services S1 (or S2) is composed of two groups of buyers the buyers interested

in S1 (or S2) and the buyers who are interested in the composite Web service (S3).


Web Service P
Vendor 1

V Customers of
Sl. S2 and S3

Web Service S2
Vendor 2 Ps

Figure 2-1. Independent service vendors

I adopt a linear demand function as in Parker and Alstyne (2001) to calculate the

number of buyers of S1, S2 and S3, denoted by q,,, qs2 and qs3, as follows.


q, =Q,- 'Q, i= 1,2 (2.2)
V

Pi +P2 +C
qs3 =3 s 1s2 3 (2.3)
3V

Note that in the ISV market structure, buyers of the composite Web service have to

spend an integration cost of c, see Eq. (2.3). Accordingly, the demands of each service

vendor D,1 and Ds2 are

Ds, = q, + q3, i = 1,2. (2.4)

Both Web service vendors seek to set the price of their products to maximize profit,

which is formulated in the following problems.









P I Pl +I+ +C
max s, = P, .D, = P(Q-, ( i)+Ps, (3 Q 3), i=1,2 (2.5)
Ps ,i V3

By solving the first order conditions of both service vendors simultaneously, one

derives the equilibrium optimal prices of Web services S1 and S2 in Eq. (2.6).

Subsequently, the optimal profits of the two service vendors *, can be calculated by

plugging the prices in Eq. (2.6) into the profit function. Unfortunately, the complexity of

n, prohibits an explicit display of its functional from.

S'*--2Q232 + 2Q26Q3V2 + 2Q1Q3yz 2cQ2Q3V + QV2 cQ32V Q2Q3V
S3 4Q Q32 + 4QQ3 V2V3 + 4Q23V3 + 3Q32V2 (26)

P = 2QIQ2V32 + 2QQy32 + 2Q2z3lV3 2cQVQV3 +3V3 -cQ -QQy1V3
sl 4Q,Q2V3 + 4Q1Q3V23 +4Q2Q3 VV3 +3Q32V 2

It should be pointed out, however, that if the integration cost is prohibitively high,

there could be no demand of the integrated product, i.e., qs3 = 0. In that case, the service

vendors' profit-maximizing problem reduces to


max = P,(Q-, Q,),i= 1,2. (2.7)


By inspection, the optimal prices and profits in case of no demand for the

composite Web service are

= -, a ', =1,2. (2.8)
2 4

Summarizing the results of Eqs. (2.6) and (2.8), one gets the optimal profits for the

each of the independent service vendors in the ISV market structure as follows.

nI, =max{f^, <}, i= 1,2. (2.9)









In order to make the three market strategies comparable, I take a "macro" market

view of the independent service vendors. That is, I use the sum of the independent

service vendor's optimal profits as a measurement of the goodness of ISV market

structure. The optimal profit of the ISV market strategy is defined as Hn = H +n s

where Hn (i = 1, 2) are described in Eq. (2.9).

2.2.2 Strategic alliance

Under the strategic alliance (SA) market structure, the two Web service vendors

form a strategic alliance by integrating the Web services into one composite service S3,

see Figure 2-2. To distinguish from the Web services marketplace strategy, I assume that

the composite service is not divisible. That is, the potential customers of the strategic

alliance are those who are interested in the composite Web service S3.


S3


Strategic Customers
Alliance C of S3





Figure 2-2. Strategic alliance

As described in the introduction, one key advantage of the Web services

technology lies in the easy integration of software components. Therefore, I assume that

the SA incurs a minor one-time integration cost to produce the composite service. In

addition, the cost of providing the integrated Web service is negligible due to the

technological simplicity of Web services, i.e., the marginal cost of providing one more

copy of the composite Web service is assumed to be zero. The strategic alliance sells the









composite service at price P,. The consumers don't incur additional integration cost

when they buy S3 from the SA. Since the integrated Web service is indivisible, the

potential buyers of the SA are the buyers of S3. The strategic alliance seeks to optimally

set the price of the composite service (P,) to maximize profit, which is formulated as


max zr = (Q3 --a Q3) (2.10)
P, V3

Solving the first order condition of Eq. (2.10) yields the optimal price and profit of

the strategic alliance, summarized as follows.


P= ,V V3Q3 (2.11)
2 4

2.2.3 Web service marketplace

The third market structure, Web service marketplace (Marketplace), can be viewed

as a combination of the ISV and SA market structures, where three types of services-S1,

S2 and S3 are offered, see Figure 2-3. Similar to the strategic alliance, the "sunk" cost of

composing Web services by the marketplace is negligible and the marginal costs are

assumed to be zero.




1 Pml, m2, m3 Customers of
Web Services S S2, S3
Marketplace
integrate




Figure 2-3. Web service marketplace

Under the Web services marketplace market structure, the Web service vendors

seek to optimally set the prices of the Web services, P,,, P,2 and P,3 to maximize total









profit. To ensure that the demand for the composite Web service S3 is non-negative, the

price of the composite Web service sold by the marketplace cannot exceed the total cost

of creating S3 by the consumers themselves, i.e., P3 < P,, + Pm2 + c. Put in math, the

optimization problem in the Marketplace structure can be formulated in Eq. (2.12).

P Q ( P P
max m =P(Q- Q)+ P( m2Q2- Qm2 )+ P3(Q3 Q3)
PmlP2m3 V, V2 V (2.12)
s.t 13P < P + P2 + c
s.t Pm3 P ml +Pm2

The constrained profit maximization problem defined in Eq. (2.12) can be solved

using KKT conditions. The similar approach is used in proving Lemma 2-3 in the next

section. The optimal total profit under the Web service marketplace market structure is

described as follows.


IM if c< V V2)
I =2 2 (2.13)
~1 VT-( V2 )
(Va+Q 2 +V3Q) if c> V3 v +V2)
4 2

where

2 V2 V3 123 VV2 + 12 V2 V3 +23 V2 V 2 + 1 V3 V2 3 2 V3V
Q 2 Q2Vy +Q 2Q3V2 + QQlQV +Q Q3'y +QlQVy +QlQ2V
4(QQ3V2 +QQ2V3 + Q2Q3V)
Q1Q2Q3(4cV3 -4cV -4cV -4c2 +2V2V3+ 2V3 2VY2)
4(QQ3V2 + Q1Q2V3 + Q2Q3)

2.3 Analytical Insights from a Simplified Model

The best profit-maximizing market structure for the Web service vendors is found

by comparing the service vendors' total profit in the three market structures. The

analysis of the general model in the previous section suggests that the service vendors'

optimal strategy is determined by several factors the size of potential buyers in each

group, the valuations of the services and the integration cost. However, the mathematical









complexity of the general model, especially the optimal total profit of the independent

service vendors (see the optimal prices in Eq. (2.6)), makes the analysis of optimal

market structure technically intractable. Therefore, the following two simplifying

assumptions are made to modify the general model in order to gain insights from

analytical study. First, I assume that there are (approximately) equal number of potential

customers of S1 and S2. Secondly, it is assumed that the potential customers of S1 and

S2 have (approximately) equal valuations of the two services. By imposing the

assumptions, we can focus on studying the impact of integration cost on optimal market

structure first. The assumptions are specified mathematically as follows.

Q1 =Q =Q and V 2 =V, =V (2.14)

In the simplified model, I study a symmetric market where the two complementary

services are valued equally and both Web service vendors enjoy the same market

potential. At the same time, the relationship between the integration cost and the

valuations of the services, described in Eq. (2.1), still holds in the simplified model.

Plugging Eq. (2.14) into the general model, I derive the optimal total profits under the

three market structures in the context of a symmetric market, which are summarized in

the following lemmas.

Lemma 2-1. In a symmetric market, the optimal total profit under the independent

service vendors (ISV) market structure is specified in Eq. (2.15). In addition, r is

decreasing and convex in the integration cost c.


nT = max vQ, (2.15)


S2V( VQ+V3Q3 c)(V32Q2 +VV3QQ3 + V2QQ3 +VV3Q -c QQ -cVQ3)
where rs =
V (2QV3 + 3VQY3)2









n


VQ /2
VQ12


Integration cost (c)
Integration cost (c)


Figure 2-4. Optimal profit of ISV

Lemma 2-2. In a symmetric market, the optimal total profit of the strategic

alliance is specified in Eq. (2.11).

Proof. Because the strategic alliance sells the composite Web service only, the

valuation and market size of the individual Web services won't affect the strategic

alliance's profit. Q.E.D.

Lemma 2-3. In a symmetric market, the optimal total profit of the Web services

marketplace is

V -2V
)T ^if c<
Hn = (2.16)
1 1 V 2IV
yVO+-VQ ifc 3



where + -V + -K -
where 2 4 3 4(QVj + 2QV)
Lemma 2-4. 'm, is increasing and concave in the integration cost c. Hn is

increasing in the valuation (V3) and market potential (Q3) of the composite service. In

addition, the total profit of the marketplace is at minimum when c = 0, which is

described in Eq. (2.17).

VV3 (Q + Q,)2
,(c = 0) = (3) (2.17)
2(Q +2VQ,)










Profit

VQ/2+V3Q3 /4


Hm


Integration cost


Figure 2-5. Optimal profit of marketplace w.r.t. c

Lemma 2-5. In a symmetric market, the optimal profit of the Web services

marketplace takes the form of n* when V3 > 4V ; the optimal profit switches from n* to

1 11
-VQ +-V33 when 2V <3 <4V; the optimalprofit takes the form of VQ +-V33 if
2 4 2 4

V < V <2V.

Proof. Lemma 2-3 suggests that the optimal profit of the Web services

marketplace is bimodal, which depends on the relationship between V, V3 and c.

SV -2V
According to Eq. (2.16), the optimal profit is *- if c < -V while the optimal profit
2

1 1 V -2V
is -VQ + -VQ3 if c> 3 Recall our assumption in Eq. (2.1) that the integration
2 4 2

cost can't exceed the values of Web services being integrated, i.e., 0
accordingly, there are three possible functional forms of the profit with respect to V3. In

V -V V -2V
particular, if V3 >4V, c< 3 2V is always satisfied; if V < V3 <2V, c> 3 2V is
2 2


V -2V
always satisfied; if 2V < V3 < 4V, either < 3
2


V -2V
or c > 3-
2


applies. Q.E.D.


Lemma 2-1 to 2-4 describes the optimal profits under three market structures.

Figures 2-4 and 2-5 plot the behavior of the profit under the ISV and marketplace market









structure with respect to integration cost respectively. Furthermore, Lemma 2-5 specifies

three functional forms of profit of the marketplace under the assumption that 0 < c < V.

Correspondingly, Figures 2-6 (a), (b), and (c) illustrate these three cases.

Profit Profit Profit
SI -I- 3 Q_+ I3Q3





V c V c V
(a) (b) (c)

Figure 2-6. Optimal profit of marketplace w.r.t. V3 (a) Vi > 4V (b) 2V < V < 4V (c)
V
Given the optimal profits under three market structures, one can derive the optimal

market structure for the Web service vendors with respect to different integration cost (c)

and market characteristics (market sizes and valuations of Web services). Propositions 2-

6 to 2-9 summarize our key findings.

Proposition 2-6. The service vendors are always better off under the Web services

marketplaces than staying as independent service providers regardless of the integration

cost.

Profit

VQ/ 2 + V3 3 / 4 .......- .............. n (Marketplace)



VQI / 2 t (Isv

Integration cost


Figure 2-7. ISV vs. marketplace









Proposition 2-7. When V < VV < *, Web services marketplace is the dominant

market structure, regardless of the integration cost. Specifically,

V* =(2Q + 4Q3)/(3V) (2.18)

Proposition 2-8. When V*
structure if the integration cost is below c marketplace is the optimal market structure

if the integration cost is above c. In particular, V* is defined in Eq. (2.18) and

1 4QV2 + 2VV33QQ3
c= V3 V (2.19)
2 2Q3

** 4Q3Q 2 + 8QQ3 + 4Q2 (2.20)
V = Q3^ ^-3 V (2.20)
Q3

Proposition 2-9. When V3 > V**, strategic alliance is the optimal market structure,

regardless of the integration cost, where V** is defined in Eq. (2.20).

Proofs of Propositions 2-6 to 2-9 are relegated to Appendix A. Figure 2-7 depicts

the total profit for the service vendors in the ISV vs. marketplace structure. We observe

that the marketplace yields more profit, regardless of the integration cost. Figures 2-8

and 2-9 give graphical illustration of Propositions 2-8 and 2-9 respectively.

Profit
..... ...... ~* (Marketplace)
1
34 V3 F1a (SA)



SV Integration cost
c V Integration cost


Figure 2-8. SA vs. marketplace (V < V3 < V )









Profit
1 V3-3 I7 (SA)
4
.................. j (M marketplace)
171 (Marketplace)





V Integration cost
Figure 2-9. SA vs. marketplace (V3 > V**)


2.4 Computational Explorations

From the simplified model, we observe that the integration cost plays a critical role

in determining the optimal market structure for the Web service vendors. In addition, the

optimal market structure also depends on the valuations and the sizes of market potential

of the individual and composite Web services in a symmetric market. To complete our

analysis on optimal market structure, one should consider the general cases where V # V2

and Q, Q2. Due to the technical intractability, we resort to numerical experiments to

draw insights from the generalized model in this section.

In the numerical experiments, I focus on the situations where the valuations and the

market potentials of the individual services are different. Without loss of generality,

experiments are conducted assuming TV > V2 since one can always exchange Web service

1 and service 2 without changing the results. In addition, we run the experiments under

the constraints specified in Eq. 2-1 (i.e., V3 > max{V, V}, c < min{V, V}).

Insights from the numerical experiments are summarized in Observations 2-1 and

2-2. Table 2-1 describes the design of the experiments, which classifies six combination

scenarios of V and Q, (i = 1, 2, 3).









Table 2-1. Design of numerical experiments
Case V vs. V2 Q vs. Q Q3 vs. Q and Q2
1 >V2 Q1 2 Q3>max{Q1,Q2}
2 V1 > V2 Q >Q2 3 < min{Q1, Q2
3 > V2 Q1 >Q2 Q1 >Q3 >Q2
4 >V2 1 max{Q1,Q2}
5 V >V Q1 <2 3 < min{Q1,Q2}
6 V >v
Observation 2-1. Ceteris paribus, the optimal market structure switches from Web

service marketplace to strategic alliance as the valuation of the composite service (V3)

increases. Further, the Marketplace always dominates the ISV market structure.

Observation 2-1 suggests that the results from the numerical experiments are

consistent with the analytical study of the simplified model. When V3 is small, the

service vendors are best off by implementing a marketplace, regardless of the integration

cost. As the valuation of the composite Web service increases, the optimal market

structure turns to a mixture of marketplace and strategic alliance, with the SA market

structure as optimal for small integration cost and the Web services marketplace as

dominant for large integration cost. When V3 is sufficiently high, the Web service

vendors always form a strategic alliance, regardless of the integration cost.

Figures 2-10, 2-11, and 2-12 plot three examples of the optimal market structure

with varying integration cost and market conditions. Notice that in all experiments, the

marketplace always dominates the ISV, implying that the service vendors always benefit

from the interoperability of the Web services.








32




Market Structure comparison (/1=1, V2=0 6, V3=3 0, 01=100,02=50, Q3=200)

180 ..... .

170 .--

160-

profitt150 ------------


Legend


Profit of the marketplace

..Profit of the SA
Profit of the SA


Profit of the ISV


0.1 0.2 0.3 0.4
integration cost


0.5 06


Figure 2-10. Marketplace is optimal

Market Structure comparison (V1=1, V2=0.6,V3=5.0,Q1=100,Q2=50,Q3=200)


280-


260-


240-
profit

220-


200-

i,"


0nt1 0.2 03 c
integration cost


0'4 0.5 0'6


Figure 2-11. SA dominates for small integration cost while marketplace dominates for
large integration cost

Market Structure comparison (V1=1 ,V2=0.6,V3=6.0,Q1=100,Q2=50,03=200)


3201

300 ------- ------------------------


profit
260

240-

220-


1 01 0.2 03
integration cost


04 05 06


Figure 2-12. Strategic alliance is optimal


------



,,- -*


-e
-r
clr~
~cc
c-r
cc









Observation 2-2. The threshold of V, for SA to surpass Marketplace is decreasing

in potential market size of the composite service (Q3). In addition, the threshold value is

smaller in a market where V, > V and Q1 < Q than in market where V, > V and


Q1>Q2.

Table 2-2. Optimal market structure w.r.t V3
Optimal Market Structure
SA if c for
Scenario Parameter Values c o
M smaller c; M SA
for bigger c
VI> V2 K = I.0, v, = 0.6
1 2 1.0, 2 0.6 (0.6,3.9) [3.9,5.35] > 5.35
Q3 > QI > Q2 Q = 100, Q2 = 50, Q3 = 200
K = 1.0, v2 = 0.6
1 2 (0.6,6.6) [6.6,8.4] > 8.4
V1 > V2 Q = 100, Q2 = 60, Q3 = 40
2
Q > Q2 > Q v1, = 1.0, v2 = 0.6
1 (0.6,5.9) [5.9,7.7] > 7.7
Q1 = 100, Q2 = 60, Q3 = 50
V1 >V2 v1 =1.0, v, =0.6
3 2 1.0, 0.6 (0.6,5.35) [5.35,7.0] > 7.0
Q1 > 3 >2 1 = 100, Q2 = 40, Q3 = 60
VI> V2 K = I.O, V2 = 0.6
4 1.0, 0.6 (0.6,3.8) [3.8,5.25] > 5.25
Q1 < Q2 < 3 1 = 50, Q2 = 100, Q3 = 200
V1 >V2 v1 =1.0, V2 =0.6
5 .0, 0.6 (0.6,6.2) [6.2,8.0] > 8.0
Q3 < Q < 2 Q = 60,Q2 = 100, Q3 = 40
V1 >V2 v1 =1.0, v, =0.6
6 2 1.0, 0.6 (0.6,4.95) [4.95,6.55] > 6.55
Q1<3

Table 2-2 shows an

scenarios. For example,


example of optimal

in the first scenario


1 =100, Q2 = 50, Q3 = 200 (Q3 >1 >Q2).


range of 0

market structure with respect to V3 in six

, we set V =1, 2 =0.6 (V >V2) and

The integration cost is restricted in the


The marketplace is the optimal market structure regardless of the


integration cost if the valuation of the composite service V3 e (0.6,3.9]; if the valuation of









the composite service V3 e (3.9,5.35], the optimal market structure is dependent on the

integration cost, with the strategic alliance as optimal for small integration cost and the

marketplace as optimal for large integration cost; the strategic alliances becomes the

dominant strategy for the service vendors if the valuation of the composite service

satisfies V3 >5.35, regardless of the integration cost. The threshold values of the

composite service valuations (V3) in this experiment are 3.9 and 5.35, where the

marketplace turns from less dominant to being dominated. Another interesting

observation from the numerical experiment is that a more "balanced" market tends to

favor the strategic alliance. This can be shown by comparing case 3, a polarized market

where service vendor 1 has apparent advantages over service vendor 2 (V > V2, Q1 > Q ),

against case 6, a balanced market where both service provider 1 and service provider 2

has certain market advantage (V > V2, Q < Q2 ). With the same market potential of the

integrated service (Q3), the Strategic Alliance beats the marketplace if V3 > 7.0 in case 3

while the marketplace is dominated by the strategic alliance if V3 > 6.55 in case 6.

Observation 2-1 suggests that a higher valuation for the composite component (V3)

tends to favor the strategic alliance. Observation 2-2 further describes how the threshold

value is affected by the market potential of the composite service. At first sight, this

observation is somewhat obvious since the optimal profit of the strategic alliance is

V33 /4, which is increasing in V3 and Q3. However, the marketplace also sells the

composite product and its optimal profit is also increasing in the valuation and market

potential of the composite service (see Lemma 2-4). Observation 2-2 might result from

the fact that the profit of the marketplace is restricted in its price setting policy









Pm3 < Pm, +Pm2 +c. In fact, it can be proved that the optimal price of the composite

software component in the marketplace is less than the optimal price in the strategic

alliance, see Appendix A. This suggests that the service vendors don't necessarily

always benefit from diversifying their products. If the composite service is highly

valuable and has a large market potential, the service vendors are better off by

establishing a strategic alliance selling simply the composite service.

Another interesting observation from the numerical experiments suggests that a

more "balanced" market seem to favor the strategic alliance more than the marketplace.

The market is balanced if one service vendor sells a valuable service with small market

potential while the other service vendor sells a less valuable service with larger market

potential. In other words, if the service vendor each has some advantage in service

valuation or market power, they are more likely to form a cooperative strategic alliance.

On the other hand, if the market is extremely asymmetric with one service vendor selling

a highly valuable service and enjoying a large potential market, the service vendors are

more likely to prefer the Web services marketplace, which gives them a certain degree of

autonomy.














CHAPTER 3
OPTIMAL LOCATION AND PRICING OF AN INTEGRATED WEB SERVICE

While the Web services technology is a promising solution to bridging the platform

discrepancy and geographical distance between software components, some constraints

may impede the widespread adoption of Web services and the service-oriented

architecture. In particular, the performance of Web services is refrained by the

computing power of local servers and the robustness and capabilities of the underlying

network through which the Web services are distributed.

From technical perspectives, the execution of Web services follows the traditional

Client/Sever paradigm. Figure 3-1 illustrates an example of Web service consumption.

First, the Web service client sends a processing request to a remote Web service

application server. The Web service application server locates the appropriate Web

service and transforms (or "serializes") the request into Web-services-compliant format

(SOAP) and then forward to a particular Web service to handle the request. The result is

transmitted over the network and finally transformed (or "deserializes") in a format

understood by the end user. Overall, the response delay in Web service execution

consists of the computation time at the server and the network delay. Sometimes,

response time is especially important for time-sensitive applications such as stock quote

and instant messaging. As the computing power keeps increasing at decreasing cost, in

accord with the well-known Moore's Law of computing, it's not hard to predict that the

network latency will become a prominent part of the response delay for applications

distributed over the Internet. This chapter studies the impact of network delay on the









pricing and location of an integrated Web service provided by a Web service

intermediary (WSI).

Web Service
Application Server Internet
End-user


request Messages request

response I I
response transport r s

WS
xecutio ,

Figure 3-1. Web service execution model


3.1 Problem Description and Related Literature

Consider two independent software vendors offering two complementary Web

services denoted by S1 and S2. Let P] and P2 be the price of S1 and S2 respectively. At

the same time, a Web service intermediary (WSI) offers a time-sensitive composite Web

service denoted by S3, which is developed by integrating S1 and S2. Examples of time-

sensitive Web services include stock quote, credit check required for payment, and so on.

The WSI charges a price of P, for its integrated Web service S3. Customers who are

interested in the integrated Web service can either buy the composite service from the

WSI or buy the two Web services S1 and S2 from the service providers and integrate by

themselves, in which case the customer incurs an integration cost c.

In the context of Web services paradigm, the customers "buy" a Web service by

executing the Web service hosted at the service provider's server. The customers do not

own and house the software component. Consequently, customers experience a response

delay resulting from the turnaround time of executing the Web service.









In this chapter, we focus on the demand for the integrated Web service. Suppose

the reservation prices of the customers for the integrated service are sufficiently high so

that all customers have one unit demand for the composite Web service and they either

buy from the WSI or from the service vendors. A customer evaluates the total cost to

decide whether to buy the integrated Web service from the WSI or buy the individual

Web services from the service vendors and then create the integrated Web service by

herself. To be more specific, the total cost to "purchase" the integrated Web service

includes the price charged by the WSI (or the Web service vendors), delay cost and

integration cost (if the customer buys from the service vendors). In case a customer

incurs the same cost to, the customer will buy the composite Web service from the WSI

due to some valued-added services by the WSI.

As the response time is a major concern for consumers of time-sensitive integrated

Web service, it is important to analyze the composition of response time of applications

over the Internet. According to Johansson, et al. (2000), the response time of applications

over the Internet is composed of two parts local processing time and network response

time. Local processing time is determined by the capacity of the local server and the

request load. Network response time can be further divided into transmit time, queuing

delay and network latency. Transmit time is related to network bandwidth while the

queuing delay is determined by capacity of network devices and amount of data

transmission jobs. Network latency is the time taken to transport data between two

locations on the network and is generally a function of physical distance between the two

nodes on the network. Johansson (2000) points out that while much research has been

done on network design in consideration of network bandwidth and device capacity, the









network latency is usually ignored in previous research. Since the cost of computing is

halved roughly every eighteen months and the network infrastructure has seen an

accelerating growth recently, the network latency will become one dominant term of

response time of applications over the Internet.

In an abstract sense, the Internet is composed of two parts, the cores and the edges.

Local servers and client machines are located at the edges, connected by the switches and

routers at the core. The network latency for exchanging data between the server and the

client is a function of number of routers and switches between them, which is correlated

with the physical distance between the server and the client. To model the impact of

network latency on the response time of Web services, let t be the delay cost per unit of

distance between the customer and the Web service provider where the distance

corresponds to the number of routers and switches between them.

The choice of optimal location for the WSI bears some resemblance to the facility

location problem in previous literature. Information systems research on network design

has traditionally taken an operations research (OR) approach, borrowing methods from

classic OR problems such as the facility location problem (Erlenkotter 1977). The

facility location problem, usually modeled as a mixed integer programming problem, is

defined as choosing the number of facilities and locations to minimize total cost (or

maximize profit) subject to capacity constraints, demand constraints and others. Solution

methodologies include developing heuristics based on dynamic programming (Li et al.

1999), Lagrangean relaxation (Liu et al. 2001), and so on. Sun (2003) and Sun and

Koehler (2003) were among the first to study the location model for Web service

intermediaries. Mixed integer programming models are proposed in Sun (2003) and Sun









and Koehler (2003) to decide the best location of a WSI by considering the usage

requirement, server's capacity, and assignment constraints. Efficient heuristics were

developed to tackle the location problem as the proposed model became computationally

intractable.

Several special characteristics of Web services suggest solving the location and

pricing problem of the integrated Web service in a different way from traditional facility

location problems. First, while the facility setup cost is an important factor in the facility

location problem, I treat it as sunk cost and focus on the WSI's revenue from selling the

composite Web service. Second, usually the facility location problem deals with cost

minimization and solves the decision problem for one single company without

consideration of competitions. The WSI, however, has to compete with the Web service

providers by choosing the right location and the right price for its integrated Web service.

Our problem is unique in that we consider the joint decision of location and pricing

problem in a market of complementary Web services. Last, as will be illustrated below,

the delay cost of accessing Web services for a customer in the network has several

complex functional forms, which is dependent on the location of the customer. Although

in facility location problems one can model the delay cost as a function of distance

between computing nodes, it does not provide the flexibility to choose different delay

cost functions for customers at different locations.

In this chapter, I propose a spatial model to study the joint location and pricing

decision problem for the WSI. The optimal location and price is first derived in a linear

city model. Then a unit circle model is applied to study the joint decision problem in a

more general context.









3.2 The Linear City Model

We assume the two Web service providers offering S1 and S2 are located at the end

points of a unit length linear city (LC) and let x be the WSI's location on the linear city

(Figure 3-2). Due to the symmetry of this linear city model, we consider without loss of

generality the case where 0 < x <1/2. The potential customers of the integrated service

are uniformly distributed in this linear city between 0 and 1. Recall that the customers

incur a delay cost t per unit of distance from consuming (or accessing) the Web services.

0 x y 1

Si S3 Customer S2
(Intermediary)

Figure 3-2. Linear city model

3.2.1 Cost to buy from WSI in linear city

The customer at location y (0
consuming the Web service S1 and t (1 -y) for S2. The network delay cost for the same

customer to access the integrated Web service offered by the WSI is t I y- x Then, the

total cost for the consumer located at y to buy the integrated service from the WSI is

composed of the price of the integrated service (P,) and the delay cost, i.e.,

P, + t y-x (3.1)

3.2.2 Cost to buy from service vendors in linear city

The total cost for customers between 0 and 1 to purchase the Web services S1 and

S2 separately and integrate by themselves includes the prices of the individual Web

services (/P and P), the integration cost (c), and the total delay cost which equals to the

sum of network delay of accessing each Web service, i.e., ty + t(1 y) = t. In math, the









total cost for customers who choose to create the integrated Web service by themselves is

as follows.

P+P2+c+t (3.2)

3.2.3 Optimal location and pricing in linear city

By evaluating the total costs described in Equations (3.1) and (3.2), each customer

decides whether to buy the integrated service from the WSI or to create the integrated

service by purchasing the individual Web services from the service vendors separately.

The marginal customers who are indifferent between buying from the WSI and creating

the integrated Web service by themselves are described as below, where y" and y2

denote the distance between the marginal customer and the WSI (Figure 3-3).

P +P2 + + t =P + ty, P + +P2 + c + t = P3 + ty2 (3.3)

Marginal y Marginal
Customer y y Marginal
0 Customer 1
I I I I
S1 i S3 S2
x (WSI)

Figure 3-3. Marginal customer in LC model

For customers between the two marginal customers at y" and y2, the total cost to

buy the composite Web service from the WSI is lower than that to buy S1 and S2

separately and then create the composite service by themselves. Therefore, they will buy

from the WSI. However, if the WSI sets price P, too high, all customers between 0 and

1 would create the integrated service by themselves, leaving no demand for the WSI. On

the other hand, if the WSI set the price P3 low enough, all customers between 0 and 1

would buy from the WSI. In summary, the demand of the WSI isD = y, + y2, where









'0, if ym y,= ym, if 0 < y, < x, i = 1, 2 (3.4)
x, if y, > x

Accordingly, the joint decision of optimal location and pricing for the integrated

Web service by the WSI is formulated as follows

max z= PD = P(y +y,)
P3, (3.5)
s.t Eq. (3.4)

Proposition 3-1. In the unit-length linear city model, the optimalprice and profit

1 1
of the integrated Web service are P = P +P, +c+-t and .* =P~ +P +c+-t. The
2 2

mid-point position between the service providers is the optimal location for the integrated


Web service, i.e., x* = Furthermore, the WSI captures the entire market demand by


charging P,' and P' increases faster i i/th c than t.

Proof. The derivation of the optimal profit and location is quite tedious and the

details are delegated to the Appendix B. I just provide a sketch of proof here. This joint

decision problem is solved in two steps. First, I derive the optimal price and profit for the

WSI given any particular position x. Then, the optimal location is selected as the one

that yields the highest profit obtained in the previous step. Q.E.D.

Proposition 3-1 suggests that if all customers are located between the service

providers, in which case all customers incur the same cost if they integrate the composite

Web services by purchasing S1 and S2 from the service providers, the optimal location of

the WSI would be the midpoint position between the service providers. In addition, the

WSI is best off by charging a penetration price to capture entire market demand. In









addition, as the delay cost or the integration cost increases, the WSI should increase the

price of the composite Web service and obtain more profit. However, integration cost

has bigger impact on profit (and price) than the delay cost. It is quite intuitive that the

WSI gains more profit when the integration cost is higher since more customers will

switch to the WSI due to the higher cost of integrating the individual Web services.

However, when the delay cost increases, the costs of buying from the WSI and the

service providers will both increase. The increased profit of the WSI in the presence of

larger delay cost suggests that the negative impact of delay cost is higher for the service

providers than the WSI.

In the linear city model, it is assumed that the service vendors are located at

"extreme points" such that all customers are located between the service providers. As a

result, all customers experience equal delay cost if they buy the two Web services from

service vendors. In the next section, I shall relax this assumption by considering a unit

circle model.

3.3 The Unit Circle Model

In this section, I study the more general case with a unit circle (UC) model in which

not all customers are located between two Web service vendors. Let 0 be the location of

the first service provider on the unit circle, and the second service provider is located at

distance d clockwise from the first provider. Without loss of generality, we restrict our

1
attention to consider the case where 0 < d <-. Suppose the integrated Web service is
2

located at x clockwise (0 < x < 1) on the unit circle. There are N potential customers of

the integrated service uniformly distributed along the unit circle.









The unit circle model is depicted in Figure 3-4, where points A and B correspond to

the locations of the two Web service providers and the integrated Web service is located

at point E. For the purpose of analysis, we mark points C, D and F, which are diagonal to

points A, B and E on the unit circle respectively.

(S1)
A (0)
E (WSI)


B (d) (S2)



(0.5+ d)


F (0.5+x) C 0.5)

Figure 3-4. Unit circle model

3.3.1 Cost to buy from WSI in unit circle

One distinctive feature of the unit circle model is that the delay cost for a customer

along the unit circle is conditional on his location, since the shortest route to reach a node

could be traveled either clockwise or counter-clockwise. For example, for a particular

customer located at y clockwise from point A, the total cost to purchase the integrated

service from the WSI (tc, ) is

P3+tly-x|, ifly-x<1/2
tc; = (3.6)
S +P, +t(l- y-x), if y-x|>1/2

3.3.2 Cost to buy from service vendors in unit circle

If a customer integrates the services by herself, the delay cost is the sum of delay

costs of accessing the Web services S1 and S2. Unlike the linear city model where all









customers are located between the two service providers and incur the same delay cost

when accessing the Web services S1 and S2, customers at different locations experience

different response delay due to different shortest route of access. For example, a

customer located between A and B incur a delay cost of td, while a customer located

between C and D incur a delay cost of t(1-d). Customers located within the BC and

DA segments incur a delay cost between td and t(1-d). Equation (3.7) formulates the

total cost (tcN) for a customer at y (0 < y <1) if he (or she) integrate the Web service

by oneself, which includes the prices of the Web services S1 and S2, the integration cost,

and delay cost.

P +P2+c+td, if 0 C +Pz +c+t(2y-d), if d tN P, +P +c+t(1-d), if 1/2
P+P+ c + t(2-2y+d), if 1/2+d
3.3.3 Optimal location and pricing in unit circle

Given the total costs tc1 and tcN, one can find the location of the marginal

customer who is indifferent between buying the integrated Web service from the WSI

and from the Web service vendors. Consequently, the demand for the integrated Web

service at a particular location on the unit circle can be calculated as a function of the

delay cost, integration cost, distance between the individual service providers and their

prices. Using the same approach as in the previous section, one can solve the joint

decision problem of optimal location and pricing for the integrated Web service.

However, it is rather tedious since the cost functions of tc, and tc, have conditional

format, which in turn leads to complex profit function for the WSI. Therefore, I use a

different approach to solve the location and pricing problem in the unit circle model. The









following lemmas explain our logic of derivation. Further, results from analytical study

are presented in Propositions 3-5 and 3-6, followed by interpretations of managerial

insights.

Lemma 3-2. The highest market-covering price for the integrated Web service at

x is

min{P + P +c+tx, Pl +P +c+t(0.5-d), P + P2+c+t(d-x)}, 0 P +P2+c+t(d-x), d S=< 1(3.8)
+P, +P2+c+t(d-0.5), 0.5 LP +P2 +c+t(x-1), 0.5+d< x<1

Proof. The highest market-covering price is the highest price the WSI can charge

while still attracting all customers in market. This price is calculated in two steps. First,

for each customer I calculate the total cost to buy Web services from the service vendors

and then create the integrated Web service by himself (or herself). Then, the highest

market-covering price is selected as the lowest total costs for all customers in market.

Detailed derivation is left to Appendix B.

Lemma 3-3. When the WSI charges the highest market-covering price, the WSI

achieves maximum profit if the integrated Web service is located between the two Web

service providers.

Proof. When the WSI captures the entire market demand, its profit is constrained

by the highest price it can charge, which is conditional on the location of the integrated

Web service, as specified by the four cases in Eq. (3.8). By inspection, one gets

P, >P +P2 +c in the first case while P,
words, if the integrated Web service is placed between the two service vendors, the

intermediary can charge a higher price while still captures entire market demand.









Therefore, the WSI will choose to locate the integrated Web service between the service

providers to maximize profit. Q.E.D.

kt
Lemma 3-4. If the WSI raises its market-covering price PR by AP = where k
N

is a positive integer, it will lose no less than min{k, N} customers. Furthermore, if

kt (k + 1)t
- < AP < the WSI loses demand of no less than min{k +1, N}.
N N

Proof. See Appendix B.

Proposition 3-5. When t < 2(PI + P + c), it is optimal to host the integrated Web

service between the service providers of Sl and S2 and the optimal price is the highest

market-covering price specified in (3.8), i.e.,

P, = min{P, +P +c+tx, P + P+c+t(O.5 -d), PI+P2+c+t(d-x)} (3.9)

Proof. Proposition 3-5 is derived from Lemma 3-2, 3-3 and 3-4. See Appendix B

for a detailed proof.


Proposition 3-6. When t < 2(PI +P2 +c) and d > there are multiple optimal
3

locations for the WSI. The optimal location andpricing of the integrated Web service are

described in (3.10).

1 1 1 1 1
If - 3 2 2 2 2

1
On the other hand, when t < 2(P/ + P + c) and 0 < d < -, the optimal location and
3

pricing of the integrated Web service are described in Eq. (3.11).

1 d
If O < d < -, P = + + c + t / 2 and x =(3.11)
3 2









Propositions 3-5 and 3-6 are consistent with the analysis in the linear city model.

However, in the linear city model, the two Web service vendors are located at the end

points such that the integrated Web service can only be stored between them. In the unit

circle model, the WSI can choose the intensity of competition by locating at different

regions on the unit circle. For example, the competition between the WSI and the service

providers is maximal if the integrated Web service is located between A and B; In

contrast, the competition is minimal in section between C and D; Further, the sections

between BC and DA represent regions of moderate competition.

Proposition 3-5 suggests that if the delay cost is small, the WSI prefers maximum

competition and will set a low penetration price to capture the entire market demand. At

first sight, this result is quite "unconventional" in that classic economic theories, such as

the theory of Bertrand competition, suggest that firms prefer lesser competition in order

to avoid price war. This unusual result can be explained by two characteristics of the

Web service market. First, as an executable program distributed over the Internet, the

performance of a Web services based platform is greatly constrained by the underlying

networking infrastructure. In particular, a customer accessing the Web service incurs a

delay cost due to network latency, which is associated with the physical distance between

the customer and Web service application server. Second, due to the platform

independence and software modularity, a Web services based platform boasts the

flexibility of integrating multiple Web services on the fly, across the street or across the

ocean. Consequently, in case when multiple Web services at different locations are

accessed to compose an integrated Web service, the network delay of accessing the

integrated Web service is the sum of network latency of accessing each constituent Web









services. On the other hand, there's only one single network delay when a customer

accesses an integrated Web service from the WSI directly. Therefore, although the WSI

faces a stronger competition when the integrated Web service is located between the two

Web service vendors, it will be compensated since it can charge a higher market-covering

price. In fact, this is not a deviant from the results in a Bertrand competition, since the

WSI sets a low penetration price to cover the entire market.

While the linear city model yields one single optimal location for the WSI,

Proposition 3-6 suggests that there could be multiple optimal locations for the integrated

Web service in the unit circle model. In fact, the result in the linear city model can be

viewed as a special case of the unit circle model since the mid-point position is always

the optimal location according to Proposition 3-6.














CHAPTER 4
OPTIMAL SUBSCRIPTION AND LOSING FEE OF A WEB SERVICE
INTERMEDIARY

Depending on technical strengths and business scope, the Web service intermediary

(WSI) can play various roles in the Web service supply chain. In the previous chapter, I

focused on studying the optimal strategy for a WSI that provides integration service and

sells an integrated Web service. In this chapter, I study another model of WSI that does

not sell Web services of its own. Instead, the WSI offers aggregation service to match

the Web service vendors with the Web service consumers. In addition, the WSI provides

value-added services to take the advantage of its technical expertise.

One example of such WSI is Salcentral.com, which maintains a comprehensive

directory of Web services so that service consumers can browse, search for and audit

particular Web services. Another WSI, GrandCentral.com, provides a centralized Web

services network and acts as a trust broker that handles issues of message delivery and

routing, security, ..., etc. In summary, the WSIs provide certain value-added services to

both Web service vendors and service requestors, allowing them to charge a fee to both

sides of the Web services supply chain.

In this chapter, I study the optimal strategies for the WSI in a supply chain of

complementary Web services. In particular, the WSI serves two groups of web service

vendors that provide Web services of complementary functionalities. The two

complementary Web services can be integrated by consumers to create a new composite

Web service. Correspondingly, the consumers are divided into three groups those who









desire the two individual Web services and those for the composite Web service. The

WSI charges the Web service vendors a listing fee and the Web service consumers a

subscription fee in order to access the added value provided by the WSI. The added

value of the WSI consists of the "intrinsic" value and the "cross network externality"

value. The intrinsic value of the WSI refers to the standalone services offered by the

WSI such as Web service development tools, maintenance, and security. The cross

network externality value is proportional to the number of service vendors listed in and

the customers subscribing to the WSI. That is, the more service vendors listed on the

WSI, the more valuable it is for service consumers to subscribe to the intermediary, and

vice versa.

The Web services supply chain has several unique characteristics. First, unlike

traditional supply chain, the object supplied and consumed in the Web services supply

chain is not tangible physical goods, but rather software components residing at the

service provider's computer server. Hence, most issues considered in traditional supply

chain literature such as inventory and ordering decisions of the distributor are no longer

relevant to the WSI. The major decision facing the WSI is how to optimally set the

listing fee for the Web service providers and the subscription fee for the Web service

consumers. Second, most supply chain literature involves one supplier and one customer,

or one group of suppliers and one group of customers. One of the most appealing

features of the Web service technology is the ease of creating a new composite web

service (i.e., new business functionality) from integrating two Web services of

complementary functionalities. To account for this reality, this model includes two

groups of Web service vendors providing complementary Web services. The consumers









of the Web service supply chain are thus comprised of three groups those demanding

the individual Web services and those in need of the composite Web service. Third, in

addition to the intrinsic value offered by the WSI, we study the impact of "cross network

externality" effect on the WSI's optimal strategies. The intensity of the cross-network

externality effect is found to be a key factor affecting the WSI's pricing strategies.

The objective of this chapter is to study what are the best strategies for the profit-

maximizing WSI. Specifically, I address the following research questions. First, what

are the optimal subscription fee and listing fee? Second, is it optimal for the WSI to

attract all the Web service providers and/or all the Web service customers? Next, what is

the impact of the WSI's intrinsic value and intensity of network externality effect on its

pricing strategies? Finally, how does the nature of the Web services market affect the

optimal strategy of the WSI?

This chapter is organized as follows. First, I provide critical background of the

Web service supply chain. Next, I review related literature and outline the uniqueness of

this research. After that, I introduce the analytical model and derive the optimal listing

fee and subscription fee, followed by discussion on managerial insights.

4.1 The Web Service Supply Chain

A Web service supply chain is composed of three parties, the Web service vendors,

the Web service consumers and the Web service intermediary. A Web service vendor

develops some Web service with certain business function and makes it accessible

through its Web site. To make the Web service interoperable and discoverable, a Web

service description file (WSDL), which describes the function of the Web service and

specifies the technical signatures such as entry point, transport protocol and encoding









style, etc., is uploaded to a registry service, which can be either the public business

registry (PBR) or a private registry provided by the WSI.

Currently, the PBR is cooperatively operated by four companies, IBM, Microsoft,

SAP and NTT. A Web services registry can be understood as a database of Web service

descriptions (WSDL files).which accepts queries on base of multiple criteria, such as

functionality, industry, geographical region. Listing and searching via the public business

registry is free while a Web service intermediary may charge subscription fee to the

service consumers and listing fee to the service vendors.

When a Web service requester (consumer) considers using a software component

offered by other companies (service vendors) instead of developing it in house, she

searches for the desired Web services via the WSI or PBR. After obtaining information

from the registry, i.e., retrieving the WSDL file, the service requestor can choose to

purchase the Web service. In the context of Web services, the "purchase" of a Web

service involves binding the service requestor's client application with the remote Web

service and then invoking the Web service hosted on the service vendor's Web site.

Figure 4-1 illustrates the interactions among the parties of the Web services supply chain.


Publish


service Publish we service Find eb service
ndor intermediary Requester
(WSI)

............................................................... ............................................
Bind/Invocation


Figure 4-1. Web service supply chain









In addition to providing registry services, a WSI also provides value-added services

to consumers of Web services. For example, the intermediary can help reduce

transaction costs among trading partners by providing account management so that

consumers only need one account to access multiple Web services (e.g., SalCentral.com).

The WSI may improve Web services management by enforcing contract to guarantee

quality of service and security (e.g., FlamencoNetworks.com). The WSI can also exploit

its technical expertise and provide integration services to realize "service on demand"

(e.g., GrandCentral.com). The WSI can set up a networking infrastructure to provide

reliable service provisioning (e.g., BlueTitan.com). At the same time, Web service

vendors benefit from publishing their Web services on the WSI since the added values by

the WSI help increase the chance of a successful transaction with subscribers of the

intermediary.

4.2 Literature Review

Vast amount of research has been conducted on traditional intermediaries, with

applications mostly in financial market, labor market and supply chain markets.

Research on Web services in general and Web services intermediaries (WSI) in particular

has primarily centered around technical issues in the computer science field, while there

is a lack of research on Web services and WSI from the perspective of business

management. Prior literature on traditional intermediaries can be generally classified into

two categories.

The first stream of research on traditional intermediaries studies the role of

intermediaries. According to Spulber (1996), intermediaries can play several roles in a

vertical market matching and searching, price setting and market clearing, providing









liquidity and immediacy as well as guaranteeing of quality. There is ample research on

each aspect of the roles played by the intermediaries. Rubinstein and Wolinsky (1987)

model the interaction between buyers and sellers as a time-consuming bilateral search

process and studies how a matchmaking intermediary can affect profit division between

the two parties of transaction. Yavas (1994) studies the impact of an intermediary on

consumer search behavior, while Naert (1971) takes the perspective of producers and

analyzes the producer's optimal decisions on advertising and markup in an intermediate

market. Biglaiser (1993) takes another avenue of research and shows that an expert

intermediary can contribute to quality guarantee. The advent of the Internet technologies

and the ensuing e-commerce heralds recent research on the functions of electronic

intermediaries (Bailey and Bakos 1997, Bakos 1998, Kaplan and Sawhney 2000). By

observing the that the Internet helps reduce search cost (Bakos 1997), Bailey (1998) sets

out to study whether the intermediation via the Internet reduces friction and finds

empirically that there is greater price dispersion in online intermediaries.

The second stream of abundant research on traditional intermediaries examines the

optimal strategies of intermediaries. Gehrig (1993) studies the tradeoff between ask-bid

spread and cost of delay in private search and finds that a monopoly intermediary will

charge a positive spread. In another paper by Wooders (1997), it is shown that a profit-

maximizing intermediary may act as a Walrasian auctioneer by setting bid and ask prices

to nearly Walrasian equilibrium prices. Prior research on intermediaries of traditional

physical goods market unfortunately cannot be directly applied to online intermediaries,

since most online intermediaries take the role of "matchmakers" instead of "market

makers". That is, they provide value-added services and don't sell products on their own.









There are no issues of inventory costs, shipping costs, order quantities and transfer prices

that underline the framework of numerous previous research.

Recent work on optimal strategies of information intermediaries is more related to

our research on WSI. Baye and Morgan (2001) study how an information gatekeeper,

which provides product and price information to consumers, should optimally set

subscription fee to consumers and advertising fee to producers. Baye and Morgan (2001)

find that the gatekeeper will set a low subscription fee to attract all customers. There are

two key differences between Baye and Morgan (2001) and this research. First, they

assume that customers are geographically segmented so that consumers only buy from

local firms if they do not subscribe to the gatekeeper. In contrast, Web services

requestors can always search via a public business registry and obtain an entire list of

available service vendors. Second, the advertised price is lower than unadvertised price

in requestors, while in the context of Web services market, the service vendors and

requestors trade directly even if they find the match via the WSI, see Fig. 1. This feature

combined with the Web service requestors' ability to search the entire list of service

providers leads to no difference between listed and unlisted prices. These two key

differences between the information gatekeeper and the WSI result in opposite

conclusions to those in Baye and Morgan (2001). Bhargava and Choudhary (2004) study

the product line design (vertical differentiation) of an information intermediary in the

presence of aggregation benefits. In their model, consumers have heterogeneous search

costs and producers have heterogeneous expectations of gains from joining the

intermediary. In this paper, we model the interaction among the service vendors, the









WSI, and consumers in response to the subscription fee and listing fee charged by the

WSI.

Recent work on optimal strategies of information intermediaries is more related to

this research on Web service intermediaries. Baye and Morgan (2001) study how should

an information gatekeeper, which provides product and price information to consumers,

optimally set subscription fee to consumers and advertising fee to producers. It is shown

that the gatekeeper should set a low subscription fee to attract all customers. There are

several key differences from this research. First, they assume that customers are

geographically segmented so that consumers only buy from local firms if they do not

subscribe to the gatekeeper. In contrast, as explained in the previous section, the

requestors of Web services can always search via a public business registry and obtain an

entire list of available service vendors. Second, they explicitly allow price dispersion,

making advertised price lower than unadvertised price while in the context of a Web

service supply chain, the service vendors and requestors trade directly even if they find

the match via an intermediary. This implies that there's no price difference between

listed and unlisted prices. It is worth noting that due to the major discrepancies of

problem setup, this research leads to opposite conclusions from those by Baye and

Morgan (2001).

Of most relevance to this research is the work by Corbett and Karmarkar (1999),

which analyzes optimal subscription fee and listing fee by an intermediary when there

exhibit cross network externalities. Their study, however, focuses on one group of sellers

and consumers of one product. Since a major benefit of Web services technology is the

ease of software integration, the WSI we study faces two groups of service vendors









providing complementary functionalities, resulting in three groups of customers namely

two groups of consumers of the individual Web services and a third group of consumers

of the integrated Web service. Consequently, the problem facing the WSI is much more

complicated.

4.3 The Model

Following the modeling approach of Corbett and Karmarkar (1999), I consider two

groups of Web service vendors which provide two complementary Web services, denoted

by S1 and S2. The two complementary Web services can be integrated into one

composite Web service S3. There are N, service vendors of S1 and N2 service vendors

of S2. The numbers of service providers N, and N2 are sufficiently large, and the Web

service market of S1 and S2 is highly competitive. The prices of S1 and S2, denoted by

P1 and P2 respectively, are thus treated as exogenously given.

The Web service consumers consist of three distinct groups Q, consumers

interested in S1, Q2 consumers interested in S2, and Q3 consumers interested in the

composite Web service S3. In case a consumer is interested in the individual Web

service (S1 or S2) and the composite Web service S3, he is counted as a consumer of S3

since the composite Web service always performs the functions of S1 and S2.

A monopolist WSI that provides value-added services charges a fixed listing fee L

to Web service providers and charges a fixed subscription fee F to service consumers.

Let xl (i=1,2) be the proportion of service vendors listed on the WSI and y,

(j = 1,2,3 ) be the proportion of consumers subscribing to the intermediary. Figure 4-2

delineates the basic setup of the model discussed above.










N1 / Service Q]
vendors of
Si y.
1S X1 yWeb yl
Service Consumers Q2
-v ofS2
X2 Intermediary
Service
N2 vendors of Cons
N' S2 ?3'^/ Consumers 3
^S2 yof S3


Figure 4-2. Model of subscription and listing fee

The sequence of events is described as follows. First, the WSI sets the subscription

fee F and listing fee L. Observing the listing fee and subscription fee, consumers

decide whether to subscribe to the WSI based on their judgment of expected value from

the subscription. At the same time, the service vendors decide whether to list on the

WSI, in consideration of the expected number of subscribers and vendors joining the

intermediary. In equilibrium, service consumers have rational expectation of proportion

of listed service vendors, and vice versa.

I use backward induction to solve the WSI's decision of optimal subscription fee

and listing fee. First we derive the proportions of Web service consumers (y, j = 1, 2,3)

and vendors (x,,i =1,2) who decide to join the WSI given the subscription fee F and

listing fee L. In the next section, I analyze how the WSI optimally sets the subscription

fee and listing fee

4.3.1 Consumer's subscription decision

From the service consumer's point of view, the added value offered by the WSI

consists of intrinsic value and cross network externality effect. The intrinsic value of the

WSI results from the reduction of transaction cost, enhanced security and management,









and improved reliability, etc. In general, the intrinsic value is related to the technical

strength of the WSI and is independent of the number of service vendors listed on it. The

consumers are heterogeneous in their judgment of the intrinsic value, denoted by the

random variable v uniformly distributed in the interval [0,v]. The ceiling of the

distribution, v, is used as a representative measure of consumers' valuation of the WSI's

intrinsic value. The second part of the WSI's added value is network effect, which is

increasing in the number of Web service vendors listed on the intermediary. Let y

indicate the intensity of the cross network externality, or consumer's valuation for the

network effect. Then, the network value to a consumer equals y multiplied by the

number of service vendors listed on the WSI.

Web service consumers decide whether to subscribe to the WSI by evaluating the

cost (subscription fee) and value from subscribing to it. For a customer who is interested

in S1, the total benefit from subscribing to the intermediary is v+yx, where v is the

consumer's valuation of the WSI's intrinsic value, and x, is the proportion of listed Web

service vendors providing SI. Let v0, be the valuation of the marginal consumer of S1

indifferent between subscribing and not subscribing to the WSI. Then equation

vol + yx, = F holds. Similarly, the marginal consumer of S2 with intrinsic value of vo2 is

described by vo + yx2 = F The proportion of consumers of S1 (or S2) who subscribe to

the WSI corresponds to those consumers who have higher valuation than that of the

marginal consumer, specified as follows. Note that if the subscription fee is prohibitively

high, all consumers will stay away from the WSI (y =0), while a sufficiently low

subscription fee attracts all consumers to subscribe (y = 1).









(v F +yx)/ if yx< F y,(x,) = if F < yx (all subscribe) (4.1)
{0, if F>v +x, (nobody subscribe)

The consumer who is interested in the composite Web service derives v + yx, + yx2

benefit from subscribing to the WSI. That is, the network value for the consumers of

composite service S3 is increasing in the proportion of listed service vendors of S1 and

the proportion of listed service vendors of S2. In accordance, the proportion of

consumers of S3 who subscribe to the intermediary is as follows.

(v- F +yx, +2x,)/v if yx,1 2 y3 ( x, )=2 0 if F>v + yx + x2 (4.2)
1 if F < yx, + yx2

4.3.2 Service vendor's listing decision

Subscribers to the WSI search the Web services listed on the intermediary and

choose to transact with those vendors listed on the WSI, while non-subscribers search

through the public business registry (PBR) which contains the entire list of service

vendors. Since the price of the same Web service in this competitive market is the same,

consumers pick the service vendor with equal likelihood. In other words, listing to the

WSI helps the service vendors increase the chance of accomplishing transactions with

more consumers (unless none of the consumers subscribe to the WSI) and reduce the

competition from peer service vendors (if not all service vendors publish on the

intermediary).

Given the proportion of consumers subscribing to the intermediary (y,, y2 and

y3), the profits for a service vendor to list (rf) or not list (;rw') are formulated in

Equations (4.3) and (4.4), respectively. The subscript i is an index of Web services (S1









or S2). Notice that the demand of Web service S1 (likewise, S2) comes from both the

consumers of S1 (S2) and those of the composite Web service S3.

rf1 = P]qQ( + P Q3( + 3 )- L ,i = 1,2 (4.3)
N, x,N, N, x,N,


z i 1- y, +ly3 p i=1, 2 (4.4)
N N

A Web service vendor of S1 will choose to list on the WSI if it gains more profit

from listing (z;r > ;r') while otherwise it will not list. Hence, one derives the proportion

of service vendors listing on the WSI from the equation ;N- = ; (i=1,2), which is

described below. Similar to the consumer side, the intermediary will attract all service

vendors if the listing fee is low enough (x = 1). It is interesting that that although a high

subscription fee will drive all consumers away from the intermediary, a high listing fee

won't deter all service vendors. This is because those consumers who subscribe to the

intermediary won't search beyond the WSI, creating a niche market of subscribers for the

service vendors listed on the WSI.

{T(YQ, +Y3Q3) if L > + y3Q3)
x,(,,y3)= LN, N, i=1,2 (4.5)
otherwise

4.4 Optimal Subscription and Listing Fee

After deriving the response functions of the service consumers and service vendors,

the WSI solves the decision problem described as follows. The initial cost to set up for

the WSI is sunk and the marginal operational cost incurred by the intermediary is

assumed to be negligible.









max H = (yQe + y2Q2 + y3Q)F + (xN, + x2N2)L
L,F (4.6)
s.t. Eqs. (4.1), (4.2),and (4.5)

Lemma 4-1. The optimal strategy of the WSI is to induce all service vendors to list

on the intermediary, i.e., x = x2 = 1. Specifically, the WSI will set the listing fee at


L* = min{L,,L2}, where L, =(yQ +3Q3) (i = 1,2).
N

Lemma 4-1 specifies that the listing fee should be low enough such that all service

vendors list on the WSI (x, = x2 =1). The exact value of the listing fee cannot be

determined until we derive the proportion of the subscribers (y 's), which in turn is

determined by the subscription fee F. In addition, the minimum of L1 and L2 is

dependent on the values of P,, N, and Q,.

Knowing that its optimal strategy is to attract all service vendors to list on it, the

WSI is naturally confronted with the following question: whether it should attract all

customers to subscribe? Lemma 4-2 suggests that this is not the case.

Lemma 4-2. The intermediary's profit is not always maximized when all customers

subscribe to its service, i.e., y, y2 =3 =1.

Proof. I prove Lemma 4-2 by showing an example where the intermediary obtains

more profit without full subscription from the service consumers. First note that F = y is

the maximum subscription the intermediary can charge to induce total subscription.

From Lemma 4-1 we know that the intermediary has incentive to allow all service

vendors to list on it. Consequently, the maximum profit the intermediary can achieve

when all consumers subscribe to it is r1 = y(Qe +Q2 +Q3)+Pf(Qi +Q3)+P (Q2 +Q3).

Next consider the case when the intermediary charge a subscription fee of F = 2y,









which induces all customers of S3 to subscribe but not all customers of S1 and S2 will

subscribe, i.e., 0 < y1,y2 <1 and y3 = 1. Then the intermediary's profit is > z2 where

2 = 2yQ3I +fQ3 +P2Q3 is calculated when we plug in F = 27, y3 =1 and y = y2 = 0

into the profit function. With simple algebra, it's easy to show that the intermediary

obtains more profit if yQ3 > (Q1 + Q2) +Q1 +P2Q2, i.e., iT > i2 > iT1. Q.E.D.

Next we reformulate the optimal decision problem faced by the WSI by applying

Lemma 4-1 and Lemma 4-2 in Eq. (4.7).

max IN = (yQ, + y2Q2 + y3Q3)F + P(yQ, + y3Q3) + P2(y2Q2 + y3Q3)
F (4.7)
s.t. (4.1), (4.2), (4.5)

Note that the functional form of proportions of subscribers (y, s) is conditional on

the value of subscription fee (F). Accordingly, the WSI's profit function takes different

format under various subscription fee. In order to solve for the optimal subscription fee,

we need to compare the profit under each possible combination of y, s (j = 1, 2,3 ). That

is, we need to consider internal solutions (0 < yj <1) as well as the boundary solutions

(yj =0 or 1). Furthermore, the proportion of consumers who subscribe to the WSI

varies under different relationship of y and v. For example, if the intermediary charge a

subscription fee of v + y, all consumers of S3 will subscribe to it if y > v. On the other

hand, only partial consumers of S3 will subscribe to it if y
charged at v+ y. In the following two subsections, I solve for the optimal subscription

fee F* under different relationships between consumer's valuation of the network effect

(y) and the WSI's intrinsic value (v).









4.4.1 Network value is less than intrinsic value

When consumers value intrinsic value more than network value ( < v ), the

proportion of subscribers with respect to subscription fee is illustrated in Figure 4-3,

which breaks down into the following three sub-cases. If the subscription fee is between

y and 2y (case I-1), partial consumers of S1 and S2 subscribe to the intermediary while

all consumers of S3 subscribe to the intermediary; If the subscription fee is between 2y

and v + (case 1-2), partial consumers of S1, S2 and S3 subscribe to the intermediary; If

the subscription fee is between v + y and v + 2 (case 1-3), only partial consumers of S3

subscribe to the intermediary. Obviously, the intermediary has no incentive to set the

subscription fee lower than y or higher than v + 2, since the market is saturated under

subscription fee at y and in the latter case no consumer will subscribe.


1-1 1-2 1-3
7 2y v+7 v+2y F

Figure 4-3. Proportion of Subscribers when y
I first derive the locally optimal subscription fee and then compare the WSI's profit

in each sub-case. The optimal subscription fee is found as the one that generates the

highest profit for the WSI.

Case -1: y
If the subscription fee charged by the intermediary is between y and 2y, all

consumers of S3 will subscribe to the intermediary while only partial customers S1 and

S2 will subscribe to the intermediary, i.e., 0 < y, y2 < 1 and y3 = 1. Correspondingly, the

intermediary's profit maximization problem is defined in Eq. (4.8). Lemma 4-3 describes









the optimal subscription fee when the intermediary desires to attract all consumers of S3

and partial consumers of S1 and S2.

max 11 v +7-F ( v+ -F (4,
max ri= (Q, +Q+Q)Q, F+ ( Q, +Q,)
F=1,2 v (4.8)
s.t < F< 27

Lemma 4-3. If y
profit of the intermediary (F* ) is specified as follows.

(V-P)Q +(V-P2 2 3
a. If (V l<+( )Q2 Q < (0Q + 2)

b. f(V- ) + ( Pz)Q2 + 3 (v-) +(v-P) )2 2+v 3 F, F
3(Q1+ 2) <+ 11 1 2
where F, is defined as F, (= + P)Q1 +(v P)Q2 Q3
2(Q + Q2)

0< (V )Q1 +(v -22 +Q 3, 2y.
c. If 0 < : < yi = 22/.
3(Q1 +Q2)

Proof. Lemma 4-3 is derived from solving the constrained optimization problem

defined in (4.8). See Appendix C for a detailed derivation.

Case 1-2: 2y < F < v +

If the subscription fee charged by the intermediary is between 27 and v+ y, part

of customers interested in each of the Web services S1, S2 and S3 will subscribe to the

intermediary, i.e., 0
maximization problem is defined as follows. Lemma 4-4 describes the optimal

subscription fee when the intermediary desires to allow all groups of consumers to

subscribe.









mv+-F+2-F V-+y-F v+2y-F
max n 12 v+y-F (Q )+3 F + ( Q,+ 2
F V V 1=1,2 V V (4.9)
s.t 27
Lemma 4-4. If y
profit of the intermediary (F12) is specified as follows.
(v -P)Q+(v-P2)62 -P2)3 -*
a. Ifo 3Q1 + 3Q2 + 2Q3
definedas 12 (V 1 Q(V -P2)2(v2-P -P2)Q3.
2(Q1 + Q2 +3)

(A -P) -P 2 -P -P
b. I( -(V 1)Q1 + (V- PV2)2 + (>- -1 P()V I Y < y, F12 = 2y2.
b. If < F 2
3Q1 + 3Q2 + 232

Proof. Lemma 4-4 is drawn from solving the constrained optimization problem

defined in (4.9). It is quite similar to proving Lemma 3. So we won't repeat here.

Case 1-3: v + y
If the subscription fee charged by the intermediary is between v+ y and v+ 2y,

no consumers of S1 and S2 will subscribe to the intermediary while partial consumers of

S3 will subscribe to the intermediary, i.e., y, = y2 = 0 and 0 < y3 <1. Correspondingly,

the intermediary's profit maximization problem is defined as follows. Lemma 4-5

describes the optimal subscription fee when the intermediary desires to get subscription

from proportional consumers of S3 only.

v+2y-F v+2y-F
max 1113 V F Q3F +(FP +P2) Q3
F v v (4.10)
s.t. v+y
Lemma 4-5. If y < v and v + y < F < v + 2y, the subscription fee that maximizes


the profit of the intermediary is F1 = v + y.









Proof. We derive Lemma 4-5 by solving the constrained optimization problem and

it's quite similar to proving Lemma 4-3. Detailed proof will be omitted.

Proposition 4-6. If the intrinsic value of the intermediary is larger than the

network value (y
specified as follows.

a. If 0 < ,,, the optimal subscription fee is F12 defined in Lemma 4-4.
Proportional consumers of S1, S2 and S3 will subscribe to the intermediary, i.e.,
0< y, <1 (j =1,2,3);

b. If Y1,,1 < y12, optimal subscription fee is 27. All consumers of S3 will subscribe
to the intermediary while only partial consumers of S1 and S2 will subscribe to
the intermediary, i.e., y3 = 1 and 0 < y, 2 < 1

c. If 212 < y13, optimal subscription fee is F1; defined in Lemma 4-3. All
customers of S3 subscribe to the intermediary while only partial customers of S1
and S2 subscribe to the intermediary, i.e., y3 = 1 and 0 < y, y2 < 1

d. If Y213 < < V, optimal subscription fee is y. All customers of S1, S2 and S3 will
subscribe to the intermediary, i.e., y, =1 (j = 1, 2, 3),

V (v- )Q +(v-) +(v --2))Q3 (v- )Q1 +(v- )Q2 +vQ
3Q + 32 + 23 3(Q +Q2)
(v ~D + (v lO)02 3 v
and ,13 is defined as 2,13 =( I)Q1
Q1 +Q02
One interesting observation from Proposition 4-6 is that the intermediary has more

incentive to serve consumers of the integrated Web service than to serve consumers of

individual Web services. Unless the network effect is very large, see (d) of Proposition

4-6, the intermediary only desires to allow partial consumers of S1 and S2 to subscribe

to. In contrast, the intermediary will set the subscription fee to attract all consumers of

S3 except for very small network effect, e.g., see (a) of Proposition 4-6.









Furthermore, Proposition 4-6 suggests that the optimal subscription fee is

conditional on intensity of the network effect (y). Note that y,, < ,, < y3, this implies

that as the intensity of network effect increases, or consumer's valuation of the network

effect increases, the intermediary has incentive to allow more and more consumers to

subscribe to it, since the proportion of subscribers increases from (a) to (d). The behavior

of optimal subscription fee and profit is summarized in Proposition 4-7.

Proposition 4-7. If the intrinsic value of the intermediary is larger than the

network value (y < v), the optimal subscription fee and optimal profit are increasing in

network intensity (Y).

Proof. By inspection, the subscription fee is increasing in network intensity under

each conditions specified in (a)-(d). In addition, note that the subscription fee coincides at

each point that delimits successive cases from (a) to (d). For example, when = y,,, the

optimal subscription fee F2, under the condition (a) equals 2y, which is the optimal

subscription fee under the condition (b). Therefore, the optimal subscription increases

with network intensity. The network effect has similar effect on the optimal profit for the

intermediary. Q.E.D.

Figures 4-4 and 4-5 illustrate the behavior of optimal subscription fee and profit

when the network effect is lower than the intrinsic value of the WSI (y
parameters in the shown example are: Q = 100, Q2 = 150, Q3 = 90, P = 5, P2 = 2,

v = 5, y, = 0.29, 12 = 1.2, ,13 = 3.6. Note that the optimal subscription fee (or profit) is

a kinked one (different functional forms with varying network intensity). But the optimal

subscription fee (or profit) is generally increasing in network intensity.









Subscription
Fee (F;) 5

4-/




2- /




0 1 2 3 4 5
Network effect (y)
Figure 4-4. Optimal subscription fee when y <


Profit (H1) 300

2300.
2800-




2000
1600-
1600-

0 1 2 3 4 5
Network effect (7)
Figure 4-5. Optimal profit when y <

It's worthy noting that although Proposition 4-6 describes the optimal subscription

fee with respect to different values of the network intensity (y), not all conditions


prescribed in Proposition 4-6 can be met in reality. For example, in case when y13 > V,


the condition specified in (d) will never be satisfied. In other works, the intermediary will

never set the subscription fee at 7, which attracts all consumers to subscribe to the

intermediary, when the condition in (d) is met. Corollaries 4-8 and 4-9 present more

insights from Proposition 4-6 by studying the impact of intrinsic value on the optimal

subscription fee.









Corollary 4-8. The intermediary will set the subscription fee to attract all


customers of S3 to subscribe if v Q1 +Q2 +Q3

Proof. If 7,, <0, the condition specified in (a) of Proposition 4-6 will never be

satisfied. Therefore, the optimal subscription fee charged by the intermediary must be

realized in one of the remaining cases of (b)-(d), which all suggest a total subscription

from consumers of S3, i.e., y3 = 1. Q.E.D.

Corollary 4-9. The intermediary won't serve all Web service consumers if v > V,

S P1Q1 + P2Q2
where V =
Q3

Proof. To achieve total subscription from all customers, the intermediary has to set

up its subscription fee at y. According to Proposition 4-6, the subscription fee y is

optimal only when 13 < y < v But if y13 > v which translates to v > V, the condition in

d of Proposition 6 can never be satisfied. Q.E.D.

Corollary 4-8 specifies the condition under which the WSI will set the subscription

to allow all subscribers of S3 to subscribe to it. This relates the intermediary's optimal

strategy with the nature of Web services provided by the service vendors since the

threshold value V is increasing in prices of Web services (PI and P2). Specifically, the

WSI will strategically attract more consumers with higher value (or price) of the Web

services.

Corollary 4-9 can be viewed to some extent as the opposite case of Corollary 8.

Instead of answering when the intermediary should allow more consumers to subscribe to

it, Corollary 9 specifies the condition under which the intermediary desires to serve less









consumers. Corollary 4-9 also relates the intermediary's optimal strategy with the nature

of Web services provided by the service vendors. Note that the threshold value V is

determined by the prices of the Web services (P, and P,). According to Corollary 9, if

the consumers have a relatively higher valuation of the value-added services from the

WSI compared to prices of Web services, the intermediary need not set a low

subscription fee to attract all consumers to subscribe to it.

4.4.2 Network value is greater than intrinsic value

Similar analysis can be applied to the second scenario where consumers place a

higher value of the network effect than the intrinsic value (y> v). The proportion of

subscribers with respect to subscription fee is illustrated in Figure 4-6, which breaks

down into the following three sub-cases. If the subscription fee is between y and v + y

(case II-1), partial consumers of S1 and S2 subscribe to the intermediary while all

consumers of S3 subscribe to the intermediary, If the subscription fee is between v +

and 2y (case 11-2), no consumer of S1 and S2 will subscribe while all consumers of S3

will subscribe to the intermediary; If the subscription fee is between 2y and v +2y (case

11-3), partial consumers of S3 subscribe to the intermediary and no consumer of S1 and

S2 will subscribe. Similar to the previous scenario, we don't consider the cases when the

subscription fee is lower than y or higher than v + 2y.


II-1 11-2 11-3
Y V+y 2y v+2y F


Figure 4-6. Proportion of subscribers when y > v









CaseII-1: y
In Case II-1 the intermediary's profit function is the same as the one defined in

Case I-1. All consumers of S3 will subscribe to the intermediary while only partial

customers S1 and S2 will subscribe to the intermediary, i.e., 0< yl,y2 <1 and y3 =1.

However, the range of subscription fee is different from that of case I-1. We define the

intermediary's decision problem in Eq. (4.11). Lemma 4-10 specifies the optimal

subscription fee when the intermediary desires to attract all consumers of S3 and partial

consumers of S1 and S2.

max I21 v+I -F F+ v+-F Q+(3v+y F (4.11)
max 1I21 (Q1 +Q2) 3F+ + (_3
F v 1 =1,2 v (4.11)
s.t y
Lemma 4-10. If y
profit of the intermediary (F2) is specified as follows.

aV : 3 (V + 1+ P1)Q (V + + P2 2 =
a. If v < -< --, F v 21 +,
01 +02

b. VQ3 (- (V + )Q (v + )01 + (v P2)2 + Q3
01 + 2 Q1 + Q2
F1 =F21, where F21 is defined as F21 = (V+Y-21)Q1 v+ Y-2)Q2 +Q3.
2(Q + Q2)

S _(v P1)Q + (v P2)Q2 +vQ3
c. If > FQ21 y.
0 +02

Proof. Lemma 4-10 is proved using the same approach in proving Lemma 4-3.

Case 11-2: v + y < F < 2

If the subscription fee is between v + y and 2y, all consumers of S3 will subscribe

while no consumers of S1 or S2 will subscribe to the WSI, i.e., y, = y2 = 0 and y, = 1.









Furthermore, the WSI must set the subscription fee at 2y, which is the highest

subscription fee the WSI can charge in order to have all consumers of S3 subscribe to it.

This is because any subscription fee less than 2y won't attract more consumers and thus

will render less profit for the WSI. Therefore, Case II-2 can be viewed as a special case

of Case II-3 to be discussed below.

Case 11-3: 27 < F < v + 27

If the intermediary set the subscription fee between 2y and v + 2y, no consumer

of S1 or S2 will subscribe while some consumers of S3 might subscribe to it, i.e.,

y, = y2 = 0 and 0 < y3 <1. The profit maximization problem is described in Eq. (4.12).

It is same with the one defined in case 1-3, except for different constraint the subscription

fee. Lemma 4-11 specifies the optimal subscription fee when the intermediary desires to

get subscription from proportional consumers of S3 only.

max H23 V+2 -F Q3F+( +P2) +27- F Q3
max nI ,F + (-F: + F) Q,
F 3 v (4.12)
s.t. 27
Lemma 4-11. If y > v and 2y < F < v + 2y, the subscription fee that maximizes

the profit of the intermediary is F2v = V + .

Proof. The proof is similar to that of Lemma 4-5.

Proposition 4-12. If consumer evaluate the value from network effect more than

that of the technical services (7 > v), the optimal subscription fee is described as follows,

3 (v + P)Q1 (v + )Q (V -)Q + (v P)Q +
where 21 = and 2 +









a. If v y < y21, the optimal subscription fee is 27. All customers of S3 subscribe to
the intermediary while no customers of Si and S2 subscribe, i.e., y, = y2 = 0 and
y3 =1,

b. If 21 < Y < Y22, the optimal strategy for the intermediary is to set the subscription
so that all consumers of S3 subscribe while some consumers of SI or S2
subscribe, i.e., y3 = 1 and 0 < yl, y2 <1. In particular, the subscription fee is

F2, if ( > 0
d. F* = 2 if where F21 is defined in Lemma 4-10 and
[2y, if0<0



c. If > 7/22 the optimal subscription fee and proportions of subscribers are

27, if <>0
F= 27 ,^where = y(Q,+Q ,-Q3)+If2P,P,


leading to all consumers of Si, S2, and S3 to subscribe if F* = y or all consumers of S3

to subscribe but none of customers of Si and S2 when F* = 2y.

Proof. See Appendix C.

Similar to the previous scenario for y
the WSI by comparing the WSI's profit in Case II-1 and Case 11-3, which is summarized

in Proposition 4-12. But this scenario (y > ) is a bit more complex than the case when

y < V, since we don't have conclusive solutions for the optimal subscription fee under

conditions (b) and (c), which are determined by functions D and Y respectively. That

is, the optimal subscription fee is determined not only by the network intensity but also

the nature of the Web services, e.g., prices of individual Web services (P 's), market size

(Q 's), and the intrinsic value of the intermediary's service .

Figures 4-7 and 4-8 draw an example of the behavior of optimal subscription fee

with respect to network effect under conditions (b) and (c), which are determined by










functions D and Y respectively. The parameters in Figure 4-7 are: Q,


Q3 = 200,f P =1, P2 =1, v =10. The parameters in Figure 4-8 are: Q,


Q3 =200, f =1, P = 0.5, v =1.


Hn 9000
2


8000-


7000-


GOOD-
6000-


5000


80, Q2


80, Q2


F=2y


F=F21


10 12 14 16 18 20
Network effect (7)

Figure 4-7. Optimal subscription fee depends on 0 when y21 < < 22

-rr


112


2400:

2200-

2000-

1800-

1600-

1400-

1200-

1000-


F=2y



F=y


Network effect (y)


Figure 4-8. Optimal subscription fee depends on Y when y > y22


112


1









Furthermore, we summarize the characteristics of the optimal subscription fee with

respect to the specific relationship of v, P and Q in Corollaries 4-13, 4-14, 4-15 and 4-

16.

Corollary 4-13. When the network value is greater than the intrinsic value

(7 > v), the intermediary will never set the subscription fee at if Q + Q2 < Q3 and

SPeQ1 +P2Q2
v>
3 Q Q2

Proof. According to condition (c) in Proposition 4-12, the WSI has incentive to set

the subscription fee at y, which will allow all consumers subscribe to it, if Y > 0 and

PQ +PQ
y > 722 However, if Q + Q2 < Q3, we must have / < -2 so that Y is positive.
Q3 -Q1 -Q2

SPQI +P2Q2
Since y > v, it follows that if v > -- 2 2Y must be negative. Q.E.D.


Corollary 4-14. When the network value is greater than the intrinsic value, the

optimal subscription fee is 2y if 2(Q + Q2) < Q3 and v >
Q3- 2Q 2Q2

Proof. I show that under the conditions in Corollary 4-14, the following

inequalities hold: 721 > v, ( < 0 and Y < 0. Detailed proof is relegated to Appendix C.


Corollary 4-15. When the network value is greater than the intrinsic value, the

optimal subscription fee is increasing in network intensity if 2(Q +Q2)
SPQ1 + PQ2
v<
Q3 2Q 2Q2

Proof. See Appendix C for detailed proof. We show that there are only three

possible cases of the optimal subscription fee: (1) 22 for the entire region 2 > v; (2) F21









for 721 <7<722, y and then 2y for Y>Y72; (3) F, and then 2yfor 721 <7<7 22, 2y

for / > 22.

Corollary 4-16. If y > 22 and Q1 + Q > Qz, the optimal subscription fee is y.

Proof. By inspection, Y is positive when Q1 +Q2 > Q under condition c in

Proposition 4-12. Q.E.D.

Proposition 4-12 specifies three strategies for the intermediary when the network

effect is more significant ( > v): charge a low subscription fee to attract all consumers

(y); allow only consumers of S3 to subscribe (2y); or allow partial consumers of S1 (or

S2) and all consumers of S3 to subscribe (F21). Although the optimal strategy should be

determined by the network intensity and functions of 0 and Y, there are cases where

the WSI is certain about the optimal subscription without having to evaluate the values of

o or Y.

Corollary 4-13 states the condition when the intermediary will never charge a low

subscription fee to attract all consumers to subscribe to its service. It suggests that if the

market for the composite service is large and consumers have high valuation of the

technical expertise (intrinsic value) of the WSI, it will never offer a low subscription fee

to attract all consumers. Corollary 4-14 further states the condition under which the

intermediary only needs to attract consumers of S3 to subscribe to its service. Corollary

4-15 characterizes the behavior of the optimal subscription fee if the market for the

composite service is large but the consumer's valuation of the WSI's intrinsic value is not

as high. It should be noted that if at some point, the optimal subscription fee is 2y, the

WSI will never set the subscription fee of y. In other words, if the WSI's strategy is to






80


attract consumers of S3 only, it won't lower the subscription fee to attract all consumers

as network effect intensifies. On the other hand, if the market size of the composite Web

service S3 is small, Corollary 4-16 suggests that the WSI should set the subscription fee

to attract all consumers if the network effect is large.














CHAPTER 5
CONCLUSIONS AND FUTURE RESEARCH

The intense competition and the constant changing nature of the global economy

call for an agile information technology infrastructure for businesses to stay competitive

and adapt to new threats and opportunities. Astute managers face an enormous pressure

to cut costs and leverage existing information technology resources, a task complicated

by legacy systems built on technologies of different ages. Web services technology,

touted as the foundation to an interoperable, location transparent service-oriented

architecture has been developed as a promising solution to the challenge of heterogeneity

and change.

Despite the enormous hype and doubt in industry, there has been a lack of

academic awareness on this new computing paradigm, especially from business angle.

The objective of my dissertation is to study the business implications of Web services on

firm strategies. Specifically, I focus on the optimal strategies to provide Web services for

Web service vendors and Web service intermediaries. The dissertation is divided into

three research topics.

The first part of the dissertation deals with the optimal market structure to provide

complementary Web services. In particular, I compare three market structures-

independent service vendors (ISV), strategic alliance (SA), and Web service marketplace.

Under the ISV market structure, two Web service vendors offer two complementary Web

services separately and it is left to consumers to integrate the two Web services. Under

the SA market structure, two Web service vendors form an alliance to sell an integrated









Web service. Under the market structure of Web service marketplace, both individual

and composite Web services are provided.

Interesting managerial insights have been derived from theoretical analysis of a

simplified model and numerical explorations on a generalized model. First, it is found

that the marketplace dominates the ISV market structure, implying that Web service

vendors can benefit from the integration of Web services. Second, the integration cost

and the market characteristics of Web services play important roles in determining the

optimal market structure. If the valuation of the integrated software service is small, the

service vendors prefer marketplace to SA. For larger valuation of the integrated Web

service, the marketplace is preferred if the integration cost is high while SA dominates if

the integration cost is low. As the valuation of the integrated software service becomes

sufficiently high, SA turns to be the optimal market structure. In addition, if the

integrated service enjoys a larger market potential, SA will beat marketplace for a smaller

valuation of the integrated service. Finally, in a more balanced market, where one

service vendor has advantage over software valuation while the other service vendor has

advantage over market potential, SA is preferred to marketplace for a smaller valuation of

the integrated service.

The second part of the dissertation is intrigued by observing Web service

consumption model. In a Web-service-based computing environment, the Web service

consumers no longer need to license or house the software modules (Web services).

Instead, the consumption of Web services involves accessing the software component on

the remote server of Web service vendors. This causes network latency because of the









turnaround time between Web service vendors and consumers. As a result, the

performance of Web services is affected by where the Web services are located.

In the second part of the dissertation, I solve the joint decision of location and

pricing for a time-sensitive composite Web service provided by a Web service

intermediary. I propose two spatial models to solve the joint optimization problem by

taking into account both delay cost and integration cost. A linear city model is first

presented to study a special case where the individual Web service providers are located

at the ends of the linear city model. Optimal location and price are derived in the linear

city model. Analytical results indicate that the optimal location for the integrated Web

service is midpoint between the service providers. When the delay cost is small, the WSI

should set a low penetration price to capture entire market demand. When the delay cost

is high, the best strategy for the WSI is to share the market with the service providers.

Furthermore, the optimal price and profit are increasing in delay cost and integration cost.

To study the general cases where not all customers reside between the service providers,

a unit circle model is applied. Analysis in the unit circle model shows that when the

delay cost is low, the highest market-covering price is the optimal price and the optimal

location is midpoint between the service providers. Interestingly, analysis of the unit

circle model suggests that there exist multiple optimal locations for the integrated Web

service if the distance between the two Web service vendors is large.

Finally, I study the optimal strategies of a Web services intermediary (WSI) that

provides aggregation service and value-added technical services in a supply chain of

complementary Web services. The value of the aggregation service is dependent on the

number of participants in the Web service supply chain. Specifically, the Web service









consumers appreciate the aggregation service more if there are more vendors listed on the

WSI. Likewise, the Web service vendors think it is more worthwhile to list the Web

services on the WSI if more consumers are aware of it. The value-added technical

services are network-dependent, such as enforcing quality of service, enhancing security,

improving Web service management.

The aggregation and value-added services give the WSI the privilege to charge

subscription fee to Web service consumers and listing fee to Web service vendors. The

last part of the dissertation aims to solve for the optimal subscription and listing fees. In

particular, I consider a WSI that faces service vendors providing two complementary

Web services and there are demand for both the individual Web services and the

composite Web service. Analytical results suggest that in the presence of inter-network

externalities, the optimal strategy for the intermediary is to set a listing fee such that all

Web service vendors list their Web services on it. On the other hand, the optimal

subscription fee is determined by the relationship between the network value and the

value of technical services. When the consumers appreciate the technical services more

than the network effect, the optimal subscription fee is increasing in network effect and

the intermediary will attract more consumers as network effect intensifies. In addition,

the intermediary's strategy is affected by the natures of Web services being traded, such

as the prices of Web services, the market size, and the relationship between consumer's

valuation of technical services by the WSI and the prices of Web services.

This dissertation should serve as a ground for extended research in the future.

There are many interesting issues worthy of future work. In the study on optimal market

structure, I shall consider more market structures. For example, instead of having the









Web service vendors form a marketplace, a third-party company could serve the same

task of providing both individual and composite Web services. In some cases, the two

complementary Web services are not of equal importance. In other words, there could be

no demand for a supplementary Web service by its own. Another interesting issue of

Web service market structure is the tradeoff between customization and integration.

While a strategic alliance or marketplace helps reduce integration cost, the Web service

consumer suffers from loss of customization. Therefore, I shall incorporate the effect of

customization for the analysis on optimal market structure in the future. Furthermore, the

current research on market structure takes a "macro" view of the problem, ignoring the

profit distribution among service vendors. It is interesting to analyze the market

structures under different profit division mechanisms.

In the analysis of optimal location and pricing of an integrated Web service, I only

derive analytical results for the case when the delay cost is low in the unit circle model.

While analytical derivations of general scenarios for large delay cost become

mathematically impracticable to solve, I can adopt other numerical approaches, such as

simulations, to draw more insights.

The second and third part of the dissertation address different types of Web service

intermediary-a "matchmaker" that provides value-added services and a "market maker"

that sells an integrated Web service. A WSI that has the technical capabilities can

provide both integration and aggregation services. This means the relationship between

the WSI and the Web service vendors could be both cooperative and competitive. It's

worth studying the optimal strategies of such Web service intermediaries. Another






86


avenue of future research is to extend the monopolistic WSI to consider competition in

the duopoly setting.













APPENDIX A
PROOFS OF CHAPTER 2

A.1 Proof of Lemma 2-1

In a symmetric market, where the valuations and market sizes of the two Web

services are equal, the profit maximization problem for the service vendors is

max P=.D =p(O ,,)+P(Q), i= ,2 ( 2 A.1)
P11 V V3

By solving the above problems for each Web service vendor simultaneously, one

gets the optimal solutions described as follows.

SVW Q+V ^Q -cVQ,
S= 3 VV3Q3 -cVQ3 (A.2)
2QV +3VQ3

V( VQ+ V3Q -cQ3)(V2Q2 +VVQQ + 2QQ3 +VV3Q -cVQQ -cVQ2) (A3)
3t3 \ 3Q3- (A.3)
V (2QV + 3VQ3)2

Take the first and second derivatives of z, with respect to c.

2VQ3[V2(Q2 + QQ3+ V3QQ3 (V -c) + VQ2 (y -c)]
ac V3(2V3Q+3VQ3)2

a2 2Q3V(Q3V+QV3) (A.5)
ac2 V3(2QV +3QV)2

By inspection, the first derivative is negative under the assumption that

0 < c
convex in c, where +. = + z2. In addition, *. is maximized when c = 0. Recall that









the optimal total profit under the ISV market structure is I1 =max VQ, ~ It


follows that the optimal total profit is *z when c = 0, since


3, (c = 0) = (A6)
c 0=o)- 1 =QQ3(V3 V)(V3 9V)8Q2VV3 -V)+4VV3Q2 (A.6)

2 2(2QV3+ 3VQ3)2


Obviously, when V3 > V, we always have *(c = 0)> -VQ. Therefore, the shape
2

of the function Hn switches from z-* to -VQ as the integration cost increases.
2

A.2 Proof of Lemma 2-3

We solve the constrained profit maximization problem by establishing Lagrangean

function, see (A.7) and then solving for the KKT conditions in Equations (A.8) to (A. 11).

Pml P P(
L(A)=P,(Q-- Q)+P,2(Q-r2 Q)+P,,3(Q3- -3Q3)+ A(P, + P,2 +c P,3) (A.7)


2P
Lp = Q Q +A<, PmLpm 0 (A.8)
V

LP, = Q Q + < 0, P,,2L 0 (A.9)
V




L, = Pm,1 + Pr2 + c ,3 > 0, AL = 0 (A.11)

Obviously, the zero price solution can't be optimal. So we must have qualities in

Equations (A.8) to (A.10). If A > 0, then according to (A.11) we have L, =0, which

implies P3 =Pml +1Pm2 +c. Substitute into Equations (A.8) to (A.10) and one gets the

optimal solutions as follows.