Services are pervasive in today's economic landscape, and services-based architectures are being rapidly adopted as IT infrastructure. The need to take a broader perspective of services to include people and organizational descriptions as opposed to technical interface descriptions has already been recognized as part of an overall vision of services science [46, 100]. This article describes the Semantic Services Science (3S) model, which seeks to demonstrate the essential benefits of semantics in view of the broader vision of services science by using service descriptions that capture technical, human, organizational, and business value aspects. We assert that ontology-based semantic modeling and descriptions can be used to energize services across the broad service spectrum. In this article, we demonstrate how the 3S approach could be used along three points in this spectrum: semantic descriptions of standard Web services with the help of WSDL-S, semantic policies, and agreements; semantic descriptions of lightweight Web services using Web 2.0 technologies (such as REST and AJAX); and ontology-based profiling of people and organizational aspects of the assets associated with the knowledge services.
Why use semantics as the basis of the service model? Interoperability has been a key challenge in IT for well over a decade. While Electronic Data Exchange standards and XML have provided the basis for data exchange and syntax-level interoperability, IT infrastructure requires semantic interoperability to fully exploit and interoperate with respect to all its data and service resources. The fast-emerging Semantic Web shows how the use of formal knowledge representation, typically in the form of ontologies, leads to machine-processable descriptions, and how the adoption of ontologies that provide common vocabulary and shared knowledge leads to improved semantic interoperability.
There are three primary advantages of creating models that employ semantics: they promote reuse and interoperability among independently created and managed services; ontology-supported representations based on formal and explicit representation lead to more automation; and explicit modeling of the entities and their relationships between them allows performing deep and insightful analysis. For example, if a particular business component is declared as critical, and the relationships between the business and IT components supporting them are explicitly modeled, simple semantic queries may allow business managers to verify if adequate IT resources have been allocated to that component [50]. This reasoning would have otherwise required a systems analyst with great deal of knowledge of both the business values and IT of the organization. The 3S approach utilizes semantic descriptions to capture relationships between services, the people, the organizational aspects, and the business values, allowing business managers to reason on them to easily create new services and to efficiently allocate resources to the services.
On the technical front, much of the modeling effort on services has so far focused on standard Web services in the context of SOA enabled by Web Service Description Language (WSDL), SOAP (Simple Object Access Protocol, an XML-based message exchange format) and UDDI (Universal Description, Discovery, and Integration), a technical specification for implementing registries that allow publication and discovery of Web services. It is, however, possible to take XML-based descriptions used by these standard Web services (and in principle, other syntactic descriptions of services) and annotate them with semantics specified in ontologies or conceptual models to gain the previously described benefits of a semantic approach. In the emerging field of Semantic Web Services (SWS), semantics is exploited to discover services using semantic (rather than syntactic) descriptions to more effectively integrate, compose, or orchestrate services to support workflows or processes. The same approach to semantic interoperability can be accorded to fast-growing Web-based services using Web 2.0 technologies (such as REST and AJAX), often termed as lightweight services, and to more expansive knowledge services that go beyond the scope of Web services to encompass human skills and organizational aspects.
Why use the 3S model? As businesses re-factor their organizational structure to become service providers, they must possess the ability to provide new services by optimizing their technical and organizational resources across the globe. The 3S model provides a comprehensive model of all the resources of a business that can be queried using Semantic Web technologies to quickly assemble the resources for providing new services. Consider the example of a global telecommunications provider that decides to provide a new video streaming service in the U.S. From an organizational perspective, the manager would try assemble a team that has a mixture of experience in delivering this technology in other countries as well as in-depth knowledge of the program availability, demographics, and market preferences in the U.S. Based on the knowledge profiling aspect of the 3S stored using ontologies, the project manager can quickly identify relevant organizational roles for this project. Similarly, the technical aspects of the 3S model can be used for service discovery (using UDDI registries) to discover technical resources whose capabilities are semantically described using WSDL-S. In addition, with the help of semantics descriptions of Web 2.0-based services, Web interfaces to aggregate data from multiple sources can be quickly created for customer and internal use. This example provides one instance of the use of the 3S model with respect to resource discovery. Other benefits include using the 3S model for composition of services, runtime adaptation of business processes, Web-based collaboration, and generating context-driven content.
The 3S model provides a comprehensive model of all the resources of a business that can be queried using Semantic Web technologies to quickly assemble the resources for providing new services.
"A service is a provider-client interaction that creates and captures value" [94]. The challenge for service providers is to create services by optimally leveraging all their resources, often in a global environment. It requires that service providers are cognizant of not just their software capabilities but also of their global work force, the current business trends, and demands of their consumers. As businesses reorganize their overall structure to focus on their core competency and become primarily service providers, a broader perspective of services beyond the traditional services known to computer science professionals is needed. This pervasive view must account for technical, people, social, organizational, and business aspects of offering a service. The 3S model makes an initial attempt to act upon IBMs Senior Vice President Paul Horn's suggestion of identifying human and software assets as a starting point of modeling a service. It builds upon previous modeling efforts in Web services, knowledge profiling, and asset management. We define a service as "value proposition provided to an organization and its definition includes the following: technical description of the implementation technology (SOAP/WSDL, REST, AJAX); organizational description of the people and their roles involved in developing, rendering, managing, or supporting the service; and the business or organizational value it renders."
Consequently the relationships between the components of a service related to the first two parts of the definition are shown in Figure 1. The central entity is the organization that consumes or offers services. A service is realized using the assets that the organization possesses (a more complete view may be extended to include external assets including those employed, co-development, and outsourcing). The assets include software, hardware, intellectual property, and human assets. Software assets include any software, applications, or infrastructural components that can be converted to services or used to create services. From an implementation point of view, the software assets may convert to services by either using Web service technologies (WSDL, SOAP, UDDI) or lightweight approaches like REST and AJAX. Human assets include project managers, software developers, customer relationship representatives and other relevant persons involved with creating, developing, marketing, maintaining, or otherwise managing the service.
Our definition of service is specifically targeted toward business managers involved in creating a new service/project, and requires targeting the problem from both functional and organizational perspectives. The functional perspective involves locating services for reuse and employees that can help create the new service/project. The organization perspective involves creating a team and defining its members' roles and responsibilities. Here semantics are used to enhance the descriptions so that business managers can realize new projects and services efficiently.
The recently defined field of the Semantic Web tries to add more meaning to resources (both information and services) on the Internet by annotating it with concepts from agreed-upon domain models. The annotations act as a common underpinning between different users and promote greater interoperability. Ontologies provide the most accepted way of creating conceptual models for domains. W3C-recommended ontology specification languages like OWL and RDF/S provide a way to specify conceptual models using formal languages, while UML maybe an alternative language for modeling assets such as software. In the context of the Semantic Web, the ontologies are formal specifications of knowledge that capture or represent agreements. Ontologies can be domain-specific with the scope of an entire industry (such as manufacturing), sub-area of a science (for example, Glyco ontology for Glycomics) or only a type of application (such as for Sarbanes-Oxley compliance). They can also be domain-independent covering e-commerce (as in the case of ontology based on RosettaNet or ebXML) or policy and agreement (for example, ontology-based WS-agreement). Note that in many cases, agreements documented as textual specifications can be captured as ontologies by using formal representations. While we do not particularly focus on other forms of capturing semantic models in this article, less expressive knowledge representations like taxonomies, thesauri, or folksonomies can be useful in capturing domain knowledge with corresponding loss in degree of interoperability and automation.
Our approach for semantically representing the complete spectrum of services is based on annotating Web service descriptions with domain-specific and domain-independent ontologies. This is exemplified in WSDL-S [2], which provides an incremental and evolutionary approach for adding semantics to existing Web service standards by using extensibility elements of (WSDL). In addition to WSDL-S, three other specifications have been recognized by W3C as member submissions to be considered as inputs for possible recommendations in this area. This is an indicator of the growing recognition by standards bodies like OASIS and W3C about the need for semantic descriptions of the services to handle real-world issues of efficient reuse and interoperability.
Table 1 shows the four major specifications submitted as input to W3C for Semantic Web services recommendations. In addition to these, there has been work in semantically capturing the nonfunctional aspects of Web services (such as semantic policy and semantic agreement). A relevant effort that expresses views on architectural issues is [20], the W3's proposed task forces in this area are at [112], and a sample of books on this topic include [22] and [99].
Web services provide a standards-based solution for creating such services by facilitating reuse, interoperability, and composition of existing services and applications. While Web services standards resolve platform heterogeneities, and XML provides the basis of syntactic interoperability, there are many other types of heterogeneities in the business environment (see Table 2). The table shows how Web service standards only provide support for platform- and system-level heterogeneities since they operate at the syntactical level. Semantics is a critical requirement for handling all the other heterogeneities.
One relatively comprehensive approach for semantically modeling Web services is based on four types of semantics [95]: functional (what a Web service does); data (how to interact with the service); nonfunctional (including quality of service attributes of the service); and execution semantics (modeling runtime behavior, exceptions, and so forth) of the Web service. The different types of semantics are critical enablers of the different value propositions of Web services: search or discovery (the ability to match service partners, often consumers and providers); reuse (easily use the services found); interoperability (ability to freely exchange data between the services and substitute services with the same functionality); and composition (being able to combine multiple services to carry out complex business processes).
For search and reuse, services and requests are annotated with functional semantics so that they can be efficiently discovered by services or components that require the desired functionality.
Table 2 provides an insight into how semantics can be used to resolve different types of heterogeneities for interoperability. Finally, composition uses functional semantics for finding the appropriate services, data semantics for passing data across the services, nonfunctional semantics to ensure that services meet the nonfunctional criteria, and execution semantics for ensuring that the ordering constraints of invoking operations of services are not violated.
An important contribution of the 3S model is proposing the use of semantics in Web 2.0-based lightweight services, which are now increasingly being realized using the REST architectural approach. REST-based services use the classical HTTP GET-POST approach to invoke services, Uniform Resource Identifiers (URIs) to represent resources and messages encoded in XML for communication. Although SOAP has advantages in terms of greater tooling support and the ability to support quality of service guarantees (for example, security and transactions), the ease of use and lightweight style of interaction over the Web using XML have made REST services very popular. One of the most popular applications of lightweight Web services is called a mashup, which is basically a Web site that aggregates content from different providers. A mashup uses lightweight services to query the providers to get content in XML format. Due to difference in data definitions (XML schemas) of different providers, a semantic approach is needed for seamless integration of the data. Using semantics to integrate and coordinate mashups gives us smashups (semantic mashups).
An important contribution of the 3S model is proposing the use of semantics in Web 2.0-based lightweight services.
We illustrate the need for semantics with the example shown in Figure 2, which depicts a fictional online bookseller applicationmyBook.comthat is created using lightweight Web services. Whenever a user wants to buy a book, the application queries different book vendors (in our example, the vendors are ubn.com and yaos.com) using REST Web services. It is often the case that the XML schemas of the data returned by different vendors are different from the schema of myBook.com. We propose adding semantics to lightweight services by annotating the XML schema of the service inputs and outputs. In the example here, the conceptual model (ontology) captures the domain of books and all the sellers annotate their XML schema. In the example, annotating the <shippingDetails> element in the yaos.com and <shipping> element in the myBook.com schema with the same concept in the ontology will help the application infer they are semantically similar. Using this approach will allow myBook.com to address the heterogeneities in the schemas of different providers and deliver the content seamlessly to the user.
Here, we provide an overview of the organizational and people aspects of the 3S model. Knowledge profiling entails formally managing knowledge resources in order to facilitate access and reuse of knowledge. Knowledge that is profiled depends on the asset being profiled. For example, to profile a Web service asset, one would need to profile features like the capability of the Web service, data semantics of the inputs and the outputs and policies associated with the service that capture enterprise features offered (like security and reliable messaging) and also quality of service guarantees like round trip network time. This can be done with ontology-driven semantic annotation of service descriptions. On the other hand, when profiling a human asset such as a software engineer, we need to capture the knowledge with respect to his areas of expertise, skill sets, relationships experience, the projects he has worked on and the roles he has played in the projects. This is along the lines of creating a social network for the human capital of an organization. The semantic descriptions would allow these human service engineers to be found, create relationships with, streamlined, and applied to appropriate parts of the service creation and enactment.
One of the main purposes of enterprise knowledge profiling is to capture knowledge so that it is accessible and reusable to the enterprise.
One of the main purposes of enterprise knowledge profiling is to capture knowledge so that it is accessible and reusable to the enterprise. At a physical level, infrastructures like knowledge warehouses can be created to store this knowledge. However, an important factor in determining reusability and accessibility is the logical structure or the conceptual models that dictate the way the knowledge is stored. In order to be able to share the knowledge that is profiled, one would need to go beyond schemas while capturing knowledge. It is in this situation that semantics help.
Figure 3 provides an outline of a conceptual model to profile a human asset in software development. The model captures the projects the person has worked on, the roles he has played (developer, project manager), his areas of expertise (networking, SOA, project management), his technical skills (like programming, network management), and his managerial and organizational skills. Further, it also captures his experience in various roles. Based on the semantic profiles of human assets, the managers can quickly assemble resources for new projects.
We reviewed curriculum of 40 prominent computer science departments to study the number of services or Web services-based courses at the graduate level (see Table 3). We divided the courses into three groups: system-level courses where the emphasis was on operating system-level support for Web services; application-level courses where the emphasis was on learning Web service technologies to build applications; and courses that discuss the business aspects of services. From our brief survey, we concluded that while 75% of the universities have at least one course on technical aspects of Web services, there is no coverage of the business and organization aspects of services as advocated in services science. In the context of globalization-induced transformations occurring in industry, there is an increased need to supply graduates from technical areas (such as computer science) with better understanding and knowledge of business and organizational aspects. Our view is that computer science departments on their own are not well equipped and well motivated do a better job in meeting this expectation of industry. Two possible agents of change are better involvement by industry in conveying requirements to computer science departments (for example, by participating on curriculum committees), supported by more collaboration and funding (industry funding of higher education research is a small fraction of funding from federal and state sources), and significant collaboration between business and computer science departments. The first of these changes is unlikely to happen if the industry continues to put all its effort in global sourcing of technical talent, ignoring its involvement in higher education at home.
The key to the 3S model presented here is the use of ontologies in representing the technical, people, and organizational aspects of services. In addition to further refining this model, we intend to pursue using semantics to link organizational layers in a services-based enterprise. Using ontologies and Semantic Web techniques to represent relationships between the models of different organizational layers of an enterprisethe strategy model, organization model, the execution model, and the implementation modelhas been proposed previously [50]. This would lead to more direct links between the business layers and the IT infrastructure leading to more efficient alignment of IT services with the business goals of the enterprise. We believe that using the information captured in the 3S model can be used as a starting point to implement the proposed architecture [50]. Our future work includes extending the 3S model to capture additional semantic descriptions; examples include semantic descriptions of business value of services, and novel forms of assets such as those involved in rendering knowledge services or in staging experience.
1. Abbott, A. The System of Professions: An Essay on the Division of Expert Labor. University of Chicago Press, Chicago, IL, 1988.
2. Akkiraju, R., et al. WSDL-S Web Services SemanticsWSDL-S. W3C Member Submission; www.w3.org/Submission/WSDL-S/.
3. Alic, J. Postindustrial technology policy. Research Policy 30 (2001), 873889.
4. Alter, S. The Work System Method: People, Process, and Technology (2006). Unpublished manuscript available by request to author; www.stevenalter.com.
5. Anderson, E.W., Fornell, C.L., and Rust, R.T. Customer satisfaction, productivity, and profitability: Differences between goods and services. Marketing Science 16, 2 (1997), 129145.
6. Aspray, W. and Williams, O.B. Arming American scientists: NSF and the provision of scientific computing facilities for universities, 19501973. IEEE Annals of the History of Computing 16, 4 (1994), 6074.
7. Aspray, W. Was early entry a competitive advantage? U.S. universities that entered computing in the 1940s. IEEE Annals of the History of Computing 22, 3 (2000), 4287.
8. Baba, M., Gluesing, J., Ratner, H., and Wagner K. The contexts of knowing: Natural history of a globally distributed team. J. Organizational Behavior 25 (2004), 547587.
9. Baldwin, Carliss Y. and Clark, Kim B. Design Rules, Vol. 1: The Power of Modularity. MIT Press, Cambridge, MA, 2000.
10. Barrett, R., Kandogan, E., Maglio, P.P., Haber, E., Takayama, L., and Prabaker, M. Field studies of computer system administrators: Analysis of system management tools and practices. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, 2004.
11. Berry, L.L. and Parasuraman, A. Building a new academic fieldThe case of services marketing. J. of Retailing 69, 1 (1993), 1360.
12. Bettencourt, L., Ostrom, A.L., Brown, S.W., and Roundtree, R.I. Client co-production in knowledge-intensive business services. California Management Review 44, 4 (2002), 100127.
13. Bijker, W.E. Of Bicycles, Bakelites, and Bulbs: Toward a Theory of Sociotechnical Change. MIT Press, Cambridge, MA, 1995.
14. Bonabeau, E. Agent-based modeling: Methods and techniques for simulating human systems. In Proceedings of the National Academy of Science 99, 3 (2002), 72807287.
15. Bordoloi, S. and Matsuo, H. Human resource planning in knowledge-intensive operations: A model for learning with stochastic turnover. European Journal of Operational Research 130, 1 (2002), 169189.
16. Boudreau, J., Hopp, W., McClain, J., and Thomas, L.J. On the interface between operations and human resources management. Manufacturing & Service Operations Mgmt 5, 3 (2003), 179202.
17. Brannen, M.Y., Liker, J.K., and Fruin, W.M. Recontextualization and factory-to-factory knowledge transfer from Japan to the United States. Remade in America: Transplanting and Transforming Japanese Management Systems. J.F. Liker, W.M. Fruin, and P.S. Adler, Eds. Oxford University Press, NY, 1999, 117154.
18. Brown, S.W. and Bitner, M.J. Mandating a services revolution for marketing. The Service-Dominant Logic of Marketing: Dialog, Debate, and Directions. R.F. Lusch and S.L. Vargo, Eds. M.E. Sharpe, Armonk, NY, 2006.
19. Bryson, J.R., Daniels, P.W., and Warf, B. Service Worlds: People, Organisations, Technology. Routledge, London, 2004.
20. Burstein, M., Bussler, C., Finin, T., Huhns, M., Paolucci, M., Sheth, A., and Williams, S. A Semantic Web services architecture. IEEE Internet Computing, (Sept.Oct. 2005), 5261.
21. Burt, R.S. The network structure of social capital. Research in Organizational Behavior, Vol. 22. R.I Sutton and B.M. Staw, Eds. JAI Press, Greenwich, CT, 2000.
22. Cardoso, J. and Sheth, A., Eds. Semantic Web Services, Processes and Applications. Springer Book Series on Semantic Web & Beyond: Computing for Human Experience, 2006.
23. Chesbrough, H. Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Cambridge, MA, 2003.
24. Colecchia, A., Guellec, D., Pilat, D., Schreyer, P., and Wyckoff, A. A New Economy: The Changing Role of Innovation and Information Technology in Growth. OECD, Paris, France, 2002.
25. Coombs, R. and Miles, I. Innovation, measurement and services: The new problematique. Innovation Systems in the Service Economy. J.S. Metcalfe and I. Miles, Eds. Kluwer, Dordrecht, 2000, 83102.
26. CSTB. Making IT Better: Expanding Information Technology Research to Meet Society's Needs. National Academy Press, Washington, DC., 2000.
27. Davenport, T. The coming commoditization of processes. Harvard Business Rev. (June 2005), 100108.
28. Davies, A. Moving base into high-value integrated solutions: A value stream approach. Industrial and Corporate Change 13, 5 (2004), 727756.
29. Dess, G.G. and Picken, J.C. Beyond Productivity: How Leading Companies Achieve Superior Performance by Leveraging their Human Capital. American Management Association, NY, NY, 1999.
30. Emery, F.E. Characteristics of socio-technical systems. Tavistock Document 527. London, 1959.
31. Erl, T. Service-Oriented Architecture: A Field Guide to Integrating XML and Web Services. Prentice Hall, Upper Saddle River, NJ, 2004.
32. Fein, L. The role of the university in computers, data processing, and related fields. Comm. ACM 2, 9 (Sept. 1959), 714.
33. Fisk, R.P., Brown, S.W., and Bitner, M.J. Tracking the evolution of the services marketing literature. J. of Retailing 69, 1 (Spring 1993), 61103.
34. Fisk, R.P., Grove, S.J., and John, J. Services Marketing Self-Portraits: Introspections, Reflections, and Glimpses from the Experts. American Marketing Association, Chicago, 2000.
35. Fitzsimmons, J.A. and Fitzsimmons, M.J. Service Management: Operations, Strategy, and Information Technology, 3rd Edition. McGraw-Hill, NY, NY, 2001.
36. Fitzsimmons, J.A. and Fitzsimmons, M.J. Services Management: Operations, Strategy, and Information Technology, 4th Edition. McGraw-Hill, NY, NY, 2004.
37. Friedman, T. The World is Flat: A Brief History of the 21st Century. Farrar, Straus and Giroux, NY, 2005.
38. Gadrey, J. The misuse of productivity concepts in services: Lessons from a comparison between France and the United States. Productivity, Innovation and Knowledge in Services: New Economic and Socio-Economic Approaches. J. Gadrey and F. Gallouj, Eds.. Edward Elgar Publisher, 2002.
39. Gans, N. and Zhou, Y-P. Managing learning and turnover in employee staffing. Operations Research 50, 6 (2002), 9911006.
40. George, B. Authentic Leadership: Rediscovering the Secrets to Creating Lasting Value. Jossey-Bass, San Francisco, 2003.
41. Granovetter, M. The impact of social structure on economic outcomes. J. of Economic Perspectives 19, 1 (2005), 3350.
42. Gustafsson, A. and Johnson, M. Competing in a Service Economy. Jossey-Bass, San Francisco, 2003.
43. Hacigumus, H., Rhodes, J., Spangler, W., and Kreulen, J. BISON: Providing business information analysis as a service. To appear in Proceedings of EDBT, 2006.
44. Herzenberg, S.A., Alic, J.A., and Wial, H. New rules for a new economy: Employment and opportunity in a postindustrial America. Century Foundation. Cornell University Press, Ithaca, NY, 1998.
45. Hill, T.P. On goods and services. The Review of Income and Wealth 23, 4 (1977), 314339.
46. Horn, P. The new discipline of services science. Business Week (Jan. 21, 2006); www.businessweek.com/technology/content/jan2005/ tc20050121 _8020.htm.
47. Kotler, P. and Bloom, P.N. Marketing Professional Services. Prentice-Hall, Englewood Cliffs, NJ, 1984.
48. Kouzes, J.M., and Posner, B.Z. The Leadership Challenge: How to Get Extraordinary Things Done in Organizations. Jossey-Bass, San Francisco, 1987.
49. Kox, H.L.M. Growth Challenges for the Dutch Business Services IndustryInternational Comparison and Policy Issues. CPB Netherlands Bureau for Economic Policy Analysis, The Hague (Apr. 2002).
50. Lee, J. Model-driven business transformation and the Semantic Web. Commun. ACM 48, 12 (Dec. 2005), 7577.
51. Lewis, W.W. The Power of Productivity: Wealth, Poverty, and the Threat to Global Stability. University of Chicago Press. Chicago, IL, 2004.
52. Lovelock, C.H. and Wirtz, J. Services Marketing: People, Technology, Strategy, 5th Edition. Prentice Hall, Englewood Cliffs, NJ, 2004.
53. Metcalfe, J.S. Modern evolutionary economic perspectives: An overview. Frontiers of Evolutionary Economics. J.S. Metcalfe and K. Dopfer, Eds. Edward Elgar, 2001.
54. Meuter, M.L., Bitner, M.J., Ostrom, A.L., and Brown, S.W. Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies. J. of Marketing, 69 (April 2005), 6183.
55. Mintzberg, H. The manager's job: Folklore and fact. Harvard Business Review (July/Aug. 1975), 4961.
56. Mittal, V., Anderson, E.W., Sayrak, A., and Tadikamalla, P. Dual emphasis and the long-term financial impact of customer satisfaction. Marketing Science 24, 4 (2005), 544555.
57. Mohr, M. and Russel, S.A. North American product classification system: Concepts and process of identifying service products. In Proceedings of the 17th Annual Meeting of the Voorburg Group on Service Statistics. (Nantes, France, 2002).
58. Murmann, J.P. Knowledge and Competitive Advantage: The Coevolution of Firms, Technology, and National Institutions. Cambridge University Press, Cambridge, UK, 2003.
59. National Academy of Engineering. The Impact of Academic Research on Industrial Performance. The National Academies Press, Washington, DC, 2003.
60. Nelson, R.R. On the Uneven Evolution of Human Know-How (2002); www.fondazionebassetti.org/0due/nelson-docs.htm (accessed Mar. 10, 2005).
61. Neu, W. and Brown, S.W. Forming successful business-to-business services in goods-dominant firms. J. of Service Research (Aug 2005), 115.
62. Niehaus, R.J. Evolution of the strategy and structure of a human resource planning DSS application. Decision Support Systems 14 (1995), 187204.
63. Nobel, D. Forces of Production: A Social History of Industrial Automation. Alfred A. Knopf, New York, 1984.
64. Nonaka, I. The knowledge creating company. Harvard Business Review 69 (NovDec 1991), 96104.
65. Nonaka, I. and Takeuchi, H. The Knowledge-Creating Company. Oxford University Press, 1995.
66. NSF. Scientists, Engineers, and Technicians in the United States: 1998. NSF 02-313, Arlington, VA, 2001.
67. OECD. Science, Technology and Industry Outlook 2001Drivers of Growth: ICT, Innovation and Entrepreneurship. OECD, Paris, 2001.
68. OECD. Enhancing the Performance of the Services Sector. OECD, Paris, 2005.
69. OECD. Innovation and Knowledge-Intensive Service Activities. OECD, Paris, 2006.
70. Oliva, R., and Sterman, J.D. Cutting corners and working overtime: Quality erosion in the service industry. Management Science 47, 7 (2001), 894914.
71. Oliver, R. A cognitive model of the antecedents and consequences of satisfaction decisions. J. Marketing Research, 17 (Nov. 1980), 460469.
72. Oliver, R., Rust, R.T., and Varki, S. Customer delight: Foundations, findings, and managerial insight. J. Retailing 73, 3 (1997), 311336.
73. Organisation for Economic Co-operation and Development. Promoting Innovation in Services. (Oct. 14, 2005), 152.
74. Orlikowski, W. Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science 11, 4 (2000), 404428.
75. OWL-S: Semantic Markup for Web Services, W3C Member Submission; www.w3.org/Submission/2004/SUBM-OWL-S-20041122/.
76. Paloheimo, K., Miettinen, I., and Brax, S. Customer-Oriented Industrial Services. Helsinki University of Technology, BIT Research Centre, 2004.
77. Pine II, B.J. and Gilmore, J.H. The Experience Economy: Work is Theatre and Every Business a Stage. Harvard Business School Press, Cambridge, MA, 1999.
78. Pugh, E. Building IBM: Shaping an Industry and Its Technology. MIT Press, Cambridge, MA, 1995.
79. Pugh, D.S. and Hickson, D.J. Writers on Organizations. 5th Edition. Sage Publications, Thousand Oaks, CA, 1996.
80. Quinn, J.B. Technology in services: Past myths and future challenges. Technology in Services: Policies for Growth, Trade, and Employment. National Academy of Engineering, 1988.
81. Riddle, D. The role of the service sector in economic development: Similarities and difference by development category. O. Giarini, Ed. The Emerging Service Economy. Pergamon Press, 1987.
82. Reinartz, W., Thomas, J.S., and Kumar, V. Balancing acquisition and retention resources to maximize customer profitability. J. of Marketing, 69 (Jan. 2005), 6379.
83. Romer, P. Increasing Returns and Long-Run Growth. Journal of Political Economy, 94, 5 (Oct 1986), 10021037.
84. Rouse, W.B. Start Where You Are: Matching Your Strategy to Your Marketplace. Jossey-Bass, San Francisco, 1996.
85. Rouse, W.B. Don't Jump to Solutions: Thirteen Delusions that Undermine Strategic Thinking. Jossey-Bass, San Francisco, 1998.
86. Rouse, W.B. A theory of enterprise transformation. Systems Engineering 8, 4 (2005), 279295.
87. Rouse, W.B., Ed. Enterprise Transformation: Understanding and Enabling Fundamental Change. Wiley, NY, 2006.
88. Rust, R.T., Lemon, K.N., and Zeithaml, V.A. Return on marketing: Using customer equity to focus marketing strategy. J. of Marketing 68 (Jan. 2004), 109127.
89. Rust, R.T., Lemon, K.N., and Narayandas, D. Customer Equity Management. Pearson Prentice Hall, NJ, 2005.
90. Rust, R.T. and T.S. Chung. Marketing models of service and relationships. Marketing Science, forthcoming.
91. Sampson, S.E. Understanding Service Businesses: Applying Principles of Unified Systems Theory, 2nd Edition. John Wiley & Sons, NY, NY, 2001.
92. Sasser, E., Olsen, R.P., and Wyckoff, D.D. Management of Service Operations. Allyn and Bacon, Boston, 1978.
93. Senge, P. Catalyzing systems thinking within organizations. Advances in Organizational Development. F. Masaryk, Ed. Ablex, Norwood, NJ, 1990, 197246.
94. Services Sciences, Management and Engineering; www.research.ibm.com/ssme/.
95. Sheth, A.P. Semantic Web Process Lifecycle: Role of Semantics in Annotation, Discovery, Composition and Orchestration. Invited Talk, Workshop on E-Services and the Semantic Web, WWW, 2003; lsdis.cs.uga.edu/lib/presentations/WWW2003-ESSW-invitedTalk-Sheth.pdf.
96. Shugan, S.M. and Xie, J. Advance pricing of services and other implications of separating purchase and consumption. J. of Service Research 2, 3 (2000), 227239.
97. Simon, H.A. Models of Man: Social and Rational. Wiley, NY, 1957.
98. Simon, H.A. The Sciences of the Artificial. MIT Press, Cambridge, MA, 1969.
99. Singh, M.P. and Huhns M.N. Service-Oriented Computing: Semantics, Processes, Agents. John Wiley & Sons, Ltd., 2005.
100. Spohrer, J. and Maglio, P. Emergence of Service Science: Services Sciences, Management, Engineering (SSME) as the Next Frontier in Innovation. Presentation at IBM Almaden Research Center, (Oct. 2005).
101. SWSL, Semantic Web Service Language, W3C Member Submission; www.w3.org/Submission/SWSF-SWSL/.
102. Tamura, S., Sheehan, J., Martinez, C., and Kergroach, S. Promoting Innovation in Services. Organization for Economic Co-operation and Development (OECD), Paris, France, 2005; www.oecd.org/ dataoecd/21/55/35509923.pdf.
103. Tapscott, D. and Ticoll, D. The Naked Corporation: How the Age of Transparency Will Revolutionize Business. Free Press, 2003.
104. Tidd, J. and Hull, F.M. Service Innovation: Organizational Responses to Technological Opportunities & Market Imperatives. Imperial College Press, London, UK, 2003.
105. Tien, J. and Berg, D. A case for service systems engineering. J. of Systems Science and Systems Engineering 12, 1 (2003), 1338.
106. Trist, E.L. and Bamforth, K.W. Some social and psychological consequences of the longwall method of coal-getting: An examination of a work group in relation to the social structure and technological content of the work system. Human Relations 4 (1951), 328.
107. Trist, E.L. The evolution of sociotechnical systems as a conceptual framework and an action research program. Perspectives on Organization Design and Behavior. A.H. Van de Ven and William F. Joyce, Eds. Wiley Interscience, NY, 1981, 1975.
108. Vargo, S.L. and Lusch, R.F. Evolving to a new dominant logic for marketing. J. of Marketing 68 (Jan. 2004), 117.
109. Vashistha, A. and Vashistha, A. The Offshore Nation. McGraw-Hill, NY, 2006.
110. Vermeulen, P. and Wietze van der Aa. Organizing innovation in services. Service Innovation. J. Tidd and F.M. Hull, Eds. Imperial College Press, London, 2003.
111. Vollman, T.E., Berry, W.L., and Whybark, D.C. Manufacturing Planning and Control Systems, 3rd Edition. Richard D. Irwin, Inc., 1992.
112. W3C Semantics for Web Services Characterization Group Charter; www.w3.org/2005/10/sws-charac-charter.html.
113. WSMO Web Service Modeling Ontology (WSMO), W3C Member Submission; www.w3.org/Submission/WSMO/.
114. Zeithaml, V.A., Berry, L.L., and Parasuraman, A. The behavioral consequences of service quality. J. Marketing, (1996).
115. Zeithaml, V.A., Bitner, M.J., and Gremler, D.D. Services Marketing: Integrating Customer Focus Across the Firm, 4th Edition. McGraw-Hill, NY, 2006.
Figure 1. A broader view of a service that includes people, technology, and the organizational perspective.
Figure 2. Scenarios demonstrating semantics for lightweight Web-based services.
Figure 3. Conceptual model (outline) to profile a human asset in software development.
Table 1. A comparison of four Semantic Web service specifications submitted to W3C as input for further activity in this area.
Table 2. Types of heterogeneity and how SWS may enable semantic annotations that support interoperability at that level
Table 3. Different types of service courses taught in top 40 computer science schools.
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