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Enterprise Transformation

Fundamental enterprise changes begin by looking at the challenges from technical, behavioral, and social perspectives.
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  1. Introduction
  2. Theory of Transformation
  3. Perspectives
  4. Conclusion
  5. References
  6. Authors
  7. Footnotes

A variety of forces are driving change in the world. Globalization has become incessant, with outsourcing and offshoring on the agendas of most large enterprises. The service economy is becoming increasingly dominant in developed economies, with knowledge assets playing a greater role relative to physical and financial assets. Security has become a primary objective crossing virtually every sector of the economy and society.

At the same time, according to Thomas Friedman [37], the world has become flatter. Information and communications technologies have enabled developing economies to quickly progress on the path toward equity with developed economies. For example, China and India graduate one million engineers per year while the U.S. graduates 65,000. Compounding such disparities over years and decades will undoubtedly undermine the competitive advantages of developed countries—unless we change the nature of the game.

However, changing the nature of the game will require fundamental transformations of many enterprises in industry, government, and academia. Business process improvement, or even business process reengineering, will not be sufficient. It is not just a matter of getting better at what we do—everyone is doing this. It is an issue of doing new things in new ways. This will require fundamental change. Unfortunately, we will not necessarily succeed with such changes. Indeed, most historical attempts at fundamental change have failed [84, 85].

This article summarizes an emerging theory of enterprise transformation—stated in terms of value deficiencies, work processes, decision making, and social networks. We then consider transformation from a technical perspective—the technical problem to be solved—and then contrast the technical perspective with the socio-technical point of view that emphasizes the contextual, behavioral, and social aspects of fundamental change. Finally, we consider the implications of this contrast.

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Theory of Transformation

Enterprise transformation is driven by experienced and/or anticipated value deficiencies that result in significantly redesigned and/or new work processes as determined by management’s decision-making abilities, limitations, and inclinations, all in the context of the social networks of management in particular and the enterprise in general [86, 87].

More specifically, enterprise transformation is driven by perceived value deficiencies relative to needs and/or expectations due to: experienced or expected downside losses of value; experienced or expected failures to meet projected or promised upside gains of value; desires to achieve new levels of value, for example, via exploitation of market and/or technological opportunities. In all these cases, there are often beliefs that change will enable remediation of such value deficiencies. Change can range from business process improvement to more fundamental enterprise transformation.

In general, there are three broad ways to approach value deficiencies, all of which involve consideration of the work of the enterprise. One can improve how work is currently performed, perform current work differently, and/or perform different work. The first choice is basically business process improvement. This choice is not likely to be transformative. The second choice often involves operational changes that can be transformative depending on the scope of changes. The third choice is most likely to result in transforming the enterprise. This depends, however, on how resources are redeployed. Liquidation, in itself, is not necessarily transformative.

We can characterize the work of the enterprise in terms of a hierarchy of purpose, objectives, functions, tasks, and activities. Transformation of work can be pursued at all levels of this hierarchy. Changing the tasks and activities of the enterprise, by themselves, relates to business process improvement. In contrast, changing the purpose, objectives, and/or functions of the enterprise is more likely to be transformational. Such changes may, of course, cause tasks and activities to then change. Thus, change at any level in the hierarchy is likely to cause changes at lower levels.

It seems reasonable to hypothesize that the higher the level of transformation, the more difficult, costly, time consuming, and risky the changes will be. For instance, changing the purpose of the enterprise is likely to encounter considerable difficulties, particularly if the extent of the change is substantial. In many cases, such change has only succeeded when almost all of the employees were replaced [84].

Attention and resources are also central to the theory of enterprise transformation. This includes both external variables related to customers, competitors, demand, interest rates, and so on, as well as internal variables such as resources and their allocation among work processes. Transformation involves allocating attention and resources so as to anticipate and adapt to changes of external variables, and cultivate and allocate resources so as to yield future enterprise states with high projected value with acceptable uncertainties and risks. Thus, the ability of an enterprise to redeploy its human, financial, and physical resources is central to the nature and possibility of transformation.

Value deficiencies and work processes define the technical problem of enterprise transformation. To fully understand transformation, however, we need to understand both the problem and the problem solvers. Mintzberg’s classic paper [55] serves to shatter the myth of the manager as a coolly analytical strategist, completely focused on optimizing shareholder value using leading-edge methods and tools. Simon [97, 98] articulates the concept of "satisficing," whereby managers find solutions that are "good enough" rather than optimal. Another important factor is the organizational environment can be rife with delusions that undermine strategic thinking [85]. Managers’ roles as leaders, rather than problem solvers and decision makers, are also central to transformation [40, 48].

Beyond the individual skills and abilities of managers and management teams, the "social networks" both internal and external to the enterprise can have enormous impacts [21, 41]. An important distinction is between weakly and strongly connected networks. It has been found that weakly connected networks are better sources of new information and novel ideas. The resulting big-picture perspective may better inform the nature of transformations pursued. In contrast, strongly connected networks are better at implementing change, at least once sense has been made of the anticipated changes and new meaning has been attached to these changes.

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Perspectives

Here, we elaborate on two perspectives on transformation: the technical problem to be solved and the behavioral and social context and mechanisms of transformation.

Technical perspectives. An enterprise may experience or anticipate value deficiencies for a variety of reasons. It may be that competitors are now providing similar products and services for lower prices, or higher quality products and services for the same prices. It may be that technology and market trends portend this situation, although it has not yet emerged. Perhaps the enterprise hopes to take advantage of these trends before other enterprises can act.


It is not just a matter of getting better at what we do. It is an issue of doing new things in new ways. This will necessitate fundamental change. Unfortunately, we will not necessarily succeed with such changes. Indeed, most historical attempts at fundamental change have failed.


The possibility of value deficiencies begs the question of the nature of value. It might involve standard functionality or services with higher quality and/or lower costs. Or, it might involve new functionality or services that others cannot provide at reasonable prices. In either case, it involves existing or potential customers who perceive value and see a particular enterprise’s offerings as deficient, adequate, or superior.

How an enterprise creates value is central to this discussion. This includes the ways in which one creates new functionality, enhances its quality, and decreases its costs. This may include contributions from marketing, engineering, manufacturing, and so on. There are also likely to be important enablers such as finance, human resources, product support, and so on.

There are often activities that do not create and/or enhance value. Some of these activities may be required for regulatory reasons. Others may reflect housekeeping needs. Frequently, however, there are activities for which there is no justification other than they have long been performed and seem to be needed. Such activities are strong candidates for elimination, enabling the reallocation of resources to value-added activities.

Value is created by work that is accomplished via work processes. Value deficiencies can be remediated by redesigned or new work processes. Work processes can be represented from several perspectives. Engineering tends to see work in terms of the flow of physical items that are machined, assembled, and so on. Computing sees work as the flow of information to support the activities associated with work. Architecture views work in terms of the flow of people through built environments.

Of course, all of these views are valid and useful. The key question is how these views come together in processes that create or enhance value. We also need to understand how these processes can be improved and supported to remediate experienced or anticipated value deficiencies. For example, it has been found that decision-making processes can be substantially improved by making them evidence based or data driven, thereby enhancing the quality and timeliness of resource allocation decisions, for example.

Technologies can be both drivers and enablers of enterprise transformation. More specially, many people see IT as both the driving force behind change and as the enabler of change. Examples include knowledge management, collaboration technology, and, increasingly, identity management.

The technical issues concern not how to make these technologies work, but how they are likely to change the ways enterprises accomplish work. The need to work across time zones and cultures, share information and knowledge, and assure both security and privacy are central issues in both how we create and enhance value, and how our work processes support value streams.

Social and behavioral perspectives—The socio-technical systems concept. All approaches to closing the gap between potential value and projected value require consideration of the work of the enterprise, and transformation of the enterprise depends upon work process change in one form or another. While strategic and operational choices by management are key in determining how work process change will be approached, there is another dimension of such change that is vital to consider—the nature of human work groups and their interaction with work processes; that is, how people are organized to accomplish work, how they interact with one another and with technology, and how they conceptualize work and understand the meaning of their actions.

The basic idea of a socio-technical system expresses the notion that work organizations are not solely technical or rational systems designed to accomplish managerial goals, but they also embed natural or social systems whose characteristics extend beyond the rational and thus connect them with all other human social groups, for example, complex goals and informal social structures. Only through the mutual interdependency of its technical and social dimensions is an organization capable of co-producing value for stakeholders. An implication is that work process change must consider both technical and social dimensions together, and make specific provisions for "jointly optimizing" changes in these dimensions, such as, finding the best overall solution that considers their interactions simultaneously. Otherwise, design solutions for work process change are likely to be sub-optimal.


Research in enterprise transformation must yield both understanding of fundamental change and the methods and tools that can make change possible. We firmly believe this will come from taking multiple perspectives on the problems of change—what drives it, what enables it, and what factors facilitate and hinder its success.


Historical background. The socio-technical systems concept as a framework for work process change arose from research conducted in the Yorkshire coalfields by the London-based Tavistock Institute after World War II [106, 107]. At that time, it was assumed that the technical and social aspects of work organizations were independent phenomena that "obeyed different laws" (that is, the physical and human sciences, respectively). Once situated in a work context, however, these phenomena were believed to become correlative and interdependent, in that each required the other in order to transform organizational inputs into outputs. The independence of technical and social dimensions meant that the requirements of these two elements could not be met fully and simultaneously in the same context (a "coupling of dissimilars," according to Trist [107]).

Therefore, "joint optimization" was considered the only effective solution, which meant that alternative designs that provided different configurations of technical and social elements should be considered, and the one that produced the best result overall should be selected. Within the framework of this theory, Fred Emery developed many of the key aspects of socio-technical systems methodology, including the first generalized model for separately identifying technical and social elements [30].

One of the Tavistock Institute’s most significant contributions to work process change was its discovery of the semiautonomous work group as a fundamental building block for organizations situated in turbulent environments. The socio-technical theory of the efficacy of this type of group is based on the cybernetic concept of self-regulation. In a traditional technocratic bureaucracy, the parts (jobs) are designed to be as simple and easy to replace as possible (that is, parts are redundant), but this type of design requires an elaborate control mechanism and it is not flexible or adaptive in a rapidly changing environment. An alternative design, based on redundancy of functions, provides each group member with a multi-skilled role and endows the group with a wide repertoire of activities that enable adaptive responses to change [79]. Since the group is self-managing, fewer supervisors are required.

Toward a reframing of classical socio-technical systems theory. During the 1970s and 1980s, empirical research in the social constructivist tradition demonstrated that technological and social systems co-evolve in a complex, dynamic process in which all that is technical is socially constructed and all that is social is technically constructed [13]. Characteristics observed in any given technical factor may result from influences derived from a social factor, which in turn may have been influenced by earlier forms of a technical factor, for example, see Bijker’s [13] analysis of the development of bicycles, bakelite, and fluorescent lighting and Nobel’s [63] discussion of the development of numerical control machines.

A fundamental premise of socio-technical systems theory is that technical and social systems in a work organization are independent of one another and obey different scientific laws. But mutually causal technical and social elements invalidate classical socio-technical systems methodology, which is predicated upon identifying these as separate factors and then mapping their interactions. If the factors are mutually causal, as argued by social constructivists [13], it is not possible to separate the technical and social elements. Consequently, socio-technical systems methodology (and practice) unravels. An implication is that the socio-technical systems approach requires rethinking within the context of current social theory.

Rather than attempting to identify discrete technical and social elements of a socio-technical system, Bijker [13] suggests we conceptualize a "socio-technical ensemble," which is more than a combination of technical and social factors, but a thing-in-itself, sui generics. To cope with the complexity of such ensembles, Bijker encourages us to think in terms of technological frames that distinguish foreground and background factors specific to each case.1 Technological frames are heterogeneous assemblies of elements that shape the interactions of all relevant factors and actors and, in so doing, influence the trajectory of technological and social outcomes. Depending upon the case, salient elements may include goals, problem-solving strategies, requirements to be met by problem solutions, theories current at the time, tacit knowledge, testing procedures, design methods and criteria, users’ practices, exemplary artifacts, and other elements. (No two frames will be exactly alike; thus methods must permit relevant factors to emerge from the context.

Empirical research has shown that people (users) continue to modify the meanings, properties, and applications of technology even after it has been developed, and especially when it crosses organizational and cultural boundaries [74]. Thus, the configuration of a technological frame within one cultural context might be altered quite dramatically in a different context, or the meanings of elements in the frame might be unstable across boundaries [8]. This observation has increasing relevance for technology that is reconfigurable, and for enterprise transformations that are global in scope.

The meanings inscribed in technologies by designers, users, and other social groups draw their salience from a particular cultural context. When this context changes, the meanings may morph in unexpected ways, and the seeming closure (consensus about the meanings and practices surrounding an artifact or system) comes undone. This is what elevates risk in global technology transfer [17]. An implication for enterprise transformation is that methodologies for work process change may require modification when implemented in different cultural contexts.

Dynamic modeling methodology for socio-technical systems. A dynamic modeling and simulation approach to socio-technical systems analysis and redesign addresses several of the challenges posed by social constructivist theory, and is applicable across a wide range of goods and services settings. One relevant approach is that of Oliva and Sterman [70], who have adapted Senge’s [93] organizational learning and adaptation theory to model the "interactions of physical and institutional structures with boundedly rational decision-making."

Their formal modeling approach meets the theoretical requirement of producing a heterogeneous framework representing the interaction of a diverse array of technical, economic, social, and psychological factors that flow from the situation, rather than assuming a priori categories of technical and social phenomena. Their dynamic model of customer service erosion in financial institutions captures physical flows, institutional structures (for example, managerial goals), employee behaviors, and cognitive factors (for example, perceptions). The method is empirically grounded, and can be tested with ethnographic, historical, and/or real-time data. One of the great advantages of this method is its ability to represent joint optimization through rigorous formal modeling and simulation, permitting consideration of various design alternatives and their consequences. Simulation delivers a policy roadmap for achieving enterprise goals, targeting those areas where key changes are needed.

Agent-based modeling (ABM) represents a complementary approach that may facilitate work process change in complex adaptive systems where organizational behavior is emergent (for example, as in cases of organizational learning). Insights may be gained through ABM where interactions among autonomous agents (human and/or non-human) generate patterns that cannot be reduced to the properties of the agents themselves (that is, emergent behavior). ABM can generate useful policy guidance for enterprise transformation in service contexts by engaging professional employees in the design and refinement of such models, and in the creation of recommendations for control, mitigation, and testing of risks or other key factors modeled by the tool [14].

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Conclusion

Research in enterprise transformation must yield both understanding of fundamental change and the methods and tools that can make change possible. We firmly believe this will come from taking multiple perspectives on the problems of change—what drives it, what enables it, and what factors facilitate and hinder its success. This article has presented two broad and complementary views of fundamental change, one drawn from a technical analysis of the problem and the other based on a socio-technical perspective. These views, and many hybrids in between, will provide a foundation for success.

The contrast of these views raises important issues concerning what to measure, how to collect data, and what tools are needed to model and manipulate these findings. These issues present considerable challenges, both from a practical perspective and in terms of negotiating the cultural silos of academic disciplines. Nevertheless, we feel that addressing and moving beyond these challenges are central to understanding and enabling fundamental change and providing a strong basis for competing and succeeding in our inevitably flattening world.

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    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 Semantics—WSDL-S. W3C Member Submission; www.w3.org/Submission/WSDL-S/.

    3. Alic, J. Postindustrial technology policy. Research Policy 30 (2001), 873–889.

    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), 129–145.

    6. Aspray, W. and Williams, O.B. Arming American scientists: NSF and the provision of scientific computing facilities for universities, 1950–1973. IEEE Annals of the History of Computing 16, 4 (1994), 60–74.

    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), 42–87.

    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), 547–587.

    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 field—The case of services marketing. J. of Retailing 69, 1 (1993), 13–60.

    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), 100–127.

    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), 7280–7287.

    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), 169–189.

    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), 179–202.

    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, 117–154.

    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), 52–61.

    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, 83–102.

    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), 100–108.

    28. Davies, A. Moving base into high-value integrated solutions: A value stream approach. Industrial and Corporate Change 13, 5 (2004), 727–756.

    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), 7–14.

    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), 61–103.

    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), 991–1006.

    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), 33–50.

    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), 314–339.

    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 Industry—International 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), 75–77.

    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), 61–83.

    55. Mintzberg, H. The manager's job: Folklore and fact. Harvard Business Review (July/Aug. 1975), 49–61.

    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), 544–555.

    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), 1–15.

    62. Niehaus, R.J. Evolution of the strategy and structure of a human resource planning DSS application. Decision Support Systems 14 (1995), 187–204.

    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 (Nov–Dec 1991), 96–104.

    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 2001—Drivers 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), 894–914.

    71. Oliver, R. A cognitive model of the antecedents and consequences of satisfaction decisions. J. Marketing Research, 17 (Nov. 1980), 460–469.

    72. Oliver, R., Rust, R.T., and Varki, S. Customer delight: Foundations, findings, and managerial insight. J. Retailing 73, 3 (1997), 311–336.

    73. Organisation for Economic Co-operation and Development. Promoting Innovation in Services. (Oct. 14, 2005), 1–52.

    74. Orlikowski, W. Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science 11, 4 (2000), 404–428.

    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), 63–79.

    83. Romer, P. Increasing Returns and Long-Run Growth. Journal of Political Economy, 94, 5 (Oct 1986), 1002–1037.

    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), 279–295.

    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), 109–127.

    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, 197–246.

    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), 227–239.

    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), 13–38.

    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), 3–28.

    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, 19–75.

    108. Vargo, S.L. and Lusch, R.F. Evolving to a new dominant logic for marketing. J. of Marketing 68 (Jan. 2004), 1–17.

    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.

    1It must be noted that Bijker [13] is concerned with the development of new technology, while socio-technical systems theory focuses on technology-in-use. These differences in foci may explain in part their contrasting conceptions of technology. However, in historical perspective, technology is not frozen after development, but continues to evolve over time as a result of contextual influences [74]. Thus, the two perspectives should complement one another.

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