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Intelligent Service Machine

By Wei-Fung Tung, Soe-Tsyr Yuan

Communications of the ACM, Vol. 53 No. 8, Pages 129-134
10.1145/1787234.1787268


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The service sector is becoming increasingly important to the economies of many countries, especially developed countries. The Organization for Economic Co-operation and Development (OECD) recently released its report Promoting Innovation in Services, which noted that government policy in developed countries has not been attuned to the service sector.1 For better satisfying the needs of customers, providers now attempt to add or create value through services. To unravel the changing worldview of marketing, a new dominant logic is service-centered model of exchange (such as intangibles, competences, dynamics, exchange processes and relationships, and operant resources) in 21st century.12

In many leading companies, services are more than half of the company's revenue, and usually the fastest growing part, for example, IBM, GE, Xerox, and GM. Whereas the two paradoxes dominate the future of competition in servicescustomers face choices that yield less satisfaction, while managers face more strategic options that yield less value.9 They argue that the traditional system of company centered value creation needs to be re-examined. Meanwhile, a service can be regarded as a service system (composed of subsystems/components) and service innovation is to co-create the service productivity and satisfaction. Spohrer indicated that service systems are complex adaptive systems made up of people and people are complex and adaptive themselves, which are dynamic and open rather than simple and optimized.11 Therefore, facilitating the development of service systems for value co-operation is crucial to the fulfillment of service innovation.

A traditional service industry generally adopts the physical medium in co-producing service systems, while the emerging service industry is increasingly exerting the electronic medium4 so as to raise service productivity. As a consequence, the competitive position of a service company now depends much on its ability to use technologies to co-create the service productivity and satisfaction through innovative electronic delivery channels. In this paper, we suggest the notion of intelligent service machine to provide service companies with a systematic and quantitative capability on designing service systems aiming for both service productivity and satisfaction (in analogy to the machine metaphor in the manufacturing era). Furthermore, we propose a construct model of intelligent service machine, followed by a machine-aware service-system design framework (named iDesign) and an ex-emplifier of intelligent service machine (named mDesignStorming).

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Machine Metaphor for Service/Experience Economy

In modern industrialized economies, various machines represent a natural evolution of economies from pre-industrial to industrial and finally to postindustrial societies. During the past a century, there has been a dramatic increase in the number of machines used in human life. Many inventions of machine allow enterprises or individuals to efficiently complete works with productivity in manufacturing or daily life. Machines accordingly were viewed as a metaphor for productivity. Today the service/experience economy emerges,8 this article expounds on service systems with a systematic and quantitative capability to contribute to service productivity in human life. In other words, we also share the view that the notion of machine can significantly effectuate the productivity in the emergent service/experience economy.

Metaphor is a cross-domain mapping. In this paper, the metaphor of a service machine is believed to empower the service system with a systematic and quantitative capability for productivity, satisfaction, and innovation arising within a series of activities of intangible nature and taking place in the interactions between the customer and service personnel. Intelligent Service Machine (ISM) then refers to an intelligent design of the service machine featuring the embodied cognition of co-production in terms of modeling and automating the cognitive process and knowledge representations as required. Meanwhile, simple service machines are often used in combination as components of more complex service machines. Accordingly, we argue that a good construct of simple service machines would create a great impact on designing service systems of the service productivity, satisfaction and innovation, hence, lead to a greater economic value of the service offering in the service/experience economy.

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Simple Service Machine as a Socio-Technical System

The traditional cognition of machines is those of mechanical parts, while the distributed cognition refers to the manufacturing cognitive process wherever it may take into consideration the functional relationships of parts participating in the manufacturing process. On the other hand, a service system can be viewed as a metaphorical container which involves the service encounter, service process, and service delivery. In the other words, a service machine concerns people, model, architecture, technologies for modeling and automating the service process. For this purpose, we define a simple service machine as a "socio-technical system (STS) that allows customer and provider to participate and interact in co-creating the values with the underlying objectives of maximized satisfaction and optimal productivity."3,5

As shown in Figure 1, a STS is a system composed of technical and social subsystems. A physical example for this is a hospital where people are organized, e.g. in social systems like teams or departments, to do tasks for which they use technical systems like computers or x-ray machines. In other words, a service work system is made up of two jointly independent, but correlative interacting systems (the social and the technical), and the system designer should conceptualize a service work system as a set of complementary or interdependent information systems3 with the objective of both the social and technical systems being jointly optimized. Accordingly, in the context of service machine a service system conducts the specific functions within a metaphorical container which involves the interaction of social and technical systems. Furthermore, Bijker2 identified the causal technical and social elements that shape the interactions of all relevant factors and actors and influence the trajectory of technological and social outcomes, and these salient elements include goals, problem solving strategy, solution requirements, theories, tacit knowledge, and design methods.

In this article, the model construct of a simple service machine is therefore made of the parts of the salient attribute elements regarded within the context of socio-technical systems, setting forth the capabilities of service systems in support of the value co-creation process with the underlying objectives of maximized satisfaction and optimal productivity. Meanwhile, in the value co-creation process customer and provider can share and integrate their knowledge and form a shared reality. This shared reality could also be examined by the perspective lens on purpose (intentions of stakeholders), context (circumstances of stakeholders), and content (information and decisions rendered by stakeholders w.r.t. their purposes and contexts). Accordingly, the construct model of a simple service machine should embrace the shared reality of purpose, context, and content in this paper.

Accordingly, the construct model of a simple service machine includes the STS salient elements embodying the shared reality of the value co-creation process empowered by the machine metaphor (Table 1): goal/purpose of service productivity and satisfaction; problem solving strategy of self-service (facilitated, mediated or assisted); solution requirements of minimized cost and maximized satisfaction; theories borrowed from other disciplines such as natural and social theories used to construct the service system; tacit knowledge of the information, knowledge and decisions of people involved in the service process; and design method of the synthesized artifact of service system characterized by its measurable functions, goals, and adaptability in accordance with the circumstances of customer and provider.

In short, a simple service machine aims to empower the service system with a systematic and quantitative capability for productivity, satisfaction, and innovation between customer-provider interactions in terms of (semi-) automation of the service delivery. Automation of service itself could be regarded as a typical delivery innovation and generate a new service of a self-service system.7 This attempt is also in line with Service Science, Management and Engineering (SSME)6 that is a new multi-disciplinary research aiming for the goal of making productivity, quality, sustainability and innovation more predictable across the service sectors (as depicted in Figure 1).

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Intelligent Design of Simple Service Machine

An intelligent design, in general, is aimed at modeling and automating the cognitive process and knowledge representations that can be applied to design problem. On the other hand, a service system is made up of interactions between customer and provider who co-produce value. Their dynamics are driven by the constantly shifting value of knowledge distributed among customer and provider, resulting in being difficult to predict the patterns owing to naturally emergent interactions among customer and provider. Consequently, the design of a simple service machine driving innovation is considered as a challenging problem (service innovation, herewith, refers to invented service system designs yielding values to real service problems; the values are primarily oriented to a high level of service productivity and customer satisfaction in terms of the customer experience advanced.). For this purpose, we define an intelligent service machine (ISM) as a simple service machine modeling and automating particular cognitive process and knowledge representations (that are characterized with the salient element of the embodied theory in support of the value co-creation process with the underlying objectives of maximum satisfaction and optimal productivity).

The design of ISM is based on the embodied theory within the socio-technical aspects of a simple service machine. For service productivity and customer satisfaction, a simple service machine can then be extended with the capability of intelligent service machine. We argue that ISM can be regarded as an important capability to service concept design in order to enforce a systematic and quantitative service delivery with intelligent specifics. Thus, ISM can be viewed as a tool to achieve systematic service innovation. To this end, we accordingly propose a machine-aware service-system design framework (named iDesign) that utilizes the notion of ISM.

In iDesign, the plot of services could be unfolded as (semi-)automated value co-production service systems underlying the mutualism principles.10 Namely, an intelligent service machine within iDesign models the embodied theory of ecological mutualism (that examines the interrelationship of organisms, their environments, and how organisms adapt to their environments in order to exhibit their mutualism behavior). From the standpoint of ecology, there are different levels of viability under which organisms adapt in response to changed circumstances. Figure 2(a) captures the analogy of a collaborative service system with ecology mutualism, adaptation, and evolution. Figure 2(b) conceptualizes that the service exchange of co-production could employ the intelligent service machines that are characterized with the (semi-)automated symbiosis-aware service systems (along with different variation of co-production continuity and mutual adaptability) in order to achieve the productivity and satisfaction. For instance, the service provider and customer can co-produce diverse artwork designs (such as, digital content design service, interior design service, or mobile-phone design service) via co-created service systems developed by the appropriate deployment of intelligent service machines.10 Figure 2(c) then shows that a systematic service innovation procedure within iDesign comprising four stages and indicates that the (semi-)automated service systems can be classified, modeled, evaluated and optimized.

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An Exemplar of ISM-Supported Service System

Collaborative service systems are defined as a kind of e-service with semi-automated value co-production for users. Composing music is a knowledge-intensive work for the specialist or amateur. However, collaborative service system assistance for composing music is a worthy application for service innovation. mDesignStorming serves as an example of collaborative service systems, with respect to joint efforts of music content creation. Information technology makes it possible to acquire responses by coordination, and systematically looks for best partners. In other words, music creators are ready to respond and share about composition problems, and the inquirer can address problem responses. However, the service system particularly tackles natural language inquiries, for example, music content originality and inspiration. For developing a collaborative service system for music content creation with semantic description, service systems accord with well-defined SSM elements. mDesignStorming can be first determined by the six salient SSM elements for the intelligent service machine (Table 2). mDesignStorming then introduces three certain service components (Ontology Developer, S-FGA Partnership Matcher, and Value Appraiser) to realize the ISM. In other words, based on the defined SSM of mDesignStorming (Table 2), the cognitive process refers to the system process of responding to music content and then choosing a matching partner; knowledge representation refers to music content definitions (such as music tags CDDB and ID3 from the music Database). The defined ISM aims at a collaborative marketplace service system for music content creation, using the three service modulesontology developer, S-FGA partnership matcher, and co-created value appraiserto meet service productivity and satisfaction goals. The service system adopts the evolutionary optimization methodology (genetic algorithm with fuzzy logic), semantic-based music contents process (ontology, RDF, OWL, and XML), as well as develops an environment and system architecture (J2EE, SOA, database, and service components) (Figure 3(a)).

In other words, mDesignStorming enables semi-automated value co-production by means of the collaborative service process. A systematic service system with successive service exchanges among collaborators mediates the partner matching process. Requested music content production can be diversified and facilitated when users adopt the best partner's responses within the collaborative marketplace service process. Users subsequently listen to music creations from other user responses. The listed responded music creation allows users to bid for purchasing in the e-marketplace (Figure 3(b)). This example also demonstrates how both SSM and ISM effectively design service systems in coordination with a variety of responses derived from other users. The details of the quantitative evaluations of mDesignStorming to justify collaborations with value co-production are omitted in this paper owing to the space limitation.

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Conclusion

Machine is a metaphor that can be used to expand the capability of service systems and thinks for innovative service system design. In this paper, the notion of service machine is defined as a socio-technical system with the shared reality of customer and provider aiming for the joint optimization of productivity and satisfaction. Intelligent service machine (ISM) moves beyond service machine by modeling and automating the cognitive process and knowledge representations of the machine's embodied theory, enabling a systematic and quantitative delivery of service operation using self services. Namely, we argue that the notion of ISM can help realize a systematic and quantitative delivery of service innovation. We also present a machine-aware service-system design framework (iDesign) and an exemplar of ISM-supported service system (mDesignStorming) in order to demonstrate and justify the ISM notion. That is, the machine metaphor can also significantly effectuate the productivity in the emergent service/experience economy considering the context of the provider-customer interactions. In addition, its application to a broadened context of service encounter by allowing customers to interact between themselves could be also explored.5 We hope that this paper can offer and inspire the future research and development of innovative service systems based on this notion of intelligent service machine.

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References

1. Bitner, J.M. and Brown, W.S. The evolution and discovery of services science in business schools. Comm. ACM 49, 7 (July 2006), 7378.

2. Bijker, W.E. Of Bicycles, Bakelites, and Bulbs: Toward a Theory of Sociotechnical Change. MIT Press, Cambridge, MA, 1995.

3. Bostrom, R. and Heinen, J.S. MIS problems and failures: A socio-technical perspective. MIS Quarterly 1, 3 (1977), 1730.

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5. Gummesson, E. The future of service is long overdue. Handbook of Service Science, Springer Publisher, 2010, 615632.

6. Lamb, R. and Kling, R. Reconceptualizing users as social actors in information systems research. MIS Quarterly 27, 2 (2003), 197235.

7. Maglio, P.P., Srinivasan, S., Kreulen, J.T. and Spohrer, J. Service systems, service scientists, SSME, and innovation. Comm. ACM 49, 7 (July 2006), 8185.

8. Metcalfe, J. Stanley and Miles. Innovation Systems in the Service economy: Measurement and Case Study Analysis. Boston, Mass: Kluwer Academic, 2000.

9. Pine, J.B. and Gilmore J.H. Welcome to the experience economy. Harvard Business Review 76, 4, (July-Aug. 1998), 97105.

10. Prahalad, C.K. and Ramaswamy, V. The Future of Competition: Co-Creating Unique Value with Customers. Harvard Business School Press. Boston, M.A., 2004.

11. Tung, W.F and Yuan, S.T. A value co-produced design framework for service innovation: Insight from the mutualism perspective. Kybernetes 37, (2008), 16.

12. Spohrer, J., Maglio, P.P., Bailey, J., and Gruhl, D. Steps toward a science of service systems. IEEE Computer (Jan. 2007), 7176.

13. Vargo, S.L. and Lusch, R.F. Service-dominant logic: Continuing the evolution. Journal of the Academy of Marketing Science 36, 1, (2008), 110.

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Authors

Wei-Fung Tung ([email protected]) is an assistant professor at the Department of Information Management at Fu Jen Catholic University.

Soe-Tsyr Yuan ([email protected]) is a professor and Director of Service Science Research Center at National Chengchi University.

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Footnotes

DOI: http://doi.acm.org/10.1145/1787234.1787268

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Figures

F1Figure 1. A simple service machine is a hybrid of STS and SSME

F2Figure 2. Conceptualization of the iDesign framework

F3Figure 3. mDesignStorming: architecture and interface

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Tables

T1Table 1. The construct model of a simple service machine

T2Table 2. SSM within mDesignStorming

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