key: cord-0845829-xxh1dn24 authors: Tien, James M.; Berg, Daniel title: A calculus for services innovation date: 2007-05-26 journal: J Syst Sci Syst Eng DOI: 10.1007/s11518-007-5041-y sha: cc262860fb41a68634cb6c159680cf523f8f6c73 doc_id: 845829 cord_uid: xxh1dn24 Innovation in the services area — especially in the electronic services (e-services) domain — can be systematically developed by first considering the strategic drivers and foci, then the tactical principles and enablers, and finally the operational decision attributes, all of which constitute a process or calculus of services innovation. More specifically, there are four customer drivers (i.e., collaboration, customization, integration and adaptation), three business foci (i.e., creation-focused, solution-focused and competition-focused), six business principles (i.e., reconstruct market boundaries, focus on the big picture not numbers, reach beyond existing demand, get strategic sequence right, overcome organizational hurdles and build execution into strategy), eight technical enablers (i.e., software algorithms, automation, telecommunication, collaboration, standardization, customization, organization, and globalization), and six attributes of decision informatics (i.e., decision-driven, information-based, real-time, continuously-adaptive, customer-centric and computationally-intensive). It should be noted that the four customer drivers are all directed at empowering the individual — that is, at recognizing that the individual can, respectively, contribute in a collaborative situation, receive customized or personalized attention, access an integrated system or process, and obtain adaptive real-time or just-in-time input. The developed process or calculus serves to identify the potential white spaces or blue oceans for innovation. In addition to expanding on current innovations in services and related experiences, white spaces are identified for possible future innovations; they include those that can mitigate the unforeseen consequences or abuses of earlier innovations, safeguard our rights to privacy, protect us from the always-on, interconnected world, provide us with an authoritative search engine, and generate a GDP metric that can adequately measure the growing knowledge economy, one driven by intangible ideas and services innovation. To a large extent, this paper integrates the work of Tien (2006) and Kim and Mauborgne (2005) , they independently considered the challenging topic of services innovation from two different but complementary approachestechnical and business, respectively. Additionally, the paper extends their work by considering services innovation from both a life cycle and a developmental (i.e., strategic, tactical and operational) perspective. As a result, a systematic process -or calculus -of services innovation is developed herein. Before discussing services innovation, it is important in this introductory section to consider separately the concepts of services and innovation. The importance of the services sector can not be overstated; it employs a large and growing proportion of workers in the industrialized nations. As reflected in Table 1 , the services sector includes a number of large industries; indeed, services employment in the U.S. is at 82.1 percent, while the remaining four economic sectors (i.e., manufacturing, construction, agriculture, and mining), which together can be considered to be the "goods" sector, employ the remaining 17.9 percent. In practice, the delineation between the different economic sectors are blurred; this is especially true between the manufacturing and services sectors, which are highly interdependent (Tien and Berg, 1995; Berg et al., 2001) . Clearly, the manufacturing sector provides critical products (e.g., autos, computers, aircrafts, telecommunications equipment, etc.) that enable What constitutes the services sector? It can be considered "to include all economic activities whose output is not a physical product or construction, is generally consumed at the time it is produced and provides added value in forms (such as convenience, amusement, timeliness, comfort or health) that are essentially intangible…" (Quinn et al., 1987) . Implicit in this definition is the recognition that services production and services delivery are so integrated that they can be considered to be a single, combined stage in the services value chain, whereas the goods sector has a value chain that includes supplier, manufacturer, assembler, retailer, and customer. More importantly, services are co-produced, whereas goods have traditionally been pre-produced; this and other differences are explored in Section 5 to identify possible new innovations in services. Tien and Berg (2003) provide a comparison between the goods and services sectors. The goods sector requires material as input, is physical in nature, involves the customer at the design stage, and employs mostly quantitative measures to assess its performance. On the other hand, the services sector requires information as input, is virtual in nature, involves the customer at the production/delivery stage, and employs mostly qualitative measures to assess its performance. As a consequence, the management of goods technology is different than the management of services technology (Berg and Einspruch, 2007) . For example, since services are to a large extent subject to customer satisfaction and since, as Tien and Cahn (1981) postulated and validated, "satisfaction is a function of expectation," service performance or satisfaction can be enhanced through the effective "management" of expectation. Parasuraman et al. (1998) employed the gap between expectation and actual service to evaluate service quality, as defined by reliability, tangibles, assurance, responsiveness and empathy. Tien and Berg (2003) also call for viewing services as systems that require integration with other systems and processes, over both time and space; in fact, they make a case for further developing a branch of systems engineering that focuses on problems and issues which arise in the services sector. In this manner, they demonstrate how the traditional systems approach to analysis, control and optimization can be applied to a system of systems that are each within the province of a distinct service provider. They underscore this special focus not only by the size and importance of the services sector but also by the unique opportunities that systems engineering can exploit in the design and joint production and delivery of services. In particular, they identify a number of service systems engineering methods to enhance the design and production/delivery of services, especially taking advantage of the unique features that characterize services -namely, services, especially emerging e(lectronic)services, are decision-driven, information-based, real-time, continuously-adaptive, customercentric and computationally-intensive. As we consider the future, it is perhaps more appropriate to focus on emerging e-services. flourishing and e-services or e-commerce is continuing to grow. As indicated in Table 2 , the electronic service enterprises interact or "co-produce" with their customers in a digital (including voice mail, In the remainder of this paper, the strategic, tactical and operational phases of developing a services innovation are, respectively, discussed in Sections 2, 3 and 4; they provide the framework for identifying past, current and future services innovation in Section 5. Some concluding remarks are made in Section 6. Strategically, one can approach services innovation from several perspectives: global consideration, customer drivers, business foci, business strategy, and life-cycle. Given In general and as detailed in Tien (2006) high-probability, low-risk life-as-usual situations and low-probability, high-risk catastrophes. From a business perspective, there are, of course, three reasons to act -to create a new good or service, to solve a particular problem, or to compete in a specific area. These three actions or foci can best be described by relating them to the aforementioned four customer drivers. As identified in Table 6 Kim and Mauborgne (2005) have introduced a simple, yet very effective way of visualizing and differentiating business strategies; they call it a strategy canvas. They consider the existent industry strategy to be in the red ocean (red, because of the bloody competition over a shrinking profit pool), while the innovative strategy to be in the blue ocean (blue, because of an uncontested market space that is ripe for growth). As an illustration, we employ the strategy canvas in Figure 1 to indicate howfrom our experience -BMW is differentiated from the other, high performance foreign vehicles (i.e., Acura, Audi, Infiniti and Lexus). In particular, we feel that BMW is lower in price, minimally discounted, subject to fewer complaints, available with fewer extra cost Obviously, other experiences and surveys may differ from that depicted in Figure 1 . As with all goods and services, a services innovation undergoes a somewhat predictable set of stages or life-cycle. In particular and as depicted in Table 7 Tactically, one can approach services innovation from two perspectives: business principles and technical enablers. Kim and Mauborgne (2005) In trying to identify innovation enablers, it should be noted that the enablers may differ between those for goods and those for services, "Lubber" (low rate of failure due to expert system) "Elco" (fair process facilitates execution) Operationally, one can approach services innovation from several perspectives; however, they are all alternative perspectives in regard to making decisions about how to implement or effect the innovation. More specifically, it is about decision making and decision informatics. As shown in Figure 2 , data represent basic transactions or sensor input captured during operations, while information represents processed data (e.g., derivations, groupings, patterns, etc.). Clearly, except for simple operational decisions, decision making at the tactical or higher levels requires, at a minimum, appropriate information or processed data. The decision informatics approach advanced by Tien (2003) they serve to refine the analysis and modeling steps. As depicted in Figure 3 (b), the driving force behind decision informatics is the decision foci. In regard to services innovation, the decisions concern how best to meet the four strategic drivers of collaboration, customization, integration and adaptation. Evidential reasoning is introduced to allow each sensor to contribute information at its own level of detail. For example, one sensor may be able to provide information that can be used to distinguish individual objects, whereas the information from another sensor may only be able to distinguish classes of objects. Decision modeling methods concern the information-based modeling and analysis of alternative decision scenarios; they include Systems engineering methods concern the integration of people, processes, products and operations from a systems perspective; they include electrical engineering, human-machine systems, cognitive science and systems biology. Again, the real-time nature of co-producing Sections 1 through 4 have, in essence, defined a process or calculus for undertaking services innovation. Figure 4 provides a summary of the process. It identifies the relationships between goods, experiences (which are typically integrated goods and services), and Table 9 identifies the primary drivers, enablers and decision attributes associated with 45 services innovation areas, assuming 5 areas in each of the 9 service categories or domains. Although both the domain areas and the identification process are quite subjective, it is interesting to note that the drivers, in order of impact, are: integration (16 out of 45), adaptation (13), customization (12) , and collaboration (4) . Similarly, the enablers are: automation (19) , organization (7), Third, as real-time decisions must be made in an accelerated and co-produced manner, the human decision maker or service provider will increasingly become a bottleneck; he/she must make way for a smart robot or software agent. 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NJ: Pearson Education Technology and happiness On automated correctional data systems Individual-centered cducation: an any one, any time, any where approach to engineering education Towards a decision iInformatics paradigm: a real-time, information-based approach to decision making Viewing urban disruptions from a decision informatics perspective Tien is the Yamada Corporation Professor at Rensselaer Polytechnic Institute. He is also an Honorary Professor at several Chinese Universities and an elected member of the U. S. National Academy of Engineering. He received the BEE from Rensselaer Polytechnic Institute (1966) and the SM He has held leadership positions at Bell Telephone Laboratories (1966-69), at the Rand Corporation (1970-73), and at Structured The authors would like to express their appreciation to Mr. Anuj Goel, a doctoral candidate in the Department of Decision Polytechnic Institute. He contributed several suggestions that enhanced the contents of the paper.