Innovation in the Internet of Services Jan Finzen1, Christoph Riedl2, Norman May3, Stephan Stathel4 1
Fraunhofer IAO, 2Technische Universität München, 3SAP AG, 4FZI
The TEXO project, one of the six application scenarios of the THESEUS research program, aims at developing an infrastructure for the Internet of Services, which builds upon three basic paradigms i) Service Oriented Architectures (SOA), ii) Semantic Web, and iii) Web 2.0. We suggest that software tools that support the service innovation method should make use of these three aspects to be successfully deployed and used within the Internet of Services. In this paper, we discuss key-characteristics of electronic services and their implications regarding the service innovation process. We introduce the service innovation framework developed and applied in the TEXO project and describe the software tools that support the framework. Le projet TEXO, un des six scénarios d'application du programme de recherche THESEUS, a pour but de développer une infrastructure pour l'Internet des services en s'appuyant sur trois paradigmes : 1) une architecture orientée services (SOA), 2) le Web sémantique et 3) le Web 2.0.Nous proposons que les logiciels facilitant l'innovation en matière de services utilisent ces trois aspects pour être déployés et utilisés facilement au sein de l'Internet des services. Dans cet article, nous présentons des caractéristiques clé des services électroniques et leurs implications concernant le processus d'innovation en matière de services. Nous présentons le cadre d'innovation pour les services (service innovation framework) développé et utilisé dans le projet TEXO et décrivons les logiciels constituant ce cadre.
1.
Introduction
TEXO, one of the six application scenarios of the THESEUS research program1, currently the largest publicly funded IT project in Germany, aims at developing an infrastructure for the Internet of Services, which builds upon three basic paradigms: 1. Service oriented architectures (SOA): The shift towards service oriented architectures, mashup applications and cloud computing enables applications to be composed in an ad-hoc fashion and to be distributed on multiple machines and providers.
1
See http://www.theseus-programm.de for more information on the THESEUS research program
1
2. Semantic Web: The shift towards computer-understandable web content, i.e., the semantic web allows services to be searched, found, and composed in a computer-aided or even automatic way. 3. Web 2.0: The shift towards active user involvement brings power to the customer and opens up completely new business models and user experiences. All three aspects can and should be exploited within new service development for the Internet of Services. In this paper, we describe the service innovation framework, developed in the TEXO use case of the THESEUS project. The remainder of this paper is structured as follows: In Chapter 2 we discuss important key-characteristics of electronic services and deduce three major implications for our service innovation process. In Chapter 3 we introduce the TEXO innovation process and framework and give a detailed overview on the software tools developed to support the different phases of the process. Chapter 4 gives a short outlook to our future work.
1.1.
Key-Characteristics of Electronic Services
Prior research found that certain distinct characteristics of electronic services mandate a customized development process for these services as opposed to traditional new service development. In particular the following five key areas of difference have been reported: (1) the cost structure of services, (2) the high degree of outsourcing, (3) rapid development of new services, (4) the availability of transparent service feedback, and (5) the continuous improvement of services (Riedl; Leimeister; Krcmar, 2010). The following sections motivate each area of difference. Low marginal costs of service delivery: The economics of information have been recognized as dramatically different as the economics of physical items. This leads to a unique cost structure both in comparison to physical products as well as other non-electronic services. The typical cost structure of an information technology supplier involves high fixed costs for developing the infrastructure and applications, and very low, sometimes near zero, marginal costs for actual service provision. Through the use of electronic intermediaries the search and transaction costs are further reduced. This further reduces variable costs of service provisioning and service use. Contrary to non-electronic services that are sometimes very labor intensive (e.g., hospitality services) this difference should explicitly be addressed during service development. High degree of outsourcing: Outsourcing is a standard concept that is being considered through make or buy decisions both in manufacturing and in services. In electronic services, outsourcing plays a particularly important role. First, since service provisioning occurs in the back office and electronic services can easily be delivered from remote locations there is no need to collocate service production with the service consumption. Second, through the high degree of technical standardization achieved through various Web-service standards and efforts to standardize Service Oriented Architectures, this high degree of outsourcing is accompanied by the necessary technical framework to make outsourcing of individual service components feasible. The rise of so-called mashup applications that con-
2
nect basic services to generate value added services is one significant impact of the Web 2.0 paradigm shift. Rapid development of new services: A differentiation strategy is difficult to attain as services can be copied easily and are not applicable to patent protection. Consequently, only continuous innovation can lead to economic success. Advances in electronic services are particularly rapid and low barriers of entry have been attributed to electronic services. This rapid development is further fueled by extremely fast technological progress and fast emerging of technologies. Transparent service feedback Through the electronic nature of service delivery the interaction between a service consumer and the service itself becomes very transparent. A simple example of this effect is the monitoring of click-through-rates in online shops. This generates a nearly complete picture of customer interactions which a traditional shopping mall operator would dream of. This creates various opportunities for service design and innovation. Interactions between users and the service can be recorded and replayed. Thus, a service itself can gather information about what else users might want or need (Riedl et al., 2008). Continuous improvement and deployment Unlike software being sold over the counter electronic services are no longer restricted to a scheduled release cycle where changes, improvements, and bug fixes require months to be integrated into the service (termed “perpetual beta” by some authors, cf. (O'Reilly, 2007)). Rather, services are developed in the open with tight integration of service users or even by the users themselves. The innovation process is full of small cycles that allow a service to be improved almost instantly. Additionally, as services are delivered through a global delivery system, there are no local differences in the services offered and the new version is instantly available to all users.
1.2.
Implications for the Service Innovation Process
In the preceding section, we discussed that services differ from products and service innovation differs from classical product innovation in several aspects. From the keycharacteristics discussed above we deduce three major aspects to be addressed by the TEXO innovation process and framework. The Need for Community Involvement As it is not possible to divorce a service’s production and consumption (an attribute usually termed “inseparability”), services involve the customers / service users by their very nature. The Web 2.0 evolution brings more power to the customer, thereby forcing companies to increase awareness of users' needs. Business Value Networks offer new opportunities and chances for innovation-related cooperation. “Firms must engage and co-create a dynamic fashion with everyone” (Tapscott, 2007). Innovation processes that leverage the capabilities of company-external stakeholders are usually referred to as “Open Innovation” processes, a term introduced by Chesbrough (Chesbrough, 2006). Possible collaboration partners in innovation projects include suppliers and customers (making up both ends of traditional supply chains) as well as academic partners like research institutes and universities. Even collaboration with actual competitors might be fruitful when the aim is, e.g., to develop and introduce new standards. The most promising approach, however, seems to be the in3
volvement of customers and potential future customers, since “especially the process orientation of most services requires close contact to customers and can be seen as a success factor for service companies" (Hipp; Grupp, 2005). Nieto and Santamaría conclude that innovation results can be delivered more quickly when listening to clients and suppliers at the early stages of the product development process (Nieto; Santamaría, 2007). Within the TEXO innovation process, we therefore seek to involve the customer community in each phase as tight as possible, e.g., by providing a community frontend for our central innovation repository and enabling community-based idea evaluations based on an information market approach. The Need for Web-Based Market Observation The Internet has become an important information source for innovation professionals: According to a survey by Fraunhofer IAO and Tübingen University, 75 percent of all surveyed companies stated to use the Internet within their innovation activities (Springer, 2006). Services are easy to imitate and hard to protect. This leads to many small companies being able to compete in the service sector. This enforces the need for but also the value of continuous market observation in strategic innovation management. To be informed of competitors’ actions or strategies becomes crucial. The same holds for the awareness of new customer requirements or ideas for service improvements which are uttered in Web-based information sources. Within the TEXO innovation process, we even foresee a dedicated “innovation mining” phase for systematic collection of service ideas uttered within the Internet. The TEXO Innovation Mining Cockpit supports the long-running information needs of innovation professionals by automating the information gathering processes and automatically notifying the user on newly found relevant information. The Need for Systematic Feedback Integration Most service innovations are incremental rather than radical innovations, especially electronic services are continuously improved and deployed. The innovation process must therefore be regarded as a cycle rather than a linear process, as it becomes essential to continuously collect and analyze user feedback. Such feedback includes implicit feedback, e.g., usage statistics, runtime information, or search queries as well as explicit feedback which embraces user tests, questionnaires, forums, blogs etc. The TEXO innovation process includes a separate phase dedicated to the collection and analysis of user feedback collected via the AGORA service market place. In addition, user feedback is an important aspect during the systematic idea development support via the TEXO Innovation Repository community frontend.
2.
The TEXO Innovation Process and Framework
This chapter presents the concepts, models, and tools developed in the context of the TEXO research project to provide innovation support in future business value networks. We first develop the core foundational aspects to presenting both requirements and opportunities for innovation support in business value networks. We then 4
present the overall process and architectural design for the four innovation phases: innovation mining, idea development, idea evaluation, and service feedback. Finally, the solutions developed for each of the four phases are presented in detail.
2.1.
Foundational Aspects
To jointly develop new products and services in an innovation network, different activities need to be performed by different types of roles. These roles characterize the types of activities involved and the type of contribution that are required. For successful innovation projects this is important to understand as the roles define the capabilities that actors need to contribute. In service ecosystems the following four roles can be used to structure the different types of contributions actors make towards innovation (Riedl et al., 2009): Customer: Customers not only receive value through the services they use but also contribute value through co-creation and the feedback they offer. Platform Provider: The platform providers triggers and catalyzes innovation. Furthermore, they envision and direct innovation and attend to the innovation network. They are the central member in an innovation network; they provide the initial momentum, and define key elements of the network and the innovations to be carried out. Service Provider: Service providers, first of all, offer services on the platform setup by the platform provider. However, as services can be re-combined, every service offered also contributes to innovation as it extends the potential solution space. Broker: Brokers engage in transforming and refining ideas. They do not necessarily create new ones but rather engage four sub-tasks: Brokers capture good ideas, keep ideas alive, imagine new uses for old ideas, and put promising concepts to the test. In order to coordinate the activities of the actors presented above, we developed a process and architecture framework that ties the different activities and tools supporting these activities together (Stathel et al., 2008). The general process is depicted in the top part of Figure 1. Although the process model is depicted in a sequential process the focus lies on systematic and continuous innovation management. This is illustrated by the feedback loops that connect every process step with the other ones. The bottom part of Figure 2 relates the innovation process to the specific tools developed within the TEXO project to support each process phase.
5
Fig. 1:
Overall architecture and process model of all innovation related tools and components.
After an idea has been created and developed using tools like the Innovation Mining Cockpit (Section2.2), an online community and the Solution Design Lab (Section 2.3), it is evaluated, for instance, by using information marked-based approaches (Section 2.4). If the evaluation result is positive, the idea is implemented using the ISE Workbench (Kett et al., 2008; Scheithauer et al., 2009). For integration with the ISE Work Bench the Innovation Repository provides a factsheet that can easily passed on to a service engineer who is responsible for implementing a service idea. The factsheet is available in two versions: A formal version based on the XML format of the Idea Ontology, which is machine readable, and an informal version, which is optimized for readability by humans (e.g., as a PDF file). Service usage information in different forms will influence change and improvement of existing ideas using the Service Usage Feedback Controller (Stathel et. al, 2008). Tight integration of the Innovation Repository with the AGORA market place (Section 2.5) allows the exploitation of usage information and explicit user feedback in form of voiced comments for the innovation process. A key issue arising from the overall innovation system landscape presented above is the need for a consolidating data access layer. This data layer needs to provide integration for all the tools developed within the TEXO project. For that purpose, an approach based on Semantic Web technology has been developed, the Idea Ontology (Riedl et al., 2009). The Idea Ontology developed for the Innovation Repository (Riedl et al., 2009) has been integrated as a module into the TEXO Service Ontology (Oberle et al., 2009) and is thus accessible for the complete TEXO platform.
6
2.2.
Innovation Mining
As electronic services are both offered and consumed via the Internet it can be safely assumed that a lot of useful information supporting the innovation process can be found on the Web. For innovation managers, the dilemma (but also the art) consists in the early innovation phases in identifying and collecting relevant information regarding the tasks with less effort to secure a competitive advantage. Within this context, the Internet can be used as a broad and inexpensive information base. But the abundance of information on the Internet complicates the targeted research for useful results. In consequence, such research is yet mostly very time consuming and often leads to a multitude of dispensable information, which undermines the aim of an efficient and effective information research. In a 2009 survey among German innovation professionals we analyzed the importance of different Web-based information sources regarding different steps of the early innovation phases “innovation push”, “idea collection”, “idea creation”, and “idea evaluation” (Finzen; Krepp; Heubach, 2009).
Fig. 2:
Importance of Web-based information sources in early innovation phases
7
Figure 2 summarizes the results: while online journals and research and technology portals are used for finding innovation impulses and collecting ideas, encyclopedias and especially patent databases are most useful for idea evaluation. Internet-based information sources are considered most useful for collecting ideas, but less useful for the actual creation of ideas.
2.2.1. Innovation Mining Process Given the importance of customer involvement for service innovation and the high competiveness of the service market, we suggest establishing Web mining activities as a basic step of the service innovation process. Building on the “tech mining” process described by Porter and Cunningham (2004) we suggest a five-step Web mining approach depicted in Figure 3: 1. Identify information need: Although the outcomes of any mining process might be surprising (after all, the idea of data mining is the detection of previously unknown facts), it should be as directed as possible. That means, the overall strategic goal of the mining process should be defined as the very first step, as it influences subsequent steps like source selection or visualization parameterization. 2. Collect information: Depending on the information need, a variety of information sources is selected for mining. If the information need embraces temporal developments, e.g., trend analysis of a given topic, the document corpus must be created over a larger timeframe. However, for the case of innovation mining, it is usually important to gain information as soon as possible, i.e., ideally in real-time – as soon as they show up on the Web. Therefore, either sophisticated crawl-andscrape approaches or (even better) a push supply of information is needed. 3. Process results: Once the source corpus is defined (and most probably being frequently expanded), the result processing starts. Though the approaches applied might vary very much, the main task of this step is a matching of a specified information need (e.g., a search query) against the documents of the corpus. In navigational search the tasks usually ends with weighting the respective document’s relevance in relation to the information need. This allows a suitable ranking of documents in a subsequent step. Information needs that are of a more informational kind typically involve additional information processing steps: information or meta data extraction forms a basis for appropriate analyses in the subsequent step. 4. Analyze and interpret: The information collected in the processing step are analyzed and condensed, and finally put into graphs that suit the information need. This step aims at supporting the analysis of the data by the user as good as possible. It includes choosing the best-fitting visualization, the tailoring of the visualization regarding the results to display as well as possibly providing additional information that helps interpreting the data in the right way. 5. Disseminate and act: Once, interesting results are found subsequent processes can be triggered: Further results showing up in the future might be automatically taken into account and thus resulting in new versions of the result analysis. When new findings are available, the user might want to be notified by an appropriate 8
alerting mechanism. Results might be saved and reloaded, printed, exported into complementary software tools, and passed on or shared to other users. Identify Information need(s)
Competitors Technologies Products Tenders Events Campaigns User ideas …
Collect information Internal data Competitors‘ websites Patent databases Press releases Scientific content Blogs, forums… …
Fig. 3:
Process results Text and data mining Semantic annotation Statistical approaches …
Analyse and interpret Dashboards/ cockpits Trends and events Reporting …
Disseminate and act Monitoring Automatic notification (e-mail, SMS, RSS) Collaboration & integration …
Web mining process for innovation management
Figure 3 might be suggestive of being a sequential process that can be traversed step by step. However, in reality adjustments have to be frequently applied, as changes might arise at each of the process steps at any time: new information sources might show up and old ones might be needed to be excluded. Different sources might entail adjustments to the processing procedures. Finally, the information need itself might be shaped or sharpened throughout the mining process. Figure 4 suggests a generic adjustment cycle that should be taken into account at any time during the passage of the innovation mining process.
Fig. 4:
TEXO Innovation mining cycle
2.2.2. Innovation Mining Cockpit The TEXO Innovation Mining Cockpit (IMC) is a search portal targeted towards innovation managers that allows searching the Internet far more target-oriented than it is possible by using general purpose search engines. A company's innovation professional can use the Innovation Mining Portal to search the internet for innovationrelevant information. The portal integrates different search-related services and provides means to interact with the TEXO innovation repository (cf. next section) to store and retrieve search artefacts. It focuses on long-running information needs of professional innovation professionals. It therefore provides means to organise and manage bookmarks, monitor changes in online information sources, notify the user on new 9
relevant information, and provide analysis for complex search queries. It uses semantic annotations for facetted search and result visualization, e.g., to analyse relation networks between organizations. Figure 5 shows a screenshot of the Innovation Mining Cockpit’s search portlet. Main features of the tool include: Search space configuration: The Search Space Configuration module supports step 1 (“identify information need”) and 2 (“collect information”) of the web mining process. It combines a sophisticated bookmarking system with several search engine specific adjustments, like crawl depth etc. Source identification: The source identification portlet implements a meta search engine approach: the keywords entered in the text field are passed to various general purpose search engines. The results of the different search engines are combined, ranked, and displayed in a typical search result lists. Either the complete URL or the Web domain can be added to the search space. Semantic Annotations: Depending on the document type and the information source, a document may embrace meta data or even semantic markups which can be utilized to offer advanced result visualizations and browsing functionality, e.g. facetted search. The IMC integrates several Web services for semantic annotations, like OpenCalais2, and Alchemy3. Change monitoring and alerting: Based on some heuristics (like text block length), significant changes and new content are detected and provided to the IMC in RSS format. Thus, the IMC internally builds its own RSS feed for any website that may not offer one explicitly. This way, the user can easily be notified on any (relevant) newly found information on any site either by RSS, E-Mail or SMS. Frequency analysis: Trend monitoring and event detection are important use cases within the area of technology and innovation management. The innovation mining cockpit uses classical bar and line diagrams for any possible search. Geographical analysis: Oftentimes geographical information can be extracted from texts fairly easily (e.g., country names can be easily maintained in look-up lists). For geographical information, the longitude and latitude can easily assigned using respective Geocoding Web services. This allows the visualization of objects on maps, e.g., by coloring maps or using mashup services like the Google Maps service4. Association analysis: Association graphs are used to show and analyze relations between objects, e.g., companies or persons. Generally, the nodes represent objects and the edges the relations between these objects. Various layout algorithms help to analyze structural information within the graph. For example, the degree of cross linking may indicate a person’s importance within a community network.
2
http://www.opencalais.com/ http://www.alchemyapi.com/ 4 http://code.google.com/apis/maps/ 3
10
Fig. 5:
2.3.
Innovation Mining Cockpit Screenshot
Systematic Idea Development
To enhance innovation capabilities to keep up with fast developments, organizations are increasingly relying on external resources to complement internal innovation resources. The integration of customers into the innovation process is one of the biggest resources for external innovations (Gassmann, 2006). Furthermore, service ecosystems are open and flexible systems with a great number of actors that participate on the platform. Consequently, there is a need for collaboration and an open system to foster innovation in service ecosystems. To facilitate the integration of the various actors and of customers advanced tool-support is required that enables this integration. One promising approach is to build a customer as well as a partner community supported by an online platform. To support systematic idea development for the actors of service ecosystems three tools have been developed in the context of the TEXO project. The Innovation Repository Community Website provides a Web-based community platform that serves as the central access point for all innovation relevant activi11
ties. It allows the actors within a service ecosystem to communicate and collaborate on the development of new service ideas. The Innovation Repository Backend implements a central data storage system, based on the Idea Ontology, that allows the integration of different tools into the innovation process. The Innovation Repository Backend also provides management functions that allow monitoring and controlling the systematic idea development process. The Solution Design Lab supports idea development within a group of participants (e.g., customers, co-operators) in various ways and can help to foster idea generation, idea elaboration and idea rating in a well-defined and easy-to-use way. Figure 6 shows a screenshot of the community homepage. The community website is accompanied by a backend system that offers advanced idea management functions and workflow support.
Fig. 6: Screenshot of the Innovation Repository Community Website. Visible is: (i) a list of ideas with various search and sort controls as well as a tool box including edit and communication icons for each idea; (ii) link to post new ideas; (iii) tag cloud; (iv) quick entry of new idea.
12
2.4.
Systematic Idea Evaluation
The assessment of innovations is a critical task in companies and organizations. Meetings and other methods are used to identify the most promising alternative. Often, employees, suppliers and vendors have valuable knowledge and expectations about the success of innovation alternatives. The TEXO Information Market Web Tool offers employees, decision makers as well as other groups to propose their knowledge in a Web 2.0 manner (Figure 7 shows a screenshot). People decide on a voluntary basis if and which information they disclose. Moreover, they interact via the market system as well as community features like blogs or forums.
Fig. 7:
Screenshot of the Information Market Web Tool (Market Overview)
In Information Markets, virtual shares representing the outcome of an event in the future are traded. Participants in such markets buy and sell shares based on their expectations and beliefs about the outcome of events. Once an event occurs, shares are paid out according a payout rule. Often, these markets are operated using virtual money which can be exchanged in prizes afterwards. Therefore, participants are incentivized to reveal their true information in order to maximize their profit. In contrast to traditional methods for innovation assessment and to exploit the advantages of using Information Markets, we show that these markets can be applied for the assessment of innovations in companies. Thus, Information Markets are proposed as a valuable method for the assessment of innovations in the TEXO innovation lifecycle. In an industrial context, Information Markets may be small with only a few participants. Hence, several field experiments are being conducted in order to investigate the effect of liquidity supplying mechanisms in order to get results even in small markets. In the following, we describe results of a field experiment about the investigation of small Information Markets in order to investigate the effect of liquidity providers to keep even small markets liquid. In an innovation context at EnBW BadenWürttemberg an Information Market was applied in order to investigate, if Information 13
Markets can be applied in a company. The results show that Information Markets are applicable in companies and are considered as a valuable method in innovation contexts.
2.4.1. EnBW Innovation Market In order to investigate the applicability of Information Markets for innovation assessment in an enterprise context, a field experiment was conducted at EnBW, one of the four large energy suppliers in Germany. 35 employees were supposed to assess the attractiveness of 12 innovation alternatives to be implemented to improve their daily work processes. The market was open for 6 weeks. Every trader was endowed with virtual money and an initial depot of tradable shares. In addition, a wiki hosted all information necessary and served as communication platform for participants via a forum. After the market duration, the final share prices served as ranking of shares. An additional ranking from decision makers was gathered to evaluate the results from the Information Market which was open to employees only. The results show that the first three innovations overlap with three out of the first four results from decision makers. This indicates, that the results from employees as well as decision makers are in line and do not differ significantly. Table 1 shows the results.
Name
EIM (virtual €)
Decision Makers‘ Ranking
All in One
36,40
3
MEREGIOPlattform
21,94
8
Web 2.0 Plakate
18,24
1
[email protected]
16,07
2
Parallele Dokumentenbearbeitung
11,08
7
Geraeteinventar
6,00
11
mobile Zaehlererfassung
5,93
6
Heim-Automation
2,28
9
new contact networking
1,62
12
Intelligente Terminplanung
0,86
4
Digitalisieren von Visitenkarten
0,81
10
Twitterinfo
0,49
5
Table 1: Combined Prices and Decision Makers Ranking.
14
The results of the field experiment show that Enterprise Information Markets (EIM) can motivate employees to take part in enterprise innovation. In fact, market participants approved the method of EIM and used it even more often than we initially expected. After the EIM closed, the results were discussed in the expert panel which decided to finally implement two innovations in 2009. The implementation of “Web 2.0 Plakate” was finished within the second half of the year 2009. “
[email protected]” won 1 out of 3 so called “Innovationsgutschein” worth 25,000 € in an internal award procedure and is going to be implemented shortly. In total, 10 innovations contenders applied for the three coupons. Therefore, the results of the market supported the results of decision makers strongly. Since the implementation of innovations can be seen as change projects, typical success factors in change management should be taken into account with regard to innovation implementation, as well. Figure 8 shows which success factors are mostly relevant for change management according to a recent study of IBM Global Business Services (Joergensen et al., 2008). The three top categories can be addressed with Information Markets. Interestingly, the top aspect is the sponsorship of the top management. During our extensive analysis, some executives in the EIM were identified as lead users. If the top management is also involved in innovation processes as lead users, this can be interpreted as a very strong sponsorship and shows the commitment of the top management.
Fig. 8:
Success Factors of Change Management (based on Joergensen et al. 2008).
The second success factor, named “Employee involvement” is also addressed with an EIM. Employees were invited to join the market, if they were interested in making their expectations available in the market. Employees took part in the market following the self selection mechanism and if an employee did not want to join the market, he was not forced to. 15
Furthermore, honest and timely communication is another essential success factor in change management. The market can also be considered as communication method in a Web 2.0 manner, because employees can communicate even “negative” information via the market mechanism. On the other side, negative information may not have been communicated without an appropriate communication method an employee is not willing to use. Often, employees do not communicate because they fear consequences from their managers, if they announce negative information. In EIM, every employee is anonymous and may fearlessly communicate even negative information. Often, the success of change processes is directly connected with the culture of a company. EnBW uses Information Markets in order to involve employees actively, which indicates a very open company culture. Often, employees cannot be involved in innovation process due to complexity aspects in managing thousands of employees via questionnaires or online surveys. Information Markets are a very scalable method to involve a huge number of employees efficiently. The market mechanism aggregates new information continuously and people participate autonomously, which indicates a culture that motivate employees to participate and involves them in innovation processes.
2.4.2. Summary All in all, for the EnBW the EIM was a new way to involve their employees in innovation processes and provide a sustainable method after the innovation workshop. Due to the success in 2009, the EnBW wants to run another EIM in 2010. The very valuable knowledge of employees is now a key main pillar in their innovation process and therefore the next EIM is expected to be as successful and confirm the results presented in this section.
2.5.
Turning Feedback into Value – Iterative Innovation
In this section, we discuss how feedback for innovative services is collected in AGORA, a service broker platform for electronic services, which is developed at SAP Research5. We also outline how new innovation scenarios are triggered which capitalize on the feedback collected from the community of the service marketplace. AGORA implements the process of finding, configuring and ordering services as well as monitoring the progress of their delivery and post-delivery activities like payment, rating, support and complaint management. The services offered in AGORA complement products offered by SAP, e.g. data migration services or report adaptation. Services may be offered by SAP, but also by its partners. AGORA focuses on mediation, and thus, it is the prime system to collect feedback. As a distinguishing feature both implicit feedback as well as explicit feedback is aggregated. As an example for implicit feedback, users may keep a watch list which can be interpreted as interest in the services in this list. Furthermore, the progress of service delivery is monitored using status indicators. More detailed information about the delivery is subject to the
5
The pilot system can be accessed at http://marketplace.sap.com/ requiring authentication.
16
service delivery platform which is an independent component linked with AGORA. Explicit feedback is, e.g., based on ratings provided by customers after service delivery.
a) Rating dialog Fig. 9:
b) Usage reports: best rated services
Sources for and usage of feedback in AGORA
Explicit feedback is collected on the level of services or service providers. Only users who ordered services are eligible to rate services as we want to rate the quality of service encounters (see Figure 9 a). Marketplace providers globally configure a set of rating attributes using a 5-star rating which represent general quality of service attributes in AGORA, e.g. timely delivery or quality of service description. These attributes may be derived from a service model like the e3-value ontology of Baida et al. (2004). Ratings are aggregated to an overall rating which is usually not assessed directly. Furthermore, consumers can enter feedback in free-text comments. Service providers are able to review the ratings in reports to assess popular offerings; this is depicted in Figure 9 b). This may help them to develop new offers, e.g. by composing popular complementary services offers. Implicit feedback is currently collected based on anonymized logs. Besides the technical log of the Web server, an important source of data is the business log which is used to trace business-level actions like page visit, add service to shopping cart, or service purchase. As we associate these events to sessions, we are able to analyze quantities of single page views as a measurement of consumer interest. Similar to the experiment reported in (Fox et al., 2005), we are able to analyze click-through times and exit events to validate their model to predict consumer satisfaction. Thus, AGORA supports the monitoring of observable behavior in the category of examination introduced by Oard and Kim (1998) with the limitation that repeated visits of a page are only detected within one browser session. Regarding retention, AGORA implements a watch list, where prospects can keep references to service descriptions for later use. Services in this list indicate specific interest in the offers. Reference as implicit feedback is difficult to assess as hyperlinks to pages can be copied and for17
warded. This cannot be detected by our service-based application. Analyzing referer logs, however, we are able to detect common sources via which our system is entered. The broker running AGORA has various tools to influence the services portfolio offered by independent service providers. General access to the marketplace requires authentication. Thus providing user accounts is regulated by AGORA as the intermediary. Providers can enter, modify, or undeploy service descriptions as they wish. However, changes are performed in a staging area and versioned, and thus the intermediary has full control on changes of the service portfolio including the removal of service offers. So far, gap analysis and adaptation of the services and service portfolio is manually performed by service providers or the broker based on the information provided in analytic reports as shown in Figure 9 b). Even though still largely manually accomplished, we are able to measure in test loops a modification of service quality based on implicit and explicit feedback that is provided to service providers. The integration of tools for a more automated process of service portfolio optimization is currently under development. As a first step we detect common topics in user comments and create propose new innovation scenarios based on the community feedback. Conceptually the collected feedback in AGORA is linked to an extended version of the well-known quality gap model of Parasuraman, Zeithaml and Berry (1985). Given the value analysis of the service portfolio using a service model like the one in (Baida et al., 2004), the need for improvements of single services or the portfolio in AGORA is derived on a continuous basis. As part of our future work we want to extend our framework to better support high-volume service brokers to further automate this process for the sake of better scalability. Another aspect of our future work will investigate how service consumers can actively be supported when selecting services. Initial attempts are based on personalized service recommendations.
3.
Conclusion and Future Work
This article presented the service innovation process and framework developed and applied within the TEXO use case of the THESEUS research program. Starting with our basic assumptions regarding service innovation in an Internet of Services we developed a framework of collaborating tools that exploit basic aspects of the THESEUS idea: SOA: The software tools supporting the different process phases use services and are available as services themselves. Interfaces between tools use web services. Semantic Web: The central point of integration consists of the Idea Ontology, a semantic data exchange standard fulfilling the requirements of the semantic web. Web 2.0: All our tools are web-based, community-driven, and obey the current tendency to open-up innovation processes.
18
All tools can collaborate following the TEXO innovation process by means of web service interfaces but have also been used in stand-alone-situations. Within the remaining term of the program, we will focus on evaluating and refinement of the software.
References Baida, Z., Gordijn, J., Saele, H., and Akkermans, H. (2005): Ontology-based analysis of e-service bundles for networked entreprises. In Yao-Hua Tan and D. R. Vogel editors, Proc. of the 17th Bled eCommerce Conference. Bled, 2004. Chesbrough, H. W. (2006): Open innovation. The new imperative for creating and profiting from technology. Harvard Business School Press, Boston, MA. Finzen, J., Krepp, T., and Heubach, D. (2009). Web Searching in Early Innovation Phases - a Survey among German Companies. In Proceedings of the 2nd ISPIM Innovation Symposium, New York, 2009. Fox, S., Karnawat, K., Mydland, M., Dumais, S., and White, T. (2005): Evaluating implicit measures to improve web search, ACM Trans. Information Systems, Vol 23, No 2, 147-168, ACM press. Gassmann, O. (2006): Opening up the innovation process: towards an agenda. R&D Management, 36, 223-228. 2006. Hipp, C. and Grupp, H. (2005): Innovation in the service sector: The demand for service-specific innovation measurement concepts and typologies. Research Policy, 34, 4, 517-535. 2005. Joergensen, H., Owen, L., and Neus, A. (2008): Making Change Work. Working paper, IBM. http://www-935.ibm.com/services/uk/gbs/pdf/ making-change-work.pdf. Kett, H., Voigt, K., Scheithauer, G., and Cardoso, J. (2008): Service Engineering in Business Ecosystems. In Proceedings of the XVIII. International RESER Conference, Stuttgart, Germany, September, 25-26 2008. Nieto, María Jesús; Santamaría, Lluis (2007): The importance of diverse collaborative networks for the novelty of product innovation. In: Technovation, 27, 6-7, 367–377. Oard D, W. and Kim, J (1998): Implicit Feedback for Recommender Systems; in Proc. of the AAAI Workshop on Recommender Systems, 81-83. Oberle, D., Bhatti, N., Brockmans, S., Niemann, M., and Janiesch, C. (2009): Countering Service Information Challenges in the Internet of Services Business & Information Systems Engineering, 2009, 1, 370-390. O'Reilly, T. (2007): What is Web 2.0: Design Patterns and Business Models for the Next Generation of Software. Communications & Strategies, First Quarter 2007, 17-37. 2007.
19
Parasuraman, A., Zeithaml, V. A., Berry, L. L. (1985): A conceptual model of service quality and its implications for future research. The Journal of Marketing, Vol. 49, No. 4, 41-50. Porter, A. L. and Cunningham, S. W. (2004): Tech Mining: Exploiting New Technologies for Competitive Advantage. Wiley Series in Systems Engineering and Management. John Wiley & Sons, 2004. Riedl, C., Böhmann, T., Rosemann, M., and Krcmar, H. (2008): Quality Aspects in Service Ecosystems: Areas for Exploitation and Exploration. In Proceedings of International Conference on Electronic Commerce (ICEC '08), ACM Press, 1-7. Riedl, C., Böhmann, T., Leimeister, J. M., and Krcmar, H. (2009): A Framework for Analysing Service Ecosystem Capabilities to Innovate. In Proceedings of 17th European Conference on Information Systems (ECIS'09), Verona, Italy. Riedl, C., Leimeister, J. M., and Krcmar, H. (2010): Why e-Service Development is Different: A Literature Review. e-Service Journal, accepted for publication, forthcoming. Riedl, C., May, N., Finzen, J., Stathel, S., Leidig, T., Kaufman, V., and Belecheanu, R (2009): Managing Service Innovations with an Idea Ontology. In Proceedings of XIX International RESER Conference, Budapest, 2009. Riedl, C., May, N., Finzen, J., Stathel, S.; Kaufman, V., and Krcmar, H. (2009): An Idea Ontology for Innovation Management", International Journal on Semantic Web and Information Systems, 2009, 5(4), 1-18. Scheithauer, G., Voigt, K., Bicer, V., Heinrich, M., Strunk, A., and Winkler, M. (2009): Integrated service engineering workbench: service engineering for digital ecosystems. In Proceedings of the international Conference on Management of Emergent Digital Ecosystems (France, October 27 - 30, 2009). MEDES '09. ACM, New York, NY, 446-449. Springer, S. (2006): Nutzung von Internet und Intranet für die Entwicklung neuer Produkte und Dienstleistungen, nova-net Werkstattreihe nova-netKonsortium, Ed.: Fraunhofer IRB. Stuttgart, 2006. www.nova-net.de. Stathel, S., Riedl, C., Finzen, J. und May, N. (2008): Service Innovation in Business Value Networks. In Proceedings of the 18th International RESER Conference, Stuttgart, 2008. Stathel, S. (2008): Service Innovation via Information Markets; PhD Summer School, XVIII International RESER Conference, September 25th-26th 2008, Stuttgart, Germany. Tapscott, Don; Williams, Anthony D. (2007): Wikinomics: How Mass Collaboration Changes Everything. How Mass Collaboration Changes Everything: Portfolio.
20
Authors: Jan Finzen Fraunhofer-Institut für Arbeitswirtschaft und Organisation (IAO) Nobelstrasse 12 D-70569 Stuttgart
[email protected] Christoph Riedl Technische Universität München (TUM) Boltzmannstr. 3 D-85748 Garching
[email protected] Stephan Stathel FZI – Forschungszentrum für Informatik Information Process Engineering (IPE) Haid-und-Neu-Str. 10-14, D-76131 Karlsruhe
[email protected] Norman May, Dr. SAP AG Dietmar-Hopp-Allee 16 D-69190 Walldorf
[email protected]
This research was funded by the German Federal Ministry of Economics and Technology under the promotional reference 01MQ07012, 01MQ07017, 01MQ07019, and 01MQ07024, and the German Federal Ministry of Education and Research under grant number 01IA08001A. The responsibility for this publication lies with the authors. The information in this document is proprietary to the following THESEUS consortium members funded by means of the German Federal Ministry of Economy and Technology: Technische Universität München (TUM), SAP AG, Fraunhofer IAO, Forschungszentrum für Informatik (FZI). The information in this document is provided “as is”, and no guarantee or warranty is given that the information is fit for any particular purpose. The above referenced consortium members shall have no liability for damages of any kind including without limitation direct, special, indirect, or consequential damages that may result from the use of these materials subject to any liability which is mandatory due to applicable law. Copyright 2009 by Technische Universität München (TUM), SAP AG, Forschungszentrum für Informatik (FZI), Fraunhofer IAO.
21
