Paula Urze

António Abreu

Knowledge Transfer Assessment in a Co-innovation Network

Keywords: Knowledge Transfer, Collaborative Networks, Case Study

Frequently, the innovation processes require knowledge in several domains that enterprises do not usually hold. In order to address this problem, the issue of the knowledge transfer in collaborative environments started to attract attention. In this context, the characterization and assessment of the knowledge transfer among members within a network is an important element for the wide adoption of the networked organizations paradigm. However, models for understanding the knowledge transfer in a collaborative environment are lacking. Starting with some discussion about the nature of knowledge production and transfer, this paper introduces an approach for analysing the level of maturity in terms of knowledge transfer in a collaborative network. Finally, based on experimental results from a Portuguese collaborative network, the Brisa case study, the benefits, challenges and difficulties found are presented and discussed.

Introduction

Nowadays, enterprises in global markets have to achieve high levels of performance and competitiveness to stay "alive" [START_REF] Tidd | Managing Innovation: Integrating Technological, Market and Organizational Change Hong Kong[END_REF]. In order to be competitive, enterprises must develop capabilities that will enable them to respond quickly to market needs. According to several authors, one of the most relevant sources of competitive advantage is the innovation capacity [START_REF] Argote | Knowledge Transfer in Organizations: Learning from the Experience of Others[END_REF]. However, the innovation capacity requires access to new knowledge that enterprises do not usually hold. As a result, the enterprises can improve their knowledge either from their own assets, making sometimes high investments, or from the knowledge that may be mobilized through other enterprises based on a collaborative process.

However, despite the collaboration among enterprises has been considered unusual and indeed suspicious by many SME managers until a few years ago, nowadays it is commonly assumed that the participation in a collaborative process is a common trend for many enterprises. Literature in the field has pointed out that the participation in a collaborative process brings benefits to the involved entities. On the basis of these expectations are, amongst others, the following factors: sharing of risks and resources, joining of complementary skills and capacities, access to new / wider markets and new knowledge, etc [START_REF] Abreu | Understanding Social Capital in Collaborative Networks[END_REF].

In fact, there is an intuitive assumption that, when an enterprise is a member of a long-term networked structure, the existence of a collaborative environment enables the increase of knowledge production as well as the transfer of knowledge, and thus the enterprises may operate more effectively in pursuit of their goals. However, in spite of this assumption, it has been difficult to prove its relevance due to the lack of models that support mechanisms that explain the production and transfer of knowledge in collaborative environment. Furthermore, the absence of indicators related to knowledge transfer -clearly showing the amount of knowledge transferred and the impact of this knowledge at a member level, for instance, in terms of capacity for generating new ideas, processes and products, organizational improvement through the combination of the existent resources, and diversity of cultures and experiences of other enterprises -might be an additional obstacle for a wider acceptance of this paradigm.

This paper discusses the nature of knowledge transfer as a contribution to a future identification of a set of indicators that are suitable for collaborative networks. This work aims at contributing to answer the following main questions:

• How is knowledge transferred from one network member to another?

• What are the factors that facilitate or constrain knowledge transfer in collaborative environment?

Knowledge Production and Transfer in Collaborative Networks

Upon reviewing the international literature, we find many studies highlighting the societal importance of innovation and knowledge within modern economies. CASTELL's [START_REF] Castells | A era da informação: economia, sociedade e cultura, Vol I. A sociedade em rede[END_REF]. "Network Society" or SOETE's [START_REF] Soete | A Knowledge Economy Paradigm and its Consequences[END_REF]. "Knowledge Economy" are highly regarded concepts, but we could mention other interesting works from Toffler [START_REF] Toffler | A terceira vaga, Lisboa, Edições livros do Brasil[END_REF], Bell [START_REF] Bell | The coming of postindustrial society[END_REF], or Giddens [START_REF] Giddens | The Consequences of Modernity[END_REF]. Knowledge always played an important role in the economy. But only over the last few years has its relative importance been recognised, just as that importance is growing. However, the stock of knowledge upon which economic activity is based today is definitely much larger than in previous eras. In the emergent economy and society, the accumulation of knowledge becomes the main motivational strength towards growth and development [START_REF] Gosman | Innovation and Growth in the Global Economy[END_REF], [START_REF] Maskel | Localized learning and industrial competitiveness[END_REF], [START_REF] Urze | Networked R&D Units: Case Studies on Knowledge Transfer Processes, Adaption and Value Creating Collaborative Networks[END_REF].

Actually, the last decades have shown a generalised concern about the study on how companies create knowledge and, particularly, on how they operate this transference. Knowledge is recognised as a principal source of economic rent, and the effective management of organizational knowledge has increasingly been linked to competitive advantage and is considered critical to the success of the business firm. One of the distinctive features of the knowledge-based economy is the recognition that the diffusion of knowledge is just as significant as its production, leading to increased attention to "knowledge distribution networks" and "national systems of innovation". These are the agents and structures which support the advance and use of knowledge in the economy and the linkages between them.

In this line of thought, Gibbons et al. [START_REF] Gibbons | The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies[END_REF] introduce a distinction between Mode 1 knowledge production, which has always existed, and Mode 2 knowledge production, a new mode that is emerging alongside it and which is becoming more and more relevant. While knowledge production used to be located primarily at scientific institutions (universities, government institutes and industrial research labs) and structured by scientific disciplines, its new locations, practices and principles are becoming much more heterogeneous. Mode 2 knowledge is produced in different organizations, resulting in a heterogeneous practice. The potential sites for knowledge production include not only the traditional universities, institutes and industrial labs, but also research centres, government agencies, think-tanks, and high-tech spin-offs. Mode 2 refers to a production of knowledge which is not exclusively reserved for qualified academic research but focuses on the different actors integrated in a contextualised problem-solving oriented process. The importance of knowledge is then assessed by its social value and interest to stakeholders engaged in the process of production.

Five main features of Mode 2 summarise how it differs from Mode 1. First, Mode 2 knowledge is generated in a context of application; Mode 1 knowledge can also result in practical applications, but these are always separated from the actual knowledge production in space and time. A second characteristic of Mode 2 is transdisciplinarity, which refers to the mobilisation of a range of theoretical perspectives and practical methodologies to solve problems. Transdisciplinarity goes further than interdisciplinarity in the sense that the interaction of scientific disciplines is much more dynamic. Theoretical consensus cannot easily be reduced to specific scientific parts. Thirdly, Mode 2 knowledge is produced in a diverse variety of organisations, resulting in a very heterogeneous practice. The potential sites for knowledge generation include not only the traditional universities, institutes and industrial labs, but also research centres, government agencies, think-tanks, high-tech spin-off companies and consultancies. These sites are linked through networks of communication, and research is conducted in dynamic interaction. The fourth feature is reflexivity. It means that researchers become more aware of the societal consequences of their work ('social accountability'). Sensitivity to the impact of the research is built in from the start. Novel forms of quality control constitute the fifth characteristic of the new production of knowledge. Traditional discipline-based peer review systems are replaced by additional criteria of economic, political, social or cultural nature.

In Mode 2, research is carried out in the context of application in which there is a continuing dialogue between interested parties -including producers and users of knowledge -from the beginning. Thus, the concept of knowledge transfer has to be reconsidered. It cannot be understood as a simple transmission of knowledge from the university to the receiver. The participants may include business people, venture capital, industry, research centres and many others in addition to the university. In short, all need to become actively engaged in the process of knowledge production and its transfer.

Figure 1 illustrates the two modes (I, II) of knowledge production and its transfer taking as environment the collaborative networks.

The purpose of the next section is to define a knowledge transfer model to be used in the context of collaborative networks.

A Model to Assess the Knowledge Transfer

In order to analyse and understand the processes and mechanisms of knowledge transfer in a systematic way, it is necessary to develop a model that deals with the complexity inherent to this kind of phenomena. As starting point, the aims of the proposed model are: To understand the running of an active collaborative network; to create a common reference framework; to serve as a basis for "what-if" analyses; and to motivate changes in the operation process of the network.

Based on the literature [START_REF] Gibbons | The New Production of Knowledge: The Dynamics of Science and Research in Contemporary Societies[END_REF][START_REF] Forzi | A Framework for the Analysis of Knowledge Management within Distributed Value-creating Networks[END_REF], and taking into account the context of collaborative networks, as a first approach, the model proposed includes the following perspectives:

• Transfer mechanisms -This perspective focuses on the identification and characterisation of distinct ways of "physical" interrelationship that support the process of knowledge transfer between enterprises within a network, such as internal publications, external publications, reports, patents, exchange of resources between organizations, log of good practices (lessons learned), repository of information (infrastructure dedicated), e-mail, videoconferencing, infrastructure to support collaborative processes (e.g. workgroup tool), telephone / mobile phone, informal meetings, and periodic meetings. • Nature of the relationships -The nature of the relationships determines the way collaborative space enables or facilitates the flow of knowledge among enterprises. Thus, this perspective focuses on the identification and characterisation of the various types of relationships that enterprises may have with other enterprises within the network: the relationships with new enterprises created from existing enterprises that belong to the network (e.g. spin-offs and start-ups) and also the relationships between the network as a whole and external entities (e.g. suppliers, customers, end-users, competitors, external institutions, and potential new partners).

Figure 2 illustrates the proposed model for the analysis of knowledge transfer in the context of network organizations.

Knowledge Transfer Assessment Using the Proposed Model

Methodology

The research is based on one case study pointed to the largest Portuguese motorway 1 is based on two main projects developed by Brisa, namely E_TOLL -Electronic Tolling System, a self-service toll lane where it is possible to pay by bank card or cash, and ALPR -Advanced License Plat Recognition, an enforcement system based on the automatic license plate recognition for situations where the vehicle is not equipped with an on-board-unit (OBU) or the OBU fails to electronically identify the vehicle.

Brisa identified E_TOLL and ALPR as the projects that contribute the most to the return on investments. It means that they were relevant in terms of innovation and created value to the company. These were the criteria for choosing E_TOLL and ALPR as pilot projects. On a first stage, companies and other institutions (technology centres, universities) involved in the projects were contacted and invited to cooperate with our research. Empirical data stems from two main sources: in-depth interviews (the basic tool for qualitative research on social systems) conducted with key participants belonging to the network, and a brief survey (for quantitative data) applied to participants by using a social network analysis. The involvement of various partners in the network is critical in order to foster a spirit of openness and cooperation in this fundamental process.

Brisa Case Study

The Brisa company currently operates, on a concession basis, a network of eleven motorways, with a total length of around 1096 km, constituting the main Portuguese road links. Given its importance and dimension, Brisa owns several companies specialising in motoring services aimed at improving the quality of the service provided to customers and increasing its own operating efficiency. The Brisa coinnovation network is a long-term collaborative network (a VBE) that has more than 30 members from several domains and business activities (e.g. research institutions, universities, associations, governmental entities, start-ups, business angels, and suppliers).

Knowledge transfer mechanisms

This section aims to discuss a main question: "how is knowledge transferred from one enterprise/partner to another?" considering the preliminary data related to the knowledge transfer mechanisms resulting from a survey applied (table I) to Brisa network partners. From the results, one can observe that the mechanisms most used by the Brisa network are the e-mail followed by the informal meetings, formal periodic meetings, telephone and exchange of human resources between organisations. The exchange of human resources in particular, when coming from industry and integrating research groups, was mentioned as a valuable collaboration strategy. On the other hand, the least used mechanisms are the video conference and other specialised infrastructures to support the collaborative processes (e.g. workgroup tool).

According to the results, the knowledge exchange among the enterprise members of the studied network is not based on much too sophisticated technologies. As argued by the manager of one enterprise partner, the Brisa network could improve the sharing of knowledge by using technologies specifically oriented for collaborative networks. In general, the interviewed partners were unanimous about the idea of an existing open network in terms of knowledge sharing, although some of them referred that the knowledge transfer process could be enriched by the use of advanced tools.

Competences Management

This section addresses the competences identified within E-TOLL and ALPR projects based on the information gathered through questionnaires.

From the sub-areas mentioned by the partners, a set of categories were created in order to structure a range of competences (from C1 to C25), making up this collaborative network within the projects under study. The resulting map shows that the partners hold a broad number of competences ranging from computer vision (C1), integration of systems (C5), software development (C14), Remote Monitoring (C18) and Electronic Toll Collection (ETC) systems (C20) to Plastic Injection (Industrial Design, (C8) and Development of Moulds (C9).

The following figure shows the competences used by each partner in the collaborative projects. The adoption of a graphical visualisation of competences provides a tool to analyse in detail the 'sub-structures' that may be present in a collaborative network. In Figure 3a, the node size of the enterprises/organizations represents the sum of competences used in collaborative projects (E_TOLL -Electronic Tolling System, and ALPR -Advanced License Plat Recognition), and the node size of the competences represents the level of abundance of each competence in the network during the execution of collaborative projects. Hence, during the execution phase of the projects, for instance, the most versatile enterprise is E6, as it is the one with the greatest number of distinct competences, followed by E4 and O1. On the contrary, E5 and E3 are the institutions that individually contribute with only one specific competence to the project. On the other hand, according to the competences perspective, it is possible to confirm that competences C2 and C4 are the most common in this network. Additionally, there are some partners that are the only ones to hold unique competences, which give them a powerful position inside the network. The node size of the enterprises/organization in Figure 3b represents the sum of competences used in the projects and the new competences achieved resulting from the collaborative project. The node size of the competences represents the level of abundance of each competence in the network at the end of the projects. Therefore, at a macro level, by looking at competence nodes it is possible to identify the emergence of new competences, such as: C21, C22, C23, C24 and C25. In addition, at a member level, it is also possible to identify the dissemination of competences among members of the network, for instance enterprise E2 owns two new competences: C3 and C4. One can observe that almost all organisations and companies held more competences after being involved in the projects, for instance: the E2 increased the number of competencies, as depicted in figure 3a. The gains of organizations and companies are visible by comparing the two scenarios (figure 3a and3b). When considering the Mode 2 knowledge production features, another interesting result is related to the competences held by universities (O1 and O2) in the sense that production and knowledge transfer involves all partners and universities receive competences from companies and vice-versa. It is a positive sum game. The collaborative work seems to be a privileged way of combining competences and integrate specific knowledge from different sources. Knowledge results from a great variety of organizations and institutions, and is heterogeneous in terms of the skills and experience people bring to it.

Nature of the relationships

In order to analyse the nature of the relationships, as illustrated in Figure 4a, 4b, on a scale from 1 to 10, the following aspects were assessed: -Frequency of contacts -measuring the number of business contacts between network members over time.

-Intensity of contacts -measuring the strength of business contacts in terms of lifespan (time) over time. Upon analysing these charts, we identify the existence of an increase in terms of both the frequency of contacts and the intensity of contacts over time among different members of the network. Considering this, those two variables can be viewed as a measure of the involvement capacity of network members; also, in a co-innovation network, the collaborative processes are mainly based on knowledge transfer, and thus it is possible to infer that the knowledge transferred between partners has increased over time.

Furthermore, since in a co-innovation network the pattern of linkages (knowledge transfer) between network members determines the configuration of the network structure, the position of enterprises within the network might be relevant to understand the role of each enterprise in the process of knowledge transfer. Based on this approach, a useful tool to analyse the knowledge transfer in detail can be obtained by applying several concepts from the Social Network Analysis area and relating these to mechanisms / processes of knowledge transfer.

However, the development of indicators related to knowledge transfer based on concepts from Social Network Analysis, to clearly show the amount of knowledge transferred and the impact of this knowledge at a member level, requires further research and development.

Conclusions

Summing up, it is referred by partners that knowledge transfer mechanisms could be improved by the use of more sophisticated technologies. Furthermore, the interview narratives point out that it will be important to promote useful tools to manage knowledge sharing among member partners. On the other hand, the results show increasing frequency of contacts as well as its intensity over time. Additionally, the network incorporates an extended list of competences that are shared within the work projects. One important aspect in terms of competence transfer is the mobility of Reaching a better characterisation of the nature of knowledge production and transfer in co-innovation networks is an important element for a better understanding of the behavioural aspects and also for improving the sustainability of this organizational form.

The development of a set of indicators to capture and measure the knowledge transfer can be a useful instrument to the manager of this network, as a way to support the promotion of collaborative behaviours, and for a member to extract the advantages of belonging to a network. However, the development of practical indicators to analyse the knowledge transfer requires further work.

Fig. 1 -

 1 Fig. 1 -Production of knowledge environment 1A) Mode I and 2B) Mode II

•

  Competences Management -This perspective addresses the principles, policies, and governance rules that may facilitate or constrain the processes of creating the competence and searching for competences by the members of the network. Therefore, general issues such as definition of accessibility levels (e.g. public, internal to network members or private), definition of policies in terms of competence dissemination among members of the network, definition of principles to assure the transparency and traceability of the competences in the network, definition of a competence taxonomy (e.g. market , ICT , management, manufacturing), levels of importance (e.g. central or marginal), time aspects (e.g. historical or current), and definition of rules in terms of Intellectual Property rights (IPR) (e.g. confidential or non-confidential) are considered here.

Fig. 2 -

 2 Fig. 2 -Knowledge transfer model

1

  The present results are based on research work developed under the ongoing project -CoRe -Competências de I&D para a Criação de Valor na Rede Brisa, FCT/UNL, BRISA, ISEL/IPL, 2011-2012.

Fig. 3a -

 3a Fig. 3a -Competences used by each partner in the collaborative projects.

Fig. 3b -

 3b Fig. 3b -Competences held by each partner in the end of the collaborative projects.

Fig. 4a -

 4a Fig. 4a -Frequency of business contacts over time (1-low and 10-high frequency) Fig. 4b -Intensity of business contacts over time (1 -spot and 10 -long-term)

  among partners. Participating partners valued the exchange of people as strategy to improve competences. As argued by the CEO of BIT (Brisa Innovation and Technology), this mobility is a relevant added value in terms of knowledge and competence transfer.

Table 1 -

 1 -Mean (based on a scale of 1-low to 10-high) for each type of transfer mechanisms identified

	Transfer mechanisms	Mean
	Internal publications	3.8
	External publications	2.9
	Reports	3.8
	Patents	2.6
	Exchange of resources between organizations	6.1
	Log of good practices (lessons learned)	5.1
	Repository of information (infrastructure dedicated)	4.0
	E-mail	7.0
	Videoconferencing	1.3
	Infrastructure to support collaborative processes (e.g. workgroup tool)	1.3
	Telephone / mobile phone	6.5
	Informal meetings	6.9
	Periodic meetings	6.5
	Other	4.5

Acknowledgments

This work was partially supported by BRISA Innovation and Technology (BIT) through a research and development project.


Frank Hilbert
email: frank.hilbert@tu-dresden.de

Raimar J Scherer
email: raimar.scherer@tu-dresden.de

Context-specific Multi-model-template Retrieval

Keywords: Context model, Multi-Model, Project Collaboration Ontology

Although the construction industry, in contrast to the stationary industry, is characterized by One of a Kind fabrication, there can be found similarities in project, product, process and organizational structures. The focus of this paper is to identify relationships, roles and model types, which are often needed in a specific project context and summarize these similarities by the use of a collaboration ontology providing for Multi-Model logistics. Reference models on the one side and a model to describe the project context on the other side should ensure that all partners retrieve precisely situation-specific selections of the application models which are necessary for their tasks. Furthermore, a pilot scenario is presented, which evaluate this approach on an Azure Cloud collaboration platform. As a result, we obtain an approach, which allows simplified handling and reuse of complex project-, product-and collaborationmodels in order to support the collaboration within a virtual Organization in Construction Industry.

Introduction

In History of the construction industry, always separate independent organizations of different domains and disciplines joined together in various short-term forms of organisation to combine their core competencies for handling of large and complex construction projects [START_REF] Binding | Construction in the Middle Ages[END_REF]. To allow a collaborative partnership with cooperative aspects, it is necessary to set up transparent rules for cooperation with clearly defined competencies, rights and duties of the participants, therefore various project-specific organizational, process and product models are necessary. Organizational models have evolved from tayloristic, hierarchical organizational structures towards to new forms of organization, like Virtual Organization, Collaborative Networked Organization or VO Breeding Environments as can found in [START_REF] Camarinha-Matos | A comprehensive modelling framework for collaborative networked organizations[END_REF]. For the definition of organizational roles in the building industry, there are several classifications developed, such as IFC-Actor roles [START_REF]buildingSMART: Specifications of the Industry Foundation Classes (IFC)[END_REF] and OmniClass [START_REF]OmniClass -A Strategy for Classifying the Built Environment[END_REF]. For the modelling of processes it is essential if there are predominantly material or information-transforming processes. For both process model types a variety of methods, languages and notations have been developed, here a detailed summary is given in [START_REF] Sharmak | Process Risk Management using Configurable Process Models[END_REF]. In our Work we focus on information transforming processes. To facilitate the representation and exchange of product information, data product models are an efficient way and in the course of time, they have evolved from the pure 2D drawings with small reuse value to rich building information models (a detailed summary is given in [START_REF] Weise | Ein Ansatz zur Abbildung von Änderungen in der modell-basierten Objektplanung[END_REF]). The current trend is concerned with modeling of implicit dependencies between different and inhomogeneous elementary models in the form of so-called Multi-Models (see chapter 2.2).

Although AEC is typically characterized by an One of a Kind fabrication, there can be found similarities in organizational, process and product structures, because a construction process is a repeated but mostly not identical course of action, while a construction product is a unique composition of common construction processes [START_REF] Sharmak | Process Risk Management using Configurable Process Models[END_REF]. Similarities can be found by comparison of model metadata. Therefore the focus of this paper is the use of such implicit similarities to support project collaboration. With the help of ontologies dynamic entity centric context models [START_REF] Abowd | Towards a Better Understanding of Context and Context-Awareness[END_REF] (actor, process and product context) should be generated, which summarize the information obtained by crosslinking the metadata. It is expected that with the help of such a context model significantly more context-specific collaboration possibilities can be determined.

In chapter 2 the developed project collaboration ontology is described and the generation of the context model is presented in Chapter 3. The last paragraph summarizes the results and gives a short outlook.

Project Collaboration Ontology

The Fundamental idea of the Project Collaboration Ontology is a generic formal description of organisation, process and product information to enable the utilization in a framework of integrated processes. Thereby Collaboration includes all the process and product related activities between entities (actors, processes and products), whose common goal is the creation of a product (or service) [START_REF] Altmann | Kooperative Softwareentwicklung -Rechnerunterstützte Koordination und Kooperation in Softwareprojekten[END_REF]. Basic elements of collaboration are coordination; communication and cooperation (see also [START_REF]Unterstützung flexibler Kooperation durch Software: Methoden, Systeme, Beispiele[END_REF]). For the representation of the entities we orient on the Semantic Web approach [START_REF] Davies | Towards the Semantic Web: Ontology-Driven Management[END_REF] and expand all entities with descriptive, machine-readable metadata, organized in ontologies, which are defined in [START_REF] Studer | Knowledge Engineering: Principles and Methods[END_REF] as "a formal, explicit specification of a shared conceptualization". This allows automated interpretation, evaluation and processing of information and enables us to extract the implicit knowledge, which can represent similarities. Thereby Ontologies stores concepts as well instances [START_REF] Baader | The Description Logic Handbook : Theory, Implementation and Applications[END_REF]. The concepts consist of classes of descriptive properties with cardinality, transitivity and symmetry qualities. Each property has a definition range and a set of values, distinguishing here between data type properties, representing simple Data types and class properties, which map classes to other and can be organized in hierarchical structures. Ontology containers can be evaluated through description logic algorithms used by Reasoner, for example RACER [START_REF] Haarslev | RACER System Description[END_REF] or JESS [START_REF] Friedman-Hill | The Expert System Shell for the Java Platform[END_REF]. Another important advantage is the possibility to integrate other ontologies. Figure 1 illustrates the structure of the used Project Collaboration Ontology integrating various ontologies necessary for the con-text determination, as as described below. If several Organizations joining collaboration the same underlying concepts and terminology has to be used. The Purpose of the Construction Core Ontology on the top is to define concepts and relationships between them, used in the construction domain. More information about this core ontology can be found in [START_REF] Schapke | Representing project information spaces based on semantic multi-model annotations[END_REF].

(1) The scope of the Organisation Ontology is describing the actors, rules of their collaboration as well as roles and permissions of a virtual Organization in an organizational model. In our approach, we use an organisational model, which closely follows the IFC standard [START_REF]buildingSMART: Specifications of the Industry Foundation Classes (IFC)[END_REF] mapping the competences of involved partners to roles, which consist of permissions on product model types (e.g. multi-model types, defined in the construction core ontology). Such permissions may also be required for the execution of processes. The distinction between potential roles (competences) and current user roles, as described in [START_REF] Hilbert | Multi-model-based Access Control in Construction in[END_REF] will be neglected here.

(2) Reusable Processes can be depicted as Configurable Reference Process Models (CRPM) as described in [START_REF] Sharmak | Process Risk Management using Configurable Process Models[END_REF]. In our scenario we use Business Process Modelling Notation (BPMN) [START_REF]Object Management Group: Business Process Model and Notation (BPMN)[END_REF] for the representation of common reference processes, which specify used resources, produced results and necessary permissions. Such Reference Processes are stored in the Process Ontology and can have a large number of attributes. Important for our scenario is the description of in-and output models as well as necessary actor-permissions.

(3) The basic idea of the Product Ontology is the description of product model instances and templates for a better assignment of actors and processes. Eessential with the use of product models are the knowledge of their dependencies. For this requirement in the German research project Mefisto [START_REF]MEFISTO: Management -Führung -Information -Simulation im Bauwesen. German BMBF research project (BMBF Project 01IA09001[END_REF] the multi-model approach was developed, which provides the externalization of implicit relationship of semantically and structurally inhomogeneous models. The Multi-Model was transferred in a multimodel container (MMC) which contains a generic link model, describing the relationships between models elements together with the application models, described by metadata (more information is given by Fuchs in [START_REF] Fuchs | Formal Description of a Generic Multi-Model, Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)[END_REF]). Reference models can be described by multi-model templates (MMT), which consist of partly filled MMC with metadata about the required application models [START_REF] Scherer | Mefisto: A Model, Information and Knowledge Platform for the Construction Industry (in German: "Mefisto: Eine Modell-, Informations-und Wissensplattform für das Bauwesen[END_REF]. Considering the different skills and tasks of the involved actors, it is not necessary for them to know all product models in every technical detailing. Therefore MMCs may consist of a task depending set of various application models, described as MMT. Input models, required for the instantiation of processes and their output models are specified as multi-model templates.

3

The Derivation of the Context Model

Entity models cannot capture all the relationships between the entities of a construction project that affect collaboration processes. Some dependencies are beyond the scope of the reference templates (e.g. process patterns or MMTs), but can help to identify exceptions or conflicts. Therefore a context model to characterize the considered project situation is necessary, as described in [START_REF] Hilbert | Context sensitive access to multi-models[END_REF]. Several context models for every entity type can be considered. As example we show the generation of the actor-context model, which should specify the available collaboration options for an actor to instantiate processes, use existing models or create new models in the current state of all project entities. As the actor context model reflects the relationship information of this actor to other entities, their structure follows the main aspects of the derived entity ontologies. In our case, the actor context model consists of relationship information from this actor to processes, to multi-models and to other actors. The generation of the context model occurs in three steps (see Fig. 2) as described below: (1) Permission determination: To determine utilizable processes and product models it is necessary in a first step, to collect actor roles with their permissions. The explicit permissions are determined from the actor instance of the organization ontology. If roles are organized hierarchic (e.g. OmniClass Organizational Roles [START_REF]OmniClass -A Strategy for Classifying the Built Environment[END_REF]) so explicitly assigned roles (e.g. 34-251100 Space Designer) includes also implicit roles (e.g. 34-250000 Interior Designer). same can apply for permissions: From a write permission can be derived the read permission, which can be necessary to start a process. It should be noted that the permission accumulation of roles should correspond to specific conditions (e.g. Separation of Duty [START_REF] Ferraiolo | Role-based access control (rbac): Features and motivations[END_REF]). To generate the extended permissions list, the organization ontology is evaluated by an ontology Reasoner who can derives further facts, either from the description of the organisation structure as well as from the instance data.

(2) Resource determination: In the Resource ontology explicit process templates are specified by their metadata. The semantic and structure of these metadata can be used to evaluate similarities, dependencies and inheritance structures. In our example, we limit ourselves to four class attributes: Domain, Project Phase, Level of Detail and Status, whose structure and vocabulary is defined in the construction core ontology. In step two, the structure of the metadata can now be used to inflate the object space of the product model templates (MMT). For example, a MMT has the domain attribute "BIM.BES.LBS", project phase "tender preparation", the Level of Detail value "5" (fine) and the status "beta", it can shortly described as follows: MMT(BIM.BES.LBS, tender preparation, LoD 5, status beta). This template also implicitly corresponds to even more coarsely defined templates, as e.g. MMT(BIM, Tender, LOD 1, beta). In addition we can use the information about the included application models to determined potentially compassable multi-model Templates (without linkmodel). In this manner, the object space of the product model templates can be expanded with a "implicit hull". A subsequent filter uses the permissions identified in Step 1 as well as actor competences and capacity and collect readable and writable product model templates.

(3) Identifying usable processes: A similar procedure is applied in the evaluation of process templates. A Reasoner generates a transitive hull by the recombination of explicit processes from the process ontology. New implicit (in this case transitive) processes can occur, which have the same input model like the first process and the same output model as the last process. The enlarged object space is reduced again by a filter using the previously determined readable (input) and writable (output) models.

If we connect in a last step, the process templates with the available input model instances, we obtain (for the current project context) all processes which can be used and all product models, which can be created. Thereby, if implicit models are potential input models for process templates, they may also increase the number of executable processes. Therefore, iterations are worthwhile.

As result a rich actor-context model is created with added further elements by the use of Ontologies. In a similar manner, the context models for the consideration of processes (collect usable actors and product models) and product models (shows authorized actors and executable processes) can be determined.

Results, Pilot and Outlook

By the use of the context model it is possible to identify similarities as well as transitivity's to determine on the basis of explicitly specified processes further implicit processes and suitable implicit models in the form of an "implicit envelope". It turns out that with the use of additional context information it is possible, to improve the selection of proportionate templates either qualitatively (by selection) and quantitatively (by the generation of the implicit envelope). So a context-dependent prediction is possible, whether a specified process can be started or a special model is available (or can be generated) in a current situation of the project. As a result, we obtain an Approach for generating rich context models, which allows the use of structural similarities and dependencies of product models within processes. Using semantically described task and model templates on the one side and a rich context model describing the project context on the other side should ensure that all partners receive a situation-specific selection of process and model templates which are necessary for their task and reflect the project situation at the current moment. The use of the presented approach is being evaluated in the German BMBF research project Mefisto aims at overcoming client-contractor interoperability problems in construction processes based on partnership [START_REF] Schapke | Mefisto -A multi-model-based platform for construction project management[END_REF]. The overall architecture of the Mefisto collaboration platform is a hybrid SOA-based system on a Microsoft Azure cloud environment [START_REF]Microsoft Azure[END_REF] with various platform services providing for the integration of local legacy applications, such as 3D CAD, scheduling, quantity take-off, ERP and PPM systems. So each project partner continues use its own familiar tools and environment meanwhile the communication with other partners is achieved by means of harmonised Multi-Model Containers, created and guided in process-centric manner by means of the project collaboration ontology. The use of the in Chapter 2 presented ontologies (jena framework) and Reasoner (jess Reasoner) are here encapsulated as methods of platform services for horizontal and vertical information integration. In a pilot scenario for bidding processes as described in [START_REF] Schapke | Mefisto -A multi-model-based platform for construction project management[END_REF] MMTs for tenders and offers are exchanged and evaluated with potential suppliers. Due to the shortness of the paper, most of the addressed themes could only be briefly highlighted. Details can be found the technical reports of the Mefisto project.

Several Approaches for collaborative Networks exist, especially for virtual enterprises, professional virtual communities or collaborative virtual laboratories [START_REF] Camarinha-Matos | A comprehensive modelling framework for collaborative networked organizations[END_REF]. Plisson present in [START_REF] Plisson | An ontology for virtual organization breeding environments[END_REF] an Ontology for virtual organisation breeding environments (VBE) focused on organisational structures. He uses actor and role concept without a context-and domain-specific relation.

The focus of this paper is not the invention of a new organizational model, but rather the integration of contextual and model-structure information in a collaboration structure. In the presented approach, only a reduced set of entity attributes was considered. By using data mining techniques it is possible to consider complex dynamic attribute sets. Also, we have only used the entity ontologies to determine the context model. Here the consideration of further (Project and World) factors is necessary. This approach can also be used for optimization purposes, for example, to define model templates that satisfy reasonable process orchestration, or to create Roles which have accurately privileges identified in the transitive processes -so that as few as possible Roles are needed. In this paper, we focus on information-transforming processes in a project planning phase, but the approach can be adapted for use in subsequent phases -including material transforming processes.

Fig. 1 .

 1 Fig. 1. Project Collaboration Ontology (PCO)

Fig. 2 .

 2 Fig. 2. Context model determination in three steps

Acknowledgments

The research described in this paper was enabled by the financial support of the German Ministry of Education and Research, Department of ICT under Contract No. 01|A09001A, which is gratefully acknowledged. Moreover, the authors would like point out that the results presented have been developed in collaboration with industry and academic partners (www.mefisto-bau.de) of the Mefisto project.


John Krogstie
email: krogstie@idi.ntnu.no

Modeling of Digital Ecosystems: Challenges and Opportunities

Digital ecosystems' is a metaphor inspired by natural ecosystems which describes a set of distributed, adaptive, and open socio-technical systems.

Being parts of such ecosystems, individual persons, public and private organisations are becoming increasingly dependent on each other. When such cooperation moves beyond simple buying and selling of goods and well-defined services, there is a need for a flexible infrastructure that supports not only information exchange, but also collaborative knowledge creation, evolution and sharing across a number of cooperation and collaborative networks that traditionally work in a bottom-up and rather improvised way. We will in this paper look at how techniques and approaches to modelling used e.g. for enterprise architecture and collaborative networks should evolve to support the development, support and evolution of digital ecosystems.

Introduction

All organizations are dependent on an application systems portfolio supporting its current and future tasks, and newcomers in any area are dependent on establishing a similar application portfolio quickly in a way that can evolve with changed business needs, technological affordances and expectations among co-operators, competitors and customers. An increasing fraction of the value creation in modern society comes from knowledge work using ICT. Such knowledge work is vital to meeting the grand challenges of today. As stated in the Digital Agenda for Europe [START_REF]A Digital Agenda for Europe[END_REF], "Smart use of technology and exploitation of information will help us to address the challenges facing society like supporting an ageing society, climate change, reducing energy consumption, improving transportation efficiency and mobility, empowering patients and ensuring the inclusion of persons with disabilities".

The current organization of knowledge work result in a waste of ideas, knowledge, and solutions, which are not put into use where they are developed, and not exploited by others. An approach to address this is using 'open innovation' [START_REF] Krogstie | Open Services Innovation. Rethinking your business to growth and compete in a new era[END_REF]. Open innovation will have to rely heavily on ICT, facilitating virtual communities of nomadic, human/ organizational actors, co-working on partially shared digital artefacts [START_REF]Software Architecture Knowledge Management -Theory and Practice Springer[END_REF]. The term digital ecosystem has recently been used to generalize such communities, with focus on that their actors constantly interact and cooperate with other actors in both local and remote ecosystems. Examples of digital ecosystems are communities for Creative Commons and Open Source (OSS), social media networks as in Facebook, blogs and around computer games, virtual organizations, or voluntary groups of citizen. Note that we use the term in a wider context than what is termed business ecosystem in the collaborative networks literature [START_REF] Camarinha-Matos | Taxonomy of Collaborative Networks Forms[END_REF] (a cluster or industry district).

A number of needs can be identified for supporting digital ecosystems. We will in this article discuss these issues highlighting the application and possible changed role of modelling techniques. In section 2 we describe the role of modelling in information systems development in general. In section 3 some traits of digital ecosystems are described, and section 4 describes a vision of the role of modelling in this landscape.

The Role of Modeling and Quality of Models

Information system modelling in general and modelling of collaborative networked organizations [START_REF] Afsarmanesh | The ARCON modeling framework[END_REF] is usually done in some organizational setting. One can look upon organizations and their information systems abstractly to be in a certain state (the current state, often represented as a descriptive 'as-is' model) that are to be evolved to some future wanted state (often represented as a prescriptive 'to be' model). These states are often modelled, and the state of the organization is perceived (differently) by different persons through these models. This open up for different usage areas of conceptual models as described e.g. in [START_REF] Nossum | Integrated Quality of Models and Quality of Maps[END_REF][START_REF] Krogstie | Model-Based Development and Evolution of Systems: A Quality Approach[END_REF]. 1. Human sense-making: The model of the current state can be useful for people to make sense of and learn about the current situation as it is perceived.

Communication between people [8]. 3. Computer-assisted analysis:

To gain knowledge about the situation through simulation or deduction, often by comparing a model of the current state and a model of a future, potentially improved state. 4. Model deployment and activation: To integrate the model of the future state in an information system directly. Models can be activated in three ways: a. Through people, where the system offers no active support. b. Automatically, where the system plays an active role, as in an automated workflow system. c. Interactively, the computer and the users co-operate on the process [START_REF] Krogstie | Quality of Interactive Models[END_REF]. 5. To give the context for a traditional system development project, without being directly activated. 6. Achieve acceptance of solution due to acting as a common ground 7. Quality assurance, ensuring that e.g. an organization acts according to a certified process achieved for instance through an ISO-certification process. SEQUAL (Semiotic Quality Framework) is a generic framework for assessing quality of models [START_REF] Krogstie | Process Models as Knowledge for Action: A Revised Quality Framework[END_REF][START_REF] Krogstie | Information systems engineering -Conceptual modeling in a quality perspective[END_REF]. The framework has earlier been used for evaluation of modelling and modelling languages of a large number of perspectives, including data, object, process, enterprise, and goal-oriented modelling. Quality has been defined referring to the correspondence between statements belonging to the following sets:

• G, the goals of modelling.

• L, the language extension, i.e., the set of all statements that are possible to make according to the rules of the modelling languages used. • D, the domain, i.e., the set of all statements that can be stated about the situation.

• M, the externalized model itself.

• K, the explicit knowledge relevant to the domain of the audience.

• I, the social actor interpretation, i.e., the set of all statements that the audience interprets that an externalized model consists of. • T, the technical actor interpretation, i.e., the model as 'interpreted' by tools.

The main quality types are:

• The deontic quality of a model relates to that all statements in the model M contribute to fulfilling one or more of the goals of modelling G, and that all the goals of modelling G are addressed through the model M. In particular, one include under deontic quality the extent that the participants after interpreting the model learn based on the model (increase K) and that the audience are able to change the domain D if this is beneficially to achieve the goals of modelling. • The goal defined for social quality is agreement among social actor's interpretations.

• Perceived semantic quality is the similar correspondence between the social actor interpretation I of a model M and his or hers current knowledge K of domain D.

• Pragmatic quality is the correspondence between the model M and the actor interpretation (I and T). One differentiates between social pragmatic quality (to what extent people understand the models) and technical pragmatic quality (to what extent tools can be made that can interpret the models). • Semantic quality is the correspondence between the model M and the domain D.

This includes validity and completeness. • Syntactic quality is the correspondence between the model M and L.

• Empirical quality deals with comprehensibility and predictable error frequencies when a model M is read or written by different social actors. • Physical quality: The main goal is that the externalized model M is physically available to the relevant social and technical actors for interpretation (I and T).

Characteristics of Digital Ecosystems

The long-term trend in ICT has been towards IT-systems being developed and evolved further and further away from the users of the system [START_REF] Davidsen | A longitudinal study of development and maintenance[END_REF]. We see a development in the direction of systems to a larger degree being supported by virtual communities of nomadic, human/organizational actors, co-working on partially shared digital artefacts [START_REF] Bosch | Architecture Challenges for Software Ecosystems[END_REF]. The term 'digital ecosystem' has recently been used to generalize such communities. Such systems are characterized by self-organization, scalability and sustainability, providing both economic and social value. Digital ecosystems are part of an even larger area called digital ecologies [START_REF] Vyatkin | Toward Digital Ecologies: Intelligent Agent Networks Controlling Interdependent Infrastructure[END_REF]. However, the existing digital ecosystems have limited scope, various degree of transparency, insufficient support for search and evaluation of useful quality artefacts, and none does fully support a wide range of shared artefacts from a wide range of actors. There are two main variants of digital ecosystems; content ecosystem and software ecosystems.

Content ecosystems are networks that deal with creation and sharing of artistic and intellectual artefacts. ICT have increasing impact on participative and democratic processes, and this impact will continue to grow with the increasing personalization, witnessed through the increase of social networking and user generated content and services. Internet already allows highly visual and multimodal interactions, and these interactions will become represented through richer means.

Software ecosystems are "a set of businesses functioning as a unit and interacting with a shared market for software and services, together with relationships among them. These relationships are frequently underpinned by a common technological platform and operate through the exchange of information, resources, and artefacts" [15]. See also work on software ecosystems for product families [START_REF] Bosch | Architecture Challenges for Software Ecosystems[END_REF], more general software systems [START_REF] Hunink | Industry taxonomy engineering: the case of the European software ecosystem[END_REF], and guidelines for using such ecosystems [15]. For instance within open source systems (OSS) a large number of co-evolved software components are freely available. The quality is variable and often poorly documented. Yet, many organizations now integrate OSS components into their own applications, and some also contribute back [START_REF] Hauge | Adoption of Open Source Software in Software-Intensive Industry -A Systematic Literature Review[END_REF]. Traditional customers -like municipalities -cooperate to provide improved e-services for their inhabitants. And end-users -even kids -are becoming their own developers.

To address combined digital content and software ecosystems, there must be substantial and concerted improvements of the state-of-the-art in three traditionally unrelated and partially isolated research areas. Enterprise architecture and enterprise modelling, new business models and data management

Modeling of Digital Ecosystems

The kind of modelling we are looking on in our work in particular applies to the first two areas above, i.e. enterprise and business modelling. Organizations are becoming less self-sufficient and increasingly dependent on partners and other actors, e.g. by outsourcing non-core activities. However, when such cooperation moves beyond simple buying and selling of goods and well-defined services, there is a need for a flexible infrastructure that supports both information exchange, knowledge creation, evolution and sharing across the different collaborative networks that tend to work in a bottom-up and rather improvised way. Within many organizations, it has become customary to develop enterprise architectures [START_REF] Lankhorst | Enterprise Architecture at Work[END_REF]. Ecosystem architecture takes the ideas of enterprise architecture to a higher level of abstraction, looking upon the support of a more fluid landscape of business actors providing and consuming services for information systems support in an organizational setting. In this way it extends the process perspective in BPM-in-the-Large [START_REF] Houy | BPM-in-the-Large -Towards a higher level of abstraction in Business Process Management[END_REF] to a wider setting. In also extend work in collaborative networks, such as ARCON [START_REF] Afsarmanesh | The ARCON modeling framework[END_REF]. ARCON offers an approach to model collaborative networks (CN), including: the CN life cycle dimension, the CN environmental perspectives dimension, and the CN modelling intent dimension.

A new approach to enterprise integration is needed. User-initiated software applications and enterprise mash-ups should be based on active knowledge modelling (AKM) and supports learning [START_REF] Lillehagen | Active Knowledge Modeling of Enterprises[END_REF]. For this to function, one must support more open business models. In this, one must consider financial success, sustainability, competition, copyright and licensing, and the impact on work processes, leadership, internal coordination, work processes, strategy and planning. The open innovation approach [START_REF] Krogstie | Open Services Innovation. Rethinking your business to growth and compete in a new era[END_REF] is often chosen as a basic cooperation mechanism. Companies should allow freer ("open") import and export of ideas and knowledge concerning products, processes and business models that flow between organization and their environments. Indeed, more openness will provide a larger set of possible business opportunities. Problems connected to IP and revenue sharing must be considered.

Furthermore, an open innovation strategy must be reflected not only in the business models, but also by revised behaviour (process practice) and in new thinking patterns. The SEM modelling language [15] attempts to analyze the business along customersupplier lines. Furthermore, the E 3 value model [21] describes value-generation and value-exchange among partners in a value network. Other relevant approaches are BMO [START_REF] Osterwalder | Business Model Generation[END_REF] and ARCON [START_REF] Afsarmanesh | The ARCON modeling framework[END_REF].

There are two main scenarios for the future use of modelling in this setting. What we term the steady state scenario, where modelling continues to be a somewhat esoteric activity for a limited number of experts is of course one possibility. The more optimistic scenario in our view is that abstraction techniques such as modelling are taken into use in an increasing number of areas, to make it possible to at all be able to manage this development. One striking aspect is that the number and variety of stakeholders that will need to relate to models of some sort will increase. Given the increased educational level in most countries, it is not unlikely that also more people will be able to relate to these types of abstractions.

Using the sets in SEQUAL, we predict the following under this scenario (in Fig. 1, the areas we foresee large changes are shown with stippled lines):

• G: The same list of goals and applications of modelling that is described in Section 2 will still be relevant, but emphasis on less formal, interactive approaches will increase to be able to support the more federated landscape needed to address digital ecosystems. • D: The range of relevant domains is increasing given that systems to an increasing degree ranges across and is expected to integrate a number of areas.

Business aspects must to a larger degree be integrated with more traditional enterprise aspects such as goals, processes, products, systems and data. • K: One need to deal with a more varied set of stakeholders, with a more varied set of skills and knowledge. Not only do you need to align IT-experts with business-experts, but also people across a large range of expertise, and across organizations, being used to express their knowledge in different notations. • L: Using domain specific modelling, the possibilities of tailoring the language to fit the domain, and the knowledge of the stakeholders have increased. To bring more people into (semi-) formal modelling these possibilities will have to be exploited to a larger degree. Thus rather than having a consolidation of modelling languages like the one done in object-oriented design with UML, there will be an increasing number of variants of modelling languages. We will also see a mix of richer media components being integrated with the more traditional "box and arrow"-conceptual modelling notations, thus supporting also richer meta-meta models defining and limiting the type of constructs to include in models. • T: An increasing number of tools will be available to extract model information from raw data, e.g. in the area of process mining and semantic web. In addition, tools for meta-meta modelling and meta-modelling will be more common.

• M: Models will be pervasively available being coordinated in a federated manner. Models will be across meta-levels in an increasing degree (compared to the models in traditional software engineering being primarily on the type level). Models, in particular interactive models [START_REF] Krogstie | Quality of Interactive Models[END_REF] will have a larger value in themselves, acting to a larger extend as knowledge commons and open models (http://www.openmodels.at/).

Fig. 1 SEQUAL with areas that is changing when modelling digital ecosystems

We believe the core dimensions of SEQUAL will be relevant for discussing also models used in digital ecosystems. On the other hand, a number of specializations might be envisaged. We will briefly discuss some main aspects here.

• Deontic quality: Models will be more important, due to increased dependencies across traditional organizational boundaries and needs to be handled in a more professional manner [8]. • Social quality: This will be important in smaller communities, and in interfaces between communities, but less needed across federation. Note on the other hand that since different stakeholder groups might see different views of the overall model, possible visualized in radically different ways, the effort to assure that they comprehend the models equally will potentially have to increase [START_REF] Krogstie | Process modeling value framework[END_REF]. • Pragmatic quality: Given that more types of stakeholders are involved, this is of increasing importance. Different techniques can be used for different types of stakeholder, supporting multiple views for different stakeholder types on the same model to ensure individual comprehension. • Semantic quality: The federated approach needed for modelling will bring new challenges as for how we look upon the semantic quality of the overall model. Whereas semantic quality in smaller domains would be followed up much as before (i.e. looking at the feasible (perceived) completeness and validity), one would to a larger degree need to be able to live with inconsistencies across federations. In connection to this, it would be important to be able to identify those aspects of the models across domains that need to be consistent. • Syntactic quality: Syntactic quality can be looked upon as trivial in a sense, since adherence can be enforced. On the other hand, one often sees that one extend languages with new aspects in an (not always conscious) attempt to turn semantic problems into syntactic problems. New tools based on meta-modelling makes this easier to do, and then makes in even more important to do right in the sense that one do not end up with too restricted languages. • Empirical quality: Support for empirical quality will be more built in, e.g. in tools that build up models from raw data in process mining, thus integrating information visualization tools and modelling tool. Different meta-meta models can necessitate rethinking guidelines for achieving empirical quality [START_REF] Nossum | Integrated Quality of Models and Quality of Maps[END_REF]. • Physical quality: Rather than being based on central repositories, more distributed, federated storage of model fragments must be available, utilizing standard interchange formats and supporting model mash-ups. What part of the total model that should be available for each partner must be addressed.

Concluding Remarks

From the above descriptions, we see that the technical challenges and opportunities with digital ecosystems give new challenges and opportunities for model-based techniques. In a way many of the core problems are not new. Even if the use of modelling needs to be extended and improved, general categories underlying discussions on quality of models as described in [START_REF] Krogstie | Process Models as Knowledge for Action: A Revised Quality Framework[END_REF] remains relevant, although need to be adapted to e.g. quality of interactive models [START_REF] Krogstie | Quality of Interactive Models[END_REF][START_REF] Lillehagen | Active Knowledge Modeling of Enterprises[END_REF]24]. We plan to pursue this work by working on case studies in selected domains including public sector, smartgrids and the petroleum industry to investigate more concretely how to extend the techniques described in section 4 for modelling of digital ecosystems.

Acknowledgements

This work is part of ongoing cross-disciplinary work on this area. I would in particular like to thank my colleagues Letizia Jaccheri, Jon Atle Gulla, Reidar Conradi, Tonje Osmundsen, Per Morten Schiefloe, Guttorm Sindre, Kjetil Nørvaag, Martha Molinas, and Alf Inge Wang in this regard.


Egon Lüftenegger
email: e.r.luftenegger@tue.nl

Paul Grefen

Caren Weisleder
email: c.weisleder@delagelanden.com

The Service Dominant Strategy Canvas: Towards Networked Business Models

Keywords: service dominant strategy, service dominant logic, business canvas, strategy model, service science

Service orientation, customer focus and collaboration between firms are profoundly changing the way of doing business. Marketing scholars are the first academics to conceptualize these changes under a new mindset, known as the Service Dominant Logic. However, management constructs are needed to apply this mindset to the business environment. Therefore, we have developed a new conceptual model of a Service Dominant Strategy with a visual representation in the form of a canvas. Our model is constructed by integrating current definitions of a Service Dominant strategy and by confronting them with traditional strategies. The model facilitates the design of Service Dominant strategies by answering the questions associated with fifteen elements. Experimental application of our approach in several industry domains shows the importance of both strategic level design and Service Dominant thinking.

Introduction

Service Science is an interdisciplinary area of study addressing the challenge to become more systematic about innovating in services. There is a strong industry and academic shift in interest towards services. However, most academics and industry professionals are still working under the manufacturing paradigm rather than the service paradigm [START_REF] Spohrer | The emergence of service science: Toward systematic service innovations to accelerate co-creation of value[END_REF]. This slow change has a negative impact on service innovation. The ability to change is constrained by the dominant logic of manufacturing, which is Goods Dominant. This issue that prevents the adoption of new ways of doing business is known as the dominant logic trap: the prevailing dominant logic act as a filter in a funnel that prevents the ideation and adoption of business concepts that do not fit with the current dominant logic [START_REF] Chesbrough | Open Services Innovation: Rethinking Your Business to Grow and Compete in a New Era[END_REF], [START_REF] Bettis | The Dominant Logic: Retrospective and Extension[END_REF].

A new innovative mindset that addresses this change towards a service dominant economy focused at the network level is known as the Service Dominant Logic (SDL) [START_REF] Vargo | Evolving to a new dominant logic for marketing[END_REF]. This theoretical foundation has been developed by marketing scholars and recognized as a key theory for the advancement of Service Science. However, this theory as originally stated is difficult to understand and communicate [START_REF] Lusch | Toward a conceptual foundation for service science: Contributions from service-dominant logic[END_REF]. Management constructs are needed to drive businesses development under the SDL. We need to focus on the organizational change and business models that will make service-oriented technologies and collaborative networks the main drivers to solve business problems. Hence, the strategy is the first management construct that we need to communicate this dominant logic change to decision makers. Nowadays, there is a lack of management tools developed specifically to design service dominant strategies. Current tools, like the Balanced Scorecard [START_REF] Kaplan | Linking the balanced scorecard to strategy[END_REF], have been constructed by using the manufacturing mindset in which improving the efficiency of the firm from an internal perspective is desired [START_REF] Sven | The tyranny of the balanced scorecard in the innovation economy[END_REF]. In today's complex and dynamic business environment, we need management tools that emphasize service orientation and networked collaboration from a service dominant perspective on doing business.

In this paper, we focus on a management tool that facilitates the design of strategies for the Service Dominant landscape. We have developed this conceptual model by reviewing existing research on SDL at the strategic level and by confronting it with traditional business strategies developed by business and marketing scholars. This approach has been chosen to have traditional strategic concepts acting as a bridge between the current dominant mindset and the new Service Dominant mindset. Our conceptual model takes the canvas approach as a visual representation to communicate and design. This approach has emerged from the Information Systems domain in academia and currently has been widely accepted in industry [START_REF] Osterwalder | Business Model Generation: A Handbook for Visionaries, Game Changers and Challengers[END_REF].

This paper is structured as follows: In Section 2, we identify and analyze the background research on SDL at the strategic level. In section 3, we establish a strategic bridge with traditional strategies to identify the elements of a Service Dominant strategy. In Section 4, we visualize the Service Dominant strategy and discuss the results of prototype application of the canvas in several industrial settings. We end this paper with conclusions.

Analysis of Background Research on SDL

In this section we study the literature in which a strategic view on the SDL is developed. We select the research by tracking the original authors of the SDL, and by searching Google Scholar using the keywords "service dominant strategy" and "service dominant strategic". Firstly, the "competing through service" research argues that to compete effectively through service, the entire organization should view the market and itself with a SDL [START_REF] Lusch | Competing through service: Insights from service-dominant logic[END_REF]. In this research, the SDL authors define derived propositions from the original foundational premises of the SDL as strategy. One of these derived proposition is "Firms can compete more effectively through the adoption of collaboratively developed, risk-based pricing value propositions".

Secondly, the "strategic service orientation" research is focused on the interactions with the customer [START_REF] Karpen | Linking S-D logic and marketing practice: Toward a strategic service orientation[END_REF]. The author defines his strategic approach on service by interactions. One of these interactions is stated as "Individuated interaction with an emphasis on understanding individual customers".

Finally, the "constructing a service dominant strategy" research complements the previous approaches by bringing the perspective of economics [START_REF] Järvesivu | Constructing a Service-Dominant Strategy: A Practice-Theoretical Study of a Start-Up Company[END_REF]. In this work the authors develop a Service Dominant strategy with a start-up company as real case scenario. In this research, the strategy is presented as goods dominant versus service dominant defined by statements like: "rigid versus flexible organizational boundaries". For our purposes, we take the service dominant aspect only: flexible organizational boundaries.

We identify as Service Dominant strategic statements the derived propositions of the SDL defined in the "strategic service orientation" research, the interactions defined in the "strategic service orientation" research and the service dominant aspects of "constructing a service dominant strategy" research. In Section 3, we use the background research to identify the elements of a Service Dominant Strategy. The complete list on the Service Dominant strategic statements is presented in [START_REF] Lüftenegger | The Service Dominant Strategy Canvas: Defining and Visualizing a Service Dominant Strategy Through the Traditional Strategic Lens[END_REF].

Bridging Service Dominant and Traditional Strategies

In this section, we identify a conceptual bridge between traditional strategies and the Service Dominant research efforts discussed in Section 2. This conceptual bridge aims to facilitate the communication of a Service Dominant strategy by using traditional strategic concepts. The conceptual bridge is being developed by analyzing five traditional strategies identified and classified in [START_REF] Stabell | Configuring value for competitive advantage: on chains, shops, and networks[END_REF] as being developed from business and marketing scholars. Firstly, within the strategies developed by business scholars we can distinguish: industry-based, competence-based and resource-based. Secondly, within the strategies developed by marketing scholars we can distinguish: market-oriented and relational marketing [START_REF] Stabell | Configuring value for competitive advantage: on chains, shops, and networks[END_REF].

We establish a conceptual bridge with three of the five traditional strategies. Firstly, we discuss why we discard the industry-based and market-oriented strategies. Secondly, we explain the how we establish a strategic bridge between the service dominant strategy and the competence-based, relational marketing and resource-based strategies.

Firstly, we discard the industry-based strategy as a strategic bridge; because Porter's approach is more suitable for the manufacturing mindset with the value chain approach rather the Service Dominant mindset and its value network focus [19]. Secondly, we discard the market-oriented strategy as strategic bridge, because the Service Dominant mindset focuses on the individual relationship with "the customer" rather than the market as a whole.

We establish below a strategic bridge with three traditional strategies: competencebase, relational marketing and resource-based. The strategic bridge is being developed by conceptualizing the Service Dominant strategy as business competences, market relationships and business resources elements. These elements are identified from the background research on the SDL depicted in Section 2. We illustrate with an example our line of reasoning on how we identify the elements for each strategic bridge. However, the identification process for all the elements is presented in [START_REF] Lüftenegger | The Service Dominant Strategy Canvas: Defining and Visualizing a Service Dominant Strategy Through the Traditional Strategic Lens[END_REF].

Business Competences. The competence-based strategic view suggests that to achieve competitive advantage firms should identify, seek develop, reinforce, maintain and leverage distinctive competences [START_REF] Hunt | The normative imperatives of business and marketingstrategy: grounding strategy in resource-advantage theory[END_REF]. Grant argues that resources are not a source of competitive advantage by their own. In line with this reasoning, capabilities are the source of competitive advantage. Hamel and Prahalad use the term "core competences" to describe the central strategic capabilities of a firm to achieve competitive advantage [START_REF] Grant | A resource-based theory of competitive advantage: implications for strategy formulation[END_REF]. Hence, competitive advantage can be achieved by distinctive competences. We can recognize distinctive competences as enablers of a Service Dominant strategy by answering the question "How do we enact our business relations in a Service Dominant business?" For example, we can identify the Co-creation and Coproduction business competence elements from the "competing through service" research by analyzing the Service Dominant strategic statement: "Firms gain competitive advantage by engaging customers and value network partners in co-creation and co-production activities". Furthermore, we can group the business competences within value and collaboration:

The Value group contains the elements related with our proposition to our primary stakeholders from the value-in-use and the pricing perspectives. Firstly, Co-creation is about what we are enabling as value-in-use by delivering solutions with our primary stakeholders. Secondly, Risk-based Pricing is based on transitive risk with our primary stakeholders in our network. Moreover, the pricing mechanism should be based on the risk of actors that are participating co-producing the solution.

The Collaboration group contains the networked competences that we need to establish with our stakeholders for doing business. Firstly, Co-production is about how we create with our stakeholders in a collaborative way. This co-production is achieved by including all the stakeholders in the production of our solution-centered approach defined as value-in-use. Secondly, Service Integration is about how and why we integrate the business processes between all the stakeholders involved in our collaboration. This service integration is achieved by enabling the composition and orchestration of business processes to achieve the best solution that maximizes the value-in-use of all our stakeholders. Thirdly, Knowledge Sharing is about how and why we need to share knowledge. Moreover, knowledge sharing is achieved by capturing, processing and distributing the information related with value-in-use with all our stakeholders. Market Relationships. The relational marketing strategic view suggests that to achieve competitive advantage, firms should develop a relationship portfolio with stakeholders such as customer, suppliers, employees and competitors [START_REF] Hunt | The normative imperatives of business and marketingstrategy: grounding strategy in resource-advantage theory[END_REF]. Competitive advantage can be achieved by distinctive relational approaches. Moreover, the shift of the SDL towards "marketing with" the customer implies a relationship. We can recognize distinctive market relationships as enablers of a Service Dominant strategy by answering the question "How do we relate with our business environment in a Service Dominant business?" For example, we can identify the Empowerment relationship element from the strategic service orientation" research by analyzing the service dominant strategic statement: "Empowered interaction with an emphasis on enabling customers to shape the nature and/or content or exchange". Furthermore, we distinguish between endogenous an exogenous market relationships:

The Endogenous group contains the inside-out relationship elements that start from inside the company to the outside world. Firstly, Contextually Individuated is about how we customize our relationship with the customer. This contextualization is achieved by understanding the needs of the customer that maximize the value-in-use. Secondly, Empowerment is about how we enable our collaborators to participate. The firm should facilitate the active role of the customer in the co-production process by taking customer input. Moreover, this empowerment should also be established with other stakeholders of our collaborative network.

The Exogenous group contains the relationship elements that we need to establish with the outside world. Firstly, Bidirectional is about how we communicate with the external parties. This interaction facilitates conversation and dialog by co-producing and co-creating with the customer. Secondly, Ethical Mutual Benefit is about how we share with our collaborators. This relationship is established by a mutual gain for all the actors in the business collaboration. Thirdly, Flexible Organizational Boundaries is about how we establish our collaborative network. This relationship is established by being flexible through the inclusion of multiple actors for the enactment of valuein-use. This relationship minimizes the barriers between firms to co-produce service offerings.

Business Resources. The resource-based strategic view suggests that a firm possess resources to achieve competitive advantage and superior long-term performance [START_REF] Hunt | The normative imperatives of business and marketingstrategy: grounding strategy in resource-advantage theory[END_REF]. The term resource is variously defined in the resource-based view literature. We take Grant's resource definition, because he distinguishes between resources and capabilities. A resource can be defined as the inputs or factors available to a company through which it performs its operations or carries out its activities [START_REF] Grant | A resource-based theory of competitive advantage: implications for strategy formulation[END_REF]. We identify business resources as enablers of a Service Dominant strategy by answering the question "What ingredients do we need to enact our Service Dominant strategy?" For example, we can identify the Employees business resource element from the "competing through service" research by analyzing the Service Dominant strategic statement: "Firms that treat their employees as operant resources will be able to develop more innovative knowledge and skills and thus gain competitive advantage". Furthermore, we can group the business resources within actors and infrastructures:

The Actors group contains the business resources who participate in the service dominant business. Firstly, the Customer is an actor that meets the profile of an active customer. The customer, as an individual rather than a group, participates by determining the value-in-use and co-producing the desired solution. The customer is the main stakeholder in the determination of value-in-use, because he is the actor that will use the solution. By involving the customer we can get knowledge related with their needs in an active manner. Secondly, the Partners are actors that meet the profile of an active partner. The partners participate in the co-production of the solution for the established value-in-use. Moreover, engaging network partners in co-production activities enable the firm to gain competitive advantage. Thirdly, the Employees are actors that meet the profile of an active employee that is willing to understand what is valuable for the customer. Employees are a source of customer knowledge and understanding. They participate in the co-production of the solution for enabling value-inuse.

The Infrastructures group contains what resources we needed to develop a service dominant business. Firstly, the Service Flows are the activities that define our valuein-use proposition. Service flows, acting as cross-organizational business process oriented to the customer, are needed to enable the collaboration and co-production. Secondly, the Information Technologies are the enablers that facilitate the collabora-tion and the enactment of our value-in-use proposition. Moreover, Information Technologies increase the likelihood of cross-organizational and customer collaboration.

4

The Service Dominant Strategy Canvas

In this section, we present our canvas as a management tool to facilitate the design of Service Dominant strategies by using the elements and categories identified in Section 3. We use the canvas approach, inspired by the success of the Business Model Canvas, which is used by practitioners due to its rich visual approach [START_REF] Osterwalder | Business Model Generation: A Handbook for Visionaries, Game Changers and Challengers[END_REF]. Figure 1, shows the resulting Service Dominant Strategy Canvas that facilitates the design of a service dominant strategy by answering the questions associated for each of the fifteen elements that we have identified and categorized before. As shown in Figure 1, the three rectangular main columns named market relationships, business competences and business resources are the strategic bridges between traditional and service dominant strategic concepts. Each main column has two groups represented by rounded rectangular boxes that contain the Service Dominant elements represented by a circular icon and a rectangular label with their associated question below. These questions facilitate the interaction and communication with the participants in a structured and active manner. As shown in Figure 1, the business competences column is located in the middle within the Service Dominant Strategy Canvas. We place this strategic pillar in the center to emphasize the value and collaboration groups. These two groups establish our strategic context for the understanding and design of a Service Dominant strategy.

We have tested our Service Dominant Strategy Canvas in three session with innovation managers and strategists within an information logistics company, an asset-based financial services provider and an international car leasing company. These industries currently are very asset oriented, making a perfect scenario to test the tool: a change in dominant logic is sought. The testing process is depicted as follows: firstly, we have presented the canvas with an explanation of all their elements using a well-known highly Service Dominant sample scenario. Secondly, we have asked the participants to design a Service Dominant strategy for their company by using the canvas. Each session was guided by a facilitator that knows the business of the company, the researcher who asked the strategic canvas questions and a senior manager for each industry setting.

As outcome of our qualitative research, we have three strategic canvases. From each of the sessions previously depicted we can conclude the following: in the three tested industry settings, we found that value-in-use explained by the co-creation element was an eye-opener for the participants. This element shifts the mindset from the focus of the good to the service that renders. After this mindset change, strategic canvas achieved successfully to communicate the collaboration perspective. In the case of the logistics industry, we observe that canvas facilitates the focus on a logistic experience where collaborators can participate in the transportation service. In the case of the asset-based finance industry, the mindset change is achieved by moving from asset centered activities towards the activities focused on the usage of the asset. Al last, in the car leasing industry the canvas facilitated the shift from cars toward a customer centric solution enabled by Information Technologies.

The participants were able to deal with the novel aspects of a Service Dominant Strategy. After the first session, we communicated the mindset change and we were able to guide the participants to answer the questions. However, we found out that we need more than just one session to refine the outcome of the filled canvas. This is explained due two main reasons. Firstly, the concept is new for the industry. Secondly, the numerous elements needed for conceptualization of a service dominant strategy. Furthermore, the service dominant strategy will be the basis for their future service dominant business models and service-oriented information systems empowered by collaborative networks.

Conclusions and Future Work

In this paper we present the Service Dominant Strategy Canvas as a management tool to facilitate the design of service dominant strategies. In our test sessions, the strategic tool appears to be an innovation catalyst for asset dominant companies towards service orientation. Innovation managers and strategists find the tool useful for defining future strategies. In a nutshell, the Service Dominant Strategy Canvas facilitates the understanding that solutions require multi-stakeholder perspective enabled by collaborative networks. This strategic perspective is constructed by taking a multidisciplinary approach on service by integrating business, marketing and information systems point of views. The presented canvas is the outcome of the development of the first layer of the Service Dominant Business Logic Framework. This framework is defined by four layers. Firstly, the Strategy layer is the long-term vision that recognizes the Service Dominant strategic paradigm. Secondly, we have the Business layer that takes the networked approach on business models by following the Service Dominant Strategy. Thirdly, we have Organization layer that focuses on the networked processes on the collaborative network. Finally, we have the Systems layer that is focused on a highly modular service-oriented architecture as enabler of our approach. Currently, we are working on the business layer by developing a tool to design Service Dominant Business Models.

Fig. 1 .

 1 Fig. 1. The Service Dominant Strategy Canvas.

Business Competences Market Relationships Business Resources Actors Infrastructures Collaboration Value Endogenous Exogenous Contextually Individuated How do we customize our relationship with the customer? Empowerment How do we enable our collaborators to participate? Bidirectional How do we communicate with external parties? How do we establish our collaborative network? Flexible Organizational Boundaries How do we share in our collaboration? Ethical Mutual Benefit Co-production How do we create collaboratively with our stakeholders ? Service Integration Knowledge Sharing Why and how do we need to share information? Service Flows What are the activities that define our value-in-use? Information Technologies Where will we enact our service dominant business? Co-creation What are we enabling as value-in-use? Risk-based Pricing How do we need to formulate our multi-party pricing Strategy for proposing value-in-use? Customer How does the customer participate within our business competences? Employees How does the partners participate within our business competences? How does the employees participate within our business competences? Partners Why and how do we integrate cross-organizational business processes?