Willy Picard
email: picard@kti.ue.poznan.pl

Simulating the Influence of Collaborative Networks on the Structure of Networks of Organizations, Employment Structure, and Organization Value

Keywords: employment structure, organization value, collaborative networks, job reallocation, simulation, clustering coefficient, small-world, network model

From the perspective of reindustrialization, it is important to understand the evolution of the structure of the network of organizations employment structure, and organization value. Understanding the potential influence of collaborative networks (CNs) on these aspects may lead to the development of appropriate economic policies. In this paper, we propose a theoretical approach to analysis this potential influence, based on a model of dynamic networked ecosystem of organizations encompassing collaboration relations among organization, employment mobility, and organization value. A large number of simulations has been performed to identify factors influencing the structure of the network of organizations employment structure, and organization value. The main findings are that 1) the higher the number of members of CNs, the better the clustering and the shorter the average path length among organizations; 2) the constitution of CNs does not affect neither the structure of the network of organizations, nor the employment structure and the organization value.

Introduction

Industrialization has given birth to an economic world in which manufacturing of goods and services is the base of the society. In many industrialized societies, major economic changes, such as globalization and specialization, have led to a shift from an industrial economy to a more service-oriented one. This evolution is referred to as deindustrialization. A commonly accepted definition of deindustrialization is "the decline in importance of manufacturing industry in the economy of a nation or area" [START_REF]Collins English Dictionary: Deindustrialization[END_REF]. Two processes are at work during deindustrialization [START_REF] Rowthorn | De-industrialization and Foreign Trade[END_REF]: on the one hand, specialization of organizations on their core competences increases their productivity. Next, this increase of productivity leads to a reduction of the quantity of human resources needed to create a given value. Therefore, less people are needed in the industry. On the second hand, the wages are superior in the service sector than in the industry. As a consequence, a reallocation of workers from the industry to the service sector may be observed. These two processes combined together lead to a decrease in the number of employees working in the industry, associated with a reduced number of industrial organizations. In parallel, there is a rise of the number of organizations and employees in the service sector [START_REF] Tregenna | Manufacturing Productivity, Deindustrialization, and Reindustrialization[END_REF].

It has been argued that deindustrialization has dramatic effect on many economic and social aspects of local, regional, national and transnational economies. Besides the rise of unemployment, deindustrialization leads to strong limitations with regards to innovativeness: the lack of industrial partners to design, develop, and prototype new products is a strong brake to innovation [START_REF] Cohen | Reindustrialization: From Shoes to Ships to Sealing Wax[END_REF].

Therefore, in many post-industrial countries, the process of reindustrialization is currently under scrutiny. Reindustrialization is the renewed development of the industrial sector. Although the idea of reindustrialization is not new, the rise of the economic importance of the People's Republic of China, especially with regard to the industrial sector, has given rise to a debate on the importance of reindustrialization.

The question of the importance of reindustrialization is often related to employment which is still at the level of 10.12% in 2010 in the Euro area, 8,18% among the G7 members, while at the level of 4.1% in the People's Republic of China, according to the International Monetary Fund [5]. In this context, there is a need for a good understanding of the variables that may influence the reallocation of workers. Pastore [START_REF] Pastore | Primum vivere… industrial change, job destruction and the geographical distribution of unemployment[END_REF] has studied the relation between the level of unemployment at the regional level and the reallocation rate in Italy. Worker reallocation in Canada have been studied by Morissette, Lu and Qiu [START_REF] Morissette | Worker Reallocation in Canada[END_REF], taking into account many variables, such as workers' age, organization activity sector, organization size. A similar study has been published by Liu about China [START_REF] Liu | Job and Worker Reallocation in China[END_REF]. Martin and Scarpetta [START_REF] Martin | Setting It Right: Employment Protection, Labour Reallocation and Productivity[END_REF] have a different approach to the question of employment and reindustrialization: they have addressed the question of the links between regulations for employment protection, workers' reallocation, and productivity. Bartolucci and Devicienti [10] have demonstrated that better workers are found to have a higher probability of moving to better firms. Gianelle [START_REF] Gianelle | Discovering the Regional Small World of Labour Mobility. Evidence from Linked Employer-Employee Data[END_REF] has studied in details the structure of workers reallocation in the Veneto industrialized region of the north of Italy. He has shown the importance of hub organizations, i.e., highly connected organizations bridging distinct local clusters of organization, for workers' mobility. In this work, connections among organizations represent reallocations of employees: each reallocation of an employee from an organization ܱ ଵ to an organization ܱ ଶ is represented as a link ܱ ଵ → ܱ ଶ between these organizations.

None of these studies have considered the potential influence of cooperation among organizations on the dynamics of employment. Specialization, which is one of the two pillars of deindustrialization, is also a key reason for the decision of some organizations to collaborate with other organizations. By collaborating with other organizations, creating a collaborative network (CN), on the one hand, organizations focus on their core competences and benefit from their competitive advantage, while, on the other hand, the group of collaborating organizations, i.e., the CN, is able to produce complex products or provide complex services. Intuitively one may expect a relation between the existence of CNs and reallocation: workers are probably more willing to move to organizations that they have already collaborated with, than to move to unknown ones. However, to our best knowledge, the relation between the existence of CNs and the dynamics of employment has never been studied.

In this paper, we take a theoretical approach to this question. We propose a model of the dynamics of employment in a networked ecosystem of organizations tied by their collaboration in CNs. We further use the model to run simulations to evaluate the influence of CNs, especially their size and their constitution, on the structure of the network of organizations, on employment and on the value of the organizations themselves

In Section 2, background on network structure and chosen metrics, i.e., clustering coefficient and degree distribution, is provided. Next, the proposed model is detailed in Section 3. In Section 4, the results of the simulation experiment are presented. In Section 5, the results are discussed. Finally, Section 6 concludes the paper.

Background on Networks

The word "network" refers to the notion of a set of interconnected objects, referred to as nodes, that may be material or immaterial. Networks are ubiquitous and have been the object of research in various disciplines, such as computer science, sociology, biology, and medicine to name a few [START_REF] Albert | The Diameter of the World Wide Web[END_REF][START_REF] Montoya | Small World Patterns in Food Webs[END_REF][START_REF] Eguíluz | Scale-Free Brain Functional Networks[END_REF]. It has been demonstrated that many of these networks share a set of common characteristics: their average shortest path is relatively low, therefore two nodes of the network are connected by a small number of links. Additionally in many networks, the immediate neighbors of a node tend to be connected to each other, i.e., their clustering coefficient is higher than in random networks. Such networks are referred to as "small-world networks" [START_REF] Watts | Collective dynamics of "small-world networks[END_REF][START_REF] Barabási | Emergence of Scaling in Random Networks[END_REF] and their structure and functioning have been the object of numerous works since Watts and Strogatz's article [START_REF] Watts | Collective dynamics of "small-world networks[END_REF] published in 1998. Networks have also been studied with regard to their dynamics. As an example, network percolation, with its potential application to explain disease epidemics and gossip propagation, has received significant attention [START_REF] Callaway | Network Robustness and Fragility: Percolation on Random Graphs[END_REF][START_REF] Doerr | Social Networks Spread Rumors in Sublogarithmic Time[END_REF][START_REF] Satorras | Epidemic Spreading in Scale-Free Networks[END_REF].

Among key characteristics of networks, the clustering coefficient, the scale-free and the small-world properties of some networks have been intensively scrutinized. The clustering coefficient is a measure of the tendency of nodes to connect to other nodes within groups of nodes. Formally, the clustering coefficient ܥ of a graph ܩ = (ܸ, )ܧ has been defined by Watts and Strogatz as follows: "Suppose that a vertex ݒ has ݇ ௩ neighbours; then at most ݇ ௩ (݇ ௩ -1)/2 edges can exist between them (this occurs when every neighbor of ݒ is connected to every other neighbour of .)ݒ Let ܥ ௩ denote the fraction of these allowable edges that actually exist. Define ܥ as the average of ܥ ௩ over all ".ݒ The higher the clustering coefficient, the higher the number of connected triplets of nodes. Scale-free networks are networks in which the probability that a randomly selected node has ݇ links, i.e., degree ݇, follows ܲ(݇)~݇ ିఊ , where ߛ is the degree exponent. In scale-free networks, a limited number of nodes have a large number of links (a large degree), while a large number of nodes have a small number of links (a small degree). Small-world networks are graphs in which the clustering coefficient is high and the average path length is small. In smallworld networks, a relatively small number of nodes separate any two of them, even if most nodes are not connected to each other.

Popular models of structure of networks are:

• the Erdős-Rényi model: this model is a random model in which the links between nodes are added in a random manner. Both clustering and average path length of Erdős-Rényi networks are low; • the Barabàsi-Albert model: in this model, a newly added node is connected to other nodes of the network, such that the probability to connect to a given node is proportional to the degree of this node. Barabàsi-Albert networks are scalefree and their clustering coefficient is higher than in Erdős-Rényi networks. More information about social networks analysis may be found in [START_REF] Scott | Social Network Analysis: a Handbook, 2nd edition[END_REF].

A Model of a Dynamic Networked Ecosystem of Organizations

Assumptions of the Proposed Model

The proposed model is based on the following set of five assumptions.

Assumption 1 -Synergy: Collaboration among organizations increases their value.

The synergy assumption is based on the idea that having organizations collaborating towards the achievement of a common goal by sharing their competences brings an added value to each of the collaborating organizations. So some extends, this assumption lies at the bottom of the concept of collaborative networks.

Assumption 2 -Erosion: The part of the value of an organization originated by former collaboration with other organizations fades out when this collaboration ends.

The erosion assumption takes on the idea that when organizations are not collaborating anymore, the synergy value created during their collaboration time, sustains in time. However, this synergy value vanishes over time. Therefore, the synergy value created by collaboration is fading away as the employees are not collaborating any longer. Assumption 3 -Specialization advantage: The value created by an employee is maximal when (s)he has the same profile as the organization that employs her/him.

The specialization advantage assumption is based on the idea that when an employee is working in an organization with a different profile that his/her own, the adaptation of the employee's professional culture and how-to to the culture and howto of the organization comes with a cost. For instance, an IT professional is less efficient when working at a pharmaceutical organization than at a software organization.

Assumption 4 -Local preference: an employee quitting a job preferably moves to an organization that has a collaboration history with the left organization.

The local preference assumption is related with the works on job mobility by Holzer [START_REF] Holzer | Hiring Procedures in the Firm: Their Economic Determinants and Outcomes[END_REF] and Bewley [START_REF] Bewley | Why Wages Don't Fall during a Recession[END_REF], who have identified that 53% (resp. 60%) of the employers are seeking future employees on the social networks of their employees. Assumption 5 -Profile preference: an employee quitting a job preferably moves to an organization whose profile is that same as her/his own.

The profile preference assumption is related to the idea that the specialization advantage leads to a higher efficiency of the employee, potentially related to higher wages. For instance, an IT professional will rather move to a software organization sharing his/her culture, instead of a pharmaceutical organization.

A Model of a Networked Ecosystem of Organizations

In the proposed model, an ecosystem of organizations is modeled as a triplet: a network of organizations, an employment structure, and a set of collaborative networks. In the network of organizations ܰ, the nodes represent the organizations, while the links represent the collaboration history of organizations. Let ܱ denote the set of organizations of the ecosystem, with  being the ݅-th organization. The total number of organizations in the ecosystem is assumed to be fixed.

Let ݈ = ݈ ∈ ܮ denote the link between organizations  and  representing collaboration between organizations  and  collaborate with each other. The network of organization is undirected as the collaboration relation is symmetric, i.e., if the organization  collaborates with the organization  , then the organization  collaborates with  . The network of organizations is then ܰ = {ܱ, .}ܮ

The employment structure ܧ of a networked ecosystem of organizations is modeled as a matrix of dimensions (|ܱ| + 1) × |ܲ|, where |ܱ| is the number of organizations in the ecosystem, and |ܲ| is the number of profiles of employees in the ecosystem. ܲ = { ଵ , … ,  || }. Examples of profile are "IT professional" and "Accountant". The elements of the employment structure ܧ are positive integers, such that, ݁ = ቊ the number of unemployed people of profile  , if ݅ = 0, the number of employees of the organization  with profile  , otherwise.

The total number of employees in the ecosystem is assumed to be fixed. The profile of an organization is defined as the predominant profile of its employees, i.e.,  =  ⇔ ݁ = max(݁ ) , for ݇ ∈ {1, … , |ܲ|}.

Let ܰܥ denote the set of collaborative networks. Each collaborative network is a triplet ܿ݊ = {ߠ , ܱܥ , ݀ }, where ߠ is the point in time in which the collaborative network has been created, ܱܥ is the set of organizations collaborating within the collaborative network, and ݀ is the duration of the collaborative network. Therefore, the collaborative network is dissolved at ݐ = ߠ + ݀ .

Organization Value

Finally, let ݒ denote the value of the organization  . The value of an organization should encompass both the value created by its employees and the value created by synergy with other organizations. Let ݒ denote the value created by the employees of the organization  , and ݒ ௦ the value created by the synergy with other organizations.

The value created by employees supports the specialization advantage assumption. In our model, the value brought by an employee to the value of an organization with the same profile is normalized to 1, while the value brought by an employee to the value of an organization with a different profile equals 0 ൏ ߩ ൏ 1. The total value of an organization created by its employees is then ݒ

= ߩ ∑ ݁ ஷଵ ஷ + ∑ ݁ ୀ .
The value created by the synergy with other organizations has to support both the synergy and the erosion assumptions. The synergy assumption implies that each new collaborative network within which an organization is collaborating increases the value of the organization. The erosion assumption assumes that when an organization is not collaborating with any other organization, its synergy value is fading out. The synergy value created by an organization  collaborating with the organization  equals to ݒ ௦ = 1 + log(|ܰܥ |), where ܰܥ is the subset of collaborative networks in which the organization  participates. Therefore the synergy value of an organization collaborating with another organization in one collaborative network equals 1 + log(1) = 1, the synergy value in 5 collaborative networks equals 1 + log (5)~2,6.

When an organization is not participating in any collaborative network, its synergy value is decreasing in time. Let ݐ ఠ ೕ denote the last time when the organization has participated to a collaborative network with organization  , with potentially ݐ ఠ = 0 for organizations that have never participated to any collaborative network. In our model, the synergy value is defined as follows:

ݒ ௦ )ݐ( = 1 - ଵ ଵାୣ ೞ .൬ మ శ ഘೕ ష൰ , ( 1
)
where ݂ ௦ is the fading slope, and ݂ ௗ is the fading duration. Additionally, a threshold ݂ ௧ is defined to filter out old, insignificant relations among organization. Therefore, if ݒ ௦ )ݐ( ൏ ݂ ௧ , then the link ݈ is removed from the set of links .ܮ The function defined by the Eq. 1 and threshold filtering are illustrated in Fig. 1. Next, the total value created by the synergy with other organizations may be calculated on the bases of the values ݒ ௦ . In out model, we distinguished collaboration between same-profile organizations from collaboration between organizations having different profiles. In terms of synergy, collaboration between organizations having different profiles is more valued than collaboration between same-profile organizations.

Therefore, with

0 ൏ ߠ ൏ 1, ݒ ௦ = ߠ ∑ ݒ ௦ ஷ ୀ ೕ + ∑ ݒ ௦ ஷ ஷ ೕ .
Finally, the total value of an organization is defined as ݒ = ݒ + ݒߙ ௦ , where ߙ is the weight of the synergy value with regard to the value created by the employees.

ݐ ఠ ೕ ݂ ௗ ݂ ௦ ݂ ௧
4 Dynamic Networked Ecosystem of Organizations

Employees Mobility in a Networked Ecosystem of Organizations

The dynamics of employees is modeled with three basic operations: hire (an unemployed person is hired by an organization), fire (an employee is fired by his/her organization), and quit (an employee is moving to another organization).

In the proposed model, we do not consider employees which are removed from the labor market, for various reasons, including retirement, death, and accidents leading to severe disability. The hiring rate is proportionally dependent on the ratio of the value of an organization to the number of employees. The higher this ratio is, the greater the productivity of the organization that may want to hire more employees. Similarly, the firing rate is proportionally dependent on the ratio of the number of employees to the value of an organization. The higher this ratio is, the lesser the productivity of the organization that may want to fire more employees. The quitting rate is different than the hiring and firing rate, as it is an employee's decision, instead of an employer's decision. In our model, the quitting rate is proportional to the ratio between the mean value of the neighbors of the organization in which the employee works to the value of this organization. Therefore, if the mean value of the neighboring organization is higher than the value of the organization in which the employee is working, i.e., "surrounding" organizations are doing better than the employee's organization, than the employee is more willing to quit.

In the proposed model, the choice of the organization in which the quitting employee will work has to encompass both the local and the profile preferences. Therefore, a quitting employee should prefer local organizations, i.e., organizations in the neighborhood of his/her current workplace, over other, distant organizations. Here, neighbor organizations do not have to be geographically close, but they have to be tied with regards to collaboration. Additionally, a quitting employee should prefer organizations with which (s)he shares the same profile. These preferences are captured in our model by ߨ and ߨ being the probability for a quitting employee to choose a local organization and the probability to choose a same-profile organization.

Dynamics of Collaborative Organizations

In the proposed model, collaborative organizations are created according to the following algorithm. At each discrete time moment ,ݐ each organization has the possibility to create a collaborative organization with a probability ߨ ே . The duration of each new collaborative organization is chosen randomly, according to the uniform distribution between ݀ min and ݀ max . The number of collaborators is also chosen randomly, according to the uniform distribution between ܰܥ min and ܰܥ max . The set of potential members of the collaborative network consists of the neighbors of order 2 of the organization, that is the immediate neighbors of the organization as well as their neighbors. Additionally, randomly picked organizations are added to the set of potential collaborators, with a probability ߨ random . Finally, the members of the collaborative networks are picked from the set of potential members formerly identified. The given percentage ߨ same of the members of the collaborative network shares the same profile with the creating organization, while 1 -ߨ same of the members of the collaborative network has a different profile than the creating organization. A collaborative network created at time ߠ with a duration ݀ is active, i.e., participates actively to the synergy value for all ߠ ≤ ݐ ≤ ߠ + ݀ . After the time ߠ + ݀ , the collaborative network is dissolved and does not participate directly to the synergy value of the organization.

Simulations

The proposed model has been implemented in the R language and environment [START_REF] Core | R: A language and environment for statistical computing[END_REF].

The following independent variables are manipulated in our experiment:

-the number of members of collaborative networks ݀ min and ݀ max , -the probability of the presence of randomly picked members of collaborative networks ߨ random , -the percentage ߨ same of the members of the collaborative network sharing the same profile with the creating organization. The dependent variables to be measured are:

-the clustering coefficient of the network of organizations, -the average path length of the network of organizations, -the employee distribution, -the organization value distribution. Two starting configuration of the ecosystem has been tested: in 50% of our simulations, the starting ecosystem was an Erdős-Rényi network; in the remaining 50% of our simulations, the starting ecosystem was a Barabàsi-Albert network. In both cases, the starting ecosystem contains 100 organizations and 197 links.

Three profiles have been used during our simulations, referred to as "red", "blue", and "green". The initial population has been created as follows: for each company, the number of employees for each profile is following the normal distribution with a zero mean and a standard deviation equals 70. Such a distribution creates a high number of micro and small enterprises. The type of each organization is defined at starting time. The number of unemployed people is set as 3% of the number of employees at starting time and is equally distributed among all the profiles.

For each configuration of the model, 25 different ecosystems have been generated. For each generated ecosystems, 500 iterations of the system have been performed before measuring the values of the dependent variables. The measured dependent variables have been further aggregated by processing their geographic mean.

We have found (cf. Table 1) that larger CNs in terms of number of their members (increases of ݀ min and/or ݀ max ) leads to an increase of the clustering coefficient and a decrease of the average path length. As a consequence, larger CNs lead to a smallworld of organizations, in which, on one hand, organizations are working in cluster, i.e., the collaborators of an organization are usually collaborating with each other, on the other hand, organizations are relatively close each other, i.e., an organization can reach any organization by a small number of collaborators of collaborators.

Large CNs leads also to the emergence of organizations with a large number of employees. It can be explained by the local preference assumption of our model, which encourages the mobility towards neighboring companies, and therefore, organizations that have a large number of collaborators are more willing to be the destination of quitting employees than organizations with few collaborators. The rise of the organization value is not surprising as an important part of the organization value depends directly on the number of its collaborators.

Modifications of ߨ random and ߨ same have no influence on the structure of the network of organizations in terms of clustering coefficient. The average path is influenced by ߨ random , as adding links to non-neighboring organizations creates "short-cuts" in the network of organizations. The number of employees per organization and the organization value are influenced by neither ߨ random nor ߨ same .

Conclusions

A major contribution presented in this paper is a theoretical model of ecosystems of organizations, encompassing employee mobility and collaborative networks. Our simulations based on this model provide some original insights concerning the influence of collaborative networks on the structure of the network of organizations, on employment and on the value of the organizations themselves. We have found that the size of the CNs operating on the business ecosystems has an importance influence on the structure of network of organizations: larger CNs lead to a small-world network of organizations. The constitution of CNs does not affect neither the structure of the network of organizations, nor the employment structure and the organization value. Among future works, there is an important need to confront the results of our simulations with data from real-world cases. However, a major obstacle to this confrontation is the lack of dataset concerning collaborative networks and their employment structure. Another interesting research area is the potential application of the proposed model to simulate the mobility of employees and understand the potential influence of collaborative networks on this mobility.

Fig. 1 .

 1 Fig. 1. The fading function (Eq. 1) of the synergy value for organizations that are not participating in collaborative networks.

Table 1 .

 1 Influence of the size and composition of CNs on the structure of the network of organizations, on employment and on the value of the organizations themselves.

	Dependent	Clustering	Average	Employees per	Organization
	variables ݀ max ݀ min ߨ random ߨ same	coefficient ↗ ↗ → →	path length ↘ ↘ ↘ →	organization ↗ → → →	value ↗ ↗ → →


Johannes Britsch
email: johannes.britsch@cas.de

Rebecca Bulander
email: rebecca.bulander@hs-pforzheim.de

Bernhard Kölmel
email: bernhard.koelmel@hs-pforzheim.de

Anything Relationship Management (xRM) as Management Layer for the Hyper-connected Society

Keywords: Anything Relationship Management, Collaborative Networks, Cyber-physical Systems, Internet of Services, Internet of Things

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Collaborative networks (CNs) have been long since recognized as an essential scientific discipline [1] with an important industrial impact [START_REF] Camarinha-Matos | Taxonomy of Collaborative Networks Forms, Draft working Document: In Future Internet Enterprise Systems (FInES) Task Force on CNs[END_REF]. However, looking at practice, failure to achieve successful relationships and collaborations still remains very high: empirical studies show rates reaching 50-70% [3; 4; 5].

At the same time the digital revolution is accelerating, the pace of technology is changing so rapidly that our skills and traditional organizations are not keeping up [START_REF] Brynjolfsson | Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy[END_REF]. Recent advances in the domain of cyber-physical systems (CPS) that are built from and depend upon the synergy of computational and physical components demonstrate this clearly. In the recent past, we have seen a plethora of isolated projects in this domain. Examples of the CPS application areas include the smart electric grid, smart transportation, smart buildings, smart medical technologies, and advanced manufacturing. The combination of the technical innovations in CPS and scientific organizational advancements in CNs will lead to a new societal paradigm, the long foreseen hyper-connected world [START_REF] Quan-Haase | Hyperconnected Net Work: Computer-Mediated Community in a High-Tech Organization[END_REF].

However, most approaches taken so far are isolated; complex business scenarios involving large collaborative networks as organizational basis for collaborative CPS societies are not adequately tackled. Interoperability across CPS, standardized management approaches for the targeted hyper-connected world are not yet adequately deployed and accepted. Therefore, especially small and medium-sized companies are reluctant or fail to have the resources to engage themselves in the intersection of CNs and CPS.

The authors propose Anything Relationship Management (xRM) as management layer for the hyper-connected society. The paper provides an overview of the evolution of the relationship management concept, analyzes the current status of the xRM solution market and proposes an xRM-framework for CNs and CPS.

Relevant Definitions

In this chapter, we will point out some important definitions concerning the hyper connected society and Anything Relationship Management.

Relationship Management (RM): According to Smyth [START_REF] Smyth | The credibility gap in stakeholder management: ethics and evidence of relationship management[END_REF], Relationship Management includes the following aspects: "Developing close relationships and understanding of client and stakeholder expectations […]; Developing services to match expectations where realistic […]; Delivering services to engender client and stakeholder satisfaction, which includes promise fulfillment […]; Increasing satisfaction and long-term maintenance of relationships to engender loyalty, repeat business and/or increased referral business, which concerns internal stakeholders; Increasing satisfaction to maintain and preferably increase market reputation, which concerns external stakeholders."

Collaborative Network (CN): A Collaborative Network is a network "consisting by a variety of entities (e.g. organizations and people [or intelligent machines]) that are largely autonomous, geographically distributed, and heterogeneous in terms of their operating environment, culture, social capital, and goals"; that network collaborates to better achieve "common or compatible goals, and its interactions are supported by a computer network". [1] Cyber-physical System (CPS): According to Lee [START_REF] Lee | Cyber Physical Systems: Design Challenges[END_REF], Cyber-physical Systems are "integrations of computation with a physical process. Embedded computers and networks monitor and control the physical process, usually with feedback loops where physical processes affect computations and vice versa." CPS integrate the dynamics of physical processes with those of software and networking, providing abstractions and modeling, design, and analysis techniques to an integrated whole.

Internet of Services (IoS): According to the European commission, Internet of Services "is a vision of the Internet of the Future where everything that is needed to use software applications is available as a service on the Internet, such as the software itself, the tools to develop the software, the platform (servers, storage, and communication) to run the software." [10] Internet of Things (IoT): According to Martinez [START_REF] Martinez | Objective ICT[END_REF], the Internet of Things "is a dynamic global network infrastructure with self-configuring capabilities based on standard and interoperable communication protocols where physical and virtual 'things' have identities, physical attributes and virtual personalities and use intelligent interfaces and are seamlessly integrated into the information network."

Anything Relationship Management

Evolution of the Relationship Management Concept

xRM builds upon the basic logic of RM solutions and the focus on profitable longterm relations with existing customers [START_REF] Berry | Relationship Marketing[END_REF]. Since its introduction in the early 1980s, the theoretical concept of relationship marketing has been further developed and increasingly supported by software solutions. By the end of the 1990s, these solutions were rebranded as "Customer Relationship Management" (CRM) [START_REF] Buttle | Customer relationship management: Concepts and tools[END_REF]. CRM systems put the emphasis on the customer side and experienced a period of high popularity growth around the millennium [START_REF] Fochler | Die DV-technologische Integration der Kundenschnittstelle im Unternehmen[END_REF]. CRM was designed to mainly deal with conventional cross-enterprise relationships between two parties, typically between one buyer and one seller organization (1:1 relations). Thus, the complexity of relationships and the diversity of relationship entities covered can be characterized as rather low.

Already early, parallel to the rise of CRM, several companies promoted a concept for the management of a broader variety of stakeholders [START_REF]BroadVision Featured in BusinessWeek[END_REF]. This approach was titled "Extended Relationship Management" (XRM, with a capital "X") and was firstly analyzed in detail by Forrester Research in 2001 [START_REF] Radjou | Apps For Dynamic Collaboration[END_REF]. Starting around 2005, XRM-labeled systems for different stakeholder groups and different kinds of organizations appeared on the market. They had in common that they transported the CRM ideas to different environments, resulting in solutions for Member RM, Citizen RM, Employee RM, etc. Also, due to the growing availability of new technologies like mobile high-speed data connections, navigation services, and social media, the possibilities of relationship-based solutions reached a new quality: relations could now be maintained anywhere, in real-time, with a broader data basis, on a more finegrained entity level, and through interactive social media channels involving communication between one sender and many receivers and vice versa (1:n and n:1 relations). In sum, the complexity of relationships and the entities involved in relationship structures had started to increase significantly. This trend has continued in the last years. Particularly since 2008, the idea of a holistic and integrated management of all stakeholder relations of an organization has been receiving growing attention [START_REF] Lager | Is CRM Too Hard for Microsoft?[END_REF]. Promoted by Microsoft as "Anything Relationship Management" (xRM, now mostly with a lowercased "x"), this new concept attempts to design and optimize complex relationship structures [START_REF] Gummesson | Qualitative research in marketing: Road-map for a wilderness of complexity and unpredictability[END_REF]. By shifting away from a single system towards a customizable platform/app architecture, xRM solutions provide a means with which various relationship-specific application scenarios can be realized. xRM supports the vision of the networked organization in which all levels of relationships involving organizations, human beings, intelligent objects, object service providers, and ubiquitous computing are coordinated and transparent, interactive, many-to-many relations and processes are created. Future xRM platforms are therefore likely to link both real and virtual entities dynamically with respect to the context, using a variety of applications including CNs and CPSs.

Market Overview and Use Cases

The evolution of a RM concept is linked closely with the market for its underlying IT solutions. The table below shows an excerpt of the worldwide market, covering ten producers of popular RM software as well as two producers of social collaboration software. Based on the producers self-description on their web pages, the authors ranked the scope of relationships which are covered by the solutions, and the usage of the label "xRM" (emphasized: ++, mentioned: +). There has been a notable shift in the market in the last years: none of the solutions sighted is limited to the exclusive management of customer relationships anymore. At least, relations with other stakeholders like employees, partners, suppliers, or -in industry-specific offerings -with members, patients, citizens, etc. are covered. Also common today is the integration of virtual assets (documents, events, patents, trials, etc.) in relationship structures. The possibility to manage physical assets (inventory, not intelligent objects) is frequently mentioned as well. Most solutions are however still marketed under the label "CRM"; the diffusion of the term "xRM" is rather low. Interestingly, solutions for social project management like Basecamp or Podio, which originally focused on employee management, are also heading into the xRM direction. The similarities with tradetional RM solutions can be observed in the feature list depicted in table 1.

Most producers emphasize their solutions to be easily customizable platforms, not standardized single CRM systems. Deployment in the cloud as Platform-as-a-Service (PaaS) has become the norm. On top, most platforms offer a marketplace for firstparty and third-party apps, which allow enhancing the solutions according to customer's needs. The integration of social media, e.g. though activity streams, social media analysis, etc. is mentioned by more than half of the companies in the sample. Looking at the broader market, many smaller software producers and consulting firms offer xRM software and services for different industries and target groups, mostly based on the listed platforms of the bigger vendors. In practice, xRM use cases can be found in many areas:

• A non-profit organization has developed xRM apps "to organize charitable events, grant management and outreach for fundraising efforts" [START_REF]Microsoft Drives Customer Success With Microsoft Dynamics CRM[END_REF]. • A health care company has designed RM applications to manage patient intake and streamline operations. Among other things, the xRM apps provide "a realtime view of the availability of each bed at its facilities" [START_REF]Salesforce[END_REF]. • A government department has developed multiple RM solutions using an xRM platform, "including a consumer affairs reporting tool, an energy safety intelligence application and a grants information management system" [START_REF] Doherty | Ten Companies To Watch[END_REF]. • A logistics company has designed an xRM solution for its truck drivers. The drivers can interact with local operations centers and select their preferred choice of loads [START_REF] Doherty | Ten Companies To Watch[END_REF]. • A manufacturing firm has implemented xRM-based apps for customer service and order management, inventory and warranty management, production planning, etc. [START_REF]Salesforce[END_REF].

Derivation of an xRM-Definition

The analysis of the software producers' web pages has indicated that the market for RM solutions is currently in an early transition phase between XRM and xRM. RM is not limited to customers anymore, it is increasingly offered as scalable PaaS, it is extensible through apps, and it covers the real-time analysis and management of complex relationship structures. These features build an optimal basis for a future integration of intelligent objects, object service providers and ubiquitous computing and are the basis for collaborations between entities. As a conclusion, we define xRM as:

Anything Relationship Management (xRM) is the next evolutionary step of CRM. The concept describes the holistic management of relations within and between organizations, human beings, and virtual and physical objects. Linking IoS and IoT, xRM provides the management layer for CNs and CPS. The implementation of the xRM concept is based on platforms and modular, domain-specific applications building upon these platforms.

Framework Proposal

In this section, the authors propose an xRM framework as a Management Layer for the hyper-connected Society. In our case, the framework is a layered structure indicating what kind of applications can or should be built and how they would interrelate. The framework should indicate a set of functions within the system and how they interrelate, the layers of an application subsystem, how communication should be standardized at some level of a network, and so forth. xRM platforms can be highly connected and integrated in multiple ways, even across business operations and domain boundaries. Achieving effectively networked, cooperating, and human-interactive systems will be an integral factor in the adoption of such solutions in the future. Some of the key questions to be considered include what is needed to enable streamlined and predictable development, and evolution of networked and integrated xRM solutions -particularly as systems become interconnected with legacy systems and across industry boundaries. How to effectively achieve compositionality within heterogeneous, dissimilar but connected systems? How to model and integrate the role of humans in systems with variable levels of autonomy?

New solutions will have characteristics that enable compositionality within dissimilar but connected systems, while also considering the integration of humans into systems with variable levels of autonomy.

So far, the technical environments do not have governance or business models in place to motivate the development of networked, cooperating, human-interactive systems. Developers must assume the risk of sharing proprietary information with competitors and the liability of integrating their systems with external systems to ensure high levels of performance and functionality. Building an infrastructure foundation that is interoperable, contains open source and proprietary information in balance, and operates under the same standards will provide a protected starting point from which interoperable issues are minimized and system development could be profitable. Building from a standard foundation will save time and cost by sharing critical information and will avoid the liability of a solely proprietary product. The proposed xRM-framework is based on the cloud-computing framework. A cloud is a powerful combination of cloud computing, networking, storage, management solutions, and business applications that facilitate innovative IT and consumer services. These services are available on demand and are delivered economically without compromising security or functionality. We are moving to an interconnected "world of many clouds". This will allow for an interconnected system giving services, companies, human beings, and intelligent objects access to services any time, on any device, anywhere in the world. To take full advantage of an xRM-based cloud computing solution, enterprises need to evolve their IT strategy.

In order to have a market impact, the xRM-framework must integrate the various components and abstractions that will be needed to enable co-design of software, communications, and interacting subsystems. The above depicted generic system topology of an xRM-framework proposes to have the interconnection between the various stakeholders (human beings, companies, intelligent objects) either on the Infrastructure as a Service level (with distinct IP-addresses once (Internet Protocol version 6) IPv6 is widely deployed) or on the PaaS-level (e.g. web services interacting between the cloud-framework and the intelligent object framework consisting of intelligent objects, embedded systems, CPS, etc.). The user interaction is based on the Software as a Service level allowing also apps to be the user frontend.

Conclusion

In this paper, the authors provided an overview of the evolution of the relationship management concept, defined xRM, analyzed the current status of the xRM solution market and proposed an xRM-framework for CNs and CPSs. The key argument was that moving beyond an IoT towards CPSs requires more than simply upgrading micro-electronic objects that communicate with one another. Linking objects to one another, or the internet, requires clear relationship structures, and therefore, an xRM framework. At the heart of this approach is the expansion of web-based services, the IoS, to the IoT. An xRM platform links both real and virtual entities dynamically with respect to the context, and this is done using a variety of applications.

Fig. 1 .

 1 Fig. 1. Evolutionary steps of relationship management.

Table 1 .

 1 xRM solution market: scope of relationships covered, usage of xRM label.

	Maker	S olution	S take-holder	Virtual Physical Label Assets Assets xRM	Plat-form	Cloud	Market-place	S ocial Media
	CAS Software Open	++	++	+	++	++	++
	Infor	Epiphany	++	+						++
	M icrosoft	Dynamics CRM	++	++	++	++	++	++	++
	Netsuite	CRM +	++	++	++		++	++	++
	Oracle	CRM On Demand	++	++	+			++
	Sage	CRM	++	+			++	++	++	++
	salesforce.com Platform	++	++	+		++	++	++	++
	SAP	Business ByDesign	++	++	++		++	++	++
	SugarCRM	Sugar	++	+	+	+	++	++	++	++
	Zoho	CRM	++	++	++		++	++	++	++
	37signals	Basecamp	++	++			++	++	++	++
	Citrix	Podio	++	++	++		++	++	++	++

Acknowledgments. The authors would like to thank the European Commission for the financial support of the R&D project GloNet (focusing on glocal CNs) within the FInES cluster.


Ovidiu Noran
email: o.noran@griffith.edu.au

Enhancing Collaborative Healthcare Synergy

Keywords: Health Care, Collaborative Networks, Enterprise Architecture

Worldwide, the constant ageing of the population brings significant challenges to the traditional style of health care systems. Rapidly spreading pandemics triggered by new disease strains, increased population mobility and displacements fuelled by conflict and climate change add another dimension to the health care predicament. In this context, proper cooperation and interoperability of the participants in the healthcare effort becomes paramount. Collaboration is an essential factor but also a major challenge, as typically healthcare institutions are hierarchical and heterogeneous, owing to various administrative, geographical and historical reasons. As the pressure on healthcare availability, quality and cost is constantly increasing, governments can no longer rely on traditional models for managing population wellbeing. Innovative holistic and integrated models and procedures taking into account all essential aspects, elements, participants and their life cycle are necessary if these challenges are to be successfully met. Based on previous research and applications, this paper argues that such necessary artefacts can be built using a life cycle-based whole-system paradigm enabled by advances in Collaborative Networks and Enterprise Architecture. This approach aims to provide a sound platform for efficient response delivered by agile and synergic teams to short and long-term challenges to population health and well-being.

Introduction

Worldwide, healthcare situation is radically changing. Earth's population is constantly ageing as people live longer [START_REF]The world health report 1998---life in the 21st century: a vision for all[END_REF]; while reflecting mankind progress and providing some benefits [START_REF] Healy | The Benefits of an Ageing Population -Discussion Paper 63[END_REF], this state of affairs also brings significant challenges to the human society -especially in social security and healthcare [START_REF]Ageing societies: The benefits, and the costs, of living longer[END_REF]. Thus, the pressure on healthcare services availability and cost is increasing worldwide, with governments no longer being able to manage population health using 'legacy' models. Another major healthcare concern is the growing risk of pandemics, owing to drug-resistant diseases and increased population mobility facilitated by modern means of transportation and fuelled by regional conflicts, economic crises and climate change.

Due to a number of regional, historical, organisational and political reasons, there are significant challenges in managing both internal and external collaboration and interoperation of the typically heterogeneous set of participants involved in the healthcare endeavour. This constitutes a critical issue in handling epidemic and pandemic events that require prompt response and typically claim resources and capabilities beyond those of any particular individual healthcare organisation. New innovative and especially integrated models, methods and tools are required in order to enable proper inter-professional and inter-organisational cooperation so as to cope with the new issues brought by the changing healthcare environment.

Previous research [START_REF] Noran | Towards A Collaborative Network Paradigm for Emergency Services[END_REF][START_REF] Noran | Effective Disaster Management: An Interoperability Perspective[END_REF] has investigated the use of Collaborative Networks (CN) [START_REF] Camarinha-Matos | Collaborative Networks: A new scientific discipline[END_REF] and Enterprise Architecture (EA) [START_REF]IT Glossary[END_REF] concepts and methodologies in tackling generic disaster management efforts. This paper aims to build on the previous results by focusing the application of CN and EA artefacts to the healthcare area. It is hypothesised that this approach will allow addressing the above-mentioned issues in a life cycle-based, holistic and integrated manner; this should enable a prompt and efficient response by agile and synergic teams to both acute and long-term challenges to population health and well-being.

Challenges in Healthcare Management Collaboration

Healthcare has made significant advances in the last century, such as the development and wide use of vaccines, eradication of serious diseases and large reductions in communicable disease epidemics and chronic diseases [START_REF]The world health report 1998---life in the 21st century: a vision for all[END_REF][START_REF] Fielding | Public Health in the Twentieth Century: Advances and Challenges[END_REF].

While solving some very important problems, some of these advances have unfortunately also contributed to a new set of challenges faced by the public and private healthcare infrastructure and organisations. Thus, nowadays we are confronted with population growth and ageing triggered by increased longevity and health hazards owing to causes such as climate change [START_REF] Donohoe | Causes and health consequences of environmental degradation and social injustice[END_REF] and new strains of diseases [START_REF] Kilbourne | Influenza Pandemics of the 20th Century[END_REF].

While healthcare as a system has become somewhat more organised, it has also become more expensive, complex and difficult to manage. New technologies hold the promise of remote medical assistance and automated care at home; however, the main problems remain human-related, namely overcoming the organisational and cultural barriers to collaboration and synergy of the healthcare professionals and organisations. Although collaborative healthcare is argued for and encouraged in various medical and other emergency response reports, conferences and journals (e.g. [START_REF] Kapucu | Collaborative emergency management and national emergency management network[END_REF][START_REF]Governor's Task Force for Pandemic Influenza Preparedness -Final report[END_REF][START_REF] Waugh | Collaboration and Leadership for Effective Emergency Management[END_REF][START_REF] Mechanic | A cooperative agenda for medicine and nursing[END_REF][START_REF] Hughes | Chapter 33: Professional Communication and Team Collaboration[END_REF][START_REF]To err is human: building a safer health system[END_REF][START_REF] Sansoni | An Assessment Framework for Aged Care[END_REF]), unfortunately the extent of actual collaboration is still limited.

The relevant literature also argues that effective collaborative healthcare could be enhanced by modelling and participatory design [START_REF] Kristensen | Participatory Design in Emergency Medical Service: Designing for Future Practice[END_REF] aimed at integrating scientific but also administrative and political aspects into a whole-system approach [START_REF]Governor's Task Force for Pandemic Influenza Preparedness -Final report[END_REF][START_REF]Pandemic Influenza preparedness Framework[END_REF][START_REF] Moghadas | Managing public health crises: the role of models in pandemic preparedness[END_REF]. For example, the long term healthcare issues may be alleviated by the vision and strategic research espoused by Matos et al. [START_REF] Camarinha-Matos | Adaptation and Value Creating Collaborative Networks[END_REF] and the BRAID project [START_REF] Braid | Bridging Research in Ageing and ICT Development -Consolidated Vision of ICT and Ageing[END_REF] who advocate the necessity for integrated assistive services and infrastructure supporting collaborative healthcare ecosystems [START_REF] Holzman | Computer-human interface solutions for emergency medical care[END_REF] as a component of a healthy living and ageing support paradigm [START_REF] Sansoni | An Assessment Framework for Aged Care[END_REF]. Psychological disaster effects such as uncertainty, anguish, confusion, panic etc are significantly augmented in pandemic-type situations and must be properly dealt with by building appropriate and specific preparedness of the organisations involved [START_REF]Pandemic Influenza preparedness Framework[END_REF]24], with ethics playing a prominent role [START_REF] Thompson | Pandemic influenza preparedness: an ethical framework to guide decision-making[END_REF][START_REF]Ethical Values for Planning for and Responding to a Pandemic in New Zealand[END_REF].

Owing to the urgency involved, often there is a tendency of the higher ranking and more powerful organisation(s) to override or exclude some participants, adopting a 'central command' approach in preference to a cooperative one [START_REF] Waugh | Coordination or Control: Organizational Design and the Emergency Management Function[END_REF]. This is not desirable as successful disaster management (including healthcare crises) relies on a wide range of community economic, social-psychological, and political resources. This cooperation brings communities together, gives them a sense of usefulness (ibid.) and thus also alleviates the negative psychological effects.

Collaboration between participants in the healthcare effort does not automatically occur. It must be "constructed, learned, and once established, protected" [START_REF] Wilson | Nurse Practitioners' Experience of Working Collaboratively with General Practitioners and Allied Health Professionals in NSW, Australia[END_REF]. Like most human-related processes, collaboration can neither be successfully forced on the participants nor achieved in a short time. The divergent perceptions and expectations of the parties involved [START_REF] Krogstad | Doctor and nurse perception of interprofessional co-operation in hospitals[END_REF], owing to a traditionally strong hierarchy and marked difference in status between partners [START_REF] Ramanujam | The Challenges Are Organizational, Not Just Clinical[END_REF], can be best dealt with by the higher ranking participants. They can promote collaboration and trust by employing a participatory and inclusive approach [START_REF] Baker | Teamwork as an essential component of high reliability organizations[END_REF] which will also build a beneficial sense of security [START_REF] Nembhard | Making It Safe: The Effects of Leader Inclusiveness and Professional Status on Psychological Safety and Improvement Efforts in Health Care Teams[END_REF].

To conclude, efficient healthcare collaboration requires that organisational cultures, processes and resources of the participants acquire suitable preparedness [START_REF] Kapucu | Collaborative emergency management and national emergency management network[END_REF]. This requires access to a plethora of interdisciplinary information and knowledge not always obvious or easily accessible to planners and disaster managers.

A Combined Collaborative Network / Enterprise Architecture Approach for Healthcare

The concept of networks in disaster management and recovery as an alternative to a centralised command and control approach has been advocated, studied and applied to some extent for a number of years with mixed results (e.g. [START_REF] Waugh | Coordination or Control: Organizational Design and the Emergency Management Function[END_REF][START_REF]Disaster Response Network (DRN)[END_REF][START_REF] Bard | Multi-level Social Networking to Enable and Foster Collaborative Organisations, in Adaptation and Value Creating Collaborative Networks[END_REF][START_REF] Cooper | Collaborative practices in unscheduled emergency care: role and impact of the emergency care practitioner -qualitative, quantitative and summative fndings[END_REF]). While providing valuable data, such attempts appear to have two main shortcomings. Firstly, they propose previously untested models focusing on a specific aspect in isolation, rather than employing a proven set of integrated models in a whole-system approach. Secondly, the life cycle aspect of the participant organisations, networks and other relevant entities (including the disaster event/s) appears to be less addressed. As all participating entities are evolving, it is essential that the interactions required for collaboration and interoperation be considered in an integrated life cycle context. In attempting to address these issues, it has been observed that the healthcare challenges identified in the critical literature review describe a situation similar to that of commercial enterprises who, owing to a global business environment, find themselves compelled to tackle projects requiring resources beyond their own staff, knowledge and time capabilities. Their usual reaction to this problem is to set up or join so-called Collaborative Networks (CNs) that act as breeding environments for Virtual Organisations (VOs) who are promptly created in order to bid for and (if successful) complete projects requiring combined resources and know-how. The view of CNs as social systems composed of commitments, who absorb uncertainty and reduce complexity [START_REF] Neumann | Towards a Theory of Collaborative Systems[END_REF] also supports their use towards large inter-disciplinary tasks.

Integrated modelling in a life cycle context can be further facilitated by artefacts provided by Enterprise Architecture (EA) research and practice. Thus, EA as a change management paradigm that bridges management and engineering best-practice [START_REF]IT Glossary[END_REF] is capable of providing a framework integrating all necessary aspects in a life cyclebased set of models ensuring the consistency and sustainability of complex projects. Furthermore, the fact that large scale medical emergencies such as pandemics are particular types of disaster events justifies and facilitates the use of previous research results in applying CN concepts in disaster management, supplemented with an EA perspective providing the essential integration and life cycle perspectives [START_REF] Noran | Towards A Collaborative Network Paradigm for Emergency Services[END_REF][START_REF] Noran | Effective Disaster Management: An Interoperability Perspective[END_REF]. Fig. 1 presents the main issues identified in collaborative healthcare and the potential solution and benefits brought by a CN approach enhanced with EA concepts. Adopting a CN approach for health disaster management provides benefits going beyond mere technical and syntactic-type interoperability. Thus, the participants in a 'healthcare management' CN (HMCN) have the time and suitable environment to overcome hierarchical, organisational and typically troublesome cultural interoperability [START_REF] Whitman | The Missing Link: Culture and Language Barriers to Interoperability[END_REF] barriers and achieve suitable preparedness. This is essential in the prompt and successful setup of 'health management task forces' (HMTF) for disasters but also for the creation and operation of continuing VOs (e.g. as described in [START_REF] Noran | Collaborative networks in the tertiary education industry sector: a case study[END_REF]) for long term collaborative healthcare challenges such as population ageing. The CNs and VOs set up for the healthcare domain would have specific features. For example, the competitive motivations of commercial CN participants who guide their decisions to create / join / remain / leave the network would transform into the stringent need to cope with increasingly complex health challenges and healthcare systems. The use of reference models, customary in commercial CNs, is feasible here but may be limited due to the diversity in scale and type of healthcare incidents [START_REF] Tierney | Needed Innovation in the Delivery of Emergency Medical Services in Disasters: Present and Future[END_REF].

Importantly, for the health management CN to function, the typical CN 'lead' partner/s (here, government emergency management / healthcare agencies) need to take a participatory and inclusive approach. Thus, scientific, faith and community representatives and all relevant non-governmental and volunteer organisations should also be included in the setup and operation of the HMCN, in addition to the typical participants such as hospitals, allied healthcare [START_REF]Allied health career structure[END_REF], fire and rescue services, etc.

Life Cycle Integration Modelling for Collaborative Healthcare

Successful integration modelling of the CN approach must be done collaboratively with all the network participants [START_REF] Moghadas | Managing public health crises: the role of models in pandemic preparedness[END_REF]. The proposed method and artefacts support this audience variety by using suggestive graphical models and complexity management.

While several EA frameworks would have been suitable, we have selected the modelling framework (MF) provided by GERAM (Generalised Enterprise Reference Architecture and Methodology), described in ISO 15704:2005 [START_REF]Industrial automation systems -Requirements for enterprise-reference architectures and methodologies[END_REF]. This MF provides a large set of aspects, importantly including life cycle, management, organisation, human and decision. In this paper we will use a subset of the GERA MF containing the life cycle and the management / services viewpoints (see Fig. 2, left). Fig. 2 right shows a sample use of the GERA MF life cycle viewpoint to define and map life cycle phases of a health incident on typical health disaster management activities [START_REF]Attorney's General's Office -Emergency Management in Australia[END_REF]. Fig. 2 Mapping a health incident on disaster management using GERA MF phases Fig. 3 shows a sample model of HMCN and HMTF creation and operation that integrates the life cycle and management aspects. Note that a complete modelling exercise (not possible here due to space limitations) should contain diagrams covering all the required aspects. The use of viewpoint combinations based on the same MF will facilitate producing and maintaining a coherent set of models.

The arrows in Fig. 3 show influences and contributions among the stakeholders previously identified to be of interest to the long and short term healthcare concerns. Thus, healthcare organisations HO (e.g. hospitals), allied health professionals (AHP) and scientific, faith and other communities representatives all contribute to the design and operation of a HMCN in its various life cycle phases. These contributions may also extend directly to the design and operation of the HMTFs created by the HMCN, and even to the health management projects (HMPs) created by the HMTF. Influences and contributions also come from 'non-physical' artefacts such as emergency management laws (EML), pandemic preparedness (PPF), or healthcare assessment frameworks (HAF) [START_REF] Sansoni | An Assessment Framework for Aged Care[END_REF]. Feedback from population, organisations and community representatives is used to improve Government agencies (GDMAs) and the HMTFs.

The arrow from HMTF's Management side of the Operation life cycle phase to some of its upper phases represents a very important 'self partial redesign' capability, showing a need for the HMTF to be agile and adapt in real time in the face of rapidly changing conditions on the ground that are typical of some disaster events. Any major HTMF reconfiguration will however need to involve the HMCN participants. Note that a high-level model such as shown in Fig. 3 does not aim to provide all the details necessary for actual implementation upfront. Rather, its main purpose is to facilitate stakeholder consensus on the problems of the present state and support the selection of the optimal future state. Such models can provide checklists of the entities that need to be considered in the collaborative healthcare endeavour and spell out the interactions between them, in the context of their life cycles. They can also represent organisational autonomy and agility and thus help reveal hidden problems. Once consensus has been achieved, the models can be decomposed and evolved into detailed design and implementation blueprints.

Conclusions and Further Work

Collaboration and interoperation are paramount in healthcare in order to meet major contemporary challenges. Politics, hierarchy, lack of trust, dissimilar organisational cultures and limited or missing integration and life cycle-based perspective of the participants' roles are decisive factors that can be addressed by prior preparation in a suitable environment. This paper has argued that the healthcare endeavour could significantly benefit from adopting a CN paradigm applied from an EA perspective and has attempted to exemplify a high-level integration modelling example involving CN and EA artefacts. The paper makes a theoretical contribution by emphasizing the connection between CN, EA and healthcare research and a practical contribution by providing an example of how CN concepts can be employed from an EA perspective in order to model a collaborative healthcare solution to health and well-being challenges. The proposed approach will be further developed and applied to several healthcare management case studies in order to verify, validate and refine it.

Fig. 1

 1 Fig. 1 Main collaborative healthcare barriers and solutions offered by EA and CN

Fig. 3

 3 Fig. 3 Possible life cycle model of collaborative health system setup and operation


Sarra Dahmani
email: dahmani@emse.fr

Xavier Boucher
email: boucher@emse.fr

Sophie Peillon
email: peillon@emse.fr

Industrial Transition through Product-Service Systems: Proposal of a Decision-Process Modeling Framework

Keywords: servitization, transition, decision, ecosystem

In this paper, we attempt to explain that Product Service Systems (PSS) are not based solely on technical and functional aspects, but that organizational and collaborative aspects are also involved. Thus, we propose a servitization analyzing approach that highlights the need to manage the complexity of the iconic functional and decisional areas related to this strategy. We propose a transition process conceptual model adapted from a modeling framework for business decision-making processes (GRAI framework and tools).

Introduction

Current trends of economic and population growth have been exerting pressure on the environment. The actual business environment overview is characterized by market volatility and instability. Increased competition is about to weaken the sustainability of classic differentiation strategies based on product innovation, technological empowerment or lower prices [START_REF] Baines | Towards an operations strategy for product-centric servitization[END_REF]. Since then, differentiation based on product and prices are presently less significant factors, while service competencies are becoming core differentiators in business relations [START_REF] Gebauer | Customer-focused and service-focused orientation in organizational structures[END_REF]. Therefore, during the last decades, we have witnessed a paradigm shift through the development and spread of servitization and Product Service System (PSS) concepts. A product-services system (PSS) is implemented as an innovation strategy that shifts the focus of a business from developing and selling physical products to developing and selling a system of products and services capable of fulfilling specific demands of clients [START_REF] Baines | Towards an operations strategy for product-centric servitization[END_REF]. But also, turning to this new paradigm requires questioning business interaction within a defined ecosystem and, organization' roles and goals.

We develop in this paper three main parts. First, we introduce the basic concepts of the servitization process. The objective of the second part is to illustrate our approach of servitization modeling, for which we propose a generic decision model construction. In the third part, we apply and validate this approach through a case study of a manufacturing company in a servitization process.

Servitization: Transformation of the Economic Relationships within Business Ecosystem

The main objective of this section is to present servitisation from a decisional point of view: servitization can be understood and formalized as a complex decision process of enterprise transformation, which changes several aspects of its economic relationships within a business ecosystem. We present below key dimensions of this decision process. To extend a systematic approach to strategy, we adopt Moore's vision of a company viewed not as a member of a single industry but as part of a business ecosystem that crosses a variety of industries [START_REF] Moore | Predators and prey: A new ecology of competition[END_REF].

In this paper, we express an analytical vision of servitization through focusing on a specific focal company interacting with its ecosystem. This transitioning focal company is considered as the central network productive node and as the main driver of the PSS offer. This transformation affects the company on different levels: through its strategy, its internal processes and competencies, as well as its external network relationships. In fact, servitization leads to rethink the enterprise positioning within its ecosystem, the various parties involved with it and the different relationships between parties. This research work aims at modeling and controlling the decision-making process deployed by the company along the servitization transformation project.

Transitioning to servitization is defined as a decision-making process, which generates information and knowledge progressively through temporal sequences according to each specific context. This is the system adopted by an organization to move from a current economic model (product-oriented offer) towards a servitized economic model (integrated product and service offer). A PSS business model is often accompanied by new types of partnerships among stakeholders such as producers, retailers, customers and end-of-life managers with connected economic interests and shared vision of desirable outcomes for 'system resource optimization' [START_REF] Xing | A service innovation model for synergistic community transformation: Integrated application of systems theory and product-service systems[END_REF]. Broadening PSS thinking beyond focusing on an individual product to a wider socialtechnological system that incorporates multiple actors, perspectives, and approaches may simultaneously increase customer satisfaction, profits and eco-efficiency [START_REF] Tukker | Product-services as a research field: Past, present and future. reflections from a decade of research[END_REF].

We propose to decompose the global servitization process in 3 decisional issues which cover key dimensions of the business transformation:

1. The product service system (PSS) technical design; 2. The PSS business model transformation; 3. The organizational changes, required to support the PSS implementation. Each of these decisional issues contributes to redesign the positioning of the focal company in its ecosystem with different interaction levels. Indeed, the first decision issue represented emphasizes the services' intangible nature, service delivery requires then a delicate process of value proposition [START_REF] Baines | Towards an operations strategy for product-centric servitization[END_REF]. The second issue encountered in the servitization process is to predict the market behavior vis-à-vis this new offering. Therefore, servitization leads to consider a new form of competition outside the usual expected rivals within an ecosystem [START_REF] Vandermerwe | Servitization of business: Adding value by adding services[END_REF]. In addition, the third issue considers the adaptation of organizational structures and processes necessary to ensure congruence between their resources and capabilities implementing this new strategy [START_REF] Oliva | Managing the transition from products to services[END_REF].

Thus, the transformation of a manufacturing company to a product service organization confronts a set of challenges that we summarize in the three problematic domains underlined. These domains are also included as areas having different functional characteristics of complementarity. In addition, they can play a role of catalyst in the dynamics of the transition process: the company often initiates the transition by using one of the domains as an entry point.

In the following sections, we present a way to model this complex decision process and to provide diagnosis tools dedicated to a better management of servitization.

A Modeling Framework for the Servitization Decision-Process

In reference to a state of the art in Enterprise decision-making modeling [START_REF] Dahmani | La trajectoire décisionnelle de servicisation: Proposition d'un cadre de modélisation décisionnelle[END_REF], we have selected the formalized modeling proposed in GRAI method as the most appropriate to our research problem. GRAI method is a decision-making modeling framework that focuses on businesses in general and specifically on production systems [START_REF] Doumeingts | Production management and enterprise modelling[END_REF]. Additionally to the decision-making orientation, the contribution of GRAI is also due to its ability to represent the complexity of servitization process, through a systemic view of the process. To represent the complexity of a decision process, GRAI grid considers simultaneously distinct functions of the system through functional areas, and, a notion of decision-making levels characterized by the concept of decisional horizons. Additionally, interacting among decisions is represented with coordination mechanisms among the different decision centers considered in the GRAI grid. GRAI modeling framework can then be applied to represent the servitization decisionmaking process in a systemic multi perspective approach.

We model this decision making process through three basic concepts:

-Macro-process (MP): corresponds to a functional decisional domain, and comprises a coherent set of decisions on an overall goal. In addition, each MP includes specific types of actors within an ecosystem.

-Critical Decision Process (CDP): represents all decision-making processes involved within a macro process, whose output is likely to have a significant impact / importance on the overall transition process.

-Decision Activity (DA): represents a basic/elementary decision. In a decisionmaking process of transition, a set of decision-making activities (DA) represents the finest decomposition level of each critical decision making process (CDP).

These concepts will be used later in the servitization modeling process. We specify below the servitization decision model using 2 formalisms proposed by the GRAI method: the decision-making grid system and the decision-making networks. In this paper we illustrate only the decision-making grid.

Constructing the GRAI Grid for Servitization

An overview of this decision model is provided by a GRAI grid adapted to servitization process. This grid allows classifying specific functional transition areas, and decisional horizons: it requires specifying two axes (vertical and horizontal) of the GRAI grid.

Vertical Axis: Three Decisional Horizons

The vertical axis in the GRAI grid is linked to the decision's horizon: long, medium and short term. This aspect of the decision clarifies crossing from high strategicdecisional level to tactical and operational ones. This distinction allows: -To define the strategic objectives considered by the manufacturer through the servitization transition on a long-term horizon ensuring the sustainability of the firm and minimizing the harmful effects on the environment.

-To establish the necessary resources to make the transition on a medium-term horizon for achieving the above objectives. Essential resources relate to capabilities, business networks and internal and external skills necessary to achieve this strategic change.

-To perform processing operations on a short-term horizon to achieve the strategic servitization objectives. Here, the target is about to define the specific action plan in terms of operational decisions, guided by performance indicators that can ensure a good consistency between the strategic objectives and the available resources. This characterization of the decision-making process sheds light on the transition process advancement in time and the strategic objectives decomposition into operational objectives.

Horizontal Axis: Three Macro-processes

According to GRAI approach, this axis is related to the decision's specific functional domains. We identify functional areas in the decomposition of servitization on three Macro-processes (MP) representing the three problematic issues in a manufacturer servitization process introduced above. These MP comprise the horizontal axis in the GRAI grid:

1. MP1: the product service system (PSS) technical design; 2. MP2: the PSS business model construction; 3. MP3: the Organizational changes, required to support PSS implementation

These MP aim at explaining the problematic complexity of a servitisation transitioning planning. They are also included as areas having different functional characteristics of complementarity and which involve different linkages to the ecosystem's actors. In fact, servitization requires an effort that moves beyond the capabilities of the only transitioning business, it involves complex coopetitive links between different actors; it involves an entire business ecosystem. Planning the PSS value design, the production forces and the commercializing requires understanding its firm's ecosystem ability and capacities to maintain sustainable working flows. In order to achieve its goals, a transitioning company should accommodate to its ecosystems' specificities, they are larger, more diverse, and more fluid than a traditional set of bilateral partnerships. By leveraging ecosystems, companies can deliver complex solutions while maintaining corporate focus [START_REF] Williamson | Ecosystem advantage: How to successfully harness the power of partners[END_REF].

Critical Decision Processes and Decision Centers

In the GRAI modeling framework, decision-centers represent the intersection between a functional decision area and a decision's horizon. To identify servitization DC, we consider that they are directly linked to the concept of critical decision-making processes (CDP) introduced above.

We have made a specific literature review on each MP to identify the critical decision-making processes nested within it. For clarity, in this paper, we limit our explanation to the MP2: Business-model transformation, and the associated DC (the approach is the same for the 2 others MP).

To decompose MP2 into several CDP, we adopt the following Business Model definition [START_REF] Moingeon | Genèse et déploiement d'un nouveau business model : L'étude d'un cas désarmant[END_REF] articulated through three dimensions:

1. The value proposition: it describes the "what?" and reflects the offer attractiveness to the customer. It includes the target market segment, and the proposed offer.

2. The value architecture: it describes the "How?" and is defined as the set of the implemented tasks by the company to achieve delivering the value proposition to the customer. It includes the internal value chain definition of the company and its value network.

3. The profit equation which explains the origin of profitability by combining the revenues, costs and capital employed. It reflects the economic viability of the BM.

These 3 BM dimensions induce the creation of 3 CDP, which will also correspond to decision centers. Then, we decided to add a fourth dimension in the above definition, which is the deployment of the BM, describing the implementation of the BM on the market in terms of operational decisions.

Finally we thus consider four critical decision-making processes; each of them corresponds to a specific DC. Within the structure of the GRAI grid (see Figure 1), the first dimension corresponds to a long-term horizon, and embodies strategic decisions while the second and third correspond to a medium-term horizon, they embody tactical decisions. Finally, the fourth dimension corresponds to a short or very short term that embodies operational decisions.

Illustrating the Servitization GRAI Grid

From the foregoing, we explained the followed process to build the framework in analogy with the GRAI modeling logic and to reflect the evolution of the servitization decision-making process.

The construction of the GRAI grid (Figure .1) allows visualizing the flows trade between different decision centers vertically and horizontally. These trade flows are primarily informational (thin arrows) and decisional (thick arrows). This model allows understanding the complexity of the overall transition process, identifying different backgrounds decision attached to them and delineating the functional areas in MP. In addition, the GRAI grid differentiates the granularity level of the decisionmaking process from strategic to operational objective level and from general to particular within each DC.

Moreover, analyzing the sevitization GRAI grid situates inherently the ecosystem's importance in the approach. Indeed, the focal enterprise linkages with its ecosystem appear at two levels: the first level concerns the informational domains (internal and external) and its influence on the collaborative mechanisms of the transitioning firm. The second level concerns more specifically the composition of each CD, from this point of view, we consider the importance of taking into account the main actors of an ecosystem. In that sense ecosystem plays a relevant role in orienting the global orientation of a servitization trajectory.

Case Study: Ecobel

General Presentation of Ecobel

To concretely illustrate our approach, we propose an application on a case study of a French SME named Ecobel. The company's main activity is the manufacturing, sale and installation of shower heads based on an innovative technique that allows water savings and protecting from legionella. The company accounts in its current market basically establishments receiving general public. Ecobel is planning to adopt a servitization model for its offering, so the company has initiated a debate on the implementation of a servitization strategy and decision-making process. This case study has aroused our interest because of its positioning in the transition process. Indeed, Ecobel currently offers two models: the classic offer selling only the product of showerhead and the integrated PSS offer selling PSS: showerheads reliable over 5 years. The PSS offer includes service contracts of regular maintenance and periodic exchanging of the product of showerheads with a visual identification. Ecobel's leader highlights the difficulty of placing it on the market. The main barriers to the commercialization of the proposed PSS lie in the difficulty of its value measuring and to convince potential customers of it. Ecobel remains cautious about the development of servitization model on its entire range.

Using a comparative approach, we performed the instantiation of the GRAI grid in two stages. We carried out the construction of the GRAI grid specifically to the firm's decision-making process. This current model translates the followed servitization approach of ECOBEL. Then this tracking model is compared to a reference model built to ECOBEL's data but anticipates a servitization path which is finite and consistent. The instantiation of the generic model refers to ECOBEL's characteristics to define the decision consistency over the grid's lines and the decision concordance over the grid's columns. In a hypothetic-deductive approach, the reference model represents the "supposed to be followed" trajectory, as shown in Figure 2.

The instantiation of the generic model highlights ECOBEL's closer exchanges of information and decision-making between decision centers (DC). The analysis of the current and reference models can be addressed on three levels:

1. Structure of the grids: the objective is to highlight the apparent differences between the reference model and the current model of servitization. The comparison of the two grids underlines the absence of certain DC in the current model. As the absence of a set of information and knowledge would have a significant impact on the overall missing critical processes, we consider it as a total failure of missing DC. In addition, although some CDP are actually represented in the current model's grid by their current DC, this does not automatically imply the efficiency of their results. In fact, some DC reflect a failure in their elemental composition in terms of decisionmaking activities. We consider this as a partial failure that spreads through generated information and decisions flows. Shades of color saturation mentioned in the current model then allow assessing the level of failure of the present DC. To locate the origin and degree of a DC failure, we should return to the networks that constitute the GRAI DC, which is not presented in this paper. 2. Information system: represented through the flow of internal and external information in the grids. Information orients the servitization decision-making system. The lack of certain information's elements can cause the DC malfunctioning. In fact, every DA generates a stream of data elements necessary to achieve the next activity. This interdependence produces the spread of possible malfunctions through the information exchanged.

3. Horizontal and vertical exchanges between DC: aiming to highlight the consistency and concordance linking between DC. The DC' vertical exchanges analysis, on a specific MP of the grid among three horizons, clarifies consistency between the decisional centers. The DC horizontal analysis brings information about exchanges between the three functional designated areas on a specific horizon (line of the grid), and clarifies concordance between DC.

The exploratory case study of Ecobel, allowed a concrete illustration of our approach to modeling servitization transition process. GRAI tools instantiated on Ecobel's features enable visualizing the composition of the GRAI grid. Then, the comparison between the reference model and the current one allowed us to highlight the origins of the difficulties in advancing the servitization process of this firm.

Conclusion

This research work emphasizes the systemic nature of servitization process. The analyzing approach proposed aims at decomposing the complexity of servitization decisional process and then at facilitating its management by the manufacturer. The complexity of the decisional servitization process impacted by the ecosystem dynamics may discourage the decision makers, because of too many risks to consider. Thereby this research work introduces a perspective of servitization process diagnosis for manufacturing companies. The objective is to emphasize and explain decisionmaking process potential failures and inconsistencies in order to avoid managerial risks.

Fig. 1 .

 1 Fig.1. Generic servitization transition decisional model

Instantiating the GRAI Grid on Ecobel: A Comparison Between the Reference Model and the Current Model

We undertook a process of qualitative data collection through 3 semi-structured interviews with ECOBEL's leader over a period of 3 months. This data collection has allowed instantiating the generic GRAI grid transitioning process relatively to ECOBEL.

GRAI grid Ecobel "current model" GRAI grid Ecobel "reference model" Fig. 2. Comparison of Ecobel's GRAI grids "reference model" and "current model"