# The Evolution Path to Collaborative Networks 4.0

Luis M. Camarinha-Matos, Hamideh Afsarmanesh

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Luis M. Camarinha-Matos, Hamideh Afsarmanesh. The Evolution Path to Collaborative Networks 4.0. Advancing Research in Information and Communication Technology, AICT-600, pp.170-193, 2021, 10.1007/978-3-030-81701-5_7. hal-03325991

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# The Evolution Path to Collaborative Networks 4.0

Luis M. Camarinha-Matos¹ [0000-0003-0594-1961] and Hamideh Afsarmanesh² [0000-0001-5402-2000]

¹ School Science and Technology, NOVA University of Lisbon and CTS-UNINOVA, Portugal, cam@uninova.pt

² University of Amsterdam, The Netherlands, h.afsarmanesh@uva.nl

**Abstract.** The last two decades have witnessed considerable boost in emergence of a networked society, reflecting the increasing growth in hyper-connectivity among the organizations, people, smart machines, and intelligent systems. This trend was enabled by advances in ICT and more specifically in computer networking. In this context, new forms of coworking and collaboration in networks, composed of distributed, autonomous, and heterogeneous entities have emerged, which first led to the formation of Collaborative Networks (CN) as a new discipline, and then followed by series of milestones leading to its gradual evolution.

Nowadays CNs play a key role in the ongoing process of digital transformation in industry and services. Although it is relatively young, a number of “generations” can be identified through the last decades for the CN discipline. We are now at the beginning of what can be identified as the Collaborative Networks 4.0, characterized by features such as: hybridization in CNs, collaboration between humans and intelligent autonomous systems, collaborative distributed cognitive systems, reflecting on collaborative accountability, handling ethics and coping with risks and disruptions faced in CNs, managing large amounts of collaborative data, monetization of collaboration, creating a collaboration culture, supporting collaboration creativity, handling mass collaboration, and supporting collaborative value creation through new business models, among others.

The IFIP WG 5.5, through its annual conference the PRO-VE, which is now in its 21st edition, has played a determinant role, along these two decades. It has contributed to shaping, promoting, and extending the CN research and development community and its practices; thus, consolidating this area, and identifying and introducing new directions and preserving it as an active research agenda.

**Keywords:** Collaborative Networks, Virtual Organizations, Virtual Enterprises, Digital Transformation, Business Ecosystems

## 1 Introduction

Along the last few decades there has been a noticeable increase in networking, which has enabled a big increase in collaboration activities supported by computer networks.
From an initial focus on collaboration among humans or organizations, the scope has been expanding to include collaboration with and among smart machines and systems. This trend results from a progressive integration of the physical and cyber worlds, namely Cyber-Physical Systems / Internet of Things (CPS/IoT), leading to what can be called a hyper connected world. Complementarily, more and more systems, devices and machines embed higher levels of intelligence, reflecting higher levels of autonomy [1].

Besides the technological drive, the collaboration trend has also been motivated by a number of other factors such as the need to strive in turbulent and even disruptive scenarios, increase in global competition, stronger environmental concerns, push for tailored and one-of-a-kind small products by customers, and tougher quality requirements, as well as the consumer demographic shifts, etc. [2], [3], [4].

Understanding and supporting collaboration activities requires contributions from multiple disciplines and areas, including computer science and engineering, industrial engineering, electrical engineering, management, economics, sociology, law, ethics, and even natural ecosystems and biology, etc. Although the subject has attracted considerable attention in different research communities, realizing the need to study and adopt all these contributions has caused a confluence towards developing an interdisciplinary perspective of the area. As such, during the last decades, the collaborative networks (CN) area has emerged and evolved as an established scientific discipline, whose first manifesto can be traced back to 2004/2005 [5], [6].

In parallel with research and development initiatives, a large number of application cases have emerged and become operational in virtually all sectors of the society. Often new experiments on collaboration have emerged even before the publication of their scientific basis is out. This has been induced and motivated by the possibilities offered by new technology and new market needs. Applications of collaborative networks in industry represent one of the largest groups, starting with the evolution of traditional supply chains to more dynamic value chains and global supply networks, including extended enterprises, virtual enterprises, business ecosystems, etc. [7]. This trend can be seen in manufacturing, namely in the so-called Industry 4.0 [8], [9], [10], [11], but also in construction, agribusiness, energy, and many other areas. The same has emerged in the area of services, with integrated multi-supplier business and software services in a large variety of sectors including commerce, tourism, insurance, healthcare, elderly care, education, and journalism, among others. Similarly, in the governmental sector for providing better services to citizens, through the integration of service offerings from different governmental organizations. We can also observe the emergence of public-private-social networks to address major societal problems which cannot be solved by any single organization.

In times of global crises, such as the case of COVID-19 pandemic, what becomes particularly noticeable is the high number of collaborative networks emerging worldwide. In different sectors such initiatives may use different terminologies, but a set of common underlying principles can be identified to be shared by all of them, showing that the CNs are nowadays widespread to all sectors of activity in the society.

This chapter aims to give an overview of the various types of CNs, a summary of current developments in the area, and a panorama of their evolution, and emerging directions and challenges.
## 2 A Classification of Collaborative Networks

There are currently many “manifestations” of collaborative networks in multiple sectors. Nevertheless, all these cases show diverse characteristics e.g., in terms of their structure, duration, purpose, internal agreements, external liabilities, and membership regulations, among many others. In order to facilitate understanding the specific characteristics of each case it is relevant to establish a taxonomy of collaborative networks.

A frequently used taxonomy (Fig. 1) was originally proposed in [12] and has been updated in more recent publications [7], [11], [13]. At its upper level, this taxonomy introduces two main classes:

* *Collaborative Networked Organizations (CNOs)* – to include all cases for which an organizational structure for the set of network members is designed and specified explicitly.

* *Ad-hoc collaborative networks* – to include those manifestations of CNs that emerge in a quasi-spontaneous way, without predefined organizational structure.

<figure><img src="image_2.png" /><figcaption>Fig. 1. A taxonomy of Collaborative Networks</figcaption></figure>

CNOs include two main sub-classes:

* *Long-term strategic networks* – these are associations of entities, usually established with a very long duration in mind, and whose main objective is to help their members prepare for effectively working together whenever relevant new collaboration opportunities arise. In other words, these associations aim to facilitate the rapid and agile formation of goal-oriented networks in response to new opportunities. In order to accomplish such preparedness for collaboration, the involved entities invest in establishing common or interoperable ICT infrastructures / platforms, agree on common business principles, define rules for sharing and working methods, and providing high level of mutual awareness about the competencies and resources of the members, accompanied by trust building processes, etc.
* *Goal-oriented networks* – these are typically established within and through long-term strategic networks, in order for its involved entities to jointly achieve a given goal, such as realizing a specific project, developing a (complex) product, or providing a service, etc.

According to this taxonomy, the long-term strategic networks are further divided into *Virtual organizations Breeding Environments* (VBEs) [14] and *Professional Virtual Communities* (PVCs), depending on whether the main membership type is respectively organizations/enterprises or people (e.g., free-lancers). Today, some variants of VBEs include *Industry Clusters*, *Industrial Districts Business Ecosystems*, etc. As exceptions, there are some cases of networks that might be considered as both sub-class of the VBE and the PVC, e.g., *Collaborative Innovation networks*.

Goal-oriented Networks include the cases of (i) response to a single opportunity (*Grasping opportunity driven network*), which involves a temporary association of entities that join capabilities and resources to better satisfy the opportunity and that dissolve after the goal is achieved (typically project-oriented); and (ii) continuous production of a product or delivery of services (*Continuous production driven network*). Examples of case (i) include *virtual enterprise* (VE), *virtual organization* (VO), and *extended enterprise*, among others. Examples of case (ii) include *supply chains*, *collaborative virtual government*, etc.

Ad-hoc Collaborative Networks, which often rely on new mobile communication facilities and social networks and are characterized by some kind of “spontaneous emergence” without an apparent (strong) organizational structure. Some of these cases are triggered by some social event or sudden need, or even by human socialization needs. Under this class we can include, for instance, flash mobs, mass collaboration, informal networks, etc.

Considering that CNs are still young phenomena, many new forms are still emerging, namely induced by new technologies. As such, a taxonomy of CNs needs to remain open and subject to continuous expansion and evolution. The presented taxonomy has been used by the research community as a “working taxonomy”. It was however defined at the early stage of this area, and it might be natural for other taxonomies to be proposed as new collaboration experiments pop-up once it becomes difficult to link them to the current classes. Furthermore, other taxonomies might also appear as a result of using different classification perspectives. For instance, in [15] Durugbu suggests a classification that uses mainly the same sub-classes but at the upper level they are organized in three different main groups: (1) Organization-driven, including VBEs and dynamic VOs; (2) Business-driven, including Extended Enterprise and Virtual Enterprise; (3) Professional-driven, including PVCs, eScience, and Virtual Labs. Then the class VO is defined as a member of the three groups.

We might also observe in time some transformation of classes. For instance, some ad-hoc cases, with a long duration, are likely to become organized and thus moved to the class of CNOs. That is the situation we observe for some cases of mass collaboration, such as in the Open source and Wikipedia development communities, which have evolved to a more structured cases and could better be classified as a sub-class of continuous production driven network.
## 3 Generations of Collaborative Networks

Collaborative Networks have been playing a key role in the ongoing process of digital transformation in industry and services. Despite CNs being a relatively young discipline, various development stages or as recently called “development generations” can be identified for it (Fig. 2). These stages/generations can be labelled as follows:

<figure><img src="image_3.png" /><figcaption>Fig. 2. Collaborative Networks Generations</figcaption></figure>

* **Collaborative Networks 1.0** – corresponding to the early stage of the CNs, which was mostly focused on goal-oriented networks and that has covered the beginning manifestations of dynamic supply chains, extended enterprises, virtual enterprises, and virtual organizations.

* **Collaborative Networks 2.0** – mainly characterized by the introduction of the notion of VO breeding environment as a strategic network, encompassing business ecosystems, industry clusters, industrial districts, and professional virtual communities as sub-classes.

* **Collaborative Networks 3.0** – focused on addressing the interplay among multiple CNs, including hybrid value systems networks, co-creation and open innovation networks, and narrowing down on issues of multiple levels of membership, inheritance and transition between CNs, multi-supplier “servitization”, etc.

At present however, we are entering a new stage of the CNs, whose precise characteristics are still not completely clear, but can so far be tentatively specified as follows:
* **Collaborative Networks 4.0** – primarily capturing: hybridization and collaboration between humans and intelligent autonomous systems, while addressing innovation in handling and support for distributed cognitive systems, reflection on accountability, ethics, coping with risks and disruptions, handling large amounts of data, monetizing collaboration, creation of collaboration culture, collaboration creativity, mass collaboration, collaborative value creation, and defining new business models among others.

In terms of research and development most ongoing challenges are in fact related to this generation.

<figure><img src="image_4.png" /><figcaption>Fig. 3. The role of IFIP WG5.5 and PRO-VE in shaping the Collaborative Networks area</figcaption></figure>
The IFIP WG 5.5, as reflected in its annual conference PRO-VE (Working Conference on Virtual Enterprises), has played a determinant role, along the last two decades, in formation and shaping of the related research community and its practices, consolidating the area, and pointing to new research and development directions, thus keeping an active research agenda (Fig. 3) [16]. Further to knowledge sharing and promoting a multi-disciplinary convergence, the PRO-VE series acquired a crucial role in the identification of needs, setting the trends, building a research community, and contributing to education.

## 4 Ongoing Developments, Trends, Challenges and Expectations

Research and development in CNs in the last decades covered a wide spectrum of activities from which a large number of achievements can be pointed out, including development of concepts, models, methods and tools, and their application to a variety of cases. Fig. 4 gives a brief high-level view of the main branches of developments on these past works.

<figure><img src="image_5.png" /><figcaption>Fig. 4. Some earlier developments in Collaborative Networks</figcaption></figure>

More recent and ongoing works involve a greater convergence of knowledge areas and technologies, in line and to some extent influenced by current trends in digital transformation and Industry 4.0 movement [11], eventually leading to a new generation of CNs.

In order to present the leading-edge areas of developments and trends that have revolutionized the CN, as well as the main challenges and expectations which these
have raised, we introduce three main categories for the research work in CN. These include addressing: (i) CN's scope, membership, organization, and governance, (ii) CN's support platforms, tools, and infrastructures, and (iii) CN's collaboration culture, strategies, and business models. Furthermore, for each of these categories, we provide a cross section among the sub-dimensions of the category against their related emerging trends and raised challenges. As such, Sections 4.1 to 4.3 represent each category and provide a set of significant examples within each of their respective cross section tables.

## 4.1 Evolution in Scope, Membership, Organization and Governance

While earlier works on CN were focused on networks of organizations or people, the same concepts are being extended to other types of collaborative networks. In Fig. 5, the main sub-dimensions of the evolution introduced in this area are mentioned, and for each sub-dimension, their recent trends as well as their main raised challenges are exemplified. A summary of our introduced aspects related to each sub-dimension is also described below.

<table><caption>Fig. 5. Overview of Evolution in Scope, Membership, Organization and Governance</caption><thead><tr><th></th><th>Sub-dimension</th><th>Recent and ongoing trends</th><th>Some emerging challenges</th></tr></thead><tbody><tr><td rowspan="7">Evolution in scope, membership, organization and governance</td><td>a. Evolution of scope</td><td><ul><li>Starting to address collaboration among components in complex CPS & SoS</li><li>Starting to address collaboration in digital twins</li></ul></td><td><ul><li>Identify liability & responsibility borders</li><li>Further embed collaboration in digital twins</li><li>Transition from control-orientation to collaboration-orientation</li></ul></td></tr><tr><td>b. Hybridization</td><td><ul><li>Human-Machine collaboration (e.g. collaborative robotics)</li><li>Understanding CNs as socio-technical systems</li><li>Early models of emotions in CNs</li></ul></td><td><ul><li>Explore affective computing & emotions in CNs</li><li>Extend Machine-Machine collaboration</li><li>Extend human-digital twin concept to enhance H-M collaboration</li></ul></td></tr><tr><td>c. Inspiration in Nature</td><td><ul><li>Ecosystem metaphor applied to businesses</li><li>Stigmergy and self-organization</li><li>Identification of structures, behavioral models & enhancement mechanisms</li></ul></td><td><ul><li>Better understand collaboration mechanisms and principles</li><li>Replicate effective mechanisms and organizational structures from Nature</li></ul></td></tr><tr><td>d. Multi-level networks</td><td><ul><li>Hyper-connectivity</li><li>Co-existence of formal and informal networks</li><li>Hybrid value systems</li></ul></td><td><ul><li>Support inter-dependence of co-existing CNs</li><li>Develop governance for interacting networks</li><li>Better understand self-organization, co-evolution, power dynamics, value systems</li></ul></td></tr><tr><td>e. CN Identification and differentiation</td><td><ul><li>Identification and differentiation of CNs from co-working situations analysis</li><li>Identification of collaborative behaviors</li></ul></td><td><ul><li>Concisely specify collaborative behaviors</li><li>Identify and characterize emerging collaboration forms</li></ul></td></tr><tr><td>f. Resilience & antifragility</td><td><ul><li>Identification of capabilities and strategies to support resilience and antifragility</li><li>CN developments in disaster management</li></ul></td><td><ul><li>Find strategies & implementation approaches to enhance resilience and antifragility</li><li>Develop new relevant assessment indicators for resilience and antifragility</li></ul></td></tr><tr><td>g. Collaboration – Competition</td><td><ul><li>Characterization of cooperative environment</li><li>Positioning the cooperative notion in CNs</li></ul></td><td><ul><li>Better understand interactions collaboration – competition</li><li>Develop new collaborative behavior models</li><li>Incentives and expectations management</li></ul></td></tr><tr><td></td><td>h. Sustainability</td><td><ul><li>CNs as an enabler of sustainability</li><li>Green collaborative networks</li><li>CNs in circular economy</li><li>Sustaining the collaboration</li></ul></td><td><ul><li>Progress from enterprise-centric to ecosystem-oriented perspective</li><li>Align developments with standards & UN Agenda 2030</li><li>Seek synergies with social innovation actions</li></ul></td></tr></tbody></table>
a) Evolution in scope

CN concepts are penetrating the area of Cyber-Physical Systems / Internet of Things (CPS/IoT). Previous works on such systems were mostly focused on issues of integration and interoperability, safe communications, control, and energy management. As systems grow in complexity, becoming systems-of-systems (SoS), and involve very large numbers of interconnected components, collaborative networks also offer a new perspective on how to organize and manage them.

Examples include the emerging notion of Collaborative CPS [17], and new approaches to integrated systems-of-systems [18, 19]. In fact, some earlier ideas in this direction can be traced back to [20] which adopted the notions of VO and VBE to materialize agility and reconfigurability of the shop floor, but the approach is attracting more attention recently, as more and more sub-systems embed high levels of intelligence and autonomy [21].

b) Evolution towards hybridization

Collaboration between humans and machines gained a new boost with recent developments in collaborative robotics [22] and facilitated by new interfacing and sensing technologies. But nowadays, more extended scenarios are envisioned, involving collaboration among multiple humans and machines / sub-systems [11], taking advantage of the best capabilities of each. In fact, this idea revisits the notion of “balanced automation systems” [23], [24], which can get new realization through advanced digital twins.

Complementarily, understanding CNs as socio-technical systems allows to borrow concepts from social sciences to better model social interactions among network members. One example is reflected in addressing the notion of emotion in CNs [25], which opens new directions for effective governance of networks.

c) Seeking inspiration from collaboration cases in Nature

Nature offers a large display of collaboration cases, which appear to have a high degree of sustainability and optimization [13]. These cases have been studied in various disciplines including ecology, biology, zoology, etc., from which we can get new knowledge on effective collaboration mechanisms, roles, behavior, and organizational structures.

One of the most popular classes of collaborative networks, the business ecosystems, are in fact inspired on natural ecosystems [26], [27]. Stigmergy is another example of mechanism that has been adopted namely in mass collaboration [28], [11].

d) Evolution towards multi-level networks

With the increasing hyper-connectivity among organizations, people, machines, and smart systems, typically multiple networks co-exist and interact. These networks have different durations, can be at different stages of their life cycle, and often share some members and resources. Some of these networks are formal and regulated by contracts, while others are informal and led by social interactions. Frequently these networks comprise members with very different value systems. As such, it is important to understand their complexity, support the interplay among these networks [29], and devise appropriate governance and analysis methods applicable to them.
One example can be found in [30], where multiple virtual organizations play a role along the life cycle of a complex product such as a solar power plant.

### e) *Identification and differentiation of CNs*

With the diversification in interconnection and socialization mechanisms, new forms of collaboration have emerged. In this context it is important to devise approaches and mechanisms to identify (make visible) and characterize emerging CNs. Examples in this direction include [31] focused on recognition and modeling of collaborative situations, and [32], which addresses the agile operation of small collaborative teams in the context knowledge organizations.

### f) *Evolution towards resilient and antifragile CNs*

Society and its organizations face increasing number of disruptive events, which appear with increasing frequency with potentially large impacts. Such extreme events can be caused by economic crises, pandemic situations, climate change, political instability, terrorism, demographic and immigration shifts, changes in regulations, or high dependency on advanced complex technologies, among others. From industry point of view, these can easily disrupt the global supply chains as well as the local business ecosystems. In order to cope with such events, CNs need to develop high resilience, i.e., the capability to absorb shocks and recover [33], and antifragility, i.e. the capability to not only absorb shocks, but to enhance and become better afterwards [34].

An overview of mechanisms and capabilities to build resilience and antifragility in collaborative business ecosystems can be found in [4]. Other examples include [35], which addresses resilience in service-oriented architectures for virtual enterprises, and [36] that proposes a method for formalization and evaluation of resilience in collaborative networks. Regarding antifragility, an example work is [37], which addresses edge-attack and local edge-repair response mechanisms in complex networks.

### g) *Interactions collaboration – competition*

The combination of collaboration and competition that often, while in an apparent paradox, take place within the same environment, led to the term “coopetition” and is attracting increasing attention [38].

Characterization of *coopetitive* environments and a better understanding of the behavior of their involved actors can help devising more effective governance for CNs. Complementarily, proper incentives policies and expectations management need to be better developed [39], [13].

### h) *Increased focus on sustainability*

CNs have been always pointed out as a core enabler for preserving sustainability [40], and mainly in the sense that resolving the involved challenges in the market and society require collaboration among multiple stakeholders. In this direction, more recently, a number of works introduced the two notions of green virtual enterprise and green VO breeding environment and have further addressed the relation between these two concepts and the circular economy [41], [42], [43].
Complementarily, the issue of sustainability of the collaboration itself is addressed in [44] for the context of collaborative business ecosystems. Furthermore, various initiatives are looking into the role that can be played by CNs in the fulfillment of the set of objectives set in the UN Agenda 2030 for Sustainable Development [45].

## 4.2 Evolution in Support Platforms, Tools, and Infrastructures

The information and communication technologies have always constituted a core enabler for collaborative networks. Therefore, advances in these technologies naturally impact the evolution of CNs, as highlighted in Fig. 6, through a set of main sub-dimensions. In relation to each sub-dimension, the recent set of trends as well as the main raised challenges are then exemplified and briefly described below.

<table><caption>Fig. 6. Overview of Evolution in Support Platforms, Tools, and Infrastructures</caption><thead><tr><th></th><th>Sub-dimension</th><th>Recent and ongoing trends</th><th>Some emerging challenges</th></tr></thead><tbody><tr><td rowspan="8">Evolution in support platforms, tools and infrastructures</td><td>a. Collaboration platforms</td><td><ul><li>New collaboration platforms extending document management systems & enterprise portal technologies</li><li>Cloud support to collaboration platforms</li></ul></td><td><ul><li>Develop collaboration environments coping with high connectivity</li><li>Integrate Intelligence, IoT, Big data, decision support, intelligent assistants, mass collaboration support</li></ul></td></tr><tr><td>b. Enhanced human-system interaction</td><td><ul><li>Increasing use of simulation, virtual and augmented reality</li><li>Improved user experience / intimacy</li><li>Applying some aspects of gaming</li></ul></td><td><ul><li>Explore more "natural" forms of interaction between humans and systems / machines</li><li>Develop collaboration-oriented avatars, new levels of tele-presence / remote interaction and gaming</li></ul></td></tr><tr><td>c. Improved service specification</td><td><ul><li>Strong trend towards servitization & service-enhanced products</li><li>Service-Dominant Logic concept</li><li>Multi-supplier service composition</li></ul></td><td><ul><li>Enhance mechanisms for service discovery, selection, composition and evolution</li><li>Support nomadic and ad-hoc collaboration</li><li>Support collaborative service design and on-the-fly orchestration</li></ul></td></tr><tr><td>d. Cyber-security and risks</td><td><ul><li>Safe communications, access rights, non-repudiation</li><li>Digital institutions (e.g. e-Notary)</li><li>Early blockchain application in CNs</li></ul></td><td><ul><li>Find new ways of managing cyber-risks, risk propagation and counter-attack strategies</li><li>Further distributed ledger technologies in CNs</li><li>Design new electronic institutions</li></ul></td></tr><tr><td>e. Dealing with data-rich contexts</td><td><ul><li>Cloud repositories and further application of data analytics and machine learning</li><li>Handling increased ubiquity of sensors</li></ul></td><td><ul><li>Adopt proper data analytics & machine learning tools</li><li>Handle data to enhance traceability and transparency along value chains</li><li>Face uncertainty, fake data, and data quality</li></ul></td></tr><tr><td>f. Smartness and sensing</td><td><ul><li>New levels of context awareness</li><li>Smart products and stigmergic collaboration</li><li>Sensing, smart, and sustainable (S3) concept</li></ul></td><td><ul><li>Leverage adoption of AI / Machine Learning in CNs</li><li>Support adaptation to rapidly changing environments</li></ul></td></tr><tr><td>g. Cognitive networks</td><td><ul><li>Early adoption of cognitive engineering tools in CNs</li></ul></td><td><ul><li>Develop cognitive collaborative networks & distributed cognition with evolving capabilities</li></ul></td></tr><tr><td>h. Linked data and ontologies</td><td><ul><li>Interlinking of data and widely varied knowledge sources</li><li>Common ontologies for VBEs</li></ul></td><td><ul><li>Experiment and assess open linked data in CNs</li><li>Collaborative interlinking, evolution, and refinement of ontologies</li></ul></td></tr></tbody></table>

### a) Towards a new generation of collaboration platforms

New collaboration platforms, mainly focused on collaboration among humans, are primarily resulted from progressive evolution and convergence between document management systems and enterprise portal technologies. Additionally, cloud computing provided these platforms with more elastic resources management and
remote access, which is crucial for geographically distributed systems such as the
CNs [46].

The combination of various other technologies, including the big data, sensing, IoT, and the AI / ML, offer new possibilities for richer collaboration environments, featuring real-time context awareness, decision-support and intelligent assistance, data handling, analysis and visualization, and data services [47], [48].

b) *Enhancement of human-system interaction*

The increasing adoption of augmented and virtual reality, simulation, and the so-
called “natural user interfaces” allows for better human-machine interaction and
system interface. The development of collaboration-oriented avatars [49], [50]
adoption of gaming mechanisms, and remote interaction with resources also
contribute to improving the user experience and establishing more effective
interaction among network members.

c) *Improved service specification*

There is a strong trend towards “servitization” as reflected in the notions of service-
enhanced products [51], product-service systems [52], [53], smart product, and
service-dominant logic [54], [55], [56].

In recent times, there has been a special focus on the provision of integrated
services and combining multi-supplier contributions, which implicitly requires
addressing the collaborative networks for service provision [57]. Other directions
include development of novel mechanisms for service discovery, service selection,
service composition [58], and service evolution in order to adapt to the evolving user
requirements as well as the technology evolution [57]. Collaborative design of
services, complemented with the “de-construction” / transformation of traditional
software systems into collections of services [59], is another trend that attracts
research work.

d) *Improved handling of cyber-security and communication risks*

Dealing with cyber-security has been a continuous concern since the early stages of
the CN discipline [60]. Various mechanisms for safe communications, access rights
management, non-repudiation, authentication, etc. have been addressed.
Complementarily, some experiments on electronic institutions were carried out [107].
One example is the electronic notary as an additional facility to support VO creation
in VBEs, and as a tool supporting in VBE / business ecosystems management [62].
Further trends include approaches to preclude risk propagation and exploitation of
distributed ledger technologies, such as the block-chain [63], [64], [48].

e) *Dealing with data-rich contexts*

The hyper-connectivity among organizations, people, devices, and systems, combined
with increased usage of sensors, is leading to data-rich environments which enable
better and timely decision-making, and development of new services. In this context,
new challenges emerge regarding the adoption of proper data storage, visualization
and analytics tools, and applicable machine learning techniques [11], [47]. One
relevant point to mention here is the possibility for increased traceability and
transparency of the contributors along the value chains [11]. But this new context also
poses new challenges regarding data privacy, data ownership, quality of data, fake
data identification and handling, and how to cope with uncertainty that may rise in the
CNs [65], [47], [66].

f) Increased smartness and sensing

Ubiquitous sensing allows for higher levels of context awareness which combined
with progressive adoption of AI and ML leads to increasing the smartness, autonomy
and self-adaptability capabilities of the infrastructures, systems, and machines. Such
contexts support the design of “sensing, smart and sustainable (S3)” networks [67],
[68] and thus resulting CNs that feature distributed intelligence. Some examples of
applying these ideas to the design of novel collaborative platforms in manufacturing
and smart products can be found in [69] and [70].

g) Towards cognitive networks

An additional level of integration of AI and machine learning elements in CN infrastructures and tools is emerging by the first attempts to embed cognitive engineering tools in such systems. One example is [71], which envisages cognitive-based collaborative networks to support mobile health services. In [72] the Cognitive Model of Creativity is explored to bring computational creativity to manufacturing networks. Another example [73] attempts to deploy cognitive capabilities in a CN for the delivery of public services.

These early examples illustrate a trend that may bring CNs to a form of collective
intelligence or distributed cognition.

h) Exploring linked data and ontologies

In a hyperconnected world heterogeneous data and knowledge sources abound. The
development of new methods to interlink and explore those sources, namely open
ones, can enhance collaboration among members of CNs and open the opportunity for
creation of new services. Additionally, the interlinking existing ontologies defined by
different members in the CN, as well as collaborative refinement and evolution of the
shared ontologies for the CN, is also important to facilitate common understanding.

As an example, in [74] linked data methods are used as the bases for development
of advanced collaboration spaces. Another example [75] addresses collaborative
editing of linked data. An early example of research and development for engineering
and evolution of VBE ontology can be found in [76]. Another example [77] discusses
extensions of a standard ontology for robotics and automation (CORA) for
collaborative robotics and collaboration between humans and cyber-physical systems.
A method for ontology evolution is illustrated in [61].

## 4.3 Evolution in Collaboration Culture, Strategies and Business Models

The third complementary area of evolutions in CNs focuses more on the soft issues
related to this paradigm. In Fig. 7, a set of main sub-dimensions for the evolution
introduced in this area are mentioned, and for each sub-dimension their recent trends
as well as their main raised challenges are exemplified. A summary of our introduced aspects related to each sub-dimension is also described further below.

<table><caption>Fig. 7. Overview of Evolution in Collaboration Culture, Strategies and Business Models</caption><thead><tr><th></th><th>Sub-dimension</th><th>Recent and ongoing trends</th><th>Some emerging challenges</th></tr></thead><tbody><tr><td rowspan="8">Evolution in collaboration culture, strategies, and business models</td><td>a. New collaborative business models</td><td><ul><li>New studies on business models for CNs</li><li>Emerging collaborative value creation forms</li></ul></td><td><ul><li>Keep a radar on emerging collaborative business models and assess learned lessons</li><li>Further develop collaborative business models</li><li>New value co-creation models & mechanisms</li></ul></td></tr><tr><td>b. Trust, benefits, risks</td><td><ul><li>Some models of trust in CNs</li><li>Preliminary identification and analysis of risks and benefits in CNs</li></ul></td><td><ul><li>Develop generalized risk management approaches for CNs</li><li>Model uncertainty and its propagation in CNs</li><li>Develop new trust management methods associated to new business models</li></ul></td></tr><tr><td>c. Open innovation</td><td><ul><li>User / customer involvement approaches</li><li>Data mining in characterization of open innovation</li></ul></td><td><ul><li>Further characterization of open innovation organization and governance</li><li>Develop novel models of intellectual property management for innovation in networks</li></ul></td></tr><tr><td>d. Monetization of collaboration</td><td><ul><li>Identification of benefits of collaboration</li><li>Early models of collaboration benefits distribution</li></ul></td><td><ul><li>Develop appropriate indicators & metrics for value of collaboration</li><li>Study effect of indicators on CN behavior</li><li>Devise effective ways for benefits distribution</li></ul></td></tr><tr><td>e. Collaboration culture & awareness</td><td><ul><li>New graduate level courses on CNs at MSc and PhD levels</li><li>Developing proof of concept demonstration projects in various domains</li></ul></td><td><ul><li>Further education and dissemination</li><li>Create a culture of collaboration & reduce uncertainty around collaboration</li><li>Establish further education curricula on CNs</li></ul></td></tr><tr><td>f. Expanding portfolio of applications</td><td><ul><li>Some published collections of CNs cases</li><li>Recent expansion of CNs to less explored domains - energy, healthcare, agribusiness, smart cities, etc.</li></ul></td><td><ul><li>Expand portfolio of success stories and demonstrations of collaboration</li><li>Normalize description of CN cases</li></ul></td></tr><tr><td>g. Multi-disciplinarity & inter-disciplinarity</td><td><ul><li>CNs discipline is the result of inter-disciplinary effort</li><li>Effectively supporting the ubiquity of CNs in all multi- and inter-disciplinary aspects</li></ul></td><td><ul><li>Re-enforce multi-disciplinarity and inter-disciplinarity</li><li>Seek synergies from diversity of knowledge sources and actors</li></ul></td></tr><tr><td>h. Responsibility, ethics, compliance</td><td><ul><li>Extend early CN legal frameworks</li><li>Early specification of the notion of responsibility borders</li></ul></td><td><ul><li>Develop new regulatory framework coping with responsibility sharing, ethics and compliance</li><li>Redesign strategies to interact with surrounding environment</li></ul></td></tr></tbody></table>

a) *New collaborative business models*

The ongoing digital transformation processes and the associated convergence of multiple technologies induce new collaborative business models and value co-creation approaches. This trend is reflected in the emergence of concepts such as co-creation, co-innovation, customer intimacy, *glocal* enterprise, shared economy, *servitization*, and hybrid value chains, among others [11], [78], [79], [80].

The continued emergence of new collaboration forms raises the challenge of keeping a radar on such new business models, assessing lessons learned, and further modeling and structuring the involved mechanisms [81].

b) *Further addressing of trust, collaboration benefits and risks*

Trust management has been a classical topic in CN research. Various issues have been addressed, such as rational trust modeling, assessment, and management [82], trust monitoring [83], evaluation of the social dimension of trust [84], etc. With the emergence of new collaboration forms it also becomes important to pursue new approaches for trust management in relation to new business models.
The benefits of collaboration are often mentioned in the CN literature. However, there is little work on methods to make a fair distribution of benefits among the CN members. Some early examples include methods based on the Shappley value [85], [86], and a proposal for modeling the “social contributed benefits” and “external (received) benefits”, together with a list of their relevant metrics [87]. Nevertheless, further developments of models for fair benefits distribution are still lacking.

Risks of collaboration is another issue that has been intuitively discussed and for which some methods have been proposed addressing the risk analysis and propagation, risk identification, and risk assessment and reduction [88], [89], [90], [91], [92]. Yet, a more generalized risk management approach for CNs is still needed, namely in order to cope with new the forms of CNs, and the complexity of multi-level networks.

### c) Evolution in collaboration for open innovation

Collaboration for the purpose of innovation in an open setting has been another active research topic [92], [93], [94]. A comprehensive analysis of interactions between the two areas of collaborative networks and innovation/open innovation is provided in [95].

Customer involvement in CNs, and namely in co-creation / co-innovation processes, has been particularly focused on in recent years [30], [93], [96],[97].

Despite of these recent developments, further characterization of the open innovation processes, and their organization and governance, together with novel approaches to handle the rights to intellectual property in this context, remain open challenges.

### d) Towards monetization of collaboration

Collaborative work adds certain overhead to the activities of its involved actors, e.g., the communication, reporting, and information exchange costs, the additional needed process formalization efforts, and the conflict resolution, among others. At the same time, various benefits generated through collaboration are difficult to measure and thus may easily be overlooked, e.g., the access to new knowledge, triggering innovative ideas, increased prestige, higher reputation, achieving diversity, as well as improving the potential of risk taking, acquisition of additional competencies, and agility, among others.

It is, therefore, important to devise ways of making collaboration benefits more explicit and measurable. Some earlier attempts in this direction can be found in [85] and [87] which address the modeling of collaboration benefits and identifying its main relevant indicators. Nevertheless, further work is still needed to appropriately define these indicators and introduce metrics for measuring the value of collaboration [11], in other words, to “monetize collaboration”. Furthermore, it is also important to study the effect of such indicators on the behavior of the CN members [44].

### e) Creation of collaboration culture and awareness

The effectiveness of collaboration does not depend solely on technological solutions. Considering the socio-technical nature of the CNs, it is fundamental to increase the general awareness about the main issues in CNs and to promote formal education on
this discipline. Various universities have launched graduate courses related to the CN subject at MSc/MBA and PhD levels. One of the first reported examples can be found in [98] and a preliminary proposal for a reference curriculum for CN education is provided in [99]. But further education curricula need to still be established [11] and sharing of experiences and educational resources needs to be promoted.

f) Expanding the portfolio of application domains

As part of a wider knowledge dissemination and awareness creation efforts for CNs, it is important to organize and make available collections of (real) case studies of CN applications. One example of such collection is available in [100], but an expansion of this portfolio would be highly beneficial for CN researchers, practitioners, and educators.

In recent years we have noticed a growing expansion of CNs to less explored domains, such as energy [101], health and elderly care [57], agribusiness [102], smart cities [47], etc. In order to facilitate the understanding and sharing of experiences across sectors, it is also important to reach some normalization on the description of the case studies.

g) Re-enforcing multi-disciplinary / inter-disciplinary approaches

The CN discipline itself is the result of an inter-disciplinary convergence [5], [6]. With the expansion of CN application to diverse domains and the involvement of new players as the result of ongoing digital transformation process [11], it is necessary to re-enforce the CN's multi-disciplinary and inter-disciplinary dialog, seeking synergies from diverse knowledge areas. Besides the traditional areas of technology and the social sciences involved in the CNs, it is increasingly important to also extend this dialog to natural sciences that study collaboration forms in Nature [13].

h) Progressing on responsibility, ethics, and compliance

Legal frameworks for regulating the creation and operation of several classes of CNs have been so far discussed, e.g., in [103], [104], [105], and various countries already have specific laws addressing both the “long-term strategic networks” and the “goal-oriented networks”.

With the increase in hyper-connectivity as well as in the intelligence / autonomy of machines and systems, it is necessary to further invest on regulatory frameworks coping with issues of responsibility, ethics, and compliance [11] among all entities involved in the CNs. For instance, in [106] there is some discussion on liability and “responsibility borders” in the case of multi-supplier systems-of-systems, but these issues need further developments as we move towards hybridization of CNs.

5 Conclusions

Currently, research and development on collaborative networks are growing in a pervasive manner, as manifested in covering a large number of collaboration forms and CN applications in multiple sectors of the society. Induced by new possibilities offered by advances in information and communication technologies, and motivated
by the current digital transformation movement, it is likely that various other new
forms of CN will also appear in the coming years. As such, it is important to the CN
taxonomy is open, in order to properly allow positioning various types of CNs in
relation to one another.

Along the last 2-3 decades, R&D in this area has co-evolved in parallel to the
evolutions in ICT developments, the new market, and the societal needs, thus
introducing a number of significant leaps in this area. In analogy with the industrial
revolutions and the ongoing Industry 4.0 trends, four “generations of CNs” can also
be identified. The currently emerging 4th generation is still in its early stages.
Nevertheless, it can already be roughly characterized by various dimensions as well as
the trends and challenges that once achieved, will boost the CN 4.0. As such, in this
chapter an extensive list of trends and challenges is identified, which are organized
along three main dimensions of: (1) Evolution in scope, memberships, organization
and governance, (2) Evolution in support platforms, tools and infrastructures, and (3)
Evolution in collaboration culture, strategies, and business models. The set of trends
and challenges that are mentioned and exemplified in relation to each of the above
dimensions and their sub-dimensions, also constitute the elements of a research
agenda for the coming years in this area.

Acknowledgements.

This work was developed in collaboration with the SOCOLNET scientific network and its ARCON-ACM initiative. Partial support also from the Center of Technology and Systems – UNINOVA, and the Portuguese FCT Foundation (project UIDB/00066/2020), and the European Commission (project DiGiFoF).

**References**

1. Monostori, L.: Cyber-physical Production Systems: Roots, Expectations and R&D Challenges. *Procedia CIRP*, vol. 17, pp 9-13, DOI: 10.1016/j.procir.2014.03.115 (2014).

2. Camarinha-Matos, L. M.: Collaborative Networks: A Mechanism for Enterprise Agility and Resilience. In: *Enterprise Interoperability VI* (K. Maertins et al. Eds.), *Proceedings of the I-ESA 7*, Springer, pp. 3-11 (2014).

3. Romero, D.; Molina, A.: Collaborative networked organisations and customer communities: value co-creation and co-innovation in the networking era. *Production Planning & Control*, vol. 22, issue 5-6, pp 447-472, https://doi.org/10.1080/09537287.2010.536619 (2011).

4. Ramezani, J.; Camarinha-Matos, L. M.: Approaches for resilience and antifragility in collaborative business ecosystems. *Technological Forecasting and Social Change*, Volume 151, February 2020, 119846. DOI: 10.1016/j.techfore.2019.119846 (2020).

5. Camarinha-Matos L.M., Afsarmanesh H.: The Emerging Discipline of Collaborative Networks. In: *Virtual Enterprises and Collaborative Networks*. PRO-VE 2004. IFIP, vol 149. Springer, Boston, MA. https://doi.org/10.1007/1-4020-8139-1_1 (2004).

6. Camarinha-Matos, L.M.; Afsarmanesh, H.: Collaborative networks: A new scientific discipline, *J. Intelligent Manufacturing*, vol. 16, Nº 4-5, pp439-452. (2005).
7. Camarinha-Matos, L.M.; Afsarmanesh, H.; Galeano, N.; Molina, A.: Collaborative Networked Organizations - Concepts and practice in Manufacturing Enterprises. *Journal of Computers & Industrial Engineering*, vol. 57, pp 46–60. DOI: 10.1016/j.cie.2008.11.024 (2009).

8. Bartodziej, C. J.: The Concept Industry 4.0 - An Empirical Analysis of Technologies and Applications in Production Logistics. Springer Gabler, Wiesbaden, Germany. DOI 10.1007/978-3-658-16502-4 (2017).

9. Kang, H.S., Lee, J.Y., Choi, S. Kim, H., Park, J. H., Son, J. Y., Kim, B. H., Noh, S. D.: Smart manufacturing: Past research, present findings, and future directions. *Int. J. of Precis. Eng. and Manuf.-Green Tech.* 3(1): 111-128. (2016).

10. Hermann, M., Pentek, T., Otto, B.: Design Principles for Industrie 4.0 Scenarios. In: *2016 49th Hawaii International Conference on System Sciences (HICSS)*, Koloa, HI, 2016, IEEE Xplore, pp. 3928-3937. DOI: 10.1109/HICSS.2016.488 (2016).

11. Camarinha-Matos, L. M.; Fornasiero, R.; Ramezani, J.; Ferrada, F.: Collaborative Networks: A Pillar of Digital Transformation. *Applied Sciences*, 2019, 9(24), 5431; DOI: 10.3390/app9245431 (2019).

12. Camarinha-Matos L.M., Afsarmanesh H.: Collaborative Networks. In: Wang K., Kovacs G.L., Wozny M., Fang M. (eds) *Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management*. PROLAMAT 2006. IFIP, vol 207, pp. 26-40. Springer, Boston, MA. https://doi.org/10.1007/0-387-34403-9_4 (2006).

13. Camarinha-Matos L.M., Afsarmanesh H.: Roots of Collaboration: Nature-Inspired Solutions for Collaborative Networks, *IEEE Access*, vol. 6, pp. 30829-30843, 2018, doi: 10.1109/ACCESS.2018.2845119. (2018).

14. Afsarmanesh, H.; Camarinha-Matos, L. M.; Msanjila, S. S.: Models, Methodologies, and Tools Supporting Establishment and Management of Second-Generation VBEs, *IEEE Transactions on Systems, Man and Cybernetics – C*, Vol. 41, Issue 5, Sept 2011, pp 692-710. (2011).

15. Durugbo, C.: Collaborative networks: A systematic review and multi-level framework. *Int. J. Prod. Res.*, 54, 3749–3776, doi:10.1080/00207543.2015.1122249 (2016).

16. Camarinha-Matos, L. M.: Collaborative Networks in Industry and the role of PRO-VE, *International Journal of Production Management and Engineering*, vol. 2, N° 2, pp 53-56 (2014).

17. Nazarenko, A.; Camarinha-Matos, L. M.: Basis for an Approach to Design Collaborative Cyber-Physical Systems. In: *Technological Innovation for Industry and Service Systems*. DoCEIS 2019. IFIP AICT, vol 553, pp. 193-205, 2019. Springer, Cham. DOI: 10.1007/978-3-030-17771-3_16 (2019).

18. Osório A.L., Belloum A., Afsarmanesh H., Camarinha-Matos L. M.: Agnostic Informatics System of Systems: The Open ISoS Services Framework. In: *Collaboration in a Data-Rich World*. PRO-VE 2017. IFIP AICT, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-65151-4_37 (2017).

19. Osório A.L., Camarinha-Matos L.M., Dias T., Tavares J.: Adaptive Integration of IoT with Informatics Systems for Collaborative Industry: The SITL-IoT Case. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_5 (2019)

20. Barata, J.; Camarinha-Matos, L. M.: Coalitions of manufacturing components for shopfloor agility - the CoBASA architecture. *Int. Journal of Networking and Virtual Organizations*, 2(1): 50-77. DOI: 10.1504/IJNVO.2003.003518 (2003).

21. Borangiu, T.; Trentesaux, D.; Thomas, A.; Leitão, P.; Barata, J.: Digital transformation of manufacturing through cloud services and resource virtualization. *Computers in Industry*, Vol. 108, pp 150-162. DOI: 10.1016/j.compind.2019.01.006. (2019).

22. Goldberg, K.: Robots and the return to collaborative intelligence. *Nature Machine Intelligence*, vol. 1, pages2–4. DOI: 10.1038/s42256-018-0008-x (2019).
23. Camarinha-Matos, L. M.; Rabelo, R.; Osório, A. L.: Balanced Automation. In: *Computer Assisted Management and Control of Manufacturing Systems*, Springer, London, pp. 376-414, DOI: 10.1007/978-1-4471-0959-4_14 (1997).

24. Romero, D.; Noran, O.; Stahre, J.; Bernus, P.; Fast-Berglund, A.: Towards a Human-Centred Reference Architecture for Next Generation Balanced Automation Systems: Human-Automation Symbiosis. In: *Advances in Production Management Systems: Innovative Production Management Towards Sustainable Growth*, Part II, APMS 2016, AICT 460, Springer, Cham, pp 556-566. DOI: 10.1007/978-3-319-22759-7_64 (2016).

25. Ferrada, F.; Camarinha-Matos, L.M.: A modelling framework for collaborative network emotions, *Enterprise Information Systems*, 13(7-8): 1164-1194, DOI: 10.1080/17517575.2019.1633583 (2019).

26. Moore, J.F.: The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems. Harper Collins Publishers. (1996).

27. Graça, P.; Camarinha-Matos, L. M.: Performance Indicators for Collaborative Business Ecosystems – Literature review and trends. *Technological Forecasting & Social Change*, 116(C): 237-255. DOI: 10.1016/j.techfore.2016.10.012 (2017).

28. Zamiri, M.; Camarinha-Matos, L. M.: Mass Collaboration and Learning: Opportunities, Challenges, and Influential Factors. *Applied Sciences*, 9(13), 2620, DOI: 10.3390/app9132620 (2019).

29. Camarinha-Matos, L.M.; Ferrada, F.; Oliveira, A. I.: Interplay of Collaborative Networks in Product Servicing. In: *Collaborative Systems for Reindustrialization*, Springer, IFIP Series 408/2013, pp. 51-60. DOI: 10.1007/978-3-642-40543-3_6 (2013).

30. Camarinha-Matos, L. M.; Oliveira, A. I.; Ferrada, F.; Sobotka, P.; Vataščinová, A.; Thamburaj, V.: Collaborative Enterprise Networks for Solar Energy, *Proceedings of ICCCT'15 - IEEE International conference on Computing and Communications Technologies*, pp 93-98, 26-27 Feb 2015, Chennai, India. DOI 10.1109/ICCCT2.2015.7292726 (2015).

31. Gou J., Liu Q., Mu W., Ying W., Afsarmanesh H., Benaben F.: A Digital-Enabled Framework for Intelligent Collaboration in Small Teams. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_18 (2019).

32. Liu, Q.; Gou, J.; Camarinha-Matos, L. M.: Towards Agile Operation for Small Teams in Knowledge Intensive Organizations: A Collaboration framework. To appear in *Boosting Collaborative Networks 4.0*, PRO-VE 2020. IFIP AICT, Springer, Cham. (2020).

33. Chroust G, and Aumayr G.: Resilience 2.0: Computer-aided Disaster Management. *Journal of Systems Science and Systems*. 26 (3): 321–335. doi:10.1007/s11518-017-5335-7. (2017).

34. Taleb, N.N.: Antifragile: things that gain from disorder. Random House Publishing Group, New York. (2012).

35. Bezerra R.O., Rabelo R.J., Cancian M.H.: Supporting SOA Resilience in Virtual Enterprises. In: *Collaborative Networks of Cognitive Systems*. PRO-VE 2018. IFIP AICT, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-99127-6_10. (2018).

36. Moradi B., Daclin N., Chapurlat V.: Formalization and Evaluation of Non-functional Requirements: Application to Resilience. In: *Collaborative Networks of Cognitive Systems*. PRO-VE 2018. IFIP AICT, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-99127-6_11. (2018).

37. Topîrceanu A., Udrescu M., Mărculescu R.: Complex Networks Antifragility under Sustained Edge Attack-Repair Mechanisms. In: *Proceedings of NetSci-X 2020: Sixth International Winter School and Conference on Network Science*. NetSci-X 2020.
Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-38965-9_13 (2020).

38. Ritala, P.; Golnam, A.; Wegmann, A.: Coopetition-based business models: The case of Amazon.com. *Industrial Marketing Management*, Volume 43, Issue 2, pp. 236-249. https://doi.org/10.1016/j.indmarman.2013.11.005 (2014).

39. Vanessa R. Levesque, Aram J. K. Calhoun, Kathleen P. Bell & Teresa R. Johnson: Turning Contention into Collaboration: Engaging Power, Trust, and Learning in Collaborative Networks. *Society & Natural Resources*, 30:2, 245-260, DOI: 10.1080/08941920.2016.1180726 (2017).

40. Camarinha-Matos L.M., Afsarmanesh H., Boucher X.: The Role of Collaborative Networks in Sustainability. In: *Collaborative Networks for a Sustainable World*. PRO-VE 2010. IFIP AICT, vol 336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15961-9_1 (2010).

41. Romero D., Molina A.: Green Virtual Enterprise Breeding Environment Reference Framework. In: *Adaptation and Value Creating Collaborative Networks*. PRO-VE 2011. IFIP AICT, vol 362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23330-2_59 (2011).

42. Romero, D.; Noran, O.: Green Virtual Enterprises and their Breeding Environments: Engineering their Sustainability as Systems of Systems for the Circular Economy. *IFAC-PapersOnLine*, Volume 48, Issue 3, pp 2258-2265. DOI: 10.1016/j.ifacol.2015.06.424 (2015).

43. Romero, D.; Nora, O.: Towards Green Sensing Virtual Enterprises: Interconnected Sensing Enterprises, Intelligent Assets and Smart Products in the Cyber-Physical Circular Economy. *IFAC-PapersOnline* Vol. 50, Nº 1, pp 11719-11724. https://doi.org/10.1016/j.ifacol.2017.08.1944 (2017).

44. Graça, P.; Camarinha-Matos, L. M.: A Model of Evolution of a Collaborative Business Ecosystem Influenced by Performance Indicators. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568, pp 245-258. Springer, Cham. DOI: 10.1007/978-3-030-28464-0_22 (2019).

45. United Nations. Transforming our world: The 2030 Agenda for Sustainable Development. UN Report A/RES/70/1. Available online: https://sustainabledevelopment.un.org/post2015/transformingourworld (last accessed 4 Aug 2020).

46. Camarinha-Matos, L. M.; Juan-Verdejo, A.; Alexakis, S.; Bär, H.; Surajbali, B.: Cloud-based Collaboration Spaces for Enterprise Networks, *Proceedings of ICCCT'15 - IEEE International conference on Computing and Communications Technologies*, 185 - 190, 26-27 Feb 2015, Chennai, India. DOI: 10.1109/ICCCT2.2015.7292743 (2015).

47. Petersen S.A., Pourzolfaghar Z., Alloush I., Ahlers D., Krogstie J., Helfert M.: Value-Added Services, Virtual Enterprises and Data Spaces Inspired Enterprise Architecture for Smart Cities. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_34 (2019).

48. Ojo A.: Next Generation Government - Hyperconnected, Smart and Augmented. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_25 (2019).

49. Szybicki D., Kurc K., Gierlak P., Burghardt A., Muszyńska M., Uliasz M.: Application of Virtual Reality in Designing and Programming of Robotic Stations. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_51 (2019).

50. Rabelo R.J., Zambiasi S.P., Romero D.: Collaborative Softbots: Enhancing Operational Excellence in Systems of Cyber-Physical Systems. In: *Collaborative Networks and*
*Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham.
https://doi.org/10.1007/978-3-030-28464-0_6 (2019).

51. Camarinha-Matos L.M., Oliveira A.I., Ferrada F.: Supporting Collaborative Networks for Complex Service-Enhanced Products. In: *Risks and Resilience of Collaborative Networks*. PRO-VE 2015. IFIP AICT, vol 463. Springer, Cham.
https://doi.org/10.1007/978-3-319-24141-8_16 (2015).

52. Boucher, X.: Economic and Organizational Transition towards Product/Service Systems: The Case of French SMEs. In: *Collaborative Networks in the Internet of Services*, IFIP AICT series 380, Springer, pp 26-34. DOI: 10.1007/978-3-642-32775-9_3 (2012).

53. Sklyar, A.; Kowalkowski, C.; Sörhammar, D.; Tronvoll, B.: Resource integration through digitalisation: a service ecosystem perspective. *Journal of Marketing Management*, vol. 35, issue 11-12, pp 1-18,. DOI: 10.1080/0267257X.2019.1600572 (2019).

54. Lusch, R. F.; Vargo, S. L.: *The Service-Dominant Logic of Marketing: Dialog, Debate, and Directions*. Routledge (2014).

55. Turetken, Oktay and Grefen, Paul: DESIGNING SERVICE-DOMINANT BUSINESS MODELS. In: *Proceedings of the 25th European Conference on Information Systems (ECIS)*, Guimarães, Portugal, June 5-10, 2017 (pp. 2218-2233). (2017).

56. Lüftenegger E., Comuzzi M., Grefen P.: The Service-Dominant Ecosystem: Mapping a Service Dominant Strategy to a Product-Service Ecosystem. In: *Collaborative Systems for Reindustrialization*. PRO-VE 2013. IFIP AICT, vol 408. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40543-3_3 (2013).

57. Baldissera, T.A.; Camarinha-Matos, L. M.: SCoPE: Service Composition and Personalization Environment. *Applied Sciences* 2018, 8(11), 2297; https://doi.org/10.3390/app8112297. (2018).

58. Afsarmanesh, H.; Sargolzaei, M.; Shadi, M.: Semi-automated software service integration in virtual organisations. *Enterprise Information Systems*, 9(5-6), pp.528-555. DOI: 10.1080/17517575.2014.928953 (2015).

59. Baek, J. S.; Kim, S.; Pahk, Y.; Manzini, E.: A sociotechnical framework for the design of collaborative services, *Design Studies*, Volume 55, pp 54-78, https://doi.org/10.1016/j destud.2017.01.001 (2018).

60. Osório A.L., Antunes C., Barata M.M.: The PRODNET Communication Infrastructure. In: *Infrastructures for Virtual Enterprises*. PRO-VE 1999. IFIP, vol 27. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35577-1_11 (1999).

61. Cao T., Mu W., Montarnal A., Barthe-Delanoë AM.: A Method of Ontology Evolution and Concept Evaluation Based on Knowledge Discovery in the Heavy Haul Railway Risk System. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_20 (2019).

62. Oliveira A.I., Camarinha-Matos L.M.: Negotiation and Contracting in Collaborative Networks. In: *Emerging Trends in Technological Innovation*. DoCEIS 2010. IFIP AICT, vol 314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11628-5_9 (2010).

63. Schaffers H.: The Relevance of Blockchain for Collaborative Networked Organizations. In: *Collaborative Networks of Cognitive Systems*. PRO-VE 2018. IFIP AICT, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-99127-6_1 (2018).

64. Silva H.D., Soares A.L., Bettoni A., Francesco A.B., Albertario S.: A Digital Platform Architecture to Support Multi-dimensional Surplus Capacity Sharing. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICTy, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_28 (2019).
65. Mäki M., Alamäki A.: Data Privacy Concerns Throughout the Customer Journey and Different Service Industries. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/2019.

66. Schuh G., Hoffmann J., Bleider M., Zeller V.: Assessment of IS Integration Efforts to Implement the Internet of Production Reference Architecture. In: *Collaborative Networks of Cognitive Systems*. PRO-VE 2018. IFIP AICT, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-99127-6_28 (2018).

67. Chavarría-Barrientos D., Camarinha-Matos L.M., Molina A.: Achieving the Sensing, Smart and Sustainable “Everything”. In: *Collaboration in a Data-Rich World*. PRO-VE 2017. IFIP AICT, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-65151-4_51 (2017).

68. Chavarría-Barrientos, D.; Batres, R.; Wright, P. K.; Molina, A.: A methodology to create a sensing, smart and sustainable manufacturing enterprise. *International Journal of Production Research*, Vol. 56, issue 1-2, pp 584-603.
https://doi.org/10.1080/00207543.2017.1386333 (2018).

69. Mladineo, M.; Celar, S.; Celent, L.; Crnjac, M.: Selecting manufacturing partners in push and pull-type smart collaborative networks. *Advanced Engineering Informatics*, Volume 38, pp. 291-305, https://doi.org/10.1016/j.aei.2018.08.001 (2018).

70. Kang, H.S., Lee, J.Y., Choi, S. Kim, H., Park, J. H., Son, J. Y., Kim, B. H., Noh, S. D.: Smart manufacturing: Past research, present findings, and future directions. *Int. J. of Precis. Eng. and Manuf.-Green Tech.* 3(1): 111-128 (2016).

71. Macedo P., Madeira R.N., Camarinha-Matos L.M.: Cognitive Services for Collaborative mHealth: The OnParkinson Case Study. In: *Collaborative Networks of Cognitive Systems*. PRO-VE 2018. IFIP AICT, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-99127-6_38 (2018).

72. Nikghadam-Hojjati S., Barata J.: Computational Creativity to Design Cyber-Physical Systems in Industry 4.0. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_4 (2019).

73. Salem S., Ojo A., Estevez E., Fillottrani P.R.: Towards a Cognitive Linked Public Service Cloud. In: *Collaborative Networks of Cognitive Systems*. PRO-VE 2018. IFIP AICT, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-99127-6_37 (2018).

74. Sousa C., Pereira C.: Sharing through Collaborative Spaces: Enhancing Collaborative Networks Interoperability. In: *Collaborative Systems for Smart Networked Environments*. PRO-VE 2014. IFIP AICT, vol 434. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44745-1_48 (2014).

75. Zarzour, H.; Al-Ayyoub, M.; Jararweh, Y.: Towards social collaborative editing of distributed linked data, 9th International Conference on Information and Communication Systems (ICICS), Irbid, 2018, pp. 90-95, doi: 10.1109/IACS.2018.8355447. (2018).

76. Afsarmanesh, H.; Ermilova, E.: The management of ontologies in the VO breeding environments domain. *International Journal of Services and Operations Management*, vol. 3, N° 3, DOI: 10.1504/IJSOM.2010.031954 (2010).

77. Antonelli D., Bruno G.: Ontology-Based Framework to Design a Collaborative Human-Robotic Workcell. In: *Collaboration in a Data-Rich World*. PRO-VE 2017. IFIP AICT, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-65151-4_16 (2017).

78. Man, A.-P.; Luvison, D.: Collaborative business models: Aligning and operationalizing alliances, *Business Horizons*, Volume 62, Issue 4, pp 473-482, https://doi.org/10.1016/j.bushor.2019.02.004 (2019).

79. Laukkanen, M.; Tura, N.: The potential of sharing economy business models for sustainable value creation, *Journal of Cleaner Production*, Volume 253, https://doi.org/10.1016/j.jclepro.2020.120004 (2020).
80. Kohtamäki, M.; Parida, V.; Oghazi, P.; Gebauer, H.; Baines, T.: Digital servitization business models in ecosystems: A theory of the firm, *Journal of Business Research*, Volume 104, pp 380-392, https://doi.org/10.1016/j.jbusres.2019.06.027 (2019).

81. Valkokari K., Rantala T., Alamäki A., Palomäki K.: Business Impacts of Technology Disruption - A Design Science Approach to Cognitive Systems' Adoption Within Collaborative Networks. In: *Collaborative Networks of Cognitive Systems*. PRO-VE 2018. IFIP AICT, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-99127-6_29 (2018).

82. Msanjila, S.; Afsarmanesh, H.: FETR: a framework to establish trust relationships among organizations in VBEs. *Journal of Intelligent Manufacturing*, vol. 21, issue 3, pp 251-265. DOI: 10.1007/s10845-008-0178-1 (2010).

83. Abreu A., Requeijo J., Calado J.M.F., Dias A.: Control Charts to Support Trust Monitoring in Dynamic Logistics Networks. In: *Collaborative Networks of Cognitive Systems*. PRO-VE 2018. IFIP AICT, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-99127-6_43 (2018).

84. Andrade-Garda, J., Anguera, Á., Ares-Casal, J. et al.: A metrology-based approach for measuring the social dimension of cognitive trust in collaborative networks. *Cogn Tech Work* 22, 235–248. https://doi.org/10.1007/s10111-018-0483-1 (2020).

85. Abreu A., Camarinha-Matos L.M.: Fair Distribution of Collaboration Benefits. In: *Encyclopedia of Networked and Virtual Organizations*, IGI Global. DOI: 10.4018/978-1-59904-885-7.ch079 (2008).

86. Wang, Y.; Ma, X.; Li, Z.; Liu, Y.; Xu, M.; Wang, Y.: Profit distribution in collaborative multiple centers vehicle routing problem, *Journal of Cleaner Production*, Volume 144, pp 203-219, https://doi.org/10.1016/j.jclepro.2017.01.001 (2017).

87. Abreu A., Camarinha-Matos L.M.: A benefit analysis model for collaborative networks. In: *Collaborative Networks: Reference Modeling*. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79426-6_18 (2008).

88. Jamshidi A., Abbasgholizadeh Rahimi S., Ait-kadi D., Ruiz A.: A New Decision Support Tool for Dynamic Risks Analysis in Collaborative Networks. In: *Risks and Resilience of Collaborative Networks*. PRO-VE 2015. IFIP AICT, vol 463. Springer, Cham. https://doi.org/10.1007/978-3-319-24141-8_5 (2015).

89. Wulan, M.; Petrovic, D.: A fuzzy logic based system for risk analysis and evaluation within enterprise collaborations, *Computers in Industry*, Volume 63, Issue 8, pp 739-748, https://doi.org/10.1016/j.compind.2012.08.012 (2012).

90. Li J., Bénaben F., Gou J., Mu W.: A Proposal for Risk Identification Approach in Collaborative Networks Considering Susceptibility to Danger. In: *Collaborative Networks of Cognitive Systems*. PRO-VE 2018. IFIP AICT, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-99127-6_7 (2018).

91. Oliveira A.I., Camarinha-Matos L.M.: Negotiation Support and Risk Reduction in Collaborative Networks. In: *Technological Innovation for the Internet of Things*. DoCEIS 2013. IFIP AICT, vol 394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37291-9_2 (2013).

92. Rosas, J.; Macedo, P.; Tenera, A.; Abreu, A.; Urze, P.: Risk Assessment in Open Innovation Networks. In: *Risks and Resilience of Collaborative Networks*. PRO-VE 2015. IFIP AICT, vol 463, pp 27-38. Springer, Cham. DOI: 10.1007/978-3-319-24141-8_3 (2015).

93. Villa A., Taurino T.: Crowd Engineering: Manage Crowd Contributions for Design and Manufacture of Innovative Products. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_9 (2019).
94. Urze P., Rosas J., Tenera A., Camarinha-Matos L.M.: Open Innovation Practitioners Mindset on Risk. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_10 (2019).

95. Appio, F. P.; Martini, A.; Massa, S.; Testa, S.: Collaborative network of firms: antecedents and state-of-the-art properties. *Int. Journal of Production Research*, vol. 55, issue 7, pp 2121-2134. DOI: 10.1080/00207543.2016.1262083 (2017).

96. Vilas-Boas J., Mirnoori V., Razy A., Silva A.: Outlining a New Collaborative Business Model as a Result of the Green Building Information Modelling Impact in the AEC Supply Chain. In: *Collaborative Networks and Digital Transformation*. PRO-VE 2019. IFIP AICT, vol 568. Springer, Cham. https://doi.org/10.1007/978-3-030-28464-0_35 (2019).

97. Fragidis G.: The User Perspective on Service Ecosystems: Key Concepts and Models. In: *Collaboration in a Data-Rich World*. PRO-VE 2017. IFIP AICT, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-65151-4_34 (2017).

98. Camarinha-Matos L.M., Cardoso T.: Education on Virtual Organizations: An Experience at UNL. In: *Virtual Enterprises and Collaborative Networks*. PRO-VE 2004. IFIP, vol 149. Springer, Boston, MA. https://doi.org/10.1007/1-4020-8139-1_61 (2004).

99. Camarinha-Matos L.M., Afsarmanesh H., Cardoso T., Klen E.: A Reference Curriculum For Education In Collaborative Networks. In: *Methods and Tools for Collaborative Networked Organizations*. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-79424-2_20 (2008).

100. Romero, D.; Rabelo, R. J.; Molina, A.: Collaborative Networks as Modern Industrial Organisations: Real Case Studies, *International Journal of Computer Integrated Manufacturing*, 26:1-2, 1-2, DOI: 10.1080/0951192X.2013.745348 (2013).

101. Adu-Kankam, K. O.; Camarinha-Matos, L. M.: Towards collaborative, Virtual Power Plants: Trends and convergence, Sustainable Energy, Grids and Networks, 16, pp 217-230, https://doi.org/10.1016/j.segan.2018.08.003 (2018).

102. Esteso A., Alemany M.M.E., Ortiz A.: Conceptual Framework for Managing Uncertainty in a Collaborative Agri-Food Supply Chain Context. In: *Collaboration in a Data-Rich World*. PRO-VE 2017. IFIP AICT, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-319-65151-4_64 (2017).

103. Weitzenboeck, E. M.: Building a Legal Framework for a Virtual Organisation in the Maritime Domain: The MARVIN Experience. Proc. of 7th International Conference on Concurrent Enterprising 27-29 June 2001, Bremen, https://pdfs.semanticscholar.org/3483/5b3c20b010d7eac64a82f574f3d5e1fc4164.pdf (2001).

104. Shelbourn M., Hassan T., Carter C.: Legal and Contractual Framework for the VO. In: *Virtual Organizations*. Springer, Boston, MA. https://doi.org/10.1007/0-387-23757-7_11 (2005).

105. Cevenini, C.: Legal Issues of Virtual Organizations. In: *Encyclopedia of Information Science and Technology*, IGI Global, DOI: 10.4018/978-1-60566-026-4.ch383 (2009).

106. Osório, A. L.; Camarinha-Matos, L. M.; Afsarmanesh, H.; Belloum, A.: Liability in Collaborative Maintenance of Critical System of Systems. To appear in *Boosting Collaborative Networks 4.0*, PRO-VE 2020. IFIP AICT, Springer, Cham. (2020).

107. Cardoso H.L., Oliveira E.: Virtual Enterprise Normative Framework Within Electronic Institutions. In: *Engineering Societies in the Agents World V*. ESAW 2004. Lecture Notes in Computer Science, vol 3451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11423355_2 (2005).