Magdalena Ciesielska
email: magdalena.ciesielska@pg.edu.pl

Tomasz Janowski
email: tomasz.janowski@pg.edu.pl

Inter-governmental Collaborative Networks for Digital Government Innovation Transfer -Structure, Membership, Operations

Keywords: digital government, innovation transfer, collaborative networks

Digital government refers to the transformation of government organizations and their relationships with citizens, business and each other through digital technology. It entails digital innovation in processes, services, organizations, policies, etc. which are increasingly developed and tested in one country and transferred, after adaptation, to other countries. The process of innovation transfer and the underlying information and knowledge sharing increasing take place through networks. The aim of this study is to identify various forms of such networks, their structures, membership criteria and modes of operation. The study relies on the analysis of literature on innovation transfer, collaborative networks and inter-governmental collaboration, and a survey of existing inter-governmental networks for digital government innovation transfer. The key finding is that such networks are a growing form of international collaboration and an instrument in global economy.

Introduction

Digital government transformation has advanced rapidly over the past 15 years, as shown by increasing number of countries with very high (between 0,75 and 1,00) value of the United Nation's e-Government Development Index (EGDI) [START_REF]Gearing e-government to support transformation towards sustainable and resilient societies[END_REF], from 10 in 2003 to 40 in 2018, and a decrease in the number of countries with "very low" (between 0,00 and 0,25) value of the EGDI, from 38 in 2003 to 16 in 2018. Thus some countries have gained deep knowhow in digital government, making their power, transport, security and other systems ready to interconnect with other systems, while others stand to learn from them, and to connect or even adopt their systems.

However, successful transfer of digital government solutions is difficult due to different conditions -technical, legal, economic, cultural, etc. existing in the donor and recipient countries [2][3]. This and continuous pressure for improvement in public infrastructure and services, highlight the importance of knowledge sharing and innovation transfer between government organizations [START_REF] Alawadhi | Smart governance: A cross-case analysis of smart city initiatives[END_REF] [START_REF] Gil-Garcia | Towards a smart State? Inter-agency collaboration, information integration, and beyond[END_REF]. Inter-organizational information sharing delivers higher information quality, improved decision-making, increased productivity, and service integration [START_REF] Gil-García | E-government success factors: Mapping practical tools to theoretical foundations[END_REF]. Inter-governmental information sharing relies on collaborative actions by diverse agencies from different countries, increasingly coordinated through inter-governmental collaboration networks. Such networks constitute multi-organizational arrangements for solving problems that cannot be achieved, or achieved easily, by a single organization. They rely heavily on informal interaction, persuasion, and information to deal with critical areas [START_REF] Dawes | Interagency information sharing: Expected benefits, manageable risks[END_REF].

While the existence of networks that specialize in digital government innovation transfer is documented in literature [START_REF] Gil-Garcia | Towards a smart State? Inter-agency collaboration, information integration, and beyond[END_REF][6] [START_REF] Yang | How is information shared across the boundaries of government agencies? An e-Government case study[END_REF], comparative studies are lacking, and questions remain concerning objectives, membership criteria, structure and mode of operation adopted by such networks. This paper aims to fill this gap based on the combination of literature review on technology transfer, collaborative networks and inter-governmental collaboration, four case studies of collaborative networks for inter-governmental technology transfer, and cross-case analysis. The case studies include networks run by countries with advanced digital government capabilities and interest in transferring such capabilities to other countries -Estonia, Korea, Singapore and USA. The main message uncovered by this study is that international digital government collaboration has become an instrument in global economy.

The rest of this paper is structured as follows. Section 2 present a literature review on technology transfer, collaborative networks and inter-governmental collaboration. The main outcome is the framework for inter-governmental networks for digital government technology transfer, which is presented in Section 3 along with research questions and how they are addressed. Section 4 presents and analyzes four case studies of such networks using the framework in Section 3. Section 5 discusses implications and lessons learns from this work, and Section 6 concludes with summary of the findings, limitations of this research, and plans for future work.

Background

This section presents the outcomes of a literature review on technology transfer, collaborative networks and inter-governmental collaboration. The review follows the approach described in [START_REF] Webster | Analyzing the past fo prepare for the future: writing a literature review[END_REF] and the outcomes, described in subsequent sections, lead to the definition of research questions and a framework in Section 3.

Technology Transfer

The introduction of digital technology into government happens in stages of digital government evolution [START_REF] Janowski | Digital government evolution: From transformation to contextualization[END_REF], from Electronic Government "when ICT is used to transform the internal organization and working of government", to Electronic Governance "when ICT is used to transform the relationships between government and citizens, businesses, other non-state actors and other arms of government" [START_REF] Janowski | Government Information Networks -Mapping Electronic Governance cases through Public Administration concepts[END_REF], to Policy-Driven Electronic Governance, which supports "efforts by countries, cities, communities and other territorial and social units to develop themselves" [START_REF] Janowski | Digital government evolution: From transformation to contextualization[END_REF].

The process of adapting a digital government application from the donor to the recipient context is referred to as digital government technology transfer [START_REF] Do | Towards a heuristic frame for transferring e-government technology[END_REF]. The process is hindered by contextual distances between participant countries which Inter-governmental Collaborative Networks for Digital Government Innovation 291 include culture, politics, organizational issues, relations, knowledge, resources, and physical and technical conditions [START_REF] Dawes | Transnational public sector knowledge networks: Knowledge and information sharing in a multi-dimensional context[END_REF].

Digital government is essentially based on imported designs, and digital government applications are isolated technical artefacts [START_REF] Heeks | Information systems and developing countries: Failure, success, and local improvisations[END_REF]. Digital government technology transfer concerns the transformation of government administration, information provision and service delivery by new technologies [START_REF] Korteland | Diffusion of e-government innovations in the Dutch public sector: The case of digital community policing[END_REF]. In this perspective, digital government initiatives are associated with the deployment of a complex digital infrastructure [START_REF] Stanforth | Using Actor-Network Theory to Implementation in Developing Countries[END_REF] involving national and local governments, agencies, NGOs, international organization, and citizens [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF]. The potential to support sustainable socio-economic development is well supported, e.g. [START_REF] Janowski | Implementing Sustainable Development Goals with Digital Government -Aspiration-capacity gap[END_REF].

According to [START_REF] Teece | Technology Transfer by Multinational Firms: The Resource Cost of Transferring Technological Know-How[END_REF], the greater the value of the donor's knowledge stock, the greater its attractiveness to other countries. This is consistent with diffusion of innovation [START_REF] Rogers | Diffusion of Innovation[END_REF], a process by which an innovation is communicated through certain channels among the members of a social system and by which alternation occurs in the structure and function of such system as a kind of social change. While diffusion is crucial to fully benefit from innovation, the diffusion of digital government innovations is uneven [START_REF] Janowski | Implementing Sustainable Development Goals with Digital Government -Aspiration-capacity gap[END_REF]. A small number of rich countries are seen as vanguards of digital government, while poor countries experience fragmented digital government implementations. As the deployment of digital government in developing countries should address specific contextual characteristics of such countries and their sectors and organizations [START_REF] Avgerou | Information technology in context: Implementing systems in the developing world[END_REF], international technology transfer should be a learning process based on trust [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF], supportive institutional design [START_REF] Holsapple | Influence Structure and Inter-group Learning[END_REF], policy and legal adjustment [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF], and the explicit characteristics of the technology being transferred [START_REF] Do | Towards a heuristic frame for transferring e-government technology[END_REF].

Technology transfer includes transfer: between individuals, from individuals to groups, between groups, across groups, and from groups to organizations [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] [START_REF] Gupta | Knowledge flows within multinational corporations[END_REF]. International technology transfers are guided by profit [START_REF] Choi | Technology Transfer Issues and a New Technology Transfer Model[END_REF], and include trade flows between parties [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF], e.g. a donor country gaining advantage for purchasing raw material from the recipient, and profiting from technology maintenance [START_REF] Akubue | Technology Transfer: A Third World Perspective[END_REF]. However, digital government technology transfer has often bilateral character, e.g. in Mozambique [START_REF] Nhampossa | Re-thinking technology transfer as technology translation: A case study of Health Information Systems in Mozambique[END_REF], Sri Lanka [START_REF] Stanforth | Using Actor-Network Theory to Implementation in Developing Countries[END_REF] or Malaysia [START_REF] Kasimin | Managing multi-organizational interaction issues: A case study of information technology transfer in public sector of Malaysia[END_REF].

Collaborative Networks

Networks refer to multi-organizational arrangements for solving problems that cannot be achieved, or achieved easily, by a single organization. A collaborative network is a network containing a variety of entities that are mostly independent, geographically dispersed, and varied in terms of operating environment, culture, social capital and goals, but that collaborate to achieve common or compatible goals [START_REF] Camarinha-Matos | Collaborative networks: A new scientific discipline[END_REF]. Participation in such networks is aligned to increasing competitiveness, reaching new knowledge, sharing risks and resources, and joining complementary skills. A crucial factor for networks and an alternative governance mechanism is trust [START_REF] Luna-Reyes | Creating public value through digital government: Lessons on inter-organizational collaboration and information technologies[END_REF].

According to [START_REF] Mandell | Evaluating the of interorganizational relations through networks[END_REF], networks are characterized by: orientation of members and their commitment to goals, organization of the network including the intensity and breadth of its linkages, and the aim including complexity of purpose and the scope of the efforts. The formation and operation of the network reflects the characteristics of its participants and their expectations of the benefits and barriers [START_REF] Zhang | Expectations and perceptions of benefits, barriers, and success in public sector knowledge networks[END_REF]. Network constitution happens through [START_REF] Agranoff | Big Questions in Public Network Management Research[END_REF]: activation -prior to successful inter-organizational policy formation; framing -establishing rules, influencing values, and shaping perceptions of the network; and synthesizing -creating the environment and enhancing the conditions for productive interactions among participants.

From the digital government perspective, the concept of public sector knowledge networks is used -inter-organizational relations, policies, structured information, professional knowledge, work processes and technologies brought together to achieve a collective public purpose [START_REF] Zhang | Expectations and perceptions of benefits, barriers, and success in public sector knowledge networks[END_REF]. They are a type of collaborative networks: led by government entities [START_REF] Agranoff | Big Questions in Public Network Management Research[END_REF], having some formal elements but not defined by the law [START_REF] Zhang | Expectations and perceptions of benefits, barriers, and success in public sector knowledge networks[END_REF], enabling members to share knowledge. Network-level knowledge sharing and collaboration assumes that at least three actors pursue a common goal and take collective actions to achieve this goal by producing and sharing skills, expertise, experience, information and data [START_REF] Ku | The emergence and evolution of cross-boundary research collaborations: An explanatory study of social dynamics in a digital government working group[END_REF].

Inter-governmental Collaboration for Technology Transfer

Previous concepts should be regarded as the context for inter-governmental collaboration within collaborative networks. In this context, network participants are countries or territorial units, represented by government authorities. Factors that affect multinational digital government collaboration, interoperability and information sharing include: collaboration factors, value network factors, cross-border factors, and integration and interoperability factors [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF].

Scarce publications address the structure of inter-governmental collaboration. Thus, a rational formal structure is assumed to be the most effective way to coordinate and control complex relational networks involved in such collaboration [START_REF] Powell | Neither market nor hierarchy: The sociology of organizations: classic, contemporary, and critical readings[END_REF]. According to [START_REF] Dawes | Building Collaborative Digital Government Systems: Systemic Constraints and Effective Practices[END_REF], three types of inter-organizational collaborations are: publicpublic, public-non-profit, and public-private. The first includes horizontal agreements between governments at the same level, and vertical agreements or intergovernmental alliances between levels. The latter need legal authorization, they operate by local agencies. According to [START_REF] Luna-Reyes | Collaborative digital government in Mexico: Some lessons from federal Web-based interorganizational information integration initiatives[END_REF], inter-organizational trust and collaboration is often not supported by institutional arrangements and organizational structures.

Inter-governmental collaboration takes place in specific contexts. Well connected members introduce trust, norms and social sanctions based on mutual expectations and obligations [START_REF] Coleman | Social Capital in the Creation of Human Capital[END_REF]. Cooperation incentives are greater within networks as "competition is usually minimized" and "organizations generally trust each other to a greater degree" [START_REF] Argote | Organizational learning: Creating, retaining and transferring knowledge[END_REF]. Within collaborative networks, information is shared easily, and members can build and manage their reputation [START_REF] Uzzi | The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect[END_REF]. Inter-governmental collaboration within networks operates under organizational missions, existing legal and policy frameworks, assigned organizational structures, management practices and each countries' technological infrastructures and capabilities [START_REF] Dawes | Interagency information sharing: Expected benefits, manageable risks[END_REF].

While research on networks as an element of public policy process is covered in literature, e.g. research on structure, function, management and outcomes of networked forms of organization [START_REF] Agranoff | Big Questions in Public Network Management Research[END_REF][17] [START_REF] O'toole | Implementing public innovations in network settings[END_REF], the topic of organizational networks as an instrument of public management in the international context is relatively recent. This study address this knowledge gap by exploring the structure and operations of inter-governmental collaborative networks for digital government innovation transfer.

Research Design and Method

This work studies inter-governmental collaborative networks for digital government innovation transfer. We pursue three research questions:

1. What are the aims, strategies and missions of such networks? 2. What are the membership, structures and operations of such networks? 3. How are the networks facilitating digital government innovation transfer? These questions were addressed through exploratory and comparative case study research. Such research is focused on understanding the dynamics present within a small number of cases in their real-life context [START_REF] Yin | Case Study Research Design and Methods Second Edition[END_REF]. It is applied when the topic is complex, there is a lot of theory available, and the context is important [42].

The main of the literature review is an integrative framework for intergovernmental collaborative networks for digital government innovation transfer. The framework, depicted in Table 1, is instantiated for particular donor, recipient and the innovation transfer initiative. It consists of general information including objectives, mission, strategy, legal framework and contextual distances [START_REF] Betsill | Transnational networks and global environmental governance[END_REF]; membership criteria including participants [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] and their status [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF]; structure including collaboration types [START_REF] Dawes | Building Collaborative Digital Government Systems: Systemic Constraints and Effective Practices[END_REF], structural and individual behavior [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF], institutional design [START_REF] Holsapple | Influence Structure and Inter-group Learning[END_REF], managerial tasks and roles [START_REF] Agranoff | Big Questions in Public Network Management Research[END_REF] and decision-making authority [START_REF] Holsapple | Influence Structure and Inter-group Learning[END_REF]; and operation including incentives and their types [START_REF] Dawes | Building Collaborative Digital Government Systems: Systemic Constraints and Effective Practices[END_REF], transactions [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF], trade flows [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF], deliverables [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF], policy and legal adjustments [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF], and information integration [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF].

The framework is applied to develop and analyze four case studies of such networks. The enquiry was limited to official websites and legal acts, agreements and statuses available online. Case study selection was based on the donor countries' digital government maturity and active international transfer to third countries. [START_REF] Betsill | Transnational networks and global environmental governance[END_REF] □ cultural

□ knowledge □ intention □ relational □ physical □ technical □ political
□ resource Membership Participants [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ international organization □ local government □ national government □ agencies □ citizens □ NGOs Participant status [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ equal □ unequal Structure

Collaboration type [START_REF] Dawes | Building Collaborative Digital Government Systems: Systemic Constraints and Effective Practices[END_REF] □ public-public □ public-non-profit □ public-private Promotes cooperation [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ yes □ no Individual behavior [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ trust □ experience Institutional design [START_REF] Holsapple | Influence Structure and Inter-group Learning[END_REF] □ level of centrality: low, middle, high □ assigned organizational structures: yes / no □ management practices: yes/no □ inclusion criteria □ exclusion criteria

Managerial tasks [START_REF] Agranoff | Big Questions in Public Network Management Research[END_REF] □ activating □ framing □ mobilizing □ synthesizing Managerial roles [START_REF] Agranoff | Big Questions in Public Network Management Research[END_REF] □ international organization □ national government □ local government □ agencies □ citizens □ NGOs Roles [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ individuals □ groups □ business units □ organizations Decision making [START_REF] Holsapple | Influence Structure and Inter-group Learning[END_REF] □ international organization □ national government □ local government □ agencies □ citizens □ NGOs Operation Incentives [START_REF] Dawes | Building Collaborative Digital Government Systems: Systemic Constraints and Effective Practices[END_REF] □ technical □ organizational □ political Incentive type [START_REF] Dawes | Building Collaborative Digital Government Systems: Systemic Constraints and Effective Practices[END_REF] □ positive (outcome) □ negative (conflict) Transactions [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ individual -group □ group -business unit □ unit -organization Trade flows [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ goods □ people □ investments Deliverables [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ tangible □ intangible Policy adjustments [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ international □ regional □ state □ national □ local Legal adjustment [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ international □ regional □ national □ local Information integration [START_REF] Navarrete | Multinational egovernment collaboration, information sharing, and interoperability: An integrative model[END_REF] □ yes □ no

Case Studies

This section presents four case studies of inter-governmental collaborative networks, and conducts cross-case analysis. All case studies identify a donor country with mature digital government and related international innovation transfer, and one instance of such transfer from the donor to recipient country. Each case study presents the organization responsible for international dissemination of the donor country's digital government innovations, and analyzes one example of innovation transfer from to a third country using the framework in Table 1. The case studies are presented in Section 4.1 (Estonia), Section 4.2 (Republic of Korea), Section 4.3 (Singapore) and Section 4.4 (USA). Section 4.5 includes cross-case analysis.

Estonia -e-Governance Academy

Estonian e-Governance Academy was established in 2002 as a non-profit think tank and consultancy organization aimed to help "governments increase their governance efficiency and improve their democratic processes" [START_REF]Governance Academy: About Us -e-Governance Academy[END_REF]. In 2015, Tunisia joined the Estonian development cooperation project, managed by e-Governance Academy, to develop the legal and organizational framework for e-governance and look into the possibilities of having a single identifier for Tunisian citizens. Five contextual distances were identified between donor and recipient countries: cultural, organizational, knowledge, resource and technical. The cultural distance results from differences in national cultures, particularly the rights of citizens to privacy and freedom of expression. Due to previous Tunisian institutional experience in digital government assistance, this distance tends to shrink, establishing an adequate level of trust to build working relations. The organizational distance refers to the constituted independence of each government agency in Tunisia, resulting in the lack of unique identification. The knowledge distance was estimated as appropriate for knowledge transfer. The resource distance is expressed by the level of funding from the Estonia to support the Tunisia project. The technical distance is primarily due to the lack of data integration and information sharing between Tunisian agencies.

The project realizes public-public collaboration. Structural and individual behavior enhanced inter-governmental collaboration by promotion of cooperation and acclamation of trust and institutional experience. We could not identify inclusion or exclusion criteria within this project, or explicitly assigned organizational structure. However, project management was performed by the Academy including activating, framing, mobilizing and synthesizing tasks. The roles were assigned to individuals and organization. The project features positive incentives, and lack of negative ones.

South Korea -e-Government Cooperation Center

Republic of Korea shares its best practices in public administration with countries around the world through its official development assistance program. The aim is "to contribute to the advancement of the global community as a pioneer in administrative innovation" [START_REF]the Interior and Safety: International Cooperation in Good Governance[END_REF]. The organization responsible for international cooperation in digital government is e-Government Cooperation Center (eGCC). eGCC selects a recipient country using existing cooperative relationships, willingness of the partner country, etc. The cooperation is launched through a high-level dialogue with the recipient. eGCC Committee is established with experts from both countries to decide on the content of cooperation, and to execute managerial tasks. Each cooperation program is aligned to trade flows where Korean government provides USD 1 million and the recipient country provide additional funds subject to negotiation. In the operation phase, e-government experts are dispatched to provide training, consulting, etc. In 2017, the eGCC cooperation was established between South Korea and Kenya with the aim to: materialize governmental e-offices, share residential ID experiences, provide consultations, and plan the national information infrastructure. Tangible and intangible deliverables, e.g. ICT infrastructure or knowledge sharing, were produced. This cooperation also forced Kenya to adjust its legal frameworks.

The eGCC cooperation type is public-public, enforced by structural and individual behavior. Contextual distances include: organizational distance -related to structure and processes, relational distance -establishing previous positive ties, resource distance -lack of funding and qualified staff, physical distance -geography, and technical distance -low information sharing between agencies.

Singapore -Infocomm Development Authority International

Countries interested in importing Singapore's digital government technology could turn to IDA International, a subsidiary of Infocomm Development Authority of Singapore (IDA). Established in 2008, IDA International served as the execution arm of public service infocomm collaborations between Singapore and governments around the world [START_REF]IDA International to spearhead the export of public sector infocomm expertise[END_REF], focused on delivering public infocomm services, including digital government consultancy, master planning, national infocomm planning, industry and cluster development, and program management.

In 2007, IDA and the Information Technology Authority of Oman signed a MoU to facilitate the use of ICT in government and various economic sectors of Oman. In particular, the transfer was about developing the urban portal, a new service delivery platform for connecting government and citizens. This public-private collaboration joined government agencies and private organizations. Four contextual distances were identified: cultural, political, relational, and knowledge. Unfortunately, the official websites of the Singapore and Oman governments do not provide further information as to the operation, inclusion criteria, and the structure of the collaborative network.

USA -USAID Global Development Lab

The United States Agency for International Development (USAID) supports intergovernmental collaboration on digital government to strengthen democratic governance through open, responsive, and accountable institutions and processes that serve the needs and preferences of the public. The USAID Global Development Lab is an innovation hub that works external partners to produce innovations and to open development to people. The Lab works with impact investors to catalyze private capital for businesses and to strengthen the environment for entrepreneurship.

The Digital Liberia Electronic Government activity is a one year program funded by the USAID Global Development Lab with the aim to improve Liberian Government's performance through sustainable utilization of ICT-related systems, processes, and procedures at targeted ministries, agencies and commissions. Technology transfer aims to improve government management and decision-making by introducing the Integrated Financial Management System, Asset Management Information System and e-services for the Revenue Authority [47].

The collaboration type is public-non-profit and public-private. Structural and individual behavior are identified as positive. Contextual distances comprise: knowledge, technical, physical, resource and cultural distances. The USAID Global Development Lab executes managerial tasks. Transactions occur between individual, groups and business units. Project deliverables are both tangible -ICT infrastructure and systems, and intangible -knowledge sharing. The project is funded by the U.S.

Cross-Case Analysis

This section provides a cross analysis of the four case studies documented in Sections 4.1 to 4.4, guided by the framework from Table 1.

General:

The objectives of technology transfer vary, e.g. the transfer from Estonia and Tunisia is aimed at developing legal and operational frameworks for digital government, while from Singapore to Oman at deploying technical solutions within the Omani infrastructure. All cases address the needs of developing countries or countries with low digital government maturity. Due to this, the donor's and recipient's status is unequal, and except for Singapore, all donors support innovation transfer financially. Except Singapore, all cases have explicit strategies and mission statements for international partnership in the digital government space.

Structure:

The cases provide information on the legal frameworks underpinning collaboration. Korea established a comprehensive legal framework for importing its digital government technology. Semi-structured legal frameworks are provided by Estonia and the US. In each case, the collaboration is outsourced to a government subsidiary which hosts the responsibility for managerial coordination and operation. The legal frameworks influence collaboration types, institutional design, managerial tasks, roles, transactions, and trade flows between donor and recipient countries. Contextual distances include knowledge and resource distances.

Membership: Only Korea identified inclusion criteria, conditioning collaboration on shared values and willingness. Both legal framework and project type influence the membership. Two members are constant -national government and agencies. The participation of businesses and NGOs is related to the project's types and objectives. Except the US, all cases represent the public-public type of collaboration. In cases of Estonia and Korea, there is clear acclamation of trust between donor and recipient parties. In every case, donors are assessing the recipient's institutional experience to adjust operations to the recipient's institutional and organizational environment.

Operations: Each case provides group transactions. Managerial tasks are assigned to the governmental subsidiary. Strategic decision-making is assigned to government entities and operational decision-making to agency or businesses. Except Singapore, positive incentives are offered in official announcements. Funding and people flows are common. Project deliverables are tangible when the transfer concerns technical solution deployment, and intangible when the transfer concerns knowledge sharing. Trust among donors and recipients is fundamental. The transfer is not only to promote own digital solutions or industries, but also to build trust between parties. Lack of clear inclusion criteria allows for subjective selection of recipients, becoming an instrument in the donor's economic expansion towards developing countries' markets.

Discussion

This study provides an analysis of inter-governmental collaboration networks for digital government innovation transfer. A literature review was conducted on technology transfer, collaborative networks and inter-governmental collaboration for technology transfer. On this basis, we developed a framework that aggregates various models, concepts, definitions and factors related to such networks.

We applied this framework to develop four case studies of donor-driven networks: Estonia, Korea, Singapore and the US. The data highlights various approaches to activating, framing, mobilizing and synthesizing interactions adopted by the donors. Despite all donors establishing purposeful agencies to handle innovation transfer, only Korea offers institutional collaboration framework. None of the cases formulates exclusion membership criteria but only Korea formulates inclusion criteria. Individual recipient's behavior, particularly acclamation of trust and experience is important. All cases established public-public collaborations, except public-non-profit by the US. Each case clearly assigns roles to participants. Given the resource-type contextual distance and the donors' financial support, participant status is unequal. Two participant types are engaged -national government and agencies. Finally, although selected donors are well-established digital government adopters, only Estonia and Republic of Korea are transparent about undertaken activities. We met substantive difficulties in accessing information on bilateral cooperation on digital government from Singapore, and minor difficulties from the US.

Conclusions

Inter-governmental collaborative networks illustrate the importance of partnerships in the global economy. International digital government innovation transfer projects feature effective partnerships, trustful relationships focused on common goals and risk sharing, and access to resource and benefits attained by all parties. As such, four major points emerge from this work: 1) inter-governmental trust and collaboration in technology transfer should be supported by institutional arrangements and established organizational structures; 2) digital government collaboration open a door to building wider bilateral partnerships; 3) inter-governmental cooperation is based on inclusion criteria which are in turn based on shared values and trust; and 4) the proposed framework has proven itself as a useful research tool.

This research has some limitations. The first is small number of case studies. The second is limited data collected on the Singapore and US cases, due to the difficulties in accessing public information. The third is partial coverage of the studied phenomena, and the consequent difficulties in generalizing the findings. The fourth is that the case studies only cover asymmetric donor-recipient relationships.

Follow up research is to address these limitations and focus on institutional frameworks and their influence on the donor, recipient and network performance. We also plan to develop case studies that represent symmetric peer-peer donor-recipient relationships, more common for North-North and South-South innovation transfer.

Table 1 .

 1 Framework for inter-governmental collaborative networks

	General	Basic information	Donor	Objectives
			Recipient	Strategy
			Innovation transfer	Mission
				Legal framework
		Contextual distance


Angeles María

Llanos Rodríguez

Ángel Cuenca
email: llcuenca@cigip.upv.es

Ortiz
email: aortiz@cigip.upv.es

Big Data Transformation in Agriculture: From Precision Agriculture towards Smart Farming

Keywords: Big Data, Smart Farming, Precision Agriculture

Big data is a concept that has changed the way to analyse data and information in different environments such as industry and recently, in agriculture. It is used to describe a large volume of data (structured or unstructured data), which are difficult to obtain, process or parse using conventional technologies and tools like relational databases or conventional statistics, in a reasonable time for their insight. However, Big Data is applied differently in each area to take advantage of its potential and capabilities. Specially in agriculture that presents more demanding conditions due to its inherent uncertainty, so Big Data methods and models from other environments cannot be used straight away in this area. In this paper, we present a review/update of term Big Data and analyse the evolution and the role of Big Data in agriculture outlined the element of collaboration.

Introduction

Agriculture has been a sector with few influences of new technologies, such as Internet of Things (IoT), Cloud Computing or Big Data. Indeed, advanced technologies for data like Big Data or Advanced Data Analysis have not been used until the last decade, although it has always been an area focused on the use and exploitation of data (manual data management in its origins).

Recently, the modern technologies have been introduced in agriculture and it raised new concepts such as Smart Farming. This new concept has burst in to stay and banish old terms like Precision Agriculture. Also, these technologies make the old procedures (re)adapt and new automatized procedures emerge for improve daily management farming and increase collaborative networks between stakeholders.

Therefore, the main contributions of this paper are: the literature update of term Big Data, the proposal of an actual review of Big Data in Agriculture, a description of evolution of Big Data from its origins towards nowadays, and the role of Big Data in collaborative networks.

In order to write this paper, we searched well-known databases, such as Scopus or Elsevier. Searching has focused on most relevant articles to the area of Agriculture research describing or using Big Data. In this way, we used the following search keywords: ["Precision Agriculture" OR "Smart Agriculture" OR "Precision Farming" OR "Smart Farming"] AND ["Big Data"] Also, the research has been carried out since the year 1980-1990, when the terminology of old term (Precision Agriculture) is already used, until now, when the use of new term (Smart Farming) has spread. Our analysis is divided in two stages: (1) providing a review and an update of term Big Data, and (2) summarizing evolution of Big Data in Agriculture highlighting the aspect of collaboration. Therefore, this paper is organized as follows. In section 2, a review of overview in Big Data are detailed. An evolution of Big Data from Precision Agriculture towards Smart Farming and its implications are described in section 3. Finally, in section 4 conclusions of this paper are exposed.

Big data: An Overview

The term Big Data was coined by Cox and Ellsworth in 1997 [START_REF] Cox | Application-Controlled Demand Paging for out-of-Core Visualization[END_REF]. And that term was used to describe a problem they observed: "data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk". The supercomputer they used for their Computational Fluid Dynamics research often generated large amounts of data (could exceed 100 Gbytes) that could not visualize or process.

However, the concept Big Data does not refer only to the amount of data. As can be seen in his characterization in 2001, Laney [START_REF] Laney | 3D Data Management: Controlling Data Volume, Velocity and Variety[END_REF] provided Big Data with 3 dimensions:

high Volume (V1): Size of data. The important increase in data size.

high Velocity (V2): Real-Time data. It has also increased the speed of data generation. This means that their access and analysis must be within a reasonable time to take advantage of this data. That is to say, the data not only must be available immediately, moreover data must be analyzed while data is useful and relevant.

high Variety (V3): Muti-source. There is a wide variety of structured or unstructured data sources.

These three dimensions are focused on the storage and processing of data, but there is a lack of analyze the data. Subsequently, in 2012 Laney [START_REF] Beyer | The Importance of "Big Data[END_REF] added two dimensions:

-Value (V4): Insight. Extract knowledge from the data.

-Veracity (V5): High Quality. The data must be reliable.

The Table 1 quantifies in numerical data the meaning of the 5 V's. In the table it can be noticed that Big Data is more than huge data, also it is different kinds of data, streaming data and data quality. Standing out the significance of last two V's, because they play an important role in decrement corporate earnings due to, for instance, the expenditures for poor data quality and its inadequate management. Specially veracity, because without it; Big Data is less accurate, confidence, consistent or reliable. By this way, inconsistent and unreliable data sets will result in inconsistent and unreliable knowledge [START_REF] Kamilaris | A Review on the Practice of Big Data Analysis in Agriculture[END_REF]. Therefore, as Laney indicates: "Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization".

After Laney, the definition of Big Data was unified and most used the characterization of Big Data in dimensions to define it. Such is the case of NIST [START_REF]The definition of Big Data[END_REF], IBM [START_REF]The definition of Big Data[END_REF] and Oracle [START_REF] Oracle | The definition of Big Data[END_REF]; which uses 5 V's, only uses 3 V's, and focuses on the challenge of traditional databases, respectively. According to this, the authors' definition is, Big Data is an effective way to store, process and analyse large amounts of data that are generated nowadays according to business needs.

The Big Data applications are many, such as healthcare [START_REF] Shahbaz | Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change[END_REF] to provide better medical attention to the patient; governments organization [START_REF] Trom | Analysis of Data Governance Implications on Big Data[END_REF] to controlled governance dynamics, industry [START_REF] Tao | A Field Programmable Gate Array Implemented Fibre Channel Switch for Big Data Communication towards Smart Manufacturing[END_REF] to opens a bright perspective for smart manufacturing or agriculture [START_REF] Kamilaris | A Review on the Practice of Big Data Analysis in Agriculture[END_REF] to improve the productivity in farms or crops.

Evolution of Big Data in Agriculture

Agriculture sector was delayed in introducing new technologies such as IoT or Big Data. The IoT concept in agriculture appears for the first time in studies dating from 2010 [START_REF] Lu | Research on the innovation of strategic business model in green agricultural products based on Internet of Things (IOT[END_REF] and it is described as a wireless automation system by monitoring crop and farm data [START_REF] Zhao | A crop monitoring system based on wireless sensor network[END_REF]. And Big Data concept in agriculture appears in decade of 2010s and it is described using characterization of 5 V's [START_REF] Chi | Big data for remote sensing: Challenges and opportunities[END_REF]. Agriculture has always been focused on data. In fact, farmers have always collected amounts of data on their crops and soils for its daily management. That data collection was initially manual and, later, it was automated, until our days is fully automated thanks to the IoT [START_REF] Rodriguez | FIWARE Open Source Standard Platform in Smart Farming -A Review[END_REF]. The use of new technologies in any area, is associated with the appearance of new terms, and in the case of agriculture, two terms must be highlighted: Precision Agriculture and Smart Farming.

Precision Agriculture

This concept is special, because over the years it has undergone a change in its meaning and use. Precision Agriculture was presented for the first time in the late 1980s [START_REF] Stafford | A GPS Backpack System for Mapping Soil and Crop Parameters in Agricultural Fields[END_REF], when new Information and Communications Technology (ICT) in agriculture were not yet developed. But it was not until 1990s, when the application of this term began to appear in scientific studies.

At the beginning, in mid 1990s, Precision Agriculture was based as the management of crop, for instance, targeting of fertilizers and herbicides, according to locally determined ('meter-by-meter') requirements, that is to say, precision refers on geolocated data of soil and crop parameters [START_REF] Robert | Precision agriculture: research needs and status in the USA[END_REF]. After ten years, in 2000s, farmer was used complex data such as images or photographs. Detailed spatial information like digitized aerial photographs on crop is needed for controlling crop operations. In this case, Precision Agriculture refers to the application of geospatial technologies (like Geographic Information System, GIS) that allowing farmers access and visualize to images that influence their decisions for the purpose of improving crop performance [START_REF] Long | Remote sensing for northern plains precision agriculture[END_REF]. Finally, in decade of 2010, as a result to the emergence of IoT and Big Data, everything was based on monitoring data in real time and management of large amount of data that it generates. Furthermore, the concept of Precision Agriculture that is known and used nowadays, is defined as management of agricultural plot by monitoring, data processing and intervention of crops to optimize the consumption of resources and provides the farmers an added value of decision making for exploitation daily operations and management. [START_REF] Ge | Remote sensing of soil properties in precision agriculture: A review[END_REF].

The Figure 1 below, shows the evolution of concept Precision Agriculture over the years. As times goes by, it can be observed that in decade of 2000 in agriculture only 2 of the 3 dimensions that were characterized at that time were used. Volume (V1) and Variety (V3) were being used with the introduction of the use of images, because it was possible to have data sources of different types and, in addition, a large size of data was collected. However, Velocity (V2) was not achieved, because data not collected in real time, although sending of the complex data like images were faster than in previous times, it was collected quasi real time not full real time. In fact, it was not until the year 2010, when the Velocity (V2) was achieved, thanks to the monitoring with sensors the sending and acquisition of data in real time. The term Smart Farming emerges after the concept Precision Agriculture and both are inherently related. At the beginning of 2010s, the term Smart Farming [START_REF] Sundmaeker | Internet of food and farm[END_REF] began to be used to refer to that new advanced definition of Precision Agriculture. The aspect of precision (location / geolocation data sent by satellites) disappears, and it materializes on aspect of Smart (real-time data sent by smart sensors -IoT architecture-, which highlighted for their ability to communicate its data). In agriculture sector, many Smart Farming applications supports a large amount of heterogeneous real-time data sent by sensors. [START_REF] Barmpounakis | Management and control applications in Agriculture domain via a FI Businessto-Business platform[END_REF] This kind of data is considered Big Data with 5 V's, because the term Smart assumes the challenges of last two V's: insight and high-quality.

Another aspect relevant in this new term is collaboration. In Precision Agriculture farmer is the main figure or responsible of data and has his own local database system (1980-2000) that he does not share with other farmers, suppliers, etc. New technologies as a Big Data, IoT and Cloud Computing (after 2000), have been able to increase collaboration quickly, because the data is in cloud database systems that can be shared and this real-time data that help to accelerate decisions making. Consequently, decision making be able to include more interlocutors. Everyone can visualize the same data (although each kind of stakeholder sees the information in a different way according to their knowledge). In addition to collaborating in decision making, the information runs between all of them in a connected way, that is, the isolated farm becomes a connected farm [START_REF] Musat | Advanced services for efficient management of smart farms[END_REF]. The idea of connected farm that appears in Smart Farming, is the fundamental vision of collaborative systems in agriculture. The role played by Big Data in the connected farm is of vital importance and, as already mentioned, it has to assume the challenge of the last two V's. Both daily decisions and strategic decisions of the farm, it is necessary to have adequate information extracted from the data (remember that depending on the stakeholder the information must be presented in a different way) -this is provided by Value -; and also, the information has to be of quality (poor data can cost wrong decisions) -this is provided by Veracity-. And, the role played by IoT in the connected farm is also relevant, mentioned in [START_REF] Rodriguez | FIWARE Open Source Standard Platform in Smart Farming -A Review[END_REF], for instance, FIspace [START_REF]FIspace[END_REF] (European business-to-business collaboration IoT platform -belongs to FP7) can exchange data or use shared and customizing solutions with minimal costs to help small or medium sized companies.

The Figure 2 below, adds the evolution of concept Smart Farming in Figure 1 previous. It can be observed that in decade of 2010 Smart Farming consumes Precision Agriculture, and it incorporates the last two V's (Value and Veracity). Moreover, this aspect of the transition makes it enhancer faster the collaboration between stakeholders.

Fig. 2. Evolution of Smart Farming

As an example of Smart Farming application is Agricolus. This system uses IoT (crop and soil monitoring), Cloud Computing (process data in cloud), and Big Data (farm management and decision support system for treatments and fertilizers) [24]. And data process of solution is similar to Big Data process. In Big Data, the first step is to capture and save the data -V1, V2 and V3-(in Agricolus the recollected data provided by sensors), the second step is to prepare and transform data -V5-(in Agricolus the store data is converted in data quality) and, finally, the third step is to analyze the data and act (in Agricolus the data is analyzed and visualized in application to support decisions about agricultural operations). Each of the phases of Big Data process presents challenges, mainly the second step (data preparation and transformation). For instance, this step requires 80% of effort and time, due to the particularities of any dataset (different formats, missing values, duplication of data, ...) and its subsequent cleaning, selection and transformation to value data [START_REF] Paton | Automating data preparation: Can we? should we? must we[END_REF]. To reduce this time, could automate data preparation in field of agriculture with a tool such as START (Soil daTA Retrieval Tool) [START_REF] Kim | A data preparation tool for crop simulation models using web-based soil databases[END_REF]. It was designed to automated preparation of soil input data files for multiple crop models. This tool consumed about 33% of time compared with 80% of time in manual preparation. However, they do not mention that automatization must be without lost data quality -V5-.

Conclusions

This paper has defined Big Data for characterization of 5 V's (Volume, Velocity, Variety, Value, Veracity) and we have provided a more up-to-date and simple definition of the term. Also, we have updated concepts of Big Data in agriculture, for that purpose we have explained the evolution of new technologies focused on data in agriculture. Finally, we have described collaboration as another aspect of Big Data application.

Without being aware of it, the Big Data approach has always been present in this sector. On a daily basis, large amounts of data are collected and analysed in crops or soils. Before the arrival of digital transformation and TICs, many of these procedures were done manually and only the farmer was responsible for the data and decisions. But nowadays, everything is automated, and the farmer is part of a collaborative network. Moreover, agriculture is to be congratulated, research and innovation in this field is being encouraged from the EU Framework Program called Horizon 2020. For instance, IoF2020 explores the potential of IoT for food and farming industry [27].

To conclude, we have observed that Smart Farming is more collaborative than Precision Agriculture, and only with new technologies as Big Data (5 V's) and IoT, connected farms is a reality. In addition, the future with automatized tools for data preparation or new concept Data Lakes (repository of structured and unstructured big data) present new challenges such as quality and ethics in data and overload of information.

Fig. 1 .

 1 Fig. 1. Evolution of Precision Agriculture

Table 1 .

 1 Quantification of meaning of the 5 V's[START_REF] Marr | How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read[END_REF]

	VOLUME	VARIETY	VELOCITY	VALUE AND
				VERACITY
	SIZE OF DATA	MULTI-SOURCE	REAL-TIME DATA	INSIGHT AND
				HIGH QUALITY
	90% of today data has	90% of	216.000 Instagram	Poor data
	been created in last 2	generate data is	posts / every 60	quality cost
	years	unstructured	seconds	business 600
				billion a year
	Every day we create		204.000.000 email
	2.5 quintibillion bytes	80% of data is video,	sent / every 60	30% of data
	of data	images and documents	seconds	collected by
				marketers are not
	Facebook has 1 petta		50.000 GB/second is	usable
	bytes of storage		the estimated rate of
			global Internet traffic

Acknowledgments

. All authors acknowledge the partial support of Project 691249, RUC-APS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems, funded by the EU under its funding scheme H2020-MSCA-RISE-2015; and the project "Development of an integrated maturity model for agility, resilience and gender perspective in supply chains (MoMARGE). Application to the agricultural sector." Ref. GV / 2017 / 025 funded by the Generalitat Valenciana. This first author was supported by the Aid Programme of Research and Development of Universitat Politècnica de València [PAID-01-18].


Paula Graça
email: mgraca@deetc.isel.pt

Luis M Camarinha-Matos

A Model of Evolution of a Collaborative Business Ecosystem Influenced by Performance Indicators

Keywords: Collaborative Networks, Business Ecosystem, Performance Indicators, System Dynamics, Agent Based Modelling

The materialization of the 4 th Industrial Revolution needs to emphasize the role of collaboration. Traditional business ecosystems have evolved to hyperconnected organizations facing more advanced collaboration models, dynamic networks, and more complex smart systems. Emerging collaborative aspects in this context need to be identified, and tools developed to help organizations coping with changing environment, market, and societal needs. As such, an assessment model is proposed to measure the expected self-adjustment of organizations in a collaborative business ecosystem, induced by performance indicators, in order to improve the organizations themselves and the ecosystem as a whole. Organizations with distinct profiles, categorized by classes of responsiveness, respond differently to the collaboration opportunities they may receive, or are more likely to invite others to collaborate. This behaviour is expected to be influenced by the variation in importance (weight) of each specific performance indicator adopted in a given business ecosystem, as the organizations, like individuals, tend to evolve according to how they are evaluated. To assess the proposed approach, an experiment has been set up using a simulation model based on system dynamics and agents. Preliminary results, based on a number of relevant scenarios, are presented and discussed.

Introduction

Business ecosystems are continuously evolving, accompanying the growing use of digital and collaborative platforms. Nowadays, they are shifting towards the age of Industry 4.0, more specifically to the notion of Collaborative Industry 4.0 [START_REF] Luis | Collaborative networks as a core enabler of industry 4.0. In Collaboration in a Data-Rich World[END_REF]. The expression Business Ecosystem was first introduced by Moore and inspired by ecological ecosystems [START_REF] James | Predators and prey: a new ecology of competition[END_REF]. On the other hand, a business ecosystem it is also considered in the research area of Collaborative Networks (CN) [START_REF] Luis M Camarinha-Matos | Collaborative networks: a new scientific discipline[END_REF], which has a wider scope. As such and aiming to emphasize the collaboration dimension, the term Collaborative Business Ecosystem (CBE) has been introduced in [START_REF] Graça | The need of performance indicators for collaborative business ecosystems[END_REF] and a model proposed [START_REF] Graça | Evolution of a collaborative business ecosystem in response to performance indicators[END_REF].

The aim of the present work is to assess the influence of performance indicators in a CBE, expecting to improve its behaviour and that of its individual organizations. There are several mechanisms to evaluate organizations individually, of which the balanced score cards (BSCs) [START_REF] Robert | The balanced scorecard: translating strategy into action[END_REF] are the best-known. However, to evaluate collaboration benefits, only limited contributions can be found in the literature. As an example, [START_REF] Luis | A conceptual model of value systems in collaborative networks[END_REF] proposes a conceptual model for value systems in CNs, and suggests a method for assessing the alignment of the value systems of their members [START_REF] Macedo | A qualitative approach to assess the alignment of value systems in collaborative enterprises networks[END_REF]. Other examples in the field of supply chain collaboration (SCC), a relatively new research area that is growing fast [START_REF] Ramanathan | Performance of supply chain collaboration â€" a simulation study[END_REF], identify collaboration to improve performance in traditional SCs and propose a wide variety of methods and metrics in [START_REF] Vereecke | Performance improvement through supply chain collaboration in europe[END_REF], [START_REF] Lorentz | Supply chain collaboration performance metrics: a conceptual framework[END_REF] and [START_REF] Ramanathan | Supply chain collaboration: Impact of success in long-term partnerships[END_REF]. Finally, the social network analysis (SNA) proposes a set of metrics related to the structure of the network, namely in [START_REF] Linton C Freeman | Centrality in social networks conceptual clarification[END_REF] and [START_REF] Matthew | Social and economic networks[END_REF], consisting of the most adequate approach as a contribution to the establishment of the performance indicators of the CBE.

For the evaluation of the CBE in this work, two of the performance indicators proposed in [START_REF] Graça | Evolution of a collaborative business ecosystem in response to performance indicators[END_REF] and [START_REF] Graça | A proposal of performance indicators for collaborative business ecosystems[END_REF] (CI -Contribution Indicator and PI -Prestige Indicator) are detailed, as well as a proposal for an influence mechanism. For experimental assessment, the CBE is simulated by a Performance Assessment and Adjustment Model (PAAM) as proposed in [START_REF] Graça | Evolution of a collaborative business ecosystem in response to performance indicators[END_REF], using agent based modelling (ABM) and system dynamics (SD) [START_REF] Borshchev | The Big Book of Simulation Modeling: Multimethod Modeling with AnyLogic 6[END_REF].

The remaining sections of this paper are organized as follows: section two describes the proposed simulation model, presenting its collaborative and assessment environment; section three shows how to calculate two of the performance indicators used to illustrate the assessment; section four presents the experimental evaluation of the model using a parametrized scenario to assess and verify the influence of indicators in its evolution, including a discussion of results. The last section summarizes the results and identifies the ongoing research and future work.

A Simulation Model of a CBE

The PAAM model illustrated in Figure 1, simulates a CBE environment populated by organizations (the agents) of different profiles, classified according to classes of responsiveness described in Table 1, thus allowing the establishment of diversified behaviors. To better respond to market opportunities, it is assumed that organizations collaborate by creating collaboration opportunities (CoOps) that they send and receive from each other. These collaborations generate "links" between organizations, weighted by the number of times they collaborate (#CoOps). The higher values of #CoOps mean stronger collaboration. For a certain CBE, a variable number of organizations of each class of responsiveness can be used among those considered in Table 1: Social, Selfish, Innovator, and Crook, to better reproduce diversity in a true CBE. Each class is composed of three parameters to characterize the agents, whose values presented in Table 1 are merely illustrative and can be adjusted for each simulation scenario. These parameters (decimal values ranging from 0 to 1), are used as the probability of successful attempts in the distribution functions adopted by the model to simulate the random behaviour of the agents. Table 1. Description of the classes of responsiveness of organizations.

Collaborative Environment

When an organization wants to collaborate with other organizations in the CBE, it requests so by sending a CoOp (taskDescription, resourcesToAssign), describing the task and specifying the amount of resources assigned. This amount is given by a binomial distribution as illustrated in formula (1), to get a value bounded between [0, resourcesToAssign] with a probability equal to the contactRate parameter. The higher the parameter, the more likely it is to get more resources to distribute. Organizations belonging to the Social class have the highest contactRate and those of the Selfish class the lowest.

On the other hand, the organizations that receive the invitations, if having available resources, accept with a probability given by the Bernoulli distribution [START_REF]Introduction to mathematical probability[END_REF] as illustrated in formula [START_REF] James | Predators and prey: a new ecology of competition[END_REF]. The result is "yes/no" with the "yes" having a probability equal to the acceptRate parameter. The higher the parameter, the more likely the collaboration is to be accepted. Organizations belonging to the Social class also have the highest acceptRate and those of the Selfish class the lowest.

(2) Finally, if the CoOp refers to a task related to innovation, which may result in the development of new products or patents, then the organizations also accept the collaboration according to the Bernoulli distribution as illustrated in formula ( 3), but with a probability equal to the newProductsRate parameter. The higher the parameter, the more likely the collaboration is to be accepted. Organizations belonging to the Innovators class have the highest newProductRate and those of the Social and the Selfish class the lowest.

(3)

Assessment Environment

A performance assessment mechanism can be used to assess the CBE and its individual organizations, based on the indicators proposed in [START_REF] Graça | Evolution of a collaborative business ecosystem in response to performance indicators[END_REF] and [START_REF] Graça | A proposal of performance indicators for collaborative business ecosystems[END_REF]: the Innovation Indicator (II), to evaluate the proficiency of the organizations to create new products or patents; the Contribution Indicator (CI), to evaluate the value generated by the collaboration; and the Prestige Indicator (PI), to evaluate the prominence of a particular organization over others, to participate in collaboration.

The weight (significance) given to each performance indicator by the CBE manager, is expected to act as a factor of influence, resulting in a certain achievement of organizations, which as individuals, tend to adjust according to the way they are evaluated. For demonstrative purposes, a scenario of simulation was created with three main components of common business activity: research and development (R&D), Consulting, and Inner tasks. For the realization of each component, the organizations allocate a given percentage of resources according to their class of responsiveness. Table 2 illustrates a sample of a possible allocation used in the current experiment (the Crook class was not considered).

Table 2. Sample of resources allocation by business activity and class of responsiveness.

It is assumed that the variation in the weights of the performance indicators by the CBE manager, will act as a factor of influence over the organizations, causing their self-adjustment trying to improve their profile, resulting in an improvement of the CBE as a whole. Therefore, as illustrated in Figure 2, it is considered that an influence mechanism acts on the percentage of resources allocated to each business activity, called respectively slice for R&D, slice for Consulting and slice for InnerTasks. The factor of influence (FI) of the mechanism, is expressed as a percentage (for instance 10%) of improvement to be distributed among the slices according to the weights of the performance indicators (wII, wCI and wPI), causing a reallocation of resources and a consequent self-adjustment of the organizations' behaviour. It is also assumed that the resources for R&D are influenced by the weight wII, and the resources for consulting, are influenced by the weights wCI and wPI. 2 as a base distribution, the influence mechanism can be expressed by formulas (4), ( 5) and ( 6).

(4)

(5) [START_REF] Robert | The balanced scorecard: translating strategy into action[END_REF] According to these formulas, the influence mechanism subtracts the FI equally from the three slices of resources, so that it can be redistributed by considering the weights of the indicators.

Performance Indicators to Assess the Influence on the CBE

Two of the performance indicators proposed in [START_REF] Graça | Evolution of a collaborative business ecosystem in response to performance indicators[END_REF], are used in this work to assess the CBE and the influence on the behaviour of its organizations in terms of collaboration. The Contribution Indicator (CI), to measure the total value created by collaboration in the CBE as a whole and that of its individual organizations, and the Prestige Indicator to measure the influence/prominence of the organizations in the CBE. Table 3 and Table 4 describe the metrics used to calculate the performance indicators CI and PI, of the organizations' collaboration and that of the CBE as a whole.

Table 3. Metrics of the Contribution Indicator.

The CIi in of an organization, assesses the contribution of the organization Oi in terms of accepted collaboration opportunities. The value CIi in is thus obtained by the weighted degree centrality of Oi calculated by formula [START_REF] Luis | A conceptual model of value systems in collaborative networks[END_REF], which is more related to the popularity of organizations [START_REF] Opsahl | Node centrality in weighted networks: Generalizing degree and shortest paths[END_REF]. [START_REF] Luis | A conceptual model of value systems in collaborative networks[END_REF] The CIi out of an organization, assesses the contribution of the organization Oi in terms of created collaboration opportunities. The value CIi out is thus obtained by the weighted outdegree centrality of Oi calculated by formula [START_REF] Macedo | A qualitative approach to assess the alignment of value systems in collaborative enterprises networks[END_REF], which is more related to the activity of organizations [START_REF] Opsahl | Node centrality in weighted networks: Generalizing degree and shortest paths[END_REF]. These values are normalized between 0 and 1 in relation to the maximum degree centrality for the current network. [START_REF] Macedo | A qualitative approach to assess the alignment of value systems in collaborative enterprises networks[END_REF] The CICBE in and the CICBE out of the CBE, assess respectively the degree to which the most popular organization in terms of accepted collaboration opportunities and the most active organization in terms of created collaboration opportunities, exceeds the contribution of the others. The values CICBE in and CICBE out are thus obtained by the weighted degree centrality of the CBE as a whole calculated by formulas ( 9) and ( 10), i.e. the sum of differences between the contribution of the most popular/active organization (O*) and that of all organizations in the CBE. These values are normalized between 0 and 1 in relation to the maximum possible sum of differences of degree centralities for the current network.

(9) [START_REF] Vereecke | Performance improvement through supply chain collaboration in europe[END_REF] The CICBEt, calculated by formula [START_REF] Lorentz | Supply chain collaboration performance metrics: a conceptual framework[END_REF], is a ratio of the total number of collaboration opportunities created in the CBE by the total number of organizations. The PIi in of an organization, assesses the prominence of the organization Oi in terms of accepted collaboration opportunities. It means the extent to which a node (organization) is part of transactions (collaboration) among other nodes [START_REF] Opsahl | Node centrality in weighted networks: Generalizing degree and shortest paths[END_REF]. Using Freeman's betweenness measure [START_REF] Linton C Freeman | Centrality in social networks conceptual clarification[END_REF], this means the number of times that an organization is on the shortest paths among all pairs of the other organizations. In a binary network, the shortest path means the smallest number of intermediate nodes between two organizations. However, in weighted networks, the transactions (collaboration) between two nodes (organizations) might be faster (more expressive) with more intermediate nodes that are strongly connected [START_REF] Opsahl | Node centrality in weighted networks: Generalizing degree and shortest paths[END_REF]. This is due to the fact that stronger intermediate nodes mean more collaboration between organizations. The value PIi in is thus obtained by the weighted betweenness centrality calculated by formula [START_REF] Ramanathan | Supply chain collaboration: Impact of success in long-term partnerships[END_REF], which stands for the sum of overall partial betweenness of Oi relative to all pairs Okj assuming that connections between any Ok organization and any other Oj have weight of #CoOpkj in. [START_REF] Ramanathan | Supply chain collaboration: Impact of success in long-term partnerships[END_REF] The PIi out of an organization, assesses the prominence of the organization Oi in terms of created collaboration opportunities. Similarly to PIi in, PIi out is calculated by formula [START_REF] Linton C Freeman | Centrality in social networks conceptual clarification[END_REF]. These values are normalized between 0 and 1 in relation to the maximum betweenness centrality for the current network. [START_REF] Linton C Freeman | Centrality in social networks conceptual clarification[END_REF] The PICBE in and PICBE out of the CBE, assess respectively the degree to which the most prominent organization in terms of accepted collaboration opportunities and the most prominent organization in terms of created collaboration opportunities, exceeds the contribution of the others. The values PICBE in and PICBE out are thus obtained by the weighted betweenness centrality of the CBE as a whole calculated by formulas ( 14) and ( 15), i.e. the average of the differences between the preponderance of the most influent organization (O*) and that of all organizations in the CBE. These values are normalized between 0 and 1 in relation to the maximum possible sum of differences of betweenness centralities for the current network. ( 14) [START_REF] Graça | A proposal of performance indicators for collaborative business ecosystems[END_REF] The PI indicator, as shown in formulas ( 12), ( 13), ( 14) and ( 15), uses the betweenness centrality to evaluate the preponderance of organizations' collaboration in the CBE. For this, the Floyd-Warshall algorithm [START_REF] Floyd | Algorithm 97: Shortest path[END_REF] was applied to find the shortest paths in the weighted graph represented by the CBE and its organizations connected by collaboration opportunities. The algorithm starts with a distance matrix D with n lines and n columns, where n is the number of nodes (#O) and each position of the matrix D[i,j] contains the weight (#CoOpij) between the node i (Oi) and node j (Oj). Because the shortest paths in the CBE mean stronger connections between the organizations, i.e. more collaboration, the inverse of the #CoOpij is used, resulting in the matrix ( 16). [START_REF] Borshchev | The Big Book of Simulation Modeling: Multimethod Modeling with AnyLogic 6[END_REF] The shortest paths matrix is then obtained after k = 0..n-1 iterations over the D n distance matrix, where in each k iteration, the D k matrix is calculated according to formula [START_REF]Introduction to mathematical probability[END_REF]. [START_REF]Introduction to mathematical probability[END_REF] Finally, to compute the betweenness centrality of each node, i.e. the number of times that an organization Oi is on the shortest paths among all pairs of the other organizations Okj, the Floyd-Warshall algorithm [START_REF] Floyd | Algorithm 97: Shortest path[END_REF] had to be improved. A path matrix P was used to register the shortest paths between all pairs, starting with the matrix P 0 calculated according to [START_REF] Opsahl | Node centrality in weighted networks: Generalizing degree and shortest paths[END_REF]. [START_REF] Opsahl | Node centrality in weighted networks: Generalizing degree and shortest paths[END_REF] The final P n matrix is reached after k = 0..n-1 iterations, where in each k iteration, the P k matrix is calculated according to formula [START_REF] Floyd | Algorithm 97: Shortest path[END_REF]. [START_REF] Floyd | Algorithm 97: Shortest path[END_REF] All the metrics and formulas described in this chapter, were used to calculate the performance indicators in the experimental evaluation of the CBE.

Experimental Evaluation of the CBE

To build the proposed PAAM described in chapter 2, for the experimental evaluation of the CBE, and to implement the performance indicators described in chapter 3, the AnyLogic Multimethod Simulation Software [START_REF] Borshchev | The Big Book of Simulation Modeling: Multimethod Modeling with AnyLogic 6[END_REF] was used. The model depicted in Figure 3, simulates an environment (the CBE), populated by agents (the organizations), whose behaviour is represented by state-charts and system dynamics, to represent stocks and flows of resources.

The income market opportunities (incomingMarketOps) are also modelled by agents arriving at a rate of 1.000/year plus a 25% of opportunities for new products or patents, following the Poisson distribution (adequate for modelling the number of times an event occurs in an interval of time) [START_REF] Frank | Handbook of the poisson distribution[END_REF]. Each incomingMarketOps is composed of a task description (research or consulting) and a number of resources (days-man) estimated to perform the task (generated by a uniform distribution bounded by [1..50 days-man]). For the present experimental evaluation, the PAAM simulation model was parametrized to represent a CBE composed of 6 Social organizations, 5 Selfish and 3 Innovative. The organizations were configured with the values described in Table 1 and Table 2, having an initial amount of resources of 1.500/year (day-man).

Running the model considering the interval of one year, the performance indicators CI and PI were calculated, resulting in the values displayed in Table 5 Running the model again for a period of one year and parameterizing the influence mechanism as shown in Table 6, the results of Table 7 were achieved. Table 6. Parametrization of the influence mechanism.

Comparing the results of Table 5 and Table 7, it can be observed that the more significant difference in the CBE after applying the influence mechanism, is that all the organizations tried to be more active creating more CoOps. The indicator CICBEt increased from 26,4 to 26,7 (showing a higher average of collaboration opportunities by organization, although not very significant), and the CIi out (invites to collaborate sent by organization) also increased for almost all the organizations, flattening CICBE out from 0,214 to 0,178 (showing a more uniform collaboration among organizations) at the same time. On the other hand, the PIi in also had an increase (more prestige concerning invitations received) but only in the Social and Innovator classes, resulting in a better PICBE in from 0,776 to 0,742 (showing a more uniformization of the prestige among organization), but still showing a high polarized distribution. Finally, no further significant differences were registered. Table 7. Values of the CI and PI for each individual organization and for the CBE, after the influence mechanism.

Although the previous observed responses of a CBE and its individual organizations, to the proposed influence mechanism are not very significant so far, these are preliminary results using arbitrary parameters so that the modelling and simulation concept can be illustrated. Other improvements to the influence mechanism should be made as well as the adjustment of the parameters used in order to obtain more meaningful conclusions.

Conclusions and Further Work

The PAAM model and the experimental evaluation in the previous section showed that a CBE can be evaluated through performance indicators, more specifically, the proposed CI and PI. It also showed that a CBE can evolve by self-adjusting of the behaviour of its organizations, when influenced by the variation of the weights (significance) of the adopted performance indicators.

The ongoing work is related to the improvement of the influence mechanism, enhancing the calculation formulas by introducing more variables in addition to the allocated resources.

Future work includes the calculation of the Innovation Indicator (II), correlating it with collaboration. On the other hand, the PAAM model should be more dynamic, basing the decision to collaborate not on distribution functions, but depending on the performance of organizations. Finally, more refined and tested simulation scenarios should be carried out using all classes of responsiveness with different and dynamic parametrizations.

Figure 1 .

 1 Figure 1. PAAM (Performance Assessment and Adjustment Model) for a CBE.

Figure 2 .

 2 Figure 2. Detail of the influence mechanism used for the presented simulation model.

( 11 ) 4 .

 114 Table Metrics of the Prestige Indicator.

Figure 3 .

 3 Figure 3. PAAM model after an iteration of one year.

  . Columns CIi in and CIi out show respectively the contribution of the organization Oi in terms of accepted CoOps, and the contribution in terms of created CoOps by inviting other organizations to collaborate. On the other hand, columns PIi in and PIi out show respectively the prominence of the organization Oi, i.e. the extent to which Oi is part of the collaboration among the other organizations in terms of accepted or invited CoOps. Finally, the performance indicators related to the whole CBE, have the following results: CICBEt = 26,4 is the ratio of the total number of CoOps generated in the CBE by the total number of organizations; CICBE in = 0,444 and CICBE out = 0,214, are respectively the degree to which the most popular organization (#CoOps received) and the most active (#CoOps created), exceeds the contribution of the others; PICBE in = 0,776 and PICBE out = 0,686, are respectively the degree to which the most prominent organization (being part of the CoOps received or created ) exceeds the contribution of the others. Table 5. Values of the CI and PI for each individual organization and for the CBE. The indicators CICBE in/out reveal a better distribution of the collaboration than the PICBE in/out, since these values are normalized between 0 and 1, with zero indicating an equal distribution of collaboration among all organizations.

Acknowledgments. This work benefited from the ongoing research within the CoDIS (Collaborative Networks and Distributed Industrial Systems Group) which is part of both the New University of Lisbon (UNL) -Faculty of Sciences and Technology, and the UNINOVA -CTS (Center of Technology and Systems). Partial support also comes from Fundação para a Ciência e Tecnologia through the PEST program UID/EEA/00066/2019.