The role of private consultants as innovation intermediaries in technology transfer

Universidade do Minho Escola de Engenharia João Carlos Cordeiro Soares The role of private consultants as innovation intermediaries in technology transfer January 2024 UMinho | 2024 João Carlos Cordeiro Soares The role of private consultants as innovation intermediaries in technology transfer João Carlos Cordeiro Soares The role of private consultants as innovation intermediaries in technology transfer Doctoral Thesis Doctoral Program in Industrial and Systems Engineering Work performed under the supervision of: Professor Fernando Romero Professor Manuel Lopes Nunes January, 2024 ii DIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidade do Minho. Licença concedida aos utilizadores deste trabalho Atribuição-NãoComercial-SemDerivações CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/ iii ACKNOWLEDGEMENTS I would like to express my sincere gratitude to my two supervisors, Professor Fernando Romero, and Professor Manuel Lopes Nunes, for their unwavering patience, guidance, and support throughout the five years it took to conduct this research. Their expertise and feedback have been invaluable in shaping the direction of my thesis. I extend heartfelt gratitude to Professor Ana Cristina Braga for her invaluable support. Despite not being her student, she generously devoted her time, attention, and expertise to guide me through the statistical software challenges I encountered. I would also like to appreciate my wife’s role, who has supported and motivated me throughout this journey. Her love, encouragement, and understanding have been instrumental in keeping me focused and motivated. I am also deeply grateful to the participants of my interviews, who generously gave their time and shared their valuable insights, even amidst the challenges posed by the COVID-19 pandemic. Their contributions have been crucial in providing me with a deeper understanding of the topic. Finally, I would like to thank the private consulting firms that opened their doors and trusted me with access to their data and knowledge. Their cooperation and willingness to share their expertise have been instrumental in providing me with the necessary information to complete this research. Once again, I would like to express my sincere gratitude to all those who have contributed to this thesis. Without your support, this work would not have been possible. iv STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho. University of Minho, January 2024. Name: João Carlos Cordeiro Soares _______________________________ Signature v O papel dos consultores privados como intermediários de inovação em transferência de tecnologia RESUMO Os domínios de sistemas de inovação e transferência de tecnologia (TT) têm evoluído consideravelmente nas últimas décadas. O dinamismo dos mercados, em especial, considerando face a uma perspetiva de inovação aberta, têm resultado numa maior complexidade daquele que é o processo de inovação, bem como do próprio papel dos agentes envolvidos na sua implementação. Novos intermediários de TT têm vindo a emergir para dar resposta a estas dinâmicas e novas lacunas por responder. Ainda assim, o conhecimento sobre o agente intermediário e o seu papel está ainda restringido a uma dimensão meramente conceptual, apesar da multiplicidade de agentes já a atuar sob este papel. Empresas de consultoria em gestão e inovação têm vindo cada vez mais a atuar como intermediários no Sistema Nacional de Inovação (SNI). Esta investigação procurou aprofundar o conhecimento relativo a este papel dos consultores privados enquanto intermediários de inovação em TT. Foram conduzidas entrevistas semiestruturadas a agentes do SNI com vista ao registo das suas experiências e perceções. Posteriormente, cinco hipóteses puderam ser formuladas e testadas através da condução de analise estatística a um arquivo de projetos de TT de uma consultora privada alvo de caso de estudo. Compilouse uma lista estruturada de treze papeis/especializações que o intermediário de inovação pode desenvolver. A consultora privada demonstrou ter um papel significativo enquanto intermediário de inovação, em seis dos treze papeis compilados. Fatores adicionais foram ainda identificados por contribuírem para o papel do consultor privado, sendo o mais significativo a sua proatividade na origem de projetos de TT. Adicionalmente, registaram-se casos de colaboração direta entre a consultora privada e outros intermediários tradicionais a atuar nos projetos de TT. Por fim, uma comparação entre papeis desempenhados por ambos intermediários revelou tanto casos de sobreposição como de mútua exclusividade. As conclusões do estudo contribuem para a compreensão das consultoras e do seu papel em TT. Além disso, os resultados abrem caminho a uma discussão sobre o posicionamento do papel destes consultores privados dentro das complexas dinâmicas no SNI. Pretende-se dar um passo para o formal reconhecimento deste novo tipo de intermediário e consequentemente da atualização das políticas de inovação que regulam a TT e o marketing de inovação e dos resultados de I&D. Palavras-chave: Sistemas de Inovação, Intermediação, Consultoria, Transferência de tecnologia. vi The role of private consultants as innovation intermediaries in technology transfer ABSTRACT Innovation systems (IS) and technology transfer (TT) fields have been undergoing considerable changes throughout the last few decades. The dynamics of markets’ functioning, especially considering an open innovation perspective, add a new complexity layer to fully understand the innovation process and its stakeholder’s role in implementing it in the market. New TT intermediaries have been emerging to tackle these dynamics and new unanswered gaps. However, the understanding of the intermediary and its role is somehow still conceptual despite the multiplicity of agents operating under the role. Private innovation and management consultancy firms have been increasingly providing services as intermediaries within national innovation systems (NIS). This study aims to further develop the understanding of private consultants’ role as innovation intermediaries in TT. Initially, exploratory semi-structured interviews were conducted with Portuguese NIS agents to collect and discuss experiences and perceptions. Five hypotheses were afterwards formulated and tested through a statistical analysis conducted on an archive with TT projects of a Portuguese private consultancy firm which was the case study subject. A framework was developed comprising thirteen role specializations an innovation intermediary can have. The private consultancy demonstrated to play a significant role as an innovation intermediary, particularly in six of these thirteen roles’ specialisations. Other additional variables were also found to be key factors contributing to the private consultant’s role, being the most relevant to their proactivity in originating TT projects. Moreover, TT projects in which the private consultancy directly collaborated with other traditional intermediaries were registered. Still, a comparison of the roles played by both intermediaries found cases of overlapping and mutual exclusivity within the same TT projects. The study findings contribute to the understanding of the role of private consultancies in TT projects. Also, the results lay a foundation for further discussion regarding the positioning of private consultants’ role within the complex relationships and dynamics of NIS, in particular, with other traditional intermediaries. Intrinsically, the findings intent to be a step further in the recognition of this new kind of intermediary and, consequently, the improvement of innovation policies regulating TT and the marketing of innovation and R&D results. Keywords: Innovation Systems, Intermediation role, Consultants, Technology Transfer. vii CONTENTS Acknowledgements ................................................................................................................................ iii Resumo................................................................................................................................................... v Abstract.................................................................................................................................................. vi List of tables.......................................................................................................................................... xii List of figures ........................................................................................................................................xiv List of abbreviations and acronyms ....................................................................................................... xv Chapter 1. Introduction and Thesis overview .......................................................................................... 2 1.1 The research background ....................................................................................................... 2 1.2 The researcher motivation ...................................................................................................... 3 1.3 The research problem ............................................................................................................ 4 1.4 The research strategy ............................................................................................................. 6 Chapter 2 - Literature Review ................................................................................................................. 9 2.1 Introduction ............................................................................................................................ 9 2.2 Innovation ............................................................................................................................ 11 2.2.1 The concept of Innovation ............................................................................................ 11 2.2.2 Innovation Systems (IS) ................................................................................................ 14 2.2.2.1. A systemic approach to innovation ....................................................................... 14 2.2.2.2. National Innovation Systems (NIS) ........................................................................ 17 2.2.2.3. The Triple-Helix framework ................................................................................... 23 2.2.2.4. The Open Innovation model (OI) ........................................................................... 26 2.2.2.5. Innovation Policymaking ....................................................................................... 32 2.2.3 The Portuguese innovation system ............................................................................... 34 2.2.3.1 Characterization of the Portuguese NIS ................................................................ 34 2.2.3.2 Financial innovation incentives ............................................................................. 37 2.3 Technology Transfer (TT) ...................................................................................................... 40 2.3.1 Transferring technology and innovation ........................................................................ 40 2.3.2 Challenges to Technology Transfer ............................................................................... 42 2.3.3 Key components of the TT concept .............................................................................. 44 2.3.3.1 Sources and Receivers of the technology .............................................................. 45 2.3.3.2 Object (or Technology) .......................................................................................... 45 2.3.3.3 Intermediaries (or mediators) ............................................................................... 47 xiv LIST OF FIGURES Figure 1 - Research synopsis ................................................................................................................. 7 Figure 2 – Technology-push perspective .............................................................................................. 16 Figure 3 - Demand-pull perspective ...................................................................................................... 17 Figure 4 - Elements of NIS ................................................................................................................... 19 Figure 5 - Planned Economy NIS .......................................................................................................... 21 Figure 6 - Market economy NIS ............................................................................................................ 22 Figure 7 - Triple-Helix model................................................................................................................. 24 Figure 8 - Closed Innovation paradigm ................................................................................................. 26 Figure 9 - Open Innovation paradigm ................................................................................................... 28 Figure 10 – Sector/Technology domains of the TT projects in the sample ......................................... 174 Figure 11 - Type of entities responsible for originating the TT projects ............................................... 175 Figure 12 - Entity in the TT projects responsible for the technology source contact ............................ 176 Figure 13 - Entity in the TT projects responsible for the technology recipient contact ......................... 177 Figure 14 - Existence of previous interaction with the private consultants .......................................... 178 Figure 15 - Key motivations/reasons leading the contact or involvement of private consultants ......... 179 Figure 16 - Participation of other intermediaries alongside the private consultant .............................. 182 Figure 17 - Roles performed by the private consultant (case study firm) in the sample ...................... 183 Figure 18 - Traditional intermediaries’ roles (subsample) ................................................................... 186 Figure 19 - Private consultant roles (subsample) ................................................................................ 188 xv LIST OF ABBREVIATIONS AND ACRONYMS AQ - Accreditation & Quality ANI – National Innovation Agency (Portugal) BG - Brokering & Gatekeeping CRM - Customer Relationship Management DI - Design & Idealization EEC - European Economic Commission EU - European Union FF - Funding & Finance FTF - Financial & Technical Feasibility ICT - Information and Communication Technologies IKT - Implementation & Knowledge Transfer IP - Intellectual Property IPR - Intellectual Property & Rights IS - Innovation Systems ITT - Innovation and Technology Transfer KDS - Knowledge Diffusion & Support KIBS - Knowledge Intensive Business Services KTT - Knowledge and Technology Transfer MBD - Marketing & Business Development MM - Mediation & Mobilization NIS - National Innovation Systems OECD - Organisation for Economic Co-operation and Development OI – Open Innovation ORT - Organizations for Research and Technology PMA - Project Management & Assessment PC – Private Consultant PS - Policy & Strategy R&D - Research and Development ROI - Return on the Investment SME - Small and Medium Enterprises TI – Traditional Intermediary xvi TIC - Technology Interface Centre TRL - Technology Readiness Level TSMF - Technology Scouting & Market Foresight TT - Technology Transfer TTO - Technology Transfer Office UNCTAD - United Nations Conference on Trade and Development UNIDO - United Nations Industrial Development Organization WOM – Word of Mouth 1 CHAPTER 1 INTRODUCTION AND THESIS OVERVIEW 2 CHAPTER 1. INTRODUCTION AND THESIS OVERVIEW 1.1 The research background The fields of Innovation Systems (IS) and Technology Transfer (TT) have generated more literature than ever before over the last few decades. However, the constant dynamism of markets, particularly when viewed from an open innovation perspective, adds a new layer of complexity to the understanding of the innovation process. The rise of new market needs, agents, and mechanisms within IS creates new systematic gaps, which are increasingly viewed by private innovation management consulting organizations as market opportunities to sell highly specialized and knowledge-intensive business services (KIBS). In the current context of Open Innovation (OI), IS dynamics combine various players with distinct external support roles, such as suppliers, clients, competitors, research institutes, universities, consulting firms, and other public organizations. From these dynamics, TT stakeholders constantly seek knowledge access and external information, which is facilitated by the growing involvement of innovation intermediaries. These intermediaries are also responsible for constantly maintaining networks, sourcing market intelligence and technology knowledge, as well as facilitating access to other players and funding programs (Chesbrough et al., 2006). The concept of the “innovation intermediary” is defined as an entity with a systematic key role as a mediator or broker in the various dimensions of the innovation process between two or more parties. Activities performed by this agent typically focus on gathering and providing knowledge, brokering negotiations and contracts between parties, playing an active mediator and go-between within networks, building project consortiums and partnerships, advising and consulting in support of decision-making, obtaining funding, and monetizing innovation outcomes (Howells, 2006; Silva et al., 2018). Howells (1999, 2006) highlights the systemic value that innovation intermediaries generate in IS and innovation policies, due to their catalysing effect on the connectivity and relationship dynamics of system agents. The same author has been emphasizing the increasing growth in the number, and range, of these intermediary players within the systems. In particular, the emergence of private players, such as consultants and Knowledge-Intensive Business Services (KIBS), who privatized the intermediation role, selling it to the market as a specialized value-added service. This almost rampant growth of entities with some intermediary role in Innovation and Technology Transfer (ITT) makes these agents too significant and too wide to be ignored by the system (Dalziel, 2010). The figure of the innovation intermediary has always had a significant relationship with the TT literature, with some mentions of private consultants' participation. Several authors have been exploring 3 the multiplicity of specializations and dimensions that can configure what is referred to as the “role” of intermediation. However, different typologies of intermediary organizations/entities may have different roles and, therefore, cannot be compared (Agogué et al., 2017; Howells, 2006; Pinto et al., 2015; Pinto, 2018; Silva et al., 2018). In particular, the Portuguese National Innovation System (NIS) is mostly governed by public and academic entities (Duarte & Carvalho, 2020; Laranja, 2007; Santos, 2016; Simões, 2003). However, there has been a multiplication in the number of private consulting firms operating in it in recent decades. These firms provide a wide range of services to businesses, mostly from a market-pull perspective (Jun & Ji, 2016; Laranja, 2009). Thus, this new figure of private consultant positions itself as an unofficial innovation intermediary within the NIS, with its particular role and positioning when participating in TT projects (Basu & Taylor, 2010; Bessant & Rush, 1995; Costa et al., 2021; Tether & Tajar, 2008). In the literature, there are several mentions and approaches to the topic of innovation intermediaries. However, consultants, who see themselves as innovation intermediaries, tend not to be the focus of research and publications. Despite this, the relevance of consultants can be recognized from the point of view of innovation policies and systems, as the number of consultants in this role has been increasing (Klerkx et al., 2015; Klerkx & Leeuwis, 2008, 2009). Nevertheless, there is still a lack of proper depth in the literature regarding an understanding of what “technology transfer intermediation” is, as well as the real role and positioning of these key intermediary agents within the innovation system (Howells, 2006; Klerkx & Leeuwis, 2009; Silva et al., 2018). 1.2 The researcher motivation The researcher started his career in the manufacturing industry, focusing primarily on designing and launching new products supported by R&D project outcomes from consortiums involving various companies, universities, R&D centres, and consulting firms. With a formal education background in design engineering, product marketing, and project management, the researcher's last decade has been dedicated to the innovation consultancy industry. As a senior innovation strategy consultant, the researcher has professionally worked on the idealization, planning, financing, and management of national-scale projects in domains such as digital transformation, innovation, and TT. The researcher's close and regular collaboration with companies, business/sectoral associations, universities, and R&D centres provided him with a unique perspective on the reality of innovation consultancy in Portugal. Furthermore, there is an increasing need to clarify the current role of private consulting firms in fostering ITT. The sale and supply of services by consultants that, in theory, should be 4 offered openly and free of charge in the NIS by traditional (i.e., public) intermediaries is a recurrent scenario. Additionally, public, and semi-public organizations, such as universities, R&D centres, Technological Interface Centres (TIC), and business associations, have been collaborating more closely and even demanding private consultant services. These experiences led the researcher to fundamental questions regarding the role of the private consultant as an innovation intermediary participating in TT processes and how it is perceived in the NIS. From the researcher's point of view, gaining a further understanding of the role of the consultant in the Portuguese NIS may contribute to a step further towards the formal recognition of private consultancy firms as accredited intermediaries. Moreover, it will enable the transfer of knowledge and good practices from private organizations to be implemented in traditional public and academic intermediaries operating in the NIS. 1.3 The research problem The research presented in this document takes an IS perspective, where cooperation between agents is considered a crucial factor in the system's proper functioning (Hidalgo & Albors, 2008; Pollard, 2006, 2015). However, cooperation between agents can often have flaws, and both scientific and political literature on IS suggests the need for intermediation organizations to facilitate cooperation between agents (Klerkx & Leeuwis, 2009). Different types of intermediary organizations, both inside and outside of innovation systems, have been identified (Betz et al., 2016; Suvinen et al., 2010; Tether & Tajar, 2008). However, the role of each of these intermediaries and how they can cooperate to enhance TT effectiveness and synergies are not easily understood (Randhawa et al., 2018). The innovation intermediation literature is highly fragmented and scattered, making it difficult to define concretely “the role” of the intermediary and what this concept entails (Soares et al., 2020). Traditionally, intermediaries served the purpose of mediating contacts, relationships, and negotiations of technology and knowledge between sources and recipients (Betz et al., 2016). However, with the development of the literature, it became clear that the concept of “role” is much more complex, requiring different specializations to respond to various projects, technology fields, regions, economies, and stakeholders. As a result, several authors have contributed to adding functions, activities, and role specializations to the concept of the intermediation role. Nevertheless, understanding the intermediation’s role specializations is complicated by the numerous entities and organizations that can perform intermediation roles (Howells, 2006; Shearmur & Doloreux, 2019). Private consultants, for instance, tend 5 to operate outside the formal IS and play roles generally intended for traditional intermediaries, making their role, and positioning, not yet fully understood. This research focuses on the private innovation consultant as a particular type of TT intermediary that is somewhat neglected in the scientific literature (Dias et al., 2017), despite their proven contribution to fostering innovation and accelerating new products to the market (Colombo et al., 2015). Moreover, the existing literature still lacks a proper in-depth comprehension of private consultants, including empirical validation of the role these agents play as intermediaries of innovation (Dias et al., 2017), This research aims to further develop practical knowledge and address the insufficient understanding of the role of private consultants as innovation intermediaries in TT. Due to the lack of sufficient literature to sustain the definition of the research gap, an interactive research approach as followed as explained in the next sub-chapter (1.4 The Research Strategy). The research staring point was deployed from the need deepen the knowledge on the role private consultants might have as innovation intermediaries within processes of technology transfer. Table 1 – Research objectives formulation Research Question 1: What is the role of the private consultant in technology transfer projects? Objective 1: Identify the key roles played by private consultants in TT projects. Objective 2: Identify what key motivations may constitute a value proposition leading the involvement of consultants in TT projects. Research Question 2: How is private consultants’ role positioned when compared to traditional intermediaries on NIS? Objective 3: Understand whether there is a complementary or overlapping interaction between the roles of traditional intermediaries and private consultants in TT projects. 6 1.4 The research strategy The research is positioned at the intersection of several fields, combining knowledge and perspectives from economics, industrial policy, innovation systems, and technology transfer. Furthermore, this set of research fields tends to combine the use of quantitative and qualitative components, presenting various challenges in data collection and analysis. The strategic basis of this research followed a line of inductive research (Saunders et al., 2009), beginning with research questions and objectives and followed by the collection of empirical data. This data was then used to generate hypotheses and test them to produce and discuss findings. For this reason, the research followed a mixed strategy, divided into two consecutive phases: 1. Qualitative Research - The first phase focused on qualitative research, combining inputs collected in the literature review to design qualitative and exploratory research, namely semistructured interviews. The results of this empirical data collection were analysed to propose five research hypotheses and consecutively feed the methodological design of the quantitative research. 2. Quantitative Research – Following the hypotheses formulated as a result of the qualitative research, the quantitative research took place using empirical data collected from an archive of a case study consultancy firm. The data was used to conduct a series of descriptive analyses and statistical tests designed to accept or reject the hypotheses previously formulated and, thus, infer a set of findings (i.e., theory). 7 Figure 1 - Research synopsis This strategic looping structure, as depicted in the research synopsis (Figure 1), aims to maximize the potential of each research approach and, consequently, enhance the scientific quality of the resulting findings. The outcomes obtained from the exploratory phase in Chapter 5, conducted through qualitative research methodology, serve as the basis for formulating hypotheses, which in turn become the key input for designing the research methodology employed in the case study (refers to Chapter 6). 14 Other authors such as Seaton and Cordey-Hayes (1993) have introduced a technology market exploitation perspective to the discussion, emphasizing the importance of developing capabilities and designing value propositions that are aligned with market needs. This perspective can be viewed as a role that relates to product management and marketing, particularly from an industrial marketing strategy point of view, as new technological advancements are turned into marketable products. 2.2.2 Innovation Systems (IS) The IS literature has also recognized the growth of intermediaries and their roles. Several authors (Intarakumnerd & Chaoroenporn, 2013; Lichtenthaler, 2013; Lynn et al., 1996) have emphasized the crucial role played by intermediary organizations, both private and public, in supporting the links and relationships within innovation networks and systems. These organizations create structures that provide collective support and coordinate the entities responsible for developing core technological innovations. Laursen and Salter (2006) also identified the support provided by external intermediary organizations in the adjustment of innovative technology solutions to current market needs in the manufacturing industry. With a broader perspective, Van der Meulen and Rip (1998) saw some of these intermediary organizations - specifically public organizations related to academia and research - being involved with more strategic roles, supporting process management from the policymaking to the operational implementation of these policies within the system. Also, some innovation policies are increasingly recognizing the key role played by innovation intermediaries in providing advice and informing policymaking studies (Kivimaa et al., 2019; Russo et al., 2019; Steinmueller, 2009), and their active role in innovation and technology management within complex industry networks (Chesbrough, 2004). 2.2.2.1. A systemic approach to innovation The systematization of innovation as a theoretical perspective resulted from a set of approaches that recognised the complexity of innovation processes, and therefore consider them more as a system than a mere set of processes (Dosi et al., 1988; Edquist, 1997; Freeman, 1987, 1988; Lundvall et al., 2002; Lundvall, 1985, 1992; Nelson & Rosenberg, 1993). Several models and theories influence the functioning of IS, including the interactive learning model and evolutionary theory (Lundvall, 1992; Nelson & Winter, 1982). Innovation Systems (IS) combine within themselves a set of economic, social, political, and any other factors that can foster the development, dissemination, and absorption of scientific and technological knowledge (Edquist, 1997, 2001). Even so, it is not possible to identify in the literature a simple and concrete definition for IS. 15 According to Silva (2003), several systems overlap simultaneously in the innovation process, with different possibilities of interpretation, decision systems, focuses, and rules of communication. This transversality of application from the perspective of IS means that they can exist in larger or smaller dimensions, from international, national, regional, sectorial, or local levels. Following this perspective, the ability of an economy to innovate is influenced not only by the way organizations act in isolation, but also by the way they relate to each other. As components of a collective innovation creation system (Calia et al., 2007; Rycroft & Kash, 2004), they follow a set of complex and dynamic innovation processes (Duarte & Carvalho, 2020; Smith, 2001). The idea behind an IS concentrates predominantly on the relationships between the players involved in the innovation activities and processes. These are seen as vital to foster the improvement of an economy’s innovative capacity, especially in an institutional and political domain (Bogers et al., 2018; Henriques & Larédo, 2013; Schröter, 2009), and thus, increasingly becoming a transversal part of the expected outputs of any IS. The IS concept becomes then a heuristic approach, conceptualized with a focus on the analysis and understanding of the subsystems, individual players and organizations contributing (i.e., directly, or indirectly) to business innovation and consequent economic development (Samara et al., 2012). These systematic players, innovation players interacting within the systems, come directly from within the companies, universities, and R&D centres. Many times, these interactions are supported by other system players, intermediary organizations, as well as other supporting financial and public institutions operating within the IS (Chaminade & Edquist, 2010; Simões, 2003). Linear innovation models For many decades, the discussion about innovation in the literature focused mostly on a linear perspective. From this perspective, the innovation process was perceived as a sequential hierarchy of successive stages (Godinho, 2003). A fact that is currently recognized as outdated. The use of this linear concept was already severely criticized by Kline and Rosenberg (1986, 1982), who highlighted it as being an oversimplified distortion of the reality of what the innovation process is. The linear perspective considers that the process itself comes from scientific research activities, ignoring that technological knowledge tends to precede scientific knowledge. In addition, it does not include feedback loops and the recurrent setbacks of technological innovation development processes (Kline & Rosenberg, 1986; Rosenberg, 1982). 16 Even so, the concept of linear innovation is still used by some, as despite its simplicity, it has its merits. The linear approach was afterwards compounded by two perspectives, the so-called “technologypush model” and the “demand-pull model” (Jun & Ji, 2016; Langrish et al., 1972). Two decades later, other authors have thoroughly researched and expanded this subject, Rothwell (1994) being one of the key authors contributing to it. The first perspective, referred to as “technology-push”, or “science and technology-push”, is driven by scientific and technological breakthroughs. It places significant emphasis on research and development (R&D) activities, as innovation is believed to originate from previous inventions, without market input. According to this perspective (as illustrated in Figure 2), the innovation process primarily centres on the value generated through R&D, which is subsequently introduced (i.e., pushed) to the market to explore potential application opportunities. Authors such as Day (1994) and Rothwell (1994) argue that the competitive performance of a company, in terms of innovation, is directly associated with the quantity and quality of the research department inside such company. Figure 2 – Technology-push perspective Source: Adapted from Rothwell (1994) In contrast, the other linear perspective, the “demand-pull” perspective of innovation (a.k.a. market-pull), completely reverses the technology-push perspective. It considers that the first stage of the process is a responsibility of the market, which must express its vision, goals, and needs to stimulate an innovation response. Thus, this “pull” perspective shown below in Figure 3 perceives the innovation process as a natural response to market needs/opportunities (Godinho, 2003; Rothwell, 1994; Silva, 2003). 17 Figure 3 - Demand-pull perspective Source: Adapted from Rothwell (1994) In summary, there are several possible perspectives and approaches to the concept of innovation, leading to different degrees of complexity, different relationships between agents and consequently different essential characteristics. Over the past few decades, National Innovation Systems (NIS) have emerged as the most extensively discussed concept in the realm of IS. This systematic approach is particularly useful in comprehending the intricate relationships and interactions involved in the innovation process (Carlsson et al., 2002). However, due to its complexity, the NIS concept remains ambiguous despite numerous studies. Therefore, it would be beneficial to break down the concept of NIS into its various components to better understand the wide range of activities and players involved in the system's complex processes. The NIS, as a concept, is intrinsically related to IS, as it describes the functioning of several institutions, public and private, which, jointly or individually, foster the development, dissemination, and absorption of new and innovative technologies, being also influenced by government ideologies. That contributes to innovation policies aimed at improving the efficiency and effectiveness of innovation processes (Simões, 2003). 2.2.2.2. National Innovation Systems (NIS) As in any society, the institutional environment is recognised as a determining factor of an economy’s competitiveness. Consequently, the interaction and relationship of economic agents involved in activities of creation, development, transformation, and dissemination of technological knowledge must also be considered as so (Freeman, 1995). The functioning of these relationships is described in the literature under the designation of National Innovation System (NIS), which was first introduced by Lundvall (1992), greatly influenced by Lizt (1841), in his publication “The National System of Political Economy”. NIS emphasize that more than protecting and nurturing developing economies and industries, the government must implement policies that foster industrialization and consequently economic growth. One of the main focuses of the NIS policymaking should be to accelerate technological research activities to bring the resulting technologies and knowledge to the market, as soon as possible (Lundvall & Borrás, 18 2006). In a more modern view, a NIS is framed as a set of private entities (e.g., businesses, research centres, consumers), public entities (e.g., academic, research units), and financial and governmental organizations (e.g., regulatory, social, legal, financial) interacting with each other throughout the innovation process. Oriented to respond to strategic economic goals, it is the NIS's responsibility to foster competitive performance through the creation, development, and dissemination of technological knowledge (Gretchenko, 2008). NIS are the product of the interaction between three primary societal domains: government, academia, and industry. Consequently, fostering NIS heavily relies on R&D and innovation activities, which are subject to dynamic and complex processes involving various types of agents from all three spheres of society. It is worth noting that in NIS, the relationships between agents are considered more crucial than the actual object of their interaction (Todeva, 2013). For this reason, the level of development of NIS can be determined by the quality and regulation of these same relationships (Motta et al., 2017). The regulation of these relationships can be developed from both political and legal perspectives. Even so, the government often resists the promotion of such regulations, neglecting that their institutional innovation is a first step towards the promotion of technological innovation and their economic development (Johnson, 1992). 19 Figure 4 - Elements of NIS Source: OCDE (1997) The infrastructure of a NIS is composed of various interconnected organizations that work together to produce and commercialize the outcomes of innovation activities. This infrastructure plays a crucial role in facilitating the efficient development of the NIS at all stages of the innovation process. Its ultimate goal is to create an environment that encourages the dissemination and application of knowledge and technology in the economy and society. Figure 4 illustrates the different elements that constitute the NIS infrastructure, which is characterized by a diverse range of organizations with various origins and functions, all sharing a common interest in innovation. Key components within any NIS infrastructure are the intermediary and supporting mechanisms and players such as industrial parks, technology hubs, clusters, interface centres or TT centres, consulting 20 intermediaries and other key supporting KIBS. From a co-creation perspective, all this core infrastructure collaborates continuously with key knowledge sources such as higher education entities and research units, both public and semi-public (Gråsjö et al., 2018). The proper functioning of this dynamic infrastructure is thus ensured by regulatory and innovation policies and incentives (e.g., financial incentives), identified in the literature as being the leading success factors in fostering TT (Carvalho et al., 2012). The focal point in NIS literature tends to be focused on the conditions, elements, and interactions within the system functioning itself. This is even more evident in the case of developing economies, despite the exponential rise of a global vision and the unbridled emergence of international competition. Even so, existing interactions between NIS and global innovation networks are also highlighted in the literature for their importance in maintaining a thriving environment for the continuous development of innovation (Freeman, 1995). The expected output of a properly structured and effective NIS is essentially the development of the economy’s innovation capabilities. Consequently, so it is market competitiveness, meaning an overall increase in economic productivity and the development of the well-being of society (Lundvall, 2007). Evolution of National Innovation Systems (NIS) As far as the concept of NIS is concerned, it earned its place in literature, being currently used as a replacement, or complement to traditional linear models of innovation. NIS does not consider innovation as being carried out in a chained way in a direct process with unidirectional interactions. With the evolution in the understanding of how innovation is carried out, the systemic approach, namely the concept of NIS, has been demonstrating the importance of feedback between stages, agents, and activities of the process. It is from this back-and-forth loop that it is possible to achieve the creation and acceleration of technological knowledge throughout the entire innovation lifecycle, and not just in the early stages of R&D (Edquist, 2014). The complexity of the innovation dynamics is now widely recognized. Thus, from a systems perspective, the generation of technological knowledge and innovation itself is to be triggered by a combination of dynamic contact networks of agents and players from different backgrounds (Dezhina & Saltykov, 2004). As innovation theory and TT have become more systematically approached, the focus has shifted towards understanding the complex dynamics of innovation. Currently, greater emphasis is placed on the interactions between agents involved in the innovation process, rather than solely on the agents or the 21 object being transferred. Additionally, regulatory effectiveness has become more prominent than the output of the innovation process. These changes are evident when comparing two ideal NIS typologies: the “planned economy” and the “market economy” approaches (Dezhina & Saltykov, 2004, 2005; Edquist, 2014). The “planned economy”, which is mostly observed in communist countries where the government plays a predominant role, subjugates the NIS and its innovation processes to a nationalist principle that dominates the spectrum of labour outputs, including intellectual knowledge. In this paradigm, the system turns inward, closes itself off, and eliminates any transfer of knowledge, either outward or from the outside (see Figure 5). In such a system, various innovation activities, including research, are indoctrinated by the government, which biases scientific and technological development. This type of NIS is also characterized by low interaction between players and industries, as well as negligent allocation of resources, favouring sectors and scientific fields that correspond to the ideology and objectives of the state (Etzkowitz & Leydesdorff, 2000; Leydesdorff & Etzkowitz, 2001). Figure 5 - Planned Economy NIS Source: Etzkowitz and Leydesdorff (2000) In short, in economies with a planned approach, the NIS and all its entities (i.e., academia, research, companies, etc.) have fewer motives or incentives to innovate when subjugated solely by the state. Even so, the planned economy model shown in Figure 5 also presents advantages, namely the possibility of mobilizing a large set of resources in a focused response to a problem or objective. Also, this planned economy NIS tend to present excellent social and economic conditions for the development of fundamental research (Dezhina & Saltykov, 2004). 22 A second perspective, commonly found in capitalist societies such as those in most western countries, is based on the “market economy” approach. In this approach, the government, although an essential part of the NIS, is of equal importance to other elements (Figure 6). Therefore, government inputs, like any other, must undergo multidirectional interaction with other agents, operating according to the “ laissez-faire” principle 1 . In this paradigm, all elements are free to interact with one another, and regular feedback is present among all stakeholders, highlighting a model with greater openness for the local economy. This model is open to interaction and integration with other economies, supported by a set of regulations focused on protecting private property, including intellectual rights, as some R&D results may require. Thus, in a win-win perspective, agents can satisfy their interests, aligned with market opportunities and consumer needs that enhance the innovation process. Figure 6 - Market economy NIS Source: Etzkowitz and Leydesdorff (2000) Within this typology of NIS, the freedom granted to agents also entails greater responsibility, as they assume the inherent risks of their research activities. Despite these risks, in the market economy model, the potential benefits resulting from innovation activities provide sufficient incentives for individuals and organizations to pursue them. Market-based NIS typically comprise a mix of large corporations and numerous small and Medium-Sized Enterprises (SME). SME are often characterized by their smaller and 1 Laissez-faire (from the French: “leave alone”) is policy principle stated that the less the government is involved in the economy, the better off business will be, and by extension, society as a whole. “Laissez-faire economics” is a key part of free-market capitalism. 23 more flexible structures, which enable them to take risks and pursue innovation in earlier stages of the technology lifecycle (Dezhina & Saltykov, 2005). More recently, NIS literature has shown a new evolutive perspective, derived from the transformation of increasingly global economic systems, fuelled by an acceleration in technological and scientific advances. It was during these transformations that systems functioning, explicitly the relations between university, government, and industry, underwent a set of evolutionary changes, leading to a new perspective of the innovation model (Binz & Truffer, 2017; Freeman, 2002). In planned economy approaches to NIS, the spheres operate dependently, with companies and academia fully controlled by the government and no room for partnership relationships. In contrast, in market-based NIS, interactions are typically bidirectional, with free feedback between agents forming double helices. However, as global economies have transformed and competitiveness in technology and industry has advanced, such interactions have become insufficient to support strategic decision-making. Consequently, theoretical models of innovation and NIS have evolved to respond to the need for triple interactions, which involves combining all three agents of the system in a networking format to create a modern innovation model known as the “Triple-Helix” (Etzkowitz & Leydesdorff, 2000). 2.2.2.3. The Triple-Helix framework The conceptualization of a triple-helix framework as a new perspective of NIS focuses on, and studies, interactions between the three helixes of academia, industry, and state (see Figure 7). This new model seeks to understand and describe the interactions between helixes and learn from the results (Etzkowitz & Leydesdorff, 2000). Literature on innovation economics has shown that technology and knowledge are increasingly being valued as key resources in the performance of any economy, as it benefits all three helixes accordingly (Leydesdorff & Etzkowitz, 2001). An unbridled race in search for innovative products and new technologies has led the innovation process from being an internal process within companies to a more complex process involving companies, academic research entities (Etzkowitz, 2003) and the government (Santoro & Bierly, 2006). Some still point out the academic sphere as being the primary source of innovation within a NIS (Leydesdorff, 2012). Yet, in this new economic paradigm, the production of innovation and knowledge becomes the third dynamic of economic development, apart from the market equilibrium and normative control mechanisms (Etzkowitz, 2003). 30 It is important to note that innovation can take different forms, with varying degrees of novelty resulting from modern approaches like OI or traditional closed approaches. Radical innovations typically result in new products and/or technologies entering the market. On the other hand, incremental innovations tend to result in improvements and adaptations to products that are already available in the market (Hall et al., 2014). Although the OI approach has gained popularity, there are still organizations that rely on traditional, closed, and linear innovation models that focus on inventing, producing, and directly commercializing their products (see Figure 8). Typically, these organizations have their R&D departments and depend solely on their internal structures, which is feasible only for large corporations (Chesbrough et al., 2006). While this closed innovation model offers advantages to shareholders, such as high control and protection over innovation processes and results, it requires significantly more financial, human, technical, and time resources (Chesbrough, 2003, 2004; Chesbrough et al., 2006; Chesbrough & Rosenbloom, 2002). As the innovation paradigm shifts towards openness (Figure 9), the number of players involved in the system increases, resulting in dynamic growth and acceleration of processes that benefits all stakeholders. The systematization of the OI model enhances organizations' ability to transform ideas quickly and cost-effectively into reality. This ideal ecosystem for industrial and business development strengthens competitiveness, expands market reach, and yields greater financial returns for organizations, ultimately improving the quality of life for society (Chesbrough, 2004; Chesbrough et al., 2006; Ozkan, 2015). Technology Transfer (TT) in an Open Innovation (OI) context Technologic innovations can quickly and severely transform the normal functioning of companies and organisations. With global economies currently undergoing a fourth industrial revolution, digital transformation has become a high priority on the agenda of many companies and governments (Hess et al., 2016). It is possible to find several examples of organizations that have succumbed to not being able to adapt to the disruptive evolution of technology (Hess et al., 2016). To highlight the importance of this transformation, experts tend to bring up “ Moore’s Law ”, which states that with the current unbridled scientific and technological development, the capacity and performance of digital technologies will double every two years (Deloitte, 2015). Challenges related to the adoption of new technologies go far beyond the technology itself, including issues associated with users, infrastructure, and the symbolic meaning of the change itself 31 (Geels, 2002). As a result, many organizations are currently experiencing a development cycle gap, between the technologies they have knowledge about and the ones they will adopt. It is, therefore, of vital importance to study this process of dissemination of technological innovation. Although the adoption of innovation can provide companies with numerous competitive advantages, the innovation process requires significant organizational resources and involves a high-risk, high-reward strategy that can lead to failure (Tatikonda & Rosenthal, 2000). The process of innovating is complicated by various barriers, which can hinder the adoption of technological innovations. Therefore, regardless of its typology, innovation requires careful planning and resource allocation to mitigate the risks associated with the process. As mentioned in the “Oslo Manual” (OECD & Eurostat, 2018), several conditions can hamper innovation and impair its results, of which the following stand out: • High risks; • Shortage of funding; • ROI is too long; • Lack of capacity for innovation; • Lack of specialized human resources; • Ignorance of technology; • Ignorance of market conditions; • Difficulties in external cooperation; • The complexity of intellectual property. Still, regarding the involvement of companies in technological R&D, the main limiting factors identified by Matheson and Matheson (1998) tend to be the: • Temporal distance between R&D activities and the achievement of their benefits; • Uncertainty and risk; • Lack of knowledge of the market; • Difficulty of companies in adapting their business model to technological domains they do not master. 32 In addition, other authors have been more recently emphasizing the importance of aligning R&D project scopes with companies’ strategy, implying a need for greater cooperation between internal and external agents of the company (Mazurkiewicz & Poteralska, 2017). 2.2.2.5. Innovation Policymaking Innovation policy can be defined as the public actions influencing an innovation process, i.e., the development and diffusion of innovation, both in terms of products and processes (Chaminade & Edquist, 2006). These actions include also public actions that may influence demand-side innovations (Edquist, 2001). According to these definitions, public action is crucial to stimulate innovation, although nothing is said about the form or the extent to which these actions should take place. Some answers can be found in the innovation economics literature, where “market failures” (neoclassical approach), “systemic failures” (evolutionary approach) and “voluntarist motives” are pointed out as justifications for the government intervention in the economy, especially when policymaking (Bonaccorsi, 2014; Schröter, 2009; Swann, 2009). The traditional approach (i.e., neoclassical) to innovation considers technology development as being an exogenous variable of the economy, adding that technological progress is just a result of R&D activities (Bach & Matt, 2005). This approach claims that there are “market failures” (Gråsjö et al., 2018; Klein Woolthuis et al., 2005) making it impossible for the market to function in equilibrium. A market failure occurs when there is no efficient allocation of resources due to a breakdown of the price mechanisms, which can be caused by a market phenomenon preventing a socially optimal equilibrium (Swann, 2009). The neoclassical approach associates market failures with knowledge creation, with knowledge being synonymous with information that can be codified, generic, accessible, or adaptable to the companies' characteristics (Chaminade & Edquist, 2010). However, tacit knowledge is also essential to any innovation process. While the fundamental idea that “knowledge” is equal to “information” implies that knowledge can be considered a public good, there are three major market failures associated with the production, dissemination, and accumulation of knowledge (Chaminade & Edquist, 2010; Nelson, 2009; Swann, 2009): • Externalities — which can be positive or negative, and which can provoke an inadequate appropriation of results by those who produce knowledge. • Uncertainty — results from the asymmetry of information access among the agents, meaning that results and risks of a research process may not be fully known. 33 • Indivisibilities — difficulty in managing economies of scale, resulting from the need for a minimal investment in knowledge to produce new knowledge. In summary, in the neoclassical approach, market failures justify the need for public innovation policies supporting R&D activities and the production of technological knowledge. However, one of the main criticisms of the “market failures argument” justifying state intervention in innovation concerns the primacy given to the market about other forms of organization of economic activity (Nelson, 2006, 2009). By the late 1990s, and especially in the early 2000s, there was a gradual shift from a vision based on science and technology policy, to a more holistic view based on innovation policy (Soete, 2007). This new trend was characterized by a greater emphasis on the relationship between R&D, innovation, economic and social development, from a systemic perspective (Steinmueller, 2009). Contributing to this new vision, public innovation policies started being designed as strategic contributions to: • The importance of interaction and cooperation between companies and their external environment (Breschi & Malerba, 2005; Chesbrough et al., 2006; Porter, 1998); • Formal and informal networks (Laursen & Salter, 2006); • Institutions and learning processes or users (von Hippel, 2005, 2009). In the last two decades, there was also a stagnation of public efforts for R&D in OECD and EU countries. Although total expenditure on R&D increased in the 2000s, the relative efforts of states in R&D stagnated or even decreased. In contrast, the relative effort put forth by higher education institutions and companies increased during this period (Santos, 2016). The systematic approach to innovation Policy The evolution of current innovation policies in response to the fourth industrial revolution, characterized by digital transformations within industries, has been greatly influenced by the development of the field of Information and Communication Technologies (ICT). These technologies have contributed to a change in the perception of the nature of innovation processes and have been instrumental in the adoption of a systemic perspective in the policy formulation process (Delanghe et al., 2009; Soete et al., 2009). ICT enabled quick access to new technologies, namely digital technologies, stimulating the development of knowledge services specialized to support the innovation process itself (Perez, 2009). In other words, while access to knowledge and innovations did not necessarily lead to the immediate 34 development of Research & Development (R&D) activities, it did encourage the recombination of existing ideas and technologies, with a focus on the commercial exploitation of R&D outcomes (Soete et al., 2009). In addition to the impact of these digital technologies on the innovation process, there was a growing demand for competitiveness at sectoral/industrial levels in international markets, which was complemented by improving interactions and intermediation between industries' needs and national policies. This demand contributed to the creation of a favourable framework for adopting a systemic view of the innovation process by the policymakers, with a greater focus on expanding markets and changes in the political, technological, and institutional context on a global scale. As result, policymakers have been increasingly proclaiming the adoption of an IS approach as a framework and guide for designing future innovation policies (Edquist & Hommen, 2008). This perspective considers that innovation policy should be comprehensive, including elements of R&D policy, technology policy, infrastructure policy, and regional and education policy, and thus showing its complexity, from an evolutionary (i.e., systemic) perspective (Edquist, 2014). The comprehensive view offered by the systematic approach to the innovation process means that it is currently used by international organizations with political influence, such as the OECD (OCDE, 1997, 2009). The IS’s approach is also being increasingly used within the policy-making context by regional organizations, national governments, public agencies, and international organizations such as the OECD and EU. In recent years, innovation policy has also been increasingly discussed in terms of “broad-based innovation policies” and in a more “demand-pull approach” (Edquist, 2014). The adoption of a systemic approach by policymakers and international entities makes the performance of public policies more transparent on the supply side, on the market, on the system functioning, and on the strategic action taken (Edquist, 2014; Santos, 2016). 2.2.3 The Portuguese innovation system 2.2.3.1 Characterization of the Portuguese NIS The development of the Portuguese NIS has followed a trajectory similar to that of other European Union countries, beginning with a traditional and linear approach focused on science as the primary driver of innovation and economic growth, and then transitioning towards a more open and systemic perspective (Henriques & Larédo, 2013). Over the past few decades, the Portuguese NIS has increasingly adopted an IS approach. Significant investments were made in the development of scientific infrastructure and technology, as well 35 as in the training and qualification of researchers, which have greatly benefitted the Portuguese NIS (Rodrigues et al., 2003). From a historical perspective, Portugal has never presented a linear evolution concerning its NIS. Still, key development clusters can be identified, representing different stages of Portuguese NIS evolution (Ferreira, 2005; Santos, 2016): • The 1960s – Strong investment in HR training and the development of national R&D infrastructures. • The 1970s – Promotion of R&D capacity in strategic sectors of the national economy, directing them to the absorption and application of technical and technological knowledge. • The 1980s – Focused heavily on an industrial policy to encourage the collaboration of industries with universities, encouraging the emergence of hybrid interface institutions (e.g., TIC) in support of technological development between agents. • The 1990s - Oriented to training, competencies and professionalization of R&D activities carried out in academia. • The 2000s - Focused on opening the system itself, mostly aiming for an internationalization of the NIS, with policies to incentivise collaborative ITT projects between companies and academic and R&D entities, both within the NIS and with foreign partners (Godinho, 2013). In Portugal, the NIS approach adopted between the 1980s and 1990s focused on a traditional and linear perspective of technology-push, with the government directly promoting and funding science, technology, and innovation. During these decades the Portuguese government regarded the academia as the primary source of innovation (Godinho & Simões, 2005; Laranja, 2007), as result most of its investment focused on national R&D technology infrastructures (Duarte & Carvalho, 2020; Laranja, 2007). However, by the late 1990s, the Portuguese economy gradually shifted towards a more modern and systemic approach, aiming to meet the state-of-the-art models of the NIS at that time. To support innovation during this period, there was an increasing demand for the integration of different political sectors, as well as greater integration of operational management, financial, and fiscal incentive programs (Duarte & Carvalho, 2020; Laranja, 2007). The adoption of this new and systemic perspective was a milestone foundation for the current NIS. Over the last few decades, policies and innovation programs have been designed in a more modern and open innovation perspective following the directives of the EU (Santos, 2016). 36 Following this, Simões (2003) categorised the five main agents constituting the Portuguese NIS: • Companies; • Academia and research institutions; • Technology interface entities (as key intermediaries); • Financial system; • Governmental (public) entities. However, the interactions and relationships between agents in the Portuguese NIS, whether formal or informal, remained limited and inconsistent. This was largely due to the specific characteristics of the organizations involved and their inadequate capacity for collaborative networking (Ferreira, 2005). The functioning, structure, and characteristics of the Portuguese NIS have been extensively analysed by several authors (Assis, 1999; Costa et al., 2022; Ferreira, 2005; Godinho, 2013; Henriques & Larédo, 2013; Santos & Mendonça, 2017; Simões, 2003; Teixeira & Lopes, 2012) who have identified common strengths and weaknesses of the system. While the strengths are primarily related to the level of infrastructure and the qualifications of the system, the weaknesses are linked to institutional factors and the characteristics of the Portuguese economy's productive structure (Costa et al., 2022; Duarte & Carvalho, 2020). Weaknesses as opportunities for the Portuguese NIS Many of the innovation models see system gaps, weaknesses, and failures as opportunities for continuous improvement. Regarding the Portuguese NIS, its greatest’s weaknesses are essentially systematic, centred on the structure of the economy and society itself, in addition to institutional factors that have been thoroughly highlighted by several authors in recent years (Duarte & Carvalho, 2020; Laranja, 2009). For instance, Simões (2003) identified as main weaknesses of Portuguese NIS: • The systemic deficiencies in the articulation, coordination and strategic guidance on the part of innovation policymakers, who continue to be unable to overcome the dichotomy between sciences and economics; • The biased vision and management of public institutions, such as the lack of a culture of risk and entrepreneurship, as well as reducing the number of cooperative interactions between players, leading to a reduced level of self-confidence; 37 • Lack of skilled resources supporting innovation activities within key public and governmental entities of the NIS, especially interface entities, policymakers, financial system and even inside most companies. The latter, the lack of qualification and competencies for innovation was also mentioned by Mamede et al., (2014), who associated it with the low weight of knowledge and technology-intensive sectors within the Portuguese economy. This is reflected in the low specialization and internationalization of Portuguese companies, mostly SME. 2.2.3.2 Financial innovation incentives Following its accession to the European Economic Community (EEC) in 1986 and the adoption of the Schengen Area and the single currency, Portugal began receiving support from the European regional policy to align its development patterns with the European average. Over the past 35 years, the structural and cohesion funds have played a crucial role in the country's progress, serving as a significant factor in its economic development and modernization. The impact of these funds can be measured both directly and indirectly, as they have contributed to the improvement of the economy and society in Portugal, both in terms of previously registered advancements and expected future benefits (Alexandre, 2021). The structural and cohesion funds are essential in promoting innovation capabilities and enhancing the performance of public and academic infrastructures, as well as industries' approach to innovation. In Portugal, these funds have played a significant role in shaping the NIS by providing financial incentives for innovation. Over the years, Portugal has submitted six strategic intervention proposals to obtain support for its development through the European Regional Policy's programming cycles. These funds have been widely adopted due to their crucial role in funding and incentivizing national innovation programs, particularly those aimed at companies. Additionally, they have a proven ability to mobilize public and private funding for innovation since national funds are required to complement community funds in approved projects (Laranja, 2009; Santos, 2016; Simões, 2003). QCA III - Community Support Framework (2000-2006) After the initial Community Support Frameworks - QCA I (1989-1993) and QCA II (1994-1999) – Portugal began the new millennium with a third QCA (III). Between 2000-2006 the primary public financing instrument for the modernization of the Portuguese economy and territorial development was 38 the third Community Support Framework (QCA III). The QCA III consisted of sectoral operational programs, divided into four key aims: 1. Qualification, employment and social cohesion (included support for education, employment and training, science and innovation, knowledge society, health and culture); 2. The changing of the productive profile of the Portuguese economy (support for agriculture, fisheries, industry, commerce and services); 3. Territorial development (support for accessibility, transport, and the environment; 4. Sustainable development of regions and national cohesion (support for regional development). QREN - National Strategic Reference Framework (2007-2013) In the period 2007-2013, the reference instrument for competitiveness policies was the National Strategic Reference Framework (QREN). The QREN took on a major strategic aim of responding to the main weaknesses of the Portuguese economy and society, namely the lack of qualification of the Portuguese population; the valorisation of knowledge, science, technology, and innovation; as well as an increase in efficiency and quality of public institutions (Alexandre, 2021).To this end, the implementation of the QREN was ensured by three thematic operational programs, which had the following objectives: 1. Operational Program for Human Potential (OPHP), which aimed to overcome the qualification deficit of the Portuguese population, support qualification in scientific and technological institutions, as well as support job creation, entrepreneurship, the transition to active life, and equality of opportunity. 2. Operational Program for Competitiveness Factors (OPCF), which focused on improving the competitiveness of the Portuguese economy, in a global market context, by stimulating innovation, S&T, internationalization and modernization of public administration. 3. Operational Program for Territorial Enhancement (OPTE), which was intended to strengthen the country's international connectivity, the national infrastructure network and overall territorial cohesion. PT2020 – Portugal 2020 (2014 – 2020) The most recent innovation incentive program in the Portuguese economy was called Portugal 2020 (PT2020). This program focused on the appropriate application of European structural funds in Portuguese NIS between 2014 and 2020, with its complete closure coming to be extended to 2023 due 39 to the 2020-2022 pandemic crisis (Alexandre, 2021). The design of this incentive program was aligned with the benchmarks of Europe 2020 strategy, oriented towards intelligent and sustainable growth, focused on the application of more than 25 billion euros in four distinct operational domains (Carvalho et al., 2012; Santos, 2016): 1. Competitiveness and Internationalization Program (POCI/COMPETE 2020) – meant to increase national economic competitiveness as well as internationalise Portuguese SMEs. The main areas supported by the program were R&D, Innovation, SME competitiveness, job creation and the modernization of government institutions. 2. Social Inclusion and Employment Program (POISE) – aiming to improve the quality of employment, labour mobility, inclusion and also the promotion of social innovation initiatives. 3. Human Capital Program (POCH) – focused on educational development, training, and learning, including adult training and absorption of highly qualified human resources (MSc and PhD) by companies. 4. Program for Sustainability and Efficiency in the Use of Resources (POSEUR) – designed to mitigate carbon emissions, promoting efficient and sustainable use of natural resources. Among the four main operational lines, POCI/COMPETE 2020 stood out as the most significant, representing 75.5% of the total budget. This line comprised five intervention axes, with a focus on enhancing the competitiveness of companies, public institutions, innovation, and technology through funded R&D and TT projects (Cabral, 2018; Carvalho et al., 2012). One of the priority axes aimed to incentivize companies and technological institutions to collaborate on R&D projects and disseminate their results, promoting effective knowledge and TT between different agents. These funded projects included various typologies such as R&D, technology pilots and demonstrations, dissemination of results, IP patenting and licensing, marketing innovations, and the establishment and maintenance of sectoral and regional innovation networks supported by recognized Portuguese clusters (Alexandre, 2021; Santos, 2016). 46 Until the late 1990s, technology as a concept was primarily described as a “tool” (Bozeman, 2000). Other authors such as Sahal (1981, 1982) had previously thought that the concept of technology could be itself considered as a “configuration”. The author stressed that the transfer of such technology cannot be simply perceived as the physical movement of such technology as a product might make, from a source to a recipient. The focus should be on its application and effective use by the receiver, which corroborates the idea of innovation thought by Schumpeter (McCraw, 2010; Soete & ter Weel, 1999). Combining perspectives, others such as Liyanage et al. (2009), support the view that the object being exchanged typically represents a concrete technology but could also be considered as technological knowledge. For instance, Keller (2001) presented three defining attributes of the concept of technology: • It is a created good thought to be shared until its marginal cost per added user becomes insignificant; • The ROI of innovative technology can be private (as companies own it), but also public (as institutions may also benefit from its knowledge); • Technological development results from the work of private players creating innovation, whether through new products or new processes. Corroborating the perspective of Madeuf (1984), several other authors (Choudhry & Ponzio, 2020; Lane, 1999; Morrissey & Almonacid, 2005; Rogers, 2016) have a contribution to the perception of the concept of technology as a combination of techniques and more or less formalized information resulting from conducting R&D activities. Still, others see technology as something that should not exist by itself or be transferred without any control, fearing that a monopolized use of it as a competitive advantage by some companies, would corrupt its meaning as a “public good” (Bozeman, 2000; Santos, 2016). The transfer process entails the involvement of two distinct entities, the sender, and the receiver, who exchange technology or knowledge as a valuable "object" with unique significance for each party. Merely transferring people, knowledge, or technology in isolation is insufficient. As noted by Malik (2002), it is also important to transfer the know-how necessary for the proper interpretation and use of the received technology and knowledge. 47 2.3.3.3 Intermediaries (or mediators) Davenport and Prusak (1998) have described the transfer of knowledge and technology products as a non-free exchange between parties. In this exchange, the supplier develops and transfers units of knowledge in exchange for remuneration, creating a knowledge and innovation market of high importance for both R&D developers and companies looking to acquire it. The agents involved in this process, in most cases, are also the intermediaries who are responsible for finding, managing, relating, and contracting buyers and suppliers. When there is a specialized intermediary agent involved in the process (a.k.a. mediator or broker), they may or may not be directly involved in the entire TT process. In many cases, intermediaries are contracted to assist with specific tasks that other players (i.e., source and recipient) have no competencies, resources, or interest in performing (Colombo et al., 2015). The intermediary agent is a specific emerging body of literature where its role in the transfer process is being analysed. Innovation intermediaries are generally depicted as agents facilitating the process of KTT between organizations and industries (Silva et al., 2018). Intermediaries are considered part of the innovation system, and their role focuses mostly on assisting in the TT process between people and organizations, dealing with facilitating or restrictive factors. They play a crucial role in the process, particularly in the context of transfers between organizations. They intervene in the process by acting as mediators between parties, facilitating the relational context and supporting the process to ensure desired results for all parties involved (Kivimaa et al., 2019; Stezano, 2018; Watkins et al., 2015). The evolving comprehension of the inherent complexity in the innovation process and its stakeholders has prompted entire governments, economies, and their NIS to embrace more contemporary and inclusive perspectives. The potential outcomes stemming from open collaboration between organizations have served as a catalyst for recognizing the significance of intermediary agents in the literature on innovation (Howells, 1999; Kivimaa et al., 2019). From an Open Innovation (OI) perspective, the specialized role of such intermediaries has acquired substantial significance by connecting and aligning source and recipient organizations, as well as facilitating the dissemination of information and market requirements among key stakeholders. In essence, intermediaries contribute to the conceptualization and oversight of high-risk/high-impact TT projects and the market introduction of resulting novel products and solutions (Colombo et al., 2015; Howells, 2006; Jenson et al., 2020; Randhawa et al., 2018; Tran et al., 2011). 48 2.4 Intermediation and Intermediaries 2.4.1 Technology Transfer intermediation As previously mentioned, Silva et al. (2018) have described TT intermediaries as mediating agents in innovation processes and systems, highlighting their role as facilitators to organizations in TT. However, Dalziel (2010) has proposed an alternative definition, focusing on intermediaries' purpose as organizations or groups working to enable and foster innovation in technology and market. Innovation intermediaries can take many forms, such as technology brokers, university interface departments, R&D centres, regional technology centres, innovation agencies, and transnational networks (Watkins et al., 2015). Some companies, especially certain KIBS, may also be included in this group, given their extensive service offerings and flexibility in operations and interactions (Shearmur & Doloreux, 2019). Private consultancy firms that specialize in innovation and technology fields may also be considered intermediaries in an OI paradigm, as they serve as a source of ideas and knowledge for entire industries and market sectors (Tether & Tajar, 2008). Innovation intermediaries can both expand and strengthen the innovation capacity of recipient companies, industries, regions and even nations. They reduce the gap between internal and external knowledge, reduce the time of access to know-how and marketing, increasing the efficiency and efficacy of innovation in recipient companies (Dalziel, 2010; Villani et al., 2017). The concept of innovation intermediaries can be traced back in the literature to the “brokers” in the agricultural and textile industries of the sixteenth to eighteenth centuries. These brokers had mostly commercial functions but also actively disseminated technical knowledge (Howells, 2006). Since then, intermediaries have gained new importance (Hakkarainen & Hyysalo, 2016), and their functions have become more extensive and varied depending on the agent. With the widespread adoption of OI by NIS worldwide, innovation intermediaries have proliferated and have been playing a broader, more dynamic, and recognized role. Intermediaries work directly with their clients on an individual basis, seeking collaborations of interest, but are increasingly involved in more complex relationships, especially in the context of national innovation networks and systems (Barlatier et al., 2017; Howells, 2006; Zajko, 2017). Fields such as KTT, Innovation and Technology Management, IS, and even Business Strategy or Product Marketing have identified various roles and specializations for entities acting as technology intermediaries (Howells, 2006; Lichtenthaler & Ernst, 2008). However, this was not always the case. In the early 1990s, most publications, especially in the TT-related literature, typically summarized the 49 functions of intermediary agents into just two roles: “brokering”, as the main function during the innovation and/or TT process; and “networking”, a typical role for an intermediary within an IS, providing and maintaining the right connections and network conditions in a defined sector or industry and among its stakeholders (Agogué et al., 2013; Barrie et al., 2019). Other authors (Wolpert, 2002; Lynn et al., 1996) added two other major activities that were found to be increasingly demanded from intermediaries: “communication” and “scanning and gathering of information”. These activities posed a milestone in understanding that there is a broader potential in their intermediary role of boosting, supporting, and sustaining IS and TT processes (Battistella et al., 2016). In most cases, authors tend to agree that the intermediary's role in innovation and TT is far more complex than just mediating and brokering - the most highlighted intermediation roles in TT literature. For instance, Bessant and Rush (1995) showed that private management consultants had a particular role in acting as “innovation bridges” by providing a set of specialized activities sold as Knowledge Intensive Business Services (KIBS). These specialized “bridging activities” observed by Bessant and Rush (1995) overlapped and went beyond their previous notion of intermediation activities, among which are: • The articulation of needs and selection of options; • The identification of needs and training selection; • The creation of business cases; • Communications and development; • Education and links to external info; • Project management activities, like managing external resources and organisational development. Despite the development of the innovation intermediary literature, the roles, and activities of intermediaries in innovation and TT literature are still relatively ambiguous and scattered (Soares et al., 2020). In general, the literature continued to describe the intermediary's focus within specific TT case studies rather than their actual roles and specialized activities. Thus, the term “intermediary role” is being used to describe a portfolio of activities, tasks, responsibilities, focus, or even goals within a TT process. Examples of this are (Diener et al., 2020; Howells, 2006; Pollard, 2015; Vidmar, 2021): • Building linkages with external knowledge providers; • Providing specific knowledge of technology and industries; 50 • Articulating communications; • Diagnosing and evaluating technologies to be transferred; • Establishing relationships between TT agents to facilitate transactions; • Providing guidance and implementing innovation policy. Howells (2006) made a significant contribution to the innovation intermediary literature by presenting a comprehensive study in which he compiled, systematized, and shed new light on the activities that innovation intermediaries can undertake and why they are becoming key agents in IS. This contribution has been further corroborated by more recent publications (Dalziel & Parjanen, 2012; Laranja, 2009; Todeva, 2013; Vidmar, 2021). Howells (2006) showed that innovation and technology intermediaries can act across a wide spectrum of domains, ranging from technical knowledge diffusion and TT process support to innovation management and marketing. In addition, intermediaries were found to provide KIBS in several steps of TT projects, expanding their role into domains of idea conception, technical problem-solving, matchmaking, intellectual property, technology brokering, and even commercialization. In what concerns innovation intermediaries’ roles, the contribution of Howells (2006) is still widely accepted and used (Pinto, 2018). Howells (2006) proposed ten key intermediary functions, namely: 1. Foresight and diagnostics; 2. Scanning and information processing; 3. Knowledge processing and combination/recombination; 4. Gatekeeping and brokering; 5. Testing and validation; 6. Accreditation; 7. Validation and regulation; 8. Protecting the results; 9. Commercialisation; 10. Evaluation of outcomes. Several authors have adopted Howells' framework proposal on the activities that intermediaries undertake in both innovation and TT (Kanda et al., 2018). These authors seek to complement and add new roles and activities to the existing framework such as: 51 • Forecasting and road mapping (Agogué et al., 2013; Kivimaa, 2014); • Information gathering and dissemination (Bessant & Rush, 1995; Geels & Deuten, 2006); • Fostering networking and partnerships (Kivimaa, 2014; Klerkx & Leeuwis, 2009); • Prototyping and piloting (Matschoss & Heiskanen, 2017); • Technical consulting (Pinto et al., 2015); • Resource mobilisation (Polzin et al., 2016; Van Lente et al., 2003); • Commercialisation (Bessant & Rush, 1995; Van Lente et al., 2003); • Branding and legitimation (Kivimaa, 2014); • Investment appraisal analysis (Pinto et al., 2015); • Definition of innovation needs (Agogué et al., 2013; Pinto et al., 2015). Current literature indicates that intermediaries do much more than simply mediating and brokering, as was traditionally believed. Intermediaries have taken on the role of innovation architects in the collective exploration and creation of knowledge at the often-diffuse front of innovation (Agogué et al., 2013). From Howells’ (2006) widely recognized perspective, innovation intermediaries support the development of new technologies by acting as specialized intermediary agents between two or more participants (Dalziel, 2010). Pinto et al. (2015), who studied KIBS's involvement in innovation marketing, proposed an updated and extended version of Howells’ proposal (2006) to shed light on the typical functions and service offerings of KIBS as innovation intermediaries: 1. Analysis and definition of innovation needs; 2. Identification of user requirements and main trends; 3. Signalling of technological options; 4. Design of new services; 5. Design of new organizational methods; 6. Definition of new marketing strategies; 7. Identification of potential partners; 8. Testing and dimensioning; 9. Selection and training of specialized resources; 10. Protection of innovation assets; 11. Accreditation and certification; 12. Investment evaluation. 52 This framework, based on Howells’ (2006) initial proposal, foresees a broader role for innovation intermediaries, suggesting some new and improved functions that result from a broader understanding of the innovation concept (Pinto et al., 2015; Pinto, 2018). Other studies have also highlighted the contribution of these “third parties” in the process of innovation and TT (Kirkels & Duysters, 2010). Despite this, there are still few publications exploring the concrete roles, functions, and specialization of these intermediary agents (Barlatier et al., 2017; Shearmur & Doloreux, 2019; Soares et al., 2020; Tether & Tajar, 2008). Despite the proven contribution of intermediaries in fostering innovation and accelerating new products to the market (Colombo et al., 2015) they continue to be a particular type of NIS agent that is neglected in the scientific literature (Dias et al., 2017). Dias et al. (2017) draw attention to the lack of literature focused on understanding the multiplicity of distinct agent organizations playing the role of intermediaries in NIS. Their research conclusions also underline the need to empirically study a new reality in which private organizations are increasingly playing a direct role as innovation intermediaries operating in NIS. 2.4.2 Understanding intermediaries and their role The significance of intermediaries has been increasingly recognized in the literature, as evidenced by the growing number of publications that delve into the subject with varying levels of depth. Over the last few decades, the concept has evolved, and scholars have come to realize that intermediaries play a more complex and dynamic role than previously thought (Howells, 2006; Soares et al., 2020; Zajko, 2017). Any agent that provides support or performs tasks between a technology source and a recipient can be considered an intermediary. This new understanding of intermediation goes beyond the traditional notion that intermediaries merely facilitate technology brokering between academia and industry by developing networks for opportunities (Zajko, 2017). While some scholars have proposed frameworks to simplify the overall understanding of intermediaries, literature on intermediation roles, activities, and responsibilities is still largely fragmented across various publications, research fields, and designations (Intarakumnerd & Chaoroenporn, 2013; Randhawa et al., 2018; Soares et al., 2020; Zajko, 2017). Some scholars have acknowledged this fragmentation and have proposed modular frameworks to categorize, structure, and connect intermediation roles using different conceptual logics. Todeva (2013) viewed intermediaries as innovation “coordinators” responsible for coordination activities and proposed a framework with three main coordination categories: “network”, “cooperation”, and “political”. 53 Other scholars have differentiated intermediaries by using specialized cluster domains such as “problemsolving”, “technology transfer”, or “coordination of networks in innovation systems” (Agogué et al., 2017; Sieg et al., 2010). The role of technology transfer intermediation encompasses various essential activities, which Howells (2006) categorized into four key areas: • Providing information about potential collaborators; • Brokering a transaction between two or more parties; • Acting as a mediator or go-between organizations already collaborating; • Helping to find advice, funding, and support for the innovation outcomes of such collaborations. Although the role of innovation intermediation has evolved, the traditional focus on disseminating and diffusing knowledge and technical information within a given sector or industry remains prominent in the literature (Stezano, 2018). Howells (2006) supports this view, highlighting intermediaries' emphasis on addressing the specific needs of TT projects. In addition, Tether (2005) identifies a particular interest in studying intermediaries' performance, particularly in the manufacturing sector, regarding the implementation of innovative technologies in new products and manufacturing processes. 2.4.3 Intermediary organizations As discussed, innovation intermediaries are a type of organization tackling the in-between tasks of TT. Their starting role tends to be finding a match between two or more parties (Howells, 2006), however, they can then evolve to provide and develop other technology opportunities alongside the project needs. Intermediaries can act as superstructures in the IS (Betz et al., 2016; Kivimaa, 2014; Lynn et al., 1996; Nilsson & Sia-Ljungström, 2013), not only by being a bonding agent between new partnerships and projects but also by posing as a catalyst agent helping to disseminate key knowledge and information. Acting from within the systems, some intermediaries are strategically positioned to identify and better respond to market needs and innovation opportunities (Canato & Giangreco, 2011; Martinez et al., 2016). Numerous authors have been noticing the rapid growth in the number and typology of entities performing some kind of intermediation role within the IS (Diener et al., 2020; Howells, 2006; Mignon, 2017; Pollard, 2015; Todeva, 2013; Vidmar, 2021). However, despite this multiplicity of agents with distinct intermediation roles in TT, proper categorization of these entities is lacking, as they are often depicted under the generalized concept of “intermediary”. 54 Both in the innovation intermediation and the TT literature, intermediary organisations can be found depicted under a set of different, yet synonymous, terms, such as “intermediaries”, “brokers”, “mediators”, “consultants”, “third parties” or “bridge organisations” (Klerkx & Leeuwis, 2009). By itself, the concept of “intermediary” as an actor playing within the IS, may not significantly imply that it is directly involved in the TT (Battistella et al., 2016). Both the importance and the complexity of its role and impact within the NIS earned innovation intermediaries their own body of literature (Battistella et al., 2016; Hargadon & Sutton, 1997; Howells, 2006; Lichtenthaler & Ernst, 2007; Roth, 2003). Several authors have been further exploring their understanding regarding these emerging and highly dynamic organizations and their role, both within the NIS and in supporting TT processes (Betz et al., 2016; Janssen et al., 2019; Nilsson & Sia-Ljungström, 2013). A review of the literature on innovation intermediaries has revealed a connection between the adoption of the open innovation model by NIS and the emergence of new players strategically responding to system gaps and market opportunities, as noted by (Howells, 2006). Pollard (2006) further suggested that this increase in the number of organizations serving as innovation intermediaries is also driving the evolution of roles available to address unmet system gaps and market needs. Consequently, this trend has piqued the interest of scholars, particularly in the fields of innovation management and related areas (Chesbrough et al., 2006; Diener et al., 2020; Howells, 2006; Shearmur & Doloreux, 2019; Todeva, 2013). The development of this new research field is contributing to the understanding of systems’ intermediaries from perspectives previously underexplored, namely the variety of intermediary organization typologies, their roles, and their importance to NIS (Agogué et al., 2013; Gasco-Hernandez et al., 2017; Li-Ying, 2012; Pinto, 2018; Stewart & Hyysalo, 2008). The concept of intermediary is no longer considered a generalized theoretical concept, as its role has evolved beyond its traditional functions of supporting networking and technology mediation between senders and receivers. As a result, the term “intermediary” has taken on a wider range of meanings, encompassing different types of intermediary agents and organizations, and a larger portfolio of roles played by them. As the multiplicity behind the concept of intermediary gained recognition, scholars began to observe and hypothesize the existence of a correlation between types of intermediary agents and the roles they played in the NIS (Katzy et al., 2013; Pollard, 2015; Silva et al., 2018). While several roles have already been scrutinized from different points of view, there is still a need for further research to 55 understand how specific types of intermediaries may be associated with particular intermediary roles (LiYing, 2012; Pinto, 2018). From the first mentions of intermediaries as a concept, which mainly focused on mediating and brokering technology negotiations, the literature now identifies several new kinds of intermediation entities, such as “innovation consultants” (Basu & Taylor, 2010; Bessant & Rush, 1995; Costa et al., 2021; Howells, 2006), “knowledge-intensive business services” (KIBS) (Klerkx & Leeuwis, 2008, 2009; Shearmur & Doloreux, 2019), “knowledge brokers” (Hargadon, 1998; A. Hargadon & Sutton, 1997), “innovation marketplaces” (Lichtenthaler & Ernst, 2008) and idea scouts or technology scouts (Nambisan & Sawhney, 2007). While many intermediaries can be identified and distinguished, most of them still fall under the umbrella term of “brokers”, which refers to their traditional intermediary role of brokering. However, categorizing all intermediary agents as “brokers” insinuates that they are all direct competitors with each other, which would suggest that they compete under the same conditions and value proposition. In the last few decades, scholars have become increasingly aware that the involvement of these distinct organizations within the NIS can be much more complex, as many organizations may present distinct roles and value propositions (Agogué et al., 2017; Bessant & Rush, 1995). Additionally, just as there are technology demands and technology supplies, there is also an emerging group of intermediary actors who perceive the NIS as an innovation market. With this freemarket perspective in mind, modern intermediaries have increasingly been observed building up their capabilities and value propositions in response to systemic failures and gap-filling opportunities (Agogué et al., 2017; Howells, 2006; Klerkx & Leeuwis, 2008). From a review of the literature over the last thirty years, sixteen major types of organizations with intermediary roles can be highlighted. These types are described below, starting with ten key players categorized as “traditional”, mostly known for being public coordinated entities operating either from academia or from government helixes: 1. Regional counties – are local councils focused on applying innovation policies locally, encouraging the rapid diffusion of knowledge, skills, and best practices within a region, following a perspective of Regional Innovation System (RIS) (Inkinen & Suorsa, 2010; Pino & Ortega, 2018). 2. Governmental innovation agencies – are government agencies strategically created to develop actions aiming to support technological and business innovation within a country, market, or sectors (Etzkowitz, 2003; Tamtik, 2018). 62 Costa and António (2014) defined consultants' role as external functional support for organizations' strategy and operations, whether proposing solutions in response to identified problems and opportunities or supporting their implementation. The same authors recognized that consultants have a key role in distributing knowledge and triggering new and innovative practices, both within and between organizations. The consultancy has its origins in the management and strategy literature, where the concept of consultant first appeared. Drucker (1981) argued that management is neither a science nor an art, but a practice learned through exposure to and experience with a wide variety of companies in various industries. According to Drucker, consultants are those who can transcend organizations and gain this much-valued exposure. He is recognized as one of the main thinkers of consultancy as an activity and has even been labelled by several scholars as “the father of modern management” (Cohen, 2009). For Drucker (1981), the consultant has an expertise-application role, similar to any other executive position within a company. The difference between the two, however, lies in the fact that consultants have a greater range of exposure to the market through their experience with various organizations and perspectives on the same or different business issues, and thus gain a special capacity for diagnosis (Costa et al., 2021). Still, their role is closely associated with their defining characteristics. The consultants pose an external perspective to clients’ organizations, and this allows them to provide an unbiased, impartial, and rational perspective (Costa et al., 2021; Drucker, 1981). For Drucker (1981), and more recently corroborated by other authors (Canato & Giangreco, 2011; Cesário et al., 2015; Costa et al., 2020), the consultant’s value proposition still resides essentially in these two characteristics: • Being a specialist with high exposure to a given subject; • His professional perspective is detached from the client’s organisational involvement. Following these previous contributions of Peter Drucker, other authors felt a need to characterize consultants as well as their role as agents of change in organizations. Bower (1982) presented six factors contributing to the understanding of the consultants’ value proposition. Other authors (Butler, 2009; Costa et al., 2021; Wright et al., 2012) found six underlying factors/motivations behind the decision of organizations to hire management and innovation consultants: 1. Being able to provide competencies that are not available inside the organization; 2. Have varied experience in the world outside the client’s organization; 63 3. Having time and resources to study problems; 4. Having a professional attitude; 5. Being independent of the client’s organization; 6. Have the ability to create action based on their recommendations. Some methods and good practices provided by consultants are linked to organizations buying access to management knowledge, bypassing the learning curve (Basu & Taylor, 2010; Butler, 2009). Additionally, Wright et al., (2012) emphasized the consultant's key role and responsibility in promoting standard setting in industries, generating isomorphism based on good practices and knowledge. This view is further supported by other authors' versions (Costa et al., 2021; Jacobson et al., 2005), who highlight consultants' roles as intermediaries and disseminators of knowledge within specific sectors or industries. Canato and Giangreco (2011) classified the consultant's role into four main perspectives: 1. Consultants as standards setters - Consultancies build up state-of-the-art debates explicitly to supply industries with a set of methods and solutions, for which they then can offer full assistance. 2. Consultants as information suppliers - Consultants have superior knowledge, experience, and expertise in specific industries. They have been exposed to general trends in the industry, contacting different environments, situations, and problems, rather than focusing on a single firm’s specific scenario. 3. Consultants as knowledge brokers - The experience gained by assisting their clients with different solutions developed in different industries gives management consultants the capacity to use and transfer useful knowledge from one project into another. 4. Consultants as knowledge integrators - Consultants act as knowledge integrators as they not only advise and instruct but also help customers to implement recommendations and support knowledge transfer processes. Consulting presents a unique environment, providing consultants with a privileged positioning to diligently track emerging trends in technology markets (Dias et al., 2017). Management and innovation consulting firms tend to have a catalyst role in the markets they operate through their understanding of market and technology dynamics (Alexandre, 2021; Basu & Taylor, 2010; Cesário et al., 2015; Gråsjö et al., 2018). As catalyst agents within NIS, private consulting firms provide specialized intelligence and 64 services to support the project’s scope and strategic goals. They are also vital in fuelling innovation and TT projects by supporting project stakeholders in accessing financing and funding (Chesbrough et al., 2006). 2.5.2 Consulting Industry in Portugal In Portugal, the first consulting firms emerged between the 1940s and 1950s, primarily founded by academics or as a spin-off from support departments within corporate organizations (Sismet, 1993). By the 1960s, the consulting industry experienced its first peak, driven by government efforts to modernize the Portuguese economy and promote industrial development (Amorim and Kipping, 1999). Despite this, the Portuguese consulting sector remained underdeveloped, creating an opportunity for foreign consulting groups to enter the national market. By the 1970s, the evolution of the consulting industry was marked by the April 25th revolution and the beginning of Portuguese democracy. This was the first key political event to impact the consulting industry and could be considered a milestone for the creation of the second generation of consultants (Sismet, 1993). The mass nationalization of the financial sector and some industrial organizations shifted the focus from the most powerful organizations. Consulting firms operating at that time in Portugal could no longer keep up fulfilling the market’s needs, of which were back then in line with supporting key processes of nationalization and were involved in the population’s concern regarding labour relations and industrial organization (Cunha & Marques, 1995). Once again, the underdeveloped national consultancy industry was overtaken by foreign consulting companies entering the national market allured by market gaps and unfulfilled opportunities to offer their services designed to foster industrial organization and labour performance. The second political event that changed the course of the consulting market in Portugal occurred in 1986 when Portugal joined the European Economic Community (EEC). Since then, the Portuguese market has undergone significant changes in its productive structure, leading to high economic growth (Freire, 2008). Many previously nationalized corporations were privatized once more, benefiting from the growth and evolution of European markets, leading to considerable growth and diversification of activities (Cunha & Marques, 1995). Portugal also experienced high levels of Foreign Direct Investment (FDI) and financial incentives from EU funding programs (Freire, 2008). Amidst this favourable context of business development and emerging opportunities, the consulting industry in Portugal underwent an accelerated growth, driven by the emergence and expansion of national SME and the entry of some multinational companies (e.g., McKinsey in 1989; Boston Consulting Group in 1995) (Costa & António, 2014). 65 With the beginning of the new millennium, the consultancy in Portugal had evolved and can currently be divided into two key segments/clusters of activity (Basilioa et al., 2019; Costa & António, 2014; Freire, 2008): 1. Big Consulting – Large consulting firms, all of them multinationals with a foreign origin have established themselves close to the main national economic, financial, and political arteries. 2. SME Consulting – Thousands of small and medium-sized consulting companies, most of them of Portuguese origin, spread throughout the territory and compose most of the consulting scene. The first, larger established consulting firms dominate the market for large national and multinational corporations and groups. They are also known for providing specialized services to public and governmental organizations, focusing on strategy, corporate finance, and IT services. On the other hand, the thousands of SME consulting firms tend to compete with each other, as well as with public and semi-public agencies and organizations. SME consulting firms are the most common type in the Portuguese market as the Portuguese economy is also primarily populated by SME. They drive and are driven by the growth and acceleration of small and medium-sized businesses and their innovations, whether in the form of products or services. These firms focus on supplying general management services and niche-specific services based on key knowledge or experience that SME may not possess. They also provide economic and market studies, access to finance and fiscal incentives, accreditation, and generalized training (Costa, 2012; Freire, 2008). The economic development dynamics and opening of markets create opportunities for companies, but they also face constant pressure from market dynamics such as competition, customers, suppliers, financial institutions, and even government agencies. As a result, companies of all sizes are increasingly turning to external consultants to remain competitive and innovative (Freire, 2008; Lapiedra et al., 2011; Pinto, 2018). Despite lagging behind other developed global markets, the consulting industry in Portugal has experienced rapid growth over the years, similar to what has been seen in other countries (Costa & António, 2014; Sousa, 2018). According to the publication “Companies in Portugal – 2020” ( Instituto Nacional de Estatística , 2022), Portuguese KIBS providing “consulting, scientific, technical, and similar activities” have sold services worthing 370 million euros in 2020, a 47% increase since 2015. Additionally, by the end of 2020, almost 41 thousand companies (+19% compared to 2015) provide these services, employing 191,338 people (+29% compared to 2015). As one of the fastest-growing sectors in the Portuguese economy, the role of private consulting firms in promoting competitiveness and 66 innovation in the NIS is of great importance for governments and regulators to understand further (Alexandre, 2021). 2.5.3 Consultants’ role in Technology Transfer The literature on IS has emphasized that the success of TT projects depends on the overall system and the quality of its interconnections (Basu & Taylor, 2010; Villani et al., 2017). In particular, Bigliardi & Dormio, (2017) have highlighted the impact of various types of intermediaries on the success of TT. These intermediaries, increasingly present in the literature, mainly focus on public and academic entities, those recognised as traditional intermediaries. Consulting services involved in TT intermediation often provide a broad range of interactions throughout the process (Cesário et al., 2015). They can provide essential information and knowledge and offer administrative support services to bridge the “valley of death” - the gap between technological opportunity and poorly defined market needs (Bessant & Rush, 1995; Dias et al., 2017). Table 2 below presents six types of activity domains performed by private consultants in TT project, identified by Bessant & Rush (1995). According to the authors, consultants involved in these processes can either replace/outsource TT processes or complement the capabilities (or the lack of them) of project stakeholders. Table 2 - Activities of consultants in technology transfer Source: Adapted from Bessant and Rush (1995) Domain Activity Supply side Technology - Articulation of specific needs - Selection of appropriate options - Sources of technology Skills and human resources - Identification of needs - Selection - Training and development - Labour market - Training resources Financial support - Investment appraisal - Making a business case - Sources of finance – venture capital, banks, government, etc. Business and innovation strategy - Identification and development - Communication and Implementation - Environmental signals – threats, opportunities, etc. 67 Knowledge about new technology - Education, information and communication - Locating key sources of new knowledge - Building linkages with the external knowledge system - Examples of best practice - Emerging knowledge base Implementation - Project management - Managing external resources - Training and skill development - Organisational development - Specialist resources In line with the core concepts of TT and innovation, consulting was traditionally viewed as a linear function, in which the consultant's role was primarily focused on the final stage of KTT, serving as a market champion who implements R&D/TT outcomes in the market (Bianchi et al., 2016). However, the role of consultants as TT intermediaries has evolved to become more complex. As consultants have gained exposure to their clients' TT challenges, they have developed their services to provide TT stakeholders with improved innovation and TT expertise and capabilities, thus offering a more complete response to process phases (Basilioa et al., 2019; Bessant & Rush, 1995). Even in the early days of the consulting industry, Schein (1969) depicted the role of consultants as a form of “process consulting”, a catalytic, non-directive approach that emphasizes teaching and convenience over-prescription. As time passed, more consultants began to reshape their roles into softer innovation intermediation services (Costa et al., 2021; Lapiedra et al., 2011; Pinto, 2018), following the perspective and opportunities of open innovation to offer businesses and institutions innovation and TTrelated expertise. The role of consultants has become increasingly focused on creating value and shared problem-solving, rather than attempting to sell one-off products or projects. This has led to a shift in their commercial interests, with consultants now seeking to create and maintain long-term networks and highvalue partnerships (Bessant & Rush, 1995; Costa et al., 2021; Freire, 2008). 68 2.6 Literature Review conclusions The literature review has reinforced and supported the research objectives by contextualizing the need to understand the roles of private consultants as intermediaries in TT. In addition, it has provided a comprehensive understanding of the field of study, identifying the main ideas and frameworks used by various authors in recent decades. Several key findings relevant to the study have been identified: • Despite the linear description of technology transfer (TT) as part of the innovation process, it is recognized in the literature as a highly dynamic process in light of current innovation perspectives. It has gained importance both as a mechanism and for its outcomes in promoting competitiveness and innovation in economies. • The intermediation of innovation and TT has become a new field of research. Its complexity and significance go far beyond the traditional roles presented by classical authors - brokering and mediating. Intermediation now includes a multitude of agents and activities that can catalyse, accelerate, or support flows of communication, information, and knowledge between two or more entities. • It is still challenging to comprehend the multitude of organizations with an intermediary role, as well as the different roles, functions, activities, or responsibilities they may be played in the system by such intermediaries. The literature on the intermediary concept and its role has proven to be insufficient and scattered among different authors, perspectives, levels of depth, and even research fields. Several authors have highlighted the need for further structuring and synthesizing information. However, many have also contributed ideas, frameworks, and compilations in response to this need. • The “role” of a TT intermediary is a theoretical concept still not fully delimited. It is composed of various responsibilities, functions, activities, and specializations that are performed in response to the complex emerging needs of TT processes and market failures (i.e., gaps). However, it is possible to compile several contributions from different authors and define a portfolio of key roles/specializations that could come close to defining this “role” of the intermediary (see Table 3). • The literature on innovation intermediation and TT intermediaries still primarily focuses on the traditional view of public intermediaries, those mostly linked to the state or academia. Nevertheless, publications mentioning the intervention of private intermediaries in TT processes can be found. Private intermediaries sell their professional services and are emerging in the literature as an unofficial player acting and contributing to the NIS. 69 • One of the main types of private intermediaries are consulting firms (i.e., management and innovation) that develop and sell a wide range of KIBS on the market. These consultants have had their role and importance recognized by some of the key authors in the TT and IS field as they bring a new perspective of “innovation-as-a-service” to the NIS. • The “servitization” of the role of intermediation by consultants has become more relevant in recent decades. The growth of private consultancy markets working within the triple-helix (i.e., companies, academia, and the state) has led to an increasingly discussed need to study their role as innovation intermediaries in TT within the NIS. 70 CHAPTER 3 THE ROLES OF AN INNOVATION INTERMEDIARY 71 CHAPTER 3 - THE ROLES OF AN INNOVATION INTERMEDIARY Innovation Intermediaries are now recognized as a fundamental component for the well-being of an innovation system, at all levels, sectors, and fields. The basic understanding of the intermediation role is still rooted in a linear perspective of innovation, which views this role as simply supporting the connection between technology sources and users of such knowledge (Howells, 2006; Vidmar, 2021). However, the innovation intermediary concept now encompasses a broader, heterogeneous, and growing set of entities, whose importance and functions are both varied and critical for proper functioning within current models of open NIS (Diener et al., 2020; Katzy et al., 2013). Therefore, a good understanding of their roles may be a way to uncover the structure behind the link between roles and types of intermediaries. The increasing emergence of new roles and designations describing activities and functions performed by innovation intermediaries may be associated with new interpretations of data that were not duly explored and/or understood in previous decades (Vidmar, 2021). The heterogeneity of organizations that can be classified as innovation intermediaries, the roles they play in TT, as well as their increasing specialization, all contribute to the complexity of the intermediation phenomenon and a recognized difficulty in properly interpreting the concept (Jenson et al., 2020). Based on the literature, it is possible to identify different terms referring to the same role and activity, in addition to several activities that can be categorized under the same umbrella. Even after combining and merging several similar roles’ designations, the range of the intermediary’s responsibilities and activities is still too wide and fragmented to be properly analysed. To provide a comprehensive yet concise analysis that responds to the research goals of this thesis, a framework is proposed following a synthesized approach to the review findings. This framework uses key roles and specializations as categorization criteria, which act as umbrella clusters for the activities, functions, and focuses that intermediaries perform alongside TT. The widely accepted contributions of Howells (2006) were used as a baseline, being also considered the extensive and more recent set of contributions and additional roles and designations proposed and highlighted by several authors reviewed. The outcome of this study is the proposal of a framework that synthesizes the review findings into thirteen key roles (Table 3). Each role describes a specific specialization that an intermediary agent can perform, along with the associated duties, responsibilities, and activities found in the literature review. By integrating the various specializations of intermediaries, this framework provides a deeper understanding of the diverse activities that make up the “intermediation role”. 78 such detail, giving the intermediary a broader and generalized role of coordinator (Tamtik, 2018; Todeva, 2013). Facing TT projects with different organizations and different interests, Silva et al. (2018) argued that for both parties to work effectively together, intermediaries must have a critical focus on monitoring and actively reducing potential opportunistic behaviours of the parts, that may put at risk the project trust relationships, which could negatively impact the project performance and its results. 3.8 Project Management & Assessment (PMA) The complexity and heterogeneity of TT projects led to a set of different characteristics and different management needs to ensure the achievement of projects’ aimed goals. Some intermediaries specialized in key functions of project development (Bessant & Rush, 1995), as well as in project management, and monitoring (Hidalgo & Albors, 2008). It is an intermediary’s responsibility to assist project stakeholders, freeing them from peripherical tasks so they can dedicate themselves to project core activities related to technology and innovation. Hossain (2012) distinguishes project managementrelated activities as being a soft-innovation role, supportive and administrative activities present in any TT process. The intermediary becomes responsible for supporting and developing project management functions such as organizational set-up and budgetary support (Intarakumnerd & Chaoroenporn, 2013); also, the intermediary may be committed to more bureaucratic activities like reporting and drafting of contracts (Silva et al., 2018). The results of innovation and TT projects have different stakeholders and therefore different points of view, needs and interests. It is safe to assume a suitable role of project assessment from an intermediary, as being a neutral third-party operating in the project. Thus, an intermediary could be responsible for evaluating the project’s performance, its results, and the overall impact of it, having in mind the project’s initial goals, and the expectations of each involved part, as well as the market and society standards. Howells (2006) defined assessment and evaluation as a key role specialization, also corroborated by other authors, as they highlight the importance of measuring innovation and evaluating its results and impacts (Janssen et al., 2014; Tamtik, 2018). 3.9 Financial & Technical Feasibility (FTF) Emerging intermediary organizations have been focusing on improving and expanding their value propositions to satisfy different needs alongside TT and innovation projects. As an example, Howells 79 (2006) mentions knowledge-intensive business services (KIBS) as being a new kind of innovation intermediary. Characterized by their high technical skills, KIBS can provide IS with services of technical and technological intensity, such as consultancy to assist companies in their innovation and TT processes. Regarding a technical and economic feasibility focus, several authors have been highlighting these technical and specific functions as being increasingly performed by intermediary entities, specifically in supporting independent analysis and validation of the viability of technologies and ideas (Winch & Courtney, 2007). Considering their focus on feasibility, the intermediary’s activities can also be as eclectic as the development of feasibility studies (Bendis et al., 2008; Bessant & Rush, 1995); making prototypes and pilot series (Matschoss & Heiskanen, 2017); or even as generalized as testing, diagnosing, and evaluating technologies to be transferred (Howells, 2006). 3.10 Accreditation & Quality (AQ) Specialized third-party organisations are increasingly being demanded by TT projects as quality controllers and standard experts. Handling supporting activities related to quality and accreditation has been also recognised in the literature, due to intermediaries’ importance in the quality and certification phases of technological innovations to be transferred (Howells, 1999; Lee et al., 2002). Howells (2006) gathers under the same umbrella the intermediary roles and activities related to accreditation and standards, regulation, and arbitration. This role was also corroborated by Pinto et al. (2015), namely the assistance in due diligence activities related to accreditation and certification. In addition, Bessant & Rush (1995) included in the role of some innovation consultants the responsibilities of auditing and quality control which are very important for new technologies and innovations with no market track. 3.11 Intellectual Property & Rights (IPR) Patents, rights, and other forms of intellectual property are the most tangible form of technology and knowledge transfer between academia and industry. It is therefore not surprising that intellectual property (IP) management is a major role emphasized by the literature (Howells, 2006). This role appears particularly in the later stages of TT projects and also involves specific types of intermediary agents, such as consultants and attorneys. This highly specialized agent must be packed with market & legal knowledge to be able to effectively respond to strategies for valuing R&D results and making them available to the interested recipients, and at the same time, protected from “copy-cats” (Li et al., 2015). 80 In the literature, there are several forms and functions indicative of this role. From more generalist functions such as the management of IP (Howells, 2006; Janssen et al., 2014); legal and IP support (Agogué et al., 2017; Hossain, 2012); to more specific activities such as the creation and valorisation of patents (Li et al., 2015); protection of innovation assets (Pinto et al., 2015) and brand management (Kivimaa, 2014). 3.12 Implementation & Knowledge Transfer (IKT) As the TT project progresses, it will come a time for the concrete application of the technology object at the receiving entity. Intermediaries have been evolving to provide recipient organizations with adequate absorptive capabilities to successfully implement and use the received technology as part of their business model. This market needs demands more specialized intermediaries, such as KIBS (Howells, 2006), and senior researchers specialized in the technology to be transferred, and who can temporarily act as consultants to the recipient company (Tether & Tajar, 2008). Intermediaries are increasingly being expected to perform a range of implementation supportrelated activities (Bessant & Rush, 1995), many involving direct knowledge transfer such as training (Battistella et al., 2016), or technology-related workshops (Tamtik, 2018). Another author also put a great emphasis on the intermediaries’ responsibility to improve the absorptive capacity of the recipient companies, through support in the selection and training of specialised workforces (Bessant & Rush, 1995; Pinto et al., 2015). 3.13 Marketing & Business Development (MBD) This role is especially related to more mature and ready-to-market technologies that need support to be converted into ready-to-launch products (Agogué et al., 2013), particularly in the first phases of the commercialization process (Howells, 2006). The commercialization aspect of innovation (Tamtik, 2018) is a function that can and should be developed by innovation intermediaries within TT. They tend to have more market intelligence and product marketing management expertise to assist the technology is gaining traction in the market. As so, within this role, two sides coexist - Marketing and Business Development (commercialization). Both have a residual presence in the literature on IS and TT (Cesário et al., 2015) but a high presence in marketing and innovation management literature (Jenson et al., 2020; Pinto et al., 2015). 81 A generalist marketing and commercial support was highlighted by Klerkx & Leeuwis (2008) as a necessary role to be performed by intermediaries as market facilitators of technologies leaving the academia. Then again, Pinto et al. (2015) described a specific need in the innovation process to support stakeholders in the definition of new marketing strategies. These marketing strategies may even include, according to Thursby et al. (2001), the development of activities to attract potential investors. The intermediary role in business development and commercialization has been recognized as needing assistance with business strategy (Battistella et al., 2016; Howells, 2006), as well as complementary activities like research commercialization (Pollard, 2015). TT processes can also proceed in a very different way when there is no technology-receiving entity involved and instead, there is a strategy to spin off the R&D project. In this scenario, those responsible for the R&D results tend to have full ownership of the innovation process, through the creation of their own company as a strategy to place their innovation in the market. Yet in this scenario, researchers as highly skilled in hard innovation tasks are mostly inexperienced with product marketing strategy and operations and thus might compromise the entire project. When it comes to entrepreneurship and business development support, the main innovation intermediaries mentioned in the literature are incubators and accelerators (Clayton et al., 2018), innovation hubs (Cantù et al., 2015) and business consultants (Clayton et al., 2018; Slaughter & Leslie, 1997). Regardless of the intermediary selected, they must possess the necessary skills to assist teams lacking in business acumen. This involves providing support in activities like business planning and development (Albors et al., 2005; Dias et al., 2017), as well as business scaling (Pinto et al., 2015). Additionally, Tamtik (2018) mentions the intermediary's role in linking new entrepreneurs with the required expertise or investors to effectively bring their technological innovations to market. 82 CHAPTER 4 METHODOLOGY 83 CHAPTER 4 – METHODOLOGY This thesis utilizes a mixed research methodology, which combines quantitative and qualitative techniques. To collect qualitative and quantitative data, structured interviews and document analysis from a case study were respectively employed. Content analysis and statistical analysis were the procedures utilized to process the data collected. The upcoming chapter will discuss the research strategy exploration and definition, where the methodological instruments designed for collecting and analysing data will be elaborated. This chapter explores the assumptions and key characteristics of the methodological perspectives, i.e., qualitative, and quantitative methods, in the fields of Innovation and Engineering. The focus will be on identifying opportunities for the simultaneous and complementary use of both techniques. 4.1 Qualitative research methods Qualitative methods in research form a distinct field of study that is interdisciplinary and applicable to diverse topics and fields. Over the past century, qualitative research has undergone continuous epistemological and methodological transformations. The term “qualitative” implies a focus on the domain of quality, highlighting the socially constructed nature of reality beyond process and meaning (Baxter & Jack, 2015). This method emphasizes how the researcher and the object of study relate to each other, which can be influenced by situational factors and biases (Debout, 2016; Denzin & Lincoln, 2000). Aspers and Corte, (2019) characterized qualitative methodologies as an approach in which the researcher becomes the research instrument, following the development of a narrative that incorporates the perspectives and experiences of the study's participants. Serapioni (2000) summarized the qualitative method: • Its fundamental characteristics of having a behavioural analysis centred on the actor’s point of view; • The focus on naturalistic observation of the situation, without any control; • The subjective “insider perspective” of the interviewees; • The importance of the process and discovery; • The exploratory, descriptive, and inductive approaches; • Its particular and not generalizable nature. 84 It is possible to identify six main pillars that govern qualitative methodology as a research phenomenon (Baxter & Jack, 2015; DiCicco-Bloom & Crabtree, 2006): 1. Complexity - Qualitative research processes involve social components that result in behavioural and cultural manifestations of high complexity. They cannot be reduced to a mere set of variables. 2. Subjectivity - Since the researcher is an instrument of observation in the process, their reality and values result in an inevitably subjective perspective that cannot be suppressed. Therefore, the researcher must accept and acknowledge that the results cannot be entirely objective. 3. Contextualization - To understand a phenomenon or object of study, the researcher must consider the influence of contextual factors, as reality itself is a combination of numerous multidimensional factors. 4. Free interpretation - Qualitative research results in multiple interpretations and meanings that can be radically different from different perspectives and realities. Interpretive and meaning analysis are essential components of qualitative research methodology. 5. Study objective - Since non-subjective explanations of causality, control, or precision are impossible, the primary objective of qualitative studies is to understand and interpret phenomena. The researcher plays a role in creating empathy and recreating the experience of others in themselves (Bresler, 2000); 6. Application objectives - Although understanding a given phenomenon and its contexts may serve as a basis for understanding others, it is crucial to note that the knowledge resulting from qualitative methods is not generalizable but rather transferable (Seabra et al., 2009). A qualitative research methodology distances itself from purposefully neutral and aseptic language to reach an empathetic understanding of the experience shared by participants. Beyond its characteristic abstraction, it should be a record of the empirical phenomenon as it was observed, leaving space for interpretations and debates, and thus increasing the range of questions that can be used to further the study (Aspers & Corte, 2019). Therefore, according to Turner (2010), there are limitations to the qualitative methodology, mostly regarding the limited precision of the data. This poses as a natural imprecision from an inexact science and thus must be complemented by constant referencing of discourses in an original and raw form. Following a path of qualitative research presupposes conducting an in-depth analysis of the meanings, experiences, and quality of the phenomenon under study, with a whole focus on the importance of the process rather than on the acquisition of tangible results. Fidalgo (2003) highlights the 85 fact that following this methodology, the information ends up being framed and interpreted according to the general context of the situation, reality, past experiences, and other factors with special significance for the participants involved. It is the role of the qualitative researcher to study each phenomenon in its natural context, focusing on the interpretation of that phenomenon and the meaning attributed by the people involved. Thus, it is a process requiring the collection of data that describe specific aspects by the individuals involved, and whose interpretation constitutes an attempt to develop knowledge about the study object (Morgan, 2022; Roberts, 2020). The choice of a qualitative study method involves an interest in what is complex, seeking to describe and understand the process more than its results. A qualitative study provides access to a diverse and complex reality of study. This reality is enriched by contextual factors and by the meaning that others give to it (Marques, 2005), and gives internal validity since it focuses on the specific characteristics involved in the study (Aspers & Corte, 2019; Park & Park, 2016). Hence, it is possible to identify key strengths and weaknesses of qualitative research, as summarized in Table 4. Table 4 - Qualitative Research - Pros and Cons Adapted from Rutberg and Bouikidis (2018) Advantages Disadvantages • Accessibility to what is complex; • Spectrum of interpretations and meanings; • Notion of context; • Internal validation of the process itself; • Access to the participants’ point of view; • Investigator poses as a research tool; • Transportability of analysis to different contexts; • Data inaccuracy and variability; • Might not be accepted by adepts of the exact sciences; • Limitations to data generalization; • Deals with small and non-statistically significant samples; • Subjectivity of the analysis; Qualitative research methods allow for the exploration and understanding of complex and subjective phenomena and experiences by collecting rich and descriptive data focused on quality, rather than quantity (Rutberg & Bouikidis, 2018). However, it is important to acknowledge that qualitative research also has some inherent weaknesses. Firstly, the limitation to generalizing its results hinders the direct transfer of data between different contexts. Nevertheless, the principle of transportability can be employed to draw analogies, patterns, and commonalities between different (Park & Park, 2016). Nonetheless, researchers must refrain from claiming that the results of their sample represent the entire 86 population. Other scholars suggest also that it is only appropriate to aim for conceptual and analytical generalization, which seeks to theorize about the process and phenomenon studied without measuring the frequency of the same in the society or reality envisaged (Azevedo et al., 2017). Another weakness of qualitative research is the imprecision, variability, and subjectivity of its data. Researchers are therefore required to continuously refer back to the raw data and maintain fidelity to the perspective of the individual studied, to avoid biases towards their interests or opinions. In addition, qualitative research tends to focus on the complexity and depth of a topic at the expense of sample size, unlike quantitative methods which prioritize larger and statistically significant samples (Turner, 2010). Despite this, qualitative research remains an ideal option for small samples and isolated case studies, where in-depth analysis is necessary for contextualizing reality and gaining depth through the techniques and instruments used in the collection and analysis of information (Azevedo et al., 2017; Rubin & Rubin, 2005; Serapioni, 2000). 4.2 Quantitative research methods A quantitative research methodology focuses on explaining, predicting, and controlling the phenomena studied. It seeks to identify rules and laws through objective, quantifiable, and measurable processes, and techniques (Herciu, 2017; Park & Park, 2016). The foundations of quantitative research methodologies include a focus on the quantification of phenomena and cause related to the object of study, the use of controlled techniques, objectivity, the hypothetical-deductive nature of the process, the possibility of generalizing and replicating results, and the notion that the object of study and surrounding reality is somehow static (Rutberg & Bouikidis, 2018; Serapioni, 2000). Through these highly objective procedures, the aim is to create knowledge that can be generalized (Rutberg & Bouikidis, 2018), meaning that it depends heavily on external validity (Moreira, 2006; Serapioni, 2000). By allowing a generalization of the study’s outputs, the quantitative method tends to move away from the singularity of the phenomenon. However, this distance leads to highly useful results due to its replication value in different realities (Moreira, 2006). The same author also highlights that this type of analysis has two main focuses: the description of the distribution of entities by the different values of the variables; and the description of the relationship between the variables. By opting for a quantitative and correlational methodology, the researcher seeks to understand and, in a certain way, predict the phenomena under study, either by testing and analysing internal or external constructs relating to the context variables (Maula & Stam, 2020). 87 The use of mathematical language by quantitative research methodologies allows the systematization of the phenomena observed, developing it concretely and analytically to generate new knowledge, which can be generalized (Maula & Stam, 2020). Mathematics then brings to the quantitative research objectivity and tangibility in the research process, by quantifying what would otherwise be seen as subjective. However, Minayo and Sanches (1993) preferred to emphasize the importance of deciding which techniques and quantification tools are relevant to certain problems, as well as what disadvantages can arise from their use. The authors base this question on the limitations of mathematical language, which, according to them, can lead to idealized conceptual models, based on an abstract construction that, in practice, only partially describes reality (Minayo & Sanches, 1993). One of the main weaknesses of quantitative techniques, particularly in the face of a positivist position, refers to the lack of consideration of the researcher’s social role in the process (Rutberg & Bouikidis, 2018). This does not consider its ideological impact on the research bias, which is disregarded by the presence of objectivity of the methods in use, as well as by the logic of mathematical thought and language (Cardoso, 2007). Furthermore, the objectivity of a quantitative methodology does not take into account the individual’s perspective, traditionally associated with qualitative techniques. This could lead to insufficient internal validation and consequent ambiguity of the measured variables (Rutberg & Bouikidis, 2018; Serapioni, 2000). Table 5 summarizes the main strengths and weaknesses of quantitative research. Table 5 - Quantitative research - Pros and Cons Adapted from Rutberg and Bouikidis (2018) Advantages Disadvantages • External validation of the process • Possibility of replicating results • Generalization of results • Methods and tools accepted by the scientific community • Capability of coverage in larger samples • Insufficient internal validation • Does not consider the individual’s point of view • Fails not consider the existence of the researcher’s subjectivity as part of the process; 94 1 and 2 - Identify the main offer of private intermediaries - Identify your outstanding key roles - Understand your attractiveness in the system - What are the key characteristics, skills and roles sought in consultants (value proposition)? - Are there other factors (external to the private consultants) contributing for their involvement in TT projects? 3 - Understand the level of threat/competition that private consultants represent to the public - To identify complementarities of the performance of the private ones for traditional performance - Are private consultants’ competition (threat) or complementary (opportunity) to public intermediaries? 2 and 3 - Understand the perceived positioning of consultants in the NIS - Understand whether innovation policies take into consideration the role of private intermediaries - (In your opinion,) does the NIS decision making level recognizes the private consultant’s role? - (In your opinion,) do innovation and TT support programs and policies recognize/take account the role of private consultants? 1, 2 and 3 - Identification of key role specializations performed by consultants as intermediaries - Which roles/specializations do you identify as being performed by private consultants as intermediaries? (Assisted with the thirteen role specialization framework developed in Table 3) Based on the interview aims, an interview script was developed with key questions to support the researcher during the collection process. The script, which included direct, semi-direct, and even assisted questions, was designed to allow the researcher to adjust the scope of the response of each interviewee in case they deviated from the central aims of each question. Additionally, the questions were sufficiently open to allow the interviewee the freedom to contextualize and justify their answer, thus providing a greater quantity and quality of data to be analysed later. The interview script can be found in Annex I. 95 4.4.1.2 Sample definition Samples can be the groups of subjects to whom the interview was conducted or the sets of recorded occurrences or behaviours to be analysed (Almeida & Feire, 2000; Turner, 2010). Sampling has a significant impact on the quality of the results, as it should be as representative of the population as possible. The inferences that may be made depend entirely on the relevance and quality of the samples from which the data was collected. The significance of a sample refers to the number of elements that constitute it, while its representativeness refers to its quality (Turner, 2010). Regarding data collection instruments, a sampling procedure appropriate to the method in question was developed. The Portuguese NIS has a high and incredibly diverse number of players with a distinct variability of positions and roles. Therefore, a too-small sample of interviews could condition and bias the research results. However, one of the main limitations of this method is the limitation of the number of interviews realistically possible to carry out while maintaining the quality of the sample and its results. Moreover, the characteristics of relevant individuals with the greater potential to be subject to an interview make it difficult to reach participants in high numbers, as they are in positions hard to reach, being most of them academic directors, company executives, and representatives of public bodies. To boost feasibility and sample significance, the sampling process was carried out with some degree of convenience. A varied set of profiles was strategically selected from different NIS backgrounds, both public and private, from academia to businesses. The variety of the sample focuses not only on the helix of their provenance but also on the type of organization and the position of the person interviewed. However, it is possible to identify some sample bias characteristics resulting from convenience picking, such as the majority of stakeholders being from the ICT field and having a higher provenance from the northern region of Portugal. This bias resulted from the researcher's network of contacts and proximity within the ICT sector in the north of Portugal. To protect their anonymity, the names of the interviewees were replaced with identification codes, and any information that could jeopardize their anonymity was omitted (e.g., the name of the organization they represent or work with). For this purpose, a sample characterization matrix was created, in which a tracking identification code (from #A01 to #A19) was assigned to each of the participants (refers to Table 8). 4.4.1.3 Interview process The interviews were conducted over a period of eight months, between 2021 and 2022, subject to the availability of the respondents. Due to the covid-19 pandemic, the interview process was made 96 more flexible and was carried out either in person, by video call, or by phone call. To minimize the influence of the interviewer, in all interviews, the pivotal interviewer was the same person – the researcher (Johnson et al., 2021; Tuckman, 2000). It is worth noting that all the interviews were conducted in Portuguese, the native language of the participants, which facilitated their understanding of the questions and interactions with the researcher. As the interviews were exploratory in nature, their duration varied according to the profile of the interviewee, with the shortest interview lasting 32 minutes and the longest interview lasting more than 160 minutes. Table 8 – Interview sample characterization matrix Organization type Sectors Subject role Code (#) Innovation and Financing Consulting ICT, Electronics, Manufacturing Industry, Senior Director A01 Innovation and Financing Consulting Manufacturing Industry, Food Industry, Textile and Materials Co-founder & CEO A02 Innovation and Financing Consulting ICT, Electronics, Urban Mobility, Energy, Systems Founder & CEO A03 University Innovation and TT, Teaching and Research Professor & Researcher A04 University Technology Transfer Office (TTO) Mechanical and Material Engineering, industrial and robotics Vice-President & TTO A05 University Technology Transfer Office (TTO) ICT, Electronics, Materials, Construction Project Manager A06 University Research Centre ICT Research Coordinator A07 Research Centre & University interface (TIC) ICT Senior Business Developer A08 Research Centre & University interface (TIC) Materials, Energy, Environment Senior Researcher A09 Research Centre Nanotechnology, Health, Food tech R&D Group Leader A10 Collaborative Laboratory ICT Executive Director A11 Collaborative Laboratory Food Tech, Biotech Principal Researcher A12 Incubator and accelerator ICT, FinTech, Health tech Executive Director A13 University Technology Transfer Office (TTO) ICT, Manufacturing Industry, Textiles and Materials Executive Director A14 97 Innovation Association Innovation, tech transfer intermediation, business qualification Project Manager A15 Governmental Innovation Agency Innovation, tech transfer intermediation, Funds management Board Member A16 Company (Corporative Group) Industrial AI, Tech developer Co-founder & CEO A17 Company (Corporative Group) Product Engineering, Digital Manufacturing, Mould-making Board Member & Innovation Director A18 Company Media AI and Software Dev Co-founder & CEO A19 All the interviews were recorded with total or partial permission of the participants, to be used for research purposes only. Some interviewees did not authorize full disclosure of their audio recordings and their respective transcripts (Johnson et al., 2021). For these specific cases, the partial transcripts and notes taken were later filtered and validated by the interviewees themselves, who wished to remain anonymous, so that all the final elements contained in this thesis and its respective annexes could be fully disclosed. Therefore, following the guidelines proposed by Ghiglione & Matalon (2006). 4.4.1.4 Content results analysis After conducting the interviews, the process of transcribing the audio recordings and compiling the respective notes was carried out. Due to the considerable number of interviews conducted, whose duration greatly varied, selective transcription of the interviews was done using a non-naturalistic and tabular method of content analysis (Azevedo et al., 2017), prioritizing the use of key content excerpts that bring value to the research question presented. This was achieved by firstly omitting speech or conversation not related to the study, such as non-responses and indirect/parallel developments to the questions asked. Additionally, were also excluded parts where the interviewee requested not to be considered in the analysis, such as opinions of a more political and/or sensationalistic nature (Turner, 2010). Despite this, for interviews with more sensitive discussions and/or interviewees more concerned with their anonymity, the transcripts and notes taken were later sent to the participants so they could further filter and validate the final content to be analysed. Thus, the resulting transcripts and notes are fully authorized for analysis and subsequent discussion disclosure (i.e., under anonymity) (Johnson et al., 2021; Turner, 2010). The analysis followed a non-naturalistic tabular transcription scheme (Azevedo et 98 al., 2017), where key relevant excerpts from the interviews and their respective previously validated and authorized notes were tabulated as answers to each question. False starts, repetitions of sentences, interruptions, and non-relevant inputs were also excluded. Additionally, slang and Portuguese vernacular sayings were translated during the transcription process to allow English readers a full understanding of the discussion as well as to ensure that the transcription and analysis process focused on the accuracy of the interview (DiCicco-Bloom & Crabtree, 2006; Roberts, 2020). This was followed by an analysis conducted question by question, where content codification parameters were identified and defined, allowing key answers/ideas to be compiled in a structured way (see Table 9). The content analysis and discussion were conducted following the most exemplary and relevant excerpts, expressions, or words for each question, and the analysis was carefully structured by its key codes, thus enhancing a properly organized and reasoned discussion (DiCicco-Bloom & Crabtree, 2006; Turner, 2010). Table 9 - Interview content codification Interview Questions / Key Domains Codification analysis (codes) Traditional Intermediaries Roles and value proposition Resources and responsiveness Market positioning Innovation Consultants Roles and Value Proposition The emergence of private players Value Proposition Proactivity Responsibility Competencies Relationships (Network) Financing Comparison Strategic complementarity Residual Overlapping External Factors Sector and geographical area Business interests Time as factors Occupation and limitations of stakeholders Financial capacity Financial opportunities Recognition (Positioning perceptions) Peers’ recognition Not properly recognized by NIS Distinguishing intermediation roles and services 99 In the interview results chapter (Chapter 5), the key ideas/answers to each question are presented and discussed following the script order proposed in Table 7. The argumentation narrative of the discussion of the results follows a combination of interviewees' ideas thoroughly referenced by their participant codes (e.g., #01), defined in the sample shown in Table 8. Additionally, whenever possible, direct interview excerpts were highlighted as tangible examples citing the ideas discussed. Throughout the text, reference may be made to the identification code of interviewees (from A01 to A19) to indicate the source of a proposition. 4.4.2 Case study through document analysis Due to criticism previously pointed out to self-report methods such as interviews and questionnaires, it is recommended that they should be complemented adequately with other noninterfering methods (i.e., other data obtained by processes that do not involve the direct collection of information from the investigated subjects) (Hanson & Grimmer, 2007; Park & Park, 2016). In this way, a documental analysis was conducted in a case study as a quantitative method to complement the qualitative research (Morgan, 2022). This method involves the use of a statistical tool to conduct a quantitative analysis of the data collected from hundreds of TT projects found in the file folder of a case study subject – a Portuguese private innovation consulting firm. The method of document analysis implies a quantitative approach that starts with the use of content analysis to extract data variables that are feasible to be analysed by a statistical program (Lee et al., 2002; Soni & Singh Yadav, 2015). In the present case study, it is important to understand the reality of consultants' work from an insider perspective regarding the roles they play in the NIS. Most of the data was analysed following previously discussed ideas, both in the literature review and in the individual interviews. Despite the existence of congruence points between the interviewees' contents, some interviewees' opinions were revealed to be mixed with personal opinions and preconceived ideas. Thus, some of these were taken into consideration when formulating research hypothesis as guidelines for the quantitative research design of the case study through document statistical analysis. 4.4.2.1 Hypotheses formulation Following a deductive research approach, as previously shown, the second phase of the research (i.e., the quantitative phase) was designed with the output results from both the interviews and the literature review. The goal of this deductive approach was to hypothesize key aspects in line with the research objectives previously proposed and thus to structure and further deepen the research process 100 to reach concrete findings to answer the initial research questions. In this sub-chapter, the major congruence points from the qualitative research results were synthesized and discussed to achieve the formulation of concrete research hypotheses as pivot guidelines for quantitative research. Therefore, the following hypotheses were discussed and formulated based on the content analysis of the interviews’ results in Chapter 5 and aimed to narrow the research aim from a subjective (i.e., qualitative) to an objective nature (i.e., quantitative) (Maula & Stam, 2020). These research hypotheses were formulated and supported by the key discussion points highlighted in the results of the qualitative research. Five research hypotheses will be carried out as guidelines for the quantitative research design to be conducted, tested, and discussed throughout Chapter 6. Chapter 5 presented the results of the interviews conducted to explore the research subject, which led to a broad range of contributions and ideas discussed. To deepen the research discussion, a set of research hypotheses was formulated to guide further developments through more quantitative research methodologies (Ang et al., 2019). These hypotheses served as subject guidelines to validate or refute the key findings from the literature review and interviews, and thus generate the final findings and conclusions aimed at with this research. Starting with the central research focus - the intermediary role of consultants - different specializations and roles were identified both in the literature (refers to Table 3) and by the interviewees' contributions. Two major disruption points were identified. There are different role specialization perspectives from different interviewees. The interviews revealed the existence of two major perspectives when identifying the roles played by private consultants. Mostly, disagreements were found between public and private entities interviewed. Particularly, these could be observed in the results of the assisted table of intermediary’s role specializations (see p. 166, Table 12). Consultants can adjust themselves to clients' needs. The literature on management and innovation consulting showed that consulting firms focus on increasing the value of their services by aligning them with clients' and market needs (Basu & Taylor, 2010; Butler, 2009; Drucker, 1981). Thus, consultants can be the product of client-consultant relationships (Costa et al., 2021; Martinez et al., 2016). Some consulting firms may develop their value proposition on more transversal and managerial roles with greater potential to respond to a wider market need, while other more specialized consulting firms and KIBS might focus on delivering more niche roles in specific fields and sectors related to knowledge-intensive services (Basilioa et al., 2019; Bianchi et al., 2016; Shearmur & Doloreux, 2019). 101 In addition to answering the first research objective of identifying the roles performed by private consultants in TT projects, the need for understanding such roles was enhanced. Unlike traditional intermediaries, the roles and specializations of private consultants are not planned and designed by regulators, nor thoroughly described in the literature, so, additional variables must be considered. As both the interviews' results and the literature review highlighted this defining importance of client-consultant relationships in the role of private consultants, the first research hypothesis was underlined: • Hypothesis H1 - The roles played by private consultants are associated with the type of entity originating the project. The role of private consultants, like the concept of an intermediary described in the literature, mostly focuses on the concrete functions, activities, and specializations they perform to respond to gaps and needs in TT processes. However, some authors with more a more “open market” perspective consider consultants' value proposition to be more complex than the hard innovation services they can provide (Basu & Taylor, 2010; Dias et al., 2017). Soft innovation roles are emerging in the literature as a gluing factor that supports consultants' services value proposition. These soft services and skill are response mechanisms and characteristics which consultants develop in response to market gaps and clients' needs (Back et al., 2014; Basu & Taylor, 2010). Interviewees supported this line of thought by mentioning additional factors contributing to the consultant's role, which were depicted as valuable as their core roles. Proactivity is regarded as the key characteristic of a private consultant. Described by the interviewees as the attitude with which the most consultants tackle the response to the needs identified in both companies and public institutions (A01). This proactivity was highly regarded by most of the interviewees as the primary factor within private consultants' value proposition. Consultancy firms were recognized by some interviewees as innovation catalysts (A19), and even as the responsible entities for originating some of the Innovation and TT project opportunities (A15, A17). This consultant's proactivity has different meanings according to different interviewee perspectives. Despite the overall agreement regarding consultants' proactivity that interviewees demonstrated, distinct perspectives were found. Public and academic entities interviewed mainly stressed this consultant's proactivity as the consultants’ responsibility in prospecting new companies and partnerships outside their current network in a never-ending expansion of their current network (A08, A10). In contrast, interviewed companies (i.e., recipients) reported consultants' proactivity as a continuous collaboration in which consultants' responsibility is to propose and maintain a pipeline 102 of TT projects and funding opportunities within their current network, in order to keep their clients innovative (A19). Consultants strive to create, nurture, and maintain their network of close contacts (clients and partners). The literature on the consulting industry emphasizes the importance of client networks as the primary critical success factor of consultants' activity (Costa et al., 2021; Tether & Tajar, 2008). Consultants tend to act according to their strategic interests, which are mostly of economic nature. For that reason, they tend to maintain close relationships with their network to foster opportunities to create and deliver value and generate revenue (Canato & Giangreco, 2011; Costa et al., 2020). From some interviewees' perspectives, a consultant's network represents both its specialization market and its comfort zone (A18). Therefore, working beyond their current network may only happen strategically or in need. Consequently, the proactivity attitude and capacity of consultants to work beyond their normal network may vary depending on their client organizations' needs and characteristics. Their proactivity approach can range from originating TT projects themselves to supporting a stream of TT opportunities to meet the needs of their current network. Reports recognizing such consultants' proactivity seem to be mostly linked to entities from their current network, with which they had previous interactions. However, some interviewees mentioned the consultant's prospection of new recipient companies' partnerships (A08). To guide research exploration in this matter, the following hypothesis was formulated: • Hypothesis H2 - The TT projects where the private consultant works beyond their current network are linked to the type of entity responsible for originating the project. As the concept of an intermediary role is further understood, identifying the activities, functions, and specializations performed by a private consultant becomes insufficient to fully comprehend their role as intermediaries. The reasons and motivations behind the consultant's increasingly active role in NIS go beyond the functions they can perform, as their value is perceived differently by various agents involved in TT processes. The reasons to involve a consultant in a TT project go beyond their role specializations. Interview exploration on the role of consultants as innovation intermediaries revealed additional factors contributing to the consultant's perceived value proposition in TT. The characteristics and needs of the projects' stakeholders were highlighted as trigger reasons/motivations to resort to 103 consultants, such as the need for financing options, their ability to free stakeholders from project responsibilities, or even the lack of contacts of project stakeholders in NIS or the market. Different entities showed different motivations to involve consultants in their TT projects. From interviewee contributions, many motivations foster the contact and involvement of private consultants as innovation intermediaries to the detriment of traditional ones. Despite some commonalities in the motivations, such as the existence of innovation incentives or the desire for project management support to free the key stakeholders, interviews revealed that different motivations in the origin of a TT project may be linked to the entity originating it. Academia and research centres/interfaces highlighted their interest in consultants' networks, while companies preferred to highlight previous experiences or good references as motivations for the option of the private consultant as a preferred intermediary. Private consultants' value proposition as intermediaries in TT processes may vary depending on their clients' characteristics and external factors that continue to evolve, such as policies and incentives. Likewise, different entities responsible for originating TT projects may have varying motivations for involving private consultants in their affairs. In addition to the concrete functions, activities, and specializations that private consultants perform, their proactivity attitude and capacity to expand their network can also differ depending on the needs and characteristics of the client organization (Back et al., 2014; Sousa, 2018). Therefore, understanding these factors and motivations is crucial to comprehensively grasp the consultant's role as an intermediary in TT processes. To explore these ideas, a third research hypothesis has been formulated: • Hypothesis H3 - The motivations for the contact and involvement of the private consultant in a TT project vary according to the type of entity that originates the project. To gain a better understanding of the role that private consultants play as innovation intermediaries, it is also necessary to examine their position in relation to traditional structures and institutions within NIS. Accordingly, the second research question aims to explore how private consultants' roles compare to those of traditional intermediaries within NIS. Both literature and the results from interviews suggest that consultants are increasingly working within NIS, although they are not formally recognized as intermediary agents. Based on these findings, several key contributions were identified in the interviews’ discussion that can help to further define research hypotheses. The role of consultants in the NIS is not yet fully understood or formally recognized. While interviewees from public and governmental institutions mentioned having knowledge of consultants 110 4.4.2.4 Statistical analysis process The data collected from the case study archive was structured according to the presented variables in Table 10, and subjected to various procedures, including data entry, sample descriptive analysis, and statistical tests using IBM® SPSS® (Statistical Package for Social Sciences) version 28.0. Given the qualitative nature of the variables which were mostly qualitative scales (i.e., nominal), specific statistical tools and tests were carefully selected based on initial descriptive analysis, as well as statistical associations and comparative analysis. The initial descriptive analysis focused on conducting individual frequency tests for each variable collected, both nominals, ordinals, and scales. The descriptive analysis is summarized in the beginning of Chapter 6 using observational tools such as bar graphs. From the descriptive analysis conducted to the 219 TT project sample, the case study firm could be initially characterised, and thus this first analysis served as a foundation to adjust and conduct the following tests necessary to respond to the five hypotheses proposed. To respond to these hypotheses, data variables were selected, and statistical tests were chosen based on variable typology and test assumptions. Table 11 presents five tests carefully chosen for their suitability to properly respond to each of the five hypothesis. Two main forms of statistical tests were used: 1) Chi-square's test of independence, and 2) McNemar's test of comparison. Table 11 - Statistical tests selection by Hypothesis H# Hypotheses Statistical test options H1 The roles played by private consultants are associated with the type of entity originating the project. The Chi-square test of independence verifying whether the variables “Roles_Pri_Con_” are likely to be associated or not with the variable “origin_type” H2 The TT projects where the private consultant works beyond its network are linked to the type of entity originating the project. The Chi-square test of independence verifying whether variable “Previous_int” is likely to be linked or not with the variable “origin_type”. H3 The motivations for the contact and involvement of the private consultant in a TT project vary according to the type of entity that originates the project. The Chi-square test of independence verifying whether the variables “Cont_Motivator_”are likely to be related or not with the variable “origin_type” 111 H4 The existence of a partnership between consultants and traditional intermediaries in the same project is linked with the type of entity that originated it. The Chi-square test of independence verifying whether variable “Other_Pub_Int” is likely to be linked or not with the variable “origin_type”. H5 The roles of consultants fundamentally differ from those performed by traditional intermediaries in projects both participate. The McNemar's test of comparing/checking if there are differences between two groups – “Roles_Pri_Con” and “Other_Pub_Roles” when the variable “Other_Pub_Int” is equal to 1 (yes). The first four tests were conducted in contingency tables of the Chi-square type, to verify the existence of a statistically significant association between the tested variables. The last hypothesis followed a comparative study of pairs, in 2x2 tables, by using McNemar’s test, in which the focus is to identify a statistically significant difference between the responses of two groups within a chosen variable. The decision rule used for both types of tests consisted of detecting significant statistical evidence for probability values (i.e., test proof value) less than 0.05 (p ≤ 0.05). In the Chi-square tests analysis, the main outputs consisted of minimal distribution tables where the responses regarding a binary variable were distributed by the response of another variable. This is a common statistical test used to verify the existence of an association/correlation between two variables in the sample and thus better understand such variables and the behaviour of the sample (i.e., the case study consultancy firm behaviour in association with external variables). In the fifth hypothesis, two groups, the private consultancy firm (i.e., the case study) and the other traditional intermediaries operating simultaneously within the same TT projects, were compared following one response variable - the nominal variable of each role specialization. McNemar’s test is the better-suited test of a non-parametric test designed for paired nominal data, used to determine if there are differences in a dichotomous dependent variable between two related groups. It can be considered to be similar to the paired-samples t-test but for a dichotomous rather than a continuous dependent variable. However, unlike the paired-samples t-test, it can be conceptualized to be testing two different properties (or in this case, groups) of a repeated measure dichotomous of an equivalent variable. McNemar's output 2x2 tables are used when samples are paired to increase the accuracy of the comparison. Thus, in the circumstance under study (Hypothesis 5), one intends to compare the responses regarding the two response variables of “Role” (i.e., yes, or no), comparing them between an 112 equivalent set of individuals – The private consultancy (the case study) and the other traditional intermediaries. This test is the most appropriate to assess whether the proportion of discordant responses is the same in each category/group (i.e., intermediary type). A p-value less than 0.05 (p ≤ 0.05) would validate that the responses regarding the performance of specific role specialization in a project (i.e., yes, or no) fundamentally differ according to the respondent (i.e., the private consultancy firm or the other traditional intermediaries participating in the TT project). 113 CHAPTER 5 QUALITATIVE ANALYSIS: INTERVIEW RESULTS 114 CHAPTER 5 - QUALITATIVE ANALYSIS: INTERVIEW RESULTS In this study, responses, excerpts, and significant ideas related to each interview question were selected (see Annex I for the interview script) and a set of codes/topics for each answer was defined (refer to Table 9). The coding of responses by key ideas is reflected in the structure of the discussion of results for each interview question. To ensure transparent and logically cohesive discussion, the content analysis and results discussion below followed the codification phase, using the key codes as sub-indexes within each question discussion. The discussion of key ideas/answers to each question in this chapter is presented in a structured order, as previously presented in Table 9. The argumentative narrative of the discussion of the results follows a combination of interviewees' ideas, which are thoroughly referenced by their participant codes (e.g., #01) defined in the sample Table 8. Additionally, direct interview excerpts are highlighted wherever possible as examples to support the ideas discussed. 5.1 Perception and experience with public intermediaries The primary objective of this first question was to assess the interviewees' knowledge and familiarity with public (i.e., traditional) intermediation organizations. To achieve this, a diagnostic question concerning public intermediaries was asked during the interviews. Although the object of the research is private consultants as innovation intermediaries, the discussion began with a strategic focus on traditional/public intermediaries as a reference level, in the same way as a control question. The intention behind asking this question was to identify the interviewees' perceptions and prejudices about public intermediaries and to use the findings as a reference point for comparison and relative positioning of the private consultants' role. To facilitate the correct identification of the object of discussion, reference examples of national public intermediaries such as Technology Transfer Offices (TTO) and Technology and Innovation Centres (TIC) from universities and research centres were used. During the interviews, it was found that the majority of the interviewees had a great familiarity with traditional intermediaries, having previously worked or interacted with them. Through the analysis of the responses from the various participants, a set of key ideas was identified. These key ideas were used in content codification and grouped in a structure that is strictly focused on reading coherency. The structure does not aim to convey any hierarchy of importance between the topics covered, but aims to provide a logical structure for the discussion: 115 • Roles and value proposition; • Resources and responsiveness: • Market positioning. Overall, the combination of the diagnostic question and content analysis of the interviews provided valuable insights into the interviewees' knowledge and perceptions of traditional intermediaries, which served as a reference point for the subsequent analysis of private consultants' role as innovation intermediaries. 5.1.1 Roles and value proposition Regardless of their helix origin, most interviewees recognized traditional intermediaries as the main players in innovation intermediation, as they hold the most formal role as intermediaries and are even recognized as such by the Portuguese NIS (A05). During the discussion, several examples of university and polytechnic TTO and TIC were provided, which led to some consistency in the perception of the roles of traditional intermediaries. However, it became clear throughout the discussion that this intermediary encompasses a large set of intermediation roles. Many of these organizations advertise themselves as performing a large number of functions, but in reality, they can only perform a much smaller number and range of functions/roles. “They offer much more than what they manage to accomplish afterwards.” – A01 “(…) essentially they take part in fostering the generation of ideas, and maybe not much else.” - A11 Most interviewees perceived that traditional intermediaries tend to offer a wide range of theoretical functions beyond what they can perform in the market. Some interviewees emphasized key roles in which they believed these intermediaries have a real impact or value, such as disseminating knowledge created in academia either through the creation and promotion of networking events, or dissemination of project results in co-promotion or training (A02, A05). 116 5.1.2 Resources and responsiveness Limited resources Regarding to this perceived exaggeration of the value proposition promoted by public intermediaries, a set of perceptions emerged based on the previous experiences of the interviewees with this type of intermediary. They mostly focused on the lack of responsiveness and the incapacity to perform the comprehensive set of roles that are often expected from them. The lack of resources to support an effective response to their role as intermediaries was a key aspect highlighted by several interviewees (A04, A05, A08). This lack of resources mainly pertains to personnel and the associated skills. It was likewise mentioned that "the lack of human skills, training, and even the lack of strategic guidance of the teams" (A04) are great limitations to what they can do. “Resources are lacking. It's almost embarrassing. Too much is demanded from them.” - A05 The emergence of new types of public/semi-public intermediaries, particularly the TIC, represented by some interviewees (A08, A05), is perceived as a NIS strategy to expand beyond those more traditional and academic-like intermediaries, providing an additional dose of resources to respond beyond what other smaller offices can do. “I think that a TIC such as ourselves, emerge to respond to what small TTO cannot do due to their lack of resources.” - A08 Nevertheless, several interviewed players, especially those linked to academia (A14) and government (A16), feel that the best resources and skills tend to leave the public sector in search of better opportunities in the private sector, where there is a greater appreciation of their competencies. While the public sector continues to struggle to attract and retain highly qualified human resources in positions supporting TT, "there will always be more competitive opportunities on the private side to unbalance the scale" (A16). Quality discrepancy This "discrepancy" (A16) is not only apparent between public and private intermediaries but also among public intermediaries themselves. Specific examples of traditional intermediaries were deemed 117 unhelpful contextualization as they were perceived as highly relative by some of the interviewees. Traditional intermediaries comprise various distinct entities, both academic and public/semi-public in nature. According to several interviewees, there is a significant discrepancy between the quality of intermediaries and their outcomes. While some national TTO and TIC are recognized abroad as excellent examples, others that should be standardized within the NIS have revealed unsatisfactory performances from several NIS perspectives. “In the last ten years, we have seen more competencies and new entities, but they are not homogeneous. They vary a lot in quality and performance.” - A16 “There are different ways of working, some closer or farther from companies.” - A12 “There are several examples and experiences, some with better results, others with worse. But the experience we have, at least so far, I perceive as being good.” - A18 The notion of discrepancy/variability in the quality of public intermediaries was widespread among interviewees. The perceived quality appears to be heavily dependent on the resources of the organization, the way it is managed, and its proximity to businesses, as the technology recipients. Also noted was the lack of strategic orientation among some traditional intermediaries, which fail to focus on concrete results instead of business events (A17). “There are dissemination events that are nothing more than show-off moments” - A17 Interviewee A14, who has a background in academia, private consulting, and links to a public intermediation entity, clarifies that, from his experience, "there is no correct or incorrect way of working ". However, an organization's existing culture dictates a large part of its results, and the academic culture has a specific set of interests that are misaligned with the key objectives of TT. This misalignment represents a barrier to TT opportunities with many companies. In addition, this misalignment can be found in many academic intermediaries known by the participant (A14). In defence of these institutions, several other interviewees recognized the efforts that personnel from traditional intermediary organizations often make, which are sometimes beyond the scope of their 118 responsibilities. The dependence on personal morale and voluntarist attitude characterizes several experiences reported by interviewees (A01, A04, A07, A14, A17, A18). “(…) they present very different realities (between the best and worst examples of traditional intermediation). I think it is associated with their lack of resources (…). I feel that the attitude of the institutions (i.e., the people) is also noteworthy. I mean, facing the mission they have, sometimes even without proper resources. (…) but when there is a will of the people, things can be accomplished.” - A07 Focused on mitigating the perception that public/traditional intermediaries have difficulties and limitations performing their role, some interviewees insisted on stressing the importance of the “voluntarist attitude” (A04) of the personnel behind this type of organization. The lack of financial and human resources is a reality. Even so, positive results can be achieved when there is a genuine interest in project performance and not just in doing “ the bare minimum necessary to maintain its funding ” (A17). Technical knowledge The high technical knowledge possessed by public intermediation institutions was highlighted. Scientific and technological knowledge, especially when it is at a state-of-the-art level, has inherent novelty and complexity. This is one of the factors that most increase the difficulty of TT processes, especially when crossing the valley of death (Gulbrandsen, 2009; Lindström & Silver, 2017), due to the perceived intangibility of academic R&D results by the market. Although many interviewees highlighted the distance from the market as a negative factor for the role of traditional intermediaries (A12), several preferred to see it positively for being closer to academia. “To keep up with the state-of-the-art, you have to be close with those who produce knowledge.” - A11 “Beyond conducting TT, I believe the more important aspect is to understand scientific and technological activities and to keep up with which researchers work (…).” - A08 An evolution in the last decade of the role of some traditional intermediaries was noticed. Having lost some of its functions and responsibilities, either by the emergence of other public intermediary 119 mechanisms (A16) or by the evolution of the role of private intermediaries (A19). However, public intermediaries, such as the University TTO and Research and Technology Interface Centres (RTIC) tend to have a similar bias towards the interests of academia, as they represent and offer a closer position to academia and the knowledge sources. This proximity to academia and somewhat distanced from the market is perceived positively by companies (A01, A03, A17, A18, A19). “Public intermediaries are closed within their universities. (…) for more than 10 years, I have only worked with private consultants because they are much closer to the market. Still, all the R&D projects we develop have the participation of universities or TIC, filling roles of technical support, R&D consultancy, testing, and feasibility.” - A19 5.1.3 Market positioning Regarding the positioning perceived by NIS agents, Portuguese NIS has evolved in the last two decades, increasingly recognizing the importance of TT in promoting market innovation. As previously mentioned, several interviewees see public intermediaries' role as not corresponding to what it is supposed to be theoretically. Since most of these intermediaries act from an academic perspective, several participants began to perceive public/traditional intermediaries as academic champions, positioned as the front-end of academia and R&D results. Most interviewees from private organizations (i.e., consultants and companies) regard this championing and specialization in the front-end of academia not as a limitation on their intermediary role but as a strength for their proximity advantage to the academia and the source of technical knowledge (A01, A17, A18, A19). Gatekeeping at University helix The positioning of proximity to academia generates a duality of interpretations on the part of the interviewees. On the one hand, this championing of the University's helix is well regarded for the technical knowledge it represents, for its proximity to technologies and R&D results, and whose positioning is benefited by the curriculum of the academia it represents (A18). On the other hand, there are perceptions and experiences among the interviewees that point out a gatekeeping role for intellectual property resulting from projects at the university. In theory, this gatekeeping on behalf of the university "has everything to work out great, however, as in almost all TIC, they end up becoming a repository of research personnel attached to the university" (A17). 126 innovation in companies. In addition, there is an additional financial incentive if an academic or R&D entity is involved in the innovation project as a TT initiative. Therefore, the involvement of private consultants in financed innovation projects is prominent in Portugal, as "very few technological innovation projects are done without a consultant involved" (A01). Private consultants are recognized as a key part of TT projects, as "without them, we would not see the results we all know today in the market " (A02). This role is acknowledged by most NIS agents interviewed, both on the academic and industrial sides. Private consultants' expertise in designing and building innovative projects to be submitted to public funding programs is highly valued (A01, A08, A11, A17, A19). “The success rate of project funding approval is much higher than that of traditional intermediaries.” - A08 The importance and recognition of this specific function of obtaining funding from national and European programs to encourage innovation have led to an increase in the number of players (i.e., consultants) in the market with different value propositions (A02). Some private consultancy firms are seen as "merely writers of project proposals" (A15), but there is still a need for these agents in the NIS, especially among small and medium-sized Portuguese companies that "have little or no culture of R&D investment" (A11). Hence, many of these smaller companies are encouraged by the proactivity of consultants in identifying opportunities and the financial support associated with these innovation investments (A11). Freeing key stakeholders According to the interviewees, consultants possess two key characteristics: proactivity and accountability. Consultants are responsible for their functions and work autonomously to solve problems and meet project stakeholder expectations and deadlines without their involvement. “The good thing about working with consultants is that they work for us, and we only need to involve a university or TTO when we have a concrete project on the table.” - A07 Several interviewees (A04, A08, A12, A13, A15, A17, A18, A19) believe that this sense of accountability stems from the fact that consultants are for-profit entities and must respond to the needs 127 of their stakeholders, who are mainly private companies acting as their clients. As intermediaries, consultants have professionalized their roles and gained recognition in the market for their ability to achieve intended results. “(…) when we need high-quality work and want to make money, we have to work with the best. I'm not saying that there aren't good professionals in the public sphere, but paying for a service provided by a well-paid and experienced consultant is a guarantee of a job well done.” - A19 There is a prevailing opinion that each entity, especially intermediaries, should take full responsibility for their actions as experts. This allows other stakeholders to focus on their core roles (A03, A18, A19). This professionalization of consultants as intermediaries has gained recognition in the market for their performance and, their ability, to achieve the intended results. There was an opinion that each intermediary entity should take full responsibility for what they do as an expert, thus freeing the remaining stakeholders so that they can also dedicate themselves to the core role they should play (A03, A18, A19). “(…) projects pose as a big bureaucratic burden. It’s highly impractical to manage these kinds of projects as the promoting company or a consortium partner.” - A18 “We feel at ease because we have someone to guide us (i.e., the private consultant). Typically, we come up with the original idea, but the consultant guides us through the design and execution of the project.” - A17 In TT projects, activities linked to project support functions are considered soft-innovation activities that are best left to specialists, namely private consultants. This approach allows other stakeholders to focus on the core activities of their projects, which are deemed essential (A17, A18). TT projects often involve project support functions that are considered soft innovation activities by key stakeholders (i.e., both sources and receivers), despite their recognized importance (A17, A18). These activities are typically delegated to specialists such as private consultants, allowing other stakeholders to focus on the core activities of their projects (A18). 128 “(…) that's where consultants come in: to make the necessary connections and get things moving. Then, it's up to us in R&D to deliver the hard results.” - A11 “We've had fantastic experiences working with private consultants. They are great support because they free us from the most mundane activities and allow us to focus on the development and innovation activities that are central to our projects.” - A18 Continuous work and maintenance of relationships The specialization of consultants, which frees project stakeholders to focus on core activities, requires a laborious role in managing expectations and communication between parties before, during, and after projects. It was evident that consultants establish continuous working relationships within the system, mainly at the R&D, innovation, and TT levels. Some interviewees expressed trust and loyalty to this new intermediary partner. “I have worked exclusively with private consultants for over 10 years.” - A19. “We have been conducting R&D projects continuously for several years, and I always choose to work with private consultants.” - A17 “Private consultants have become increasingly involved with us, especially in copromoting technology transfer projects.” - A06 Continuous work with customers or strategic contacts allows for a more regular presence and participation in innovation projects, as well as intrinsic knowledge of stakeholders and the sector in question. Consequently, private consultants identify opportunities for innovation for their network of clients and partners and often conceive and propose project opportunities themselves. “It has become natural for us to work closely with several consultants because they are the ones who ‘walk the walk’. They have more contacts and often bring us pre-designed projects that are ready to start. We maintain regular communication with several consultants in different sectors where we aim to carry out projects regularly.” - A08 129 Acknowledgement of this role in creating and maintaining network relationships has led to higher involvement and impact of consultants, resulting in new roles and broader scopes. Currently, consultants are even partnering with business associations to strategically define the future of entire regions or sectors of activity (A02, A15). “The strategic relationship between associations and innovation consultants has grown stronger. Private consultants now tend to have a regular presence in associations (i.e., business/regional), in addition to their current involvement with academia and even government. Many entities nowadays cannot do anything without private consultants, making them an essential part of innovation in our country.” - A15 5.2.2 The emergence of private players The emergence of private players The ideas shared by the interviewees revealed a set of perceptions linked to the evolution of this intermediary and its role in the NIS, particularly its exponential emergence over the last decade (A16). Along with the consultancy firms' emergence, there is awareness regarding their role within the system and their increasing involvement, not only with recipient companies but also with public and academic entities. “Consultants are indispensable key players in the system, as well as for traditional intermediary agents. This is evident from the high number of consultancy firms emerging in the market." - A15 “(…) these days, I deal with private consultants every day. Over the last decade, they have become much more active, and new entities are popping up all the time. Consulting has always existed, but in the past, consultants were merely reactive to the challenges they faced. Nowadays, they are more proactive, bringing project opportunities to companies and even to academia." - A16 During the interviews with entities of the R&D system, as well as some companies, it was stated that this relationship of private consultants with other public and academic players often stems from the organization founders. Many times, specialized human resources move from academia and traditional 130 intermediaries to the private sector, not only through expert phishing but also through the spin-off of experts and academics looking to capitalize on their knowledge and skills. “Most of these consultancies are founded by professors or former members of the academic community." - A06 “Consultancies seek out HR from traditional intermediaries because the salaries are always better in the private sector for those who are good professionals.” - A16 This topic will be discussed in greater depth in a further section related to the competition or complementarity between public and private intermediaries. Still, the perception remains that this emergence of consulting entities in the market to collaborate with public and academic entities is seen almost as a natural evolution of the market resulting from a real need felt by companies, mostly. “Many private consultants are emerging because the industry has the capabilities and is creating mixed teams and partnerships to solve their problems without dependencies on public organizations. Consultants make it happen." - A17 Systematic implications Finally, some notes and opinions linked to the perceptions and prejudices discussed by various interviewees are highlighted, during which some ideas of potential implications for the NIS were presented. Starting with innovation consulting themselves (interviewees) stated that despite favouring the market growth trend, the increase of private players is making it difficult to manage meaningful relationships in the private sphere. This seems paradoxical as consultants themselves promote the model of open innovation (Bianchi et al., 2016; Diener et al., 2020). Yet, their expertise and networks are often under strict confidentiality agreements, as knowledge is power, and there is a growing competition within the private consultancy sector (A02, A03). “(…) consultants still have a lot of room for improvement. They have difficulties relating to each other, even though they are great with all the NIS agents. However, between themselves, they compete fiercely, which ends up limiting open innovation.” - A02 131 There is difficulty in understanding the innovation consultant as a concept of a private intermediary due to a "great discrepancy of agents and fields of intervention, as well as of the capabilities and resources they possess" (A04). This generates a perception of a significant discrepancy/distinction regarding the private consultants, their role, and the quality of their work. This culminates in a perception that, in the Portuguese NIS, the innovation consultant as a private intermediary has a less comprehensive role compared to other more evolved NIS in foreign markets. “(…) The range of services provided by private consultants in our country is comparatively limited in comparison to those offered in many other European countries.” - A04 5.3 Role value proposition After assessing the interviewees' familiarity and knowledge of traditional intermediaries and private consultants, the interview shifted focus towards the research's main objective of comprehending the role of private consultants as intermediaries. Through the preceding questions, a range of roles and responsibilities were identified by the interviewees, mainly focused on specialized tasks such as project design, securing funding, project management and monitoring, creating and sustaining contacts, and market opportunity surveillance. Additionally, other significant characteristics were emphasized as value propositions, contributing to their role perception, particularly the way they position themselves in the NIS by providing supplementary and complementary services to those offered by traditional intermediaries, with a focus on financially supporting TT projects. The value of the consultants' role was also recognized for their ability to relieve TT stakeholders of less core project activities and ensure successful execution, allowing stakeholders to concentrate on innovation activities. Furthermore, the continuous involvement of private consultants in maintaining dynamic relationships within their networks to encourage ongoing investment in R&D and innovation was emphasized. Respondents were also asked about the additional characteristics, skills, and specialized roles they deemed most essential and desirable in consultants, which was a continuation of the preceding question discussion. Five main components of the value proposition of private consultants' roles emerged from a direct analysis of the discussion content: • Proactivity; • Responsibility; • Competence; 132 • Relationships (Network); • Financing. 5.3.1 Proactivity The most prominent factor, as previously mentioned, was the sense of proactivity and attitude demonstrated by private consultants in the NIS as intermediaries for innovation. The majority of interview participants identified "proactivity" (A01) as the most significant value proposition factor. This factor even seems to impact participants' perception of other roles and their impact on TT outcomes, as this private intermediary is widely recognized for its ability to "make it happen" (A01), "idealize opportunities" (A12), and prevent "ideas from ending up in the drawer" (A08). The proactivity of private intermediaries, specifically consultants, provides them with greater agility compared to traditional intermediaries. As a result, they can achieve different results (A02). In an interview with a private consultant (A01), "proactive, open, and accountable" were identified as highlights of their organization's value proposition. This value proposition aims to differentiate the consultant's intermediary role in the market, assuring their customers, mainly companies, that they can "rest easy when they leave things to the consultants” (A01). However, it is important to note, as emphasized by traditional intermediaries and academic entities (A07, A10, and A13), that despite having departments focused solely on supporting technology transfer, private consultants continue to be viewed as specialized subcontractors. This perception of consultants as a strategic opportunity for subcontracting by public organizations and academics is also supported by their perceived proactivity in finding and securing financing solutions for academic innovation and R&D projects. Proactivity and innovation catalysts Additionally, several indirect ideas and topics of discussion among interviewees perfectly align with the same notion of proactivity. These impacts of their proactivity are specifically seen when consultants support organizations, mainly companies, in identifying where and how to innovate. Private consultants continuously generate a stream of innovation opportunities to attract and pitch to potential clients. This characteristic of proactivity is perceived by some as a catalyst for innovation in the NIS. “We don't usually reach out to consultants. They are the ones who typically approach us with new challenges from potential client companies or even their own project ideas to be developed with our skills.” - A11 133 “Companies require assistance not only in identifying internal opportunities but also in establishing connections with the scientific community to explore opportunities that they would not have found otherwise.” - A16 According to some interviewees, this proactivity of innovation consultants is merely a natural response to the market since companies are too busy with day-to-day activities and lack the time for strategic and innovation tasks. Innovation consultants offer these companies the opportunity to select pre-screened and substantiated innovation opportunities. "Companies often lack the time and resources to focus on strategy and innovation. Consultants bring culture and legitimacy to break the status quo as many organizations have their staff dedicated to such activities. External consultants have the time and outsider perspective to follow trends and deal with everything, including project due diligence. Additionally, they have more training, and they offer these skills for sale.” - A15 This value proposition of presenting innovation and technology transfer opportunities in the form of investment project opportunities, along with identifying potential financial incentives and estimated prospects of financial return, is highly attractive in keeping companies generating innovation. “Even companies with in-house R&D seek the services of consultants. However, they primarily look for specialized support in strategic planning, developing business opportunities, finding financing, and generating innovation. I also believe that companies will increasingly demand for TT support. This is why private consultants have been creating new offers and differentiating themselves through their proactivity and agility in the market.” - A16 5.3.2 Responsibility Closely related to the value proposition of proactivity is the responsibility and accountability with which consultants perform their tasks and represent the interests of their clients. Private consultants offer an intermediary role that is especially geared towards providing support and accountability for soft innovation activities (Pinto, 2018) to manage the execution of TT projects. This role aims to free their 134 clients and other stakeholders from tasks that are less related to the concrete objectives of R&D and TT activities, also known as hard innovation tasks (Pinto, 2018; Silva et al., 2018)). “The value of my private partners (i.e., consultants) is that they enable me to focus on other things. It's not up to me, or anyone else in my company, to keep up with legislation or do technological surveillance. My private partners have been working with me for a few years now. When I ask for a service, I don't even question the price anymore because I know exactly what I'm going to receive and that I'm paying for quality.” - A19 “We do what clients do not want to bother with. (…) such as project financing, responsible management of internal processes, management of partner networks or the preparation of studies of strategic nature.” - A03 Private consultants cannot successfully exercise this role of responsibility and accountability if their clients and the system itself do not have confidence and trust in their work. There is a perception that the evolution of the role of private consultants has been carefully thought out to gain the confidence of NIS players. As such, a closer involvement of private consultants in innovation and TT projects is necessary, both in terms of accountability and risk sharing (A12, A17). "They offer established and trustworthy relationships in partnerships and consortiums." - A12 “They are aware of the consequences and risks of the project and are willing to take responsibility." - A17 Alignment with companies Part of the factors that have led to a higher level of trust between NIS players and private consultants stems from the perception that private consultants stand out for their focus on achieving concrete outputs in TT projects, namely the technologies or products to be placed on the market. 135 "Companies seek more commitment and focus on technology transfer projects, not just R&D services, to quickly bring the final product to the market. This is why consultants are becoming more involved." - A10 “The roles are well defined. Companies want to focus on development and outsource administrative support for other essential parts to achieve the objectives required by a financed project. Professional support from consultants is crucial for achieving good results by the end of the project." - A18 The orientation of private consultants towards results leads to a set of alignments of their internal activity with that of their clients. Several interviewees have noticed the importance that consultants place on being properly aligned with their clients' objectives (A14) and their companies' work pace (A14, A17). This is perceived as an integral part of the responsibility and accountability that consultants offer to the system. "Consultants comprehend companies' expectations for time and objectives." - A14 "They work at the same pace as companies." - A17 This alignment with the objectives and the way of working of private companies make private consultants highly regarded as ideal intermediaries to partner with. This is especially because there is a recognized gap resulting from the misalignment between the work pace of traditional intermediaries and that of companies. 5.3.3 Competence The interviewees recognized the competence of private consultants in the specialized execution of their intermediation roles. The main idea identified about competence and competencies is that different consulting organizations might offer different sets of skills, but always look to complement their clients with skills that they do not have in-house or do not have time to perform (A12, A18). Proactive positioning would not be as highly regarded by the interviewees if they did not also recognize the competence of private consultants in their specialized roles. 238 Drechsler, W., & Natter, M. (2012). Understanding a firm’s openness decisions in innovation. Journal of Business Research , 65 (3), 438–445. https://doi.org/10.1016/J.JBUSRES.2011.11.003 Drejer, I. (2004). Identifying innovation in surveys of services: a Schumpeterian perspective. Research Policy , 33 (3), 551–562. https://doi.org/10.1016/J.RESPOL.2003.07.004 Drucker, P. (1981). Why managements consultants. The Science of Evolving Management , 1–4. Duan, Y., Nie, W., & Coakes, E. (2010). Identifying key factors affecting transnational knowledge transfer. Information & Management , 47 (7–8), 356–363. https://doi.org/10.1016/J.IM.2010.08.003 Duarte, M., & Carvalho, F. (2020). The Drivers of National System of Innovation in Portugal: A Panel Data Analysis. Journal of Information Systems Engineering and Management , 5 (2). https://doi.org/10.29333/jisem/8248 Edquist, C. (1997). Systems of Innovation: Technologies, Institutions, and Organizations. Pinter Publisher Ltd . Edquist, C. (2001). Innovation Systems and Innovation Policy: the state of the art. Invited Paper, In Conference, for DRUID’s Nelson-Winter . Edquist, C. (2014). Striving Towards a Holistic Innovation Policy in European Countries - But Linearity Still Prevails! STI Policy Review , 5 (2), 1–19. Edquist, C., & Hommen, L. (2008). Globalization and innovation policy. In Small Country Innovation Systems: Globalization, Change and Policy in Asia and Europe . Edward Elgar Publishing. Edwards-Schachter, M. (2018). The nature and variety of innovation. International Journal of Innovation Studies , 2 (2), 65–79. https://doi.org/10.1016/J.IJIS.2018.08.004 Etzkowitz, henry, & Zhou, C. (2008). The Triple Helix: University–Industry–Government Innovation and Entrepreneurship . Routledge. Etzkowitz, H. (1998). The norms of entrepreneurial science: cognitive effects of the new university– industry linkages. Research Policy , 27 (8), 823–833. https://doi.org/10.1016/S00487333(98)00093-6 Etzkowitz, H. (2002). Incubation of incubators: innovation as a triple helix of university-industrygovernment networks. Science and Public Policy , 29 (2), 115–128. https://doi.org/10.3152/147154302781781056 Etzkowitz, H. (2003). Innovation in innovation: The Triple Helix of university-industry-government relations. In Social Science Information (Vol. 42, Issue 3, pp. 293–337). SAGE Publications. https://doi.org/10.1177/05390184030423002 239 Etzkowitz, H., & Leydesdorff, L. (1995). The triple helix---university-industry-government relations: a laboratory for knowledge based economic development. EASST Review , 14 , 14–19. Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: from National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy , 29 (2), 109–123. https://doi.org/10.1016/S0048-7333(99)00055-4 Eveland, J. D. (1986). Diffusion, Technology Transfer, and Implementation. Knowledge , 8 (2), 303–322. https://doi.org/10.1177/107554708600800214 Faria, P., Lima, F., & Santos, R. (2010). Cooperation in innovation activities: The importance of partners. Research Policy , 39 (8), 1082–1092. https://doi.org/10.1016/J.RESPOL.2010.05.003 Ferreira, V. A. R. (2005). Portugal e Irlanda — trajetórias dos sistemas nacionais de inovação, in Cadernos Sociedade e Trabalho. Cadernos Sociedade e Trabalho, Competitividade, Inovação e Emprego , 183–218. Fidalgo, L. (2003). (Re)Construir a Maternidade Numa Perspectiva Discursiva . nstituto Piaget. Freeman, C. (1987). Technology gaps, international trade and long waves. Technology, Policy, and Economic Performance , 91–117. Freeman, C. (1988). Japan: A New National System of Innovation? In Technical Change and Economic Theory . Freeman, C. (1995). The ‘National System of Innovation’ in historical perspective. Cambridge Journal of Economics , 19 (1), 5–24. https://doi.org/10.1093/OXFORDJOURNALS.CJE.A035309 Freeman, C. (2002). Continental, national and sub-national innovation systems—complementarity and economic growth. Research Policy , 31 (2), 191–211. https://doi.org/10.1016/S00487333(01)00136-6 Freeman, C., & Soete, Luc. (1997). The economics of industrial innovation . MIT Press. Freire, S. S. (2008). Evolução do sector de consultoria de gestão em Portugal : que futuro? Universidade de Aveiro. García-Vega, M., & Huergo, E. (2017). Trust and technology transfers. Journal of Economic Behavior & Organization , 142 , 92–104. https://doi.org/10.1016/J.JEBO.2017.07.029 Garriga, H., Von Krogh, G., & Spaeth, S. (2013). How constraints and knowledge impact open innovation. Strategic Management Journal , 34 (9), 1134–1144. https://doi.org/10.1002/SMJ.2049 Gasco-Hernandez, M., Sandoval-Almazan, R., & Gil-Garcia, J. R. (2017). Open Innovation and Co-creation in the Public Sector: Understanding the Role of Intermediaries (pp. 140–148). https://doi.org/10.1007/978-3-319-64322-9_12 240 Geels, F., & Deuten, J. J. (2006). Local and global dynamics in technological development: a sociocognitive perspective on knowledge flows and lessons from reinforced concrete. Science and Public Policy , 33 (4), 265–275. https://doi.org/10.3152/147154306781778984 Geels, F. W. (2002). Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Research Policy , 31 (8–9), 1257–1274. https://doi.org/10.1016/S0048-7333(02)00062-8 Gerli, F., Chiodo, V., & Bengo, I. (2021). Technology transfer for social entrepreneurship: Designing problem-oriented innovation ecosystems. Sustainability (Switzerland) , 13 (1). https://doi.org/10.3390/su13010020 Ghiglione, R. , & Matalon, B. (2006). O Inquérito - Teoria e Prática. In As histórias das crianças: Um estudo sobre competência e capacidade narrativa, com crianças em situação de pobreza (pp. 467– 484). Análise Psicológica. Giuri, P., Munari, F., Scandura, A., & Toschi, L. (2019). The strategic orientation of universities in knowledge transfer activities. Technological Forecasting and Social Change , 138 , 261–278. https://doi.org/10.1016/j.techfore.2018.09.030 Glass, A. J., & Saggi, K. (1999). Multinational Firms and Technology Transfer . Policy Research Working Paper , 2067 . https://papers.ssrn.com/sol3/papers.cfm?abstract_id=620494 Godinho, M. M. (2003). Inovaco e difuso da inovaco: Conceitos e perspectivas fundamentais. Para Uma Política de Inovação Em Portugal, Lisboa: Dom Quixote, 53–62. Godinho, M. M. (2013). A Inovação em Portugal . Fundação Francisco Manuel dos Santos. Godinho, M. M., & Simões, V. C. (2005). I&D, Inovação e empreendedorismo 2007-2013Relatorio final . Good, M., Knockaert, M., Soppe, B., & Wright, M. (2018). The technology transfer ecosystem in academia. An organizational design perspective. Technovation . https://doi.org/10.1016/J.TECHNOVATION.2018.06.009 Gopalakrishnan, S., & Santoro, M. D. (2004). Distinguishing between knowledge transfer and technology transfer activities: The role of key organizational factors. IEEE Transactions on Engineering Management , 51 (1), 57–69. https://doi.org/10.1109/TEM.2003.822461 Gråsjö, U., Karlsson, C., Bernhard, I., Russo, M., Caloffi, A., Rossi, F., & Righi, R. (2018). Innovation intermediaries as a response to system failures: creating the right incentives. In Geography, Open Innovation and Entrepreneurship (pp. 19–42). Edward Elgar Publishing. https://doi.org/10.4337/9781786439901.00006 241 Greiner, L. E., & Metzger, R. O. (1983). Consulting to management. In Published in 1983 in Englewood Cliffs (N.J.) by Prentice-Hall . Prentice-Hall. Gretchenko, A. (2008). Methodology and mechanisms of formation of the national inno-vation system: international experience and Russian prospects. Conference Paper . Gulbrandsen, K. E. (2009). Bridging the valley of death: The rhetoric of technology transfer . Iowa State University. Hägerstrand, T. (1952). The Propagation of Innovation Waves. Royal University of Lund . Hakkarainen, L., & Hyysalo, S. (2016). The Evolution of Intermediary Activities: Broadening the Concept of Facilitation in Living Labs. Technology Innovation Management Review . https://doi.org/10.22215/timreview/960 Hall, B., Helmers, C., Rogers, M., & Sena, V. (2014). The Choice between Formal and Informal Intellectual Property: A Review. Journal of Economic Literature , 52 (2), 375–423. https://doi.org/10.1257/JEL.52.2.375 Hanson, D., & Grimmer, M. (2007). The mix of qualitative and quantitative research in major marketing journals, 1993-2002. European Journal of Marketing , 41 (1–2). https://doi.org/10.1108/03090560710718111 Hargadon, A. B. (1998). Firms as knowledge brokers: Lessons in pursuing continuous innovation. California Management Review , 3 , 209–227. https://doi.org/10.2307/41165951 Hargadon, A., & Sutton, R. I. (1997). Technology brokering and innovation in a product development firm. Administrative Science Quarterly , 42 (4), 716–749. https://doi.org/10.2307/2393655 Henriques, L., & Larédo, P. (2013). Policy-making in science policy: The ‘OECD model’ unveiled. Research Policy , 42 (3), 801–816. https://doi.org/10.1016/j.respol.2012.09.004 Herciu, M. (2017). Drivers of Firm Performance: Exploring Quantitative and Qualitative Approaches. Studies in Business and Economics , 12 (1). https://doi.org/10.1515/sbe-2017-0006 Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2016). Options for Formulating a Digital Transformation Strategy. In MIS Quarterly Executive (Vol. 15). Hidalgo, A., & Albors, J. (2008). Innovation management techniques and tools: A review from theory and practice. R and D Management , 38 (2), 113–127. https://doi.org/10.1111/j.14679310.2008.00503.x Hossain, M. (2012). Performance and Potential of Open Innovation Intermediaries. Procedia - Social and Behavioral Sciences , 58 , 754–764. https://doi.org/10.1016/j.sbspro.2012.09.1053 242 Howells, J. (1999). Research and Technology Outsourcing. Technology Analysis and Strategic Management , 11 (1), 17–29. https://doi.org/10.1080/095373299107555 Howells, J. (2006). Intermediation and the role of intermediaries in innovation. Research Policy , 35 (5), 715–728. https://doi.org/10.1016/j.respol.2006.03.005 Hsu, D. H., & Ziedonis, R. H. (2013). Resources as dual sources of advantage: Implications for valuing entrepreneurial-firm patents. Strategic Management Journal , 34 (7), 761–781. https://doi.org/10.1002/SMJ.2037 Hudson, J., & Khazragui, H. F. (2013). Into the valley of death: research to innovation. Drug Discovery Today , 18 (13–14), 610–613. https://doi.org/10.1016/J.DRUDIS.2013.01.012 Inkinen, T., & Suorsa, K. (2010). Intermediaries in Regional Innovation Systems: High-Technology Enterprise Survey from Northern Finland. European Planning Studies , 18 (2), 169–187. https://doi.org/10.1080/09654310903491556 Instituto Nacional de Estatística. (2022). Empresas em Portugal : 2020 . Intarakumnerd, P., & Chaoroenporn, P. (2013). The roles of intermediaries in sectoral innovation system in developing countries: public organizations versus private organizations. Asian Journal of Technology Innovation , 21 (1), 108–119. https://doi.org/10.1080/19761597.2013.810949 Jacobson, N., Butterill, D., & Goering, P. (2005). Consulting as a Strategy for Knowledge Transfer. The Milbank Quarterly , 83 (2), 299–321. https://doi.org/10.1111/j.1468-0009.2005.00348.x Janssen, M. J., Bogers, M., & Wanzenböck, I. (2019). Do systemic innovation intermediaries broaden horizons? A proximity perspective on R&D partnership formation. Industry and Innovation , 1–25. https://doi.org/10.1080/13662716.2019.1618701 Janssen, W., Bouwman, H., van Buuren, R., & Haaker, T. (2014). An organizational competence model for innovation intermediaries. European Journal of Innovation Management , 17 (1), 2–24. https://doi.org/10.1108/EJIM-09-2012-0087 Jenson, I., Doyle, R., & Miles, M. P. (2020). An entrepreneurial marketing process perspective of the role of intermediaries in producing innovation outcomes. Journal of Business Research , 112 , 291–299. https://doi.org/10.1016/j.jbusres.2019.09.054 Johanna, K., & G, H. E. (2012). Intermediaries driving eco‐innovation in SMEs: a qualitative investigation. European Journal of Innovation Management , 15 (4), 442–467. https://doi.org/10.1108/14601061211272376 Johnson, B. H. (1992). National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning. Pinter Publisher Ltd . 243 Johnson, D. K. N., & Lybecker, K. M. (2009). Challenges to Technology Transfer: A Literature Review of the Constraints on Environmental Technology Dissemination. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.1456222 Johnson, D. R., Scheitle, C. P., & Ecklund, E. H. (2021). Beyond the In-Person Interview? How Interview Quality Varies Across In-person, Telephone, and Skype Interviews. Social Science Computer Review , 39 (6). https://doi.org/10.1177/0894439319893612 Jonhn D.Wolpert. (2002). Breaking Out of the Innovation Box . Harvard Business Review. Jun, Y., & Ji, I. (2016). Demand-pull Technology Transfer and Needs-articulation of Users: A Preliminary Study. Procedia Computer Science , 91 , 287–295. https://doi.org/10.1016/j.procs.2016.07.079 Kanda, W., Hjelm, O., Clausen, J., & Bienkowska, D. (2018). Roles of intermediaries in supporting ecoinnovation. Journal of Cleaner Production , 205 , 1006–1016. Kani, M., & Motohashi, K. (2012). Understanding the technology market for patents: New insights from a licensing survey of Japanese firms. Research Policy , 41 (1), 226–235. https://doi.org/10.1016/J.RESPOL.2011.08.002 Karlan, D., McConnell, M., Mullainathan, S., & Zinman, J. (2016). Getting to the top of mind: How reminders increase saving. Management Science , 62 (12). https://doi.org/10.1287/mnsc.2015.2296 Katzy, B., Turgut, E., Holzmann, T., & Sailer, K. (2013). Innovation intermediaries: a process view on open innovation coordination. Technology Analysis & Strategic Management , 25 (3), 295–309. https://doi.org/10.1080/09537325.2013.764982 Keller, W. (2001). International Technology Diffusion. National Bureau of Economic Research . https://doi.org/10.3386/W8573 Kidwell, D. K. (2013). Principal investigators as knowledge brokers: A multiple case study of the creative actions of PIs in entrepreneurial science. Technological Forecasting and Social Change , 80 (2), 212– 220. https://doi.org/10.1016/J.TECHFORE.2012.05.004 Kirkels, Y., & Duysters, G. (2010). Brokerage in SME networks. Research Policy , 39 (3), 375–385. https://doi.org/10.1016/J.RESPOL.2010.01.005 Kivimaa, P. (2014). Government-affiliated intermediary organisations as actors in system-level transitions. Research Policy , 43 (8), 1370–1380. https://doi.org/10.1016/j.respol.2014.02.007 Kivimaa, P., Boon, W., Hyysalo, S., & Klerkx, L. (2019). Towards a typology of intermediaries in sustainability transitions: A systematic review and a research agenda. Research Policy , 48 (4). https://doi.org/10.1016/j.respol.2018.10.006 244 Klein Woolthuis, R., Lankhuizen, M., & Gilsing, V. (2005). A system failure framework for innovation policy design. Technovation , 25 (6), 609–619. https://doi.org/10.1016/j.technovation.2003.11.002 Klerkx, L., Álvarez, R., & Campusano, R. (2015). The emergence and functioning of innovation intermediaries in maturing innovation systems: The case of Chile. Innovation and Development , 5 (1), 73–91. https://doi.org/10.1080/2157930X.2014.921268 Klerkx, L., & Leeuwis, C. (2008). Matching demand and supply in the agricultural knowledge infrastructure: Experiences with innovation intermediaries. Food Policy , 33 (3), 260–276. https://doi.org/10.1016/j.foodpol.2007.10.001 Klerkx, L., & Leeuwis, C. (2009). Establishment and embedding of innovation brokers at different innovation system levels: Insights from the Dutch agricultural sector. Technological Forecasting and Social Change , 76 (6), 849–860. Kline, S. J., & Rosenberg, N. (1986). An overview of innovation. In The Positive Sum Strategy: Harnessing Technology for Economic Growth (pp. 275–306). National Academy of Sciences. https://doi.org/10.1142/9789814273596_0009 Knockaert, M., & Spithoven, A. (2014). Under Which Conditions Do Technology Intermediaries Enhance Firms’ Innovation Speed? The Case of Belgium’s Collective Research Centres. Regional Studies , 48 (8), 1391–1403. https://doi.org/10.1080/00343404.2012.708405 Kumar, J., & Ganesh, L. S. (2009). Research on knowledge transfer in organizations: a morphology. Journal of Knowledge Management , 13 (4), 161–174. https://doi.org/10.1108/13673270910971905 Lai, W. H. (2011). Willingness-to-engage in technology transfer in industry–university collaborations. Journal of Business Research , 64 (11), 1218–1223. https://doi.org/10.1016/J.JBUSRES.2011.06.026 Lane, J. P. (1999). Understanding Technology Transfer. Assistive Technology , 11 (1), 5–19. https://doi.org/10.1080/10400435.1999.10131981 Langrish, J., Gibbons, M., Evans, W. G., & Jevons, F. R. (1972). Wealth from Knowledge. Wealth from Knowledge . https://doi.org/10.1007/978-1-349-01054-7 Lapiedra, R., Alegre, J., & Chiva, R. (2011). The importance of management innovation and consultant services on ERP implementation success. The Service Industries Journal , 31 (12), 1907–1919. https://doi.org/10.1080/02642069.2011.556189 245 Laranja, M. (2009). The development of technology infrastructure in Portugal and the need to pull innovation using proactive intermediation policies. Technovation , 29 (1), 23–34. https://doi.org/10.1016/J.TECHNOVATION.2008.07.010 Laranja, Manuel. (2007). Uma nova poltica de inovac  o em Portugal : a justificac  o, o modelo e os instrumentos . 304. Laroche, E., & Amara, N. (2011). Transfer activities among Canadian researchers: Evidence in occupational safety and health. Safety Science , 49 (3), 406–415. https://doi.org/10.1016/J.SSCI.2010.10.005 Laurent, G., Kapferer, J. N., & Roussel, F. (1995). brand awareness definition.pdf. In Marketing Science (Vol. 14, Issue 3). Laursen, K., & Salter, A. (2006). Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms. Strategic Management Journal , 27 (2), 131–150. https://doi.org/10.1002/SMJ.507 Lee, H., & Miozzo, M. (2019). Which types of knowledge-intensive business services firms collaborate with universities for innovation? Research Policy , 48 (7), 1633–1646. https://doi.org/10.1016/J.RESPOL.2019.03.014 Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002). AIMQ: a methodology for information quality assessment. Information & Management , 40 (2), 133–146. https://doi.org/10.1016/S03787206(02)00043-5 Lessard-Hébert, M., Goyette, G., & Boutin, G. (1990). Investigação Qualitativa Fundamentos e práticas . Instituto Piaget. Leydesdorff, L. (2012). The Triple Helix, Quadruple Helix, ..., and an N-Tuple of Helices: Explanatory Models for Analyzing the Knowledge-Based Economy? Journal of the Knowledge Economy , 3 (1), 25–35. https://doi.org/10.1007/S13132-011-0049-4/FIGURES/2 Leydesdorff, L., & Etzkowitz, H. (2001). The Transformation Of Universityindustry government Relations. Electronic Journal of Sociology , 5 (4). Leydesdorff, L., & Meyer, M. (2006). Triple Helix indicators of knowledge-based innovation systems: Introduction to the special issue. Research Policy , 35 (10), 1441–1449. https://doi.org/10.1016/J.RESPOL.2006.09.016 Li, C., Lan, T., & Liu, S.-J. (2015). Patent attorney as technology intermediary: A patent attorney-facilitated model of technology transfer in developing countries. World Patent Information , 43 , 62–73. https://doi.org/10.1016/j.wpi.2015.08.003 246 Liang, X., Xing, Z., & Shi, Y. (2015). Origins and Trends of the Multinational’s R&D Internationalisation Researcha Systematic Literature Review (pp. 597–606). https://doi.org/10.2991/978-94-6239-102-4_122 Lichtenthaler, U. (2013). The collaboration of innovation intermediaries and manufacturing firms in the markets for technology. Journal of Product Innovation Management , 30 (SUPPL 1), 142–158. https://doi.org/10.1111/jpim.12068 Lichtenthaler, U., & Ernst, H. (2007). External technology commercialization in large firms: Results of a quantitative benchmarking study. R and D Management , 37 (5), 383–397. https://doi.org/10.1111/j.1467-9310.2007.00487.x Lichtenthaler, U., & Ernst, H. (2008). Intermediary services in the markets for technology: Organizational antecedents and performance consequences. Organization Studies , 29 (7), 1003–1035. https://doi.org/10.1177/0170840608090531 Lin, M., & Wei, J. (2018). The impact of innovation intermediary on knowledge transfer. Physica A: Statistical Mechanics and Its Applications , 502 , 21–28. https://doi.org/10.1016/j.physa.2018.02.207 Lindström, T., & Silver, S. (2017). Avoiding the innovation valley of death Private sector commercialization of publicly funded research . KTH Industrial Engineering and Management. Liyanage, C., Elhag, T., Ballal, T., & Li, Q. (2009). Knowledge communication and translation – a knowledge transfer model. Journal of Knowledge Management , 13 (3), 118–131. https://doi.org/10.1108/13673270910962914 Li-Ying, J. (2012). What do we need from intermediaries for technology transfer to China? A European firm perspective. Prometheus , 30 (2), 199–209. https://doi.org/10.1080/08109028.2012.661553 Lizt, F. (1841). The National System of Political Economy. In Longmans, Green, and Company . Lundvall, B. Å. (2007). National Innovation Systems—Analytical Concept and Development Tool. Industry and Innovation , 14 (1), 95–119. https://doi.org/10.1080/13662710601130863 Lundvall, B. Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy , 31 (2), 213–231. https://doi.org/10.1016/S0048-7333(01)00137-8 Lundvall, B.-Å. (1985). Product Innovation and User-Producer Interaction. Aalborg University Press , Series No (Industrial Development Research). 247 Lundvall, B.-Å. (1992). National systems of innovation: Towards a Theory of Innovation and Interactive Learning. Pinter Publisher Ltd . Lundvall, B.-Å. (2013). Innovation Studies: A Personal Interpretation of ‘The State of the Art.’ In Innovation Studies (pp. 20–70). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199686346.003.0002 Lundvall, B.-Å., & Borrás, S. (2006). Science, Technology, and Innovation Policy . Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199286805.003.0022 Lundvall, B.-Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy , 31 (2), 213–231. https://doi.org/10.1016/S0048-7333(01)00137-8 Lynn, L. H., Reddy, N. M., & Aram, J. D. (1996). Linking technology and institutions: The innovation community framework. Research Policy , 25 (1), 91–106. https://doi.org/10.1016/00487333(94)00817-5 Macchi, M., Rizzo, U., & Ramaciotti, L. (2014). From services dealers to innovation brokers. Journal of Intellectual Capital , 15 (4), 554–575. https://doi.org/10.1108/JIC-06-2014-0078 Madeuf, B. (1984). International technology transfers and international technology payments: Definitions, measurement and firms’ behaviour. Research Policy , 13 (3), 125–140. https://doi.org/10.1016/0048-7333(84)90021-0 Malik, K. (2002). Aiding the technology manager: a conceptual model for intra-firm technology transfer. Technovation , 22 (7), 427–436. https://doi.org/10.1016/S0166-4972(01)00030-X Mamede, R. P., Simões, V. C., & Godinho, M. M. (2014). Assessment and challenges of industrial policies in Portugal: Is there a way out of the ‘stuck in the middle’ trap? In Structural Change, Competitiveness and Industrial Policy: Painful Lessons from the European Periphery . Routledge Advances in Regional Economics, Science and Policy,. Mantel S, & Rosegge G. (1987). The role of third-parties in the diffusion of innovations: a survey. In Innovation: Adaptation and Growth (pp. 123–124). Elsevier. Martin, S., & Scott, J. T. (2000). The nature of innovation market failure and the design of public support for private innovation. In Research Policy (Vol. 29). Martinez, L. F., Ferreira, A. I., & Can, A. B. (2016). Consultant–Client Relationship and Knowledge Transfer in Smalland Medium-Sized Enterprises Change Processes. Psychological Reports , 118 (2), 608–625. https://doi.org/10.1177/0033294116639429 254 Sieg, J. H., Wallin, M. W., & Von Krogh, G. (2010). Managerial challenges in open innovation: a study of innovation intermediation in the chemical industry. R&D Management, Blackwell Publishing Ltd , 40 (3). http://doi.wiley.com/10.1111/j.1467-9310.2010.00596.x Silva, M., Howells, J., & Meyer, M. (2018). Innovation intermediaries and collaboration: Knowledge–based practices and internal value creation. Research Policy , 47 (1), 70–87. https://doi.org/10.1016/J.RESPOL.2017.09.011 Silva, M. J. (2003). Capacidade Inovadora Empresarial - Estudo dos Fatores impulsionadores e limitadores nas empresas Portuguesas . Tese de Doutoramento, Universidade Da Beira Interior, Covilh. Simões, V. C. (2003). O sistema nacional de inovação em Portugal: Diagnostico e prioridades. Para Uma Política de Inovação Em Portugal, Lisboa: Dom Quixote , 53–62. Sismet. (1993). O Sector da Consultoria e Projectos em Portugal, Associação Portuguesa de Projetistas e Consultores. Lisboa . Slaughter, S., & Leslie, L. (1997). Academic Capitalism: Politics, Policies, and the Entrepreneurial University. In MD: Johns Hopkins University Press . The Johns Hopkins University Press. https://books.google.com/books/about/Academic_Capitalism.html?hl=pt-BR&id=A-7bFoyY8wcC Smith, K. (2001). Comparing economic performance in the presence of diversity. Science and Public Policy , 28 (4), 267–276. https://doi.org/10.3152/147154301781781417 Smits, R., & Kuhlmann, S. (2004). The rise of systemic instruments in innovation policy. In Int. J. Foresight and Innovation Policy (Vol. 1, Issue 2). Soares, J., Romero, F., & Lopes Nunes, M. (2020). Innovation and technology transfer: a framework for clustering intermediation roles. 2020 IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD) , 1–6. https://doi.org/10.1109/ICTMOD49425.2020.9380600 Soete, L. (2007). From industrial to innovation policy. Journal of Industry, Competition and Trade , 7 (3– 4), 273–284. https://doi.org/10.1007/S10842-007-0019-5/FIGURES/1 Soete, L., & ter Weel, B. (1999). Schumpeter and the Knowledge-Based Economy: On Technology and Competition Policy . Soete, L., Verspagen, B., & ter Weel, B. (2009). Systems of Innovation. MERIT Working Papers , 2009– 062 . Soete, L., & Weel, B. ter. (1999). Schumpeter and the Knowledge-Based Economy: On Technology and Competition Policy. Research Memorandum . 255 Soni, P., & Singh Yadav, N. (2015). Quantitative Analysis of Document Stored Databases. International Journal of Computer Applications , 118 (20). https://doi.org/10.5120/20865-3587 Sousa, H. M. (2018). Management consulting business models: operations through and for digital transformation . Instituto Universitário de Lisboa. Spithovenm, A., & Knockaert, M. (2012). Technology intermediaries in low tech sectors: The case of collective research centres in belgium. Innovation: Management, Policy and Practice . https://doi.org/10.5172/impp.2012.14.3.375 Steinmueller, W. E. (2009). Technology policy: The roles of industrial analysis and innovation studies. In The New Economics of Technology Policy . Edward Elgar Publishing. Stewart, J., & Hyysalo, S. (2008). Intermediaries, users and social learning in technological innovation. International Journal of Innovation Management , 12 (3), 295–325. https://doi.org/10.1142/S1363919608002035 Stezano, F. (2018). The Role of Technology Centers as Intermediary Organizations Facilitating Links for Innovation: Four Cases of Federal Technology Centers in Mexico. Review of Policy Research , 35 (4), 642–666. https://doi.org/10.1111/ropr.12293 Stoian, M.-C., Rialp, J., & Dimitratos, P. (2017). SME Networks and International Performance: Unveiling the Significance of Foreign Market Entry Mode. Journal of Small Business Management , 55 (1), 128–148. https://doi.org/10.1111/jsbm.12241 Sung, T. K., Gibson, D. V., & Kang, B. S. (2003). Characteristics of technology transfer in business ventures: the case of Daejeon, Korea. Technological Forecasting and Social Change , 70 (5), 449– 466. https://doi.org/10.1016/S0040-1625(02)00360-8 Suvinen, N., Konttinen, J., & Nieminen, M. (2010). How necessary are intermediary organizations in the commercialization of research? European Planning Studies . https://doi.org/10.1080/09654313.2010.492584 Swann, G. M. P. (2009). The economics of innovation : an introduction . Edward Elgar. Tamtik, M. (2018). “Innovation policy is a team sport” - insights from non-governmental intermediaries in Canadian innovation ecosystem. Triple Helix , 5 (1), 8. https://doi.org/10.1186/s40604-0180062-8 Tatikonda, M. V, & Rosenthal, S. R. (2000). Successful execution of product development projects: Balancing firmness and flexibility in the innovation process. Journal of Operations Management , 18 (4), 401–425. https://doi.org/10.1016/S0272-6963(00)00028-0 256 Teixeira, A. A. C., & Mota, L. (2012). A bibliometric portrait of the evolution, scientific roots and influence of the literature on university-industry links. Scientometrics , 93 (3), 719–743. https://doi.org/10.1007/S11192-012-0823-5/FIGURES/9 Teixeira, A., & Lopes, M. (2012). Open innovation in Portugal. Acta Oeconomica , 62 (4). https://doi.org/10.1556/AOecon.62.2012.4.2 Tether, B. S. (2005). Do Services Innovate (Differently)? Insights from the European Innobarometer Survey. Industry & Innovation , 12 (2), 153–184. https://doi.org/10.1080/13662710500087891 Tether, B. S., & Tajar, A. (2008). Beyond industry–university links: Sourcing knowledge for innovation from consultants, private research organisations and the public science-base. Research Policy , 37 (6–7), 1079–1095. https://doi.org/10.1016/J.RESPOL.2008.04.003 Thursby, J. G., Jensen, R., & Thursby, M. C. (2001). Objectives, characteristics and outcomes of University Licensing: A survey of major U.S. Universities. Journal of Technology Transfer , 26 (1–2), 59–72. https://doi.org/10.1023/a:1007884111883 Tidd, J., Tidd, J., & Tavares, trad. C. (2003). Gestão da inovação: integração das mudanças tecnológica, de mercado e organizacionais . Monitor. Todeva, E. (2013). Governance of Innovation and Intermediation in Triple Helix Interactions. Industry and Higher Education , 27 (4), 263–278. https://doi.org/10.5367/ihe.2013.0161 Tran, Y., Hsuan, J., & Mahnke, V. (2011). How do innovation intermediaries add value? Insight from new product development in fashion markets. R&D Management , 41 (1), 80–91. https://doi.org/10.1111/j.1467-9310.2010.00628.x Tuckman, B. W. (2000). Manual de Investigação em Educação . Fundação Calouste Gulbenkian. Turner, D. W. (2010). Qualitative interview design: A practical guide for novice investigators. Qualitative Report , 15 (3). https://doi.org/10.46743/2160-3715/2010.1178 Vahlne, J.-E., & Johanson, J. (2017). From internationalization to evolution: The Uppsala model at 40 years. Journal of International Business Studies , 48 (9), 1087–1102. https://doi.org/10.1057/s41267-017-0107-7 Valdez-Juárez, L. E., & Castillo-Vergara, M. (2021). Technological capabilities, open innovation, and ecoinnovation: Dynamic capabilities to increase corporate performance of smes. Journal of Open Innovation: Technology, Market, and Complexity , 7 (1). https://doi.org/10.3390/joitmc7010008 van de Vrande, V., de Jong, J. P. J., Vanhaverbeke, W., & de Rochemont, M. (2009). Open innovation in SMEs: Trends, motives and management challenges. Technovation , 29 (6–7), 423–437. https://doi.org/10.1016/J.TECHNOVATION.2008.10.001 257 van der Jagt, A. P. N., Raven, R., Dorst, H., & Runhaar, H. (2020). Nature-based innovation systems. Environmental Innovation and Societal Transitions , 35 . https://doi.org/10.1016/j.eist.2019.09.005 Van Der Meer, H. (2007). Open innovation - the Dutch treat: Challenges in thinking in business models. Creativity and Innovation Management , 16 (2), 192–202. https://doi.org/10.1111/J.14678691.2007.00433.X Van Der Meulen, B., & Rip, A. (1998). Mediation in the Dutch science system. Research Policy , 27 (8), 757–769. https://doi.org/10.1016/S0048-7333(98)00088-2 van Lente, H., Hekkert, M., Smits, R., & van Waveren, B. (2003). Roles of Systemic Intermediaries in Transition Processes. International Journal of Innovation Management , 07 (03), 247–279. https://doi.org/10.1142/S1363919603000817 Vidmar, M. (2021). Enablers, Equippers, Shapers and Movers: A typology of innovation intermediaries’ interventions and the development of an emergent innovation system. Acta Astronautica , 179 , 280– 289. https://doi.org/10.1016/j.actaastro.2020.10.011 Villani, E., Rasmussen, E., & Grimaldi, R. (2017). How intermediary organizations facilitate university– industry technology transfer: A proximity approach. Technological Forecasting and Social Change . https://doi.org/10.1016/j.techfore.2016.06.004 von Hippel, E. (2005). Democratizing Innovation. MIT Press . von Hippel, E. (2009). Adapting policy to user-centered innovation. In The New Economics of Technology Policy (pp. 327–336). Edward Elgar Publishing. Watkins, A., Papaioannou, T., Mugwagwa, J., & Kale, D. (2015). National innovation systems and the intermediary role of industry associations in building institutional capacities for innovation in developing countries: A critical review of the literature. Research Policy , 44 (8), 1407–1418. https://doi.org/10.1016/j.respol.2015.05.004 Watkins, D., & Horley, G. (1986). Transfering technology from large to small firms: the role of intermediaries. In Small Business Research (pp. 215–251). Aldershot. Winch, G. M., & Courtney, R. (2007). The Organization of Innovation Brokers: An International Review. Technology Analysis & Strategic Management , 19 (6), 747–763. https://doi.org/10.1080/09537320701711223 Wood, E. M. (2002). The Origin of Capitalism: A Longer View. In Contemporary Sociology (Vol. 29, Issue 4). 258 Wright, C., Sturdy, A., & Wylie, N. (2012). Management innovation through standardization: Consultants as standardizers of organizational practice. Research Policy , 41 (3). https://doi.org/10.1016/j.respol.2011.12.004 Youn, Y.-H., Na, D.-S., & Jung, J.-T. (2015). An empirical study for the relations between consultant’s expertise and consulting knowledge transfer : Focused on FTA consulting. Journal of Digital Convergence , 13 (11), 119–132. https://doi.org/10.14400/JDC.2015.13.11.119 Yu, X., & Yan, J. (2018). Research on University Knowledge Transfer Strategy Selection based on Innovation Intermediary. Educational Sciences: Theory & Practice , 18 (5). https://doi.org/10.12738/estp.2018.5.097 Yun, J. H. J., Zhao, X., Jung, K. H., & Yigitcanlar, T. (2020). The culture for open innovation dynamics. In Sustainability (Switzerland) (Vol. 12, Issue 12). https://doi.org/10.3390/su12125076 Yusuf, S. (2008). Intermediating knowledge exchange between universities and businesses. Research Policy , 37 (8), 1167–1174. https://doi.org/10.1016/j.respol.2008.04.011 Zahra, S. A., & Nielsen, A. P. (2002). Sources of capabilities, integration and technology commercialization. Strategic Management Journal , 23 (5), 377–398. https://doi.org/10.1002/SMJ.229 Zajko, M. (2017). Open Innovation Intermediaries – Current Roles and Benefits. In Innovation Management, Entrepreneurship and Sustainability (pp. 1129–1143). Vysoká škola ekonomická v Praze. Zartha Sossa, J. W., López Montoya, O. H., & Acosta Prado, J. C. (2021). Determinants of a sustainable innovation system. Business Strategy and the Environment , 30 (2). https://doi.org/10.1002/bse.2689 Zhao, L., & Reisman, A. (1992). Toward meta research on technology transfer. IEEE Transactions on Engineering Management , 39 (1), 13–21. https://doi.org/10.1109/17.119659 Zobel, A. K., Lokshin, B., & Hagedoorn, J. (2017). Formal and informal appropriation mechanisms: The role of openness and innovativeness. Technovation , 59 , 44–54. https://doi.org/10.1016/J.TECHNOVATION.2016.10.001 Zylberberg, E. (2017). Beyond RTO Benchmarking: Towards a Typology of Innovation Intermediaries . 259 ANNEX I – INTERVIEW GUIDE As in the previous contact, my name is João Soares, I am a PhD student at University of Minho (ID8079) researching the role of private consultants acting as innovation intermediaries in TT projects. The purpose this interview is to collect and analyse the perceptions and experiences regarding the use of private consultants as innovation intermediaries in TT projects, as well as other more traditional (public) intermediaries (such as TTO and TIC). Several professionals, such as yourself, representing key player's institutions within the Portuguese NIS will be interviewed. Institutions such as research centres, universities, companies, public and governmental institutions, industrial associations, and private consultants. Due to the ongoing COVID-19 pandemic restrictions, the interviews can be conducted in person or through alternative communication channels such as video conference or by phone call. Interview’s raw data such as recordings and transcriptions are to be used by the researcher for research purposes only. Key excerpts and notes can be used and made public only with express authorization/validation from the interviewee. All data will be used under total anonymity for both the interviewees and the organisations they represent/are part of. The interview is composed of 5 open-ended questions to allow for a more detailed and in-depth exploration of the topic and 1 assisted survey part in the end. The interview has an estimated duration of 45 minutes and will be conducted by the researcher himself in a semi-structured way. Research context: TT is a critical aspect of innovation, and intermediaries play a crucial role in facilitating the transfer of technology between academia and industry. Private consultants have emerged as important intermediaries in recent years, and their role has been increasingly recognized as crucial to the success of TT projects. However, little research has been conducted on the role of these players as innovation intermediaries in the TT processes within NIS. NOTE: Please keep in mind that all the questions in this interview focus on the TT process within the Portuguese NIS and the role intermediary organisations play with key TT stakeholders such as academic/research institutions and companies. 260 1. What is your perception and experience with public/traditional intermediaries? Support guides: How familiar you are? Do you have previous experiences (good or bad)? Elaborate on your perceptions/opinion about this kind of intermediaries (e.g., TTO, TIC). 2. What is your perception and experience with private intermediaries, namely private consultants? Support guides: How familiar you are? Do you have previous experiences (good or bad)? Elaborate on your perceptions/opinions about this kind of intermediaries. 3. What are the key characteristics, skills and roles sought in consultants (value proposition)? Support guides: Many consultants are being involved in TT projects? What do they have to offer? (roles, specializations, resources, other?). 4. Are private consultants’ competition (threat) or complementary (opportunity) to public/traditional intermediaries? Support guides: How do they compare? How do they differ? How they relate within the NIS? 5. Are there other factors, external to the private consultants, that might be contributing for their involvement in TT projects? Support guides: Why a private consultant might be chosen instead of a traditional intermediary? Are there external factors that can favour private consultants (e.g., project phase, TRL, sector, political, etc) 6. (In your opinion,) do the NIS’s key actors recognize the private consultant’s role as an innovation intermediary? Is this recognition reflected in the innovation and TT support programs and incentives policies? Support guides: Are consultants generally seen as TT innovation intermediaries by other organisations? Do you think private consultants’ role is recognized/taken into account at policy/decision-making levels? 261 7. ASSISTED SURVEY: Which roles/specializations do you identify as being performed by private consultants as intermediaries? Support guides: I’m going to present you with a checklist of intermediary roles. I’ll be checking if you consider that a private consultant play – totally on just in part – any of these roles/specializations. Specializations Description X Policy & Strategy Support and lobby policymakers in the development and implementation of regional, sectorial, or nationalwide innovation policy strategies, providing a connection to government and public entities in matters of innovation. Mediation & Mobilization Create and coordinate networks and other strategic intermediation platforms, providing neutral grounds to foster collaboration between innovation system’s stakeholders and potentiate the mobilization of its key resources. Knowledge Diffusion & Support Act as a two-way communication channel between university and industry, providing a centralized point of contact to both knowledge diffusion and knowledge support. Funding & Finance The focus is to identify and bid to funding opportunities aligned with project needs, or in the due diligence and activities related to the strategic selection and sourcing of public or private financing schemes. Technology Scouting & Market Foresight Constantly monitor the technology state of the art evolution, scan and gather information to support innovation decisions and technology procurement. Playing as an input source of market opportunities through strategic foresight activities, such as identifying and diagnosing market trends, industry’ needs and innovation challenges. Design & Idealization Support the conceptualization and generation of new project ideas, by assisting in the idealization process contributing with knowledge and creative support. Brokering & Gatekeeping Brokering & Gatekeeping technology, R&D results, and intellectual property, arranging and negotiate technology deals between sources and interested recipients. Project Management & Assessment Assisting with the design, set-up and management of projects properly aligned with defined goals and needs, interacting regularly with key stakeholders from project administration and execution control tasks. Also, since acting as neutral third parties, intermediaries can independently assess and evaluate technology transfer projects performance and its impacts. Financial & Technical Feasibility Assisting with concept proofing, supplying qualified feasibility analysis, and testing, diagnosing, and evaluating ideas, models, and technologies’ prototypes in order to validate and evaluate its potential. Accreditation & Quality Support in accreditation and standards, providing assistance in technology regulation and arbitration due diligences and through quality processes. Intellectual Property & Rights Support R&D and technology needs through legal strategies, representing and supporting bureaucratic processes to protect and value intellectual property, rights, and other innovation assets. Implementation & Knowledge Transfer Be part of the technology transfer and implementation processes fostering the recipient absorptive capacity through knowledge transfer strategies such as the selection and training of specialised workforces. Marketing & Business Development Bridge and help to sell ready-to-market technology innovations, by assisting in key business activities like marketing research and strategy and after by assisting in the commercialization process. Also, in the case of entrepreneurial technology transfer strategies, being in the form of spin-offs and/or start-ups, it adds up the need for business development support to create, accelerate and grow the ventures. 262 ANNEX II – SPSS OUTPUTS Descriptive Analysis of the Sample Count Column N % 95,0% Lower CL for Column N % 95,0% Upper CL for Column N % Sector/Technology Domain ICT 206 94,1% 90,3% 96,6% Electronics and Computers 6 2,7% 1,2% 5,6% Mechanical 4 1,8% 0,6% 4,3% Industrial Automation/Robotics 3 1,4% 0,4% 3,6% Total 219 100,0% . . Type of Origin Entity University/TTO 4 1,8% 0,6% 4,3% Research Centre / Tech Interface 10 4,6% 2,4% 7,9% Recipient Company 128 58,4% 51,8% 64,8% Private Consultant 77 35,2% 29,1% 41,6% Total 219 100,0% . . Contact of the Source University/TTO 5 2,3% 0,9% 4,9% Research Centre / Tech Interface 6 2,7% 1,2% 5,6% Recipient Company 5 2,3% 0,9% 4,9% Private Consultant 203 92,7% 88,7% 95,6% Total 219 100,0% . . Contact of the Recipient University/TTO 0 0,0% . . Research Centre / Tech Interface 0 0,0% . . Recipient Company 128 58,4% 51,8% 64,8% Private Consultant 91 41,6% 35,2% 48,2% Total 219 100,0% . . Year of the project 2014 5 2,3% 0,9% 4,9% 2015 18 8,2% 5,1% 12,4% 2016 43 19,6% 14,8% 25,3% 2017 37 16,9% 12,4% 22,3% 2018 58 26,5% 21,0% 32,6% 2019 5 2,3% 0,9% 4,9% 2020 52 23,7% 18,5% 29,7% 2021 1 0,5% 0,0% 2,1% Total 219 100,0% . . 263 Count Column N % 95,0% Lower CL for Column N % 95,0% Upper CL for Column N % Existence of previous interaction No 20 9,1% 5,9% 13,5% Yes 199 90,9% 86,5% 94,1% Total 219 100,0% . . Looking for Financing Options No 9 4,1% 2,1% 7,4% Yes 210 95,9% 92,6% 97,9% Total 219 100,0% . . Looking for Partners/Contacts No 154 70,3% 64,0% 76,1% Yes 65 29,7% 23,9% 36,0% Total 219 100,0% . . Looking for technical know-how No 184 84,0% 78,7% 88,4% Yes 35 16,0% 11,6% 21,3% Total 219 100,0% . . Looking for Project Management Support No 143 65,3% 58,8% 71,4% Yes 76 34,7% 28,6% 41,2% Total 219 100,0% . . Came due to WoM or good References No 117 53,4% 46,8% 60,0% Yes 102 46,6% 40,0% 53,2% Total 219 100,0% . . Count Column N % 95,0% Lower CL for Column N % 95,0% Upper CL for Column N % Initial TRL 2 1 0,5% 0,0% 2,1% 3 46 21,0% 16,0% 26,8% 4 139 63,5% 56,9% 69,6% 5 22 10,0% 6,6% 14,5% 6 11 5,0% 2,7% 8,5% Participation of another Private Intermediaries No 216 98,6% 96,4% 99,6% Yes 3 1,4% 0,4% 3,6% Participation of Other traditional / Public Intermediaries No 190 86,8% 81,8% 90,8% Yes 29 13,2% 9,2% 18,2% 270 Statistical Tests – Hypothesis 1 - Type of Origin Entity * Funding & Finance (Role) Crosstab Funding & Finance Total No Yes Type of Origin Entity University/TTO Count 1 3 4 % within Type of Origin Entity 25,0% 75,0% 100,0% Research Centre / Tech Interface Count 1 9 10 % within Type of Origin Entity 10,0% 90,0% 100,0% Recipient Company Count 3 125 128 % within Type of Origin Entity 2,3% 97,7% 100,0% Private Consultant Count 2 75 77 % within Type of Origin Entity 2,6% 97,4% 100,0% Total Count 7 212 219 % within Type of Origin Entity 3,2% 96,8% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square 8,032a 3 ,045 Likelihood Ratio 3,978 3 ,264 Linear-by-Linear Association 3,076 1 ,079 N of Valid Cases 219 a. 5 cells (62,5%) have expected count less than 5. The minimum expected count is ,13. 271 Statistical Tests – Hypothesis 1 - Type of Origin Entity * Technology Scouting & Market Foresight (Role) Crosstab Technology Scouting & Market Foresight Total No Yes Type of Origin Entity University/TTO Count 1 3 4 % within Type of Origin Entity 25,0% 75,0% 100,0% Research Centre / Tech Interface Count 4 6 10 % within Type of Origin Entity 40,0% 60,0% 100,0% Recipient Company Count 60 68 128 % within Type of Origin Entity 46,9% 53,1% 100,0% Private Consultant Count 30 47 77 % within Type of Origin Entity 39,0% 61,0% 100,0% Total Count 95 124 219 % within Type of Origin Entity 43,4% 56,6% 100,0% Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square 1,845a 3 ,605 Likelihood Ratio 1,882 3 ,597 Linear-by-Linear Association ,118 1 ,731 N of Valid Cases 219 a. 3 cells (37,5%) have expected count less than 5. The minimum expected count is 1,74. 272 Statistical Tests – Hypothesis 1 - Type of Origin Entity * Design & Idealization (Role) Crosstab Design & Idealization Total No Yes Type of Origin Entity University/TTO Count 2 2 4 % within Type of Origin Entity 50,0% 50,0% 100,0% Research Centre / Tech Interface Count 6 4 10 % within Type of Origin Entity 60,0% 40,0% 100,0% Recipient Company Count 101 27 128 % within Type of Origin Entity 78,9% 21,1% 100,0% Private Consultant Count 42 35 77 % within Type of Origin Entity 54,5% 45,5% 100,0% Total Count 151 68 219 % within Type of Origin Entity 68,9% 31,1% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square 14,434a 3 ,002 Likelihood Ratio 14,339 3 ,002 Linear-by-Linear Association 4,022 1 ,045 N of Valid Cases 219 a. 3 cells (37,5%) have expected count less than 5. The minimum expected count is 1,24. 273 Statistical Tests – Hypothesis 1 - Type of Origin Entity * Brokering & Gatekeeping (Role) Crosstab Brokering & Gatekeeping Total No Yes Type of Origin Entity University/TTO Count 2 2 4 % within Type of Origin Entity 50,0% 50,0% 100,0% Research Centre / Tech Interface Count 9 1 10 % within Type of Origin Entity 90,0% 10,0% 100,0% Recipient Company Count 122 6 128 % within Type of Origin Entity 95,3% 4,7% 100,0% Private Consultant Count 71 6 77 % within Type of Origin Entity 92,2% 7,8% 100,0% Total Count 204 15 219 % within Type of Origin Entity 93,2% 6,8% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square 12,874a 3 ,005 Likelihood Ratio 6,750 3 ,080 Linear-by-Linear Association 1,657 1 ,198 N of Valid Cases 219 a. 3 cells (37,5%) have expected count less than 5. The minimum expected count is ,27. 274 Statistical Tests – Hypothesis 1 - Type of Origin Entity * Project Management & Assessment (Role) Crosstab Project Management & Assessment Total No Yes Type of Origin Entity University/TTO Count 4 0 4 % within Type of Origin Entity 100,0% 0,0% 100,0% Research Centre / Tech Interface Count 8 2 10 % within Type of Origin Entity 80,0% 20,0% 100,0% Recipient Company Count 6 122 128 % within Type of Origin Entity 4,7% 95,3% 100,0% Private Consultant Count 7 70 77 % within Type of Origin Entity 9,1% 90,9% 100,0% Total Count 25 194 219 % within Type of Origin Entity 11,4% 88,6% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square 83,697a 3 ,000 Likelihood Ratio 50,181 3 ,000 Linear-by-Linear Association 27,981 1 ,000 N of Valid Cases 219 a. 3 cells (37,5%) have expected count less than 5. The minimum expected count is ,46. 275 Statistical Tests – Hypothesis 1 - Type of Origin Entity * Financial & Technical Feasibility (Role) Crosstab Financial & Technical Feasibility Total No Yes Type of Origin Entity University/TTO Count 2 2 4 % within Type of Origin Entity 50,0% 50,0% 100,0% Research Centre / Tech Interface Count 6 4 10 % within Type of Origin Entity 60,0% 40,0% 100,0% Recipient Company Count 96 32 128 % within Type of Origin Entity 75,0% 25,0% 100,0% Private Consultant Count 73 4 77 % within Type of Origin Entity 94,8% 5,2% 100,0% Total Count 177 42 219 % within Type of Origin Entity 80,8% 19,2% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square 17,761a 3 ,000 Likelihood Ratio 19,680 3 ,000 Linear-by-Linear Association 17,293 1 ,000 N of Valid Cases 219 a. 3 cells (37,5%) have expected count less than 5. The minimum expected count is ,77. 276 Statistical Tests – Hypothesis 1 - Type of Origin Entity * Accreditation & Quality (Role) Crosstab Accreditation & Quality Total No Yes Type of Origin Entity University/TTO Count 3 1 4 % within Type of Origin Entity 75,0% 25,0% 100,0% Research Centre / Tech Interface Count 10 0 10 % within Type of Origin Entity 100,0% 0,0% 100,0% Recipient Company Count 128 0 128 % within Type of Origin Entity 100,0% 0,0% 100,0% Private Consultant Count 77 0 77 % within Type of Origin Entity 100,0% 0,0% 100,0% Total Count 218 1 219 % within Type of Origin Entity 99,5% 0,5% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square 53,997a 3 ,000 Likelihood Ratio 8,275 3 ,041 Linear-by-Linear Association 12,949 1 ,000 N of Valid Cases 219 a. 5 cells (62,5%) have expected count less than 5. The minimum expected count is ,02. 277 Statistical Tests – Hypothesis 1 - Type of Origin Entity * Intellectual Property & Rights (Role) Crosstab Intellectual Property & Rights Total No Yes Type of Origin Entity University/TTO Count 2 2 4 % within Type of Origin Entity 50,0% 50,0% 100,0% Research Centre / Tech Interface Count 10 0 10 % within Type of Origin Entity 100,0% 0,0% 100,0% Recipient Company Count 128 0 128 % within Type of Origin Entity 100,0% 0,0% 100,0% Private Consultant Count 75 2 77 % within Type of Origin Entity 97,4% 2,6% 100,0% Total Count 215 4 219 % within Type of Origin Entity 98,2% 1,8% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square 54,591a 3 ,000 Likelihood Ratio 15,853 3 ,001 Linear-by-Linear Association 6,037 1 ,014 N of Valid Cases 219 a. 5 cells (62,5%) have expected count less than 5. The minimum expected count is ,07. 278 Statistical Tests – Hypothesis 1 - Type of Origin Entity * Implementation & Knowledge Transfer Crosstab Implementation & Knowledge Transfer Total No Type of Origin Entity University/TTO Count 4 4 % within Type of Origin Entity 100,0% 100,0% Research Centre / Tech Interface Count 10 10 % within Type of Origin Entity 100,0% 100,0% Recipient Company Count 128 128 % within Type of Origin Entity 100,0% 100,0% Private Consultant Count 77 77 % within Type of Origin Entity 100,0% 100,0% Total Count 219 219 % within Type of Origin Entity 100,0% 100,0% Chi-Square Tests Value Pearson Chi-Square .a N of Valid Cases 219 a. No statistics are computed because Implementation & Knowledge Transfer is a constant. 279 Statistical Tests – Hypothesis 1 - Type of Origin Entity * Marketing & Business Development Crosstab Marketing & Business Development Total No Yes Type of Origin Entity University/TTO Count 2 2 4 % within Type of Origin Entity 50,0% 50,0% 100,0% Research Centre / Tech Interface Count 8 2 10 % within Type of Origin Entity 80,0% 20,0% 100,0% Recipient Company Count 105 23 128 % within Type of Origin Entity 82,0% 18,0% 100,0% Private Consultant Count 74 3 77 % within Type of Origin Entity 96,1% 3,9% 100,0% Total Count 189 30 219 % within Type of Origin Entity 86,3% 13,7% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square 13,027a 3 ,005 Likelihood Ratio 13,500 3 ,004 Linear-by-Linear Association 11,872 1 ,001 N of Valid Cases 219 a. 3 cells (37,5%) have expected count less than 5. The minimum expected count is ,55. 286 Statistical Tests – Hypothesis 3 – Type of Origin Entity * Looking for Project Management Support Crosstab Looking for Project Management Support Total No Yes Type of Origin Entity University/TTO Count 3 1 4 % within Type of Origin Entity 75,0% 25,0% 100,0% % within Looking for Project Management Support 3,1% 2,2% 2,8% % of Total 2,1% 0,7% 2,8% Research Centre / Tech Interface Count 6 4 10 % within Type of Origin Entity 60,0% 40,0% 100,0% % within Looking for Project Management Support 6,3% 8,7% 7,0% % of Total 4,2% 2,8% 7,0% Recipient Company Count 87 41 128 % within Type of Origin Entity 68,0% 32,0% 100,0% % within Looking for Project Management Support 90,6% 89,1% 90,1% % of Total 61,3% 28,9% 90,1% Total Count 96 46 142 % within Type of Origin Entity 67,6% 32,4% 100,0% % within Looking for Project Management Support 100,0% 100,0% 100,0% % of Total 67,6% 32,4% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square ,372a 2 ,830 Likelihood Ratio ,367 2 ,832 Linear-by-Linear Association ,005 1 ,941 N of Valid Cases 142 a. 3 cells (50,0%) have expected count less than 5. The minimum expected count is 1,30. 287 Statistical Tests – Hypothesis 3 – Type of Origin Entity * Came due to WoM or good References Crosstab Came due to WoM or good References Total No Yes Type of Origin Entity University/TTO Count 0 4 4 % within Type of Origin Entity 0,0% 100,0% 100,0% % within Came due to WoM or good References 0,0% 5,0% 2,8% % of Total 0,0% 2,8% 2,8% Research Centre / Tech Interface Count 3 7 10 % within Type of Origin Entity 30,0% 70,0% 100,0% % within Came due to WoM or good References 4,8% 8,8% 7,0% % of Total 2,1% 4,9% 7,0% Recipient Company Count 59 69 128 % within Type of Origin Entity 46,1% 53,9% 100,0% % within Came due to WoM or good References 95,2% 86,3% 90,1% % of Total 41,5% 48,6% 90,1% Total Count 62 80 142 % within Type of Origin Entity 43,7% 56,3% 100,0% % within Came due to WoM or good References 100,0% 100,0% 100,0% % of Total 43,7% 56,3% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square 4,167a 2 ,125 Likelihood Ratio 5,685 2 ,058 Linear-by-Linear Association 4,018 1 ,045 N of Valid Cases 142 a. 3 cells (50,0%) have expected count less than 5. The minimum expected count is 1,75. 288 Statistical Tests – Hypothesis 4 – Type of Origin Entity * Participation of other public/traditional intermediaries Case Processing Summary Cases Valid Missing Total N Percent N Percent N Percent Type of Origin Entity * Participation of Public Intermediary(ies) 219 100,0% 0 0,0% 219 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square 85,649a 3 ,000 Likelihood Ratio 56,836 3 ,000 Linear-by-Linear Association 21,826 1 ,000 N of Valid Cases 219 a. 3 cells (37,5%) have expected count less than 5. The minimum expected count is ,53. 289 Type of Origin Entity * Participation of Public Intermediary Crosstabulation Participation of Public Intermediary Total No Yes Type of Origin Entity University/TTO Count 0 4 4 % within Type of Origin Entity 0,0% 100,0% 100,0% % within Participation of Public Intermediary(ies) 0,0% 13,8% 1,8% % of Total 0,0% 1,8% 1,8% Research Centre / Tech Interface Count 1 9 10 % within Type of Origin Entity 10,0% 90,0% 100,0% % within Participation of Public Intermediary(ies) 0,5% 31,0% 4,6% % of Total 0,5% 4,1% 4,6% Recipient Company Count 122 6 128 % within Type of Origin Entity 95,3% 4,7% 100,0% % within Participation of Public Intermediary(ies) 64,2% 20,7% 58,4% % of Total 55,7% 2,7% 58,4% Private Consultant Count 67 10 77 % within Type of Origin Entity 87,0% 13,0% 100,0% % within Participation of Public Intermediary(ies) 35,3% 34,5% 35,2% % of Total 30,6% 4,6% 35,2% Total Count 190 29 219 % within Type of Origin Entity 86,8% 13,2% 100,0% % within Participation of Public Intermediary(ies) 100,0% 100,0% 100,0% % of Total 86,8% 13,2% 100,0% 290 Statistical Tests – Hypothesis 5 – Private Consultant (PC) * Other Traditional Intermediary (OTI) - Policy & Strategy Warnings No measures of association are computed for the crosstabulation of Policy & Strategy (PC) * Policy & Strategy (OTI). At least one variable in each 2-way table upon which measures of association are computed is a constant. Policy & Strategy (PC) * Policy & Strategy (OTI) Crosstabulation Policy & Strategy (OTI) Total No Policy & Strategy (PC) No Count 29 29 % within Policy & Strategy (PC) 100,0% 100,0% % within Policy & Strategy (OTI) 100,0% 100,0% % of Total 100,0% 100,0% Total Count 29 29 % within Policy & Strategy (PC) 100,0% 100,0% % within Policy & Strategy (OTI) 100,0% 100,0% % of Total 100,0% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square .a McNemar-Bowker Test . . .b N of Valid Cases 29 a. No statistics are computed because Policy & Strategy (PC) and Policy & Strategy (OTI) are constants. b. Computed only for a PxP table, where P must be greater than 1. 291 Statistical Tests – Hypothesis 5 – Private Consultant (PC) * Other Traditional Intermediary (OTI) - Mediation & Mobilization Mediation & Mobilization (PC) * Mediation & Mobilization (OTI) Crosstabulation Mediation & Mobilization (OTI) Total No Yes Mediation & Mobilization (PC) No Count 22 6 28 % within Mediation & Mobilization (PC) 78,6% 21,4% 100,0% % within Mediation & Mobilization (OTI) 95,7% 100,0% 96,6% % of Total 75,9% 20,7% 96,6% Yes Count 1 0 1 % within Mediation & Mobilization (PC) 100,0% 0,0% 100,0% % within Mediation & Mobilization (OTI) 4,3% 0,0% 3,4% % of Total 3,4% 0,0% 3,4% Total Count 23 6 29 % within Mediation & Mobilization (PC) 79,3% 20,7% 100,0% % within Mediation & Mobilization (OTI) 100,0% 100,0% 100,0% % of Total 79,3% 20,7% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Exact Sig. (2sided) Exact Sig. (1sided) Pearson Chi-Square ,270a 1 ,603 Continuity Correctionb ,000 1 1,000 Likelihood Ratio ,473 1 ,492 Fisher's Exact Test 1,000 ,793 Linear-by-Linear Association ,261 1 ,610 McNemar Test ,125c N of Valid Cases 29 a. 2 cells (50,0%) have expected count less than 5. The minimum expected count is ,21. b. Computed only for a 2x2 table c. Binomial distribution used. 292 Statistical Tests – Hypothesis 5 – Private Consultant (PC) * Other Traditional Intermediary (OTI) - Knowledge Diffusion & Support Warnings No measures of association are computed for the crosstabulation of Knowledge Diffusion & Support (PC) * Knowledge Diffusion & Support (OTI). At least one variable in each 2-way table upon which measures of association are computed is a constant. Knowledge Diffusion & Support (PC) * Knowledge Diffusion & Support (OTI) Crosstabulation Knowledge Diffusion & Support (OTI) Total No Yes Knowledge Diffusion & Support (PC) No Count 24 5 29 % within Knowledge Diffusion & Support (PC) 82,8% 17,2% 100,0% % within Knowledge Diffusion & Support (OTI) 100,0% 100,0% 100,0% % of Total 82,8% 17,2% 100,0% Total Count 24 5 29 % within Knowledge Diffusion & Support (PC) 82,8% 17,2% 100,0% % within Knowledge Diffusion & Support (OTI) 100,0% 100,0% 100,0% % of Total 82,8% 17,2% 100,0% Chi-Square Tests Value df Asymptotic Significance (2-sided) Pearson Chi-Square .a McNemar-Bowker Test . . .b N of Valid Cases 29 a. No statistics are computed because Knowledge Diffusion (PC) & Support (OTI) is a constant. b. Computed only for a PxP table, where P must be greater than 1. 293 Statistical Tests – Hypothesis 5 – Private Consultant (PC) * Other Traditional Intermediary (OTI) - Funding & Finance Funding & Finance (PC) * Funding & Finance (OTI) Crosstabulation Funding & Finance (OTI) Total No Yes Funding & Finance (PC) No Count 0 2 2 % within Funding & Finance (PC) 0,0% 100,0% 100,0% % within Funding & Finance (OTI) 0,0% 9,1% 6,9% % of Total 0,0% 6,9% 6,9% Yes Count 7 20 27 % within Funding & Finance (PC) 25,9% 74,1% 100,0% % within Funding & Finance (OTI) 100,0% 90,9% 93,1% % of Total 24,1% 69,0% 93,1% Total Count 7 22 29 % within Funding & Finance (PC) 24,1% 75,9% 100,0% % within Funding & Finance (OTI) 100,0% 100,0% 100,0% % of Total 24,1% 75,9% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Exact Sig. (2sided) Exact Sig. (1sided) Pearson Chi-Square ,684a 1 ,408 Continuity Correctionb ,000 1 1,000 Likelihood Ratio 1,151 1 ,283 Fisher's Exact Test 1,000 ,569 Linear-by-Linear Association ,660 1 ,417 McNemar Test ,180c N of Valid Cases 29 a. 2 cells (50,0%) have expected count less than 5. The minimum expected count is 48. b. Computed only for a 2x2 table c. Binomial distribution used. 294 Statistical Tests – Hypothesis 5 – Private Consultant (PC) * Other Traditional Intermediary (OTI) - Technology Scouting & Market Foresight Warnings No measures of association are computed for the crosstabulation of Technology Scouting (PC) & Market Foresight * Technology Scouting & Market Foresight (OTI). At least one variable in each 2-way table upon which measures of association are computed is a constant. Technology Scouting & Market Foresight * Technology Scouting & Market Foresight Crosstabulation Technology Scouting & Market Foresight (OTI) Total No Technology Scouting & Market Foresight (PC) No Count 12 12 % within Technology Scouting & Market Foresight (PC) 100,0% 100,0% % within Technology Scouting & Market Foresight (OTI) 41,4% 41,4% % of Total 41,4% 41,4% Yes Count 17 17 % within Technology Scouting & Market Foresight (PC) 100,0% 100,0% % within Technology Scouting & Market Foresight (OTI) 58,6% 58,6% % of Total 58,6% 58,6% Total Count 29 29 % within Technology Scouting & Market Foresight (PC) 100,0% 100,0% % within Technology Scouting & Market Foresight (OTI) 100,0% 100,0% % of Total 100,0% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square .a McNemar-Bowker Test . . .b N of Valid Cases 29 a. No statistics are computed because Technology Scouting & Market Foresight is a constant. b. Computed only for a PxP table, where P must be greater than 1. 295 Statistical Tests – Hypothesis 5 – Private Consultant (PC) * Other Traditional Intermediary (OTI) - Design & Idealization Design & Idealization (PC) * Design & Idealization (OTI) Crosstabulation Design & Idealization (OTI) Total No Yes Design & Idealization (PC) No Count 5 10 15 % within Design & Idealization (PC) 33,3% 66,7% 100,0% % within Design & Idealization (OTI) 31,3% 76,9% 51,7% % of Total 17,2% 34,5% 51,7% Yes Count 11 3 14 % within Design & Idealization (PC) 78,6% 21,4% 100,0% % within Design & Idealization (OTI) 68,8% 23,1% 48,3% % of Total 37,9% 10,3% 48,3% Total Count 16 13 29 % within Design & Idealization (PC) 55,2% 44,8% 100,0% % within Design & Idealization (OTI) 100,0% 100,0% 100,0% % of Total 55,2% 44,8% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Exact Sig. (2sided) Exact Sig. (1sided) Pearson Chi-Square 5,992a 1 ,014 Continuity Correctionb 4,302 1 ,038 Likelihood Ratio 6,248 1 ,012 Fisher's Exact Test ,025 ,018 Linear-by-Linear Association 5,785 1 ,016 McNemar Test 1,000c N of Valid Cases 29 a. 0 cells (,0%) have expected count less than 5. The minimum expected count is 6,28. b. Computed only for a 2x2 table c. Binomial distribution used. 302 Statistical Tests – Hypothesis 5 – Private Consultant (PC) * Other Traditional Intermediary (OTI) - Marketing & Business Development Warnings No measures of association are computed for the crosstabulation of Marketing & Business Development (PC) * Marketing & Business Development (OTI). At least one variable in each 2-way table upon which measures of association are computed is a constant. Marketing & Business Development (PC) * Marketing & Business Development (OTI) Crosstabulation Marketing & Business Development (OTI) Total No Marketing & Business Development (PC) No Count 25 25 % within Marketing & Business Development (PC) 100,0% 100,0% % within Marketing & Business Development (OTI) 86,2% 86,2% % of Total 86,2% 86,2% Yes Count 4 4 % within Marketing & Business Development (PC) 100,0% 100,0% % within Marketing & Business Development (OTI) 13,8% 13,8% % of Total 13,8% 13,8% Total Count 29 29 % within Marketing & Business Development (PC) 100,0% 100,0% % within Marketing & Business Development (OTI) 100,0% 100,0% % of Total 100,0% 100,0% Chi-Square Tests Value df Asymptotic Significance (2sided) Pearson Chi-Square .a McNemar-Bowker Test . . .b N of Valid Cases 29 a. No statistics are computed because Marketing & Business Development is a constant. b. Computed only for a PxP table, where P must be greater than 1.