Abstract
Purpose
This paper aims to explore the key anchors of the National Innovation System shaping the nature of collaboration between academic high-performance computing centres (academic HPC centres) and small- to medium-sized enterprises (SMEs) working in the automotive and electronics sectors of the Danube region. With two main research questions, it discusses the importance of knowledge transfer and technology transfer for collaboration between University and Industry (U-I collaboration) in three groups of developmentally distinct countries: competitively advanced, competitively intermediate and competitively lagging. As main anchors of the innovation system, stable legal environment, exciting innovation policies and strong R&D funding are recognised.
Design/methodology/approach
A qualitative empirical study in 14 Danube region countries included 92 focus group participants, expert representatives of academic HPC centres and SMEs. The data were audio recorded, transcribed and analysed.
Findings
The findings show the main prerequisites of the framework conditions for efficient U-I collaboration evolve through a goal-oriented National Innovation Policy and developed and functioning legal environment supporting labour market and intellectual property (IP) protection and enforcement. Additionally, skilled people are needed to be able to operate with HPC, where it seems all the countries lack such skilled workforce. In competitively lagging countries, the high levels of brain drain exhibit strong impact to U-I collaboration.
Research limitations/implications
Research into relationships between academic HPC centres and SMEs conducted was qualitative; therefore, limitations in terms of generalisation arise from it. On the other hand, the research is promising in terms of offering the guidance for policy makers who can use the findings when delivering innovation policy mix, adjusted to developmental level of own innovation ecosystem.
Originality/value
The study is among the pioneering work in U-I collaboration between academic HPC centres and SMEs from automotive and electronics industries in the Danube region. The research addresses the dynamics of collaboration and offers policy implications to strengthen the particular U-I collaboration.
研究目的
本文旨在探究國家創新系統的主要支柱; 這些支柱決定了學術性的高速網路與計算中心 (註: 此為直譯) (以下簡稱學術高網算中心) 與於多瑙河地區的汽車製造業和電子產品行業內營運的中小型企業之間的合作性質。本文透過兩條主要的研究問題、去探討知識轉移和技術轉讓對大學與產業界之間的合作的重要性而這些產業是屬於在發展階段上三個明顯不同的國家組別裏的這三個組別是 競爭先進的、競爭性中級的和競爭落後的。穩定的法律環境、令人興奮的創新政策和強大的研究與開發資金被認為是創新系統的三個主要支柱。
研究設計
研究人員在14個位於多瑙河地區的國家裏進行一個質性觀察研究研究涵蓋92個焦點小組參與者、來自學術高網算中心和中小型企業的專家代表。有關的數據被錄音繼而被轉寫下來最後被分析。
研究結果
研究結果顯示效率高的大學產業界合作的框架條件的主要先決條件是透過一個以目標為導向的國家創新政策而逐漸形成繼而發展起來; 另外所需的條件是一個支援勞工市場、保障知識產權、並執行有關的法律的正常運作的法律環境。其次若想與學術高網算中心一起工作技術人才是必須的因學術高網算中心內的所有國家似乎欠缺技術勞動力。在落後於競爭對手的國家裏高度的人才外流對大學與產業界之間的合作會產生重大的影響。
研究的局限/啟示
由於研究採用的研究方法為質性研究法故研究結果、就普遍化的歸納而言是有其局限的。唯研究結果在實務方面有其作用因政策制定者在推行與科技進步與對策有關的策略時他們可把研究結果作為指引就其自身創新生態系統的發展水準而作出適當的調整。
研究的原創性/價值
本研究探討涉及學術高網算中心與於多瑙河地區的汽車製造業和電子產品行業內營運的中小型企業之間合作的大學產業界合作就此而言可說是開創性研究之一。本研究探究有關的大學產業界合作的變革動力並為政策制定者提供啟示以能強化有關的合作。
Keywords
Citation
Besednjak Valič, T., Kolar, J., Lamut, U. and Pandiloska Jurak, A. (2023), "Key policy mechanisms supporting the University–Industry collaboration in the Danube region: case study of academic HPC centres and SMEs", European Journal of Management and Business Economics, Vol. 32 No. 5, pp. 509-524. https://doi.org/10.1108/EJMBE-09-2022-0283
Publisher
:Emerald Publishing Limited
Copyright © 2023, Tamara Besednjak Valič, Janez Kolar, Urša Lamut and Alenka Pandiloska Jurak
License
Published in European Journal of Management and Business Economics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Collaboration intrigues, especially in innovation studies, where it is well established that the collaboration and knowledge exchange are the drivers of progress. With the rise of the knowledge society, knowledge economy and the importance of the global research networks (Guimón and Paunov, 2022), the processes only sped up, demanding strategic collaborations on all levels. However, one, in particular, remains for us. It is the intrigue of numerous researchers exploring the dynamics of University–Industry collaboration (U-I collaboration). U-I collaboration (from here onwards U-I collaboration) is an intriguing case of two paradigmatically distinct business models (Buehling and Geissler, 2022) that were only recently addressed through the perspectives of Entrepreneurial University or even open innovation (De Bernardi et al., 2020). But most of all U-I collaboration is recently being addressed to as a mean to uplift the “third mission” of the universities (Nsanzumuhire and Groot, 2020). Among the factors recognised as game changers, also when addressing the U-I collaboration, there is the rapid development of ICT (Hönigsberg and Dinter, 2019) and its utility in innovation processes and competitiveness (Tarutė and Gatautis, 2014), let alone communication (Camarinha-Matos et al., 2019).
In terms of the desire to re-industrialise the Danube Region countries (Besednjak Valič, 2019), the U-I collaborations are welcomed and looked for, taking into account the need of the Industry to follow the global trend of digital transformation (Guimón and Paunov, 2022) and to move towards Industry 4.0 (Crupi et al., 2020). To keep up with original equipment manufacturers' (OEMs’) demands (Hafner and Modic, 2020; Kurpjuweit et al., 2018), small- to medium-sized enterprises (SMEs) need to keep up with new technologies, such as high-performance computing (HPC) or supercomputing. HPC services can be of help (Suklan, 2019).
The present study explores the role institutional framework and actors of the triple helix of the National Innovation System (NIS) play when framing the U-I collaboration. As research shows (Nsanzumuhire and Groot, 2020), a gap in the developing countries with the respect of the U-I collaboration exists. On that note, our research focussed on the dynamics of such collaborations within developmentally distinct groups of Danube region countries. Focus group discussions included triple helix experts and representatives on U-I collaboration within those particular countries. We conducted the research within the scope of the Danube region academic HPC centres representing the University sphere and SMEs working in the automotive industry as suppliers to OEMs as Industry representatives.
Focussing on institutional frameworks surrounding the U-I collaboration, the main research questions deriving from the given situations are the following:
What are the key anchors of the institutional forces for establishing effective U-I collaboration for the cases of academic HPC centres and SMEs in the Danube region?
What policy mechanisms seem most appropriate to encourage the U-I collaboration among academic HPC centres and SMEs in the Danube region?
To respond to both research questions, we adopted the inductive approach, underpinning the qualitative research process. The analysis of the focus group discussion will deliver responses to the main research questions. In providing the answers, we will rely on a case-based approach.
The authors structured the paper the following way: first, we set the research's main concepts and starting points. Description of data collection follows next, and after that, the data analysis report. The final part of the paper delivers the discussion with conclusions.
2. Institutions, National Innovation Systems and HPC in the context of University–Industry collaboration in the Danube region
2.1 Institutions and key anchors for collaboration
Sociologically, when addressing U-I collaboration, we lean on neo-institutional theory as dominating organisational studies (Alvesson and Spicer, 2019). The presented research defines institutions as formalised, regulative (example legislation) (Alvesson and Spicer, 2019). On the other hand, we describe the institutions working within the legal setting as organisations. Adopting such an approach, we believe a sharper distinction between institutions (codified agreements) and organisations (formalised groups of employees working under established rules) is needed. Having said all this, to understand further the U-I collaboration dynamics, we need to remain aware that both organisations do not operate independently of the social environment. Therefore, the NIS approach is further adopted.
2.2 Innovation systems
With full awareness of different definitions of innovation system as one “genotype”, we focus on the concept of NIS, as several “phenotypes” (Modic and Rončević, 2018) among the for example also: regional innovation system, sectoral innovation system or even corporate innovation system (Granstrand and Holgersson, 2020). Having said that, we follow Freeman (1987) who defined the NIS as a network of institutions interacting, importing, modifying and diffusing new technologies. However, some institutions mentioned earlier act as organisations with established organisational fields (DiMaggio and Powell, 1983). The organisations operating within NIS can be in the public or private sector (Freeman, 1987). Following the definition of NIS, we understand innovation as a sophisticated and complex process in which different elements of the system are linked to each other, enabling the sharing of knowledge and the mutual support for innovation activities (Lundvall, 1992) but especially emerging technologies and innovations (Lundvall, 2007; Nelson, 1993). NIS is composed of the linkages and flow of information among the different actors of the system concerning the generation of ideas and the innovation process (Lundvall, 2007). Other governmental policies address the importance of NIS, and policymakers invest efforts in connecting different actors of the economy (Arranz et al., 2020).
Several attempts (Kuhlmann, 2001) structure the relationships among stakeholders of NIS by demonstrating the complexity of relationships among them. The most interesting is the model by (Warnke et al., 2016). The model focusses on the two main subsystems of NIS: University and Industry. Intermediary organisations interlink both subsystems. The political system and influence shape the subsystems of the demand system, the framework conditions and the existing infrastructure system (Warnke et al., 2016).
Different systemic arrangements, such as configurations of stakeholders and organisations, can deliver similar levels of innovative performance. Research of the European NIS over the last ten years reveals that innovation systems show inherent complexity, which leads to a high level of complementarity among their constituent components and configuration. This result implies that successful innovation policies should be systemic, leaving little flexibility in policy design and scope (Cirillo et al., 2019). However, there are specific strategic competences to be developed at the level of territorial actors (Fric et al., 2023). As substantive knowledge and strategic connections are among the most relevant (ibid), the plan for the future development of NIS suggests adaptation to global trends, adaptation of NIS to country specifics and fitting entrepreneurial innovation into NIS (López-Rubio et al., 2022).
However, in cases of obstructed evolution of the innovation systems, the weaknesses of markets, institutions, organisations and networks are emphasised (Carlsson and Jacobsson, 1997; Jacobsson and Bergek, 2006; Jacobsson and Johnson, 2000; Rotmans et al., 2001; Unruh, 2000). A weakened system structure may lead to co-called system failure, meaning a system fails to develop or does so in a stunted fashion (Carlsson and Jacobsson, 1997). There are several levels of a system failure, while for the present discussion, we focus on system failure concerning structural components (Bergek et al., 2008). To prevent system failure, proper policies need to be elaborated, strategically steered to achieve economic and societal development (Rončević and Besednjak Valič, 2022).
2.3 National Innovation Systems as a triple helix
The model of NIS (Lundvall, 1992) can also be used to explain the creation of socially relevant knowledge. To illustrate the dynamics of socioeconomic relations in knowledge creation processes between the academic, economic and governmental spheres, we will use the triple helix model (Leydesdorff and Etzkowitz, 1998, 2001; Ranga and Etzkowitz, 2013). The triple helix model spotlights a trilateral network of relations between University, Industry and public authorities with the expectation to create a novel knowledge infrastructure. This involves bringing these three subsystems together, each assuming own respective role, and establishing effective organizations at the intermediate level. We place the production and transfer of knowledge at the centre of the concept of the triple spiral as a fundamental issue that converts the three subsystems. The triple helix model also tries to illuminate and explain the dynamics of internal changes in individual social subsystems as well as changes in the relationships between them.
Having said all the above, we describe the above three types of actors within the institutional framework for further conceptualisation and analysis. All three are structured as organisations and operate within the same system, undertaking their roles. Their actions and interaction thus contribute to the innovativeness of the NIS and might, in the same manner, contribute to system failure. The actors are the following: academic HPC centres, SMEs working in the automotive and electronics industries and public authorities.
2.4 Diffusing high-performance computing in the European Union
In cases when regulation obstructs the deployment of particular technologies, promising developmental trajectories may be foreclosed (Martin et al., 2019). However, the law can also provide additional incentives for innovation, leading to the creation of new technologies, products and markets and the discovery of overlooked efficiencies—see also Porter hypothesis (Porter and van der Linde, 1995). Early adopters may enjoy first-mover advantages in export markets. Regulation can foster consumer trust, increasing demand for new technologies (Martin et al., 2019). The countries of the EU have started to recognise the importance of intertwinement of University and Industry (Besednjak Valič et al., 2023) and include this in their innovation policy mix (Modic and Rončević, 2018).
However, the HPC remains costly infrastructure, expensive also to maintain (Sajay and Babu, 2016), and it is the cost that are seen as the major burden for SMEs when deciding to adopt HPC (Botelho Junior and O’Gorman, 2022). Additionally, it is Sakellariou et al. (2018) who is focussing on challenges in interplay between Industry 4.0 and HPC, especially in the context of smart manufacturing systems. The authors detect the most common challenges in the interplay between Industry (4.0) and HPC: The first arises from the environment adaption of the HPC as HPCs consumes large amounts of data and resources. As HPC systems become interconnected with manufacturing systems, they will act in line with overall systems performance. And overall systems might have other objectives but time, for example energy consumptions, network traffic etc. following this Gitler et al. (2020) explore different regional ecosystems in Latin America to understand the overall regional specifics.
Others studies also approach the non-technical aspects of HPC, see also Botelho Junior and O'Gorman (2022), who outline detect a range of studies focussing on HPC in the context of manufacturing innovation (Kim, 2016). Basili et al. (2008) detect common traits in HPC project as lack of targeted HPC training and issues related to code development. Lastly, there is a specific lack of competences detected among the SMEs, especially when it comes to accessing the HPC as it predominantly occurs through the cloud (Lu et al., 2022).
2.5 Innovativeness of the Danube region
The Danube region is a joint name for numerous countries in the Danube basin. Danube river is the second longest European river and runs through a total of 10 countries. The territory of the Danube basin is, in policy terms, covered by the European Union strategy for the Danube region (EUSDR, 2022).
The EUSDR covers the area spreading from the Black Forest in Germany to the Black Sea (Romania, Moldova and partially Ukraine) (Besednjak Valič, 2019). Up to 115m people inhabit the region. The Danube region and the EUSDR cover the following EU member states: Austria, Bulgaria, Croatia, Czech Republic, Germany (regions of Baden-Wurttemberg and Bayern), Hungary, Romania, Slovakia, and Slovenia), Pre-accession countries (Bosnia and Herzegovina, Montenegro and Serbia) and neighbouring countries (Moldova and, since 2022, also whole Ukraine) (Besednjak Valič, 2019).
All the above-listed countries rank very differently according to numerous statistics and reports. We could group them into three groups for further data analysis and interpretations. We did this based on their ranking by adopting the Global Competitiveness Index (Schwab, 2018). The first group was named the group of competitively advanced countries. This group includes the countries: Germany (Baden- Württemberg), Austria, the Czech Republic and Slovenia. The second group was named the group of competitively intermediate countries. The group includes Slovakia, Hungary, Bulgaria and Romania. Lastly, the third group was called the group of competitively lagging countries. It contains countries: Serbia, Croatia, Montenegro, Ukraine, Moldova and Bosnia and Herzegovina.
To sum up, researching institutional aspects of NIS needs to distinguish the difference between institutions and organisations of NIS and should focus on the interactions of two main subsystems of NIS – University and Industry while keeping in mind the distinctive differences between NIS. Adopting a triple helix approach is necessary, especially in developing countries where public authorities' intervention will enable the framework conditions for NIS development. The process is tested further on in the case of three developmentally different groups of Danube region countries. We have researched the particularities of U-I collaboration in the case of collaboration between academic HPC centres and SMEs.
3. Methodology
We analysed the data collected within the InnoHPC (InnoHPC, 2017) project to respond to the proposed research questions. As the project was transnational, the data collection took place in 14 countries of the Danube region, mostly by different performers and with groups gathered on the local availability. The data collection period ranged from May to October 2017. We conducted fourteen focus groups, and Table 1 delivers the number of participants for each focus group. Additionally, Table 1 displays the groupings based on Global Competitiveness Index. The data collection for Germany took place in Baden- Württemberg focussing on the dynamics of that particular region. In each of the countries, one expert focus group was conducted, with expert speakers and representatives of the triple helix (Ranga and Etzkowitz, 2013) actors from the fields of academic HPC centres, Industry and policy-making. We invited the expert speakers to the discussion based on their expertise in HPC and technology transfer.
The whole research process (Alase, 2017) followed the interpretative paradigm (Lamut and Macur, 2012; Smith and Shinebourne, 2012). Within the analysis process, the authors also introduced the phenomenological aspect (Miller et al., 2018). Guba and Lincoln (2004) point out that reality is constructed by the individual, so there are different interpretations of the same problem. The individual's interpretation of the problem under study is not only conditioned by their knowledge, experience and values but also depends on the specific historical, cultural and political context (Guba and Lincoln, 2004) of the environment in which the individual operates. By leaning on an interpretative paradigm, authors put emphasis on examining subjective experiences, reflections and understandings and as well as determining what meaning is attached to research topic, from the perspective of the participants included in the study.
The authors adopted a multistage qualitative content analysis approach when analysing and interpreting the data. The data collection and analysis procedure included, first, audio recorded and transcribed data collection. To obtain good transcriptions, the authors ensured the correct meanings and opinions. Second, the transcriptions were organised and arranged into a coding table. The coding table was structured to enable the position of the same question and pertaining response within a single line. Once we arranged and transcribed the data, we conducted the first reading. In the next step, we structured the data according to the two main research questions, based on the key detected topics. The final phase included the open coding of data. Lastly, we created the paradigm model based on the open coding results. The authors contributed equally to safeguarding the process and ensuring the objectivity of the analysis.
4. Results
The interviewees belonging to the group of competitively advanced countries consider the role of public authorities as weak when it comes to using HPC for industrial purposes. National policies do not seem to promote cooperation between the academic HPC centres and Industry. Further on, interviewees of the competitively advanced countries criticise the inadequate national policies in terms of limited budgetary support for U-I collaboration in the field of HPC. Subsequently, the academic HPC centres and Industry identify EU project funding as a viable source of financing for HPC infrastructure. As a result, organisations form international links and cooperation. Interviewees from the group of competitively advanced countries criticise their national strategies for lack of vision on using HPC in R&D and U-I collaboration. However, they also highlight the presence and support of national policies for tuition fee-free public offerings of HPC training/education. The workforce skilled in HPC is crucial as interviewees identify the numerous potentials of the professional labour force. According to the interviewees, the low level of HPC skills is present even among the experts.
In order to mitigate the issues related to skills to use the HPC, the respondents detect potential in organisation of non-formal training related to the use of HPC technology. Such activity is necessary, as specific HPC-related knowledge is deficient. Both sectors face this problem—University and Industry. The interviewees of the competitively intermediate countries highlight the supportive role of the public and intermediary organisations for HPC accessibility as opposed to their counterparts from advanced countries. Public organisations provide (public) financing for HPC infrastructure and also promote cooperation between the academic HPC centres and Industry through national calls and tenders. Such type of cooperation is also encouraged by EU-funded projects. At the same time, the interviewees mention the lack of financial support from the EU. The reason for this is the industry's struggle with the administrative requirements of managing EU project documentation. Another reason for the struggle is the slowness of EU funding processes.
The strong links between University and Industry are recognised within the technology transfer offices (TTOs) services by interviewees of the competitively intermediate countries. TTOs have the potential to serve as promotors of cooperation between the academic HPC centres and industry, according to the interviewees. Interviewees agree such an approach is crucial as academic demands in competitively intermediate countries do not necessarily value the applicative research and collaboration with the industry as much as basic research.
National innovation strategies usually define the HPC as a research infrastructure and one of the key enabling technologies for innovation. Based on the opinions of the interviewees, the lack of national strategies reveals itself in weak support of industrial R&D. In industrial R&D, the experts do not recognise the HPC as a priority technology. The interviewees expressed their critique of the national innovation policies as they noted the lack of vision and goals for proper positioning the HPC technology within the industry. Apart from that, the emerging industry sectors seem to be able to establish close links between clusters of potential beneficiaries of HPC infrastructure and technology. Based on the interviewees' opinion, those emerging industry sectors do not provide support for the use of HPCs outside the established clusters. Consequently, according to interviewees, competitively intermediate countries do not exploit the potential of clusters when disseminating both innovation and knowledge linked to HPC technologies.
Public authorities of the group of competitively lagging countries express the rudimentary willingness to improve and transfer of HPC technologies. Interviewees recognise the readiness of public authorities to change legal frameworks towards supporting the use of existing HPC infrastructure. Despite the declarative supportive role of public authorities, the interviewees note that the public authorities themselves act as a critical obstacle to the exploitation of HPC technologies. Public authorities do not provide sufficient financial support in funding R&D. Moreover; the interviewees note that the public authorities do not undertake investments in HPC infrastructure. According to the interviewees, public authorities expect industry initiatives to create and support the R&D and HPC technology clusters. Based on the responses from the interviewees, in the competitively lagging countries, there is a lack of mutual understanding for HPC-related R&D within different sectors. The attitudes of public authorities and the lack of knowledge, according to interviewees, reflect in the absence of strategic documents. The interviewees also note the absence of concrete strategic measures to overcome the status quo.
Focussing on the proposed RQ1, the following response is below:
What are the key anchors of the institutional forces for establishing effective U-I collaboration for the cases of academic HPC centres and SMEs in the Danube region?
We presented the key anchors in Table 2. As key anchors for the institutional forces, we outline the technology transfer and knowledge transfer, both depending on the levels of collaboration potentials and collaboration itself. For this purpose, stable legal environment, existing innovation policies and strong R&D funding are recognised as anchors for establishment of U-I collaborations.
The U-I collaboration in the field of HPC offers an opportunity. This opportunity, however, depends on the ability to adopt HPC as HPC is costly and skills demanding infrastructure. Both setbacks are problematic for economically less developed countries experiencing problems of brain drain. The ability to use HPC is prerogative for technology transfer and knowledge transfer processes.
Lastly, in competitively advanced countries accessibility to HPC capabilities lies often within organisations, where, HPC technology helps achieving organisation's strategic goals. In contrast, in competitively intermediate and competitively lagging countries, the case HPC technology is primarily introduced via higher education institutions (HEI). Further communication and cooperation with industrial actors are hindered because of the lack of competent professionals, but also due to intellectual property rights (IPR) concerns.
What policy mechanisms seem most appropriate to encourage the U-I collaboration among academic HPC centres and SMEs in the Danube region?
Regarding policy mechanisms considered appropriate to address the given situations, Table 2 reveals issues on several levels. First, the public authorities must ensure the proper information flow among all triple helix actors. Second, the policies must support the demand for a skilled workforce, supporting curricula adjustments. Based on that, the policies should be set in a way to promote HPC competencies, not only among junior but also among senior staff. A stable legal environment must support for U-I collaboration and the establishment of competitive labour markets. The interviewees particularly emphasised the need to ensure the funds invested in R&D return as profits. Supported U-I applicable research can secure such returns in profits. The interviewees expect a systematic solution for providing and funding HPC infrastructure.
The below paradigm model was developed based on the analyses and responses to both research questions. The functioning NIS can play a central role through sufficient information flow among all system stakeholders. Second, a competitive National Innovation Policy that fosters technological readiness and people skills is essential. Also, the National Innovation Policy should support R&D investment to be turned into profitable products. Third, the legal environment must provide IPR enforcement and competitive labour market conditions (see Figure 1).
5. Discussion and conclusions
In a time of fierce global competition and increased transition towards new business models for numerous sectors and organisations, the U-I collaboration facilitated by public authorities seems to remain at the heart of innovation processes. Enabling more vital collaboration is a task for numerous prosperous OECD countries (Haeussler and Colyvas, 2011). To facilitate the paradigmatic shift in research orientation, University and Industry need specific framework conditions for the collaboration to realise. Research results show that the most critical institutional anchors are two—efficient and straightforward National Innovation Strategy and a functioning legal environment. The National Innovation Strategy must focus on supporting the skilled workforce, implementing and maintaining HPC technology, and actively supporting relevant R&D research. In this context, a need for multilevel responsibility for innovation policy (as also in Modic and Rončević, 2018) is required. The practical legal environment must ensure a stimulating labour market that attracts and attain talented people and must provide the protection and enforcement of IP rights. The same conclusions can be confirmed by Viale and Campodall’Orto (2002) who are aware, together with Mali (2009), based on such legislation, that knowledge transfer is either supported or hindered. Additionally, National Innovation Strategy must support U-I collaborations in terms of equal valuation of basic and applicative research along with labour legislation enabling easier transfers of researchers from University to Industry and vice versa (Viale and Campodall’Orto, 2002).
Concerning cases of NIS failure, the results of our presented research confirmed the findings of (Bergek et al., 2008). The establishment of efficient U-I collaboration between academic HPC centres and SMEs of automotive and electronics industries in the Danube region is hindered by (a) infrastructural failures, such as lack of HPC infrastructure; (b) institutional failures, such as corruption and non-functioning legislation; and (c) capabilities failures, such as lack of skilled workforce and collaboration. The findings go in line with detected barriers to U-I collaboration (Nsanzumuhire and Groot, 2020).
For the presented case of three groups of countries of the Danube region, we met two distinctions worth further exploring: (a) developmentally differentiated countries seem to have different needs when supporting U-I collaboration. Economically less developed countries rely more on public authorities and their support in the U-I collaboration endeavours. (b) In competitively lagging countries of the Danube region, a distinct pattern of rigidity in considering collaborations between academic HPC centres and SMEs was noted, especially from the side of academia. The role of mental frameworks, especially considering national/cultural characteristics in U-I collaborations, could be more important than previously considered and can be subject to further explorations of the collaboration dynamics. Additionally, following the conceptualisations of ideal types of competence model for smart territorial development (Fric et al., 2023), particularly tailored policy mix can be formulated for each of the specific regional contexts.
Apart from many insightful findings, the authors are aware of the study's main limitations—results are limited to the case studies of relationships between academic HPC centres and SMEs working in automotive or electronics industries in the Danube region. However, the results still can serve as the cases of deep knowledge and guidance for future work. Apart from the sample size, another major limitation of the study is in the age of collected data. Since 2017, many world-changing events took place; however, the field of U-I collaboration within the scope of key enabling technologies (HPC included) has not changed much let alone change the main findings of the present paper.
Figures
Number of expert participants per focus group
| No. | Group according to global competitiveness index (2018) | Country | No. of participants | Participants profiles |
|---|---|---|---|---|
| 1 | Competitively advanced | Austria | 6 | 3 industry representatives 2 academic HPC representatives 1 policy maker—national level |
| 2 | Czech Republic | 6 | 3 industry representatives 1 academic HPC representatives 2 policy maker—national level | |
| 3 | Germany (Baden-Württemberg) | 7 | 3 industry representatives 4 academic HPC representatives | |
| 4 | Slovenia | 15 | 10 industry representatives 3 academic HPC representatives 2 policy makers—national level | |
| 5 | Competitively intermediate | Bulgaria | 6 | 2 industry representatives 2 academic HPC representatives 2 policy makers—national level |
| 6 | Hungary | 10 | 6 industry representatives 3 academic HPC representatives 1 policy makers—national level | |
| 7 | Romania | 5 | 2 industry representatives 1 academic HPC representatives 2 policy makers—national level | |
| 8 | Slovakia | 6 | 3 industry representatives 1 academic HPC representatives 2 policy makers—national level | |
| 9 | Competitively lagging | Croatia | 5 | 3 industry representatives 2 academic HPC representatives |
| 10 | Bosnia and Herzegovina | 4 | 2 industry representatives 1 academic HPC representatives 1 policy maker—local level | |
| 11 | Moldova | 4 | 1 industry representative 1 academic HPC representative 2 policy makers—national level | |
| 12 | Montenegro | 3 | 2 industry representatives 1 academic HPC representatives | |
| 13 | Serbia | 10 | 5 industry representatives 3 academic HPC representatives 2 policy makers—national level | |
| 14 | Ukraine | 5 | 4 industry representatives 1 academic HPC representatives |
Source(s): InnoHPC (2017), own grouping according to Schwab (2018)
Key themes defining institutional forces for collaboration and policy mechanisms
| Key themes | Institutional forces for a collaboration | Policy mechanisms |
|---|---|---|
| Information flow between organisations |
|
|
| Institutional cooperation |
|
|
| Knowledge creation in HEI and RI |
|
|
| Knowledge transfer, HPC training |
|
|
| Ability to use HPC |
|
|
| HPC setbacks for SMEs |
|
|
| Transnational collaboration |
|
|
| The competitive advantage of HPC usage |
|
|
| National innovation policy |
|
|
| Role of public authorities |
|
|
| Issue of brain drain and migration |
|
|
Source(s): Own research results interpretation
References
Alase, A. (2017), “The interpretative phenomenological analysis (IPA): a guide to a good qualitative research approach”, International Journal of Education and Literacy Studies, Vol. 5 No. 2, pp. 9-19.
Alvesson, M. and Spicer, A. (2019), “Neo-institutional theory and organization studies: a mid-life crisis?”, Organization Studies, Vol. 40 No. 2, pp. 199-218, doi: 10.1177/0170840618772610.
Arranz, N., Arroyabe, M.F. and Schumann, M. (2020), “The role of NPOs and international actors in the national innovation system: a network-based approach”, Technological Forecasting and Social Change, Vol. 159, 120183, doi: 10.1016/j.techfore.2020.120183.
Basili, V.R., Carver, J.C., Cruzes, D., Hochstein, L.M., Hollingsworth, J.K., Shull, F. and Zelkowitz, M.V. (2008), “Understanding the high-performance-computing community: a software engineer's perspective”, IEEE Software, Presented at the IEEE Software, Vol. 25 No. 4, pp. 29-36, doi: 10.1109/MS.2008.103.
Bergek, A., Jacobsson, S., Carlsson, B., Lindmark, S. and Rickne, A. (2008), “Analyzing the functional dynamics of technological innovation systems: a scheme of analysis”, Research Policy, Vol. 37 No. 3, pp. 407-429, doi: 10.1016/j.respol.2007.12.003.
Besednjak Valič, T. (2019), “Innovation, digitalisation, and the HPC in the Danube region”, in Rončević, B., Coscodaru, R. and Fric, U. (Eds), Go with the Flow: High Performance Computing and Innovations in the Danube Region, pp. 22-46.
Besednjak Valič, T., Cepoi, V., Džajić Uršič, E., Fric, U., Gangaliuc, C., Pandiloska Jurak, A. and Rončević, B. (2023), Development and Implementation of the EU Grand Strategies: Sociological, Policy, and Regional Considerations of Agenda 2030, Peter Lang, Berlin.
Botelho Junior, S. and O'Gorman, B. (2022), “Region-specific guidelines to encourage SMEs to use high performance computing”, Digital Policy, Regulation and Governance, Vol. 24 No. 6, pp. 513-524, doi: 10.1108/DPRG-02-2021-0032.
Buehling, K. and Geissler, M. (2022), “PhDs with industry partners – assessing collaboration and topic distribution using a text mining methodology”, Azagra-Caro, J.M., D’Este, P. and Barberá-Tomás, D. (Eds), University-Industry Knowledge Interactions: People, Tensions and Impact, International Studies in Entrepreneurship, Springer International Publishing, Cham, pp. 9-24, doi: 10.1007/978-3-030-84669-5_2.
Camarinha-Matos, L.M., Fornasiero, R., Ramezani, J. and Ferrada, F. (2019), “Collaborative networks: a pillar of digital transformation”, Applied Sciences, Multidisciplinary Digital Publishing Institute, Vol. 9 No. 24, p. 5431, doi: 10.3390/app9245431.
Carlsson, B. and Jacobsson, S. (1997), “In search of useful public policies — key lessons and issues for policy makers”, in Carlsson, B. (Ed.), Technological Systems and Industrial Dynamics, Springer US, Boston, MA, pp. 299-315, doi: 10.1007/978-1-4615-6133-0_11.
Cirillo, V., Martinelli, A., Nuvolari, A. and Tranchero, M. (2019), “Only one way to skin a cat? Heterogeneity and equifinality in European national innovation systems”, Research Policy, Vol. 48 No. 4, pp. 905-922, doi: 10.1016/j.respol.2018.10.012.
Crupi, A., Del Sarto, N., Di Minin, A., Gregori, G.L., Lepore, D., Marinelli, L. and Spigarelli, F. (2020), “The digital transformation of SMEs – a new knowledge broker called the digital innovation hub”, Journal of Knowledge Management, Vol. 24 No. 6, pp. 1263-1288, doi: 10.1108/JKM-11-2019-0623.
De Bernardi, P., Forliano, C. and Bertello, A. (2020), “Digital transformation in SMEs: a systematic review and future directions”, in Vrontis, D., Weber, Y. and Tsoukatos, E. (Eds), Business Theory and Practice across Industries and Markets, EuroMedPress, pp. 306-323.
DiMaggio, P.J. and Powell, W.W. (1983), “The iron cage revisited: institutional isomorphism and collective rationality in organizational fields”, American Sociological Review, [American Sociological Association, Vol. 48 No. 2, pp. 147-160, doi: 10.2307/2095101.
EUSDR (2022), “European union strategy for the Danube region”, available at: Https://Danube-Region.Eu/
Freeman, C. (1987), Technology Policy and Economic Performance: Lessons from Japan, Printer, London.
Fric, U., O'Gorman, W. and Rončević, B. (2023), “Strategic competence model for understanding smart territorial development”, Societies, Vol. 13 No. 3, p. 76, doi: 10.3390/soc13030076.
Gitler, I., Gomes, A.T.A. and Nesmachnow, S. (2020), “The Latin American supercomputing ecosystem for science”, Communications of the ACM, Vol. 63 No. 11, pp. 66-71, doi: 10.1145/3419977.
Granstrand, O. and Holgersson, M. (2020), “Innovation ecosystems: a conceptual review and a new definition”, Technovation, Vols 90-91, 102098, doi: 10.1016/j.technovation.2019.102098.
Guba, E.G. and Lincoln, Y.S. (2004), “Competing paradigms in qualitative research: theories and issues”, Approaches to Qualitative Research: A Reader on Theory and Practice, Vol. 17, p. 38.
Guimón, J. and Paunov, C. (2022), “The policy mix to promote university-industry knowledge transfer: a conceptual framework”, in Azagra-Caro, J.M., D'Este, P. and Barberá-Tomás, D. (Eds), University-Industry Knowledge Interactions: People, Tensions and Impact, Springer International Publishing, Cham, pp. 67-88, doi: 10.1007/978-3-030-84669-5_5.
Haeussler, C. and Colyvas, J.A. (2011), “Breaking the ivory tower: academic entrepreneurship in the life sciences in UK and Germany”, Research Policy, Vol. 40 No. 1, pp. 41-54, doi: 10.1016/j.respol.2010.09.012.
Hafner, A. and Modic, D. (2020), “European automotive technological innovation systems in the age of disruption: the suppliers' view”, Research in Social Change, Vol. 12 No. 3, pp. 53-77, doi: 10.2478/rsc-2020-0014.
Hönigsberg, S. and Dinter, B. (2019), “Toward a method to foster the digital transformation in SME networks”, 40th International Conference on Information Systems, Munich, p. 10.
InnoHPC (2017), High-Performance Computing for Effective Innovation in the Danube Region – InnoHPC, Danube Transnational Programme, Interreg Danube, Budapest.
Jacobsson, S. and Bergek, A. (2006), “A framework for guiding policy-makers intervening in emerging innovation systems in ‘catching-up’ countries”, The European Journal of Development Research, Vol. 18 No. 4, pp. 687-707, doi: 10.1080/09578810601094902.
Jacobsson, S. and Johnson, A. (2000), “The diffusion of renewable energy technology: an analytical framework and key issues for research”, Energy Policy, Vol. 28 No. 9, pp. 625-640.
Kim, Y.-Y. (2016), “Manufacturing innovation and HPC (high performance computing) utilization”, Journal of Korea Technology Innovation Society, Korea Technology Innovation Society, Vol. 19 No. 2, pp. 231-253.
Kuhlmann, S. (2001), “Future governance of innovation policy in Europe — three scenarios”, Research Policy, Vol. 30 No. 6, pp. 953-976, doi: 10.1016/S0048-7333(00)00167-0.
Kurpjuweit, S., Reinerth, D. and Wagner, S.M. (2018), “Supplier innovation push”, Research-Technology Management, Vol. 61 No. 2, pp. 47-55, doi: 10.1080/08956308.2018.1399025.
Lamut, U. and Macur, M. (2012), Metodologija Družboslovnega Raziskovanja: Od Zasnove Do Izvedbe, Vega, Ljubljana.
Leydesdorff, L. and Etzkowitz, H. (1998), “The Triple Helix as a model for innovation studies”, Science and Public Policy, Vol. 25 No. 3, pp. 195-203, doi: 10.1093/spp/25.3.195.
Leydesdorff, L. and Etzkowitz, H. (2001), “The transformation of university-industry-government relations”, Electronic Journal of Sociology, Vol. 5 No. 4.
López-Rubio, P., Roig-Tierno, N. and Mas-Verdú, F. (2022), “Assessing the origins, evolution and prospects of national innovation systems”, Journal of the Knowledge Economy, Vol. 13 No. 1, pp. 161-184, doi: 10.1007/s13132-020-00712-7.
Lu, Q., Damij, N. and Whalley, J. (2022), “An exploration of high performance computing cloud contract for SMEs: a systematic literature review”, Digital Policy, Regulation and Governance, Vol. 24 No. 6, pp. 525-540, doi: 10.1108/DPRG-11-2020-0163.
Lundvall, B.-Å. (1992), National Systems of Innovation: towards a Theory of Innovation and Interactive Learning, Pinter Publishers, London.
Lundvall, B. (2007), “National innovation systems—analytical concept and development tool”, Industry and Innovation, Vol. 14 No. 1, pp. 95-119, doi: 10.1080/13662710601130863.
Mali, F. (2009), Bringing converging technologies closer to civil society: the role of the precautionary principle, Innovation: The European Journal of Social Science Research Vol. 22 No. 1, pp. 53-75, doi: 10.1080/13511610902770586.
Martin, N., Matt, C., Niebel, C. and Blind, K. (2019), “How data protection regulation affects startup innovation”, Information Systems Frontiers, Vol. 21 No. 6, pp. 1307-1324, doi: 10.1007/s10796-019-09974-2.
Miller, R.M., Chan, C.D. and Farmer, L.B. (2018), Interpretative phenomenological analysis: a contemporary qualitative approach, Counselor Education and Supervision Vol. 57 No. 4, pp. 240-254.
Modic, D. and Rončević, B. (2018), “Social topography for sustainable innovation policy: putting institutions, social networks and cognitive frames in their place”, Comparative Sociology, Vol. 17 No. 1, pp. 100-127, doi: 10.1163/15691330-12341452.
Nelson, R.R. (1993), National Innovation Systems. A Comparative Analysis, Oxford University Press, New York.
Nsanzumuhire, S.U. and Groot, W. (2020), “Context perspective on University-Industry Collaboration processes: a systematic review of literature”, Journal of Cleaner Production, Vol. 258, 120861, doi: 10.1016/j.jclepro.2020.120861.
Porter, M.E. and van der Linde, C. (1995), “Toward a new conception of the environment-competitiveness relationship”, Journal of Economic Perspectives, Vol. 9 No. 4, pp. 97-118, doi: 10.1257/jep.9.4.97.
Ranga, M. and Etzkowitz, H. (2013), “Triple helix systems: an analytical framework for innovation policy and practice in the knowledge society”, Industry and Higher Education, Vol. 27 No. 4, pp. 237-262, doi: 10.5367/ihe.2013.0165.
Rončević, B. and Besednjak Valič, T. (2022), An Active Society in a Networked World: the Cultural Political Economy of Grand Strategies, Peter Lang, Berlin.
Rotmans, J., Kemp, R. and van Asselt, M. (2001), “More evolution than revolution: transition management in public policy”, Foresight, Vol. 3 No. 1, pp. 15-31, doi: 10.1108/14636680110803003.
Sajay, K.R. and Babu, S.S. (2016), “A study of cloud computing environments for High Performance applications”, Presented at the 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), pp. 353-359, doi: 10.1109/SAPIENCE.2016.7684127.
Sakellariou, R., Buenabad-Chávez, J., Kavakli, E., Spais, I. and Tountopoulos, V. (2018), “High performance computing and industry 4.0: experiences from the DISRUPT project”, Proceedings of the 18th International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, Association for Computing Machinery, New York, NY, pp. 218-219, doi: 10.1145/3229631.3264660.
Schwab, K. (2018), The Global Competitiveness Report 2018, World Economic Forum, Geneva, p. 671.
Smith, J.A. and Shinebourne, P. (2012), Interpretative Phenomenological Analysis, American Psychological Association, Washington.
Suklan, J. (2019), “Gap Analysis: HPC suppy and demand”, Rončević, B., Coscodaru, R. and Fric, U. (Eds), Go with the Flow: High Performance Computing and Innovation in the Danube Region, Vega Press, London, Budapest, Ljubljana, pp. 47-59.
Tarutė, A. and Gatautis, R. (2014), “ICT impact on SMEs performance”, Procedia – Social and Behavioral Sciences, Vol. 110, pp. 1218-1225, doi: 10.1016/j.sbspro.2013.12.968.
Unruh, G.C. (2000), “Understanding carbon lock-in”, Energy Policy, Vol. 28 No. 12, pp. 817-830.
Viale, R. and Campodall'Orto, S. (2002), “An evolutionary Triple Helix to strengthen academy-industry relations: suggestions from European regions”, Science and Public Policy, Vol. 29 No. 3, pp. 154-168, doi: 10.3152/147154302781781029.
Warnke, P., Koschatzky, K., Dönitz, E., Zenker, A., Stahlecker, T., Som, O., Cuhls, K. and Guth, S. (2016), “Opening up the innovation system framework towards new actors and institutions”, Fraunhofer ISI, No. 49, pp. 1-50.
Acknowledgements
Since acceptance of this article, the following author(s) have updated their affiliation(s): Janez Kolar is at the Rudolfovo – Science and Technology Centre, Novo mesto, Slovenia and Alenka Pandiloska Jurak is at the Rudolfovo – Science and Technology Centre, Novo mesto, Slovenia and Faculty of Information Studies, Novo mesto, Slovenia.