The history of technologies having a major economic or technological impact shows that technologies emerge and become established with important role of supporting infrastructure. The provision of highways, electricity distribution networks or cable networks have powerful effects in initating and driving many technologies. The knowledge infrastructure, compared to physical one, is often less tangible, more differentiated and specific, serving narrower constituencies and answering needs less clearly articulated. It also involves the articulation of new needs that can only be met through the generation of new capabilities within markets that have yet to be created (Morris and Teubal, 1995, p. 260). Historically, many major technological innovations were developed in government laboratories, publicaly-owned enterprises, univeristies equipped with modern knowledge infrastructures (Smith, 2005, p. 88; Smith, 2009, p. 89). However, the knowledge infrastructures can also be a source of valuable, external knowledge for firms and could help them to recognize the value of new, external information, assimilate it, and apply it to commercial ends (Cohen and Levinthal, 1990, p. 128).

For many years, knowledge infrastructure was referred as research or scientific infrastructure and linked mainly with large-scale and complex research facilities. The examples of such facilities, called also Big Science, are: The European Organization for Nuclear Research (CERN), The European Spallation Source (ESS), The European Space Observatory (ESO), International Thermonuclear Experimental Reactor (ITER). Research infrastructures on a European scale have been a subject of political intrest since the early 2000s with the launch of the Lisbon Strategy and the European Research Area. The intrest in European research infrastructure reflects in the new modes of coordination (the European Strategy Forum for Research Infrastructures – ESFRI and Roadmaps), the incentivization through generous financial schemes (the Framework Programmes) and the implementation of tailor-made legislation (specific legal framework the European Research Infrastructure Consortium – ERIC) (Cramer and Ruffin, 2025, p. 45).

In recent years, a new dimension of knowledge infrastructure has gained increasing prominence among European research organizations, industry stakeholders, entrepreneurs, and policymakers. It reffers to technology infrastructure, which encompasses facilities, equipment, expertise, and resources that are essential for the development, testing, scaling, and validation of technologies. Its scope ranges from research services at the pre-competitive applied research stage to large-scale demonstration and validation activities. The concept of technology infrastructure appeared in European policy documents in 2015 (EC, 2015) in the context of Key Enabling Technologies and in 2019 in Staff Working Document concerning technology infrastructures. Then it was included in many Europan political documents (Viscido et al., 2022, p. 2). It was also popularised in reports and documents presenting case studies of technology infrastructure prepared by the European Association of Research and Technology Organisations (EARTO, 2022). Pararelly, it has been examined in several projects funded through European programs — such as the RITIFI project (RITIFI, 2025) — and has been the subject of discussion at numerous conferences, including the event jointly organized by the European Commission and the Łukasiewicz Research Network in Warsaw in May 2025 (EC 2025a). Most recently, technology infrastructure has been formally incorporated into „the European Strategy on Research and Technology Infrastructures” published by the European Commission (EC, 2025b).

The role of infrastructures, in particular technology infrastructures, seems to be very valuable for many countries and regions in strengthening cooperation between science and business, but also from the perspective of smart specialisation strategies as well as technological independence and sovereignty (EGTI, 2025, p. 43; Lewandowski and Falkowski, 2025, p. 26). It concerns situation in many Central and Eastern European countries facing challenges such as outdated or missing infrastructure for specialized research, weak cooperation among firms and research organisations, high pressure to spend budget funds effectively as well as exploiting synergies and complementarities between innovation policy instruments implemented at European, national and regional levels (EC, 2017, p. 15; Timofejevs and Avotins, 2020, p. 1547; Gittova K., Sipikal M., 2022, p. 648). However, the domain of technology infrastructure remains less clearly defined than research infrastructures and raises a number of unresolved issues. A central challenge concerns the boundaries between technology and research infrastructures, drivers and barriers associated with access to infrastructures and instruments supporting the use of infrastructures, especially by small and medium sized enterprises. This corresponds to the firms’ ability to value, assimilate and apply new knowledge and its absorptive capacities. The literature on absorptive capacity discusses the impact of research and development activities on this capacity, but does not refer to the role of infrastructures, especially technology ones. The objective of this article is to address these ambiguities and contribute to a clearer understanding of the concept of technology infrastructures and their role as enablers of firms’ absorptive capacity.

The article is structured as follows: part two provides a review of the relevant literature, part three details the research methodology, part four reports the empirical findings and the final sections offer a summary of discussion and conclusions.

2. Literature review

The infrastructures used for research and development are a central components of the national innovation systems (Smits, 2005, p. 103). However, they are not homogeneous category and include scientific or research infrastructures, technology infrastructures or industrial infrastructures (EGTI, 2025, p. 22). Among them research (or scientific) infrastructures are well recognized and widely described in the literature, mainly due to the popularity of large-scale and complex research facilities. One of the most popular definitions, used among others by ESFRI, indicates that research infrastructures are facilities, resources and services that are used by the research communities to conduct research and foster innovation in their fields. Research infrastructures include major scientific equipment (or sets of instruments), knowledge-based resources such as collections, archives and scientific data, einfrastructures, such as data and computing systems and communication networks and any other tools that are essential to achieve excellence in research and innovation (Regulation, 2013). The main challenges facing research infrastructures, especially large-scale infrastructures, are among other fings engagement of users throughout the entire life cycle and strengthening their impact (ESFRI, 2021, p. 20; Ulnicane, 2020, p. 9).

Significantly fewer studies concern technology infrastructures. Tassey (1992, p. 10) defined technology infrastructure as the body of scientific, engineering, and technical knowledge accessible to private industry. It encompasses generic technologies, infratechnologies, and technical information, as well as data relevant to strategic planning and market development, mechanisms for collaboration, and frameworks for the allocation of intellectual property rights. Typically, technology originates outside the boundaries of individual firms and is disseminated through direct transfer, codification in standards, or institutionalized programs such as quality assurance systems. However, technologically complex infrastructures face significant challenges, most notably insufficient resource allocation, which stems from a limited recognition of their role in fostering long-term economic growth (Tassey, 2008, p. 10).

According to Justman and Teubal (1995, p. 260), technology infrastructure can be understood as a set of collectively provided, industry-relevant capabilities designed for application across multiple firms or user organizations. They distinguish between two ideal types of technology infrastructure:

• Basic or sectoral infrastructure, which primarily supports small and medium-sized enterprises (SMEs) engaged in low- to medium-technology activities, typically by delivering technological services through sectoral technology centers;
• Advanced or functional infrastructure, which serves high-technology, leading-edge firms by providing research and innovation inputs.

Both forms of technology infrastructure correspond to two fundamental governmental roles in its promotion: market-building and capability creation. The former involves fostering the capabilities required for the provision of technological services within the local economy—for example, by stimulating demand through awareness-raising initiatives and user-needs assessments, supporting the development of independent supply sources through learning-by-doing, training consultants, and facilitating the spin-off of independent consulting services. The latter role, capability creation, focuses on developing collaborative capacities among users. This may include promoting interaction, establishing temporary joint laboratories and research teams that integrate researchers from different organizations, encouraging information exchange, and undertaking parallel research and development activities in critical areas such as fine-processing equipment, with sub-teams led by different user groups (Justman & Teubal, 1995, p. 277).

Antonelli, Link and Metcalfe (2009, p. 6) indicates that technology infrastructure supports the design, deployment and use of both individual technology-based components and the systems of such components that form the knowledgebased economy. In narrow sense, technology infrastructure is a set of physical and virtual tools, methods, and data to conduct research and development as well as to control of production processes to achieve target quality and yield. In broad sense, it includes organizational or institutional forms that leverage knowledge creation and knowledge flows in technology developers and users, including research/science parks, incubators, university research centers, and focused public–private partnerships (Antonelli, Link, Metcalfe, 2009, p. VII).

Technology infrastructures have appeared in many European documents for the last few years. The reference to technology infrastructure was made in the smart specialization strategies as a part of a wider eco-system complementing research infrastructures and encompassing science parks, incubators, sectoral excellence centres, living labs, prototyping centres, intellectual property right (IPR) centres, technology transfer offices, etc. which often facilitate the commercialisation of research results into market applications (EC, 2012, p. 76). It appeared also in the context Key Enabling Technologies (KETs) and defined as „public or private organisations carrying out research and innovation in technology readiness levels (TRLs) 3 to 8 for one or more KETs (not necessarily the whole TRL range) and providing at least one type of technological service and infrastructure in one or more KETs to industry and SMEs that corresponds to a TRL equal to 5 or higher” (EC, 2015, p. 5). From this perspective, SMEs represent a “demand side” for the KETs expertise that technology infrastructures (the “supply side”) need to respond to. According to Staff Working Document published in 2019 technology infrastructures are understood as „facilities, equipment, capabilities and support services required to develop, test and upscale technology to advance from validation in a laboratory up to higher TRLs prior to competitive market entry. They can have public, semi-public or private status. Their users are mainly industrial players, including SMEs, which seek support to develop and integrate innovative technologies towards commercialisation of new products, processes and services, whilst ensuring feasibility and regulatory compliance”. (EC 2019, p. 3). The reference to the above definition also includes the „European Strategy on Research and Technology Infrastructures”, which indicates that the primary purpose of technology infrastructure is to boost industrial competitiveness through enabling and accelerating technological innovations towards societal/market adoption. It also presents the main barriers faced by industrial users when accessing technology infrastructures such as insufficient financial and human resources, asymmetry of information and cultural barriers (EC, 2025b, p. 13).

Given the purpose of technology infrastructures discussed in the literature and policy documents, it seems that technology infrastructures could serve to enhance absorptive capacity of firms. Absorptive capacity means firm’s ability to value, assimilate and apply information towards commercial ends (Cohen and Levinthal, 1990, p. 128). It could also be defined as distinct but complementary capabilities including four components: acquisition, assimilation, transformation and exploitation (Zahra and Geogre, 2002, p. 189). Absorptive capacity depends on a firm’s prior knowledge and skills and it’s created as byproducts of a firm’s R&D investment and manufacturing operations. However, it also depends on external sources of information like acquisitions, purchasing, through licencing and contractual agreements, interogranisational relationships such as research and development consortia, alliances and joint ventures. Firms could take advatage of external sources of information when there is a knowledge complementarity between firm’s capabilities and these sources of information (Zahra and Geogre, 2002, p. 193). Technology infrastructures could help firms to recognize new information and take advatage of it. They could also maintain and diffuse elements of scientific knowledge or specific industry-relevant knowledge as well as help firms to identify technological opportunities, possible applications, their consequences and strategies requires to do them (Bergek, 2009, p. 122).

The relationships between R&D activities, knowledge transer and absorptive capacity are widely described in the literature (Lau and Lo, 2015, p.110; Miller et al., 2016, 386). However, issues related to technology infrastructure and absorptive capacity are rarely integrated and mainly limited to the use of infrastructure concerning information and communication technologies. There is a gap concerning the relationship between the role technology infrastructures as enablers of firms’ absorptive capacity. It will be particularly interesting to learn about the motives why companies use technology infrastructure, the barriers they encounter in this regard, the activities that can help them overcome these barriers and how it translates into enhancing firms’ absorptive capacity (Viscido et al., 2022, p. 19).

Building on the prior research on the literature on technology infrastructures and absorptive capcity the following research questions were proposed:

Q1: What are the primary drivers of technology infrastructure use from the perspective of firms’ absorptive capacity?
Q2: What are the main barriers associated with the use of technology infrastructures from the perspective of firms’ absorptive capacity?
Q3: In what ways can the current system of support for technology infrastructures be organised to more effectively foster the absorptive capacity of enterprises?

3. Methods

The present study was exploratory in nature and aims to improve understanding of relations between the role of technology infrastructures as enablers of firms’ absorptive capacity. The first step in the study was an investigation of current knowledge base, especially articles and reports concerning research and technology infrastructures. The sources of information on the articles were the Scopus and Web of Science databases. The results of the search in both databases is presented in Table 1.

Analysis of 109 publications from Scopus and 46 from WoS showed that most of them concern mainly information technology (IT) infrastructures or e-infrastructures. Only few of them relate to both categories: absorptive capacity and technology infrastructures, but even in these cases they concern IT infrastructure. Due to the small number of publications, a bibliometric analysis was not performed. These publications were supplemented by reports and documents published by the European Commission and EARTO, which were made available thanks to the author ’s cooperation with EARTO and participation in the European Commission Expert Group on Technoalogy Infrastructures (EGTI) as a Member.

The study takes advantage of data obtained from a survey on technology infrastructures organised by EGTI. Invitations to participate in the survey, launched on 19 August 2024, were sent through umbrella organisations, Member States contact points for ERA action 12 and disseminated nationally by Members of EGTI. The survey was open until the 30 November 2024. In total, 328 answers were received, including 5 from Poland. The survey consisted of 28 questions, which are presented in analitical report „User Needs for Technology Infrastructures” (EC 2025c, p. 52). The author obtained access to source data concerning Polish enterprises participating in the survey.

The number of enterprises from Poland that responded to the survey was too small to conduct quantitative research, therefore it was decided to conduct qualitative research to gain a better understanding of the responses provided by the respondents that completed the survey. For this purpose, in-depth 12 interviews with managers and employees from these enterprises were conducted (at least 2 from each). It was decided that, for the purposes of this study, only representatives of companies that completed the survey would be interviewed. The aim was to better understand the motives that prompted them to complete the survey and to clarify additional issues relevant to the research questions posed in the article. The interviews lasted from 30 to 90 minutes and were based on open-ended questions that allowed respondents to express themselves freely. All interviews were conducted in Polish, recorded with the respondents’ consent, and subsequently transcribed for analysis. In addition, six short interviews were conducted with representatives of companies that did not complete the survey. The purpose of these interviews was to obtain information on why the survey had not been submitted. The research method used in this study allowed to capture the overview of the role of technology infrastructures as enablers of firms’ absorptive capacity. It also provides a starting point for further research and more in-depth research in this topic in the future.

4. Results

4.1. General information about enterprises covered by the survey and interviews

The article takes advantage of the results of a survey conducted in 2024 by the European Commission and Members of the European Commission Expert Group on Technology Infrastructure (EGTI) to understand the industrial perspective on user needs for technology infrastructures. Among 328 responses were 208 SMEs or start-ups and 120 enterprises with more than 250 eployees. Table 2 presents the status of enterprises responding to the survey.

Summary information about the enterprises is provided in Appendix 1. The Polish firms participating in the survey were small or medium-sized enterprises (up to 250 employees). One described itself as a start-up (established in 2021), but falls within the category of small and medium-sized enterprises. The other enterprises were established between 1984 and 2022 and did not identify themselves as start-ups in the survey. According to the information obtained in the interviews, all of them operated on the basis of simple organizational structures and flexible project teams ensuring good communication and quick decision-making processes. Their founders were involved in day-to-day operations as managers or supervisors. Four enterprises offer products or services to customers, while one is working on a product that will be available on the market as a finished product in a few years (with funding secured from external investors). From the perspective of the start-up life cycle (although only one identifies with it), these enterprises can be placed in the growth stage.

For three enterprises, the current main market was the domestic market, for one the regional market, and for another the European market. In turn, two enterprises indicated the European market as their target market, one indicated the regional European market (several European countries), and the remaining two indicated the domestic market. For the latter two, the domestic market is a starting point for expansion into the European market: one began its foreign expansion in 2025, while the other is launching a new product that will be offered on foreign markets from the outset. All enterprises indicated the need to enter new markets and acquire new customers, while three of them indicated the need to stabilize revenue generation.

Two enterprises operated in the electronics industry, two in the energy industry, and one in the metal industry. Despite operating in the same industries, the enterprises did not compete directly and their products/services concerned various segments of the electronics or energy market. All of them are planning or already implementing work in the field of artificial intelligence and Industry 4.0 solutions. All of them had their own research and development departments, employing from a few to a dozen or so employees. Research and development activities were usually carried out in the form of projects and based on matrix structures (selected tasks are also carried out by employees from other departments). Three of them carried out research and development activities mainly on their own, while two outsourced them to external contractors. Four enterprises indicated that they use technology infrastructure 1-3 times a year, allocating less than 20% of their research and development expenditures to this purpose.

4.2. The awareness about technology infrastructure

Among the surveyed enterprises, 122 repondents (37% of total answers) indicated that they were familiar with the concept of technology infrastructure and had knowledge of the available infrastructure. In turn, 104 (32% of total answers) enterprises had knowledge of the infrastructure corresponding to their needs, but were not familiar with the concept of technology infrastructure, 65 (20% of total answers) enterprises indicated that they were familiar with the concept, but had no knowledge of the infrastructure available to them, and 37 (11% of total answers) were not familiar with the concept and had no knowledge of the infrastructure (EGTI, 2025, p. 11).

Three of the Polish enterprises participating in the survey indicated that they were familiar with the concept, but did not have information about the technology infrastructures available for them. The interviews confirmed that the managers of these enterprises have extensive knowledge of European-level activities resulting from their involvement in project consortia or European partnerships, which explains their familiarity with the concept of technology infrastructures. In turn, two enterprises indicated that they have information about technology infrastructure relevant to them, but are not familiar with the concept of technology infrastructures. One of the interviewees explained that his company has been cooperating with universities for many years and, from his perspective, the terminology (i.e. research or technology infrastructure) is a secondary issue. Another interviewee pointed out on the practical benefits of the concept of technology infrastructure, which could help identify those areas of activities at universities and institutes that are closest to cooperation with enterprises and, through appropriate support, contribute to its improvement: “Universities and institutes are large organizations, and not all scientists who work there need to cooperate with business, but it would be good for enterprises to know which of them are ready and open to such cooperation and are prepared for it.” [C1].

The interviewees also pointed out that, from their perspective, the poor availability of information about the infrastructure and the rules for using it is a major obstacle to its use. They usually get information about infrastructure based on many years of cooperation, but it is much more difficult for them to find such information about universities and institutes with which the enterprises have not had any previous contact. One interviewee pointed out: “Information about the equipment is scattered and incomplete, which makes it difficult to figure out who can specifically help us, when, and with what. There is no single place where we could find out what different institutes have to offer.” [D1].

Among Polish enterprises, four ipointed out that they used technology infrastructures, while one responded that it did not use technology infrastructure due to lack of knowledge about such infrastructure available in Poland in the field of its activities. This company outsources research work to other entities operating abroad. According to the information obtained during the interviews, this is due to the highly specialized knowledge and experience that the company needed and which was not available at Polish universities and institutes, but the company planned to include Polish universities and research organisations in its cooperation in the future.

4.3. The reasons for using technology infrastructure

The enterprises that indicated in the survey that they use technology infrastructure (264 or 80% of total answers) cited the following as the main reasons for using it: development of new technologies, methods, products, processes, and solutions they are working on (253, 77% of total answers), testing products or processes in an environment close to real life conditions (239, 65 of total answers), increasing competences to be able to adopt new technology (233, 61% of total answers), and performing some tests on their products manufacturing methods and processes (232, 60% of total answers). Enterprises that did not use the technology infrastructure indicated that this was due to a lack of knowledge and information about this infrastructure, remote distance, difficulties with access, lack of adequate resources, or more favorable conditions for conducting tests directly at their customers’ premises (EGTI, 2025, p. 13).

Among the five Polish enterprises that completed the survey, one indicated that it does not use technology infrastructure because it lacks knowledge about the availability of infrastructure that could address its needs. This is due to the fact that the solutions the company is intrested in “[…] are innovative on a global scale, and we are currently unaware of any center that would be able to comprehensively meet our needs. To our knowledge, such infrastructure is not currently available in Poland or Europe.” [E1]. Nevertheless, the company does not rule out using the infrastructure in the future, provided that it meets its needs.

The four remaining Polish enterprises pointed out that they use technology infrastructure and indicated as highly necessary for testing products or processes in an environment close to real life conditions (4 responses) and for making products and/or processes comply with standards (3 responses). Compared to all enterprises, Polish enterprises indicated less interest in the needs related to the development of new technologies, methods, products, processes, and solutions, as well as the development of prototypes. During the interviews, representatives of enterprises explained that they work independently on the development of new products and processes, while the use of technology infrastructure takes place at later stages of product launch, especially testing and adaptation to standards: “We worked independently on the development of our product and did not need the help of an external organisations. However, it was useful when we already had an advanced product and needed to test it outside our laboratory. In this respect, the assistance […] was very helpful.” [D1]. Compared to the other respondents, Polish enterprises indicated a lower needs to increase the competences, which was largely due to the high assessment of the current level of knowledge and competence of employees. One of the enterprises explained in an interview: “We have qualified employees who have been developing our technology for years […], and the solutions we are working on are so niche and require such specialized knowledge that currently none of the academic centers or institutes in Poland have it.” [B1].

All Polish enterprises participating in the survey planned to develop products or new technologies within the next two years and to use technology infrastructure. However, they indicated that there is insufficient technology infrastructure to address their needs or that they are unaware of its existence.

4.4. The forms of access to technology infrastructure

The most popular form of access to technology infrastructure among the surveyed enterprises (273 answerd to the question) was collaboration with research organizations and/or universities hosting technology infrastructure, followed by own testing and scale-up facilities and participation in collaborative projects (EU, regional, national). Less popular forms of using technology infrastructure were direct procurement of research and technology services (research contracts) from technology infrastructure hosts, paid access to facilities on market terms, use of the services of intermediaries to get acces to facilities and through publicly supported schemes (e.g. using innovation vouchers or through specific programs) (EGTI, 2025, p. 16).

For the Polish enterprises, the most common form of access to technology infrastructure was collaboration with research organizations and/or universities hosting technology infrastructure. However, next came direct procurement of research and technology services (research contracts) from technology infrastructure hosts, which were less popular among foreign enterprises. As one of the interviewees pointed out: “Research contract is a very attractive form of cooperation for us, as we clearly define what we want to receive from the university or institute and when. We also have secured intellectual property rights, which remain on our side and, in the case of research, are transferred to us.” [D2]. Another interviewee pointed out: “Cooperation on joint research projects is a big challenge, while contract research is much faster and simpler for us. When we use funding, we have to follow procurement procedures, but even so, from my perspective, they are faster and simpler than negotiating joint projects and then writing proposals, especially with several consortium members.” [B1]. One of the interviewees explained that the popularity of research contracts may also result from the funding rules for research projects in Poland: “Many calls published by the National Centre for Research and Development or Polish Agancy for Enterprise Development are addressed only to enterprises, and even if we wanted to form consortia, there is no formal possibility to do so, and the only form of university involvement in these projects is to purchase research services from them.” [B2]. Another interviewee pointed out: “Often when we obtain a large foreign contract, only one entity can participate in the procurement procedure, not a consortium, and then we have no choice. If we want to cooperate with universities or instituties, we take advanateg of contract research.” [A1].

At the same time, two Polish enterprises indicated that they do not intend to use access to infrastructure through publicly supported schemes in the near future. In interviews, they explained that the reasons for this are excessive bureaucracy associated with obtaining and accounting for support: “When our customers are interested in a new product, we cannot wait for a call to be launched and then for the application to be evaluated, with the risk that we may not receive the support in the end because we fill incorrectly some fields in application. We have to act quickly and efficiently because that is what our customers expect from us.” [D1]. The second interviewee pointed out, “We operate in the European market, where quality and timely delivery are what count. National calls are complicated, and even small grants require the engagement of significant resources to obtain them. We don’t have the time and money for that.” [A1].

4.5. The barriers to access to technology infrastructure

Among the main barriers to access to technology infrastructure, the surveyed enterprises primarily indicated two challenges: lack of financial resources and lack of staff within the enterprise. Lack of financial resources was primarily reported by SMEs and start-ups (69%), and to a lesser extent by large enterprises with more than 250 employees (47%). In turn, large enterprises indicated lack of staff within the enterprise (42%) and fear of losing research and development results and industrial secrets (44%) or legal issues like IPR (38%) as barriers to a greater extent than SMEs and start-ups (respectively 30%, 21% and 18%) (EGTI, 2025, p. 17).
The main barriers to accessing technology infrastructure identified by Polish enterprises are:

• lack of financial resources (4 enterprises),
• legal issues and fear of losing control over own IPR results (3 enterprises),
• lack of staff within our enterprise (2 enterprises).

The responses of Polish enterprises are similar to those provided by small and medium-sized enterprises and start-ups from other countries. According to the access to finance, three Polish enterprises took advantage of public funding to develop new products and services, mainly domestic and regional. They acted as leaders and partners in these projects. Of the remaining two enterprises, one is considering obtaining funding in the near future, while the other indicated that it does not use and does not plan to use funding at the national and regional level. Among the main challenges related to obtaining funding, firms indicated the need to provide own contribution (in cash), the complicated process of preparing applications, and the long application evaluation processes. Three respondents stressed that in the future they will focus on obtaining international projects, because despite high competition and low success rates, the application evaluation process is, in their opinion, more transparent and faster. [A1].

One of the enterprises also pointed out the following barriers related to access to technology infrastructure: lack of the required expertise or support in the field of technology to be taken into account, geographical proximity of the appropriate technology infrastructure for company, outdated or insufficiently modern equipment, lack of resources within the technology infrastructure to support the industry/our company. The interview explained that these result, among other things, from the geographical presence of the company, which is located in a considerable distance from major academic and research centers.

During the interviews, other barriers to accessing university and institute infrastructure were also pointed out, resulting from regulations concerning the system of evaluating scientific results in Poland: “Sometimes it is difficult to encourage them to cooperate because they have other priorities and activities, such as scientific publications. We are not interested in this, and often disclosing information is problematic for us, while scientists are held accountable for this and it is sometimes difficult to convince them to give up publishing.” [D2].

4.6. The actions to increase the access to technology infrastructure

The results of the survey suggests that the most useful options to increase the usage of technology infrastructure depend on the size of the responding enterprise. For SMEs and start-ups the most important was better knowledge of/insight into the offering of technology infrastructures (60% answers from 208), but above issue among larger enterprises is of less importance (only 5% answers from 120). SMEs and start-ups are also more intrested in funding to purchase access to technology infrastructures than larger enterprises (48% versus 22%). On the other hand, larger enterprises are more intrested in „one stop shop” access point than SMEs or start-ups (63% versus 37%). This confirms observations that large enterprises seek to overcome administrative barriers in accessing technology infrastructure, while SMEs have less experience in working with technology infrastructures and need information and financial support for that (EGTI, 2025, p. 20).

Among the measures that could contribute to better use of technology infrastructure, Polish enterprises primarily pointed to better knowledge of/insight into the offering of technology infrastructures (equipment, capabilities, services). During the interviews, it was explained that although databases containing descriptions of research equipment and services are available, they often relate to individual universities or institutes and vary in terms of content, often presenting mainly detailed technical parameters of the equipment. As a result, there is no single database presenting the research equipment and apparatus available in Poland and the services that can be provided by universities and institutes. One of the interviewees pointed out: “Several universities offer well-developed catalogs of services and available equipment. They provide contact details for technology brokers or researchers. This significantly speeds up the process of obtaining information, but it only applies to a few universities, and only the largest ones.” [D1].

Among other factors that could facilitate access to technology infrastructure, Polish enterprises pointed to:

• geographical proximity of technolog infrastructure with fitting offer (or help in using/collaborating on further distance) (2 enterprises),
• services from/cooperation with higher education institutions, related to technology development, testing and scaling up (2 enterprises),
• a “One stop shop” access point (for technology infrastructure and related services) (2 enterprises),
• training (development of skills) (2 enterprises),
• funding to ‘purchase’ access to technology infrastructure (1 enterprises).

According to the survey, two types of services were highly needed by European enterprises to enhance its capabilities to innovate: support to develop new technology, method, product, proces, solution (145 answers, 44% of total answers) and support to test products or processes in an environment close to real life conditions (131 answers, 40% of total answers). For all other types of services listed, the positive replies significantly outnumber the neutral or negative ones (EGTI, 2025, p. 22). In turn, Polish enterprises pointed to support to make products and/or process(es) comply with standards, legal norms, or similar as well as to perform some tests on product(s) manufacturing methods and/or process(es). The least interest was shown in acivities dedicated to increase the competences of companies to adopt new technology and/or automate industrial production. These responses were consistent with their earlier statements regarding the reasons for using the technology infrastructure. The Polish enterprises participating in the study developed new products or processes independently and based on internal resources, while support from technology infrastructures was desired during the testing and market launch phases of new solutions. The lower interest in support for competence development among Polish enterprises participating in the survey results, among other things, from the fact that enterprises have qualified employees and implement staff development activities based on their own resources or take advantage of personnel support programmes offered by various national and regional agencies. One of the interviewees pointed out: “Currently, there are many opportunities to take advantage of support in the field of human resources development offered, for example, by the Polish Agency for Enterprise Development or the Marshal’s Office. However, our products are so niche that it is difficult to find valuable external training, so we mainly educate ourselves through internal workshops and daily work.” [B1]. Another interviewee explained: “The stage of development we are at now does not require hiring additional employees, but we may need them in the future. That is why we are constantly expanding our network of contacts.” [D1].

5. Discussion

The primary goal of technology infrastructure is to “accelerate technological innovations toward societal and market adoption while promoting industrial competitiveness” (EGTI, 2025, p. 24). From this perspective, the concept of technology infrastructures is familiar with initiatives designed to strengthen firms’ absorptive capacity. Table 3 provides a summary of the analysis regarding the components of absorptive capacity (acquisition, assimilation, transformation, and exploitation), as well as the motives for using technology infrastructures, the barriers to accessing them, and the instruments available to overcome these barriers.

It appears that Polish enterprises are interested in utilizing technology infrastructures; however, there is a lack of accessible information about them. The interviews confirmed that establishing a database of such infrastructures would be highly beneficial in facilitating cooperation between enterprises and technology infrastructure providers. Interviewees cited examples of Polish universities that maintain clear and up-to-date offers for infrastructure access and related services, though these cases are isolated and there is no systematic, nationwide approach in this area. They also emphasized that the first step of mapping technology infrastructure should involve developing a methodology for inventorying existing infrastructures. The database should be accompanied by transparent rules for access—ensuring openness, non-discriminatory use, and adaptability for diverse users, which could help firms more effectively identify and acquire externally generated knowledge.

An interesting finding from the survey and interviews is that Polish firms, compared to those in other countries, tend to show greater interest in using technology infrastructures during the later phases of the innovation process, while demonstrating less engagement in the early phases, such as research and development activities. The Polish firms participating in the survey placed more emphasis on testing products or processes under near-real conditions and on ensuring compliance with standards, whereas they tended to develop new products and services independently. According to the opinions of respondents, it resulted from the lack of adequate and valuable knowledge offered by infrastructure providers, which is essential from the perspective of the development of new products and processes. On one hand, this highlights the crucial role that the competences of infrastructure providers in effectively utilizing these facilities (skills and competences are just as important as equipment). On the other hand, it reveals a mismatch between the services offered by technology infrastructures and the actual needs and expectations of firms. To overcome these barriers to cooperation, instruments such as small grants for the rapid verification of technological concepts and ideas proposed by companies, as well as programs supporting mobility and staff exchanges, could be beneficial. A notable example is ESA BIC Poland, which provides grants and support for startups developing space-related business ideas—offering up to €50,000 in non-repayable funding for business, technical, and legal assistance, along with access to office space and an international network of partners and incubators.

Facilities and equipments are only tangble elements of technology infrastructures, while intangible elements such as the knowledge and skills of the employees who operate and use it on a daily basis are of key importance from the perspective of its utilization by firms. Referring to the idea of absorptive capacity, the use of external sources of information by firms requires appropriate knowledge and skills on the part of both firms and technology infrastructures. This presents a significant challenge for technology infrastructure operators, who must not only modernize and maintain their facilities and equipment but also focus on developing the skills and competences of personnel responsible for operating the infrastructure and conducting research. In terms of typology proposed by Justman and Teubal (1995, p. 260), the results of the study suggest that local technology infrastructure available for firms does not meet the needs of capability creation (high-technology firms), but rather delivers technological services valuable for market-building (low-medium technology activities).

Both European and Polish enterprises primarily identified a lack of financial resources, legal challenges, and concerns about losing control over intellectual property rights as major barriers—issues that are particularly significant for the exploitation component of absorptive capacity. These challenges could be addressed through programs that fund collaborative research projects and offer support for research and intellectual property rights (IPR) services. However, insights from the interviews reveal that Polish companies often encounter administrative constraints, such as the labor-intensive process of preparing project proposals, as well as cultural barriers, including conflicting goals and expectations between firms and researchers. Firms highlighted the importance of securing demand for their products or services, which suggests the need to consider a broader use of innovative public procurement in the future as an instrument to stimulate demand and support innovation-driven growth of companies.

This article is among the first to explore the topic of technology infrastructures in Poland. The research conducted is therefore exploratory in nature and subject to several limitations. The survey results should be interpreted with caution, as they were dominated by small and medium-sized enterprises (SMEs), whose responses differed from those of large firms. The Polish participants in the survey consisted exclusively of SMEs and start-ups, and only five enterprises ultimately took part. All of these firms were engaged in R&D activities and possessed considerable experience in conducting research projects. The small Polish sample and the qualitative nature of interviews limit the possibility of generalising results and they should be treated as a hypothesis for a more comprehensive study. For future research, it would be valuable to include larger group of firms including those that do not conduct R&D activities, in order to better understand the barriers they perceive and the measures that could encourage them to utilize technology infrastructures. To investigate the reasons for low response rate to the survey among Polish firms (the survey was distributed to several dozen Polish firms, only five responses were received), short interviews were conducted with six companies. One cited technical difficulties in submitting the survey, while the remaining five explained that, after reviewing the survey, they found the survey was not relevant—or only partially relevant—to their operations, and therefore chose not to fill in it. In light of these findings, it would be advisable to raise awareness about technology infrastructures and repeat the survey with a larger and more diverse group of Polish enterprises, particularly large companies.

6. Conclusions

This article aims to apply the concept of technology infrastructures to the Polish context and to raise awareness among organizations that manage technology infrastructures, as well as those responsible for national and regional innovation policy development. The study connects the main motives and barriers related to the use of technology infrastructures with examples of instruments that could address these challenges, with the goal of improving their utilization and enhancing firms’ absorptive capacity.

Taking into account the limitations of the study discussed above, it can be pointed out that the drivers and barriers associated with the use of technology infrastructures were very similar for Polish firms and those from other European countries. However, several differences were identified from the perspective of Polish firms:

• collaboration focused primarily on more advanced phases of research and innovation processes (e.g. testing products or processes in environments close to real-life conditions),
• a lower perceived need to enhance competences, largely resulting from a high self-assessment of employees’ existing knowledge and skills,
• research contracts being a particularly common form of collaboration with technology infrastructures,
• the need for improved awareness and understanding of the technology infrastructure offer available in Poland,
• administrative constraints, such as the labor-intensive process of preparing project proposals (national and regional levels), as well as cultural barriers like conflicting goals and expectations between firms and researchers (i.e. resulting from the evaluation criteria).

The concept of technology infrastructures is a relatively new in Poland, although the construction, maintenance, and the use of research infrastructure has been a topic of discussions for many years. In Poland, as at the European level, these discussions have focused mainly on the so-called large-scale research facilities included in the Map of Polish Research Infrastructure, as well as Poland’s participation in ESFRI projects. Nevertheless, in many universities and research institutes, research infrastructures have been thoroughly modernized and upgraded, mainly due to European funds and operational programs such as Integrated Regional Development Programme (2004-2006), the Operational Programme Innovative Economy (2007-2013), the Operational Programme Smart Growth (2014-2020), the European Funds for Smart Economy (2021-2027), Regional Operational Programmes, and the National Recovery and Resilience Plan. Therefore, the concept of technology infrastructures can be applied to a wide range of facilities and equipment financed under these programs, facilitating more effective collaboration with firms and supporting the enhancement of their absorptive capacity.

Although the concept of technology infrastructure is relatively new, it builds on initiatives that are already well established at the national and regional levels, such as smart specialization strategies and Key Enabling Technologies, which can help facilitate its practical implementation. From this perspective, it also constitutes a promising area for future research and practical application of the concept by national and regional organisations responsible for research and innovation policies.

References

Antonelli, C., Link, A. N., & Metcalfe, S. (2009). Technology infrastructure (pp. 6–10, VI–X). Routledge Taylor & Francis Group.
Chaminade, C., & Edquist, C. (2009). Rationales for public policy intervention in the innovation process: Systems of innovation approach. In R. E. Smits, S. Kuhlmann, & P. Shapira (Eds.), The theory and practice of innovation policy (pp. 95–114). Edward Elgar.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.
Cramer, K. C., & Ruffin, N. V. (2025). The Europeanisation of research infrastructure policy. Minerva, 63, 45–68. https://doi.org/10.1007/s11024-024-09544-0
EARTO. (2022). EARTO case studies on technology infrastructure. https://www.earto.eu/wp-content/uploads/EARTO-Case-Studies-on-Technology-Infrastructures-Final.pdf
European Commission. (2012). Guide to research and innovation strategies for smart specialisations (RIS3). Publications Office of the European Union. https://doi.org/10.2776/65746
European Commission (2013). Regulation (EU) No 1291/2013 of 11 December 2013: Establishing Horizon 2020 – the Framework Programme for Research and Innovation (2014‒2020), Article 2 (6). https://eur-lex.europa.eu/eli/reg/2013/1291/oj/eng
European Commission. (2015). Promoting the access of SMEs to KETs technology infrastructures action plan. Publications Office of the European Union. https://doi.org/10.2769/92236
European Commission. (2017). Peer review: Poland’s higher education and science system. Publications Office of the European Union. https://doi.org/10.2777/193011.
European Commission (2019). Staff Working Document „Technology Infrastructures” (SWD(2019) 158 final). https://data.consilium.europa.eu/doc/document/ST-8411-2019-INIT/en/pdf
European Commission (2023). The Commision Regulation 2023/1315 of 23 June 2023 amending Regulation (EU) No 651/2014 declaring certain categories of aid compatible with the internal market in application of Articles 107 and 108 of the Treaty and Regulation (EU) 2022/2473 declaring certain categories of aid to undertakings active in the production, processing and marketing of fishery and aquaculture products compatible with the internal market in application of Articles 107 and 108 of the Treaty. https://eur-lex.europa.eu/eli/reg/2023/1315/oj/eng
European Commission. (2025a). Technology infrastructures: A strategic asset for European competitiveness – Conference report. Publications Office of the European Union. https://doi.org/10.2777/8029491
European Commission (2025b). The European Strategy on Research and Technology Infrastructures. Communiaction (COM(2025) 497 final). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri= COM:2025:497:REV1
European Commission. (2025c). User needs for technology infrastructures: Analytical report. Publications Office of the European Union. https://doi.org/10.2777/4186567
EGTI. (2025). Towards a European policy for technology infrastructures. Publications Office of the European Union. https://doi.org/10.2777/0876395
ESFRI. (2021). Roadmap 2021. Strategy Report on Research Infrastructures. https://roadmap2021.esfri.eu/ media/1295/esfri-roadmap-2021.pdf
Gittova, K., & Sipikal, M. (2022). University science parks as an innovative tool for university–business cooperation. In Proceedings of the 17th European Conference on Innovation and Entrepreneurship (ECIE 2022) (pp. 648–656).
Justman, M., & Teubal, M. (1995). Technological infrastructure policy: Creating capabilities and building markets. Research Policy, 24, 259–281. https://doi.org/10.1016/0048-7333(93)00765-L
Lau, A. K. W., & Lo, W. (2015). Regional innovation system, absorptive capacity and innovation performance: An empirical study. Technological Forecasting & Social Change, 92, 99–114. https://doi.org/10.1016/j.techfore.2014.12.003
Lewandowski, P., & Falkowski, R. (2025). Bezpieczeństwo technologiczne UE. Przegląd inicjatyw UE w obszarze działań na rzecz suwerenności technologicznej. Sieć Badawcza Łukasiewicz-ITECH Instytut Innowacji i Technologii, Warszawa. https://itech.lukasiewicz.gov.pl/publikacje/ bezpieczenstwo-technologiczne-ue-przeglad-inicjatyw-ue-w-obszarze-dzialan-na-rzecz-suwerennosci-technologicznej/
Miller, K., McAdam, R., Moffett, S., Alexander, A., & Puthusserry, P. (2016). Knowledge transfer in university quadruple helix ecosystems: An absorptive capacity perspective. R&D Management, 46(2), 383–399. https://doi.org/10.1111/radm.12182
Smith, K. (2009). Globalisation and innovation systems: Policy issues. In R. E. Smits, S. Kuhlmann, & P. Shapira (Eds.), The theory and practice of innovation policy (pp. 75–94). Edward Elgar.
Tassey, G. (2008). The roles and economic impacts of technology infrastructure (Working paper). https://www.nist.gov/system/files/documents/2017/05/09/Measurement_Infrastr_Roles_Impacts_v3.pdf
Tassey, G. (1992). Technology infrastructure and competitive position, Springer Science+Business Media.
Timofejevs, A., & Avotins, V. (2020). Institutional cooperation models for enhanced utilization of research infrastructure in the BSR: Case of Latvia. Engineering for Rural Development, 20, 1547–1533. https://doi.org/10.22616/ERDev.2020.19.TF383
Ulnicane, I. (2020). Ever-changing big science and research infrastructures: Evolving European Union policy. In K. C. Cramer & O. Hallonsten (Eds.), Big science and research infrastructures in Europe (pp. 76–100).
Viscido, S., Taucer, F., Grande, S., & Jenet, A. (2022). Towards the implementation of an EU strategy for technology infrastructures. JRC and EARTO. Publications Office of the European Union. https://doi.org/10.2760/4834
Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualisation, and extension. Academy of Management Review, 27(2), 185–203. https://doi.org/10.2307/4134351

Every organization – be it industrial or service-oriented – that seeks to develop, to compete effectively, and to create value needs to systemically generate and implement innovations across all its areas of operation. In economically developed countries, innovation is treated as a driving force (Hilmersson & Hilmersson, 2021, pp. 43‒49; Latif, 2024) that enables:

1) improved organizational economics,
2) opening up of new markets,
3) enriched knowledge resources and their creative application,
4) renewed industrial structures,
5) effective achievement of developmental goals,
6) expansion and diversification of products, services, and related markets,
7) implementation of new production, supply, and distribution methods,
8) the introduction of new methods of management and work organization, as well as changes in working conditions and staff qualifications,
9) staff integration and stronger relationships with customers,
10) improved quality of work, production standards, workplace health and safety, and environmental protection,
11) enhanced teamwork and collaboration with customers,
12) value creation,
13) increased living standards of societies, etc.

Therefore, it should be a guiding principle of management to systematically enhance the innovativeness of the organizations managed, continually striving to transform them into truly innovative enterprises. However, these issues are often marginalized in business operations. One hallmark of innovative organizations is the active involvement of managers and employees in fostering innovative activity, while remaining guided by shared values (Peters & Waterman, 2000, p. 47). This is supported by the strong correlation that has been found between the introduction of new products and the success of organizations (Tidd & Bessant, 2013, p. 26). Moreover, every manager needs to remember that any market advantage attained through innovation will inevitably diminish over time, due to innovative efforts on the part of other organizations. Therefore, successful organizations have to establish systematic, methodical, and organized processes to ensure the continued creation and practical implementation of innovations.

Innovation is of great economic and social importance, for organizations and societies alike. However, the various processes involved in creating, implementing, and managing innovations often encounter significant challenges. Numerous technical, technological, legal, economic, social and organizational barriers can hinder operational and development activities. These barriers, as highlighted in the literature, can be categorized into three groups (Ordoñez-Gutiérrez et al., 2023, pp. 1–22): 1) cost-related barriers (involving insufficient internal or external financial resources), 2) knowledge-related barriers (a lack of employment opportunities for employees with appropriate qualifications, limited knowledge of market rules, inadequate information about the company’s innovation needs), and 3) market knowledge related barriers (an inability to introduce innovations to the market effectively, making it impossible to recover the costs of their development).

Other scholars have proposed other classifications. Indrawati et al. (2020, p. 555) identified the following barriers: 1) those related to the financing of innovation in enterprises (high costs of innovation, difficulties in obtaining credit from financial institutions, high interest rates), 2) barriers related to government support (minimum financial assistance from the government, a lack of training from the government in the field of innovation, non-targeted government aid for innovative equipment), 3) those related to business partners (no suppliers as business partners, no marketing agencies as business partners), 4) those related to the quality of human resources (difficulties in recruiting high-quality employees, a lack of employee competence, resistance of employees to innovative changes, relatively high resistance of business owners to innovative changes, a lack of knowledge of business owners about innovation), 5) economic conditions (difficulty in obtaining innovative equipment, unstable economy, low purchasing power). Das et al. (2018, p. 99), in turn, have classified barriers into 1) internal (organizational strategy, organizational architecture, leadership, organizational culture, R&D organization, motivational incentives, negative attitude, inadequate innovative competences, unfavorable organizational structure); and 2) external (market dynamics, competitor behavior, market and technological turbulence, customer resistance, ecosystem dynamics, underdeveloped information network).

Recognizing such barriers can support rational decision-making and enhance innovation processes. One way to reduce the negative effects of these barriers to innovative activity is to engage in organized and rationally-managed collaboration among enterprises in the field of creating and implementing innovations.

The research problem addressed herein can be framed as follows: To what extent is collaboration in innovative activity prevalent among Polish enterprises? How dynamic is this collaboration, and is it an element of rational management? To address these questions, this study examines: 1) the proportion of innovative enterprises among the total number of companies, 2) the percentage of innovatively active enterprises, 3) the percentage of enterprises engaging in innovation-related collaboration, 4) the structure of collaborative partners, 5) the percentage of enterprises collaborating within cluster initiatives.

Via these issues, this study explores the role of managerial involvement in overcoming innovation barriers. By addressing these challenges, organizations can improve innovation management, enhancing competitiveness, value creation, knowledge expansion, and market growth.

A key assumption underlying this study is that the relatively low percentage of innovatively active organizations in Poland that decide to collaborate in the field of innovative activity is a consequence of a certain gap between theoretical advancements in innovative management and the willingness of managers to adopt and implement these concepts in practice.

Therefore, the objective of this publication is to assess the prevalence and dynamics of collaboration in innovation among Polish industrial and service enterprises and to demonstrate that such cooperation is not yet a common component of managerial decision-making. This state of affairs stands in contrast to the model solutions presented in the literature.

The article is structured into an introduction, followed by a critical literature review, research methodology, presentation of research findings and their analysis; and summary of the findings as well as suggestions for further research.

2. Literature review

Due to its high importance for the development of organizations, regions and entire economies – as well as in value creation – innovation has been extensively explored in the research literature, from a variety of technical, economic, management, organizational, sociological and psychological perspectives (Brzeziński, 2001; Sosnowska et al. 2000; Świtalski, 2005; Janasz & Kozioł-Nadolna, 2011; Zangara & Filice, 2024, pp. 360–383; Ullah et al., 2024, pp. 1967–1985). Authors have focused their attention on explaining the essence of innovation, classifying innovations, identifying innovation’s role in the development of organizations, increasing their competitiveness, improving management efficiency, improving management and the qualifications of employees, creating innovations as a team and organizing the national innovation system, etc. (Baruk, 2009, pp. 93–103; Świadek, 2021; Kozioł-Nadolna, 2022). Note that despite the extent of this literature, no uniform, universally applicable definition of innovation has yet been developed. The vagueness and diversity of definitions presented in the literature makes it difficult to understand the essence of innovation and hinders communication between theoreticians and practitioners, contributing to interpretative confusion (Baruk, 2022, pp. 10–23; Baregheh et al., 2009, p. 1334).

Since innovation activity faces various types of external and internal obstacles, some authors have attempted to identify such obstacles and their impact on the universality of creating and implementing innovations, as well as on the effectiveness of innovative activity. As noted above, three groups of barriers to innovative activity are most often indicated (Das et al., 2018, p. 99; Carvache-Franco et al., 2022, p. 1–17; Martínez-Azúa & Sama-Berrocal, 2022, p. 1–25): (a) cost and financial barriers, (b) knowledge barriers, (c) market barriers. Social barriers are also significant – managerial resistance, especially among middle management, and lack of employee engagement (Alshwayat et al., 2023, pp. 159–170).

Given the negative impact of these barriers on the efficiency of innovative activities, it is crucial to seek ways to eliminate or at least mitigate them. One such strategy widely discussed in the literature involves fostering collaboration with other business entities / research centers. Well-organized cooperation of this sort has been shown to have numerous advantages (Hardwick et al., 2013, pp. 4–21): reduced risk of failure, lower costs of innovation activities, improved effectiveness, rational use of knowledge possessed by cooperating organizations, expanding and enriching this knowledge, and shortened innovation development cycles, etc.

In general, cooperation in innovation can adopt four broad forms: distant, translated, definite and developed (Lind et al. 2013, pp. 70–91; Wong et al., 2018, pp. 316–332; Yunus, 2018, pp. 350–370). Collaboration is recognized as one of the four key elements of innovation – alongside ideas, implementation and value creation (Cowan et al., 2009; Watkins, 2024).

Since innovation relies on knowledge, numerous authors have explored the relationship between knowledge creation, innovation, and knowledge management (Baruk, 2023, pp. 43–55; Nonaka & Takeuchi, 2000; Kowalczyk & Nogalski, 2007; Baruk, 2009; Baruk, 2018, pp. 83–110; Baruk, 2011, pp.113–127). The function linking knowledge creation to innovation is undoubtedly the efficient management of knowledge and innovation within a single system. However, how to best achieve such a systemic approach remains an open problem challenge in the literature. Theoretical and empirical research on knowledge and innovation management continues to offer valuable insights, especially for leaders managing modern enterprises (Baruk, 2021, pp. 14–21; Baruk, 2022, pp. 10–23; Tidd & Bessant, 2013; Baruk, 2015, pp. 121–145).

5. Research methodology

This study is based on empirical research conducted by Poland’s Central Statistical Office (GUS) on the innovative activity of enterprises in Poland in 2014–2022. These surveys covered all industrial and service enterprises with 10 or more employees and were conducted as part of the international research program Community Innovation Survey (CIS), using the standardized PNT–02 questionnaire. The results of these studies were presented in the publications GUS (2017, 2019, 2020, 2023).

Numerical data taken from these publications enabled a detailed interpretation of the studied phenomenon in terms of its universality and dynamics. Additionally, they also allowed for the verification of the following detailed research questions:

1) Was innovation-related collaboration an element of rational decision-making processes in Polish industrial and service organizations?
2) How prevalent was collaboration in innovative activities?
3) What were the dynamics of such cooperation?
4) Who were the collaborating partners?
5) Was a cluster participation a significant form of cooperation?

The following research methods were employed in this study: 1) critical and cognitive analysis of selected literature on the subject, 2) descriptive and comparative methods, 3) statistical analysis, 4) the projective method. These methods facilitated the interpretation of key concepts, an overview of the current knowledge base, barriers accompanying innovative activity, as well as the universality and dynamics of the use of cooperation in Polish industrial and service organizations. Additionally, the projective method was used to outline potential strategies for improving the management of collaborative innovative activities.

Research results and discussion

1) The level of innovation at Polish enterprises and its dynamics in 2016–2022

The level of innovation can be assessed using various indicators, one of which is the proportion of innovative enterprises among the total number of business entities. As Table 1 shows, in 2016–2022, the share of innovative industrial enterprises in the total number of Polish enterprises in this sector was at an average level of 23.7%. This measure notably fluctuated over subsequent years, reaching its highest value in 2022 – over 32%. Compared to 2016, this represents an increase of 13.5 percentage point.

A key finding is the low percentage of industrial enterprises engaging in innovation cooperation, which remained below 10% throughout the study period (peaking at 9.1% in 2022). Additionally, industrial enterprises that were active in innovation undertook cooperation within the framework of cluster initiatives, with a participation rate of 14%. The highest values were observed in 2018 (21%) and 2019 (20.5%).

In the service sector, the average proportion of innovative enterprises was 19.7% – 4 percentage points less than for the industrial sector. Throughout the analyzed period, the percentage of innovative enterprises in services was consistently lower than in industry, with the largest gap coming in 2017, at 8.1 percentage points.

Engagement in collaborative innovation is typically influenced by the overall level of innovative activity within business entities. As shown in Table 2, the average percentage of innovatively active enterprises was 26% in the industrial sector and 20.8% in the services sector. A positive trend was the growing percentage of innovatively active entities in both sectors and within individual categories of enterprises. However, a decline was observed in the services sector in 2017–2019, where the share of innovatively active enterprises decreased by 0.8 percentage points compared to the previous period. A similar trend occurred among small enterprises, where in the corresponding period the proportion of innovatively active companies decreased by 1 percentage points.

A key characteristic of innovative activity is its correlation with enterprise size, measured by the number of employees. As shown in Table 2, the prevalence of innovatively active companies increases with enterprise size in both the industrial and service sectors. The lowest percentage of innovatively active firms was recorded among small enterprises, while the highest was observed among large enterprises. Notably, on average, in the European Union (2018–2020), 52.7% of enterprises engaged in innovative activities, whereas in Poland, the figure was only 34.9%, reflecting a 17.8 percentage point (p.p.) gap (EUROSTAT).

2) Prevalence of innovation-related collaboration among innovatively active enterprises

One of the key questions for this study is: What percentage of innovatively active enterprises engage in innovation-related cooperation? As Table 3 shows, in 2014–2016, almost one-third of innovatively active enterprises engaged in cooperation. However, this percentage declined by 9.4 p.p. in 2017–2019 and further by 8.1 p.p. in 2020–2022. Small enterprises were the least likely to cooperate, with only one in four firms engaging in collaborative innovation in 2014–2016. This figure further declined by 10.2 p.p. in 2017–2019 and by 8.3 p.p. in 2020–2022. The likelihood of cooperation increased with enterprise size. Among large enterprises, nearly 51% engaged in innovation cooperation in 2014–2016. However, this figure declined by 7.4 p.p. in 2017–2019 and by 0.5 p.p. in 2020–2022.

Similar trends in innovation-related collaboration were observed in the services sector. The most favorable period was between 2014 and 2016, when nearly 27% of innovatively active service companies engaged in cooperation. However, in the following years, this share declined by 8.4 percentage points and then by an additional 4.7 percentage points. A similar pattern was evident across service enterprises of different sizes, with small companies showing the lowest levels of collaboration and large enterprises the highest.

Between 2014 and 2016, in the industrial sector, companies that were actively engaged in innovation and cooperated with business entities in this field exhibited varying levels of collaboration depending on their industry. The highest levels of cooperation were seen among enterprises producing tobacco products, other transport equipment, and coke and refined petroleum products, where collaboration rates reached 60.0%, 59.3%, and 57.1%, respectively. In contrast, companies involved in furniture production, leather and leather products, and clothing manufacturing had the lowest levels of innovation-related cooperation, with rates of 16.1%, 15.4%, and 4.9% (GUS, 2017, pp. 87–88). A similar distribution was observed in the services sector during the same period 2014–2016, where enterprises in air transport, film and TV production, and scientific research and development demonstrated the highest levels of cooperation. In these industries, all air transport enterprises participated in innovation-related collaboration, while 66.7% of film and TV production companies and 59.6% of research and development firms engaged in such activities. On the other end of the spectrum, businesses involved in publishing, postal and courier services, and land and pipeline transport had the lowest levels of collaboration, with rates of 17.7%, 9.1%, and 8.0%, respectively (GUS, 2017, pp. 87–88).

Between 2017 and 2019, industrial enterprises exhibited significant variation in their engagement in innovation-related collaboration. The highest levels of cooperation were recorded in metal ore mining, where all innovatively active companies collaborated with other enterprises or institutions. Similarly, in the mining of hard coal and lignite, 60% of companies engaged in innovation-related cooperation, while 44.4% of enterprises producing coke and refined petroleum products did the same. The lowest levels of collaboration were observed in food product manufacturing, where only 14.2% of businesses participated, as well as in water collection, treatment, and supply at 13.8%, and furniture production at 13.5% (GUS, 2020, pp. 77–78). In the services sector during the same period, 2017–2019, scientific research and development remained the area with the highest prevalence of innovation cooperation, with nearly 59.7% of firms in this field engaging in such activities. Companies in architecture and engineering, as well as those involved in technical research and analysis, also showed significant collaboration, with 32.3% participating in innovation-related partnerships. Telecommunications companies followed, with 29.2% engaging in cooperation. In contrast, businesses in postal and courier services, warehousing, and transport support services demonstrated the lowest levels of participation, with cooperation rates of 5.6%, 4.2%, and 2.2%, respectively (GUS, 2020, pp. 77–78).

During the 2020–2022 period, in turn, innovation cooperation among industrial enterprises was most common in tobacco product manufacturing and the mining of hard coal and lignite, where 66.7% of innovatively active companies engaged in collaboration. The production of other transport equipment also exhibited a high level of cooperation, with 49.7% of enterprises in this sector participating. However, cooperation was much less common in industries such as wood, cork, straw, and wicker product manufacturing, where only 13.1% of firms engaged in collaborative innovation efforts. Similarly, clothing production had a cooperation rate of 12.5%, while reclamation activities showed the lowest level of engagement at just 10.0% (GUS, 2023, p. 77). In the services sector during the same period, scientific research and development remained the dominant area for innovation-related collaboration, with 65.6% of innovatively active organizations participating. Other sectors also demonstrated considerable cooperation, including insurance, reinsurance, and pension funds, where 41.1% of companies engaged in innovation partnerships, as well as architecture and engineering, where 33.6% collaborated. On the lower end of the spectrum, postal and courier services had a cooperation rate of 17.2%, land and pipeline transport had 12.9%, and water transport had the lowest level of participation at just 11.1% (GUS, 2023, p. 78).

In terms of geographical distribution, between 2014 and 2016 the highest percentage of industrial enterprises engaged in innovation cooperation was recorded in the Podkarpackie (41.4% of innovatively active companies), Śląskie (38.5%), and Małopolskie (38.1%) provinces. In contrast, the lowest levels of cooperation were observed in the Wielkopolskie (29.2%), Zachodniopomorskie (28.6%), and Lubuskie (27.1%) provinces. In the services sector during the same period, innovation-related collaboration was most common among enterprises in the Podkarpackie (73.4%), Warmińsko-Mazurskie (46.2%), and Świętokrzyskie (40.7%) provinces. On the other end of the spectrum, the lowest levels of cooperation were recorded in Łódzkie (14.9%), Opolskie (8.3%), and Lubelskie (7.3%) (GUS, 2017, p. 89).

In the 2017–2019 period, in turn, the pattern changed slightly for industrial enterprises, with the highest levels of innovation cooperation occurring in Lubelskie (29.2%), Śląskie (28.2%), and Opolskie (27.7%). The lowest rates were observed in Podlaskie (19.0%), Zachodniopomorskie (18.9%), and Warmińsko-Mazurskie (13.7%). For service enterprises during the same period, the highest levels of collaboration were recorded in Podkarpackie (39.7%), Łódzkie (31.8%), and Lubuskie (26.7%). In contrast, the lowest levels of cooperation were found in Podlaskie (10.0%), Wielkopolskie (7.4%), and Zachodniopomorskie (2.7%) (GUS, 2020, p. 79).

During 2020–2022, the territorial distribution of innovation cooperation changed once again. Among industrial enterprises, the highest prevalence was recorded in Kujawsko-Pomorskie (34.5%), Opolskie (30.1%), and Podlaskie (29.2%), while the lowest rates were seen in Łódzkie (18.8%), Świętokrzyskie (18.2%), and Warmińsko-Mazurskie (17.8%). In the services sector during the same period, the highest levels of cooperation in innovation were found in Zachodniopomorskie (48.5%), Podkarpackie (40.8%), and Lubuskie (35.5%), whereas the lowest levels were recorded in Opolskie (8.3%), Podlaskie (8.2%), and Kujawsko-Pomorskie (7.1%) (GUS, 2023, p. 79).

Analyzing innovation cooperation by technological level, it is evident that in the industrial processing sector, high-tech enterprises were the most engaged in collaboration, while low-tech enterprises showed the lowest participation. Between 2014 and 2016, innovation cooperation was undertaken by 46.7% of high-tech companies, compared to just 2.2% of low-tech firms (GUS, 2017, p. 90). A similar pattern persisted in 2017–2019, with 33.9% of high-tech enterprises engaging in cooperation, while low-tech firms exhibited a significantly lower rate of 14.6% (GUS, 2020, p. 80). Between 2020 and 2022, the trend remained consistent, with 36.4% of high-tech enterprises participating in innovation-related collaboration, compared to 20.2% of low-tech firms (GUS, 2023, p. 80).

3) Cooperation partners of enterprises in the field of innovative activities

Now that we know that Polish enterprises engaged in innovation-related collaboration, the next important question arises: Who were their key cooperation partners? Table 4 provides a detailed breakdown of these partnerships.

Among industrially active enterprises, collaboration with Polish universities was the most common. Between 2016 and 2018, nearly 48% of industrial enterprises cooperated with universities in Poland. This percentage increased to 56% between 2017 and 2019, before declining to 39% in 2020–2022. Collaboration with foreign universities was significantly less frequent. On average, 3.2% of industrial enterprises cooperated with universities in the EU and EFTA countries, while only 0.5% partnered with universities outside these regions.

Industrial enterprises also collaborated with public research institutes (including those of the Polish Academy of Sciences). Most often these were domestic Polish institutes. In the years 2016–2022, on average, less than 31% of innovatively active companies engaged in such cooperation. There were also cases, albeit rare, of cooperation with foreign institutes. On average, about 2.2% of industrial enterprises cooperated with institutes from the EU and EFTA countries in the analyzed period. On the other hand, about 0.5% of companies in the industrial sector undertook such cooperation with institutes in other countries.

Another common type of cooperation partner was companies belonging to the same group of companies. Most often these were domestic Polish companies, with which almost 26% of enterprises cooperated on average. Some enterprises also collaborated with affiliates from the EU and EFTA countries (16.2%) and from other countries (5.2%).

Industrial enterprises also cooperated with companies from outside their own group of enterprises. In this respect, domestic companies were preferred, with about 54% of industrial enterprises choosing Polish firms as cooperation partners. Meanwhile, 23.1% of partnerships involved EU and EFTA-based firms, and 9.5% involved companies from other countries.

Public sector entities also played a role in innovation cooperation, though at a smaller scale. On average, 11% of industrial enterprises collaborated with Polish public sector units, while partnerships with public sector entities from EU and EFTA countries involved only 0.83% of companies. Cooperation with public sector organizations outside these regions was rare, with only 0.33% of industrial enterprises reporting such collaborations.

Non-profit organizations accounted for a small share of innovation cooperation. In 6.1% of cases, Polish non-profits were cooperation partners for industrial enterprises. Cooperation with EU and EFTA-based organizations was recorded in 0.46% of cases, while 0.56% of partnerships involved non-profits from other countries.

In the service sector, the prevalence patterns of collaboration followed a similar trend. As seen in Table 5, service enterprises most frequently partnered with Polish firms from outside their corporate group. Between 2016 and 2018, only one in four innovatively active service enterprises engaged in such cooperation. However, this figure rose significantly in the following years, exceeding 70% in the 2020–2022 period.

A significant percentage of innovatively active service companies engaged in collaboration with Polish universities. However, this collaboration declined over time, decreasing from 46.4% in 2016–2018 to 43.1% in 2017–2019, and further dropping to 25.9% in 2020–2022. Cooperation with foreign universities remained limited, with only a small percentage of service enterprises establishing partnerships outside Poland. Service enterprises also collaborated with companies within their own corporate group, particularly those based in Poland. On average, 27.6% of service enterprises partnered with domestic companies from within their group, while 17.9% collaborated with firms from EU and EFTA countries, and 8.4% engaged in partnerships with companies from other regions. Collaboration with public research institutes, including those affiliated with the Polish Academy of Sciences, was another avenue for innovation-related cooperation. On average, 21.6% of service enterprises worked with Polish public research institutes, though partnerships with foreign institutions remained minimal. A smaller proportion of service enterprises partnered with public sector entities and non-profit organizations. On average, 11.1% of service enterprises collaborated with Polish public sector institutions, while 7.7% partnered with non-profits in the field of innovation.

4) Companies cooperating within the cluster initiative

One form that innovation-related collaboration among enterprises my take is participation in cluster initiatives. A cluster is a network that harnesses the innovative and organizational potential of a regional environment, supporting intellectual capital accumulation and its efficient utilization. (Encyclopedia.com, n.d.). This structure fits well into the modern innovation paradigm, emphasizing systematicity, holism, and interactivity. Clusters combine the flexibility of small businesses with the innovation and global reach of large enterprises, creating a network where businesses collaborate with the local community cooperates with the companies: state institutions, R&D centers, standardization bodies, quality control laboratories and universities, and even industry, political and cultural organizations.

The key question here is: To what extent did Polish companies take advantage of these opportunities? As shown in Table 6, between 2016 and 2018, only 3.5% of industrial enterprises participated in cluster initiatives. This figure declined to 3.2% in 2017–2019 and further dropped to 2.8% in 2020–2022.

In terms of enterprise size, large industrial companies demonstrated the greatest interest in cluster-based cooperation, with an average participation rate of 10.87% over the analyzed period. This figure showed an upward trend, indicating increasing involvement. In contrast, small industrial enterprises were far less engaged, with their participation rate averaging only 1.9%. Among service enterprises, in turn, only 2.2% of companies, on average, engaged in cluster cooperation. Large service enterprises participated the most, while small enterprises showed the least interest in this form of collaboration.

In terms of geographical distribution, in 2016–2018, the highest levels of industrial cluster cooperation were observed for industrial companies in the Lubelskie (8.1%) and Podkarpackie (7.6%) provinces. Meanwhile, the lowest levels were recorded in Opolskie (1.1%) and Wielkopolskie (2.0%). For service enterprises, the highest rates of participation were in Świętokrzyskie (6.6%) and Lubelskie (4.1%), whereas in Opolskie, no companies were recorded as participating in cluster initiatives during this period (GUS, 2019, p. 84).

By 2020–2022, the highest levels of cluster cooperation among industrial enterprises were in Podlaskie (6.3%) and Lubelskie (4.9%), while the lowest levels were recorded in Łódzkie (1.0%) and Lubuskie (1.6%). Among service enterprises, Lower Silesia (3.1%) and West Pomeranian (2.5%) recorded the highest levels of cooperation in this period, whereas Świętokrzyskie (0.5%) and Opolskie (0.6%) had the lowest (GUS, 2023, pp. 83–84).

Broken down by industry, between 2016 and 2018, cluster participation was highest among industrial companies engaged in hard coal and lignite mining (18.2%) and metal ore mining (16.7%). On the other hand, industries such as paper and paper product manufacturing and textile production had the lowest rates of participation, at only 1.2% each (GUS, 2019, pp. 86–87). By 2020–2022, the highest cluster participation in the industrial sector was recorded in the production of other transport equipment (12.5%), while no companies in the tobacco or clothing manufacturing sectors participated in cluster initiatives.

In the service sector, the highest level of cluster participation between 2016 and 2018 was found in scientific R&D (17.1%) and air transport (13.6%). In contrast, wholesale trade (1.5%) and land and pipeline transport (0.9%) had the lowest levels of participation. By 2020–2022, the research and development sector remained the most active in cluster initiatives, with 21.7% of enterprises engaged. However, in the water transport sector, no companies were recorded as participating in cluster cooperation (GUS, 2023, pp. 83–84).

More broadly, the statistical data presented in this paper indicate a certain regularity: industrial enterprises engaged in high-technology activities were the most likely to participate in cluster initiatives, with an average participation rate of 10.8%. Conversely, companies operating in low-technology industries had the lowest participation rate, at only 1.2% (GUS, 2023, p. 87).

4. Conclusions

The primary aims of this study were: 1) to analyze the prevalence of innovative activity among Polish industrial and service enterprises and, in this context, the prevalence of their innovation-related collaboration with other business entities, 2) to critically assess the extent of Polish enterprises’ innovation-related collaboration, demonstrating that such collaboration has occupied only a rather marginal place the managerial decision-making processes.

To achieve these goals, key measures were used to assess both innovation activity and innovation-related collaboration. These measures, outlined in six tables, were critically analyzed, resulting in the following key conclusions:

1. The share of innovative enterprises in the total number of businesses, both industrial and service-based, fluctuated across the years analyzed, without showing a clear upward trend.
2. The percentage of industrial enterprises engaged in innovation-related cooperation varied randomly over time and never exceeded 9.1% during the analyzed period.
3. Although the share of industrial enterprises participating in formalized cooperation structures was slightly higher, it remained inconsistent over time.
4. The percentage of innovatively active enterprises was slightly higher in the industrial sector than in the service sector. In both sectors, larger enterprises exhibited higher levels of innovative activity compared to smaller ones.
5. Cooperation in innovation activities was slightly more common in the industrial sector than in the service sector. Even during the most favorable period (2014–2016), cooperation rates did not exceed 33% in industry and 27% in services, and in subsequent periods, they were even lower. This suggests that decision-making processes related to innovation cooperation were highly inconsistent. Larger enterprises had a significantly higher level of participation, while small enterprises remained the least engaged.
6. When comparing innovation cooperation rates in Poland with those in other European countries, Poland ranked relatively low. In 2018–2020, 23.6% of industrial enterprises in Poland cooperated in innovation, whereas in Norway, this rate was 53.8%. Similarly, in the services sector, the Polish cooperation rate was 20.9%, compared to 43.2% in Cyprus (GUS, 2023, p. 87). Earlier data from 2016–2018 showed similarly unfavorable trends (GUS, 2020, p. 86). In 2020, the EU average for innovation cooperation stood at 25.7%, while in Poland, it was 22.4%, 3.3 percentage points lower (EUROSTAT 2020a, 2020b).
7. Both industrial and service companies undertook limited innovation-related collaboration with various partners. Most often these were: enterprises from outside their own group of enterprises; undertakings belonging to its own group of companies; higher education institutions; public research institutes; public sector entities and non-profit organizations. Collaboration was mainly with domestic partners from Poland, much less often with those from other countries. The low level of strategic organization in these collaborations suggests that cooperation was often ad hoc than systematically managed.
8. Cluster initiatives remained an underutilized form of innovation cooperation. The average participation rate was 3.16% for industrial enterprises and 2.16% for service enterprises, with a downward trend in later periods.

The relatively low level of innovation among Polish enterprises appears to stem from a lack of understanding of innovation’s role, its impact on business development, and weak innovation management practices that are not grounded in knowledge-based, research-driven models (Baruk, 2022, pp. 10–23; Baruk, 2021, pp. 14–27). Consequently, innovation activity and innovation-related collaboration remain limited.

Often, employees within companies lack the necessary knowledge to develop systematic or radical innovations. In such cases, it is crucial to access external knowledge through structured collaboration with scientific and economic institutions that possess the necessary expertise or can help co-create it. Therefore, managers must develop competencies in key areas related to innovation, such as knowledge management, strategy, organizational culture, systemic learning, teamwork, and cooperation with business partners and clients.

Effective innovation management should be based on a deep understanding of the impact of innovation on businesses, their employees, and end users. As a result, managers should strive to transform their organizations into leading innovators (Peters & Waterman, 2000, pp. 45–48). Achieving this goal requires implementing structured, research-backed innovation management strategies, as outlined in relevant scientific literature (Baruk, 2022, pp. 10–23; Baruk, 2021, pp. 14–27; Baruk, 2009; Baruk, 2015, pp. 121–145; Tidd & Bessant, 2013).

5. Suggestions for further research

Given the levels of enterprise innovation analyzed in this study, further theoretical and empirical research is warranted to assess the stability or variability of these measures over time. Conducting such an analysis would provide valuable insights into several key questions, including:

1. Are trends in enterprise innovation activity consistent over time, and if so, what patterns emerge?
2. What are the long-term tendencies in innovation-related cooperation?
3. How do these trends evolve dynamically across different periods?
4. Which scientific and economic entities are most commonly involved in innovation cooperation?
5. How widespread and dynamic is such cooperation across industries and sectors?
6. What tangible effects does innovation collaboration have on business performance?
7. How do information-sharing and decision-making processes influence the initiation of cooperation?
8. Do managers systematically analyze and evaluate innovation activity and collaboration trends?
9. Are the findings from these analyses effectively used to improve management processes and decision-making within enterprises?

Addressing these questions would deepen our understanding of innovation ecosystems, help identify barriers and facilitators of collaboration, and provide practical recommendations for improving innovation management strategies in business environments.

References

Alshwayat, D., Elrehail, H., Shehadeh, E., Alsalhi, N., Shamout, M. D., & Ur Rehman, S. (2023). An exploratory examination of the barriers to innovation and change as perceived by senior management. International Journal of Innovation Studies, 7(2), 159–170. https://doi.org/10.1016/j.ijis.2022.12.005

Baregheh, A., Rowley, J., & Sambrook, S. (2009). Towards a multidisciplinary definition of innovation. Management Decision, 4(8), 1334. https://doi.org/10.1108/00251740910984578

Baruk, J. (2009). Zarządzanie wiedzą i innowacjami [Management of knowledge and innovations]. Wydawnictwo Adam Marszałek w Toruniu.

Baruk, J. (2011). Wiedza w procesach tworzenia innowacji [Knowledge in the processes of creating innovations]. Organizacja i Kierowanie (4), 113–127.

Baruk, J. (2015). Zarządzanie działalnością innowacyjną w organizacjach naukowych i badawczo-rozwojowych [Managing innovative activities in scientific and research and development organizations]. Marketing Instytucji Naukowych i Badawczych, 17(3), 121–145; https://doi.org/10.14611/ minib.17.03.2015.04

Baruk, J. (2018). Wiedza i innowacje jako czynniki rozwoju organizacji – Podejście zintegrowane [Knowledge and innovation as factors of organizational development – An integrated approach]. Marketing Instytucji Naukowych i Badawczych, 29(3), 83–110. https://doi.org/10.14611/minib.29.09.2018.05

Baruk, J. (2021). Wspieranie zarządzania innowacjami rozwiązaniami modelowymi [Supporting innovation management with model solutions]. Marketing i Rynek, XXVIII(3), 14–27. https://doi.org/10.33226/1231-7853.2021.3.2

Baruk, J. (2022). Racjonalizacja zarządzania innowacjami – koncepcje modelowe [Rationalization of innovation management – Model concepts]. Marketing i Rynek, XXIX(5), 10–23. https://doi.org/10.33226/1231-7853.2022.5.2

Baruk, J. (2023). Znaczenie wiedzy w podnoszeniu sprawności działalności innowacyjnej przedsiębiorstw [The importance of knowledge in improving the efficiency of innovative activities of enterprises]. Marketing i Rynek, XXX(6), 43–55. https://doi.org/10.33226/1231-7853.2023.6.5

Brzeziński, M. (ed.). (2001). Zarządzanie innowacjami technicznymi i organizacyjnymi [Management of technical and organizational innovations]. Warszawa, Difin.

Carvache-Franco, O., Carvache-Franco, M., & Carvache-Franco, W. (2022). Barriers to Innovations and Innovative Performance of Companies: A Study from Ecuador. MDPI. Social Science, 11(2), 1–17.

Cowan, K. M., Haralson, L.E., & Weekly, F. (2009). The Four Key Elements of Innovation: Collaboration, Ideation, Implementation and Value Creation. Retrieved July 24, 2024, from https://www.stlouisfed.org/ publications/bridges/summer-2009/the-four-key-elements-of-innovation-collaboration-ideation-implementation-and-value-creation

Das, P., Verburg, R., Verbraeck, A., & Bonebakker, L. (2018). Barriers to innovation within large financial services firms: An in-depth study into disruptive and radical innovation projects at a bank. European Journal of Innovation Management, 21(1), 96–112. https://doi.org/10.1108/EJIM-03-2017-0028

Encyclopedia.com. (n.d.). Clusters. Retrieved July 28, 2024, from https://www.encyclopedia.com/ entrepreneurs/encyclopedias-almanacs-transcripts-and-maps/clusters

EUROSTAT. (2020a) Enterprises with innovation activities during 2018 and 2020 by NACE Rev. 2 activity and size class (2020). Retrieved July 26, 2024, from https://ec.europa.eu/eurostat/databrowser/ view/inn_cis12_inact/default/table?lang=en&category=scitech.inn.inn_cis12.inn_cis12_inno

EUROSTAT. (2020b). Innovative enterprises that co-operated on R&D and other innovation activities with other enterprises or organisations, by kind and location of co-operation partner, NACE Rev. 2 activity and size class (2020). Retrieved July 26, 2024, from https://ec.europa.eu/eurostat/databrowser/ view/inn_cis12_coop/default/table?lang=en&category=scitech.inn.inn_cis12.inn_cis12_inno

GUS. (2017). Działalność innowacyjna przedsiębiorstw w latach 2014-2016 [Innovative activity of enterprises in the years 2014-2016]. Central Statistical Office (GUS). Warszawa, Szczecin.

GUS. (2019). Działalność innowacyjna przedsiębiorstw w latach 2016-2018 [Innovative activity of enterprises in the years 2016-2018]. Central Statistical Office (GUS). Warszawa, Szczecin.

GUS. (2020). Działalność innowacyjna przedsiębiorstw w latach 2017-2019 [Innovative activity of enterprises in the years 2017-2019]. Central Statistical Office (GUS). Warszawa, Szczecin.

GUS. (2023). Działalność innowacyjna przedsiębiorstw w latach 2020-2022 [Innovative activity of enterprises in the years 2020-2022]. Central Statistical Office (GUS). Warszawa, Szczecin.

Hardwick, J., Anderson, A. R., & Cruickshank, D. (2013). Trust formation processes in innovative collaborations. European Journal of Innovation Management, 16(1), 4–21. https://doi.org/10.1108/ 14601061311292832

Hilmersson F.P. & Hilmersson M. (2021). Networking to accelerate the pace of SME innovations. Journal of Innovation and Knowledge, 6(1), 43–49. https://doi.org/10.1108/14601061311292832

Indrawati H., Caska, & Suarman. (2020), Barriers to technological innovations of SMEs: how to solve them?, International Journal of Innovation Science, 12(5), 545–564. https://doi.org/10.1108/IJIS-04-2020-0049

Janasz, W. & Kozioł-Nadolna, K. (2011). Innowacje w Organizacji [Innovations in the Organization]. Warszawa, PWE Polskie Wydawnictwo Ekonomiczne.

Kowalczyk, A. & Nogalski, B. (2007). Zarządzanie wiedzą. Koncepcje i narzędzia [Managing Knowledge: Concepts and Tools]. Warszawa, Difin.

Kozioł-Nadolna, K. (2022). Przywództwo a innowacyjność organizacji. Perspektywa teoretyczna i praktyczna [Leadership and Organizational Innovativeness: Theoretical and Practical Perspectives]. Warszawa, Difin.

Latif, Y. (2024). Importance of innovation. Retrieved July 26, 2024, from https://www.linkedin.com/ pulse/importance-innovation-yasir-latif-cipm-cipc-v0ed

Lind, F., Styhre, A., & Aaboen, L. (2013). Exploring university-industry collaboration in research centres. European Journal of Innovation Management. 16(1), 70–91. https://doi.org/10.1108/14601061311292869

Martínez-Azúa, B. C. & Sama-Berrocal, C. (2022). Objectives of and Barriers to Innovation: How Do They Influence the Decision to Innovate? Journal of Open Innovation: Technology, Market, and Complexity, 8(134), 1–25. https://doi.org/10.3390/joitmc8030134

Nonaka, I. & Takeuchi, H. (2000). Kreowanie wiedzy w organizacji [Generating Knowledge in the Organization]. Warszawa, Poltext.

Ordoñez-Gutiérrez A.V., Mendez-Morales A., & Herrera M.M. (2023). Barriers to Innovation: A Systematic Literature Review. Trilogia, Ciencia Tecnología Socieda, 29(15), 1–22. https://doi.org/10.22430/21457778.2614

Peters, T.J. & Waterman, R.H. (2000). Poszukiwanie doskonałości w biznesie [Seeking Excellence in Business]. Warszawa: Wydawnictwo MEDIUM. Polish edition of Peters, T. J., & Waterman, R. H. Jr. (1982). In search of excellence: Lessons from America’s best-run companies. Harper & Row.

Sosnowska, A., Łobejko, S., & Kłopotek, A. (2000). Zarządzanie firmą innowacyjną [Managing an Innovative Firm]. Warszawa, Difin.

Świadek, A. (2021). Krajowy system innowacji 2.0 [The National Innovation System 2.0]. Warszawa, CeDeWu.

Świtalski, W. (2005). Innowacje i konkurencyjność [Innovations and Competitiveness], Warszawa, WUW.

Tidd, J. & Bessant, J. (2013). Zarządzanie innowacjami. Integracja zmian technologicznych, rynkowych i organizacyjnych. Warszawa. Oficyna a Wolters Kluwer Business. Polish edition of Tidd, J. & Bessant, J. (1997), Managing Innovation: Integrating Technological, Market and Organizational Change.

Ullah, I., Hameed, R.M., Mahmood, A. (2024). The impact of proactive personality and psychological capital on innovative work behavior: evidence from software houses of Pakistan, European Journal of Innovation Management, 27(6), 1967–1985. https://doi.org/10.1108/EJIM-01-2022-0022

Watkins, M. (2024). The Power of Collaboration: How an Open Innovation Platform Fuels Your Company’s Success. Retrieved July 25, 2024, from https://www.wazoku.com/blog/open-innovation-crowdsourcing-external-innovation-collaboration/

Wong, J.-Y., Wan, T.-H., & Chen, H.-C. (2018). The innovative grant of university–industry–research cooperation: A case study for Taiwan’s technology development programs. International Journal of Innovation Science, 10(3), 316–332. https://doi.org/10.1108/IJIS-01-2017-0004

Yunus, E. N. (2018). Leveraging supply chain collaboration in pursuing radical innovation. International Journal of Innovation Science, 10(6), 350–370. https://doi.org/10.1108/IJIS-05-2017-0039

Zangara, G. & Filice, L. (2024). Innovating the management of supply chains for social sustainability: from the state of the art to an integrated Framework. European Journal of Innovation Management, 27(9), 360–383. https://doi.org/10.1108/EJIM-02-2024-0120

The pursuit of beauty is as old as mankind – throughout history, people have tried to improve their attractiveness and enhance their beauty. Aesthetic medicine, often considered as ancient as medicine itself (Krueger et al., 2013), traces its roots back to early civilizations. The ancient Egyptians, for instance, used oils, salt, alabaster, and milk to aesthetically improve the skin of their body and face (Brody et al., 2000). Nowadays, with rapid technological advancements and ongoing developments in medical science, aesthetic medicine offers patients/clients the possibility of seemingly eternal youth and an ideal figure (Napiwodzka-Bulek, 2017). These modern methods provide women as well as men the opportunity to maintain a young look and to age in the best possible way, while also significantly enhancing patients’ quality of life (Welsch, 2005; Asscher, 2014).

The aesthetic medicine market has emerged as a significant segment of the self-financed medical industry (Lin & Yen, 2020). The global aesthetic medicine market size was valued at USD 82.46 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 8.3% from 2024 to 2030 (Grand View Research, 2024). Medical tourism, an influential factor in this growth, exhibited a compound annual growth rate of 15% by 2018 (Chistobaev & Semenova, 2018). In Europe, Spain is notably prominent in aesthetic medicine, with regulations permitting cosmetic procedures for patients as young as 14 years old (Pustułka & Jędrzejczak, 2018).

In Poland, the value of the aesthetic medicine market was estimated at 112 milions USD in 2022 (Posełek, 2024). The rates of individuals interested in improving their external appearance have steadily increased over time. Notably, male patients are becoming more frequent visitors to aesthetic medicine clinics. Men are increasingly self-aware, with their external appearance influencing their sense of attractiveness, self-confidence, and masculinity (Załuski, 2023).

The development of the aesthetic medicine market is shaped by several current trends, including:

• personalized treatments,
• cell-based therapies,
• increased integration of artificial intelligence (AI),
• the use of augmented reality (AR) and virtual reality (VR) technologies, and
• advances in non-surgical technologies.

The distinctive characteristics of the aesthetic medicine market – such as comparatively high costs, lack of public funding, individualized services and outcomes, and subjective assessments of appearance – play a significant role in determining patient satisfaction. Importantly, satisfaction in aesthetic medicine differs significantly from satisfaction with other medical treatments. This distinction underscores the need for further investigation into consumer behavior within this market (Ankiel et al., 2021). A number of factors contribute to the limited understanding of this field, including the following:

• Aesthetic medicine is a relatively new area of medical study, having been the subject of limited research from economic, sociological, and psychological perspectives.
• Services in aesthetic medicine often remain an intimate topic; minimal downtime allows patients to recover privately, which may lead to reluctance in participating in studies and consequently smaller sample sizes (Liao et al., 2019).

The primary objective of the present study was to identify and evaluate the determinants of satisfaction among patients/clients with aesthetic medicine treatments that are based on innovative technologies. A secondary aim was to analyze commonly performed procedures (from the perspectives of both doctors and patients) and assess patient satisfaction levels.

2. Theoretical background

Aesthetic medicine employs minimally invasive methods to prevent skin aging and reduce fat tissue. It is often referred to as “wish-fulfilling medicine,” as its procedures are directed by patients’ desires, seeking to improve their quality of life and mental well-being. One way to define aesthetic medicine is medical interventions to prevent and manage skin aging, thereby enhancing patients’ appearance and subjective well-being (Boon & Tan, 2007; Ankiel et al., 2021). As such, unlike traditional medicine, aesthetic medicine does not seek to treat diseases. Patients seeking such treatments are typically healthy images who wish to enhance their physical image (Rymkiewicz, 2018).

Aesthetic medicine often works in tandem with cosmetology, which focuses on contemporary and evolving treatments. These treatments are tailored to meet consumer demands for preventative care against skin aging (Pfenninger & Fowler, 2011). The integration of medical and cosmetological techniques ensures a comprehensive approach to patient needs.

Table 1 outlines a classification of aesthetic medicine treatments.

The number of aesthetic medicine clinics is increasingly significantly each year, driven by growing consumer interest. Non-surgical treatments, including botulinum toxin A (BoNT-A), hyaluronic acid, other dermal fillers, and laser treatments, have been the fastest-growing procedures in aesthetic medicine over the past five years (Vlahos & Bove, 2015). For instance, the use of botulinum toxin A injections has surged remarkably since their first use in this setting in the mid-1980s (Dover et al., 2018; Carruthers & Carruthers, 1998). Current aesthetic uses of BoNT-A include treating glabellar lines, forehead wrinkles, periorbital and perioral lines, platysmal bands, horizontal necklines, and the masseter muscle, among many other applications (Blitzer et al., 1993; Dorizas et al., 2014). Aesthetic medicine is a rapidly evolving field, propelled by innovative technologies and advancements in treatment materials (Przylipiak, 2017). Modern aesthetic medicine employs a wide range of lasers, energy-based devices, and natural or synthetic fillers (Redaelli & Ignaciuk, 2000). Technological progress has significantly expanded the possibilities for aesthetic treatments, transforming the delivery of skincare within clinical dermatology. In particular, the harmful effects of UV irradiation have spurred the development of therapies aimed at reversing photodamage. Among these, laser technologies have become a cornerstone of skin rejuvenation treatments (Theodorou et al., 2021).

Table 2 presents the growth in innovative aesthetic medicine services since 1965. Research papers since 2018 have highlighted the introduction of the newest technology, High-Intensity Focused Electromagnetic Field (HIFEM) used in body contouring and urinary incontinence treatments, and address the safety and effectiveness of this technology (Jacob & Paskowa, 2018; Samuels, 2018; Samuels et al., 2019). A variety of technologies and less invasive methods to treat a wide range of beauty defects and difficulties in daily life are constantly being improved. The advancement of medical technology, the provision of professional services, and the functional training of aesthetic medical practitioners all significantly contribute to the profitability of enterprises in this field (Skountridaki, 2017).

Consumer satisfaction in aesthetic medicine is understood as a psychological state in which the perceived characteristics of the product or services match consumer expectations (Hunt, 1998). In this context, patient satisfaction depends on the post-treatment effects. Many objective and sub-objective factors influence patient satisfaction, such as treatment availability, the ongoing care process, health improvements health, and service price (Małecka & Marcinkowski, 2007; Ankiel & Kuczyńska, 2018). One of the most important determinants of patient satisfaction is the experience and expertise of the medical doctor who is carrying the treatments, who should provide patients with positive emotions and a sense of safety. Patient trust, doctors’ empathy, attentive listening, and professionalism are critical factors in aesthetic medicine. Moreover, effective communication between doctor and patient provides an opportunity to thoroughly understand the patients’ concerns and fosters a sense of trust in the doctor (Bukowska-Piestrzyńska, 2017).

Patient satisfaction is a key metric for evaluating a healthcare organization’s success. In the United States, physician bonuses are linked to patients’ evaluation of their interaction with doctors (Funk et al., 2012). A variety of tools and measures have been developed to estimate patients satisfaction and evaluate specific outcomes, which are important to receiving aesthetic medicine services (Cohen & Scuderi, 2017). For instance, Likert-type scales may be used to rate patients’ satisfaction, such as the Facial Lines Treatment Satisfaction Questionnaire (Cox et al., 2003). Another instrument is the Facial Line Outcome questionnaire, which evaluates specific outcomes such as perception of attractiveness, self-perception of aging, and extended facial lies, which may project expressions such as stress, anger, or simply looking tired which the patient does not actually feel (Dayan et al., 2019). For a more objective and reproducible assessment of aesthetic procedures and patient satisfaction, the Aesthetic Numeric Analog Scale has been introduced. This scale builds on established tools such as the Wong-Baker FACES Pain Rating Scale and the 11-Point-Box-Scale to create a standardized approach (Funk et al., 2012).

The Net Promoter Score (NPS) is a versatile tool employed across various service industries, including healthcare, to gauge how likely a patient or other consumer is to recommend a service. In this framework, consumers are categorized as Promoters (those who actively recommend the services), Passives (those who are satisfied, but do not actively recommend the services); and Detractors (consumers who actively discourage others from using the services) (Reichheld, 2003). NPS is increasingly being used to measure patient satisfaction , serving as a simple yet effective indicator of overll cosumer experience.

One notable application of NPS is the Family and Friends Test, widely used by the National Health Services (NHS) of the United Kingdom to measure overall patient satisfaction and likelihood of recommendation (Stirling et al., 2013; Wilberforce et al., 2019). The straightforward and unambiguous nature of the NPS methodology has contributed to its growing popularity in consumer satisfaction studies, often outperforming alternative models like SERVQUAL or SERVPERF in terms of usability and ease of interpretation (Parasuraman et al., 1985; Cronin & Taylor, 1992).

However, while NPS and similar tools have been extensively studied and applied in fields like plastic surgery, dentistry, and orthodontics (De Vries et al., 2014; Sharp et al., 2014; Abbas & Karadavut, 2017), aesthetic medicine remains underexplored in the context of consumer satisfaction. Existing research on consumer behavior in healthcare highlights significant gaps in understanding the determinants of satisfaction among patients undergoing aesthetic medicine treatments and the relationship between satisfaction and loyalty of consumers/patients.

The present study, therefore, aims to identify the satisfaction indicators of the clients using innovative aesthetic medicine services based on innovative technologies. Evaluating client satisfaction in this context will enable a more precise identification of patient expectations, allowing practitioners to better align their offerings with client needs.

3. Materials and methods

The research process was structured in two stages, combining both quantitative and qualitative methods to ensure a comprehensive analysis of satisfaction indicators among clients and practitioners of aesthetic medicine services.

Stage 1: quantitative survey
The first stage involved a quantitative survey conducted through direct interviews at aesthetic medicine clinics located in selected large cities in Poland (with more than 500,000 inhabitants) – Poznań, Kraków, Warsaw, Łódź and Gdynia – in the period from May to June 2019. As a research tool, we used a direct interview questionnaire prepared and verified in piloting. The research population included consumers declaring regular use of aesthetic medicine services. The research sample included 745 respondents; the selection of respondents was based on purposive sampling (Tongco, 2007). The reliability of the construction of the questionnaire was verified using the Alpha-Cronbach test (the coefficient was 0.81, indicating good reliability).

Two crucial aspects of the research are worth emphasizing (Ankiel et al., 2021):

• the distinctive research site: large aesthetic medicine clinics in the above-mentioned cities (each with more than 500 patients in their client bases),
• the significant size of the research sample composed of patients of these clinics (n=745).

A review of the existing literature shows that there have been relatively few comparable research attempts in aesthetic medicine, mainly due to the difficulties of obtaining consent for participation of the patients in a direct questionnaire. These challenges arise from the very personal nature of the procedures and the sensitive reasons for their implementation (Angelini & Carmignani, 2017; Betancur et al., 2014).

Stage 2: qualitative survey

The second stage involved a qualitative survey conducted among aesthetic medicine doctors. This phrase aimed to identify the key determinants influencing the development of treatment offerings in aesthetic medicine. The survey was carried out in August 2019, among physicians attending the XXI 5 Continent – Congress Barcelona, and in September 2019, among attendees of the XX International Congress of the Polish Society of Aesthetic Medicine and Anti-Aging in Warsaw. The research sample included 20 respondents – doctors from Poland, the United States, and Spain. Individual in-depth interviews (IDIs) were conducted using a pre-tested direct interview questionnaire to explore the perspectives of medical professionals.

The satisfaction survey of patients using aesthetic medicine services in Poland was carried out using the Net Promoter Score technique.

4. Results and discussion

4.1. Identification the most popular innovative aesthetic medicine treatments

Continuous technological advances and the evolution of aesthetic medicine techniques have significantly expanded the options available to patients, enabling the use of non-invasive or minimally invasive techniques to improve the external appearance and address signs of skin and body aging. The primary goals driving modern aesthetic medicine are to reduce the invasiveness of treatments and shorten the recovery time while maintaining the best possible results.

Striving to meet evolving trends in consumer expectations, manufactures of Energy Based-Devices (EBDs) are continually introducing innovative technologies. Among the latest technologies to be introduced into the aesthetic medicine market is the High-Intensity Focused Electromagnetic Field (HIFEM). In Poland, distribution of HIFEM devices began in 2019. HIFEM is used for body contouring treatments and treating urinary incontinence (Jacob & Paskowa, 2018). Owners of aesthetic medicine clinics and medical doctors can choose among various medical devices and fillers, and other injection materials. However, the wide array of available equipment and materials poses challenges in creating the most effective and appealing service offerings. The selection process requires careful consideration of consumer preferences, market trends, and the specific benefits of each technology. The most popular aesthetic medicine services in medical doctors’ opinions are shown in Table 3.

The most popular aesthetic medicine services selected by medical doctors is hyaluronic acid treatment. Hyaluronic acid is commonly used to fill the wrinkles, giving the client/patient a younger and smoother skin surface. It is also used to enhance the volume of the cheeks and to augment the lips. Its popularity is largely due to the low percentage of post-treatment complications. Other fillers, such as autologous fat tissue or collagen, cause post-treatment complications more often (Carruthers & Carruthers, 2011).

The second most popular aesthetic medicine treatment indicated by medical doctors is botulinum toxin type A injections – one of the most widely used treatments for reducing signs of skin aging. BoNT-A works by inhibiting the secretion of acetylcholine, leading to the relaxation of the facial muscles and making the face appear smoother and younger (Baumann et al., 2016). Additionally, neurologists use it to treat migraine headaches.

The third most popular aesthetic treatment services is laser hair removal (60% of indications by the medical doctors). Laser hair removal is based on selective thermolysis, which targets melanin in the hair to produce a localized thermal effect, damaging the hair follicle and stopping hair growth (Lanigan, 2000). This treatment provides satisfactory post-treatment outcomes.

Next most frequently, medical doctors selected PDO thread treatments (25% indications). PDO threads are applied under the face skin, providing a revitalizing and lifting effect (Przylipiak, 2017). The least popular aesthetic medicine treatment, as indicated by the physicians, is laser telangiectasia removal (with only 10% indications). However, despite its lower popularity, this treatment remains the only effective option for addressing flushing – an involuntary or temporary reddening of the face, neck, and décolletage caused by factors such as temperature changes, physical activity, or consuming hot and spicy foods – and telangiectasia, which involves the chronic dilation of small blood vessels, resulting in visible dark red or light red blotches on the skin.

The popularity of innovative aesthetic medicine services as ranked by the patients surveyed, on the other hand, is shown in Table 4.

Based on patient responses, botulinum toxin type A injections (75% indications) are the most popular aesthetic medicine treatment. This is followed closely by procedures based on hyaluronic acid injections (67% indications). The third most popular selection is laser hair removal (37% indications), with lip augmentation ranking next (34% indications). While this latter service is typically performed by using hyaluronic acid, doctors might also apply collagen or silicon.

The next innovative treatment in the patients’ ranking is microneedle mesotherapy (25% indications). This treatment has gained its popularity due to its versatility in treating various conditions, such as alopecia or selected skin inflammations (Jager et al., 2011). Ranking next are laser telangiectasia removal (20% indications) and fractional ablative laser eCO2 (18% indications). The fractional ablative laser is particularly notable for its effectiveness in scar removal and skin resurfacing. This treatment works by damaging targeted areas of the skin to stimulate the repair process, which shortens collagen fibers and results in smoother, more delicate skin (Alexiades-Armenakas et al., 2008). Despite being one of the most invasive treatments in aesthetic medicine, it remains a highly rated and sought-after service.

The next most popular aesthetic medicine treatments are High Intensity Focused Ultrasounds and Dermapen® treatment and face volumetry (both with 17% indications). Contemporary High Intensity Focused Ultrasounds (HIFU) is one of the most effective methods of non-invasive skin lifting. This treatment’s main aim is to provide to focused thermal damage in deep layers of the skin or fat tissue (Wasiluk, 2007). Dermapen®, in turn, is a treatment that uses superficial skin needling to start skin self-recovery (Hou et al., 2017). Volumetry treatment gives increasing volume to tissues in the checks, jawline, etc. (Baumann et al., 2016). Other treatments were indicated by less than 15% of respondents and were therefore not included in this analysis.

In summary, aesthetic medicine treatments incorporating innovative techniques such as laser therapy and HIFU are very popular due to their fast post-treatment effects and minimal recovery time. The findings of this part of the present study may be invaluable for medical doctors and the owners of aesthetic medicine clinics in shaping their service offerings to better align with patient preferences and expectations.

4.2. Aesthetic medicine services – consumer satisfaction

This study study aimed to evaluate the satisfaction of patients receiving aesthetic medicine treatments based on innovative technologies, performed at aesthetic medicine clinics in large Polish cities. Figure 1 shows the extent to which post-treatment effects complied with patient expectations.

Most of the respondents making use of aesthetic medicine services in large cities in Poland positively assessed the adequacy of those services in meeting their expectations (59% reported that the service was as expected, 16% that it was above their expectations, and 18% that it exceeded significantly beyond their expectations) (Fig. 1). Respondents highlighted such aspects as the clinic’s prestige, its range of facilities (devices and fillers), positive energy, and the quality of the information provided by the clinic. Addressing any post-treatment complications was also deemed crucial, as it enhances patients’ sense of safety (Ankiel et al., 2021). About 6% of respondents indicated that the post-treatment effects were below their expectations, while only 1% indicated that they fell significantly below their expectations.

In this context, it is important to stress that the main contraindication for all aesthetic medicine services is having unreal expectations. Sometimes, the patient’s expectations may be simply impossible to meet. Moreover, in some cases, the patient’s state of health may preclude certain medical procedure from being carried out.

In summary, a majority of respondents were found to be pleased with the outcomes of aesthetic medicine services. Treatments that align with or exceed patient expectations positively influence satisfaction, enhance quality of life, and contribute to better overall well-being. Hence, addressing both procedural outcomes and the holistic patient experience is key to maintaining high levels of satisfaction in aesthetic medicine services.

The main objective of this study was to identify the level of patient satisfaction with aesthetic medicine services rendered in Poland. Additionally, the study assessed the role of clinics as significant determinants in the development of the aesthetic medicine market. It is noteworthy that this study is one of the few large-scale surveys conducted in Poland targeting a broad group of aesthetic medicine clients through direct questionnaire interviews, carried out shortly before the COVID-19 pandemic.

As mentioned earlier, the Net Promoter Score (NPS) methodology was employed for this assessment. In accordance with the NPS procedure, clients of aesthetic medicine clinics were first asked about the likelihood of their recommending the treatments to their friends. Subsequently, respondents were asked about their likelihood of recommending the specific clinic to potential clients. Recommendations were rated on a 0–10 scale, reflecting the level of likelihood. The results for the first issue, analyzed across the general population and various age groups, are presented in Table 5. Gender analysis was excluded due to the relatively small representation of male respondents (12% of the total).

The results in Table 5 show a clear dominance of the Promoters group across all age categories, with values exceeding 70% in every subgroup. This indicates a high likelihood of respondents recommending aesthetic medicine treatments (scored 9 or 10). Conversely, the proportion of Detractors is remarkably low, never exceeding 7%, except for the 51–60 age group, where it slightly increased to 11.8%.

The overall NPS score for the entire population reached 71.4%, reflecting a very positive evaluation and high satisfaction with aesthetic medicine services in Poland. The NPS score was even higher among respondents aged 41–50, exceeding 75%. These findings suggest a consistently high level of consumer satisfaction with aesthetic medicine services, regardless of the patients’ age. Additionally, the results indicate a promising outlook for the growth of aesthetic medicine services in Poland in the coming years.

The study also explored the likelihood of respondents recommending the specific aesthetic medicine clinics they had utilized. These results, presented in Table 6, closely align with the general recommendations for aesthetic medicine treatments. The overall NPS score for clinics was 77.1%, reflecting a high probability of recommendation. This value remained consistently high across all age groups, with a slightly lower score observed in the 51+ age group.

These findings reinforce the conclusion that both treatments and specific clinics offering aesthetic medicine services in Poland are highly regarded by consumers, underscoring the importance of maintaining high service quality and customer satisfaction to drive market growth.

5. Conclusions

The theoretical and empirical literature offers numerous studies on patient satisfaction with medical services, but they mainly focus on aspects related to the patient’s attitude, nurses, emergency room service, or general healthcare staff. Many studies specifically explore plastic surgery services and factors determining patient satisfaction with this type of services, such as clear explanations of problems/conditions, addressing patient questions/worries, involvement in decision-making, information about medications, follow-up care instructions, the use of clear language, the time clinicians spend with the patient, etc. (Clapham et al., 2010; Chen et al., 2018). Aesthetic medicine, being a much younger field of study compared to plastic surgery, has had relatively few studies assessing patient satisfaction.

In Poland, our own study was one of the first to examine patient satisfaction with services offered by aesthetic medicine clinics (Ankiel & Kuczyńska, 2018). However, given the very similar nature of the outcomes achieved by aesthetic medicine and plastic surgery procedures – such as appearance enhancement and the correction of natural defects – based on our findings, it can be assumed that the sets of determinants of patient satisfaction are largely similar across both fields. On the other hand, satisfaction surveys for services provided in offices and clinics must take into account specific factors such as location, decor, equipment quality, professionalism of medical staff, and the level of patient care and service.

More broadly, the present findings support the conclusion that the aesthetic medicine market in Poland is poised for strong growth, both in terms of the number of procedures performed and the overall value of the services provided. The high level of patient recommendations reflects the exceptional quality of aesthetic medicine services available in Poland: the satisfaction levels reported by respondents differ significantly from those observed in other medical services in the country. This is particularly notable given that the overall evaluation of healthcare services in Poland is often considered relatively low compared to other European Union countries.

The present study has a number of practical implications. First of all, it shows that a professional approach to patient care and the delivery of high-quality treatments and accompanying processes are essential for achieving patient satisfaction. In Poland, aesthetic medicine is characterized by high satisfaction ratings, whereas other types of medical services are generally evaluated as being of low satisfaction and poor quality. We believe that this derives from the fact that these services are predominantly provided by private offices and aesthetic medicine clinics. From the marketing point of view, therefore, it is important to build positive relationships with patients before, during, and after each procedure, which results in very positive WOM (word of mouth), necessary for increasing the demand for this type of service. Moreover, in the private sector, implementing innovative technologies as part of various types of treatments is typically a short and simple process (usually with a single decision-maker). Offering cutting-edge treatments is another dimension important for building a positive image and reputation for an office or clinic.

Despite its potential for growth, however, the aesthetic medicine market in Poland faces some limitations. One significant challenge is the affordability of services; many innovative treatments represent a substantial financial burden for patients, limiting access. Moreover, intense market competition could lead to reduced prices for aesthetic medicine services in the future.

The market’s development will also be influenced by factors such as increasing household wealth and advancements in medical technology. However, the lack of public education on aesthetic medicine, particularly in rural areas, and the absence of reimbursement for such services from the state healthcare system are additional barriers.

Further research by the authors of the present study will focus on examining the determinants of consumer satisfaction in aesthetic medicine services in international markets, with a particular emphasis on Western Europe, Turkey, the United States, and Brazil.

References

Abbas, O. L., & Karadavut, U. (2017). Analysis of the Factors Affecting Men’s Attitudes Toward Cosmetic Surgery: Body Image, Media Exposure, Social Network Use, Masculine Gender Role Stress and Religious Attitudes, Aesthetic Plastic Surgery, 41(6), 1454–1462.

Alexiades-Armenakas, M. R., Dover, J. S., & Arndt, K. A. (2008). The spectrum of laser skin resurfacing: Nonablative, fractional, and ablative laser resurfacing, Journal of the American Academy of Dermatology, 58(5), 719–737.

Angelini, A., & Carmignani, G.G. (2017). The consumer experience of aesthetic medicine services. In 20th Excellence in Services International Conference, Conference Proceedings (pp. 11–22), University of Verona.

Ankiel, M. & Kuczyńska, A. (2017). Wyznaczniki satysfakcji klientów korzystających z usług medycyny estetycznej, Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach, 330, 7–15.

Ankiel, M., Sojkin B., & Gogołek A. (2021). Determinants of purchasing decisions of innovative aesthetic medicine services in Poland, International Journal of Innovation and Learning, 29(3), 373–386.

Asscher, E.C. (2014). Wish-fulfilling medicine in practice: opinions and arguments of lay people, Journal of Ethics, 40(12), 837–841.

Baumann, L., Dayan, S., Connolly, S., Silverberg, N., Lei, X., Drinkwater, A., & Gallagher, C. J. (2016). Duration of Clinical Efficacy of OnabotulinumtoxinA in Crow’s Feet Lines, Dermatologic Surgery, 42(5), 598–607.

Betancur, D. M., Castaneda, K.M., & Tavera-Mesias, J. (2014). Correlational study of the factors that influence in the recommendation and loyalty of patients of aesthetic medicine Medellín Colombia, Cuadernos de Administracioìn Journal of Management, 33(58), 3–17.

Blitzer, A., Brin, M. F., Keen, M. S., & Aviv, J. E. (1993). Botulinum Toxin for the Treatment of Hyperfunctional Lines of the Face, Archives of Otolaryngology – Head and Neck Surgery, 119(9), 1018–1022.

Boon, K., & Tan, H. (2007). Aesthetic medicine: a health regulator’s perspective, Clinical Governance: An International Journal, 12(1), 13–25.

Brody, H.J., Monheit, G.D., Resnik, S.S., & Alt, T.H. (2000). A history of chemical peeling, Dermatologic Surgery, 26(5), 405–409.

Bukowska-Piestrzyńska, A. (2017). Quality of Medical Services – Based on the Example of Dental Services, Problemy Jakości, 6, 8-16.

Carruthers, J., & Carruthers, A. (1999). Practical Cosmetic Botox Techniques. Journal of Cutaneous Medicine and Surgery, 3(4_suppl), S4–49–S4–52.

Chen, K., Congiusta, S., Nash, I. S., Coppa, G. F., Smith, M. L., Kasabian, A. K., Tanna, N. (2018). Factors Influencing Patient Satisfaction in Plastic Surgery, Plastic and Reconstructive Surgery, 142(3), 820–825.

Chistobaev, A. I., & Semenova, Z. A. (2018). Spatio-Temporal Dynamics of the Global Medical Tourism, Journal of Environmental Management & Tourism, 9, 267–75.

Clapham, P. J., Pushman, A. G., & Chung, K. C. (2010). A Systematic Review of Applying Patient Satisfaction Outcomes in Plastic Surgery, Plastic and Reconstructive Surgery, 125(6), 1826–1833

Cohen, J. L., & Scuderi, N. (2017). Safety and Patient Satisfaction of AbobotulinumtoxinA for Aesthetic Use: A Systematic Review, Aesthetic Surgery Journal, 37(suppl_1), 32–44.

Cox, S.E., Finn, J.C., Stetler, L., Mackowiak, J., & Kowalski, J.W. (2003). Development of the Facial Lines Treatment Satisfaction Questionnaire and initial results for botulinum toxin type A-treated patients, Dermatologic Surgery, 29(5), 444–459.

Cronin, J. J., & Taylor, S. A. (1992). Measuring Service Quality: A Reexamination and Extension, Journal of Marketing, 56(3), 55–68.

Dayan, S., Yoelin, S. G., De Boulle, K., & Garcia, J. K. (2019), The Psychological Impacts of Upper Facial Lines: A Qualitative, Patient-Centered Study, Aesthetic Surgery Journal Open Forum, 1(2).

De Vries, D., Jochen, P., Nikken, P., & de Graaf, H. (2014). The Effect of Social Network Site Use on Appearance Investment and Desire for Cosmetic Surgery Among Adolescent Boys and Girls, Sex Roles: A Journal of Research, 71(9), 283–295

Dorizas, A., Krueger, N., & Sadick, N. S., (2014). Aesthetic Uses of the Botulinum Toxin, Dermatologic Clinics, 32(1), 23–36.

Dover, J. S., Monheit, G., Greener, M., & Pickett, A. (2018). Botulinum Toxin in Aesthetic Medicine, Dermatologic Surgery, 44(2), 249–260.

Funk, W., Podmelle, F., Guiol, C., & Metelmann, H. R. (2012). Aesthetic satisfaction scoring – Introducing an aesthetic numeric analogue scale (ANA-scale), Journal of Cranio-Maxillofacial Surgery, 40(5), 439–442.

Grand View Research (2024). Aesthetic Medicine Market Size, Share & Trends Analysis Report By Procedure Type (Invasive Procedures, Non-invasive Procedures), By Region (North America, Asia Pacific, Europe), And Segment Forecasts, 2024-2030. Grand View Research.
https://www.grandviewresearch.com/industry-analysis/medical-aesthetics-market.

Hou, A., Cohen, B., Haimovic, A., & Elbuluk, N. (2017). Microneedling, Dermatologic Surgery, 43(3), 321–339.

Hunt, H. K. (1991). Consumer Satisfaction, Dissatisfaction, and Complaining Behavior, Journal of Social Issues, 47(1), 107–117.

Jacob C.I., Paskowa K. (2018). Safety and efficacy of a novel high-intensity focused Electromagnetic technology device for non-invasive abdominal contouring, Journal of Cosmetic Dermatology, 17, 783–787.

Jäger, C., Brenner, C., Habicht, J., & Wallich, R. (2011). Bioactive reagents used in mesotherapy for skin rejuvenation in vivo induce diverse physiological processes in human skin fibroblasts in vitro ‒ a pilot study, Experimental Dermatology, 21(1), 72–75.

Krueger N., Luebberding S., Sattler G., Hanke C.W., Alexiades-Armenacas M., Sadick N. (2013). The History of Aesthetic Medicine and Surgery, Journal of Drugs in Dermatology, 7(7), 737–742.

Lanigan, S. W. (2000). Lasers in dermatology. Springer.

Liao, K., Chen, S., & Jhou, Y. (2019). An exploration of factors influencing the potential customers perceived value in aesthetic medicine, Commerce & Management Quarterly, 20(2), 109–130.

Lin, T. T., & Yen, H.-T. (2020). The Criteria of Optimal Training Cost Allocation for Sustainable Value in Aesthetic Medicine Industry, Journal of Risk and Financial Management, 13(7), 149.

Małecka, B., & Marcinkowski, J.T. (2007). Satysfakcja pacjenta czynnikiem kształtującym współczesny rynek usług medycznych, Problemy Higieny Epidemiologicznej, 88(1), 17–19.

Napiwodzka-Bulek, K. (2017). Medycyna estetyczna – humanistyczne dążenie czy „enhancement”? Filozofia Publiczna i Edukacja Demokratyczna, 6(1), 151–166.

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A Conceptual Model of Service Quality and Its Implications for Future Research, Journal of Marketing, 49(4), 41–50.

Pfenninger, J. L., & Fowler, G. C. (2011). Pfenninger and Fowler’s procedures for primary care (2nd ed.). Elsevier/Saunders.

Posełek, M. (2024, August 4). Ile jest wart rynek medycyny estetycznej? Fashion Biznes. https://fashionbiznes.pl/wartosc-rynku-medycyny-estetycznej-ile-wynosi/

Prendergast, P.M., & Shiffman, M.A. (2012). Aesthetic medicine: Art and techniques, Springer.

Przylipiak, A. (2017). Medycyna Estetyczna: Podręcznik dla studentów kosmetologii. Wydawnictwo Lekarskie PZWL.

Pustułka, A., & Jędrzejczak, A. (2018, October 14). Medycyna estetyczna to duży biznes. Chcemy być coraz piękniejsi. Retrieved from https://plus.dzienniklodzki.pl/medycyna-estetyczna-to-duzy-biznes-chcemy-byc-coraz-piekniejsi/ar/13601996

Redaelli, A., & Ignaciuk, A. (2000). Medycyna Estetyczna. Wydawnictwo Medycyna Estetyczna.

Reichheld, F.F. (2003). The one number you need to grow, Harvard Business Review (81), 46–55.

Rymkiewicz, D. (2018). Relevance of information and contractual modality of aesthetic medicine: A comparative analysis between Spanish and Polish case-law. Review of European and Comparative Law, 35(4), 103–111.

Samuels J.B. (2018). Nonsurgical Vaginal Rejuvenation. In G. H. Branham, J. S. Dover, H. J. Furnas, M. M. J. Tenenbaum, & A. E. Wulc (Eds.), Advances in Cosmetic Surgery (Vol. 1, pp. 75–84). Elsevier.

Samuels, J. B., Pezzella, A., Berenholz, J., & Alinsod, R. (2019). Safety and Efficacy of a Non-Invasive High-Intensity Focused Electromagnetic Field (HIFEM) Device for Treatment of Urinary Incontinence and Enhancement of Quality of Life, Lasers in Surgery and Medicine, 51(9), 760–766.

Sharp, G., Tiggemann, M., & Mattiske, J. (2014). The role of media and peer influences in Australian womens’ attitudes towards cosmetic surgery, Body Image, 11(4), 482–487

Skountridaki, L. (2017). Barriers to business relations between medical tourism facilitators and medical professionals, Tourism Management, 59, 254–266.

Stirling, P., Jenkins, P.J., Clement, N.D., Duckworth, A.D., & McEachan, J.E. (2019). The Net Promoter Scores with Friends and Family Test after four hand surgery procedures, Journal of Hand Surgery, 44(3), 290–295.

Theodorou, S.J., Chia, Ch.T., & Dayan, E. (2021). Emerging technologies in face and body contouring. Thieme Medical Publishers.

Tongco D. C., (2007). Purposive sampling as tool for informant selection, Ethnobotany Research and Applications, 5, 147–158.

Vlahos, A., & Bove, L. L. (2016). Went in for Botox and left with a rhinoplasty Marketing Intelligence & Planning, 34(7), 927–942.

Wasiluk, M. (2016). Medycyna estetyczna bez tajemnic. Wydawnictwo Lekarskie PZW.

Welsh, W. (2005). Estetyka poza estetyką: o nową postać estetyki. Towarzystwo Autorów i Wydawców Prac Naukowych ‘Universitas’.

Wilberforce, M., Poll, S., Langham, H., Worden, A., & Challis, D. (2018). Measuring the patient experience in community mental health services for older people: A study of the Net Promoter Score using the Friends and Family Test in England, International Journal of Geriatric Psychiatry, 34, 31–37.

Załuski, K. (2023, November 28). Mężczyźni w gabinecie medycyny estetycznej. Co poprawiają i ile to kosztuje? Onet – Jesteś na bieżąco. https://www.onet.pl/styl-zycia/facet-xl/mezczyzni-w-gabinecie-medycyny-estetycznej-co-poprawiaja-i-ile-to-kosztuje/jjm64np,30bc1058

The development of unmanned aviation during the 20th century and into the early 21st century has proceeded in pace with major scientific and technical advancements – especially the advent of remote and automatic control systems, the introduction of a new elemental base (particularly microcircuits and microchips), advancements in real-time data collection and processing technologies, and the development of innovative composite materials. Nowadays, progress in unmanned aerial vehicle (UAV) technology is increasingly being guided by the principle of open innovation. This approach involves the active exchange of knowledge between various entities participating in the innovation process and the establishment of sustainable relationships between various scientific and research institutions, manufacturers, and users, all within the framework of innovative ecosystems. Open innovation leads to greater differentiation of products, services, and processes, which ensures stable growth of production volumes and expands the scope of drone use. For example, the technical capabilities of modern UAVs can enhance the work of law enforcement agencies both in maintaining public order and safety, and in conducting search and rescue operations. Since 2019, for instance, the UK has launched a program to involve police drones in traffic monitoring, where drones assist in recording traffic violations and transmit the received information to police units for further documentation and termination of illegal activity. Additionally, many innovative solutions in the field of unmanned aviation have emerged out of the use of drones in military operations, especially in Afghanistan, Libya, and Syria. Open-source data indicates that the leading countries in the field of unmanned military aviation are the United States, Israel, Turkey, and EU member states. In the EU, drone ecosystem creates new jobs, promotes and protects European technological know-how, and creates opportunities for the growth of the EU economy in general.

Analysis of the current landscape in the design and production of UAVs reveals a vibrant scene in Ukraine, with more than 200 medium and small businesses actively engaged in designing and manufacturing UAVs of various capacities. The ongoing military action in Ukraine has stimulated the rapid development of innovations across various sectors, including the creation and use of unmanned aerial vehicles. We strongly believe that, as a country suffering from the military aggression of its northern neighbor and as a candidate country for EU membership, Ukraine not only can, but indeed must use the current situation as an impetus to accelerate the transformation of its economy by actively implementing innovations and stimulating innovative entrepreneurship. As the Minister of Digital Transformation of Ukraine has emphasized, by the end of 2023, as compared to the previous year the production of drones in Ukraine will increase approximately 120-140 times, underscoring the country’s strong commitment to advancing this critical sector.

At the same time, despite the military activity, the civilian sphere of application of UAVs is a priority for Ukraine’s economy. The UAV technologies that are rapidly developing in wartime are also widely used in non-military realms as well. They will be of particular importance in the post-war reconstruction of the independent nation’s economy. The practical experiences of UAV use in Ukraine and worldwide have demonstrated their potential to significantly reduce the costs associated with monitoring the environment and natural disasters, precision agriculture, wireless communications, remote sensing, power-line surveillance, highway and border traffic control, search and rescue operations, etc. For instance, the Ukrainian company “DroneUA” has entered the world ranking of the best startups in the field of artificial intelligence for agriculture. As a system integrator of unmanned solutions, it has its own engineering and production divisions as well as an open data processing center. “DroneUA” technologies are currently being utilized across more than 4 million hectares of cultivated land in Ukraine.

The dynamic development of Ukrainian-made and -designed UAVs is poised to make a significant contribution not only to the development of the national economy in particular, but also the economy of the European Union as a whole. Moreover, Ukraine is positioned to make a real contribution to the development of the European UAV market, which experts predict will provide numerous services for various end-users in the civil and defense sectors (Steer 2023). Innovative Urban Air Mobility (UAM) services will be part of the future ecosystem of urban multimodal intelligent mobility, and the ground and air infrastructures supporting these transport services will be widely deployed and integrated.

Although the broader deployment of UAVs offers numerous advantages, such as increased efficiency, cost-effectiveness and enhanced data collection capabilities, it also comes with its share of challenges, such as the regulatory framework, data security, flight safety, and the need for continuous refinement of technical and technological features. These challenges emerge at the intersection of multiple fields of scientific research. The resulting interdisciplinary nature of UAVs research requires the convergence of various R&D directions, including aerospace engineering, informatics and cybernetics, robotics, management and logistics, etc. Understanding the interplay between these R&D directions and forging effective collaborative strategies is critical for the development of UAV technologies. Consequently, identifying an optimal, effective organizational and economic configuration of participants in the innovative process of UAV development is one of the most urgent theoretical and practical tasks.

International experience with creating competitive products shows that innovative ecosystems are an effective form of cooperation between different participants (or “stakeholders”), in the innovation process, the active development of which is confirmed by the annual rating of “The Global Startup Ecosystem Report.” Note that the term “ecosystem” is increasingly being used in various contexts by international organizations – in particular, by the European Commission, the OECD, the United Nations Conference on Trade and Development (UNCTAD), the World Economic Forum, international consulting and auditing firms, etc. The growing interest in the functioning of innovative ecosystems stems from the need to navigate the complexities of modern markets through novel cooperative mechanisms, and from numerous instances of successful ecosystem-based business strategies that have yielded synergistic outcomes unattainable by entities in isolation.

The development of effective innovation ecosystems is the primary task of the “Strategy for the Development of the Sphere of Innovative Activity for the Period until 2030,” approved by the Cabinet of Ministers of Ukraine (2019). The strategy defines priority innovation sectors, including: Defense Tech, Artificial Intelligence, FinTech, Green Tech, AgriTech, Cyber Security, and Industry 4.0. The onset of the active phase of the war in 2022 in Ukraine has intensified the need for accelerated innovative development of both the military economy (especially in the areas of Military Tech and Cyber Security) and the post-war recovery economy. Innovations have become a matter of national security and this necessitates an appropriate environment – an ecosystem conducive to innovations.

However, it’s not just governmental bodies that need to adapt; business organizations, scientific communities, and expert groups also must evolve their approaches to collaboration. adopting practices that significantly enhance the cooperation among key stakeholders. This shift towards more effective collaboration underscores the importance of exploring innovative ecosystems at large, especially those tied to the development of unmanned aerial vehicles (UAVs).

The objective of this article, therefore, is to advocate for the establishment of an innovative ecosystem of UAV technology in Ukraine. First, we review the existing literature to analyze different perspectives and identify the main approaches to understanding innovative ecosystems, especially in terms of UAV development, systematizing the existing knowledge. We elucidate the benefits of adopting an ecosystem approach over the traditional models of UAV design and development, then propose a coherent definition of the innovation ecosystem and its conceptual model from the point of view of the co-creation of a focal innovation. Lastly, we highlight the pivotal role of research universities in catalyzing UAV innovations in such an ecosystem. Overall, this study offers a foundation for developing an ecosystem strategy and refining practical tools for ecosystem management across various administrative levels – setting the stage for future ecosystem research, providing a strategic foundation for innovation and the sustainable, technologically advanced development of UAVs.

Literature Review

Numerous scholars emphasize that the business ecosystem approach is a synthesis of mechanistic (regulations, instructions, schedules) and organic (values, motives, communities, network interaction) management styles. Therefore, the ecosystem paradigm is increasingly being used in enterprise management, the greening of business activities, technological innovations, and collaborative processes of value creation. Embracing the ecosystem approach requires a review of the traditional business models of various organizations and state institutions, as well as an assessment of the opportunities for cooperation of enterprises from various sectors of the economy, which can potentially unlock additional opportunities to boost competitiveness and create new sources of income. The toolkit of the ecosystem approach makes it possible to analyze various socio-economic entities, ranging from the scale of the world economy as a whole down to the level of small enterprises.

The business ecosystem approach is therefore increasingly being discussed in various fields, particularly in aviation. Drawing on Moore’s (1996) definition, we assume that a “business ecosystem” is as follows:

[a]n economic community supported by a foundation of interacting organizations and individuals – the organisms of the business world. This economic community produces goods and services of value to customers, who are themselves members of the ecosystem. The member organisms also include suppliers, lead producers, competitors, and other stakeholders. Over time, they coevolve their capabilities and roles, and tend to align themselves with the directions set by one or more central companies. Those companies holding leadership roles may change over time, but the function of ecosystem leader is valued by the community because it enables members to move toward shared visions to align their investments, and to find mutually supportive roles. (Moore, 1996).

Moore’s key point here is that competition among enterprises does not disappear, but rather evolves to a more complex level, when business entities realize their place and relationships in an already existing ecosystem, or develop new ones. As components of ecosystems, companies create innovative opportunities: they cooperate and compete at the same time with the goal of creating new products and meeting customer needs, which results in the enhancement of production and management practices.

The evolution towards Industry 5.0 further reinforces the innovative significance of the business ecosystem approach. Adner (2017: 41) describes the emergence, within the ecosystem, of “coevolution of opportunities and abilities of participants in the process of value creation.” This shift from economic competition to mutually beneficial cooperative engagement helps to mitigate resource scarcity. The ecosystem’s “equalization structure” is pivotal, whereby a network of partners committed to mutual benefits and shared objectives collaborates to realize an innovative strategy (Reeves & Pidun, 2002).

Many researchers emphasize that innovative ecosystems are a synergistic confluence of state, business, and research environments, collectively fostering knowledge flows, supporting technological development and the commercialization of innovations. For instance, in the project “Development of the Innovation Ecosystem in Ukraine” published in April 2023, the Ministry of Digital Transformation of Ukraine defined the “innovation ecosystem as a synergy of the state, business and research environment with the use of regulatory, educational and financial resources and the introduction of a knowledge transfer mechanism with the aim of transformation into innovative products” (Poberezhets & Rakytska, 2003: 436).

Another important feature of innovative ecosystems is their digital orientation, facilitating digital relations between their participants and the use of digital technologies. In particular, Lee and Trimy (2021: 16) have proposed a revolutionary paradigm: an innovative platform ecosystem that unites people, objects, ideas, functions, technologies. In their view, an innovative ecosystem is a self-organized mechanism for finding and solving problem situations and creating added value using end-to-end digital technologies (artificial intelligence, Internet of Things, big data analytics, e-learning, etc.).

Pidorycheva’s (2020) approach categorizes innovative ecosystems into four distinct models, analyzing each of them in detail:

1)ecosystems organized around a focal (central) firm;
2)ecosystems as “structures” built around a focal value proposition (focal innovation);
3)ecosystems as specific environments that arise at different levels, from local to global;
4)ecosystems as platforms around which stakeholders’ activities are organized.

In this article, it is the second, structural approach that we adopt: viewing innovation ecosystems in terms of partners needing to cooperate in order to implement a focal value proposition (focal innovation). The distinctiveness of this approach lies in its reliance upon cooperation mechanisms, thanks to which enterprises and organizations combine their individual offerings into a coordinated solution for customers (Adner, 2017; Adner & Kapoor, 2016; Adner & Feiler, 2019). In such an ecosystem, there is a coordinating firm, but it does not control other participants, only coordinates their joint activities. Coordination truly matters here: if it is insufficient, the partners will fail and the value proposition will not be created. Moreover, the ecosystem is multilateral, that is, the relationships within it cannot be divided into a set of simple bilateral relations.

Many researchers draw attention to the role of higher education institutions in fostering and developing innovative ecosystems (Chychkalo-Kondratska & Levchenko, 2023). Modern universities should act as generators of new ideas, as inclusive educational spaces where there is a synergy of science, education, innovation, and business. Historical examples of this model include Silicon Valley, which developed around Stanford University, the Boston ecosystem, with the centers of MIT and Harvard, as well as Israel’s innovative ecosystem, focused on high-class medical technologies, which was formed with the active participation of scholars from Technion University.

Bazhal (2022) has examined the role of research universities and other scientific and educational organizations in the structural innovation transformation of Ukraine’s economy, suggesting that in the process of formulating Ukraine’s state innovation policy a separate priority direction should be established, related to the creation of next-generation higher education institutions (universities). These institutions are envisioned as innovative entrepreneurial universities and centers, aimed at nurturing cumulative human capital amidst the temporary state of crisis. Kyzim et al. (2021) emphasize the crucial role of educational institutions in the startup ecosystem. They underscore how these institutions support entrepreneurship and innovation, providing valuable resources to startups and providing students with theoretical knowledge in various fields. Such higher education institutions (universities) create opportunities for the development of their own innovative ideas through the creation of startup schools and accelerator programs.

Some research has pertained directly related to aviation, both unmanned aviation and the broader aviation industry. Hrinchenko (2020) believes that the ecosystem approach helps to determine the key factors for the sustainable development of the aviation industry and to develop an appropriate and effective industry policy. Some researchers believe that aviation is the ideal prototype of an “ecosystem,” as attracting investment in new technologies will not only allow for mobility to be improved, but also for the harmful impact of the industry on the environment to be reduced (Kalf & Renda, 2020; Kim, et al., 2022).

Telli et al.’s (2023) thorough review of the directions of UAV-related research, carried out on the basis of the Scopus database and analyzing 47,635 references published in the field of UAVs between 2020 and 2023, identified the following main areas of UAV research: technical design aspects, artificial intelligence applications, and the development of control systems and software.

The scholarly literature often highlights the intricate web of interactions and collaborative efforts required among corporations, research entities, and governmental institutions to nurture and sustain an innovation ecosystem. Despite the recognized importance of these interactions, however, there remains a gap in understanding the optimal strategies for generating synergy among science, education, business and the state in the field of UAV development. It is this gap that we address in this article.

Methodology

The informational base for this study comprised legislative and other normative acts on the international, regional and national levels governing the provision of UAV services, regulating their functioning and development, monographs and economic reviews of scientific institutions, Internet resources, and publications in both Ukrainian and foreign periodicals.

To address the objectives of this study and develop a comprehensive understanding of the establishment of an innovative ecosystem for UAV technology in Ukraine, the following methodologies were employed:

Analytical review

An extensive review of existing literature, including scholarly articles, reports, and case studies related to innovative ecosystems, UAV technologies, and the role of research institutions in fostering innovation. This review aimed to identify successful models and practices from around the world that could be adapted to the Ukrainian context. Particular attention was given to the analysis of ecosystems in sectors relevant to UAVs, such as aerospace, defense technology, and digital transformation initiatives.

Stakeholder analysis

A detailed stakeholder analysis was conducted to map out the key players in the UAV ecosystem within Ukraine and internationally. This included universities (such as the National Aviation University and the National Technical University “KPI”), government agencies (e.g., the Ministry of Digital Transformation of Ukraine), private enterprises engaged in UAV design and manufacturing, venture capital funds, and international partners like the Łukasiewicz Research Network ‒ Institute of Aviation in Warsaw. The analysis focused on understanding the roles, interests, capabilities, and potential contributions of each stakeholder to the ecosystem.

Comparative analysis

A comparative analysis was performed to benchmark Ukraine’s UAV ecosystem against those in other countries with established and successful UAV innovation ecosystems. This analysis helped identify best practices, key success factors, and lessons learned that could inform the development of Ukraine’s UAV ecosystem.

Synthesis and strategic recommendations

The findings from the literature review, stakeholder analysis, and comparative analysis were synthesized to develop a comprehensive understanding of the current landscape and potential pathways for establishing a vibrant UAV innovative ecosystem in Ukraine. Based on this synthesis, strategic recommendations were formulated to address the identified challenges and leverage opportunities for accelerating the development and commercialization of UAV technologies in Ukraine.

This multi-methodological approach enabled a thorough examination of the factors influencing the establishment of an innovative ecosystem for UAVs in Ukraine, offering a robust foundation for actionable strategies to enhance innovation, collaboration, and sustainability in the UAV sector.

Results and Discussion
The formation of an innovative UAVs ecosystem

Currently, the production of UAVs in Ukraine can be described as artisanal, as it is engaged in by many different small enterprises with limited orientation towards new knowledge and innovations. Adopting the ecosystem approach requires a review of the traditional business models of various organizations and government institutions, as well as a reassessment of the opportunities for cooperation among enterprises from various sectors of the economy, which can potentially provide additional opportunities to increase competitiveness and create new sources of income. The main idea of the approach is that the transformation of scientific knowledge into innovation requires cooperative efforts on the part of all stakeholders in the innovation process (universities, private enterprises, venture funds, etc.). In other words, innovations are fostered collectively, in a certain networked environment formed by legally independent participants between whom there are formal and informal arrangements. The ecosystem approach makes it possible to treat the issue comprehensively, taking into account various internal relationships and interactions with the external environment. It facilitates the integration of various methodological approaches and evaluating alternative ways of implementing UAV design and production strategies.

In an innovative business ecosystem, the dynamic interactions between business, science, education and the state are akin to those in a natural ecosystem, with each entity both influencing and being influenced by others, creating an ever-changing configuration in which each business must be flexible and adaptable to survive. The main factor in the evolution of business ecosystems is the minimization of aggregate public costs for the creation and dissemination of innovations, and its activities are aimed at collective actions in the field of creation of knowledge flows, support for technological development and commercialization of innovations. The ecosystem logic not only generates new ideas in the design of UAVs with a certain set of consumer values, but also allows for the identification of participants who have enough resources, capabilities, competences and capital to implement those ideas.

Fig. 1 presents a conceptual diagram of the formation of the UAV innovation ecosystem, which assumes that the value proposition (innovation) is determined first, then the types of activities necessary for its creation, and lastly the stakeholders, whose participation depends on whether the focal innovation will be produced. So, the key idea of forming an innovative ecosystem for UAVs is that cooperation among participants in the innovation process ensures the continuous creation of new ideas based on a complementary combination of resources, opportunities, competencies in various combinations, and thus contributes to the constant increase of the innovative potential of all participants. That is, the integration of science, education and business in the field of UAV development and production will contribute to the constant generation of new knowledge and the continuous flow of innovations at the request of consumers, driven by the synergistic efforts of all ecosystem participants.

The value proposition (focus innovation) of the UAV innovation ecosystem

In the scheme presented, the participants of the UAV innovation ecosystem pool their resources on mutually beneficial terms, exchange common knowledge and work collectively with the aim of jointly achieving innovative results. They create value that none of them would be able to generate on their own. Therefore, the innovative UAV ecosystem should, on the one hand, create consumer value, since it is the consumer’s receptivity and willingness to pay for scientific advancements for further use that determines the consumer value of UAVs. On the other hand, the ecosystem must have value for its participants, i.e. create benefits for all stakeholders involved in generating ideas, designing products or manufacturing UAVs.

As we have adopted the structural approach – which involves the formation of an ecosystem based on a value proposition (focal innovation) – we first define a value proposition, then the types of activities necessary for its creation, and then finish with the stakeholders whose participation depends on whether the focal innovation will be produced (Fig. 2).

This scheme shows that the basis for cooperation among various organizations and enterprises in a single innovative ecosystem is the focus on the final outcome of that cooperation – the creation of the next generation of UAVs with high competitiveness and innovation that meet the requirements of the military and civilian sectors of the economy. The approach underscores the consumer’s perception of UAV value, which encompasses the structural, technological, ecological, and ergonomic characteristics of the new product.

The value proposition of the UAV innovation ecosystem, in turn, should be based on the following: the creation of new market opportunities and the promotion of innovative products to domestic and international markets, production cooperation in supply chains to ensure the continuity of production and commercialization of innovations, the development of personnel potential and the creation of a creative environment for the generation of ideas among university students, their involvement in real projects, improving the qualifications of company personnel, boosting the organizational capacity of ecosystem participants, improving their business processes, as well as joint use of assets and better access to resources.

Current directions of scientific research for UAV innovation ecosystem stakeholders

A great deal of research is currently underway on the design of both whole UAV systems and their individual components (airframes, engines, control systems, etc.), as well as in related areas (use of new energy sources, sensors, communication interfaces, etc.). In particular, Telli et al. (2023), in their extensive review of the worldwide literature, identified unresolved challenges in augmenting UAV efficiency, enhancing security, and countering cyber threats. They see potential solutions to these problems as lying in the use of modern information technologies, in particular, the Internet of Things, blockchain and artificial intelligence. Core AI technologies include machine learning and deep learning, natural language processing, image processing, object recognition and video analytics. Figure 3 summarizes the main directions of current and future scientific research related to the development of drones and the expansion of their functionality.

As can be seen from the figure, the most promising areas of research are related to the use of state-of-the-art information technologies. The use of wireless sensors (IoT) is crucial for creating intelligent traffic control systems and improving the performance of UAVs. Sensor-equipped drones can collect data on crop health, soil moisture and temperature that can be analyzed in real time to inform irrigation and fertilizer decisions. Similarly, UAVs can be used to monitor natural disasters and assess damage, enabling a faster and more accurate response. The integration of various sensors, cameras, and radars allows information about the environment to be collected quickly, which can be processed and interpreted by computer vision algorithms. Modern big-data technologies create opportunities for fast transfer and processing of large arrays of information in real time. Artificial intelligence, in particular machine learning and deep learning algorithms, can be used to optimize flight modes, automatically adjust flight paths, and make autonomous decisions in collision avoidance scenarios. Detecting and avoiding collisions with manned aircraft is a key task in the operation of UAVs, especially in shared airspace.

Ukraine is currently engaged in a lot of research on the use of artificial intelligence technologies to track moving objects and automatically identify various types of targets. In particular, reconnaissance drones can not only scout for information, but also recognize hardware and transmit information in real time. For instance, the aforementioned Ukrainian company “DroneUA” has been successful in the field of artificial intelligence for agriculture.

Another crucial aspect of research focuses on enhancing the energy efficiency of UAVs, which is pivotal for improving UAV performance and capabilities, including flight time, payload, and range. In this context, the use of composite materials, the integration of electric and hybrid power systems, the use of light solar panels and energy storage systems are important. Researchers from Yunnan University and the Chinese Academy of Sciences recently presented a new object-detection system based on boundary computing – potentially enabling UAVs to detect objects with minimal power consumption, thanks to edge computing’s faster and more efficient data processing capabilities.

A separate area of research relates to the safety of UAVs and combating cyber threats through the implementation of reliable encryption, authentication mechanisms and effective countermeasures against cyber attacks. A very important challenge restraining the development of UAVs is ensuring the transmission of information via communication channels between the UAV and the ground control point, in the required amount, at the necessary speed and without distortion. This task can be solved by enhancing the bandwidth and immunity of information transmission channels, as well as by concentrating onboard the UAV a maximal set of component devices that work in autonomous (software) mode without the need for constant information exchange with the control point.

Another promising direction of research is swarm behavior, involving groups of UAVs able to cooperate and continually exchange information with one other. One of the challenges of swarm behavior is how to ensure effective cooperation between UAVs while adapting to changing conditions and environments. To address this problem, researchers are developing new algorithms for cooperation and coordination, as well as new approaches to task allocation and resource management. In addition, the development of algorithms that allow UAVs to operate autonomously and make decisions based on their environment, including the integration of AI techniques such as ML and DL, is an important aspect of swarm behavior. Last July, Ukraine’s Ministry of Digital Transformation, Ministry of Defense, General Staff and State Special Communications launched the “Army of Drones” project, which focuses on the state’s long-term needs and priorities in terms of drones and robotic platforms. The strategic plans of the Ministry of Defense for saturation with such weapons over the next decades will stimulate manufacturers and investors to invest in the creation of new production facilities and the development of new platforms.

On the other hand, after its victory in the current war, the Ukrainian drone market will require deregulation of the permit system and increased civilian oversight of the circulation and use of UAVs, especially given the emergence of a large number of qualified pilot operators among the military. Drones will find applications outside of military use, including in the field of infrastructure monitoring, expedited delivery services, and agriculture. The existing legislation needs to be revised in accordance with the needs of the market and the further development of the industry.

The International Civil Aviation Organization (ICAO) defines the main requirements for the organization and implementation of the use of remotely piloted aircraft systems (RPAS). The aim is to develop an international regulatory framework based on Standards and Recommended Practices (SARPS), complemented by Air Navigation Services Rules (PANS) and guidance material, which will enable safe, coordinated and effectively integrated flights of UAVs similar to flights of manned aircraft. The most important task is ensuring that the integration of UAVs in non-segregated airspace does not lead to an increase in the level of risk to the safety of manned aviation flights. According to the recommendations within the framework of the civil aviation system, UAVs will play the role of an equal partner, able to interact with air traffic control authorities and with other aircraft in real time (Kharchenko, 2017).

The structure of UAV innovation ecosystem participants

So far, we have outlined a wide range of current scientific problems in the field of UAV design and production that can be solved more efficiently and effectively within the framework of the innovative ecosystem. The next important aspect is forming a set of partners interested in mutually beneficial cooperation, who have a common vision and understanding of how to achieve goals in the course of implementing the innovation ecosystem strategy. All innovative ecosystems, regardless of their level of creation, are formed at the initiative of participants, have a high degree of self-organization, an internal self-regulation mechanism and sufficient potential for self-development. Self-development arises as a result of continuous updates, and is also characterized by a decentralized way of decision-making. In our case, in order to develop an innovative ecosystem of UAVs, we must identify potential participants who have enough resources, capabilities, competence and capital to implement the general idea in a hierarchically structured manner (Fig. 4).

This structure of the participants of the UAVs innovation ecosystem entails a transition from a linear model of creating innovations to a nonlinear one, where dynamic horizontal connections between the participants of the innovation process based on a complementary combination of resources, opportunities and competencies. At the same time, in our ecosystem model, an important role is played by the coordinator, who should facilitate the joint work of various participants. The coordinator ensures the resolution of conflicts, the creation of an conducive environment and the pursuit of common interests, and it actively engages external developers, startups, research institutions and other third-party organizations to participate in the open ecosystem.

The main hubs of the UAV innovation ecosystem are enterprises and organizations that engage in the ecosystem’s main activity of producing UAVs – namely design bureaus, developers and serial manufacturers of UAVs, aircraft engines and avionics. The activities of these hubs need to be integrated into the system, as a guarantee of coherent action towards achieving the common goal. However, the UAV production ecosystem is an open-type system that requires the development of interfaces with other groups, with which it cooperates and/or on whose activities it depends.

The first is the group of customers, which in Ukraine’s UAV production ecosystem consists of civil aviation enterprises, state aviation, aviation and multimodal logistics enterprises, organizations and individual customers of aviation and transport services. The civil aviation enterprises of Ukraine (as in other countries of the world) include airlines (state, non-state, mixed-capital, etc.) and aviation companies that provide services for various sectors of the national economy: agriculture, environmental monitoring of pipelines, forest areas, reservoirs, aerial photography, etc. The State Aviation of Ukraine, in turn, includes the Air Force, Army Aviation, Naval Aviation, National Guard Aviation, Police Aviation, Aviation of the State Emergency Service, Aviation of the State Border Guard Service, other State Aviation. Aviation and multimodal logistics enterprises play an increasingly powerful role in the air transportation market and are of particular importance in supporting the national economy in wartime conditions. Also, with the ecosystem approach, it is customary to take into account not only the needs and requests of direct operators of UAVs, but also those of the potential customers of UAVs services: organizations and individual customers of aviation and transport services.

The second group consists of entities supporting the production of UAVs, which includes: suppliers of units, systems and spare parts, organizations responsible for the certification of UAVs, organizations responsible for energy supply, nature protection organizations, organizations of fuel and lubricant support, the system of airports and airfields, and the air traffic control system. Looking forward, it is imperative to integrate service enterprises supporting the life cycle of unmanned aerial vehicles; their primary functions will include development and implementation of activities for the operation, modernization, repair and maintenance of UAVs. Special attention should be paid to the end of the life cycle of innovative products and the organization of recycling and disposal processes. Recycling should be aimed at the maximum possible secondary use of UAV materials and components or their use for the production of new goods or components, effectively creating a closed-loop drone supply chain infrastructure. In this cycle, manufacturers not only produce and distribute UAVs to customers but also encourage the return of these products for repair, resale, or component reuse – thereby adhering to the “Zero Waste” principles of minimizing waste and loss and fostering a new attitude towards the management of production and consumption waste.

The third group consists of entities providing innovative support and training of personnel of the aviation industry of Ukraine, which includes: scientific research institutes of the National Academy of Sciences of Ukraine and institutions of other ministries and departments, research universities, experts and scientists of the aviation industry.

The role of research universities in the innovation ecosystem

Let’s consider in more detail the role of research universities, which should become centers of innovation generation. Universities provide interdisciplinary and fundamental education while also supporting the research efforts of teachers, graduate students and undergraduates. In the context of the ecosystem approach, it is important to determine the avenues through which universities interact with scientific institutions and the business environment. The general scheme of such interaction is presented in Fig. 5.

The innovation process at universities is built around the activities of entities that generate new knowledge (such as individual researchers, groups of researchers, undergraduate and graduate students, academic departments, laboratories, and divisions) and those focused on commercializing those innovations (departments dedicated to R&D, technology transfer centers, etc.). Key strategies for involving students in research and innovation include establishing start-up centers, organizing startup schools, holding startup competitions, organizing startup festivals and exhibitions of scientific works, supporting business incubator activities, etc.

For universities to effectively manage their scientific and educational endeavors, several foundational conditions must be met:

1) Leadership within the universities should facilitate the generation and dissemination of new knowledge and technologies, going beyond the organizational structures, creating conditions conducive to the development of the necessary competencies and supporting continuous learning at higher education institutions.

2) The establishment of integrated structures is essential; these should the encompass technologies and competences needed to advance specific target areas, based on the coordination of interests and providing incentives to all participants.

Moreover, these units should formulate guidelines governing the relations between generators of new knowledge and technologies and the entities responsible for commercializing them, ensuring effective cooperation with other participants of the innovation ecosystem.

The organizational and economic mechanism for integrating education, science and business within the innovation ecosystem proposed here is a general outline, which requires further detailing, refinement, and customization in accordance with the specific needs of universities and the specifics of their functioning, as well as with the set purpose and goals of creating an innovative ecosystem.

UAVs scientific technical direction at the National Aviation University (Kyiv, Ukraine)

At the National Aviation University (NAU) in Kyiv, Ukraine, the incorporation of cutting-edge technologies into the educational curriculum is deemed essential for equipping future aviation specialists with practical skills. Recognizing the importance of unmanned aerial vehicles (UAVs) in the global civil aviation industry, NAU has dedicated significant efforts to researching and developing experimental remotely piloted aircraft systems. The university’s research and production center, “Virage,” has developed a series of UAVs, including the single-engine M-3 “Border” and M-6 “Skylark,” as well as the twin-engine M-7, M-7D, M-7V5 “Sky Patrol,” and the electric motor-powered “Eye” UAVs. These UAVs serve as practical training tools for aviation specialists (see Fig. 6) (Isaienko et al., 2018).

A significant component of NAU’s training program involves the use of simulators, including a complex of Air Traffic Control and Flight Simulators designed and operated at NAU (see Fig. 7). Simulator software with elements of artificial intelligence was jointly developed by NAU scientists and students. These simulators are designed for:

• air traffic management (ATM) training for students and staff, simulating the real-world scenarios,
• training and qualification testing for both manned aircraft pilots and UAV ground control operators.

The ATM simulator includes an air traffic control (ATC) tower simulator, a UAV flight simulator, and a Yak-18T airplane flight simulator.

NAU also boasts an Aerodynamic Research Center for conducting scientific and practical training in UAV aerodynamics, alongside a UAV Components Durability Complex Test System, which enables researchers and students to explore new UAV designs (Kharchenko et al., 2014). Collaborative efforts with the International University of Logistics and Transport in Wroclaw have focused on assessing the capacity and effectiveness of Remotely Piloted Aircraft Systems (RPAS) in addressing logistical challenges within territorial infrastructure (Kharchenko et al., 2014). Furthermore, NAU develops methods for integrating, searching, recognizing, and processing data from satellite systems, navigation, and UAV onboard avionics (Isaienko et al., 2018).

Noise generation due to civil use of UAVs has been identified as an important issue, particularly over densely populated areas and especially given the potential for a rapid increase in UAV applications. NAU conducts intensive research on UAV noise prediction for commercial operations and community exposure (Makarenko et al., 2020; Tokarev & Makarenko, 2021), based on UAV acoustic characteristics in operational modes. The concept of acoustic noticeability of UAVs is introduced in order to mitigate the influence of noise and for urban noise management, making it possible to evaluate the extent of population exposure to UAV noise – especially important in low-altitude urban airspace. Traffic distribution of UAV flights based on the entropy approach (maximum entropy method) for air traffic system optimization with various operational scenarios is proposed to minimize noise exposure (Tokarev & Kazhan, 2014; Zaporozhets et al., 2011). NAU’s research extends to environmental impact considerations, including air pollution (Synylo et al., 2020), electromagnetic fields (Glyva et al., 2023), third-party risk (Zaporozhets et al. 2019) and visual pollution, highlighting the comprehensive scope of NAU’s commitment to advancing UAV technology and its responsible integration into civil aviation.

UAVs scientific and technical direction at the National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute” (KPI) (Kyiv, Ukraine)

At the National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute” (KPI), the development and research in UAV technologies have been significant, underpinned by the “Sikorsky Challenge Ukraine” (SCU) innovation ecosystem. This ecosystem integrates various structural divisions of the university, including the Department of Innovation and Technology Transfer, the scientific research sector, the Center of Intellectual Property, TechnoHab, the Sikorsky School Startup, KPI Challenge, and the Institute of Advanced Defense Technologies.

Moreover, the SCU encompasses 15 regional and city innovation clusters, numerous enterprises, business associations, foundations, and maintains 10 representative offices across five countries: the United States, Israel, Poland, China, Azerbaijan.

KPI’s student design bureau for unmanned aerial vehicles and onboard equipment is a hub of innovation, where graduate and undergraduate students and faculty collaborate on modernizing existing aerial vehicles and devising new hardware models. This collaboration leverages advanced design methodologies, contemporary and emerging technologies for manufacturing airframe elements and onboard UAV equipment, and draws on the expertise of industry professionals from Yuavia and the Zlit Design Bureau. A notable achievement includes the development of a miniature integrated navigation system by the student design bureau. This system makes it possible to determine the parameters of UAV movement with high accuracy under difficult traffic conditions.

KPI’s connection with UAV development for the Ukrainian army is multifaceted:

• KPI offers educational programs and courses in robotics, autonomous systems and robots, which allow students to acquire the necessary knowledge and skills in these areas.
• The university is actively engaged in research in the field of unmanned systems. This includes the development of new technologies for the creation of drones and robots, the development of algorithms for their autonomous navigation and control.
• KPI supports student projects and startups related to the development and production of unmanned systems. The university provides access to resources and expert support for the development of such initiatives.
• KPI cooperates with other universities and research organizations in Ukraine and abroad for joint projects in the field of unmanned systems.

One of the promising startups is the UAV “Spectator,” developed by graduate student Roman Karnaushenka together with undergraduates Ihor Bogachuk and Yevhen Sedochenko. In order to achieve the ideal shape and good aerodynamics of the plane, they consulted with experienced scientists and engineers from KPI and Antonov State Enterprise. The “Spectator,” a UAV primarily intended for reconnaissance, features compact size that helps it remain inconspicuous and opaque to electromagnetic detection.

Currently, KPI scientists are working with engineers from the Meridian company to create civilian versions of unmanned aerial vehicles, with a focus on their use in agriculture, forestry and water management in Ukraine and other countries. Special drones are also being developed for monitoring oil pipelines, gas pipelines and power lines, as well as for cartography. Several so-called “civilian” drones have already been purchased by farmers, as they have proven to be very useful for agricultural monitoring. These UAVs allow multispectral surveying of a large area of agricultural fields in real time and analyzing crop condition. In one flight, such devices can cover an area of up to 500 hectares.

This work illustrates KPI’s significant contributions to both military and civilian UAV applications, fostering innovation and practical solutions in various sectors.

Conclusions

In conclusion, this paper has illustrated the vital need for Ukraine to cultivate an “innovative ecosystem” specifically tailored for the advancement of unmanned aerial vehicle (UAV) technology. Embracing an ecosystem approach, we argue, is not merely beneficial but essential for navigating the complexities inherent in the development and production of UAVs. This approach emphasizes the importance of creating a dynamic, open, and non-linear networking environment, thriving on the horizontal connections among diverse participants. These connections, characterized not by simple cooperation but by deep, collaborative engagement, underpin the ecosystem’s capacity to foster the creation and diffusion of knowledge flows, their transformation into innovations, and their ultimate commercialization.

Central to our argument is the conviction that such an ecosystem must leverage the intellectual and infrastructural capabilities of Ukraine’s premier research institutions, such as the National Aviation University (NAU) and the National Technical University “KPI.” The collaboration between these institutions – as well as with their international counterparts, notably the Łukasiewicz Research Network ‒ Institute of Aviation in Warsaw, Poland – is pivotal. Such collaboration accelerates the development of innovation “factories” that support the training, incubation, and growth of startups, encouraging the formation of micro- and small high-tech enterprises and facilitating their entry into global and regional markets. Furthermore, this collaboration enhances the mechanisms of interaction between startup teams and financial backers, including venture capital funds and investment companies, fostering a culture of trust and establishing common norms to navigate complex challenges collectively.

The business ecosystem approach, with its focus on co-specialization and the joint creation of new value, underscores the collaborative nature of innovation. Participants engage in distinct yet complementary activities, each contributing resources to the innovation process. This interactive exchange of knowledge and resources, characterized by dialogue, agreement, and feedback, shares risks and obligations among independent stakeholders. Such a process not only cultivates a collaborative culture but also creates structures to address the growing complexity of technology and the vast amounts of information and knowledge required for innovations in UAV design and operation. The UAV technologies that are rapidly developing in wartime are also widely used in non-military realms as well, and so will be of particular importance in the post-war reconstruction of the independent nation’s economy.

Our findings lay the groundwork for further theoretical and methodological research within the ecosystem framework, offering a strategic foundation for developing an ecosystem strategy and refining practical tools for ecosystem management across various administrative levels. This approach not only fosters innovation but also ensures that the development and production of UAVs contribute to broader objectives of economic sustainability and technological advancement.

In this endeavor, the role of modern universities cannot be overstated. Acting as generators of new ideas and inclusive educational spaces, they create a synergy of science, education, innovation, and business. This paper underscores the pivotal role of research universities in catalyzing UAV innovations within such an ecosystem, highlighting their indispensable contribution to creating a sustainable, technologically advanced future for UAV development in Ukraine.

References

1.Adner R. (2017). Ecosystem as Structure: an Actionable Construct for Strategy. Journal of Management, 43(1), 39–58. doi: 10.1177/0149206316678451
2.Adner R., & Feiler D. (2019). Interdependence, Perception, and Investment Choices: An Experimental Approach to Decision Making in Innovation Ecosystems. Organization Science, 30(1), 109–125. doi: 10.1287/orsc.2018.1242
3.Adner R., & Kapoor R. (2016). Innovation ecosystems and the pace of substitution: Reexamining technology S-curves. Strategic Management Journal, 37, 625–648.
4.Bazhal Yu.M. (2022). Innovative ecosystem as a factor of ensuring progressive structural changes in the economy. Scientific PAPERS NaUKMA: Economics, 7(1), 3–9. doi: 10.18523/2519-4739.2022.7.1.3-9
5.Cabinet of Ministers of Ukraine (2019). Strategy for the development of the sphere of innovative activity for the period up to 2030. Adopted by order of the Cabinet of Ministers of Ukraine, No. 526-r dated July 10, 2019. https://zakon.rada.gov.ua/ laws/show/526-2019-%D1%80#Text
6.Chychkalo-Kondratska, I., & Levchenko, I. (2023). The role of universities in innovative development: USA and Canada experience. International Science Journal of Management, Economics & Finance, 2(2), 1–9. doi: 10.46299/j.isjmef.20230202.01
7.Glyva V.A., Zaporozhets O.I., Levchenko L.O., Burdeina N.B., & Nazarenko V. (2023). Methodological foundations protective structures development for shielding electromagnetic and acoustic fields. Strength of Materials and Theory of Structures, 110, 245–255. doi: 10.32347/2410-2547.2023.110.245-255
8.Hrinchenko, Y. (2020). Journal of Applied Management and Investments, 9(2), 71–84. doi: 10.35774/visnyk2020.01.046
9.Isaienko, V., Pawęska, M., Kharchenko, V., & Bugayko, D. (2018). Challenges of international science and education in the field of aviation transport safety. Logistics and Transport, 38, 23–32.
10.Kalff, D., Renda A. (2020). Supporting and restructuring the aviation ecosystem. CEPS website, 09 July 2020. https://www.ceps.eu/supporting-and-restructuring-the-aviation-ecosystem/
11.Kharchenko, V., Pawęska, M., Bugayko, D., & Prusov, D. (2014). The Efficiency and Effectiveness of Remotely Piloted Aircraft Systems Used in Logistics Problems Solving Due to Territorial Infrastructure. Logistics and Transport, 22, 13–20.
12.Kharchenko, V.P (ed.) (2017). Metodolohiia sytuatsiinoho kolektyvnoho upravlinnia pilotovanymy i ezpilotnymy litalnymy aparatamy v yedynomu povitrianomu prostori. Tom 2. Intehrovani korporatyvni modeli dlia kolektyvnoho upravlinnia pilotovanymy i bpla v yedynomu povitrianomu prostori v umovakh ryzyku i nevyznachenosti [Methodology of situational collective management of manned and unmanned aircraft in a single airspace: scientific materials. Volume 2: Integrated corporate models for the collective management of manned aircraft and UAVs in a single airspace under conditions of risk and uncertainty]. Kyiv: NAU.
https://www.researchgate.net/publication/321913275_Metodologia_situacijnogo _kolektivnogo_upravlinna_pilotovanimi_i_bezpilotnimi_litalnimi_aparatami _v_edinomu_povitranomu_prostori_V_3-h_tomah_Tom_2_Integrovani_ korporativni_modeli_dla_kolektivnogo_uprav
13.Kim, E., Beckman, S., Kim, K., & Santema, S. (2022). Designing for dynamic stability in an uncertain world: A media content study of the aviation industry, in Lockton, D., Lenzi, S., Hekkert, P., Oak, A., Sádaba, J., Lloyd, P. (eds.), DRS2022: Bilbao, 25 June – 3 July, Bilbao, Spain. doi: 10.21606/drs.2022.376
14.Kyzym M. O., Haustova V. E., & Reshetnyak O. I. ( 2021). Organizational and economic mechanism of integration of education, science and business: a model of a modern university. Problems of economics, 4(50), 29–41. doi: 10.32983/2222-0712-2021-4-29-41
15.Lee, S.M., & Trimi, S. (2021). Convergence Innovation in the Digital Age and in the COVID-19 Pandemic Crisis. Journal of Business Research, 123, 14–22. doi: 10.1016/j.jbusres.2020.09.041
16.Makarenko V., Tokarev V., & Zaporozhets O. (2020). Determination of acoustic characteristics of Autonomous Aircraft for environment protection and collision avoidance in flight restricted zones. AEC 2020 Conference Proceedings, Bordeaux, France, 25 to 28 February 2020. Paper number 594.
17.Moore, J. F. (1996). The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems. New York, NY: Wiley Harper Business.
18.Pidorycheva, І. (2020). Innovation ecosystem in contemporary economic researches. Econ. promisl., 2(90), 54–92. doi: 10.15407/econindustry2020.02.054
19.Poberezhets, O., & Rakytska, A. (2023). Rozvytok Innovatsiinoi Ekosystemy Ukrainy na Natsionalnomu Rivni [Development of innovative ecosystem of Ukraine at the national level]. Market Economy: Modern Management Theory and Practice, 21, 431-448. doi: 10.18524/2413-9998.2022.3(52).275824
20.Reeves, M., & Pidun, U. (2022). Business Ecosystems. Berlin, Boston: De Gruyter. doi: 10.1515/9783110775167.
21.Steer (2023). Research for TRAN Committee – Unmanned Aircraft Systems integration into European airspace and operation over populated areas. European Parliament. Policy Department for Structural and Cohesion Policies. Brussels.
https://www.europarl.europa.eu/thinktank/en/document/IPOL_STU(2023)733124/
22.Synylo K., Ulianova K., & Zaporozhets O. 2020. Air Quality Studies at Ukrainian Airports. International Journal of Aviation Science and Technology, 2(1), 4–14. doi: 10.23890/IJAST.vm02is01.0101
23.Telli, K., Kraa, O., Himeur, Y., Ouamane, A., Boumehraz, M., Atalla, S. & Mansoor, W. (2023). A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs). Systems, 11, 400. doi: 10.3390/systems11080400
24.Tokarev V., & Kazhan K. (2014) Entropy approach for mitigation of environmental aviation impact and airport capacity increase. International Journal of Sustainable Aviation, 1(2), 119–138. doi: 10.1504/IJSA.2014.065479
25.Tokarev V., & Makarenko V. (2021). Prediction of noise generated by small unmanned aerial vehicles’ operation in vertiport vicinity. International Journal of Sustainable Aviation, 7(2), 165–185. doi: 10.1504/IJSA.2021.117243
26.Zaporozhets O., Levchenko L. & Synylo K. (2019). Risk and exposure control of aviation impact on environment. Advanced Information Systems, 3(3), 17–24. doi: 10.20998/2522-9052.2019.3.02
27.Zaporozhets O., Tokarev V. & Attenborough K. (2011). Aircraft noise: assessment, prediction and control. Glyph International, Taylor & Francis. doi: 10.1201/b12545

In the 21st century, the technological world is evolving with increasing rapidity. This is especially true in the field of artificial intelligence (AI), which is transforming markets in revolutionary ways. The aim of this article is to explore the impact of the development of these new AI new technologies on medical services, and products, and to classify them according to patient needs and benefits. We contribute to the literature by demonstrating the added value for the patient, for the healthcare system, and for the physicians (service providers), the interconnectedness of the factors influencing the development of new technologies, and the benefits for key stakeholders. We focus on demonstrating key innovative solutions that enable new functionalities, higher standards of service and improved clinician competence.

The article is both theoretical and practical in nature. Our primary research method is analysis of the literature and information we collected while managing the project “Implementation of a telemedicine model in the field of cardiology by ‘Polish Mother’s Memorial Hospital – Research Institute, 5/NMF/2066/00/62/2023/295, subsidized by the Norwegian Financial Mechanism and the state budget.”

First, we consider the theoretical aspects of the empowerment that emerges from new technologies, products and services, and then focus more on AI based technology for healthcare. Next, we propose an original classification of new e-health technologies according to their added value to the main healthcare stakeholders (patients, clinicians, and the healthcare system itself). Then we discuss some of the challenges faced by the implementation of new e-health technologies, products and services, and finally offer some conclusions.

Empowering new technologies, products and services — theoretical aspects

The process of harnessing new technologies and products in providing healthcare services is deeply embedded within the healthcare system as a whole. In particular, this involves healthcare providers and the services they provide, aimed at strengthening and improving the health of individuals and societies through disease prevention, early detection, treatment, and rehabilitation. Unfortunately, the healthcare situation in most European countries is expected to deteriorate due to population ageing, price increases, and the increasing complexity of healthcare technologies (Marmot et al., 2012). This entails a demand for a disproportionately high level of financial, material, and human resources.

Hence, meeting the health needs of citizens, and thus ensuring the availability of health-related services, depends primarily on a number of critical factors (Sobiech, 1990, p. 10): the volume of financial resources flowing into the health care system of a given country, the number and qualifications of medical staff, their spatial distribution and efficiency, the availability and application of medical technology and apparatus, and access to medical expertise (know-how) (Bukowska-Piestrzyńska, 2013, p. 66). With healthcare funding becoming more constrained every day, it is becoming increasingly important to look more closely at the processes of purchasing, distributing, and harnessing new technologies. Metrics such as stock coverage, urgent purchases, and non-standard purchases are particularly important in the management of healthcare facilities (Santosa et al., 2022, pp. 1-6).

The integration of AI systems in handling some aspects of communications during the diagnosis and treatment process could prove crucial for patient well-being and thus for the doctor-patient relationship. AI-based technologies, products and services will raise new questions about the limits of usability, cost-effectiveness and the ever-increasing cost of healthcare – above all, the issue of optimality in the creation of new products and services (Ayad et al., 2023).

A promising avenue of opportunities to apply new solutions to this challenge lies in digital healthcare, particularly through the implementation of artificial intelligence. These implementations involve a wide range of technologies. Digital tools are “fine-tuning” the capabilities of medical staff and facilitating a shift towards “consumerized” healthcare. This allows citizens to become more involved in managing their family’s healthcare. Digitalization, however, also brings risks, particularly if the challenges it presents are not adequately understood. If these wonderful technological advances are misused, they may expose society to the “dark side” of digital innovation. For example, if smart homes are not designed with the patient’s needs firmly in mind, but instead for the convenience of the “system,” they may give patients a prison-like experience, with robotic and sensor monitoring and control (Stahl & Cockelberg, 2016). Moreover, while AI can improve doctors’ technical skills to operate new technological solutions, it may also reduce their exposure to varied clinical experience (which in turn may make it more difficult to detect rare and atypical diseases).

Accountability in the health sector therefore raises critical questions: accountability for what, and to whom?

Ramachandran et al. (2015), for instance, reported that as many as 60% of patients with chronic diseases show interest in receiving healthcare via telephone, highlighting the a growing demand for e-health services in recent years. The management of chronic diseases is costly for individual patients and their families, as well as for the national health service. Therefore, there is great potential for developing new e-health technologies to improve the management of chronic diseases. Implementing these e-health technologies in healthcare systems can yield significant improvements and facilitate the integration of different aspects of healthcare (Hunt, 2015).

The responsible development of new technologies and bringing innovations to market require the active involvement of all stakeholders, from the very beginning of the innovation process. This helps to accurately identify the needs and priorities of innovation for society (Owen et al., 2012; Stahl et al., 2017). In health care, this means involving patients, carers and other stakeholders in the innovation process, anticipating the risks associated with new solutions, and ensuring that the solutions offered are implemented in a responsible and safe way, with the patient at the forefront (Pawelec, 2022).

New technologies and products in medicine mean smarter, safer, and more patient-centered healthcare services. By improving fit-for-purpose design, efficiency, and effectiveness, they help to reduce errors and shorten the length of hospital stays. The marketing management of healthcare services increasingly focuses on the individual purchaser – a shift emerging in many healthcare organizations thanks in part to new technologies. It should be recognized that there is an important difference between an “ordinary” customer, who can opt out of a purchase, and the patient-consumer of a healthcare service, who relies on medical consultations that directly affect his or her health or life (Białowolski et al., 2012).

Traditionally, patients have often been passive recipients at the endpoint of the service delivery system, rather than active stakeholders. One of the dangers of powerful new technologies is that patients may become even more marginalized, as healthcare is provided and delivered in an increasingly administrative, programmed manner. The doctor may also become more like a robot, carrying out programmed tasks in what could be described as “inhumane services.” The alternative approach places the patient at the center and puts technologies, products, and services at their disposal that allow them to design and control their healthcare based on their own needs. In this, it is important to shift away from seeing patients as a homogeneous group, instead categorizing them as distributed across a spectrum, including:

1)“Informed Users,” who are in a position to use technology with a better understanding;

2)“Engaged Users,” who play an activist role in the wider healthcare system, empowered by technology;

3)“Innovative Users,” who contribute their own ideas based on a deep understanding of healthcare problems.

Artificial Intelligence based technology for healthcare

Advancing safety in the organization of health technology use underscores that while consumers generally trust mature and complex technologies, advances in this area often obscure our understanding of the basics of how such technologies operate. We rely on them not because we are unaware of the potential risks, but because we believe that these risks are properly managed both by control procedures and by human oversight (by a physician). For example, we use increasingly advanced medicines without fear, often without fully grasping the complex clinical trial process that validates their safety. Similarly, we consent to robotic surgeries without fear that our health will be compromised (Turpin et. al., 2020).

The application of new technologies in healthcare should create new value, which may vary depending on the stakeholder. On the one hand, there are private companies that develop and market a technology, product or service, offering it to patients and hospitals in exchange for payment. This technology or product usually enables new functionality, a higher standard of healthcare, or a higher level of proficiency among doctors. On the other hand, there are hospitals that seek to generate maximum value, provided that does not exceed costs. Value is created when it increases revenue, enables more patients to access services, or allows diseases to be detected more quickly, improving quality of life. Value can also be derived from adhering to new global trends, such as the use of AI (Kulkov, 2021).

Artificial intelligence (AI) in healthcare involves the deployment of advanced mathematical algorithms and computer software to analyze complex medical data. The analysis of large datasets (“big data”) makes it possible to predict the probability of particular medical events. Programs that operate with the support of AI have the ability to learn autonomously (machine learning), by harnessing the collected data and the performed analyses.

Some of the first medical applications of artificial intelligence emerged in the field of radiology. AI systems are able to automatically assimilate X-ray data from databases containing thousands of images and then use this knowledge to assess a particular case and even evaluate a patient’s skeletal age (Jankowski, 2018). Physicians from the Department of Radiology, School of Medicine, Stanford University conducted a study in which 33 patients with nonspecific or common interstitial pneumonia were enrolled. Participants were selected by radiologists with 15-year experience. The same group of patients was qualified by n AI algorithm and two medics who had attended a one-year training course in the field. The AUC (area under the curve) obtained by the AI was 0.81, indicating its strong diagnostic ability. Interestingly, different diagnostic errors were found between the trained doctors and the algorithm, involving different patients. Such findings suggest the possibility of diminishing the risk associated with human error and the possibility of AI collaborating with physicians to further minimize incorrect diagnoses (Depeursinge et al, 2015).

Artificial intelligence in the field of radiology facilitates the search and analysis process for lesions, and is additionally able to detect the smallest lesions that may have been overlooked by experts (Arbabshirani et al., 2018). Recent studies also show that deep learning can adaptively improve image reconstruction during MRI examinations, leading to shorter scan times and increased quality of the obtained images, and thereby to a higher diagnostic value of the examination performed. Such improvements are particularly notable in images obtained with the FLAIR (fluid-attenuated inversion recovery) MRI sequence, which is commonly used for imaging specific brain structures (Hagiwara et. al., 2019).

A significant advantage of AI in healthcare is its potential to relieve doctors of many of their duties, allowing for more patients to be examined. An example of such an application is a study conducted on 154 diabetic patients, which investigated the efficacy of diabetic retinopathy detection based on ocular fundus examinations by the Remidio NM FOP 10, an AI-based device. Results showed concurrence in 85 cases between the device’s assessments and those of ophthalmologists. There were four instances where diabetic retinopathy lesions were identified and 81 cases with no lesions detected. Discrepancies arose in 21 cases, involving poor-quality images. The study revealed that the Remidio NM FOP 10 has a detection accuracy of 80.2%. Additionally, the device can be operated by a trained individual without an ophthalmologist’s direct involvement, potentially increasing the accessibility of preventive measures for individuals with diabetes (Kaczmarek, 2021). Deep learning holds promise for the automatic detection of diabetic retinopathy, offering consistency and precision due to its methodological approach and detailed analysis capabilities.

Another example of the application of intelligent algorithms is their use in supporting Czech medical unit doctors during appointments with specialists. Here, the AI system listens to the patient and the doctor during the appointment at the medical facility and then files a transcription of their dialog. After a few seconds, the AI generates a report from the visit, capturing the most important information provided by the patient as well as the diagnosis, recommendations, and treatment suggested by the doctor. The specialist can edit the report, add or remove specific information that the algorithm has generated. This process not only improves the visit but also allows for detailed review of previous visits, increasing the potential for seeing more patients and reducing their waiting time.

The methodologies described above have not yet been implemented in standard use. Many systems are still in the testing and observation stage in order to verify their correct functioning. Nevertheless, intelligent algorithms often yield results that are on par with, or sometimes even better than, those achieved by medical experts. The cooperation of AI systems and medical experts can minimize the risk of human error when making a diagnosis. Nevertheless, despite the attractive solutions that AI offers, there are several challenges that cannot be overlooked. It is crucial to collect, store and share medical data correctly, in accordance with current regulations. Intelligent algorithms are trained based on huge databases, with content of quality that can be difficult to access. The more information AI assimilates, the more precise the final results and diagnoses will be. Ultimately, it is crucial for results generated by AI to be verified and approved by experts in the relevant medical field (Char et al., 2018).

During the COVID-19 pandemic, new technology played an important role in allowing health services to function through increased Internet capabilities. Telemedicine, in particular, has seen significant advancements, catalyzing dynamic changes in the medical field. In addition, a variety of applications have been developed to facilitate the monitoring of patient health, as well as websites providing necessary information for those interested in such innovations. Some of the solutions are developing globally, making it possible not only to treat, but also to improve procedures or save patients’ lives, thus raising the standard of medical care in the healthcare sector.

A classification of new e-health technologies according to benefits to the main healthcare stakeholders

This section of the article explores the emerging importance of these and other cutting-edge technologies and products in healthcare. The multifaceted nature of such technologies, exhibiting high complexity, mean that a broad range of traditional health care stakeholders must be taken into consideration in the analysis of their implementation. We evaluate the benefits and added value for various groups, including medical institutions, physicians, nurses, medical technicians, distributors, e-health providers, e-health systems managers, and patients. The needs of these stakeholders vary, necessitating tailored solutions that cater to specific requirements.

While some stakeholders are involved in R&D on new technology and products, others function primarily as distributors or supporters, while still others are end-users. The literature on this topic offers various classifications of new technologies and products – notably including Herrmann et al.’s (2018) classification of over 400 different digital health projects and solutions. These were categorized according to their purpose into ten different types: software as a medical device, advanced analytics, artificial intelligence, cloud services, cybersecurity, interoperability, medical devices data systems, mobile medical applications, wireless technologies, and novel digital health solutions. However, this classification primarily focuses on products aimed at healthcare professionals, mitting those designed for the industry, the insurance companies and other stakeholders.

Severika and Ceranic (2020), in contrast, offer a broader classification of new technologies pertaining to healthcare professionals, industries, insurance companies and other stakeholders. Their proposed categories include: lifestyle intervention tools, diagnostics and prevention tools, research and development & production optimization tools, remote tracing tools, clinical decision support tools, telemedicine tools, and workflow tools. The World Health Organization (2018) emphasizes that digital and mobile technologies are increasingly crucial in supporting the needs of health systems.

From a market perspective, new technologies and products that are implemented in medical units should first and foremost add value for patients and physicians. (The economic value of new technologies cannot be overlooked, of course, but it is not the focus of this article.) From this perspective of the added value for clinicians, we propose to segment the new technologies and products into seven categories: wearable devices, mobile applications, remote monitoring systems, technologies based on artificial intelligence algorithms, telemedicine platforms, electronic health records, and 3D printing technologies. These categories underscore the specialized development and implementation needs within medicine and their potential to offer significant value to clinicians and patients. In many cases, technologies and products span multiple categories.

A detailed table of benefits for patients, doctors and the healthcare system is presented in Table 1.

We will illustrate our classification further by providing a few examples of the first product group in it: wearable devices, where the use of the product by the customer has medical applications. Several solutions will be discussed to illustrate their importance for patients.

HigoSense has created a device with 5 interchangeable tips that capture images and measurements of given areas of the patient’s body and is equipped with a module for listening to breathing, etc. This allows anyone, anywhere, to collect detailed medical data of similar quality to that obtained by a doctor during direct contact with the patient in the office or during a home visit. Data collection, delivery and sharing are carried out by means of the Higo app, which also supports medical interviews, management of patient’ history, scheduling examinations and communications with the doctor. (https://higosense.com/pl/produkt/) MedApp’s CarnaLife Holo solution employs a revolutionary technology for the three-dimensional visualization of diagnostic data to assist in the planning and execution of medical procedures. The HoloLens 2 goggles, developed by Microsoft, provide the ability to view 3D holograms of anatomical structures in a real-world environment. The doctor is able to interact with the holograms, such as rotating, scaling, moving and even entering inside the anatomical structures using gestures and voice commands. The entire process is carried out without the risk of compromising sterility and without the need to cooperate with additional technical staff. The goggles are an interactive screen that can be used for procedure planning and anywhere in the operating room, or even during the procedure. (https://medapp.pl/carnalife-holo/)

telDoc presents an innovative solution related to the Virtual Medical Assistant, which, during the patient’s contact with the medical facility, provides initial, ad hoc assistance and then refers the patient to the appropriate tests and specialist. During the visit, the doctor receives the test results and the initial medical history, which has been conducted by the Virtual Assistant via voice or chat, reducing the time spent on the administrative part of the visit. In addition, the company has created a Virtual Nurse Assistant, which regularly calls patients to ask how they are feeling, collects basic information about the patient’s vital functions, asks or reminds them to take prescribed medication, suggests contacting a medic in an alarming situation, and notifies relatives of the patient’s situation. (https://www.teldoc.eu/projekty)

Nestmedic’s Pregnabit medical device is designed for remote/hybrid KTG monitoring for women from 32 weeks of pregnancy, with indications for examination or hospitalization. The system consists of a mobile KTG device and a Medical Telemonitoring Centre service, where the test results are analyzed by medical experts. The use of specially developed medical algorithms aids doctors and midwives in monitoring and decision-making. (https://nestmedic.com/pregnabit/)

For patients, the main advantages of using modern technology of this sort include reduced access time to the doctor, increased intensity of treatment, a higher level of care resulting in better treatment outcomes, a better standard of living with chronic diseases with all-day health monitoring. Doctors, in turn, can optimize medical care and have faster, often immediate access to data in the form of epics or images. Artificial intelligence is also entering the operating theatre to assist the doctor, making procedures easier and reducing the number of repeat operations. For the healthcare system, however, it is the cost implications that are important. Estimation of the real costs of new implementations, reduction of unit costs with an increase in the number of interventions, possibility of detection of new diseases (including rare diseases).

Challenges for the implementation of new e-health technologies, products and services

Some of the key challenges include:

• Data accuracy and reliability: The accuracy and reliability of data collected by medical devices incorporating intelligent technologies is critical to the effective and efficient management of healthcare services. Patient accountability must ensure the timeliness, accuracy, relevance, appropriateness and consistency of measurements provided by AI devices (Etemadi & Khashei, 2020).
• Data security and privacy: Smart technologies generate and transmit sensitive health data, raising concerns about data security and privacy. Protecting personal data and health information from unauthorized access, breaches and misuse is paramount in the development of cyberhealth. Security measures must be implemented to protect patient data. These must be in line with data protection regulations and encryption techniques (Fatima & Colombo-Palacios, 2018).
• Integration: Integration of different smart healthcare technologies and systems is essential for seamless data exchange and collaboration (Shah et al., 2021). However, the challenges of integrating different devices, digital platforms and electronic health record systems can hinder effective data sharing and communication between patients and their doctors or healthcare organizations. One solution to this involves standardization.
• Adaptability: Devices, systems and platforms must be adapted to the type of patient, the level of health care reference, and the level of technological development of the organization implementing the new solutions (Chronaki et al., 2004).
• User acceptance and involvement: The success of the implementation of intelligent technologies depends on user acceptance and involvement. Patients need to be motivated to make consistent use of these technologies and to take an active part in their own care (Jankowska-Polańska et al., 2014). Clinicians need to follow protocols and monitor the activity and accuracy of patients’ use of the technologies. Overcoming barriers such as technology familiarity, usability concerns and resistance to change is key to widespread use of digital technologies.
• Legislation and regulation: Regulatory changes need to keep pace with the rapid development of smart technologies. Legislation needs to be put in place to ensure the safe, effective and ethical use of technology in healthcare, particularly artificial intelligence. In addition, reimbursement policies should take into account the value and cost-effectiveness of smart technologies, as this may have an impact on their availability and adoption (Orędziak, 2018).
• Accessibility: Ensuring equal access to smart technologies is essential to address inequalities in healthcare. Price, usability and accessibility of new technologies need to be considered (Bokolo, 2021).
• Validation: Rigorous clinical trials are needed for smart technologies, especially those based on artificial intelligence. Rigorous scientific research, randomized controlled trials and analysis of real-world data are needed to demonstrate the clinical value and safety of using smart technologies in healthcare. The margin for error in the use of new technologies in medicine, for example, is very small or may not exist at all. This has to do not only with protecting health, but also with protecting life (ICH Guidelines, 2016).
• Cost-effectiveness: Cost-effectiveness is an important factor in the introduction of new medical technologies. However, its role in improving quality of life and standards of care should also be emphasized (Trzmielak, 2014).

Conclusions

In the coming years, medical professionals can expect to be able to access more advanced and highly specialized tools will be available to medical professionals, increasing their competence and capabilities. Continued advances in artificial intelligence (AI) research in medicine are also likely contribute to the thorough validation of both existing and future systems, which could lead to their widespread adoption. However, the integration of advanced technologies, particularly AI, into healthcare practices represents a significant paradigm shift towards improving patient care and enhancing healthcare delivery systems. Throughout this article, we have explored the multifaceted impact of these technologies, demonstrating how they not only augment clinical practices but also empower patients by offering more personalized and accessible healthcare solutions. The bibliographic analysis and examples discussed herein offer a certain overview of the practical applications and theoretical implications of AI in healthcare, emphasizing the dual benefit to both clinicians and patients.

Our findings illustrate that AI-driven tools can significantly relieve the workload of healthcare professionals, allowing for the expansion of healthcare services and specializations that cater more directly to patient needs. This not only improves the efficiency of healthcare delivery but also enhances the quality of patient care by enabling more accurate diagnoses and tailored treatment plans. The classification of new e-health technologies that we have proposed herein may serve as a clear framework for understanding the various ways in which these innovations can be implemented to maximize their benefits across different sectors of the healthcare industry.

Moving forward, the continuous advancement and deployment of these technologies necessitates a committed approach to research and validation, ensuring that they meet the highest standards of efficacy and safety. The collaborative acceptance by healthcare professionals and patients is crucial for these technological innovations to be successfully integrated into everyday medical practices. Such acceptance is dependent on clear demonstrations of the improvements these technologies bring to patient outcomes and healthcare workflows.

In conclusion, the successful deployment of AI and other innovative technologies in medicine requires ongoing analysis and adaptation to the evolving needs of the healthcare sector. By aligning these technological advancements with the real-world requirements of both healthcare providers and recipients, we can ensure that they lead to more effective, efficient, and empathetic healthcare services. The promising developments discussed in this article not only highlight the current achievements but also pave the way for future innovations that will continue to transform healthcare.

Aknowledgements

The article was funded by the project entitled “Implementation of a telemedicine model in the field of cardiology by Polish Mother’s Memorial Hospital – Research Institute subsidized by the Norwegian Financial Mechanism and the state budget,” under contract no. 5/NMF/2066/00/62/2023/295.

References

Arbabshirani, M. R., Fornwalt, B. K., Mongelluzzo, G. J., Suever, J. D., Geise, B. D., Patel, A. A., & Moore, G. J. (2018). Advanced machine learning in action: Identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digital Medicine, 1, 9. https://doi.org/10.1038/s41746-017-0015-z

Auwal, F. I., Copeland, C., Clark, E. J., Naraynassamy, C., & McClelland, G. R. (2023, September). A systematic review of models of patient engagement in the development and life cycle management of medicines. Drug Discovery Today, 28(9), 103702. https://doi.org/10.1016/j.drudis.2023.103702

Ayad, N., Schwendicke, F., Krois, J., S. van den Bosch, Bergé, S., Bohner, L., Hanisch, M., & Vinayahalingam, S. (2023, December). Patients’ perspectives on the use of artificial intelligence in dentistry: A regional survey. Head & Face Medicine, 19, Article 23. https://doi.org/10.1186/s13005-023-00368-z

Białowolski, P., Grabowska, I., Kotowska, I., Strzelecki, P. &Węziak-Białowolska, D. (2012). Social Diagnosis 2011: The objective and subjective quality of life in Poland. Ed. Czapiński, J., & Panek, T. The Council for Social Monitoring Warsaw.

Bokolo, A. Jnr. (2020). Application of telemedicine and eHealth technology for clinical services in response to the COVID-19 pandemic. Health and Technology, 11, 359–366. https://doi.org/10.1007/s12553-020-00516-4

Bukowska-Piestrzyńska, A. (2013). Zmiany w systemie opieki zdrowotnej a dostępność usług stomatologicznych w Polsce w XXI wieku [Changes in the healthcare system and the availability of dental services in Poland in the 21st century]. Vol. XIV(10), pt. I. [in Polish]

Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care — Addressing ethical challenges. New England Journal of Medicine, 378(11), 981–983. https://doi.org/10.1056/NEJMp1714229

Chronaki, C. E., Lelis, P., Chiarugi, F., Trypakis, D., Moumouris, K., Stavrakis, H., Kavlentakis, G., Stathiakis, N., Tsiknakis, M., & Orphanoudakis, S. C. (2004). An open eHealth platform for health management using adaptable service profiles. International Congress Series, 1268. https://doi.org/10.1016/j.ics.2004.03.201

Depeursinge, A., Chin, A. S., Leung, A. N., Terrone, D., Bristow, M., Rosen, G., & Rubin, D. L. (2015). Automated classification of usual interstitial pneumonia using regional volumetric texture analysis in high-resolution computed tomography. Investigative Radiology 50(4), 261–267. https://doi.org/10.1097/RLI. 0000000000000127

Etemadi, S., & Khashei, M. (2020). Data accuracy and reliability. Computers in Biology and Medicine, 141. https://doi.org/10.1016/j.compbiomed.2021.105138

Fatima, A., & Colomo-Palacios, R. (2018). Security aspects in healthcare information systems: A systematic mapping. Procedia Computer Science, 138, 12–19. https://doi.org/10.1016/j.procs.2018.10.003

Hagiwara, A., Otsuka, Y., Hori, M., Tachibana, Y., Yokoyama, K., Fujita, S., Andica, C., Kamagata, K., Irie, R., Koshino, S., Maekawa, T., Chougar, L., Wada, A., Takemura, M., Hattori, N., & Aoki, S. (2019). Improving the quality of synthetic FLAIR images with deep learning using a conditional generative adversarial network for pixel-by-pixel image translation. American Journal of Neuroradiology, 40(2), 224–230.

Herrmann, M., Boehme, P., Mondritzki, T., Ehlers, J. P., Kavadias, S., Truebel, H. (2018). Digital transformation and disruption of the health care sector: Internet-based observational study. Journal of Medical Internet Research, 20, 104–112. https://doi.org/10.2196/jmir.9498 Hunt, C. W. (2015). Technology and diabetes self-management: An integrative review. World Journal of Diabetes, 6(2), 225–33. https://doi.org/10.4239/wjd.v6.i2.225

ICH Guidelines. (2016). Harmonized ICH Guidelines, Integrated Addendum to ICH E6(R1): Good Clinical Practice, E6(R2), version 4. International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH). https://database.ich.org/sites/default/files/E6_R2_Addendum.pdf

Jankowski M., (2018). Sztuczna inteligencja jako narzędzie wspomagające proces diagnostyczno-terapeutyczny, Menadżer Zdrowia, październik – listopad 8-9, 66–67.

Jankowska-Polańska, B., Ilko, A., & Wleklik, M. (2014). Wpływ akceptacji choroby na jakość życia chorych z nadciśnieniem tętniczym [Influence of the acceptance of the disease on quality of life of patients with hypertension]. Nadciśnienie Tętnicze, 18(3), 143–150. [in Polish]

Kaczmarek, E. (2021). Sztuczna inteligencja – pomoc w wykryciu retinopatii cukrzycowej [Artificial Intelligence – Assistance in Detecting Diabetic Retinopathy]. Optyka, 6(73), 48–49. [in Polish]

Kulkov, I. (2023). Next-generation business models for artificial intelligence start-ups in the healthcare. International Journal of Entrepreneurial Behavior & Research, 29(4).

Marmot M., Allen J., Bell R., Bloomer E., Goldblatt P., (2012). WHO European review of social determinants of health and the health divide, Lancet 2012; 380, 1011–29. 10.1016/S0140-6736(12)61228-8

Orędziak, B. (2018). Telemedycyna a konstytucyjne prawo do opieki zdrowotnej w kontekście wykluczenia cyfrowego [Telemedicine and the Constitutional Right to Healthcare in the Context of Digital Exclusion]. Zeszyty Prawnicze, 18(1). https://doi.org/10.21697/zp.2018.18.1.06 [in Polish]

Owen, R., Macnaghten, Ph., Stilgoe, J., (2012). Responsible Research and Innovation: From Science in Society to Science for Society, with Society. Science and Public Policy, 39(6), 751–760. https://doi.org/10.1093/scipol/scs093.

Pawelec, G. (2022). Rola nowych technologii w podnoszeniu jakości usług zdrowotnych w dobie pandemii COVID-19. [The role of new technologies in improving the quality of health services in the era of the COVID-19 pandemic]. Marketing i Rynek, XXIX(2). https://doi.org/10.33226/1231-7853.2022.2.2 [in Polish]

Ramachandran, N., Srinivasan, M., Thekkur, P., Johnson, P., Chinnakali, P., & Naik, B. N. (2015). Mobile phone usage and willingness to receive health-related information among patients attending a chronic disease clinic in rural Puducherry, India. Journal of Diabetes Science and Technology, 9(6), 1350–1. https://doi.org/10.1177/1932296815599005

Santosa, E. S., Fariab, S. C. M., Carvalhob, M. I. S., Molc, M. P. G., Silvab, M. N., & Silva, K. R. (2022, December). Management of unused healthcare materials and medicines discarded in a Brazilian hospital from 2015 to 2019. Cleaner Waste Systems, 3. https://doi.org/10.1016/j.clwas.2022.100046

Severika, B., & Ceranic, K. (2020, April). Digital Health Classification Systems. Statistics & Science. https://www.5-ht.com/en/media/blog/digital-health-classification-systems

Stahl, B.C. and Coeckelbergh, M. (2016) Ethics of Healthcare Robotics: Towards Responsible Research and Innovation. Robotics and Autonomous Systems, 86, 152–161. https://doi.org/10.1016/j.robot.2016.08.018

Shah, J. L., Bhat, H. F., & Khan, A. I. (2021). Integration of Cloud and IoT for smart e-healthcare. In V.E. Balas, S. Pal (Eds.). Healthcare Paradigms in the Internet of Things Ecosystem (pp. 101–136). Elsevier. https://doi.org/10.1016/B978-0-12-819664-9.00006-5.

Stahl, S., Morrissette, D. A., Faedda, G. L., Fava, M., Goldberg, J., Keck, P., Lee, Y., Malhi, G., Marangoni, C., Mcelroy, S., Ostacher, M., Rosenblat, J., Solé, E. Suppes, T. Takeshima, M. Thase, M., Vieta, E., Young, A. Zimmerman, M. McIntyre, R. (2017). Guidelines for the recognition and management of mixed depression. CNS Spectrums. 22(2), 203–219. https://doi.org/10.1017/S1092852917000165.

Sobiech, J. (1990). Warunki wyboru ekonomiczno-finansowych mechanizmów kierowania opieką zdrowotną. Zeszyty Naukowe, 109. Wydawnictwo Akademii Ekonomicznej w Poznaniu.

Trzmielak, D. M. (2013). Komercjalizacja wiedzy i technologii – determinanty i strategia [Commercialization of knowledge and technology – determinants and strategy]. Łódź: Wydawnictwa Uniwersytetu Łódzkiego. [in Polish]

Turpin, R., Hoefer, E., Lewelling, J., & Baird, P. (2020). Machine Learning AI in Medical Devices, Adapting Regulatory Frameworks and Standards to Ensure Safety and Performance. AAMI, BSI. https://www.medical-device-regulation.eu/wp-content/uploads/2020/09/machine_learning_ai_in_medical_devices.pdf

Ullah, M., Hamayun, S., Wahab, A., Khan, S. U., Rehman, M. U., Haq, Z. U., Rehman, K. U., Ullah, A., Mehreen, A., Awan, U. A., Qayum, M., & Naeem, M. (2023, November). Smart Technologies used as Smart Tools in the Management of Cardiovascular Disease and their Future Perspective. Current Problems in Cardiology, 48. https://doi.org/10.1016/j.cpcardiol.2023.101922

Verbraecken, J. (2021, September). Telemedicine in Sleep-Disordered Breathing: Expanding the Horizons. Sleep Medicine Clinics, 16(3), 418–445.

World Health Organization. (2018). Classification of Digital Health Interventions v 1.0: A shared language to describe the uses of digital technology for health. https://apps.who.int/iris/bitstream/handle/10665/260480/WHO-RHR-18.06-eng.pdf

Online sources:

https://higosense.com/pl/produkt/
https://medapp.pl/carnalife-holo/
https://nestmedic.com/pregnabit/
https://www.teldoc.eu/projekty

A rationally managed enterprise, systemically focussed on dynamic development at the stage of progress, competitiveness, conquering new markets, or increasing its position on existing markets, must be able to combine three spheres of activity into one systemic whole. Such spheres include: the pre-production sphere, the production sphere, and the postproduction sphere. A special role should be assigned to the pre-production sphere, because it involves the processes of generating/acquiring knowledge necessary to effectively solve all managerial, organisational, technical, and technological problems arising in the company and its environment. The ability to quickly and effectively conduct the processes of generating/acquiring knowledge requires a rational link between research and development (R&D) and innovation activities into one common whole research-development-innovation (R&D&I). The concept of treating the above phases together makes up the cycle of the research system, which can be considered the most important stimulus for the generation of knowledge in the structures of the modern economic entity (Mate & Molero, 2021, p. 1–14). The presented concept requires changes in the approach to managing individual spheres of the company’s activity, it requires innovation in management.

Particular emphasis should be placed on developing the ability to identify problems and acquire the knowledge necessary to solve them efficiently, materialised in the form of streamlining and radical innovations. Such knowledge is acquired as a result of organised R&D activities carried out within the enterprise or organised acquisition of external knowledge. Research and development is considered the cornerstone of the competitive advantage, the long-term success of the organisation (Heij et al., 2020, p. 277–294). In the absence of sufficient resources of up-to-date knowledge, effective solutions to even the smallest problems are not possible. Therefore, the management faces an important task consisting of organising R&D activities in such a way as to ensure a systematic supply of knowledge necessary to identify and solve problems and create innovations, especially product and process innovations.

Rational organisation of R&D and innovation activities, efficient management of these functions, should be based on the ability to identify the actual state, the knowledge of which would be the basis for designing and implementing structural and process changes covering both R&D and innovation activities (Heij et al., 2020, p. 277–294). Rational organisation of R&D activities and linking them with innovative activities is an important challenge for the management of modern business entities. However, in the context of daily practice, it may be doubtful whether managers feel the need to take such actions or improve them. Do they see a connection between the results of R&D and innovation activities and the technical and economic results of the company and its market position?

In general, there are significant differences in the perception of the essence of R&D&I activities presented in the literature and the daily activities of many business entities in this field. The European Commission’s report concludes that sound research and innovation policies can help build an inclusive, sustainable, competitive, and resilient Europe. R&D and innovation are a source of prosperity and a catalyst for social, economic, and environmental sustainability. Research and innovation are the basis for productivity growth and the competitiveness of the economy. They support the creation of new and better jobs and the development of knowledge-based sectors (Report, 2022a, p. 5).

According to statistical data presented by EUROSTAT (2020), the percentage of enterprises conducting internal R&D was at the level of 17.1% in the European Union (EU). In Poland, on the other hand, the percentage of such companies was only 8.8% (https://ec.europa.eu/ databrowser/). According to the European Innovation Scoreboard, Poland is in the group of so-called emerging innovators (the fourth group-the lowest classified). In 2022, it reached 60.5% of the EU average (Report, 2022b, p. 20). Recent surveys conducted among 846 respondents from 17 countries (including Poland) indicate a 10% decrease in internal resources in the R&D sphere compared to the previous year (Ayming Report, 2023, p. 7).

Examples of data indicate the need to undertake systemic improvement activities leading to the popularisation of R&D&I activities in business entities. An important instrument for such changes should be innovations in management with particular emphasis on social aspects (shaping innovative behaviour among employees), organisational and financial aspects, which are the basis for ensuring the smooth course of R&D&I activities.

Literature Review

The literature on R&D activities can be divided into two groups focussed on: (1) analysis of theoretical aspects of the essence of R&D activities and its role in the development of the organisation, and (2) identification of the actual involvement and achievements of the organisation in the field of R&D. In the publications of the first group, many issues related to this activity, its level, and its management are raised (Kisielnicki, 2018, p. 25–43). In practice, managers of organisations are not always aware of the role of R&D in the development of business entities and entire societies, therefore they do not show interest in this sphere of activity, or this interest is weak and not supported by scientific research (Jasiński, 2021, p. 22; Mate & Molero, 2021, p. 1–14). One of the reasons for this may be the uncertainty about the unambiguously positive impact of R&D activities on innovation and the results of the entire organisation (Heij et al., 2020, p. 277–294; Salisu & Abu Bakar, 2019, p. 56–61). In the literature on the subject of R&D activity, an important function is attributed to generating knowledge (Świadek, 2017, p. 75–84). According to Nonaka and Takeuchi, an organisation’s ability to effectively solve problems and create innovations depends on the ability to efficiently create knowledge (Nonaka & Takeuchi, 2000, p. 66), which is created within the framework of rationally managed R&D activities (Baruk, 2006, p. 55–90; Ferreira et al., 2023, p. 322–338; Suomala & Jokioinen, 2003, p. 213–227). The lack of organised R+D activities in business entities is one of the main barriers to the efficient creation and implementation of innovations (Das et al., 2018, p. 96–112; Kozioł-Nadolna, 2022, p. 3192–3201; Okoń-Horodyńska, 2004, p. 141–163). However, the knowledge generated within the organisation is not always enough to efficiently create innovations. It is therefore necessary to acquire external knowledge (Klessova et al., 2023, p. 1–23; Serrano-Bedia et al., 2010, p. 439–465; Śliwa & Patalas-Maliszewska, 2015, p. 267–280; Smiljic et al., 2023, p. 260–278). It is believed that economic entities conducting their own R&D activity are more likely to reach for external knowledge (Yamaguchi et al., 2021, p. 114–126). Organisations that decide to implement technological changes based on R&D should also introduce changes in the social system. Such changes include innovations in management (Heij et al., 2020, p. 277–294). It seems that these issues are a weakness of the managers of Polish enterprises.

The second group of publications on R&D activities are empirical research reports. An example of such a study is the report on R&D activities in Poland. Surveys show that 65% of companies from the industrial sector and 49% from the trade and service sector were involved in R&D projects (own work or outsourced) (KPMG w Polsce, 2013, p. 1–48). The relatively low level of R&D activity in Poland is evidenced by the results of research conducted by the Polish Agency for Enterprise Development, published in a report in 2013 (PARP, 2013, p. 1–173).

Although subsequent studies indicate an increase in some measures of R&D activity in Poland, their level does not match, for example, the average values in the EU (Deloitte Polska, 2016, p. 1–40; European Commission – Joint Research Centre, 2021, p. 1–32; IDEA Instytut, 2021, p. 1–174; Polski Instytut Ekonomiczny, 2019, p. 1–38; Raport Ayming, 2019, p. 1–44). This thesis is also confirmed by the publications of the author of this text (Baruk, 2016, p. 57–78; Baruk, 2019, p. 1–26; Baruk, 2020, p. 21–48; Baruk, 2022, p. 25–52). As a consequence, Poland ranks among the ’emerging innovators’. A systemic improvement in the level and universality of R&D work should therefore be sought, among others, in the change in the concept of management of the R&D&I sphere.

Research problem: the article attempts to solve the research problem contained in the question: what is the level and dynamics of R&D activity in Polish business entities, treated as an important source of knowledge in the processes of generating innovations? Answering the question formulated in this way required the adoption of the following measures of R&D activity: (1) the number of entities conducting R&D activities; (2) percentage share of entities conducting R&D in the total number of business entities in the industry; (3) employment in R&D activities and its structure; 4) expenditure on R&D and its structure.

Aim: The aim of the publication is to analyse the level and dynamics of the adopted measures of R&D activity, to assess the prevalence of R&D activity in Polish business entities, and to propose directions of improvement in this area.

Research methods: The following research methods were used to develop the publication: (1) cognitive-critical analysis of selected literature on the subject, (2) descriptive and comparative method, (3) statistical method, and (4) projection method.

The first three methods were used to interpret R&D activities, treated as an important source of creating knowledge necessary to effectively solve problems occurring in innovative processes, with particular emphasis on assessing the level and dynamics of adopted measures of R&D activity. The projection method was used to propose improvement changes in R&D&I business management processes and to develop a rational management model for R&D functions and creating innovations.

Results: R&D activity is one of the basic functions generating knowledge necessary for the efficient creation and implementation of innovations in all functional areas of business entities. A prerequisite for the rational development of this activity is efficient management and combining it with innovative activities into one common system. In practice, R&D activity is rather sporadic, intuitive, and deviating from theoretical assumptions. The high rank of R&D activities as a source of knowledge materialised in innovative processes largely depends on the ability of the management to innovatively manage this functional area. However, the level and dynamics of the analysed measures of R&D activity indicate the accidental, rather than innovative nature of this management.

Research limitations/implications: It seems that, for many business managers, the current technical and economic performance of the organisation is a priority in the decision-making process. On the other hand, new management concepts focussed on the future of the organisation, on the knowledge generated in R&D processes, systemically related to innovative activities, are not the strengths of the managerial staff. Systemic changes in the mentality and substantive preparation of managers are necessary so that R&D&I activities fulfil the role assigned to them in the development of business entities.

Practical implications: Awareness of the high importance of R&D&I activities in the improvement of social, organisational, technological, and economic development of the company, its understanding and striving for practical use, determining the improvement of the efficiency of management of R&D&I processes, increasing their impact on the economics of the organisation, and improving customer relations.

Social implications: Efficient management of R&D&I activities can inspire to generate/acquire knowledge, learn, share knowledge, and systematically materialise it in innovations that provide new values to both employees and customers who are more willing to engage in shaping the internal R&D&I environment.

Originality: The content of the publication contributes to the theory of R&D management and its systemic connection with innovative activities. The concept of basing the information and decision-making process on the results of the assessment of the existing state in the field of R&D activities using specific measures and linking this activity with innovative activity was proposed. The concept of a model approach to the management of R&D spheres and an innovative one, constituting a model for practical management activities, was also proposed.

Type of publication: Theoretical-Research

Research Results and Discussion: Entities involved in R&D activities

According to the statistics of the Central Statistical Office (GUS), entities involved in R&D activities include those organisations in which R&D activity is the main type of economic activity, which implement R&D projects in addition to other basic activities or finance R&D works performed by other entities (GUS, 2022c, p. 20). As can be seen from Table 1, in the analysed period of time, the number of entities involved in R&D activities showed a slight upward trend from 5,779 in 2018 to 7,370 in 2021. Year-on-year, these increases amounted to: 1.5% in 2019, 8.8% in 2020, and 15.5% in 2021, which is a positive phenomenon.

A positive phenomenon is the systematic, albeit small, increase in the number of organisations involved in R&D. Compared to 2018, this increase was 1.45 % in 2019, 10.42% in 2020, and 27.53% in 2021. The analysis of the percentage share of entities carrying out R&D works in the total number of business entities in the industry indicates a small percentage of such organisations that dealt with this form of activity. This share remained at around 2.5%. Only in 2021, it slightly exceeded 3%.

Taking into account the size of business entities conducting R&D activities, measured by the number of employees, it should be stated that medium-sized entities employing from 50 to 249 employees were the most common in this area. Next came small entities. Large entities did it the least. On average, in the period under review, the prevalence of such work was at the level of 28.85% in medium-sized entities with a slight downward trend over time. In large organisations, this percentage was 19.2% also with a downward trend. Slightly higher prevalence of R&D activities was characteristic of the smallest entities employing up to 9 employees. On average, in the analysed period, it was at the level of 24.3% with a slightly upward trend. Greater universality of R&D activities was demonstrated by small entities employing from 10 to 49 employees. On average, 27.7% of such organisations carried out some work in this area. This prevalence was rather stable over the time interval considered.

R&D staff

The efficiency of R&D activities depends on the employment of properly prepared personnel. This group includes persons: (1) directly involved in the implementation of R&D work (persons working in an organisation conducting R&D activities and external collaborators), (2) persons providing direct services for R&D activities (e.g. managers of R&D works, administrative service employees, office workers, and technicians).

In general, these staff can be divided into two groups (GUS, 2022c, p. 34–35): (a) internal staff-including people working in an organisation carrying out R&D work and directly contributing to the implementation of R&D activities (persons employed on the basis of an employment relationship or service relationship; employers and self-employed persons; agents working on the basis of agency contracts; homeworkers; persons who are members of agricultural cooperatives) and (b) external staff, that is, independent (self-employed) or dependent workers (salaried) participating in the R&D activities of a given organisation, but not being formally employed by the organisation carrying out R&D work.

As can be seen from Table 2, 266,283 people worked in the R&D sphere in 2018. Most of them (76.5%) were internal staff. In 2019, the number of such employees increased to 271,025 people. Compared to the previous year, an increase of 4,742 people was recorded, that is, by 1.8%. Here, too, internal staff dominated. It accounted for 79.3% of the total staff. In 2020, there was a further increase in the number of people employed in the R&D sphere by 4.6% compared to the previous year. These were mainly internal staff (79.8% of the total staff). The next year of analysis (2021) was characterised by a further increase in the total number of employees in the R&D sphere, which is a positive phenomenon. Compared to the previous year, this number increased by 22,132 people, that is, by 7.8%. In that year, internal staff accounted for 81.5% of the total number of employees in the R&D sphere.

In general, in the analysed period, the total number of R&D staff increased in subsequent years from 266,283 people in 2018 to 305,563 people in 2021. The number of internal staff also increased steadily from 203,588 people in 2018 to 249,014 people in 2021. The second group of R&D employees were external staff, whose number varied from year to year. Compared to the previous year, the number of external staff decreased by 10.4% in 2019. In 2020, it increased by 1.95% compared to the previous year. In 2021, there was a further decrease in the number of external staff by 1.3% compared to 2020. Compared to 2018, the number of external staff decreased by 9.8% in 2021.

Referring to the number of R&D staff to the size of business entities, it should be stated that this number was the lowest in micro-enterprises employing up to 9 employees. During the period under review, this number increased from 5,979 in 2018 to 9,101 in 2021 – an increase of 52.1%. The largest number of R&D staff was characterised by large organisations employing 250 or more employees. In 2018, 197,433 people involved in research and development worked in them. In 2021, this number increased to 222,998 people. Thus, there was an increase in employment by 12.9%.

Overall, in 2018, the share of R&D staff of micro-enterprises in the total number of R&D staff was 2.2%. In small enterprises, this ratio was at the level of 6.4%; 17.2% in medium-sized enterprises and 74.1% in large enterprises. In 2019, these shares amounted to: 2.5% in micro-enterprises, 6.2% in small enterprises, 16.9% in medium-sized enterprises, and 74.4% in large enterprises. In 2020, R&D staff of micro-enterprises accounted for 3.3% of all R&D personnel; 7.2% in small enterprises, 15.5% in medium-sized enterprises, and 73.9% in large enterprises. Similar relations took place in 2021. In the case of micro-enterprises, this share was 3.0%; 7.7% in small enterprises, 16.3% in medium-sized enterprises, and 73.0% in large enterprises.

It should be noted that, in all groups of analysed business entities conducting R&D activities, the majority were the internal staff. The smallest difference between the number of internal and external staff appeared in micro-enterprises. It amounted to 107 employees in 2018, 834 in 2019, and 1,125 in 2021. In 2020 alone, external staff outnumbered internal staff by 356. The small differences in the number of internal and external staff involved in R&D in micro-enterprises are understandable. They result from the limited financial capacity of these economic operators and the small number of employees in total. In other groups of companies, the predominance of internal staff increased as their size increased. Between 2018 and 2021, there was an average of 8.65 internal staff per 1,000 employees. On the other hand, the number of researchers in internal R&D staff per 1,000 employees was on average 6.35 people in the same period.

The development of the number of R&D staff can also be analysed across groups and executive sectors. As Table 3 shows, the largest number of R&D staff was employed in the higher education sector. In 2018, 141,877 employees worked there. However, in the next 2 years, this number decreased by 1,071 people in 2019 and by 3,881 people in 2020. In 2021, this sector employed 2,197 employees less than in the base year. However, compared to the previous year, there was a slight increase (by 1,684 people) in R&D staff.

The second largest R&D staff was occupied by the corporate sector. In 2018, 113,395 R&D staff were employed there, that is, 28,482 people less than in the higher education sector. In contrast to the higher education sector, the number of R&D staff in the enterprise sector increased in the following years of the analysis. For 2018, this number increased by: 7,815 people in 2019 (6.9%), 21,332 people in 2020 (18.8%), and 42,289 people in 2021 (37.3%). Compared to the higher education sector, the employment of R&D staff in the enterprise sector was lower in subsequent years of the analysis by: 28,482 people in 2018, 19,596 people in 2019, and 3,269 people in 2020. The exception was 2021, when the number of employees in the R&D sphere of the enterprise sector was higher by 16,004 people compared to the higher education sector.

In terms of the number of employed R&D personnel, the government ministry was in third place. In 2018, 8,080 people worked there. In the following year, this number decreased by 1,356 people, that is, by 16.8%. The year 2020 was characterised by an increase in the number of B&R staff to 8,813 people. Compared to the base year, this increase amounted to 9.1%, and 31.1%, compared to the previous year. However, in 2021, employment decreased by 280 people, that is, by 3.2%, compared to the previous year. The variable level of R&D staff in the government sector indicates the lack of a prospective research and development policy in this sector.

By far, the least R&D staff was employed in the sector of private nonprofit institutions. In this sector, the total number of R&D employees decreased in subsequent years of the analysis. In 2018, it amounted to 2,931 people. In 2019, it decreased by 646 people (22.0%), in 2020, by 1,036 people (35.3%), and in 2021, by 1,265 people (43.2%). A characteristic feature of this sector is the higher number of external staff compared to internal staff during the period considered. At the same time, the number of employees in the R&D sphere, both internal and external, decreased in subsequent years of the analysis. It can therefore be assumed that R&D activity in this sector was of marginal importance. In other sectors, the number of internal staff far exceeded the number of external staff.

Expenditures on R&D activities

R&D activity, like any other form of activity, requires adequate financial resources. The basic statistical indicator in this area is gross domestic expenditure on R&D activities (GERD). They constitute the amount of total internal expenditures on R&D activities carried out on the territory of a given country in the indicated reporting period. Internal expenditures on R&D activities include all current expenditures and gross capital expenditures on fixed assets related to R&D activities conducted in a statistical unit in a given reporting period, regardless of the source of financing (GUS, 2022c, p. 19). As shown in Table 4, in 2018, GERD amounted to PLN 25,647.8 million. Compared to 2018, in subsequent years of the analysis, these expenditures gradually increased: by 18.1% in 2019, by 26.3% in 2020, and by 46.9% in 2021. Year-on-year, these increases were 18.1% in 2019, 7.0% in 2020, and 16.3% in 2021, respectively. The share of these inputs in the gross domestic product (GDP) in the period considered was on average at the level of 1.34%. A positive phenomenon is a slight increase in this indicator in subsequent years of analysis from 1.21% in 2018 to 1.44% in 2021. In 2018, PLN 668 of these expenditures accounted for per capita. In subsequent years of analysis, this amount gradually increased and in 2021, reached the level of PLN 992.

The source of financing for R&D activities was also the rest of the world. In 2018, PLN 1,804.5 million came from this source. In subsequent years, this amount increased by 18.3% in 2019, by 28.9% in 2020, and by 70.6% in 2021, reaching PLN 3,079.1 million. The share of foreign funds in GERD was on average at the level of 7.35% in the analysed period. It reached its highest value in 2021–8.2%.

The funds of the EC were also used to finance R&D works. In 2018, it was PLN 1,035.7 million. In subsequent years, these amounts increased by 37.5% in 2019, by 65.3% in 2020, and by 130.9% in 2021. However, the share of these measures in gross domestic inputs averaged 5.1%. On a positive note, it has gradually increased from 4.0% in 2018 to 6.3% in 2021. The European Commission’s funds for R&D activities were used by a relatively small number of entities. In 2018, it was 891 organisations, which accounted for only 15.4% of entities conducting R&D work. In subsequent years of analysis, the number of entities using EU funds for R&D activities increased slightly (year-on-year) by 15.7% in 2019, by 9.0% in 2020, and by 21.8% in 2021. In the last year, 1,369 entities benefited from EU funds, which accounted for 18.6% of all organisations conducting R&D work.

The level of internal expenditures on R&D activities considered in the cross-section of individual executive sectors and size classes of enterprises is interesting. As can be seen from Table 5, in total, in the country, the amount of expenditure on R&D increased along with the increase in the size of enterprises. For example, in 2021, the share of expenditures incurred by micro-enterprises accounted for 2.5% of the total expenditure incurred on R&D. In the case of small entities, this share increased to 7.7%. An even higher value–15.7%–was achieved in medium-sized entities and the highest in large entities–74.1%. In each enterprise size class, the volume of expenditures incurred increased, with the exception of mediumsized enterprises, where in 2020, PLN 172.4 million less was allocated to R&D than in the previous year.

Turning to the sector analysis, it should be stated that the largest expenditure on R&D took place in the corporate sector. In 2018, they amounted to PLN 16,950.8 million, which accounted for 66.1% of total expenditures on R&D. In subsequent years of analysis, these shares amounted to: 62.8% in 2019, 62.8% in 2020, and 63.1% in 2021, respectively. Total expenditures in the enterprise sector increased in subsequent years of analysis. In 2019, they increased (year-on-year) by PLN 2,080.1 million, by PLN 1,328.2 million in 2020, and by PLN 3,410 million in 2021.

In the enterprise sector, micro-enterprises spent the least funds on R&D. In 2018, they accounted for only 2.6% of the total expenditures incurred in the entire enterprise sector. In 2019, this share was 3.1%, and 3.5% in 2020. Much larger amounts on R&D were spent by small entities. In 2018, the share of expenditures for this purpose accounted for 9.9% of all expenditures incurred in the enterprise sector. In 2019, it was 9.6% and, in 2020, it was 10.5%. In terms of absolute value, medium-sized companies allocated even higher funds to R&D. In 2018, they accounted for 20.7% of total expenditures. In the following year, this share was 21% and in 2020, it was 19.2%. The highest expenditure on R&D was incurred by large entities in the enterprise sector. In 2018, it was PLN 11,319.0 million, which accounted for 66.8% of the total expenditures of this sector. In the following year, this share was at a similar level of 66.4%, to increase to 66.8% in 2020. The development of the considered measure in 2021 was omitted due to the lack of numerical data presented by the GUS.

The higher education sector ranked second in terms of internal expenditure on R&D activities, analysed in the cross-section of executive sectors and size classes of enterprises. Its expenditure for this purpose accounted for 31.7% of total expenditures in 2018. In subsequent years, these shares amounted to 35.6% in 2019 and 34.9% in 2020. In this sector, it is impossible to fully analyse the development of expenditures on R&D in the cross-section of size classes of entities due to the lack of data. Data for 2018–2020 indicate that large enterprises allocated the largest amounts to R&D. In 2018, PLN 7199.2 million was spent for this purpose. This amount accounted for 88.6% of total expenditure in the higher education sector. In 2019, PLN 2407.8 million more was allocated to R&D than in the previous year. They accounted for 89.1% of total expenditure in this sector.

Even higher expenditures were incurred by large enterprises in the higher education sector in 2020. Compared to the previous year, they increased by PLN 804.1 million, which amounted to 91.9% of the total expenditures of the sector considered. Much less internal expenditure on R+D was borne by the government sector. In 2018, PLN 498.6 million was spent there. The share of this amount in total expenditures is only 1.9%. In the following year of analysis, PLN 114.4 million less was spent for this purpose than in the previous year. These funds accounted for only 1.3% of total expenditures. In 2020, PLN 639.1 million was allocated to R&D in the government sector, that is, PLN 254.9 million more than in the previous year. The share of these expenditures in total expenditures is less than 2% (1.97%). Compared to the previous year, R&D expenses in 2021 increased by PLN 131.2 million. They accounted for only 2% of total domestic expenditure. The available figures indicate that large companies have invested the most in R&D in this sector. In 2018, they accounted for 44.8% of total government sector expenditures. In 2019, this ratio was 63.2%, and in 2020, 51.2%.

The least active entities in the field of financing R&D activities were recorded by entities from the sector of private non-commercial institutions. In subsequent years of analysis, a total of PLN 76.7 million was spent in 2018, that is, 0.3% of total expenditure. In 2019, these expenses increased to PLN 90.3 million. They accounted for 0.3% of total expenditures. In the following year, these expenditures decreased by PLN 10.8 million and their share in total expenditures amounted to 0.2%. In 2021, R&D expenditures fell further to PLN 77.5 million. This amount represented 0.2% of the total expenditure for this purpose. In the cross-section of size classes of entities in this sector, micro-enterprises and small enterprises incurred greater expenditures, but without regular increases in the amounts spent.

The results of the analysis of internal expenditures on R&D activities considered according to types of costs and implementation sectors are interesting. The structure of internal expenditures on R&D consists of current (including personnel) and investment expenditures. Current expenditures include personnel expenditure on R&D personnel as well as other current expenditures related to R&D activities: services and items consumed within one year, annual fees, and rents. Personnel expenses include the remuneration of R&D staff and related costs or additional benefits. On the other hand, investment outlays are the annual gross amount paid for acquired fixed assets, repeatedly used in R&D activities for a period longer than one year (GUS, 2022c, p. 27).

As can be seen from Table 6, the total country was dominated by current expenditures over capital expenditures in individual years of the analysis. Current expenditures were characterised by their systematic growth (yearonyear): in 2019 by 22.4%, in 2020 by 9.3%, and in 2021 by 16.6%. The share of current expenditures in total expenditures was: 79.5% in 2018, 82.4% in 2019, 84.2% in 2020, and 84.4% in 2021. On the other hand, the share of capital expenditures in internal expenditures was much smaller, irregular, and amounted to: 20.5% in 2018, 17.6% in 2019, 15.8% in 2020, and 15.6% in 2021.

Current expenditures in R&D activities also dominated in individual executive sectors. In the enterprise sector, they increased in subsequent years of analysis, and their share in the total expenditures of this sector amounted to: 74.2% in 2018, 78.4% in 2019, 82.4% in 2020, and 84.3% in 2021. The higher education sector ranked second in terms of R&D expenditures. Most of them are current expenditures. In 2018, they accounted for 89.7% of total expenditures in this sector. In 2019, this share was 89.3%. In the next two years, current expenditures accounted for 87.4% and 84.9% of total expenditures, respectively.

Significantly, lower R&D expenditures were incurred in the government sector and in the sector of private non-profit institutions. In the first sector, the share of total expenditures in total domestic expenditures amounted to: 1.9% in 2018, 1.3% in 2019, and 2% each in 2020 and 2021. The private nonprofit institutions sector had even lower values of these shares: 0.3% in 2018, 0.3% in 2019, 0.2% in 2020, and 0.2% in 2021. Significantly less money was spent on investments in all sectors. They accounted for a small percentage of total expenditures.

The results of the structure of internal expenditures on R&D activities considered by types of activities and implementation sectors are interesting. As can be seen from Table 7, in total, development work absorbed the most funds in the country. The share of expenditures on these works in total expenditures was: 54.2% in 2018, 46.5% in 2019, 51.0% in 2020, and 53.4% in 2021. Year-on-year, these expenditures increased by: 1.21% in 2019, 17.5% in 2020, and 21.8% in 2021. In the second place in terms of the amount of expenditure on R&D was basic research. The share of these expenditures in total expenditures was: 32.5% in 2018, 40.1% in 2019, 33.2% in 2020, and 32.1% in 2021. In contrast to expenditure on development works, expenditure on basic research did not increase in subsequent years of analysis. In 2019, they increased by 45.5% compared to the previous year. In 2020, PLN 1,377.5 million less was spent on basic research than in the previous year. Therefore, there was a decrease in expenditures for this purpose by 11.3% compared to the previous year. In 2021, PLN 1,315.0 million more was spent on basic research than in 2020. Thus, there was an increase in expenditures by 12.2%.

The least popular was the funding of applied research. This is evidenced by the lowest amounts allocated for this purpose in individual years of the analysis. A positive feature of these expenditures was their systematic, albeit slight increase in subsequent years. They increased by 19.7% in 2019, 25.5% in 2020, and 6.9% in 2021.

Across the implementation sectors, the priorities in terms of the amount of expenditure on R&D were variable. The enterprise sector was dominated by expenditures on development works. The amount of these expenditures increased in subsequent years of the analysis (year-on-year): by PLN 134.7 million in 2019, by 17.3% in 2020, and by 18.1% in 2021. The share of expenditures on development works in the enterprise sector in total development expenditures in the country amounted to: 92.7% in 2018, 92.6% in 2019, 92.4% in 2020, and 89.6% in 2021.

In the enterprise sector, applied research was the second largest in terms of expenditure, with the exception of 2019, when more resources were spent on basic research. The difference amounted to PLN 662.9 million. A positive feature of expenditures on applied research was their successive but slight increase in subsequent years of analysis. Year-on-year, these amounts increased by: 21.9% in 2019, 22.3% in 2020, and 7.6% in 2021. In 2018, expenditures on applied research in the enterprise sector accounted for 64.5% of such expenditures incurred in total in the country. In subsequent years, these relations amounted to: 65.7% in 2019, 64.0% in 2020, and 64.4% in 2021.

In the enterprise sector, relatively, the smallest expenditure was spent on basic research. Most funds were spent in 2019, the least in 2020. The share of these expenditures in the total expenditures of the enterprise sector was at the level of: 11.0% in 2018, 17.5% in 2019, 8.9% in 2020, and 9.3% in 2021. Compared to the total expenditure on basic research in the country, expenditures for this purpose in the enterprise sector amounted to: 22.4% in 2018, 27.4% in 2019, 16.9% in 2020, and 18.3% in 2021.

In contrast to the business sector, basic research was the most financial resource in the higher education sector, followed by applied research. In third place were development works. A characteristic feature of all types of R&D activities in the higher education sector was a gradual increase in expenditures incurred in subsequent years of analysis. In relation to overall expenditure in the higher education sector, expenditure on basic research was 76.4% in 2018, 79.6% in 2019, 76.3% in 2020, and 73.2% in 2021. The amount of these expenditures in subsequent years of the analysis increased by: 38.3% in 2019, 0.6% in 2020, and 10.7% in 2021. Much lower investment in the higher education sector was spent on applied research.

Their share in the total expenditures of this sector was: 13.6% in 2018, 12.0% in 2019, 14.4% in 2020, and 13.7% in 2021. The least popular in the higher education sector was the development work, on which the least financial resources were spent during the period under review.

The share of R&D expenditures of the private non-profit sector in the total domestic expenditure was 0.3% in 2018, 0.3% in 2019, 0.2% in 2020, and 0.2% in 2021, respectively, with no clear emphasis on any type of R&D activity.

Conclusions

The analysis of empirical material indicates that, in the analysed period of time, a small percentage of business entities (less than 4%) conducted R&D activities. Medium-sized organisations were most often involved in R&D activities. However, their share in this work did not exceed 30% and had decreasing trends in subsequent years of analysis. There were between 8.1 internal staff per 1,000 employees in 2018 and 9.3 in 2021. By contrast, the share of researchers in internal R&D staff stood at 6.1 in 2018 and 2019, rising to 6.8 in 2021. A positive phenomenon was the increasing number of total R&D staff in subsequent years. These were mainly internal staff, constituting on average 79.3% of all R&D employees in the analysed period. While the number of internal staff increased in subsequent years, the number of external staff did not show an upward trend. In 2021, it accounted for 90.2% of the 2018 figure.

The number of R&D staff changed along with the change in the size of business entities measured by the number of employees. Micro-organisations had an increasing overall workforce until 2020 and a decline in 2021. The second feature was the slightly higher number of internal staff compared to the number of external staff. Along with the increase in the size of business entities, the number of B&R staff in total increased, which is a normal phenomenon. There was also a growing gap between the number of internal and external staff in favour of the former. On the other hand, there were no regular increases in the number of R&D employees in subsequent years of the analysis, especially with regard to external staff. In large organisations, the number of external R&D employees has been gradually decreasing, which suggests greater independence of such organisations in conducting R&D activities.

Across the overall implementation sectors, the higher education sector employed the largest number of R&D staff, with the exception of 2021, when the corporate sector took the lead. The sector of private non-profit institutions came last. In this sector, as the only one, the number of external staff exceeded the number of internal staff and decreased in subsequent years of the analysis, as did the total number of staff. Downward trends in the number of external R&D employees were also recorded in the higher education sector. In other sectors, there were irregularities in the development of this measure.

In general, the number of R&D employees increased with the size of business entities. This is a specific regularity resulting from the fact that the smallest organisations do not have the staff, organisational and financial conditions to separate R&D cells in their structures. Such entities are more likely to be assisted by external staff.

Another positive phenomenon is the increase in expenditure on R&D in the analysed period of time and their increasing amount per capita. There was also a slight increase in the share of gross domestic capital formation for R&D activities in GDP. However, this share was significantly lower than the EU average. In addition to national funds, the source of financing R&D activities were also funds from the European Commission (EC) and funds from the rest of the world. The share of foreign funds in domestic outlays was only 8.2% in 2021. The European Commission’s funds accounted for an even smaller share in national expenditures. In the most favourable 2021, this share was 6.3%. A small but increasing percentage of entities benefiting from EC funds was also beneficial. In 2021, it was 18.6%.

A characteristic feature of total internal expenditures on R&D was their increase in subsequent years of analysis. Similar trends occurred in operators considered by size classes, with the exception of medium-sized organisations. These outlays increased as the size of enterprises increased. In the cross-section of executive sectors, the largest internal expenditure on R&D was incurred in the corporate sector, and the lowest in the sector of private non-profit institutions.

In the structure of total internal expenditures on R&D, current expenditures dominated over capital expenditures. Similar relations occurred in all executive sectors without clear upward trends. The priority in this expenditure was the financing of development work, followed by basic research. In last place was applied research. The accents in R&D expenditures in individual sectors were slightly different. In the enterprise sector, the largest amount of funds was allocated to development work, followed by applied research (with the exception of 2019). At the end of the priorities were basic research. The exception was 2019, when expenditure on basic research exceeded expenditure on applied research. In the government sector, basic research has been the most resourced, as has the higher education sector. In the sector of private non-profit institutions, preferences in financing R&D activities were not as diverse as in other sectors.

In general, the level and dynamics of selected measures of R&D activity indicate that this activity was not a priority in the information and decisionmaking processes of managers. The actions taken were more focussed on overcoming current problems than on solving strategic issues. In many entities, the problem of systemic R&D work did not exist at all, as indicated by a small percentage of companies conducting R&D activities. The insufficient interest of management in systemic business development is also evidenced by the relatively low position of Poland compared to the average results in the EU.

The level, dynamics, and universality of R&D activity depend on many internal and external factors. One of the internal factors is the ability of the management to rationally, systemic, future-oriented management of the R&D&I sphere (Tidd & Bessant, 2013, p. 114–120). There are significant reserves in this area for improving management efficiency. It is necessary to give up accidental management, focussed on solving current problems and move to rational, systemic, future-oriented management of the enterprise, based on the knowledge and widespread use of modern management methods, especially management through innovation, innovation management, and knowledge management. The concept of such management is illustrated in Figure 1.

This concept emphasises the rational combination of the general strategy of development of the business entity with functional strategies. These strategies set the directions of internal R&D&I activities and cooperation with external organisations conducting external R&D. The consequence of such activity and cooperation should be the resources of knowledge necessary to efficiently identify and solve internal and market problems of an operational and strategic nature. The solution to problems results in streamlining radical innovations that efficiently meet current and future of own and market needs. The areas of activity of the business entity listed in the model should be subject to rational management. In process terms, it includes four basic management functions: setting goals and planning ways to achieve them; organising work in a structural and process sense; conduction; controlling (Griffin, 2007, p. 8). An indispensable condition for rational management according to the proposed concept is to change the mentality of the management staff and to realise the need to master and use modern management methods in information and decision-making processes (Bieniok, 2011; Błaszczyk, 2022; Zimniewicz, 2009).

Suggestions for further research

In the context of the issues discussed in this publication, it seems reasonable to undertake further empirical research aimed at verifying the correctness of the theoretical model of systemic management of R&D and innovative activities in the context of efficient implementation of the overall strategy of organisational development and the resulting functional strategies. The concept of such research should be guided by the following questions: 1. Do managers understand the importance of R&D activities and related innovation activities in the development of each business entity? 2. Do managers systematically follow the literature on the subject and get acquainted with new concepts of managing integrated R&D and innovation activities? 3. Do managers have the will to have these concepts empirically verified and why? 4. What advantages and disadvantages can result from the use of modern concepts of R&D and innovation management? It is also reasonable to answer the question: does the management of the organisation want, can, and can efficiently manage R&D&I activities?

References

1. Ayming Report. (2023). International innovation barometer 2023. Ayming Institute. p. 7.
https://www.aymingusa.com/insights/whitepapers/international-innovation-barometer-2023/ #download. Dostęp z dnia 05.04.2023 r.
2. Baruk, J. (2006). Zarządzanie wiedzą i innowacjami. Toruń: Wydawnictwo Adam Marszałek w Toruniu. pp. 55–90.
3. Baruk, J. (2016). Miejsce działalności badawczo-rozwojowej w polityce rozwojowej przedsiębiorstw. Marketing Instytucji Naukowych i Badawczych, 20(2), 57–78. https://doi.org/10.14611/minib.20.03.2016.04
4. Baruk, J. (2019). Finansowe aspekty polityki badawczej i rozwojowej w Unii Europejskiej. Marketing Instytucji Naukowych i Badawczych, 33(3), 1–26. https://doi.org/10.2478/minib-2019-0037
5. Baruk, J. (2020). The volume and dynamics of domestic expenditures on research and development in the European Union. Marketing of Scientific and Research Organizations, 38(4), 21–48. https://doi.org/10.2478/minib-2020-0025
6. Baruk, J. (2022). Research and development expenditures in the sector of polish enterprises as an instrument of research and development policy. Marketing of Scientific and Research Organizations, 43(1), 25–52. https://doi.org/10.2478/minib-2022-0002;
7. Bieniok, H. (2011). Metody sprawnego zarządzania. Warszawa: Placet.
8. Błaszczyk, W. (2022). Metody organizacji i zarządzania. Kształtowanie relacji organizacyjnych. Warszawa: Wydawnictwo Naukowe PWN.
9. Das, P., Verburg, R., Verbraeck, A., & Bonebakker, L. (2018). Barriers to innovation within large financial services firms. European Journal of Innovation Management, 21(1), 96–112. https://doi.org/10.1108/EJIM-03-2017-0028
10. Deloitte Polska. (2016). Badania i rozwój w przedsiębiorstwach 2016. Warszawa: Deloitte. pp. 1–40.
11. European Commission – Joint Research Centre. (2021). The 2020 EU Survey on Industrial R&D Investment Trends. Luxembourg: Publications Office of the European Union. pp. 1–32.
12. EUROSTAT. (2020). https://ec.europa.eu/eurostat/databrowser/view/INN_CIS12_INRD __custom_5561818/bookmark/table?lang=en&bookmarkId=588637df-57e9-4118-a02acd6270006c22.
Dostęp z dnia 27.03.2023 r.
13. Ferreira, J. J., Fernandes, C. I., Veiga, P. M., & Dooley, L. (2023). The effects of entrepreneurial ecosystems, knowledge management capabilities, and knowledge spillovers on international open innovation. R&D Management, 53(2), 322–338.
https://doi.org/10.1111/radm.12569.
14. Griffin, R. W. (2007). Podstawy zarządzania organizacjami. Warszawa: Wydawnictwo Naukowe PWN, s. 8.
15. GUS (2019). Działalność badawcza i rozwojowa w Polsce w 2018 r. Warszawa, Szczecin: GUS. tab. 1, s. 20, tab. 3, s. 22.
16. GUS (2020). Działalność badawcza i rozwojowa w Polsce w 2019 r. Warszawa, Szczecin: GUS. tab. 1, s. 20, tab. 3, s. 22.
17. GUS (2021). Działalność badawcza i rozwojowa w Polsce w 2020 r. Warszawa, Szczecin: GUS. tab. 3, s. 22.
18. GUS (2022a). Rocznik Statystyczny Rzeczypospolitej Polskiej 2022. Warszawa: GUS. tab.
6 (421), s. 516.
19. GUS (2022b). Rocznik Statystyczny Przemysłu 2021. Warszawa: GUS. tab. 1, s 33.
20. GUS (2022c). Działalność badawcza i rozwojowa w Polsce w 2021 r. Warszawa, Szczecin: GUS. tab. 1., s. 20; tab. 3, s. 22.
21. Heij, C. V., Volberda, H. W., Van den Bosch, F. A. J., & Hollen, R. M. A. (2020). How to leverage the impact of R&D on product innovation? The moderating effect of management innovation. R&D Management, 50(2), 277–294. https://doi.org/10.1111/ radm.12396
22. IDEA Instytut. (2021). Wpływ wsparcia działalności badawczo-rozwojowej w polityce spójności 2014–2020 na konkurencyjność i innowacyjność gospodarki – I etap: badanie w trakcie wdrażania. Warszawa: IDEA Instytut. pp. 1–174.
23. Jasiński, A. H. (2021). Współczesna scena innowacji. Warszawa: Poltext. p. 22.
24. Kisielnicki, J. (2018). Projekty badawczo-rozwojowe: charakterystyka i znaczenie. Studia i Prace. Kolegium Zarządzania i Finansów, (159), 25–43.
25. Klessova, S., Engell, S., & Thomas, C. (2023). The interplay between the contextual conditions and the advancement of the technological maturity in inter-organisational collaborative R&D projects: A qualitative study. R&D Management, 53(3), 1–23.
https://doi.org/10.1111/radm.12598
26. Kozioł-Nadolna, K. (2022). Innovation strategies used by companies in Poland during the pandemic. Procedia Computer Science, (207), 3192–3201. https://doi.org/ 10.1016/j.procs.2022.09.377
27. KPMG w Polsce. (2013). Działalność badawczo-rozwojowa w Polsce. Perspektywa 2020. Kpmg.pl, s. 1–48. https://assets.kpmg.com/content/dam/kpmg/pdf/2016/03/DzialalnoscBR-przedsiebiorstw-w-Polsce.pdf. Dostęp z dnia 14.04.2023r.
28. Mate, M., & Molero, J. (2021). The impact of public and private internal R&D investments on Spanish business performance during the period of crisis 2008–2012. International Journal of Advanced Research in Engineering & Management, 07(2), 1–14.
29. Nonaka, I., & Takeuchi, H. (2000). Kreowanie wiedzy w organizacji. Warszawa: Poltext. p. 66.
30. Okoń-Horodyńska, E. (2004). Działalność badawczo-rozwojowa i innowacje w Polsce a Strategia Lizbońska. Nauka i Szkolnictwo Wyższe, (1/23), 141–163.
31. PARP. (2013). Ocena zapotrzebowania przedsiębiorstw na wsparcie działalności badawczo-rozwojowej. Warszawa: PARP. pp. 1–173.
32. Polski Instytut Ekonomiczny. (2019). Polskie B+R. Dostępne narzędzia wsparcia i nowe możliwości. Warszawa: Polski Instytut Ekonomiczny. pp. 1–38.
33. Raport Ayming. (2019). Ulga B+R. Małymi krokami do większej innowacyjności. Warszawa: Ayming Polska. pp. 1–44
34. Report. (2022a). Science, Research and Innovation Performance of the EU 2022 – Building a sustainable future in uncertain Times. European Commission. DirectorateGeneral for Research and Innovation. B-1049 Brussels. p. 5.
35. Report. (2022b). European Innovation Scoreboard 2022. European Commission. Luxembourg: Publications Office of the European Union. 20. https://www.kpk.gov.pl/european-innovation-scoreboard-2022
36. Salisu, Y., & Abu Bakar, L. J. (2019). Technological, capability, innovativeness and the performance of manufacturing small and medium enterprises (SMEs) in developing economies of Africa. IOSR Journal of Business and Management, 21(1), 56–61.
https://doi.org/10.9790/487X-2101015661
37. Serrano-Bedia, A.M., Lopez-Fernandez, M.C., & Garcia-Piqueres, G. (2010). Decision of institutional cooperation on R&D. Determinants and sectoral differences. European Journal of Innovation Management, 13(4), 439–465. https://doi.org/10.1108/ 14601061011086285
38. Śliwa, M., & Patalas-Maliszewska, J. (2015). Model doboru jednostki badawczorozwojowej dla przedsiębiorstwa opartego na wiedzy. Modern Management Review, XX(3), 267–280. https://doi.org/10.7862/rz.2015.mmr.49
39. Smiljic, S., Aas, T. H., & Mention, A.L. (2023). To join or not to join? Insights from coopetitive RD&I Project. R&D Management, 53(2), 260–278. https://doi.org/ 10.1111/radm.12560
40. Suomala, P., & Jokioinen, I. (2003). The patterns of success in product development: A case study. European Journal of Innovation Management, 6(4), 213–227. https://doi.org/ 10.1108/14601060310500931
41. Świadek, A. (2017). Krajowy system innowacji w Polsce. Warszawa: CEDEWU. pp. 75–84.
42. Tidd, J., & Bessant, J. (2013). Zarządzanie innowacjami. Warszawa: Oficyna a Wolters Kluwer business. pp. 114–120.
43. Yamaguchi, S., Nitta, R., Hara, Y., & Shimizu, H. (2021). Who explorer further?
Evidence on R&D outsourcing from the survey of research and development. R&D Management, 51(1), 114–126.
44. Zimniewicz, K. (2009). Współczesne koncepcje i metody zarządzania. Warszawa: Polskie Wydawnictwo Ekonomiczne.

Entrepreneurs running a business strive to achieve success, and the basic measure of their success is not only their remaining on the market but, most of all, achieving a positive financial result of their business activity. In order to enable entrepreneurs to make a profit, the products and services offered by them should have characteristics that consumers perceive as desirable and satisfactory to them. It is connected with the necessity of recognising the needs, preferences and awareness of consumers as precisely as possible, while at the same time searching for innovative

solutions that allow entrepreneurs to stay ahead of competitors (Zimon & Gawron-Zimon, 2014). Additionally, it should be remembered that entrepreneurs striving to modernise their offer should take care of closer relations with clients who have new ideas and willingly share their observations, treating it as a source of innovation and development (Dobiegała-Korona, 2012, p. 68).

Pérez-Luno, Valle Cabrer and Wiklund (2007, p. 80) indicate that of the two possible paths-imitation and innovation-only the latter leads to a competitive advantage in the market. In turn, according to Dereli (2015, p. 1367), in order to survive in a competitive market, companies must carefully follow and implement innovations or must be innovative themselves. Only enterprises that offer innovations can achieve a competitive advantage (Adegbesan, 2009; Dereli, 2015, p. 1367; AnningDorson, 2018, p. 580). Grzegorczyk (2011, p. 26) emphasises that the possibility of achieving a competitive advantage is closely related to the competitive situation on the market and the company’s resources, especially with its product offer. An entrepreneur creates an opportunity to obtain the opinion of an innovator and achieve a dominant position on the market by introducing a new or significantly improved product to the market. It should be highlighted, that how long this dominance in the market will last largely depends on the behaviour of competitors and their innovative activities. Friar (1995, p. 33) notes that the product innovation alone may not be enough to keep a company in a dominant position. The success in the field of product innovation decreases with the increase of the intensity of market competition. This may result from the inability of customers to distinguish products based on, for example, their functional performance.

Therefore, in order to keep pace with the competition, it is necessary to constantly implement not only product innovations, but also non-product innovations and appropriate innovation management (Friar, 1995; Dereli, 2015, p. 1369), which allows the enterprise to choose the appropriate protection method for the innovative solutions being developed or implemented in the enterprise.

The article primarily aimed to present selected aspects of the innovative activity of Polish enterprises. To achieve it, we attempted to answer the following research questions:

1. What is the share of enterprises implementing innovations in the total number of enterprises? Do the profile and size of the enterprise affect their innovative activities?
2. Did the outbreak of the COVID-19 pandemic in 2020 have a positive impact on innovative activities in Polish enterprises?
3. What are the sources of finances for innovative activities in Polish enterprises?
4. What kind of innovations do entrepreneurs finance from outlays on innovative activities?
5. How do entrepreneurs protect innovative solutions?

Materials and Method

The study is based on a critical literature review, which is based on a query of selected scientific publications on innovation and innovativeness as a source of competitive advantage in enterprises, as well as statistical analyses of secondary data, including structure and chain index.

To calculate the chain index, a modified formula was used (Major & Niezgoda, 2003, pp. 96–97), in which 100% was subtracted for easier interpretation of the obtained results:

where: i represents dynamics, t the current year, t–1 the previous year, yt the value in the current year and y_t–1 the value in the previous year.

We analysed data published by Statistics Poland (previously: the Central Statistical Office of Poland) and the Patent Office of the Republic of Poland (PORP) for 2016–2020. The indicators of the structure made it possible to observe the changes that took place in the analysed period, while the chain index analysis made it possible to determine the intensity of changes that took place over the observed years.

Analyses presented in this article cover small enterprises (employing 10–49 people), medium-sized enterprises (employing 50–249 people) and large enterprises (employing ≥250 people), because Statistics Poland collects and publishes data on innovations based on the Oslo Manual 2018 methodology only within these groups of enterprises.

Innovations in the Enterprise

The term ‘innovation’ can be understood in many ways, but there is no doubt that it derives from the Latin word ‘innovatio’, meaning renewal or ‘innovare’, indicating renewal, refreshing, changing (Kopaliński, 2006). The literature includes numerous, very different approaches to defining the essence of innovation, as well as its terminological interpretation or functions (Montoya-Weiss & Calantone, 1994; Drucker 1998). It results in the lack of a single definition of innovation in economics and management (Table 1).

In the short review of definitions of innovation presented above, we can see both a narrow (i.e. the first introduction of a solution in the world) and a broad (i.e. the first use of a solution in a specific enterprise) understanding of innovation. Most often, however, they are equated with a novelty, a change, and refer to new products or new technologies (Sikora & Uziębło, 2013, p. 351). However, attention should be paid to the fact that these definitions usually contain some regular elements determining the essence of innovation and defining its character. They include: subject (enterprise, human), object (technology, organisation, market), novelty (originality), economics or positive evaluation of changes and the fact of their implementation, assimilation (Kamiński, 2018).

The large diversity of definitions resulted in the introduction of many different types of classifications to the theory, allowing
researchers to divide innovations by the subject, the effects, the originality of changes or the nature of innovations and their
significance in terms of changes for the commercial sector (Sławińska, 2015).

Until 2018 the division of innovation according to the subject indicating its technological (product and process innovation) or non-technological nature (organisational and marketing innovations) (Oslo Manual, 2005) was the most basic and most frequently used classification. It was replaced by product innovations and business process innovations after publication of the new Oslo Manual (2018). The new definitions specify more precisely what conditions must be met to consider a product, a service or a process to be an innovation. It is indicated that the innovations must significantly differ from the products or services already existing on the market or they must significantly differ from the company’s business processes already used by the enterprise.

Analysing the data of Statistics Poland (Table 2) for 2016–2018, it can be noticed that the share of enterprises implementing innovations in the total number of enterprises, both industrial and service ones, was systematically increasing. After this period, a temporary decrease in the share of enterprises implementing innovations was observed in 2019, and in 2020 the innovative activity of enterprises in Poland increased again. It is worth noting that in 2020 the indicators of the share of innovative enterprises in the total number of enterprises were the highest and amounted to 31.4% in the case of industrial enterprises and 30.8% in the case of service enterprises.

Over the analysed time the largest number of innovations in both industrial and service enterprises resulted from the implementation of innovations in business processes. The share of innovative industrial enterprises increased from 15.2% in 2016 to 26.3% in 2020 and the share of innovative service enterprises increased from 10.4% in 2016 to 39% in 2020.

In 2018–2020 (Figure 1), industrial enterprises innovating business processes most often implemented new methods of manufacturing products or providing services (including the development of new products or services), assigning tasks and managing decision-making powers (including their effective delegation) and human resources, and changed principles of operation extant within the company and/or those governing the entity’s external relations. In turn, service enterprises changed mainly the rules of operation extant within the company and/or those governing the entity’s external relations, as well as the rules laying down the modus operandi for the assignment of tasks, management of decision-making powers (including their effective delegation) and human resources, and development and implementation of solutions for information-processing and communication requirements.

When analysing the data on product innovations in 2016–2020 (Table 2), it should be noted that the share of enterprises implementing them was much lower than that of enterprises implementing innovations in the business process. However, the growing share of enterprises implementing product innovations in the total number of enterprises should be assessed positively (in the case of industrial enterprises, the share of enterprises implementing innovations increased from 12.4% in 2016 to 18.4% in 2020, while in the case of service enterprises the share almost doubled: from 6.9% in 2016 to 12.1% in 2020). It may indicate the introduction of newer or significantly improved products that can meet the expectations of the contemporary consumer.

The data from Statistics Poland also show that large enterprises were most innovative in 2020 (they constituted 66.7% in the case of industrial enterprises and 60.5% in service enterprises). It might have resulted from the need to ensure the sufficient outlays financing innovative activities of enterprises, which large enterprises planned and included in the long-term development of their organisations (Figure 2).

Impact of COVID-19 on Innovative Activities of Enterprises

The outbreak of the COVID-19 pandemic in 2020 changed the rules of functioning of enterprises all over the world. In Poland, a number of restrictions were introduced (Ministry of Health Regulation, 2020), which forced entrepreneurs to take measures to adapt their businesses to the new situation.

Results of the survey carried out by Statistics Poland indicate no noticeable impact of the outbreak of the COVID-19 pandemic (Figure 3) on the functioning of enterprises. Such effect was declared by over 66% of industrial enterprises and over 71% of service enterprises. On the other hand, almost a third of industrial companies and 26% of service companies believed that the outbreak of the COVID-19 pandemic had a negative impact on the company’s operations. Positive influence of COVID-19 was recorded in 1.6% of industrial enterprises and 2.4% of service enterprises.

Due to the situation related to COVID-19, in 2020, 10.6% of industrial enterprises implemented new or significantly changed products, whereas 18.2% implemented new or significantly changed business processes (Figure 4).

Among service enterprises that, in 2020, implemented changes in their functioning forced by the situation related to COVID-19, 9.2% introduced new or significantly improved products, whereas 37.0% introduced new or significantly changed business processes.

Types of Support for Innovative Enterprises

Hu, Yu, Jin, Pray and Deng (2022, p. 709) note that governments use some incentives to make enterprises create and develop innovation. These incentives include, among others, specific funds for enterprise innovation, promotion of cooperation between the enterprise sector and public research sectors and institutes, strengthening protection of intellectual property rights, reforms of state-owned enterprises and establishing research and development institutes.

In Poland, support for innovative entrepreneurs may take the form of (i.e. Journal of Laws of 2021, item 706, as amended):

(1) granting a technological loan by commercial banks and a technological premium by Bank Gospodarstwa Krajowego;
(2) granting the entrepreneur the status of a research and development centre;
(3) aid granted under programmes in the field of innovation of the economy, established by the minister competent for the economy.

This act also explains the terms ‘investment activity’ and ‘technological investment’. Innovative activity is ‘the development of a new technology and launching on its basis the production of new or significantly improved goods, processes or services’ (Journal of Laws of 2021, item 706, as amended). A technological investment, on the other hand, means:

(a) purchase of a new technology, its implementation and launching on its basis the production of new or significantly improved products, processes or services and providing conditions for the production of these products, processes or services, or
(b) implementation of own new technology and launching on its basis the production of new or significantly improved products and providing conditions for the production of these products, processes or services.

When analysing the sources of outlays financing innovative activities in Polish enterprises, it should be noted that the vast majority of them are financed from domestic funds (in the structure of the distribution of outlays, their value in the analysed years was around 90%). This situation was caused by the distribution of public aid for the development of innovation from the internal funds of enterprises, as well as from those of government institutions and local governments. In 2016–2020, the annual value of outlays on innovative activities from this source (Table 3) remained at the level of around 35%.

In 2016, the total value of outlays was over PLAN 19 billion, and in 2020 it increased to a level exceeding PLN 35 billion. It resulted in an increase in the value of outlays from both domestic and foreign funds and budget funds co-financed from the EU funds. Findings on the dynamics of changes in the value of outlays on innovative activities show a large increase in outlays between 2017 and 2018. It may indirectly be related to the introduction of the 2016 amendment to the R&D tax relief into the Polish tax system, which replaced the technology relief. From 2018, this relief allowed entities that do not have the status of a research and development centre to deduct 100% of eligible costs, and in the case of research and development centres, even 150% of eligible costs (except for the costs of patents, protection rights for utility models, and rights from registration of industrial designs incurred by research and development centres being large entrepreneurs; for these entities, these costs could be deducted up to 100%) (Goyke, 2018, p. 31).

It should also be noted that in 2018–2020, only 6.2% of Polish enterprises (3.7% industrial and 2.5% service) benefitted from the R&D relief, and only 7% of enterprises (4.2% of industrial and 2.8% of services) benefitted from the reliefs or incentives for other operating activities (GUS, 2021).

The Use of Funds for Innovative Activity

The implementation of innovative activities in enterprises is closely related to research and development (R&D), which, as Wiśniewska aptly notes (2017, p. 308), covers both basic research and applied research, as well as development works. The author also emphasises that the above-mentioned activities constitute one of the main conditions for innovation.

Taking into account the dominant share of internal outlays financing innovative activities in Polish enterprises in 2016–2020. Table 4 presents the use of these funds. In the analysed period, the value of total internal outlays on R&D increased from PLN 9.6 billion in 2016 to PLN 16.5 billion in 2020, which was influenced by the increase in the value of outlays in the subsequent years of the analysed period.

It is worth noting that the largest share of internal outlays was allocated by entrepreneurs to the implementation of development works (approx. 50%) and basic research (approx. 33%). Taking into account the dynamics of changes in the use of internal outlays, the highest increase in the value of funds allocated to the financing of basic research was observed between 2019 and 2018, in the case of applied research between 2017 and 2016; and when analysing the data on financing development works, the largest increase in funds was between 2018 and 2017.

By the outlays on innovative activities in 2020 (Figure 5), entrepreneurs of the industrial and service sector most often financed purchases of fixed assets (63.4% and 67.1% of all innovation-active enterprises, respectively) and employee training (38.6% and 41.4% of all innovation-active enterprises, respectively), and invested in building the brand and marketing activities (31.8% and 36% of all innovation-active enterprises, respectively).

The lowest value of outlays was spent by entrepreneurs representing both industrial and service enterprises on the acquisition or obtaining of intellectual property rights from other entities and registration of their own intellectual property rights (4.3% and 4.1% of the total number of innovatively active enterprises, respectively).

Protection of Innovations in Enterprises

Inventions, utility and industrial models, trademarks, topographic signs or topographies of integrated circuits, and works as well as knowhow may be created as a result of the innovative involvement of enterprises. It should therefore be remembered that entrepreneurs may use various types of protection, which in the case of a single item may indicate the need to choose from among the provisions on combating unfair competition, including trade confidentiality (Journal of Laws of 2022, item 1233), industrial property rights (Journal of Laws of 2021, item 324) or copyright (i.e. Journal of Laws of 2021, item 1062, as amended).

J. Schumpeter (p. 19) clearly separated the meaning of the term ‘innovation’ from the term ‘invention’. In practice, many inventions never become innovations because they are not put into production, and not all innovations meet the conditions of patentability to be classified as inventions. In addition, Schumpeter focussed only on technical innovations and their importance in achieving positive economic effects.

From the point of view of products and services created in enterprises, the most important thing is the appropriate management of innovations, enabling, inter alia, the selection of appropriate protection for upcoming innovations.

The data of Statistics Poland and the PORP for 2018–2020 show that entrepreneurs running a business in Poland, both in the industrial and service sectors, most often chose business confidentiality as a form of protection for innovative solutions (20.2% in the case of industrial enterprises and 18.1% of service enterprises). Copyright protection was chosen by 12.6% of industrial enterprises and the same share of service enterprises (Table 5).

The least innovative enterprises decided to be protected in the form of industrial property rights by submitting their innovative solutions for protection to the PORP or another entity granting a wider scope of territorial protection, i.e. the European Patent Office (EPO), the World Intellectual Property Organization (WIPO) or the European Union Intellectual Property Office (EUIPO).

It should be noted, that a large group of both industrial and service enterprises engaged in innovative activities decided to protect their trademarks (4.3% of industrial enterprises and 3.6% of service enterprises) and inventions (2.9% of industrial and 0.8% of service enterprises).

It is worth emphasising that resources or competences difficult to imitate are the basis of the company’s competitive advantage (Grzegorczyk, 2020, pp. 198–199). Preventing imitation is, in turn, a fundamental role of intellectual property rights (World Intellectual Property Office; O’Donoghue, Scotchmer, & Thisse 1998; Maurer & Scotchmer, 2002; Anton & Yao, 2004; Granstrand, 2006; Mokyr, 2009; Holgersson, 2013; Muça, Pomianek, & Peneva, 2022). These rights give a sense of security and certainty to innovators, inventors and entrepreneurs. Their time, financial outlays and knowhow used to develop a new solution will have a chance to pay for themselves (Saha & Bhattacharya, 2011, p. 88), as long as the law will remain in force.

A patent granted for an invention (in Poland-for a maximum of 20 years counted from the date of submitting the application to the PORP) is not limited in its purpose to obtaining a document authorising the exclusive use of the patent by the inventor (entrepreneur), e.g. for manufacturing a unique product using modern technology and thus competing effectively in the market. A patent is a component of the company’s assets, increasing its market value. It also improves the image of the company-as innovative, profitable and with development prospects-in the eyes of investors and contractors. It should be emphasised that the novelty of the invention is tested on a global scale, and thus even if the patent is valid in a limited area-e.g. a national patent in a given country in which an application was made for protection, or a European patent in states that are parties to the European Patent Convention of 1973 (The European Patent Convention, 17th edition 2020) — this is still a clear signal for competitors and business partners that the company was the first to apply this innovative technical solution on a global scale. As Czub (2021, p. 197) notes, the invention consists in using matter in a new way to satisfy various human needs (individual or collective), and the speciality of the innovator’s contribution is that the presence of such needs might have been reported by society not long before the invention is made or created for the first time by the innovator developing the invention, thus making the invention a particularly timely contribution. Especially, the latter situation gives the entrepreneur a chance to gain a competitive advantage-and even, to some extent, a monopoly on the market (Long, 1991, p. 846). As Wiśniewska rightly points out (2017, p. 316), patent protection does not guarantee its successful commercialisation, but resignation from such protection may deprive the entity of the possibility of benefitting from this right. The protection of intellectual property is also intended to promote fair competition (Al-Aali & Teece, 2013, p. 17; Komor, 2022, p. 13). As
emphasised by Sojkin (2012, p. 132), it should also be remembered that one of the key issues in the process of commercialisation of innovations is the assessment of the impact of innovation on the existing product portfolio, in particular the risk of ‘cannibalism’ within the portfolio. Financial opportunities that guarantee the implementation of the tasks provided for in the adopted strategy for introducing a new product to the market are an important element of commercialisation. This is especially important when it is necessary to make a decision to introduce innovation in an investment.

Conclusions

The role of innovation is extremely important in the process of social and economic development and progress, because innovation is an inherent condition for the development of not only enterprises but also the entire economy (Firlej, 2012). Innovations are combined with innovativeness, where the latter is understood as a tendency to create new or improve existing products, technological processes and systems of organisation, management or marketing, whose outcome is typically expected to be creative changes that lead to the emergence of new solutions in enterprises and adaptation of external scientific and technical achievements to the needs of the enterprise.

Based on the literature review and analyses of secondary data provided by Statistics Poland (GUS) and the PORP for 2016–2020, the following conclusions can be drawn:

1. The share of innovatively active industrial and service enterprises in Poland is growing systematically. In 2020, in both groups of enterprises, approximately one-third of small-sized, 40% of medium-sized and 60% of large enterprises introduced innovations.
2. A high frequency of implementation of innovations in business processes was observed in both industrial and service enterprises. Industrial companies implemented mainly changes in the processes of manufacturing products, while service companies changed the methods of internal and external communication.
3. The majority of entrepreneurs did not feel a significant impact of the COVID-19 pandemic on their operations in 2020, while nearly one-third described this impact as negative, and only about 2% as positive. In the latter two groups, the outbreak of the pandemic determined enterprises to introduce innovations both in their business processes (approximately 20% of industrial enterprises and approximately 40% of service enterprises) and in products (approximately 11% of industrial enterprises and approximately 9% of service enterprises).
4. The main source of outlays on innovative activities in enterprises were domestic funds, the value of which slightly decreased to 92% in 2020, because of the increased absorption of funds from abroad, including EU funds. These funds were primarily used by entrepreneurs for the implementation of development works. Acquired outlays on innovative activities allowed industrial and service entrepreneurs to finance, among others, the purchase of fixed assets, employee training, building the brand and marketing activities.
5. The implementation of innovative activities in enterprises is one of the areas that may constitute a source of competitive advantage; however, it is necessary to remember about appropriate protection for the innovations being developed. In the analysed period, among the forms of protection for innovative solutions, entrepreneurs most often chose business confidentiality and legal and copyright protection. They rarely applied for industrial property rights granted by the PORP or other entities granting such exclusive rights. Among industrial property rights, entrepreneurs most often protected trademarks and inventions.

Summing up the considerations presented in the paper, it should be stated that the conducted preliminary analyses based on GUS data show a great research potential, which should undoubtedly be explored in further studies. At the same time, it should also be emphasised that the
introduction of the new Oslo Manual 2018 guidelines influenced methodological changes in the study of innovativeness of enterprises in Poland, which allowed, on the one hand, the introduction of new research areas to the analyses carried out so far, and on the other, also limited the possibility of comparing the variability of some determinants in the long term.

References

1. Adegbesan, J. A. (2009). On the origins of competitive advantage: Strategic factor markets and heterogeneous Resource complementarity. Academy of Management Review, 34(3), 40632465. doi: 10.5465/amr.2009.40632465
2. Al-Aali, A. Y., & Teece, D. J. (2013). Towards the (strategic) management of intellectual property: Retrospective and prospective. California Management Review, 55(4), 15–30. doi: 10.1525/cmr.2013.55.4.15
3. Anning-Dorson, T. (2018). Innovation and competitive advantage creation: The role of organisational leadership in service firms from emerging markets. International Marketing Review, 35(4), 580–600. doi: 10.1108/IMR-11-2015-0262
4. Anton, J. J., & Yao, D. A. (2004). Little patents and big secrets: Managing intellectual property. The RAND Journal of Economics, 35(1), 1–22. doi: 10.2307/1593727
5. Baruk, J. (2001). Wiedza i innowacje jako źródło przewagi konkurencyjnej. Gospodarka Narodowa, (167)4, 20–34. doi: 10.33119/GN/113895
6. Białoń, L. Janczewska, D. (2006). Menedżer marketingu w firmie przyszłości. [W], L. Białoń (red.) Metody oceny kształcenia specjalistów w zakresie zarządzania, 105–117. Warszawa, Poland: Oficyna Wydawnicza WSM.
7. Czub, K. (2021). Prawo własności intelektualnej. Warszawa, Poland: Wolters Kluwer.
8. Dereli, D. D. (2015). Innovation management in global competition and competitive advantage. Procedia — Social and Behavioral Sciences, 195, 1365–1370. doi: 10.1016/ j.sbspro.2015.06.323
9. Dobiegała-Korona, B. (2012). Nowa rola marketingu w budowie wartości przedsiębiorstwa. Kwartalnik Nauk o Przedsiębiorstwie, 23(2), 64–75.
10. Drucker, P. F. (1998). On the Profession of Management. Boston, USA: Harvard Business Scholl Press.
11. Drucker, P. (2002). Innowacje i przedsiębiorczość. Praktyka i zasady. Warszawa: PWE.
12. Firlej, K. A. (2012). Innowacyjność polskiej gospodarki jako wyzwanie rozwojowe w warunkach integracji europejskiej. [W] A. Prusek (red.), Wyzwania rozwoju społeczno-ekonomicznego Polski 143–144. Kraków–Mielec, Poland: Katedra Polityki Ekonomicznej
i Programowania Rozwoju Uniwersytetu Ekonomicznego w Krakowie.
13. Freeman, Ch. (1982). The Economics of Industrial Innovation. London, UK: Cassell.
14. Friar, J. H. (1995). Competitive advantage through product performance innovation in a competitive market. Journal of Product Innovation Management, 12(1), 33–42. doi: 10.1016/0737-6782(94)00026-C
15. Gomułka, S. (1998). Teoria innowacji i wzrostu gospodarczego. Warszawa, Poland: CASE.
16. Goyke, K. (2018). Ulga na działalność badawczo-rozwojową jako podatkowa zachęta do ponoszenia nakładów w obszarze B+R. Europa Regionum, 3, 23–36. doi: 10.18276/ er.2018.36-02
17. Granstrand, O. (2006). Innovation and intellectual property rights. [W] J. Fagerberg., & D. C. Mowery (Eds.), The Oxford Handbook of Innovation, 266–290. Oxford, UK: Oxford Academic. doi: 10.1093/oxfordhb/9780199286805.003.0010
18. Grudzewski, W. M., & Hejduk, I. K. (2004). Metody projektowania systemów zarządzania. Warszawa, Poland: Difin.
19. Grzegorczyk, W. (2011). Strategie marketingowe przedsiębiorstw na rynku międzynarodowym. Łódź, Poland: Wydawnictwo Uniwersytetu Łódzkiego.
20. Grzegorczyk, T. (2020). Strategie zarządzania własnością intelektualną. [W] R. Śliwiński (red.), Strategiczne zarządzanie przedsiębiorstwem międzynarodowym, 173–203. Warszawa, Poland: Difin.
21. Grzywacz, W. (1995). Metodyka polityki gospodarczej. Szczecin, Poland: Wydawnictwo PTE.
22. GUS. Działalność innowacyjna przedsiębiorstw w latach 2018–2020. Pobrano 10 sierpnia 2022. Retrieved from https://stat.gov.pl/obszary-tematyczne/nauka-i-technika-spoleczenstwo-informacyjne/nauka-i-technika/dzialalnosc-innowacyjna-przedsiebiorstw-w-latach-2018-2020,2,20.html
23. Hildreth, P., Kimble, Ch. (2004). Knowledge networks: Innovation through communities of practice. Hershey, USA and London, UK: Idea Group Publishing.
24. Holgersson, M. (2013). Patent management in entrepreneurial SMEs. R&D Manage, 43, 21–36. doi: 10.1111/j.1467-9310.2012.00700.x
25. Hu, R., Yu, C., Jin, Y., Pray, C., & Deng, H. (2022). Impact of government policies on research and development (R&D) investment, innovation, and productivity: Evidence from pesticide firms in China. Agriculture, 12(5), 709. doi: 10.3390/agriculture12050709
26. Janasz, W., Kozioł-Nadolna, K. (2011). Innowacje w organizacji. Warszawa, Poland: Polskie Wydawnictwo Ekonomiczne.
27. Kamiński, R. (2018). Istota innowacji (definicje, wyznaczniki i rodzaje). [W] R. Kamiński (red.), Innowacje gospodarcze. Wybrane aspekty ekonomiczne i prawne, 13–24. Poznań, Poland: Wydawnictwo Naukowe UAM.
28. Kasprzyk, S. (1980). Innowacje od koncepcji do produkcji. Warszawa, Poland: CRZZ
29. Koch, J. (2004). Innowacje siłą napędową rozwoju. [W] J. Gawlik, S. Szelest, M. Trompeteur (red.) Materiały Konferencji Naukowo-Technicznej, Jakość, innowacyjność i transfer technologii w rozwoju przedsiębiorstw INTELTRANS 2006, 91–102, Kraków, Poland: Wydawnictwo Politechniki Krakowskiej.
30. Komor, M. (2022). Political and legal conditions of marketing activity of businesses in the European market. Marketing of Scientific and Research Organizations, 44(2), 1–20.
31. Kotler, Ph. (2004). Marketing od A do Z. Warszawa, Poland: PWE.
32. Kopaliński, W. (2006). Podręczny słownik wyrazów obcych, Warszawa, Poland: Oficyna Wydawnicza Rytm.
33. Lange, O. (1961). Pisma ekonomiczne i społeczne 1930–1960. Warszawa, Poland: PWN.
34. Long, P. O. (1991). Invention, authorship, “Intellectual Property,” and the origin of patents: Notes toward a conceptual history. Technology and Culture, 32(4), 846–884. doi: 10.2307/3106154
35. Major, M., & Niezgoda, J. (2003). Elementy Statystyki Część I. Statystyka opisowa. Kraków: Krakowskie Towarzystwo Edukacyjne Sp. z o.o. na zlecenie Krakowskiej Szkoły Wyższej im. Andrzeja Frycza Modrzewskiego.
36. Marciniak, S. (2010). Innowacyjność i konkurencyjność gospodarki. Warszawa, Poland: Wydawnictwo C.H. BECK.
37. Maurer, S. M., & Scotchmer, S. (2002). The independent invention defence in intellectual property. Economica, 69, 535–547. doi: 10.1111/1468–0335.00299
38. Mokyr, J. (2009). Intellectual property rights, the industrial revolution, and the beginnings of modern economic growth. American Economic Review, 99(2), 349–355.
39. Montoya-Weiss M. M., & Calantone, R. (1994). Determinants of new product performance: A review and meta-analysis, Journal of Product Innovation Management, 11, 397–417.
40. Muça, E., Pomianek, I., & Peneva, M. (2022). The role of GI products or local products in the environment-consumer awareness and preferences in Albania, Bulgaria and Poland. Sustainability, 14(4). doi: 10.3390/su14010004
41. O’Donoghue, T., Scotchmer, S., & Thisse, J. F. (1998). Patent breadth, patent life, and the pace of technological progress. Journal of Economics & Management Strategy, 7, 1–32. doi: 10.1111/j.1430-9134.1998.00001.x
42. Oslo Manual. (2005). Proposed Guidelines for Collecting and Interpreting Technological Innovation Data. Pobrano 10 sierpnia 2022. Retrieved from https://ec.europa.eu/eurostat/documents/3859598/5889925/OSLO-EN.PDF
43. Oslo Manual. (2018). Guidelines for Collecting, Reporting and using Data on Innovation, 4th ed, The Measurement of Scientific, Technological and Innovation Activities, by OECD/Eurostat za: Podręcznik Oslo 2018, Zalecenia dotyczące pozyskiwania, prezentowania i wykorzystywania danych z zakresu innowacji, Pomiar działalności naukowo-technicznej i innowacyjnej, GUS, 2020. Pobrano 20 września 2022. Retrieved from https://stat.gov.pl/files/gfx/portalinformacyjny/pl/defaultaktualnosci/5496/18/1/1/podrecznik_oslo_2018_internet.pdf
44. Penc, J. (1999). Innowacje i zmiany w firmie. Transformacja i sterowanie rozwojem przedsiębiorstwa. Warszawa, Poland: Agencja Wydawnicza Placet.
45. Pérez-Luno, A., Valle Cabrera, R., & Wiklund, J. (2007). Innovation and imitation as sources of sustainable competitive advantage. Management Research, 5(2), 71–82. doi: 10.2753/JMR1536-5433050201
46. Pietrasiński, Z. (1970). Ogólne i psychologiczne zagadnienia innowacji. Warszawa, Poland: PWN.
47. Pomykalski, A. (2001). Zarządzanie innowacjami. Warszawa, Poland: PWN.
48. Rozporządzenie Ministra Zdrowia z dnia 20 marca 2020 r. w sprawie ogłoszenia na obszarze Rzeczypospolitej Polskiej stanu epidemii, Dz.U. 2020, poz. 491.
49. Saha, C. N., & Bhattacharya, S. (2011). Intellectual property rights: An overview and implications in pharmaceutical industry. Journal of Advanced Pharmaceutical Technology & Research, 2(2), 88–93. doi: 10.4103/2231-4040.82952
50. Schumpeter, J. (1911). The theory of economic development. New Brunswick, USA and London, UK: Transaction Publishers.
51. Schumpeter, J. (1949). Economic Theory and Entrepreneurial History. [W] R. R. Wohl (Ed.), Change and the Entrepreneur. Postulates and the Patterns for Entrepreneurial History, 131–142. Cambridge, USA: Harvard University Press.
52. Schumpeter, J. Clemence, R. V. (1989). Essays on entrepreneurs, innovations, business cycles and the evolution of capitalism. New Brunswick, USA: Transaction Publishers.
53. Sikora, J., & Uziębło, A. (2013). Innowacja w przedsiębiorstwie — próba zdefiniowania. Zarządzanie i Finanse, 2(2), 351–376.
54. Simpson, P. M., Siguaw, J. A., & Enz, C. A. (2006). Innovation orientation outcomes: The Good and the Bad. Journal of Business Research, 59. doi: 10.1016/j.jbusres.2006.08.001
55. Sławińska, M. (2015). Innowacje marketingowe w działalności przedsiębiorstw handlowych. Annales, Sectio H, XLIX(1), 157–167.
56. Sojkin, B. (2012). Informacyjne podstawy decyzji marketingowych w procesie komercjalizacji produktu. Marketing Instytucji Naukowych i Badawczych, 222, 125–133.
57. Świtalski, W. (2005). Innowacje i konkurencyjność. Warszawa, Poland: Wydawnictwo Uniwersytetu Warszawskiego.
58. Ustawa z dnia 16 kwietnia 1993 r. o zwalczaniu nieuczciwej konkurencji. Tekst jedn. Dz.U. z 2022 r. poz. 1233.
59. Ustawa z dnia 30 czerwca 2000 r. Prawo własności przemysłowej, Tekst jedn.: Dz.U. z 2021 r. poz. 324.
60. Ustawa z dnia 30 maja 2008 r. o niektórych formach wspierania działalności innowacyjnej. Tekst jedn.: Dz.U. z 2021 r. poz. 706 z późn. zm.
61. Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych. Tekst jedn. Dz.U. z 2021 r. poz. 1062 z późn. zm.
62. Ustawa z dnia 6 marca 2018 r. Prawo przedsiębiorców. Tekst jedn.: Dz.U. z 2022 r. poz. 24 z późn. zm.
63. Whitfield, P. R. (1979). Innowacje w przemyśle. Warszawa, Poland: PWE.
64. Wiśniewska, J. (2017). Ochrona wynalazku w procesie zarządzania działalnością badawczo-rozwojową. Studia i Prace WNEIZ US, 48(3), 307–318. doi:10.18276/ sip.2017.48/3-25
65. Zimon, D., & Gawron-Zimon, Ł. (2014). Wykorzystanie metody QFD do doskonalenia logistycznej obsługi klienta. [W] R. Knosala (red.) Innowacje w zarządzaniu i inżynierii produkcji, Konferencja IZiP, 1077–1084, Opole, Poland: Oficyna Wydawnicza Polskiego Towarzystwa Zarządzania Produkcją.

Podstawowymi elementami każdej gospodarki są podmioty gospodarcze różniące się celami i zakresem działania a także zasobami niezbędnymi do ich realizacji. Podmioty te mogą działać na rynku lokalnym, regionalnym, krajowym i światowym. Ogólnym celem ich funkcjonowania może być działalność produkcyjna, usługowa lub regulacyjna. Zazwyczaj podmioty te rozwijają się w warunkach: dynamicznych zmian zachodzących w ich otoczeniu ekonomicznym, politycznym i społecznym; silnej konkurencji na rynku; szybkich zmian techniki i technologii; utrudnionego dostępu do zasobów materialnych i niematerialnych — zwłaszcza wiedzy; dynamicznych zmian oczekiwań aktualnych i potencjalnych klientów; szybko zmieniających się metod zarządzania itp. W konsekwencji podmioty te muszą posługiwać się sprawnym systemem informacyjnym/informatycznym, pozwalającym możliwie szybko identyfikować wszelkie zmiany zachodzące zarówno w otoczeniu wewnętrznym, jak i zewnętrznym (ogólnym i zadaniowym) (Griffin, 2007, s. 75–89) celem: rejestracji wszelkich sygnałów (nawet słabych) o zmianach zachodzących w otoczeniu; reagowania na te zmiany poprzez dostosowywanie swoich rozwiązań strukturalnych, procesowych, technicznych, technologicznych, społecznych, kulturowych i zarządczych, których wdrożenie pozwoli zachować równowagę z otoczeniem, a nawet wyprzedzać zmiany zachodzące w środowisku, jak również tworzyć środowiska wzajemnych interakcji między przedsiębiorstwem a jego klientami (Li, Zhang i Wei, 2018, s. 22).

Niewątpliwie podstawowymi instrumentami zmian dostosowawczych i wyprzedzających są innowacje produktowe, procesowe, organizacyjne i marketingowe (Baruk, 2018, s. 88). Tworzenie takich innowacji powinno mieć systemowy charakter i wynikać z racjonalnej polityki innowacyjnej prowadzonej na poziomie kraju, regionu, każdego podmiotu gospodarczego (Chen, Xia i Yang, 2018, s. 39). Sprawne kreowanie innowacji uwarunkowane jest posiadaniem określonych zasobów wiedzy naukowej, rynkowej, technologicznej, ekonomicznej, bowiem każda innowacja powstaje w procesie materializowania posiadanych zasobów różnych kategorii wiedzy. Wiedza organizacyjna jest jednym z jej najważniejszych zasobów, podstawą stabilnego rozwoju, źródłem utrzymania konkurencyjnego charakteru organizacji (Wang i Chen, 2017, s. 96).

Właśnie systemowe poszukiwanie i transfer nowej wiedzy lub twórcze połączenie istniejących pomysłów lub technologii stało się kluczowym warunkiem udanych innowacji (Xie, Hall, McCarthy, Skitmore i Shen, 2016, s.

71) Takimi zasobami wiedzy należy racjonalnie zarządzać poprzez realizację zbioru logicznych działań obejmujących pozyskiwanie wiedzy, jej magazynowanie, oczyszczanie (aktualizowanie), dystrybucję, wykorzystanie i monitorowanie. Ułatwieniem realizacji procesu zarządzania wiedzą mogą być modele zarządzania wiedzą (Baruk, 2009, s. 32, 35–46). Postępowanie zarządzających zgodne ze wskazaniami modeli sprzyja kształtowaniu gospodarki opartej na wiedzy, charakteryzującej się systemowo prowadzoną działalnością badawczą i rozwojową (B+R) oraz innowacyjną. Taka konstatacja jest szczególnie istotna w świetle względnie niskiej świadomości prac B+R, ich rozumienia i potrzeby identyfikacji przez kadrę kierowniczą w polskich firmach (Deloitte, 2016, s. 10).

Działalność badawcza i rozwojowa stanowi więc źródło wiedzy dla procesów innowacyjnych dlatego powinna być istotnym elementem polityki badawczo-rozwojowej i innowacyjnej na poziomie makro- i mikroekonomicznym. Polityka ta umożliwia kreowanie nowej wiedzy, rozwój technologii zwiększających zdolności podmiotów gospodarczych w zakresie tworzenia innowacji i ich praktycznego wykorzystania. Generalnie, prace B+R wspomagają organizacje w systemowym zwiększaniu zasobów wiedzy (zwłaszcza podstawowej), wiedzy pracowników, umożliwiają ujawnianie i wykorzystanie talentów, pozyskiwanie wiedzy zewnętrznej i usprawnianie zdolności innowacyjnych. Dzięki racjonalnie organizowanym pracom B+R organizacje biznesowe nabywają lub opracowują ważne technologie wewnętrznie lub zewnętrznie — poprzez wspólne przedsięwzięcia, licencje, sojusz strategiczny i przejęcia (Salisu i Bakar, 2019, s. 58).

Wysoka ranga działalności B+R, traktowanej jako źródło wiedzy materializowanej w procesach tworzenia i wdrażania innowacji, wymaga kreatywnego zaangażowania się menedżerów w systemowy jej rozwój. Zakres takiego zaangażowania kadry kierowniczej można wyrazić pośrednio za pomocą miernika w postaci procentowego udziału wydatków ponoszonych na badania i rozwój w produkcie krajowym brutto. Analizie poddano kształtowanie się tego miernika w odniesieniu do: wszystkich sektorów działania; w sektorze przedsiębiorstw; w sektorze rządowym; w sektorze szkolnictwa wyższego; w sektorze prywatnych instytucji niekomercyjnych. Poziom tych mierników, ukształtowanych w latach 2008; 2010; 2013; 2015 i 2017, odniesiono do UE, Polski oraz wybranych krajów członkowskich charakteryzujących się względnie najwyższymi i najniższymi udziałami.

Celem publikacji jest więc próba identyfikacji i krytycznej oceny udziału wydatków na B+R w produkcie krajowym brutto (PKB), ponoszonych przez podmioty gospodarcze skupione w czterech sektorach (przedsiębiorstw, rządowym, szkolnictwa wyższego i prywatnych instytucji niekomercyjnych) oraz łącznie we wszystkich sektorach, traktowanych jako pośrednia miara stopnia aktywności kadry kierowniczej w kształtowanie polityki badawczo-rozwojowej. Analizą objęto średnie wyniki notowane w UE, a także w wybranych krajach członkowskich (w tym w Polsce) oraz w wybranych krajach pozaeuropejskich.

Drugim celem opracowania jest próba weryfikacji tezy, że wydatki na B+R są zmienne i zróżnicowane w poszczególnych państwach członkowskich i nie dają jednoznacznie pozytywnego obrazu systematycznego i dynamicznego wzrostu aktywności badawczo-rozwojowej w tych krajach.

Do opracowania publikacji wykorzystano następujące metody badawcze: analizę krytyczno-poznawczą piśmiennictwa; analizę statystycznoporównawczą wtórnego materiału empirycznego Eurostatu; metodę projekcyjną.

Istota działalności badawczo-rozwojowej

Działalność badawczo-rozwojowa obejmuje systematycznie prowadzone prace twórcze, realizowane w celu zwiększenia zasobów wiedzy, w tym wiedzy o człowieku, kulturze i społeczeństwie, a także — znalezienia nowych możliwości zastosowania pozyskanej (odkrytej) wiedzy (GUS, 2019, s. 27).

Działalność B+R powinna być ukierunkowana na nowe odkrycia, oparte na oryginalnych koncepcjach lub hipotezach a także na ich interpretację. Cechą tej działalności jest brak pewności co do ostatecznego wyniku lub przynajmniej co do ilości czasu i zasobów potrzebnych do jego osiągnięcia. Celem tej działalności jest osiągnięcie wyników, które można byłoby swobodnie przenosić do praktyki lub sprzedawać na rynku. Działalność tę można uznać za działalność badawczą i rozwojową, jeżeli spełnia ona następujące kryteria (OECD, 2015, s. 47):

1) nowatorskość — ukierunkowanie na nowe odkrycia,
2) twórczość — oparcie się na oryginalnych, nieoczywistych koncepcjach i hipotezach,
3) nieprzewidywalność — niepewność co do ostatecznego wyniku oraz kosztu, w tym poświęconego czasu,
4) metodyczność — prowadzona w sposób zaplanowany (z określonym celem projektu B+R oraz źródłem finansowania),
5) możliwość do przeniesienia lub odtworzenia — skutkująca wynikami, które mogą być odtwarzane.

Na działalność B+R składają się:

1) badania podstawowe (czyste i ukierunkowane),
2) badania stosowane,
3) prace rozwojowe.

Badania podstawowe (basic research) to prace eksperymentalne lub teoretyczne podejmowane głównie w celu zdobycia nowej wiedzy na temat podłoża określonych zjawisk i obserwowalnych faktów, bez nastawienia na konkretne jej zastosowanie lub wykorzystanie. Badania te dzielą się na:

• „czyste” badania podstawowe (pure basic research) prowadzące do postępu wiedzy, bez nastawienia na osiąganie korzyści ekonomicznych czy społecznych i bez podejmowania aktywnych działań w celu zastosowania wyników badań do rozwiązywania problemów o charakterze praktycznym lub w celu przekazania wyników do sektorów zajmujących się ich zastosowaniem;
• ukierunkowane badania podstawowe (oriented basic research) nastawione na stworzenie szerokiej bazy wiedzy, stanowiącej podstawę rozwiązywania problemów lub wykorzystywania możliwości, zarówno istniejących, jak i przewidywanych w przyszłości.

Badania stosowane (applied research) to oryginalne prace badawcze podejmowane w celu zdobycia nowej wiedzy. Są one ukierunkowane głównie na osiągnięcie konkretnych celów praktycznych. Badania te polegają na uwzględnieniu istniejącej już wiedzy i jej „poszerzeniu” z myślą o rozwiązywaniu konkretnych problemów. Badania stosowane umożliwiają operacjonalizację pomysłów. Takie rozwiązania oparte na wiedzy mogą być chronione za pomocą instrumentów ochrony własności intelektualnej, włącznie z zapewnieniem tajemnicy handlowej. Skutkami badań stosowanych mogą być modele próbne wyrobów, procesów lub metod.

Prace rozwojowe (experimental development) obejmują metodyczną pracę opierającą się na wiedzy uzyskanej w wyniku działalności badawczej oraz doświadczeniach praktycznych i mającą na celu wytworzenie dodatkowej wiedzy ukierunkowanej na stworzenie nowych lub istotnie udoskonalonych materiałów, urządzeń, wyrobów, procesów, systemów lub usług, łącznie z przygotowaniem prototypów doświadczalnych oraz instalacji pilotażowych (Baruk, 2016, s. 61; Bogers, 2011, s. 94).

Działalność B+R, traktowana jako systemowe tworzenie wiedzy wykorzystywanej do tworzenia innowacji oraz rozwiązywania aktualnych i przyszłych problemów, może być prowadzona przez pojedynczy podmiot gospodarczy, jeżeli posiada on odpowiednie warunki organizacyjne, technologiczne, finansowe i kadrowe. W przypadku braku takich warunków podmiot gospodarczy może korzystać z wyników działalności B+R realizowanej w innych podmiotach gospodarczych. Możliwe jest też rozwiązanie pośrednie, polegające na wspólnym prowadzeniu prac B+R z innymi organizacjami (przemysłowymi, naukowymi i badawczymi) w ramach struktur sieciowych. Korzystanie z takich rozwiązań wymaga racjonalnej polityki B+R, innowacyjnej i rozwojowej na wszystkich szczeblach zarządzania. Koncepcję takiego podejścia do zarządzania przedstawiono na rysunku 1.

Funkcjonowanie podmiotu gospodarczego obrazują cztery logicznie następujące po sobie zbiory działań: działalność badawczo-rozwojowa kreująca zasoby wiedzy; działalność innowacyjna materializująca pozyskaną wiedzę; oparta na innowacjach działalność operacyjna polegająca na wytwarzaniu innowacyjnych wyrobów i świadczeniu innowacyjnych usług; działalność marketingowa/zbyt — umieszczenie na rynku innowacyjnych wyrobów lub usług. Zarządzanie tymi zbiorami działań powinno opierać się na założeniach wzajemnie powiązanych polityk: B+R, innowacyjnej i rozwojowej, a także na systemowo pozyskiwanej wiedzy naukowej, technologicznej, ekonomicznej, rynkowej, handlowej i klientów.

Udział wydatków na badania i rozwój w PKB poniesionych we wszystkich sektorach działania

Działalność B+R jest działalnością kosztochłonną dlatego wymaga racjonalnych decyzji w zakresie pozyskiwania środków na ten cel, wymaga też skoordynowanej polityki w skali całej gospodarki, w skali regionów oraz w skali poszczególnych podmiotów gospodarczych. Zasadne jest więc przeanalizowanie jak radzą sobie państwa członkowskie w kształtowaniu polityki B+R. Syntetycznym miernikiem takiego zaangażowania może być udział wydatków na B+R w dochodzie krajowym brutto. W tabeli 1 przedstawiono kształtowanie się tego miernika dla UE, Polski i wybranych krajów członkowskich w wybranych latach.

W rozważanych latach udział wydatków na B+R w PKB był zróżnicowany pod względem wartości i tendencji zwyżkowych. Na poziomie UE w latach 2008 i 2010 udział ten nie przekraczał 2%, natomiast w trzech pozostałych latach przekroczył granicę 2% z nieznaczną tendencją wzrostową. Do takiego stanu przyczyniły się kraje członkowskie, wyraźnie zróżnicowane pod względem poziomu PKB przeznaczanego na B+R. Pozytywnie wyróżniały się takie kraje jak: Finlandia, Szwecja, Dania, Niemcy i w mniejszym stopniu Francja. W krajach tych poziom analizowanego miernika był wyższy od średniej wartości dla UE w poszczególnych latach. Szczególnie wyróżniała się Szwecja, gdzie udział ten przekraczał 3%, jednak bez wyraźnej tendencji wzrostowej. W Finlandii w początkowych trzech latach przekraczał on 3%, ale w kolejnych dwóch latach miał tendencje malejące. Przeciwna sytuacja miała miejsce w Danii, gdzie w latach 2008, 2010 i 2013 miernik ten utrzymywał się na poziomie poniżej 3%, ale z nieznaczną tendencją wzrostową, by w kolejnych latach przekroczyć granicę 3%. Nieco niższy poziom miernik ten osiągał w Niemczech, wykazując nieznaczną tendencję wzrostową i w 2017 r. przekroczył granicę 3%.

Przeciwstawną grupę stanowiły kraje o względnie małych udziałach wydatków na B+R w PKB. Głównie chodzi tu o Cypr, Rumunię, Łotwę i Bułgarię. W krajach tych poziom analizowanego miernika nie przekraczał 1% i miał nieregularne tendencje wzrostowe. W poszczególnych latach udziały te znacznie odbiegały od średnich wartości w UE.

W Polsce wydatki na B+R kształtowały się na znacznie niższym poziomie niż średnio w UE. Pozytywnym zjawiskiem był niewielki ale wzrostowy charakter rozważanego miernika od 0,6% w 2008 r. (mniej o 1,23 pproc. w porównaniu do średniego wyniku w UE) do 1,03% w 2017 r. (mniej o 1,04 pproc. w stosunku do średniego wyniku w UE). Udziały wydatków na B+R w PKB plasują Polskę w grupie państw, które muszą nadrabiać dystans dzielący ich od czołówki europejskiej.

W kontekście prowadzonej analizy nasuwa się pytanie, jaką pozycję zajmuje UE i poszczególne kraje członkowskie na tle poziomu rozważanego miernika charakteryzującego kraje przodujące pod tym względem, takie jak: USA, Japonia, czy Korea Południowa? Okazuje się, że średnie wyniki dla UE były niższe od wyników osiąganych w USA w 2008 r. — o 0,94 pproc., w 2010 r. — o 0,82 pproc., w 2013 r. — o 0,71 pproc., w 2015 r. — o 0,72 pproc., dla 2017 r. brak danych dla USA. Przedstawione liczby wskazują na utrzymywanie się luki technologicznej między UE a USA, Japonią i Koreą Południową, mimo optymistycznych założeń strategii „Europa 2020” zakładającej poprawę warunków prowadzenia działalności badawczo-rozwojowej, między innymi, poprzez przeznaczanie 3% PKB UE na inwestycje w badania i rozwój (Strategia, 2015, s. 1).

Wśród państw członkowskich tylko Szwecja spełniła ten warunek w analizowanych latach. Natomiast Finlandia — w latach 2008, 2010 i 2013. Dania osiągnęła poziom zakładanego miernika w latach 2015 i 2017, podczas gdy Niemcy tylko w 2017 r.

Jeszcze większe różnice w poziomie udziału wydatków na B+R w PKB pojawiły się między UE a Japonią oraz Koreą Południową. W Japonii wydatki na B+R stanowiły ponad 3% PKB. Jeszcze korzystniejsza sytuacja panowała w Korei Południowej, gdzie w latach 2013 i 2015 udziały te przekroczyły 4%.

Udział wydatków na badania i rozwój w PKB w sektorze przedsiębiorstw

Do sektora przedsiębiorstw zalicza się (OECD, 2015, s. 34): 1) wszystkie przedsiębiorstwa mające status rezydenta, w tym nie tylko przedsiębiorstwa posiadające osobowość prawną, bez względu na miejsce zamieszkania lub siedzibę ich akcjonariuszy czy udziałowców. Zalicza się tutaj zarówno przedsiębiorstwa prywatne (przedsiębiorstwa notowane na giełdzie i będące przedmiotem obrotu giełdowego lub też nie), jak i przedsiębiorstwa sektora publicznego (tj. przedsiębiorstwa kontrolowane przez sektor rządowy),

2) nieposiadające osobowości prawnej oddziały przedsiębiorstw niemających statusu rezydenta, które uznaje się za rezydentów i element tego sektora, ponieważ zajmują się produkcją na danym obszarze gospodarczym w perspektywie długofalowej,

3) wszystkie instytucje niekomercyjne mające status rezydenta, które są producentami wyrobów lub usług na rynku bądź świadczą usługi na rzecz biznesu.

W kontekście prowadzonej analizy zasadne jest pytanie: jaki był udział wydatków na B+R w PKB, ponoszonych w sektorze przedsiębiorstw? Jak wynika z tabeli 2, poziom tego miernika przyjmował różne wartości w poszczególnych państwach członkowskich.

Średnio w UE wydatki te osiągały poziom przekraczający 1% PKB i miały tendencję wzrostową od 1,16% w 2008 r. do 1,36% w 2017 r. Jednak w krajach członkowskich uwidoczniły się znaczne różnice w poziomie tego miernika względem innych państw, a także w poszczególnych latach. Pozytywnie wyróżniały się sektory przedsiębiorstw w Szwecji i w Finlandii. W państwach tych rozważany miernik znacznie przekraczał średnie wyniki dla UE osiągając ponad 2% wartość z wyjątkiem Finlandii, gdzie w 2015 r. obniżył się do 1,93%. Pozytywne tendencje zanotowano w przedsiębiorstwach austriackich i niemieckich, gdzie analizowane udziały cechowały się stałą, aczkolwiek niewielką tendencją wzrostową. W przypadku Austrii od 1,78% w 2008 r. do 2,22% w 2017 r.

W przypadku Niemiec od 1,8% w 2008 r. do 2,09% w 2017 r. Na wyższym poziomie niż średnio w UE miernik ten kształtował się również w Danii lecz miał on względnie stabilny charakter.

Część państw członkowskich UE charakteryzowała się znacznie niższymi poziomami rozważanego miernika. Konstatacja ta dotyczy szczególnie Cypru, Rumunii, Łotwy, Litwy i Słowacji. W krajach tych wydatki na B+R ponoszone przez sektor przedsiębiorstw kształtowały się na względnie niskim i zróżnicowanym poziomie. Najgorsza sytuacja panowała wśród przedsiębiorstw cypryjskich, gdzie w 2008 r. rozważany miernik osiągnął poziom 0,09%, by w 2017 r. wzrosnąć do zaledwie 0,2%. Pozytywnym zjawiskiem w sektorach przedsiębiorstw litewskich i słowackich były niewielkie ale systematyczne wzrosty badanego miernika w analizowanych latach. W przypadku Litwy od 0,19 w 2008 r. do 0,31% w 2017 r. a w przypadku Słowacji od 0,2% w 2008 r. do 0,48% w 2017 r.

Udział wydatków na B+R w PKB w sektorze polskich przedsiębiorstw był wyraźnie mniejszy w porównaniu ze średnimi wynikami w UE.

W Polsce w 2008 r. miernik ten był niższy o 0,97 pproc., w 2010 r. — o 1 pproc., w 2013 r. — o 0,9 pproc., w 2015 r. — o 0,84 p. proc. i w 2017 r. — o 0,67 pproc. Mimo stosunkowo niskiego udziału wydatków na B+R w PKB ponoszonych przez sektor przedsiębiorstw w Polsce można dostrzec pozytywne tendencje przejawiające się malejącą luką w stosunku do średnich wyników w UE i nieznacznym wzrostem miernika w kolejnych latach z wyjątkiem 2010 r. Jednak bezwzględne wartości tego miernika plasują polskie przedsiębiorstwa w grupie państw o względnie niskim poziomie finansowania działalności B+R.

Również w tym przekroju analizy średnie wyniki dla UE w porównaniu do wyników cechujących USA, a zwłaszcza Japonię i Koreę Południową, nie są zadowalające. W USA miernik ten zbliżony był do 2% w poszczególnych latach i przewyższał średnie wartości w UE o 0,79 pproc. — w 2008 r., o 0,67 pproc. — w 2010 r., o 0,64 pproc. — w 2013 r., o 0,66 pproc. — w 2015 r. W Japonii i w Korei Południowej wyniki te kształtowały się średnio na poziomie odpowiednio 2,53% i 2,87%.

Udział wydatków na badania i rozwój w PKB poniesionych przez sektor rządowy

Jednym z ważnych podmiotów kreujących politykę badawczą i rozwojową są rządy poszczególnych państw i ich agendy. Miarą takiego zaangażowania może być udział wydatków na B+R w PKB ponoszonych przez sektor rządowy, na który składają się (OECD, 2015, s. 35):

1) wszystkie jednostki władz szczebla centralnego/federalnego, regionalnego/stanowego oraz lokalnego/gminnego, w tym zakłady ubezpieczeń społecznych, z wyjątkiem tych jednostek, które odpowiadają opisowi instytucji szkolnictwa wyższego,

2) pozostałe organy administracji publicznej: agencje wykonujące lub finansujące B+R oraz wszystkie nierynkowe instytucje niekomercyjne, które są kontrolowane przez jednostki sektora rządowego, a które same nie należą do sektora szkolnictwa wyższego.

Poziom tego miernika przedstawiono w tabeli 3.

Na poziomie UE, średnia wartość udziału wydatków na B+R w PKB poniesionych przez rządy państw członkowskich wynosiła około 0,25% i miała stabilny charakter. W przekroju państw członkowskich wartości analizowanego miernika odbiegały od średnich wyników dla UE. Różniły się też między poszczególnymi krajami. W krajach takich jak: Niemcy, Czechy, Luksemburg, Słowenia i Finlandia udział rządowych wydatków na B+R w PKB był nieco wyższy od średnich wyników w UE, jednak nie miał on jednoznacznie wzrastającego charakteru w kolejnych latach. Najbardziej wyróżniającym się krajem były Niemcy, gdzie sektor rządowy na B+R przeznaczał około 0,4% PKB w poszczególnych latach.

Na przeciwnym końcu skali znalazły się: Malta, Irlandia, Cypr, Dania i Portugalia. W państwach tych poziomy analizowanego miernika były wyraźnie niższe od średnich wartości dla UE. Wyniki te wskazują na śladowe zaangażowanie sektora rządowego w finansowanie badań i rozwoju. Przykładowo na Malcie udział sektora rządowego w finansowaniu B+R w latach 2008, 2010 utrzymywał się na poziomie 0,02% PKB. Jeszcze gorsza sytuacja była w 2017 r. W Portugalii wartość analizowanego miernika spadła z 0,11% w 2008 r. do 0,07% w 2017 r.

W Polsce finansowanie badań i rozwoju przez sektor rządowy mierzony procentowym udziałem wydatków na B+R w PKB zbliżony był do średnich wyników w UE i utrzymywało się na poziomie nieco przekraczającym 0,2% z wyjątkiem 2017 r., kiedy wartość tego miernika spadła do zaledwie 0,02%. Wynik ten plasuje Polskę na drugim miejscu od końca państw członkowskich przed Maltą.

Dla porównania w kilku państwach na świecie wartość badanego miernika kształtowała się na wyższym poziomie niż średnio w UE. Do takich państw należą: Korea Południowa, USA, Rosja i Hong Kong, gdzie poziom rządowych wydatków na B+R kształtował się nieco powyżej 0,3% PKB, natomiast w Korei Południowej oscylował między 0,38% w 2008 r. a 0,5% w 2015 r.

Luka w poziomie analizowanego miernika między UE a USA wynosiła: 0,07 pproc. w 2008 r., 0,1 pproc. w 2010 r., 0,06 pproc. w 2013 r. i 0,07 pproc. w 2015 r.

Udział wydatków na badania i rozwój w PKB poniesionych w sektorze szkolnictwa wyższego

Jednym z sektorów, który powinien być silnie zaangażowany w działalność B+R jest sektor szkolnictwa wyższego, do którego zalicza się (OECD, 2015, s. 36):

1) wszystkie uniwersytety, uczelnie techniczne i inne instytucje prowadzące formalne programy kształcenia na poziomie wyższym, bez względu na ich źródło finansowania i status prawny,

2) wszystkie instytuty badawcze, ośrodki, stacje doświadczalne i kliniki, które prowadzą działalność B+R pod bezpośrednią kontrolą lub zarządem instytucji szkolnictwa wyższego.

Przejawem takiego zaangażowania może być finansowanie badań i rozwoju. Rodzi się więc pytanie: jaki był udział wydatków na B+R w PKB ponoszonych przez sektor szkolnictwa wyższego w UE i w wybranych państwach członkowskich w analizowanym okresie? Wartości tego miernika przedstawiono w tabeli 4.

Okazuje się, że średnio w UE wydatki na B+R ponoszone przez sektor szkolnictwa wyższego kształtowały się na poziomie powyżej 0,4% PKB i miały raczej stabilny charakter w poszczególnych latach. Natomiast w poszczególnych państwach członkowskich udziały te wyraźnie różniły się, co pozwoliło na wyodrębnienie grupy państw o najwyższych udziałach, znacznie przekraczających średnie wyniki w UE, takich jak: Dania, Szwecja, Finlandia i Austria oraz grupy państw o najniższych udziałach, kształtujących się wyraźnie poniżej średniej dla UE, takich jak: Bułgaria, Rumunia, Luksemburg i Węgry. W pierwszej grupie państw szczególnie wyróżniała się Dania, gdzie w latach 2013, 2015 i 2017 wydatki sektora szkolnictwa wyższego na B+R przekroczyły 1% PKB. Takiego poziomu finansowania na zanotowano w żadnym z pozostałych państw członkowskich.

Wśród państw drugiej grupy najniższe wartości analizowanego miernika cechowały Bułgarię. Kształtowały się one na poziomie 0,04% w latach 2008 i 2017 oraz 0,07% w 2010 r. Niewiele lepsze wyniki w tym zakresie zanotowała Rumunia, zwłaszcza w 2017 r.

W Polsce sektor szkolnictwa wyższego przeznaczał na B+R od 0,2% PKB w 2008 r. do 0,34% PKB w 2017 r. Wyniki te były niższe od średnich dla UE o 0,22 pproc. w 2008 r., o 0,2 pproc. w 2010 r. o 0,22 pproc. w 2013 r., o 0,18 p. proc. w 2015 r. i o 0,12 pproc. w 2017 r. Pozytywną tendencją jest fakt stopniowego, aczkolwiek nieznacznego zwiększania się poziomu analizowanego miernika w kolejnych latach analizy.

Porównując średni poziom badanego miernika w UE z wynikami charakterystycznymi dla krajów przodujących należy zauważyć, że w UE, w porównaniu do USA, udział wydatków na B+R w PKB poniesionych przez sektor szkolnictwa wyższego był wyższy o 0,05 pproc. w 2008 r., o 0,07 pproc. w 2010 r., o 0,09 pproc. w 2013 r. i o 0,1 pproc. w 2015 r.

W rozważanym przekroju analizy średnie wartości analizowanego miernika w UE przewyższały też taki sam parametr charakteryzujący Japonię i Koreę Południową, co jest zjawiskiem korzystnym.

Udział wydatków na badania i rozwój w PKB poniesionych przez prywatne instytucje niekomercyjne

Instytucje niekomercyjne (non-profit institutions) to osoby prawne lub podmioty społeczne utworzone w celu wytwarzania wyrobów i usług, przy czym ich status nie pozwala na to, aby były one źródłem dochodu, zysku lub innych korzyści finansowych dla jednostek je zakładających, kontrolujących lub finansujących. Instytucje te mogą prowadzić produkcję rynkową lub nierynkową. W skład tego sektora wchodzą (OECD, 2015, s. 110):

1) wszystkie instytucje niekomercyjne działające na rzecz gospodarstw domowych, z wyjątkiem instytucji zaliczonych do sektora szkolnictwa wyższego;

2) gospodarstwa domowe i osoby prywatne zaangażowane w działalność rynkową lub nieuczestniczące w niej.

Przykładami jednostek zaliczanych do tego sektora mogą być niezależne stowarzyszenia zawodowe i naukowe oraz organizacje dobroczynne, które nie są kontrolowane przez jednostki należące do sektora rządowego lub sektora przedsiębiorstw.

Udział wydatków na B+R w PKB takich organizacji przedstawiono w tabeli 5.

Średnio w UE miernik ten kształtował się na poziomie 0,02% w latach 2008–2015. Jego wartość różniła się jednak w przekroju państw członkowskich. Największe wartości zanotowano na Cyprze od 0,04 w 2008 r. do 0,07 w 2015 i w 2017 r. We Włoszech udział wydatków na B+R w PKB w sektorze prywatnych instytucji niekomercyjnych utrzymywał się na poziomie 0,04%, by w 2017 r. obniżyć się do 0,02%. Miernik ten nieznacznie mniejsze wartości przyjmował w Wielkiej Brytanii i we Francji.

W grupie państw członkowskich były też takie, w których prywatne instytucje niekomercyjne zachowywały całkowitą bierność w finansowaniu badań i rozwoju. Do takich państw należały: Hiszpania, Rumunia, Słowacja i Polska. Natomiast w Słowenii tylko w 2017 r. organizacje te na B+R przeznaczyły 0,01% PKB.

Dla porównania w USA miernik ten utrzymywał się na poziomie 0,11% a w 2010 r. na poziomie 0,12%. W porównaniu ze średnimi wartościami w UE był on wyższy o 0,09 p. proc. w 2008 r., o 0,1 p. proc. w 2010 r., o 0,09 p. proc. w 2013 i 2015 r.

W Japonii prywatne instytucje niekomercyjne na B+R wydatkowały 0,05% PKB w 2008 r. i w 2010 r. oraz 0,04% w latach 2013 i 2015. O ile w Japonii udziały te nieznacznie zmalały to w Korei Południowej zanotowano nieznaczny ich wzrost od 0,04% w 2008 r. do 0,06% w 2010 i 2013 r. oraz do 0,07% w 2015 r.

Zakończenie

W publikacji podjęto próbę realizacji dwóch celów polegających na:

1) dokonaniu analizy i krytycznej oceny udziału wydatków na B+R w produkcie krajowym brutto (PKB), ponoszonych przez podmioty gospodarcze skupione w czterech sektorach (przedsiębiorstw, rządowym, szkolnictwa wyższego i prywatnych instytucji niekomercyjnych) oraz łącznie we wszystkich sektorach, traktowanych jako pośrednia miara stopnia aktywności kadry kierowniczej w kształtowanie polityki badawczorozwojowej na wszystkich szczeblach struktury zarządzania. Analizą objęto średnie wyniki notowane w UE, a także w wybranych krajach członkowskich (w tym w Polsce) oraz w wybranych krajach pozaeuropejskich.

2) weryfikacji tezy, że wydatki na B+R w poszczególnych państwach członkowskich są zmienne w czasie oraz zróżnicowane pod względem udziału w PKB i nie dają jednoznacznie pozytywnego obrazu dynamicznego wzrostu aktywności badawczo-rozwojowej w tych krajach.

Analiza krytyczno-poznawcza dostępnego materiału empirycznego potwierdziła powyższą tezę. Wartości liczbowe przyjętego miernika charakterystyczne dla UE, a także dla wybranych państw członkowskich o najwyższych i najniższych udziałach wydatków na badania i rozwój w PKB pozwalają uszeregować rozważane sektory od największej aktywności do najmniejszej. Na pierwszym miejscu znalazł się sektor przedsiębiorstw, następnie sektor szkolnictwa wyższego przed sektorem rządowym i sektorem prywatnych instytucji niekomercyjnych. W sektorze przedsiębiorstw średnie wartości miernika w UE miały wzrastający charakter, co jest zjawiskiem pozytywnym, sugerującym pewną racjonalność polityki B+R. Podobna sytuacja miała miejsce w Niemczech i w Austrii — wśród państw o najwyższych udziałach oraz na Litwie. Jednak w wielu krajach członkowskich udziały te zmieniały się nieregularnie pod względem wartości w poszczególnych latach np. od 2,8% w Niemczech do 0,14% na Łotwie (w 2017 r.); od 2,63% w Finlandii do 0,09% na Cyprze (w 2008 r.).

Pod względem udziału wydatków na B+R w PKB sektor przedsiębiorstw w Polsce cechował się niewielkim, aczkolwiek systematycznym wzrostem w kolejnych latach analizy (co jest zjawiskiem pozytywnym), jednak udziały te były znacznie niższe (grubo poniżej 1%) od średnich wyników dla UE, co plasuje Polskę w grupie państw maruderów, wyraźnie odstających od średniego poziomu w UE, zwłaszcza od państw przodujących.

Na drugim miejscu pod względem poziomu analizowanego miernika znalazł się sektor szkolnictwa wyższego, w którym udział wydatków na B+R w PKB (średnie wyniki dla UE) utrzymywał się na poziomie poniżej 0,5% i był w miarę stabilny w poszczególnych latach. Jednak w przekroju państw członkowskich analizowane udziały znacznie odbiegały od średnich wyników w UE zarówno w górę, jak i w dół. Przykładowo w 2008 r. w Danii wynosił on 0,75%, natomiast w Bułgarii tylko 0,04%; w 2017 r. w Danii kształtowały się na poziomie 1,01% a w Bułgarii na poziomie 0,04%, w Rumunii miał on wartość 0,05%.

Należy podkreślić, że w poszczególnych latach wartości tego miernika zmieniały się nieregularnie, nie posiadały jednoznacznie wzrostowych tendencji.

W Polsce sektor szkolnictwa wyższego charakteryzował się znacznie mniejszymi udziałami wydatków na B+R w PKB w porównaniu do średnich wyników w UE. Jednak pozytywnym zjawiskiem był ich wzrost od 0,2% w 2008 r. do 0,34% w 2017 r.

Na kolejnym miejscu pod względem udziału wydatków na B+R w PKB uplasował się sektor rządowy. Średnio w UE jego udziały nie przekraczały 0,25% i od 2013 r. miały malejącą tendencję.

W przekroju wybranych państw członkowskich udziały te były zróżnicowane co do wartości i w poszczególnych latach. Przykładowo, w Niemczech miernik ten utrzymywał się na poziomie nieco powyżej 0,4%, ale na Malcie nie przekraczał 0,1% z wyjątkiem 2015 r.

Najniższe i nieregularne wartości badanego miernika zanotowano w sektorze prywatnych instytucji niekomercyjnych. Średnio w UE utrzymywały się one na poziomie 0,02%, natomiast w krajach wyróżniających się od 0,03% do 0,07%. W takich krajach jak: Hiszpania, Rumunia i Słowacja prywatne instytucje niekomercyjne nie wydały na B+R żadnych środków.

Poziomy badanego miernika wskazują na istnienie luki między UE a USA, Japonią i Koreą Południową. Luka ta występuje zarówno w przekroju wszystkich sektorów działania (tabela 1), jak i w poszczególnych sektorach, tj. przedsiębiorstw, rządowym oraz prywatnych instytucji niekomercyjnych. Wyjątkiem jest sektor szkolnictwa wyższego, w którym udział wydatków na B+R w PKB średnio w UE był wyższy niż w USA, Japonii i Korei Południowej.

Zmienny i zróżnicowany w czasie poziom badanego miernika pozwala przypuszczać, że w państwach członkowskich UE nie wypracowano skutecznych instrumentów polityki B+R ukierunkowanej na racjonalne kreowanie wiedzy, która byłaby materializowana w innowacjach, zwłaszcza radykalnych (strategicznych), systemowo zaspokajających bieżące i przyszłe potrzeby klientów. Konstatacja ta dotyczy szczególnie państw cechujących się względnie niskimi udziałami wydatków na B+R w PKB, w tym również Polski. W państwach tych polityki rozwojowe bardziej ukierunkowane są na realizację zadań operacyjnych niż na kreowanie przyszłości. Przyczynami takiego stanu mogą być bariery: zewnętrzne, wewnętrzne, ekonomiczne, społeczne, kulturowe, organizacyjne, techniczne, mentalne itp. Zapewne wielu menedżerów w obawie przed ryzykiem towarzyszącym działalności B+R unika inwestowania w systemowy rozwój tej działalności traktowanej jako źródło wiedzy niezbędnej w kreowaniu innowacji, zwłaszcza radykalnych. Można przypuszczać, że jedną z takich barier jest brak umiejętności kształtowania polityki badawczo-rozwojowej zarówno na poziomie kraju, jak i regionu oraz podmiotu gospodarczego, koordynacji jej na wszystkich szczeblach zarządzania.

Do względnie niskiego poziomu prac B+R przyczyniają się błędy w zarządzaniu, przejawiające się słabą znajomością nowoczesnych metod zarządzania (w tym zarządzania wiedzą i innowacjami), dominacją w procesach decyzyjnych działalności operacyjnej, ograniczone zainteresowanie zarządzaniem strategicznym, niedocenianie wpływu kultury organizacyjnej (innowacyjnej) na wzrost zainteresowania pracowników i indywidualnych klientów tworzeniem wiedzy i jej wykorzystaniem w rozwiązywaniu pojawiających się problemów. Zarządzanie wiedzą należy traktować na równi z zarządzaniem zasobami ludzkimi i materialnymi organizacji, nie tylko jako dyskretną funkcję zarządzania, ale także jako wyjątkową umiejętność, ponieważ stanowi ono znaczący katalizator tworzenia innowacji i zawartej w nich wartości dla organizacji i dla klientów.

Zintegrowane zarządzanie wiedzą i innowacjami musi służyć usprawnianiu i wspieraniu procesów tworzenia i wdrażania innowacji, rozwoju tych procesów jako podstawowej kompetencji podmiotów gospodarczych (Gloet i Samson, 2019, s. 20). Znacznym ułatwieniem dla zarządzających działalnością B+R i innowacyjną może być postępowanie zgodne z wybranymi modelami innowacji, bowiem każdy z nich oparty jest na ścisłym związku B+R z działalnością innowacyjną. Modele innowacji stanowią grupę zasad, przepisów, procedur i praktyk, racjonalizujących procesy innowacji (Barbieri i Alvares, 2016, s. 116).

W kontekście względnie niskich i zróżnicowanych wydatków na B+R zasadne byłoby skoncentrowanie się menedżerów na systemowym postępowaniu zgodnym z założeniami czwartej generacji metod zarządzania działalnością B+R. Istotą tej koncepcji jest racjonalna koordynacja strukturalnych i procesowych aspektów tej działalności realizowanej wewnątrz podmiotu gospodarczego z organizacjami zewnętrznymi. W ten sposób powstaje badawcza struktura sieciowa wspomagana systemem informatycznym, racjonalnie wykorzystująca zasoby osobowe, organizacyjne, techniczne i finansowe. W konsekwencji powstają elastyczne struktury składające się z jednostek badawczo-rozwojowych funkcjonujących w strukturach różnych podmiotów gospodarczych. Jednostki te, dzięki posiadanym zasobom intelektualnym, metodycznie rozwiązują pojawiające się problemy, wymieniają się danymi, informacjami oraz wiedzą o wynikach prowadzonych prac, umieszczając je we wspólnych bazach danych. Powstałe w ten sposób struktury nazywane są niekiedy wirtualnymi strukturami B+R. Praca w takich strukturach może przebiegać według dwóch koncepcji polegających na (Baruk, 2009, s. 62–67):

1) przydzielaniu zadań do wykonania poszczególnym partnerom zlokalizowanym niekiedy w różnych krajach, w różnych strefach geograficznych, według modułowej struktury produktu, co oznacza, że określona jednostka odpowiada za opracowanie określonego modułu we wszystkich fazach jego rozwoju,

2) przydzielaniu zadań do wykonania poszczególnym partnerom według fazy cyklu prac B+R. W konsekwencji takiego rozdziału zadań każda organizacja należąca do sieci odpowiada za realizację innej fazy procesu B+R (np. opracowanie: koncepcji, projektu, prototypu, przeprowadzenie prób i badań itp.).

W obu przypadkach sprawność działania uwarunkowana jest zachowaniem interaktywnej komunikacji między uczestnikami procesów B+R, zapewnianej przez systemy informatyczne.

Wydaje się, że słabością dotychczas stosowanych polityk w zakresie B+R jest niewystarczające ich ukierunkowanie znajomością podstawowych relacji, takich jak:

1) produkt — technologia,

2) produkt — rynek,

i wynikających z nich strategii działalności B+R. Szczególną rolę należy przypisać strategiom ofensywnym, typowym dla wysokiej atrakcyjności rynku i wysokiej pozycji konkurencyjnych podmiotu gospodarczego.

Z uwagi na wysokie koszty prac B+R, często przekraczające możliwości finansowe pojedynczych podmiotów gospodarczych, zasadne jest ukierunkowanie polityki B+R na współpracę wielu instytucji dysponujących odpowiednimi zasobami, zwłaszcza kadrowymi (głównie w zakresie badań podstawowych i stosowanych), których brakuje w wielu przedsiębiorstwach. Zasadne jest też, w większym niż dotychczas stopniu, wspomaganie działalności B+R racjonalną polityką B+R rządu obejmującą: opracowanie rozwiązań regulacyjnych, inicjowanie programów B+R, szkoleniowych, kształtowanie infrastruktury sprzyjającej działalności B+R, kultury B+R i innowacyjnej, finansowanie lub współfinansowanie prac B+R itp.

Bibliografia

1. Barbieri, J. C., Alvares, A. C. T. (2016). Sixth generation innovation model: description of a success model. Innovation & Management Review, Vol. 13, No. 2, (116).
2. Baruk, J. (2009). Zarządzanie wiedzą i innowacjami. Toruń: Wydawnictwo Adam Marszałek w Toruniu.
3. Baruk, J. (2018). Wybrane aspekty innowacyjności przedsiębiorstw funkcjonujących w UE. Kwartalnik Nauk o Przedsiębiorstwie, nr 1, (88).
4. Baruk, J. (2016). Miejsce działalności badawczo-rozwojowej w polityce rozwojowej przedsiębiorstw. Marketing Instytucji Naukowych i Badawczych, 20 (2), (61).
5. Bogers, M. (2011). The open innovation paradox: knowledge sharing and protection in R&D collaborations. European Journal of Innovation Management, Vol. 14, No. 1, (94).
6. Chen, X., Xia, Y., Yang, J. (2018). Analysis on the Impact of Government-Enterprise Cooperation on Technological Innovation and its Economic Consequences. Business and Management Studies, Vol. 4, No. 4, (39).
7. Deloitte (2016). Badania i rozwój w przedsiębiorstwach 2016. Deloitte. (10).
8. Gloet, M., Samson, D. (2019). Knowledge and Innovation Management: Creating Value. Effective Knowledge Management Systems in Modern Society. IGI Global. Chapter 2, (20).
9. Griffin, R. W. (2007). Podstawy zarządzania organizacjami. Warszawa: Wydawnictwo Naukowe PWE.
10. GUS (2019). Nauka i Technika w 2017 r. Warszawa, Szczecin: Główny Urząd Statystyczny.
11. Li, W., Zhang, Y., Wei, Y. (2018). Management Capabilities and Corporate Environmental Performance: The Moderating Role of Top Management Team Faultlines. Science Journal of Business and Management, Vol. 6 No. 1, (22).
12. OECD (2015). Podręcznik Frascati 2015. Zalecenia dotyczące pozyskiwania i prezentowania danych z zakresu działalności badawczej i rozwojowej. Warszawa: GUS.
13. OECD (2015). Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development. OECD. 2018 Statistics Poland for this Polish editio.
14. Salisu, Y., Abu Bakar, L. J. (2019). Technological Capability, Innovativeness and the Performance of Manufacturing Small and Medium Enterprises (SMEs) in Developing Economies of Africa. Journal of Business and Management, Vol. 21, No. 1, (58).
15. Strategia „Europa 2020” (2015). Ministerstwo Gospodarki. Warszawa http://www.mg.gov.pl/Bezpieczeństwo+gospodarcze/Strategia+Europa+2020 (dostęp z dnia 09.10.2015).
16. Wang, T., Chen, M. (2017). Perceiving Organisational Culture Influence on Knowledge Management Performance. Science Journal of Business and Management, Vol. 5 No. 3, (96).
17. Xie, Z., Hall, J., McCarthy, I. P., Skitmore, M., Shen L. (2016). Standardization efforts: The relationship between knowledge dimensions, search processes and innovation outcomes. Technovation, Vol. 48–49.
18. https://ec.eurostat/tgm/printTable. (dostęp z dnia 31.12.2018 r.).