The dynamic deployment of solutions based on Artificial Intelligence (AI) across global digital markets has opened up a new stage in the evolution of trade and consumer behavior (UNCTAD, 2024, p. 3). Contemporary purchasing decisions are increasingly being shaped by digital tools, often promoted as employing “Artificial Intelligence” to optimize the decision-making process from the consumer’s perspective (Paterson, 2022, p. 558).

Following Warszycki (2019, p. 115), AI may be understood as “a field of science encompassing disciplines, methods, tools, and techniques aimed at creating and developing a complete computer program that accurately reflects the model of human functioning and the human mind.” It has become an integral part of the modern consumer market, applied in both front-office processes (interfacing with consumers, clients, and supervisory bodies) and back-office processes (supporting the internal functioning of companies and institutions) (Keller et al., 2024, p. 417).

In consumer-facing applications, AI systems recommend products inferred from users’ preferences and histories, perform automated credit assessments, and provide customer support via virtual assistants (chatbots), among other functions (Myszakowska-Kaczała, 2024). On the operational side, companies are increasingly using AI-based analytics to understand consumer behavior, optimize pricing strategies, and improve supply chain management (GlobeNewswire, 2025).

Although the use of AI in customer service is often considered a hallmark of modern technological implementation, Artificial Intelligence itself is not a twenty-first-century innovation. Most technology historians trace the origins of the concept to the work of the British mathematician and cryptanalyst Alan Turing, who formulated its theoretical foundations in 1950 (Accenture, 2024, p. 8). Nevertheless, the dynamic development of AI was not widely recognized until 2011, when global technology companies such as Google, Facebook, Microsoft, and IBM began using it for business purposes (Ness et al., 2024, p. 1064).

From the perspective of the Polish AI landscape, 2023 marked a turning point, with 88% of respondents declaring familiarity with the term sztuczna inteligencja (“artificial intelligence”) – with this figure rising to 96% among individuals aged 18 to 24 (Digital Poland, 2023, p. 57). It is also notable that the jury of the Polish Language Council declared this term the Polish “Word of the Year” in 2023 (Kruszyńska, 2024).
This coincided with the rapid rise of ChatGPT, an AI–based application that achieved unprecedented global recognition. Between late 2022 and early 2023, the platform attracted approximately 100 million users (mp/dap, TVN24.pl, 2023). The scale and pace of its user growth may position ChatGPT as the fastest-growing consumer-facing web application to date (The Guardian, 2023). Its widespread adoption spurred the creation of numerous derivative solutions tailored to the needs of specific industries, including the banking sector (Capgemini, 2024, p. 44).

In 2025, the global AI market was valued at USD 757.58 billion, with forecasts projecting growth to approximately USD 3,680.4 billion by 2034 (Precedence Research, 2025). Within the global banking sector alone, AI is estimated to generate up to USD 1 trillion in additional value annually (Biswas et al., 2020, pp. 2–3).
The expanding use of AI in consumer services brings not only financial gains but also a range of other benefits – from mitigating risks associated with human error and improving service accessibility, to process automation that enhances efficiency and speeds up customer service. However, the adoption of AI-based tools by market entities also introduces new risks for consumers. The decision-making processes of AI algorithms may be opaque or difficult for the average client to comprehend (Ahn et al., 2024), which can hinder their ability to assess whether a system is operating correctly.

The opacity of AI systems, combined with their capacity to exploit biases and generate unintended side effects, has intensified debates on the need for responsible governance of AI technologies (Cheong, 2024, p. 2). A key challenge, therefore, lies in guaranteeing the effective protection of consumer rights when decisions affecting individuals are being made by algorithms, as well as in determining which parties bear responsibility in cases of algorithmic error or either unintentional or deliberate misuse.

This article seeks to address the following research question: Do Polish and EU legal acts, together with institutional oversight, provide consumers with adequate protection against the negative consequences of decisions made by AI systems, and are there legal gaps in this area? The approach taken is descriptive and analytical, based on selected legal acts (including the Act on Competition and Consumer Protection and the AI Act), relevant academic literature, and selected legal opinions. These sources form the basis for further, more detailed research on the topic.

The choice of a qualitative descriptive analysis stems from its suitability for examining phenomena within their real-world context – in this case, the institutional and regulatory environment. Its purpose is to capture ongoing processes, identify the actors involved, and situate them within their operational conditions. While serving as a starting point for more advanced analyses, this approach itself constitutes a valuable and independent methodological framework (Sandelowski, 2000, p. 339). It involves the following stages (Villamin et al., 2024, pp. 51–91):

• defining the research objective (application-oriented),
• determining the research method (descriptive analysis),
• establishing the theoretical framework (accountability for algorithmic decisions in the context of legal frameworks and institutional oversight),
• selecting the research sample (domestic and international literature, legal provisions, and opinions of Polish legal scholars),
• collecting data (reviewing available sources),
• analyzing data (evaluating sources in light of the research objective), and
• presenting the research findings.

The outcomes of this analysis are threefold: (i) a presentation of the current regulatory framework governing responsibility for AI-mediated decisions affecting consumers; (ii) the identification of potential gaps within the existing system of consumer protection; and (iii) the formulation of recommendations aimed at addressing these gaps in the Polish legal system, alongside proposals for new regulatory measures to strengthen consumer safeguards against the adverse consequences of AI-driven decision-making.

2. The use of AI – benefits and risks

Artificial Intelligence is now being applied across nearly all areas of human activity. It is already assisting the work of both teachers and students, including in schools and even in early childhood education (Iron Mountain, 2025). AI can automatically perform tasks such as grading tests and homework assignments or generating reports on student progress (Stecyk, 2025). Higher education institutions are also increasingly utilizing AI algorithms to enhance the efficiency of administrative and academic work. One example is the use of autonomous AI agents that assist in creating professional academic presentations based on an outline (Stecyk, 2025). AI can likewise improve communication processes within universities – for example, through the implementation of “intelligent” dean’s offices or automated student admissions systems. A student wishing to access publicly available university knowledge and documentation in real time needs only one condition to be met: access to the Internet (KALASOFT, n.d.).

It should be emphasized that in the context of higher education, where the student may be regarded as a client or consumer of educational services (Sojkin et al., 2012, pp. 565, 567), the use of Artificial Intelligence entails risks analogous to those observed in other sectors of digital services, particularly regarding data protection, algorithmic transparency, and the right to reliable information. Theoretically, information generated by software based on AI algorithms should be factually accurate. In practice, however, AI systems may rely on unreliable or outdated sources, creating a risk that users receive incorrect or misleading information.

Another risk associated with the use of Artificial Intelligence in higher education concerns the protection of student data collected by institutions employing AI tools, as well as the potential dehumanization of the educational process – where human interaction is diminished and the lecturer’s role shifts away from that of a mentor, becoming instead a mere supervisor of AI-driven systems (Kornaś, 2024).

An argument in favor of limiting the use of Artificial Intelligence in education is that decisions made without human intervention may result in the absence of a clearly identifiable responsible entity, as well as a lack of transparency regarding how such decisions are made (PARP Grupa PFR, 2023, p. 29). Insufficient oversight of these processes may, in turn, result in different types of misuse or abuse, potentially harming the interests of those affected (Iron Mountain, 2025). Table 1 presents examples of AI applications in the consumer market, along with their associated potential benefits and risks. 

The examples of Artificial Intelligence applications presented in Table 1 illustrate the dual nature of AI’s impact on the consumer market. On the one hand, algorithms can enhance convenience, accessibility, and service efficiency, reduce operating costs, and minimize human error. On the other, AI-related risks include a lack of transparency in decision-making processes, potential discrimination, and incorrect decisions that may result in harm to the consumer. AI-powered tools may not only pose a threat to customer privacy but also increase the risk of consumers falling victim to deceptive or unfair market practices or even financial exclusion in cases where an insurer, on the basis of an AIgenerated analysis, determines that a given consumer represents too great a risk of potential payout (BEUC, 2021, p. 35).

An example of potential gender-based discrimination by an AI algorithm was the 2019 case in the United States involving the credit limit determination process for the Apple Card, issued jointly by Apple and Goldman Sachs. Customers observed that the algorithm responsible for assigning credit limits granted significantly higher limits to men than to women with comparable financial situations. One potential applicant reported that his credit limit was 20 times higher than that of his wife, even though they shared joint marital property and, in his view, her credit history was even better than his. Following the publication of this report, other couples also began to confirm such disparities, sharing examples suggesting that the algorithm favored men. The case attracted the attention of the New York Department of Financial Services, which launched an investigation to determine whether anti-discrimination laws had been violated in this instance (The Guardian, 2019), but it ultimately concluded that there was no discrimination against customers based on gender (Campbell, 2021).

The Apple Card case demonstrated, however, that a lack of algorithmic transparency can lead to public controversy. Customers did not receive a clear explanation as to why the decisions varied so significantly between genders. Being unable to understand the automated decision-making process led some users to perceive the differences in credit limits as negative gender discrimination, even though closer scrutiny showed that no such discrimination had actually occurred. A positive takeaway from this example is that regulators are prepared to intervene, treating the use of AI like any other credit procedure subject to the law.

It should be noted, however, that despite incidents raising concerns about the impartiality of AI-based solutions, there is also evidence suggesting that consumers perceive such systems as more objective than human-driven processes. The rationale in this context is the perceived absence of bias and emotions in AI decision-making (Nogueira et al., 2025, p. 2).

Another type of potential incident involving the use of AI tools and consumers could be having a chatbot incorrectly dismiss a complaint. This might occur, for example, if the chatbot misinterprets an image submitted by the customer and wrongly concludes that a product defect was caused by user error. Another possible example, negative from the customer’s perspective, would involve the misclassification of a complaint into the wrong category. In both cases, one of the possible consequences is the expiration of the statutory 14-day period for responding to a consumer complaint, which, under Polish consumer law, results in the complaint being deemed accepted by default (Polish Consumer Rights Act, Article 7a). A consumer’s lack of awareness of, or failure to invoke the above legal provision could result in their not receiving appropriate support in such a case, due to the algorithm’s improper functioning.

The next section of this article will examine the extent to which current Polish regulations address the challenges outlined above and what changes may be necessary to ensure that consumer rights are effectively protected in the era of widespread algorithmic use in the consumer market. This is a highly important issue, as the number of incidents involving AI systems is increasing alongside the growing adoption of Artificial Intelligence. Between 2022 and 2023 alone, the number of such incidents rose by approximately 1278% (OECD, n.d.).

3. Legal regulations and institutional oversight as pillars of accountability for algorithmic decisions impacting consumers

3.1. The current legal framework for consumer protection in relation to AI

Given the risks associated with the practical use of Artificial Intelligence, it is often perceived as a source of threats to individual rights (Contissa et al., 2018, p. 11). The noticeable rise in technological sophistication and the emergence of new risks have led regulatory bodies to recognize the necessity of legislative action in this domain (Lagioia et al., 2022, p. 482). Artificial Intelligence poses new and complex challenges to both consumers and the system of consumer law – challenges that existing regulatory mechanisms are not always capable of addressing effectively (Terryn & Martos Marquez, 2025, p. 210).

Based on the analysis of the current legal framework, it can be indicated that there is no comprehensive legal act that specifically addresses the use of Artificial Intelligence in the consumer context. Nevertheless, existing legal provisions offer a certain degree of protection to consumers against the negative consequences of decisions made by algorithms. These include data protection regulations and consumer protection laws (Table 2).

Moreover, successive parts of the relatively new EU Artificial Intelligence Regulation (AI Act) are now gradually entering into force. The aim of the regulation is “to improve the functioning of the internal market by laying down a uniform legal framework, in particular for the development, placing on the market, putting into service and use of Artificial Intelligence systems (…) to promote the uptake of human-centric and trustworthy Artificial Intelligence (…) and to support innovation” (Regulation (EU) 2024/1689 of the European Parliament and of the Council – The Artificial Intelligence Act). Although the AI Act will fully apply as of 2 August 2026, the provisions of Chapters I and II are already binding and should be applied now (AI Act, art. 113).

Despite the fact that the AI Act includes several significant provisions from a consumer protection standpoint, such as the prohibition of social scoring and the right to file a complaint with a market surveillance authority if an AI system is believed to violate the regulation, European consumer advocacy groups have raised concerns about legal gaps that fail to fully address the risks consumers are exposed to in the context of AI deployment. According to these organizations, the AI Act is not capable of fully eliminating the risks associated with the use of AI tools in consumer interactions. In their view, the regulation focuses primarily on high-risk systems, while many widespread applications of AI, such as the use of chatbots, fall outside its scope (BEUC, 2023).

Such a situation may lead to the emergence of national legislative solutions addressing selected risks associated with the use of AI, which in turn could result in the fragmentation of legal provisions and hinder the assurance of a uniform level of protection for European Union citizens with respect to the same technological products and services (Bertolini, 2025, pp. 9–10).

Referring back to the earlier example of potential gender discrimination in the Apple Card credit approval process, it is worth noting that, under EU law, a consumer in a similar situation could rely on Article 22 of the General Data Protection Regulation (GDPR). This provision entitles the data subject, whether a potential or actual client, to request clarification regarding the logic behind the algorithmic decision on their credit limit, and to demand a reassessment of the outcome by a human decision-maker.

Additionally, the European Union has in place anti-discrimination regulations – such as Directive 2004/113/EC of 13 December 2004, implementing the principle of equal treatment between men and women in the access to and supply of goods and services. Moreover, if such an incident were to occur in Poland, an entity that actually employed a discriminatory algorithm could face sanctions from the Office of Competition and Consumer Protection (UOKiK), as its actions may constitute a violation of collective consumer interests (Polish Act on Competition and Consumer Protection, Article 24). The activities of this Office will be discussed in more detail in the following sections of this article.

Similarly, in scenarios involving potentially incorrect decisions issued by an AI-driven complaint resolution system, or where a university student receives inaccurate information from an “intelligent” dean’s office, current legal frameworks would regard such instances as the equivalent of human error. Ultimately, responsibility for the functioning and consequences of AI systems rests with the individual or institution that has introduced and operates them (Paprocki, 2025).

The consumer submitting a complaint would retain the right to exercise their entitlement (e.g., to repair or replacement of the product) (Polish Consumer Rights Act, Article 43d). The consumer could also notify the UOKiK, which would assess whether the company had violated the collective interests of consumers (Polish Competition and Consumer Protection Act, Article 24). In cases where complaint processing is delegated to a malfunctioning algorithm, the UOKiK has begun examining such situations and emphasizes that the use of AI does not relieve businesses of their responsibility to review consumer complaints in a fair and timely manner (Infor.pl, 2023).

However, for a student who received incorrect information via an AI system, pursuing legal remedies in response to the negative consequences of such inadequate support may prove to be a significant challenge. Legal provisions do not always recognize a student as a consumer eligible for protection under all the legal acts listed in Table 2. However, if a student were to enter into an agreement based on incorrect information provided by a chatbot, the issue of determining liability for being misled by AI could have a valid legal basis (Warchoł-Lewucka, 2024). In the case of an incorrect response provided by a “smart” dean’s office – regarding, for instance, the current class schedule – the consequences of a student’s absence from mandatory classes held on a date not indicated by the chatbot would likely be borne solely by the student.

3.2. Regulatory and supervisory institutions and their role

Since the broad application of AI in areas such as the consumer market is a relatively new phenomenon, the institutional structure aimed at protecting consumers from AIrelated risks is still evolving. Additionally, the complexity of AI use cases necessitates coordination and cooperation among the various regulatory and supervisory authorities.

In the Polish legal system, the Office of Competition and Consumer Protection (UOKiK), established in 1990, serves as the main institution responsible for safeguarding consumer rights (UOKiK, n.d.). While no existing legal act explicitly names the UOKiK as the principal supervisory authority overseeing the impact of Artificial Intelligence on the consumer market, the Office has been actively engaged in addressing issues related to the use of algorithms in consumer-facing processes. Despite the lack of explicit regulatory designation, the UOKiK actively monitors and engages with developments concerning the application of algorithms in consumer interactions. Its current activities include assessments of chatbot functionality in the telecommunications market and in ecommerce services – most notably in food delivery apps and online marketplaces (Infor.pl, 2023).

The UOKiK is also striving to harness AI to enhance consumer protection on the Polish market. An example of this effort is the implementation of the project entitled “Detection and elimination of dark patterns using Artificial Intelligence,” which aims to develop an AI-based tool capable of identifying unfair uses of so-called dark patterns on commercial websites (UOKiK, 2024). These are user-interface designs intentionally created to mislead consumers, hinder the expression of genuine preferences, or manipulate users into taking predetermined actions. Such practices are intended to pressure consumers into making purchases they do not truly desire, or to manipulate them into revealing personal information they would not voluntarily provide in a more transparent context (Luguri & Strahilevitz, 2021, p. 43).

It can be assumed that in the near future, the scope of UOKiK’s activities and responsibilities related to the use of Artificial Intelligence in the consumer market will continue to expand. It is likely that the authority will gradually acquire additional statutory powers aimed at enhancing the effectiveness of its supervisory activities in this area.

An additional authority involved in addressing the use of Artificial Intelligence with respect to personal data protection in Poland is the Personal Data Protection Office (UODO). Its counterpart at the EU level is the European Data Protection Board (EDPB), which coordinates data protection policies across member states.

The President of the UODO is the “competent authority for personal data protection” (Polish Personal Data Protection Act, Article 34(1)), with tasks including monitoring and enforcing the provisions of the GDPR, as well as promoting public awareness and understanding of the risks, rules, safeguards, and rights related to data processing (GDPR, Article 57(1)(a) and (b)).

In the context of Artificial Intelligence, the Personal Data Protection Office (UODO), examines the impact of AI on individuals’ privacy and the protection of their personal data (UODO, n.d.). The UODO is authorized, among other things, to impose administrative fines for violations of the GDPR, including the aforementioned Article 22 (e.g., failure to provide human verification of automated data processing in cases where the decision produces legal effects for the consumer).

Among the responsibilities of the European Data Protection Board (EDPB, or EROD) is providing guidance to the European Commission on issues concerning data protection – particularly with regard to proposed amendments to the GDPR and broader legislative initiatives within the EU (EDPB, n.d.). Notably, at its inaugural plenary meeting in 2018, the EDPB adopted guidelines addressing automated decision-making and profiling (EDPB, 2018).

At the EU level, the European Artificial Intelligence Board was established to oversee the proper implementation of the AI Act (European Commission). Moreover, the European Data Protection Supervisor (EDPS) plays a key role in ensuring that all EU institutions and bodies respect citizens’ privacy rights during personal data processing. The EDPS is also responsible for tracking the development of emerging technologies that may impact data protection and for carrying out investigations into relevant matters falling within its jurisdiction (european-union.europa.eu). Accordingly, it may be concluded that the enforcement of legal standards regarding the protection of Polish consumers’ personal data and the appropriate use of AI-assisted tools involves multiple institutions operating at both the national and European levels. 

Determining which body is responsible in a specific case should depend exclusively on the type of suspected violation (Table 3).

Table 3. Comparison of the scope of responsibilities of Polish institutions overseeing the consumer market.

However, due to the fast-paced development of Artificial Intelligence in an evergrowing range of consumer-facing applications, it is highly probable that not all risks stemming from AI usage are adequately addressed in existing legal frameworks, and that responsibility for such risks may not fall solely within the remit of a single regulatory body. A relevant example would be a chatbot’s improper handling of a consumer complaint, accompanied by a breach of personal data protection regulations – particularly involving sensitive data. In such circumstances, the case would require joint consideration by at least two competent authorities, such as the UOKiK and UODO.

Thus, it is crucial to ensure not only the constant oversight of emerging AI-related risks and the ongoing adjustment of relevant legislation and institutional responsibilities, but also effective interdisciplinary collaboration between the entities tasked with safeguarding consumer rights.

4. Responsibility for algorithmic decision-making

When analyzing the risks associated with the use of Artificial Intelligence in consumer services, it is essential to consider the issue of responsibility for erroneous decisions made by algorithms. AI itself does not possess legal personality and therefore cannot be held directly accountable (Bączyk-Rozwadowska, 2022, p. 9). Responsibility may lie solely with a natural or legal person who exercises control over the operation and deployment of AIdriven systems (Kulicki, 2025). As one analyst has put it, “In principle, liability for errors stemming from the system’s architecture or software should rest with the manufacturer, whereas responsibility for misuse of the system lies with the end user” (Trzaska, 2024). However, given that there is currently no specific legal act that directly assigns responsibility for damages caused by Artificial Intelligence, it remains challenging to clearly designate a natural or legal person as directly liable for errors resulting from AI operations (Trzaska, 2024).

The existing academic literature offers a range of proposals concerning the entity that could be considered “responsible” for decisions made by AI systems: ranging from the software developer who implemented faulty algorithms (programistajava.pl, 2025), through the system operator or controller (Kaniewski & Kowacz, 2023), to the end user (Infinity Insurance Brokers, n.d.), which may be responsible for the proper use of artificial intelligence systems (Buiten, 2024, pp. 256–257).

Certain authors suggest a model in which responsibility is distributed among various groups of stakeholders (programistajava.pl, 2025). Meanwhile, other sources highlight the possibility that, given the considerable complexity of the AI value chain, it may not always be possible to clearly identify the entity responsible for a specific error (Jelińska-Sabatowska, 2025). In many AI-driven processes involved in the provision of products and services, multiple entities participate (Buiten et al., 2023, p. 11). Legal counsels also point to a new type of risk associated with the use of AI – namely, the risk of a “liability gap” (Nogacki, 2024).

The challenge of assigning liability for the outcomes of Artificial Intelligence stems from factors including the following (Nogacki, 2025):

• autonomy – AI systems make decisions without human oversight,

• opacity – the AI decision-making process may be difficult to understand,

• data dependency – flawed data can lead AI to make erroneous decisions,

• value chain complexity – the development and implementation of AI involves multiple entities.

Nevertheless, the most frequently cited example of a party considered responsible for decisions made by AI is the entrepreneur who implements an AI-based process within their organization. As such, they must take into account the possibility of incurring contractual liability in the event that damage is caused by Artificial Intelligence – such as when an error results in the failure to fulfill a contract concluded with a business partner (Tak Prawnik, 2025). They may also face tort liability, for example in the case of an accident caused by an autonomous vehicle (Kaniewski et al., 2023). However, some sources argue that the previously mentioned “opacity” of AI decision-making undermines the application of standard principles of tort liability (Nogacki, 2025).

Apart from the legal challenge of clearly identifying the entity liable for damage caused by Artificial Intelligence, another significant obstacle is the difficulty in proving the “fault” of the AI system itself. To do so, the consumer – or their legal representative – must gain access to and understand how the AI tool functions, which may require insight into complex and often non-transparent decision-making processes. In practice, however, this may prove difficult or even impossible. Among other factors, this is due to the so-called “black box problem” (Taveira da Fonseca et al., 2024, p. 300) – that is, the system’s recommendations may not be explainable within the framework of traditional linear cause-and-effect logic (Kroplewski, 2023, p. 112).

An additional risk for banking customers related to the use of Artificial Intelligence is the potential overdependence on AI systems in decision-making, predictive analytics, and recommendation processes. Even if a human remains the final decision-maker, they may defer too strongly to the suggestions provided by AI – perceiving them as inherently correct or derived from deep and reliable analysis (Szostek et al., 2022, p. 55). In practice, however, there may be uncertainty as to whether the data used by automated models is of adequate quality, which may result, for example, in an inaccurate assessment of a customer’s creditworthiness (Szostek et al., 2022, p. 26). In such circumstances, the harmed consumer may face significant challenges in demonstrating that the unfair treatment resulted from the actions of both the AI system and the bank’s staff.

In the context of seeking redress against an erroneous AI-generated decision, the consumer must first be aware that such an irregularity has occurred. The literature on accountability for AI-driven decisions highlights the so-called “information gap,” whereby an individual may not realize that their adverse situation results from the actions of Artificial Intelligence (Ziosi et al., 2023, p. 9). What is crucial, therefore, is not only the existence of legal provisions designed to prevent the effects of erroneous AI decisions, but also the consumer’s own awareness of the protections available under the relevant legal framework.

5. Conclusions and recommendations

While the application of Artificial Intelligence in the consumer market brings various advantages – such as personalized product offerings – it also entails significant risks. These include the reliance of algorithms on outdated or biased data, which may result in the unequal treatment of certain customer groups.

Additionally, consumers’ inability to logically explain how algorithms operate may also lead to their misinterpretation of AI-generated decisions, as exemplified by the case concerning the determination of credit limits in the Apple Card program.

Although existing legislation ensures a certain level of protection for consumers against the risks posed by Artificial Intelligence – such as the right to human oversight and the prohibition of discriminatory practices – there are still notable legal gaps. In particular, the opacity of AI decision-making processes creates challenges in proving errors and seeking redress. The lack of algorithmic explainability may also result in consumers misinterpreting automated decisions, as exemplified by the Apple Card case referenced earlier, in which the credit limit allocation raised concerns about fairness and transparency.

One of the legal gaps identified in the article concerns the question of who should be held accountable for decisions made by Artificial Intelligence. Since AI does not have legal personality, it cannot itself bear responsibility for erroneous algorithmic decisions, and no existing provision in either Polish or EU legislation explicitly designates the entity liable for the malfunction of AI systems. In the scholarly literature, the entity most frequently identified as “responsible” is the provider making the AI-based solution available to consumers. However, responsibility for the harms caused by Artificial Intelligence is also sometimes attributed to the software developers whose algorithms prove faulty, as well as to end users.

In summary, the answer to the research question posed in this article is as follows: Polish and EU legal acts, together with institutional oversight, provide consumers with protection against the negative consequences of decisions made by AI systems. However, this protection does not extend to the full spectrum of potential risks arising from the use of Artificial Intelligence in consumer markets. Legal gaps remain in this area, and the introduction of new legislation that keeps pace with the ongoing development of AI capabilities represents a major regulatory challenge, making the complete elimination of such gaps difficult – if not impossible – in the foreseeable future.

As the use of Artificial Intelligence becomes more widespread, the frequency of incidents involving AI systems continues to rise. Regulatory bodies at both the European and national levels, along with consumer protection authorities, are still building the expertise and acquiring the instruments required to monitor and control AI effectively. This transitional phase contributes to the persistence of certain regulatory blind spots and legal uncertainties. To enhance consumer protection in a market environment where an ever-growing number of processes are supported by Artificial Intelligence – systems that may still be prone to error – it is crucial to implement reforms across legislative, institutional, and educational spheres.

With regard to recommendations, priority should be given to measures designed to address the identified shortcomings in the Polish legal system and to strengthen safeguards for consumers affected by AI-driven decision-making, such as the following:

• Clarifying legal liability for individual entities involved in the development, provision, and use of AI – for example, by introducing a provision into the Polish Competition and Consumer Protection Act stating that liability for errors made by Artificial Intelligence rests with the entity that makes the AI-based tool available to consumers, or with another entity explicitly designated by that provider in the applicable terms and conditions.

• Introducing a legal provision that facilitates the burden of proof for consumers in disputes concerning the malfunctioning of Artificial Intelligence – given that proving an AI-related error is often difficult or even impossible for the average consumer, a reasonable solution would be to shift the burden of proof to the entity providing the AI-based tool to consumers (or to another entity explicitly designated in the relevant terms and conditions). In the event of a dispute, this entity would be required to demonstrate that the AI system did not make an error; otherwise, the case would be resolved in favor of the consumer.

• Requiring algorithmic transparency – consumers should have the right to understand the logic behind decisions made by Artificial Intelligence that affect them personally; for example, by being granted access to terms and conditions that include information about the characteristics or factors the AI takes into account when making specific decisions

• Establishing a statutory definition of the competences of supervisory authorities – for example, a dedicated department could be established within Poland’s Office of Competition and Consumer Protection (UOKiK), staffed with experts in artificial intelligence systems, tasked with analyzing cases in the consumer market suspected of involving faulty operation of AI-based systems.

• Promoting consumer education on AI – through initiatives aimed at increasing consumer awareness of the risks associated with artificial intelligence, as well as of the rights they have with regard to protection against such risks.

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Systematic literature reviews (SLR) shape scholarship in many disciplines, functioning as a rigorous method for synthesizing existing primary research. They are particularly important in fields such as the health sciences and management, where the proliferation of publications entails a need for more effective and dependable methods to condense vast bodies of information into practical insights (Tantawy et al., 2023; Tsafnat et al., 2013, 2014; Tranfield et al., 2003). The introduction of artificial intelligence (AI) into the SLR process promises to transform and greatly enhance its efficiency and accuracy through automation – especially in repetitive and time-consuming tasks, such as data extraction and synthesis (Clark et al., 2020; Lau, 2019).

The use of AI in SLRs represents more than just a technological advancement; it signifies a shift in the researcher’s role from a traditional examiner of literature to a manager of research processes. In process management, the manager plans, organizes, coordinates, and controls the work (Sommerville et al., 2010), whereas the employees execute the assigned tasks. Transferring this logic to the process of creating a systematic literature review, the researcher, acting as manager, can plan that process, organize the work, coordinate the use of AI applications, and monitor their effects on the outcomes. The AI algorithms carry out the instructions provided by the manager. The whole process remains grounded in the established methodological logic of systematic literature reviews (see Denyer & Tranfield, 2009; Vrontis & Christofi, 2021).

This shift brings both new opportunities and challenges that are redefining the academic research landscape (Vrontis & Christofi, 2021; Wagner et al., 2022). AI tools can quickly become collaborative partners, enabling complex analyses that extend beyond simple automation, even supporting the generation of novel research questions and hypotheses (Saeidnia et al., 2024).

In this paper, we consider the role of AI in the SLR process. AI functions as a collaborator, with the potential to redefine the researcher’s role. Based on a systematic review of the relevant literature, this study explores how AI is currently utilized in SLRs and proposes a framework for future collaboration between humans and AI in academic writing and research. These practical and philosophical considerations highlight the evolving relationship between human researchers and AI technologies.

With the advancement of AI technologies, traditional ideas of authorship and the researcher’s role in knowledge creation are increasingly being challenged. AI can not only support the research process but also autonomously carry out certain tasks, raising questions about maintaining integrity and accountability in scientific output (Howard, 2024; Masukume, 2024).

This article also discusses the variability and difficulties associated with incorporating AI into management-focused systematic reviews, where the nuanced and contextual aspects of research may pose challenges for automation. The goal is to present a balanced perspective that acknowledges both the potential of AI to improve research methods and the need for researchers to ensure that AI applications align with academic standards and ethical considerations.

Building on this foundation, we formulated the following research question: How can AI support the SLR process in management? This question itself was then addressed through a systematic literature review.

This study adopts a transdisciplinary approach to research methodology, integrating perspectives from management, information science, and technology studies. By exploring how artificial intelligence can be meaningfully embedded in the process of conducting systematic literature reviews, the article addresses not only academic concerns but also the practical needs of external stakeholders – including research institutions, consulting firms, and organizations seeking evidence-based insights. The proposed human–AI collaboration framework encourages more inclusive and participatory models of knowledge creation, potentially involving non-academic actors in the innovation process by enabling faster and more accessible synthesis of research findings. In doing so, the paper aligns with broader efforts to make academic inquiry more responsive, collaborative, and relevant to real-world challenges in business and society.

2. Research design

Data collection

We conducted searches using commonly accepted search algorithms in the Scopus and Web of Science databases, which contain the largest collections of peer-reviewed academic publications (Glińska & Siemieniako, 2018, Paul & Criado, 2020). We formulated two search queries (one for each database) corresponding to the most common keywords of our basic research concepts, and we followed the database protocols regarding the use of Boolean operators AND, OR, and appropriate truncations (*).

1. (“Automation” OR “Automating” OR “Automated” OR “Automatic”OR “Automates” OR “Mining”)

2. (“Systematic review*” OR “Systematic Literature Review*”)

3. (“Artificial intelligence” OR “AI”)

This yielded the following query for Scopus, which returned 1,297 studies:

TITLE-ABS-KEY((“Automation” OR “Automating” OR “Automated” OR “Automatic” OR “Automates” OR “Mining”) AND (“Systematic review*” OR “Systematic Literature Review*”) AND (“Artificial intelligence” OR “AI”)).

On Web of Science, we used the following query, which returned 785 studies:

TS = ((“Automation” OR “Automating” OR “Automated” OR “Automatic” OR “Automates” OR “Mining”) AND (“Systematic review*” OR “Systematic Literature Review*”) AND (“Artificial intelligence” OR “AI”)).

Together, both queries produced an initial sample of 2,082 studies.

Data selection

Three inclusion criteria were applied to select which articles to review. Papers had to be scientific in nature, published in peer-reviewed scientific journals, and written in English. This reduced the initial sample to 1,649 papers. Next, two exclusion criteria were introduced during title and abstract screening. We excluded papers that merely mentioned AI automation in SLRs without describing its application, as well as studies focusing solely on specific phases of the SLR process rather than automation or AI in general. These were mainly technical articles not including any broader context or concept. We also eliminated duplicates from the two databases.

Following this exclusion process, 34 publications remained. Then we added four articles found through AI engines (Elicit and SciSpace software). We then conducted a backward citation search analysis on these 38 articles, yielding 17 additional papers (for a total of 55 in all). Finally, we performed a one-layer forward citation search, which produced 38 additional articles, proceedings, preprints and one doctoral thesis. The final sample consisted of 93 publications, collected as of April 8, 2024.

We chose not to conduct a formal quality assessment due to the emerging nature of the topic. At this nascent stage of the research field, we deemed it more valuable to analyse all available sources to ensure comprehensive coverage.

The final set of 93 publications was analysed using thematic analysis. We coded the material to identify recurring themes related to the integration of AI into the SLR process. The themes were grouped according to the stages of the review process. The findings were then synthesized to build a framework supporting researchers’ collaboration with AI in academic writing. Based on our analysis of 93 studies, we identified how AI contributes to different stages of the SLR process. The review revealed that AI tools are used in scoping, research question formulation, literature identification and selection, data extraction, synthesis, and reporting. These findings of our analysis form the basis for the human researcher–AI collaboration framework we propose.

3. Results

Systematic literature review as a form of scientific writing in management

A systematic literature review (SLR) is a rigorous method for identifying, selecting, evaluating, analysing, and synthesizing existing research findings on a specific topic. It follows a precisely defined and replicable procedure for systematically gathering knowledge on a given topic. The results are transparent and can be verified by other researchers (van Dinter et al., 2021). In contrast to traditional literature reviews used in empirical articles, SLRs employ detailed criteria for selecting and evaluating the quality of source articles and the possibility of using the results in different contexts. They are used to identify research gaps, develop new ideas, and generate comprehensive reviews of the state of the art in specific research fields (Denyer & Tranfield, 2009).

Automation of the SLR process has so far been most widely implemented in the health sciences (Laynor, 2022; Tsafnat et al., 2013, 2014). This trend is reflected in our findings, as more than 70% of the articles in our sample are from that domain. Systematic literature reviews in the health sciences are a comprehensive and scientifically rigorous approach to summarizing existing evidence on a specific topic. As volume of research publications continues to increase, SLRs help researchers, healthcare providers, and medical practitioners stay informed about the latest evidence and practices (Laynor, 2022).

SLRs in management sciences, although no less important than in health sciences, are nevertheless considerably less developed. There therefore remains an under-satisfied need for rigorous synthesis of research findings in the field (Siemieniako et al., 2022), providing a comprehensive and relatively unbiased analysis of the existing literature on particular topics in management. SLRs help identify research gaps, inform directions for future research, and reduce the time spent synthesizing existing sources (Denyer & Tranfield, 2009). Scholars have advocated for the use of systematic review methods in management and organizational studies to advance evidence-based management practices (Tranfield et al., 2003). While certain adjustments may be expected in traditional systematic review methodologies to accommodate the unique characteristics of the management field, the benefits of using systematic literature reviews are widely recognized (Tranfield et al., 2003).

Given the significant progress achieved in automating SLRs within the health sciences and their growing importance in management, it is worth exploring how similar automation could be implemented in this context. To address our research question, the following section presents the various phases of SLRs in management sciences and examines the current possibilities of their automation, based on practices in the health sciences.

Systematic literature review phases in management

As outlined by Tranfield et al. (2003) and Denyer and Tranfield (2009), the general phases of a systematic literature review in management typically include:

Planning the review: The researcher plans the review and defines the scope, protocol, and process for conducting the literature review. In this step, the researcher considers which databases and tools to use, what skills are needed, how to allocate time, and how to search for high-quality resources.

Conducting the search: Next the researcher collects and selects primary studies that are relevant to the review topic. The researcher performs database searches, screens the citations, assesses the quality of the studies, extracts data, and monitors the activities.

Analyzing & synthesizing the literature: In the next phase, the researcher correlates the evidence from multiple sources, synthesizes results, and then arranges the data in order to address the research questions.

Reporting the findings: This final stage involves preparing and disseminating the review results. This includes formatting the main report, reviewing the report, summarizing the findings, discussing limitations, formulating recommendations for policy and practice, and identifying future research areas.

For this study, we adopted the concise and clear procedure developed by Vrontis and Christofi (2021), which also corresponds to the process outlined by Denyer and Tranfield (2009). This procedure consists of the following steps.

Conducting a scoping review: Scoping analysis defines the boundaries and focus of a research study, systematically determining which studies to include according to established criteria and the timeframe to be covered (Vrontis & Christofi, 2021). The main aim is to develop a comprehensive, structured review of relevant literature. This analysis facilitates mapping the field; identifying the main trends, gaps, and opportunities for theoretical development; and providing solid and reliable evidence for further research. A scoping analysis, therefore, allows researchers to efficiently and effectively assemble, assess, and collate the available literature to inform study objectives and methodologies (Vrontis & Christofi, 2021).

Identifying the research purpose and research question: In the next step, the researcher identifies the research purpose and research question by defining the scope and the focus of the study. This process follows a comprehensive scoping review, which enhances awareness of gaps, trends, and what is already known on the subject of interest (Pereira et al., 2023). Finally, research questions are formulated based on this preliminary study to meet the review’s overall research objectives.

One effective way to formulate a research question is through the interplay between the researchers and feedback from experts in academia and from the relevant industries (Vrontis & Christofi, 2021). Such an iterative process may better focus the research question so as to better capture the intent. The research question should be grounded in an understanding of the interface between different variables or concepts under study (Billore et al., 2023).

At this stage, it is also important to consider the inclusion criteria, regarding what the study will seek to address and what kinds of sources to include (Vrontis & Christofi, 2021). Well-honed inclusion criteria ensure that a research question remains focused and relevant to the set research objectives. Generally, by following a structured methodology, researchers can formulate well-defined research questions in line with the overall research aim.

Identifying the research context: The research context is the particular setting, condition, or background in which the study takes place. It incorporates the industry under study, participants’ cultural traits, geographical locations, time periods, and all those elements which may have an effect on the research topic or its findings (Vrontis et al., 2020).

Understanding the research context is therefore crucial for interpreting and generalizing findings, since different contexts may lead researchers to varying outcomes with different implications (Christofi et al., 2017). Researchers usually design their studies with contextual factors in mind to ensure that their findings are relevant and applicable in particular situations (Baima et al., 2020). By examining different research contexts, scholars can gain new insights, refine theories, and enhance their understanding of a particular area of study (Vrontis et al., 2022).

Identifying the literature: Literature identification is a systematic process of searching for, selecting, and analysing relevant publications and research studies with respect to a given topic or issue. This typically includes assessing the relevance and quality of the literature found and synthesizing key findings into insights about the current state of knowledge on the subject under investigation. Identifying the literature allows researchers to better grasp the theoretical approaches taken and the extant research gaps, trends, and challenges in the respective field of study. In other words, this step enables scholars to map out the current state of the subject and, consequently, to identify gaps and trends in order to support the development of scientific projects (Jain et al., 2022).

Selecting the literature: In the fifth step, the relevant sources of information – such as research articles, books, and other publications – are selected for inclusion in the study or review. This process requires the setting of clear selection criteria, such as the studies’ research questions, objectives, and the quality of the sources. These criteria help to identify and screen potential sources and, finally, select relevant and high-quality literature to be further studied (Christofi et al., 2017). The systematic methodologies used in conducting literature reviews help researchers ensure a very rigorous and comprehensive selection process for this step of the review (Battisti et al., 2023). Through careful selection, researcher build a solid foundation of existing knowledge and findings relevant to their own study.

Extracting and synthesizing data: Data extraction involves the systematic collection of relevant data from the selected articles or research papers, according to predefined criteria. This includes identifying and recording specific information such as publication details, author details, article type, methods used, key findings, and other relevant data points (Christofi et al., 2021). Data synthesis, by contrast, involves analysing the extracted material to identify patterns, relationships, or common themes in the literature. This stage aims at synthesizing the data from the different sources of information into a coherent framework or model that will then guide further research or provide practical implications (Christofi et al., 2021). This is then followed by thematic analysis to integrate the results into an overall framework, further enabling in-depth understanding of interrelating concepts (Battisti et al., 2023). In general, data synthesis facilitates the generation of meaningful inferences from the literature review and provides directions for future research.

Reporting and making recommendations: This final stage involves preparing the report and recommendations, which requires summarizing and synthesizing the results of the reviewed studies in a structured and transparent manner. Principal results, themes, and lessons learned from the literature are organized and presented comprehensively. The authors of the review identify gaps in the literature, propose future directions, and offer recommendations for both academics and practitioners based on their analysis of the reviewed studies. The ultimate aim is to contribute valuable insights to the existing knowledge base of the research area and guide further research efforts (Christofi et al., 2017; Pereira et al., 2023).

Automation of SLRs in management

In this section, we illustrate how artificial intelligence tools can be used to automate specific stages of the systematic literature review process. The examples come mainly from health sciences literature, but the same SLR procedures are increasingly being used in management (Denyer & Tranfield, 2009).

Scientific automation refers to the application of technological instruments and procedures to mechanize and enhance a number of scientific processes related to data collection, analysis, and reporting. Within the context of systematic reviews, it entails the use of software and algorithms to accelerate the review process and to efficiently and accurately synthesize evidence (Lau, 2019). The tasks that can be automated for systematic reviews include literature screening, data extraction, and meta-analysis (Tóth et al., 2023). More generally, science automation aims to improve efficiency, transparency, and reproducibility, while reducing costs by taking advantage of better technology and artificial intelligence (Laynor, 2022).

Scoping analysis

AI algorithms can help automate several tasks within scoping analysis, facilitating the extraction of key information from large bodies of scientific literature – such as author names, affiliations, keywords, citation counts, or topics (Saeidnia et al., 2024). By analysing citation networks, AI systems can identify highly cited and influential papers and reveal the dynamics of scientific knowledge diffusion. They may also predict the potential impact of scientific research based on a variety of factors. Moreover, they may detect and visualize research collaborations through co-authorship networks and publication histories. Applying natural language processing (NLP) techniques can make it easier for researchers to identify emerging trends and topics during the scoping analysis (Saeidnia et al., 2024).

Identifying the research purpose and research questions

AI can assist researchers in posing research questions by providing data-driven insights and optimized methodologies. It can identify gaps in the available literature, generate hypotheses, and even predict probable correlations or causal relationships. AI tools can, therefore, enhance the brainstorming process with insights drawn from existing trends, historical data, and cross-disciplinary studies that may ultimately set researchers onto new investigative paths (Wagner et al., 2022). Moreover, given AI’s advanced capacity to analyse data faster and more accurately than is humanly possible, it can reveal hidden patterns, correlations, and emerging research trends that enable the researcher to find new directions to pursue (Saeidnia et al., 2024; Tomczyk et al., 2024).

However, while AI can significantly increase the efficiency of the research process, human judgment and critical thinking remain indispensable for determining which research gaps merit exploration and how they should be addressed (Spillias et al., 2023). While AI can open up ways to fast-track the process of identifying relevant literature and proposing hypotheses, human judgment is necessary for generating meaningful questions through problematization (Wagner et al., 2022).

Identifying the research context

AI can also contribute to defining research contexts by generating ideas, reviewing the literature, analysing data, and mapping out collaboration networks (Saeidnia et al., 2024). AI algorithms are able to process large amounts of data to pinpoint underexplored areas within a field (Khalifa & Albadawy, 2024). In this respect, using natural language processing techniques, AI can extract keywords, topics, and trends from scientific publications that may be helpful for the research community to find new directions and emerging areas of focus in the respective domains (Saeidnia et al., 2024). Moreover, AI can contribute to the generation of ideas and hypotheses and to the development of robust designs by proposing relevant research problems as well as methodologies (Khalifa & Albadawy, 2024). It can be applied to predict emerging research trends; identify potential collaborators and influential research networks, and measure the impact and visibility of scientific papers, authors, and journals (Saeidnia et al., 2024).

Identifying literature

AI techniques can identify relevant literature in various ways. Algorithms can distinguish between authors with similar names by considering variables such as institutional affiliations and publication histories (Saeidnia et al., 2024). This guarantees that scholarly work is attributed correctly and also enhances the reliability of bibliometric analysis.

Researchers are increasingly applying AI techniques such as machine learning (ML) and data mining in bibliometrics in order to predict future publication trends, emerging research areas, and research impact (Saeidnia et al., 2024). AI algorithms can recognize patterns and relationships in large bibliographic datasets, and then deliver critical insights regarding what the scientific enterprise of research may look like in the years to come. Such studies may significantly enhance researchers’ capacity to recognize and remain abreast of key trends and research collaborations.

As Saeidnia et al. (2024) observed, AI algorithms can automatically collect bibliographic data from a variety of sources, such as online databases, academic libraries, and digital repositories, and this may save a lot of time and effort for researchers engaged in data collection. AI analysis of citation networks also helps locate influential papers, authors, and journals, highlighting the impact and visibility of research outputs and spotting key trends.

Selecting literature

AI can facilitate the literature-selection stage through advanced methods for knowledge representation and inference, text manipulation, and learning from large amounts of data. These techniques are particularly useful for tasks that are laborious or repetitive for humans, such as the critical analysis of scientific literature (de la Torre-Lópezet al., 2023). AI tools support the clear specification of problem domains and literatureselection criteria, thus enabling researchers to apply search and selection criteria, save time, and ensure transparency and quality in the literature review (Ngwenyama & Rowe, 2024). AI-based tools can potentially deal with fuzzy, weakly structured, and unstructured data, providing abstraction and semantic meaning-based analysis that can support searching and screening tasks for literature selection (Wagner et al., 2022). Advanced supervised machine learning methods, such as deep learning (DL), are used to automate decisions on the relevance of papers. This alleviates researchers from the tedious task of rule-codification and also makes the literature-selection processes more efficient (Wagner et al., 2022). Essentially, AI tools offer capabilities that can be harnessed to advance the effectiveness, efficiency, and accuracy of the literature-selection processes, thus proving very instrumental for researchers in their quest to navigate the veritable sea of literature available in many domains.

Extracting and synthesizing data

In the data-extraction phase, AI tools can automatically extract information from articles, whether structured through elements of the PICO framework or specific data points, using ML/DL/NLP methods (Santos et al., 2023). AI tools can assist in summarizing and interpreting the extracted information in formats that will enable graphic and statistical synthesis, including the generation of tables, diagrams, and graphs examining between-study heterogeneity, and in updating meta-analyses and related forest plots (Amezcua-Prieto et al., 2020). These capabilities of AI thus support faster data-extraction and synthesis processes in literature reviews, improving efficiency and quality in synthesizing evidence in scholarly research.

Reporting and preparing recommendations

AI-driven tools can contribute significantly to improved manuscript preparation, assisting in such stages as grammar correction, text rewriting, and recommendation generation – often tailored to the users’ individual preferences and writing style (Chemaya & Martin, 2023). AI systems can also automatically identify missing data, synthesize evidence from source studies, and identify topics through automated text clustering (Santos et al., 2023). Moreover, AI algorithms can digest large numbers of scientific publications to retrieve information about author names, affiliations, keywords, or citations, all of which may help researchers gain a better grasp the publication patterns, underlying research networks, and collaborations in a scientific area (Saeidnia et al., 2024).

AI-powered recommender systems can be used to recommend relevant scientific websites, online resources, and research collaborations based on user preferences, reading behaviour, and web data (Saeidnia et al., 2024). Natural language processing and machine learning techniques may play a central role in these systems, supporting the analysis of web-based documents, extraction of key information, understanding of research outputs, and assessment of impact and visibility of online scientific research (Saeidnia et al., 2024).

The reviewed literature shows that AI capabilities in data extraction, analysis, and recommendation generation are transforming the process of reporting, explaining, and communicating research findings – bringing a revolution in how academic and research outputs are reported and shared. Table 1 presents a summary of this analysis. 

In summary, this section has demonstrated how artificial intelligence can support the automation of the various phases of systematic literature reviews, which therefore answers our core research question. More specifically, we investigated how AI applications implemented in the SLR procedures for health sciences can be applied to management sciences. The SLR procedure adopted here follows the framework proposed by Vrontis and Christofi (2021), who extended that of Denyer and Tranfield (2009).

4. Conclusions, Limitations, and Future Research

The integration of artificial intelligence into systematic literature reviews represents not Merely an evolution, but a revolution – one that challenges the very foundation of academic Research. The traditional painstaking process of identifying, analysing, and synthesizing Literature is being rapidly overtaken by ai-driven automation, fundamentally shifting the Researcher’s role from that of an intellectual labourer to that of a process manager.

To further illustrate the balance between human oversight and machine capability, This transformation can be productively examined in terms of the data–prediction–Judgment–action model (agrawal, gans, & goldfarb, 2018). According to this model, ai Improves the prediction stage by processing large amounts of information, whereas the Stages of judgment and action remain the responsibility of humans. Applied to slrs, this Implies that researchers are not passive supervisors. Rather, they must critically evaluate Ai outputs, interpret them, and decide how to integrate them into existing theory. Ai can Automate the identification of literature and point to potential gaps, yet it cannot replace Human judgment in assessing relevance or drawing conclusions. A useful analogy can be Found in the military domain, where ai improves predictive capacities but decisionmaking authority ultimately remains with humans (agrawal, gans, & goldfarb, 2018). This framework thus reinforces our view that ai does not eliminate the researcher’s role. Instead, it redefines it. Researchers remain managers of the process, with their judgment And action ensuring rigor and depth.

However, this transformation is not universally welcomed. While it may lead to Improved efficiency, scalability, and precision, one must ask: at what cost? Increased reliance on AI threatens to erode the depth of critical engagement with literature, potentially reducing researchers to mere supervisors of algorithms rather than active participants in knowledge creation. Yet AI systems are not neutral; they inherit the biases of their training data, the priorities of their programmers, and the constraints of their algorithms. If left unchecked, these embedded biases could reshape academic discourse in ways we are only beginning to understand.

The present study has a number of limitations, which reflect broader concerns about AI’s role in research. The fact that most extant SLR automation techniques stem from health sciences raises a crucial question: Is management research even compatible with such mechanization? The field of management thrives on context, interpretation, and theoretical nuance – elements that AI, for all its computational power, struggles to grapple with. Applying automation techniques designed for medical trials to a discipline that values qualitative insight may, at best, be an oversimplification and, at worst, an intellectual misstep. Moreover, our reliance on peer-reviewed studies from established databases inadvertently sidelines alternative perspectives and cutting-edge discussions happening outside traditional academic publishing. If AI is trained only on what is deemed “acceptable” by established gatekeepers, are we not reinforcing the very same academic silos that researchers have long criticized? The omission of formal quality assessment further highlights the immaturity of this research area. We have embraced AI before rigorously questioning whether it genuinely improves the research process – or simply accelerates flawed methodologies.

As far as further limitations are concerned, the number of references included in this study could possibly have been larger, but it was the direct outcome of our systematic selection procedure. The final set of publications was determined through predefined keywords and strict inclusion and exclusion criteria, ensuring objectivity and transparency. As a result, the number of sources may have been smaller than expected, but it accurately reflects the available and relevant research within the scope of this emerging field.

The fact that our own study was itself conducted through the systematic literature review method also invites some brief reflection on this process. We relied on established databases (Scopus and Web of Science) and complemented them with AI-based tools such as Elicit and SciSpace to identify additional sources. While this approach provided a broad coverage of relevant studies, it also revealed challenges that are characteristic of AI-assisted reviews. For example, integrating results from traditional databases and AI tools required additional effort to ensure consistency and avoid duplication. Furthermore, while AI engines accelerated the retrieval of relevant articles, they sometimes produced results lacking sufficient context or theoretical framing, which required careful human judgment. These experiences confirm our broader argument: AI can support the prediction and data retrieval stages, but the stages of judgment and action remain dependent on researchers. By reflecting on our own process, we emphasise the importance of methodological transparency and show that the opportunities and limitations of AI-assisted SLRs are not only conceptual but also practical realities encountered during research.

Looking ahead, future research must confront these uncomfortable realities rather than blindly celebrate AI’s capabilities. Instead of merely asking how AI can make SLRs more efficient, we should ask whether AI-assisted reviews do actually produce better knowledge at all. If AI is allowed to dictate research agendas by prioritizing what is most frequently cited, we risk creating an academic echo chamber where innovation is stifled in favour of algorithmic consensus.

The ethical implications are equally alarming. Who takes responsibility when AIgenerated literature reviews misrepresent findings or reinforce biases? The obsession with automation must be tempered with a serious conversation about accountability and intellectual integrity. Scholars must resist the temptation to let AI do their thinking for them. The most pressing challenge is not improving AI but ensuring that human researchers remain the architects of inquiry rather than its passive facilitators. The future of AI-driven research is not inevitable – it is a choice. Whether that choice leads to a new era of intellectual empowerment or a hollowing out of academic rigor depends entirely on how critically we engage with this technology now.

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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.

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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.

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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?

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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.

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