SPACE-RL Innovation Transfer Model "Science - Business"

Abstract

The article deals with creating the innovation transfer model science - production - business . It is based on quantitative indicators for assessing the external environment and market analysis, the landscape for implementing the development and introduction of innovation and evaluating the internal environment and the level of readiness for implementation. The authors provide ways of commercializing the scientific research results and describe the role of an entrepreneurial university in achieving the main tasks of innovation and technology transfer. A combined SPACE-RL model for assessing the prospects for new scientific products concerning external and internal influencing factors is proposed. The SPACE matrix, from a subjective assessment tool based on qualitative indicators determined by experts, has turned into a SPACE-RL model, in which it is possible to compare the entrepreneurial university's capabilities in terms of the strategy of bringing the invention to the market and the readiness of the invention to become innovation The SPACE-RL model maximally objectifies the innovation transfer process, reducing the influence of qualitative factors on decision-making.

Headnote

Abstract

The article deals with creating the innovation transfer model science - production - business . It is based on quantitative indicators for assessing the external environment and market analysis, the landscape for implementing the development and introduction of innovation and evaluating the internal environment and the level of readiness for implementation. The authors provide ways of commercializing the scientific research results and describe the role of an entrepreneurial university in achieving the main tasks of innovation and technology transfer. A combined SPACE-RL model for assessing the prospects for new scientific products concerning external and internal influencing factors is proposed. The SPACE matrix, from a subjective assessment tool based on qualitative indicators determined by experts, has turned into a SPACE-RL model, in which it is possible to compare the entrepreneurial university's capabilities in terms of the strategy of bringing the invention to the market and the readiness of the invention to become innovation The SPACE-RL model maximally objectifies the innovation transfer process, reducing the influence of qualitative factors on decision-making.

Keywords: Transfer model; innovation; entrepreneurial university; commercializing; SPACE-RL model;

JEL Classification: O31; O32;

DOI: http://doi.org/10.24818/ejis.2023.01

1.Introduction

The transfer of innovation is one of the university's missions, which is implemented in educational and scientific directions. Many works are devoted to this problem, some of which describe the innovative activity of enterprises (including universities) (Dźwigoł, 2021; Ojeda, 2021; Goncharenko, 2020), transfer algorithms (Kaya, 2021; Niftiyev et al. 2021; Sineviciene et al., 2018; Brimah et al., 2020), socio-economic indicators of the transfer process (Smiianov et al., 2020; Kozmenko and Vasylyeva, 2008; Skrynnyk and Vasilyeva, 2020a; Bilan et al., 2022), models of strategic development of enterprises regarding the use of innovations (Moskovicz, 2021; Brown and Kasztelnik, 2020; Slavinskaite et al., 2022; Ibraghimov, 2022; Koyluoglu and Dogan, 2021), solving the goals of sustainable development through the innovation transfer, etc. (Vorontsova et al., 2020; Skrynnyk and Vasilyeva, 2020b).

The process of technology and knowledge transfer are similar. Therefore, when building a transfer model, one should take into account the main patterns of ensuring the quality of education when describing various aspects of the "environment" (Chen et al., 2013; Benda and Šmejkalová, 2015; Häkkinen, 2013; Badea, 2011; Dragolea et al., 2014; Sedziuviene and Vveinhardt, 2018; Zafiropoulos and Vrana, 2008; Figueira-Cardoso, 2022; Praneviciene et al., 2017; Gad and Yousif, 2021; Gavurova et al., 2019; Krisnaresanti et al., 2020; Jantoń-Drozdowska and Majewska, 2013; Caballero-Morales et al., 2020; Grundey, 2009; Štimac and Šimić, 2012; Hitka et al., 2021; Zaharia et al., 2022; Suciu and Lacatus, 2014; Abdimomynova et al., 2021; Kozma, 2005; Jiang et al., 2021; Sarvašová and Királová, 2018; Hallova et al., 2017; Ivanov et al., 2012; Přívara and Kiner, 2020; Gryshchenko, 2012; Diaconu and Duţu, 2015).

In any case, university educational and scientific products form the object of commercial transfer. The state pays for educational products for higher education students, if it is a state order, and by individuals and legal entities. Educational services for other categories (for example, lifelong learning) are also implemented at the expense of individuals and legal entities. All this takes place in a competitive environment with a specifically formulated technical task (service description) and task performance results (criteria for a successful graduate). As for scientific products, universities have stricter rules of competition in this direction because, in addition to other educational institutions and scientific institutions, there is a business in this market that is immune to anything except indicators of socio-economic efficiency and the risks of not achieving it. A university from a purely educational organization turns into an educational-scientific-production complex, an entrepreneurial university (Rhiannon et al., 2018), where the algorithmization of the scientific product commercialization is a strict necessity.

There are various innovation transfer strategies. The choice of a specific way of promoting a development or service is based on a multifactorial analysis. This analysis can be carried out using different approaches, which in some cases can be fundamentally different.

It is necessary to start with the one who is the "catalyst" for the product's market entry. In the "marketing pull" mode, the creation of products is initiated by the consumer of the development or service, a specific representative of the industry or a branch of industry that needs the product. The "technological push" mode involves the developer's initiative to start a new segment of providing services and creating products for the industry. Both regimes are based on an in-depth analysis of the selected market segment. The research result in the first case is a list of enterprises that need specific investigations, and in the second - a list of objects where development can be embedded. "Marketing pull" involves receiving a technical task from the customer. Within the framework of the implementation of this technical task, it is possible to introduce innovation in the development as a whole and in its parts. "Technology push" is more conducive to innovation but associated with risks at the stage of implementation and determination of performance indicators.

2. Background

For further classification of strategies, it is necessary to place some accents in the conceptual apparatus. The concepts of "innovation" and "invention" should be distinguished. In the first case, it is an already developed solution that has been implemented or is at the implementation stage, the economic efficiency and social significance of which are higher than analogues or have none. An invention is an idea based on increased economic efficiency and social relevance. The idea is the basis of innovation and the invention as an object of intellectual property right is the first stage of innovation implementation. It is the technology of creating or functioning of an innovation. These ideas are born in the vast majority of scientific departments and laboratories of universities; this fact determines the urgency of starting the transfer from the "place of birth". Thus, the transfer of a certain product on the path "science - production" can develop as a technology or innovation transfer. In any case, this process is the commercialization of the results regarding the scientific and technical activity of the performer of the development or service.

Commercialization of the scientific research results can be implemented in one of the following ways (Caulfield and Ogbogu, 2015):

1. Provision of services and performance of research and development works to order.

2. Transfer of rights to the intellectual activity results with various options for the distribution of property rights.

3. Organization of production based on the protected intellectual property rights.

Each of the methods is implemented with the help of "marketing pull", "technological push" or a combination of these strategies, as shown by Figure 1 in application to the entrepreneurial university.

However, there are many barriers on the way from an idea to its commercialization:

* lack of a common language between the authors of the invention and the investors because the authors offer a description of the investigation uniqueness, and the investors find out the financial conditions and risks of implementation;

* it is difficult for a scientist to be a scientist and a businessman at the same time, which causes a misunderstanding of the transfer details;

* scientists' efforts to publish applied research results before they protected the intellectual property right for the invention. It results in the publication of information about the invention, which in the future does not give the author the opportunity to patent his idea;

* low level of scientists' motivation to be engaged in transforming an invention into an innovation due to insufficient funding, possession of data on the low percentage of commercialization of inventions, etc.;

* the egoism of a scientist who is unable to independently develop a road map for the commercialization of his/her invention, but does not contact specialists because they should receive remuneration for their services;

* the invention has not been brought to the level of commercialization and cannot receive a positive conclusion from the customer due to the lack of technological audit results, a commercialization plan, etc.;

* inventions (with any strategy of their commercialization, "marketing pull" or "technological push") are created without taking into account market demand;

* universities and scientific institutions are not ready to act in the conditions of competition among themselves and competition from scientific research centres of industrial enterprises;

* universities and scientific institutions are reluctant to create clusters to solve interdisciplinary tasks and develop turnkey projects;

* the future commercialization of the invention and its transformation into an innovation requires additional funding, which is not always available to the university or scientific institution;

* due to the lack of market analysis data, the invention cannot be implemented either as a response to a request ("marketing pull"), or as an offer ("technological push").

The potential of universities to commercialize research results depends significantly on their organizational capabilities, applied approaches to generate new ideas, and used internal processes to transform them into innovation. This implies assessing their dynamic capabilities and "organizational ambidexterity" concerning the entire innovation cycle.

Emphasizing the entrepreneurial aspect, universities' dynamic capabilities relate to their ability to integrate, build, and reconfigure internal and external competencies to respond to a rapidly changing environment (Teece et al., 1997). In some cases, external factors can essentially shift the universities' potential as well as the whole country's intellectual potential patterns (Mishchuk et al., 2019; Oliinyk et al., 2022). The impetus to innovative activity and development of the university-business cooperation can be achieved in the case of increasing attention to entrepreneurial education development (Barrientos-Báez et al., 2022; Carvalho et al., 2021) resulted in increasing the entrepreneurial intention of the students (Rodríguez Loor and Muñoz-Fernández, 2022). These reasons enforce the universities for seeking and maintaining partnerships with businesses (Czako et al., 2021). At the same time, dynamic capabilities imply the presence of appropriate internal processes and procedures to ensure the implementation of the innovation process (Eisenhardt and Martin, 2000). Dynamic capabilities in themselves do not create end results but help to organize and use university resources in such a way as to lead to the desired end results. This idea reflects the possibility for universities to apply different models for managing their resources and adapting their organizational processes to the dynamics of the external environment. Thus, universities' dynamic capabilities in creating innovations for moderately dynamic markets rely on routines and practices based on pre-existing knowledge, consistent application, and relatively more easily predictable outcomes. In high-velocity markets, however, the dynamic capabilities of universities imply experimental, unstable processes that rely on rapidly acquired knowledge, trial-and-error method, and hard-to-predict results (Eisenhardt and Martin, 2000).

The organizational ambidexterity of universities reflects their ability to reconcile and simultaneously use two seemingly opposite approaches in the innovation transfer process - reactive and proactive. In a broader context, they are associated with the combined application of both - alignment (exploitation) and adaptability (exploration). Exploitation is associated with refining work, execution and implementation, whereas exploration is search, experimentation, innovation and risk-taking (March, 1991). Alignment means that universities exploit the benefits of their established processes, good past practices and assets. In contrast, adaptability reflects the ability to move quickly toward new opportunities and adjust to volatile markets (Birkinshaw and Gibson, 2004).

Thus, the organizational ambidexterity of universities expresses their ability to pursue incremental and revolutionary innovation simultaneously, which requires them to use different, sometimes opposing, structures and processes to support individual phases of the innovation cycle. Moreover, exploitation activities are linked to incremental innovation, which is suitable for the idea implementation phase, while exploration activities are linked to radical innovation, associated with the idea generation phase (Bledow et al., 2011). The initiation phase of the innovation implies a high degree of complexity, a low degree of formalization, and a weak centralization of decision-making. In contrast, the implementation phase of the innovation requires a low degree of complexity, a high degree of formalization, and a strong centralization.

Organizational ambidexterity can be seen as a driver to increase the innovation activity of universities because it contributes to resolving internal contradictions arising from the peace of mind of relying on established and verified past good practices against the uncertainty of the future.

According to the described situation, the optimal solution is to use a tool for assessing the level of invention and the prospect of its transformation into an innovation. This tool should become a "common language" between the authors of the invention and prospective customers of the product. At the same time, attention should be paid to both internal and external factors that accompany the process of creating an innovation from an invention and its subsequent commercialization. For the successful implementation of the innovation transfer process, a tool is also needed that can evaluate the innovation and the prospects of its introduction to the market according to several specific indicators. These indicators should be determined either according to the rating scale or according to specific signs of compliance of the indicator with one or another level. The tool should describe the innovation transfer strategy and at the same time determine the level of the product:

1. Technological level of readiness.

2. Innovative level of readiness.

3. A Review of Approaches to the Analysis of Influence Factors as Applied to the Transfer of Innovations

Among many approaches to analyzing internal and external factors influencing the decision-making process, only certain tools were selected for analysis in application to the transfer of innovations. The main criterion for choosing the following list of tools is the possibility of their application to assess the prospects of turning an invention into innovation with subsequent market entry.

Approaches to the analysis of influencing factors in decision-making can be classified as follows.

1. Qualitative analysis approaches:

* SWOT (Satria and Shahbana, 2020; Ali et al., 2019);

* PEST, PESTEL, PESTELI, STEEP, LONGPESTEL (Graham, 2007; Graham 2018);

* SMART (Bjerke and Renger, 2017);

2. Approaches to finding cause-and-effect relationships and quality control:

* the Ishikawa method (Milosavljevic et al., 2018; Reilly et al., 2014);

* The Deming-Shewhart cycle (Aggarwal, 2020).

3. Approaches involving strategic analysis with obtaining qualitative indicators of strategy elements:

* Ansoff matrix (Ecobici, 2017);

* Abell matrix (Abell, 2014);

* Balanced scorecard matrix (Farooq and Hussain, 2011);

* GE McKinsey matrix (Tsakalerou, 2015);

* SPACE matrix (Pyo, 2022);

The first group of approaches enables the assessment of the internal and external landscape of the prospects / futility of decision-making but does not operate with parameters that directly relate to the features of the invention and the stage of its possible transformation into an innovation.

The second group of approaches allows for a more in-depth assessment of the risks on the way to the emergence of innovation and to determine what criteria an invention must meet to become an innovation. However, this approach is subjective because only the author of the invention conducts the analysis.

The third group of approaches is the most suitable for implementing innovation transfer processes. These approaches can take into account two (Ansoff matrix), three (Abel matrix and four (Balanced scorecard measures)) groups of factors and determine the interval value of a group of factors for expanding strategic positions (GE McKinsey matrix). At the same time, the proposed methods are characterized mainly by qualitative indicators assessment and taking into account the opinion of experts, which also gives subjectivity to the results of the analysis. These methods also do not consider the importance of factors in each group.

Nevertheless, for example, if the approach to its application is improved (determining the degree of importance of this or that factor according to the opinions of experts, quantitative assessment of factors, parametric identification of the innovation transfer model), the SPACE matrix can become a reliable tool for use in the construction of road maps of innovation output, created in a university or scientific institution, to the market.

It is proposed to choose a strategy for innovation transfer based on the SPACE matrix, in which the university will be described as entrepreneurial. The approach to using this matrix will be improved to provide a quantitative assessment of strategy selection indicators and to compensate for the shortcomings noted above. At the same time, it is proposed to determine the technological and innovative level of readiness of the invention for implementation. In this way, a multifactor model of the algorithm for deciding on the strategy of entering the invention into the market is created, the description of which will be presented below.

4. The SPACE-RL Model of Decision-Making about the Strategy of Bringing an Invention to the Market as an Innovation

The proposed SPACE-RL model is shown by Figure 2. The model has three planes. The SPACE matrix was used as a strategy selection tool in the conditionally lower plane (according to the formal location in space in the figure). In the conditionally upper plane, the technological and innovative level of development is defined. The middle plane is used to compare the analysis results of the invention readiness and determine whether it is a technology (without innovative solutions) or an innovation.

The model works as follows. In the first stage, the SPACE matrix is used to determine the type of strategy for bringing a scientific product to the market. The originality of the approaches in using the SPACE matrix is as follows:

1. The matrix is used to describe business processes in the entrepreneurial university.

2. The subjectivity degree to select factors for each of the groups specified in the matrix is reduced due to the selection of those factors whose impact on the innovation creation process is statistically confirmed. For example, such confirmation can be data from the Global Innovation Index (Figure 3) or data from rating agencies, in the methodology of which key factors are defined for each group of the SPACE matrix.

3. The market experts and representatives of potential or real customers define the importance of the factors selected for strategy development. At the same time, the degree of experts' opinion consistency must be determined using the Kendall concordance coefficient (Field, 2014), obtained on the basis of the experts' assessment matrix (Table 1). At the same time, the expert has the right to recognize several factors as equivalent, assigning them the same rank number.

4.Factors for each group have a numerical value and a rating scale in the range "minimum - maximum". An example of such a factor can be one of the indicators of the formula for the distribution of state budget expenditures for higher education among higher education institutions (Ukrainian Government, 2020): "The indicator of the scientific activity of the higher education institution is determined depending on the amount of income to the special fund based on the results of scientific and scientific-technical works under international cooperation projects, on the results of scientific and scientific-technical works under economic contracts and on the results of the scientific service provision per scientific and pedagogical employee at the main place of work (PEi) on average for the previous three calendar years and is accepted in the following amounts:

1 - if the PEi does not exceed 500 UAH (12,5 EUR) per person;

1.1 - if PEi is equal to 501-2000 UAH (12,51-50 EUR) per person;

1.2 - if PEi is equal to 2001-5000 UAH (50,01-125 EUR) per person;

1.3 - if PEi is 5,001-10,000 UAH (125,01-250 EUR) per person;

1.4 - if PEi is equal to 10,001-20,000 UAH (250,01-500 EUR) per person;

1.5 - if PEi exceeds 20,001 UAH (500,01 EUR) per person".

At the second stage, the technological and innovative level of the invention readiness for implementation is determined according to specifically defined compliance indicators of each level for TRL and IRL (Lavoie and Daim, 2017), for example, using specialized calculators (Technology Readiness Level (TRL) Calculator, 2021). An example of indicators of compliance with a specific TRL level is shown in Figure 4.

The comparison stage allows us to determine the relationship between the possibilities for introducing innovation and the actual state of its development. The comparison can look like in Figure 5. The comparison results determine further actions (including achieving specific numerical indicators in the SPACE matrix) regarding the desired strategy based on the assessment of the invention readiness level. Another option is to define specific measures to change the level of invention readiness to achieve the desired implementation strategy. In addition, the commercialization path (or a combination of paths) and the corresponding mode of the product's entry into the market ("market thrust" or "technological push") are chosen. If both resulting points coincide, the commercialization path is determined immediately (with the possibility of changing it due to a road map of measures for transition to another category in case of dissatisfaction with the modeling results).

It is possible to use the parametric identification of the product release model according to the strategy based on the SPACE matrix (if accepted as the final one) as an adjusting tool for the strategy of invention promotion to the market. Parametric identification is carried out based on a model for calculating the economic indicators of the invention implementation, as well as experimental data - actual economic features for implementing investigation analogues or in a specific market. Parametric identification allows you to determine the discrepancy between the model and the experiment with the determination of measures to eliminate this discrepancy by adjusting the mathematical model. The technology transfer process is presented for analysis in the form of a "black", "grey", or "white" box, depending on the completeness of the mathematical description. The process of parametric identification can be represented as shown in Figure 6.

5. Conclusion

Thus, the SPACE matrix from a subjective assessment tool based on qualitative indicators determined by experts has turned into a SPACE-RL model. It enables to compare capabilities of an entrepreneurial university in terms of the strategy of bringing the invention to the market and the real level of the invention readiness to become an innovation. The SPACE-RL model maximally objectifies the innovation transfer process, reducing the influence of qualitative factors on decision-making.

Acknowledgement: This research was funded by the Ministry of Education and Science of Ukraine and performed the results of the project «Business-Education-Science Coopetition: Institutional and Economic Models of Innovation Transfer for National Security and Sustainable Development» (No 0122U000772), «Cognitive Model of Innovations' Commercialization in the Conditions of Industry 4.0: Intellectual Capital Protection, Marketing and Communications» (No 0122U000780) and by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and Slovak Academy of Sciences and performed the results of the project «Changes in the approach to the creation of companies' distribution management concepts influenced by the effects of social and economic crises caused by the global pandemic and increased security risks» (VEGA 1/0392/23).

Sidebar

Received: 3 March 2023; Revised: 12 May 2023; Accepted: 15 May 2023

References

References:

Abdimomynova, A., Duzelbayeva, G., Berikbolova, U. Kim, V., & Baimakhanova, A. (2021). "Entrepreneurship Education Prospects in The Public-Private Partnership System, Montenegrin Journal of Economics, 17(2): 83-92.

Abell, D. (2014). The past, present, and future of strategy: broadening challenges; advancing insight. Revista Ibero Americana de Estrategia, 13(3), 7-18.

Ukrainian Government (2019). About state budget expenditures distribution amongst higher education institutions based on educational, scientific and international activity indicators: Decree of Cabinet of Ministers of Ukraine, 24.12.2019 # 1146. Official Herald of Ukraine. 2020. #4. P. 424.

Aggarwal, A.K. (2020). Using Deming's Cycle for Improvement in a Course: A Case Study. International Journal of Web-Based Learning and Teaching Technologies, 15(3): 31-45.

Ali, G., Buruga, B.A., & Habibu, T. (2019). SWOT Analysis of Blended Learning in Public Universities of Uganda: A Case Study of Muni University. J, 2(4): 410-429. doi: https://doi.org/10.3390/j2040027

Badea, L. (2011). Economic Crisis and Education. Case Study: the Romanian Educational System. Equilibrium. Quarterly Journal of Economics and Economic Policy, 6(3): 71-84.

Barrientos-Báez, A., Martínez-González, J.A., García-Rodríguez, F.J., & GómezGalán, J. (2022). Entrepreneurial competence perceived by university students: Quantitative and descriptive analysis. Journal of International Studies, 15(2), 40-49. doi:10.14254/2071-8330.2022/15-2/3

Benda, P., & Smejkalová, M. (2015) Web Interface for Education of Mentally Disabled Persons for Work in Horticulture", AGRIS on-line Papers in Economics and Informatics, 7(1): 13 - 19.

Bilan, S., Šuleř, P., Skrynnyk, O., Krajňáková, E., & Vasilyeva, T. (2022). Systematic bibliometric review of artificial intelligence technology in organizational management, development, change and culture. Business: Theory and Practice, 23(1): 1-13. doi:10.3846/btp.2022.13204

Birkinshaw, J., & Gibson, C. (2004). Building ambidexterity into an organization: a company's ability to simultaneously execute today's strategy while developing tomorrow's arises from the context within which its employees operate. MIT Sloan Management Review, 45(4), 47-56.

Bjerke, M.B., & Renger, R. (2017). Being smart about writing SMART objectives. Evaluation and Program Planning, 61: 125-127. https://doi.org/10.1016/j.evalprogplan.2016.12.009.

Brimah, B. A., Olanipekun, W. D., Bamidele, A. G., & Ibrahim, M. (2020). Knowledge Management and its Effects on Financial Performance: Evidence from Dangote Flour Mills, Ilorin. Financial Markets, Institutions and Risks, 4(2): 34-42. https://doi.org/10.21272/fmir.4(2).34-42.2020.

Brown, E., & Kasztelnik, K. (2020). The Observational Microeconomics Study of the Phenomenon of Entrepreneur Resilience and Collaborative Innovative Financial Leadership in the United States. Financial Markets, Institutions and Risks, 4(3): 24-41. https://doi.org/10.21272/fmir.4(3).24-41.2020

Caballero-Morales, S.O., Cordero-Guridi, J.J., Alvarez-Tamayo, R.I., & Cuautle-Gutiérrez, L. (2020). Education 4.0 to Support Entrepreneurship, Social Development and Education in Emerging Economies. International Journal of Entrepreneurial Knowledge, 8(2): 89-100.

Carvalho, L., Mavlutova, I., Lesinskis, K., & Dias, R. (2021). Entrepreneurial perceptions of students regarding business professional career: The study on gender differences in Latvia. Economics and Sociology, 14(3), 220-241. doi:10.14254/2071-789X.2021/14-3/12

Caulfield, T., & Ogbogu, U. (2015). The commercialization of university-based research: Balancing risks and benefits. BMC Med Ethics 16, 70 https://doi.org/10.1186/s12910-015-0064-2

Czako, K., Kohus, Z., & Baracskai, Z. (2021). Understanding intentions towards seeking university-business partnerships - analysing four European cases. Journal of International Studies, 14(2), 228-244. doi:10.14254/2071-8330.2021/14-2/15

Chen, F., Wu, C., Yang, W., & Zhang, W. S&T Collaboration Platform for Higher Education Institutions and Industry: a Case Study of Wenzhou, China Inžinerine Ekonomika-Engineering Economics, 2013, 24(5): 424-436

Diaconu, M., & Duţu, A. (2015). The Role of the Modern University in Supporting the Entrepreneurial Ecosystem European Journal of Interdisciplinary Studies, 17(1): 11-24.

Dragolea L., Achim M.I., Căbulea L., & Popa M. (2014). Adapting educational services to labor market. Polish Journal of Management Studies 10 (1): 19-23

Dźwigoł, H. (2021). Leadership in the Research: Determinants of Quality, Standards and Best Practices. Business Ethics and Leadership, 5(1), 45-56. https://doi.org/10.21272/bel.5(1).45-56.2021

Ecobici, M.L. (2017). The Use Of Ansoff Matrix In The Field Of Business. Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, 2, 141-149.

Eisenhardt, K. M., Martin, J. A. (2000). Dynamic Capabilities: What are They? Strategic Management Journal, 21: 1105-1121.

Farooq, A., & Hussain, Z. (2011). Balanced scorecard perspective on change and performance: a study of selected Indian companies. Procedia - Social and Behavioral Sciences, 24, 754-768. https://doi.org/10.1016/j.sbspro.2011.09.043.

Field, A.P. (2014). Kendall's Coefficient of Concordance. In Wiley StatsRef: Statistics Reference Online (eds N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri and J.L. Teugels). https://doi.org/10.1002/9781118445112.stat06537

Figueira-Cardoso, F.C.S. (2022) University Outreach, Indigenous Knowledge, and Education: A Project with the Pataxó in Brazil. European Journal of Interdisciplinary Studies, 14(1): 39-55.

Gad, S., & Yousif, N.B.A. (2021). Public management in the education sphere: Prospects for realizing human capital in the development of knowledge management technologies. Administrate si Management Public, 37, 151-172. DOI: 10.24818/amp/2021.37-10

Gavurova, B., Tucek, D., & Kovac, V. (2019). Economic aspects of public procurement parameters in tertiary education sector. Administratie si Management Public, (32), 42-62, DOI: 10.24818/amp/2019.32-04.

Goncharenko, T. (2020). From Business Modelling to the Leadership and Innovation in Business: Bibliometric Analysis (Banking as a Case). Business Ethics and Leadership, 4(1), 113-125. http://doi.org/10.21272/bel.4(1).113-125.2020.

Graham, D. (2007). PESTE Factors in Developing a Framework for E-Learning. E-Learning and Digital Media, 4(2): 194-201. doi: https://doi.org/10.2304/elea.2007.4.2.194

Graham, D. (2018). PESTEL factors for e-learning revisited: The 4Es of tutoring for value added learning. E-Learning and Digital Media, 15(1): 17-35. doi: https://doi.org/10.1177/2042753017753626

Grundey, D. (2009). Developing Sustainability Principles at Lithuanian Universities: An Interdisciplinary Approach. Journal of International Studies, 2(1), 9-20.

Gryshchenko, I. (2012). The Economic Nature of Education Production, Economics & Sociology, 15(2): 50-57.DOI: dx.doi.org/10.14254/2071-789X.2012/5-2/4

Häkkinen, P. (2013). Technologies to Support Learning, Well-Being, and Communication. Human Technology, 9(2), 109-112.

Hallová, M., Polakovič, P., & Slováková, I. (2017) Current Trends in Training of Managers in the Field of Information And Communication Technologies And Identifying the Barriers to Education of Managers, AGRIS on-line Papers in Economics and Informatics, 9(4), 45-52.

Hitka, M., Štarchoň, P., Lorincová, S., & Caha, Z. Education as a key in career building. Journal of Business Economics and Management, 2021, 22(4): 1065-1083

Ibraghimov, E. A. (2022). Management of Innovation in Azerbaijan: Relationships with Competitiveness and Sustainable Development. Marketing and Management of Innovations, 1, 247-256. http://doi.org/10.21272/mmi.2022.1-18

Ivanov, S., Gutkevych, S., & Dichkovskiy, S (2012). An input of european educational standards at higher school of Ukraine - modernisation of educational system, Journal of International Studies, 5(2): 66-71.

Jantoń-Drozdowska, E., & Majewska, M. (2013). Effectiveness of Higher Education in the European Union Countries in Context of National Competitiveness. Equilibrium. Quarterly Journal of Economics and Economic Policy, 8(2), 81-100.

Jiang, J., Tan, W., Zhu, X., Liu, J., & Liu, T. (2021), Innovative Models and Practices for Engineering and Technology Talent Cultivating in Transnational Higher Education, Transformations in Business & Economics,20(1), 52:37-52.

Kaya, H.D. (2021). How Does The Use Of Technology In Entrepreneurial Process Affect Firms' Growth? SocioEconomic Challenges, 5(1), 5-12. https://doi.org/10.21272/sec.5(1).5-12.2021

Koyluoglu, S., & Dogan. M. (2021). The Impact of Innovation Strategies on Business Performance: Practices in High Technology Companies in Turkey. Marketing and Management of Innovations, 4, 168-183. http://doi.org/10.21272/mmi.2021.4-13

Kozma, R. (2005). National policies that connect ICT-based education reform to economic and social development. Human Technology, 1(2), 117-156.

Kozmenko, S., & Vasyl'yeva, T. (2008). Specialized innovative investment banks in Ukraine. Banks and Bank Systems, 3(1), 48-56. doi:10.21511/bbs.3(1).2008.01

Krisnaresanti, A., Julialevi, K.O., Naufalin, L.R., & Dinanti, A. (2020). Analysis of Entrepreneurship Education in Creating New Entrepreneurs. International Journal of Entrepreneurial Knowledge, 8(2): 67-76. https://www.ijek.org/index.php/IJEK/article/view/112

Lavoie, J.R. & Daim, T.U. (2017). Technology Readiness Levels Improving R&D Management: A Grounded Theory Analysis. 2017 Portland International Conference on Management of Engineering and Technology (PICMET), Portland, 1-9.

March, J. G. (1991). Exploration and exploitation in organizational learning. Organization science, 2(1), 71-87.

Milosavljevic, P., Pavlovic D, Rajic, M., Pavlovic, A. and Fragassa, C. (2018). Implementation of quality tools in higher education process. International Journal of Continuing Engineering Education and Life-Long Learning, 28(1), 24 - 36. do: https://doi.org/10.1504/IJCEELL.2018.090248

Mishchuk, H., Roshchyk, I. Sulkowska, J. & Vojtovič, S. (2019). Prospects of Assessing the Impact of External Student Migration on Restoring the Country's Intellectual Potential (Case Study of Ukraine). Economics & Sociology, 12(3), 209-219. DOI: 10.14254/2071-789X.2019/12-3/14

Moskovicz, A. (2021). Post-pandemic Scenario for University Startup Accelerators. Financial Markets, Institutions and Risks, 5(2), 52-57. https://doi.org/10.21272/fmir.5(2).52-57.2021

Niftiyev, I., Yagublu, N., & Akbarli, N. (2021). Exploring The Innovativeness of The South Caucasus Economies: Main Trends And Factors. SocioEconomic Challenges, 5(4), 122-148. https://doi.org/10.21272/sec.5(4).122-148.2021

Ojeda, F.A. (2021). Origin, Use and Meaning of the Innovation Diamond. Business Ethics and Leadership, 5(4), 48-58. https://doi.org/10.21272/bel.5(4).48-58.2021

Skrynnyk, O.; Vasilyeva, T. (2020a). Comparison of open learning forms in organizational education. CEUR Workshop Proceedings, 2732, 1314-1328. https://ceur-ws.org/Vol-2732/20201314.pdf

Skrynnyk, O.; Vasilyeva, T. (2020b). Neuro-genetic hybrid system for management of organizational development measures. CEUR Workshop Proceedings, 2732 411-422. https://ceur-ws.org/Vol-2732/20200411 .pdf

Oliinyk, O., Mishchuk, H., Bilan, Y., & Skare, M. (2022). Integrated assessment of the attractiveness of the EU for intellectual immigrants: A taxonomy-based approach. Technological Forecasting and Social Change, 182, 121805. https://doi.org/10.1016/j.techfore.2022.121805

Praneviciene, B., Puraite, A., & Vasiliauskiene, V. (2017). State Financing Impact on Autonomy of Higher Education Institutions and its Link with Economic Activities of Universities. Inzinerine Ekonomika-Engineering Economics, 28(5), 564-574

Přívara, A., & Kiner, A. (2020). Immigrant Employment in the Slovak Hospitality Industry: Profiles, Experience, and Education. Journal of Tourism and Services, 20(11), 167-182.

Pyo, N.H.L. (2022). The SPACE (Strategic Position and Action Evaluation)-Driven Strategic Insight for the Pineapple Manufacturers and Traders. International Journal of Multidisciplinary in Management and Tourism, 4(1), 28-40.

Reilly, J.B., Myers, J. S., Salvador, D., & Trowbridge, R. L. (2014). Use of a novel, modified fishbone diagram to analyze diagnostic errors. Diagnosis, 1(2): 167-171. doi: https://doi.org/10.1515/dx-2013-0040

Rhiannon P., Wadid L., Sarah J., & Eleanor H. (2018) The entrepreneurial university and the region: what role for entrepreneurship departments?, European Planning Studies, 26:9, 1835-1855, DOI: 10.1080/09654313.2018.1447551

Rodríguez Loor, R., & Muñoz-Fernández, G. A. (2022). Entrepreneurship in university: A logit methodological evaluation in an emerging economy. Economics and Sociology, 15(4), 114-128. doi:10.14254/2071-789X.2022/15-4/5

Sarvašová, I., & Kiráľová, A. (2018). Educational Tourism in Arboretum - The Case of Borová Hora. Journal of Tourism and Services 9(16): 31-44.

Satria, R. & Shahbana, E. (2020). SWOT Analysis of Strengthening Education Character in Junior High School. Jurnal Iqra': Kajian Ilmu Pendidikan, 5(2): 56-67. doi: https://doi.org/10.25217/ji.v5i2.827

Sedziuviene, N., & Vveinhardt, J. (2018). The Reactions of Post-Soviet Countries Employees to Changes Carried Out by Organizations in Higher Education: Cases of Lithuanian, Ukrainian and Belarusian State Colleges. Montenegrin Journal of Economics, 14(4): 225-235.

Sineviciene, L., Shkarupa, O., & Sysoyeva, L. (2018). Socio-economic and Political Channels for Promoting Innovation as a Basis for Increasing the Economic Security of the State: Comparison of Ukraine and the Countries of the European Union. SocioEconomic Challenges, 2(2), 81-93. DOI: 10.21272/sec.2(2).8193.2018

Slavinskaite, N., Lapinskiene, G., Hlawiczka, R., & Vasa, L. (2022). Financial Innovation Management: Impact of Fiscal Decentralization on Economic Growth of the Baltic Countries. Marketing and Management of Innovations, 1, 257-271. http://doi.org/10.21272/mmi.2022.1-19

Smiianov, V. A., Vasilyeva, T. A., Chygryn, O. Y., Rubanov, P. M., & Mayboroda, T. M. (2020). Socio-economic patterns of labor market functioning in the public health: challenges connected with COVID-19. Wiadomosci Lekarskie (Warsaw, Poland : 1960), 73(10), 2181-2187.

Štimac, H., & Šimić, M.L. (2012). Competitiveness in Higher Education: a Need for Marketing Orientation and Service Quality, Economics & Sociology, 5(2): 23-34. DOI: dx.doi.org/10.14254/2071-789X.2012/5-2/2

Suciu M.C., & Lacatus M.L. (2014). Soft skill s and economic education. Polish Journal of Management Studies, 10 (1): 161-168

Technology Readiness Level (TRL) Calculator (2021). Available from: https://www.dau.edu/cop/stm/lists/tools/allitems.aspx

Teece, D. J., Pisano, G., Shuen, A. (1997). Dynamic Capabilities and Strategic Management. Strategic Management Journal, 18(7), pp. 509-533.

Tsakalerou, M. (2015). Multi-variable analysis and modelling of intellectual capital effects on firm performance. International Journal of Learning and Intellectual Capital, 12(4), 372 - 385.

Vorontsova, A., Vasylieva, T., Bilan, Y., Ostasz, G., & Mayboroda, T. (2020). The influence of state regulation of education for achieving the sustainable development goals: Case study of central and eastern european countries. Administrate Si Management Public, (34), 6-26. doi:10.24818/amp/2020.34-01

Zafiropoulos, C., & Vrana, V. (200). Service quality assessment in a greek higher education institute. Journal of Business Economics and Management, 9(1): 33-45

Zaharia, R., Zaharia, R.M., Edu, T., & Negricea, I.C. (2022), Exploring Student Satisfaction with Online Education During the Covid-19 Pandemic in Romania: A Logistic Regression Approach, Transformations in Business & Economics, 21, 2 (56): 41- 62.