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Article

Tool for Assessment of the Green Technology Transfer Structure in Brazilian Public Universities

by
Luan Carlos Santos Silva
1,2,*,
Carla Schwengber Ten Caten
2,
Silvia Gaia
3 and
Rodrigo de Oliveira Souza
2
1
Faculty of Business Administration, Federal University of Grande Dourados (UFGD), Dourados 79825-070, Brazil
2
Department of Production Engineering, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre 90010-150, Brazil
3
Department of Production Engineering, Federal University of Technology—Paraná (UTFPR), Ponta Grossa 84017-220, Brazil
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(8), 6873; https://doi.org/10.3390/su15086873
Submission received: 3 March 2023 / Revised: 30 March 2023 / Accepted: 12 April 2023 / Published: 19 April 2023
(This article belongs to the Section Sustainable Management)
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Abstract

:
Green technologies have assumed an important role in combating global climate change. The process of transferring environmentally sustainable technologies from universities is crucial to mitigate climate change and for promoting sustainable development. This study aims to propose a tool to evaluate the transfer structure of the green technologies that are generated within universities. The tool has the purpose of supporting managers in the dissemination and absorption of these technologies in the market. The research methodology is established as applied and exploratory, with a quantitative approach. To analyze and validate the developed tool, a diagnosis was conducted in the Brazilian scenario. The research identified 255 groups registered in universities that develop research in green areas. To analyze the information collected in the Green Technology Transfer Radar survey, Pearson’s linear correlation coefficients between the dimensions were checked, followed by multivariate statistical techniques and factor analysis. The factor extraction method considered was that of principal components, and factor rotation was performed using the varimax orthogonal method with Kaiser’s criterion. As results were obtained, the evaluation tool contains eleven dimensions (People, Process, Budget, Relationship, Integrated Management, Research, and Development “R&D” in Green Technologies, Intellectual Property, Valuation, Commercialization, Environment, and Society) and seven stages for operationalizing the tool. The results revealed gaps in the transfer process, and universities should develop strategies to reduce the gaps pointed out in the application of the tool. Additionally, joint action with the TTOs and research groups within their institutions is necessary.

1. Introduction

The planet has been going through constant technological transformations and, in the midst of this process, climate changes have been directly affecting the production system. According to the United Nations (UN), the world population reached 8 billion in 2022, and it is estimated that it will reach 9 billion in 2037. This increasing population, linked to climate issues and the scarcity of resources already present in the world, has led to the UN and other international organizations to discuss human nutrition, hunger, access to natural resources, sustainability, and the adoption of technology to combat several problems that exist and will emerge in the future, among others.
UN actions, such as the creation and implementation of 17 sustainable development goals in the 2030 Agenda, are being conducted with the aim of eliminating extreme poverty and hunger, developing quality education for all, protecting the planet, and promoting peaceful and inclusive societies [1]. This is in addition to the annual actions taken by several nations through the Conference of the Parties (COP)—created in 1995—which have enabled the creation of major international treaties, such as the 1997 Kyoto Protocol, which aimed to establish stricter guidelines for reducing the emission of gases that produce the greenhouse effect, and the 2015 Paris Agreement, which included the goal of curbing the increase in global warming.
Governments must rethink the way they interact with the ecosystem and industry, and act in a more sustainable way. In 2009, the national patent offices of Japan, Israel, South Korea, the United Kingdom, the United States of America, and Canada created pilot programs to accelerate the examination of patent applications for green technologies. These programs were initially focused on a few specific areas, such as alternative energies, transportation, energy conservation, waste management, and sustainable agriculture.
In Brazil, this program started in 2012 with the goal of reducing the time for patent examination by two years. Currently, in Brazil, the average time for patent granting is eleven years (according to the National Institute of Industrial Property), which is too long. One of the central reasons for this movement was to quickly identify and categorize technologies with an environmental appeal that can be analyzed and granted by the patent system faster than other technologies. Once analyzed in this way, these technologies can replace current polluting technologies in the market.
Green technologies have assumed an important position in developing sustainable solutions for the planet and in decreasing CO2 emissions. Governments worldwide recognize the importance of the development of these technologies in the fight against global climate change and have started to recognize the relevance of granting patents as a mechanism to stimulate green technologies in the country. Therefore, it is necessary to think of models to transfer these generated green technologies, as well as to evaluate their internal and external transfer capabilities, especially those of public universities.
Evaluating the green technology transfer structure through tools before the registration process of contractors can reduce many gaps in this process and provide a favorable scenario for the best choices of mechanisms to transfer the negotiated green technology. The process of technology transfer (TT) can be extremely important and strategic for industries and universities to deal with sustainability and with the scarcity of resources [2,3,4,5]. This process of TT to universities can have numerous benefits, including the potential to generate revenue through the licensing and commercialization of new technologies, fostering collaboration between academia and industry, and promoting innovation and economic growth through the development of new products and services [6,7,8,9,10].
The advanced industrialization process requires that companies not only know their potential and management structure, but also seek cooperative partnerships with universities and research institutes, aiming to develop innovation projects through technology transfer in order to act in an increasingly effective and sustainable way [11,12,13,14,15,16].
Cooperative partnerships with universities offer several benefits, including increased access to funding opportunities, expanded research capabilities and expertise, opportunities for knowledge exchange, and the sharing of best practices. These collaborations can also enhance the reputation and visibility of universities, as well as facilitate the development of innovative solutions to complex societal challenges [17,18,19,20,21,22]. Cooperation also allows companies to gain new academic knowledge and experience, to keep up with the rapid changes in new technologies, and to integrate new products into their portfolios [23,24,25].
Establishing cooperative partnerships between universities and industry is essential for advancing scientific research and technological innovation. However, numerous barriers can hinder the establishment of such collaborations. One of the main challenges is the differences in institutional cultures and priorities. Universities often prioritize academic freedom, while industry partners focus on commercialization and profit [26,27,28]. Another significant barrier is the lack of trust between universities and industry. Universities may be hesitant to share their intellectual property and research findings, while industry partners may view academic research as too theoretical and impractical. Communication breakdowns and conflicting expectations can also impede cooperation, as can differences in the speed and flexibility of decision-making processes [29,30,31].
Furthermore, limited resources can pose significant challenges for universities seeking to establish cooperative partnerships with industry. Faculty members may be reluctant to devote time and effort to industry collaborations, as these collaborations may divert attention from their academic research and teaching duties [32,33,34,35].
Currently, innovation models [36,37,38] and tools for evaluation in the process of innovation management are available in the literature, such as the Innovation Radar [39] and the innovation octagon [40]. There are also models to enable technology transfer [41], but there are still no tools to assess the management of the green technology transfer process. Evaluating this process effectively will make it possible to better optimize the human, technological, financial, and environmental resources available, as well as will ensure sustainability in the innovation process.
Considering this, the objective of this research was to develop a tool to evaluate the structure of green technology transfer in Brazilian universities and public institutes, as well as to make a diagnosis about the researched activities. The study aims to answer two questions: (i) How can the green technology transfer process be evaluated? and (ii) what is the Brazilian green technology transfer scenario?
In the next section of the study, the process of designing the tool will be presented.

2. Green Technology Transfer Radar

The dimensions of the evaluation tool model in Table 1 were based on the dimensions of innovation models, technology transfer models, and sustainability. To validate the model, different alternatives were found in the literature by cross-searching the key terms innovation, technology transfer, and sustainability.
In the innovation field, five main models were consulted. The first model was that of Berreyre [36], who was one of the first to create typologies for innovation, which can be classified into four groups:
The technological domain, which comprises transformations related to the technical aspects of products, processes, and methods in production and services;
The commercial domain, which includes transformations regarding the commercialization process;
The organizational domain, which comprises transformations related to organization, management, and procedures;
The institutional domain, which includes transformations in the systems and norms that constitute the company.
The second model was that of Schumpeter, who, in 1984 [37], proposed a system for innovation comprising five forms or dimensions: the entry of new products, the entry of new production methods, the opening of new markets, the development of new sources of raw materials and agricultural inputs, and the creation of new market structures in the industry. The author also emphasizes the great radical innovations that include transformations in the economic model, and the incremental innovations that comprise the improvements of the radical innovations.
The third model was the Oslo Manual [38], which is a milestone for the systematization of innovation, being the first formal tool with the capacity to evaluate innovative companies. The Manual is part of a series of publications by the Organization for Economic Co-operation and Development (OECD), and it comprises five dimensions for innovation. In 2004, in its third edition, the Manual brought forward a conceptual broadening for innovation. In addition to the dimensions of products and processes, organizational innovations and marketing innovations were incorporated.
The fourth model was that of Sawhney [39], who created the Innovation Radar tool. The author’s model directs to a set of twelve distinct dimensions so that a company can be innovative in the market. The dimensions refer to the following aspects:
−
Offers: How to develop new products and/or services?
−
Platform: How to use common elements or develop sets?
−
Solutions: How to design personalized offers to suit customers?
−
Customers: How to prospect unmet demands from new customers?
−
Customer Experience: How to reevaluate and redesign the relationship with the customer?
−
Added value: How to redesign the activities to generate value?
−
Process: How to redesign the processes to achieve efficiency and effectiveness?
−
Organization: How to modify the form, functions, or scope of activities?
−
Supply Chain: How to think about different supply alternatives and how to improve the supply chain?
−
Presence: How to create new channels of distribution or present locations?
−
Network: How to create integrated and intelligent networks for the offer?
−
Brand: How to boost the company’s brand?
The fifth researched innovation model was proposed by Scherer and Carlomagno [40]; the authors created a new tool called the “innovation octagon”. The tool presents the main points to be managed to increase innovative productivity, from strategy to the process of transforming ideas into results. The dimensions refer to the following aspects:
−
Strategy: How does a company articulate the direction of innovation initiatives?
−
Leadership: What is the understanding of the leadership regarding the need for and relevance of innovation? How do managers support the atmosphere of innovation?
−
Culture: What does the top administration say and how do they create an atmosphere that is conducive to innovation?
−
Relationships: How does the company use partners, customers, and competitors in the creation and refinement of ideas?
−
Structure: Where is the innovation activity located and how is it being organized?
−
People: How is support provided for innovation, its incentives, and recognition?
−
Process: How are innovation opportunities created, developed, and evaluated?
−
Funding: How are innovation initiatives financed?
The technology transfer models were based on the Bozeman [41] and Baek [42] models. These models were chosen for their structure and as points of reference.
In Bozeman’s model [41], the structure includes five broad dimensions to determine the effectiveness of the activity: characteristics of the transfer agent, characteristics of the transfer media, characteristics of the transfer object, the demand environment, and the characteristics of the transfer recipient. The model includes a social impact assessment.
Baek et al. [42] developed a technology valuation model in the process of technology transfer negotiations. The structure of the technology valuation model is based on an income approach and real options, and can express the value of a specific technology in economic terms. The model was divided into three steps: the expected returns analysis, the technology contribution analysis, and the buyer’s technology assessment.
Finally, the sustainability model “Triple Bottom Line” by John Elkington [43] was analyzed, which was chosen because it is a reference model in the literature regarding sustainability. The dimensions comprise the following points:
−
Economy (Profit): This comprises the company’s profitability aspects;
−
Social (People): This comprises the treatment of the human capital of a company or society;
−
Environmental (Planet): This comprises the natural capital of a company or society.
The researched models serve as a basis for the study, preparation, and choice for the dimensions of the proposed tool, as shown in Table 1. The dimensions of the tool and their connection with the studied models of innovation, transfer of technology, and sustainability are shown below.
Table
Table 1. Dimensions of the based models for the proposed RGTT.
Table 1. Dimensions of the based models for the proposed RGTT.
Innovation ModelsTT ModelsSustainability ModelProposed Dimensions
[36][37][38][39][40][41][42][43]
xx xPeople
xxxxxx Processes
x xBudget
xx Relationship
x x Integrated Management
x R&D in Green Technologies
x Intellectual Property
x Valuation
xxxx x Commercialization
xEnvironment
x xSociety
Source: The authors’ own elaboration.

2.1. Conceptual Model of the Proposed Tool

The Green Technology Transfer Radar tool was structured with eleven dimensions (People, Processes, Budget, Relationships, Integrated Management, Research and Development in Green Technologies, Intellectual Property, Valuation, Commercialization, Environment, and Society), presenting the main points to be managed in the green technology transfer process in the university–industry context. These ranged from the strategy to the process of transforming ideas into patenting, as well as the monitored impacts generated by the transferred technology. The dimensions proposed in the tool refer to the following aspects:
(1)
People: How is support provided for green technology transfer, including incentives and knowledge diversity for the sustainable area?
(2)
Processes: How are green technology transfer opportunities created, developed, and evaluated?
(3)
Budget: How are green technology transfer initiatives funded?
(4)
Relationships: How does the university use its stakeholders in creating and improving sustainable ideas?
(5)
Integrated Management: How are activities and decisions in conducting projects involving green technologies planned and managed in laboratories, TTOs, and academic boards?
(6)
Research and Development in Green Technologies: How are scientific projects researched and developed for green technologies?
(7)
Intellectual Property: How are measures for the patenting process and for the registration of technology transfer contracts conducted?
(8)
Valuation: How are tools and measures applied to the valuation of technologies before going to the market?
(9)
Commercialization: How are negotiations and the commercialization of transferred technologies conducted?
(10)
Environment: What are the impacts on the environment that result from the insertion of transferred green technologies, and how are they measured and monitored?
(11)
Society: How was the history of society, as well as its consumption pattern, studied and evaluated before the transfer of green technology? Additionally, how were the impacts of the use of technology measured and monitored in the lives of the people living in society?
A questionnaire was prepared with 33 closed questions that constitute the dimensions of the Green Technology Transfer Radar tool (Supplementary File S1); three questions were distributed for each dimension. The tool has a Likert scale, with scores from 1 to 5, where 1 means never, 2 rarely, 3 occasionally, 4 frequently, and 5 very frequently. The higher the score presented in the question, the better the potential for green technology transfer in universities. The illustrative model of the tool can be seen in Figure 1.

2.2. Stages for Operating the Green Technology Transfer Radar (RGTT) Tool

To improve the management process in the application of the green technology transfer radar in universities, seven operational stages were elaborated. These steps comprise:
(1)
Research Planning: Those involved must plan all phases of the research application, defining the human, financial, and technological resources in the short and medium term, as well as the return for those involved;
(2)
Conducting Research in TTOs and/or Research Laboratories: Those involved in the process must conduct the research with the established target audience in the planning stage, considering the best time and place for its application;
(3)
The Measurement, Analysis, and Dissemination of Results: The results should be measured and analyzed using a radar chart. If necessary, other statistical tools should be added to the analysis to improve the investigation. After this stage, the best communication method for disseminating the results should be chosen;
(4)
Defining Methodologies to Remove Bottlenecks: After analyzing and observing the green technology transfer scenario, methodologies for eliminating the identified gaps should be defined;
(5)
Cost–Benefit Analysis and Corrective Plan Elaboration: In this stage, methodologies should be defined for the cost–benefit analysis of regarding the operationalization of the activities to eliminate the identified gaps and to develop a corrective plan for these activities;
(6)
Approval of the Project/Resources with the University: With the project of the corrective plan, along with the cost–benefit analysis, the project should be presented to the administration for approval of the project funding;
(7)
Conducting the Action Plan: In this last stage, the approved resources should be directed and a plan developed in order to conduct the operational activities. The green technology transfer radar tool operationalization model can be seen in Figure 2. The model has three gates: stage 1, stage 4, and stage 6.

3. Materials and Methods

The research is applied by nature. From an objective point of view, it is descriptive and exploratory. The approach is quite qualitative. The research was conducted in three phases. The first phase consisted of exploring the literature on the topic of the tool dimensions.
In the second stage, the tool dimensions were elaborated, an operationalization/application model was developed, and a questionnaire with 33 closed questions was prepared (Supplementary File S1). Three questions were distributed for each dimension. To test the validation of the questionnaire, it was first sent to 8 TTO managers and 20 researchers from Brazilian research groups/laboratories. After receiving feedback, corrections were made, and the final questionnaire was validated.
The third stage consisted of validating the green technology transfer tool with its application in the research groups/laboratories, and with the TTOs of their respective universities and/or research institutes to validate the instrument and to diagnose their activities regarding TT.
The research groups/laboratories and universities were extracted from the Directory of Research Groups in Brazil, which was obtained from the National Council for Scientific and Technological Development, the Brazilian government’s main platform for registering groups. Information was sought on the activities directly related to the development of green technologies. Priority was given to the areas that are part of the green patents program of the National Institute of Industrial Property of Brazil: agriculture, energy (conservation and alternative energies), waste management, and transportation.
A total of 255 researcher groups/laboratories were found in the directories of the National Council for Scientific and Technological Development (CNPq) out of the 37,640 existing groups operating in all of the areas of knowledge (0.7%) corresponding to green areas in Brazil. This percentage is considered very low for existing groups in the green areas in Brazil.
In Table 2, the distribution of the groups and the areas of subarea knowledge can be found. The 255 groups consist of 2338 researchers and 2645 registered students. The areas of knowledge that had the largest number of identified groups were agriculture and waste management.
The tool’s questionnaire was applied to the total population of the 255 research groups/laboratories surveyed in the search, as well as to their respective TTOs of universities and research institutes. The survey return rate was 22% for the universities.
The sample was composed of the following equation: where n = size of the population (255 groups), Zα/² = critical value that corresponds to the desired confidence level, σ = population standard deviation of the variable, and E = margin of error or the maximum estimation error. After application, with a desired confidence level of 95%, a margin of error of 11.7%, and a sample size corresponding to 55 universities, the chosen sample was considered safe with a good margin of error.
The objective of applying the questionnaire in the TTOs and research groups of the same institution was to trace a comparative scenario through the green technology transfer radar between the response of the university’s technology transfer agent (TTO) and the distinct research groups/laboratories that develop science, technology, and innovation activities. These groups, in many contexts, have had no approach with respect to the TTOs.
Due to the complexity of information related to the TTO functions and management activities in the research groups, the questionnaire was applied to the leaders, referring to the real scenario, in order to enable more authenticity in analyzing the information. Thus, the managers were selected to answer the questionnaires. The questionnaire was applied electronically using the Google Forms tool between 2 April 2022 and 3 June 2022.
To obtain a single average on each of the eleven dimensions of the green technology transfer radar tool, the scores given by the respondents were scored on a Likert scale between 1 and 5 for each of the three questions, which were then added up and divided by the total number of questions (three) to obtain the final average.
Table 3 shows the distribution of the numbering of the questions in the questionnaire for each tool dimension observed (Supplementary File S1). With the averages of each tabulated dimension, it was possible to develop the radar graph of the green technology transfer radar.
To analyze the information collected in the green technology transfer radar survey, Pearson’s linear correlation coefficients between the dimensions were verified. Then, the multivariate statistical technique factor analysis was used to generate a smaller number of new latent variables that were unobserved and calculated from the raw data. The factor extraction method considered was that of principal components. The factor rotation was performed using the orthogonal varimax method, and Kaiser’s criterion (eigenvalue above 1) determined the number of factors to be used in this analysis.
To verify the existence of the differences between the averages of the TTOs and the research groups, an analysis of variance (ANOVA) was first carried out. Tukey’s test was applied because it allowed for multiple comparisons between all averages taken two by two, considering a significance level of 5%. The hypothesis of equality between the averages of the dimensions was not rejected. The statistical analyses were developed with the support of Minitab software 16.0. When considering a significance level of 5%, the hypothesis of equality between the average levels was not rejected.

4. Results and Discussion

This section presents the results of the green technology transfer radar validation with universities and research institutes in Brazil. A response rate of 22% was obtained from universities, of which 64% were responses from the research groups and 36% were from the TTOs. The names of the groups and universities are identified by letters and numbers to ensure the confidentiality of the respondents’ identification.
The survey with the managers of the TTOs and research groups/laboratories made it possible to observe more effectively the functioning of environments concerning the green technology transfer process within universities and research institutions. Figure 3, Figure 4, Figure 5 and Figure 6 present a scenario of the green technology transfer structure of the research groups/laboratories in the areas of waste management, agriculture, energy, and transportation. Acronyms have been assigned to the group names to simplify the statistical analysis. Figure 7 and Figure 8 present scenarios of the TTOs of the education/research institutions, where the interferences between the dimensions of the green technology transfer radar tool can be observed. The average of the values given in all dimensions in the green technology transfer radar are distributed and presented in Supplementary Files S2 and S3.
The ideal result would be for the averages of the 11 dimensions of the TTOs and research groups to be equal to 5, making the transfer of green technologies efficient. However, as can be seen in the graphs above, there is a constant oscillation in the averages. Among the groups, the GR6 group had the lowest oscillation of the averages, and the overall average of the dimensions was 4.30. Among the TTOs, U8 was the one that presented a better performance with an overall average of the dimensions of 4.40.
In Pearson’s correlation coefficient table (Supplementary File S4), there is a strong correlation between the dimensions, with only 8 of the 55 correlations found being less than 0.30. The highest correlation coefficients, considering the coefficients above 0.60, are presented in Table 4.
The number of factors to be extracted is shown in Table 5. The Kaiser criterion suggests that three factors should be extracted: The first presents an eigenvalue of 5.52, accounting for about 50% of the variance. The second factor presents an eigenvalue of 1.24, accounting for about 11% of the variance. The third eigenvalue of 1.04 accounts for about 10% of the variance. Together, these three factors explain 71% of the variance of the original variables. Figure 9 illustrates the dispersion of the components in the screen test.
Table 6 presents the values of three factors without and with orthogonal varimax rotation.
It can be seen that, without rotation, there is a concentration of dimensions in factor F1, whereas only intellectual property is found in factor F2. With the application of varimax rotation, a better distribution occurred. Figure 10 better represents the grouping of the dimensions of the data used in this research.
The results of the test for comparison of the means between the research groups show the results of the test of the means of the TTOs, with a confidence level of 95%.
The averages of the budget dimension among the different research groups are significantly different, whereas in the environment dimension, the AG5 group has an average equal to 1.00. In addition, EN3 has an average equal to 1.00, where they differ from the other groups; moreover, GR6 has an average equal to 5.00. Among the TTOs, the averages of the dimensions of budget and intellectual property dimensions are significantly different.
With these results, it is possible to identify a great variation between the dimensions, and, consequently, the universities face many barriers to developing TT activities. To overcome these barriers, universities must engage in proactive efforts to foster collaboration and build relationships with industry partners. This can involve creating partnerships that are mutually beneficial, building trust through transparent communication and shared goals, as well as by providing support and resources for the faculty members involved in industry collaborations [44,45].
Ultimately, successful partnerships between universities and industry require a willingness to bridge the cultural divide between academia and industry, as well as a commitment to working together to achieve common goals [46].

5. Conclusions

The purpose of this research was achieved: a green technology transfer radar tool was developed and validated, through the eleven dimensions, with the purpose of creating a mechanism to evaluate the structure of green technology transfer within the scope of universities and institutions of science and technology in Brazil. The statistical analysis applied allowed us to verify the characteristics of the TTOs and research groups regarding the dimensions adopted in the tool. The averages of the dimensions were small in some groups and TTOs, indicating that actions must be taken to improve their dimensions. This reinforces that technology transfer becomes efficient if the dimensions are close to five.
In the scope of interaction experience, strengthening the green technology transfer structure goes through the training of qualified labor to act in strategic sectors and incentives to direct research carried out at universities, institutes, and industries toward priority areas for the country’s development.
Brazil has recognized the importance of developing and implementing strategies for the transfer of green technology in universities. One proposed strategy is to create innovation centers to bring together university researchers and industry representatives. This type of collaboration can increase the likelihood of successful technology transfer by providing access to resources and networks that would otherwise be unavailable. Researchers [47] explain that universities must engage in knowledge transfer activities that are meaningful to industry partners to ensure successful technology transfer. This means that universities must understand the needs and priorities of industry partners and must develop targeted strategies to meet those needs. The authors suggest that universities can also play a role in promoting the development of green technology startups through the provision of support services such as incubation and acceleration programs.
Another strategy proposed for the transfer of green technology in Brazilian universities is the creation of technology transfer offices (TTOs). These offices can serve as intermediaries between university researchers and industry partners, providing support for patenting, licensing, and the commercialization of technology. The TTOs can also play a role in identifying potential industry partners and building relationships that facilitate technology transfer. The authors emphasize the importance of creating a culture of innovation within universities to encourage technology transfer and the development of green technologies. This can involve creating incentives for researchers to engage in technology transfer activities and promoting a culture of entrepreneurship and innovation [48].
In addition to these strategies, several authors have proposed specific actions that universities can undertake to promote the transfer of green technology. Authors [49,50,51] suggests that universities can encourage the development of green technologies by offering courses and training programs that focus on sustainability and green technologies. The authors also propose the development of research centers that focus on green technologies and the creation of mechanisms to support collaboration between researchers and industry partners. Moreover, universities can develop partnerships with government agencies to support the development and commercialization of green technologies.
The transfer of green technology is an important issue that requires collaboration between universities, industry partners, and government agencies. Strategies such as the creation of innovation centers and TTOs, as well as actions such as offering training programs and developing research centers, can help promote successful technology transfer and the development of green technologies. However, it is important for universities to understand the needs and priorities of industry partners and to create a culture of innovation and entrepreneurship to encourage technology transfer. By implementing these strategies and actions, Brazilian universities can contribute to the development of sustainable and environmentally friendly technologies.
The researched universities must analyze the aspects and critical points raised and develop a green technology transfer model process. This can be achieved by having the TTO as the facilitating agent in this process and by integrating it into the green technology transfer model. Creating a stage of evaluation of the transfer structure between the agents involved in this process, and also evaluating this transfer structure through tools before the negotiation and approval process of the contracted parties, may reduce gaps in this process and enable a favorable scenario for the best choices of mechanisms to transfer the negotiated green technology transfer.
However, universities must develop strategies to reduce the gaps mentioned in the application of the green technology transfer radar tool. Additionally, they should promote a joint action with TTOs and research groups within their institutions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su15086873/s1, Supplementary File S1: Form for the evaluation of the Green Technology Transfer structure; Supplementary File S2: Table of distribution of RGTT dimension scores for research groups/laboratories; Supplementary File S3: Table of distribution of RGTT dimension scores for Technology Transfer Offices (TTOs); Supplementary File S4: Table of Pearson’s correlation coefficients of dimensions.

Author Contributions

Conceptualization, L.C.S.S.; data curation, C.S.T.C.; formal analysis, R.d.O.S.; funding acquisition, L.C.S.S.; investigation, L.C.S.S.; methodology, L.C.S.S. and C.S.T.C.; project administration, L.C.S.S.; resources, L.C.S.S. and C.S.T.C.; supervision, L.C.S.S. and C.S.T.C.; visualization, C.S.T.C.; writing—original draft preparation, C.S.T.C.; writing—review and editing, L.C.S.S. and S.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by CAPES—Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil [n° 2013-2016], through a scholarship to carry out studies of Ph.D. in Production Engineering at the Federal University of Rio Grande do Sul (UFRGS), for Luan Carlos Santos Silva.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Radar representation of green technology transfer (RGTT), Source: The authors’ own elaboration.
Figure 1. Radar representation of green technology transfer (RGTT), Source: The authors’ own elaboration.
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Figure 2. Operational steps of the RGTT tool. Source: The authors’ own elaboration.
Figure 2. Operational steps of the RGTT tool. Source: The authors’ own elaboration.
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Figure 3. Research waste management groups. Source: own study.
Figure 3. Research waste management groups. Source: own study.
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Figure 4. Research agriculture groups. Source: own study.
Figure 4. Research agriculture groups. Source: own study.
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Figure 5. Research energy groups. Source: own study.
Figure 5. Research energy groups. Source: own study.
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Figure 6. Research transport groups. Source: own study.
Figure 6. Research transport groups. Source: own study.
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Figure 7. Research TTO’s RGTT. Source: own study.
Figure 7. Research TTO’s RGTT. Source: own study.
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Figure 8. Research TTO´s RGTT. Source: own study.
Figure 8. Research TTO´s RGTT. Source: own study.
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Figure 9. Scree plot. Source: own study.
Figure 9. Scree plot. Source: own study.
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Figure 10. Rotated dimensions. Source: the authors’ own elaboration.
Figure 10. Rotated dimensions. Source: the authors’ own elaboration.
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Table
Table 2. Distribution of research groups in green areas in Brazil.
Table 2. Distribution of research groups in green areas in Brazil.
Agriculture Waste Management Transport Energy
AREAN°AREAN°AREAN°AREAN°
Agricultural Sciences82Agricultural Sciences23Agricultural Sciences0Agricultural Sciences2
Biological Sciences3Biological Sciences6Biological Sciences0Biological Sciences0
Exact and Earth Sciences7Exact and Earth Sciences13Exact and Earth Sciences1Exact and Earth Sciences5
Human Sciences5Human Sciences0Human Sciences0Human Sciences1
Applied Social Sciences4Applied Social Sciences2Applied Social Sciences1Applied Social Sciences2
Engineering5Engineering60Engineering9Engineering24
Total groups:106Total groups:104Total groups:11Total groups:34
Total Researchers1201Total Researchers796Total Researchers92Total Researchers249
Total Students1177Total Students1150Total Students95Total Students223
Source: The authors’ own elaboration.
Table
Table 3. Distribution of questions in the dimensions of the RGTT tool.
Table 3. Distribution of questions in the dimensions of the RGTT tool.
RGTT DimensionDistribution of Questions
People1, 2, 3
Processes4, 5, 6
Budget7, 8, 9
Relationship10, 11, 12
Integrated Management13, 14, 15
R&D in Green Technologies16, 17, 18
Intellectual Property19, 20, 21
Valuation22, 23, 24
Commercialization25, 26, 27
Environment28, 29, 30
Society31, 32, 33
Source: The authors’ own elaboration.
Table
Table 4. Highest correlation coefficients.
Table 4. Highest correlation coefficients.
VariablesF1F2F3F4F5F6F7F8F9F10F11
People0.640.19−0.42−0.220.43−0.34−0.03−0.19−0.070.000.03
Processes0.76−0.22−0.23−0.010.020.070.510.16−0.010.16−0.02
Budget0.73−0.04−0.400.30−0.01−0.09−0.180.380.07−0.17−0.03
Relationship0.68−0.330.390.290.270.13−0.070.02−0.29−0.010.10
Integrated Management0.730.130.410.340.12−0.17−0.05−0.090.280.16−0.07
R&D in Green Technologies0.640.46−0.220.32−0.360.010.01−0.24−0.210.03−0.05
Intellectual Property0.490.690.08−0.260.140.35−0.130.21−0.020.11−0.01
Valuation0.80−0.19−0.10−0.070.090.400.05−0.260.15−0.21−0.06
Commercialization0.660.310.49−0.21−0.14−0.230.230.07−0.01−0.240.04
Environment0.86−0.19−0.11−0.17−0.290.00−0.15−0.050.130.100.22
Society0.73−0.420.15−0.37−0.17−0.10−0.210.03−0.120.08−0.18
Eigenvalues5.521.241.040.710.560.510.430.390.270.210.11
(%) Var50.0011.0010.07.005.005.004.004.002.002.000.00
Source: own study.
Table
Table 5. Eigenvalues and variance.
Table 5. Eigenvalues and variance.
Transfer DimensionsCorrelation Coefficient
Integrated managementRelationship0.64
ValuationProcesses0.65
CommercializationIntegrated management0.62
EnvironmentProcesses0.65
EnvironmentBudget0.62
EnvironmentValuation0.71
SocietyEnvironment0.79
Source: own study.
Table
Table 6. Factor matrix with and without rotation (varimax).
Table 6. Factor matrix with and without rotation (varimax).
Non-Rotated MatrixMatrix Rotated (Varimax)
Transfer DimensionsF1F2F3F1F2F3
People0.640.19−0.420.720.070.3
Processes0.76−0.22−0.230.690.460.06
Budget0.73−0.04−0.40.780.250.15
Relationship0.68−0.330.390.170.830.12
Integrated Management0.730.130.410.160.630.55
R&D in Green Technologies0.640.46−0.220.560.040.59
Intellectual Property0.490.690.080.2200.82
Valuation0.80−0.19−0.10.610.550.14
Commercialization0.660.310.490.040.540.69
Environment0.86−0.19−0.110.650.580.17
Society0.73−0.420.150.390.760.00
Source: own study.
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Santos Silva, L.C.; Ten Caten, C.S.; Gaia, S.; de Oliveira Souza, R. Tool for Assessment of the Green Technology Transfer Structure in Brazilian Public Universities. Sustainability 2023, 15, 6873. https://doi.org/10.3390/su15086873

AMA Style

Santos Silva LC, Ten Caten CS, Gaia S, de Oliveira Souza R. Tool for Assessment of the Green Technology Transfer Structure in Brazilian Public Universities. Sustainability. 2023; 15(8):6873. https://doi.org/10.3390/su15086873

Chicago/Turabian Style

Santos Silva, Luan Carlos, Carla Schwengber Ten Caten, Silvia Gaia, and Rodrigo de Oliveira Souza. 2023. "Tool for Assessment of the Green Technology Transfer Structure in Brazilian Public Universities" Sustainability 15, no. 8: 6873. https://doi.org/10.3390/su15086873

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