Assessing academics' third mission engagement by individual and organisational predictors

Karlsdottir, Verena; Magnus Thor Torfason; Ingi Rúnar Eðvarðsson; Thamar Melanie Heijstra Article Assessing academics' third mission engagement by individual and organisational predictors Administrative Sciences Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Karlsdottir, Verena; Magnus Thor Torfason; Ingi Rúnar Eðvarðsson; Thamar Melanie Heijstra (2023) : Assessing academics' third mission engagement by individual and organisational predictors, Administrative Sciences, ISSN 2076-3387, MDPI, Basel, Vol. 13, Iss. 1, pp. 1-22, https://doi.org/10.3390/admsci13010009 This Version is available at: https://hdl.handle.net/10419/275475 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Karlsdottir, Verena, Magnus Thor Torfason, Ingi Runar Edvardsson, and Thamar Melanie Heijstra. 2023. Assessing Academics’ Third Mission Engagement by Individual and Organisational Predictors. Administrative Sciences 13: 9. https://doi.org/10.3390/ admsci13010009 Received: 29 November 2022 Revised: 21 December 2022 Accepted: 24 December 2022 Published: 29 December 2022 Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). administrative sciences Article Assessing Academics’ Third Mission Engagement by Individual and Organisational Predictors Verena Karlsdottir 1,* , Magnus Thor Torfason 1, Ingi Runar Edvardsson 1and Thamar Melanie Heijstra 2 1School of Business, University of Iceland, 102 Reykjavík, Iceland 2Faculty of Sociology, Anthropology and Folkloristics, University of Iceland, 102 Reykjavík, Iceland *Correspondence: ver[email protected] Abstract: In recent years, the coming of the entrepreneurial university has brought about a third role in academia, which involves greater visible exchange of academics with society and industry. In this paper, the authors investigate to what extent individual and organisational factors influence the propensity of academics to engage in different types of Third Mission (TM) activities. This study is based on a large-scale survey of academics in Iceland regarding engagement in socio-economic activities. The results indicate that “soft” activities such as community activities and external teaching and training can be better predicted by individual factors, while hard activities such as applied contract research and commercialisation can be better predicted by organisational factors. Overall, academics are most likely to participate in community-related activities. Hereby, academics from the STEM and health disciplines, with work experience outside of academia and who are open to new experiences are more likely to be engaged in applied contract research and commercialisation. Academics belonging to disciplines other than STEM and health sciences and those that on an average publish more peer-reviewed articles are more likely to disseminate their knowledge to a wider audience outside of academia through public science communication. Gender, rank, and teaching do not affect TM participation, but openness, performance, or discipline do. Keywords: third mission; entrepreneurial university; personality traits; commercialisation; contract research; science communication 1. Introduction The introduction of the entrepreneurial university has magnified the work obligations of academics by adding socio-economic activities to their traditional research and teaching responsibilities (Addie 2017). The model anticipates more intensive interaction with industry and society, with academics taking up a third role (Westnes et al. 2009) or a “regional development role” (Jaeger and Kopper 2014). The purpose of this third role, or Third Mission (TM) is then to increase knowledge and technology transfer to society, for instance, by incorporating formal and informal commercialisation activities (Perkmann et al. 2013) to enhance innovation and create a more profitable university by allowing more diverse sources of allocation. This then supplements universities in their role within the triple helix (Etzkowitz 2002;Leydesdorff and Etzkowitz 1996). What complicates the situation is that the literature on TM and entrepreneurial universities is broad, the stakeholders many, and the activity outcomes at times difficult to measure. This makes it challenging to capture TM and the entrepreneurial university at their full scope. As per definition, the TM and entrepreneurial universities are in line with each other. Philpott et al. (2011) explain: “a university that embraces its role within the triple helix model and adopts the mission of contributing to regional/national development is referred to as an ‘entrepreneurial university’” (p. 162). Hereby, the “entrepreneurial university adopts the third mission” (p. 162). However, what represents an entrepreneurial university is not restricted by innovation and entrepreneurship, but also includes direct Adm. Sci. 2023,13, 9. https://doi.org/10.3390/admsci13010009 https://www.mdpi.com/journal/admsci Adm. Sci. 2023,13, 9 2 of 22 and indirect academic engagement with their immediate environment. This relates to actors from industry, research organisations, other educational establishments, the public, and society in general. Examples are science communication, organising lifelong-learning activities and policy development. As the recipients from industry and other stakeholders interacting with academia have different purposes and intentions themselves, it is important that universities and researchers do not solely link the TM concept or entrepreneurial activities, with activities related to economic development or research commercialisation (Philpott et al. 2011). It is therefore crucial for the university to follow a balanced approach to satisfy multiple economic and social interests of its many stakeholders with respect to its social responsibility within the community (Barrena Martínez et al. 2016). The impact of the TM is broad and can also be linked to non-profit and informal aspects. Consequently, there have been calls from within the university that the current academic performance system, which rewards scientific publications and teaching activities, is insufficient, as it does not capture the socio-economic impact of the TM efforts well-enough (Dahlborg et al. 2017). Different opinions and criteria exist on how to evaluate and measure the performance of an entrepreneurial university and academics’ TM engagement, as no common frameworks exist yet (Gür et al. 2017;Mazdeh et al. 2013;Secundo et al. 2017). Academics are used to high levels of autonomy, especially at public universities. In this regard, it seems crucial to analyse personal characteristics and link them to TM engagement. Moreover, entrepreneurial intention seems to be highly driven by intrinsic motivation and can be mediated by academic position and work context (Antonioli et al. 2016). There are studies that have reviewed the influence of individual and organisational motivational causes on innovation and entrepreneurship participation (e.g., Liñán and Fayolle 2015; Molino et al. 2018), or that have focused on the academic context in particular (Johnson et al. 2017;Miller et al. 2018;Neves and Brito 2020). This article does both by concentrating on individual and organisational factors influencing academic engagement in five types of TM activities. Moreover, while previous research on TM activities of academics has focused on either commercialisation or university-industry collaboration (UIC) (Baycan and Olcay 2021; Knudsen et al. 2021;Ranga et al. 2016;Stefanelli et al. 2020), we will focus on a broader variety of TM activity engagement. However, to do so, it is crucial to conceptualise TM first, as for the operationalisation to be valid and reliable. For this study, we follow the definition of Molas-Gallart et al. (2002) who define TM as the “interactions between universities and the rest of society” (p. iv), whereby TM activities are mainly driven by the “generation, use, application and exploitation of knowledge and other university capabilities outside academic environments” (p. 2). To design policies and performance reward systems, it is important to know which role academics play when it comes to socio-economic engagement and what factors impact the extent and frequency of their engagement. Therefore, we pose the following research question: To what extent do individual and organisational factors influence the propensity of academics to engage in different types of Third Mission activities? We will also evaluate what academic profile suits the different TM activities by examining how immutable individual and organisational factors that are inherent to the organisation relate to TM participation. We assume that academics cannot be engaged in all different TM activities simultaneously, hence the tendency of academics to engage in some activities more than in others. By examining both individual and organisational determinants, we are answering to a call by Huyghe and Knockaert (2015) for the need of broader analysis. Much of the existing research concentrates on company creation or patenting, and it has also been criticised that industry interaction is commonly only partly evaluated. Subsequently, there is the risk of missing out on important other types of knowledge interactions (Schartinger et al. 2002). Getting a better understanding of TM activities does not only facilitate the creation of an entrepreneurial university structure but it helps building Adm. Sci. 2023,13, 9 3 of 22 a powerful and dynamic research environment in regional innovation systems through purposeful allocation of funding, the creation of appropriate organisational structures and incentives, and through the development of policies (Karlsen et al. 2017;Lehmann et al. 2009;Nilsson 2006). The objective of this study is thus to shed light on the way academic institutions can encourage academic employees to participate in TM activities. This study makes several contributions. First, it considers academic organisational variables such as rank, discipline, academic work experience, outside academia experience and academic performance (Dahlborg et al. 2017;Holmen and Ljungberg 2015). Bourelos et al. (2012) emphasize that variables related to the individual and the organisational support structure should be included in the context of policy formulation due to the complexity of entrepreneurship. In this study, we therefore examine how individual factors and organisational factors influence researchers’ entrepreneurial and societal engagement (Fogelberg and Lundqvist 2013). Second, no prior research evaluates all academic disciplines or compares Science, technology, engineering and mathematics (STEM) and health disciplines towards all other scientific disciplines, and propensity of researchers towards TM. Most research has been conducted in engineering and natural or medical sciences with an emphasis on commercial aspects of TM (D’Este and Patel 2007). Third, this article contributes to theory building as, to date, only single aspects of the TM have been researched, while this research considers TM from a broader perspective. Although all of these aspects have been applied in other studies, our study is the first to integrate all three aspects in one model. Results reveal that academics participate most in community-related activities. Further, participation in TM activities is not affected by factors such as gender, teaching, or rank, but is affected by factors such as openness to experience, performance, or discipline. 2. Literature Review So far, myriad research has been conducted on factors influencing academic productivity and success (Feist 2011;Feist and Gorman 1998). Such factors can be on the individual level and be affiliated with age, gender, personality characteristics, academic rank, and specialisation. External factors are in contrast related to the working environment, group dynamics, or incentives (Antonio-García et al. 2014). So far, there are opposing results on industry collaboration having a positive effect on academic productivity and teaching performance (Bikard et al. 2019;Garcia et al. 2019) or not or even a negative effect (Brooks and Randazzese 1998;Hottenrott and Lawson 2014;Nelson 2004;Zhang and Wang 2017). Regarding academic consulting, results depend on the field of science if it is negatively (STEM) or positively (SSH) correlated with number of publications (Rentocchini et al. 2014). Research has shown that knowledge transfer, which is based on mutual collaboration, is mostly fostered by individual researchers and not universities or university departments (Breschi and Catalini 2010;D’Este and Patel 2007). Moreover, Bourelos et al. (2012) also showed that it is crucial to include variables based on individual level of academics such as research performance and personal networks. Likewise, the innovative activity of individuals and, in this context, entrepreneurship has been studied profoundly. When summarising previous research in regard to gender, male academics are more likely to collaborate with the industry and are more committed to innovation and entrepreneurship (Calvo et al. 2019;Pita et al. 2021). Possible reasons are that men seem to have a denser network outside of academia (Abreu and Grinevich 2013;Bozeman and Gaughan 2011). Additionally, the proportion of men is higher in disciplines of natural and health sciences, where innovation and applied research are practiced more than in other sciences (Klofsten and Jones-Evans 2000). Further, women are generally more risk averse than men, especially in regard to financial decisions (Brindley 2005; Gimenez-Jimenez et al. 2020; Humbert and Brindley 2015), and they also dislike competition more than men (Gneezy et al. 2003;Niederle and Vesterlund 2007,2011). On the other hand, female academics are more engaged in teaching and teaching related activities (Hughes et al. 2016). Adm. Sci. 2023,13, 9 4 of 22 Regarding the age of academics and research productivity, age does not seem to be a good predictor as results are either ambiguous (Kotrlik et al. 2002) or only weakly correlated (Stephan 1996). Seniority seems to be a better predictor, whereas higher seniority positively influences patenting behaviour due to bigger networks and university-industry collaboration (UIC) activities (Boardman and Ponomariov 2009;Carayol 2007;D’Este and Perkmann 2011;Grimm and Jaenicke 2015). This is facilitated as mature academics have developed higher human capital (e.g., in the form of scientific publications and patents) and social capital (e.g., in the form of research partnerships, collaborations, and networks) (Calvo et al. 2019). However, the older a person becomes, the less likely it is that he or she will start a new company (Karlsson and Wigren 2012). Younger academics have smaller networks and less experience in collaborative activities. They further need to establish themselves first in academia by following academic excellence to move up to higher positions (Klofsten and Jones-Evans 2000). Therefore, less time is invested into entrepreneurial activities. Studies have further examined different types of universities, thereby looking into distinctions within older, more established universities, and younger, less research-oriented universities where commercialisation is in the foreground (Sanchez-Barrioluengo et al. 2019). Regarding the size of universities or university departments, results reveal mixed effects (Bonaccorsi et al. 2014), with medium-sized universities being more engaged in TM. Additionally, location can give evidence, whereby regional universities are less collaborating with industry (Sanchez-Barrioluengo et al. 2019). Regarding the type of university, it is easier for private universities to follow a business model that incorporates TM such as commercial transfer, as private universities are—especially financially—more independent (Gaus and Raith 2016). Specialisation of university can further influence organisation of TM activities, whereas for example, a concentration on engineering disciplines gives evidence for a much higher TM involvement (Rolfo and Finardi 2014). Academic funding does not only increase overall publication rate (Gush et al. 2017;Payne and Siow 2003), but government funding further stimulates industry collaboration (Fan et al. 2019;Muscio et al. 2013; Nugent et al. 2021;Silva et al. 2018), and it is positively correlated to research performance (Mejlgaard and Ryan 2017;Muscio et al. 2017), but has no effect on entrepreneurial outputs (Gulbrandsen and Smeby 2005). The work experience of academics outside academia is another important factor when it comes to academic entrepreneurship, which according to Bourelos et al. (2012, p. 774) further “helps the researcher at the research institute or university to specify and define new sets of research problems”. Previous research has confirmed that academics with former industry experience have a positive influence on academic entrepreneurship (Jonsson et al. 2015;Klofsten and Jones-Evans 2000;Krabel and Mueller 2009;Nielsen 2015). In addition, academics have built up more diverse and stronger network ties affecting future collaboration (Dietz and Bozeman 2005). This is especially true in fields of medical science and engineering and, consequently, the propensity of academics in engineering, technology, or natural sciences to participate in contracts with companies is significantly higher than those in social sciences or humanities (Azagra-Caro 2007). Generally, it is well known that the type of discipline or field of study has an influence on knowledge and technology transfer, and academic entrepreneurship (Bekkers and Freitas 2008;Bercovitz and Feldman 2008;Giuliani et al. 2010;Martinelli et al. 2008;Perkmann et al. 2013;Stuart and Ding 2006). Therefore, health sciences and disciplines belonging to STEM are often leading when it comes to entrepreneurial or commercialisation activities such as patenting, licensing, or starting businesses (Bercovitz and Feldman 2008;Delmar et al. 2003;Hughes et al. 2016; Laukkanen 2003;Owen-Smith and Powell 2001;Powers 2003;Stuart and Ding 2006). Previous studies have tried to explain scientific success (Feist and Gorman 1998) and entrepreneurial activity (Crant 1996;Wu et al. 2019) in relation to personality traits. Feist (1998,2011) has thereby studied the “psychology of science” and suggested that particular personality traits such as conscientiousness, but a lower degree of openness to experience are more prevalent among scientists than non-scientists. High levels of conscientiousness Adm. Sci. 2023,13, 9 5 of 22 and openness to experience give scientists the feeling to be more “embedded within norms of academic system” so that they “report greater appreciation of the impact of their work on their academic peers” (Azagra-Caro and Llopis 2017, p. 568). However, creative scientists are more likely to score higher in openness and confidence, but less in conscientiousness than less creative scientists (Feist and Gorman 1998). Further, scientists that score higher in openness to experience and conscientiousness state higher perceived academic impact, yet with higher chance of experiencing a conflict of interest regarding industry (Azagra-Caro and Llopis 2017). Feist was a forerunner when it comes to analysing the personality of scientists and gives a good overview and summary of previous research on the topic. Therefore, it can be said that scientists are more ambitious, driven and dominant than non-scientists, work more independently, are more introverted and less sociable (Feist 2011; Feist and Gorman 1998,2012). Considering TM activities as a dependent variable, the authors formed the following hypotheses: H1. Male academics are more likely to be engaged in TM activities related to research commercialisation and knowledge and technology transfer. H2. Female academics are more likely to be engaged in TM activities related to teaching and community engagement. H3. Academics in disciplines of natural and health sciences as well as more senior academics are more likely to be engaged in TM activities related to research commercialisation and knowledge and technology transfer. H4. Academics with work experience outside of academia are more likely to be engaged in TM activities related to research commercialisation and knowledge and technology transfer. H5. Academics who score higher in openness to experiences are more likely to be engaged in TM activities in general. 3. Data and Methods At the start of 2021, a quantitative survey among academics in Icelandic universities was conducted with the aim of obtaining information about their engagement in TM activities. The target group comprised the total population of academics, working as either adjunct, assistant, associate- or full professor, at one of the seven Icelandic universities. The majority (n = 674) 1 worked for the University of Iceland, the others (n = 360) for other universities to the time of data collection. The survey question design was based on the outcomes of a literature review (Schnurbus and Edvardsson 2020) and inspired by a prior study on university-industry collaboration (Karlsdottir et al. 2021). The survey was pre-tested among several academics and staff from university administration, and adjustments were made accordingly. Email addresses were obtained from the institutions’ public homepages. The survey was conducted through QuestionPro and was open for 21 days. Two reminders were sent out after the initial invitation, but the response rate remained low. We collected 183 responses whereby not all participants completed the questionnaire. The response rate was therefore 17.7%. Possible reasons for the low response are the survey length, survey fatigue (Olson 2014), a general lack of participation in TM and collaboration activities in Iceland, and a lack of interest in the survey topic. Even though Roscoe (1975) and Hair et al. (2019) argue that the rate is acceptable for further analysis, we are aware of the limitations this raises when it comes to extensive data analysis. A non-response analysis was performed by comparing late and early responses (Hair et al. 2019). T-tests did not reveal any statistically significant differences between means of these two groups in terms of demographic characteristics such as gender,age,rank, and outside academic experience (p> 0.05). Moreover, it is worth emphasizing that this study builds on a target population, not a sample, and that academic disciplines that have to date mainly been neglected in studies on TM, are included in this study. Adm. Sci. 2023,13, 9 6 of 22 Table 1presents the demographics and academic profile of the participants. Women tend to participate in surveys in a larger extent than men (Groves et al. 2011), and this is also a case in our study where a higher proportion of female academics participated (57%) even though the current proportion of female academics is less than male academics at Icelandic universities. Almost half of the participants hold a full professor position. Most responses (63%) came from academics aged 50 years or older, and almost one-third of responding academics were 60+. More than half of participants are affiliated with either the School of Social Sciences (27%) or the School of Natural Sciences (26%). Table 1. Participants’ profile. Percentage (N) Women 56% (84) Men 44% (66) Younger than 40 years 6% (9) 40–49 31% (47) 50–59 32% (49) 60 years old or older 31% (48) School of Natural Sciences 26% (39) School of Health 13% (20) School of Humanities 13% (19) School of Education 19% (29) School of Social Sciences 27% (40) School of Agriculture 3% (4) STEM/Health Sciences 39% (59) Other Sciences 61% (92) Adjunct lecturer (Aðjúnkt) 7% (10) Assistant professor (Lektor) 24% (36) Associate professor (Dósent) 23% (34) Full professor (Prófessor) 47% (70) 3.1. Measures In this study, there are five dependent variables, each representing one of the five TM activities: community activities, science communication, external teaching and training, applied contract research and commercialisation. They are composite variables, with Table 2listing the items the variables represent, as well as Cronbach’s alpha values. N implies the number of answers. As we also included partial respondents (pairwise deletion), the number of answers slightly differs between components. Most items were inspired by previous study measurements on the commercialisation of research (Nilsson et al. 2010), industry interaction (D’Este and Patel 2007;Inzelt 2004; Schartinger et al. 2002), and academic entrepreneurship activities (Klofsten and Jones-Evans 2000), and adapted to the Icelandic context. The answer scale comprised a five-point Likert scale measuring how often a certain activity was performed, in a 3-year period prior to the survey (i.e., 2018–2020), ranging from “never” (1) to “very often” (5). Community Activities represent collaborative activities with educational- and other public institutions, and also includes voluntary work for the benefit of the community. In general, lectures and public debates could also be considered external teaching and training activities, which is another activity within the TM model, but as the item only corresponded to the community component in our study, it was accommodated there. Adm. Sci. 2023,13, 9 7 of 22 Table 2. Five components (dependent variables) of the Third Mission. Name of Component (Dependent Variable) NNumber of Items Items Cronbach’s αMean SD Community Activities 152 5 Collaboration and communication with preceding school levels 0.753 2.566 0.866 Volunteer contribution to the community Organising conferences and workshops Contribution to public policy Lectures, public debates or talks to non-academic organisations Science Communication 142 6 Print media interviews 0.857 1.891 0.921 Interviews for web-based media TV programs Radio programs Podcasts Writing of newspaper articles External Teaching and Training 155 3 Training/coaching of company employees 0.804 1.951 0.982 Joint teaching courses or programs with industry or public organisations Taking part in lifelong-learning activities Applied Contract Research 178 2 Application for funding together with industry/public organisation 0.768 (r = 0.623) ** 2.610 1.247 Formal R&D co-operations such as contract research or joint research projects Commercialisation 143 6 Publishing patents or patent applications as co-inventor or applicant 0.656 1.614 0.541 Licensing Participating or initiating cluster creation or development of Science Park or Technology Transfer Office Creation of or participation in the creation of spin-off or start-up Application for funding together with industry/public organisation Formal R&D co-operations such as contract research or joint research projects Pearson’s correlation was calculated for components with fewer than 3 items (** represents significant results, p< 0.01). The component science communication contains mainly items of public science communication. These refer to a type of science communication that is often referred to as popular science communication. In academia, it is considered less prestigious than peerreviewed content, but media appearance often reaches a greater audience instead. In turn, the awareness in society for certain scientific results can lead to additional trust in science and changed social behaviour (Huber et al. 2019;Marcinkowski and Kohring 2014;Schäfer 2016). External Teaching and training includes TM activities such as training and guidance on the job, and teaching outside of academia, for instance, by developing programs for life-long learning activities (Icelandic: endurmenntun). The component applied contract research includes activities related to funding and participation in collaborations on formal research and development projects. From a theoretical point of view, it would be preferable to highlight the commercialisation aspect (e.g., registration of patents, licensing, and cluster or start-up creation) but retrospectively, Adm. Sci. 2023,13, 9 8 of 22 the answers in this study do not allow for such focus. Only a very small percentage of participants takes part in commercialisation activities. The independent variables contain measurements at the individual and organisational level. First, starting with the individual factors, there is a dummy variable for gender (1 = men, 0 = women), and five personality traits (extraversion, agreeableness, conscientiousness, emotional stability and openness) were measured according to a 10-item measure of the Big-Five dimensions based on Gosling et al. (2003) on a Likert-scale from 1 (disagree strongly) to 7 (agree strongly). After reversing some items, results for single items were averaged to produce a general outcome for each trait and reliability analysis was conducted (Table 3). As agreeableness and emotional stability have a low reliability, caution is in order when interpreting the results. Table 3. The Big Five Personality traits (based on Payne and Harper (2020) and Gosling et al. (2003)). Factors N Items Description of Factors Cronbach’s αCorrelation Mean SD Extraversion 148 Extraverted, enthusiastic The ability of a person to engage with the external world; the opposite is introversion. 0.643 0.480 ** 4.595 1.349 Reserved, quiet (reversed) Agreeableness 148 Critical, quarrelsome (reversed) Demonstrates how people are different regarding cooperation and social harmony; the opposite is disagreeableness. 0.323 0.205 ** 5.243 1.087 Sympathetic, warm Conscientiousness 149 Dependable, self-disciplined The capability of a person to manage, regulate, organise, and direct emotions or impulses; the opposite is easy going, disorderly, and with no self-control. 0.716 0.574 ** 5.768 1.178 Disorganised, careless (reversed) Emotional Stability 148 Anxious, easily upset (reversed) Describes how a person experiences positive feelings; the opposite is being emotionally unstable. 0.375 0.241 ** 5.368 1.168 Calm, emotionally stable Openness to Experience 146 Open to new experiences, complex Describes and distinguishes people’s creativity and intellectual awareness; the opposite is not accepting change, being traditional, liking familiar routines, and a narrower choice of interests. 0.682 0.529 ** 5.558 1.124 Conventional, uncreative (reversed) ** Correlation is significant at the 0.01 level (2-tailed). Second, six organisational dummy variables were created. The first one yime spent on teaching (1 = I spend most of my time on teaching, 0 = I do not spend most of my time on teaching) captures the focus of the academic work. Academic rank (1 = full professor, 0 = other position) measures the progression through the academic system. Academic work experience measures the number of years academics have worked in academia (1 = more than 10 years, 0 = 10 years or less). The only organisational variable that is not measured as a dummy variable is Academic performance. Inspired by Bourelos et al. (2012) and Karlsson and Wigren (2012), participants were asked about the number of publications in academic peer-reviewed journals in the last three years. In the survey, data on article authorship were collected separately for single-authored articles, for first-author articles, and for authorship in non-leading author positions. The Academic performance variable was created by adding the mid-scores of the ranges for all authorship types. The scale ranges from 0 up to 7+ articles and shows the average number of articles per year. Academic discipline captures differences between STEM and health disciplines (1) compared to other disciplines (e.g., Social Sciences, Humanities, Education, and Agriculture) (0), similar to the research conducted by Huyghe and Knockaert (2015). About 39% of participants belonged to STEM and health disciplines, the rest (61%) to other schools. Finally, the dummy variable Outside academia experience (1 = yes, 0 = no) measures if academics have experience working outside of academia, i.e., in companies or organisations such as the national hospital. Adm. Sci. 2023,13, 9 15 of 22 doing research abroad. These are important aspects as it is often different not only between universities but also between various countries how TM engagement is developed and thus has an impact on academics. However, to do so, the data collection would need to be expanded. Individual level aspects such as marital status and number of children could be considered as these aspects are related to time issues, which can result in negative effect on TM participation. Additionally, it would be possible to collect qualitative data to get more insight into the reasons that hinder TM participation as well as into academics’ position towards TM activities. Further, future studies should also focus on sustainable entrepreneurship strategies as part of TM (Pascucci et al. 2022). Author Contributions: Conceptualization, V.K.; Methodology, V.K. and M.T.T.; Software, V.K.; Validation, V.K.; Formal analysis, V.K.; Investigation, V.K.; Resources, V.K.; Writing—original draft, V.K.; Writing—review & editing, M.T.T., I.R.E. and T.M.H.; Visualization, V.K.; Supervision, M.T.T., I.R.E. and T.M.H.; Funding acquisition, Thamar Melanie Heijstra. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by The University of Iceland [1010-101280]. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Data can be provided upon reasonable request. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Table A1. Multiple regression models for predicting engagement in community activities. Variable N Model 1a Model 1b Model 1c B ß SE B ß SE B ß SE Constant 152 0.378 0.524 2.161 *** 0.272 0.026 0.635 Male 150 0.047 0.027 0.141 −0.059 −0.034 0.166 Extraversion 148 0.125 ** 0.195 0.054 0.125 ** 0.194 0.061 Agreeableness 148 0.139 ** 0.175 0.067 0.149 * 0.187 0.076 Conscientiousness 149 −0.071 −0.096 0.065 −0.071 −0.096 0.075 Emotional Stability 148 −0.023 −0.031 0.064 −0.025 −0.034 0.071 Openness 146 0.250 *** 0.325 0.066 0.238 ** 0.309 0.074 Most Time Spent Teaching 120 0.009 0.005 0.191 0.036 0.020 0.180 Professor 150 0.041 0.024 0.203 0.040 0.023 0.197 Academic Work Experience 151 0.081 0.045 0.192 0.066 0.036 0.182 Performance 182 0.144 ** 0.251 0.061 0.133 ** 0.232 0.056 STEM/Health Sciences 151 0.018 0.010 0.172 0.179 0.101 0.166 Experience outside Academia 155 0.112 0.060 0.182 0.045 0.024 0.168 R20.199 0.076 0.264 Adjusted R20.163 0.023 0.174 F-Statistics 5.439 *** 1.430 2.954 *** Note. In Model 1a, we entered individual variables; in Model 1b, only organisational;, in Model 1c all independent variables. * p≤0.1. ** p≤0.05. *** p≤0.01. Adm. Sci. 2023,13, 9 16 of 22 Table A2. Multiple regression models for predicting engagement in science communication. Variable NModel 2a Model 2b Model 2c B ß SE B ß SE B ß SE Constant 142 0.564 0.613 1.872 *** 0.282 1.081 0.716 Male 150 0.179 0.097 0.165 0.134 0.072 0.187 Extraversion 148 0.180 ** 0.264 0.063 0.146 ** 0.214 0.068 Agreeableness 148 0.053 0.062 0.079 0.072 0.085 0.085 Conscientiousness 149 0.002 0.002 0.077 −0.050 −0.064 0.084 Emotional Stability 148 −0.014 −0.018 0.075 −0.019 −0.024 0.080 Openness 146 0.037 0.046 0.077 0.013 0.016 0.083 Most Time Spent Teaching 120 −0.061 −0.032 0.198 −0.031 −0.016 0.203 Professor 150 0.241 0.131 0.210 0.201 0.109 0.222 Academic Work Experience 151 −0.338 * −0.175 0.199 −0.348 * −0.180 0.205 Performance 182 0.160 ** 0.262 0.063 0.156 ** 0.256 0.063 STEM/Health Sciences 151 −0.446 ** −0.237 0.178 −0.389 ** −0.207 0.187 Experience outside Academia 155 0.083 0.042 0.188 0.067 0.034 0.190 R20.083 0.166 0.214 Adjusted R20.038 0.116 0.114 F-Statistics 1.863 * 3.321 ** 2.136 ** Note. In Model 2a we entered individual variables; in Model 2b, only organisational; in Model 2c, all independent variables. * p≤0.1. ** p≤0.05. *** p≤0.01. Table A3. Multiple regression models predicting engagement in external teaching and training activities. Variable NModel 3a Model 3b Model 3c B ß SE B ß SE B ß SE Constant 155 0.584 0.628 1.787 *** 0.313 0.323 0.780 Male 150 −0.176 −0.089 0.169 −0.135 −0.069 0.204 Extraversion 148 0.036 0.049 0.065 0.034 0.047 0.074 Agreeableness 148 0.130 0.144 0.080 0.146 0.162 0.093 Conscientiousness 149 −0.054 −0.064 0.078 −0.029 −0.035 0.092 Emotional Stability 148 −0.055 −0.065 0.077 −0.066 −0.078 0.087 Openness 146 0.216 ** 0.248 0.079 0.197 ** 0.226 0.091 Most Time Spent Teaching 120 0.018 0.009 0.220 0.013 0.006 0.221 Professor 150 −0.254 −0.129 0.233 −0.252 −0.128 0.242 Academic Work Experience 151 0.248 0.121 0.221 0.254 0.123 0.223 Performance 182 0.035 0.054 0.070 0.032 0.050 0.069 STEM/Health Sciences 151 −0.134 −0.067 0.198 −0.027 −0.013 0.204 Experience outside Academia 155 0.299 0.140 0.209 0.257 0.121 0.206 R20.099 0.039 0.128 Adjusted R20.058 −0.016 0.023 F-Statistics 2.407 ** 0.713 1.220 Note. In Model 3a we entered individual variables; in Model 3b, only organisational; in Model 3c, all independent variables. ** p≤0.05. *** p≤0.01. Adm. Sci. 2023,13, 9 17 of 22 Table A4. Multiple regression models for predicting engagement in applied contract research activities. Variable N Model 4a Model 4b Model 4c B ß SE B ß SE B ß SE Constant 178 1.121 0.789 2.099 *** 0.355 0.320 0.884 Male 150 0.136 0.054 0.213 −0.104 −0.042 0.231 Extraversion 148 −0.072 −0.078 0.081 −0.047 −0.051 0.084 Agreeableness 148 −0.026 −0.023 0.101 −0.002 −0.002 0.105 Conscientiousness 149 −0.034 −0.032 0.099 −0.022 −0.021 0.104 Emotional Stability 148 0.044 0.041 0.096 0.068 0.064 0.099 Openness 146 0.333 *** 0.300 0.099 0.328 *** 0.295 0.103 Most Time Spent Teaching 120 −0.273 −0.105 0.249 −0.253 −0.098 0.251 Professor 150 0.194 0.078 0.264 0.248 0.099 0.274 Academic Work Experience 151 −0.098 −0.038 0.250 −0.132 −0.051 0.253 Performance 182 0.108 0.130 0.079 0.086 0.104 0.078 STEM/Health Sciences 151 0.726 *** 0.285 0.224 0.849 *** 0.333 0.231 Experience outside Academia 155 0.538 ** 0.199 0.236 0.458 * 0.169 0.234 R20.089 0.207 0.298 Adjusted R20.048 0.164 0.214 F-Statistics 2.204 ** 4.782 *** 3.566 *** Note. In Model 4a we entered individual variables; in Model 4b, only organisational; in Model 4c, all independent variables. * p≤0.1. ** p≤0.05. *** p≤0.01. Table A5. Multiple regression models for predicting engagement in commercialisation activities. Variable N Model 5a Model 5b Model 5c B ß SE B ß SE B ß SE Constant 143 1.114 ** 0.352 1.301 *** 0.157 0.631 * 0.379 Male 150 0.127 0.117 0.095 0.037 0.034 0.099 Extraversion 148 −0.038 −0.094 0.036 −0.023 −0.057 0.036 Agreeableness 148 −0.017 −0.033 0.045 −0.001 −0.002 0.045 Conscientiousness 149 −0.043 −0.093 0.044 −0.034 −0.075 0.045 Emotional Stability 148 −0.001 −0.003 0.043 0.008 0.017 0.042 Openness 146 0.172 *** 0.358 0.044 0.169 *** 0.351 0.044 Most Time Spent Teaching 120 −0.087 −0.077 0.110 −0.066 −0.059 0.108 Professor 150 −0.022 −0.020 0.117 −0.001 −0.001 0.118 Academic Work Experience 151 −0.012 −0.010 0.111 −0.047 −0.042 0.108 Performance 182 0.083 ** 0.232 0.035 0.072 ** 0.202 0.034 STEM/Health Sciences 151 0.401 *** 0.362 0.099 0.432 *** 0.390 0.099 Experience outside Academia 155 0.216 ** 0.184 0.105 0.184 * 0.157 0.100 R20.129 0.259 0.369 Adjusted R20.086 0.214 0.288 F-Statistics 3.033 ** 5.759 *** 4.532 *** Note. In Model 5a, we entered individual variables; in Model 5b, only organisational; in Model 5c, all independent variables. * p≤0.1. ** p≤0.05. *** p≤0.01. Note 1Thereof 62 adjunct lecturers, 147 assistant professors, 126 associate professors, and 339 professors. Adm. Sci. 2023,13, 9 18 of 22 References Abreu, Maria, and Vadim Grinevich. 2013. The nature of academic entrepreneurship in the UK: Widening the focus on entrepreneurial activities. Research Policy 42: 408–22. [CrossRef] Addie, Jean-Paul D. 2017. From the urban university to universities in urban society. Regional Studies 51: 1089–99. [CrossRef] Antonio-García, M. Teresa, Irene López-Navarro, and Jesús Rey-Rocha. 2014. Determinants of success for biomedical researchers: A perception-based study in a health science research environment. Scientometrics 101: 1747–79. [CrossRef] Antonioli, Davide, Francesco Nicolli, Laura Ramaciotti, and Ugo Rizzo. 2016. The effect of intrinsic and extrinsic motivations on academics’ entrepreneurial intention. Administrative Sciences 6: 15. [CrossRef] Azagra-Caro, Joaquin M. 2007. What type of faculty member interacts with what type of firm? Some reasons for the delocalisation of university–industry interaction. Technovation 27: 704–15. [CrossRef] Azagra-Caro, Joaquín M., and Oscar Llopis. 2017. Who do you care about? Scientists’ personality traits and perceived impact on beneficiaries: Personality traits and perceived beneficiary impact. R&D Management 48: 566–79. [CrossRef] Barrena Martínez, Jesús, Macarena López Fernández, and Pedro Miguel Romero Fernández. 2016. Corporate social responsibility: Evolution through institutional and stakeholder perspectives. European Journal of Management and Business Economics 25: 8–14. [CrossRef] Baycan, Tüzin, and Gökçen Arkali Olcay. 2021. Linking the performance of entrepreneurial universities to technoparks and university characteristics in turkey. Region 8: 97–117. [CrossRef] Bekkers, Rudi, and Isabel Maria Bodas Freitas. 2008. Analysing knowledge transfer channels between universities and industry: To what degree do sectors also matter? Research Policy 37: 1837–53. [CrossRef] Bercovitz, Janet, and Maryann Feldman. 2008. Academic Entrepreneurs: Organizational Change at the Individual Level. Organization Science 19: 69–89. [CrossRef] Bikard, Michaël, Keyvan Vakili, and Florenta Teodoridis. 2019. When Collaboration Bridges Institutions: The Impact of University- Industry Collaboration on Academic Productivity. Organization Science 30: 426–45. [CrossRef] Boardman, P. Craig, and Branco L. Ponomariov. 2009. University researchers working with private companies. Technovation 29: 142–53. [CrossRef] Bonaccorsi, Andrea, Luca Secondi, Enza Setteducati, and Alessio Ancaiani. 2014. Participation and commitment in third-party research funding: Evidence from Italian Universities. Journal of Technology Transfer 39: 169–98. [CrossRef] Bourelos, Evangelos, Mats Magnusson, and Maureen McKelvey. 2012. Investigating the complexity facing academic entrepreneurs in science and engineering: The complementarities of research performance, networks and support structures in commercialisation. Cambridge Journal of Economics 36: 751–80. [CrossRef] Bozeman, Barry, and Monica Gaughan. 2011. How do men and women differ in research collaborations? An analysis of the collaborative motives and strategies of academic researchers. Research Policy 40: 1393–402. [CrossRef] Breschi, Stefano, and Christian Catalini. 2010. Tracing the links between science and technology: An exploratory analysis of scientists’ and inventors’ networks. Research Policy 39: 14–26. [CrossRef] Brindley, Clare. 2005. Barriers to women achieving their entrepreneurial potential. International Journal of Entrepreneurial Behavior & Research 11: 144–61. [CrossRef] Brooks, Harvey, and Lucien P. Randazzese. 1998. University-industry relations: The next four years and beyond. In Investing in Innovation: Creating and Innovation Policy That Works. Edited by Lewis M. Branscomb and James H. Keller. Cambridge: MIT Press, pp. 361–99. Calvo, Nuria, Sara Fernández-López, and David Rodeiro-Pazos. 2019. Is university-industry collaboration biased by sex criteria? Is university-industry collaboration biased by sex criteria? Knowledge Management Research & Practice 17: 408–20. [CrossRef] Carayol, Nicolas. 2007. Academic incentives, research organization and patenting at a large French university. Economics of Innovation and New Technology 16: 119–38. [CrossRef] Crant, J. Michael. 1996. The Proactive Personality Scale as a Predictor of Entrepreneurial Intention. Journal of Small Business Management 34: 42–49. D’Este, Pablo, and Markus Perkmann. 2011. Why do academics engage with industry? The entrepreneurial university and individual motivations. Journal of Technology Transfer 36: 316–39. [CrossRef] D’Este, Pablo, and Pari Patel. 2007. University-industry linkages in the UK: What are the factors underlying the variety of interactions with industry? Research Policy 36: 1295–313. [CrossRef] Dahlborg, Charlotta, Danielle Lewensohn, Rickard Danell, and Carl Johan Sundberg. 2017. To invent and let others innovate: A framework of academic patent transfer modes. Journal of Technology Transfer 42: 538–63. [CrossRef] Delmar, Frédéric, Per Davidsson, and William B. Gartner. 2003. Arriving at the High-Growth Firm. Journal of Business Venturing 18: 189–216. [CrossRef] Dietz, James S., and Barry Bozeman. 2005. Academic careers, patents, and productivity: Industry experience as scientific and technical human capital. Research Policy 34: 349–67. [CrossRef] Etzkowitz, Henry. 2002. Incubation of incubators: Innovation as a triple helix of university-industry-government networks. Science and Public Policy 29: 115–28. [CrossRef] Adm. Sci. 2023,13, 9 19 of 22 Fan, Hsueh-Liang, Mu-Hsuan Huang, and Dar-Zen Chen. 2019. Do funding sources matter?: The impact of university-industry collaboration funding sources on innovation performance of universities. Technology Analysis & Strategic Management 31: 1368–80. [CrossRef] Feist, Gregory J. 1998. A Meta-Analysis of Personality in Scientific and Artistic Creativity. Personality and Social Psychology Review 2: 290–309. [CrossRef] Feist, Gregory J. 2011. Psychology of science as a new subdiscipline in psychology. Current Directions in Psychological Science 20: 330–34. [CrossRef] Feist, Gregory J., and Michael E. Gorman. 1998. The Psychology of Science: Review and Integration of a Nascent Discipline. Review of General Psychology 2: 3–47. [CrossRef] Feist, Gregory J., and Michael E. Gorman. 2012. Handbook of the Psychology of Science. Berlin/Heidelberg: Springer Publishing Company. Fogelberg, Hans, and Mats A. Lundqvist. 2013. Integration of academic and entrepreneurial roles: The case of nanotechnology research at Chalmers University of Technology. Science and Public Policy 40: 127–39. [CrossRef] Garcia, Renato, V. Araújo, S. Mascarini, E. G. Santos, and A. R. Costa. 2019. How long-term university-industry collaboration shapes the academic productivity of research groups. Innovation-Organization & Management 22: 56–70. [CrossRef] Gaus, Olaf, and Matthias G. Raith. 2016. Commercial transfer—A business model innovation for the entrepreneurial university. Industry and Higher Education 30: 183–201. [CrossRef] Ghasemi, Asghar, and Saleh Zahediasl. 2012. Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism 10: 486–89. [CrossRef] Gimenez-Jimenez, Daniela, Linda F. Edelman, Alexandra Dawson, and Andrea Calabrò. 2020. Women entrepreneurs’ progress in the venturing process: The impact of risk aversion and culture. Small Business Economics 58: 1091–111. [CrossRef] Giuliani, Elisa, Andrea Morrison, Carlo Pietrobelli, and Roberta Rabellotti. 2010. Who are the researchers that are collaborating with industry? An analysis of the wine sectors in Chile, South Africa and Italy. Research Policy 39: 748–61. [CrossRef] Gneezy, Uri, Muriel Niederle, and Aldo Rustichini. 2003. Performance in competitive environments: Gender differences. The Quarterly Journal of Economics 118: 1049–74. [CrossRef] Gosling, Samuel D., Peter J. Rentfrow, and William B. Swann Jr. 2003. A very brief measure of the Big-Five personality domains. Journal of Research in Personality 37: 504–28. [CrossRef] Grimm, Heike M., and Johannes Jaenicke. 2015. Testing the causal relationship between academic patenting and scientific publishing in Germany: Crowding-out or reinforcement? Journal of Technology Transfer 40: 512–35. [CrossRef] Groves, Robert M., Floyd J. Fowler Jr., Mick P. Couper, James M. Lepkowski, Eleanor Singer, and Roger Tourangeau. 2011. Survey Methodology. Hoboken: John Wiley & Sons, vol. 561. Gulbrandsen, Magnus, and Jens-Christian Smeby. 2005. Industry funding and university professors’ research performance. Research Policy 34: 932–50. [CrossRef] Gür, Ufuk, Ikbal Sinemden Oylumlu, and Özlem Kunday. 2017. Critical assessment of entrepreneurial and innovative universities index of Turkey: Future directions. Technological Forecasting and Social Change 123: 161–68. [CrossRef] Gush, Jason, Adam Jaffe, Victoria Larsen, and Athene Laws. 2017. The effect of public funding on research output: The New Zealand Marsden Fund. New Zealand Economic Papers 52: 1–22. [CrossRef] Hair, Joseph F., Jr., Rolph E. Anderson, Barry J. Babin, and William C. Black. 2019. Multivariate Data Analysis: A Global Perspective, 8th ed. Upper Saddle River: Pearson Education. Holmen, Magnus, and Daniel Ljungberg. 2015. The teaching and societal services nexus: Academics’ experiences in three disciplines. Teaching in Higher Education 20: 208–20. [CrossRef] Hottenrott, Hanna, and Cornelia Lawson. 2014. Research grants, sources of ideas and the effects on academic research. Economics of Innovation and New Technology 23: 109–33. [CrossRef] Huber, Brigitte, Matthew Barnidge, Homero Gil de Zúñiga, and James Liu. 2019. Fostering public trust in science: The role of social media. Public Understanding of Science 28: 759–77. [CrossRef] Hughes, Alan, Cornelia Lawson, Michael Kitson, Ammon Salter, Anna Bullock, and Robert Hughes. 2016. The Changing State of Knowledge Exchange: UK Academic Interactions with External Organisations 2005–2015. London: NCUB. Humbert, Anne Laure, and Clare Brindley. 2015. Challenging the concept of risk in relation to women’s entrepreneurship. Gender in Management: An International Journal 30: 2–25. [CrossRef] Huyghe, Annelore, and Mirjam Knockaert. 2015. The influence of organizational culture and climate on entrepreneurial intentions among research scientists. Journal of Technology Transfer 40: 138–60. [CrossRef] Inzelt, Annamaria. 2004. The evolution of university-industry-government relationships during transition. Research Policy 33: 975–95. [CrossRef] Jaeger, Angelika, and Johannes Kopper. 2014. Third Mission Potential in Higher Education: Measuring the Regional Focus of Different Types of HEIs. Review of Regional Research 34: 95–118. [CrossRef] Johnson, Mark, Erik W. Monsen, and Niall G. MacKenzie. 2017. Follow the Leader or the Pack? Regulatory Focus and Academic Entrepreneurial Intentions. Journal of Product Innovation Management 34: 181–200. [CrossRef] Jonsson, Lars, Enrico Baraldi, Lars-Eric Larsson, Petter Forsberg, and Kristofer Severinsson. 2015. Targeting Academic Engagement in Open Innovation: Tools, Effects and Challenges for University Management. Journal of the Knowledge Economy 6: 522–50. [CrossRef] Adm. Sci. 2023,13, 9 20 of 22 Karlsdottir, Verena, Ingi Runar Edvardsson, and Thamar Melanie Heijstra. 2021. “Nothing happens in a vacuum here”: University– industry collaboration in Iceland. International Journal of Knowledge-Based Development 12: 35–56. [CrossRef] Karlsen, James, Jan Beseda, Karel Šima, and Barbara Zyzak. 2017. Outsiders or Leaders? The Role of Higher Education Institutions in the Development of Peripheral Regions. Higher Education Policy 30: 463–79. [CrossRef] Karlsson, Tomas, and Caroline Wigren. 2012. Start-ups among university employees: The influence of legitimacy, human capital and social capital. Journal of Technology Transfer 37: 297–312. [CrossRef] Klofsten, Magnus, and Dylan Jones-Evans. 2000. Comparing Academic Entrepreneurship in Europe—The Case of Sweden and Ireland. Small Business Economics 14: 299–309. [CrossRef] Knudsen, Mette Præst, Marianne Harbo Frederiksen, and RenéChester Goduscheit. 2021. New forms of engagement in third mission activities: A multi-level university-centric approach. Innovation: Organization and Management 23: 209–40. [CrossRef] Kotrlik, Joe W., James E. Bartlett, Chadwick C. Higgins, and Heather A. Williams. 2002. Factors Associated With Research Productivity Of Agricultural Education Faculty. Journal of Agricultural Education 43: 1–10. [CrossRef] Krabel, Stefan, and Pamela Mueller. 2009. What drives scientists to start their own company?: An empirical investigation of Max Planck Society scientists. Research Policy 38: 947–56. [CrossRef] Laukkanen, Mauri. 2003. Exploring Academic Entrepreneurship: Drivers and Tensions of University-Based Business. Journal of Small Business and Enterprise Development 10: 372–82. [CrossRef] Lehmann, Martin, Per Christensen, Mikkel Thrane, and Tine Herreborg Jørgensen. 2009. University engagement and regional sustainability initiatives: Some Danish experiences. Journal of Cleaner Production 17: 1067–74. [CrossRef] Leydesdorff, Loet, and Henry Etzkowitz. 1996. Emergence of a Triple Helix of university-industry-government relations. Science and Public Policy 23: 279–86. [CrossRef] Liñán, Francisco, and Alain Fayolle. 2015. A systematic literature review on entrepreneurial intentions: Citation, thematic analyses, and research agenda. International Entrepreneurship and Management Journal 11: 907–33. [CrossRef] Link, Albert N., Donald S. Siegel, and Barry Bozeman. 2007. An empirical analysis of the propensity of academics to engage in informal university technology transfer. Industrial and Corporate Change 16: 641–55. [CrossRef] Marcinkowski, Frank, and Matthias Kohring. 2014. The changing rationale of science communication: A challenge to scientific autonomy. Journal of Science Communication 13: C04. [CrossRef] Martinelli, Arianna, Martin Meyer, and Nick Von Tunzelmann. 2008. Becoming an entrepreneurial university? A case study of knowledge exchange relationships and faculty attitudes in a medium-sized, research-oriented university. Journal of Technology Transfer 33: 259–83. [CrossRef] Mazdeh, Mohammad Mahdavi, Seyed-Mostafa Razavi, Roozbeh Hesamamiri, Mohammad-Reza Zahedi, and Behin Elahi. 2013. An empirical investigation of entrepreneurship intensity in Iranian state universities. Higher Education 65: 207–26. [CrossRef] Mejlgaard, Niels, and Thomas Kjeldager Ryan. 2017. Patterns of third mission engagement among scientists and engineers. Research Evaluation 26: 326–36. [CrossRef] Meng, Yu. 2016. Collaboration patterns and patenting: Exploring gender distinctions. Research Policy 45: 56–67. [CrossRef] Miller, Kristel, Allen Alexander, James A. Cunningham, and Ekaterina Albats. 2018. Entrepreneurial academics and academic entrepreneurs: A systematic literature review. International Journal of Technology Management 77: 9–37. [CrossRef] Molas-Gallart, Jordi, Ammon Salter, Pari Patel, Alister Scott, and Xavier Duran. 2002. Measuring Third Stream Activities. Final Report to the Russell Group of Universities. Brighton: Science and Technology Policy Research (SPRU), University of Sussex. Molino, Monica, Valentina Dolce, Claudio Giovanni Cortese, and Chiara Ghislieri. 2018. Personality and social support as determinants of entrepreneurial intention. Gender differences in Italy. PLoS ONE 13: e0199924. [CrossRef] Muscio, Alessandro, Davide Quaglione, and Giovanna Vallanti. 2013. Does government funding complement or substitute private research funding to universities? Research Policy 42: 63–75. [CrossRef] Muscio, Alessandro, Laura Ramaciotti, and Ugo Rizzo. 2017. The complex relationship between academic engagement and research output: Evidence from Italy. Science and Public Policy 44: 235–45. [CrossRef] Nelson, Richard R. 2004. The market economy, and the scientific commons. Research Policy 33: 455–71. [CrossRef] Neves, Sara, and Carlos Brito. 2020. Academic entrepreneurship intentions: A systematic literature review. Journal of Management Development 39: 645–704. [CrossRef] Niederle, Muriel, and Lise Vesterlund. 2007. Do Women Shy Away From Competition? Do Men Compete Too Much? The Quarterly Journal of Economics 122: 1067–101. [CrossRef] Niederle, Muriel, and Lise Vesterlund. 2011. Gender and Competition. Annual Review of Economics 3: 601–30. [CrossRef] Nielsen, Kristian. 2015. Human capital and new venture performance: The industry choice and performance of academic entrepreneurs. Journal of Technology Transfer 40: 453–74. [CrossRef] Nilsson, Anna S., Annika Rickne, and Lars Bengtsson. 2010. Transfer of academic research: Uncovering the grey zone. Journal of Technology Transfer 35: 617–36. [CrossRef] Nilsson, Jan-Evert, ed. 2006. Higher Education in the Nordic Countries. In The Role of Universities in Regional Innovation Systems—A Nordic Perspective. Copenhagen: Copenhagen Business School Press, pp. 29–54. Nugent, Annita, Ho Fai Chan, and Uwe Dulleck. 2021. Government funding of university-industry collaboration: Exploring the impact of targeted funding on university patent activity. Scientometrics 127: 29–73. [CrossRef] Adm. Sci. 2023,13, 9 21 of 22 Olson, Curtis A. 2014. Survey Burden, Response Rates, and the Tragedy of the Commons. Journal of Continuing Education in the Health Professions 34: 93–95. [CrossRef] Owen-Smith, Jason, and Walter W. Powell. 2001. To Patent or Not: Faculty Decisions and Institutional Success at Technology Transfer. The Journal of Technology Transfer 26: 99–114. [CrossRef] Pascucci, Tancredi, Giuseppina Maria Cardella, Brizeida Hernàndez-Sànchez, and Jose Carlos Sànchez-Garcìa. 2022. Environmental Sensitivity to Form a Sustainable Entrepreneurial Intention. Sustainability 14: 10398. [CrossRef] Payne, A. Abigail, and Aloysius Siow. 2003. Does Federal Research Funding Increase University Research Output? Advances in Economic Analysis & Policy 3: 1018–18. [CrossRef] Payne, Arlys S., and Christofer M. Harper. 2020. Studying the Impact of Personality Traits on Team Performance for Construction and Engineering. Journal of Construction Engineering and Project Management 10: 1–24. [CrossRef] Perkmann, Markus, Valentina Tartari, Maureen McKelvey, Erkko Autio, Anders Broström, Pablo D’este, Riccardo Fini, Aldo Geuna, Rosa Grimaldi, Alan Hughes, and et al. 2013. Academic engagement and commercialisation: A review of the literature on university–industry relations. Research Policy 42: 423–42. [CrossRef] Philpott, Kevin, Lawrence Dooley, Caroline O’Reilly, and Gary Lupton. 2011. The entrepreneurial university: Examining the underlying academic tensions. Technovation 31: 161–70. [CrossRef] Pita, Mariana, Joana Costa, and António Carrizo Moreira. 2021. The effect of university missions on entrepreneurial initiative across multiple entrepreneurial ecosystems: Evidence from europe. Education Sciences 11: 762. [CrossRef] Powers, Joshua B. 2003. Commercializing Academic Research: Resource Effects on Performance of University Technology Transfer. The Journal of Higher Education 74: 26–50. [CrossRef] Ranga, Marina, Serdal Temel, Ilker Murat Ar, Rustem Baris Yesilay, and Fazilet Vardar Sukan. 2016. Building Technology Transfer Capacity in Turkish Universities: A critical analysis. European Journal of Education 51: 90–106. [CrossRef] Rentocchini, Francesco, Pablo D’Este, Liney Manjarrés-Henríquez, and Rosa Grimaldi. 2014. The relationship between academic consulting and research performance: Evidence from five Spanish universities. International Journal of Industrial Organization 32: 70–83. [CrossRef] Reymert, Ingvild, and Taran Thune. 2022. Task complementarity in academic work: A study of the relationship between research, education and third mission tasks among university professors. Journal of Technology Transfer. [CrossRef] Rolfo, Secondo, and Ugo Finardi. 2014. University Third mission in Italy: Organization, faculty attitude and academic specialization. Journal of Technology Transfer 39: 472–86. [CrossRef] Roscoe, John T. 1975. Fundamental Research Statistics for the Behavioral Sciences, 2nd ed. New York: Holt, Rinehart and Winston. Sanchez-Barrioluengo, Mabel, Elvira Uyarra, and Fumi Kitagawa. 2019. Understanding the evolution of the entrepreneurial university. The case of English Higher Education institutions. Higher Education Quarterly 73: 469–95. [CrossRef] Schäfer, Mike S. 2016. Mediated trust in science: Concept, measurement and perspectives for thescience of science communication. Journal of Science Communication 15: C02. [CrossRef] Schartinger, Doris, Christian Rammer, and J. Fröhlich. 2002. Knowledge Interactions between Universities and Industry in Austria: Sectoral Patterns and Determinants. Research Policy 31: 303–28. [CrossRef] Schnurbus, Verena, and Ingi Runar Edvardsson. 2020. The Third Mission Among Nordic Universities: A Systematic Literature Review. Scandinavian Journal of Educational Research 66: 1–23. [CrossRef] Secundo, Giustina, Susana Elena Perez, Žilvinas Martinaitis, and Karl Heinz Leitner. 2017. An Intellectual Capital framework to measure universities’ third mission activities. Technological Forecasting and Social Change 123: 229–39. [CrossRef] Silva, Diego R., AndréT. Furtado, and Nicholas S. Vonortas. 2018. University-industry R&D cooperation in Brazil: A sectoral approach. Journal of Technology Transfer 43: 285–315. [CrossRef] Stefanelli, Valeria, Vittorio Boscia, and Pierluigi Toma. 2020. Does knowledge translation drive spin-offs away from the “valley of death”? A nonparametric analysis to support a banking perspective. Management Decision 58: 1985–2009. [CrossRef] Steinþórsdóttir, Finnborg Salome, Thamar Melanie Heijstra, and Þorgerður Jennýjardóttir Einarsdóttir. 2017. The making of the’excellent’university: A drawback for gender equality. Ephemera 17: 557–82. Stephan, Paula E. 1996. The Economics of Science. Journal of Economic Literature 34: 1199–235. Stuart, Toby E., and Waverly W. Ding. 2006. When Do Scientists Become Entrepreneurs? The Social Structural Antecedents of Commercial Activity in the Academic Life Sciences. American Journal of Sociology 112: 97–144. [CrossRef] [PubMed] Tabachnick, Barbara G., Linda S. Fidell, and Jodie B. Ullman. 2007. Using Multivariate Statistics. Boston: Pearson, vol. 5. Tartari, Valentina, and Ammon Salter. 2015. The engagement gap: Exploring gender differences in University—Industry collaboration activities. Research Policy 44: 1176–91. [CrossRef] Westnes, Petter, Sachi Hatakenaka, Martin Gjelsvik, and Richard K. Lester. 2009. The role of Universities in strengthening local capabilities for innovation—A comparative case study. Higher Education Policy 22: 483–503. [CrossRef] Wu, Wenqing, Hongxin Wang, Chundong Zheng, and Yenchun Jim Wu. 2019. Effect of Narcissism, Psychopathy, and Machiavellianism on Entrepreneurial Intention-The Mediating of Entrepreneurial Self-Efficacy. Frontiers in Psychology 10: 360. [CrossRef] Adm. Sci. 2023,13, 9 22 of 22 Zhang, Ben, and Xiaohong Wang. 2017. Empirical study on influence of university-industry collaboration on research performance and moderating effect of social capital: Evidence from engineering academics in China. Scientometrics 113: 257–77. [CrossRef] Zhou, Ping, Robert Tijssen, and Loet Leydesdorff. 2016. University-Industry Collaboration in China and the USA: A Bibliometric Comparison. PLoS ONE 11: e165277. [CrossRef] [PubMed] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). 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