top of page
Writing on the Board

Commercializing research

Communicating research

White Structure

Introduction

Typically, the main roles of universities revolve around education and research.  However, in recent decades, and especially in recent years, commentators have recognized that universities, in addition to education and research, also engage in entrepreneurial activity—primarily to improve the economic performance of their region and nation.  This conceptualization of universities is sometimes called the entrepreneurial university

 

Taxonomy of entrepreneurial activities

Many scholars have proposed various taxonomies or catalogues to delineate the range of entrepreneurial activities that universities undertake (e.g., Philpott et al., 2011).  To illustrate, universities may implement one or more of the following activities

 

  • Technology parks or sites at a university campus in which technology and other businesses can interact with the university.  These parks facilitate clusters of collaborations, attract skilled individuals, and increase the likelihood of research collaborations and other arrangements

  • Spin-off firms or joint ventures that commercialize the research of this university and thus generate a potential revenue stream (for evidence of their importance to a region, see Fuster et al., 2019)

  • Patents, licences, and other avenues to secure property rights from the discoveries in the university—ultimately to enable other firms to apply this knowledge as well as to attract income, such as royalties

 

Many other entrepreneurial activities at universities overlap more closely with their traditional roles and include

 

  • contract research in which industry fund academics to complete specific research projects, many of which may generate commercial benefits

  • industry training courses in which universities impart knowledge to industry employees about relevant advances in their fields, funded by industry partners, government agencies, or both

  • consulting in which academics are engaged by industries to solve challenging problems.

 

Finally, many of the core activities that universities undertake are relevant to the economic performance of their region and nation, such as research grants, research publications, conferences, informal exchanges, and the teaching of relevant skills. Some researchers even contend that such core activities transfer knowledge to industry more effectively than alternative entrepreneurial practices (Cohen et al. 2002). 

 

Caveats about entrepreneurial activities

Some commentators raise concerns about entrepreneurial activities that deviate from the traditional role of universities around education and research.  First, scholars have suggested that universities are not positioned or configured appropriately to thrive in these activities, such as technology parks, spin-off firms, joint ventures, patents, and licences.  Even universities that are renowned to be leaders in entrepreneurial activity do not always succeed.  To illustrate

 

  • as Agrawal and Henderson (2002) underscored, patents explain only 10% of instances in which knowledge was transferred from MIT labs to industry

  • patenting and licencing is primarily confined to software and biosciences—and therefore is not relevant to many academic disciplines (Mowery et al., 2004). 

  • even at some of the most entrepreneurial universities, patents and licences are only marginally profitable (Mowery et al., 2004)

  • some research even indicates that national governments may be funding entrepreneurial activities such as technology parks, spin-off firms, and joint ventures to the detriment of education and research—ultimately compromising the benefits of universities to the economy (Bubela & Caulfield, 2010)

 

Similarly, researchers have proposed that universities can benefit the economy more if they develop entrepreneurial graduates rather than venture into entrepreneurial activities themselves.  According to one estimate, even in the last century, MIT graduates had founded over 4000 companies, generating $232 billion in annual revenues across the globe (BankBoston, 1997)

 

Academics also express concerns about the entrepreneurial university.  To illustrate, Philpott et al. (2011) conducted interviews with 12 academics at a European university on this topic.  Academics indicated that

 

  • entrepreneurial activities often stifle progression and promotion, because of the time that must be dedicated to this entrepreneurial pursuit

  • the entrepreneurial activities of individual staff should be facilitated and embraced—but university management should not impose these activities onto staff

  • entrepreneurial activities are challenging, partly because academics perceive a dearth of suitable role models to emulate in their field

  • entrepreneurial activities may not be as relevant to the humanities or social sciences

Measures of entrepreneurial practices at universities

Scholars have developed several indices to gauge the degree to which a university engages in entrepreneurial activity.  For example, the Spanish Entrepreneurial University Scoreboard measure, as utilised by Guerrero and Urbano (2012), considers

 

  • the degree to which universities have introduced entrepreneurial initiatives such as science parks, incubators, courses in entrepreneurial activity, or interdisciplinary centers

  • the extent to which universities generate outcomes that demonstrate entrepreneurial activity, including patents, licences, and spin-offs

  • the degree to which the universities have fostered a culture of entrepreneurial activity and have developed or employed entrepreneurial staff

  • the level of entrepreneurship in the local region, as gauged by the number of new enterprises

  • the ranking of a university

 

Determinants of entrepreneurial activity at universities: University characteristics and practices

Research has explored the characteristics of universities that promote entrepreneurial activity or foster an entrepreneurial culture.  To illustrate, according to Clark (1998, 2001), universities can more readily develop an entrepreneurial culture if

 

  • the university is small and thus can change more efficiently

  • the university specialized in particular academic disciplines, such as technology

  • the administration is primarily centralized rather than devolved to faculties, colleges, or schools.

 

Guerrero and Urbano (2012) conducted a study to ascertain the features and practices of universities that coincide with the degree to which universities engage in entrepreneurial activity.  These researchers explored courses about entrepreneurial activity, reward schemes, entrepreneurial teaching methods, attitudes towards entrepreneurial activity, governance, and many other practices that could foster entrepreneurial behavior. 

 

The degree to which universities were entrepreneurial, as measured by the Spanish Entrepreneurial University Scoreboard, was largely dependent on the degree to which academics and students express favorable attitudes towards entrepreneurial activity.  If these individuals tended to regard entrepreneurial activity as beneficial and valuable, the universities were more likely to be entrepreneurial.  Many features of universities can foster these attitudes, such as the degree to which the university offers courses in entrepreneurial activity, rewards entrepreneurial activity, and employs some role models in entrepreneurial activity

 

Determinants of entrepreneurial activity at universities: University support

Other studies have explored how universities and other bodies can help graduates translate entrepreneurial intentions to entrepreneurial behavior.  For instance, in one study, conducted by Yi (2021), administered a survey to graduates of Yancheng Teachers University and Xiangtan University.  The survey measured intentions to pursue green entrepreneurial enterprises, such as “I wished to start a green enterprise that assists in alleviating environmental issues during my study in university” as well as behaviors that are designed to pursue these intentions, such as written a business plan, sought funding, or purchased materials. The survey also gauged support from the university, such as “My university offers courses on green entrepreneurship”, and support from other bodies, such as “(A body) played a significant role in providing financial support for your start-up activities”

 

As hypothesized, both university support and other support increased the likelihood that entrepreneurial intentions translated to entrepreneurial behavior.  More importantly, the survey uncovered four practices universities could apply to support entrepreneurial activity: courses of entrepreneurial behavior, entrepreneurial projects to which students could perhaps contribute, advice on finances as well as policies, and campaigns to motivate entrepreneurial behavior. 

 

Some researchers have validated the benefits of entrepreneurial training—especially training about the T shaped approach—representing a blend of expertise about one topic and familiarity with many topics.  To illustrate, Rippa et al. (2022) validated an entrepreneurial training program that was directed to graduate researchers.   First, during this training, participants learned about innovation business models—that is, the various pathways that transform intellectual knowledge, such as a scientific discovery, into a commercial asset.  Second, participants learned about how they can use copyright, patent, and trademark to protect and to utilize their intellectual knowledge.  Third, participants leanted about how to navigate the transformation of intellectual knowledge to commercial opportunities—such as how to manage this knowledge before they submit a patent application, how to submit a patent application, and how to establish an academic spin-off.  Finally, participants were exposed to detailed case studies of academic spin-offs or other avenues owned by alumni of the university.  This program significantly increased the degree to which these candidates intended to pursue an entrepreneurial venture and boosted their confidence in their capacity to achieve this goal. 

 

University support of entrepreneurial activity, however, does not always inspire students to engage in this activity.  To illustrate, Wegner et al. (2020) compared two universities—only one of which has invested in courses that help students develop entrepreneurial skills, entrepreneurial competitions, and incubation opportunities.  Interestingly, the intention of students to pursue entrepreneurial ventures did not significantly differ between the two universities.

 

Determinants of entrepreneurial activity at universities: Motivations

Some research has explored the motivations that inspire academics to pursue entrepreneurial activities as well as the antecedents to these motivations.  As D’este and Perkmann (2011) reveal, many academics in the physical and engineering sciences pursue entrepreneurial activities primarily to accelerate their research and not specifically to commercialize their insights.  Specifically, academics tend to engage in consulting, contract research, or industry research to enhance their research careers.  In contrast, academics tend to engage in spin-off companies or seek patents mainly to commercialize their work. 

 

Determinants depend on which entrepreneurial activities are prioritized

Arguably, the determinants of entrepreneurial behavior might vary across the various entrepreneurial activities.  To illustrate, in an intriguing but exploratory study, Sánchez-Barrioluengo and Benneworth, (2019) developed three indices to measure the degree to which universities are entrepreneurial.  Specifically

 

  • the first index gauges the degree to which the university is commercially entrepreneurial—as measured by number of patents, number of spinoffs, and IP revenue each year

  • the second index gauges the degree to which the university is regionally engaged—as measured by consultancy income, contract research income, and facilities income each year

  • the third index blends these measures and also includes income from collaborative research that is publicly funded to measure the degree to which the university is engaged.

 

Arguably, which characteristics of universities promote entrepreneurial behavior might depend on which of these indices researchers utilize.  To test this possibility, Sánchez-Barrioluengo and Benneworth, (2019) measured a range of configurations that might shape entrepreneurial activity including

 

  • whether the university reports or discloses IP revenue—that is, revenue from inventions, software, copyright, design, trademark, and other IP

  • staff receive rewards if they generate revenue from intellectual property

  • whether the university has established a central body to receive enquiries from enterprises, to coordinate interactions with business or the community, and to pursue licencing opportunities

  • the percentage of academics who provide services to business or community partners.

 

Interestingly, few of these configurations were associated with level of entrepreneurial activity.  One exception was that a central body to support entrepreneurial activity was inversely associated with the degree to which the universities are commercially entrepreneurial or engaged in general.  In contrast, disclosure and rewards were positively associated with the degree to which the university was regionally engaged. 

 

Overall, these results imply that structural configurations do not greatly affect entrepreneurial success—and centralized bodies might even impede this activity.  More importantly, as these findings indicate, a particular configuration might not suit all universities.  Which configuration to pursue might depend on which entrepreneurial activities the university wants to prioritize.

 

Practices that foster entrepreneurial activity at universities

According to Gianiodis and Meek (2019), many of the attempts of universities to promote entrepreneurial activity have been relatively unsuccessful.  That is, universities that have established innovation hubs, units, or institutes, embedded entrepreneurial activity into their strategic plans, and rewarded academics who engaged in entrepreneurial activity have attracted only modest benefits.

 

Instead, Gianiodis and Meek (2019) proposes that universities should instead prioritize entrepreneurial capital and education. That is, universities should teach students, alumni, and staff how to think critically, how to evaluate ideas, and how to solve problems creatively—all of which have been shown to foster entrepreneurial solutions and innovations.   Specifically, according to Gianiodis and Meek (2019), universities could

 

  • propose a strategy to increase the percentage of enrolled and past students who are founders, employees, or interns of new ventures

  • propose a plan to increase the percentage of students who participate in technology parks, incubators, and collaborative workspaces—such as makerspaces or hackerspaces

  • introduce a chair or director of entrepreneurship at the university

  • increase the number of informal workshops and bootcamps that facilitate entrepreneurial activity in students, alumni, and staff

  • develop a strategy on how to encourage mentorship and internship experiences to students or staff interested in new ventures

  • construct a strategy on how             to attract and to recruit surrogate entrepreneurs to consider and to evaluate the innovations in this university.

 

Other studies have also explored which practices influence the benefits of entrepreneurial education.  Hahn et al. (2019), for example, investigated whether elective courses or mandatory courses on entrepreneurial education are more effective.  In this study, Swiss university students had either never completed a course on entrepreneurial activity, completed an elective course on entrepreneurial activity, or completed a mandatory course on entrepreneurial activity.  After the course, the students evaluated the degree to which they felt they developed entrepreneurial skills, such as the capacity to

 

  • identify business opportunities

  • create and commercialize new products or services

  • manage innovation within a firm and a business more broadly

  • act as a leader and communicator

  • establish a professional network

 

As hypothesized, elective courses on entrepreneurial activity did indeed improve entrepreneurial skills.  Interestingly, mandatory courses on entrepreneurial activity improved entrepreneurial skills, but only in students whose parents were self-employed.  As this finding indicates, entrepreneurial courses do not improve entrepreneurial skills in all students.  These courses are effective only if the courses are optional or if students are familiar with entrepreneurial activity, because of their family environment. 

 

Overview of the literature

Because the literature on entrepreneurial universities has soared in recent years, Cerver Romero et al. (2021) conducted a bibliometric analysis to explore this literature more systematically.  The authors uncovered several distinct perspectives on entrepreneurial universities.  For example

 

  • one perspective considers the different skills and motivations in academics that are necessary if they want to pursue entrepreneurial activity rather than only teaching and research

  • another perspective revolves around the tensions and synergies between traditional objectives—teaching as well as research—and entrepreneurial objectives.   For example, some research underscores tensions, such as the notion that university rankings do not greatly depend on spinoffs or patents.  Hence, entrepreneurial activity might be deterred.  Other research underscores how amendments to teaching and research could enhance entrepreneurial pursuits.

  • another perspective revolves around the notion that attempts to uncover a unified theory are futile, because of the inherent and profound variations across the academic disciplines.

References

  • Agrawal, A., & Henderson, R. (2002). Putting patents in context: Exploring knowledge transfer from MIT. Management science, 48(1), 44-60.

  • Audretsch, D. B. (2014). From the entrepreneurial university to the university for the entrepreneurial society. The Journal of Technology Transfer, 39(3), 313-321.

  • Audretsch, D. B., & Belitski, M. (2021). Three-ring entrepreneurial university: in search of a new business model. Studies in Higher Education, 46(5), 977-987.

  • BankBoston (1997). MIT: The impact of innovation. Bank Boston Economics Department Special Report, Boston MA, USA.

  • Bubela, T. M., & Caulfield, T. (2010). Role and reality: technology transfer at Canadian universities. Trends in biotechnology, 28(9), 447-451.

  • Bukhari, E., Dabic, M., Shifrer, D., Daim, T., & Meissner, D. (2021). Entrepreneurial university: The relationship between smart specialization innovation strategies and university-region collaboration. Technology in Society, 65.

  • Cai, Y., & Ahmad, I. (2021). From an entrepreneurial university to a sustainable entrepreneurial university: Conceptualization and evidence in the contexts of European University Reforms. Higher Education Policy, 1-33.

  • Cerver Romero, E., Ferreira, J. J., & Fernandes, C. I. (2021). The multiple faces of the entrepreneurial university: a review of the prevailing theoretical approaches. The Journal of Technology Transfer, 46(4), 1173-1195.

  • Clark, B. (2001). The entrepreneurial university: New foundations for collegiality, autonomy, and achievement. Higher education management, 13(2).

  • Clark, B. R. (1998). Creating entrepreneurial universities: organizational pathways of transformation. Issues in Higher Education. Elsevier Science Regional Sales, 665 Avenue of the Americas, New York, NY.

  • Cohen, W. M., Nelson, R. R., & Walsh, J. P. (2002). Links and impacts: the influence of public research on industrial R&D. Management science, 48(1), 1-23

  • Cunningham, J. A., & Miller, K. (2021). Entrepreneurial university business models: core drivers, challenges and consequences. In A Research Agenda for the Entrepreneurial University (pp. 103-128). Edward Elgar Publishing.

  • D’este, P., & Perkmann, M. (2011). Why do academics engage with industry? The entrepreneurial university and individual motivations. The Journal of Technology Transfer, 36(3), 316-339.

  • Etzkowitz, H. (2003). Research groups as ‘quasi-firms’: the invention of the entrepreneurial university. Research policy, 32(1), 109-121.

  • Etzkowitz, H. (2013). Anatomy of the entrepreneurial university. Social Science Information, 52(3), 486-511.

  • Etzkowitz, H., Dzisah, J., & Clouser, M. (2022). Shaping the entrepreneurial university: Two experiments and a proposal for innovation in higher education. Industry and Higher Education, 36(1), 3-12.

  • Feola, R., Parente, R., & Cucino, V. (2021). The entrepreneurial university: How to develop the entrepreneurial orientation of academia. Journal of the Knowledge Economy, 12(4), 1787-1808.

  • Fuster, E., Padilla-Meléndez, A., Lockett, N., & del-Águila-Obra, A. R. (2019). The emerging role of university spin-off companies in developing regional entrepreneurial university ecosystems: The case of Andalusia. Technological Forecasting & Social Change, 141, 219–231.

  • Gianiodis, P. T., & Meek, W. R. (2019). Entrepreneurial education for the entrepreneurial university: a stakeholder perspective. The Journal of Technology Transfer, 45(4), 1167–1195

  • Gibb, A., & Hannon, P. (2006). Towards the entrepreneurial university. International Journal of Entrepreneurship Education, 4(1), 73-110.

  • Gibb, A., Haskins, G., & Robertson, I. (2013). Leading the entrepreneurial university: Meeting the entrepreneurial development needs of higher education institutions. In Universities in change (pp. 9-45). Springer, New York, NY.

  • Guerrero, M., & Urbano, D. (2012). The development of an entrepreneurial university. The Journal of Technology Transfer, 37(1), 43-74.

  • Guerrero, M., & Urbano, D. (2021). The entrepreneurial university in the digital era: Looking into teaching challenges and new higher education trends. In A research agenda for the entrepreneurial university. Edward Elgar Publishing.

  • Hahn, D., Minola, T., Bosio, G., & Cassia, L. (2019). The impact of entrepreneurship education on university students’ entrepreneurial skills: a family embeddedness perspective. Small Business Economics, 55(1), 257–282.

  • Hannon, P. D. (2013). Why is the entrepreneurial university important? Journal of Innovation Management, 1(2), 10-17.

  • Heydarian, N. et al., (2021). Explaining the challenges facing the development of Entrepreneurial University: The Application of Grounded Theory. Journal of Contemporary Issues in Business & Government, 27(3).

  • Karim, M. S., Sena, V., & Hart, M. (2022). Developing entrepreneurial career intention in entrepreneurial university: the role of counterfactual thinking. Studies in Higher Education, 47(5), 1023-1035.

  • Liu, S., & van der Sijde, P. C. (2021). Towards the entrepreneurial University 2.0: Reaffirming the responsibility of universities in the era of accountability. Sustainability, 13(6).

  • Mowery, D. C., Nelson, R. R., Sampat, B. N., & Ziedonis, A. A. (2015). Ivory tower and industrial innovation: University-industry technology transfer before and after the Bayh-Dole Act. Stanford University Press.

  • Padilla-Meléndez, A., Fuster, E., Lockett, N., & del-Aguila-Obra, A. R. (2021). Knowledge spillovers, knowledge filters and entrepreneurial university ecosystems. Emerging role of University-focused venture capital firms. Knowledge Management Research & Practice, 19(1), 94-105.

  • Philpott, K., Dooley, L., O'Reilly, C., & Lupton, G. (2011). The entrepreneurial university: Examining the underlying academic tensions. Technovation, 31(4), 161-170.

  • Pinheiro, R., & Stensaker, B. (2014). Designing the entrepreneurial university: The interpretation of a global idea. Public Organization Review, 14(4), 497-516.

  • Rippa, P., Landi, G., Cosimato, S., Turriziani, L., & Gheith, M. (2022). Embedding entrepreneurship in doctoral students: the impact of a T-shaped educational approach. European Journal of Innovation Management, 25(1), 249-270.

  • Sam, C., & Van Der Sijde, P. (2014). Understanding the concept of the entrepreneurial university from the perspective of higher education models. Higher Education, 68(6), 891-908.

  • Sánchez-Barrioluengo, M., & Benneworth, P. (2019). Is the entrepreneurial university also regionally engaged? Analysing the influence of university’s structural configuration on third mission performance. Technological Forecasting & Social Change, 141, 206–21

  • Thorp, H. H., & Goldstein, B. (2013). Engines of innovation: The entrepreneurial university in the twenty-first century (Second edition.). Baltimore, Maryland: Project Muse.

  • Valera-Loza, D. H., JUNCO, J. G. D., & Palacios-Florencio, B. (2021). Conceptual model about the entrepreneurial university: design and validation with the PLS methodology. Anais da Academia Brasileira de Ciências, 93.

  • Wegner, D., Thomas, E., Teixeira, E. K., & Maehler, A. E. (2020). University entrepreneurial push strategy and students’ entrepreneurial intention. International Journal of Entrepreneurial Behaviour & Research, 26(2), 307–325.

  • Yi, G. (2020). From green entrepreneurial intentions to green entrepreneurial behaviors: the role of university entrepreneurial support and external institutional support. International Entrepreneurship and Management Journal, 17(2), 963–979.

White Structure

Academic or university spinoffs

Introduction

In the last few decades, many universities and research institutions have generated, or attempted to generate, spinoffs—ventures that are designed to commercialize a discovery at the university.  These spinoffs share many features with other new technology businesses.  Nevertheless, spinoffs from universities and research institutions differ from these other ventures on several features (e.g., Wennberg et al., 2011; Zhang, 2009).  In particular, compared to other technology ventures, university spinoffs, also called academic spinoffs or research-based spinoffs,   

 

  • often benefit more from government funding or support rather than venture capital

  • tend to benefit from more advanced research activity, partly because they often sustain a relationship with the university

  • tend to be more sustainable (Zhang, 2009) but sometimes not as profitable

  • tend to remain physically close to the university (e.g., Zhang, 2009)

 

Variation of academic spinoffs

Academic spinoffs vary on many characteristics.  For example

 

  • although often founded by universities, these spinoffs might also emanate from public research institutes, especially in France, Germany, and Norway.  These public research institutes tend to be more specialized than universities and devoid of teaching responsibilities

  • usually, but not always, universities establish a technology transfer office to manage these spinoffs. These officers can arrange patents, provide seed funding, facilitate networking with possible collaborators, and facilitate access to business services (see Mathisen & Rasmussen, 2019)

  • in some instances, the inventor acts as an entrepreneur; in other instances, the inventor does not contribute heavily to the business

 

Consequences of academic spinoffs to investors

Occasionally, spinoffs such as Google and Genentech, the biotechnology company, are remarkably successful.  To illustrate

 

  • the net present value of Canadian academic spinoffs exceeds the total research investment into all scientific fields (Vincett, 2010)

  • about 25% of all initial public offerings in European technology industries emanate from academic spinoffs

 

Although most commentators recognize that academic spinoffs can facilitate the transfer of technology to industry and accelerate economic growth (Mathisen & Rasmussen, 2019), some research has challenged the viability of academic spinoffs in general.  Many spinoffs do not grow substantially (e.g., Hayter 2011) and thus may not justify the government funding they receive (e.g., Mustar et al. 2008).  Indeed, in the US, academic ventures are not as financially successful as other business start-ups (Ensley & Hmieleski, 2005).

 

Consequences of academic spinoffs to the founder

Universities and, occasionally, other tertiary education institutions encourage academics to pursue entrepreneurial activities, such as spinoffs and startups.  One question is whether these spinoffs benefit the academic founder.  To illustrate, academics are often uncertain as to whether the research performance of these founders improves or deteriorates after the spinoff is established.

 

Abramo et al. (2012) conducted a study that was designed to explore this question.  This study investigated all spinoffs that emanated from Italian universities during 2001 to 2008 and applied a bibliometric approach to evaluate the research performance of founders.  In general, after the spinoff was established, the research performance of founders tended to improve.  These changes in research performance did vary across fields.  But, in no fields did spinoffs diminish the research performance of founders appreciably.   

 

Spinoffs may not only enhance the research performance of founders but attract other benefits.  For example, founders are often attracted to spinoffs because of the potential to experience a greater sense of autonomy and independence (O’Shea et al., 2005).

 

Development of spinoffs: Recognize the opportunity

Academic spinoffs tend to experience a series of iterative phases (e.g., Vohora et al., 2004). First, the individuals must first recognize the opportunity to apply some discovery to address some problem. During this time, the team often seek the advice of a coach or consultant, either formally or informally, to explore market opportunities.  Vanaelst et al. (2006) refers to the coach or consultant as a privileged witness to reinforce the separation between this individual and the creative pursuit. Nevertheless, during this time, many academic inventors confine their network to other members of their university (Hayter, 2016).  This homogeneity may not be detrimental during this phase but is likely to impede commercial success later.

 

Development of spinoffs: Commit to an entrepreneurial orientation

Second, the team must either commit to operate as entrepreneurs or entice other entrepreneurs to commit to this venture, often called surrogate entrepreneurs.  Surrogate entrepreneurs tend to increase the likelihood the spinoff will succeed financially (e.g., Lundqvist, 2014) and survive (Prokop et al., 2019).  

 

To identify entrepreneurs, many academics will first contact their technology trading office or a comparable team.  Consequently, the networks this office have established will significantly influence whether the spinoff will proceed and thrive (Hayter, 2016).  Indeed, the duration a technology trading office has been operating significantly determines the likelihood the spinoff will survive, as Prokop et al. (2019) revealed.  Unfortunately, as Hayter (2016) observed, these technology trading offices tend to orient their attention more to contractual arrangements, such as equity and licensing.  These officers do not often introduce academics to key business contacts, partly because their networks are sometimes inadequate.   

 

Rather than depend on the technology trading office, some academics utilize networks they have developed previously, from contract research or other activities.  These networks are not as dependent on a single intermediary and thus not as vulnerable to obstacles, such as staff turnover in a technology trading office (Hayter, 2016).

 

Furthermore, during this phase, the venture may then become a legal entity and comprise a management team and board of directors.  The researchers are typically, but not invariably, members of both the management team and board of directors. The privileged witnesses, the technology transfer office, scientific peers, and financial partners might be members of the board of directors but unlikely to be members of the management team. 

 

Development of spinoffs: Foster credibility

Third, the team must develop the credibility they need to attract funding, either from industry partners or from specialist investors.  The composition of the board might change, partly to attract funding.  The composition of the management team might also change, especially if a funder, such as a venture capitalist, appoints a CEO or business developer.  If the board or management team have developed existing relationships with venture capitalists, the spinoff is more likely to succeed financially and sustainably (e.g., Mueller et al., 2012). 

 

The reliance on the university will typically wane during this phase or during similar activities (Fernandez-Alles et al. 2015), unless the spinoff revolves around particularly advanced and evolving technology (e.g., Treibich et al., 2013).  However, initial capital investment from the university can increase degree to which the spinoff will be able to attract venture capital (Gubitta et al., 2016) and predict future growth in the number of staff (Zerbinati et al., 2012). Nevertheless, some conflicting results have been uncovered (e.g., Clarysse et al., 2007): Initial capital investment can sometimes increase the likelihood the team overestimate the value of their intellectual property, deterring venture capitalists later.

 

One complication during this phase relates to the conflicting inclinations of venture capitalists and technology transfer offices.  As Wright et al. (2006) reveal, venture capitalists are more inclined to invest after the venture has received seed funding.  Venture capitalists are wary of university spinoffs because they assume these ventures will demand the creation of a management team, a longer time before profits are earned, and closer monitoring.  Venture capitalists are concerned they might need to interact with multiple stakeholders who offer different perspectives. Yet, technology transfer offices feel that venture capital is vital early in the project—and are more likely to support projects in which venture capital has been received.

 

Development of spinoffs: Sustain the business

Finally, the team must introduce operations to sustain the business.  During each transition between the phrases, the team must acquire the resources and capabilities they need to succeed in the next phase (Vohora et al., 2004).  In particular, as the venture proceeds, the teams become increasingly diverse in their experience with entrepreneurial ventures, their knowledge, and their skills.  That is, teams are often homogenous initially and progressively diverse as they develop.

 

Determinants of success: Government and university policies

The funding policies of federal governments and universities significantly affect the degree to which academics pursue entrepreneurial activities and the frequency of start-ups.  To illustrate, as Goldfarb and Henrekson (2003) argued, in Sweden, despite significant investment in research and development as well as strong performance in scientific activity, the number and success of academic spinoffs is limited.  In Sweden, and in many European nations, the universities are not privately owned but dictated by the government.  That is, the government significantly determines the rules of admission, the size of universities, and the size of specific fields of education.  Consequently, the pay and working conditions of academics is relatively uniform across the nation.  Academics, therefore, are not as inclined to differentiate themselves and seek entrepreneurial pursuits.  Even Swedish attempts to commercial academic research have been relatively ineffective, because academics are not as interested in these possibilities. 

 

In contrast, in the US, some universities are privately owned and all universities are granted more flexibility, often culminating in competition.  Academics who pursue entrepreneurial ventures are more likely to be awarded, culminating in many spinoffs. 

 

The official university policies and rules may also shape the success or failure of spinoffs (e.g., Degroof & Roberts, 2004). Muscio et al. (2016) explored which university policies and rules in particular might facilitate academic spinoffs. In particular, the researchers examined a database of 62 universities to ascertain which rules to predict the number of spinoff companies these institutions generate each year.  In particular, the researchers explored three sets of predictors

 

  • rules around monetary incentives, such as rules about the minimum share of the spinoff equity that is assigned to academic participants, whether founders of a spinoff must work at the university only part time, the amount the university withholds from the sales of a patent or other sources of revenue

  • rules around risk management, such as whether the university is liable to fund losses from the spinoff and whether time limits on access to university facilities

  • more general rules, such as whether the university has established a committee to evaluate spinoffs, conflicts of interest between spinoffs and the university are prohibited, and availability of templates to assist business proposals

 

Many of these rules affected the number of spinoffs that institutions generate.  In particular, universities tend to generate more spinoffs if templates to develop business proposals are available, if universities stipulate a minimum share of the spinoff equity to academic participant, and if the university does not withhold significant revenue.

 

Determinants of success: University support

Ventures or spinoffs can survive only if supported by the university.  That is, once academic inventors disclose their invention to the university, particular specialist, often called technology licensing officers, consider how to proceed.  For instance, these officers may consider whether the intervention should be patented or whether other avenues should be considered.

 

However, as Shane et al. (2015) revealed, technology licensing officers are susceptible to a specific bias, called the representativeness heuristic.  In particular, these officers are more likely to support projects in which the inventor typifies an entrepreneur.  For instance, they tend to support ventures when the inventor is male, Asian, and experienced in industry.  Shane et al. (2015) corroborated these premises in an experimental study in which technology licensing officers were exposed to a series of proposals and then asked to indicate whether they would dissuade the inventor or recommend a startup to the internal capital fund in the university.  If the inventors were female, not Asian, and inexperienced in industry, technology licensing officers were more inclined to dissuade, rather than encourage, the inventor. 

 

The level of support that universities offer all ventures, instead of biases towards specific ventures, also affects the number and success of academic spinoffs.  Lockett and Wright (2005), for instance, explored which resources and investments facilitated the generation of spinoffs. The authors discovered that universities were likely to generate more spinoffs if

 

  • the university was experienced in negotiation around commercial arrangements

  • the university was experienced in assessment of intellectual property rights—or invested heavy in advice on intellectual property rights

  • the university delivered proficient support in marketing and technical matters

  • academics received sizeable royalties or percentages of the revenue earned.

 

Determinants of success: Technology parks and incubators

In the second half of the previous century, universities began to launch technology or science parks.  Typically, a technology park refers to property, connected to a university or research institution, that delivers services to help academics transfer knowledge to businesses.  The park enables businesses to develop interactions with centers of knowledge.  

 

Limited research has explored the degree to which technology parks facilitate or impede spinoffs (Link & Scott, 2007).  One exception was a study, reported by Colombo and Delmastro (2002), into startup firms in Italy.  The results indicated that technology or science parks attract entrepreneurs who have accrued great industry experience and educational qualifications.  Furthermore, the revenue of these startups that were located in science parks, compared to spinoffs that were not located in science parks, increased more rapidly.  These startups were more likely to adopt more advanced technologies and attract public subsidies more effectively.  Overall, these results may vindicate the decision of many universities to establish or support technology parks.

 

Determinants of success: Business arrangements and investment

As Wright et al. (2004) discuss, some, but not all, spinoffs are joint ventures between universities and industry partners.   Relative to spinoffs that are not joint ventures with industry partners, these joint ventures are more likely to become viable.  The industry partner is more likely to supply the management expertise as and financial support that is necessary to survive.   

 

The level of investment may also influence the success of spinoffs.  That is, angel investors, venture capital, public funds, and university venture funding may all fund spinoffs, usually in exchange for equity in the business.  Because investors tend to averse to risk, other investors will typically regard these investments as a sign the spinoff is credible, viable, and high in quality and entrepreneurial capability. These signals may attract further investment, enhancing the sustainability of these spinoffs.  Consistent with these arguments, Prokop et al. (2019) revealed that spinoffs that attract many investors are more likely to survive than spinoffs that attract fewer than two investors.

 

Determinants of success: Competencies

Rasmussen, Mosey, and Wright (2011) explored the circumstances, conditions, and characteristics of individuals that contributed to the success and sustainability of four academic spinoffs.  The first competency was that founders were able to discover or utilize a scientific insight and then translate this opportunity to a cogent and viable business proposal, called opportunity refinement.  To develop this competency

 

  • some academics work in the relevant industry over several months or years

  • some academics arrange regular meetings with industry representatives and customers, often to discuss and develop the opportunity iteratively

  • some academics receive advice and assistance from a local innovation hub or scientific park

  • some academics recruit collaborators, such as research fellows, who have worked in industry

 

The second competency revolved around the capacity of academics to acquire resources, integrate resources, and to utilize resources that were collated for other purposes, called leveraging. Academics may, for example

 

  • engage a collaborator who can attract funding

  • engage an entrepreneur to refine and to legitimize the business proposal before they speak to investors or industry partners

  • seek funding from the university technology transfer office

 

The final competency revolved around the importance of a personal commitment and leadership to sustain the endeavor, called championing.  Specifically

 

  • in the early phases, the most effective teams comprised academics who were passionate about the topic.  A

  • as the spinoff commences, the most effective teams also comprised individuals who have accrued commercial expertise to be passionate about the venture

  • each of these individuals need to be committed to a precise role

  • some of these individuals need to be charismatic enough to instill this passion in other people

 

Other studies have explored the qualities of champions—the individuals who promote the spinoff tirelessly and passionately—that enhance the success of these ventures.  As Walter et al. (2011) revealed, when champions have developed the capacity to excite other individuals in their network about the ideas, the spinoff is more likely to generate increasing sales over time.  In addition, champions who persist in response to moderate adversity, but abandon the pursuit in response to more severe adversity, also increase the likelihood of sales. Presumably, champions who persist in response to severe adversity may not be flexible enough to succeed in business.  Finally, champions who assume many responsibilities, but can still delegate effectively, also enhance the success of spinoffs.

 

References

  • Abramo, G., D'Angelo, C. A., Ferretti, M., & Parmentola, A. (2012). An individual‐level assessment of the relationship between spin‐off activities and research performance in universities. R&D Management, 42(3), 225-242.

  • Bathelt, H., Kogler, D. F., & Munro, A. K. (2010). A knowledge-based typology of university spin-offs in the context of regional economic development. Technovation, 30(9-10), 519-532.

  • Bekkers, R., Gilsing, V., & van der Steen, M. (2006). Determining factors of the effectiveness of IP-based spin-offs: Comparing the Netherlands and the US. Journal of Technology Transfer,31, 545–566.

  • Benneworth, P., & Charles, D. (2005). University spin-off policies and economic development in less successful regions: Learning from two decades of policy practice. European Planning Studies,13, 537–557.

  • Bigdeli, A. Z., Li, F., & Shi, X. H. (2016). Sustainability and scalability of university spinouts: A business model perspective. R & D Management, 46, 504–518.

  • Bjornali, E. S., & Gulbrandsen, M. (2010). Exploring board formation and evolution of board composition in academic spin-offs. Journal of Technology Transfer,35, 92–112.

  • Bock, C., Huber, A., & Jarchow, S. (2018). Growth factors of research-based spin-offs and the role of venture capital investing. The Journal of Technology Transfer, 43(5), 1375-1409.

  • Boh, W. F., De-Haan, U., & Strom, R. (2016). University technology transfer through entrepreneurship: Faculty and students in spinoffs. Journal of Technology Transfer,41, 661–669.

  • Clarysse, B., & Moray, N. (2004). A process study of entrepreneurial team formation: the case of a research-based spin-off. Journal of Business Venturing, 19, 55-79.

  • Clarysse, B., Wright, M., Lockett, A., de Velde, E. V., & Vohora, A. (2005).  Spinning out new ventures: a typology of incubation strategies from European research institutions. Journal of Business Venturing, 20, 183- 216.

  • Clarysse, B., Wright, M., Lockett, A., Mustar, P., & Knockaert, M. (2007). Academic spin-offs, formal technology transfer and capital raising. Industrial and Corporate Change,16, 609–640.

  • Colombo, M. G., & Delmastro, M. (2002). How effective are technology incubators? Evidence from Italy. Research Policy, 31(7), 1103–1122.

  • Czarnitzki, D., Rammer, C., & Toole, A. A. (2014). University spin-offs and the “performance premium”. Small Business Economics,43, 309-326.

  • Degroof, J. J., & Roberts, E. B. (2004). Overcoming weak entrepreneurial infrastructures for academic spin-off ventures. Journal of Technology Transfer, 29, 327–352.

  • Druilhe, C. L. and Garnsey, E. (2004). Do academic spin-outs differ and does it matter? Journal of Technology Transfer, 29, 269-285.

  • Ensley, M. D., & Hmieleski, K. M. (2005). A comparative study of new venture top management team composition, dynamics and performance between university-based and independent start-ups. Research policy, 34(7), 1091-1105.

  • Fernandez-Alles, M., Camelo-Ordaz, C., & Franco-Leal, N. (2015). Key resources and actors for the evolution of academic spin-offs. Journal of Technology Transfer,40, 976–1002.

  • Franklin, S., Wright, M., & Lockett, A. (2001). Academic and surrogate entrepreneurs in university spin-out companies. Journal of Technology Transfer, 26, 127-141.

  • Goldfarb, B., & Henrekson, M. (2003). Bottom-up versus top-down policies towards the commercialization of university intellectual property. Research Policy, 32(4), 639-658.

  • Gubitta, P., Tognazzo, A., & Destro, F. (2016). Signaling in academic ventures: The role of technology transfer offices and university funds. Journal of Technology Transfer,41, 368–393.

  • Hayter, C. S. (2011). In search of the profit-maximizing actor: Motivations and definitions of success from nascent academic entrepreneurs. Journal of Technology Transfer, 36, 340–352.

  • Hayter, C. S. (2016). A trajectory of early-stage spinoff success: the role of knowledge intermediaries within an entrepreneurial university ecosystem. Small Business Economics, 47(3), 633–656

  • Karnani, F. (2013). The university’s unknown knowledge: Tacit knowledge, technology transfer and university spin-offs findings from an empirical study based on the theory of knowledge. The Journal of Technology Transfer, 38(3), 235-250.

  • Link, A. N., & Scott, J. T. (2006). US university research parks. Journal of Productivity Analysis, 25(1–2), 43–55.

  • Link, A. N., & Scott, J. T. (2007). The economics of university research parks. Oxford Review of Economic Policy, 23(4), 661–674.

  • Lockett, A., & Wright, M. (2005). Resources, capabilities, risk capital and the creation of university spin-out companies. Research Policy, 34, 1043–-1057.

  • Lockett, A., Wright, M., & Franklin, S. (2003). Technology transfer and universities' spin-out strategies. Small Business Economics, 20, 185-200.

  • Lundqvist, M. A. (2014). The importance of surrogate entrepreneurship for incubated Swedish technology ventures. Technovation,34, 93–100.

  • Mathisen, M. T., & Rasmussen, E. (2019). The development, growth, and performance of university spin-offs: A critical review. The Journal of Technology Transfer, 44(6), 1891-1938.

  • Mueller, C., Westhead, P., & Wright, M. (2012). Formal venture capital acquisition: can entrepreneurs compensate for the spatial proximity benefits of South-East England and ‘star’ golden-triangle universities? Environment and Planning A,44, 281–296.

  • Muscio, A., Quaglione, D., & Ramaciotti, L. (2016). The effects of university rules on spinoff creation: The case of academia in Italy. Research Policy, 45(7), 1386-1396.

  • Mustar, P., Renault, M., Colombo, M. G., Piva, E., Fontes, M., Lockett, A. et al. (2006). Conceptualizing the heterogeneity of research-based spin-offs: a multi-dimensional taxonomy. Research Policy, 35, 289-308.

  • Mustar, P., Wright, M., & Clarysse, B. (2008). University spin-off firms: Lessons from ten years of experience in Europe. Science and Public Policy, 35, 67–80.

  • O’Shea, R. P., Allen, T. J., Chevalier, A., & Roche, F. (2005). Entrepreneurial orientation, technology transfer and spinoff performance of US universities. Research Policy, 34(7), 994–1009.

  • Prokop, D., Huggins, R., & Bristow, G. (2019). The survival of academic spinoff companies: An empirical study of key determinants. International Small Business Journal, 37(5), 502–535.

  • Ramaciotti, L., & Rizzo, U. (2015). The determinants of academic spin‐off creation by Italian universities. R&D Management, 45(5), 501-514.

  • Rasmussen, E., Mosey, S., & Wright, M. (2011). The evolution of entrepreneurial competencies: A longitudinal study of university spin‐off venture emergence. Journal of Management Studies, 48(6), 1314-1345.

  • Salvador, E. (2011). Are science parks and incubators good “brand names” for spin-offs? The case study of Turin. The Journal of Technology Transfer, 36(2), 203-232.

  • Shane, S. (2004). Academic entrepreneurship--university spinoffs and wealth creation. Cheltenham: Edward Elgar Publishing.

  • Shane, S., Dolmans, S. A., Jankowski, J., Reymen, I. M., & Romme, A. G. L. (2015). Academic entrepreneurship: Which inventors do technology licensing officers prefer for spinoffs? The Journal of Technology Transfer, 40(2), 273-292.

  • Smilor, R. W., Gibson, D. V., & Dietrich, G. B. (1990). University spin-out companies – technology start-ups from University-of-Texas-at-Austin. Journal of Business Venturing, 5, 63-76.

  • Treibich, T., Konrad, K., & Truffer, B. (2013). A dynamic view on interactions between academic spin-offs and their parent organizations. Technovation,33, 450–462.

  • Vanaelst, I., Clarysse, B., Wright, M., Lockett, A., Moray, N., & S'Jegers, R. (2006). Entrepreneurial team development in academic spinouts: an examination of team heterogeneity. Entrepreneurship Theory and Practice, 30, 249-271.

  • Vernon, M. M., Balas, E. A., & Momani, S. (2018). Are university rankings useful to improve research? A systematic review. PloS One, 13(3), e0193762–e0193762

  • Vincett, P. S. (2010). The economic impacts of academic spin-off companies, and their implications for public policy. Research Policy,39, 736–747.

  • Visintin, F., & Pittino, D. (2014). Founding team composition and early performance of university-based spin-off companies. Technovation, 34(1), 31-43.

  • Vohora, A., Wright, M., & Lockett, A. (2004). Critical junctures in the development of university high-tech spinout companies. Research Policy, 33, 147-175.

  • Walter, A., Parboteeah, K. P., Riesenhuber, F., & Hoegl, M. (2011). Championship behaviors and innovations success: An empirical investigation of university spin-offs. Journal of Product Innovation Management,28, 586–598.

  • Wennberg, K., Wiklund, J., & Wright, M. (2011). The effectiveness of university knowledge spillovers: Performance differences between university spinoffs and corporate spinoffs. Research Policy,40, 1128–1143.

  • Woolley, J. L. (2017). Origins and outcomes: The roles of spin-off founders and intellectual property in high-technology venture outcomes. Academy of Management Discoveries,3, 64–90.

  • Wright, M., Lockett, A., Clarysse, B., & Binks, M. (2006). University spin-out companies and venture capital. Research Policy, 35, 481–501.

  • Wright, M., Vohora, A., & Lockett, A. (2004). The formation of high-tech university spinouts: The role of joint ventures and venture capital investors. Journal of Technology Transfer,29, 287–310.

  • Zerbinati, S., Souitaris, V., & Moray, N. (2012). Nurture or nature? The growth paradox of research-based spin-offs. Technology Analysis & Strategic Management,24, 21–35.

  • Zhang, J. F. (2009). The performance of university spin-offs: An exploratory analysis using venture capital data. Journal of Technology Transfer, 34, 255–285.

White Structure

Introduction to patents

Unlike tangible property, such as building, vehicles, and equipment, some property are inventions of the mind, called intellectual property.  Examples include inventions, designs, phrases, symbols, literature, and music.  Tertiary education institutions, like all organizations, like to register and to protect their intellectual property because

 

  • they might want to sell or license this intellectual property

  • they might want to earn revenue from this intellectual property and prevent rivals from competing

  • they might want to increase the value of their organization, because such property is an asset

 

The value of this intellectual property depends on the level of income this property might generate, the likelihood that an alternative could be developed, the value of similar intellectual property, and many other considerations.  To protect this value, institutions can utilize a variety of instruments, such as trademarks, designs, copyright, trade secrets, and patents.  Specifically.

 

  • trademarks protect logos, slogans, shapes like the iPod, sounds like the ticks in 60 minutes, color palettes, gestures, and symbols

  • designs protect new and distinctive configurations, patterns, or decorations that confer a unique appearance to products—rather than one particular shape or sound like trademarks

  • copyright enables authors to determine whether and when other people or organizations can utilize their articles, novels, poetry, movies, songs, computer software, visual art, plays, choreography, and architecture but not ideas. 

  • patents protect a device, substance, method, or process that is new, useful, and inventive or innovative

 

The benefits of patents vary across nations.  To illustrate, in Australia

 

  • patents will protect most inventions for 20 years and other innovations for 8 years

  • patents will protect drugs for 25 years

  • during this time, no other person or organization can produce, use, or sell this invention in this nation, without the permission of the patent holder; otherwise, this person or organization is liable to pay damages

 

In Australia, and in most nations, institutions should utilize specialist assistance to apply for a patent, because the application process is challenging and can cost about $30 000.  Nevertheless, even in Australia, about 30 000 or so patents are approved each year.   In essence, to apply, institutions should

 

  • first decide whether to apply for a standard patent—in which the invention comprises an inventive change that is not obvious to someone with knowledge and experience in the field and is thus more than merely an incremental advance—or an innovation patent—in which the invention comprises an original and useful feature but not necessarily an inventive change.   Innovation patents are often approved more promptly

  • determine whether the benefits of this patent, such as the commercial returns, are likely to offset the costs of this patent—often about $30 000 over 20 years.  Otherwise, trade secrets and non-disclosure agreements may be sufficient.

  • conceal the invention until the patent is approved

  • visit the website in which applications are submitted.  In Australia, this website is www.ipaustralia.gov.au/patents

  • search other patents in your nation, at https://www.ipaustralia.gov.au/patents in Australia for example or Google Patents, to check your invention is indeed original.

  • write your patent application, often comprising a title, background, summary, description, claims that stipulate which activities would constitute an infringement, and drawings. 

  • In Australia and many other nations, submit a provisional application before the final application. Then submit the final application—and seek examination—within five years. 

  • After the final application is submitted, an examiner assesses whether the patent achieves the relevant criteria, usually within 12 months. 

 

Which inventions or innovations can be patented has changed over the years.  For example, in Australia, algorithms, formulas, or software could not be patented.  Now, algorithms, formulas, or software can be patented if related to a specific application. 

Introduction to patents in universities and research institutions

Universities first patented their discoveries in the 1980s.  Until 1980, individuals and organizations could not generally patent discoveries they distilled from research that was funded by federal government.  The Bayh–Dole Act of 1980 essentially erased this impediment.  According to this act, scientists, universities, and businesses could patent from discoveries they derived from federally funded research.  Analogous legislation was enacted in Europe and other nations soon afterwards, igniting the pursuit of patents in universities and other research institutions. 

 

Over the last couple of decades, universities have become increasingly motivated to patent their inventions and innovations.  In some projects, the researchers had planned to patent the work even before they commenced the research, called intention-based inventions (Wu et al., 2015).  On other projects, the researchers had not foreseen the possibility of a patent until after they had analyzed the data and uncovered an important discovery, called opportunity-based inventions (Wu et al., 2015). 

 

University patents are especially beneficial to society.  To illustrate, relative to patents derived from corporations, patents derived from universities tend to be cited more often in subsequent patent applications and utilized by a broader range of disciplines and fields (Schmid & Fajebe, 2019).  This finding implies the knowledge and insights that emanate from university patents permeate across industry and society, inspiring significant advances in technology.   

 

Despite this increase in patents, only a small fraction of academics patent their discoveries.  To illustrate, as a review, conducted by Perkmann et al. (2013), reveals:

 

  • Only about 12% of academics in Sweden and 7% of academics in Norway have participated in patent applications

  • Only 5% of US academics, employed at research universities, have contributed to patent applications

  • Higher percentages have been observed in some other nations, such as 25% in Ireland

  • These variations depend on the sample of interest in each study and not only systematic differences across nations

 

Patents do not always emanate from large, prestigious research institutions.  To illustrate, Daniel and Alves (2020) interviewed academic inventors who had secured patents in Portuguese public universities.  They discovered that most of the patents had emanated from Masters or PhD research, conducted in small work teams, often with no or limited industry engagement. 

 

Determinants of the value and impact of patents: Ownership

Some of the patents that emanate from universities or research institutions are owned by these organizations.  Other patents that emanate from universities or research institutions are owned by the inventors or by corporations that are associated with the inventors.  As research has revealed, ownership might affect the commercial benefits and consequences of patents.

 

To explore this possibility, Giuri et al. (2013) examined patents in a sample of 858 universities or research institutions that had filed patent applications with the European Patent Office between 2003 and 2005.  The authors explored whether the ownership of these patents affected whether these patents were licensed—an important source of income, sold, or translated into a university spinoff.  The analysis generated some key insights.  Specifically

 

  • when universities own the patents, the likelihood the patent is licensed increases.  Organizations will not license a patent unless they feel they can maintain an ongoing relationship with the owners of this patent over an extended period.  Universities may seem to offer the continuity and support these organizations value.

  • When research institutions, rather than inventors or associates, own the patents, sales are more common.  Conceivably, inventors do not want to sustain their relationship with industry partners over an extended period.  These inventors, in contrast to the institutions, might thus prefer to sell the patent to obviate the need to maintain these relationships

 

Admittedly, many considerations may influence universities decide to own the patent or not.  As Schoen and Buenstorf (2013) revealed, whether universities choose to own a patent varies considerably across institutions.  For example, as Thursby et al. (2009) revealed, in the US, public universities are more likely than private universities to own the patents generated in their institutions.  Likewise, universities that offer inventors larger shares of the royalties are more likely to own the patents as well.   

 

Determinants of the number and commercial value of patents: Government investment

Government funding schemes may affect the frequency with which universities submit patent applications and secure patents.  Specifically, as Nugent et al. (2021) revealed, government research grants that fund research collaborations between universities and industries—such as the Australian Research Council Linkage Grants—tend to increase the number of patent applications submitted and the number of patents secured or sealed.  In contrast, government research grants that are not restricted to research collaborations between universities and industries—such as the Australian Research Council Discovery Grants—do not generate these benefits.           

 

To explore how these government research grants contribute to patents, Nugent et al. (2021) also examined this relationship longitudinally.  The findings were nuanced:

 

  • When the grant was confined to collaborations between universities and industries, grant applications and patent applications were submitted in the same year.

  • Presumably, these grants inspired engagement with industry, and this engagement with industry might have oriented the researchers to commercial imperatives

  • When the grant was not confined to collaborations between universities and industries, patent applications were submitted a few years after grant applications

  • In these instances, the researchers may not have considered the commercial possibilities of their work until after they discovered an important finding

 

Determinants of the number and commercial value of patents: University investment

Universities have introduced a range of policies to encourage patents.  To illustrate, universities might offer inventors a high royalty, in which they earn a significant proportion of the revenue or returns the patent generates.  For example, before 2004, the University of Washington did not pay inventors a share of the revenue.  After 2004, the university paid inventors 33% of the revenue.  Since 2005, the University of Iowa pay all the initial patent revenue to the inventor.  

 

Some researchers have explored how these policies affects the number of patents these institutions produce.  To illustrate, Lee (2021) examined how academic remuneration affects the number of patents.  Specifically, Lee showed that universities generate more patents if

 

  • academics receive a considerable bonus or royalty if they secure patents or fulfill other research goals; modest bonuses are not as beneficial

  • academics are paid more than average in the sector—although, the effects of greater remuneration tend to diminish at high levels

  • the institutions dedicate more funding to research and research administration than competitors

 

Taken together, these results suggest that increases in bonuses are more valuable when bonuses are already reasonably generous.  In contrast, increases in salary are more valuable when salary is otherwise low, compared to the sector average.

 

Ouellette and Tutt (2020) explore how the prospect of royalties, in which inventors earn some of the revenue that a patent generates, affects the behavior of academics.  Specifically, the researchers examined whether various royalty polices from 152 universities over time.  Their analysis uncovered no relationship between royalty policies and patent submissions or patent success. If these findings are replicated, the results indicate that perhaps universities should retain most of the royalties because they can invest this money in research and education. 

 

Determinants of the number and commercial value of patents: Industry relationships

How academics collaborate with industry may also affect whether these individuals submit patent applications, secure or seal these patents, and commercialize these patents successfully.  To illustrate, Zhao and Cui (2021) examined whether the number of industry partners with which the university collaborates is related to the commercial value of patents, as measured by the number of patent licenses the university has arranged.  The research was conducted in China.  The researchers uncovered an inverted U relationship.  That is

 

  • universities that collaborate with few industry partners arrange almost no patent licenses

  • universities that collaborate with a moderate number of partners arrange many patent licenses

  • but universities that collaborate with a very large number of partners arrange few if any patent licenses.   

 

As these findings indicate, a moderate number of industry partners is more likely to culminate in commercial returns from patents.  The study did not uncover the precise optimal number of industry partners.  However, if universities and research institutions feel they cannot maintain regular conversations or service their industry partners adequately, the number of these partners is probably excessive. 

 

Besides the number of relationships, the quality of relationships should obviously affect the commercial value of patents.  Petruzzelli (2011), however, conducted an insightful study that explores the similarities between the university and industry partner that might enhance these relationships and improve the commercial value of patents they secure jointly.  Specifically, Petruzzelli explored three kinds of similarities

 

  • technological relatedness of the degree to which the university and industry partner have secured patents in overlapping fields and thus have developed capabilities in similar technologies

  • geographical proximity

  • previous collaboration on a patent

 

The analyses revealed that an inverted U relationship between technological relatedness and value of these joint patents—as measured by the number of times other patents applications cited this joint patent.  Presumably, if the university and industry partner share no overlapping capability in technology, their capacity to communicate effectively with one another and collaborate productively declines.  Yet, if the university and industry partner have developed almost the same capabilities in technology, they are not as likely to uncover novel insights, because their skills may not be diverse enough.

 

Interestingly, geographical proximity was inversely associated with the value of joint patents between the university and industry.  Conceivably, if the university and industry operate in separate locations, they may be exposed to distinct perspectives and opportunities.  They might, collectively, uncover more novel approaches to improve and to market the patented discovery. 

 

Previous collaborations on a patent enhanced the value of future joint patents.  These previous collaborations might enhance the capacity of universities and industry partners to understand one another, communicate effectively, and accommodate the styles and preferences of each other.  Consequently, the universities and industry partners may trust one another and be more willing to withstand the challenges and complications they will inevitably experience. 

 

Determinants of the number and commercial value of patents: Regional characteristics

In addition to the characteristics of inventors, universities, and industry partners, economic and social qualities of the surrounding region might also influence the generation and commercial value of patents.  That is, most scholars assume that knowledge tends to shift from universities to their local surrounds.  But, in many instances, knowledge shifts in the reverse direction, from the local community to the universities (Coronado Guerrero et al., 2017).  To illustrate, as Coronado Guerrero et al. (2017) revealed, when the local, regional economy specializes in advanced technologies, universities tend to secure more patents in advanced technologies as well. 

 

Determinants of the number and commercial value of patents: Motivations

The motivations of academics could also shape the likelihood that universities and other research institutions will secure patents.  In a study of academic inventors in Portugal, conducted by Daniel and Alves (2020), participants indicated their primary motivations revolved around the prestige and reputation of their team and themselves as well as university incentives that reward commercial ventures.

 

Barriers that impede patents: University practices

Some university practices can obstruct the capacity of inventors to secure patents or to utilize their patents effectively.  For example, as Daniel and Alves (2020) concluded, after conducting interviews of Portuguese academic inventors, many of these individuals felt that universities demonstrated inflexibility while negotiating with industry.   In addition, several inventors bemoaned the limited research and development funding that was directed to the refinement of these technologies—necessary to market and to optimize the invention.   Fortunately, as these inventors revealed, strong collaborative relationships with industry diminish the impact of inflexible universities and limited R&D funding. 

 

Consequences of patents: licensing and selling

Patents are not merely exclusive rights granted to the inventor but also an asset that can be monetized directly. Indeed, many of the patents that universities are granted are monetized—usually licensed to another institution or sold to another institution.  To illustrate, Caviggioli et al. (2020) explored the degree to which university patents have been monetized in the US.  These researchers examined the patent transactions in the top 58 universities in the nation between 2002 and 2010.  About 29% of these patents have been licensed. About 6% have been sold to other universities, National Laboratories, federal agencies, or non-profit entities.  Finally, about 1.3% have been sold to companies. 

 

Caviggioli et al. (2020) also examined the characteristics of patents that predict whether they will be licensed or sold.  The patents that were sold and transferred to companies tended to in fields outside the core priorities of the university. Patents that are licensed or, to a lesser extent, sold are more likely to be especially

 

  • high in value

  • high in technical merit, as measured by the number of times the application was cited by other patent applications

  • high in legal robustness, as measured by geographic scope and number of other inventions cited in the application

  • high in complexity, as measured by the number of researchers who contributed

 

Universities and research institutions often need to decide whether they would prefer to license a patent or sell a patent outright (e.g., Wu et al., 2015).  For example, when institutions are uncertain of whether the patent will generate significant revenue and profit, they may prefer to sell the patent and receive a lump sum (Megantz, 2002).  Accordingly, the university will receive income regardless of whether the patent is successful. Conversely, when institutions are more confident the patent will generate revenue and profit, they will prefer to license this patent (Jeong et al., 2013)

 

Several obstacles may impede the capacity of universities and other research institutions to license or to sell their patents.  For example, organizations that contemplate whether to purchase a patent or to seek a license

 

  • are often uncertain about the scope of this patent—that is, the degree to which the patent can inhibit similar competitors—and hence may not be sure the patent is valuable and viable (Gambardella et al., 2007)

  • are not certain they can utilize the patent effectively because they have not developed the tacit knowledge or other information that is not included in the patent application (Agrawal, 2006)

  • are uncertain the degree to which the invention is marketable, because potential customers often do not understand or appreciate the implications of academic discoveries (Buenstorf & Geissler, 2012)

 

Consequences of patents: knowledge transfer

The priorities of universities tend to differ from the priorities of businesses.  The key objective of universities is to disseminate knowledge.  Universities, therefore, tend to conceptualize patents as an opportunity to disseminate knowledge.

 

One question that researchers often consider is how to measure this dissemination of knowledge from patents to society.  One measure is the number of times subsequent patent applications cite or refer to university patents.  As Mukherji and Silberman (2021), citations of patents is an imperfect measure of knowledge transfer but perhaps the most common and convenient. 

 

Mukherji and Silberman (2021) conducted a study to explore how relationships between universities and businesses can affect knowledge transfer from patents, as measured by the number of times these patents were cited in subsequent patent applications.  In particular, this study revealed that, in the United States

 

  • businesses are more likely to cite patents from universities in the same region—especially if the university is public rather than private

  • once the distance between a business and university exceeds 50 miles, further increases in distance do not affect the likelihood the business will cite the university patent application

  • businesses are more likely to cite patents from universities that utilize similar technologies

 

Despite these findings, some research indicates that patents may actually impede the diffusion of knowledge from academics to industry and society (Huang & Murray, 2009; Murray & Stern, 2007). 

That is, patents often demand and instill a sense of secrecy, precluding forthcoming conversations about discoveries and thus impeding the transmission of knowledge from academia to society.  This pattern is sometimes called the anti-commons hypothesis.  

 

Consequences of patents: reputation

Patents may also improve the prestige, visibility, and reputation of university and research institutions.  Despite this possibility, patents do not affect university rankings appreciably.  Indeed, as Vernon et al. (2018) revealed, measures of intellectual property, such as patents, only explain 3.5% of the variance in university rankings overall.  Only four of the ranking schemes—the Clarivate Analytics Most Innovative Universities, the Center for World University Rankings, U-Multirank, and Scimago Institutions Rankings—depend on the number of patent applications or other measures of intellectual property.  For example

 

  • the Clarivate Analytics Most Innovative Universities assesses a patent success ratio—the number of patents awarded divided by the number of patent applications

  • the Center for World University Rankings and U- Multirank consider the number of patent applications

  • the Scimago Institutions Rankings consider the number of university publications cited in patent applications and, therefore, is not dependent on the number of actual patent applications or patent approvals.

 

The other ranking schemes—such as Academic Ranking of World Universities, CWTS Leiden Ranking, QS World University Rankings, and Times Higher Education World University Rankings—do not consider patent activity.  Admittedly, patents tend to boost other research activity and, therefore, may improve rankings obliquely.  Nevertheless, the underlying message is unambiguous: patents do not affect traditional rankings to a marked extent.

 

References

  • Agrawal, A. (2006). Engaging the inventor: Exploring licensing strategies for university inventions and the role of latent knowledge. Strategic Management Journal, 27(1), 63-79.

  • Azagra-Caro, J. M., Archontakis, F., & Yegros-Yegros, A. (2007). In which regions do universities patent and publish more? Scientometrics, 70(2), 251–266.

  • Buenstorf, G., & Geissler, M. (2013). Not invented here: technology licensing, knowledge transfer and innovation based on public research. In The Two Sides of Innovation (pp. 77-107). Springer, Cham.

  • Calvo, N., Rodeiro-Pazos, D., Rodríguez-Gulías, M. J., & Fernández-López, S. (2019). What knowledge management approach do entrepreneurial universities need? Information Systems, 85, 21-29.

  • Caviggioli, F., De Marco, A., Montobbio, F., & Ughetto, E. (2020). The licensing and selling of inventions by US universities. Technological Forecasting and Social Change, 159.

  • Coronado Guerrero, D., Flores, E., & Martínez, M. Á. (2017). The role of regional economic specialization in the production of university-owned patents. The Annals of Regional Science, 59(2), 513–533.

  • Daniel, A. D., & Alves, L. (2020). University-industry technology transfer: the commercialization of university’s patents. Knowledge Management Research & Practice, 18(3), 276–296.

  • Gambardella, A., Giuri, P., & Luzzi, A. (2007). The market for patents in Europe. Research policy, 36(8), 1163-1183.

  • Giuri, P., Munari, F., & Pasquini, M. (2013). What determines university patent commercialization? Empirical evidence on the role of IPR ownership. Industry and Innovation, 20(5), 488–502

  • Huang, K. G., & Murray, F. E. (2009). Does patent strategy shape the long-run supply of public knowledge? Evidence from human genetics. Academy of management Journal, 52(6), 1193-1221.

  • Jeong, S., Lee, S., & Kim, Y. (2013). Licensing versus selling in transactions for exploiting patented technological knowledge assets in the markets for technology. The Journal of Technology Transfer, 38(3), 251-272.

  • Lee, Y. H. (2021). Determinants of research productivity in Korean Universities: the role of research funding. The Journal of Technology Transfer, 46(5), 1462-1486.

  • Megantz, R. C. (2002). Technology management: Developing and implementing effective licensing programs (Vol. 21). Wiley.

  • Mukherji, N., & Silberman, J. (2021). Knowledge flows between universities and industry: The impact of distance, technological compatibility, and the ability to diffuse knowledge. The Journal of Technology Transfer, 46(1), 223-257.

  • Murray, F., & Stern, S. (2007). Do formal intellectual property rights hinder the free flow of scientific knowledge? An empirical test of the anti-commons hypothesis. Journal of Economic Behavior & Organization, 63(4), 648-687.

  • Nugent, A., Chan, H. F., & Dulleck, U. (2021). Government funding of university-industry collaboration: exploring the impact of targeted funding on university patent activity. Scientometrics, 127(1), 29–73.

  • Ouellette, L. L., & Tutt, A. (2020). How do patent incentives affect university researchers? International Review of Law and Economics, 61.

  • Perkmann, M., Tartari, V., McKelvey, M., Autio, E., Broström, A., D’este, P., ... & Sobrero, M. (2013). Academic engagement and commercialisation: A review of the literature on university–industry relations. Research policy, 42(2), 423-442.

  • Petruzzelli, A. M. (2011). The impact of technological relatedness, prior ties, and geographical distance on university–industry collaborations: A joint-patent analysis. Technovation, 31(7), 309–319.

  • Ryan, C. J., & Frye, B. L. (2017). An empirical study of university patent activity. Journal of Intellectual Property and Entertainment Law, 7(1)

  • Schmid, J., & Fajebe, A. (2019). Variation in patent impact by organization type: An investigation of government, university, and corporate patents. Science & Public Policy, 46(4), 589–598

  • Schoen, A., & Buenstorf, G. (2013). When do universities own their patents? An explorative study of patent characteristics and organizational determinants in Germany. Industry and Innovation, 20(5), 422–437.

  • Scotchmer, S. (2013). Patents in the University: Priming the Pump and Crowding Out. The Journal of Industrial Economics, 61(3), 817–844.

  • Sterzi, V., Pezzoni, M., & Lissoni, F. (2019). Patent management by universities: evidence from Italian academic inventions. Industrial and Corporate Change, 28(2), 309–330

  • Thursby, J., Fuller, A. W., & Thursby, M. (2009). US faculty patenting: Inside and outside the university. Research policy, 38(1), 14-25.

  • Wu, Y., Welch, E. W., & Huang, W. L. (2015). Commercialization of university inventions: Individual and institutional factors affecting licensing of university patents. Technovation, 36, 12-25.

  • Zhao, X., & Cui, H. (2021). Impact of university-industry collaborative research with different dimensions on university patent commercialisation. Technology Analysis & Strategic Management

White Structure

Background

During the 1990s, governments began to doubt the assumption that publicly funded research would inevitably generate useful applications to society and, instead, sought objective evidence that demonstrates the impact and benefits of scientific research (Mostert et al., 2010). Accordingly, the UK introduced the Research Assessment Exercise, in specialists reviewed publications and assigned a ranking.  This approach inspired similar methods in Europe and around the world and was eventually supplanted by the Research Excellence Framework in 2014. 

 

Research may culminate in many benefits and impacts.  These impacts tend to be divided into two categories: impact on academia and impact on society in general.  To gauge impact on academic, governments and other bodies primarily consider the extent to which publications are cited or variants of this measure.  To gauge impact on society, governments and other bodies may consider a range of metrics, all of which remain contentious.

 

The notion of Altmetrics surfaced as a means to measure societal impact.  In contrast to metrics that measure academics citations, alternative metrics, or Altmetrics, also gauge the extent to which publications surface in other media and platforms.  For example, as Moed (2017) argued, Altmetrics evaluates the impact of publication in four constellations of platforms: social media to gauge social activity, reference managers such as Research Gate to gauge scholarly activity, scholarly blogs to gauge scholarly commentary, and mass media such as newspapers to gauge public interest.   Other scholars have also suggested other relevant measures—such as the degree to which the data or codes have been shared, wikis, and ratings (e.g., Haustein, 2016; Serghiou & Ioannidis, 2018)

 

Measures of Altmetrics

Researchers can utilize multiple the services of several provieders to access the Altmetrics of publications, including Altmetric.com—a data science company that tracks the degree to which publications are mentioned in various platforms, PlumX, and Crossref Event Data.  As Ortega (2018) showed, Altmetric.com measures blog posts, news, and tweets most comprehensively, PlumX measures Mendeley readers most comprehensively, and Crossref Event Data extracts the most Wikipedia citations. 

 

Researchers can access PlumX Scopus, a database of publications.  That is, when researchers utilize Scopus, and then click the title of a research publication, they can also click an option to display PlumX metrics.  Metrics that equal 0—such as zero mentions in Twitter—will not appear.   Otherwise, PlumX can display a variety of metrics including

 

  • citations—such as the number of citations from the United States Patent and Trademark Office, from PubMed Clinical Guidelines, from the National Institute for Health and Care Excellence, and from policies

  • usage—such as the number of times the abstract or full text has been viewed or downloaded

  • captures—such as the number of times this publication has been bookmarked, marked as a favourite in various websites, followed in Github, or exported to bibliographic management tools

  • mention—such as the number of blog posts, comments in various websites, reviews, references in Wikipedia, and mentions in Stack Exchange

  • social media—such as the number of upvotes in Reddit, number of tweets and rewets, number of recommendations in SourceForge and so on

 

Altmetric generates some overlapping but distinct information.  This information can be accessed from https://app.dimensions.ai/discover/publication

 

Researchers have also developed indices that are intended to combine many Altmetrics into a single measure.  For example, Hassan et al. (2020) proposed a measure called the alt-index—the Altmetrics variant of the h-index—that combines the number of mentions from many platforms including Facebook, F1000, Reddit, Twitter, Weibo, and Wikipedia.  This index was highly correlated to the h-index as well.

 

Benefits of Altmetrics: Overview

Bornmann (2014) outline four key benefits of Altmetrics over traditional approaches.  Specifically

 

  • articles tend to be cited in scholarly journals several years after publication and, therefore, measures of citation tend to be delayed; Altmetrics, in contrast, can be measured within days or weeks of publication.  The benefits of this immediacy is that authors and institutions can utilize this information to adjust their marketing campaigns and other practices

  • Altmetrics can measure the societal impact not only of scholarly publications but many other products of research, such as datasets, software, algorithms, grey literature, and even presentation slides

  • to measure societal impact, administrators previously depended solely on case studies; but case studies are hard to quantify and data about the impact of these case studies are hard to collect—a problem that Altmetrics circumvents

  • obviously, Altmetrics does not only measure the impact of research in scholarly circles but in society more generally.

 

Benefits of Altmetrics: Cost to institutions

Furthermore, Altmetrics may be significantly less expensive than other measures of social impact.  That is, rather than depend on Altmetrics, the Research Evaluation Framework in the UK applies a more comprehensive analysis to assess societal impact.  To demonstrate impact, this exercise costs UK higher education institutions about 55 million pounds a year and the UK more broadly 246 million pounds a year (for a review, see Bornmann et al., 2019).  Institutions need to collate evidence, such as discussions of academic work in commercial documents, practitioner documents, media, meetings, conferences, seminars, working groups, and other bodies.  This evidence is embedded in case studies, comprising four pages, that encapsulates the work of research groups over recent years in a standard template.

 

Although the case studies impart knowledge about the multifaceted relationships between research and impact—and thus can be remarkably informative, several complications emanate from this method, besides the expense.  The quality of case studies is hard to compare.  Case studies overlook complications and, therefore, do not generate accurate estimates of return on investment.

 

Bornmann et al. (2019) explored whether six Altmetrics might act as a proxy measure of societal impact.  These metrics included the number of tweets, Wikipedia articles, policy documents, blog posts, Facebook posts, and news articles that referred to a scientific publication.  To access these metrics, the researchers utilized the data that are collected by Altmetric—a data science company that tracks the degree to which publications are mentioned in various platforms.  The researchers also applied the Mantel-Haenszel quotient to normalize these metrics.  This adjustment is pertinent to data that comprises many zeros.

 

Unfortunately, as Bornmann et al. (2019) showed, the six Altmetrics, when adjusted, were negligibly related to the measures of social impact derived from the case studies.  These findings indicate that Altmetrics are not appropriate substitutes of case studies. 

 

Concerns about Altmetrics: Manipulation

Some commentators argue that researchers, institutions, and journals can more readily manipulate and artificially inflate Altmetrics than more traditional bibliometrics, such as citation rates. To illustrate, Thelwall (2014) argues that administrators of social websites seldom impose quality controls to check whether online identities correspond to actual individuals.  Authors, institutions, ad journals could develop a range of fake accounts and utilize these fake accounts to raise Almetrics.  Similarly, authors, institutions, ad journals could utilize a range of technologies, such as robot tweeting, to increase Almetrics (Liu & Adie, 2013).

 

Certainly, in the future, administrators may introduce a range of measures to curb this concern.  Techniques that compare data from multiple sources could uncover suspicious patterns (Priem & Hemminger, 2010). 

 

Concerns about Altmetrics: Dependence on social media platforms

Similarly, rather than deliberate manipulation, to pursue their commercial obligations social media platforms may significantly impinge on Altmetrics.  That is, social media platforms strive to attract significant traffic to their websites.  They may, therefore, introduce campaigns that increase the visibility of publications that are likely to attract interest and skew the Altmetrics (Bornmann, 2014).  The problem is that Altmetrics, therefore, may be more dependent on the priorities and actions of a few social media platforms than on the capabilities and efforts of authors or institutions.   

 

Concerns about Altmetrics: Reliability and validity of the data

Even if administrators can prevent manipulation, several features of media, especially social media, may compromise the reliability and validity of Almetric data.  Researchers have referred to many causes of concern around the reliability and validity of Almetric data.  For instance

 

  • the definitions of specific Altmetric can be ambiguous: mentions in Facebook might refer only to public wall posts or all posts.  Minor differences in the definition can generate large discrepancies in the measure, compromising the reliability of these metrics (Liu & Adie, 2013)

  • the metrics often insensitive to distinct behaviors.  The number of mentions in a social media could refer to merely a link to an article or a detailed analysis of this article.  Because of this variety, a simple measure, such as number of mentions, could reflect negligible or significant impact on society (Neylon et al., 2014).  Unlike scholarly citations, nobody has developed or applied standards on how to cite publications in social media (Neylon et al., 2014).

  • Altmetrics scores may be biased towards newer publications, emotional topics, and many other characteristics.  Therefore, two similar Altmetric scores might actually reflect different levels of societal impact.  In contrast to metrics that gauge scholarly citations, metrics that gauge Altmetrics are seldom normalized to overcome this bias (Bornmann, 2014). One service, called ImpactStory, utilizes percentiles to partly address this problem however: see https://profiles.impactstory.org/

 

Concerns about Altmetrics: Variations across stakeholders

Whether Altmetrics generate valuable information about the societal impact of research may vary across stakeholders.  To illustrate, Pollitt et al. (2016) administered a survey in which respondents assessed statements about the various impacts of research.  For example, they indicated the degree to which they value the impact of research on job creation, and so forth.  This approach revealed that various stakeholders, such as researchers and the general public, prioritize distinct outcomes of research.  This perspective implies that perhaps administrators need to develop a variety of metrics to evaluate impact, each relevant to a different circumstance or stakeholder group.    

 

Determinants of Altmetrics: Quality of publications

Some researchers have explored the features of publications that might increase Altmetrics. One possible feature is simply the quality of these publications.  Bornmann and Haunschild (2018) conducted a study that explored whether Altmetrics is related to the quality of publications.   To measure the quality of publications, the authors utilized the project, F1000Prime.  Specifically, this project is, in essence, a peer-review that follows, rather than precedes, the publication of papers.  About 5000 academics assess publications they read and post comments as well.  The papers are deemed as good, very good, or excellent.

 

Bornmann and Haunschild (2018) then explored which clusters of Altmetrics correlate with these ratings.  As the authors showed, a factor or variable that reflects the number of readers—as manifested by the number of times a publication was included in Mendeley, for example— was positively and significantly related to the quality of these publications.  In contrast,  a factor or variable that reflects the level of attention that publications have received, such as number of tweets,  are negligibly associated with quality.  As this finding shows, the quality of publications may increase some Altmetrics but not other Altmetrics.   

 

Determinants of Altmetrics: Title of publications

The title of publications, if vivid and emotive, can attract readers and may thus increase Altmetrics scores.  To explore this possibility empirically, Araujo et al. (2018) examined whether features of the title affect Altmetrics.  This study examined 200 publications, all of which reported clinical trials that relate to lower back pain.  The titles were divided into two clusters: titles with a question or reference to the main conclusion and other titles.  If the titles included a question or reference to the key conclusion, the publication was more likely to be mentioned in the media. Including social media.

Determinants of Altmetrics: Tags

Araujo et al (2020) recommend a series of practice that can increase Altmetrics.  For example, whenever authors post an article in social media or discuss the article in YouTube, they should include a link to the article or the DOI.  Readers and viewers are thus more likely to access the article.

 

Determinants of Altmetrics: Actions and characteristics of journals

Besides features of the publications, the actions of journal editors or other characteristics of journal might also influence Altmetrics.  For example, to examine this matter, Araujo et al. (2021) conducted a systematic review.  Specifically, they identified 19 articles that have explored the associations between features of the journal or article and Altmetric scores.  This review uncovered some key insights.  For example

 

  • journal impact factor, reflecting the extent to which scholars tend to cite this journal, was positively associated with Altmetrics

  • the degree to which individuals have downloaded the article or access the full text online was also positively associated with Altmetrics

  • open access publications tend to attract higher Altmetrics

  • press releases about articles tend to increase Altmetrics.  Specifically, as Haneef et al. (2017) revealed, if the author, institution, or publisher had submitted a press release about the article—as uncovered by EurekAlert, a database of science press releases, the publication was mentioned more frequently in social media, online news outlets, and science blogs.

 

Consequences of Altmetrics: Article processing charges

Some research has explored whether the Altmetrics of journals influences article processing charges.  Specifically, in many journals, if researchers are willing to pay a fee, called an article processing charge, the publication will be accessible to all readers at no cost—called gold open access.  The magnitude of this fee can vary appreciably, often ranging from $500 to $5000.  Research has investigated the causes of variation.

 

One possibility is that journals that attract many citations and high Altmetrics will tend to impose a steeper article processing charge.  That is, researchers may agree to pay a higher fee to publish in these journals.  Yet, contrary to this prediction, Maddi and Sapinho (2022), in an analysis of over 83 000 articles and 4751 journals across 267 publishers, showed that article processing fees is not strongly associated with Altmetrics. The correlation between article processing fees and Altmetrics is only .09.  Indeed, after controlling a range of other variables, this relationship became slightly negative.  Similarly, citation rates were not highly associated with article processing charges either.  These results indicate that classical economic models do not readily explain article processing charges

References

  • Araujo, A. C., Gonzalez, G. Z., Nascimento, D. P., & Costa, L. O. P. (2020). Deep impact: 4 tips for authors and journal editors to improve Altmetric scores. Physical Therapy, 100(11), 2060-2062.

  • Araujo, A. C., Nascimento, D. P., Gonzalez, G. Z., Maher, C. G., & Costa, L. O. P. (2018). Impact of low back pain clinical trials measured by the altmetric score: cross-sectional study. Journal of medical Internet research, 20(4)

  • Araujo, A. C., Vanin, A. A., Nascimento, D. P., Gonzalez, G. Z., & Costa, L. O. P. (2021). What are the variables associated with Altmetric scores? Systematic Reviews, 10(1), 1–9.

  • Bornmann, L. (2014). Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics. Journal of Informetrics, 8(4), 895–903

  • Bornmann, L., & Haunschild, R. (2018). Do altmetrics correlate with the quality of papers? A large-scale empirical study based on F1000Prime data. PloS One, 13(5), e0197133–e0197133

  • Bornmann, L., Haunschild, R., & Adams, J. (2019). Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK research excellence framework (REF). Journal of Informetrics, 13(1), 325–340.

  • Haneef, R., Ravaud, P., Baron, G., Ghosn, L., & Boutron, I. (2017). Factors associated with online media attention to research: a cohort study of articles evaluating cancer treatments. Research Integrity and Peer Review, 2(1), 1-8.

  • Hassan, S.-U., Iqbal, S., Aljohani, N. R., Alelyani, S., & Zuccala, A. (2020). Introducing the “alt-index” for measuring the social visibility of scientific research. Scientometrics, 123(3), 1407–1419.

  • Haustein, S. (2016). Grand challenges in altmetrics: heterogeneity, data quality and dependencies. Scientometrics, 108(1), 413-423

  • Liu, J., & Adie, E. (2013). Five challenges in altmetrics: A toolmaker's perspective. Bulletin of the American Society for Information Science and Technology, 39(4), 31-34.

  • Maddi, A., & Sapinho, D. (2022). Article processing charges, altmetrics and citation impact: Is there an economic rationale? Scientometrics.

  • Moed, H. F. (2017). Applied evaluative informetrics (p. 312). Berlin: Springer International Publishing.

  • Mostert, S. P., Ellenbroek, S. P., Meijer, I., Van Ark, G., & Klasen, E. C. (2010). Societal output and use of research performed by health research groups. Health research policy and systems, 8(1), 1-10.

  • Neylon, C., Willmers, M., & King, T. (2014). Rethinking impact: Applying altmetrics to southern African research. Paper/Scholarly Communication in Africa Programme; 1, January 2014.

  • Nuzzolese, A. G., Ciancarini, P., Gangemi, A., Peroni, S., Poggi, F., & Presutti, V. (2019). Do altmetrics work for assessing research quality? Scientometrics, 118(2), 539–562

  • Ortega, J. L. (2018). Reliability and accuracy of altmetric providers: a comparison among Altmetric. com, PlumX and Crossref Event Data. Scientometrics, 116(3), 2123-2138.

  • Pollitt, A., Potoglou, D., Patil, S., Burge, P., Guthrie, S., King, S., ... & Grant, J. (2016). Understanding the relative valuation of research impact: a best–worst scaling experiment of the general public and biomedical and health researchers. BMJ open, 6(8)

  • Priem, J., & Hemminger, B. H. (2010). Scientometrics 2.0: New metrics of scholarly impact on the social Web. First Monday.

  • Serghiou, S., & Ioannidis, J. P. (2018). Altmetric scores, citations, and publication of studies posted as preprints. Jama, 319(4), 402-404.

  • Sugimoto, C. R., Work, S., Larivière, V., & Haustein, S. (2017). Scholarly use of social media and altmetrics: A review of the literature. Journal of the association for information science and technology, 68(9), 2037-2062.

  • Thelwall, M. (2014). A brief history of altmetrics Research Trends, 37, 3-4.

The model university 2040: An encyclopedia of research and ideas to improve tertiary education

©2022 by The model university. Proudly created with Wix.com

bottom of page